Predicting Healthcare Utilization by Patients Admitted for COPD Exacerbation OC13-OC17
Dr. Karthikeyan Ramaraju,
Associate Professor, Department of Respiratory Medicine, PSG Institute of Medical Sciences and Research,
Peelamedu, Coimbatore – 641004, Tamilnadu, India.
Background: Healthcare utilization, especially length of hospital stay and ICU admission, for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) determine overall outcomes in terms of morbidity, mortality and cost burden. Predicting prolonged hospital stay (PHS) and prolonged intensive care (PIC) for AECOPD is useful for rational allocation of resources in healthcare centres.
Aim: To characterize the pattern of healthcare utilization by COPD patients hospitalized for acute exacerbation, and to identify clinical and laboratory predictors of ‘prolonged hospital stay’ (PHS) and ‘prolonged intensive care’(PIC)
Materials and Methods: This study attempted through retrospective data analysis, to identify risk factors and evolve prediction models for increased healthcare utilization namely PHS and PIC for AECOPD. The data were extracted from 255 eligible admissions for AECOPD by 166 patients from Aug 2012 to July 2013. Logistic regression analysis was used for identifying predictors and models were tested with area under receiver operating characteristic curve.
Results: Independent predictors of prolonged hospital stay (= 6 days) were chronic respiratory failure at baseline, low saturation at admission, high HbA1c level and positive isolates in sputum culture. Independent predictors of prolonged intensive care (for = 48 hours) were past history of pulmonary tuberculosis, chronic respiratory failure at baseline, low saturation at admission, high leukocyte count and positive culture isolates in sputum. Prediction models evolved from variables available at admission showed AUC 0.805 (95% CI 0.729 – 0.881) and 0.825 (95% CI 0.75 – 0.90) for PHS and ICU admissions respectively.
Conclusion: Our prediction models derived from simple and easily available variables show good discriminative properties in predicting PHS and PIC for AECOPD. When prospectively validated, these models are useful for rational allocation of services especially in resource limited settings.