Background: Serum lactate level can predict clinical outcomes in some critical cases. In the clinical setting, we noted that patients undergoing extracorporeal cardiopulmonary resuscitation (ECPR) and with poor serum lactate improvement often do not recover from cardiopulmonary arrest. Therefore, we investigated the association between lactate clearance and in-hospital mortality in cardiac arrest patients undergoing ECPR. Methods: Serum lactate levels were measured on admission and every hour after starting ECPR. Lactate clearance [(lactate at first measurement − lactate 6 h after)/lactate at first measurement × 100] was calculated 6 h after first serum lactate measurement. All patients who underwent ECPR were registered retrospectively using opt-out in our outpatient’s segment. Result: In this retrospective study, 64 cases were evaluated, and they were classified into two groups according to lactate clearance: high-clearance group, > 65%; low-clearance group, ≤ 65%. Surviving discharge rate of high-clearance group (12 cases, 63%) is significantly higher than that of low-clearance group (11 cases, 24%) (p <0.01). Considering other confounders, lactate clearance was an independent predictor for in-hospital mortality (odds ratio, 7.10; 95% confidence interval, 1.71–29.5; p < 0.01). Both net reclassification improvement (0.64, p < 0.01) and integrated reclassification improvement (0.12, p < 0.01) show that adding lactate clearance on established risk factors improved the predictability of in-hospital mortality. Conclusion: In our study, lactate clearance calculated through arterial blood gas analysis 6 h after ECPR was one of the most important predictors of in-hospital mortality in patients treated with ECPR after cardiac arrest. Keywords: Lactate clearance, Extracorporeal cardiopulmonary resuscitation, Cardiac arrest, In-hospital mortality * Correspondence: email@example.com; firstname.lastname@example.org Cardiovascular Center, Ichinomiya Municipal Hospital, Ichinomiya, Japan Department of Cardiology, Ichinomiya Municipal Hospital, 2-2-22 Bunkyo, Ichinomiya City, Aichi 491-8558, Japan Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Mizutani et al. Journal of Intensive Care (2018) 6:33 Page 2 of 7 Background hypothesized that LC might be an indicator of ECPR Extracorporeal cardiopulmonary resuscitation (ECPR) effectiveness. is one of the most powerful therapies after cardiopul- We investigated the association between LC and sur- monary arrest (CPA) . A prior study showed that viving discharge in cardiac arrest patients treated with earlier return of circulation leads to improvement of ECPR. 30-day survival, surviving discharge rate, and clinical performance category (CPC) . Because ECPR re- starts systemic circulation forcibly, it is a very strong Methods strategy when cardiopulmonary resuscitation or defib- In this single-center retrospective observational study, rillation is not effective. However, the guideline of the we collected data on 98 patients treated with percutan- American Heart Association (AHA) cited insufficient eous cardiopulmonary support (PCPS) at our hospital evidence and limited the indication for ECPR . between 2011 and 2016. According to AHA guidelines, ECPR may be con- Among the 98 patients treated with PCPS at our sidered for selected cardiopulmonary arrest patients hospital, 24 received PCPS before cardiac arrest; these in whom the suspected etiology of cardiac arrest is cases were not defined as cases of ECPR and were potentially reversible during a limited period of mech- excluded. Further, we also excluded patients with an anical cardiorespiratory support . Because the CPC etiology of aortic dissection. Finally, 4 patients who or surviving discharge rate is very low after CPA or died before 6 h after ECPR and one patient whose ECPR, the adequacy of continuation of ECPR should serum lactate