Abstract
Sepsis has emerged as a major global public health concern due to its elevated mortality and high cost of care. This study aimed to evaluate the risk factors associated with the mortality of sepsis patients in the Intensive Care Unit (ICU), and to intervene in the early stages of sepsis in order to improve patient outcomes and reduce mortality. From January 1st, 2021 to December 31st, 2021, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Huashan Hospital Affiliated to Fudan University, and The Seventh People’s Hospital Affiliated to Shanghai University of Traditional Chinese Medicine were designated as sentinel hospitals, and sepsis patients in their respective ICU and Emergency ICU were selected as research subjects, and divided into survivors and non-survivors according to their discharge outcomes. The mortality risk of sepsis patients was subsequently analyzed by logistic regression. A total of 176 patients with sepsis were included, of which 130 (73.9%) were survivors and 46 (26.1%) were non-survivors. Factors identified as having an impact on death among sepsis patients included female [Odds Ratio (OR) = 5.135, 95% confidence interval (CI): 1.709, 15.427, P = .004)], cardiovascular disease (OR = 6.272, 95% CI: 1.828, 21.518, P = .004), cerebrovascular disease (OR = 3.133, 95% CI: 1.093, 8.981, P = .034), pulmonary infections (OR = 6.700, 95% CI: 1.744, 25.748, P = .006), use of vasopressors (OR = 34.085, 95% CI: 10.452, 111.155, P < .001), WBC < 3.5 × 109/L (OR = 9.752, 95% CI: 1.386, 68.620, P = .022), ALT < 7 U/L (OR = 7.672, 95% CI: 1.263, 46.594, P = .027), ALT > 40 U/L (OR = 3.343, 95% CI: 1.097, 10.185, P = .034). Gender, cardiovascular disease, cerebrovascular disease, pulmonary infections, the use of vasopressors, WBC, and ALT are important factors in evaluating the prognostic outcome of sepsis patients in the ICU. This suggests that medical professionals should recognize them expeditiously and implement aggressive treatment tactics to diminish the mortality rate and improve outcomes.
Keywords: intensive care unit, predictive value, risk factors, sepsis
1. Introduction
Sepsis is an acute, life-threatening disorder of host immunological response to infectious agents, caused by multiple microorganisms and potentially culminating in severe sepsis and septic shock if left untreated.[1] Sepsis has become a critical public health concern globally, due to its high mortality rate and the cost of treatment. Worldwide, more than 19 million people suffer from sepsis annually and approximately 6 million succumb to the condition, with a fatality rate of over 25%. Furthermore, up to 3 million of those who survive are left with cognitive deficits.[2,3] Annually, over 2 hundred thousand people in the United States perish as a result of sepsis, with fatality rates of 14.9% and 34.2% when advancing to severe sepsis and septic shock.[4–6] Despite advancements in recent years pertaining to the etiology of sepsis and its treatment, the underlying causes of the illness are intricate, involving aberrant systemic reactions such as inflammatory outbreaks,[7] immune imbalance,[8] and coagulation disorders,[9] with rapid progression and high heterogeneity. Due to the extensive utilization of antibiotics in clinical care, patients have a high prevalence of pathogenic microorganism resistance and display multiple drug resistance, which elevates the challenge of treatment.[10] In China, the mortality rate of sepsis is significantly elevated compared to other regions, which is closely associated with the size of the population and the country’s socioeconomic factors.[11] A nationwide survey revealed that, among individuals admitted to the ICU, 20% were diagnosed with sepsis, and the mortality rate during the 90-day follow-up was as high as 35.5%.[12]
Numerous epidemiological investigations into sepsis have been undertaken by scholars over the past decades, however, the majority of the data has been sourced from developed countries.[13] These studies are lacking in the use of multiple center data, potentially skewing the analysis. Additionally, retrospective studies are often utilized, and discrepancies between the information obtained from the study subjects and the true state of affairs could lead to debatable research results. Thus, the pertinent research outcomes may not accurately reflect the extant risk factors related to the mortality of ICU sepsis.
Here, we conducted a multicenter observational study, involving critical care patients with sepsis and screening the associated factors impacting the prognosis of such patients, with the aim of exploring the predictive value of mortality risk in the ICU sepsis patients. Through early identification and active intervention of the onset and progression of sepsis and the combination of multiple factors to construct a model, we aimed to provide clinical utility and scientific guidance for the diagnosis, treatment, and prognosis of sepsis patients.
2. Methods
2.1. Study sites
From January 1st, 2021 to December 31st, 2021, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Huashan Hospital Affiliated to Fudan University, and Seventh People’s Hospital Affiliated to Shanghai University of Traditional Chinese Medicine were chosen as sentinel hospitals for this study. Patients with sepsis in ICU and Emergency ICU were selected as study objects for the multicenter observational study.
2.2. Study subjects
Patients with sepsis in the above hospitals from January 1st, 2021 to December 31st, 2021, were diagnosed according to the 2016 International Guidelines for Severe Sepsis and Sepsis Shock.[1]
Inclusion criteria: meeting the diagnostic criteria of the 2016 International Guidelines for Severe Sepsis and Septic Shock.[1]
Exclusion criteria: diagnosis of Sepsis time > 48 hours; patients refused to participate in the study or decided to stop treatment during the observation; patients with severe underlying diseases such as immune deficiency and blood diseases; pregnancy and lactation women.
