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Derseh NM, Agimas MC, Tesfie TK. Risk score prediction for bacteriologically confirmed tuberculosis among adults with HIV on antiretroviral therapy in northwest Ethiopia: prognostic model development. AIDS 2024; 38:1333-1341. [PMID: 38691024 DOI: 10.1097/qad.0000000000003917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
OBJECTIVE This study was aimed at developing a risk score prediction model for bacteriologically confirmed tuberculosis (TB) among adults with HIV receiving antiretroviral therapy in Ethiopia. METHODS An institutional-based retrospective follow-up study was conducted among 569 adults with HIV on ART. We used demographic and clinical prognostic factors to develop a risk prediction model. Model performance was evaluated by discrimination and calibration using the area under the receiver operating characteristic (AUROC) curve and calibration plot. Bootstrapping was used for internal validation. A decision curve analysis was used to evaluate the clinical utility. RESULTS Opportunistic infection, functional status, anemia, isoniazid preventive therapy, and WHO clinical stages were used to develop risk prediction. The AUROC curve of the original model was 87.53% [95% confidence interval (CI): 83.88-91.25] and the calibration plot ( P -value = 0.51). After internal validation, the AUROC curve of 86.61% (95% CI: 82.92-90.29%) was comparable with the original model, with an optimism coefficient of 0.0096 and good calibration ( P -value = 0.10). Our model revealed excellent sensitivity (92.65%) and negative predictive value (NPV) (98.60%) with very good specificity (70.06%) and accuracy (72.76%). After validation, accuracy (74.85%) and specificity (76.27%) were improved, but sensitivity (86.76%) and NPV (97.66%) were relatively reduced. The risk prediction model had a net benefit up to 7.5 threshold probabilities. CONCLUSION This prognostic model had very good performance. Moreover, it had very good sensitivity and excellent NPV. The model could help clinicians use risk estimation and stratification for early diagnosis and treatment to improve patient outcomes and quality of life.
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Affiliation(s)
- Nebiyu Mekonnen Derseh
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
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Gebregergs GB, Berhe G, Gebrehiwot KG, Mulugeta A. Predictors contributing to the estimation of pulmonary tuberculosis among adults in a resource-limited setting: A systematic review of diagnostic predictions. SAGE Open Med 2024; 12:20503121241243238. [PMID: 38764538 PMCID: PMC11100385 DOI: 10.1177/20503121241243238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/14/2024] [Indexed: 05/21/2024] Open
Abstract
Background Although tuberculosis is highly prevalent in low- and middle-income countries, millions of cases remain undetected using current diagnostic methods. To address this problem, researchers have proposed prediction rules. Objective We analyzed existing prediction rules for the diagnosis of pulmonary tuberculosis and identified factors with a moderate to high strength of association with the disease. Methods We conducted a comprehensive search of relevant databases (MEDLINE/PubMed, Cochrane Library, Science Direct, Global Health for Reports, and Google Scholar) up to 14 November 2022. Studies that developed diagnostic algorithms for pulmonary tuberculosis in adults from low and middle-income countries were included. Two reviewers performed study screening, data extraction, and quality assessment. The study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2. We performed a narrative synthesis. Results Of the 26 articles selected, only half included human immune deficiency virus-positive patients. In symptomatic human immune deficiency virus patients, radiographic findings and body mass index were strong predictors of pulmonary tuberculosis, with an odds ratio of >4. However, in human immune deficiency virus-negative individuals, the biomarkers showed a moderate association with the disease. In symptomatic human immune deficiency virus patients, a C-reactive protein level ⩾10 mg/L had a sensitivity and specificity of 93% and 40%, respectively, whereas a trial of antibiotics had a specificity of 86% and a sensitivity of 43%. In smear-negative patients, anti-tuberculosis treatment showed a sensitivity of 52% and a specificity of 63%. Conclusions The performance of predictors and diagnostic algorithms differs among patient subgroups, such as in human immune deficiency virus-positive patients, radiographic findings, and body mass index were strong predictors of pulmonary tuberculosis. However, in human immune deficiency virus-negative individuals, the biomarkers showed a moderate association with the disease. A few models have reached the World Health Organization's recommendation. Therefore, more work should be done to strengthen the predictive models for tuberculosis screening in the future, and they should be developed rigorously, considering the heterogeneity of the population in clinical work.