lose at 6 h were also excluded. Thus, in be considered . Nevertheless, there have been very total, we evaluated 64 patients undergoing ECPR after limited data of ECPR prognosis or risk factors . cardiac arrest (Fig. 1). Their data, including their Risk factors such as initial rhythm (shockable rhythm serum lactate levels and clinical courses, including or non-shockable rhythm), old age, CPA without by- that after ECPR, were retrospectively collected. stander, without bystander CPR, longer CPR duration The exclusion criteria for ECPR were as follows: pa- time, and without defibrillation are well-known inde- tients with estimated age > 75 years, those with no pendent prognostic factors. long-term prognosis, and those with dementia. Our in/ A prior study showed that early goal-directed exclusion flow chart is shown in Fig. 2. In addition, hemodynamic optimization therapy is effective in car- ECPR was not performed even if the cardiovascular diac arrest [5, 6]. Meanwhile, earlier improvement in lac- physician decided it would not be effective for the tate clearance (LC) was reported to lead to better patient. prognosis in the treatment of sepsis [7, 8]. In addition, After commencement of ECPR, we usually check serum lactate-guided intensive care reduces hospital the patients’ consciousness levels before leaving our mortality in the treatment of sepsis . Post-cardiac ar- catheter laboratory. In cases in which the Glasgow rest syndrome (PCAS) is reported to be one of the Coma Scale motor score was below 6, targeted sepsis-like syndromes . Of course, lactate reduction temperature management (TTM) was initiated. Our is one of most important predictors of survival and TTM protocol is as follows: 34 °C for 48 h and a re- neurological outcome after cardiac arrest . Thus, we covery temperature of 36 °C during the next 24 h. Fig. 1 Decision tree of ECPR or conventional CPR with inclusion and exclusion criteria for ECPR Mizutani et al. Journal of Intensive Care (2018) 6:33 Page 3 of 7 Fig. 2 Design of this observation study The primary outcome was survival discharge, and the analysts, we concluded that LC = 65% is a better cut- secondary outcomes were 30-day mortality and neuro- off value for ECPR. logical outcomes. Statistical analysis Serum lactate measurement and LC calculation Statistical analysis was performed using JMP version Arterial blood gas samples were immediately obtained 13.0 software (SAS Institute, Cary, NC, USA). Con- from the arteries of all CPA patients in the emer- tinuous variables were presented as median values gency department of our hospital. The artery blood with interquartile ranges according to the results of samples were immediately transferred to our labora- the normality test. Mann-Whitney U tests were con- tory and measured using RapidLab (Siemens AG, Er- ducted for comparison of continuous variables. Cat- furt, Germany). In case of ECPR, arterial blood gas egorical variables were presented as frequencies and analysis was performed every hour until the end of percentages and compared using chi-squared or ECPR. Serum lactate was measured simultaneously Fisher exact tests. Intergroup differences in the con- with artery blood gas analysis. Blood sample was tinuous and categorical variables were evaluated using transferred to the laboratory and measured as soon as the Student t test and chi-squared test, respectively. possible every time. Intergroup differences in mortality were evaluated We calculated LC using the serum lactate level at the using a logistic regression model. The significant vari- emergency department and at 6 h after admission using ables from univariate analysis and the established risk the following formula: ðÞ lactate at first measurement−lactate 6 hours after LC ¼ 100: lactate at first measurement Patients with LC > 65% were included in the high-clearance group, and those with LC ≤ 