Whole human heart histology to validate electroanatomical voltage mapping in patients with non-ischaemic cardiomyopathy and ventricular tachycardia

Whole human heart histology to validate electroanatomical voltage mapping in patients with... Abstract Aims Electroanatomical voltage mapping (EAVM) is an important diagnostic tool for fibrosis identification and risk stratification in non-ischaemic cardiomyopathy (NICM); currently, distinct cut-offs are applied. We aimed to evaluate the performance of EAVM to detect fibrosis by integration with whole heart histology and to identify the fibrosis pattern in NICM patients with ventricular tachycardias (VTs). Methods and results Eight patients with NICM and VT underwent EAVM prior to death or heart transplantation. EAVM data was projected onto slices of the entire heart. Pattern, architecture, and amount of fibrosis were assessed in transmural biopsies corresponding to EAVM sites. Fibrosis pattern in NICM biopsies (n = 507) was highly variable and not limited to mid-wall/sub-epicardium. Fibrosis architecture was rarely compact, but typically patchy and/or diffuse. In NICM, biopsies without abnormal fibrosis unipolar voltage (UV) and bipolar voltage (BV) showed a linear association with wall thickness (WT). The amount of viable myocardium showed a linear association with both UV and BV. Accordingly, any cut-off to delineate fibrosis performed poorly. An equation was generated calculating the amount of fibrosis at any location, given WT and UV or BV. Conclusion Considering the linear relationships between WT, amount of fibrosis and both UV and BV, the search for any distinct voltage cut-off to identify fibrosis in NICM is futile. The amount of fibrosis can be calculated, if WT and voltages are known. Fibrosis pattern and architecture are different from ischaemic cardiomyopathy and findings on ischaemic substrates may not be applicable to NICM. Arrhythmias, Mapping, Histology, Fibrosis, Cardiomyopathy Introduction Sustained ventricular tachycardia (VT) in patients with non-ischaemic cardiomyopathy (NICM) is often due to myocardial re-entry and occasionally to triggered activity both associated with the presence of fibrosis.1,2 Animal models and human data in end-stage heart failure suggest that the degree of arrhythmogeneity depends on amount and architecture of fibrosis with highest propensity for intermediate degrees and patchy architecture.3–5 However, histological data from patients with NICM and sustained monomorphic VT (MVT) is lacking. Electroanatomical bipolar voltage (BV) and unipolar voltage (UV) mapping is considered an invasive reference method to detect fibrosis.6 Different endocardial BV and UV cut-off values for detecting fibrosis have been proposed.7–9 It has been suggested that the presence of a viable sub-endocardial layer overlying fibrosis may prevent its detection by BV mapping using the currently uniformly applied BV cut-off of 1.5 mV.8,9 Unipolar voltage mapping is considered to have a larger field of view and thus superior in detecting mid-wall and sub-epicardial fibrosis.9–11 However, neither the currently used cut-off values for detecting fibrosis in NICM, nor the ‘field of view’ of UV or BV have been validated. To date, the only histological validation of electroanatomical voltage mapping (EAVM) for scar detection arises from animal infarct models.12 Infarct scars, with a transmural pattern and compact fibrosis interspersed with viable myocardial bundles, may be substantially different from NICM scars.4,13 Animal models mimicking NICM scar patterns are lacking. The purpose of this study was two-fold: (i) to evaluate the location, pattern, architecture, and amount of fibrosis in patients with NICM and sustained VT and (ii) to evaluate the performance of EAVM to detect fibrosis by exvivo registration of EAVM data with whole heart histology. Methods Patients and controls Non-ischaemic cardiomyopathy patients who underwent detailed EAVM and ablation for MVT and either died or received heart transplantation after the procedure were included (Supplementary material online, methods). All patients were treated according to our standard clinical protocol and provided informed consent for mapping and ablation. All patients and/or next of kin provided informed consent for post-mortem analysis. Transmural biopsies (TB) from seven age-matched hearts, in which cardiac pathology was excluded by an experienced pathologist, served as controls. Electroanatomical mapping and ablation High-density EAVM was performed during sinus rhythm or right ventricular pacing using a 3.5 mm irrigated-tip catheter (1 mm ring electrode, 2 mm inter-electrode spacing; NaviStar Thermocool, Biosense Webster Inc., CA, USA) and the CARTO® system (Figure 1A) (Supplementary material online, methods).7,11,14,15 Figure 1 View largeDownload slide (A) Endocardial and epicardial bipolar voltage maps, colour-coded according to bar in an anterior–posterior view. (B) Creation of 3D-mesh from 5-mm pathological slices. (C) Integration of voltage maps with 3D anatomical mesh (grey). (D) Example of four slices with electroanatomical voltage mapping points and ablation locations projected. Accurate integration confirmation by visual inspection of projection of ablation locations over pathology ablation lesions. Electroanatomical voltage mapping mapping locations (a–h) shown on CARTO® maps and on histological slices. Stained 5-mm wide biopsy corresponding to non-ablation site A. Collagen stains red and myocardium yellow. Figure 1 View largeDownload slide (A) Endocardial and epicardial bipolar voltage maps, colour-coded according to bar in an anterior–posterior view. (B) Creation of 3D-mesh from 5-mm pathological slices. (C) Integration of voltage maps with 3D anatomical mesh (grey). (D) Example of four slices with electroanatomical voltage mapping points and ablation locations projected. Accurate integration confirmation by visual inspection of projection of ablation locations over pathology ablation lesions. Electroanatomical voltage mapping mapping locations (a–h) shown on CARTO® maps and on histological slices. Stained 5-mm wide biopsy corresponding to non-ablation site A. Collagen stains red and myocardium yellow. Ex vivo image integration Three-dimensional meshes were created from 5 mm thick slices of the fixed heart, imported into CARTO®, and merged with EAVM data (Figure 1B and C) (Supplementary material online, methods).9 Histological analysis Transmural biopsies with a width of 5 mm (7 μm thick) were taken from left ventricular (LV) sites corresponding to non-ablation EAVM sites and stained with Picrosirius Red (Figure 1D) (Supplementary material online, methods). In all patients, TB were taken from seven locations: the anterior-septal, lateral and inferior wall at the mid, and basal level and the apex. Transmural biopsies with signs of acute or old ablation lesions were excluded. Transmural biopsies were systematically assessed on the following parameters: Location The fibrotic involvement of the seven LV segments was macroscopically assessed in the stained TB to determine the location of fibrosis. Pattern According to the dominant site of fibrosis throughout the myocardium, five patterns were defined by visual assessment: minimal interstitial fibrosis (not restricted to one area of the biopsy), sub-endocardial, mid-wall, sub-epicardial, and transmural fibrosis (Figure 2A). Figure 2 View largeDownload slide (A) Patterns of fibrosis. (B) Architecture of fibrosis. Interstitial: fibrosis in the extracellular space between myocardial bundles. Patchy: areas of replacement fibrosis, surrounded by confluent viable myocardium. Diffuse: intermingling of myocardial and collagen fibres. Compact: dense areas of fibrosis, spanning the full width of the TB devoid of any viable myocardium. (C) Custom software used to calculate the amount of fibrosis. Figure 2 View largeDownload slide (A) Patterns of fibrosis. (B) Architecture of fibrosis. Interstitial: fibrosis in the extracellular space between myocardial bundles. Patchy: areas of replacement fibrosis, surrounded by confluent viable myocardium. Diffuse: intermingling of myocardial and collagen fibres. Compact: dense areas of fibrosis, spanning the full width of the TB devoid of any viable myocardium. (C) Custom software used to calculate the amount of fibrosis. Architecture The dominant architecture of fibrosis present within a TB was defined as: interstitial, patchy, diffuse, compact, or a combination thereof (for definitions and examples see Figure 2B). Pattern and architecture of fibrosis in a random selection of TB were reviewed by a co-author (C.B.) to assess inter-observer agreement (Supplementary material online, methods). Amount Wall thickness (WT) of each TB was measured. Custom pixel-by-pixel software calculated the percentage of fibrosis and the amount of viable myocardium (mm2) within each TB (Supplementary material online, methods and Figure S1). The area of viable myocardium was used as a surrogate for the volume of viable myocardium at that location (Supplementary material online, methods and Figure S2). From the control biopsies, 7 μm sections were taken and the percentage fibrosis calculated. Statistical analysis Normally distributed data was reported as mean ± standard deviation; not normally distributed data as median and interquartile range (IQR). Categorical data was expressed as percentages or frequencies. Continuous variables were compared using (multivariable) linear regression analysis in a model that allowed for intragroup correlation. Statistical analysis was performed using IBM SPSS version 23 (SPSS Inc., Chicago, IL, USA) or STATA Statistical Software (StataCorp, College Station, TX, USA), version 14. Results Patients Electroanatomical voltage mapping data and whole heart histology of eight male NICM patients with MVT [median age 63 (IQR 58–68) years] were analysed (Table 1). Seven patients died and one patient was successfully transplanted a median of 25 (IQR 6–217) days after EAVM. Cause of death was sepsis (n = 2) 28 and 497 days after ablation, cardiogenic shock, and/or severe vasoplegia with multi-organ failure (n = 4) within 5 days of ablation and in one case cardiogenic shock 21 days after ablation without VT or obvious luxation. Table 1 Baseline characteristics Baseline characteristics     Male (%)  8 (100%)   Age (years)  63 (58–65)   Co-morbiditya  4 (50%)   Family history of sudden cardiac death  2 (25%)   ICD  7 (87.5%)   Genetic testing performed  8 (100%)    Pathogenic mutationb  5 (63%)    Unclassified variantc  1 (13%)  Clinical presentation   LV ejection fraction (%)  35 (20–43)   VT storm/incessant VT  6 (75%)   Prior ablation in different centre  3 (38%)   Amiodarone use  8 (100%)   Escalation of AAD  5 (63%)  Baseline characteristics     Male (%)  8 (100%)   Age (years)  63 (58–65)   Co-morbiditya  4 (50%)   Family history of sudden cardiac death  2 (25%)   ICD  7 (87.5%)   Genetic testing performed  8 (100%)    Pathogenic mutationb  5 (63%)    Unclassified variantc  1 (13%)  Clinical presentation   LV ejection fraction (%)  35 (20–43)   VT storm/incessant VT  6 (75%)   Prior ablation in different centre  3 (38%)   Amiodarone use  8 (100%)   Escalation of AAD  5 (63%)  Escalation of AAD: amiodarone plus ≥1 Class 1 anti-arrhythmic drug. a Renal disease, COPD, and diabetes mellitus. b