TY - JOUR
AU1 - Sato, Koji
AU2 - Kubota, Koji
AU3 - Oda, Hiroyuki
AU4 - Taniguchi, Takumi
AB - Abstract Background Because of progress in cardiovascular management, many critically ill geriatric patients undergo various procedures and intensive cardiovascular care treatments. Although delirium frequently affects geriatric patients post-procedurally and after intensive cardiovascular care, the impact of delirium on acute cardiac patients has not been well understood. The objective of this study was to investigate the impact of delirium on outcomes in acute, non-intubated cardiac patients. Methods This was a prospective cohort study including non-surgical cardiac patients aged 65 years or older admitted to the intensive care unit or intensive cardiac care unit. We excluded mechanically ventilated patients. Delirium was evaluated using the confusion assessment method for the intensive care unit. The primary outcome analysed was 60-day mortality. The secondary outcomes analysed were risk and precipitating factors for delirium development. Results Of 163 patients, 35 (21.5%) developed delirium. Patients with delirium had higher 60-day mortality rates than those without delirium (22.9% versus 3.9%, P<0.001) and spent an average of 10 days longer in the hospital (32±20 versus 22±16 days, P=0.002). On the multivariable Cox analysis, delirium was independently associated with 60-day mortality (adjusted hazard ratio 3.91; 95% confidence interval 1.06−17.36; P=0.04), which was also confirmed by the propensity score-matched analysis. Dementia, history of cerebrovascular disease, and higher sequential organ failure assessment score were significantly associated with delirium development. Conclusions Acute delirium is common and predicts mortality in non-intubated cardiac patients. Cardiac critical care providers should be aware of this neurological condition. Aging, cardiovascular disease, delirium, mortality, cerebrovascular disorder Introduction A longer life expectancy and effective modern cardiovascular care techniques are changing the epidemiology of cardiovascular diseases in older patients, who are likely to have comorbidities and age-related health issues. Current investigations underscore the importance of considering non-cardiac factors, such as general health, comorbidity, cognitive status and frailty, for improving cardiovascular disease care in elderly cardiac patients.1,2 Delirium, an acute brain dysfunction, is a unique psychiatric condition common among critically ill elderly patients. Delirium in mechanically ventilated patients and cardiac surgical patients has been well evaluated and shown to be associated with long hospital stays, poor cognitive outcomes and high mortality rates.3–5 Therefore, delirium has gained increasing attention as an identifiable, perhaps predictable, and potentially modifiable critical care condition. Recently, patient characteristics have changed considerably in the intensive cardiac care unit (ICCU), including illness severity on admission and proportion of diseases.6 Moreover, the rate of mechanically ventilated patients in the ICCU is lower than that in the general or surgical intensive care unit (ICU). The relationship between delirium and outcomes has not been well evaluated in acute cardiac patients. We thus aimed to evaluate the impact of delirium on outcomes and to identify risk factors in acute cardiac patients. Methods Study population This study was conducted at the Public Central Hospital of Matto Ishikawa and was approved by the local institutional review board (reference number 24-16). The study centre is a 305-bed Japanese regional hospital that performs 400 percutaneous coronary intervention (PCI) procedures annually and is capable of primary PCI and acute cardiovascular care 24 hours per day. This study included patients who were 65 years of age or older and were admitted to the ICU or ICCU with non-surgical cardiovascular disease between December 2012 and December 2013. The exclusion criteria were as follows: patients staying in the ICU for less than 48 hours; patients with coexisting infections or advanced cancer; mechanically ventilated patients; and