A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients

TL;DR

This paper proposes a pragmatic methodology for estimating treatment effects from observational EHR data, specifically assessing the impact of prone positioning on COVID-19 ventilated patients, aiding clinical decision-making.

Contribution

It introduces a novel, practical approach for causal inference from observational data, addressing the lack of standardized methods for treatment effect estimation in clinical settings.

Findings

Preliminary estimates of proning effect on COVID-19 patients.

Method demonstrates robustness in observational data analysis.

Provides clinicians with evidence-based guidance.

Abstract

Despite the recent progress in the field of causal inference, to date there is no agreed upon methodology to glean treatment effect estimation from observational data. The consequence on clinical practice is that, when lacking results from a randomized trial, medical personnel is left without guidance on what seems to be effective in a real-world scenario. This article proposes a pragmatic methodology to obtain preliminary but robust estimation of treatment effect from observational studies, to provide front-line clinicians with a degree of confidence in their treatment strategy. Our study design is applied to an open problem, the estimation of treatment effect of the proning maneuver on COVID-19 Intensive Care patients.