65% were in- cluded in the low-clearance group. We determined the cutoff value retrospectively. Re- ceiver operating characteristic curve (ROC) analysis yielded a cutoff value of LC = 69% (sensitivity 0.48, 1 − specificity 0.43, AUC 0.75) (Fig. 3). In addition, we observed that the serum lactate level improved on early lactate-guided therapy . The reported im- provements in the lactate levels were 4.7–1.7 (64%) in the control group and 4.6–1.6 (65%) in the targeted Fig. 3 ROC comparison between established risk factors and + LC group. Through consensus with ECPR specialists and Mizutani et al. Journal of Intensive Care (2018) 6:33 Page 4 of 7 factors were included in multivariate logistic regres- predictor when added to a baseline model with sion analysis. We included the established risk factors established risk factors. NRI indicates the relative (age,sex,initial rhythm, in-/out-hospital CPA, pH, number of patients with improved predicted probabil- and CRP duration) as confounders during multivariate ities for LC, whereas IDI represents the average logistic regression analysis. In addition, we calculated improvement in predicted probabilities for LC after the area under the ROC (AUC), net reclassification adding LC variables into the baseline model. In all improvement (NRI), and integrated discrimination analyses, p < 0.05 was considered statistically improvement (IDI) to assess the accuracy of LC as a significant. Table 1 Patients’ background All cases Low-clearance group High-clearance group p value n =64 n =45 n =19 Age 70.8 (58.5–77.9) 71.7 (63.8–77.3) 64.1 (50.6–78.5) 0.30 Female (%) 17 (27) 14 (31) 3 (18) 0.35 Height cm 165 (157–172) 164 (156–170) 164 (156–170) 0.08 Weight kg 63 (54–72) 61 (53–72) 65 (55–75) 0.45 BMI 23 (21–25) 23 (21–25) 23 (21–26) 0.88 Diabetes (%) 23 (36) 17 (38) 6 (32) 0.78 Hypertension (%) 34 (53) 25 (56) 9 (47) 0.59 Dyslipidemia (%) 20 (31) 12 (27) 8 (42) 0.25 Current smoke (%) 19 (30) 10 (22) 9 (47) 0.07 Hemodialysis (%) 4 (6.3) 2 (4.4) 2 (11) 0.58 Prior PCI (%) 13 (20) 10 (22) 3 (16) 0.56 Prior CABG (%) 6 (9.4) 4 (8.9) 2 (11) 1.00 OMI (%) 15 (23) 10 (22) 5 (26) 0.75 Initial rhythm 0.48 VF/pulseless VT (%) 38 (59) 25 (56) 13 (68) PEA/asystole (%) 26 (41) 20 (44) 6 (32) Location 0.42 In-hospital (%) 45 (55) 23 (51) 12 (63) Out-hospital (%) 29 (45) 22 (49) 7 (37) CPR duration (min) 24 (12–45) 24 (16–46) 25 (10–46) 0.67 Laboratory data pH 7.03 (6.92–7.15) 7.03 (6.89–7.14) 7.09 (6.92–7.20) 0.54 Lactate mmol/L 11.8 (9.9–14.8) 11.7 (9.7–14.9) 12.8 (10.1–14.2) 0.74 Total protein g/dL 5.2 (4.3–6.1) 4.9 (4.0–5.6) 5.7 (5.1–6.5) 0.02 Albumin g/dL 2.9 (2.1–3.5) 2.5 (2.0–3.2) 3.3 (2.6–3.8) 0.01 BUN mg/dL 19 (16–28) 20 (17–27) 19 (14–31) 0.59 Creatinine mg/dL 1.1 (1.0–1.5) 1.1 (0.9–1.4) 1.1 (1.0–1.5) 0.74 Total cholesterol mg/dL 129 (80–164) 98 (70–164) 142 (97–178) 0.15 Low-density lipoprotein mg/dL 70 (55–109) 62 (54–105) 95 (70–127) 0.11 High-density lipoprotein mg/dL 28 (20–35) 25 (18–33) 28 (24–40) 0.16 Triglyceride mg/dL 71 (42–96) 58 (32–103) 84 (71–94) 0.26 Hemoglobin g/dL 11.6 (9.5–13.8) 11.0 (8.3–13.9) 12.2 (11.2–13.6) 0.14 White blood cell count 10 /μL 124 (97–168) 121 (96–16) 137 (106–194 0.21 Platelet 10 /μL 16.7 (11.9–21.6) 14.8 (9.8–20.9) 21.3 (14.3–25.3) 0.02 C-reaction protein mg/dL 0.19 (0.06–3.13) 0.19 (0.07–2.56) 0.16 (0.05–4.83) 0.91 Mizutani et al. Journal of Intensive Care (2018) 6:33 Page 5 of 7 Table 2 Final diagnosis Diagnosis All cases Low-clearance group High-clearance group p value n =69 n =45 n =19 0.48 Cardiac rapture (%) 11 (15.9) 6 (13) 1 (5.3) Electrical storm (%) 6 (8.7) 3 (6.7) 3 (16) Heart failure (%) 4 (5.8) 5 (11) 2 (11) Ischemic heart disease (%) 32 (46.4) 21 (47) 10 (53) PE (%) 3 (4.4) 2 (4.4) 1 (5.3) Myocarditis (%) 2 (2.9) 1 (2.2) 1 (5.3) Other 11 (15.9) 7 (16) 1 (5.3) Results high-clearance group than in the low-clearance group Patient characteristics are shown in