Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department
Abstract Background Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-01-01
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| Series: | Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13049-024-01177-2 |
| _version_ | 1797276340344324096 |
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| author | Paul M.E.L. van Dam William P.T.M. van Doorn Floor van Gils Lotte Sevenich Lars Lambriks Steven J.R. Meex Jochen W.L. Cals Patricia M. Stassen |
| author_facet | Paul M.E.L. van Dam William P.T.M. van Doorn Floor van Gils Lotte Sevenich Lars Lambriks Steven J.R. Meex Jochen W.L. Cals Patricia M. Stassen |
| author_sort | Paul M.E.L. van Dam |
| collection | DOAJ |
| description | Abstract Background Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology. Methods The study is a prospective, randomized, open-label, non-inferiority pilot clinical trial. We will investigate the clinical impact of a prediction model based on ML technology, the RISKINDEX, which has been developed to predict the risk of 31-day mortality based on the results of laboratory tests and demographic characteristics. In previous studies, the RISKINDEX was shown to outperform internal medicine specialists and to have high discriminatory performance. Adults patients (18 years or older) will be recruited in the ED. All participants will be randomly assigned to the control group or the intervention group in a 1:1 ratio. Participants in the control group will receive care as usual in which the study team asks the attending physicians questions about their clinical intuition. Participants in the intervention group will also receive care as usual, but in addition to asking the clinical impression questions, the study team presents the RISKINDEX to the attending physician in order to assess the extent to which clinical treatment is influenced by the results. Discussion This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISKINDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models. Trial registration ClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). Registered on August 11, 2022. URL: https://clinicaltrials.gov/study/NCT05497830 . |
| first_indexed | 2024-03-07T15:26:52Z |
| format | Article |
| id | doaj.art-81bfcb8f14514d02bfc6b145725147a8 |
| institution | Directory Open Access Journal |
| issn | 1757-7241 |
| language | English |
| last_indexed | 2024-03-07T15:26:52Z |
| publishDate | 2024-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine |
| spelling | doaj.art-81bfcb8f14514d02bfc6b145725147a82024-03-05T16:40:32ZengBMCScandinavian Journal of Trauma, Resuscitation and Emergency Medicine1757-72412024-01-013211710.1186/s13049-024-01177-2Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency departmentPaul M.E.L. van Dam0William P.T.M. van Doorn1Floor van Gils2Lotte Sevenich3Lars Lambriks4Steven J.R. Meex5Jochen W.L. Cals6Patricia M. Stassen7Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center +Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center +Department of Family Medicine, Care and Public Health Research Institute (CAPHRI), Maastricht UniversityDepartment of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +Abstract Background Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology. Methods The study is a prospective, randomized, open-label, non-inferiority pilot clinical trial. We will investigate the clinical impact of a prediction model based on ML technology, the RISKINDEX, which has been developed to predict the risk of 31-day mortality based on the results of laboratory tests and demographic characteristics. In previous studies, the RISKINDEX was shown to outperform internal medicine specialists and to have high discriminatory performance. Adults patients (18 years or older) will be recruited in the ED. All participants will be randomly assigned to the control group or the intervention group in a 1:1 ratio. Participants in the control group will receive care as usual in which the study team asks the attending physicians questions about their clinical intuition. Participants in the intervention group will also receive care as usual, but in addition to asking the clinical impression questions, the study team presents the RISKINDEX to the attending physician in order to assess the extent to which clinical treatment is influenced by the results. Discussion This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISKINDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models. Trial registration ClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). Registered on August 11, 2022. URL: https://clinicaltrials.gov/study/NCT05497830 .https://doi.org/10.1186/s13049-024-01177-2Machine learningArtificial intelligenceEmergency departmentImplementationRisk stratificationPrediction |
| spellingShingle | Paul M.E.L. van Dam William P.T.M. van Doorn Floor van Gils Lotte Sevenich Lars Lambriks Steven J.R. Meex Jochen W.L. Cals Patricia M. Stassen Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine Machine learning Artificial intelligence Emergency department Implementation Risk stratification Prediction |
| title | Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department |
| title_full | Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department |
| title_fullStr | Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department |
| title_full_unstemmed | Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department |
| title_short | Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department |
| title_sort | machine learning for risk stratification in the emergency department mars ed study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31 day mortality in the emergency department |
| topic | Machine learning Artificial intelligence Emergency department Implementation Risk stratification Prediction |
| url | https://doi.org/10.1186/s13049-024-01177-2 |
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