ABCC9, TTN, Lamine A/C, RBM20, and MYBPC–3. c MYH7.2. ICD, internal cardiac defibrillator. Table 1 Baseline characteristics Baseline characteristics     Male (%)  8 (100%)   Age (years)  63 (58–65)   Co-morbiditya  4 (50%)   Family history of sudden cardiac death  2 (25%)   ICD  7 (87.5%)   Genetic testing performed  8 (100%)    Pathogenic mutationb  5 (63%)    Unclassified variantc  1 (13%)  Clinical presentation   LV ejection fraction (%)  35 (20–43)   VT storm/incessant VT  6 (75%)   Prior ablation in different centre  3 (38%)   Amiodarone use  8 (100%)   Escalation of AAD  5 (63%)  Baseline characteristics     Male (%)  8 (100%)   Age (years)  63 (58–65)   Co-morbiditya  4 (50%)   Family history of sudden cardiac death  2 (25%)   ICD  7 (87.5%)   Genetic testing performed  8 (100%)    Pathogenic mutationb  5 (63%)    Unclassified variantc  1 (13%)  Clinical presentation   LV ejection fraction (%)  35 (20–43)   VT storm/incessant VT  6 (75%)   Prior ablation in different centre  3 (38%)   Amiodarone use  8 (100%)   Escalation of AAD  5 (63%)  Escalation of AAD: amiodarone plus ≥1 Class 1 anti-arrhythmic drug. a Renal disease, COPD, and diabetes mellitus. b ABCC9, TTN, Lamine A/C, RBM20, and MYBPC–3. c MYH7.2. ICD, internal cardiac defibrillator. Electroanatomical voltage mapping All eight patients underwent LV endocardial mapping. Five underwent combined endo-epicardial mapping (Supplementary material online, Table S1). In two patients, a second mapping procedure was performed within 29 days and data from both procedures were included. Controls Control tissue for histology was obtained from seven age-matched hearts [five male, median age 65 (IQR 59–67) years]. From each heart, four LV TB were taken from the anterior, lateral, inferior, and septal walls. The amount of fibrosis in each biopsy was assessed, with a median of 6.5% (IQR 4.9–9.3) fibrosis. Based on the 95th percentile, 21% was defined as the upper limit of normal quantity of fibrosis for this age group. Ex vivo integration Integration of the exvivo 3D meshes with the invivo mapping data was accurate based on good agreement between macroscopically or histologically identified ablation lesion and ablation sites on EAVM (Figure 1D). A median of 138 (IQR 98–198) endocardial and 172 (IQR 142–338) epicardial mapping points per patient were included for analysis. Histology of non-ischaemic cardiomyopathy In total, 507 TB [56 (IQR 34–96) TB per patient] were taken; 277 corresponding to endocardial non-ablation EAVM sites (endocardial TB) and 230 corresponding to epicardial non-ablation EAVM sites (epicardial TB) (Supplementary material online, Table S2). Location On histological analysis, all NICM hearts showed pathological amounts of fibrosis. The basal-anterior and basal-septal segments were most frequently affected, followed by mid-anterior and apical-anteroseptal involvement. Unipolar voltage mapping overestimated and BV mapping underestimated involvement as derived from histology (Supplementary material online, Table S2). Pattern Of all TB, 32 (6%) were classified as having minimal interstitial fibrosis on visual assessment, 153 (30%) were classified as dominant sub-endocardial fibrosis, 96 (19%) as mid-wall, 83 (17%) as sub-epicardial, and 143 (28%) as transmural fibrosis. Architecture A patchy architecture was the most common dominant architecture and occurred in 277 (55%) of TB, followed by diffuse in 171 (34%), and interstitial in 45 (9%) (Supplementary material online, Table S3). Of interest, compact fibrosis was the dominant architecture in only 14 (3%) TB and never extended transmurally. In 457 (90%) TB, a combination of two or three architectures was observed. The most common combination of fibrosis architecture was patchy and interstitial (44% of all TB). Amount Transmural biopsies had a median WT of 13.8 mm (IQR 9.8–17.5), 25% fibrosis (IQR 19.1–32.1), and 45.8 mm2 (IQR 32.5–59.7) viable myocardium. In total, 160 (32%) TB had normal amounts of fibrosis if matched for age. Voltages and corresponding histology Voltages and histological parameters of TB with normal and abnormal amounts of fibrosis are given in Table 2. Table 2 Histological and voltage characteristics of biopsies   All biopsies (n = 507)  Endocardial biopsies (n = 277)   Epicardial biopsies (n = 230)   All biopsies (n = 277)  <21% fibrosis (n = 78)  >21% fibrosis (n = 199)  All biopsies (n = 230)  <21% fibrosis (n = 82)  >21% fibrosis (n = 148)  Fibrosis (%)  24.8 (19.1–32.1)  25.5 (20.1–33.7)  15.7 (9.3–18.8)  30.0 (24.6–37.8)  23.9 (18.7–30.6)  17.0 (13.7–18.8)  28.2 (24.4–34.1)  Wall thickness (mm)  13.8 (9.8–17.5)  14.4 (10.7–17.3)  15.1 (11.5–18.5)  14.0 (10.0–17.0)  13.2 (9.0–17.8)  14.8 (9.1–17.5)  12.8 (9.0–17.9)  Viable myocardium (mm2)  45.8 (32.5–59.7)  46.8 (33.5–59.6)  60.0 (43.3–73.7)  42.1 (31.6–55.8)  45.1 (31.8–59.9)  54.5 (33.6–66.6)  40.8 (29.6–57.4)  Bipolar voltage (mV)  2.7 (1.3–4.1)  2.6 (1.6–4.3)  3.3 (1.8–6.7)  2.4 (1.4–4.0)  2.7 (0.9–4.0)  3.4 (2.3–5.5)  1.8 (0.8–3.5)  Unipolar voltage (mV)  6.5 (4.5–9.2)  6.2 (4.5–9.0)  8.3 (5.9–10.9)  5.4 (4.3–8.1)  6.9 (4.6–9.3)  8.6 (6.6–11.9)  5.9 (4.1–8.1)  Epicardial fat (mm)    N.A.  N.A.  N.A.  0.5 (0.0–2.0)  0.2 (0.0–1.5)  0.6 (0.0–2.4)    All biopsies (n = 507)  Endocardial biopsies (n = 277)   Epicardial biopsies (n = 230)   All biopsies (n = 277)  <21% fibrosis (n = 78)  >21% fibrosis (n = 199)  All biopsies (n = 230)  <21% fibrosis (n = 82)  >21% fibrosis (n = 148)  Fibrosis (%)  24.8 (19.1–32.1)  25.5 (20.1–33.7)  15.7 (9.3–18.8)  30.0 (24.6–37.8)  23.9 (18.7–30.6)  17.0 (13.7–18.8)  28.2 (24.4–34.1)  Wall thickness (mm)  13.8 (9.8–17.5)  14.4 (10.7–17.3)  15.1 (11.5–18.5)  14.0 (10.0–17.0)  13.2 (9.0–17.8)  14.8 (9.1–17.5)  12.8 (9.0–17.9)  Viable myocardium (mm2)  45.8 (32.5–59.7)  46.8 (33.5–59.6)  60.0 (43.3–73.7)  42.1 (31.6–55.8)  45.1 (31.8–59.9)  54.5 (33.6–66.6)  40.8 (29.6–57.4)  Bipolar voltage (mV)  2.7 (1.3–4.1)  2.6 (1.6–4.3)  3.3 (1.8–6.7)  2.4 (1.4–4.0)  2.7 (0.9–4.0)  3.4 (2.3–5.5)  1.8 (0.8–3.5)  Unipolar voltage (mV)  6.5 (4.5–9.2)  6.2 (4.5–9.0)  8.3 (5.9–10.9)  5.4 (4.3–8.1)  6.9 (4.6–9.3)  8.6 (6.6–11.9)  5.9 (4.1–8.1)  Epicardial fat (mm)    N.A.  N.A.  N.A.  0.5 (0.0–2.0)  0.2 (0.0–1.5)  0.6 (0.0–2.4)  Histological and voltage parameters of all biopsies, and biopsies subdivided based on location of voltage data (endocardial and epicardial) and on quantity of fibrosis [normal (<21%) and abnormal (>21%)]. Data given as median (IQR). N.A., not applicable. Table 2 Histological and voltage characteristics of biopsies   All biopsies (n = 507)  Endocardial biopsies (n = 277)   Epicardial biopsies (n = 230)   All biopsies (n = 277)  <21% fibrosis (n = 78)  >21% fibrosis (n = 199)  All biopsies (n = 230)  <21% fibrosis (n = 82)  >21% fibrosis (n = 148)  Fibrosis (%)  24.8 (19.1–32.1)  25.5 (20.1–33.7)  15.7 (9.3–18.8)  30.0 (24.6–37.8)  23.9 (18.7–30.6)  17.0 (13.7–18.8)  28.2 (24.4–34.1)  Wall thickness (mm)  13.8 (9.8–17.5)  14.4 (10.7–17.3)  15.1 (11.5–18.5)  14.0 (10.0–17.0)  13.2 (9.0–17.8)  14.8 (9.1–17.5)  12.8 (9.0–17.9)  Viable myocardium (mm2)  45.8 (32.5–59.7)  46.8 (33.5–59.6)  60.0 (43.3–73.7)  42.1 (31.6–55.8)  45.1 (31.8–59.9)  54.5 (33.6–66.6)  40.8 (29.6–57.4)  Bipolar voltage (mV)  2.7 (1.3–4.1)  2.6 (1.6–4.3)  3.3 (1.8–6.7)  2.4 (1.4–4.0)  2.7 (0.9–4.0)  3.4 (2.3–5.5)  1.8 (0.8–3.5)  Unipolar voltage (mV)  6.5 (4.5–9.2)  6.2 (4.5–9.0)  8.3 (5.9–10.9)  5.4 (4.3–8.1)  6.9 (4.6–9.3)  8.6 (6.6–11.9)  5.9 (4.1–8.1)  Epicardial fat (mm)    N.A.  N.A.  N.A.  0.5 (0.0–2.0)  0.2 (0.0–1.5)  0.6 (0.0–2.4)    All biopsies (n = 507)  Endocardial biopsies (n = 277)   Epicardial biopsies (n = 230)   All biopsies (n = 277)  <21% fibrosis (n = 78)  >21% fibrosis (n = 199)  All biopsies (n = 230)  <21% fibrosis (n = 82)  >21% fibrosis (n = 148)  Fibrosis (%)  24.8 (19.1–32.1)  25.5 (20.1–33.7)  15.7 (9.3–18.8)  30.0 (24.6–37.8)  23.9 (18.7–30.6)  17.0 (13.7–18.8)  28.2 (24.4–34.1)  Wall thickness (mm)  13.8 (9.8–17.5)  14.4 (10.7–17.3)  15.1 (11.5–18.5)  14.0 (10.0–17.0)  13.2 (9.0–17.8)  14.8 (9.1–17.5)  12.8 (9.0–17.9)  Viable myocardium (mm2)  45.8 (32.5–59.7)  46.8 (33.5–59.6)  60.0 (43.3–73.7)  42.1 (31.6–55.8)  45.1 (31.8–59.9)  54.5 (33.6–66.6)  40.8 (29.6–57.4)  Bipolar voltage (mV)  2.7 (1.3–4.1)  2.6 (1.6–4.3)  3.3 (1.8–6.7)  2.4 (1.4–4.0)  2.7 (0.9–4.0)  3.4 (2.3–5.5)  1.8 (0.8–3.5)  Unipolar voltage (mV)  6.5 (4.5–9.2)  6.2 (4.5–9.0)  8.3 (5.9–10.9)  5.4 (4.3–8.1)  6.9 (4.6–9.3)  8.6 (6.6–11.9)  5.9 (4.1–8.1)  Epicardial fat (mm)    N.A.  N.A.  N.A.  0.5 (0.0–2.0)  0.2 (0.0–1.5)  0.6 (0.0–2.4)  Histological and voltage parameters of all biopsies, and biopsies subdivided based on location of voltage data (endocardial and epicardial) and on quantity of fibrosis [normal (<21%) and abnormal (>21%)]. Data given as median (IQR). N.A., not applicable. Voltages and ex vivo wall thickness Within TB with normal amounts of fibrosis, there was a linear relationship between WT and endocardial UV (Figure 3A). For every millimetre increase in exvivo WT the UV increased by 0.28 mV (P = 0.010). Of interest, the same linear relationship was observed between BV and WT: for every millimetre increase in exvivo WT the BV increased by 0.23 mV (P = 0.009). Figure 3 View largeDownload slide (A) Wall thickness and voltages in transmural biopsies with normal amounts of fibrosis. (B) Viable myocardium and corresponding voltages. The 1.5 mV and 8.27 mV mark indicate clinically applied cut-off values. (C) Transmural biopsies with <21% in the 4 mm sub-endocardial rim (n = 79): unipolar voltage and bipolar voltage against viable myocardium within the entire biopsy. Figure 3 View largeDownload slide (A) Wall thickness and voltages in transmural biopsies with normal amounts of fibrosis. (B) Viable myocardium and corresponding voltages. The 1.5 mV and 8.27 mV mark indicate clinically applied cut-off values. (C) Transmural biopsies with <21% in the 4 mm sub-endocardial rim (n = 79): unipolar voltage and bipolar voltage against viable myocardium within the entire biopsy. Voltages and amount of viable myocardium In all TB, a linear relationship between amount of viable myocardium and UV generated was observed. A 1 mm2 increase in viable myocardium resulted in a 0.09 mV (P = 0.002) increase in endocardial UV (Figure 3B) and a 0.08 mV (P = 0.016) increase in epicardial UV. Notably, there was a comparable linear relationship between amount of viable myocardium and the endocardial BV generated. A 1 mm2 increase in amount of viable myocardium resulted in a 0.06 mV (P = 0.001) increase in endocardial BV. A single cut-off to detect an amount of viable myocardium performed poorly irrespective of the amount of viable myocardium that was considered relevant (Supplementary material online, results). Field of view of unipolar voltage and bipolar voltage It has previously been suggested that BV is limited by a field of view.7,16 To test the impact of fibrosis remote from the endocardial surface, the relationship between amount of viable myocardium within TB and the endocardial voltages generated was analysed within a sub-selection of TB which had a normal amount of fibrosis in the 4 mm sub-endocardium (Figure 3C). Both UV and BV were impacted by changes in the amount of viable myocardium occurring at distances of >4 mm from the endocardial surface. A 1 mm2 increase in the amount of viable myocardium beyond the 4 