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HOME > Acute Crit Care > Volume 35(2); 2020 > Article
Original Article
Cardiology Predictors and outcomes of sepsis-induced cardiomyopathy in critically ill patients
Myung Jin Songorcid, Sang Hoon Leeorcid, Ah Young Leemorcid, Song Yee Kimorcid, Kyung Soo Chungorcid, Eun Young Kimorcid, Ji Ye Jungorcid, Young Ae Kangorcid, Young Sam Kimorcid, Joon Changorcid, Moo Suk Parkorcid
Acute and Critical Care 2020;35(2):67-76.
DOI: https://doi.org/10.4266/acc.2020.00024
Published online: May 15, 2020

Division of Pulmonology, Department of Internal Medicine, Institute of Chest Diseases, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea

Corresponding author Moo Suk Park Division of Pulmonology, Department of Internal Medicine, Institute of Chest Diseases, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea Tel: +82-2-2228-1955 Fax: +82-2-393-6884 E-mail: pms70@yuhs.ac
• Received: January 14, 2020   • Revised: March 17, 2020   • Accepted: April 7, 2020

Copyright © 2020 The Korean Society of Critical Care Medicine

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • Background
    Sepsis-induced cardiomyopathy (SIC) occurs frequently in critically ill patients, but the clinical features and prognostic impact of SIC on sepsis outcome remain controversial. Here, we investigated the predictors and outcomes of SIC.
  • Methods
    Patients admitted to a single medical intensive care unit from June 2016 to September 2017 were retrospectively reviewed. SIC was diagnosed by ejection fraction (EF) <50% and ≥10% decrease in baseline EF that recovered within 2 weeks.
  • Results
    In total, 342 patients with sepsis met the inclusion criteria, and 49 patients (14.3%) were diagnosed with SIC; the latter were compared with 259 patients whose EF was not deteriorated by sepsis (non-SIC). Low systolic blood pressure and increased left ventricular end-diastolic diameter (LVEDD) were identified as predictors of SIC. SIC and non-SIC patients did not differ significantly in terms of 28-day all-cause mortality (24.5% vs. 26.3%, P=0.936). Acute Physiology and Chronic Health Evaluation II (APACHE II; hazard ratio [HR], 1.10; 95% confidential interval [CI], 1.02 to 1.18; P=0.009) and delta neutrophil index (DNI; HR, 1.02; 95% CI, 1.00 to 1.08; P=0.026) were independent risk factors for 28-day mortality with SIC. DNI, APACHE II, and lactate were identified as risk factors for 28-day mortality in sepsis patients as a whole.
  • Conclusions
    SIC was not associated with increased mortality compared to non-SIC. Low systolic blood pressure and increased LVEDD were predictors of SIC. High APACHE II score and elevated DNI, which reflect sepsis severity, predict 28-day all-cause mortality.
  • Key Words: APACHE; delta neutrophil fraction; ejection fraction; mortality; prognosis; sepsis
Sepsis is a syndrome of physiologic, pathologic, and biochemical abnormalities induced by infection that can result in multiple organ dysfunction via inflammatory cytokines, mitochondria dysfunction, and tissue hypoxia [1]. Due to specific injury of cardiomyocytes, approximately 3.8%–65% of patients with sepsis develop sepsis-induced cardiomyopathy (SIC) [2-7]. SIC has been defined as myocardial dysfunction characterized by decreased ejection fraction (EF) and increased left ventricular end-diastolic diameter (LVEDD) that recovers within 7–10 days [2]. Current understanding of the pathogenesis of SIC is limited, and conflicting results remain regarding the prognostic impact of SIC on sepsis outcomes.
Improved understanding of SIC is important for multiple reasons. First, cardiac function is crucial for maintaining hemodynamic stability in patients with septic shock. Second, by understanding the clinical features and predictors of SIC, we can discriminate SIC from other cardiac diseases and avoid unnecessary invasive procedures, such as coronary angiography, a risky procedure in critically ill patients. Thus, we aimed to define clinical predictors of SIC and assess the clinical course and outcome of SIC in patients with sepsis.
Study Population
In this study, the medical records of patients who were admitted to the medical intensive care unit (ICU) of Yonsei University College of Medicine from June 2016 to September 2017 were reviewed. A total of 576 adult patients (>18 years old) admitted to the ICU during this period were screened for inclusion (Figure 1). Patients who did not (1) meet the sepsis definition, (2) have baseline transthoracic echocardiography (TTE) data, or (3) undergo TTE within 48 hours after ICU admission were excluded. This study was approved by the Institutional Review Board and Ethics Committee of Severance Hospital (IRB No. 4-2018-0751). The requirement for written informed consent from patients was waived. All methods were performed in accordance with the Declaration of Helsinki.
Definitions of Variables
The sepsis-3 definition was applied in patients with sepsis and septic shock [1]. Sepsis was defined by an increase in Sequential Organ Failure Assessment (SOFA) score of ≥2 points due to infection. Infection was defined as detection of microorganisms in culture or as radiologic or clinical manifestations suggesting infection despite negative culture results [8]. Septic shock was defined as sepsis with persistent hypotension requiring vasopressor drugs to maintain mean arterial pressure ≥65 mm Hg accompanied with serum lactate level >2 mmol/L despite adequate volume resuscitation. Left ventricular function deterioration was defined as EF <50% and ≥10% decrease in baseline EF. Among patients with left ventricular function deterioration, those whose EF recovered to the baseline level within 2 weeks were defined as having SIC [7,9,10].
Data Collection
Demographic data (age, sex, body weight, height, pre-existing comorbidities), vital signs, laboratory findings, echocardiographic findings, and clinical findings (presence of acute kidney injury, acute respiratory distress syndrome, and use of mechanical ventilation) were collected. Laboratory tests were performed within 24 hours of ICU admission, and SOFA and Acute Physiology and Chronic Health Evaluation (APACHE) II scores were evaluated at ICU admission. The delta neutrophil index (DNI) was assessed using an automated blood cell analyzer (ADVIA 120; Siemens, Forchheim, Germany) and calculated using the following formula: (neutrophil subfraction+ eosinophil subfraction measured in the myeloperoxidase channel)–(polymorphonuclear subfraction measured in nuclear lobularity channel) [11,12]. If there was no contraindication, TTE was routinely performed by a cardiologist at the time of ICU admission. There was no fixed schedule for follow up TTE, but in the majority of patients it was performed 1–2 weeks after ICU admission, especially when cardiac function deteriorated during ICU admission.
Statistical Analysis
The primary endpoint was 28-day all-cause mortality of SIC. The secondary endpoint was to identify predictors of SIC development and risk factors for 28-day all-cause mortality in patients with SIC. Factors contributing to the incidence of SIC were also analyzed. Continuous variables were analyzed using Student t-test or Mann-Whitney U-test; categorical variables were analyzed using the chi-squared distribution and Fisher’s exact test. A logistic regression model was used to identify variables contributing to the development of SIC. Cox proportional hazards regression analyses were conducted to assess the relationships between variables and 28-day allcause mortality. Area under the curve of receiver operating characteristic curves was used to identify the effect of major variables in multivariate Cox proportional hazards regression analysis (P<0.05) on 28-day all-cause mortality. Cumulative time-to-event distribution (survival) was estimated using Kaplan-Meier survival curves, and differences in survival between groups were assessed using the log-rank test. In all cases, P-values <0.05 were considered statistically significant. Statistical analyses were performed using R statistical software ver. 3.5.1 (R Foundation, Vienna, Austria).
Baseline Characteristics of Study Population
A total of 342 patients met the study eligibility criteria. Of these, 83 showed left ventricular function deterioration compared to baseline TTE. Among these, 49 patients had an EF that recovered to the baseline value and were categorized as SIC. Fifteen patients with persistently decreased EF in follow-up TTE and 19 patients without follow-up TTE were excluded (Figure 1). A total of 259 patients whose EF did not deteriorate with sepsis were categorized as the non-SIC group.
Baseline characteristics of 49 SIC and 259 non-SIC patients with sepsis are shown in Table 1. There was no significant difference between SIC and non-SIC patients in terms of age, sex, and body mass index. APACHE II and SOFA scores also did not significantly differ between SIC and non-SIC patients. Underlying heart failure was more frequent in SIC patients (8.2% vs. 1.5%, P=0.029), but the incidence of other comorbidities, including coronary artery disease, was not significantly different between the groups. On ICU admission, systolic blood pressure was significantly lower in patients with SIC (83.9±18.3 vs. 91.1±22.9, P=0.039). There was no significant difference in vasopressor and inotrope use between SIC and non-SIC patients. Primary site of infection was not significantly different between SIC and non-SIC patients. Both C-reactive protein and serum bilirubin levels were higher (192.4± 134.2 vs. 137.5±110.6, P=0.002; 1.2±1.4 vs. 2.1±4.1, P=0.008, respectively), whereas serum albumin levels were lower (2.4± 0.5 vs. 2.5±0.5, P=0.032) in SIC patients than in non-SIC patients.
Echocardiographic findings and cardiac biomarkers are shown in Table 2. LVEDD and left ventricular end-systolic diameter (LVESD) were significantly increased in SIC patients (49.6±6.0 vs. 43.4±6.3, P<0.001; 39.5±6.4 vs. 28.7±4.8, P<0.001, respectively). N-terminal pro b-type natriuretic peptide (NTproBNP; pg/ml) and troponin-T (pg/ml) were significantly higher in SIC patients (19,911.2±18,194.9 vs. 7,940.3±13,613.9, P<0.001; 278.9±492.7 vs. 104.8±178.4, P=0.019, respectively), but creatine kinase (CK) and creatine kinase-muscle/brain (CK-MB) levels were not significantly different between SIC and non-SIC patients.
Predictors of SIC
Univariate logistic regression analyses for predictors of SIC revealed that low systolic blood pressure, hypoalbuminemia, elevated NT-proBNP, troponin-T, and increased LVEDD were major covariates (P<0.05). Multivariate logistic regression analysis revealed that low systolic blood pressure upon ICU admission and increased LVEDD were independent risk factors for the development of SIC (Table 3).
Outcomes of SIC
There was no significant difference between SIC patients and non-SIC patients in 28-day all-cause mortality (24.5% vs. 26.3%, P=0.936), length of ICU stay in days (15.4±13.8 vs. 12.4±20.2, P=0.249), ICU mortality (26.5% vs. 24.3%, P=0.882), and inhospital mortality (36.7% vs 36.7%, P=1.000) (Table 4). Among 31 patients who survived in the hospital, 25 were discharged home and six were transferred to another hospital. Cause of death for those who died in the hospital were as follows: primary infection-related multiple organ failure (n=10), respiratory failure (n=3), end of life decision (n=4), sudden cardiac arrest (n=1).
SIC patients were divided into survivors and non-survivors according to 28-day all-cause mortality, as shown in Table 5. Non-survivors showed higher APACHE II (32.1±8.3 vs. 24.4± 8.9, P=0.012) and SOFA scores (11.7±3.3 and 9.1±3.0, P=0.015) than survivors. Non-survivors showed significantly lower platelet counts (54.0; interquartile range [IQR], 29.0–138.5 vs. 155.0; IQR, 76.0–271.0; P=0.024) and higher DNI (26.1; IQR, 3.0–45.9 vs. 3.2; IQR, 1.1–9.5; P=0.049) than survivors. Regarding vital signs on admission, non-survivors showed a higher heart rate than survivors (134.3±25.8 vs. 101.5±25.2, P<0.001). Cox hazard proportional regression analysis for 28-day mortality in SIC patients revealed that APACHE II (hazard ratio [HR], 1.10; 95% confidential interval [CI], 1.02 to 1.18; P=0.009) and DNI (HR, 1.02; 95% CI, 1.00 to 1.08; P=0.026) were independent risk factors, while TEE parameters and cardiac biomarkers did not show statistically significant correlations with 28- day mortality. Cox hazard proportional regression analysis for 28-day mortality in sepsis patients as a whole revealed that APACHE II (HR, 1.04; 95% CI, 1.01 to 1.07; P=0.004), DNI (HR, 1.02; 95% CI, 1.00 to 1.03; P=0.044), and lactate (HR, 1.07; 95% CI, 1.02 to 1.13; P=0.007) were independent risk factors (Table 6). These variables are known to represent sepsis severity [12-14]. This suggests that sepsis severity, rather than TTE parameters and cardiac biomarkers, has the most important effect on SIC mortality.
Using the definition of SIC delineated in this study, patients with reversible LV function deterioration due to sepsis were compared with non-SIC patients. Of 83 patients who showed LV function deterioration, 15 patients with persistent LV dysfunction in follow-up TTE and 19 patients without follow-up TTE were excluded. The mortality outcomes of these two excluded groups are shown in Figure 1. While patients with persistent LV dysfunction revealed similar mortality outcomes as SIC and non-SIC patients, the group of patients without follow-up TTE showed a significantly poorer mortality outcome (28-day mortality, 73.7%). Follow-up TTE data were not available within 14 days from the first TTE in this group of patients for the following reasons. Eight patients (42.1%) died within 72 hours after ICU admission, end-of-life decisions were made for six patients (31.6%) mostly due to terminal underlying diseases (e.g., cancer), two patients were transferred to other hospitals, and three patients (15.8%) recovered from sepsis.
In this study, we demonstrated the clinical features, predictors, and survival outcomes of SIC. Low systolic blood pressure and increased LVEDD were revealed to be independent predictors of SIC. There was no significant difference between SIC and sepsis patients as a whole in terms of 28-day all-cause mortality. However, high APACHE II scores and DNI, which reflect sepsis severity, were independent risk factors for 28-day all-cause mortality in SIC patients.
To define SIC, we used a definition commonly applied in previous studies [2,7,10]. In these studies, the inclusion of preexisting cardiac diseases was controversial. In some studies, patients with pre-existing cardiac disease were excluded to reduce false-positive errors in detecting SIC [4,5,10,15-17]. However, those with pre-existing cardiac disease should be included and evaluated if this condition alters the risk of SIC development and survival. Our study revealed that neither heart failure nor coronary artery disease was a risk factor for SIC development or mortality.
Recently, several studies have indicated differences in the pathophysiology of SIC and stress-induced cardiomyopathy, also known as Takotsubo cardiomyopathy, in which sepsis is the source of stress [7,9,10]. Endotoxins, inflammatory cytokines, and nitric oxides are major contributors to the development of SIC [18,19]. In contrast, elevated catecholamine release is a key contributor to the development of Takotsubo cardiomyopathy [20]. However, it is difficult to distinguish these two diseases based on clinical parameters. Previous studies that distinguished SIC and Takotsubo cardiomyopathy used the criterion of typical apical ballooning in TTE. However, Takotsubo cardiomyopathy has both apical ballooning type and atypical type, which are difficult to discriminate from SIC based on TTE findings [20,21]. Thus, we proposed that SIC is a syndrome characterized by reversible LV dysfunction and includes Takotsubo cardiomyopathy caused by sepsis. Endotoxins, inflammatory cytokines, nitric oxides, and catecholamine are all considered associated with SIC development.
Echocardiography revealed that LVEDD was significantly higher in SIC patients than in non-SIC patients. Diastolic ventricle size increased to compensate for decreased systolic contractility. After fluid resuscitation, stroke volume can recover while EF is temporarily decreased due to increased LVEDD. Based on this pathophysiology, the importance of diastolic dysfunction rather than systolic function has been emphasized recently with reports of correlation between a lower e’, higher E/e’ ratio, and mortality in sepsis patients [19,22]. However, in this study, the LVEDD and E/e’ ratio did not show significant differences between survivors and non-survivors. This might be due to missing TTE parameters, especially in non-survivors.
In this study, low systolic blood pressure upon ICU admission and increased LVEDD were considered predictors of SIC development. Low systolic blood pressure resulting in inadequate coronary blood flow has been proposed as a mechanism of SIC based on animal studies [23]. Dilated LVEDD is diastolic compliance to decreased systolic function [19]. The predictors of SIC identified in this study are discrepant from those previously identified, probably due to the small sample size of each study and the use of discordant definitions [7,10,24].
In SIC patients, tachycardia on ICU admission was significantly more common among non-survivors than survivors. This is explained by adapting to insufficient diastolic filling to increase stroke volume. In the Cox proportional hazard regression analysis of 28-day mortality, APACHE II score and DNI were revealed as independent risk factors for mortality in SIC patients. Both APACHE II and DNI are markers of sepsis severity. DNI is a novel biomarker that reflects the number of circulating granulocytes in blood and correlates with sepsis severity in critically ill patients [11,12,25,26]. This suggests that sepsis severity has a greater effect on SIC prognosis than TEE parameters and cardiac biomarkers.
Among the 83 patients who showed LV dysfunction, 19 had no follow-up TTE. This group of patients showed significantly poorer mortality outcome, suggesting that the cardiac function of these 19 patients deteriorated due to sepsis and they usually did not recover, resulting in mortality. This may imply that this group of patients had the same pathophysiology as patients with SIC development and poor outcome. Due to the small number of these patients, the characteristics of persistent LV dysfunction could not be analyzed in detail in this group with no follow-up TTE. Further studies should be performed to clarify the characteristics of these patients.
This study had several limitations. First, detailed information regarding fluid resuscitation and vasopressor initiation were not available. These factors strongly affect sepsis patient prognosis. However, the study was performed in a single ICU of a tertiary university hospital in which sepsis management, including fluid resuscitation and vasopressor initiation, was generally performed according to the more recent sepsis-3 guidelines. Second, baseline TTE and TTE within 48 hours were not available in 8.7% of the initial population. This selection bias may have influenced the final results. Third, TTE parameters were not documented equally among patients and laboratory data. For example, inflammatory cytokines and catecholamines were not available due to the retrospective nature of this study. Lastly, this was a single center, retrospective study, which could affect the generalizability of results. Further prospective studies are needed to validate the results of this study.
In conclusion, this study identified predictors and outcome of SIC with inclusion of pre-existing cardiac diseases and Takotsubo cardiomyopathy. We found that low systolic blood pressure and dilated LVEDD were SIC predictors. The prognosis of SIC was affected by sepsis severity rather than cardiac function. Reversible SIC does not increase mortality risk compared to non-SIC. Further studies are needed on sepsis patients with persistent LV dysfunction and on patients whose LV function could not be restored, resulting in death.
▪ Sepsis-induced cardiomyopathy (SIC) occurred in 14.3% of intensive care unit patients with sepsis.
▪ Low systolic blood pressure and increased left ventricular end-diastolic diameter were predictors of SIC.
▪ High Acute Physiology and Chronic Health Evaluation II and elevated delta neutrophil index were risk factors of 28-day mortality in SIC.