patients who were unable to communicate. Study design This was a single-centre prospective cohort study. We enrolled patients each morning and recorded their baseline demographic information. Data regarding the diagnosis for ICU or ICCU admission, use of cardiovascular disease medications, habitual use of prescription sedatives, cardiovascular risk factors, previous cerebrovascular disease, and left ventricular ejection fraction (LVEF) and blood pressure values on admission were collected. We divided admission diagnoses into four categories: acute decompensated heart failure (ADHF); acute coronary syndrome (ACS); arrhythmia; and others. Although a diagnosis of ADHF was based on standard guidelines,7 ADHF related to ACS was classified into the ACS group. The most abnormal values obtained during the first 24 hours of the ICU or ICCU stay were used to measure sequential organ failure assessment (SOFA) scores.8 The SOFA score is a simple and objective score that allows for calculation of both the number of dysfunctional organs and the severity of organ dysfunction in six organ systems (respiratory, coagulatory, liver, cardiovascular, renal and neurological), and the score can measure individual or aggregate organ dysfunction. The Charlson comorbidity index was calculated, which represents the number and severity of pre-existing comorbid conditions.9 We also recorded the estimated glomerular filtration rate (eGFR) and haematocrit, blood urea nitrogen (BUN), serum sodium and C-reactive protein levels (CRP) for the first 24 hours. Well-trained nurses assessed the patients’ neurological status at least three times per day and diagnosed delirium using the confusion assessment method for the ICU (CAM-ICU).10 The CAM-ICU is one of the most valid and reliable delirium monitoring tools for use in adult ICU patients.11 Patients were divided into the delirium group if they experienced delirium while in the ICU or ICCU and the non-delirium group if they did not. The primary outcome of the study was 60-day all-cause mortality. The secondary outcomes were the risk and precipitating factors for delirium development. Statistical analysis The baseline characteristics of patients with and without delirium were analysed using an independent sample t-test and Wilcoxon rank-sum test for continuous variables, as appropriate. Categorical variables were analysed using the χ2 test. The 60-day mortality rates were examined using a time-to-event analysis. All survivors were followed using the hospital’s electronic record system or telephone calls. Associations between delirium and 60-day mortality were analysed using a Cox proportional hazards regression analysis. Because of its clinical relevance and potential importance, the final model included age, sex, diagnosis of ADHF, ACS, dementia, Charlson comorbidity index, systolic blood pressure, SOFA score, LVEF, eGFR and delirium. After the data were initially analysed, we performed a propensity score-matching analysis. The propensity for delirium development was determined using a multivariate logistic regression analysis. Covariates in the model included age, sex, BMI, diagnosis of ADHF, ACS, hypertension, dyslipidaemia, diabetes mellitus, dementia, cerebrovascular disease, Charlson comorbidity index, systolic blood pressure, SOFA score, LVEF, eGFR, BUN, serum sodium, haematocrit and CRP. This model yielded a C statistic of 0.843, indicating a strong ability to differentiate between delirium and non-delirium. A propensity score for delirium was then calculated. For the propensity score, one to two nearest neighbour case–control matches without replacement were used due to considerable sample size differences. A summary of baseline characteristics before matching (Table 1) and after matching (Table 2) was reported. The association between delirium and 60-day mortality was evaluated by a propensity score-stratified Cox analysis in the matched patients. Then, we used a multivariate logistic regression model to assess the risk factors for delirium development. Variables found to be significant the level of P<0.15 on univariate analysis were entered into multiple logistic regression analysis