Predicting responders to prone positioning in mechanically ventilated patients with COVID-19 using machine learning
Authors: Dam Tariq A. , Roggeveen Luca F. , van Diggelen Fuda , Fleuren Lucas M. , Jagesar Ameet R. , Otten Martijn , de Vries Heder J. , Gommers Diederik , Cremer Olaf L. , Bosman Rob J. , Rigter Sander , Wils Evert-Jan , Frenzel Tim , Dongelmans Dave A. , de Jong Remko , Peters Marco A. A. , Kamps Marlijn J. A. , Ramnarain Dharmanand , Nowitzky Ralph , Nooteboom Fleur G. C. A. , de Ruijter Wouter , Urlings-Strop Louise C. , Smit Ellen G. M. , Mehagnoul-Schipper D. Jannet , Dormans Tom , de Jager Cornelis P. C. , Hendriks Stefaan H. A. , Achterberg Sefanja , Oostdijk Evelien , Reidinga Auke C. , Festen-Spanjer Barbara , Brunnekreef Gert B. , Cornet Alexander D. , van den Tempel Walter , Boelens Age D. , Koetsier Peter , Lens Judith , Faber Harald J. , Karakus A. , Entjes Robert , de Jong Paul , Rettig Thijs C. D. , Arbous Sesmu , Vonk Sebastiaan J. J. , Machado Tomas , Herter Willem E. , de Grooth Harm-Jan , Thoral Patrick J. , Girbes Armand R. J. , Hoogendoorn Mark , Elbers Paul W. G. ,   The Dutch ICU Data Sharing Against COVID-19 Collaborators, Koeter Julia , van Rietschote Roger , Reuland M. C. , van Manen Laura , Montenij Leon , van Bommel Jasper , van den Berg Roy , van Geest Ellen , Hana Anisa , van den Bogaard B. , Pickkers Peter , van der Heiden Pim , van Gemeren Claudia , Meinders Arend Jan , de Bruin Martha , Rademaker Emma , van Osch Frits H. M. , de Kruif Martijn , Schroten Nicolas , Arnold Klaas Sierk , Fijen J. W. , van Koesveld Jacomar J. M. , Simons Koen S. , Labout Joost , van de Gaauw Bart , Kuiper Michael , Beishuizen Albertus , Geutjes Dennis , Lutisan Johan , Grady Bart P. , van den Akker Remko , Rijpstra Tom A. , Boersma Wim , Pretorius Daniël , Beukema Menno , Simons Bram , Rijkeboer A. A. , Aries Marcel , van den Oever Niels C. Gritters , van Tellingen Martijn , Dijkstra Annemieke , van Raalte Rutger , Hassouni Ali el , Guzman David Romero , Bhulai Sandjai , Ouweneel Dagmar M. , Driessen Ronald , Peppink Jan , Zijlstra G. J. , van Tienhoven A. J. , van der Heiden Evelien , Spijkstra Jan Jaap , van der Spoel Hans , de Man Angelique M. E. , Klausch Thomas , Lalisang Robbert C. A. , Tonutti Michele , de Bruin Daan P. , Fornasa Mattia , de Neree tot Babberich Michael , Thijssens Olivier , Wagemakers Lot , van der Pol Hilde G. A. , Hendriks Tom , Berend Julie , Silva Virginia Ceni , Kullberg Robert F. J. , Houwert Taco , Hovenkamp Hidde , Londono Roberto Noorduijn , Quintarelli Davide , Scholtemeijer Martijn G. , de Beer Aletta A. , Cinà Giovanni , Izdebski Adam , Heunks Leo , Juffermans Nicole , Slooter Arjen J. C. , Beudel Martijn 
Published: October 2022

Background

For mechanically ventilated critically ill COVID-19 patients, prone positioning has quickly become an important treatment strategy, however, prone positioning is labor intensive and comes with potential adverse effects. Therefore, identifying which critically ill intubated COVID-19 patients will benefit may help allocate labor resources.

Methods

From the multi-center Dutch Data Warehouse of COVID-19 ICU patients from 25 hospitals, we selected all 3619 episodes of prone positioning in 1142 invasively mechanically ventilated patients. We excluded episodes longer than 24 h. Berlin ARDS criteria were not formally documented. We used supervised machine learning algorithms Logistic Regression, Random Forest, Naive Bayes, K-Nearest Neighbors, Support Vector Machine and Extreme Gradient Boosting on readily available and clinically relevant features to predict success of prone positioning after 4 h (window of 1 to 7 h) based on various possible outcomes. These outcomes were defined as improvements of at least 10% in PaO2/FiO2 ratio, ventilatory ratio, respiratory system compliance, or mechanical power. Separate models were created for each of these outcomes. Re-supination within 4 h after pronation was labeled as failure. We also developed models using a 20 mmHg improvement cut-off for PaO2/FiO2 ratio and using a combined outcome parameter. For all models, we evaluated feature importance expressed as contribution to predictive performance based on their relative ranking.

Results

The median duration of prone episodes was 17 h (11–20, median and IQR, N = 2632). Despite extensive modeling using a plethora of machine learning techniques and a large number of potentially clinically relevant features, discrimination between responders and non-responders remained poor with an area under the receiver operator characteristic curve of 0.62 for PaO2/FiO2 ratio using Logistic Regression, Random Forest and XGBoost. Feature importance was inconsistent between models for different outcomes. Notably, not even being a previous responder to prone positioning, or PEEP-levels before prone positioning, provided any meaningful contribution to predicting a successful next proning episode.

Conclusions

In mechanically ventilated COVID-19 patients, predicting the success of prone positioning using clinically relevant and readily available parameters from electronic health records is currently not feasible. Given the current evidence base, a liberal approach to proning in all patients with severe COVID-19 ARDS is therefore justified and in particular regardless of previous results of proning.