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A predictive model for 28-day mortality after discharge in patients with sepsis associated with cerebrovascular
1Department of Neurology, Weifang People's Hospital, Weifang, China.
Background:
The present study investigated the association between cerebrovascular diseases and sepsis, including its occurrence, progression, and impact on mortality. However, there is currently a lack of predictive models for 28-day mortality in patients with cerebrovascular disease associated with sepsis.
Objective:
The objective of this study is to examine the mortality rate within 28 days after discharge in this population, while concurrently developing a corresponding predictive model.
Methods:
The data for this retrospective cohort study were obtained from the MIMIC-IV database. Patients with sepsis and cerebrovascular disease in the ICU were included. Laboratory indicators, vital signs, and demographic data were collected within 24 hours of ICU admission. Mortality rates within 28 days after discharge were calculated based on patient death times. Logistic regression analysis was used to identify potential variables for a predictive model. A nomogram visualized the prediction model. The performance of the model was evaluated using ROC curves, Calibration plots, and DCA.
Results:
The study enrolled a total of 2660 patients diagnosed with cerebrovascular disease complicated by sepsis, consisting of 1434 males (53.91%) with a median age of 70.97 (59.60, 80.73). Among this cohort of patients, a total of 751 fatalities occurred within 28 days following discharge. The multivariate regression analysis revealed that age, creatinine, arterial oxygen partial pressure (Pa O2), arterial carbon dioxide partial pressure (Pa CO2), respiratory rate, white blood cell (WBC) count, Body Mass Index (BMI), and race demonstrated potential predictive variables. The aforementioned model yielded an area under the ROC curve of 0.744, accompanied by a sensitivity of 66.2% and specificity of 71.2%. Furthermore, both calibration plots and DCA demonstrated robust performance in practical applications.
Conclusion:
The proposed prediction model allows clinicians to promptly assess the mortality risk in patients with cerebrovascular disease complicated by sepsis within 28 days after discharge, facilitating early intervention strategies. Consequently, clinicians can implement additional advantageous medical interventions for individuals with cerebrovascular disease and sepsis.
