Artificial intelligence and amikacin exposures predictive of outcomes in multidrug-resistant tuberculosis patients

Antimicrobial Agents and Chemotherapy

Volumn 60, Issue 10, 2016, Pages 5928-5932

  Modongo, Chawangwa ^a,b   Pasipanodya, Jotam G ^c   Magazi, Beki T ^d   Srivastava, Shashikant ^c   Zetola, Nicola M ^a,b,e   Williams, Scott M ^f   Sirugo, Giorgio ^g   Gumbo, Tawanda ^c,h  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b BOTSWANA UPENN PARTNERSHIP   (Botswana)

^c BAYLOR RESEARCH INSTITUTE   (United States)

^d UNIVERSITY OF PRETORIA   (South Africa)

^e UNIVERSITY OF BOTSWANA   (Botswana)

^f DARTMOUTH MEDICAL SCHOOL   (United States)

^g FATEBENEFRATELLI HOSPITAL   (Italy)

^h UNIVERSITY OF CAPE TOWN   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

AMIKACIN; CYCLOSERINE; ETHIONAMIDE; LEVOFLOXACIN; PYRAZINAMIDE; TUBERCULOSTATIC AGENT;

ADOLESCENT; ADULT; AGED; ANTIBIOTIC THERAPY; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL INTELLIGENCE; BOTSWANA; CLINICAL ARTICLE; CLINICAL FEATURE; COMORBIDITY; DEMOGRAPHY; DRUG EXPOSURE; FEMALE; HOLLOW FIBER; HUMAN; LIQUID CULTURE; MALE; MULTIDRUG RESISTANT TUBERCULOSIS; OUTCOME ASSESSMENT; PATIENT IDENTIFICATION; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; SPUTUM CULTURE; TREATMENT DURATION; TREATMENT FAILURE; DRUG EFFECTS; MICROBIAL SENSITIVITY TEST; MICROBIOLOGY; MIDDLE AGED; MYCOBACTERIUM TUBERCULOSIS; REGRESSION ANALYSIS; THEORETICAL MODEL; TREATMENT OUTCOME; TUBERCULOSIS, MULTIDRUG-RESISTANT; TUBERCULOSIS, PULMONARY; VERY ELDERLY; YOUNG ADULT;

ADOLESCENT; ADULT; AGED; AGED, 80 AND OVER; AMIKACIN; ANTITUBERCULAR AGENTS; ARTIFICIAL INTELLIGENCE; BOTSWANA; FEMALE; HUMANS; MALE; MICROBIAL SENSITIVITY TESTS; MIDDLE AGED; MODELS, THEORETICAL; MYCOBACTERIUM TUBERCULOSIS; REGRESSION ANALYSIS; TREATMENT OUTCOME; TUBERCULOSIS, MULTIDRUG-RESISTANT; TUBERCULOSIS, PULMONARY; YOUNG ADULT;

EID: 84994514607     PISSN: 00664804     EISSN: 10986596     Source Type: Journal    
DOI: 10.1128/AAC.00962-16     Document Type: Article

References (30)

