A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem

Abstract

Frequent COPD exacerbations have a large impact on morbidity, mortality and health-care expenditures. By 2020, the World Health Organization expects COPD and COPD exacerbations to be the third leading cause of death world-wide. Furthermore, In 2005 it was estimated that COPD exacerbations cost the U.S. health-care system 38 billion dollars. Studies attempting to determine factors related to COPD readmissions are still very limited. Moreover, few have used a organized machine-learning, sensitivity analysis approach, such as a Random Forest (RF) statistical model, to analyze this problem. This study utilized the RF machine learning algorithm to determine factors that predict risk for multiple COPD exacerbations in a single year.This was a retrospective study with a data set of 106 patients. These patients were divided randomly into training (80%) and validating (20%) data-sets, 100 times, using approximately sixty variables intially, which in prior studies had been found to be associated with patient readmission for COPD exacerbation. In an interactive manner, an RF model was created using the training set and validated on the testing dataset. Mean area-under-curve (AUC) statistics, sensitivity, specificity, and negative/positive predictive values (NPV, PPV) were calculated for the 100 runs.The following variables were found to be important predictors of patients having at least two COPD exacerbations within one year: employment, body mass index, number of previous surgeries, administration of azithromycin/ceftriaxone/moxifloxacin, and admission albumin level. The mean AUC was 0.72, sensitivity of 0.75, specificity of 0.56, PPV of 0.7 and NPV of 0.63. Histograms were used to confirm consistent accuracy.The RF design has consistently demonstrated encouraging results. We expect to validate our results on new patient groups and improve accuracy by increasing our training dataset. We hope that identifying patients at risk for frequent readmissions will improve patient outcome and save valuable hospital resources.

A prediction model for Clostridium difficile recurrence

“…There have been multiple publications that have demonstrated the association of these different variables with the acquisition of CDI and subsequent re-infections; however, the use of an organized machine learning, sensitivity analysis approach, such as a Random Forest (RF) statistical model, has not been used. In our study, we emulated the techniques of Amalakuhan et al ( 6 ) and the prediction model they created using an RF model in predicting patients at risk for chronic obstructive pulmonary disease (COPD) exacerbation. We employed the RF machine learning algorithm to predict C. difficile recurrence (CDR).…”

Prediction models for exacerbations in patients with COPD

“…Exacerbations were not adjudicated by a committee in any study. The prediction models were mainly based on prospective cohort studies (control arm of a randomised controlled trial for one model [30]), while two prediction models were based on retrospective cohort studies [28,46]). Follow-up periods ranged from 14 days to up to 9 years (the most common follow-up was up to 1 year).…”

“…the two categories statistical method and performance evaluation). Finally, two studies [28,30] performed an internal validation [54] and two studies [23,29] an external validation (other studies had a validation cohort, but they made a prediction for other outcomes or they did not provide any performance measure for the outcome exacerbation).…”

Development and prospective validation of a model estimating risk of readmission in cancer patients

“…Many such models have been developed and typically estimate risk using data from the electronic medical record (EMR) or hospital administrative data sources . Some models are designed for broad groups of hospital inpatients and others have been designed specifically for patients with congestive heart failure, coronary artery disease or intervention, stroke, pneumonia, and COPD …”