CONFLICT OF INTEREST No potential conflict of interest relevant to this article was reported.

AUTHOR CONTRIBUTIONS

Conceptualization: MJS, MSP. Data curation, Formal analysis, & Methodology: all authors. Project administration, Visualization, & Writing–original draft: MJS, MSP. Writing–review & editing: all authors.

Figure 1.
Patient recruitment flowchart. ICU: intensive care unit; TTE: transthoracic echocardiography; LV: left ventricle; SIC: sepsis-induced cardiomyopathy. aPatients without follow-up TTE showed poor mortality outcome. Lack of follow-up TTE in this group was due to several reasons: eight patients (42.1%) died within 72 hours after ICU admission, end-of-life decisions were made for six patients (31.6%), two patients were transferred to other hospitals, and three patients (15.8%) recovered from sepsis.
acc-2020-00024f1.jpg
Table 1.
Baseline characteristics of patients
Variable Total (n=308) SIC (n=49) Non-SIC (n=259) P-value
Age (yr) 64.6±14.5 65.1±11.2 64.6±15.0 0.772
Male sex 195 (63.3) 32 (65.3) 163 (62.9) 0.877
BMI (kg/m2) 22.2±4.3 22.4±3.9 22.2±4.4 0.782
APACHE II 24.9±8.8 26.4±9.3 24.6±8.6 0.209
SOFA 9.4±3.2 9.7±3.2 9.3±3.2 0.378
Comorbidity
 Hypertension 159 (51.6) 25 (51.0) 134 (51.7) 1.000
 Diabetes mellitus 108 (35.1) 17 (34.7) 91 (35.1) 1.000
 Chronic kidney disease 67 (21.8) 11 (22.4) 56 (21.6) 1.000
 Liver cirrhosis 25 (8.1) 2 (4.1) 23 (8.9) 0.399
 Chronic liver disease 29 (9.4) 1 (2.0) 28 (10.8) 0.097
 Cancer 83 (26.9) 16 (32.7) 67 (25.9) 0.420
 Heart failure 8 (2.6) 4 (8.2) 4 (1.5) 0.029
 Coronary artery disease 30 (9.7) 7 (14.3) 23 (8.9) 0.364
 Cerebrovascular disease 42 (13.6) 6 (12.2) 36 (13.9) 0.934
Charlson comorbidity index 2.8±2.2 3.2±2.1 2.8±2.2 0.231
Acute kidney injury 73 (23.7) 14 (28.6) 59 (22.8) 0.490
ARDS 28 (9.1) 8 (16.3) 20 (7.7) 0.099
Septic shock 295 (95.8) 49 (100.0) 246 (95.0) 0.224
Blood culture (+) 113 (36.7) 22 (44.9) 91 (35.1) 0.255
Mechanical ventilation 200 (64.9) 36 (73.5) 164 (63.3) 0.229
Vital sign on admission
 SBP (mm Hg) 90.0±22.3 83.9±18.3 91.1±22.8 0.039
 MAP (mm Hg) 66.7±15.4 64.3±15.3 67.1±15.4 0.249
 HR (bpm) 103.0±27.9 109.6±28.9 101.7±27.6 0.071
Use of vasopressor and inotrope
 None 13 (4.2) 0 15 (5.1) 0.224
 Norepinephrine 262 (85.1) 44 (89.8) 247 (84.3) 0.420
 Dobutamine 3 (0.9) 2 (4.1) 1 (0.4) 0.105
Primary focus of infection 0.507
 Pulmonary 155 (50.3) 29 (59.2) 126 (48.6)
 GI tract 43 (14.0) 3 (6.1) 40 (15.4)
 Urogenital 35 (11.4) 7 (14.3) 28 (10.8)
 Pancreatobiliary 25 (8.1) 2 (4.1) 23 (8.9)
 Soft tissue/bone 17 (5.5) 4 (8.2) 13 (5.0)
 Liver 9 (2.9) 1 (2.0) 8 (3.1)
 Kidney 6 (1.9) 1 (2.0) 5 (1.9)
 Miscellaneous 18 (5.8) 2 (4.1) 16 (6.2)
Laboratory parameter
 WBC (103/µl) 15.1±10.4 12.9±10.3 15.5±10.4 0.117
 Platelet (103/µl) 159.2±120.4 162.6±139.1 158.5±116.8 0.831
 Serum albumin (g/dl) 2.5±0.5 2.4±0.5 2.5±0.5 0.032
 Serum bilirubin (mg/dl) 2.0±3.8 1.2±1.4 2.1±4.1 0.008
 Serum creatinine (mg/dl) 2.1±1.9 2.0±1.8 2.1±2.0 0.757
 Lactate (mmol/L) 4.0±4.1 3.8±3.4 4.0±4.3 0.826
 CRP 146.3±116.2 192.4±134.2 137.5±110.6 0.002
 Procalcitonin (ng/ml) 17.5±33.0 23.9±35.5 16.2±32.4 0.135
 DNI (%) 11.0±15.1 13.9±18.9 10.5±14.2 0.224

Values are presented as mean±standard deviation or number (%).

SIC: sepsis-induced cardiomyopathy; BMI: body mass index; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment; ARDS: acute respiratory distress syndrome; SBP: systolic blood pressure; MAP: mean arterial pressure; HR: heart rate; GI: gastrointestinal; WBC: white blood cell; CRP: C-reactive protein; DNI: delta neutrophil index.

Table 2.
Echocardiographic parameters and cardiac biomarkers of patients
Echocardiographic parameter SIC (n=49)
 Non-SIC (n=259)
 P-value
Individual Value Individual Value
Ejection fraction (%) 49 34.3±8.4 259 64.7±7.5 <0.001
 LVEDD (mm) 46 49.6±6.0 248 43.4±6.3 <0.001
 LVESD (mm) 46 39.5±6.4 248 28.7±4.8 <0.001
 Mitral E/e’ ratio 36 12.9±5.5 198 12.0±4.3 0.341
 FAC (%) 7 25.3±5.3 17 24.0±8.7 0.712
Cardiac biomarker
 NT-proBNP (pg/ml) 46 19,911.2±18,194.9 233 7,940.3±13,613.9 <0.001
 CK (IU/L) 49 341.7±491.8 245 635.9±3,711.8 0.235
 CK-MB (ng/ml) 49 8.0±12.8 252 11.9±60.3 0.357
 Troponin-T (pg/ml) 48 278.9±492.7 230 104.8±178.4 0.019

Values are presented as mean±standard deviation.

SIC: sepsis-induced cardiomyopathy; LVEDD: left ventricular end-diastolic diameter; LVESD: left ventricular end-systolic diameter; E: early mitral inflow velocity; e’: mitral annular early diastolic velocity; FAC: fractional area change; NT-proBNP: N-terminal pro b-type natriuretic peptide; CK: creatine kinase; CK-MB: creatine kinase-muscle/brain.

Table 3.
Logistic regression analyses for predictors of sepsis induced cardiomyopathy
Variable HR (95% CI) P-value
Univariate logistic regression analysis
 Age 0.99 (0.97–1.02) 0.714
 Male sex 0.73 (0.31–1.70) 0.460
 Diabetes mellitus 0.59 (0.25–1.38) 0.221
 Heart failure 4.79 (0.29–78.86) 0.273
 Systolic blood pressure 0.98 (0.96–1.00) 0.027
 Heart rate 1.00 (0.98–1.01) 0.748
 CRP 1.00 (1.00–1.01) 0.088
 Procalcitonin 1.01 (0.99–1.02) 0.357
 DNI 1.00 (0.97–1.02) 0.821
 Lactate 0.93 (0.83–1.05) 0.264
 Albumin 0.43 (0.20–0.94) 0.035
 Blood culture (+) 1.28 (0.57–2.87) 0.553
 NT-proBNP/1000 1.03 (1.01–1.06) 0.001
 Troponin T/10 1.03 (1.01–1.06) 0.007
 LVEDD 1.19 (1.09–1.28) <0.001
 E/e’ 1.05 (0.97–1.14) 0.195
Multivariate logistic regression analysis
 Systolic blood pressure 0.96 (0.93–0.99) 0.007
 Albumin 0.38 (0.14–1.07) 0.066
 NT-proBNP/1000 1.04 (0.99–1.04) 0.296
 Troponin T/10 1.01 (1.00–1.03) 0.068
 LVEDD 1.24 (1.13–1.37) <0.001

HR: hazard ratio; CI: confidential interval; CRP: c-reactive protein; DNI: delta neutrophil index; NT-proBNP: N-terminal pro b-type natriuretic peptide; LVEDD: left ventricular end-diastolic diameter; E: early mitral inflow velocity; e’: mitral annular early diastolic velocity.

