Development and validation of a prediction algorithm to identify birth in countries with high tuberculosis incidence in two large California health systems.

ABSTRACT:

Objective

Though targeted testing for latent tuberculosis infection ("LTBI") for persons born in countries with high tuberculosis incidence ("HTBIC") is recommended in health care settings, this information is not routinely recorded in the electronic health record ("EHR"). We develop and validate a prediction model for birth in a HTBIC using EHR data.

Materials and methods

In a cohort of patients within Kaiser Permanente Southern California ("KPSC") and Kaiser Permanent Northern California ("KPNC") between January 1, 2008 and December 31, 2019, KPSC was used as the development dataset and KPNC was used for external validation using logistic regression. Model performance was evaluated using area under the receiver operator curve ("AUCROC") and area under the precision and rec

SUBMITTER: Fischer H 

PROVIDER: S-EPMC9409495 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature