Development of a respiratory severity score for hospitalized adults in a high HIV-prevalence setting-South Africa, 2010-2011

Abstract

Background: Acute lower respiratory tract infections (LRTI) are a frequent cause of hospitalization and mortality in South Africa; however, existing respiratory severity scores may underestimate mortality risk in HIV-infected adults in resource limited settings. A simple predictive clinical score for low-resource settings could aid healthcare providers in the management of patients hospitalized with LRTI.

Methods: We analyzed 1,356 LRTI hospitalizations in adults aged ≥18 years enrolled in Severe Acute Respiratory Illness (SARI) surveillance in three South African hospitals from January 2010 to December 2011. Using demographic and clinical data at admission, we evaluated potential risk factors for in-hospital mortality. We evaluated three existing respiratory severity scores, CURB-65, CRB-65, and Classification Tree Analysis (CTA) Score assessing for discrimination and calibration. We then developed a new respiratory severity score using a multivariable logistic regression model for in-hospital mortality and assigned points to risk factors based on the coefficients in the multivariable model. Finally we evaluated the model statistically using bootstrap resampling techniques.

Results: Of the 1,356 patients hospitalized with LRTI, 101 (7.4%) died while hospitalized. The CURB-65, CRB-65, and CTA scores had poor calibration and demonstrated low discrimination with c-statistics of 0.594, 0.548, and 0.569 respectively. Significant risk factors for in-hospital mortality included age ≥ 45 years (A), confusion on admission (C), HIV-infection (H), and serum blood urea nitrogen >7 mmol/L (U), which were used to create the seven-point ACHU clinical predictor score. In-hospital mortality, stratified by ACHU score was: score ≤1, 2.4%, score 2, 6.4%, score 3, 11.9%, and score ≥ 4, 29.3%. Final models showed good discrimination (c-statistic 0.789) and calibration (chi-square 1.6, Hosmer-Lemeshow goodness-of-fit p-value = 0.904) and discriminated well in the bootstrap sample (average optimism of 0.003).

Conclusions: Existing clinical predictive scores underestimated mortality in a low resource setting with a high HIV burden. The ACHU score incorporates a simple set a risk factors that can accurately stratify patients ≥18 years of age with LRTI by in-hospital mortality risk. This score can quantify in-hospital mortality risk in an HIV-endemic, resource-limited setting with limited clinical information and if used to facilitate timely treatment may improve clinical outcomes.