1. Dheda K, Gumbo T, Gandhi NR, Murray M, Theron G, Udwadia Z, Migliori GB, Warren R. 2014. Global control of tuberculosis: from extensively drug-resistant to untreatable tuberculosis. Lancet Respir Med 2:321-338. http://dx.doi.org/10.1016/S2213-2600(14)70031-1.
2. Falzon D, Jaramillo E, Schunemann HJ, Arentz M, Bauer M, Bayona J, Blanc L, Caminero JA, Daley CL, Duncombe C, Fitzpatrick C, Gebhard A, Getahun H, Henkens M, Holtz TH, Keravec J, Keshavjee S, Khan AJ, Kulier R, Leimane V, Lienhardt C, Lu C, Mariandyshev A, Migliori GB, Mirzayev F, Mitnick CD, Nunn P, Nwagboniwe G, Oxlade O, Palmero D, Pavlinac P, Quelapio MI, Raviglione MC, Rich ML, Royce S, Rusch-Gerdes S, Salakaia A, Sarin R, Sculier D, Varaine F, Vitoria M, Walson JL, Wares F, Weyer K, White RA, Zignol M. 2011. WHO guidelines for the programmatic management of drug-resistant tuberculosis: 2011 update. Eur Respir J 38:516-528. http://dx.doi.org/10.1183/09031936 .00073611.
3. Ahuja SD, Ashkin D, Avendano M, Banerjee R, Bauer M, Bayona JN, Becerra MC, Benedetti A, Burgos M, Centis R, Chan ED, Chiang CY, Cox H, D'Ambrosio L, DeRiemer K, Dung NH, Enarson D, Falzon D, Flanagan K, Flood J, Garcia-Garcia ML, Gandhi N, Granich RM, Hollm-Delgado MG, Holtz TH, Iseman MD, Jarlsberg LG, Keshavjee S, Kim HR, Koh WJ, Lancaster J, Lange C, de Lange WC, Leimane V, Leung CC, Li J, Menzies D, Migliori GB, Mishustin SP, Mitnick CD, Narita M, O'Riordan P, Pai M, Palmero D, Park SK, Pasvol G, Pena J, Perez-Guzman C, Quelapio MI, Ponce-de-Leon A, Riekstina V, Robert J, Royce S, Schaaf HS, Seung KJ, Shah L, Shim TS, Shin SS, Shiraishi Y, Sifuentes-Osornio J, Sotgiu G, Strand MJ, Tabarsi P, Tupasi TE, ARvan Van der Walt M, van der Werf TS, Vargas MH, Viiklepp P, Westenhouse J, Yew WW, Yim JJ. 2012. Multidrug resistant pulmonary tuberculosis treatment regimens and patient outcomes: an individual patient data meta-Analysis of 9,153 patients. PLoS Med 9:e1001300. http://dx.doi.org/10.1371/journal.pmed.1001300.
4. Modongo C, Pasipanodya JG, Zetola NM, Williams SM, Sirugo G, Gumbo T. 2015. Amikacin concentrations predictive of ototoxicity in multidrug-resistant tuberculosis patients. Antimicrob Agents Chemother 59:6337-6343. http://dx.doi.org/10.1128/AAC.01050-15.
5. Srivastava S, Modongo C, Siyambalapitiyage Dona CS, Pasipanodya JG, Deshpande D, Gumbo T. 2016. Amikacin optimal exposure targets in the hollow-fiber system model of tuberculosis. Antimicrob Agents Chemother 60:5922-5927. http://dx.doi.org/10.1128/AAC.00961-16.
6. Gumbo T, Pasipanodya JG, Nuermberger E, Romero K, Hanna D. 2015. Correlations between the hollow fiber model of tuberculosis and therapeutic events in tuberculosis patients: learn and confirm. Clin Infect Dis 61(Suppl 1):S18-S24. http://dx.doi.org/10.1093/cid/civ426.
7. Gumbo T, Pasipanodya JG, Romero K, Hanna D, Nuermberger E. 2015. Forecasting accuracy of the hollow fiber model of tuberculosis for clinical therapeutic outcomes. Clin Infect Dis 61(Suppl 1):S25-S31. http://dx.doi.org/10.1093/cid/civ427.
8. Pasipanodya JG, Nuermberger E, Romero K, Hanna D, Gumbo T. 2015. Systematic analysis of hollow fiber model of tuberculosis experiments. Clin Infect Dis 61(Suppl 1):S10-S17. http://dx.doi.org/10.1093/cid/civ425.
9. Campbell DK. 1987. Nonlinear science: from paradigms to practicalities. Los Alamos Sci Spec Issue 1987:218-262.
10. Chigutsa E, Pasipanodya JG, Visser ME, van Helden PD, Smith PJ, Sirgel FA, Gumbo T, McIlleron H. 2015. Impact of nonlinear interactions of pharmacokinetics and MICs on sputum bacillary kill rates as a marker of sterilizing effect in tuberculosis. Antimicrob Agents Chemother 59:38-45. http://dx.doi.org/10.1128/AAC.03931-14.
11. Pasipanodya JG, McIlleron H, Burger A, Wash PA, Smith P, Gumbo T. 2013. Serum drug concentrations predictive of pulmonary tuberculosis outcomes. J Infect Dis 208:1464-1473. http://dx.doi.org/10.1093/infdis/jit352.
