A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes
© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissionsoup.com..
BACKGROUND: Despite widespread availability of curative therapy, tuberculosis (TB) 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 human immunodeficiency virus (HIV)-related severity and isoniazid acetylator status.
METHODS: Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB 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 7 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, .73-.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: Using information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2022 |
|---|---|
| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:74 |
|---|---|
| Enthalten in: |
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America - 74(2022), 6 vom: 23. März, Seite 973-982 |
| Sprache: |
Englisch |
|---|
| Links: |
|---|
| Anmerkungen: |
Date Completed 05.05.2022 Date Revised 16.07.2022 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1093/cid/ciab598 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM327608404 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM327608404 | ||
| 003 | DE-627 | ||
| 005 | 20231225201145.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2022 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1093/cid/ciab598 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1091.xml |
| 035 | |a (DE-627)NLM327608404 | ||
| 035 | |a (NLM)34214166 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Peetluk, Lauren S |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Clinical Prediction Model for Unsuccessful Pulmonary Tuberculosis Treatment Outcomes |
| 264 | 1 | |c 2022 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 05.05.2022 | ||
| 500 | |a Date Revised 16.07.2022 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissionsoup.com. | ||
| 520 | |a BACKGROUND: Despite widespread availability of curative therapy, tuberculosis (TB) 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 human immunodeficiency virus (HIV)-related severity and isoniazid acetylator status | ||
| 520 | |a METHODS: Data originated from the Regional Prospective Observational Research for Tuberculosis Brazil cohort, which enrolled newly diagnosed TB patients in Brazil from 2015 through 2019. This analysis included participants with culture-confirmed, drug-susceptible pulmonary TB who started first-line anti-TB therapy and had ≥12 months of follow-up. The end point was unsuccessful TB 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 | ||
| 520 | |a RESULTS: Of 944 participants, 191 (20%) had unsuccessful treatment outcomes. The final model included 7 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, .73-.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 | ||
| 520 | |a CONCLUSIONS: Using information readily available at treatment initiation, the prediction model performed well in this population. The findings may guide future work to allocate resources or inform targeted interventions for high-risk patients | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Observational Study | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a HIV coinfection | |
| 650 | 4 | |a epidemiologic research | |
| 650 | 4 | |a prediction model | |
| 650 | 4 | |a prognosis | |
| 650 | 4 | |a pulmonary tuberculosis | |
| 650 | 7 | |a Antitubercular Agents |2 NLM | |
| 650 | 7 | |a Isoniazid |2 NLM | |
| 650 | 7 | |a V83O1VOZ8L |2 NLM | |
| 700 | 1 | |a Rebeiro, Peter F |e verfasserin |4 aut | |
| 700 | 1 | |a Ridolfi, Felipe M |e verfasserin |4 aut | |
| 700 | 1 | |a Andrade, Bruno B |e verfasserin |4 aut | |
| 700 | 1 | |a Cordeiro-Santos, Marcelo |e verfasserin |4 aut | |
| 700 | 1 | |a Kritski, Afranio |e verfasserin |4 aut | |
| 700 | 1 | |a Durovni, Betina |e verfasserin |4 aut | |
| 700 | 1 | |a Calvacante, Solange |e verfasserin |4 aut | |
| 700 | 1 | |a Figueiredo, Marina C |e verfasserin |4 aut | |
| 700 | 1 | |a Haas, David W |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Dandan |e verfasserin |4 aut | |
| 700 | 1 | |a Rolla, Valeria C |e verfasserin |4 aut | |
| 700 | 1 | |a Sterling, Timothy R |e verfasserin |4 aut | |
| 700 | 0 | |a Regional Prospective Observational Research in Tuberculosis (RePORT)-Brazil Network |e verfasserin |4 aut | |
| 700 | 1 | |a Spener-Gomes, Renata |e investigator |4 oth | |
| 700 | 1 | |a de Souza, Alexandra Brito |e investigator |4 oth | |
| 700 | 1 | |a Silva Jesus, Jaquelane |e investigator |4 oth | |
| 700 | 1 | |a Benjamin, Aline |e investigator |4 oth | |
| 700 | 1 | |a Marinho Sant'Anna, Flavia |e investigator |4 oth | |
| 700 | 1 | |a Peixoto Ignácio, Francine |e investigator |4 oth | |
| 700 | 1 | |a Cristina Lourenço, Maria |e investigator |4 oth | |
| 700 | 1 | |a Gomes-Silva, Adriano |e investigator |4 oth | |
| 700 | 1 | |a de Oliveira, Jamile G |e investigator |4 oth | |
| 700 | 1 | |a Moreira, Adriana S R |e investigator |4 oth | |
| 700 | 1 | |a Calçada Carvalho, Anna Cristina |e investigator |4 oth | |
| 700 | 1 | |a Silva, Elisangela C |e investigator |4 oth | |
| 700 | 1 | |a Mello, Mayla |e investigator |4 oth | |
| 700 | 1 | |a Rocha, Michael S |e investigator |4 oth | |
| 700 | 1 | |a Nogueira, Betania |e investigator |4 oth | |
| 700 | 1 | |a Nascimento, Vanessa |e investigator |4 oth | |
| 700 | 1 | |a Nery, Saulo |e investigator |4 oth | |
| 700 | 1 | |a Andrade, Alice M S |e investigator |4 oth | |
| 700 | 1 | |a Malta-Santos, Hayna |e investigator |4 oth | |
| 700 | 1 | |a Rebouças-Silva, Jéssica |e investigator |4 oth | |
| 700 | 1 | |a Ramos, André M C |e investigator |4 oth | |
| 700 | 1 | |a Melo, Sayonara |e investigator |4 oth | |
| 700 | 1 | |a Cubillos-Angulo, Juan M |e investigator |4 oth | |
| 700 | 1 | |a de Moraes, Laise |e investigator |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t Clinical infectious diseases : an official publication of the Infectious Diseases Society of America |d 1992 |g 74(2022), 6 vom: 23. März, Seite 973-982 |w (DE-627)NLM012603007 |x 1537-6591 |7 nnns |
| 773 | 1 | 8 | |g volume:74 |g year:2022 |g number:6 |g day:23 |g month:03 |g pages:973-982 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1093/cid/ciab598 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 74 |j 2022 |e 6 |b 23 |c 03 |h 973-982 | ||