Table 1. There were (Table 3). significant differences in total protein (low-clearance In the univariable and multivariable logistic regres- group:median,4.9 g/dL;range,4.0–5.6 g/dL; sion analyses for surviving discharge, LC was an inde- high-clearance group: median, 5.7 g/dL; range, pendent predictor for surviving discharge (odds ratio, 5.1–6.4 g/dL; p = 0.02), serum albumin (low-clearance 7.10; 95% confidence interval, 1.71–29.5; p <0.01). group:median,2.5 g/dL;range,2.0–3.2 g/dL; CPR duration time, location (in-/out-hospital CPR), high-clearance group: median, 3.3 g/dL; range, and pH were also successful independent predictors 2.6–3.8 g/dL, p = 0.01), and platelet (low-clearance for surviving discharge (Table 4). The NRI and IDI group: median, 167,000; range, 148,000–209,000; are shown in Table 5.Adding LCtothe established high-clearance group: median, 213,000; range, 143,000– risk factors improved predictability of surviving dis- 253,000 g/dL; p = 0.02). Except for total protein, serum charge after ECPR. albumin, and platelet, the baseline characteristics in both groups were well-matched. Discussion The results of the study showed that LC was one of the Diagnosis and follow-up data independent predictors of ECPR for in-hospital mortal- The final diagnosis is shown in Table 2. In the first ity. Addition of LC to the established risk factors such as 30 days, the survival rate was significantly higher in the age, initial rhythm, in-/out-hospital CPA, pH, and CRP high-clearance group (12 cases, 63%) than in the duration time improved NRI and IDI. Because LC is easy low-clearance group (12 cases, 27%; p ≤ 0.01). The sur- to calculate, is reliable, and has small fluctuations in the vival discharge rate was significantly higher in the clinical settings, our findings might be of clinical high-clearance group (12 cases, 63%) than in the significance. low-clearance group (11 cases, 24%; p < 0.01) (Table 3). In the case of cardiac arrest, ECPR is one of the Neurological outcome at discharge was better in the most powerful intensive and effective treatments, as Table 3 Primary outcome and secondary outcome All cases Low-clearance group High-clearance group p value n =64 n =45 n =19 Surviving discharge 23 (36) 11 (24) 12 (63) < 0.01 30-day survival (%) 24 (38) 12 (27) 12 (63) < 0.01 Neurological outcome (CPC) < 0.01 1 (%) 16 (25) 8 (18) 11 (58) 2 (%) 3 (4.7) 2 (4.4) 1 (5.3) 3 (%) 1 (1.6) 1 (2.2) 0 (0.0) 4 (%) 1 (1.6) 1 (2.2) 0 (0.0) 5 (%) 43 (67) 34 (76) 7 (37) Mizutani et al. Journal of Intensive Care (2018) 6:33 Page 6 of 7 Table 4 Multi-logistic analysis for surviving discharge Univariable Multivariable Odds ratio Confidential interval p value Odds ratio Confidential interval p value Age 0.98 0.95–1.03 0.63 1.02 0.96–1.10 0.49 Initial rhythm (VF and pulseless VT/asystole and PEA) 1.47 0.51–4.22 0.46 1.42 0.32–6.27 0.64 Location(out/in) 1.17 0.42–3.26 0.64 10.0 1.35–75.0 0.01 CPR duration 1.02 0.99–1.05 0.22 1.08 1.01–1.12 0.02 pH 25.2 1.24–509 0.03 247 4.16–15,000 < 0.01 Lactate clearance (high/low) 5.3 1.67–16.8 0.01 7.10 1.71–29.5 < 0.01 shown by previous studies [11, 12]. However, AHA study showed that LC is a more important indicator guidelines limited its indication because of poor evi- than SvO in sepsis . We believe that early dence . In the study, we recognized that ECPR was goal-directed therapy based on lactate will be an im- a very useful and effective treatment for patients who portant strategy in ECPR. Our results showed that a after cardiac arrest. lower serum lactate level at 6 h than the primary Because ECPR and intensive care require higher lactate level could be used as one of the prognostic cost, need more time, and are more labor-intensive, indicators. Thus, we should consider that the progno- the cost/benefit should be considered . Further- sis after ECPR can be improved by lowering the lac- more, ceasing