using a backward elimination stepwise algorithm. Because of their clinical relevance and potential importance, we retained age, hypertension, LVEF, BUN and habitual use of sedatives in this model, regardless of their statistical significance. Factors included in the final model were age, hypertension, dementia, cerebrovascular disease, SOFA score, LVEF, BUN and habitual use of sedatives. A value of P<0.05 was considered statistically significant. A power analysis determined an appropriate sample size of at least 130 patients. All statistical analyses were performed using JMP version 9 software (SAS Institute Inc., Cary, NC, USA) and R statistical software (version 3.2.2; R Foundation for Statistical Computing, Vienna, Austria). Baseline characteristics of all study patients Table 1. Baseline characteristics of all study patients Characteristics . Delirium (n=35) . Non-delirium (n=128) . P value . Age (years) 84 ± 7 79 ± 9 <0.001 Male sex (%) 19 (54) 64 (50) 0.65 BMI (kg/m2) 22.2 ± 4.2 22.5 ± 3.7 0.63 Diagnosis on admission  ADHF (%) 22 (63) 52 (41) 0.02  ACS (%) 6 (17) 59 (46) 0.001  Arrhythmia (%) 1 (3) 8 (6) 0.09  Others (%) 6 (17) 9 (7) 0.09 Hypertension (%) 28 (80) 84 (66) 0.09 Dyslipidaemia (%) 7 (20) 26 (34) 0.09 Diabetes mellitus (%) 13 (37) 47 (37) 0.96 Habitual use of sedatives (%) 9 (26) 35 (27) 0.85 Dementia (%) 12 (34) 12 (9) <0.001 Cerebrovascular disease (%) 12 (34) 13 (10) 0.001 Charlson comorbidity index 7.4 ± 2.3 6 ± 2.4 <0.001 Systolic blood pressure (mmHg) 136 ± 27 136 ± 27 0.97 Diastolic blood pressure (mmHg) 76 ± 15 73 ± 16 0.37 SOFA score 3.9 ± 2.3 2.5 ± 1.9 <0.001 LVEF (%) 51 ± 15 52 ± 15 0.58 eGFR (ml/min/1.73 m2) 43.5 ± 27 55.6 ± 26 0.004 BUN (mg/dl) 33.4 ± 21.5 24 ± 13 0.002 Serum sodium (mEq/l) 139 ± 4.2 138 ± 3.8 0.37 Haematocrit (%) 34 ± 7 37 ± 7 0.01 CRP (mg/dl) 3.8 ± 6.7 1.5 ± 3.2 0.004 Medications  Antiplatelets (%) 15 (43) 44 (34) 0.36  Beta-blockers (%) 11 (31) 30 (23) 0.34  ACEIs/ARBs (%) 10 (29) 48 (38) 0.32  Statins (%) 10 (29) 38 (30) 0.9  Diuretics (%) 14 (40) 46 (36) 0.66 Characteristics . Delirium (n=35) . Non-delirium (n=128) . P value . Age (years) 84 ± 7 79 ± 9 <0.001 Male sex (%) 19 (54) 64 (50) 0.65 BMI (kg/m2) 22.2 ± 4.2 22.5 ± 3.7 0.63 Diagnosis on admission  ADHF (%) 22 (63) 52 (41) 0.02  ACS (%) 6 (17) 59 (46) 0.001  Arrhythmia (%) 1 (3) 8 (6) 0.09  Others (%) 6 (17) 9 (7) 0.09 Hypertension (%) 28 (80) 84 (66) 0.09 Dyslipidaemia (%) 7 (20) 26 (34) 0.09 Diabetes mellitus (%) 13 (37) 47 (37) 0.96 Habitual use of sedatives (%) 9 (26) 35 (27) 0.85 Dementia (%) 12 (34) 12 (9) <0.001 Cerebrovascular disease (%) 12 (34) 13 (10) 0.001 Charlson comorbidity index 7.4 ± 2.3 6 ± 2.4 <0.001 Systolic blood pressure (mmHg) 136 ± 27 136 ± 27 0.97 Diastolic blood pressure (mmHg) 76 ± 15 73 ± 16 0.37 SOFA score 3.9 ± 2.3 2.5 ± 1.9 <0.001 LVEF (%) 51 ± 15 52 ± 15 0.58 eGFR (ml/min/1.73 m2) 43.5 ± 27 55.6 ± 26 0.004 BUN (mg/dl) 33.4 ± 21.5 24 ± 13 0.002 Serum sodium (mEq/l) 139 ± 4.2 138 ± 3.8 0.37 Haematocrit (%) 34 ± 7 37 ± 7 0.01 CRP (mg/dl) 3.8 ± 6.7 1.5 ± 3.2 0.004 Medications  Antiplatelets (%) 15 (43) 44 (34) 0.36  Beta-blockers (%) 11 (31) 30 (23) 0.34  ACEIs/ARBs (%) 10 (29) 48 (38) 0.32  Statins (%) 10 (29) 38 (30) 0.9  Diuretics (%) 14 (40) 46 (36) 0.66 Values are mean ± SD or n (%). ACEI: angiotensin-converting enzyme inhibitor; ACS: acute coronary syndrome; ADHF: acute decompensated heart failure; ARB: angiotensin II receptor blocker; BMI: body mass index; BUN: blood urea nitrogen; CRP: C-reactive protein; eGFR: estimated glomerular filtration rate; LVEF: left ventricular ejection fraction; SOFA: sequential organ failure assessment. Open in new tab Table 1. Baseline characteristics of all study patients Characteristics . Delirium (n=35) . Non-delirium (n=128) . P value . Age (years) 84 ± 7 79 ± 9 <0.001 Male sex (%) 19 (54) 64 (50) 0.65 BMI (kg/m2) 22.2 ± 4.2 22.5 ± 3.7 0.63 Diagnosis on admission  ADHF (%) 22 (63) 52 (41) 0.02  ACS (%) 6 (17) 59 (46) 0.001  Arrhythmia (%) 1 (3) 8 (6) 0.09  Others (%) 6 (17) 9 (7) 0.09 Hypertension (%) 28 (80) 84 (66) 0.09 Dyslipidaemia (%) 7 (20) 26 (34) 0.09 Diabetes mellitus (%) 13 (37) 47 (37) 0.96 Habitual use of sedatives (%) 9 (26) 35 (27) 0.85 Dementia (%) 12 (34) 12 (9) <0.001 Cerebrovascular disease (%) 12 (34) 13 (10) 0.001 Charlson comorbidity index 7.4 ± 2.3 6 ± 2.4 <0.001 Systolic blood pressure (mmHg) 136 ± 27 136 ± 27 0.97 Diastolic blood pressure (mmHg) 76 ± 15 73 ± 16 0.37 SOFA score 3.9 ± 2.3 2.5 ± 1.9 <0.001 LVEF (%) 51 ± 15 52 ± 15 0.58 eGFR (ml/min/1.73 m2) 43.5 ± 27 55.6 ± 26 0.004 BUN (mg/dl) 33.4 ± 21.5 24 ± 13 0.002 Serum sodium (mEq/l) 139 ± 4.2 138 ± 3.8 0.37 Haematocrit (%) 34 ± 7 37 ± 7 0.01 CRP (mg/dl) 3.8 ± 6.7 1.5 ± 3.2 0.004 Medications  Antiplatelets (%) 15 (43) 44 (34) 0.36  Beta-blockers (%) 11 (31) 30 (23) 0.34  ACEIs/ARBs (%) 10 (29) 48 (38) 0.32  Statins (%) 10 (29) 38 (30) 0.9  Diuretics (%) 14 (40) 46 (36) 0.66 Characteristics . Delirium (n=35) . Non-delirium (n=128) . P value . Age (years) 84 ± 7 79 ± 9 <0.001 Male sex (%) 19 (54) 64 (50) 0.65 BMI (kg/m2) 22.2 ± 4.2 22.5 ± 3.7 0.63 Diagnosis on admission  ADHF (%) 22 (63) 52 (41) 0.02  ACS (%) 6 (17) 59 (46) 0.001  Arrhythmia (%) 1 (3) 8 (6) 0.09  Others (%) 6 (17) 9 (7) 0.09 Hypertension (%) 28 (80) 84 (66) 0.09 Dyslipidaemia (%) 7 (20) 26 (34) 0.09 Diabetes mellitus (%) 13 (37) 47 (37) 0.96 Habitual use of sedatives (%) 9 (26) 35 (27) 0.85 Dementia (%) 12 (34) 12 (9) <0.001 Cerebrovascular disease (%) 12 (34) 13 (10) 0.001 Charlson comorbidity index 7.4 ± 2.3 6 ± 2.4 <0.001 Systolic blood pressure (mmHg) 136 ± 27 136 ± 27 0.97 Diastolic blood pressure (mmHg) 76 ± 15 73 ± 16 0.37 SOFA score 3.9 ± 2.3 2.5 ± 1.9 <0.001 LVEF (%) 51 ± 15 52 ± 15 0.58 eGFR (ml/min/1.73 m2) 43.5 ± 27 55.6 ± 26 0.004 BUN (mg/dl) 33.4 ± 21.5 24 ± 13 0.002 Serum sodium (mEq/l) 139 ± 4.2 138 ± 3.8 0.37 Haematocrit (%) 34 ± 7 37 ± 7 0.01 CRP (mg/dl) 3.8 ± 6.7 1.5 ± 3.2 0.004 Medications  Antiplatelets (%) 15 (43) 44 (34) 0.36  Beta-blockers (%) 11 (31) 30 (23) 0.34  ACEIs/ARBs (%) 10 (29) 48 (38) 0.32  Statins (%) 10 (29) 38 (30) 0.9  Diuretics (%) 14 (40) 46 (36) 0.66 Values are mean ± SD or n (%). ACEI: angiotensin-converting enzyme inhibitor; ACS: acute coronary syndrome; ADHF: acute decompensated heart failure; ARB: angiotensin II receptor blocker; BMI: body mass index; BUN: blood urea nitrogen; CRP: C-reactive protein; eGFR: estimated glomerular filtration rate; LVEF: left ventricular ejection fraction; SOFA: sequential organ failure assessment. Open in new tab Baseline characteristics of the propensity-matched patients Table 2. Baseline characteristics of the propensity-matched patients Characteristics . Delirium (n=35) . Non-delirium (n=70) . P value . Age (years) 84 ± 7 83 ± 8 0.574 Male sex (%) 19 (54) 34 (49) 0.295 BMI (kg/m2) 22.2 ± 4.2 22.1 ± 4.1 0.81 Diagnosis on admission  ADHF (%) 22 (63) 41 (59) 0.833   ACS (%) 6 (17) 15 (21) 0.797 Hypertension (%) 28 (80) 52 (74) 0.63 Dyslipidaemia (%) 7 (20) 15 (21) 1.00 Diabetes mellitus (%) 13 (37) 26 (37) 1.00 Habitual use of sedatives (%) 9 (26) 22 (31) 0.652 Dementia (%) 12 (34) 12 (17) 0.083 Cerebrovascular disease (%) 12 (34) 13 (19) 0.091 Charlson comorbidity index 7.4 ± 2.3 6.8 ± 2.3 0.115 Systolic blood pressure (mmHg) 136 ± 27 139 ± 29 0.559 Diastolic blood pressure (mmHg) 76 ± 15 73 ± 16 0.361 SOFA score 3.9 ± 2.3 3.4 ± 1.9 0.239 LVEF (%) 51 ± 15 51 ± 16 0.979 eGFR (ml/min/1.73 m2) 43.5 ± 27 50 ± 27 0.325 BUN (mg/d ) 33.4 ± 21.5 28 ± 15 0.152 Serum sodium (mEq/l) 139 ± 4.2 138 ± 4.1 0.833 Haematocrit (%) 34 ± 7 34 ± 7 0.492 CRP (mg/dl) 3.8 ± 6.7 2.0 ± 3.8 0.08 Medications  Antiplatelets (%) 15 (43) 28 (40) 0.835  Beta-blockers (%) 11 (31) 22 (31) 1.00  ACEIs/ARBs (%) 10 (29) 29 (41) 0.284  Statins (%) 10 (29) 18 (26) 0.817  Diuretics (%) 14 (40) 31 (44) 0.835 Characteristics . Delirium (n=35) . Non-delirium (n=70) . P value . Age (years) 84 ± 7 83 ± 8 0.574 Male sex (%) 19 (54) 34 (49) 0.295 BMI (kg/m2) 22.2 ± 4.2 22.1 ± 4.1 0.81 Diagnosis on admission  ADHF (%) 22 (63) 41 (59) 0.833   ACS (%) 6 (17) 15 (21) 0.797 Hypertension (%) 28 (80) 52 (74) 0.63 Dyslipidaemia (%) 7 (20) 15 (21) 1.00 Diabetes mellitus (%) 13 (37) 26 (37) 1.00 Habitual use of sedatives (%) 9 (26) 22 (31) 0.652 Dementia (%) 12 (34) 12 (17) 0.083 Cerebrovascular disease (%) 12 (34) 13 (19) 0.091 Charlson comorbidity index 7.4 ± 2.3 