A Clinical scoring model to predict mortality in HIV/TB co-infected patients at end stage of AIDS in China: An observational cohort study

Effectiveness and safety of dolutegravir plus lamivudine in treating HIV in China, including outcomes of patients coinfected with tuberculosis

Antiretroviral regimens for human immunodeficiency virus (HIV) infection have continuously evolved; however, antiretrovirals can cause severe adverse reactions. Two-drug regimen therapy can decrease lifetime cumulative drug exposure and long-term toxicities associated with multiple antiretrovirals. The preferred 2-drug regimen constitutes dolutegravir (DTG) and lamivudine (3TC). This study determined the rate of virological suppression and incidence of adverse events at week 48 in treatment-naïve people living with HIV initiated on DTG + 3TC. This was a single-center, retrospective, observational study. Treatment-naïve people aged ≥18 years who received at least 1 DTG + 3TC dose between May 2020 and May 2022 were included. Eighty-nine people living with HIV were enrolled. Twenty-five (28.1%) patients with a DTG + 3TC regimen at baseline were analyzed because of comorbidities, and 48% because of concomitant tuberculosis (TB). Viral suppression at 48 weeks was achieved in 91.67% of patients, and TB was well controlled. At week 48, 84 (94.38%) patients had viral loads < 50 copies/mL, and 21 (91.31%) of the 23 participants with a baseline HIV-1-RNA level ≥ 1 × 105 copies/mL achieved virological success. Fifteen (88.23%) of the 17 participants with a baseline CD4 + cell count of <200 cells/µL achieved virological suppression. The median CD4 + cell count change from baseline was 539.5 cells/µL. No significant changes in triglycerides, low-density lipoprotein cholesterol, weight, or creatinine were observed from baseline to 48 weeks. One patient had severe insomnia at 4 weeks. Our findings support the real-world effectiveness and low metabolic impact of DTG + 3TC. Using DTG + 3TC in patients coinfected with TB and HIV has favorable therapeutic outcomes.

High socioeconomic status is associated with stroke severity among stroke patients in the National Brain Centre Hospital, Jakarta, Indonesia

Rapid economic growth has widened the gap between the rich and the poor, contributing to inequalities in socioeconomic status (SES) in Indonesia and possibly inequalities in health care. Here, we aimed to assess the potential association between SES and stroke severity in Indonesia, one of the largest low- and middle-income countries. Patients diagnosed with stroke at National Brain Centre (NBC) Hospital, Jakarta, Indonesia, in 2020 were included in the study. SES was measured based on marital status, occupation, education level, source of payment, and hospitalized class with smoking status and sex as confounder. Stroke severity was classified based on the National Institutes of Health Stroke Scale score into minor stroke (Adams et al., 1993, Amarenco et al., 2014, Andersen and Olsen, 2018, Austin and Steyerberg, 2017) and moderate to severe stroke (>4). A total of 2,443 patients with moderate to severe stroke (58%) were analyzed. Currently employed patients had a lower adjusted OR (aOR) of 0.65 (95% confidence interval [CI], 0.51-0.83) than unemployed patients. Patients with the highest education level, at least a diploma degree, had a lower aOR of 0.67 (95% CI, 0.49-0.92) than those with an elementary or no education. Our findings showed that patients with a higher SES had a lower risk of more severe stroke than those with a lower SES. Hence, we must focus on improving SES as part of stroke management.

Predictors of death among TB/HIV co-infected patients on tuberculosis treatment in Sichuan, China: A retrospective cohort study