2.3. Sample size calculations
This study was an observational study, and previous literature has shown that the mortality rate of patients with sepsis is 25%.[2,3] The sample size was calculated using the “Confidence Intervals for One Proportion” module in the PASS 15 software (NCSS, LLC., Kaysville, UT). Set α = 0.05, 1–β = 0.90, P = .25, a sample size of 139 produces a two-sided 95% confidence interval with a width equal to 0.150 when the sample proportion is 0.250. Loss to follow-up and rejection are considered at 20% According to the interview situation, at least 174 subjects will be needed in the end.
2.4. Data collections
From January 1st, 2021 to December 31st, 2021, the general data and laboratory data of sepsis patients admitted to the Emergency ICU of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, the ICU of Huashan Hospital Affiliated to Fudan University, and the ICU of Seventh People’s Hospital Affiliated to Shanghai University of Traditional Chinese Medicine were systematically collected and analyzed. The data were retrieved from the clinical case study forms of sepsis patients obtained from 3 designated medical facilities, and the medical information was methodically retrieved by trained expert investigators. The participants in this study were critically ill specialists with clinical research aptitude, a consistent staff makeup, and who consistently logged information into electronic databases. The relevant data included the basic information of patients (such as age, gender, primary infection site, Acute Physiology and Chronic Health Evaluation II score (APACHE II score), Sequential Organ Failure Asses (SOFA) score, whether to use of vasopressors, etc.), medical history data (basic diseases including whether to combine hypertension, diabetes, cardiovascular disease, cerebrovascular disease, chronic kidney disease, chronic liver disease, cancer, etc.), laboratory test indicators within 24 hours of admission [Erythrocytes (RBC), Hemoglobin (Hb), Leukocyte (WBC), Neutrophil% (NEUT%), Lymphocyte%, Platelet, Hematocrit, Alanine aminotransferase (ALT), Aspartate aminotransferase, Serum Total Bilirubin, Blood Urea Nitrogen (BUN), Creatinine (Cr), Potassium (K), Sodium, Fibrinogen, Prothrombin Time, Activated Partial Thromboplastin Time, D-dimer, C-reactive protein (CRP), Procalcitonin]. The patients were divided into the survival and non-survival groups based on their discharge outcomes.
2.5. Sample collection and laboratory testing
The research subjects who met the conditions had their central venous blood collected, with the sample size amounting to at least 5 mL. The samples were swiftly transported to the medical laboratory of their respective hospital within 12 hours of collection, followed by the detection of the relevant laboratory indices.
2.6. Pathogenic culture
Blood samples were cultured with BactAlert 3D 240 automatic blood culture analyzer (Bio-Merieux, Craponne, France) and Bactec FX400 automatic blood culture instrument (BD, Franklin Lakes, NJ) and their special culture flasks; Liquid broth tube (containing 5% sheep blood); sputum, various secretions, and pus samples were inoculated on Columbia blood plate, MacConkey plate and chocolate plate, using Vitek 2 Compact automatic microbial biochemical identification instrument (French BioMérieux company) for strain identification.
2.7. Statistical analysis
The database was established with Excel 2021 software (Microsoft Corp, Redmond, Seattle, USA), and all statistical analyses were performed with IBM SPSS Statistics version 26 (IBM SPSS Inc., Armonk, NY). The measurement data is normally distributed, expressed as “mean ± standard deviation (“x ± s),” and the difference between groups is compared by t test; the measurement data is non-normal distribution, expressed as the medians and interquartile ranges, and between groups differences were compared using the Wilcoxon rank-sum test. The count data are expressed as “composition ratio or rate (%),” and the differences between groups were compared using the χ2 test. The univariate Logistic regression was used to analyze the risk factors affecting the death of patients with sepsis, and the odds ratio (OR) value and 95% confidence interval (CI) were calculated, and the related factors with P < .20 were involved. The multivariate logistic regression model was used to fit the relationship between the treatment outcome and related factors in patients with sepsis. The forward stepwise regression method was used to select statistically significant influencing factors and evaluate their independent risk factors affecting the death of patients with sepsis were screened out. The criteria for variable retention and exclusion were α = 0.05 and α = 0.20. The selected independent risk factors were fitted to the receiver operating characteristic curve analysis of the predictive effect of the treatment outcome, and the area under curve (AUC) and 95% CI were determined. The predictive efficiency of independent risk factors for treatment outcomes was evaluated by comparing the AUC. Statistical significance was defined as P < .05 (two-sided test).
3. Results
3.1. Baseline characteristics
From January 1st, 2021 to December 31st, 2021, a total of 176 sepsis patients were included in the study in the Emergency ICU of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, the ICU of Huashan Hospital Affiliated to Fudan University, and the ICU of the Seventh People’s Hospital of Shanghai University of Traditional Chinese Medicine, according to the inclusion criteria and exclusion criteria.
The median age of 176 patients was 80 years (interquartile ranges: 72–88), including 103 males (58.52%) and 73 females (41.48%). Hypertension 109 (61.93%) was the most common comorbidity, followed by diabetes 56 (31.82%), and cerebrovascular disease 54 (30.68%). In terms of the primary infection sites, lung infection 141 (80.11%) was the common primary infection site, followed by other infection 23 (13.07%), abdominal infection 9 (5.11%), and urinary tract infection 3 (1.70%). In the APACHE II score, 57 (32.39%) scored 0 to 10, 89 (50.57%) scored 11 to 20, 25 (14.20%) scored 21 to 30, and 5 (2.84%) scored > 30. Among the SOFA scores, 101 (57.39%) scored 0 to 5, 64 (36.36%) scored 6 to 10, 10 (5.68%) scored 11 to 15, and 1 (0.57%) scored > 16. Vasopressors were used in 67 (38.07%). Among the 176 sepsis patients, 130 patients (73.86%) survived and 46 patients (26.14%) died according to discharge outcomes. As shown in Table 1.
