ObjectiveTo systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.DesignSystematic review.Data sourcesPubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.Study selection and data extractionStudies that developed a model to predict pulmonary TB treatment outcomes were included. Study screening, data extraction and quality assessment were conducted independently by two reviewers. Study quality was evaluated using the Prediction model Risk Of Bias Assessment Tool. Data were synthesised with narrative review and in tables and figures.Results14 739 articles were identified, 536 underwent full-text review and 33 studies presenting 37 prediction models were included. Model outcomes included death (n=16, 43%), treatment failure (n=6, 16%), default (n=6, 16%) or a composite outcome (n=9, 25%). Most models (n=30, 81%) measured discrimination (median c-statistic=0.75; IQR: 0.68–0.84), and 17 (46%) reported calibration, often the Hosmer-Lemeshow test (n=13). Nineteen (51%) models were internally validated, and six (16%) were externally validated. Eighteen (54%) studies mentioned missing data, and of those, half (n=9) used complete case analysis. The most common predictors included age, sex, extrapulmonary TB, body mass index, chest X-ray results, previous TB and HIV. Risk of bias varied across studies, but all studies had high risk of bias in their analysis.ConclusionsTB outcome prediction models are heterogeneous with disparate outcome definitions, predictors and methodology. We do not recommend applying any in clinical settings without external validation, and encourage future researchers adhere to guidelines for developing and reporting of prediction models.Trial registrationThe study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782)

Angionvasive mucormycosis is an emerging fungal disease known to affect mainly diabetics or subjects with profound neutropenia. Infection usually occurs through the inhalation route, but cutaneous inoculation may occur after trauma or burns. However, mucormycosis remains unusual in HIV infection. We report a fatal case of cutaneous mucormycosis due to Rhizopus arrhizus involving the scalp following herpes zoster infection. The patient was a 42-year-old man with advanced AIDS failing on salvage antiretroviral therapy. The fungus was diagnosed on the basis of histopathology and culture. Our case emphasizes the need to consider mucormycosis in the differential diagnosis of necrotic cutaneous lesions in patients with late-stage HIV disease.

Background Despite widespread availability of curative therapy, tuberculosis treatment outcomes remain suboptimal. Clinical prediction models can inform treatment strategies to improve outcomes. Using baseline clinical data, we developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of HIV-related severity and isoniazid acetylator status. Methods Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly-diagnosed tuberculosis patients in Brazil from 2015-2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary tuberculosis who started first-line anti-tuberculosis therapy and had ≥12 months of follow-up. The endpoint was unsuccessful tuberculosis treatment: composite of death, treatment failure, regimen switch, incomplete treatment, or not evaluated. Missing predictors were imputed. Predictors were chosen via bootstrapped backward selection. Discrimination and calibration were evaluated with c-statistics and calibration plots, respectively. Bootstrap internal validation estimated overfitting, and a shrinkage factor was applied to improve out-of-sample prediction. Incremental value was evaluated with likelihood ratio-based measures. Results Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included seven baseline predictors: hemoglobin, HIV-infection, drug use, diabetes, age, education, and tobacco use. The model demonstrated good discrimination (c-statistic=0.77; 95% confidence interval: 0.73-0.80) and was well-calibrated (optimism-corrected intercept and slope: -0.12 and 0.89, respectively). HIV-related factors and isoniazid acetylation status did not improve prediction of the final model. Conclusions The prediction model, using information readily available at treatment initiation, performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients.

Concurrent infections of DENV and malaria have rarely been reported; the actual impact of these sequential or simultaneous infections remains unknown. Therefore, DF must be considered as a potential co-morbidity for malaria, because of its influence on fluid electrolyte management. The case presented showed consistent temporal, clinical, and laboratory evidence that the relapse or the long incubation period of P. ovale malaria may have been triggered by a recent DF episode. To the authors' knowledge, this is the first report of DENV and P. ovale co-infection.