Mycobacterium tuberculosis is the most common opportunistic infection among patients with human immunodeficiency virus (HIV) infection, and it is also the leading cause of death, causing approximately one-third of acquired immune deficiency syndrome deaths worldwide. China is on the World Health Organization's global list of 30 high-tuberculosis (TB) burden countries. The objective of this study was to evaluate the mortality rate, survival probabilities, and factors associated with death among patients with TB/HIV co-infection undergoing TB treatment in Sichuan, China. A retrospective cohort study was conducted using the Chinese National TB Surveillance System data of TB/HIV co-infected patients enrolled in TB treatment from January 2020 to December 2020. We calculated the mortality rate and survival probabilities using the Kaplan-Meier estimator, and a Cox proportional hazard model was conducted to identify independent risk factors for TB/HIV co-infection mortality. Hazard ratios and their respective 95% confidence intervals were also reported in this study. Of 828 TB/HIV co-infected patients, 44 (5.31%) died during TB treatment, and the crude mortality rate was 7.76 per 1000 person-months. More than half of the deaths (n = 23) occurred in the first 3 months of TB treatment. Overall survival probabilities were 97.20%, 95.16%, and 91.75% at 3rd, 6th, and 12th month respectively. The independent risk factors for mortality among TB/HIV co-infected patients were having extra-pulmonary TB and pulmonary TB co-infection, history of antiretroviral therapy interruption, and baseline cluster of differentiation 4 T-lymphocyte counts <200 cells/μL at the time of HIV diagnosis. Antiretroviral therapy is important for the survival of TB/HIV co-infected patients, and it is recommended to help prolong life by restoring immune function and preventing extra-pulmonary TB.

Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis

Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class.

Systematic review of prediction models for pulmonary tuberculosis treatment outcomes in adults

OBJECTIVE

To systematically review and critically evaluate prediction models developed to predict tuberculosis (TB) treatment outcomes among adults with pulmonary TB.

DESIGN

Systematic review.

DATA SOURCES

PubMed, Embase, Web of Science and Google Scholar were searched for studies published from 1 January 1995 to 9 January 2020.

STUDY SELECTION AND DATA EXTRACTION

Studies 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.

RESULTS

14 739 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.

CONCLUSIONS

TB 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 REGISTRATION

The study was registered on the international prospective register of systematic reviews PROSPERO (CRD42020155782).

A retrospective cohort study of early mortality among patients with HIV/TB co-infection in Shanghai municipality

OBJECTIVES

Tuberculosis (TB) is the most common and fatal opportunistic co-infection among HIV-infected individuals. While TB-associated mortality predominantly occurs in the first 90 days after admission, such a correlation remains unclear in HIV/TB co-infected patients. Thus, we aimed to investigate the 90-day mortality and associated risk factors among HIV/TB co-infected patients in China.

METHODS

Adult patients with HIV and a newly confirmed TB diagnosis admitted to the Shanghai Public Health Clinical Center between September 2009 and August 2017 were enrolled. Clinical and laboratory characteristics, key treatments and outcomes were collected retrospectively. The associations between different factors and early mortality were analysed.

RESULTS

Of the 485 laboratory-confirmed HIV/TB patients [median (range) age = 39 (19-79) years], 413 (85.15%) were male. Diagnosis was confirmed by culture, pathology and acid-fast bacilli smear alone in 362 (74.6%), 6 (1.2%) and 117 (24.1%) patients, respectively. Multiple drug-/rifampin-resistant TB was detected in 21 (5.8%) of the 367 patients with a positive culture. Rifampin or rifabutin was administered to 402 (82.9%) patients. Additionally, 66 (13.6%) and 86 (17.7%) died within 90 days and 1 year of admission, respectively. Of the 64 TB-related deaths, 59 (92.2%) occurred within 90 days of admission. In Cox regression, central nervous system (CNS) TB [odds ratio (OR) = 2.49, 95% confidence interval (CI): 1.46-4.23, P < 0.001], no antiretroviral therapy (ART) within 3 months after admission (OR = 11, 95% CI: 6.4-18.9, P < 0.001), and plasma albumin level < 25 g/L (OR = 1.91, 95% CI: 1.07-3.40, P = 0.021) were associated with early death.

CONCLUSIONS

Tuberculosis co-infection was prevalent and fatal in HIV-infected patients, with most deaths occurring within 90 days of admission. Early mortality was associated with CNS-TB, no ART, and serum albumin level < 25 g/L.