Table 1.
Baseline clinical and laboratory characteristics of sepsis patients.
| Variables | Total (N = 176) | Survivors (N = 130) | Non-survivors (N = 46) | P value |
|---|---|---|---|---|
| Age (yr, IQR) | 80 (72, 88) | 78 (69, 87) | 83 (77.75, 90) | .004 |
| Gender, n (%) | ||||
| Male | 103 (58.52) | 82 (63.08) | 21 (45.65) | .039 |
| Female | 73 (41.48) | 48 (36.92) | 25 (54.35) | |
| Comorbidity, n (%) | ||||
| Hypertension | 109 (61.93) | 78 (60.00) | 31 (67.39) | .375 |
| Diabetes | 56 (31.82) | 40 (30.77) | 16 (34.78) | .615 |
| Cardiovascular diseases | 45 (25.57) | 28 (21.54) | 17 (36.96) | .039 |
| Cerebrovascular diseases | 54 (30.68) | 33 (25.38) | 21 (45.65) | .010 |
| Chronic kidney diseases | 13 (7.39) | 8 (6.15) | 5 (10.87) | .293 |
| Chronic liver diseases | 10 (5.68) | 8 (6.15) | 2 (4.35) | .933 |
| Cancer | 10 (5.68) | 7 (5.38) | 3 (6.52) | 1.000 |
| Primary site of infection, n (%) | ||||
| Lung infections | 141 (80.11) | 100 (76.92) | 41 (89.13) | .075 |
| Abdominal infections | 9 (5.11) | 8 (6.15) | 1 (2.17) | .507 |
| Urinary tract infections | 3 (1.70) | 3 (2.31) | 0 (0.00) | .568 |
| Other infections | 23 (13.07) | 19 (14.62) | 4 (8.70) | .442 |
| APACHE II score, n (%) | ||||
| 0–10 | 57 (32.39) | 51 (39.23) | 6 (13.04) | .001 |
| 11–20 | 89 (50.57) | 62 (47.69) | 27 (58.70) | .200 |
| 21–30 | 25 (14.20) | 14 (10.77) | 11 (23.91) | .028 |
| >30 | 5 (2.84) | 3 (2.31) | 2 (4.35) | .842 |
| SOFA score, n (%) | ||||
| 0–5 | 101 (57.39) | 85 (65.38) | 16 (34.78) | <.001 |
| 6–10 | 64 (36.36) | 39 (30.00) | 25 (54.35) | .003 |
| 11–15 | 10 (5.68) | 6 (4.62) | 4 (8.70) | .511 |
| >16 | 1 (0.57) | 0 (0.00) | 1 (2.17) | .586 |
| Use of vasopressors, n (%) | 67 (38.07) | 28 (21.54) | 39 (84.78) | <.001 |
| RBC (×1012/L, IQR) | 3.84 (3.18, 4.24) | 3.96 (3.19, 4.32) | 3.58 (3.06, 4.06) | .059 |
| Hb (g/L, IQR) | 116.50 (96.25, 130.00) | 121.00 (96.00, 131.00) | 108.00 (96.75, 122.00) | .078 |
| WBC (×109/L, IQR) | 11.26 (7.20, 15.94) | 10.93 (7.08, 15.25) | 12.83 (8.14, 18.17) | .099 |
| NEUT (%, IQR) | 85.10 (76.43, 90.40) | 85.00 (73.40, 89.73) | 86.88 (81.60, 91.98) | .030 |
| LY (%, IQR) | 7.45 (4.13, 13.08) | 7.85 (4.47, 14.00) | 6.10 (2.75, 10.83) | .046 |
| PLT (×109/L, IQR) | 161.00 (100.00, 232.00) | 158.00 (95.00, 223.25) | 163.50 (114.00, 277.50) | .134 |
| HCT (%, IQR) | 34.75 (28.30, 38.78) | 35.95 (28.30, 39.28) | 32.60 (28.93, 37.75) | .151 |
| ALT (U/L, IQR) | 25.00 (14.25, 47.98) | 24.5 (14.00, 42.25) | 29.55 (14.75, 70.25) | .352 |
| AST (U/L, IQR) | 32.00 (22.00, 60.00) | 31.00 (21.23, 45.43) | 37.25 (22.00, 103.48) | .098 |
| STB (μmol/L, IQR) | 17.25 (10.83, 26.78) | 17.45 (10.78, 27.25) | 16.60 (10.75, 25.78) | .841 |
| BUN (mmol/L, IQR) | 9.42 (5.87, 14.66) | 7.92 (5.39, 12.56) | 14.25 (8.99, 23.00) | <.001 |
| Cr (μmol/L, IQR) | 83.40 (57.08, 127.35) | 79.25 (55.05, 124.68) | 99.05 (68.90, 169.75) | .020 |
| K (mmol/L, IQR) | 3.70 (3.43, 4.30) | 3.65 (3.40, 4.12) | 4.10 (3.60, 4.50) | .003 |
| Na (mmol/L, IQR) | 138.15 (134.00, 143.23) | 138.05 (134.00, 143.00) | 138.60 (132.78, 146.48) | .675 |
| FIB (g/L, IQR) | 4.42 (3.45, 5.90) | 4.41 (3.54, 6.53) | 4.46 (3.25, 5.50) | .689 |
| PT (S, IQR) | 13.50 (12.40, 15.60) | 13.50 (12.48, 15.10) | 14.45 (12.15, 16.93) | .251 |
| APTT (S, IQR) | 30.50 (26.93, 36.88) | 30.05 (26.58, 37.80) | 32.30 (27.73, 36.55) | .273 |
| D-D (mg/L, IQR) | 3.04 (1.67, 6.72) | 2.90 (1.57, 6.43) | 3.43 (2.12, 8.22) | .141 |
| CRP (mg/L, IQR) | 65.22 (16.74, 121.19) | 65.78 (16.03, 136.82) | 58.11 (22.07, 105.76) | .976 |
| PCT (ng/mL, IQR) | 0.87 (0.21, 4.96) | 0.78 (0.15, 5.59) | 1.14 (0.34, 3.19) | .304 |
ALT = alanine aminotransferase, APACHE II score = Acute Physiology and Chronic Health Evaluation II score, APTT = Activated Partial Thromboplastin Time, AST = aspartate aminotransferase, BUN = Blood Urea Nitrogen, Cr = Creatinine, CRP = C-reactive protein, D-D = D-dimer, FIB = fibrinogen, Hb = hemoglobin, HCT = hematocrit, K = Potassium, LY% = Lymphocyte, Na = Sodium, NEUT% = Neutrophil, PCT = Procalcitonin, PLT = platelet, PT = prothrombin time, RBC = Erythrocytes, SOFA score = Sequential Organ Failure Asses score, STB = Serum Total Bilirubin, WBC = leukocyte.
