Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning
Background Global tuberculosis (TB) drug resistance (DR) surveillance focuses on rifampicin. We examined the potential of public and surveillance Mycobacterium tuberculosis (Mtb) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) usi...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2024-03-01
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| Series: | BMJ Global Health |
| Online Access: | https://gh.bmj.com/content/9/3/e013532.full |
| _version_ | 1826914533112807424 |
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| author | Luca Freschi Alena Skrahina Maria Nakhoul Nazir Ismail Avika Dixit Roger Vargas Matthias I Gröschel Sabira Tahseen S M Masud Alam S M Mostofa Kamal Ramon P Basilio Dodge R Lim Maha R Farhat |
| author_facet | Luca Freschi Alena Skrahina Maria Nakhoul Nazir Ismail Avika Dixit Roger Vargas Matthias I Gröschel Sabira Tahseen S M Masud Alam S M Mostofa Kamal Ramon P Basilio Dodge R Lim Maha R Farhat |
| author_sort | Luca Freschi |
| collection | DOAJ |
| description | Background Global tuberculosis (TB) drug resistance (DR) surveillance focuses on rifampicin. We examined the potential of public and surveillance Mycobacterium tuberculosis (Mtb) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction.Methods We curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa.Results Mtb isolates from 29 countries (n=19 149) met sequence quality criteria. Global marginal genotypic resistance among mono-resistant TB estimates overlapped with the South African DR survey, except for isoniazid, ethionamide and second-line injectables, which were underestimated (n=3134). Among multidrug resistant (MDR) TB (n=268), estimates overlapped for the fluoroquinolones but overestimated other drugs. Globally pooled mono-resistance to isoniazid was 10.9% (95% CI: 10.2-11.7%, n=14 012). Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% (0.1–11%), n=111 and India 2.8% (0.08–9.4%), n=114). Given the recent interest in drugs enhancing ethionamide activity and their expected activity against isolates with resistance discordance between isoniazid and ethionamide, we measured this rate and found it to be high at 74.4% (IQR: 64.5–79.7%) of isoniazid-resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3964).Conclusions This is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics, but public WGS data demonstrates oversampling of isolates with higher resistance levels than MDR. Nevertheless, our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug-susceptible TB in South Asia and indicate underutilisation of ethionamide in MDR treatment. |
| first_indexed | 2024-04-24T13:52:15Z |
| format | Article |
| id | doaj.art-18f8a98070f74b2fbac7400b045a066f |
| institution | Directory Open Access Journal |
| issn | 2059-7908 |
| language | English |
| last_indexed | 2025-02-17T11:46:35Z |
| publishDate | 2024-03-01 |
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| spelling | doaj.art-18f8a98070f74b2fbac7400b045a066f2024-12-28T10:10:09ZengBMJ Publishing GroupBMJ Global Health2059-79082024-03-019310.1136/bmjgh-2023-013532Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learningLuca Freschi0Alena Skrahina1Maria Nakhoul2Nazir Ismail3Avika Dixit4Roger Vargas5Matthias I Gröschel6Sabira Tahseen7S M Masud Alam8S M Mostofa Kamal9Ramon P Basilio10Dodge R Lim11Maha R Farhat12Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USARepublican Scientific and Practical Center for Pulmonology and Tuberculosis, Minsk, BelarusInformatics and Analytics Department, Dana-Farber Cancer Institute, Boston, Massachusetts, USAClinical Microbiology and Infectious Diseases, University of the Witwatersrand Johannesburg Faculty of Health Sciences, Johannesburg, South AfricaDivision of Infectious Diseases, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts, USADepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USADepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USANational Tuberculosis Control Programme, Islamabad, PakistanMinistry of Health and Family Welfare, Kolkata, West Bengal, IndiaNational Institute of Diseases of the Chest and Hospital, Dhaka, BangladeshResearch Institute for Tropical Medicine, Muntinlupa City, PhilippinesResearch Institute for Tropical Medicine, Muntinlupa City, PhilippinesDepartment of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USABackground Global tuberculosis (TB) drug resistance (DR) surveillance focuses on rifampicin. We examined the potential of public and surveillance Mycobacterium tuberculosis (Mtb) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction.Methods We curated and quality-controlled Mtb WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa.Results Mtb isolates from 29 countries (n=19 149) met sequence quality criteria. Global marginal genotypic resistance among mono-resistant TB estimates overlapped with the South African DR survey, except for isoniazid, ethionamide and second-line injectables, which were underestimated (n=3134). Among multidrug resistant (MDR) TB (n=268), estimates overlapped for the fluoroquinolones but overestimated other drugs. Globally pooled mono-resistance to isoniazid was 10.9% (95% CI: 10.2-11.7%, n=14 012). Mono-levofloxacin resistance rates were highest in South Asia (Pakistan 3.4% (0.1–11%), n=111 and India 2.8% (0.08–9.4%), n=114). Given the recent interest in drugs enhancing ethionamide activity and their expected activity against isolates with resistance discordance between isoniazid and ethionamide, we measured this rate and found it to be high at 74.4% (IQR: 64.5–79.7%) of isoniazid-resistant isolates predicted to be ethionamide susceptible. The global susceptibility rate to pyrazinamide and levofloxacin among MDR was 15.1% (95% CI: 10.2-19.9%, n=3964).Conclusions This is the first attempt at global Mtb antibiogram estimation. DR prevalence in Mtb can be reliably estimated using public WGS and phenotypic resistance prediction for key antibiotics, but public WGS data demonstrates oversampling of isolates with higher resistance levels than MDR. Nevertheless, our results raise concerns about the empiric use of short-course fluoroquinolone regimens for drug-susceptible TB in South Asia and indicate underutilisation of ethionamide in MDR treatment.https://gh.bmj.com/content/9/3/e013532.full |
| spellingShingle | Luca Freschi Alena Skrahina Maria Nakhoul Nazir Ismail Avika Dixit Roger Vargas Matthias I Gröschel Sabira Tahseen S M Masud Alam S M Mostofa Kamal Ramon P Basilio Dodge R Lim Maha R Farhat Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning BMJ Global Health |
| title | Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning |
| title_full | Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning |
| title_fullStr | Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning |
| title_full_unstemmed | Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning |
| title_short | Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning |
| title_sort | estimation of country specific tuberculosis resistance antibiograms using pathogen genomics and machine learning |
| url | https://gh.bmj.com/content/9/3/e013532.full |
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