Abstract
BackgroundAcute 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.MethodsWe 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.ResultsOf 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).ConclusionsExisting 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.
Highlights
Acute lower respiratory tract infections (LRTI) are a frequent cause of hospitalization and mortality in South Africa; existing respiratory severity scores may underestimate mortality risk in HIV-infected adults in resource limited settings
We evaluated all included variables for interaction with one another, and performed manual backward elimination in which non-significant variables were removed from the model one at a time starting with the variable with the smallest magnitude of effect until either all remaining variables had p < 0.05 or removing an additional variable significantly affected the model’s Akaike information criterion (AIC)
Study population and risk factors associated with mortality From January 2010 to December 2011, there were 3,029 Severe Acute Respiratory Illness (SARI) cases including 218 (7.2%) deaths from Chris Hani Baragwanath Academic Hospital (CHBAH) and Selby Hospital and 71 cases including 5 deaths from Tshepong Hospital
Summary
Acute lower respiratory tract infections (LRTI) are a frequent cause of hospitalization and mortality in South Africa; existing respiratory severity scores may underestimate mortality risk in HIV-infected adults in resource limited settings. Many respiratory severity scores that use clinical and laboratory data have been developed to risk stratify and predict outcomes of patients hospitalized with pneumonia including the Pneumonia Severity Index (PSI) and CURB-65 scores [2,3,4,5]. These scores are used frequently in clinical practice, including in South Africa, they were developed and validated among HIVuninfected adults in high income settings and may underestimate mortality risk in HIV-infected adults in resource-limited settings [6]. Having an accurate predictive respiratory severity score for low-resource settings could help healthcare providers and health systems better allocate limited resources and improve care by identifying patients at risk for severe outcomes
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