3.2. Univariate analysis of prognostic factors in sepsis patients
In total, the basic information, comorbidities, infection locations, clinical indicators, and other factors of the patients were all included. This study identified 16 variables that may be related to the prognosis of ICU sepsis patients (P < .20). The specifically associated variables are age (P = .012), gender (P = .041), cardiovascular disease (P = .042), cerebrovascular disease (P = .012), pulmonary infections (P = .082), APACHE II score (P = .001), SOFA score (P = .001), use of vasopressors (P < .001), RBC (P = .055), Hb (P = .038), WBC (P = .086), NEUT% (P = .023), ALT (P = .001), BUN (P = .008), K (P = .027), CRP (P = .099). Analysis showed that age, gender, cardiovascular disease, cerebrovascular disease, pulmonary infection, APACHE II score, SOFA score, use of vasopressors, RBC, Hb, WBC, NEUT%, ALT, BUN, K, CRP were the factors affecting the prognosis of patients with sepsis, with a statistically significant difference. There were no significant differences in other variables. As shown in Table 2.
Table 2.
Univariate analysis of prognostic factors in sepsis patients.
| Variables | β | SE | Wald | P value | OR (95% CI) |
|---|---|---|---|---|---|
| Age | 1.584 | 0.630 | 6.320 | .012 | 4.876 (1.418–16.771) |
| Gender | 0.710 | 0.347 | 4.177 | .041 | 2.034 (1.030–4.017) |
| Comorbidity | |||||
| Hypertension | 0.320 | 0.362 | 0.784 | .376 | 1.378 (0.678–2.800) |
| Diabetes | 0.182 | 0.363 | 0.252 | .616 | 1.200 (0.589–2.446) |
| Cardiovascular diseases | 0.759 | 0.373 | 4.146 | .042 | 2.135 (1.029–4.432) |
| Cerebrovascular diseases | 0.904 | 0.358 | 6.371 | .012 | 2.469 (1.224–4.981) |
| Chronic kidney diseases | 0.620 | 0.598 | 1.077 | .299 | 1.860 (0.576–6.004) |
| Chronic liver diseases | −0.366 | 0.810 | 0.205 | .651 | 0.693 (0.142–3.390) |
| Cancer | 0.204 | 0.712 | 0.082 | .775 | 1.226 (0.303–4.953) |
| Primary site of infection | |||||
| Lung infections | 0.900 | 0.517 | 3.027 | .082 | 2.460 (0.892–6.782) |
| Abdominal infections | −1.082 | 1.075 | 1.013 | .314 | 0.339 (0.041–2.786) |
| Urinary tract infections | −20.187 | 23205.422 | 0.000 | .999 | NA |
| Other infections | −0.586 | 0.579 | 1.025 | .311 | 0.556 (0.179–1.731) |
| APACHE II score | 0.766 | 0.237 | 10.448 | .001 | 2.151 (1.352–3.423) |
| SOFA score | 0.940 | 0.275 | 11.702 | .001 | 2.561 (1.494–4.389) |
| Use of vasopressors | 3.010 | 0.463 | 42.345 | <.001 | 20.296 (8.196–50.256) |
| RBC | −0.345 | 0.180 | 3.670 | .055 | 0.708 (0.498–1.008) |
| Hb | −0.377 | 0.182 | 4.322 | .038 | 0.686 (0.480–0.979) |
| WBC | −0.554 | 0.323 | 2.952 | .086 | 0.574 (0.305–1.081) |
| NEUT% | −0.950 | 0.419 | 5.148 | .023 | 0.387 (0.170–0.879) |
| LY% | −17.595 | 5962.504 | 0.000 | .998 | NA |
| PLT | 0.116 | 0.183 | 0.398 | .528 | 1.123 (0.784–1.609) |
| HCT | −0.245 | 0.244 | 1.005 | .316 | 0.783 (0.485–1.263) |
| ALT | −0.893 | 0.270 | 10.921 | .001 | 0.409 (0.241–0.695) |
| AST | −0.377 | 0.306 | 1.521 | .217 | 0.686 (0.376–1.249) |
| STB | −0.225 | 0.369 | 0.370 | .543 | 0.799 (0.387–1.647) |
| BUN | −0.918 | 0.346 | 7.035 | .008 | 0.399 (0.203–0.787) |
| Cr | −0.230 | 0.266 | 0.746 | .388 | 0.794 (0.471–1.339) |
| K | 0.491 | 0.222 | 4.918 | .027 | 1.635 (1.059–2.524) |
| Na | −0.063 | 0.190 | 0.109 | .741 | 0.939 (0.646–1.364) |
| FIB | −0.050 | 0.302 | 0.027 | .869 | 0.952 (0.527–1.719) |
