A clinical prediction model for unsuccessful pulmonary tuberculosis treatment outcomes.
TL;DR: In this article, the authors developed a prediction model for unsuccessful TB treatment outcome and evaluated the incremental value of HIV-related severity and isoniazid acetylator status via bootstrapped backward selection.
Abstract: 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.
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TL;DR: The dynamic prediction framework utilizes longitudinal laboratory test results to predict patient outcomes at various landmarks to facilitate a more effective treatment monitoring program and provide insights for policymakers toward improved guidelines on follow-up tests.
5 citations
TL;DR: Wang et al. as discussed by the authors conducted a two-stage study by recruiting 346 pulmonary tuberculosis patients diagnosed between 2016 and 2018 in Dafeng city as the training cohort and 132 patients diagnosed in 2018 and 2019 in Nanjing city as external validation population.
Abstract: Identifying prognostic factors helps optimize the treatment regimen and promote favorable outcomes. We conducted a prospective cohort study on patients with pulmonary tuberculosis to construct a clinical indicator-based model and estimate its performance.We performed a two-stage study by recruiting 346 pulmonary tuberculosis patients diagnosed between 2016 and 2018 in Dafeng city as the training cohort and 132 patients diagnosed between 2018 and 2019 in Nanjing city as the external validation population. We generated a risk score based on blood and biochemistry examination indicators by the least absolute shrinkage and selection operator (LASSO) Cox regression. Univariate and multivariate Cox regression models were used to assess the risk score, and the strength of association was expressed as the hazard ratio (HR) and 95% confidence interval (CI). We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). Internal validation was conducted by 10-fold cross-validation.Ten significant indicators (PLT, PCV, LYMPH, MONO%, NEUT, NEUT%, TBTL, ALT, UA, and Cys-C) were selected to generate the risk score. Clinical indicator-based score (HR: 10.018, 95% CI: 4.904-20.468, P < 0.001), symptom-based score (HR: 1.356, 95% CI: 1.079-1.704, P = 0.009), pulmonary cavity (HR: 0.242, 95% CI: 0.087-0.674, P = 0.007), treatment history (HR: 2.810, 95% CI: 1.137-6.948, P = 0.025), and tobacco smoking (HR: 2.499, 95% CI: 1.097-5.691, P = 0.029) were significantly related to the treatment outcomes. The AUC was 0.766 (95% CI: 0.649-0.863) in the training cohort and 0.796 (95% CI: 0.630-0.928) in the validation dataset.In addition to the traditional predictive factors, the clinical indicator-based risk score determined in this study has a good prediction effect on the prognosis of tuberculosis.
TL;DR:
Abstract: BACKGROUND
Successful tuberculosis (TB) treatment is necessary for disease control. The World Health Organization (WHO) has a target TB treatment success rate of ≥90%. We assessed whether the different types of unfavorable TB treatment outcome had different predictors.
METHODS
Using data from Regional Prospective Observational Research for Tuberculosis-Brazil, we evaluated biological and behavioral factors associated with each component of unsuccessful TB outcomes, recently-updated by WHO (death, loss to follow-up [LTFU], and treatment failure). We included culture-confirmed, drug-susceptible, pulmonary TB participants receiving standard treatment in 2015-2019. Multinomial logistic regression models with inverse probability weighting were used to evaluate the distinct determinants of each unsuccessful outcome.
RESULTS
Of 915 participants included, 727 (79%) were successfully treated, 118 (13%) were LTFU, 44 (5%) had treatment failure, and 26 (3%) died. LTFU was associated with current drug-use (aOR = 5.3, 95%CI: 3.0, 9.4), current tobacco-use (aOR = 2.9, 95%CI: 1.7-4.9), and being a person living with HIV (PLWH) (aOR = 2.0, 95%CI: 1.1-3.5). Treatment failure was associated with PLWH (aOR = 2.7, 95%CI: 1.2-6.2) and having diabetes (aOR = 2.2, 95%CI: 1.1-4.4). Death was associated with anemia (aOR = 5.3, 95%CI: 1.4-19.7), diabetes (aOR = 3.1, 95%CI: 1.4, -6.7), and PLWH (aOR = 3.9, 95%CI: 1.3-11.4). Direct Observed Therapy (DOT) was protective for treatment failure (aOR = 0.5, 95%CI: 0.3-0.9) and death (aOR = 0.5, 95%CI: 0.2-1.0).
CONCLUSIONS
The treatment success rate was below the WHO target. Behavioral factors were most associated with LTFU, whereas clinical comorbidities were correlated with treatment failure and death. Because determinants of unsuccessful outcomes are distinct, different intervention strategies may be needed to improve TB outcomes.
19 May 2023
TL;DR: In this paper , a machine learning methodology for predicting the treatment outcomes of TB patients is proposed, which can assist healthcare providers in implementing more targeted follow-ups and more appropriate resource allocation strategies to improve the overall treatment outcome.