Table 4.
Outcome of sepsis induced cardiomyopathy
Variable Total (n=308) SIC (n=49) Non-SIC (n=259) P-value
28-Day mortality 80 (26.0) 12 (24.5) 68 (26.3) 0.936
In-hospital mortality 113 (36.7) 18 (36.7) 95 (36.7) 1.000
ICU mortality 76 (24.7) 13 (26.5) 63 (24.3) 0.882
Length of ICU stay (day) 13.2±19.3 15.4±13.8 12.7±20.2 0.249

Values are presented as number (%) or mean±standard deviation.

SIC: sepsis-induced cardiomyopathy; ICU: intensive care unit.

Table 5.
Baseline characteristics of sepsis-induced cardiomyopathy patients according to 28-day all-cause mortality
Characteristics Survivor (n=37) Non-survivor (n=12) P-value
Age (yr) 65.0±11.2 65.3±11.7 0.935
Male sex 24 (64.9) 8 (66.7) 1.000
BMI (kg/m2) 22.3±3.8 22.6±4.3 0.837
APACHE II 24.4±8.9 32.1±8.3 0.012
SOFA 9.1±3.0 11.7±3.3 0.015
Charlson comorbidity index 3.0 (2.0–4.0) 3.0 (2.0–4.5) 0.715
Acute kidney injury 10 (27.0) 4 (33.3) 0.721
ARDS 8 (21.6) 0 0.173
Mechanical ventilation 25 (67.6) 11 (91.7) 0.142
Blood culture (+) 16 (43.2) 6 (50.0) 0.940
Vital sign on admission
 Systolic blood pressure (mm Hg) 83.7±18.5 84.7±18.3 0.876
 Mean arterial pressure (mm Hg) 64.8±15.1 63.1±16.7 0.746
 Heart rate (bpm) 101.5±25.2 134.3±25.8 <0.001
Use of vasopressor
 Norepinephrine 32 (86.5) 12 (100.0) 0.427
 Dobutamine 2 (5.4) 0 1.000
Laboratory parameter
 WBC (103/µl) 10.3 (7.0–17.1) 9.4 (3.0–14.7) 0.429
 Platelet (103/µl) 155.0 (76.0–271.0) 54.0 (29.0–138.5) 0.024
 Serum albumin (g/dl) 2.4±0.6 2.4±0.5 0.872
 Serum bilirubin (mg/dl) 0.7 (0.4–1.1) 1.0 (0.4–1.9) 0.470
 Serum creatinine (mg/dl) 1.3 (0.9–1.9) 2.0 (1.2–3.6) 0.212
 Lactate (mmol/L) 2.5 (1.6–3.8) 4.0 (2.0–7.6) 0.089
 CRP 179.1 (100.7–257.4) 185.9 (49.5–343.2) 1.000
 Procalcitonin (ng/ml) 5.1 (1.3–30.3) 5.6 (0.8–40.6) 0.917
 DNI (%) 3.2 (1.1–9.5) 26.1 (3.0–45.9) 0.049
Echocardiographic finding
 Ejection fraction (%) 34.9±8.7 32.6±7.5 0.410
 LVEDD (mm) 50.2±5.1 (n=35) 47.6±8.1 (n=11) 0.339
 LVESD (mm) 39.7±5.8 (n=35) 39.1±8.2 (n=11) 0.800
 Mitral E/e’ ratio 12.9±4.8 (n=31) 13.2±10.0 (n=5) 0.943
Cardiac biomarker
 NT-proBNP (pg/ml) 11,983.0 (7,514.5–30,442.0) (n=35) 22,073.0 (3,705.5–35,000.0) (n=11) 0.857
 CK (IU/L) 76.0 (35.0–331.0) 171.0 (59.0–688.0) 0.231
 CK-MB (ng/ml) 4.2 (2.0–7.2) 2.1 (1.5–5.3) 0.236
 Troponin-T (pg/ml) 156.0 (47.0–318.0) 108.0 (62.5–304.0) (n=11) 0.980

Values are presented as mean±standard deviation, number (%), or median (interquartile range).

BMI: body mass index; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment; ARDS: acute respiratory distress syndrome; WBC: white blood cell; CRP: C-reactive protein; DNI: delta neutrophil index; LVEDD: left ventricular end-diastolic diameter; LVESD: left ventricular end-systolic diameter; E: early mitral inflow velocity; e’: mitral annular early diastolic velocity; NT-proBNP: N-terminal pro b-type natriuretic peptide; CK: creatine kinase; CK-MB: creatine kinase-muscle/brain.

Table 6.
Cox proportional hazard regression analysis for 28-day mortality in SIC patients and sepsis patients as a whole
Variable Univariate
 Multivariate
HR (95% CI) P-value HR (95% CI) P-value
SIC patient
 APACHE II 1.08 (1.02–1.14) 0.006 1.10 (1.02–1.18) 0.009
 DNI 1.03 (0.01–2.21) 0.027 1.02 (1.00–1.08) 0.026
 Lactate 1.12 (1.01–1.24) 0.036 1.12 (0.74–1.07) 0.210
 EF 0.97 (0.91–1.04) 0.381
 LVEDD 0.94 (0.85–1.04) 0.231
Sepsis patients as a whole
 APACHE II 1.06 (1.03–1.09) <0.001 1.04 (1.01–1.07) 0.004
 DNI 1.03 (0.01–1.04) <0.001 1.02 (1.00–1.03) 0.044
 Lactate 1.12 (1.07–1.16) <0.001 1.07 (1.02–1.13) 0.007
 EF 1.01 (0.99–1.03) 0.321
 LVEDD 0.96 (0.93–0.98) 0.008 0.97 (0.93–1.01) 0.118
 SIC 0.89 (0.0.48–1.65) 0.723 0.88 (0.43–1.74) 0.688

SIC: sepsis-induced cardiomyopathy; HR: hazard ratio; CI: confidential interval; APACHE: Acute Physiology and Chronic Health Evaluation; DNI: delta neutrophil index; EF: ejection fraction; LVEDD: left ventricular enddiastolic diameter.