mm sub-endocardial rim resulted in a UV increase of 0.09 mV (P = 0.012) and BV increase of 0.05 mV (P = 0.046). Voltages, ex vivo wall thickness, and amount of fibrosis Multiple linear regression was performed to predict the amount of fibrosis based on the voltage and WT measured. Both UV and BV can be used to predict the amount of fibrosis when exvivo WT is known (P ≤ 0.017). An equation taking into account both WT and the amount of fibrosis present was generated (Figure 4). Figure 4 View largeDownload slide (A) Equations to predict amount of fibrosis when wall thickness (mm) and voltages (mV) are known. (B) Voltages (mV) generated when ex vivo wall thickness (mm) and % fibrosis are known. Figure 4 View largeDownload slide (A) Equations to predict amount of fibrosis when wall thickness (mm) and voltages (mV) are known. (B) Voltages (mV) generated when ex vivo wall thickness (mm) and % fibrosis are known. Discussion Main findings This study is the first to provide detailed histological data on fibrosis in NICM patients with sustained MVT and to couple EAVM data with the true gold standard for fibrosis identification—histology. The fibrosis pattern is highly variable and not restricted to the mid-wall and sub-epicardium. The fibrosis architecture is most often patchy or diffuse and a combination of more than one architecture occurs frequently. The compact fibrosis architecture observed in infarct scar is very rare in NICM and never reaches transmurality. Unipolar voltages, and contrary to most commonly held beliefs, BV, are affected by WT. Additionally, the amount of fibrosis affects UV and BV. We demonstrate a linear relationship between the amount of viable myocardium and both the UV and BV amplitudes (Take home figure). In this patient population, neither BV nor UV is restricted by a ‘field of view’. Take home figure View largeDownload slide Voltage mapping has been integrated with full human heart histology in non-ischaemic cardiomyopathy. The histological substrate has been described, and the relationship between the amount of fibrosis as well as wall thickness and both bipolar and unipolar voltages demonstrated. Take home figure View largeDownload slide Voltage mapping has been integrated with full human heart histology in non-ischaemic cardiomyopathy. The histological substrate has been described, and the relationship between the amount of fibrosis as well as wall thickness and both bipolar and unipolar voltages demonstrated. Our findings have important clinical implications. Firstly, a single BV or UV cut-off to detect fibrosis, as currently applied in practice, cannot be valid considering the range of observed WT. Secondly, as the relationship between UV and BV amplitude and WT remains linear with increasing fibrosis, we may be able to determine the amount of intramural fibrosis if the local WT is known. Fibrosis in non-ischaemic cardiomyopathy The only histological data in patients with NICM come from dated autopsy studies in patients with terminal heart failure.17–19 These studies showed a dominant sub-endocardial fibrosis pattern which may be attributed to pressure overload or ischaemia. In end-stage heart failure patients a variable degree of interstitial fibrosis was seen at sites of induced focal, non-sustained, polymorphic VTs.20 Late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) is the imaging reference method for the non-invasive detection of regional fibrosis in NICM.21 A study including 63 unselected patients with dilated cardiomyopathy reported no LGE in the majority of patients (59%), sub-endocardial LGE in 13% (attributed to ischaemia), and mid-wall or sub-epicardial LGE in 28% typically involving the basal and mid-LV22. The presence and extent of particularly mid-wall fibrosis has been associated with inducible VT23 and with mortality and (aborted) sudden cardiac death in a recent cohort of 472 NICM patients.24 Our study is the first to specifically describe the fibrosis pattern and architecture in patients with NICM and sustained MVT. In contrast to what is reported in imaging studies, the mid-wall or sub-epicardial fibrosis pattern was seen in only 36% of TB. A further 30% showed a sub-endocardial pattern and a transmural pattern was seen in 28%. Of importance, only 3% of TB showed compact fibrosis with the density of ischaemic scars, and this fibrosis was never transmural. A patchy (55%), followed by a diffuse architecture (34%) was most frequently found. To date, LGE-CMR to detect and determine scar size has only been histologically validated in an exvivo animal model for post-infarct scar.25 Current LGE-CMR methods to detect fibrosis require either bright areas with dense fibrosis or normal reference myocardium. The present study demonstrates that in NICM, areas with compact fibrosis are rare and that ‘normal’ reference myocardium may contain variable degrees of fibrosis. The comparison of invivo LGE-CMR data from one patient obtained 133 days before mapping and ablation illustrates the variation in scar size dependent on the applied LGE-CMR method and supports the limitation of LGE-CMR to accurately identify and delineate diffuse fibrosis (Figure 5 and Supplementary material online, methods).23,26–28 Figure 5 View largeDownload slide Red dotted line: ICD artefact. Red: scar core. Yellow: scar borderzone according to different methods (Supplementary material online, methods). Green squares: locations of high-resolution histology inserts from non-ablation locations. Areas of dense mid-septal fibrosis surrounded by viable myocardium corresponded well with areas of late gadolinium-enhanced on cardiac magnetic resonance (Insert 2). Despite high quantity, less well-delineated fibrosis (Insert 1) was only identified as core scar when using the 2–3SD method; as borderzone when using the MaxSI or modified Full-Width Half Maximum (FWHM) method. Despite comprising more than 50% fibrosis, a diffuse pattern was not detected on late gadolinium-enhanced cardiac magnetic resonance irrespective of method used (Insert 3). Figure 5 View largeDownload slide Red dotted line: ICD artefact. Red: scar core. Yellow: scar borderzone according to different methods (Supplementary material online, methods). Green squares: locations of high-resolution histology inserts from non-ablation locations. Areas of dense mid-septal fibrosis surrounded by viable myocardium corresponded well with areas of late gadolinium-enhanced on cardiac magnetic resonance (Insert 2). Despite high quantity, less well-delineated fibrosis (Insert 1) was only identified as core scar when using the 2–3SD method; as borderzone when using the MaxSI or modified Full-Width Half Maximum (FWHM) method. Despite comprising more than 50% fibrosis, a diffuse pattern was not detected on late gadolinium-enhanced cardiac magnetic resonance irrespective of method used (Insert 3). Voltage mapping in non-ischaemic cardiomyopathy Voltage mapping is considered the gold standard for the invasive identification of fibrosis. The cut-off values for UV and BV proposed in the literature vary in their absolute value and in the population in which they were derived. Cut-offs UV >8.27 mV and BV >1.5 mV were derived from patients without structural heart disease. However, sampling locations and WT in these young patients were not reported and these cut-offs were poor predictors of normal amounts of fibrosis in our cohort (Supplementary material online, results). Ischaemic cardiomyopathy studies (pig models and humans) have shown that BV <1.5 mV is useful in identifying compact, transmural, thin-walled scars. However, BV <1.5 mV could not detect non-transmural, small sub-epicardial scar, or grey-zone.26,29 This was attributed to the smaller field of view of BV, and it has been suggested that cut-offs based on ischaemic scars may not be valid for NICM scars.8,9,30 Linear relationship between wall thickness and unipolar voltage and bipolar voltage amplitude Electroanatomical voltage mapping is considered an indirect measure of the amount of viable myocardium depolarized in the vicinity of the recording electrodes. The amplitude of an electrogram generated by activity in a myocardial bundle is inversely proportional to the square or cube of the distance between the myocardial bundle and the recording site for UV (UV∝1/r2) and BV (BV∝1/r3) (Supplementary material online, results and Figures S3 and S4).31 As such, myocardial bundles located closer to the catheter contribute more to the amplitude of both UV and BV electrograms than myocardial bundles located further away from the catheter. Additionally, BV is less sensitive to the activity of viable myocardium occurring at distances remote from the catheter tip than UV, leading to the concept of a limited ‘field of view’.7,16 Of interest, we found a linear relation between WT and electrogram amplitude for both UV and BV. Importantly, for relatively large distances between 10–20 mm, the relationship between WT and amplitude is near linear (Supplementary material online, Figure S5), which is in line with the linear relationship we found between BV and UV within the clinically relevant range of WT in our cohort. Linear relationship between amount of fibrosis and unipolar voltage and bipolar voltage amplitude Not only the WT, but also the amount of fibrosis, affects the amount of viable myocardium present and thus influences both UV and BV. An increase in fibrosis reduces the amount of viable myocardium resulting in a linear decrease in UV and BV. Our data demonstrated such a linear relationship. Accordingly, it is important to take both these parameters into account when interpreting EAVM data. ‘Field of view’ of unipolar voltage and bipolar voltage In this study, we show that both UV and BV are sensitive to histological changes occurring more distantly from the catheter tip. We could demonstrate that endocardial BV is also affected by fibrosis occurring >4 mm from the catheter. Whether BV and UV amplitudes, as well as voltages generated using a catheter with smaller electrodes, provide complementary information on fibrosis location needs further evaluation. Smaller electrodes are likely to reduce far field contamination and may be beneficial for areas with sub-endocardial involvement but would be potentially less helpful in areas with a mid-wall pattern of fibrosis. Conclusions Fibrosis pattern in patients with NICM and VTs are variable with similar prevalence of sub-endocardial, mid-wall/sub-epicardial, and transmural patterns. Patchy and diffuse architectures dominate whereas compact fibrosis is rare. These specific characteristics of fibrosis are likely to impact its accurate delineation by current LGE-CMR methods. Both BV and UV mapping are sensitive not only to the amount of fibrosis but also to myocardial WT. Similarly, both BV and UV mapping are not restricted by a ‘field of view’. Accordingly, and of high-clinical importance, a single BV and UV cut-off for detecting the amount and location of fibrosis without considering the local WT and architecture of fibrosis cannot be valid. This study has taken an important step in this regard, providing an equation for detection of fibrosis based on UV and/or BV and WT. Further study is needed to generate a comprehensive algorithm, appropriate for invivo, real time, mapping, and imaging. Limitations Whilst we have described the fibrosis present in NICM