12. Campbell D, Farmer D, Crutchfield J, Jen E. 1985. Experimental mathematics: the role of computation in nonlinear science. Commun ACM 28:374-384.
13. Breiman L, Friedman J, Stone CJ, Olshen RA. 1984. Classification and regression trees. Chapman and Hall/CRC, Boca Raton, FL.
14. Lewis RJ. 2000. An introduction to classification and regression tree (CART) analysis. Annu Meet Soc Acad Emergency Med, Des Plaines, IL.
15. Breiman L 2001. Random forests. Machine Learning 45:5-32. http://dx.doi.org/10.1023/A:1010933404324.
16. Modongo C, Sobota RS, Kesenogile B, Ncube R, Sirugo G, Williams SM, Zetola NM. 2014. Successful MDR-TB treatment regimens including amikacin are associated with high rates of hearing loss. BMC Infect Dis 14:542. http://dx.doi.org/10.1186/1471-2334-14-542.
17. Jain MK, Pasipanodya JG, Alder L, Lee WM, Gumbo T. 2013. Pegylated interferon fractal pharmacokinetics: individualized dosing for hepatitis C virus infection. Antimicrob Agents Chemother 57:1115-1120. http://dx.doi.org/10.1128/AAC.02208-12.
18. Gumbo T, Pasipanodya JG, Wash P, Burger A, McIlleron H. 2014. Redefining multidrug-resistant tuberculosis based on clinical response to combination therapy. Antimicrob Agents Chemother 58:6111-6115. http://dx.doi.org/10.1128/AAC.03549-14.
19. Gumbo T, Chigutsa E, Pasipanodya J, Visser M, van Helden PD, Sirgel FA, McIlleron H. 2014. The pyrazinamide susceptibility breakpoint above which combination therapy fails. J Antimicrob Chemother 69: 2420-2425. http://dx.doi.org/10.1093/jac/dku136.
20. Mpagama SG, Ndusilo N, Stroup S, Kumburu H, Peloquin CA, Gratz J, Houpt ER, Kibiki GS, Heysell SK. 2014. Plasma drug activity in patients on treatment for multidrug-resistant tuberculosis. Antimicrob Agents Chemother 58:782-788. http://dx.doi.org/10.1128/AAC.01549-13.
21. Craig WA. 1998. Pharmacokinetic/pharmacodynamic parameters: rationale for antibacterial dosing of mice and men. Clin Infect Dis 26:1-10. http://dx.doi.org/10.1086/516284.
22. Ambrose PG, Bhavnani SM, Rubino CM, Louie A, Gumbo T, Forrest A, Drusano GL. 2007. Pharmacokinetics-pharmacodynamics of antimicrobial therapy: it's not just for mice anymore. Clin Infect Dis 44:79-86. http://dx.doi.org/10.1086/510079.
23. Hall L, Jude KP, Clark SL, Dionne K, Merson R, Boyer A, Parrish NM, Wengenack NL. 2012. Evaluation of the Sensititre MycoTB plate for susceptibility testing of the Mycobacterium tuberculosis complex against firstand second-line agents. J Clin Microbiol 50:3732-3734. http://dx.doi.org/10.1128/JCM.02048-12.
24. Breiman L. 2001. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat Sci 16:199-231.
25. Srivastava S, Pasipanodya JG, Meek C, Leff R, Gumbo T. 2011. Multidrug-resistant tuberculosis not due to noncompliance but to betweenpatient pharmacokinetic variability. J Infect Dis 204:1951-1959. http://dx.doi.org/10.1093/infdis/jir658.
26. Pasipanodya J, Srivastava S, Gumbo T. 2012. New susceptibility breakpoints and the regional variability of MIC distribution in Mycobacterium tuberculosis isolates. Antimicrob Agents Chemother 56:5428. http://dx.doi.org/10.1128/AAC.00976-12.
27. Schmalstieg AM, Srivastava S, Belkaya S, Deshpande D, Meek C, Leff R, van Oers NS, Gumbo T. 2012. The antibiotic resistance arrow of time: efflux pump induction is a general first step in the evolution of mycobacterial drug resistance. Antimicrob Agents Chemother 56:4806-4815. http://dx.doi.org/10.1128/AAC.05546-11.
28. Gumbo T. 2013. Biological variability and the emergence of multidrugresistant tuberculosis. Nat Genet 45:720-721. http://dx.doi.org/10.1038/ng.2675.
29. Pasipanodya JG, Gumbo T. 2013. A meta-Analysis of self-Administered vs directly observed therapy effect on microbiologic failure, relapse, and acquired drug resistance in tuberculosis patients. Clin Infect Dis 57:21-31. http://dx.doi.org/10.1093/cid/cit167.
30. Dull WL, Alexander MR, Kasik JE. 1979. Bronchial secretion levels of amikacin. Antimicrob Agents Chemother 16:767-771. http://dx.doi.org/10.1128/AAC.16.6.767.