Development and validation of predictive model for 30-day mortality in elderly patients with sepsis-associated liver dysfunction.
10.3760/cma.j.cn121430-20240923-00789
- Author:
Beiyuan ZHANG
1
;
Chenzhe HE
2
,
3
;
Zimeng QIN
2
,
3
;
Ming CHEN
1
;
Wenkui YU
1
;
Ting SU
1
Author Information
1. Department of Critical Care Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing 210008, China.
2. Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing 210008, China. Corresponding author: Yu Wenkui, Email: yudrnj@
3. com.
- Publication Type:English Abstract
- MeSH:
Humans;
Sepsis/complications*;
Retrospective Studies;
Nomograms;
Aged;
Prognosis;
Risk Factors;
Liver Diseases/mortality*;
Intensive Care Units;
ROC Curve;
Male;
Female;
Logistic Models
- From:
Chinese Critical Care Medicine
2025;37(9):802-808
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To develop and validate a nomogram model for predicting 30-day mortality among elderly patients with sepsis-associated liver dysfunction (SALD), to identify high-risk patients and improve prognosis.
METHODS:A retrospective cohort study was conducted using data extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database for elderly patients with SALD who were first admitted to the intensive care unit (ICU) of Beth Israel Deaconess Medical Center between 2008 and 2019, including basic characteristics, severity scores, underlying diseases, infection foci, 24-hour vital signs, initial laboratory indicators, 24-hour complications, and prognosis related indicators. Patients were randomly assigned to training group and validation group in a ratio of 7 : 3. The training group used the LASSO regression analysis, as well as multivariate Logistic regression analysis to screen for independent risk factors for 30-day mortality. A nomogram prediction model was constructed, and receiver operator characteristic curve (ROC curve), calibration curves, and decision curve analysis (DCA) were used to evaluate the model, and validate the model using the validation cohort.
RESULTS:A total of 630 elderly patients with SLAD were included in the study, including 441 in the training group and 189 in the validation group. Oxford acute severity of illness score (OASIS) for training group [odds ratio (OR) = 1.060, 95% confidence interval (95%CI) was 1.034-1.086], 24-hour pulse oxygen saturation (SpO2; OR = 0.876, 95%CI was 0.797-0.962), initial mean corpuscular volume (MCV; OR = 1.043, 95%CI was 1.009-1.077), initial red blood cell distribution width (RDW; OR = 1.237, 95%CI was 1.123-1.362), initial blood glucose (OR = 1.008, 95%CI was 1.004-1.013), and initial aspartate aminotransferase (AST; OR = 1.000, 95%CI was 1.000-1.001) were independent risk factors for 30-day mortality in patients (all P < 0.05). Based on the above variables, a nomogram model was constructed, and the ROC curve showed that the area under the curve (AUC) of the model in the training group was 0.757 (95%CI was 0.712-0.803), with a sensitivity of 65.05% and a specificity of 74.90%; the AUC of the model in the validation group was 0.712 (95%CI was 0.631-0.792), with a sensitivity of 58.67% and a specificity of 81.58%. The calibration curves of the training and validation groups show that both the fitted curves were close to the standard curves. The Hosmer-Lemeshow test: the training group (χ 2 = 6.729, P = 0.566), the validation group (χ 2 = 13.889, P = 0.085), indicating that the model can fit the observed data well. The DCA curve shows that when the threshold probability of the training group was 16% to 94% and the threshold probability of the validation group was 27% to 99%, the net benefit of the model was good.
CONCLUSIONS:OASIS, 24-hour SpO2, initial MCV, initial RDW, initial blood glucose and initial AST are independent risk factors for 30-day mortality in elderly patients with SALD. The nomogram based on these six variables demonstrates good predictive performance.