patients with VT, the specific fibrosis needed to sustain VT has not been identified. This study reported exvivo WT. Wall thickness measured in formalin-fixed hearts are comparable to the end-systolic WT measured on echocardiography.32 Supplementary material Supplementary material is available at European Heart Journal online. Funding The Department of Cardiology (Leiden University Medical Centre) receives unrestricted research grant from Edwards Lifesciences, Medtronik, Biotronik, and Boston Scientific. Conflict of interest: none declared. References 1 Hsia HH, Callans DJ, Marchlinski FE. Characterization of endocardial electrophysiological substrate in patients with nonischemic cardiomyopathy and monomorphic ventricular tachycardia. Circulation  2003; 108: 704– 710. Google Scholar CrossRef Search ADS PubMed  2 Soejima K, Stevenson WG, Sapp JL, Selwyn AP, Couper G, Epstein LM. Endocardial and epicardial radiofrequency ablation of ventricular tachycardia associated with dilated cardiomyopathy: the importance of low-voltage scars. J Am Coll Cardiol  2004; 43: 1834– 1842. Google Scholar CrossRef Search ADS PubMed  3 Morita N, Mandel WJ, Kobayashi Y, Karagueuzian HS. Cardiac fibrosis as a determinant of ventricular tachyarrhythmias. J Arrhythm  2014; 30: 389– 394. Google Scholar CrossRef Search ADS PubMed  4 de Jong S, van Veen TA, van Rijen HV, de Bakker JM. Fibrosis and cardiac arrhythmias. J Cardiovasc Pharmacol  2011; 57: 630– 638. Google Scholar CrossRef Search ADS PubMed  5 Kawara T, Derksen R, de Groot JR, Coronel R, Tasseron S, Linnenbank AC, Hauer RN, Kirkels H, Janse MJ, de Bakker JM. Activation delay after premature stimulation in chronically diseased human myocardium relates to the architecture of interstitial fibrosis. Circulation  2001; 104: 3069– 3075. Google Scholar CrossRef Search ADS PubMed  6 Priori SG, Blomstrom-Lundqvist C, Mazzanti A, Blom N, Borggrefe M, Camm J, Elliott PM, Fitzsimons D, Hatala R, Hindricks G, Kirchhof P, Kjeldsen K, Kuck KH, Hernandez-Madrid A, Nikolaou N, Norekval TM, Spaulding C, Van Veldhuisen DJ, Kolh P, Lip GYH, Agewall S, Baron-Esquivias G, Boriani G, Budts W, Bueno H, Capodanno D, Carerj S, Crespo-Leiro MG, Czerny M, Deaton C, Dobrev D, Erol C, Galderisi M, Gorenek B, Kriebel T, Lambiase P, Lancellotti P, Lane DA, Lang I, Manolis AJ, Morais J, Moreno J, Piepoli MF, Rutten FH, Sredniawa B, Zamorano JL, Zannad F, Cardiology ES. 2015 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: association for European Paediatric and Congenital Cardiology (AEPC). Eur Heart J  2015; 36: 2793– 2867. Google Scholar CrossRef Search ADS PubMed  7 Hutchinson MD, Gerstenfeld EP, Desjardins B, Bala R, Riley MP, Garcia FC, Dixit S, Lin D, Tzou WS, Cooper JM, Verdino RJ, Callans DJ, Marchlinski FE. Endocardial unipolar voltage mapping to detect epicardial ventricular tachycardia substrate in patients with nonischemic left ventricular cardiomyopathy. Circ Arrhythm Electrophysiol  2011; 4: 49– 55. Google Scholar CrossRef Search ADS PubMed  8 Desjardins B, Yokokawa M, Good E, Crawford T, Latchamsetty R, Jongnarangsin K, Ghanbari H, Oral H, Pelosi FJr, Chugh A, Morady F, Bogun F. Characteristics of intramural scar in patients with nonischemic cardiomyopathy and relation to intramural ventricular arrhythmias. Circ Arrhythm Electrophysiol  2013; 6: 891– 897. Google Scholar CrossRef Search ADS PubMed  9 Piers SR, Tao Q, van Huls van Taxis CF, Schalij MJ, van der Geest RJ, Zeppenfeld K. Contrast-enhanced MRI-derived scar patterns and associated ventricular tachycardias in nonischemic cardiomyopathy: implications for the ablation strategy. Circ Arrhythm Electrophysiol  2013; 6: 875– 883. Google Scholar CrossRef Search ADS PubMed  10 Campos B, Jauregui ME, Park KM, Mountantonakis SE, Gerstenfeld EP, Haqqani H, Garcia FC, Hutchinson MD, Callans DJ, Dixit S, Lin D, Riley MP, Tzou W, Cooper JM, Bala R, Zado ES, Marchlinski FE. New unipolar electrogram criteria to identify irreversibility of nonischemic left ventricular cardiomyopathy. J Am Coll Cardiol  2012; 60: 2194– 2204. Google Scholar CrossRef Search ADS PubMed  11 Desjardins B, Morady F, Bogun F. Effect of epicardial fat on electroanatomical mapping and epicardial catheter ablation. J Am Coll Cardiol  2010; 56: 1320– 1327. Google Scholar CrossRef Search ADS PubMed  12 Reddy VY, Malchano ZJ, Holmvang G, Schmidt EJ, d'Avila A, Houghtaling C, Chan RC, Ruskin JN. Integration of cardiac magnetic resonance imaging with three-dimensional electroanatomic mapping to guide left ventricular catheter manipulation: feasibility in a porcine model of healed myocardial infarction. J Am Coll Cardiol  2004; 44: 2202– 2213. Google Scholar CrossRef Search ADS PubMed  13 de Bakker JM, van Capelle FJ, Janse MJ, Wilde AA, Coronel R, Becker AE, Dingemans KP, van Hemel NM, Hauer RN. Reentry as a cause of ventricular tachycardia in patients with chronic ischemic heart disease: electrophysiologic and anatomic correlation. Circulation  1988; 77: 589– 606. Google Scholar CrossRef Search ADS PubMed  14 Marchlinski FE, Callans DJ, Gottlieb CD, Zado E. Linear ablation lesions for control of unmappable ventricular tachycardia in patients with ischemic and nonischemic cardiomyopathy. Circulation  2000; 101: 1288– 1296. Google Scholar CrossRef Search ADS PubMed  15 Piers SR, van Huls van Taxis CF, Tao Q, van der Geest RJ, Askar SF, Siebelink HM, Schalij MJ, Zeppenfeld K. Epicardial substrate mapping for ventricular tachycardia ablation in patients with non-ischaemic cardiomyopathy: a new algorithm to differentiate between scar and viable myocardium developed by simultaneous integration of computed tomography and contrast-enhanced magnetic resonance imaging. Eur Heart J  2013; 34: 586– 596. Google Scholar CrossRef Search ADS PubMed  16 Haqqani HM, Tschabrunn CM, Tzou WS, Dixit S, Cooper JM, Riley MP, Lin D, Hutchinson MD, Garcia FC, Bala R, Verdino RJ, Callans DJ, Gerstenfeld EP, Zado ES, Marchlinski FE. Isolated septal substrate for ventricular tachycardia in nonischemic dilated cardiomyopathy: incidence, characterization, and implications. Heart Rhythm  2011; 8: 1169– 1176. Google Scholar CrossRef Search ADS PubMed  17 Roberts WC, Siegel RJ, McManus BM. Idiopathic dilated cardiomyopathy: analysis of 152 necropsy patients. Am J Cardiol  1987; 60: 1340– 1355. Google Scholar CrossRef Search ADS PubMed  18 Unverferth DV, Baker PB, Swift SE, Chaffee R, Fetters JK, Uretsky BF, Thompson ME, Leier CV. Extent of myocardial fibrosis and cellular hypertrophy in dilated cardiomyopathy. Am J Cardiol  1986; 57: 816– 820. Google Scholar CrossRef Search ADS PubMed  19 de Leeuw N, Ruiter DJ, Balk AH, de Jonge N, Galama JMD, Melchers WJG. Histopathologic findings in explanted heart tissue from patients with end-stage idiopathic dilated cardiomyopathy. Transpl Int  2001; 14: 299– 306. Google Scholar CrossRef Search ADS PubMed  20 Pogwizd SM, McKenzie JP, Cain ME. Mechanisms underlying spontaneous and induced ventricular arrhythmias in patients with idiopathic dilated cardiomyopathy. Circulation  1998; 98: 2404– 2414. Google Scholar CrossRef Search ADS PubMed  21 Iles LM, Ellims AH, Llewellyn H, Hare JL, Kaye DM, McLean CA, Taylor AJ. Histological validation of cardiac magnetic resonance analysis of regional and diffuse interstitial myocardial fibrosis. Eur Heart J Cardiovasc Imaging  2015; 16: 14– 22. Google Scholar CrossRef Search ADS PubMed  22 McCrohon JA, Moon JC, Prasad SK, McKenna WJ, Lorenz CH, Coats AJ, Pennell DJ. Differentiation of heart failure related to dilated cardiomyopathy and coronary artery disease using gadolinium-enhanced cardiovascular magnetic resonance. Circulation  2003; 108: 54– 59. Google Scholar CrossRef Search ADS PubMed  23 Nazarian S, Bluemke DA, Lardo AC, Zviman MM, Watkins SP, Dickfeld TL, Meininger GR, Roguin A, Calkins H, Tomaselli GF, Weiss RG, Berger RD, Lima JA, Halperin HR. Magnetic resonance assessment of the substrate for inducible ventricular tachycardia in nonischemic cardiomyopathy. Circulation  2005; 112: 2821– 2825. Google Scholar CrossRef Search ADS PubMed  24 Gulati A, Jabbour A, Ismail TF, Guha K, Khwaja J, Raza S, Morarji K, Brown TD, Ismail NA, Dweck MR, Di Pietro E, Roughton M, Wage R, Daryani Y, O'Hanlon R, Sheppard MN, Alpendurada F, Lyon AR, Cook SA, Cowie MR, Assomull RG, Pennell DJ, Prasad SK. Association of fibrosis with mortality and sudden cardiac death in patients with nonischemic dilated cardiomyopathy. JAMA  2013; 309: 896– 908. Google Scholar CrossRef Search ADS PubMed  25 Fieno DS, Kim RJ, Chen EL, Lomasney JW, Klocke FJ, Judd RM. Contrast-enhanced magnetic resonance imaging of myocardium at risk: distinction between reversible and irreversible injury throughout infarct healing. J Am Coll Cardiol  2000; 36: 1985– 1991. Google Scholar CrossRef Search ADS PubMed  26 Wijnmaalen AP, van der Geest RJ, van Huls van Taxis CF, Siebelink HM, Kroft LJ, Bax JJ, Reiber JH, Schalij MJ, Zeppenfeld K. Head-to-head comparison of contrast-enhanced magnetic resonance imaging and electroanatomical voltage mapping to assess post-infarct scar characteristics in patients with ventricular tachycardias: real-time image integration and reversed registration. Eur Heart J  2011; 32: 104– 114. Google Scholar CrossRef Search ADS PubMed  27 Schmidt A, Azevedo CF, Cheng A, Gupta SN, Bluemke DA, Foo TK, Gerstenblith G, Weiss RG, Marban E, Tomaselli GF, Lima JA, Wu KC. Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation  2007; 115: 2006– 2014. Google Scholar CrossRef Search ADS PubMed  28 Yan AT, Shayne AJ, Brown KA, Gupta SN, Chan CW, Luu TM, Di CMF, Reynolds HG, Stevenson WG, Kwong RY. Characterization of the peri-infarct zone by contrast-enhanced cardiac magnetic resonance imaging is a powerful predictor of post-myocardial infarction mortality. Circulation  2006; 114: 32– 39. Google Scholar CrossRef Search ADS PubMed  29 Wrobleski D, Houghtaling C, Josephson ME, Ruskin JN, Reddy VY. Use of electrogram characteristics during sinus rhythm to delineate the endocardial scar in a porcine model of healed myocardial infarction. J Cardiovasc Electrophysiol  2003; 14: 524– 529. Google Scholar CrossRef Search ADS PubMed  30 Sasaki T, Miller CF, Hansford R, Zipunnikov V, Zviman MM, Marine JE, Spragg D, Cheng A, Tandri H, Sinha S, Kolandaivelu A, Zimmerman SL, Bluemke DA, Tomaselli GF, Berger RD, Halperin HR, Calkins H, Nazarian S. Impact of nonischemic scar features on local ventricular electrograms and scar-related ventricular tachycardia circuits in patients with nonischemic cardiomyopathy. Circ Arrhythm Electrophysiol  2013; 6: 1139– 1147. Google Scholar CrossRef Search ADS PubMed  31 Durrer D, Van Der Twell LH. Spread of activation in the left ventricular wall of the dog. I. Am Heart J  1953; 46: 683– 691. Google Scholar CrossRef Search ADS PubMed  32 Maron BJ, Henry WL, Roberts WC, Epstein SE. Comparison of echocardiographic and necropsy measurements of ventricular wall thicknesses in patients with and without disproportionate septal thickening. Circulation  1977; 55: 341– 346. Google Scholar CrossRef Search ADS PubMed  Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2018. For permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Heart Journal Oxford University Press

Whole human heart histology to validate electroanatomical voltage mapping in patients with non-ischaemic cardiomyopathy and ventricular tachycardia

Loading next page...