Abstract: Tuberculosis (TB) is a severe and highly contagious disease that affects millions of people worldwide. The current TB treatment programs are challenging to complete for many patients due to numerous factors, including limited human resources and financial resources. To address these challenges, a solution is needed to aid in resource allocation strategies. This study suggests a machine learning methodology for predicting the treatment outcomes of TB patients. This will enable healthcare facilities to optimize resource allocation based on the prediction made. A large multi-variate TB patient dataset from the Brazilian Information System for Notifiable Disease (SINAN) was used in this study, containing attributes related to patient characteristics, clinical information, and laboratory data. The proposed model used the Naive Bayes algorithm due to its simplicity and efficiency in predicting treatment outcomes. The dataset was pre-processed, and the Synthetic Minority Oversampling Technique (SMOTE) was applied to overcome the class imbalance issues in the dataset. The combination of the borderline SMOTE and Naive Bayes algorithms on the preprocessed dataset was found to have achieved the highest levels of accuracy among the combinations sampled. This demonstrated the capability of the new algorithm in predicting the treatment outcome of TB patients. The proposed model could assist healthcare providers in implementing more targeted follow-ups and more appropriate resource allocation strategies to improve the overall treatment outcome of TB patients.
13 Apr 2023
TL;DR: Wang et al. as mentioned in this paper constructed a nomogram prognostic model to recognize risk factors and accurately predict the mortality of patients initially diagnosed with primary pulmonary tuberculosis, which is expected to guide early clinical intervention and treatment for high-risk patients.
Abstract: Abstract Background: According to the Global Tuberculosis Report for three consecutive years, tuberculosis (TB) is the second leading infectious killer. Primary pulmonary tuberculosis( PTB) leads to the highest mortality among TB diseases. Regretfully,no previous studies targeted the PTB of a specific type or in a specific course, so models established in previous studies cannot be accurately feasible for clinical treatments.This study aimed to construct a nomogram prognostic model to quickly recognize death-related risk factors in patients initially diagnosed with PTB to intervene and treat high-risk patients as early as possible in the clinic to reduce mortality. Methods: We retrospectively analyzed the clinical data of 1,809 in-hospital patients initially diagnosed with primary PTB at Hunan Chest Hospital from January 1, 2019, to December 31, 2019. Binary logistic regression analysis was used to identify the risk factors. A nomogram prognostic model for mortality prediction was constructed using R software and was validated using a validation set. Results: Univariate and multivariate logistic regression analyses revealed that drinking, hepatitis B virus (HBV), body mass index (BMI), age, albumin (ALB), and hemoglobin (Hb) were six independent predictors of death in in-hospital patients initially diagnosed with primary PTB. Based on these predictors, a nomogram prognostic model was established with high prediction accuracy, of which the area under the curve (AUC) was 0.881 (95% confidence interval [Cl]: 0.777-0.847), the sensitivity was 84.7%, and the specificity was 77.7%internal and external validations confirmed that the constructed model fit the real situation well. Conclusion: The constructed nomogram prognostic model can recognize risk factors and accurately predict the mortality of patients initially diagnosed with primary PTB. This is expected to guide early clinical intervention and treatment for high-risk patients.
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TL;DR: The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006, and a broader consortium sharing and support model was created.
8,712 citations
01 Jan 2006
TL;DR: Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas.
Abstract: Regression models are frequently used to develop diagnostic, prognostic, and health resource utilization models in clinical, health services, outcomes, pharmacoeconomic, and epidemiologic research, and in a multitude of non-health-related areas. Regression models are also used to adjust for patient heterogeneity in randomized clinical trials, to obtain tests that are more powerful and valid than unadjusted treatment comparisons.
4,211 citations
TL;DR: It is suggested that reporting discrimination and calibration will always be important for a prediction model and decision-analytic measures should be reported if the predictive model is to be used for clinical decisions.
Abstract: The performance of prediction models can be assessed using a variety of methods and metrics. Traditional measures for binary and survival outcomes include the Brier score to indicate overall model performance, the concordance (or c) statistic for discriminative ability (or area under the receiver operating characteristic [ROC] curve), and goodness-of-fit statistics for calibration.Several new measures have recently been proposed that can be seen as refinements of discrimination measures, including variants of the c statistic for survival, reclassification tables, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Moreover, decision-analytic measures have been proposed, including decision curves to plot the net benefit achieved by making decisions based on model predictions.We aimed to define the role of these relatively novel approaches in the evaluation of the performance of prediction models. For illustration, we present a case study of predicting the presence of residual tumor versus benign tissue in patients with testicular cancer (n = 544 for model development, n = 273 for external validation).We suggest that reporting discrimination and calibration will always be important for a prediction model. Decision-analytic measures should be reported if the predictive model is to be used for clinical decisions. Other measures of performance may be warranted in specific applications, such as reclassification metrics to gain insight into the value of adding a novel predictor to an established model.
3,473 citations
TL;DR: In this article, the authors give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.
Abstract: Summary. In the paper I give a brief review of the basic idea and some history and then discuss some developments since the original paper on regression shrinkage and selection via the lasso.
3,054 citations
TL;DR: In virtually all medical domains, diagnostic and prognostic multivariable prediction models are being developed, validated, updated, and implemented with the aim to assist doctors and individuals in estimating probabilities and potentially influence their decision making.
Abstract: The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
2,982 citations