Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data.

BACKGROUND

Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes.

OBJECTIVES

The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased.

DERIVATION COHORT

All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699).

VALIDATION COHORT

External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389).

PREDICTION MODEL

All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score.

RESULTS

Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31-0.21) similar to that of Modified Early Warning Score greater than 4 (0.29-0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25-4.51× more true positives.

CONCLUSIONS

Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment.

Informations de copyright

Copyright © 2021 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.

Déclaration de conflit d'intérêts

Dr. Wong is supported by the National Institute of General Medical Sciences (NIGMS) 2T32GM095442 and the Clinical and Translational Science Award pilot informatics grant by National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) under UL1TR002378. He holds equity and management roles in Ataia Medical. Dr. Kamaleswaran is supported by the Michael J. Fox Foundation (Grant No. 17267). Dr. Reyna is supported by NIH U54EB027690 and HHS0100201900015C. Dr. Josef is supported by the NIGMS 2T32GM095442. Dr. Holder is supported by the NIGMS under award number K23GM137182 for Advancing Translational Sciences of the NIH under Award Number UL1TR002378. Dr. Nemati is supported by the NIH (No. K01ES025445) and the Gordon and Betty Moore Foundation (No. GBMF9052). Dr. Buchman is supported by the Society of Critical Care Medicine and the Biomedical Advanced Research and Development Authority. He is an Editor in Chief for Critical Care Medicine and has recused himself from editorial influence on this article. Dr. Blum is supported by the NCATS of the NIH under Award Number UL1TR002378. He is a consultant for Clew Medical. The remaining authors have disclosed that they do not have any potential conflicts of interest.