6.8 ± 2.3 0.115 Systolic blood pressure (mmHg) 136 ± 27 139 ± 29 0.559 Diastolic blood pressure (mmHg) 76 ± 15 73 ± 16 0.361 SOFA score 3.9 ± 2.3 3.4 ± 1.9 0.239 LVEF (%) 51 ± 15 51 ± 16 0.979 eGFR (ml/min/1.73 m2) 43.5 ± 27 50 ± 27 0.325 BUN (mg/d ) 33.4 ± 21.5 28 ± 15 0.152 Serum sodium (mEq/l) 139 ± 4.2 138 ± 4.1 0.833 Haematocrit (%) 34 ± 7 34 ± 7 0.492 CRP (mg/dl) 3.8 ± 6.7 2.0 ± 3.8 0.08 Medications  Antiplatelets (%) 15 (43) 28 (40) 0.835  Beta-blockers (%) 11 (31) 22 (31) 1.00  ACEIs/ARBs (%) 10 (29) 29 (41) 0.284  Statins (%) 10 (29) 18 (26) 0.817  Diuretics (%) 14 (40) 31 (44) 0.835 Values are mean ± SD or n (%). ACEI: angiotensin-converting enzyme inhibitor; ACS: acute coronary syndrome; ADHF: acute decompensated heart failure; ARB: angiotensin II receptor blocker; BMI: body mass index; BUN: blood urea nitrogen; CRP: C-reactive protein; eGFR: estimated glomerular filtration rate; LVEF: left ventricular ejection fraction; SOFA: sequential organ failure assessment. Open in new tab Table 2. Baseline characteristics of the propensity-matched patients Characteristics . Delirium (n=35) . Non-delirium (n=70) . P value . Age (years) 84 ± 7 83 ± 8 0.574 Male sex (%) 19 (54) 34 (49) 0.295 BMI (kg/m2) 22.2 ± 4.2 22.1 ± 4.1 0.81 Diagnosis on admission  ADHF (%) 22 (63) 41 (59) 0.833   ACS (%) 6 (17) 15 (21) 0.797 Hypertension (%) 28 (80) 52 (74) 0.63 Dyslipidaemia (%) 7 (20) 15 (21) 1.00 Diabetes mellitus (%) 13 (37) 26 (37) 1.00 Habitual use of sedatives (%) 9 (26) 22 (31) 0.652 Dementia (%) 12 (34) 12 (17) 0.083 Cerebrovascular disease (%) 12 (34) 13 (19) 0.091 Charlson comorbidity index 7.4 ± 2.3 6.8 ± 2.3 0.115 Systolic blood pressure (mmHg) 136 ± 27 139 ± 29 0.559 Diastolic blood pressure (mmHg) 76 ± 15 73 ± 16 0.361 SOFA score 3.9 ± 2.3 3.4 ± 1.9 0.239 LVEF (%) 51 ± 15 51 ± 16 0.979 eGFR (ml/min/1.73 m2) 43.5 ± 27 50 ± 27 0.325 BUN (mg/d ) 33.4 ± 21.5 28 ± 15 0.152 Serum sodium (mEq/l) 139 ± 4.2 138 ± 4.1 0.833 Haematocrit (%) 34 ± 7 34 ± 7 0.492 CRP (mg/dl) 3.8 ± 6.7 2.0 ± 3.8 0.08 Medications  Antiplatelets (%) 15 (43) 28 (40) 0.835  Beta-blockers (%) 11 (31) 22 (31) 1.00  ACEIs/ARBs (%) 10 (29) 29 (41) 0.284  Statins (%) 10 (29) 18 (26) 0.817  Diuretics (%) 14 (40) 31 (44) 0.835 Characteristics . Delirium (n=35) . Non-delirium (n=70) . P value . Age (years) 84 ± 7 83 ± 8 0.574 Male sex (%) 19 (54) 34 (49) 0.295 BMI (kg/m2) 22.2 ± 4.2 22.1 ± 4.1 0.81 Diagnosis on admission  ADHF (%) 22 (63) 41 (59) 0.833   ACS (%) 6 (17) 15 (21) 0.797 Hypertension (%) 28 (80) 52 (74) 0.63 Dyslipidaemia (%) 7 (20) 15 (21) 1.00 Diabetes mellitus (%) 13 (37) 26 (37) 1.00 Habitual use of sedatives (%) 9 (26) 22 (31) 0.652 Dementia (%) 12 (34) 12 (17) 0.083 Cerebrovascular disease (%) 12 (34) 13 (19) 0.091 Charlson comorbidity index 7.4 ± 2.3 6.8 ± 2.3 0.115 Systolic blood pressure (mmHg) 136 ± 27 139 ± 29 0.559 Diastolic blood pressure (mmHg) 76 ± 15 73 ± 16 0.361 SOFA score 3.9 ± 2.3 3.4 ± 1.9 0.239 LVEF (%) 51 ± 15 51 ± 16 0.979 eGFR (ml/min/1.73 m2) 43.5 ± 27 50 ± 27 0.325 BUN (mg/d ) 33.4 ± 21.5 28 ± 15 0.152 Serum sodium (mEq/l) 139 ± 4.2 138 ± 4.1 0.833 Haematocrit (%) 34 ± 7 34 ± 7 0.492 CRP (mg/dl) 3.8 ± 6.7 2.0 ± 3.8 0.08 Medications  Antiplatelets (%) 15 (43) 28 (40) 0.835  Beta-blockers (%) 11 (31) 22 (31) 1.00  ACEIs/ARBs (%) 10 (29) 29 (41) 0.284  Statins (%) 10 (29) 18 (26) 0.817  Diuretics (%) 14 (40) 31 (44) 0.835 Values are mean ± SD or n (%). ACEI: angiotensin-converting enzyme inhibitor; ACS: acute coronary syndrome; ADHF: acute decompensated heart failure; ARB: angiotensin II receptor blocker; BMI: body mass index; BUN: blood urea nitrogen; CRP: C-reactive protein; eGFR: estimated glomerular filtration rate; LVEF: left ventricular ejection fraction; SOFA: sequential organ failure assessment. Open in new tab Results During the study period, 487 cardiovascular patients were admitted to the ICU and ICCU, of which 163 were enrolled in this study (Figure 1). The reasons for admission were ADHF (45%), ACS (40%), arrhythmias (6%) and other causes (9%). A total of 35 patients (21.5%) experienced delirium during their ICU or ICCU stay. The baseline characteristics of the study population are presented in