Construction and Validation of an Early Warning Model for Predicting the 28-Day Mortality in Sepsis Patients with Chronic Obstructive Pulmonary Disease.

Abstract

Background: In the intensive care unit (ICU), approximately 45.6% of patients diagnosed with chronic obstructive pulmonary disease (COPD) also presented with sepsis, and this cohort exhibited a significantly higher 28-day mortality rate compared to sepsis patients without COPD (23.6% versus 16.4%). A novel nomogram is necessary to predict the risk of mortality within 28 days for sepsis patients with COPD.

Methods: Clinical data from 501 sepsis patients with COPD were sourced from the MIMIC-IV database. These data were randomly allocated into a training cohort and a validation cohort in a 3:1 ratio. Independent predictors of 28-day mortality were identified through both univariate and multivariate logistic regression analyses. Subsequently, a nomogram model was developed, and its performance was assessed using receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis.

Results: The 28-day mortality rates in the training and validation cohorts were 32.7% and 27.2%, respectively. Multivariate regression analysis identified age, heart rate (HR), respiratory rate (RR), blood urea nitrogen (BUN), creatinine (Cr), lactate levels, pH, and urine output as independent risk factors for 28-day mortality in sepsis patients with COPD. Furthermore, the nomogram demonstrated superior predictive performance, with an area under the curve (AUC) of 0.784 for the training group and 0.689 for the validation group.

Conclusion: This nomogram integrates laboratory indicators pertinent to the patient's metabolic status, hypoxia status, and organ function, thereby enhancing the accuracy of early prediction of 28-day mortality in sepsis patients with COPD. Additionally, the model's comparative advantage over existing scoring systems (eg, SOFA) would enhance its impact. Our findings hold substantial implications for early prognostic assessment and clinical decision-making in this patient population. Therefore, earlier diagnosis within 24 hours of admission and proper identification of high-risk patients may reduce disease-related mortality by promoting timely treatment.