Construction and evaluation of short - term and long - term mortality risk prediction model for patients with sepsis based on MIMIC - IV database

Abstract

Objectives: Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk.

Methods: Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model.

Results: A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value.

Conclusions: The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.

Keywords: Medical Information Mart for Intensive Care-IV database; predictive model; prognostic factors; sepsis; short-term and long-term deaths.

图1

全子集回归变量筛选图 Figure 1 Full subset regression variable selection plot SOFA: Sepsis-related organ failure assessment; SAPS Ⅱ: Simplified acute physiology score II; GCS: Glasgow coma scale; Hb: Hemoglobin; WBC: White blood cell count; PT: Prothrombin time; SCr: Serum creatinine.

图2

预测脓毒症患者28 d及1年总生存率的列线图 Figure 2 Nomogram of predicted 28-day and 1-year overall survival rates in sepsis patients PT: Prothrombin time; SAPS Ⅱ: Simplified acute physiology score II.

图3

脓毒症患者死亡风险预测模型的时间依赖性AUC Figure 3 Time-dependent AUC for the mortality risk prediction model in sepsis patients A: Time-dependent AUC of mortality risk prediction model in the modeling group; B: Time-dependent AUC of mortality risk prediction model in the validation group. AUC: Area under the curve.

图4

脓毒症患者28 d及1年死亡风险预测模型的校准曲线 Figure 4 Calibration curves for the 28-day and 1-year mortality risk prediction models in sepsis patients A: Calibration curve of the 28-day mortality risk prediction model in the modeling group; B: Calibration curve of the 1-year mortality risk prediction model in the modeling group; C: Calibration curve of the 28-day mortality risk prediction model in the validation group; D: Calibration curve of the 1-year mortality risk prediction model in the validation group.

图5

脓毒症患者28 d及1年死亡风险预测模型的决策曲线 Figure 5 Decision curves for the 28-day and 1-year mortality risk prediction models in sepsis patients A: Decision curves of the mortality risk prediction model in the modeling group; B: Decision curves of the mortality risk prediction model in the validation group.