Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study

Background & Aims: We aimed at identifying specific emergency department (ED) risk factors for developing complicated acute diverticulitis (AD) and evaluate a machine learning model (ML) for predicting complicated AD. Methods: We analyzed data retrieved from unselected consecutive large bowel AD...

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Autores principales:	Eyal Klang, Robert Freeman, Matthew A. Levin, Shelly Soffer, Yiftach Barash, Adi Lahat
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning artificial intelligence acute diverticulitis outcome prediction emergency complications Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/857adfd9633b4d3687e50364bf14cd6e
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spelling	oai:doaj.org-article:857adfd9633b4d3687e50364bf14cd6e2021-11-25T17:21:38ZMachine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study10.3390/diagnostics111121022075-4418https://doaj.org/article/857adfd9633b4d3687e50364bf14cd6e2021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2102https://doaj.org/toc/2075-4418Background & Aims: We aimed at identifying specific emergency department (ED) risk factors for developing complicated acute diverticulitis (AD) and evaluate a machine learning model (ML) for predicting complicated AD. Methods: We analyzed data retrieved from unselected consecutive large bowel AD patients from five hospitals from the Mount Sinai health system, NY. The study time frame was from January 2011 through March 2021. Data were used to train and evaluate a gradient-boosting machine learning model to identify patients with complicated diverticulitis, defined as a need for invasive intervention or in-hospital mortality. The model was trained and evaluated on data from four hospitals and externally validated on held-out data from the fifth hospital. Results: The final cohort included 4997 AD visits. Of them, 129 (2.9%) visits had complicated diverticulitis. Patients with complicated diverticulitis were more likely to be men, black, and arrive by ambulance. Regarding laboratory values, patients with complicated diverticulitis had higher levels of absolute neutrophils (AUC 0.73), higher white blood cells (AUC 0.70), platelet count (AUC 0.68) and lactate (AUC 0.61), and lower levels of albumin (AUC 0.69), chloride (AUC 0.64), and sodium (AUC 0.61). In the external validation cohort, the ML model showed AUC 0.85 (95% CI 0.78–0.91) for predicting complicated diverticulitis. For Youden’s index, the model showed a sensitivity of 88% with a false positive rate of 1:3.6. Conclusions: A ML model trained on clinical measures provides a proof of concept performance in predicting complications in patients presenting to the ED with AD. Clinically, it implies that a ML model may classify low-risk patients to be discharged from the ED for further treatment under an ambulatory setting.Eyal KlangRobert FreemanMatthew A. LevinShelly SofferYiftach BarashAdi LahatMDPI AGarticlemachine learningartificial intelligenceacute diverticulitisoutcome predictionemergencycomplicationsMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2102, p 2102 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning artificial intelligence acute diverticulitis outcome prediction emergency complications Medicine (General) R5-920
spellingShingle	machine learning artificial intelligence acute diverticulitis outcome prediction emergency complications Medicine (General) R5-920 Eyal Klang Robert Freeman Matthew A. Levin Shelly Soffer Yiftach Barash Adi Lahat Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study
description	Background & Aims: We aimed at identifying specific emergency department (ED) risk factors for developing complicated acute diverticulitis (AD) and evaluate a machine learning model (ML) for predicting complicated AD. Methods: We analyzed data retrieved from unselected consecutive large bowel AD patients from five hospitals from the Mount Sinai health system, NY. The study time frame was from January 2011 through March 2021. Data were used to train and evaluate a gradient-boosting machine learning model to identify patients with complicated diverticulitis, defined as a need for invasive intervention or in-hospital mortality. The model was trained and evaluated on data from four hospitals and externally validated on held-out data from the fifth hospital. Results: The final cohort included 4997 AD visits. Of them, 129 (2.9%) visits had complicated diverticulitis. Patients with complicated diverticulitis were more likely to be men, black, and arrive by ambulance. Regarding laboratory values, patients with complicated diverticulitis had higher levels of absolute neutrophils (AUC 0.73), higher white blood cells (AUC 0.70), platelet count (AUC 0.68) and lactate (AUC 0.61), and lower levels of albumin (AUC 0.69), chloride (AUC 0.64), and sodium (AUC 0.61). In the external validation cohort, the ML model showed AUC 0.85 (95% CI 0.78–0.91) for predicting complicated diverticulitis. For Youden’s index, the model showed a sensitivity of 88% with a false positive rate of 1:3.6. Conclusions: A ML model trained on clinical measures provides a proof of concept performance in predicting complications in patients presenting to the ED with AD. Clinically, it implies that a ML model may classify low-risk patients to be discharged from the ED for further treatment under an ambulatory setting.
format	article
author	Eyal Klang Robert Freeman Matthew A. Levin Shelly Soffer Yiftach Barash Adi Lahat
author_facet	Eyal Klang Robert Freeman Matthew A. Levin Shelly Soffer Yiftach Barash Adi Lahat
author_sort	Eyal Klang
title	Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study
title_short	Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study
title_full	Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study
title_fullStr	Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study
title_full_unstemmed	Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study
title_sort	machine learning model for outcome prediction of patients suffering from acute diverticulitis arriving at the emergency department—a proof of concept study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/857adfd9633b4d3687e50364bf14cd6e
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Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept Study

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