| PT | 0.287 | 0.408 | 0.494 | .482 | 1.322 (0.599–2.966) |
| APTT | 0.029 | 0.254 | 0.013 | .908 | 1.030 (0.626–1.692) |
| D-D | 20.211 | 16408.712 | 0.000 | .999 | NA |
| CRP | 1.263 | 0.766 | 2.716 | .099 | 3.536 (0.787–15.876) |
| PCT | 0.447 | 0.361 | 1.535 | .215 | 1.564 (0.771–3.173) |
ALT = alanine aminotransferase, APACHE II score = Acute Physiology and Chronic Health Evaluation II score, APTT = Activated Partial Thromboplastin Time, AST = aspartate aminotransferase, BUN = Blood Urea Nitrogen, Cr = creatinine, CRP = C-reactive protein, D-D = D-dimer, FIB = fibrinogen, Hb = hemoglobin, HCT = hematocrit, K = potassium, LY% = lymphocyte, Na = sodium, NEUT% = Neutrophil, PCT = procalcitonin, PLT = platelet, PT = Prothrombin Time, RBC = Erythrocytes, SOFA score = Sequential Organ Failure Asses score, STB = Serum Total Bilirubin, WBC = Leukocyte.
3.3. Univariate analysis of pathogen distribution in sepsis patients
A total of 82 pathogenic strains were isolated, including 34 (19.32%) gram-positive, 21 (11.93%) gram-negative, and 27 (15.34%) fungi. The most common gram-negative pathogens were Klebsiella pneumoniae and Acinetobacter baumannii, the most common gram-positive pathogens were Staphylococcus aureus and Streptococcus pneumoniae, while the most common fungi pathogen was Candida albicans, and there were 17 (9.66%) cases of co-infections. As shown in Table 3.
Table 3.
Isolation number and composition ratio of pathogens in sepsis patients.
| Pathogens, n (%) | Total | Survivors | Non-survivors | P value |
|---|---|---|---|---|
| (N = 176) | (N = 130) | (N = 46) | ||
| Gram-negative bacteria | 34 (19.32) | 26 (20.00) | 8 (17.39) | .700 |
| Klebsiella pneumoniae | 12 (6.82) | 10 (7.69) | 2 (4.35) | .665 |
| Acinetobacter baumannii | 9 (5.11) | 7 (5.38) | 2 (4.35) | 1.000 |
| Pseudomonas aeruginosa | 7 (3.98) | 5 (3.85) | 2 (4.35) | 1.000 |
| Escherichia coli | 2 (1.14) | 2 (1.54) | 0 (0.00) | 1.000 |
| Other | 4 (2.27) | 2 (1.54) | 2 (4.35) | .601 |
| Gram-positive bacteria | 21 (11.93) | 13 (10.00) | 8 (17.39) | .184 |
| Staphylococcus aureus | 9 (5.11) | 7 (5.38) | 2 (4.35) | 1.000 |
| Streptococcus pneumoniae | 6 (3.41) | 4 (3.08) | 2 (4.35) | 1.000 |
| Enterococcus | 3 (1.70) | 2 (1.54) | 1 (2.17) | 1.000 |
| Corynebacterium striatum | 3 (1.70) | 0 (0.00) | 3 (6.52) | .017 |
| Fungi | 27 (15.34) | 20 (15.38) | 7 (15.22) | .978 |
| Candida albicans | 20 (11.36) | 15 (11.54) | 5 (10.87) | .902 |
| Candida tropicalis | 4 (2.27) | 3 (2.31) | 1 (2.17) | 1.000 |
| Other | 3 (1.70) | 2 (1.54) | 1 (2.17) | 1.000 |
| Co-infections | 17 (9.66) | 13 (10.00) | 4 (8.70) | 1.000 |
The isolates with a detection rate greater than 5% in the survivors were C albicans, K pneumoniae, A baumannii, and S aureus in sequence. As for the non-survivors, the strains with a detection rate of more than 5% were C albicans, and Corynebacterium striatum. There was no significant difference in the detection rate between the survival group and the non-survival group for specific pathogens (K pneumoniae, A baumannii, Pseudomonas aeruginosa, Escherichia coli, S aureus, S pneumoniae, Enterococcus, C striatum, C albicans, and C tropicalis) (P > .20). In co-infections, there was no statistically significant difference between the survival group and the non-survival group (P = .797). As shown in Table 4.
Table 4.
