[Construction and verification of a nomogram of factors influencing the risk of death in patient with sepsis-associated thrombocytopenia]

Abstract

Objective: To construct a nomogram prediction model for predicting the risk of death in patients with sepsis-associated thrombocytopenia (SAT) in intensive care unit (ICU) for early indentification and active intervention.

Methods: Clinical data of SAT patients admitted to ICU of the First Affiliated Hospital of Nanjing Medical University from December 2019 to August 2021 were retrospectively collected, including demographic data, laboratory indicators, etc. According to the prognosis at 28 days, the patients were divided into the death group and the survival group, and the differences of clinical variables between the two groups were compared. Multivariate Logistic regression analysis was performed to analyze the independent risk factors influencing mortality of patients within 28 days, then a nomogram predictive model was constructed and its performance was verified with internal data. Receiver operator characteristic curve (ROC curve) was used to evaluate the diagnostic effectiveness of the nomogram model, and the clinical applicability of this model was evaluated by clinical decision curve analysis (DCA).

Results: A total of 275 patients were included, with 95 deaths at 28 days and a 28-day mortality of 34.5%. Compared with the survival group, acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA), lactic acid (Lac), platelet distribution width (PDW) on day 5 of ICU admission, blood urea nitrogen (BUN), total bilirubin (TBIL), aspartate aminotransferase (AST), C-reactive protein (CRP) of patients in the death group were higher, activated partial thromboplastin time (APTT) and prothrombin time (PT) were longer, platelet count (PLT) on day 3 and day 5 of ICU admission, direct bilirubin (DBIL), fibrinogen (FIB) were lower, the history of chronic lung disease, mixed site infection, lung infection, bloodstream infection, Gram-negative bacterial infection and fungal infection accounted for a higher proportion, the history of diabetes mellitus, urinary tract infection and no pathogenic microorganisms cultured accounted for a lower proportion, and the proportion of the use of vasoactive drugs, mechanical ventilation (MV), continuous renal replacement therapy (CRRT), bleeding events and platelet transfusion were higher. Multivariate Logistic regression analysis showed that APACHE II score at the day of ICU admission [odds ratio (OR) = 1.417, 95% confidence interval (95%CI) was 1.153-1.743, P = 0.001], chronic lung disease (OR = 72.271, 95%CI was 4.475-1 167.126, P = 0.003), PLT on day 5 of ICU admission (OR = 0.954, 95%CI was 0.922-0.987, P = 0.007), vasoactive drug (OR = 622.943, 95%CI was 10.060-38 575.340, P = 0.002), MV (OR = 91.818, 95%CI was 3.973-2 121.966, P = 0.005) were independent risk factors of mortality in SAT patients. The above independent risk factors were used to build a nomogram prediction model, and the area under the curve (AUC), sensitivity and specificity were 0.979, 94.7% and 91.7%, respectively, suggesting that the model had good discrimination. The Hosmer-Lemeshow goodness of fit test showed a good calibration with P > 0.05. At the same time, DCA showed that the nomogram model had good clinical applicability.

Conclusions: Patients with SAT has a higher risk of death. The nomogram model based on APACHE II score at the day of ICU admission, chronic lung disease, PLT on day 5 of ICU admission, the use of vasoactive drug and MV has good clinical significance for the prediction of 28-day mortality, and the discrimination and calibration are good, however, further verification is needed.