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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/11/11/2102 |
| _version_ | 1827676816530210816 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:34:34Z |
| format | Article |
| id | doaj.art-857adfd9633b4d3687e50364bf14cd6e |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-10T05:34:34Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-857adfd9633b4d3687e50364bf14cd6e2023-11-22T23:02:23ZengMDPI AGDiagnostics2075-44182021-11-011111210210.3390/diagnostics11112102Machine Learning Model for Outcome Prediction of Patients Suffering from Acute Diverticulitis Arriving at the Emergency Department—A Proof of Concept StudyEyal Klang0Robert Freeman1Matthew A. Levin2Shelly Soffer3Yiftach Barash4Adi Lahat5Sheba Medical Center, Department of Diagnostic Imaging, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv 52621, IsraelDepartment of Population Health Science and Policy, Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USADepartment of Population Health Science and Policy, Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USAAssuta Medical Center, Ashdod 7747629, IsraelSheba Medical Center, Department of Diagnostic Imaging, Tel Hashomer, Israel, and Sackler Medical School, Tel Aviv University, Tel Aviv 52621, IsraelChaim Sheba Medical Center, Department of Gastroenterology, Sackler Medical School, Tel Aviv University, Tel Hashomer 52620, IsraelBackground & 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.https://www.mdpi.com/2075-4418/11/11/2102machine learningartificial intelligenceacute diverticulitisoutcome predictionemergencycomplications |
| spellingShingle | 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 Diagnostics machine learning artificial intelligence acute diverticulitis outcome prediction emergency complications |
| title | 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_short | 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 |
| topic | machine learning artificial intelligence acute diverticulitis outcome prediction emergency complications |
| url | https://www.mdpi.com/2075-4418/11/11/2102 |
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