  • 1. Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016;315:801-10.ArticlePubMedPMC
  • 2. Parker MM, Shelhamer JH, Bacharach SL, Green MV, Natanson C, Frederick TM, et al. Profound but reversible myocardial depression in patients with septic shock. Ann Intern Med 1984;100:483-90.ArticlePubMed
  • 3. Jardin F, Brun-Ney D, Auvert B, Beauchet A, Bourdarias JP. Sepsis-related cardiogenic shock. Crit Care Med 1990;18:1055-60.ArticlePubMed
  • 4. Etchecopar-Chevreuil C, François B, Clavel M, Pichon N, Gastinne H, Vignon P. Cardiac morphological and functional changes during early septic shock: a transesophageal echocardiographic study. Intensive Care Med 2008;34:250-6.ArticlePubMedPDF
  • 5. Vieillard-Baron A, Caille V, Charron C, Belliard G, Page B, Jardin F. Actual incidence of global left ventricular hypokinesia in adult septic shock. Crit Care Med 2008;36:1701-6.ArticlePubMed
  • 6. Bouhemad B, Nicolas-Robin A, Arbelot C, Arthaud M, Féger F, Rouby JJ. Acute left ventricular dilatation and shock-induced myocardial dysfunction. Crit Care Med 2009;37:441-7.ArticlePubMed
  • 7. Sato R, Kuriyama A, Takada T, Nasu M, Luthe SK. Prevalence and risk factors of sepsis-induced cardiomyopathy: a retrospective cohort study. Medicine (Baltimore) 2016;95:e5031.ArticlePubMedPMC
  • 8. Calandra T, Cohen J, International Sepsis Forum Definition of Infection in the ICU Consensus Conference. The international sepsis forum consensus conference on definitions of infection in the intensive care unit. Crit Care Med 2005;33:1538-48.ArticlePubMed
  • 9. Sato R, Nasu M. A review of sepsis-induced cardiomyopathy. J Intensive Care 2015;3:48. ArticlePubMedPMC
  • 10. Jeong HS, Lee TH, Bang CH, Kim JH, Hong SJ. Risk factors and outcomes of sepsis-induced myocardial dysfunction and stress-induced cardiomyopathy in sepsis or septic shock: a comparative retrospective study. Medicine (Baltimore) 2018;97:e0263.ArticlePubMedPMC
  • 11. Nahm CH, Choi JW, Lee J. Delta neutrophil index in automated immature granulocyte counts for assessing disease severity of patients with sepsis. Ann Clin Lab Sci 2008;38:241-6.PubMed
  • 12. Park BH, Kang YA, Park MS, Jung WJ, Lee SH, Lee SK, et al. Delta neutrophil index as an early marker of disease severity in critically ill patients with sepsis. BMC Infect Dis 2011;11:299. ArticlePubMedPMCPDF
  • 13. Kruse JA, Thill-Baharozian MC, Carlson RW. Comparison of clinical assessment with APACHE II for predicting mortality risk in patients admitted to a medical intensive care unit. JAMA 1988;260:1739-42.ArticlePubMed
  • 14. Mikkelsen ME, Miltiades AN, Gaieski DF, Goyal M, Fuchs BD, Shah CV, et al. Serum lactate is associated with mortality in severe sepsis independent of organ failure and shock. Crit Care Med 2009;37:1670-7.ArticlePubMed
  • 15. Furian T, Aguiar C, Prado K, Ribeiro RV, Becker L, Martinelli N, et al. Ventricular dysfunction and dilation in severe sepsis and septic shock: relation to endothelial function and mortality. J Crit Care 2012;27:319. Article
  • 16. Pulido JN, Afessa B, Masaki M, Yuasa T, Gillespie S, Herasevich V, et al. Clinical spectrum, frequency, and significance of myocardial dysfunction in severe sepsis and septic shock. Mayo Clin Proc 2012;87:620-8.ArticlePubMedPMC
  • 17. Weng L, Liu YT, Du B, Zhou JF, Guo XX, Peng JM, et al. The prognostic value of left ventricular systolic function measured by tissue Doppler imaging in septic shock. Crit Care 2012;16:R71. ArticlePubMedPMC
  • 18. Liu YC, Yu MM, Shou ST, Chai YF. Sepsis-induced cardiomyopathy: mechanisms and treatments. Front Immunol 2017;8:1021. ArticlePubMedPMC
  • 19. Walley KR. Sepsis-induced myocardial dysfunction. Curr Opin Crit Care 2018;24:292-9.ArticlePubMed
  • 20. Boland TA, Lee VH, Bleck TP. Stress-induced cardiomyopathy. Crit Care Med 2015;43:686-93.ArticlePubMed
  • 21. Templin C, Ghadri JR, Diekmann J, Napp LC, Bataiosu DR, Jaguszewski M, et al. Clinical features and outcomes of Takotsubo (Stress) cardiomyopathy. N Engl J Med 2015;373:929-38.ArticlePubMed
  • 22. Kakihana Y, Ito T, Nakahara M, Yamaguchi K, Yasuda T. Sepsis-induced myocardial dysfunction: pathophysiology and management. J Intensive Care 2016;4:22. ArticlePubMedPMC
  • 23. Bruni FD, Komwatana P, Soulsby ME, Hess ML. Endotoxin and myocardial failure: role of the myofibril and venous return. Am J Physiol 1978;235:H150-6.ArticlePubMed
  • 24. Mehta NJ, Khan IA, Gupta V, Jani K, Gowda RM, Smith PR. Cardiac troponin I predicts myocardial dysfunction and adverse outcome in septic shock. Int J Cardiol 2004;95:13-7.ArticlePubMed
  • 25. Kim HW, Yoon JH, Jin SJ, Kim SB, Ku NS, Jeong SJ, et al. Delta neutrophil index as a prognostic marker of early mortality in gram negative bacteremia. Infect Chemother 2014;46:94-102.ArticlePubMedPMC
  • 26. Yune HY, Chung SP, Park YS, Chung HS, Lee HS, Lee JW, et al. Delta neutrophil index as a promising prognostic marker in out of hospital cardiac arrest. PLoS One 2015;10:e0120677.ArticlePubMedPMC

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      Predictors and outcomes of sepsis-induced cardiomyopathy in critically ill patients
      Acute Crit Care. 2020;35(2):67-76.   Published online May 15, 2020
      DOI: https://doi.org/10.4266/acc.2020.00024
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    Predictors and outcomes of sepsis-induced cardiomyopathy in critically ill patients
    Image
    Figure 1. Patient recruitment flowchart. ICU: intensive care unit; TTE: transthoracic echocardiography; LV: left ventricle; SIC: sepsis-induced cardiomyopathy. aPatients without follow-up TTE showed poor mortality outcome. Lack of follow-up TTE in this group was due to several reasons: eight patients (42.1%) died within 72 hours after ICU admission, end-of-life decisions were made for six patients (31.6%), two patients were transferred to other hospitals, and three patients (15.8%) recovered from sepsis.

    Figure 1.