Table 1. ADHF patients developed delirium more frequently and ACS patients developed it less frequently than did patients with other diagnoses. Patients with delirium were significantly older and had a higher incidence of dementia and previous cerebrovascular disease. The Charlson comorbidity index scores of delirium patients were significantly higher than those of non-delirium patients. Haemodynamic parameters, including blood pressure and LVEF values, were not significantly different between groups. The SOFA score was significantly higher in the delirium group than in the non-delirium group. Regarding laboratory data, the delirium patients had significantly lower eGFR and haematocrit levels than did the non-delirium patients. The BUN and CRP levels in delirium patients were significantly higher than those in non-delirium patients. No differences in medications were observed between the two groups. Flow chart of patients in the study cohort. ICU: intensive care unit; ICCU: intensive cardiac care unit Figure 1. Open in new tabDownload slide The 60-day mortality rate was significantly higher in the delirium patients than in the non-delirium patients (22.9% versus 3.9%, P<0.001). Moreover, delirium patients had longer ICU or ICCU stays (9.1±5.2 versus 5.5±3.1 days, P<0.001) and overall hospital stays (32±20 versus 22±16 days, P=0.002) compared to non-delirium patients. The multivariate Cox proportional hazards regression model adjusted for potential confounders showed that delirium was independently associated with 60-day mortality (Table 3). Baseline characteristics comparing the propensity-matched delirium and non-delirium groups are shown in Table 2. As opposed to the entire population, the propensity-matched patients were well balanced, and delirium was significantly associated with 60-day mortality (22.9% versus 7.1%, P=0.024) (Figure 2 and Table 3). Cox proportional hazards regression analysis of delirium and 60-day mortality Table 3. Cox proportional hazards regression analysis of delirium and 60-day mortality Model . Hazard ratio (95% CI) . P value . 60-Day mortality in all patients  Unadjusted 6.69 (2.23−22.15) <0.001  Adjusted for selected variablesa 3.91 (1.06−17.36) 0.04 60-Day mortality in matched patients 4.00 (1.21−13.28) 0.024 Model . Hazard ratio (95% CI) . P value . 60-Day mortality in all patients  Unadjusted 6.69 (2.23−22.15) <0.001  Adjusted for selected variablesa 3.91 (1.06−17.36) 0.04 60-Day mortality in matched patients 4.00 (1.21−13.28) 0.024 CI: confidence interval. a Selected variables included age, sex, diagnosis of acute decompensated heart failure, acute coronary syndrome, dementia, Charlson comorbidity index, systolic blood pressure, sequential organ failure assessment score, left ventricular ejection fraction and estimated glomerular filtration rate. Open in new tab Table 3. Cox proportional hazards regression analysis of delirium and 60-day mortality Model . Hazard ratio (95% CI) . P value . 60-Day mortality in all patients  Unadjusted 6.69 (2.23−22.15) <0.001  Adjusted for selected variablesa 3.91 (1.06−17.36) 0.04 60-Day mortality in matched patients 4.00 (1.21−13.28) 0.024 Model . Hazard ratio (95% CI) . P value . 60-Day mortality in all patients  Unadjusted 6.69 (2.23−22.15) <0.001  Adjusted for selected variablesa 3.91 (1.06−17.36) 0.04 60-Day mortality in matched patients 4.00 (1.21−13.28) 0.024 CI: confidence interval. a Selected variables included age, sex, diagnosis of acute decompensated heart failure, acute coronary syndrome, dementia, Charlson comorbidity index, systolic blood pressure, sequential organ failure assessment score, left ventricular ejection fraction and estimated glomerular filtration rate. Open in new tab Kaplan–Meier curves of 60-day survival by presence (dotted line) or absence (solid line) of delirium among propensity-matched patients Figure 2. Open in new tabDownload slide The independent risk factors for acute delirium assessed using multivariate logistic regression are listed in Table 4. Dementia, history of cerebrovascular disease and higher SOFA scores were significantly associated with delirium development. Independent risk factors for