 
/lp/ou_press/whole-human-heart-histology-to-validate-electroanatomical-voltage-0f8DY8elP2
Publisher
Oxford University Press
Copyright
Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2018. For permissions, please email: journals.permissions@oup.com.
ISSN
0195-668X
eISSN
1522-9645
D.O.I.
10.1093/eurheartj/ehy168
Publisher site
See Article on Publisher Site

Abstract

Abstract Aims Electroanatomical voltage mapping (EAVM) is an important diagnostic tool for fibrosis identification and risk stratification in non-ischaemic cardiomyopathy (NICM); currently, distinct cut-offs are applied. We aimed to evaluate the performance of EAVM to detect fibrosis by integration with whole heart histology and to identify the fibrosis pattern in NICM patients with ventricular tachycardias (VTs). Methods and results Eight patients with NICM and VT underwent EAVM prior to death or heart transplantation. EAVM data was projected onto slices of the entire heart. Pattern, architecture, and amount of fibrosis were assessed in transmural biopsies corresponding to EAVM sites. Fibrosis pattern in NICM biopsies (n = 507) was highly variable and not limited to mid-wall/sub-epicardium. Fibrosis architecture was rarely compact, but typically patchy and/or diffuse. In NICM, biopsies without abnormal fibrosis unipolar voltage (UV) and bipolar voltage (BV) showed a linear association with wall thickness (WT). The amount of viable myocardium showed a linear association with both UV and BV. Accordingly, any cut-off to delineate fibrosis performed poorly. An equation was generated calculating the amount of fibrosis at any location, given WT and UV or BV. Conclusion Considering the linear relationships between WT, amount of fibrosis and both UV and BV, the search for any distinct voltage cut-off to identify fibrosis in NICM is futile. The amount of fibrosis can be calculated, if WT and voltages are known. Fibrosis pattern and architecture are different from ischaemic cardiomyopathy and findings on ischaemic substrates may not be applicable to NICM. Arrhythmias, Mapping, Histology, Fibrosis, Cardiomyopathy Introduction Sustained ventricular tachycardia (VT) in patients with non-ischaemic cardiomyopathy (NICM) is often due to myocardial re-entry and occasionally to triggered activity both associated with the presence of fibrosis.1,2 Animal models and human data in end-stage heart failure suggest that the degree of arrhythmogeneity depends on amount and architecture of fibrosis with highest propensity for intermediate degrees and patchy architecture.3–5 However, histological data from patients with NICM and sustained monomorphic VT (MVT) is lacking. Electroanatomical bipolar voltage (BV) and unipolar voltage (UV) mapping is considered an invasive reference method to detect fibrosis.6 Different endocardial BV and UV cut-off values for detecting fibrosis have been proposed.7–9 It has been suggested that the presence of a viable sub-endocardial layer overlying fibrosis may prevent its detection by BV mapping using the currently uniformly applied BV cut-off of 1.5 mV.8,9 Unipolar voltage mapping is considered to have a larger field of view and thus superior in detecting mid-wall and sub-epicardial fibrosis.9–11 However, neither the currently used cut-off values for detecting fibrosis in NICM, nor the ‘field of view’ of UV or BV have been validated. To date, the only histological validation of electroanatomical voltage mapping (EAVM) for scar detection arises from animal infarct models.12 Infarct scars, with a transmural pattern and compact fibrosis interspersed with viable myocardial bundles, may be substantially different from NICM scars.4,13 Animal models mimicking NICM scar patterns are lacking. The purpose of this study was two-fold: (i) to evaluate the location, pattern, architecture, and amount of fibrosis in patients with NICM and sustained VT and (ii) to evaluate the performance of EAVM to detect fibrosis by exvivo registration of EAVM data with whole heart histology. Methods Patients and controls Non-ischaemic cardiomyopathy patients who underwent detailed EAVM and ablation for MVT and either died or received heart transplantation after the procedure were included (Supplementary material online, methods). All patients were treated according to our standard clinical protocol and provided informed consent for mapping and ablation. All patients and/or next of kin provided informed consent for post-mortem analysis. Transmural biopsies (TB) from seven age-matched hearts, in which cardiac pathology was excluded by an experienced pathologist, served as controls. Electroanatomical mapping and ablation High-density EAVM was performed during sinus rhythm or right ventricular pacing using a 3.5 mm irrigated-tip catheter (1 mm ring electrode, 2 mm inter-electrode spacing; NaviStar Thermocool, Biosense Webster Inc., CA, USA) and the CARTO® system (Figure 1A) (Supplementary material online, methods).7,11,14,15 Figure 1 View largeDownload slide (A) Endocardial and epicardial bipolar voltage maps, colour-coded according to bar in an anterior–posterior view. (B) Creation of 3D-mesh from 5-mm pathological slices. (C) Integration of voltage maps with 3D anatomical mesh (grey). (D) Example of four slices with electroanatomical voltage mapping points and ablation locations projected. Accurate integration confirmation by visual inspection of projection of ablation locations over pathology ablation lesions. Electroanatomical voltage mapping mapping locations (a–h) shown on CARTO® maps and on histological slices. Stained 5-mm wide biopsy corresponding to non-ablation site A. Collagen stains red and myocardium yellow. Figure 1 View largeDownload slide (A) Endocardial and epicardial bipolar voltage maps, colour-coded according to bar in an anterior–posterior view. (B) Creation of 3D-mesh from 5-mm pathological slices. (C) Integration of voltage maps with 3D anatomical mesh (grey). (D) Example of four slices with electroanatomical voltage mapping points and ablation locations projected. Accurate integration confirmation by visual inspection of projection of ablation locations over pathology ablation lesions. Electroanatomical voltage mapping mapping locations (a–h) shown on CARTO® maps and on histological slices. Stained 5-mm wide biopsy corresponding to non-ablation site A. Collagen stains red and myocardium yellow. Ex vivo image integration Three-dimensional meshes were created from 5 mm thick slices of the fixed heart, imported into CARTO®, and merged with EAVM data (Figure 1B and C) (Supplementary material online, methods).9 Histological analysis Transmural biopsies with a width of 5 mm (7 μm thick) were taken from left ventricular (LV) sites corresponding to non-ablation EAVM sites and stained with Picrosirius Red (Figure 1D) (Supplementary material online, methods). In all patients, TB were taken from seven locations: the anterior-septal, lateral and inferior wall at the mid, and basal level and the apex. Transmural biopsies with signs of acute or old ablation lesions were excluded. Transmural biopsies were systematically assessed on the following parameters: Location The fibrotic involvement of the seven LV segments was macroscopically assessed in the stained TB to determine the location of fibrosis. Pattern According to the dominant site of fibrosis throughout the myocardium, five patterns were defined by visual assessment: minimal interstitial fibrosis (not restricted to one area of the biopsy), sub-endocardial, mid-wall, sub-epicardial, and transmural fibrosis (Figure 2A). Figure 2 View largeDownload slide (A) Patterns of fibrosis. (B) Architecture of fibrosis. Interstitial: fibrosis in the extracellular space between myocardial bundles. Patchy: areas of replacement fibrosis, surrounded by confluent viable myocardium. Diffuse: intermingling of myocardial and collagen fibres. Compact: dense areas of fibrosis, spanning the full width of the TB devoid of any viable myocardium. (C) Custom software used to calculate the amount of fibrosis. Figure 2 View largeDownload slide (A) Patterns of fibrosis. (B) Architecture of fibrosis. Interstitial: fibrosis in the extracellular space between myocardial bundles. Patchy: areas of replacement fibrosis, surrounded by confluent viable myocardium. Diffuse: intermingling of myocardial and collagen fibres. Compact: dense areas of fibrosis, spanning the full width of the TB devoid of any viable myocardium. (C) Custom software used to calculate the amount of fibrosis. Architecture The dominant architecture of fibrosis present within a TB was defined as: interstitial, patchy, diffuse, compact, or a combination thereof (for definitions and examples see Figure 2B). Pattern and architecture of fibrosis in a random selection of TB were reviewed by a co-author (C.B.) to assess inter-observer agreement (Supplementary material online, methods). Amount Wall thickness (WT) of each TB was measured. Custom pixel-by-pixel software calculated the percentage of fibrosis and the amount of viable myocardium (mm2) within each TB (Supplementary material online, methods and Figure S1). The area of viable myocardium was used as a surrogate for the volume of viable myocardium at that location (Supplementary material online, methods and Figure S2). From the control biopsies, 7 μm sections were taken and the percentage fibrosis calculated. Statistical analysis Normally distributed data was reported as mean ± standard deviation; not normally distributed data as median and interquartile range (IQR). Categorical data was expressed as percentages or frequencies. Continuous variables were compared using (multivariable) linear regression analysis in a model that allowed for intragroup correlation. Statistical analysis was performed using IBM SPSS version 23 (SPSS Inc., Chicago, IL, USA) or STATA Statistical Software (StataCorp, College Station, TX, USA), version 14. Results Patients Electroanatomical voltage mapping data and whole heart histology of eight male NICM patients with MVT [median age 63 (IQR 58–68) years] were analysed (Table 1). Seven patients died and one patient was successfully transplanted a median of 25 (IQR 6–217) days after EAVM. Cause of death was sepsis (n = 2) 28 and 497 days after ablation, cardiogenic shock, and/or severe vasoplegia with multi-organ failure (n = 4) within 5 days of ablation and in one case cardiogenic shock 21 days after ablation without VT or obvious luxation. Table 1 Baseline characteristics Baseline characteristics     Male (%)  8 (100%)   Age (years)  63 (58–65)   Co-morbiditya  4 (50%)   Family history of sudden cardiac death  2 (25%)   ICD  7 (87.5%)   Genetic testing performed  8 (100%)    Pathogenic mutationb  5 (63%)    Unclassified variantc  1 (13%)  Clinical presentation   LV ejection fraction (%)  35 (20–43)   VT storm/incessant VT  6 (75%)   Prior ablation in different centre  3 (38%)   Amiodarone use  8 (100%)   Escalation of AAD  5 (63%)  Baseline characteristics     Male (%)  8 (100%)   Age (years)  63 (58–65)   Co-morbiditya  4 (50%)   Family history of sudden cardiac death  2 (25%)   ICD  7 (87.5%)   Genetic testing performed  8 (100%)    Pathogenic mutationb  5 (63%)    Unclassified variantc  1 (13%)  Clinical presentation   LV ejection fraction (%)  35 (20–43)   VT storm/incessant VT  6 (75%)   Prior ablation in different centre  3 (38%)   Amiodarone use  8 (100%)   Escalation of AAD  5 (63%)  Escalation of AAD: amiodarone plus ≥1 Class 1 