Hospital Practices for Respiratory Isolation for Patients With Suspected Tuberculosis and Potential Application of Prediction Models

Joshua Davidson; Bryan Chesen; Samir Kumar; Daniel J Shayowitz

doi:10.7759/cureus.32294

Hospital Practices for Respiratory Isolation for Patients With Suspected Tuberculosis and Potential Application of Prediction Models

Cureus. 2022 Dec 7;14(12):e32294. doi: 10.7759/cureus.32294. eCollection 2022 Dec.

Authors

Joshua Davidson¹, Bryan Chesen², Samir Kumar³, Daniel J Shayowitz⁴

Affiliations

¹ Pulmonary and Critical Care Medicine, BronxCare Health System, New York, USA.
² Pulmonary and Critical Care Medicine, State University of New York Downstate Health Sciences University, Brooklyn, USA.
³ Pulmonary and Critical Care Medicine, Mount Sinai Health System, New York, USA.
⁴ Internal Medicine, State University of New York Downstate Health Sciences University, Brooklyn, USA.

Abstract

Hospitalized persons with suspected pulmonary tuberculosis (PTB) are placed in airborne isolation to prevent nosocomial infection, as recommended by the Centers for Disease Control and Prevention (CDC). There is significant evidence that clinicians overuse this resource due to an abundance of caution when confronted with a patient with possible PTB. Many researchers have developed predictive tools based on clinical and radiographic data to assist clinicians in deciding which patients to place in respiratory isolation. We assessed the isolation practices for an urban hospital serving a large immigrant population and then retrospectively applied seven previously derived prediction models of isolation of PTB to our population. Our current clinical practice results in 76% of patients with PTB being placed in isolation on admission. However, 208 patients without PTB were placed in isolation unnecessarily for a total of 584 days. Four models had sensitivities greater than 90%, and two models had sensitivities of 100%. The use of these models would have potentially saved more than 150 days of patient isolation per year.

Keywords: : tuberculosis; active pulmonary tuberculosis; airborne infection control; airborne isolation; decision tree; flexible fiberoptic bronchoscopy (ffb); hospital infection control; hospital isolation; prediction tools.