Procalcitonin predicts mortality in HIV-infected Ugandan adults with lower respiratory tract infections.
Respirology. 2014 Apr;19(3):382-8
Authors: Tokman S, Barnett CF, Jarlsberg LG, Taub PR, den Boon S, Davis JL, Cattamanchi A, Worodria W, Maisel A, Huang L, International HIV-Associated Opportunistic Pneumonias (IHOP) Study Group
BACKGROUND AND OBJECTIVE: In low and middle-income countries where HIV infection is prevalent, identifying patients at high risk of dying from lower respiratory tract infections is challenging and validated prognostic models are lacking. Serum procalcitonin may be a useful prognostic tool in these settings. We sought to determine if elevated serum procalcitonin is associated with increased in-hospital mortality and to combine serum procalcitonin with available clinical characteristics to create a clinically useful prognostic model.
METHODS: We conducted a prospective, nested case-control study of 241 HIV-infected adults admitted to Mulago Hospital in Kampala, Uganda with cough ≥2 weeks in duration. We collected demographic and clinical information, baseline serum for procalcitonin analysis, and followed patients to determine in-hospital mortality.
RESULTS: Serum procalcitonin was a strong and independent predictor of inpatient mortality (aOR = 7.69, p = 0.01, sensitivity = 93%, negative predictive value = 97%). Best subset multivariate analysis identified 3 variables that were combined into a prognostic model to risk stratify patients; these variables included respiratory rate ≥30 breaths/minute (aOR = 2.07, p = 0.11), oxygen saturation <90% (aOR = 3.07, p = 0.02), and serum procalcitonin >0.5 ng/ml (aOR = 7.69, p = 0.01). The predicted probability of inpatient mortality ranged from 1% when no variables were present, to 42% when all variables were present.
CONCLUSIONS: Elevated serum procalcitonin >0.5 ng/ml is an independent predictor of in-hospital mortality. Elevated serum procalcitonin, tachypnea, and hypoxemia may be combined into a prognostic model to identify patients at high risk of dying in the hospital. This model may be used to estimate the probability of death and to guide triage and treatment decisions.
PMID: 24460728 [PubMed - indexed for MEDLINE]