Nomograms for Predicting High Hospitalization Costs and Prolonged Stay among Hospitalized Patients with pAECOPD

Area of Science:

• Pulmonology and Critical Care Medicine
• Health Services Research
• Biostatistics

Background:

• Hospitalized patients with acute exacerbations of chronic obstructive pulmonary disease and community-acquired pneumonia (pAECOPD) face significant healthcare burdens, including high costs and prolonged hospital stays.
• Predictive models are needed to identify patients at risk for these adverse outcomes, enabling targeted interventions and resource allocation.
• Existing prediction tools may not adequately address the specific complexities of pAECOPD.

Purpose of the Study:

• To develop and validate nomograms for predicting high hospitalization costs in pAECOPD patients.
• To develop and validate nomograms for predicting prolonged hospital stays in pAECOPD patients.
• To identify key clinical and demographic risk factors associated with these outcomes.

Main Methods:

• An observational study included 635 hospitalized pAECOPD patients, divided into training and testing sets.
• Univariate analysis and backward stepwise regression were used for variable selection.
• Multivariable logistic regression established nomograms, with performance assessed by ROC curves, AUC, calibration plots, and decision curve analysis (DCA).

Main Results:

• Risk factors for high hospitalization costs included elevated white blood cell count, hypoalbuminemia, pulmonary encephalopathy, respiratory failure, diabetes, and RICU admissions (AUC: 0.756 training, 0.792 testing).
• Risk factors for prolonged stays included decreased total protein, pulmonary encephalopathy, reflux esophagitis, and RICU admissions (AUC: 0.629 training, 0.620 testing).
• Both nomograms demonstrated good predictive performance and calibration.

Conclusions:

• Validated nomograms effectively predict high hospitalization costs and prolonged stays in pAECOPD patients.
• Identified risk factors provide valuable insights for clinical risk stratification and management strategies.
• These predictive tools can support healthcare providers in optimizing patient care and resource utilization for pAECOPD.