Univariate analysis of pathogen distribution in sepsis patients.
| Variables | β | SE | Wald | P value | OR (95% CI) |
|---|---|---|---|---|---|
| Gram-negative bacteria | |||||
| Klebsiella pneumoniae | −0.606 | 0.794 | 0.582 | .445 | 0.545 (0.115–2.588) |
| Acinetobacter baumannii | −0.225 | 0.821 | 0.075 | .784 | 0.799 (0.160–3.991) |
| Pseudomonas aeruginosa | 0.128 | 0.855 | 0.022 | .881 | 1.136 (0.213–6.069) |
| Escherichia coli | −20.180 | 28420.722 | 0.000 | .999 | NA |
| Other | 1.068 | 1.015 | 1.106 | .293 | 2.909 (0.398–21.274) |
| Gram-positive bacteria | |||||
| Staphylococcus aureus | −0.225 | 0.821 | 0.075 | .784 | 0.799 (0.160–3.991) |
| Streptococcus pneumoniae | 0.359 | 0.884 | 0.165 | .685 | 1.432 (0.253–8.090) |
| Enterococcus | 0.352 | 1.237 | 0.081 | .776 | 1.422 (0.126–16.064) |
| Corynebacterium striatum | 22.309 | 23205.422 | 0.000 | .999 | NA |
| Fungi | |||||
| Candida albicans | −0.067 | 0.547 | 0.015 | .902 | 0.935 (0.320–2.734) |
| Candida tropicalis | −0.061 | 1.168 | 0.003 | .958 | 0.941 (0.095–9.276) |
| Other | 0.352 | 1.237 | 0.081 | .776 | 1.422 (0.126–16.064) |
| Co-infections | −0.154 | 0.599 | 0.066 | .797 | 0.857 (0.265–2.775) |
3.4. Multivariate analysis of prognostic factors in sepsis patients
The above 16 variables in the univariate analysis P < .20 were included in the multivariate analysis. The specific variables were age, gender, cardiovascular disease, cerebrovascular disease, pulmonary infections, APACHE II score, SOFA score, use of vasopressors, RBC, Hb, WBC, NEUT%, ALT, BUN, K, CRP. The results of multivariate analysis showed that gender, cardiovascular disease, cerebrovascular disease, pulmonary infections, the use of vasopressor drugs, WBC and ALT were important factors that affected the prognosis of patients with sepsis. Female, cardiovascular disease, cerebrovascular disease, pulmonary infections as the primary site of infection, use of vasopressors, WBC < 3.5 × 109/L, ALT < 7 U/L, and ALT > 40 U/L were predictors of death in ICU patients with sepsis. As shown in Table 5.
Table 5.
Multivariate analysis of prognostic factors in sepsis patients.
| Variables | β | SE | Wald | P valve | OR (95% CI) |
|---|---|---|---|---|---|
| Female | 1.636 | 0.561 | 8.495 | .004 | 5.135 (1.709–15.427) |
| Cardiovascular diseases | 1.836 | 0.629 | 8.521 | .004 | 6.272 (1.828–21.518) |
| Cerebrovascular diseases | 1.142 | 0.537 | 4.517 | .034 | 3.133 (1.093–8.981) |
| Lung infections | 1.902 | 0.687 | 7.669 | .006 | 6.700 (1.744–25.748) |
| Use of vasopressors | 3.529 | 0.603 | 34.235 | <.001 | 34.085 (10.452–111.155) |
| WBC < 3.5 × 109/L | 2.277 | 0.995 | 5.234 | .022 | 9.752 (1.386–68.620) |
| ALT < 7 U/L | 2.038 | 0.920 | 4.901 | .027 | 7.672 (1.263–46.594) |
| ALT > 40 U/L | 1.207 | 0.568 | 4.509 | .034 | 3.343 (1.097–10.185) |
| Constant | −5.267 | 0.992 | 28.197 | <.001 |
ALT = alanine aminotransferase, WBC = leukocyte.
3.5. Analysis of the diagnostic value of predictive factors in sepsis patients
The overall multivariate analysis showed high diagnostic value, with an area under the receiver operating curve of 0.917 (95% CI: 0.871, 0.964), as shown in Figure 1. The AUC for each predictor was 0.587 (95% CI: 0.491, 0.684) for gender, 0.577 (95% CI: 0.478, 0.676) for cardiovascular disease, and 0.601 (95% CI: 0.503, 0.699) for cerebrovascular disease, pulmonary infection was 0.561 (95% CI: 0.468, 0.654), the use of vasopressors was 0.816 (95% CI: 0.743, 0.889), WBC was 0.425 (95% CI: 0.331, 0.519), and ALT was 0.363 (95% CI: 0.265, 0.460). The model prediction effect is effective.
Figure 1.
Receiver operator characteristic (ROC) curve of the independent risk factors for the prediction of death. AUC = area under curve.
4. Discussion
Sepsis is a life-threatening infectious disorder that is frequently a cause of admission and mortality in the ICU patients.[14] Adherence to the latest international guidelines and best practices informed the inclusion criteria and study design employed in this study.[1] It is of paramount importance to identify a reliable approach to predict the prognosis of ICU patients with sepsis with a high degree of accuracy. This study observed that the accuracy rate of the model based on multiple variables was as high as 90%, thereby providing potential application and filling the gap of analyzing the prognosis and predictive value of risk factors in ICU sepsis patients. Our findings could serve as a reference for clinical diagnosis and treatment of sepsis.
4.1. Pathogen distribution
There were 82 pathogenic strains were isolated in our study, and the results showed that sepsis infection was mainly caused by gram-negative bacteria, accounting for 35.37% (29/82), which was consistent with the above study.[15] Among gram-negative bacteria, the most common pathogens were K pneumoniae and A baumannii. There was one study pointed out that although gram-positive pathogens remain the most common cause of sepsis, fungal organisms were increasing rapidly.[16] Our results show that fungal infection is the second leading cause of sepsis after gram-negative bacteria, and C albicans was the most common fungi pathogen. We also found that S aureus and S pneumoniae were the most common gram-positive bacteria in this study, these results were consistent with the previously reported studies.[17,18] However, our findings showed that there was no statistical difference between pathogen distribution and risk of death in sepsis patients, the discrepancies between studies might due to the sample size of this study was small.