    Predictors and outcomes of sepsis-induced cardiomyopathy in critically ill patients
    Variable Total (n=308) SIC (n=49) Non-SIC (n=259) P-value
    Age (yr) 64.6±14.5 65.1±11.2 64.6±15.0 0.772
    Male sex 195 (63.3) 32 (65.3) 163 (62.9) 0.877
    BMI (kg/m2) 22.2±4.3 22.4±3.9 22.2±4.4 0.782
    APACHE II 24.9±8.8 26.4±9.3 24.6±8.6 0.209
    SOFA 9.4±3.2 9.7±3.2 9.3±3.2 0.378
    Comorbidity
     Hypertension 159 (51.6) 25 (51.0) 134 (51.7) 1.000
     Diabetes mellitus 108 (35.1) 17 (34.7) 91 (35.1) 1.000
     Chronic kidney disease 67 (21.8) 11 (22.4) 56 (21.6) 1.000
     Liver cirrhosis 25 (8.1) 2 (4.1) 23 (8.9) 0.399
     Chronic liver disease 29 (9.4) 1 (2.0) 28 (10.8) 0.097
     Cancer 83 (26.9) 16 (32.7) 67 (25.9) 0.420
     Heart failure 8 (2.6) 4 (8.2) 4 (1.5) 0.029
     Coronary artery disease 30 (9.7) 7 (14.3) 23 (8.9) 0.364
     Cerebrovascular disease 42 (13.6) 6 (12.2) 36 (13.9) 0.934
    Charlson comorbidity index 2.8±2.2 3.2±2.1 2.8±2.2 0.231
    Acute kidney injury 73 (23.7) 14 (28.6) 59 (22.8) 0.490
    ARDS 28 (9.1) 8 (16.3) 20 (7.7) 0.099
    Septic shock 295 (95.8) 49 (100.0) 246 (95.0) 0.224
    Blood culture (+) 113 (36.7) 22 (44.9) 91 (35.1) 0.255
    Mechanical ventilation 200 (64.9) 36 (73.5) 164 (63.3) 0.229
    Vital sign on admission
     SBP (mm Hg) 90.0±22.3 83.9±18.3 91.1±22.8 0.039
     MAP (mm Hg) 66.7±15.4 64.3±15.3 67.1±15.4 0.249
     HR (bpm) 103.0±27.9 109.6±28.9 101.7±27.6 0.071
    Use of vasopressor and inotrope
     None 13 (4.2) 0 15 (5.1) 0.224
     Norepinephrine 262 (85.1) 44 (89.8) 247 (84.3) 0.420
     Dobutamine 3 (0.9) 2 (4.1) 1 (0.4) 0.105
    Primary focus of infection 0.507
     Pulmonary 155 (50.3) 29 (59.2) 126 (48.6)
     GI tract 43 (14.0) 3 (6.1) 40 (15.4)
     Urogenital 35 (11.4) 7 (14.3) 28 (10.8)
     Pancreatobiliary 25 (8.1) 2 (4.1) 23 (8.9)
     Soft tissue/bone 17 (5.5) 4 (8.2) 13 (5.0)
     Liver 9 (2.9) 1 (2.0) 8 (3.1)
     Kidney 6 (1.9) 1 (2.0) 5 (1.9)
     Miscellaneous 18 (5.8) 2 (4.1) 16 (6.2)
    Laboratory parameter
     WBC (103/µl) 15.1±10.4 12.9±10.3 15.5±10.4 0.117
     Platelet (103/µl) 159.2±120.4 162.6±139.1 158.5±116.8 0.831
     Serum albumin (g/dl) 2.5±0.5 2.4±0.5 2.5±0.5 0.032
     Serum bilirubin (mg/dl) 2.0±3.8 1.2±1.4 2.1±4.1 0.008
     Serum creatinine (mg/dl) 2.1±1.9 2.0±1.8 2.1±2.0 0.757
     Lactate (mmol/L) 4.0±4.1 3.8±3.4 4.0±4.3 0.826
     CRP 146.3±116.2 192.4±134.2 137.5±110.6 0.002
     Procalcitonin (ng/ml) 17.5±33.0 23.9±35.5 16.2±32.4 0.135
     DNI (%) 11.0±15.1 13.9±18.9 10.5±14.2 0.224
    Echocardiographic parameter SIC (n=49)
    		 Non-SIC (n=259)
    		 P-value
    Individual Value Individual Value
    Ejection fraction (%) 49 34.3±8.4 259 64.7±7.5 <0.001
     LVEDD (mm) 46 49.6±6.0 248 43.4±6.3 <0.001
     LVESD (mm) 46 39.5±6.4 248 28.7±4.8 <0.001
     Mitral E/e’ ratio 36 12.9±5.5 198 12.0±4.3 0.341
     FAC (%) 7 25.3±5.3 17 24.0±8.7 0.712
    Cardiac biomarker
     NT-proBNP (pg/ml) 46 19,911.2±18,194.9 233 7,940.3±13,613.9 <0.001
     CK (IU/L) 49 341.7±491.8 245 635.9±3,711.8 0.235
     CK-MB (ng/ml) 49 8.0±12.8 252 11.9±60.3 0.357
     Troponin-T (pg/ml) 48 278.9±492.7 230 104.8±178.4 0.019
    Variable HR (95% CI) P-value
    Univariate logistic regression analysis
     Age 0.99 (0.97–1.02) 0.714
     Male sex 0.73 (0.31–1.70) 0.460
     Diabetes mellitus 0.59 (0.25–1.38) 0.221
     Heart failure 4.79 (0.29–78.86) 0.273
     Systolic blood pressure 0.98 (0.96–1.00) 0.027
     Heart rate 1.00 (0.98–1.01) 0.748
     CRP 1.00 (1.00–1.01) 0.088
     Procalcitonin 1.01 (0.99–1.02) 0.357
     DNI 1.00 (0.97–1.02) 0.821
     Lactate 0.93 (0.83–1.05) 0.264
     Albumin 0.43 (0.20–0.94) 0.035
     Blood culture (+) 1.28 (0.57–2.87) 0.553
     NT-proBNP/1000 1.03 (1.01–1.06) 0.001
     Troponin T/10 1.03 (1.01–1.06) 0.007
     LVEDD 1.19 (1.09–1.28) <0.001
     E/e’ 1.05 (0.97–1.14) 0.195
    Multivariate logistic regression analysis
     Systolic blood pressure 0.96 (0.93–0.99) 0.007
     Albumin 0.38 (0.14–1.07) 0.066
     NT-proBNP/1000 1.04 (0.99–1.04) 0.296
     Troponin T/10 1.01 (1.00–1.03) 0.068
     LVEDD 1.24 (1.13–1.37) <0.001
    Variable Total (n=308) SIC (n=49) Non-SIC (n=259) P-value
    28-Day mortality 80 (26.0) 12 (24.5) 68 (26.3) 0.936
    In-hospital mortality 113 (36.7) 18 (36.7) 95 (36.7) 1.000
    ICU mortality 76 (24.7) 13 (26.5) 63 (24.3) 0.882
    Length of ICU stay (day) 13.2±19.3 15.4±13.8 12.7±20.2 0.249
    Characteristics Survivor (n=37) Non-survivor (n=12) P-value
    Age (yr) 65.0±11.2 65.3±11.7 0.935
    Male sex 24 (64.9) 8 (66.7) 1.000
    BMI (kg/m2) 22.3±3.8 22.6±4.3 0.837
    APACHE II 24.4±8.9 32.1±8.3 0.012
    SOFA 9.1±3.0 11.7±3.3 0.015
    Charlson comorbidity index 3.0 (2.0–4.0) 3.0 (2.0–4.5) 0.715
    Acute kidney injury 10 (27.0) 4 (33.3) 0.721
    ARDS 8 (21.6) 0 0.173
    Mechanical ventilation 25 (67.6) 11 (91.7) 0.142
    Blood culture (+) 16 (43.2) 6 (50.0) 0.940
    Vital sign on admission
     Systolic blood pressure (mm Hg) 83.7±18.5 84.7±18.3 0.876
     Mean arterial pressure (mm Hg) 64.8±15.1 63.1±16.7 0.746
     Heart rate (bpm) 101.5±25.2 134.3±25.8 <0.001
    Use of vasopressor
     Norepinephrine 32 (86.5) 12 (100.0) 0.427
     Dobutamine 2 (5.4) 0 1.000
    Laboratory parameter
     WBC (103/µl) 10.3 (7.0–17.1) 9.4 (3.0–14.7) 0.429
     Platelet (103/µl) 155.0 (76.0–271.0) 54.0 (29.0–138.5) 0.024
     Serum albumin (g/dl) 2.4±0.6 2.4±0.5 0.872
     Serum bilirubin (mg/dl) 0.7 (0.4–1.1) 1.0 (0.4–1.9) 0.470
     Serum creatinine (mg/dl) 1.3 (0.9–1.9) 2.0 (1.2–3.6) 0.212
     Lactate (mmol/L) 2.5 (1.6–3.8) 4.0 (2.0–7.6) 0.089
     CRP 179.1 (100.7–257.4) 185.9 (49.5–343.2) 1.000
     Procalcitonin (ng/ml) 5.1 (1.3–30.3) 5.6 (0.8–40.6) 0.917
     DNI (%) 3.2 (1.1–9.5) 26.1 (3.0–45.9) 0.049
    Echocardiographic finding
     Ejection fraction (%) 34.9±8.7 32.6±7.5 0.410
     LVEDD (mm) 50.2±5.1 (n=35) 47.6±8.1 (n=11) 0.339
     LVESD (mm) 39.7±5.8 (n=35) 39.1±8.2 (n=11) 0.800
     Mitral E/e’ ratio 12.9±4.8 (n=31) 13.2±10.0 (n=5) 0.943
    Cardiac biomarker
     NT-proBNP (pg/ml) 11,983.0 (7,514.5–30,442.0) (n=35) 22,073.0 (3,705.5–35,000.0) (n=11) 0.857
     CK (IU/L) 76.0 (35.0–331.0) 171.0 (59.0–688.0) 0.231
     CK-MB (ng/ml) 4.2 (2.0–7.2) 2.1 (1.5–5.3) 0.236
     Troponin-T (pg/ml) 156.0 (47.0–318.0) 108.0 (62.5–304.0) (n=11) 0.980
    Variable Univariate
    		 Multivariate
    HR (95% CI) P-value HR (95% CI) P-value
    SIC patient
     APACHE II 1.08 (1.02–1.14) 0.006 1.10 (1.02–1.18) 0.009
     DNI 1.03 (0.01–2.21) 0.027 1.02 (1.00–1.08) 0.026
     Lactate 1.12 (1.01–1.24) 0.036 1.12 (0.74–1.07) 0.210
     EF 0.97 (0.91–1.04) 0.381
     LVEDD 0.94 (0.85–1.04) 0.231
    Sepsis patients as a whole
     APACHE II 1.06 (1.03–1.09) <0.001 1.04 (1.01–1.07) 0.004
     DNI 1.03 (0.01–1.04) <0.001 1.02 (1.00–1.03) 0.044
     Lactate 1.12 (1.07–1.16) <0.001 1.07 (1.02–1.13) 0.007
     EF 1.01 (0.99–1.03) 0.321
     LVEDD 0.96 (0.93–0.98) 0.008 0.97 (0.93–1.01) 0.118
     SIC 0.89 (0.0.48–1.65) 0.723 0.88 (0.43–1.74) 0.688
    Table 1. Baseline characteristics of patients

