Development and validation of prediction algorithm to identify tuberculosis in two large California health systems.
Gespeichert in:
| Titel: | Development and validation of prediction algorithm to identify tuberculosis in two large California health systems. |
|---|---|
| Autoren: | Fischer H; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA. Heidi.Fischer@kp.org., Qian L; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA., Li Z; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA., Bruxvoort K; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.; Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA., Skarbinski J; Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA.; Department of Infectious Diseases, Oakland Medical Center, Kaiser Permanente Northern California, Oakland, CA, USA., Ni Y; Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA., Ku JH; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA., Lewin B; Department of Family Medicine, Kaiser Permanente Los Angeles Medical Center, Los Angeles, CA, USA.; Department of Clinical Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA., Garba S; Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA., Mahale P; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA., Shaw SF; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA., Spence B; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA., Tartof SY; Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, USA.; Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA, USA. |
| Quelle: | Nature communications [Nat Commun] 2025 Apr 10; Vol. 16 (1), pp. 3385. Date of Electronic Publication: 2025 Apr 10. |
| Publikationsart: | Journal Article; Validation Study |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: [London] : Nature Pub. Group |
| MeSH-Schlagworte: | Regional Health Planning* , Tuberculosis*/diagnosis , Tuberculosis*/epidemiology , Prediction Algorithms*, Humans ; Male ; Female ; Adolescent ; Young Adult ; Adult ; Middle Aged ; Aged ; California/epidemiology ; Mass Screening ; Risk Factors ; Electronic Health Records ; Datasets as Topic |
| Abstract: | Competing Interests: Competing interests: The authors declare no competing interests. California data demonstrate failures in latent tuberculosis screening to prevent progression to tuberculosis disease. Therefore, we developed a clinical risk prediction model for tuberculosis disease using electronic health records. This study included Kaiser Permanente Southern California and Northern California members ≥18 years during 2008-2019. Models used Cox proportional hazards regression, Harrell's C-statistic, and a simulated TB disease outcome accounting for cases prevented by current screening which includes both observed and simulated cases. We compared sensitivity and number-needed-to-screen for model-identified high-risk individuals with current screening. Of 4,032,619 and 4,051,873 Southern and Northern California members, tuberculosis disease incidences were 4.1 and 3.3 cases per 100,000 person-years, respectively. The final model C-statistic was 0.816 (95% simulation interval 0.805-0.824). Model sensitivity screening high-risk individuals was 0.70 (0.68-0.71) and number-needed-to-screen was 662 (646-679) persons-per tuberculosis disease case, compared to a sensitivity of 0.36 (0.34-0.38) and number-needed-to-screen of 1632 (1485-1774) with current screening. Here, we show our predictive model improves tuberculosis screening efficiency in California. (© 2025. The Author(s).) |
| References: | Stat Med. 2018 May 10;37(10):1671-1681. (PMID: 29424000) N Engl J Med. 2021 Dec 9;385(24):2271-2280. (PMID: 34879450) Stat Med. 2013 Dec 30;32(30):5381-97. (PMID: 24027076) Ann Intern Med. 2023 Jan;176(1):105-114. (PMID: 36571841) Int J Infect Dis. 2022 Nov;124 Suppl 1:S12-S19. (PMID: 35257904) Open Forum Infect Dis. 2023 Nov 01;10(11):ofad545. (PMID: 38023560) Stat Methods Med Res. 2016 Aug;25(4):1692-706. (PMID: 23907781) Int J Tuberc Lung Dis. 2006 Oct;10(10):1140-5. (PMID: 17044208) Appl Clin Inform. 2014 Jul 23;5(3):651-9. (PMID: 25298806) Lancet Infect Dis. 2017 Oct;17(10):e327-e333. (PMID: 28495525) N Engl J Med. 2004 May 13;350(20):2060-7. (PMID: 15141044) Am J Respir Crit Care Med. 1998 Apr;157(4 Pt 1):1244-8. (PMID: 9563746) MMWR Morb Mortal Wkly Rep. 2024 Mar 28;73(12):265-270. (PMID: 38547024) MMWR Morb Mortal Wkly Rep. 2017 Mar 24;66(11):295-298. (PMID: 28333913) BMJ. 2024 Apr 16;385:e078378. (PMID: 38626948) PLoS One. 2022 Aug 25;17(8):e0273363. (PMID: 36006985) Perm J. 2012 Summer;16(3):37-41. (PMID: 23012597) Clin Infect Dis. 2017 Jan 15;64(2):111-115. (PMID: 28052967) Clin Infect Dis. 2025 Mar 17;80(3):644-652. (PMID: 39561254) JAMA. 2016 Sep 06;316(9):962-9. (PMID: 27599331) Clin Infect Dis. 2024 May 15;78(5):1304-1312. (PMID: 38207124) |
| Grant Information: | R01 AI151072 United States AI NIAID NIH HHS; 5R01AI151072 Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.) |
| Entry Date(s): | Date Created: 20250409 Date Completed: 20250418 Latest Revision: 20250418 |
| Update Code: | 20250418 |
| PubMed Central ID: | PMC11982269 |
| DOI: | 10.1038/s41467-025-58775-6 |
| PMID: | 40204727 |
| Datenbank: | MEDLINE |
Full Text 
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Competing interests: The authors declare no competing interests.<br />California data demonstrate failures in latent tuberculosis screening to prevent progression to tuberculosis disease. Therefore, we developed a clinical risk prediction model for tuberculosis disease using electronic health records. This study included Kaiser Permanente Southern California and Northern California members ≥18 years during 2008-2019. Models used Cox proportional hazards regression, Harrell's C-statistic, and a simulated TB disease outcome accounting for cases prevented by current screening which includes both observed and simulated cases. We compared sensitivity and number-needed-to-screen for model-identified high-risk individuals with current screening. Of 4,032,619 and 4,051,873 Southern and Northern California members, tuberculosis disease incidences were 4.1 and 3.3 cases per 100,000 person-years, respectively. The final model C-statistic was 0.816 (95% simulation interval 0.805-0.824). Model sensitivity screening high-risk individuals was 0.70 (0.68-0.71) and number-needed-to-screen was 662 (646-679) persons-per tuberculosis disease case, compared to a sensitivity of 0.36 (0.34-0.38) and number-needed-to-screen of 1632 (1485-1774) with current screening. Here, we show our predictive model improves tuberculosis screening efficiency in California.<br /> (© 2025. The Author(s).) |
|---|---|
| ISSN: | 2041-1723 |
| DOI: | 10.1038/s41467-025-58775-6 |