anti-arrhythmic drug. a Renal disease, COPD, and diabetes mellitus. b ABCC9, TTN, Lamine A/C, RBM20, and MYBPC–3. c MYH7.2. ICD, internal cardiac defibrillator. Table 1 Baseline characteristics Baseline characteristics     Male (%)  8 (100%)   Age (years)  63 (58–65)   Co-morbiditya  4 (50%)   Family history of sudden cardiac death  2 (25%)   ICD  7 (87.5%)   Genetic testing performed  8 (100%)    Pathogenic mutationb  5 (63%)    Unclassified variantc  1 (13%)  Clinical presentation   LV ejection fraction (%)  35 (20–43)   VT storm/incessant VT  6 (75%)   Prior ablation in different centre  3 (38%)   Amiodarone use  8 (100%)   Escalation of AAD  5 (63%)  Baseline characteristics     Male (%)  8 (100%)   Age (years)  63 (58–65)   Co-morbiditya  4 (50%)   Family history of sudden cardiac death  2 (25%)   ICD  7 (87.5%)   Genetic testing performed  8 (100%)    Pathogenic mutationb  5 (63%)    Unclassified variantc  1 (13%)  Clinical presentation   LV ejection fraction (%)  35 (20–43)   VT storm/incessant VT  6 (75%)   Prior ablation in different centre  3 (38%)   Amiodarone use  8 (100%)   Escalation of AAD  5 (63%)  Escalation of AAD: amiodarone plus ≥1 Class 1 anti-arrhythmic drug. a Renal disease, COPD, and diabetes mellitus. b ABCC9, TTN, Lamine A/C, RBM20, and MYBPC–3. c MYH7.2. ICD, internal cardiac defibrillator. Electroanatomical voltage mapping All eight patients underwent LV endocardial mapping. Five underwent combined endo-epicardial mapping (Supplementary material online, Table S1). In two patients, a second mapping procedure was performed within 29 days and data from both procedures were included. Controls Control tissue for histology was obtained from seven age-matched hearts [five male, median age 65 (IQR 59–67) years]. From each heart, four LV TB were taken from the anterior, lateral, inferior, and septal walls. The amount of fibrosis in each biopsy was assessed, with a median of 6.5% (IQR 4.9–9.3) fibrosis. Based on the 95th percentile, 21% was defined as the upper limit of normal quantity of fibrosis for this age group. Ex vivo integration Integration of the exvivo 3D meshes with the invivo mapping data was accurate based on good agreement between macroscopically or histologically identified ablation lesion and ablation sites on EAVM (Figure 1D). A median of 138 (IQR 98–198) endocardial and 172 (IQR 142–338) epicardial mapping points per patient were included for analysis. Histology of non-ischaemic cardiomyopathy In total, 507 TB [56 (IQR 34–96) TB per patient] were taken; 277 corresponding to endocardial non-ablation EAVM sites (endocardial TB) and 230 corresponding to epicardial non-ablation EAVM sites (epicardial TB) (Supplementary material online, Table S2). Location On histological analysis, all NICM hearts showed pathological amounts of fibrosis. The basal-anterior and basal-septal segments were most frequently affected, followed by mid-anterior and apical-anteroseptal involvement. Unipolar voltage mapping overestimated and BV mapping underestimated involvement as derived from histology (Supplementary material online, Table S2). Pattern Of all TB, 32 (6%) were classified as having minimal interstitial fibrosis on visual assessment, 153 (30%) were classified as dominant sub-endocardial fibrosis, 96 (19%) as mid-wall, 83 (17%) as sub-epicardial, and 143 (28%) as transmural fibrosis. Architecture A patchy architecture was the most common dominant architecture and occurred in 277 (55%) of TB, followed by diffuse in 171 (34%), and interstitial in 45 (9%) (Supplementary material online, Table S3). Of interest, compact fibrosis was the dominant architecture in only 14 (3%) TB and never extended transmurally. In 457 (90%) TB, a combination of two or three architectures was observed. The most common combination of fibrosis architecture was patchy and interstitial (44% of all TB). Amount Transmural biopsies had a median WT of 13.8 mm (IQR 9.8–17.5), 25% fibrosis (IQR 19.1–32.1), and 45.8 mm2 (IQR 32.5–59.7) viable myocardium. In total, 160 (32%) TB had normal amounts of fibrosis if matched for age. Voltages and corresponding histology Voltages and histological parameters of TB with normal and abnormal amounts of fibrosis are given in Table 2. Table 2 Histological and voltage characteristics of biopsies   All biopsies (n = 507)  Endocardial biopsies (n = 277)   Epicardial biopsies (n = 230)   All biopsies (n = 277)  <21% fibrosis (n = 78)  >21% fibrosis (n = 199)  All biopsies (n = 230)  <21% fibrosis (n = 82)  >21% fibrosis (n = 148)  Fibrosis (%)  24.8 (19.1–32.1)  25.5 (20.1–33.7)  15.7 (9.3–18.8)  30.0 (24.6–37.8)  23.9 (18.7–30.6)  17.0 (13.7–18.8)  28.2 (24.4–34.1)  Wall thickness (mm)  13.8 (9.8–17.5)  14.4 (10.7–17.3)  15.1 (11.5–18.5)  14.0 (10.0–17.0)  13.2 (9.0–17.8)  14.8 (9.1–17.5)  12.8 (9.0–17.9)  Viable myocardium (mm2)  45.8 (32.5–59.7)  46.8 (33.5–59.6)  60.0 (43.3–73.7)  42.1 (31.6–55.8)  45.1 (31.8–59.9)  54.5 (33.6–66.6)  40.8 (29.6–57.4)  Bipolar voltage (mV)  2.7 (1.3–4.1)  2.6 (1.6–4.3)  3.3 (1.8–6.7)  2.4 (1.4–4.0)  2.7 (0.9–4.0)  3.4 (2.3–5.5)  1.8 (0.8–3.5)  Unipolar voltage (mV)  6.5 (4.5–9.2)  6.2 (4.5–9.0)  8.3 (5.9–10.9)  5.4 (4.3–8.1)  6.9 (4.6–9.3)  8.6 (6.6–11.9)  5.9 (4.1–8.1)  Epicardial fat (mm)    N.A.  N.A.  N.A.  0.5 (0.0–2.0)  0.2 (0.0–1.5)  0.6 (0.0–2.4)    All biopsies (n = 507)  Endocardial biopsies (n = 277)   Epicardial biopsies (n = 230)   All biopsies (n = 277)  <21% fibrosis (n = 78)  >21% fibrosis (n = 199)  All biopsies (n = 230)  <21% fibrosis (n = 82)  >21% fibrosis (n = 148)  Fibrosis (%)  24.8 (19.1–32.1)  25.5 (20.1–33.7)  15.7 (9.3–18.8)  30.0 (24.6–37.8)  23.9 (18.7–30.6)  17.0 (13.7–18.8)  28.2 (24.4–34.1)  Wall thickness (mm)  13.8 (9.8–17.5)  14.4 (10.7–17.3)  15.1 (11.5–18.5)  14.0 (10.0–17.0)  13.2 (9.0–17.8)  14.8 (9.1–17.5)  12.8 (9.0–17.9)  Viable myocardium (mm2)  45.8 (32.5–59.7)  46.8 (33.5–59.6)  60.0 (43.3–73.7)  42.1 (31.6–55.8)  45.1 (31.8–59.9)  54.5 (33.6–66.6)  40.8 (29.6–57.4)  Bipolar voltage (mV)  2.7 (1.3–4.1)  2.6 (1.6–4.3)  3.3 (1.8–6.7)  2.4 (1.4–4.0)  2.7 (0.9–4.0)  3.4 (2.3–5.5)  1.8 (0.8–3.5)  Unipolar voltage (mV)  6.5 (4.5–9.2)  6.2 (4.5–9.0)  8.3 (5.9–10.9)  5.4 (4.3–8.1)  6.9 (4.6–9.3)  8.6 (6.6–11.9)  5.9 (4.1–8.1)  Epicardial fat (mm)    N.A.  N.A.  N.A.  0.5 (0.0–2.0)  0.2 (0.0–1.5)  0.6 (0.0–2.4)  Histological and voltage parameters of all biopsies, and biopsies subdivided based on location of voltage data (endocardial and epicardial) and on quantity of fibrosis [normal (<21%) and abnormal (>21%)]. Data given as median (IQR). N.A., not applicable. Table 2 Histological and voltage characteristics of biopsies   All biopsies (n = 507)  Endocardial biopsies (n = 277)   Epicardial biopsies (n = 230)   All biopsies (n = 277)  <21% fibrosis (n = 78)  >21% fibrosis (n = 199)  All biopsies (n = 230)  <21% fibrosis (n = 82)  >21% fibrosis (n = 148)  Fibrosis (%)  24.8 (19.1–32.1)  25.5 (20.1–33.7)  15.7 (9.3–18.8)  30.0 (24.6–37.8)  23.9 (18.7–30.6)  17.0 (13.7–18.8)  28.2 (24.4–34.1)  Wall thickness (mm)  13.8 (9.8–17.5)  14.4 (10.7–17.3)  15.1 (11.5–18.5)  14.0 (10.0–17.0)  13.2 (9.0–17.8)  14.8 (9.1–17.5)  12.8 (9.0–17.9)  Viable myocardium (mm2)  45.8 (32.5–59.7)  46.8 (33.5–59.6)  60.0 (43.3–73.7)  42.1 (31.6–55.8)  45.1 (31.8–59.9)  54.5 (33.6–66.6)  40.8 (29.6–57.4)  Bipolar voltage (mV)  2.7 (1.3–4.1)  2.6 (1.6–4.3)  3.3 (1.8–6.7)  2.4 (1.4–4.0)  2.7 (0.9–4.0)  3.4 (2.3–5.5)  1.8 (0.8–3.5)  Unipolar voltage (mV)  6.5 (4.5–9.2)  6.2 (4.5–9.0)  8.3 (5.9–10.9)  5.4 (4.3–8.1)  6.9 (4.6–9.3)  8.6 (6.6–11.9)  5.9 (4.1–8.1)  Epicardial fat (mm)    N.A.  N.A.  N.A.  0.5 (0.0–2.0)  0.2 (0.0–1.5)  0.6 (0.0–2.4)    All biopsies (n = 507)  Endocardial biopsies (n = 277)   Epicardial biopsies (n = 230)   All biopsies (n = 277)  <21% fibrosis (n = 78)  >21% fibrosis (n = 199)  All biopsies (n = 230)  <21% fibrosis (n = 82)  >21% fibrosis (n = 148)  Fibrosis (%)  24.8 (19.1–32.1)  25.5 (20.1–33.7)  15.7 (9.3–18.8)  30.0 (24.6–37.8)  23.9 (18.7–30.6)  17.0 (13.7–18.8)  28.2 (24.4–34.1)  Wall thickness (mm)  13.8 (9.8–17.5)  14.4 (10.7–17.3)  15.1 (11.5–18.5)  14.0 (10.0–17.0)  13.2 (9.0–17.8)  14.8 (9.1–17.5)  12.8 (9.0–17.9)  Viable myocardium (mm2)  45.8 (32.5–59.7)  46.8 (33.5–59.6)  60.0 (43.3–73.7)  42.1 (31.6–55.8)  45.1 (31.8–59.9)  54.5 (33.6–66.6)  40.8 (29.6–57.4)  Bipolar voltage (mV)  2.7 (1.3–4.1)  2.6 (1.6–4.3)  3.3 (1.8–6.7)  2.4 (1.4–4.0)  2.7 (0.9–4.0)  3.4 (2.3–5.5)  1.8 (0.8–3.5)  Unipolar voltage (mV)  6.5 (4.5–9.2)  6.2 (4.5–9.0)  8.3 (5.9–10.9)  5.4 (4.3–8.1)  6.9 (4.6–9.3)  8.6 (6.6–11.9)  5.9 (4.1–8.1)  Epicardial fat (mm)    N.A.  N.A.  N.A.  0.5 (0.0–2.0)  0.2 (0.0–1.5)  0.6 (0.0–2.4)  Histological and voltage parameters of all biopsies, and biopsies subdivided based on location of voltage data (endocardial and epicardial) and on quantity of fibrosis [normal (<21%) and abnormal (>21%)]. Data given as median (IQR). N.A., not applicable. Voltages and ex vivo wall thickness Within TB with normal amounts of fibrosis, there was a linear relationship between WT and endocardial UV (Figure 3A). For every millimetre increase in exvivo WT the UV increased by 0.28 mV (P = 0.010). Of interest, the same linear relationship was observed between BV and WT: for every millimetre increase in exvivo WT the BV increased by 0.23 mV (P = 0.009). Figure 3 View largeDownload slide (A) Wall thickness and voltages in transmural biopsies with normal amounts of fibrosis. (B) Viable myocardium and corresponding voltages. The 1.5 mV and 8.27 mV mark indicate clinically applied cut-off values. (C) Transmural biopsies with <21% in the 4 mm sub-endocardial rim (n = 79): unipolar voltage and bipolar voltage against viable myocardium within the entire biopsy. Figure 3 View largeDownload slide (A) Wall thickness and voltages in transmural biopsies with normal amounts of fibrosis. (B) Viable myocardium and corresponding voltages. The 1.5 mV and 8.27 mV mark indicate clinically applied cut-off values. (C) Transmural biopsies with <21% in the 4 mm sub-endocardial rim (n = 79): unipolar voltage and bipolar voltage against viable myocardium within the entire biopsy. Voltages and amount of viable myocardium In all TB, a linear relationship between amount of viable myocardium and UV generated was observed. A 1 mm2 increase in viable myocardium resulted in a 0.09 mV (P = 0.002) increase in endocardial UV (Figure 3B) and a 0.08 mV (P = 0.016) increase in epicardial UV. Notably, there was a comparable linear relationship between amount of viable myocardium and the endocardial BV generated. A 1 mm2 increase in amount of viable myocardium resulted in a 0.06 mV (P = 0.001) increase in endocardial BV. A single cut-off to detect an amount of viable myocardium performed poorly irrespective of the amount of viable myocardium that was considered relevant (Supplementary material online, results). Field of view of unipolar voltage and bipolar voltage It has previously been suggested that BV is limited by a field of view.7,16 To test the impact of fibrosis remote from the endocardial surface, the relationship between amount of viable myocardium within TB and the endocardial voltages generated was analysed within a sub-selection of TB which had a normal amount of fibrosis in the 4 mm sub-endocardium (Figure 3C). Both UV and BV were impacted by changes in the amount of viable myocardium occurring at distances of >4 mm from the endocardial surface. A 1 mm2 increase in the amount of viable myocardium