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Affiliation(s)
| | - Gebretsadik Berhe
- School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Ethiopia
| | | | - Afework Mulugeta
- School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Ethiopia
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Wang Y, Liu S, Zhang W, Zheng L, Li E, Zhu M, Yan D, Shi J, Bao J, Yu J. Development and Evaluation of a Nomogram for Predicting the Outcome of Immune Reconstitution Among HIV/AIDS Patients Receiving Antiretroviral Therapy in China. Adv Biol (Weinh) 2024; 8:e2300378. [PMID: 37937390 DOI: 10.1002/adbi.202300378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/12/2023] [Indexed: 11/09/2023]
Abstract
This study aims to develop and evaluate a model to predict the immune reconstitution among HIV/AIDS patients after antiretroviral therapy (ART). A total of 502 HIV/AIDS patients are randomized to the training cohort and evaluation cohort. Least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression analysis are performed to identify the indicators and establish the nomogram for predicting the immune reconstitution. Decision curve analysis (DCA) and clinical impact curve (CIC) are used to evaluate the clinical effectiveness of the nomogram. Predictive factors included white blood cells (WBC), baseline CD4+ T-cell counts (baseline CD4), ratio of effector regulatory T cells to resting regulatory T cells (eTreg/rTreg) and low-density lipoprotein cholesterol (LDL-C) and are incorporated into the nomogram. The area under the curve (AUC) is 0.812 (95% CI, 0.767∼0.851) and 0.794 (95%CI, 0.719∼0.857) in the training cohort and evaluation cohort, respectively. The calibration curve shows a high consistency between the predicted and actual observations. Moreover, DCA and CIC indicate that the nomogram has a superior net benefit in predicting poor immune reconstitution. A simple-to-use nomogram containing four routinely collected variables is developed and internally evaluated and can be used to predict the poor immune reconstitution in HIV/AIDS patients after ART.
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Affiliation(s)
- Yi Wang
- Institute of Hepatology and Epidemiology, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Shourong Liu
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Wenhui Zhang
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
- Department of Nursing, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Liping Zheng
- Department of Nursing, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Er Li
- Department of Nursing, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Mingli Zhu
- Medical Laboratory, Affiliated Hangzhou Xixi Hospital, Zhejiang University School of Medicine, Hangzhou, 310023, China
| | - Dingyan Yan
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
- Department of Nursing, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Jinchuan Shi
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Jianfeng Bao
- Institute of Hepatology and Epidemiology, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
| | - Jianhua Yu
- Department of Infection, Affiliated Xixi Hospital in Hangzhou, Zhejiang University of Traditional Chinese Medicine, Hangzhou, 310023, China
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Wolde HF, Clements ACA, Alene KA. Development and validation of a risk prediction model for pulmonary tuberculosis among presumptive tuberculosis cases in Ethiopia. BMJ Open 2023; 13:e076587. [PMID: 38101842 PMCID: PMC10729072 DOI: 10.1136/bmjopen-2023-076587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Accepted: 09/22/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Early diagnosis and treatment of tuberculosis (TB) is one of the key strategies to achieve the WHO End TB targets. This study aimed to develop and validate a simple, convenient risk score to diagnose pulmonary TB among presumptive TB cases. METHODS This prediction model used Ethiopian national TB prevalence survey data and included 5459 presumptive TB cases from all regions of Ethiopia. Logistic regression was used to determine which variables are predictive of pulmonary TB. A risk prediction model was developed, incorporating significant variables (p<0.05). The Youden Index method was used to choose the optimal cut-off point to separate the risk score of the patients as high and low. Model performance was assessed using discrimination power and calibration. Internal validation of the model was assessed using Efron's enhanced bootstrap method, and the clinical utility of the risk score was assessed using decision curve analysis. RESULTS Of total participants, 94 (1.7%) were confirmed to have TB. The final prediction model included three factors with different scores: (1) TB contact history, (2) chest X-ray (CXR) abnormality and (3) two or more symptoms of TB. The optimal cut-off point for the risk score was 6 and was found to have a good discrimination accuracy (c-statistic=0.70, 95% CI: 0.65 to 0.75). The risk score has sensitivity of 51.1%, specificity of 79.9%, positive predictive value of 4.3% and negative predictive value of 98.9%. After internal validation, the optimism coefficient was 0.003, which indicates the model is internally valid. CONCLUSION We developed a risk score that combines TB contact, number of TB symptoms and CXR abnormality to estimate individual risk of pulmonary TB among presumptive TB cases. Though the score is easy to calculate and internally validated, it needs external validation before widespread implementation in a new setting.
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Affiliation(s)
- Haileab Fekadu Wolde
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Nedlands, Western Australia, Australia
- Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
| | | | - Kefyalew Addis Alene
- School of Population Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- Geospatial and Tuberculosis Research Team, Telethon Kids Institute, Nedlands, Western Australia, Australia
- Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
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Chen J, Wang X, Lv H, Zhang W, Tian Y, Song L, Wang Z. Development and external validation of a clinical-radiomics nomogram for preoperative prediction of LVSI status in patients with endometrial carcinoma. J Cancer Res Clin Oncol 2023; 149:13943-13953. [PMID: 37542548 DOI: 10.1007/s00432-023-05044-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE To develop and validate a model that incorporates radiomics based on MRI scans and clinical characteristics to predict lymphovascular invasion (LVSI) in endometrial cancer (EC) patients. METHODS There were 332 patients with EC enrolled retrospectively in this multicenter study. Radiomics score (Radscore) were computed using the valuable radiomics features. The independent predictors of LVSI were identified by univariate logistic analysis. Multivariate logistic regression was used to develop a clinical-radiomics predictive model. Based on the model, a nomogram was developed and validated internally and externally. The nomogram was evaluated with discrimination, calibration, decision curve analysis (DCA), and clinical impact curves (CIC). RESULTS Three predictive models were constructed based on clinicopathological features, radiomic factors and a combination of them, and that the clinic-radiomic model performed best among the three models. Four independent factors comprised the clinical-radiomics model: dynamic contrast enhancement rate of late arterial phase (DCE2), deep myometrium invasion (DMI), lymph node metastasis (LNM), and Radscore. Clinical-radiomics model performance was 0.901 (95% CI 0.84-0.96) in the training cohort, 0.80 (95% CI 0.68-0.92) in the internal validation cohort, and 0.81 (95% CI 0.73-0.9) in the external validation cohort for identifying patients with LVSI, respectively. The model is used to develop a nomogram for clinical use. CONCLUSIONS The MRI-based radiomics nomogram could serve as a noninvasive tool to predict LVSI in EC patients.