4.2. Gender
The prognosis of ICU sepsis patients in the female patient group was found to be poor in this study, which was consistent with the findings of Sakr et al,[19] Mahmood et al,[20] Nachtigall et al,[21] and Combes et al.[22] This may be related to the fact that female patients are often complicated by underlying diseases such as hypertension, diabetes, coronary heart disease, changes in hormones in the body, and decreased immunity. Women have a large advantage in sepsis survival, according to Weniger et al,[23] since estrogen has an anti-inflammatory impact on the body. However, the majority of the included cases were elderly women in this study, and the estrogen in the patients was in the declining stage. Furthermore, as the body ages and immunity declines, the prognosis of ICU sepsis in female patients is poor with a low clinical cure rate.
4.3. Cardiovascular disease
In this study, by analyzing the relationship between the comorbidities and prognosis of ICU sepsis patients, it was found that previous cardiovascular disease was a related risk factor for death in sepsis patients. This conclusion is consistent with Wu et al,[24] Ho et al,[25] and Sinning et al[26] Trinder et al[27] believed that high-density lipoprotein cholesterol is a major risk factor for cardiovascular disease, and inhibition of cholesteryl ester transfer protein can maintain high-density lipoprotein cholesterol levels and improve outcomes in patients with sepsis. The effect of comorbid cardiovascular disease on the prognosis of sepsis patients may be related to pathophysiological mechanisms such as inflammatory response burst, hemodynamic disturbance, and accelerated atherosclerosis.[28,29] Therefore, for patients with previous cardiovascular disease, precise monitoring and early active intervention can be strengthened, which will help reduce the death caused by sepsis patients and improve the treatment effect of sepsis.
4.4. Cerebrovascular disease
According to the findings of this study, concomitant cerebrovascular illness is one of the indicators of poor outcomes in ICU patients with sepsis. This may be related to mechanisms such as coagulation disorders, intestinal flora/endotoxin translocation, pro-inflammatory/anti-inflammatory balance imbalance, oxidative stress imbalance, and vascular endothelial dysfunction.[30–32] This conclusion is consistent with the findings of Xu et al[33] and Berger et al.[34] Crapser et al[30] showed that because ischemic stroke affects intestinal permeability and bacterial translocation in mice, immune dysfunction occurs secondary to sepsis. Therefore, for sepsis patients with a history of cerebrovascular disease, early diagnosis and treatment can be carried out by preventive measures such as improving microcirculation and hemorheology, and improving immunity. We can try to reduce the mortality of sepsis patients with cerebrovascular disease by balancing the relationship between excessive inflammation and immunosuppression.
4.5. Lung infections
Sepsis is a systemic inflammatory response caused by pathogenic microorganism infection. It is very important to clarify the primary infection site for the prognosis of patients with sepsis. The results of this study showed that the primary infection site of ICU sepsis patients with poor prognosis was more common in pulmonary infection, which was consistent with the conclusions of Montull et al,[35] Pereira et al,[36] Kim et al.[37] Pulmonary infection often leads to immune dysfunction, characterized by inflammatory storms caused by excessive release of inflammatory mediators, and immunosuppression caused by weakened innate immune function and T cell apoptosis.[38] However, the inflammatory storm in turn will also aggravate the disorder of innate immune function, leading to excessive activation of platelets, loss of tight connection of endothelial cells and, increase of vascular permeability, resulting in sepsis and even secondary multiple organ failure (MOF), which ultimately leads to death.[7,39] Moreover, the lung is one of the most vulnerable target organs in pathological changes. Severe pulmonary infection often results in an uncontrolled inflammatory response. Acute respiratory distress syndrome (ARDS) and multiple organ dysfunction syndrome (MODS) are serious consequences. Without timely treatment, patients not only have a poor prognosis but also a high mortality rate.[40] Therefore, for patients with sepsis admitted to ICU with pulmonary infection, attention should be paid and timely treatment should be given. By understanding and using the regulatory mechanism of the immune system in inflammatory storms, the inflammatory response should be balanced and controlled to maintain the state of effectively killing pathogenic microorganisms without causing damage to the immune system, which is conducive to predicting and improving the prognosis of sepsis patients in ICU.
4.6. Use of vasopressors
Septic shock is a critical stage in the process of the systemic inflammatory response caused by severe injury and infection.[41] The timely use of vasopressors can increase the body’s perfusion pressure and maintain circulatory stability, which is essential to reduce organ damage and reduce death due to septic shock.[42] The international guidelines point out that vasopressor drugs are an important part of the multi-faceted bundle management of sepsis and septic shock.[1] However, Anantasit et al[43] found that vasopressors had certain side effects through a retrospective cohort study of the multi-center and single-center validation cohort. The incidence of serious side effects of vasopressin (0.01–0.03 U/min) and norepinephrine (5–26.7 μg/min) was 10.8% to 12.6% and 9% to 10.2%. Bouchard et al[44] showed that the application of vasopressor drugs was an independent risk factor for in-hospital death in ICU patients, which was consistent with the conclusion of this study. The use of vasopressors was shown to be a major factor influencing the outcome of ICU sepsis patients in this study. The reason for the above phenomenon may be that patients with vasopressor drugs often have septic shock, and such patients are seriously ill and have a greater possibility of poor prognosis. In addition, the limitation of vasopressor drugs in dosage and drug selection is also one of the important reasons for the deterioration of the disease.
