Author(s): Asiimwe AC, Brims FJ, Andrews NP, Prytherch DR, Higgins BR,
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Abstract Chronic obstructive pulmonary disease (COPD) has a rising global incidence and acute exacerbation of COPD (AECOPD) carries a high health-care economic burden. Classification and regression tree (CART) analysis is able to create decision trees to classify risk groups. We analysed routinely collected laboratory data to identify prognostic factors for inpatient mortality with AECOPD from our large district hospital. Data from 5,985 patients with 9,915 admissions for AECOPD over a 7-year period were examined. Randomly allocated training (n = 4,986) or validation (n = 4,929) data sets were developed and CART analysis was used to model the risk of all-cause death during admission. Inpatient mortality was 15.5\%, mean age was 71.5 (±11.5) years, 56.2\% were male, and mean length of stay was 9.2 (±12.2) days. Of 29 variables used, CART analysis identified three (serum albumin, urea, and arterial pCO(2)) to predict in-hospital mortality in five risk groups, with mortality ranging from 3.0 to 23.4\%. C statistic indices were 0.734 and 0.701 on the training and validation sets, respectively, indicating good model performance. The highest-risk group (23.4\% mortality) had serum urea >7.35 mmol/l, arterial pCO(2) >6.45 kPa, and normal serum albumin (>36.5 g/l). It is possible to develop clinically useful risk prediction models for mortality using laboratory data from the first 24 h of admission in AECOPD.
This article was published in Lung
and referenced in Journal of Pharmaceutical Care & Health Systems