beyond the 4 mm sub-endocardial rim resulted in a UV increase of 0.09 mV (P = 0.012) and BV increase of 0.05 mV (P = 0.046). Voltages, ex vivo wall thickness, and amount of fibrosis Multiple linear regression was performed to predict the amount of fibrosis based on the voltage and WT measured. Both UV and BV can be used to predict the amount of fibrosis when exvivo WT is known (P ≤ 0.017). An equation taking into account both WT and the amount of fibrosis present was generated (Figure 4). Figure 4 View largeDownload slide (A) Equations to predict amount of fibrosis when wall thickness (mm) and voltages (mV) are known. (B) Voltages (mV) generated when ex vivo wall thickness (mm) and % fibrosis are known. Figure 4 View largeDownload slide (A) Equations to predict amount of fibrosis when wall thickness (mm) and voltages (mV) are known. (B) Voltages (mV) generated when ex vivo wall thickness (mm) and % fibrosis are known. Discussion Main findings This study is the first to provide detailed histological data on fibrosis in NICM patients with sustained MVT and to couple EAVM data with the true gold standard for fibrosis identification—histology. The fibrosis pattern is highly variable and not restricted to the mid-wall and sub-epicardium. The fibrosis architecture is most often patchy or diffuse and a combination of more than one architecture occurs frequently. The compact fibrosis architecture observed in infarct scar is very rare in NICM and never reaches transmurality. Unipolar voltages, and contrary to most commonly held beliefs, BV, are affected by WT. Additionally, the amount of fibrosis affects UV and BV. We demonstrate a linear relationship between the amount of viable myocardium and both the UV and BV amplitudes (Take home figure). In this patient population, neither BV nor UV is restricted by a ‘field of view’. Take home figure View largeDownload slide Voltage mapping has been integrated with full human heart histology in non-ischaemic cardiomyopathy. The histological substrate has been described, and the relationship between the amount of fibrosis as well as wall thickness and both bipolar and unipolar voltages demonstrated. Take home figure View largeDownload slide Voltage mapping has been integrated with full human heart histology in non-ischaemic cardiomyopathy. The histological substrate has been described, and the relationship between the amount of fibrosis as well as wall thickness and both bipolar and unipolar voltages demonstrated. Our findings have important clinical implications. Firstly, a single BV or UV cut-off to detect fibrosis, as currently applied in practice, cannot be valid considering the range of observed WT. Secondly, as the relationship between UV and BV amplitude and WT remains linear with increasing fibrosis, we may be able to determine the amount of intramural fibrosis if the local WT is known. Fibrosis in non-ischaemic cardiomyopathy The only histological data in patients with NICM come from dated autopsy studies in patients with terminal heart failure.17–19 These studies showed a dominant sub-endocardial fibrosis pattern which may be attributed to pressure overload or ischaemia. In end-stage heart failure patients a variable degree of interstitial fibrosis was seen at sites of induced focal, non-sustained, polymorphic VTs.20 Late gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) is the imaging reference method for the non-invasive detection of regional fibrosis in NICM.21 A study including 63 unselected patients with dilated cardiomyopathy reported no LGE in the majority of patients (59%), sub-endocardial LGE in 13% (attributed to ischaemia), and mid-wall or sub-epicardial LGE in 28% typically involving the basal and mid-LV22. The presence and extent of particularly mid-wall fibrosis has been associated with inducible VT23 and with mortality and (aborted) sudden cardiac death in a recent cohort of 472 NICM patients.24 Our study is the first to specifically describe the fibrosis pattern and architecture in patients with NICM and sustained MVT. In contrast to what is reported in imaging studies, the mid-wall or sub-epicardial fibrosis pattern was seen in only 36% of TB. A further 30% showed a sub-endocardial pattern and a transmural pattern was seen in 28%. Of importance, only 3% of TB showed compact fibrosis with the density of ischaemic scars, and this fibrosis was never transmural. A patchy (55%), followed by a diffuse architecture (34%) was most frequently found. To date, LGE-CMR to detect and determine scar size has only been histologically validated in an exvivo animal model for post-infarct scar.25 Current LGE-CMR methods to detect fibrosis require either bright areas with dense fibrosis or normal reference myocardium. The present study demonstrates that in NICM, areas with compact fibrosis are rare and that ‘normal’ reference myocardium may contain variable degrees of fibrosis. The comparison of invivo LGE-CMR data from one patient obtained 133 days before mapping and ablation illustrates the variation in scar size dependent on the applied LGE-CMR method and supports the limitation of LGE-CMR to accurately identify and delineate diffuse fibrosis (Figure 5 and Supplementary material online, methods).23,26–28 Figure 5 View largeDownload slide Red dotted line: ICD artefact. Red: scar core. Yellow: scar borderzone according to different methods (Supplementary material online, methods). Green squares: locations of high-resolution histology inserts from non-ablation locations. Areas of dense mid-septal fibrosis surrounded by viable myocardium corresponded well with areas of late gadolinium-enhanced on cardiac magnetic resonance (Insert 2). Despite high quantity, less well-delineated fibrosis (Insert 1) was only identified as core scar when using the 2–3SD method; as borderzone when using the MaxSI or modified Full-Width Half Maximum (FWHM) method. Despite comprising more than 50% fibrosis, a diffuse pattern was not detected on late gadolinium-enhanced cardiac magnetic resonance irrespective of method used (Insert 3). Figure 5 View largeDownload slide Red dotted line: ICD artefact. Red: scar core. Yellow: scar borderzone according to different methods (Supplementary material online, methods). Green squares: locations of high-resolution histology inserts from non-ablation locations. Areas of dense mid-septal fibrosis surrounded by viable myocardium corresponded well with areas of late gadolinium-enhanced on cardiac magnetic resonance (Insert 2). Despite high quantity, less well-delineated fibrosis (Insert 1) was only identified as core scar when using the 2–3SD method; as borderzone when using the MaxSI or modified Full-Width Half Maximum (FWHM) method. Despite comprising more than 50% fibrosis, a diffuse pattern was not detected on late gadolinium-enhanced cardiac magnetic resonance irrespective of method used (Insert 3). Voltage mapping in non-ischaemic cardiomyopathy Voltage mapping is considered the gold standard for the invasive identification of fibrosis. The cut-off values for UV and BV proposed in the literature vary in their absolute value and in the population in which they were derived. Cut-offs UV >8.27 mV and BV >1.5 mV were derived from patients without structural heart disease. However, sampling locations and WT in these young patients were not reported and these cut-offs were poor predictors of normal amounts of fibrosis in our cohort (Supplementary material online, results). Ischaemic cardiomyopathy studies (pig models and humans) have shown that BV <1.5 mV is useful in identifying compact, transmural, thin-walled scars. However, BV <1.5 mV could not detect non-transmural, small sub-epicardial scar, or grey-zone.26,29 This was attributed to the smaller field of view of BV, and it has been suggested that cut-offs based on ischaemic scars may not be valid for NICM scars.8,9,30 Linear relationship between wall thickness and unipolar voltage and bipolar voltage amplitude Electroanatomical voltage mapping is considered an indirect measure of the amount of viable myocardium depolarized in the vicinity of the recording electrodes. The amplitude of an electrogram generated by activity in a myocardial bundle is inversely proportional to the square or cube of the distance between the myocardial bundle and the recording site for UV (UV∝1/r2) and BV (BV∝1/r3) (Supplementary material online, results and Figures S3 and S4).31 As such, myocardial bundles located closer to the catheter contribute more to the amplitude of both UV and BV electrograms than myocardial bundles located further away from the catheter. Additionally, BV is less sensitive to the activity of viable myocardium occurring at distances remote from the catheter tip than UV, leading to the concept of a limited ‘field of view’.7,16 Of interest, we found a linear relation between WT and electrogram amplitude for both UV and BV. Importantly, for relatively large distances between 10–20 mm, the relationship between WT and amplitude is near linear (Supplementary material online, Figure S5), which is in line with the linear relationship we found between BV and UV within the clinically relevant range of WT in our cohort. Linear relationship between amount of fibrosis and unipolar voltage and bipolar voltage amplitude Not only the WT, but also the amount of fibrosis, affects the amount of viable myocardium present and thus influences both UV and BV. An increase in fibrosis reduces the amount of viable myocardium resulting in a linear decrease in UV and BV. Our data demonstrated such a linear relationship. Accordingly, it is important to take both these parameters into account when interpreting EAVM data. ‘Field of view’ of unipolar voltage and bipolar voltage In this study, we show that both UV and BV are sensitive to histological changes occurring more distantly from the catheter tip. We could demonstrate that endocardial BV is also affected by fibrosis occurring >4 mm from the catheter. Whether BV and UV amplitudes, as well as voltages generated using a catheter with smaller electrodes, provide complementary information on fibrosis location needs further evaluation. Smaller electrodes are likely to reduce far field contamination and may be beneficial for areas with sub-endocardial involvement but would be potentially less helpful in areas with a mid-wall pattern of fibrosis. Conclusions Fibrosis pattern in patients with NICM and VTs are variable with similar prevalence of sub-endocardial, mid-wall/sub-epicardial, and transmural patterns. Patchy and diffuse architectures dominate whereas compact fibrosis is rare. These specific characteristics of fibrosis are likely to impact its accurate delineation by current LGE-CMR methods. Both BV and UV mapping are sensitive not only to the amount of fibrosis but also to myocardial WT. Similarly, both BV and UV mapping are not restricted by a ‘field of view’. Accordingly, and of high-clinical importance, a single BV and UV cut-off for detecting the amount and location of fibrosis without considering the local WT and architecture of fibrosis cannot be valid. This study has taken an important step in this regard, providing an equation for detection of fibrosis based on UV and/or BV and WT. Further study is needed to generate a comprehensive algorithm, appropriate for invivo, real time, mapping, and imaging. Limitations Whilst we have described the fibrosis present in NICM patients with VT, the specific fibrosis needed