Analysis of a Large Patient-Level Dataset to Predict Outcome of Treatment for Drug-Resistant Tuberculosis

Abstract

ABSTRACT BACKGROUND: Drug-resistant (DR) tuberculosis treatment is challenging and frequently leads to poor outcomes. An international collaboration, the National Institute of Allergy and Infectious Diseases (NIAID) TB Portals develops, maintains, and supports a multi-national database of tuberculosis cases, with an emphasis on drug-resistant tuberculosis. Patient records include clinical, radiological, genomic, and socioeconomic features. Establishing factors associated with unsuccessful treatment may help optimize treatment for the most challenging infections. METHODS: Association analysis and machine learning algorithms were applied to identify important factors associated with treatment outcome and predict the outcome for three patient cohorts, selected by drug resistance level representing 1575 patients in total. The predicted probabilities of poor treatment outcome from models were calibrated as a risk score ranging from 0 to 100 corresponding to confidence level of the model for treatment outcome. RESULTS: The features most associated with treatment success in all cohorts were body mass index (BMI), onset age, employment, education, smear-negative microscopy, and percent of abnormal volume in X-ray images, confirming previously reported findings, and identifying novel factors such as pathogen genomic markers. CONCLUSIONS: The identified features might help in establishing high-risk patients at the time of admission for tuberculosis treatment. This study integrates clinical, radiological, and pathogen genomics into a patient risk model, a way of determining risk through the application of machine learning on real-world data.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This project has been funded in part with Federal funds from the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health, Department of Health and Human Services under BCBB Support Services Contract HHSN316201300006W/75N93022F00001 to MEDICAL SCIENCE & COMPUTING. This research was supported in part by the Office of Science Management and Operations of NIAID at the NIH. No additional external funding was received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study used de-identified data stripped of all PHI/PII, which is made publicly available through the TB Portals Program, a trans-national initiative led by the NIAID (https://tbportals.niaid.nih.gov/). Before public-sharing and reuse of the de-identified data, each participating clinical research institution (https://tbportals.niaid.nih.gov/where-do-our-cases-come-from) receives approval from the participating institution's IRB and must follow strict adherence to ethics rules requirements of CRDF Global and the International Science and Technology Center who are the grant-issuing institutions (https://journals.asm.org/doi/10.1128/JCM.01013-17). The data was analyzed in accordance to the guidelines specified in TB Portals Data Use Agreement (https://tbportals.niaid.nih.gov/pdf/TB-Portals-Data-Use-Agreement.pdf).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.

Yes

Data Availability

The TB portals program necessitates all users of the data sign a DUA before access to the underlying, de-identified clinical data is provided and the data can be requested at the following URL (https://tbportals.niaid.nih.gov/download-data). Therefore, this study provides code used in the analysis without the underlying raw data (https://github.com/niaid/tb-portals-association-and-prediction) in compliance with the DUA.

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