acute delirium Table 4. Independent risk factors for acute delirium Variable . Adjusted OR (95% CI) . P value . Dementia 4.13 (1.35−12.94) 0.013 Cerebrovascular disease 3.87 (1.35−11.17) 0.013 SOFA score, per 1 point 1.31 (1.04−1.68) 0.025 Variable . Adjusted OR (95% CI) . P value . Dementia 4.13 (1.35−12.94) 0.013 Cerebrovascular disease 3.87 (1.35−11.17) 0.013 SOFA score, per 1 point 1.31 (1.04−1.68) 0.025 OR: odds ratio; CI: confidence interval; SOFA: sequential organ failure assessment. Open in new tab Table 4. Independent risk factors for acute delirium Variable . Adjusted OR (95% CI) . P value . Dementia 4.13 (1.35−12.94) 0.013 Cerebrovascular disease 3.87 (1.35−11.17) 0.013 SOFA score, per 1 point 1.31 (1.04−1.68) 0.025 Variable . Adjusted OR (95% CI) . P value . Dementia 4.13 (1.35−12.94) 0.013 Cerebrovascular disease 3.87 (1.35−11.17) 0.013 SOFA score, per 1 point 1.31 (1.04−1.68) 0.025 OR: odds ratio; CI: confidence interval; SOFA: sequential organ failure assessment. Open in new tab Discussion We found that delirium was frequently observed in acute, non-intubated cardiac patients and was associated with increased mortality rates. Dementia, history of cerebrovascular disease and higher SOFA scores were independent risk factors for acute delirium development. The management of acute cardiac conditions can be very demanding for the patient, requiring strict haemodynamic control, adherence to medications, diet and weight control. Hyperactive delirium patients often demonstrate uncooperative, combative behaviour and can cause harm to themselves and the hospital staff, which can be a barrier to optimal invasive care, such as PCI. In contrast, hypoactive delirium patients can have issues associated with immobility, such as infections and pressure sores.12 Moreover, long-term cognitive impairment after acute delirium has been reported.13,14 This prolonged neurological disorder may have a tremendous impact on cardiac rehabilitation. Limitations during rehabilitation can lead to the development of sarcopenia and malnutrition. These conditions are related to ICU-acquired weakness.15 Delirium is associated with an increased risk of institutionalisation and dementia, in addition to the increased risk of death.16 Delirium and weakness interact with each other, potentiating a feedback loop and resulting in worse outcomes. Cognitive impairment because of heart failure has been described in numerous studies. Gure et al.17 found that mild cognitive impairment was common (24%) in older adults with heart failure. A retrospective study using the CAM-ICU reported that delirium was a strong predictor of adverse outcomes in patients with ADHF.18 The present study demonstrated that delirium was also an important condition in mixed cardiac patients. Although sicker patients are more likely to suffer from delirium, and delirium itself can indicate that the patient is worsening, delirium was an independent predictor of worse outcomes after adjusting for SOFA scores. Pre-existing dementia and a highly severe illness are well known risk factors of delirium in adult ICU patients.19 The present study demonstrated that a history of cerebrovascular disease was an independent risk factor for delirium. Previous cerebrovascular disease is an established predisposing factor for delirium in cardiac surgery patients.20 Studies using magnetic resonance imaging have shown a positive association between the duration of delirium and both cerebral atrophy and white matter disruption in ICU patients.21,22 The cholinergic, dopaminergic and noradrenergic systems, which play central roles in arousal, cognition and attention, are highly vulnerable to cerebrovascular pathology.23,24 Therefore, patients with a history of cerebrovascular disease should be considered more prone to stress and inflammation. Cardiac critical care providers should note such medical history in addition to dementia and the severity of illness. The present study also investigated systemic haemodynamic parameters and cardiac functions to evaluate their relationships with delirium. Fong et al.25 reported that cerebral perfusion abnormalities occur in delirium patients. Uthamalingam