    Values are presented as mean±standard deviation or number (%).

    SIC: sepsis-induced cardiomyopathy; BMI: body mass index; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment; ARDS: acute respiratory distress syndrome; SBP: systolic blood pressure; MAP: mean arterial pressure; HR: heart rate; GI: gastrointestinal; WBC: white blood cell; CRP: C-reactive protein; DNI: delta neutrophil index.

    Table 2. Echocardiographic parameters and cardiac biomarkers of patients

    Values are presented as mean±standard deviation.

    SIC: sepsis-induced cardiomyopathy; LVEDD: left ventricular end-diastolic diameter; LVESD: left ventricular end-systolic diameter; E: early mitral inflow velocity; e’: mitral annular early diastolic velocity; FAC: fractional area change; NT-proBNP: N-terminal pro b-type natriuretic peptide; CK: creatine kinase; CK-MB: creatine kinase-muscle/brain.

    Table 3. Logistic regression analyses for predictors of sepsis induced cardiomyopathy

    HR: hazard ratio; CI: confidential interval; CRP: c-reactive protein; DNI: delta neutrophil index; NT-proBNP: N-terminal pro b-type natriuretic peptide; LVEDD: left ventricular end-diastolic diameter; E: early mitral inflow velocity; e’: mitral annular early diastolic velocity.

    Table 4. Outcome of sepsis induced cardiomyopathy

    Values are presented as number (%) or mean±standard deviation.

    SIC: sepsis-induced cardiomyopathy; ICU: intensive care unit.

    Table 5. Baseline characteristics of sepsis-induced cardiomyopathy patients according to 28-day all-cause mortality

    Values are presented as mean±standard deviation, number (%), or median (interquartile range).

    BMI: body mass index; APACHE: Acute Physiology and Chronic Health Evaluation; SOFA: Sequential Organ Failure Assessment; ARDS: acute respiratory distress syndrome; WBC: white blood cell; CRP: C-reactive protein; DNI: delta neutrophil index; LVEDD: left ventricular end-diastolic diameter; LVESD: left ventricular end-systolic diameter; E: early mitral inflow velocity; e’: mitral annular early diastolic velocity; NT-proBNP: N-terminal pro b-type natriuretic peptide; CK: creatine kinase; CK-MB: creatine kinase-muscle/brain.

    Table 6. Cox proportional hazard regression analysis for 28-day mortality in SIC patients and sepsis patients as a whole

    SIC: sepsis-induced cardiomyopathy; HR: hazard ratio; CI: confidential interval; APACHE: Acute Physiology and Chronic Health Evaluation; DNI: delta neutrophil index; EF: ejection fraction; LVEDD: left ventricular enddiastolic diameter.

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    ACC : Acute and Critical Care
    © The Korean Society of Critical Care Medicine.
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