Author
Listed:
- Shama D Ahuja
- David Ashkin
- Monika Avendano
- Rita Banerjee
- Melissa Bauer
- Jamie N Bayona
- Mercedes C Becerra
- Andrea Benedetti
- Marcos Burgos
- Rosella Centis
- Eward D Chan
- Chen-Yuan Chiang
- Helen Cox
- Lia D'Ambrosio
- Kathy DeRiemer
- Nguyen Huy Dung
- Donald Enarson
- Dennis Falzon
- Katherine Flanagan
- Jennifer Flood
- Maria L Garcia-Garcia
- Neel Gandhi
- Reuben M Granich
- Maria G Hollm-Delgado
- Timothy H Holtz
- Michael D Iseman
- Leah G Jarlsberg
- Salmaan Keshavjee
- Hye-Ryoun Kim
- Won-Jung Koh
- Joey Lancaster
- Christophe Lange
- Wiel C M de Lange
- Vaira Leimane
- Chi Chiu Leung
- Jiehui Li
- Dick Menzies
- Giovanni B Migliori
- Sergey P Mishustin
- Carole D Mitnick
- Masa Narita
- Philly O'Riordan
- Madhukar Pai
- Domingo Palmero
- Seung-kyu Park
- Geoffrey Pasvol
- Jose Peña
- Carlos Pérez-Guzmán
- Maria I D Quelapio
- Alfredo Ponce-de-Leon
- Vija Riekstina
- Jerome Robert
- Sarah Royce
- H Simon Schaaf
- Kwonjune J Seung
- Lena Shah
- Tae Sun Shim
- Sonya S Shin
- Yuji Shiraishi
- José Sifuentes-Osornio
- Giovanni Sotgiu
- Matthew J Strand
- Payam Tabarsi
- Thelma E Tupasi
- Robert van Altena
- Martie Van der Walt
- Tjip S Van der Werf
- Mario H Vargas
- Pirett Viiklepp
- Janice Westenhouse
- Wing Wai Yew
- Jae-Joon Yim
- Collaborative Group for Meta-Analysis of Individual Patient Data in MDR-TB
Abstract
Dick Menzies and colleagues report findings from a collaborative, individual patient-level meta-analysis of treatment outcomes among patients with multidrug-resistant tuberculosis.Background: Treatment of multidrug resistant tuberculosis (MDR-TB) is lengthy, toxic, expensive, and has generally poor outcomes. We undertook an individual patient data meta-analysis to assess the impact on outcomes of the type, number, and duration of drugs used to treat MDR-TB. Methods and Findings: Three recent systematic reviews were used to identify studies reporting treatment outcomes of microbiologically confirmed MDR-TB. Study authors were contacted to solicit individual patient data including clinical characteristics, treatment given, and outcomes. Random effects multivariable logistic meta-regression was used to estimate adjusted odds of treatment success. Adequate treatment and outcome data were provided for 9,153 patients with MDR-TB from 32 observational studies. Treatment success, compared to failure/relapse, was associated with use of: later generation quinolones, (adjusted odds ratio [aOR]: 2.5 [95% CI 1.1–6.0]), ofloxacin (aOR: 2.5 [1.6–3.9]), ethionamide or prothionamide (aOR: 1.7 [1.3–2.3]), use of four or more likely effective drugs in the initial intensive phase (aOR: 2.3 [1.3–3.9]), and three or more likely effective drugs in the continuation phase (aOR: 2.7 [1.7–4.1]). Similar results were seen for the association of treatment success compared to failure/relapse or death: later generation quinolones, (aOR: 2.7 [1.7–4.3]), ofloxacin (aOR: 2.3 [1.3–3.8]), ethionamide or prothionamide (aOR: 1.7 [1.4–2.1]), use of four or more likely effective drugs in the initial intensive phase (aOR: 2.7 [1.9–3.9]), and three or more likely effective drugs in the continuation phase (aOR: 4.5 [3.4–6.0]). Conclusions: In this individual patient data meta-analysis of observational data, improved MDR-TB treatment success and survival were associated with use of certain fluoroquinolones, ethionamide, or prothionamide, and greater total number of effective drugs. However, randomized trials are urgently needed to optimize MDR-TB treatment. Background: In 2010, 8.8 million people developed tuberculosis—a contagious bacterial infection—and 1.4 million people died from the disease. Mycobacterium tuberculosis, the bacterium that causes tuberculosis, is spread in airborne droplets when people with the disease cough or sneeze and usually infects the lungs (pulmonary tuberculosis). The characteristic symptoms of tuberculosis are a persistent cough, weight loss, and night sweats. Tuberculosis can be cured by taking several powerful antibiotics regularly for at least 6 months. The standard treatment for tuberculosis comprises an initial intensive phase lasting 2 months during which four antibiotics are taken daily followed by a 4-month continuation phase during which two antibiotics are taken. However, global efforts to control tuberculosis are now being thwarted by the emergence of M. tuberculosis strains that are resistant to several antibiotics, including isoniazid and rifampicin, the two most powerful, first-line (standard) anti-tuberculosis drugs. Why Was This Study Done?: Although multi-drug resistant tuberculosis (MDR-TB) can be cured using second-line anti-tuberculosis drugs, these are more expensive and more toxic than first-line drugs and optimal treatment regimens for MDR-TB have not been determined. Notably, there have been no randomized controlled trials of treatments for MDR-TB. Such trials, which compare outcomes (cure, treatment failure, relapse, and death) among patients who have been randomly assigned to receive different treatments, are the best way to compare different anti-tuberculosis drug regimens. It is