ArticleEpidemiology and infection2020
Development and external-validation of a nomogram for predicting the survival of hospitalised HIV/AIDS patients based on a large study cohort in western China.
Article in Epidemiology and infection, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis.BMC infectious diseases · 2024Pooled it
- Assessing three-decade HIV testing transformation: TOPSIS-radar evaluation of tiered health system performance in rural-urban Southwestern China.BMC public health · 2025Article
- Risk factors and clinical prediction models for low-level viremia in people living with HIV receiving antiretroviral therapy: an 11-year retrospective study.Frontiers in microbiology · 2024Article
- Machine learning-based in-hospital mortality prediction of HIV/AIDS patients with Talaromyces marneffei infection in Guangxi, China.PLoS neglected tropical diseases · 2022Article
- Prediction Model for the Risk of HIV Infection among MSM in China: Validation and Stability.International journal of environmental research and public health · 2022Article
- A Prognostic Model to Assess Long-Term Survival of Patients on Antiretroviral Therapy: A 15-Year Retrospective Cohort Study in Southwestern China.Open forum infectious diseases · 2021Article
- Response to the letter from Dr J. Wang.Epidemiology and infection · 2020Article
- Inappropriate data selection and statistical method lead to overestimated mortality for hospitalised HIV/AIDS patients.Epidemiology and infection · 2020Article
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15 authors.
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Abstract
The aim of this study was to develop and externally validate a simple-to-use nomogram for predicting the survival of hospitalised human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS) patients (hospitalised person living with HIV/AIDS (PLWHAs)). Hospitalised PLWHAs (n = 3724) between January 2012 and December 2014 were enrolled in the training cohort. HIV-infected inpatients (n = 1987) admitted in 2015 were included as the external-validation cohort. The least absolute shrinkage and selection operator method was used to perform data dimension reduction and select the optimal predictors. The nomogram incorporated 11 independent predictors, including occupation, antiretroviral therapy, pneumonia, tuberculosis, Talaromyces marneffei, hypertension, septicemia, anaemia, respiratory failure, hypoproteinemia and electrolyte disturbances. The Likelihood χ2 statistic of the model was 516.30 (P = 0.000). Integrated Brier Score was 0.076 and Brier scores of the nomogram at the 10-day and 20-day time points were 0.046 and 0.071, respectively. The area under the curves for receiver operating characteristic were 0.819 and 0.828, and precision-recall curves were 0.242 and 0.378 at two time points. Calibration plots and decision curve analysis in the two sets showed good performance and a high net benefit of nomogram. In conclusion, the nomogram developed in the current study has relatively high calibration and is clinically useful. It provides a convenient and useful tool for timely clinical decision-making and the risk management of hospitalised PLWHAs.
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