to sustain VT has not been identified. This study reported exvivo WT. Wall thickness measured in formalin-fixed hearts are comparable to the end-systolic WT measured on echocardiography.32 Supplementary material Supplementary material is available at European Heart Journal online. Funding The Department of Cardiology (Leiden University Medical Centre) receives unrestricted research grant from Edwards Lifesciences, Medtronik, Biotronik, and Boston Scientific. Conflict of interest: none declared. References 1 Hsia HH, Callans DJ, Marchlinski FE. Characterization of endocardial electrophysiological substrate in patients with nonischemic cardiomyopathy and monomorphic ventricular tachycardia. Circulation  2003; 108: 704– 710. Google Scholar CrossRef Search ADS PubMed  2 Soejima K, Stevenson WG, Sapp JL, Selwyn AP, Couper G, Epstein LM. Endocardial and epicardial radiofrequency ablation of ventricular tachycardia associated with dilated cardiomyopathy: the importance of low-voltage scars. J Am Coll Cardiol  2004; 43: 1834– 1842. Google Scholar CrossRef Search ADS PubMed  3 Morita N, Mandel WJ, Kobayashi Y, Karagueuzian HS. Cardiac fibrosis as a determinant of ventricular tachyarrhythmias. J Arrhythm  2014; 30: 389– 394. Google Scholar CrossRef Search ADS PubMed  4 de Jong S, van Veen TA, van Rijen HV, de Bakker JM. Fibrosis and cardiac arrhythmias. J Cardiovasc Pharmacol  2011; 57: 630– 638. Google Scholar CrossRef Search ADS PubMed  5 Kawara T, Derksen R, de Groot JR, Coronel R, Tasseron S, Linnenbank AC, Hauer RN, Kirkels H, Janse MJ, de Bakker JM. Activation delay after premature stimulation in chronically diseased human myocardium relates to the architecture of interstitial fibrosis. Circulation  2001; 104: 3069– 3075. Google Scholar CrossRef Search ADS PubMed  6 Priori SG, Blomstrom-Lundqvist C, Mazzanti A, Blom N, Borggrefe M, Camm J, Elliott PM, Fitzsimons D, Hatala R, Hindricks G, Kirchhof P, Kjeldsen K, Kuck KH, Hernandez-Madrid A, Nikolaou N, Norekval TM, Spaulding C, Van Veldhuisen DJ, Kolh P, Lip GYH, Agewall S, Baron-Esquivias G, Boriani G, Budts W, Bueno H, Capodanno D, Carerj S, Crespo-Leiro MG, Czerny M, Deaton C, Dobrev D, Erol C, Galderisi M, Gorenek B, Kriebel T, Lambiase P, Lancellotti P, Lane DA, Lang I, Manolis AJ, Morais J, Moreno J, Piepoli MF, Rutten FH, Sredniawa B, Zamorano JL, Zannad F, Cardiology ES. 2015 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: The Task Force for the Management of Patients with Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death of the European Society of Cardiology (ESC). Endorsed by: association for European Paediatric and Congenital Cardiology (AEPC). Eur Heart J  2015; 36: 2793– 2867. Google Scholar CrossRef Search ADS PubMed  7 Hutchinson MD, Gerstenfeld EP, Desjardins B, Bala R, Riley MP, Garcia FC, Dixit S, Lin D, Tzou WS, Cooper JM, Verdino RJ, Callans DJ, Marchlinski FE. Endocardial unipolar voltage mapping to detect epicardial ventricular tachycardia substrate in patients with nonischemic left ventricular cardiomyopathy. Circ Arrhythm Electrophysiol  2011; 4: 49– 55. Google Scholar CrossRef Search ADS PubMed  8 Desjardins B, Yokokawa M, Good E, Crawford T, Latchamsetty R, Jongnarangsin K, Ghanbari H, Oral H, Pelosi FJr, Chugh A, Morady F, Bogun F. Characteristics of intramural scar in patients with nonischemic cardiomyopathy and relation to intramural ventricular arrhythmias. Circ Arrhythm Electrophysiol  2013; 6: 891– 897. Google Scholar CrossRef Search ADS PubMed  9 Piers SR, Tao Q, van Huls van Taxis CF, Schalij MJ, van der Geest RJ, Zeppenfeld K. Contrast-enhanced MRI-derived scar patterns and associated ventricular tachycardias in nonischemic cardiomyopathy: implications for the ablation strategy. Circ Arrhythm Electrophysiol  2013; 6: 875– 883. Google Scholar CrossRef Search ADS PubMed  10 Campos B, Jauregui ME, Park KM, Mountantonakis SE, Gerstenfeld EP, Haqqani H, Garcia FC, Hutchinson MD, Callans DJ, Dixit S, Lin D, Riley MP, Tzou W, Cooper JM, Bala R, Zado ES, Marchlinski FE. New unipolar electrogram criteria to identify irreversibility of nonischemic left ventricular cardiomyopathy. J Am Coll Cardiol  2012; 60: 2194– 2204. Google Scholar CrossRef Search ADS PubMed  11 Desjardins B, Morady F, Bogun F. Effect of epicardial fat on electroanatomical mapping and epicardial catheter ablation. J Am Coll Cardiol  2010; 56: 1320– 1327. Google Scholar CrossRef Search ADS PubMed  12 Reddy VY, Malchano ZJ, Holmvang G, Schmidt EJ, d'Avila A, Houghtaling C, Chan RC, Ruskin JN. Integration of cardiac magnetic resonance imaging with three-dimensional electroanatomic mapping to guide left ventricular catheter manipulation: feasibility in a porcine model of healed myocardial infarction. J Am Coll Cardiol  2004; 44: 2202– 2213. Google Scholar CrossRef Search ADS PubMed  13 de Bakker JM, van Capelle FJ, Janse MJ, Wilde AA, Coronel R, Becker AE, Dingemans KP, van Hemel NM, Hauer RN. Reentry as a cause of ventricular tachycardia in patients with chronic ischemic heart disease: electrophysiologic and anatomic correlation. Circulation  1988; 77: 589– 606. Google Scholar CrossRef Search ADS PubMed  14 Marchlinski FE, Callans DJ, Gottlieb CD, Zado E. Linear ablation lesions for control of unmappable ventricular tachycardia in patients with ischemic and nonischemic cardiomyopathy. Circulation  2000; 101: 1288– 1296. Google Scholar CrossRef Search ADS PubMed  15 Piers SR, van Huls van Taxis CF, Tao Q, van der Geest RJ, Askar SF, Siebelink HM, Schalij MJ, Zeppenfeld K. Epicardial substrate mapping for ventricular tachycardia ablation in patients with non-ischaemic cardiomyopathy: a new algorithm to differentiate between scar and viable myocardium developed by simultaneous integration of computed tomography and contrast-enhanced magnetic resonance imaging. Eur Heart J  2013; 34: 586– 596. Google Scholar CrossRef Search ADS PubMed  16 Haqqani HM, Tschabrunn CM, Tzou WS, Dixit S, Cooper JM, Riley MP, Lin D, Hutchinson MD, Garcia FC, Bala R, Verdino RJ, Callans DJ, Gerstenfeld EP, Zado ES, Marchlinski FE. Isolated septal substrate for ventricular tachycardia in nonischemic dilated cardiomyopathy: incidence, characterization, and implications. Heart Rhythm  2011; 8: 1169– 1176. Google Scholar CrossRef Search ADS PubMed  17 Roberts WC, Siegel RJ, McManus BM. Idiopathic dilated cardiomyopathy: analysis of 152 necropsy patients. Am J Cardiol  1987; 60: 1340– 1355. Google Scholar CrossRef Search ADS PubMed  18 Unverferth DV, Baker PB, Swift SE, Chaffee R, Fetters JK, Uretsky BF, Thompson ME, Leier CV. Extent of myocardial fibrosis and cellular hypertrophy in dilated cardiomyopathy. Am J Cardiol  1986; 57: 816– 820. Google Scholar CrossRef Search ADS PubMed  19 de Leeuw N, Ruiter DJ, Balk AH, de Jonge N, Galama JMD, Melchers WJG. Histopathologic findings in explanted heart tissue from patients with end-stage idiopathic dilated cardiomyopathy. Transpl Int  2001; 14: 299– 306. Google Scholar CrossRef Search ADS PubMed  20 Pogwizd SM, McKenzie JP, Cain ME. Mechanisms underlying spontaneous and induced ventricular arrhythmias in patients with idiopathic dilated cardiomyopathy. Circulation  1998; 98: 2404– 2414. Google Scholar CrossRef Search ADS PubMed  21 Iles LM, Ellims AH, Llewellyn H, Hare JL, Kaye DM, McLean CA, Taylor AJ. Histological validation of cardiac magnetic resonance analysis of regional and diffuse interstitial myocardial fibrosis. Eur Heart J Cardiovasc Imaging  2015; 16: 14– 22. Google Scholar CrossRef Search ADS PubMed  22 McCrohon JA, Moon JC, Prasad SK, McKenna WJ, Lorenz CH, Coats AJ, Pennell DJ. Differentiation of heart failure related to dilated cardiomyopathy and coronary artery disease using gadolinium-enhanced cardiovascular magnetic resonance. Circulation  2003; 108: 54– 59. Google Scholar CrossRef Search ADS PubMed  23 Nazarian S, Bluemke DA, Lardo AC, Zviman MM, Watkins SP, Dickfeld TL, Meininger GR, Roguin A, Calkins H, Tomaselli GF, Weiss RG, Berger RD, Lima JA, Halperin HR. Magnetic resonance assessment of the substrate for inducible ventricular tachycardia in nonischemic cardiomyopathy. Circulation  2005; 112: 2821– 2825. Google Scholar CrossRef Search ADS PubMed  24 Gulati A, Jabbour A, Ismail TF, Guha K, Khwaja J, Raza S, Morarji K, Brown TD, Ismail NA, Dweck MR, Di Pietro E, Roughton M, Wage R, Daryani Y, O'Hanlon R, Sheppard MN, Alpendurada F, Lyon AR, Cook SA, Cowie MR, Assomull RG, Pennell DJ, Prasad SK. Association of fibrosis with mortality and sudden cardiac death in patients with nonischemic dilated cardiomyopathy. JAMA  2013; 309: 896– 908. Google Scholar CrossRef Search ADS PubMed  25 Fieno DS, Kim RJ, Chen EL, Lomasney JW, Klocke FJ, Judd RM. Contrast-enhanced magnetic resonance imaging of myocardium at risk: distinction between reversible and irreversible injury throughout infarct healing. J Am Coll Cardiol  2000; 36: 1985– 1991. Google Scholar CrossRef Search ADS PubMed  26 Wijnmaalen AP, van der Geest RJ, van Huls van Taxis CF, Siebelink HM, Kroft LJ, Bax JJ, Reiber JH, Schalij MJ, Zeppenfeld K. Head-to-head comparison of contrast-enhanced magnetic resonance imaging and electroanatomical voltage mapping to assess post-infarct scar characteristics in patients with ventricular tachycardias: real-time image integration and reversed registration. Eur Heart J  2011; 32: 104– 114. Google Scholar CrossRef Search ADS PubMed  27 Schmidt A, Azevedo CF, Cheng A, Gupta SN, Bluemke DA, Foo TK, Gerstenblith G, Weiss RG, Marban E, Tomaselli GF, Lima JA, Wu KC. Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation  2007; 115: 2006– 2014. Google Scholar CrossRef Search ADS PubMed  28 Yan AT, Shayne AJ, Brown KA, Gupta SN, Chan CW, Luu TM, Di CMF, Reynolds HG, Stevenson WG, Kwong RY. Characterization of the peri-infarct zone by contrast-enhanced cardiac magnetic resonance imaging is a powerful predictor of post-myocardial infarction mortality. Circulation  2006; 114: 32– 39. Google Scholar CrossRef Search ADS PubMed  29 Wrobleski D, Houghtaling C, Josephson ME, Ruskin JN, Reddy VY. Use of electrogram characteristics during sinus rhythm to delineate the endocardial scar in a porcine model of healed myocardial infarction. J Cardiovasc Electrophysiol  2003; 14: 524– 529. Google Scholar CrossRef Search ADS PubMed  30 Sasaki T, Miller CF, Hansford R, Zipunnikov V, Zviman MM, Marine JE, Spragg D, Cheng A, Tandri H, Sinha S, Kolandaivelu A, Zimmerman SL, Bluemke DA, Tomaselli GF, Berger RD, Halperin HR, Calkins H, Nazarian S. Impact of nonischemic scar features on local ventricular electrograms and scar-related ventricular tachycardia circuits in patients with nonischemic cardiomyopathy. Circ Arrhythm Electrophysiol  2013; 6: 1139– 1147. Google Scholar CrossRef Search ADS PubMed  31 Durrer D, Van Der Twell LH. Spread of activation in the left ventricular wall of the dog. I. Am Heart J  1953; 46: 683– 691. Google Scholar CrossRef Search ADS PubMed  32 Maron BJ, Henry WL, Roberts WC, Epstein SE. Comparison of echocardiographic and necropsy measurements of ventricular wall thicknesses in patients with and without disproportionate septal thickening. Circulation  1977; 55: 341– 346. Google Scholar CrossRef Search ADS PubMed  Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2018. For permissions, please email: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

European Heart JournalOxford University Press

Published: Mar 29, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off