ECPR may sometimes be recommended tate level using catecholamines, intra-aortic balloon because of the patient’s poor prognosis. Hence, a pumping, optimization of percutaneous cardiopulmo- prognostic predictor can help decide whether to con- nary support, infusions, transfusions, and so forth, tinue ECPR. Although well-known predictors which that is, if serum lactate-guided early goal-directed have been shown in the previous reports are factors therapy will improve the prognosis of CPA and/or already determined before return of spontaneous cir- ECPR cost/benefit. culation (ROSC) [14, 15], LC is auniquepredictor PCAS includes brain/myocardial disorders and sys- because it can be calculated 6 h after starting ECPR, temic reperfusion injury. ECPR consists of a therapy not before ROSC. This time lag gives us a chance to for PCAS and treatment of the original disease reconsider the continuation of ECPR. caused the CPA. Therefore, with better LC, treatment In the clinical setting, patients with a lower lactate of the original disease, care for the myocardial dis- level might have a better outcome in both PCAS and order after CPA, improvement of systemic circulation, sepsis. We hypothesize that the high serum lactate level and coping with systemic reperfusion injury like sep- with cytokinetic storm in PCAS is due to reperfusion sis may be successful. Meanwhile, in the case of injury, whereas that in sepsis is due to the cytokinetic worse LC, failure in one or more of the abovemen- storm caused by the systemic infection. In view of the tioned items may occur. cytokinetic phenomenon, sepsis is similar to PCAS, and this hypothesis is shown in a prior study . Mean- Limitations while, a previous study showed that serum lactate level Some limitations should be considered. First, the is a better indicator of early goal-directed therapy than small number of enrolled patients was not enough to SvO . Considering the similarity between sepsis determine a new evidence. Second, there were strong andPCAS, atherapy strategy basedonthe serumlac- biases due to our ECPR exclusion criteria, e.g., age > tate level will be more effective, even in PCAS. 75 years, end-stage cancer, and strong frailty. Third, A previous study showed that early goal-directed medical treatments might have also affected the re- therapy is an effective strategy in sepsis . Another sults; however, we could not evaluate such data, e.g., Table 5 Net reclassification improvement and integrated reclassification improvement AUC p value NRI p value IDI p value Established risk factors 0.76 reference reference + Lactate clearance 0.82 0.23 0.64 < 0.01 0.121 < 0.01 Established risk factors were consisted age, sex, initial rhythm, in-/out-hospital CPA, pH, and CRP duration Mizutani et al. Journal of Intensive Care (2018) 6:33 Page 7 of 7 different treatment methods for each doctor. Further Received: 28 February 2018 Accepted: 24 May 2018 studies are required to address these limitations. 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Teijin Pharma Ltd. 17. Rvers E, Nguyen B, Havstad S, et al. Early goal-directed therapy in the The other authors declare that they have no competing interests. treatment of severe sepsis and septic shock. N Engl J Med. 2001;345:1368–77. Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author details Cardiovascular Center, Ichinomiya Municipal Hospital, Ichinomiya, Japan. Department of Emergency, Ichinomiya Municipal Hospital, Ichinomiya, Japan. Department of Cardiology, Nagoya University Graduate School of Medicine, Nagoya, Japan. Department of Medical Engineering, Ichinomiya Municipal Hospital, Ichinomiya, Japan. Department of Cardiology, Ichinomiya Municipal Hospital, 2-2-22 Bunkyo, Ichinomiya City, Aichi 491-8558, Japan.
Journal of Intensive Care – Springer Journals
Published: Jun 1, 2018
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