et al.18 reported that a LVEF less than 40% was one of the risk factors for delirium. However, the present study did not find a relationship between delirium and these parameters. McPherson et al.26 reported that haemodynamic status was not associated with delirium in either cardiac or cardiac surgical patients. These findings suggest that cardiac function may not be significantly associated with delirium. The pathophysiological mechanism of delirium remains unclear. It has been postulated that inflammation, sedative use and neurohormonal dysregulation may play significant roles in its pathogenesis.27 The present study excluded patients with coexisting infections and included non-intubated, non-surgical cardiovascular patients who did not routinely require sedative drugs and narcotics in order to eliminate the effects of inflammation and sedative use. In recent years in the ICU/ICCU, mechanical support, such as non-invasive positive pressure ventilation and intra-aortic balloon pump, can be performed easily. In addition to being in an unfamiliar noisy environment, stress from these mechanical support systems could deteriorate a patient’s sleep quality. With regard to sedation, drugs were rarely used for non-invasive positive pressure ventilation adaptation in our study. Dexmedetomidine, for example, has no effect on the respiratory drive and can be implemented safely in patients without intubation and may have an impact on outcomes compared with conventional therapies. Further investigations of these points are needed. There are several limitations in the present study. First, the sample size was relatively small because this was a single-centre, observational cohort study. Second, we did not assess the type (hyper, hypo and mixed) or length of delirium in patients, which may be related to prognosis. Third, we gathered information about dementia from patients or their families as part of the Charlson comorbidity index score without an in-depth neurocognitive assessment. Finally, we did not assess frailty, which might be a possible risk factor for delirium. Frailty is an emerging theme and an important factor in predicting mortality in patients with cardiovascular diseases.1 A number of tools are available to measure frailty. However, in acute critical care settings, there is no consensus on how best to measure frailty, and there are also concerns about the assessment of frailty: Several components of the frailty score, notably those that evaluate performance, are difficult to explore in ICU/ICCU patients; the possible interference between frailty scores and the acute illness requiring ICU/ICCU; and questionnaires to obtain a frailty score are often completed by the family or surrogate decision-makers and may be over or underscored. Le Maguet et al.28 investigated frailty in elderly ICU patients and reported that the prevalence of frailty varied widely depending on the frailty score. Although further investigation on these points is needed, we believe that this study extends knowledge on delirium and its risk factors in cardiac patients. Conclusions The present study suggests that acute delirium is common and predicts mortality in acute, non-intubated cardiac patients. Patients with delirium had longer ICU or ICCU stays and overall hospital stays than did patients without delirium. Dementia, a history of cerebrovascular disease and higher SOFA scores were significantly associated with delirium development. To improve outcomes and reduce medical costs, we recommend that cardiac critical care providers should be aware of this predictable neurological condition. Acknowledgement The authors would like to thank the physicians, the nursing staff and research assistants for invaluable support. 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TI - The impact of delirium on outcomes in acute, non-intubated cardiac patients
JF - European Heart Journal. Acute Cardiovascular Care
DO - 10.1177/2048872615624239
DA - 2017-09-01
UR - https://www.deepdyve.com/lp/oxford-university-press/the-impact-of-delirium-on-outcomes-in-acute-non-intubated-cardiac-5Z0Ie097fs
SP - 553
EP - 559
VL - 6
IS - 6
DP - DeepDyve
ER -