Development and validation of prediction algorithm to identify tuberculosis in two large California health systems

Heidi Fischer (), Lei Qian, Zhuoxin Li, Katia Bruxvoort, Jacek Skarbinski, Yuching Ni, Jennifer H. Ku, Bruno Lewin, Saadiq Garba, Parag Mahale, Sally F. Shaw, Brigitte Spence and Sara Y. Tartof
Additional contact information
Heidi Fischer: Kaiser Permanente Southern California
Lei Qian: Kaiser Permanente Southern California
Zhuoxin Li: Kaiser Permanente Southern California
Katia Bruxvoort: Kaiser Permanente Southern California
Jacek Skarbinski: Kaiser Permanente Northern California
Yuching Ni: Kaiser Permanente Northern California
Jennifer H. Ku: Kaiser Permanente Southern California
Bruno Lewin: Kaiser Permanente Los Angeles Medical Center
Saadiq Garba: Kaiser Permanente Bernard J. Tyson School of Medicine
Parag Mahale: Kaiser Permanente Southern California
Sally F. Shaw: Kaiser Permanente Southern California
Brigitte Spence: Kaiser Permanente Southern California
Sara Y. Tartof: Kaiser Permanente Southern California

Nature Communications, 2025, vol. 16, issue 1, 1-9

Abstract: Abstract 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.

Date: 2025
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DOI: 10.1038/s41467-025-58775-6

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