ArticleRenal failure2022
A risk prediction model for acute kidney injury in patients with pulmonary tuberculosis during anti-tuberculosis treatment.
Article in Renal failure, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 9 citations in OpenAlex.
- Establishing a clinical tool to predict acute kidney injury in pulmonary tuberculosis: insights from a large-scale retrospective study.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2026Article
- Prediction of acute kidney injury in patients with liver cirrhosis using machine learning models: evidence from the MIMIC-III and MIMIC-IV.International urology and nephrology · 2024Article
- Applying a Combined Model to Evaluate the Risk of Poor Treatment Outcomes in Rifampicin Resistant Tuberculosis Patients: A Multicenter Retrospective Study.Infection and drug resistance · 2024Article
- Incidence of and Risk Factors for Acute Kidney Injury During Antituberculosis Treatment: A Prospective Cohort Study and Literature Review.Infectious diseases and therapy · 2023Article
- Association between Serum Cys C and PTB Cavitation.Disease markers · 2023Article
- A nomogram for predicting mortality of patients initially diagnosed with primary pulmonary tuberculosis in Hunan province, China: a retrospective study.Frontiers in cellular and infection microbiology · 2023Article
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute kidney injury (AKI) is not a rare complication during anti-tuberculosis treatment in some patients with pulmonary tuberculosis (PTB). We aimed to develop a risk prediction model for early recognition of patients with PTB at high risk for AKI during anti-TB treatment.
methodsThis retrospective cohort study assessed the clinical baseline, and laboratory test data of 315 inpatients with active PTB who were screened for predictive factors from January 2019 to June 2020. The elements were analyzed by logistic regression analysis. A nomogram was established by the results of the logistic regression analysis. The prediction model discrimination and calibration were evaluated by the concordance index (C-index), ROC curve, and Hosmer-Lemeshow analysis.
resultsA total of 315 patients with PTB were enrolled (67 patients with AKI and 248 patients without AKI). Seven factors, including microalbuminuria, hematuria, cystatin-C (CYS-C), albumin (ALB), creatinine-based estimated glomerular filtration rates (eGFRs), body mass index (BMI), and CA-125 were acquired to develop the predictive model. According to the logistic regression, microalbuminuria (OR = 3.038, 95%CI 1.168-7.904), hematuria (OR = 3.656, 95%CI 1.325-10.083), CYS-C (OR = 4.416, 95%CI 2.296-8.491), and CA-125 (OR = 3.93, 95%CI 1.436-10.756) were risk parameter, while ALB (OR = 0.741, 95%CI 0.650-0.844) was protective parameter. The nomogram demonstrated good prediction in estimating AKI (C-index= 0.967, AUC = 0.967, 95%CI (0.941-0.984), sensitivity = 91.04%, specificity = 93.95%, Hosmer-Lemeshow analysis SD = 0.00054, and quantile of absolute error = 0.049).
conclusionsMicroalbuminuria, hematuria, ALB reduction, elevated CYS-C, and CA-125 are predictive factors for the development of AKI in patients with PTB during anti-TB treatments. The predictive nomogram based on five predictive factors is achieved good risk prediction for AKI during anti-TB treatments.
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