Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study

Blom, Mathias; Ashfaq, Awais; Sant'Anna, Anita Pinheiro; Anderson, Philip D.; Lingman, Markus

doi:10.1136/bmjopen-2018-028015

Cited by 16 publications

(19 citation statements)

References 46 publications

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“…Out of the numerous potential predictors of outcome, 3,[8][9][10][11][12][13] our findings suggest that there are four very important ones, all of which are readily available. Our findings also highlight the importance of accurately recording a complete set of vital signs and assessing mobility on all ED attendees.…”

Section: Ta B L E 1 Differences Between Cohortsmentioning

confidence: 98%

“…The decisions to admit or discharge patients are often not made by explicit evidence‐based criteria, show considerable variation, and many patients admitted to hospital have normal or near normal vital signs . Little had been published on what happens to patients discharged from emergency departments, and reports range from a 0.1% chance of unexpected mortality within 7 days to a 30‐day mortality of 0.2% . Reliable prognostication, therefore, is an essential component of emergency care.…”

Section: Introductionmentioning

confidence: 99%

“…1 Little had been published on what happens to patients discharged from emergency departments, and reports range from a 0.1% chance of unexpected mortality within 7 days 2 to a 30day mortality of 0.2%. 3 Reliable prognostication, therefore, is an essential component of emergency care. Wise management, especially of those older patients with supportive and palliative care needs, is impossible without considering their likely prognosis.…”

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confidence: 99%

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A simple prognostic score predicts one‐year mortality of alert and calm emergency department patients: A prospective two‐center observational study

et al. 2020

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Study Objective To derive and validate a prognostic score to predict 1‐year mortality using vital signs, mobility and other variables that are readily available at the bedside at no additional cost. Methods Post hoc analysis of two independent prospective observational studies in two emergency departments, one in Denmark and the other in Switzerland. Participants Alert and calm emergency department patients. Measurements The prediction of mortality from presentation to 365 days by vital signs, mobility and other variables that are readily available at the bedside at no additional cost. Results One thousand six hundred and eighteen alert and calm patients were in the Danish cohort and 1331 in the Swiss cohort. Logistic regression identified age >68 years, abnormal vital signs, impaired mobility and the decision to admit as significant predictors of 365‐day mortality. A simple prognostic score awarded one point to each of these predictors. Less than two of these predictors were present in 45.6% of patients, and only 0.4% of these patients died within a year. If two or more of these predictors were present, 365‐day mortality increased exponentially. Conclusion Age >68 years, the decision for hospital admission, any vital sign abnormality at presentation and impaired mobility at presentation are equally powerful predictors of 1‐year mortality in alert and calm emergency department patients. If validated by others these predictors could be used to discharge patients with confidence since nearly half of these patients had less than two predictors and none of them died within 30 days. However, when two or more predictors were present 365‐day mortality increased exponentially.

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Section: Ta B L E 1 Differences Between Cohortsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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A simple prognostic score predicts one‐year mortality of alert and calm emergency department patients: A prospective two‐center observational study

et al. 2020

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“…Because terminal disease is multifaceted and difficult to define as prediction endpoint, mortality is used as a proxy in a growing number of studies and implementations. ML mortality predictions has been performed among Medicare and Medicaid beneficiaries and community-dwelling older adults, as well as patients at triage in the ED, admitted into the hospital, after hospital discharge, and at discharge from the ED (5)(6)(7)(8)(9)(10)(11)(12).…”

Section: Introductionmentioning

confidence: 99%

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths

Heyman

Ashfaq

Khoshnood

et al. 2021

The Journal of Emergency Medicine

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Background: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit. Objectives: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge. Methods: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either "surprising" or "unsurprising" by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM). Results: Of all 30day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For 1 Shared last authorship.all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant ( P < .001) for all three models. Conclusion: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly.

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“…There has been great interest in comparing model performance among different ML algorithms [4–7]. ML approaches, gradient boosting decision trees (GBDT), support vector machine, K-nearest neighbors, and artificial neural network have been found to outperform traditional risk scoring systems [4, 5, 8, 9]. Among the strongest approaches is GBDT, which according to a review comparing 13 different state-of-art ML methods, was ranked as the best of all methods in tasks related to predictive analytics [10].…”

Section: Introductionmentioning

confidence: 99%

Can we use machine learning to discover risk factors? Testing the proof of principle using data on >11,000 predictors and mortality in the UK Biobank

Madakkatel

Zhou

McDonnell

et al. 2021

Preprint

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Background: Machine learning (ML) can harness information from large databases with complex structures. We present a simple and fast hypothesis-free machine learning pipeline for risk factor discovery that accounts for non-linearity and interaction in large biomedical databases with minimal variable pre-processing. Methods: Mortality models were built using gradient boosting decision trees (GBDT) and important predictors were identified using SHAP values. Cox models controlled for false discovery rate were used for interpretability and further validation. The pipeline was tested using information from 502,506 UK Biobank participants, aged 37 to 73 years at recruitment and followed over seven years for mortality registrations. Results: From the 11,639 predictors included in GBDT, 193 potential risk factors had SHAP values 0.05 or greater and were selected for further modelling. Of the total variable importance summed up, 60% was directly health related, and baseline characteristics, sociodemographics, and lifestyle factors each contributed about 10%. Cox models adjusted for baseline characteristics, showed evidence for an association with mortality for 166 out of the 193 predictors. For 19 predictors we saw evidence for an association in the unadjusted but not adjusted analyses, suggesting confounding by basic characteristics. Identified "important" predictors included traditional risk factors such as age, sex, ethnicity, education, material deprivation, smoking, physical activity, self-rated health, BMI, hypertension, cardio-vascular diseases, cancer diagnoses and type 2 diabetes, as confirmed by previous studies. Conclusion: Our approach provides a fast and pragmatic solution for hypothesis free risk factor identification.

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Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study

Cited by 16 publications

References 46 publications

A simple prognostic score predicts one‐year mortality of alert and calm emergency department patients: A prospective two‐center observational study

A simple prognostic score predicts one‐year mortality of alert and calm emergency department patients: A prospective two‐center observational study

Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths

Can we use machine learning to discover risk factors? Testing the proof of principle using data on >11,000 predictors and mortality in the UK Biobank

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