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Background  The World Health Organization (WHO) algorithm for the diagnosis of tuberculosis in seriously ill HIV-infected patients recommends that treatment for  Pneumocystis jirovecii  pneumonia (PJP) should be considered without giving clear guidance on selecting patients for empiric PJP therapy. PJP is a common cause of hospitalisation in HIV-infected patients in resource-poor settings where diagnostic facilities are limited.    Methods  We developed clinical prediction rules for PJP in a prospective cohort of HIV-infected inpatients with WHO danger signs and cough of any duration. The reference standard for PJP was > 1000 copies/mL of  P. jirovecii  DNA on real-time sputum polymerase chain reaction (PCR). Four potentially predictive variables were selected for regression models: dyspnoea, chest X-ray, haemoglobin and oxygen saturation. Respiratory rate was explored as a replacement for oxygen saturation as pulse oximetry is not always available in resource-poor settings.    Results  We enrolled 500 participants. After imputation for missing values, there were 56 PJP outcome events. Dyspnoea was not independently associated with PJP. Oxygen saturation and respiratory rate were inversely correlated. Two clinical prediction rules were developed: chest X-ray possible/likely PJP, haemoglobin ≥ 9 g/dL and either oxygen saturation < 94% or respiratory rate. The area under the receiver operating characteristic curve of the clinical prediction rule models was 0.761 (95% CI 0.683–0.840) for the respiratory rate model and 0.797 (95% CI 0.725–0.868) for the oxygen saturation model. Both models had zero probability for PJP for scores of zero, and positive likelihood ratios exceeded 10 for high scores.    Conclusion  We developed simple clinical prediction rules for PJP, which, if externally validated, could assist decision-making in the WHO seriously ill algorithm.

southern african journal of hiv medicine

Development of a clinical prediction rule to diagnose Pneumocystis jirovecii pneumonia in the World Health Organization’s algorithm for seriously ill HIV-infected patients