The MAGENTA model for individual prediction of in-hospital mortality in chronic obstructive pulmonary disease with acute exacerbation in resource-limited countries: A development study

Abstract

Background Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common undesirable event associated with significant morbidity and mortality. Several clinical prediction tools for predicting in-hospital mortality in patients with AECOPD have been developed in the past decades. However, some issues concerning the validity and availability of some predictors in the existing models may undermine their clinical applicability in resource-limited clinical settings. Methods We developed a multivariable model for predicting in-hospitality from a retrospective cohort of patients admitted with AECOPD to one tertiary care center in Thailand from October 2015 to September 2017. Multivariable logistic regression with fractional polynomial algorithms and cluster variance correction was used for model derivation. Results During the study period, 923 admissions from 600 patients with AECOPD were included. The in-hospital mortality rate was 1.68 per 100 admission-day. Eleven potential predictors from the univariable analysis were included in the multivariable logistic regression. The reduced model, named MAGENTA, incorporated seven final predictors: age, body temperature, mean arterial pressure, the requirement of endotracheal intubation, serum sodium, blood urea nitrogen, and serum albumin. The model discriminative ability based on the area under the receiver operating characteristic curve (AuROC) was excellent at 0.82 (95% confidence interval 0.77, 0.86), and the calibration was good. Conclusion The MAGENTA model consists of seven routinely available clinical predictors upon patient admissions. The model can be used as an assisting tool to aid clinicians in accurate risk stratification and making appropriate decisions to admit patients for intensive care.

A pooled analysis of the risk prediction models for mortality in acute exacerbation of chronic obstructive pulmonary disease

“…A total of 4376 pieces of literature were obtained through database searching, and 37 pieces of literature 11–47 were finally included after the screening. The process and results are shown in Figure 1.…”

“…A list of excluded studies and reasons for exclusion are provided in Table S4. This study included 26 studies 11–36 that developed models with or without validation and 11 studies 37–47 that only validated models. Fifteen studies 30,32,34,36–47 were finally included for quantitative statistical analysis.…”

“…We found 26 studies 11–36 describing the development of risk prediction models for mortality in patients with AECOPD. In‐hospital death ( n = 15) was the most common predictor of mortality.…”

“…We evaluated 37 studies 11–47 for the risk of bias using the PROBAST checklist, and all studies were at a high risk of bias, as shown in Figure 3. The main sources of risk were failure to correctly assess predictive model performance ( n = 29), insufficient sample size ( n = 27), selection of predictors using univariate analysis (n = 17), inappropriate data sources ( n = 17), lack of internal validation ( n = 8), continuous predictors handled inappropriately ( n = 7) and missing data not handled appropriately ( n = 2).…”

“…We performed a meta‐analysis of the C‐statistics of the five models included in the 15 studies 30,32,34,36–47 . More details are provided in Table S9.…”