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Affiliation(s)
- Jingya Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | | | - Haoyi Lv
- University of Science and Technology of China, Hefei, Anhui, China
| | - Wei Zhang
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing, Jiangsu Province, China
| | - Ying Tian
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | - Lina Song
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | - Zhongqiu Wang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China.
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Wu L, Xia D, Xu K. Multi-Clinical Factors Combined with an Artificial Intelligence Algorithm Diagnosis Model for HIV-Infected People with Bloodstream Infection. Infect Drug Resist 2023; 16:6085-6097. [PMID: 37719647 PMCID: PMC10503519 DOI: 10.2147/idr.s423709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/29/2023] [Indexed: 09/19/2023] Open
Abstract
Purpose Although highly active antiretroviral therapy (HA-ART) can effectively suppress the disease process in patients with acquired immunodeficiency syndrome (AIDS), opportunistic infections, mainly bloodstream infections (BSI), are still the main cause of death in people living with HIV. There is no effective diagnostic strategy for HIV-infected people with BSI. This study aimed to develop an AI diagnostic model with high sensitivity to improve the early detection of HIV-infected people with BSI. Patients and Methods This study retrospectively analyzed the 40 clinical factors of 498 HIV-infected people (171 with BSI positive and 327 with BSI negative) who admitted to Wenzhou Central Hospital from September 2014 to July 2021. This study used the hospital information management system to collect the clinical characteristics, laboratory and imaging examination results, and clinical diagnosis of the two groups. The diagnostic results of all patients were in line with the diagnostic criteria of the Chinese Guidelines for the Diagnosis and Treatment of AIDS (2021 Edition), and the BSI diagnosis was in line with the diagnostic criteria of sepsis and bacteremia in Practical Internal Medicine (13th Edition). On this basis, various risk prediction models were established by combining 8 artificial intelligence (AI) algorithms in the training set and validating the diagnosis performance in the testing set. The model with the best diagnostic performance was selected as the final diagnostic model. Results The clinical characteristics of HIV-infected people with BSI are atypical, and the pathogens in this area are mainly fungi. Ten risk factors were selected: low level of hemoglobin, CD4+T cell and platelets, high level of lactate dehydrogenase and blood urea nitrogen, splenomegaly, without ART treatment, strip shadow, nodular shadow, and shock. The combination of the ten risk factors, age, gender and the "svmRadial" model can identify the HIV-infected people with BSI from the HIV-infected people without BSI with an area under the curve of 0.916 and a sensitivity and specificity of 0.824 and 0.855, respectively. Conclusion The model showed excellent performance in diagnosing HIV-infected people with BSI. Internal and external validation showed that the diagnosis model had high clinical application value.
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Affiliation(s)
- Lianpeng Wu
- Department of Clinical Laboratory Medicine, The Ding Li Clinical College of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
- Department of Clinical Laboratory Medicine, Wenzhou Central Hospital, Wenzhou, 325000, People’s Republic of China
- Key Laboratory of Diagnosis and Treatment of New and Recurrent Infectious Diseases of Wenzhou, Wenzhou, 325000, People’s Republic of China
| | - Dandan Xia
- Department of Clinical Laboratory Medicine, The Ding Li Clinical College of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
- Department of Clinical Laboratory Medicine, Wenzhou Central Hospital, Wenzhou, 325000, People’s Republic of China
- Key Laboratory of Diagnosis and Treatment of New and Recurrent Infectious Diseases of Wenzhou, Wenzhou, 325000, People’s Republic of China
| | - Ke Xu
- Department of Clinical Laboratory Medicine, The Ding Li Clinical College of Wenzhou Medical University, Wenzhou, 325000, People’s Republic of China
- Department of Clinical Laboratory Medicine, Wenzhou Central Hospital, Wenzhou, 325000, People’s Republic of China
- Key Laboratory of Diagnosis and Treatment of New and Recurrent Infectious Diseases of Wenzhou, Wenzhou, 325000, People’s Republic of China
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