New Computer App Could Predict Risk Factors for Adverse Outcomes of COPD Exacerbations

A novel computer application could allow physicians to conveniently and accurately predict short-term risks of adverse outcomes, and help them choose treatment options, for patients with chronic obstructive pulmonary disease (COPD) who had suffered exacerbations and sought emergency care, according to a study in JMIR Medical Informatics.

Exacerbations of COPD (eCOPD) severely impact quality of life and can be predictors of mortality.² While previous studies have established proven clinical prediction rules that emergency department (ED) physicians have used to make treatment decisions for COPD patients who had suffered an exacerbation, they have often had to rely on both their experience and patients’ personal criteria to predict eCOPD evolution and select the best treatment.

“The translation of clinical prediction rules into easy-to-use computer tools would allow the use of these models in clinical practice. The goal of this work was to create a computer application to predict various outcomes related to adverse events of short-term evolution in eCOPD patients attending an ED based on valid and reliable clinical prediction rules,” researchers said.

Researchers created a computer application, Prediction of Evolution of patients with eCOPD (PrEveCOPD), to approximate the risks of 2 specific, negative outcomes. The outcomes included mortality either during hospital admission or within a week following an ED visit and also admission to either an intensive care unit (ICU) or intermediate respiratory care unit (ICRU) due to an eCOPD.

The application could be downloaded and installed on a computer, smart phone, or tablet. It could also be accessed on any device with internet access without requiring installation. The application used Windows and Android operating systems.

PrEveCOPD used algorithms based on results of the Investigación en Resultados y Servicios de Salud COPD (IRYSS-COPD) study, which developed and validated clinical protection rules for short-term outcomes of COPD patients who had visited the ED due to an exacerbation. Researchers collected data from IRYSS-COPD from 2 time points: if the patient was hospitalized, data were collected for up to 1 week; if patients were discharged, they were contacted by phone for a week after the ED visit.

Researchers performed univariate logistic regression analysis on the collected data. Variables with statistically significant results were entered into a multiple logistic regression model. Researchers developed a final predictive model by performing internal validation of the variable selection process and modeling. They developed a score by assigning a weight to each variable or category in the final multiple logistic regression model. The score was then categorized into risk class mainly based on the estimated event risk for either outcome.

PrEveCOPD had a user-friendly interface that allowed input of specific, predetermined, predictive variables. After supplying the required information, the application displayed estimated scores for the 2 outcomes: the short-term mortality score and the ICU/IRCU admission score. A screen shot of the app contained in the study shows that a score of 8 is considered "moderate," while a score of 10 is considered "severe." PrEveCOPD also showed the stratification of risk into categories for both scores.

Researchers determined the main strength of the computer application was that it was based on clinical predictive rules derived from validated models that tested for both outcomes. They believe that measuring the risk of adverse events through a single, convenient device in a timely and efficient manner could be more beneficial toward making treatment decisions than the use of individual tools or crude predictive models.

“The implementation of a theoretical model into an easy-to-use application would allow its rapid and easy incorporation to the clinical management of eCOPD patients at the ED to guide their treatment,” researchers said. “As technology advances in each health system, our instrument could serve as the basis to automatically include information relative to the individual patient at the bed-side where decisions should be made.”

References

Arostegui I, Legarreta MJ, Barrio I, et al. A computer application to predict adverse events in the short-term evolution of patients with exacerbation of chronic obstructive pulmonary disease. JMIR Med Inform. 2019;7(2). doi: 10.2196/10773.

Alameda C, Carlos Matía Á, Casado V. Predictors for mortality due to acute exacerbation of COPD in primary care: protocol for the derivation of a clinical prediction rule. NPJ Prim Care Respir Med. 2016;26. doi: 10.1038/npjpcrm.2016.70.