Pneumocystis jirovecii pneumonia in people living with HIV: a review.

Pneumocystis jirovecii is a ubiquitous opportunistic fungus that can cause life-threatening pneumonia. People with HIV (PWH) who have low CD4 counts are one of the populations at the greatest risk of Pneumocystis jirovecii pneumonia (PCP). While guidelines have approached the diagnosis, prophylaxis, and management of PCP, the numerous studies of PCP in PWH are dominated by the 1980s and 1990s. As such, most studies have included younger male populations, despite PCP affecting both sexes and a broad age range. Many studies have been small and observational in nature, with an overall lack of randomized controlled trials. In many jurisdictions, and especially in low- and middle-income countries, the diagnosis can be challenging due to lack of access to advanced and/or invasive diagnostics. Worldwide, most patients will be treated with 21 days of high-dose trimethoprim sulfamethoxazole, although both the dose and the duration are primarily based on historical practice. Whether treatment with a lower dose is as effective and less toxic is gaining interest based on observational studies. Similarly, a 21-day tapering regimen of prednisone is used for patients with more severe disease, yet other doses, other steroids, or shorter durations of treatment with corticosteroids have not been evaluated. Now with the widespread availability of antiretroviral therapy, improved and less invasive PCP diagnostic techniques, and interest in novel treatment strategies, this review consolidates the scientific body of literature on the diagnosis and management of PCP in PWH, as well as identifies areas in need of more study and thoughtfully designed clinical trials.

Diagnostic Prediction Model for Tuberculous Meningitis: An Individual Participant Data Meta-Analysis.

No accurate and rapid diagnostic test exists for tuberculous meningitis (TBM), leading to delayed diagnosis. We leveraged data from multiple studies to improve the predictive performance of diagnostic models across different populations, settings, and subgroups to develop a new predictive tool for TBM diagnosis. We conducted a systematic review to analyze eligible datasets with individual-level participant data (IPD). We imputed missing data and explored three approaches: stepwise logistic regression, classification and regression tree (CART), and random forest regression. We evaluated performance using calibration plots and C-statistics via internal-external cross-validation. We included 3,761 individual participants from 14 studies and nine countries. A total of 1,240 (33%) participants had "definite" (30%) or "probable" (3%) TBM by case definition. Important predictive variables included cerebrospinal fluid (CSF) glucose, blood glucose, CSF white cell count, CSF differential, cryptococcal antigen, HIV status, and fever presence. Internal validation showed that performance varied considerably between IPD datasets with C-statistic values between 0.60 and 0.89. In external validation, CART performed the worst (C = 0.82), and logistic regression and random forest had the same accuracy (C = 0.91). We developed a mobile app for TBM clinical prediction that accounted for heterogeneity and improved diagnostic performance (https://tbmcalc.github.io/tbmcalc). Further external validation is needed.