title: Predicting Healthcare Utilization by Patients Admitted for COPD Exacerbation.
abstract: 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.
  
  
  OBJECTIVE
  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).
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Associate Professor, Department of Respiratory Medicine, PSG Institute of Medical Sciences and Research , Coimbatore, Tamilnadu, India .
authors.forename: Karthikeyan
authors.initials: K
authors.lastname: Ramaraju
authors.affiliation: Professor, Department of Respiratory Medicine, PSG Institute of Medical Sciences and Research , Coimbatore, Tamilnadu, India .
authors.forename: Anupama Murthy
authors.initials: AM
authors.lastname: Kaza
authors.affiliation: Senior Resident, Department of Respiratory Medicine, PSG Institute of Medical Sciences and Research , Coimbatore, Tamilnadu, India .
authors.forename: Nithilavalli
authors.initials: N
authors.lastname: Balasubramanian
authors.affiliation: Assistant Professor, Department of Respiratory Medicine, PSG Institute of Medical Sciences and Research , Coimbatore, Tamilnadu, India .
authors.forename: Siddhuraj
authors.initials: S
authors.lastname: Chandrasekaran
keywords: Intensive care; Length of hospital stay