A Method to Explore Variations of Ventilator-Associated Event Surveillance Definitions in Large Critical Care Databases in the United States

An-Kwok Ian Wong; Han Kim; Marie-Laure Charpignon; Leandro Carvalho; Enrique Monares-Zepeda; R W M A Madushani; Lasith Adhikari; Ryan D Kindle; Michael Kutner; Leo Anthony Celi; Mary E Lough; Eduardo Mireles-Cabodevila

doi:10.1097/CCE.0000000000000790

A Method to Explore Variations of Ventilator-Associated Event Surveillance Definitions in Large Critical Care Databases in the United States

Crit Care Explor. 2022 Nov 15;4(11):e0790. doi: 10.1097/CCE.0000000000000790. eCollection 2022 Nov.

Authors

An-Kwok Ian Wong¹, Han Kim², Marie-Laure Charpignon³, Leandro Carvalho⁴, Enrique Monares-Zepeda⁵, R W M A Madushani⁶, Lasith Adhikari⁷, Ryan D Kindle^{8

9}, Michael Kutner¹⁰, Leo Anthony Celi^{8

9

11}, Mary E Lough^{12

13}, Eduardo Mireles-Cabodevila¹⁴

Affiliations

¹ Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, Department of Medicine, Duke University, Durham, NC.
² Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD.
³ MIT Institute for Data, Systems and Society, Cambridge, MA.
⁴ Sociedade Mineira de Terapia Intensiva, Belo Horizonte, Brazil.
⁵ Medicina Crítica por la Universidad, Centro Médico ABC, Ciudad de México, México.
⁶ Department of Infectious Diseases, Boston Medical Center, Boston, MA.
⁷ Connected Care and Personal Health, Philips Research North America, Cambridge, MA.
⁸ Massachusetts Institute of Technology, Laboratory for Computational Physiology, Cambridge, MA.
⁹ Beth Israel Deaconess Medical Center, Division of Pulmonary, Critical Care, and Sleep Medicine, Boston, MA.
¹⁰ Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA.
¹¹ Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
¹² Medicine - Primary Care and Population Health, Stanford University, Stanford, CA.
¹³ Office of Research, Stanford Health Care, Palo Alto, CA.
¹⁴ Respiratory Institute, Cleveland Clinic, Cleveland, OH.

Abstract

The Centers for Disease Control has well-established surveillance programs to monitor preventable conditions in patients supported by mechanical ventilation (MV). The aim of the study was to develop a data-driven methodology to examine variations in the first tier of the ventilator-associated event surveillance definition, described as a ventilator-associated condition (VAC). Further, an interactive tool was designed to illustrate the effect of changes to the VAC surveillance definition, by applying different ventilator settings, time-intervals, demographics, and selected clinical criteria.

Design: Retrospective, multicenter, cross-sectional analysis.

Setting: Three hundred forty critical care units across 209 hospitals, comprising 261,910 patients in both the electronic Intensive Care Unit Clinical Research Database and Medical Information Mart for Intensive Care III databases.

Patients: A total of 14,517 patients undergoing MV for 4 or more days.

Measurements and main results: We designed a statistical analysis framework, complemented by a custom interactive data visualization tool to depict how changes to the VAC surveillance definition alter its prognostic performance, comparing patients with and without VAC. This methodology and tool enable comparison of three clinical outcomes (hospital mortality, hospital length-of-stay, and ICU length-of-stay) and provide the option to stratify patients by six criteria in two categories: patient population (dataset and ICU type) and clinical features (minimum Fio₂, minimum positive end-expiratory pressure, early/late VAC, and worst first-day respiratory Sequential Organ Failure Assessment score). Patient population outcomes were depicted by heatmaps with mortality odds ratios. In parallel, outcomes from ventilation setting variations and clinical features were depicted with Kaplan-Meier survival curves.

Conclusions: We developed a method to examine VAC using information extracted from large electronic health record databases. Building upon this framework, we developed an interactive tool to visualize and quantify the implications of variations in the VAC surveillance definition in different populations, across time and critical care settings. Data for patients with and without VAC was used to illustrate the effect of the application of this method and visualization tool.

Keywords: data science; mechanical; pneumonia; surveillance; ventilator-associated; ventilator-associated condition; ventilator-associated event; ventilators.