ArticleJournal of clinical and diagnostic research : JCDR2016
Predicting Healthcare Utilization by Patients Admitted for COPD Exacerbation.
Article in Journal of clinical and diagnostic research : JCDR, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Prognostic risk prediction model for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD): a systematic review and meta-analysis.Respiratory research · 2024Pooled it
- Cluster Analysis of Healthcare Utilization Patterns in Patients with Comorbid Chronic Obstructive Pulmonary Disease and Atrial Fibrillation.Journal of clinical medicine · 2026Article
- Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning.Journal of inflammation research · 2025Article
- Utility of Computed Tomography in a Differential Diagnosis for the Patients with an Initial Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation.Tuberculosis and respiratory diseases · 2019Article
- Insights about the economic impact of chronic obstructive pulmonary disease readmissions post implementation of the hospital readmission reduction program.Current opinion in pulmonary medicine · 2018Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHealthcare 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.
aimTo 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
methodsThis 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.
resultsIndependent 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.
conclusionOur 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.
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