possible, however, to get useful information about the association of various treatments for MDR-TB with outcomes from observational studies using a statistical approach called “individual patient data meta-analysis.” In observational studies, because patients are not randomly assigned to different treatments, differences in outcomes between treatment groups may not be caused by the different drugs they receive but may be due to other differences between the groups. An individual patient data meta-analysis uses statistical methods to combine original patient data from several different studies. Here, the researchers use this approach to investigate the association of specific drugs, numbers of drugs and treatment duration with the clinical outcomes of patients with pulmonary MDR-TB. What Did the Researchers Do and Find?: The researchers used three recent systematic reviews (studies that use predefined criteria to identify all the research on a given topic) to identify studies reporting treatment outcomes of microbiologically confirmed MDR-TB. They obtained individual patient data from the authors of these studies and estimated adjusted odds (chances) of treatment success from the treatment and outcome data of 9,153 patients with MDR-TB provided by 32 centers. The use of later generation quinolones, ofloxacin, and ethionamide/prothionamide as part of multi-drug regimens were all associated with treatment success compared to failure, relapse or death, as were the use of four or more likely effective drugs (based on drug susceptibility testing of mycobacteria isolated from study participants) during the initial intensive treatment phase and the use of three or more likely effective drugs during the continuation phase. The researchers also report that among patients who did not die or stop treatment, the chances of treatment success increased with the duration of the initial treatment phase up to 7.1–8.5 months and with the total duration of treatment up to 18.6–21.5 months. What Do These Findings Mean?: These findings suggest that the use of specific drugs, the use of a greater number of effective drugs, and longer treatments may be associated with treatment success and the survival of patients with MDR-TR. However, these findings need to be interpreted with caution because of limitations in this study that may have affected the accuracy of its findings. For example, the researchers did not include all the studies they found through the systematic reviews in their meta-analysis (some authors did not respond to requests for individual patient data, for example), which may have introduced bias. Moreover, because the patients included in the meta-analysis were treated at 32 centers, there were many differences in their management, some of which may have affected the accuracy of the findings. Because of these and other limitations, the researchers note that, although their findings highlight several important questions about the treatment of MDR-TB, randomized controlled trials are urgently needed to determine the optimal treatment for MDR-TB. Additional Information: Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001300.
Suggested Citation
Shama D Ahuja & David Ashkin & Monika Avendano & Rita Banerjee & Melissa Bauer & Jamie N Bayona & Mercedes C Becerra & Andrea Benedetti & Marcos Burgos & Rosella Centis & Eward D Chan & Chen-Yuan Chia, 2012.
"Multidrug Resistant Pulmonary Tuberculosis Treatment Regimens and Patient Outcomes: An Individual Patient Data Meta-analysis of 9,153 Patients,"
PLOS Medicine, Public Library of Science, vol. 9(8), pages 1-16, August.
Handle:
RePEc:plo:pmed00:1001300
DOI: 10.1371/journal.pmed.1001300
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Cited by:
- Kalpita S Shringarpure & Petros Isaakidis & Karuna D Sagili & R K Baxi, 2015.
"Loss-To-Follow-Up on Multidrug Resistant Tuberculosis Treatment in Gujarat, India: The WHEN and WHO of It,"
PLOS ONE, Public Library of Science, vol. 10(7), pages 1-10, July.
- Tom Decroo & Aung Kya Jai Maug & Mohamed Anwar Hossain & Cécile Uwizeye & Mourad Gumusboga & Tine Demeulenaere & Nimer Ortuño-Gutiérrez & Bouke C de Jong & Armand Van Deun, 2020.
"Injectables’ key role in rifampicin-resistant tuberculosis shorter treatment regimen outcomes,"
PLOS ONE, Public Library of Science, vol. 15(8), pages 1-11, August.
- Maeve K Lalor & Jane Greig & Sholpan Allamuratova & Sandy Althomsons & Zinaida Tigay & Atadjan Khaemraev & Kai Braker & Oleksander Telnov & Philipp du Cros, 2013.
"Risk Factors Associated with Default from Multi- and Extensively Drug-Resistant Tuberculosis Treatment, Uzbekistan: A Retrospective Cohort Analysis,"
PLOS ONE, Public Library of Science, vol. 8(11), pages 1-5, November.
- Guanbo Wang & Mireille E. Schnitzer & Dick Menzies & Piret Viiklepp & Timothy H. Holtz & Andrea Benedetti, 2020.
"Estimating treatment importance in multidrug‐resistant tuberculosis using Targeted Learning: An observational individual patient data network meta‐analysis,"
Biometrics, The International Biometric Society, vol. 76(3), pages 1007-1016, September.
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