Predicting Healthcare Utilization by Patients Admitted for COPD Exacerbation

Area of Science:

• Pulmonary Medicine
• Healthcare Management

Background:

• Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) significantly impacts healthcare utilization, including hospital length of stay and ICU admission.
• Predicting prolonged hospital stay (PHS) and prolonged intensive care (PIC) is crucial for efficient healthcare resource allocation.

Purpose of the Study:

• To characterize healthcare utilization patterns in COPD patients hospitalized for AECOPD.
• To identify clinical and laboratory predictors of PHS and PIC.

Main Methods:

• Retrospective data analysis of 255 AECOPD admissions from 166 patients.
• Logistic regression was used to identify risk factors and develop prediction models.
• Model performance was assessed using the area under the receiver operating characteristic curve (AUC).

Main Results:

• Key predictors for PHS (≥ 6 days) included chronic respiratory failure, low admission saturation, high HbA1c, and positive sputum culture.
• Predictors for PIC (≥ 48 hours) comprised a history of pulmonary tuberculosis, chronic respiratory failure, low admission saturation, high leukocyte count, and positive sputum culture.
• Developed prediction models demonstrated good discriminative ability with AUCs of 0.805 for PHS and 0.825 for PIC.

Conclusions:

• Prediction models utilizing readily available admission variables effectively identify patients at risk for PHS and PIC in AECOPD.
• These models offer valuable tools for optimizing healthcare service allocation, particularly in resource-constrained environments.