4.7. WBC < 3.5 × 109/L
In contrast to the results of Goldberg et al,[45] the results of this study showed that WBC < 3.5 × 109/L was a risk factor affecting the prognosis of ICU patients with sepsis. As immune cells and important immune markers in human blood, leukocyte can well resist the invasion of foreign pathogens. When continuous decline occurs, the body’s defense ability becomes poor, immune function is unbalanced, and it is prone to serious infection and sepsis, which threatens the lives of patients. In this study, the patients with WBC < 3.5 × 109/L were mainly > 80 years, the body was aging and the immunity was low, and most of them had underlying diseases such as hypertension and cardiovascular and cerebrovascular diseases. Leading to a large consumption of leukocyte. It can be seen that sepsis patients admitted to the ICU with low leukocyte levels are more critically ill, and have a poor prognosis. Hence, accurate judgment and early intervention can be made according to the baseline leukocyte level of hospitalized sepsis patients.
4.8. Abnormal ALT
The liver is an important organ that regulates the body’s metabolism and host defense. It has the functions of removing endotoxins and bacteria, metabolizing drugs, maintaining homeostasis, and analyzing proteins required for immunity and blood coagulation. As one of the most vulnerable target organs, damage to liver function is often manifested by abnormalities in indicators such as alanine aminotransferase, aspartate aminotransferase, and bilirubin, and liver function indicators can be used to determine the prognosis and assess the severity of sepsis patients.[46,47] The results of this study showed that abnormal ALT indexes (ALT < 7 U/L and ALT > 40 U/L) were predictors of poor prognosis in ICU sepsis patients. Patients with abnormal ALT indexes often present with hepatic impairment, therefore, such patients are prone to develop septic liver injury in the follow-up treatment, which is related to severe inflammatory response imbalance, oxygen-free radical damage, and hepatic microcirculation disorders.[48–50] At the same time, during the treatment of sepsis, it is necessary to use a large number of drugs that may cause side effects, above treatments increase the burden on patients with hepatic insufficiency and increase the mortality rate.
4.9. Limitations
There were several limitations in this study. First, the trial was a multicenter observational study, which may result in the possibility of biased assessments. In addition, due to the small sample size of this study, we need to conduct a larger sample size to confirm this result. Finally, we did not conduct follow-up and survival analysis on the 28- and 60-day prognoses of ICU sepsis patients, thus a high-level clinical trial is necessary in the future to improve the reliability of results.
5. Conclusion
In conclusion, gender, cardiovascular diseases, cerebrovascular disease, pulmonary infection, use of vasopressors, WBC, and ALT were risk factors for sepsis patients in ICU. Female, complicated with cardiovascular disease, complicated with cerebrovascular disease, pulmonary infection as the primary infection site, use of vasopressors, WBC < 3.5 × 109/L, ALT < 7 U/L, ALT > 40 U/L were predictive factors for ICU sepsis death. The accuracy of the model based on the above variables can reach 90%, which has certain application potential. Early detection and active intervention in the onset and development of sepsis are advantageous for healthcare staff members to stop it from developing into a condition with a poor prognosis. It can effectively improve outcome of ICU sepsis patients, and provide technical support for the diagnosis and treatment.
Acknowledgments
We acknowledge all medical centers, researchers, and patients for their contribution to collect data in this study.
Author contributions
Data curation: Wen Zhang, Yuting Pu, Xiangru Xu.
Funding acquisition: Shuang Zhou, Bang jiang Fang.
Investigation: Yuting Sun, Yuerong Fei.
Software: Caiyu Chen.
Supervision: Shuang Zhou, Bang jiang Fang.
Writing – original draft: Caiyu Chen, Xinxin Wu.
Writing – review & editing: Caiyu Chen, Xinxin Wu.
Abbreviations:
- ALT
- alanine aminotransferase
- APACHE II score
- Acute Physiology and Chronic Health Evaluation II score
- AUC
- area under curve
- BUN
- Blood Urea Nitrogen
- CI
- confidence interval
- Cr
- Creatinine
- CRP
- C-reactive protein
- Hb
- hemoglobin
- K
- potassium
- NEUT%
- neutrophil
- OR
- odd ratios
- RBC
- erythrocytes
- SOFA score
- Sequential Organ Failure Asses score
- WBC
- Leukocyte
CC and XW contributed equally to this work.
This study was supported by the National Key Research and Development Program Project (2018YFC1705900); Shanghai Key Specialized Clinical Program (shslczdzk04401); the Construction of National TCM Emergency Medical Rescue Base [ZY (2021-2023)-0101-01].
The study was approved by the Medical Ethics Committee of Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine (approval number: 2019-012). All data were anonymized to maintain participants’ privacy. Written informed consent was obtained from the patients or guardian. We certify that the study was performed in accordance with the 1964 Declaration of Helsinki and later amendments.
The authors have no conflicts of interest to disclose.
The data that support the findings of this study are available from a third party, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of the third party.
How to cite this article: Chen C, Wu X, Zhang W, Pu Y, Xu X, Sun Y, Fei Y, Zhou S, Fang B. Predictive value of risk factors for prognosis of patients with sepsis in intensive care unit. Medicine 2023;102:23(e33881).
Contributor Information
Caiyu Chen, Email: chency911@163.com.
Xinxin Wu, Email: 15926223238@163.com.
Wen Zhang, Email: zhangwen_44@163.com.
Yuting Pu, Email: Syt_354@163.com.
Xiangru Xu, Email: 178848003@qq.com.
Yuting Sun, Email: Syt_354@163.com.
Yuerong Fei, Email: fyrkysl@163.com.
Shuang Zhou, Email: zhoushuang8008@163.com.
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