Machine learning-based prediction of massive perioperative allogeneic blood transfusion in cardiac surgery

Tschoellitsch, Thomas; Böck, Carl; Mahečić, Tina Tomić; Hofmann, Axel; Meier, Jens

doi:10.1097/eja.0000000000001721

Cited by 11 publications

(9 citation statements)

References 29 publications

(47 reference statements)

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“…The majority of identified studies employed ML to predict transfusion related to a specific specialty or procedure, notably within orthopedics, 19–25 cardiac surgery, 26–30 spinal surgery, 31–34 and liver transplant, 35–37 focusing on a specific procedure or a variety within that specialty (Table 2). A small number of studies consider procedures from multiple specialties 38–44 with Walczak and Velanovich 43 including 56 different surgeries from the publicly available United States National Surgical Quality Improvement Program (NSQIP).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine learning in transfusion medicine: A scoping review

Maynard,

Farrington,

Alimam

et al. 2023

Transfusion

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Section: Resultsmentioning

confidence: 99%

“…N/A 26 (28) 11 (12) Abbreviations: EHR, electronic health record; GI, gastrointestinal; NN, neural network.…”

Section: Hospital Blood Bank Inventory Managementmentioning

confidence: 99%

Machine learning in transfusion medicine: A scoping review

Maynard,

Farrington,

Alimam

et al. 2023

Transfusion

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“…Elective noncardiac surgery Mortality 30 days and/or 1 yr [26][27][28][29][30][31][32][33][34][35][36][37][38] In surgical patients with perioperative SarS-CoV-2 39 Morbidity Multiple postoperative complications 26,27,29,[40][41][42][43][44][45][46][47][48][49][50][51] acute and chronic pain 52-57 acute kidney failure 52,58-63 aSa score prediction 64 Delirium and cognitive decline [65][66][67][68][69][70] Cerebral/myocardial infarction 71 Difficult intubation prediciton 72 Ileus 73 Infection risk [74][75][76] Myocardial injury 77 Nausea and vomiting 78 Obstructive apnoea screening 79 Perioperative transfusion 80,81 Postoperative atrial fibrillation 82 respiratory failure and depression Liver failure 117 Major bleeding 118,119 Kidney failure…”

Section: Surgery Outcomes and Eventsmentioning

confidence: 99%

“…Three studies developed models to predict the risk of perioperative transfusion in general surgery 80,81 and cardiac surgery. 119 Tan et al 51 developed a model to predict early phase postoperative hypertension PROBAST Assessment of Machine Learning…”

Section: Perioperative Medicinementioning

confidence: 99%

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Arina,

Kaczorek,

Hofmaenner

et al. 2023

Anesthesiology

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Background The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpose of this systematic review is to assess the current state of machine learning in perioperative medicine, its utility in prediction of complications and prognostication, and limitations related to bias and validation. Methods A multidisciplinary team of clinicians and engineers conducted a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) protocol. Multiple databases were searched, including Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, PubMed, Medline, Embase, and Web of Science. The systematic review focused on study design, type of machine learning model used, validation techniques applied, and reported model performance on prediction of complications and prognostication. This review further classified outcomes and machine learning applications using an ad hoc classification system. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used to assess risk of bias and applicability of the studies. Results A total of 103 studies were identified. The models reported in the literature were primarily based on single-center validations (75%), with only 13% being externally validated across multiple centers. Most of the mortality models demonstrated a limited ability to discriminate and classify effectively. The PROBAST assessment indicated a high risk of systematic errors in predicted outcomes and artificial intelligence or machine learning applications. Conclusions The findings indicate that the development of this field is still in its early stages. This systematic review indicates that application of machine learning in perioperative medicine is still at an early stage. While many studies suggest potential utility, several key challenges must be first overcome before their introduction into clinical practice. Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New

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“…Artificial intelligence (AI) and one of its elements, machine learning (ML), features a plethora of methods that allow for analysis of large databases spanning decades with targets previously impossible to predict. ML generates predictions, sometimes using feature combinations that bear no immediately identifiable physiological correlate [7].…”

Section: Introductionmentioning

confidence: 99%

Using emergency department triage for machine learning-based admission and mortality prediction

Tschoellitsch

Seidl²,

Böck

et al. 2023

European Journal of Emergency Medicine

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Aims Patient admission is a decision relying on sparsely available data. This study aims to provide prediction models for discharge versus admission for ward observation or intensive care, and 30 day-mortality for patients triaged with the Manchester Triage System. Methods This is a single-centre, observational, retrospective cohort study from data within ten minutes of patient presentation at the interdisciplinary emergency department of the Kepler University Hospital, Linz, Austria. We trained machine learning models including Random Forests and Neural Networks individually to predict discharge versus ward observation or intensive care admission, and 30 day-mortality. For analysis of the features’ relevance, we used permutation feature importance. Results A total of 58323 adult patients between 1 December 2015 and 31 August 2020 were included. Neural Networks and Random Forests predicted admission to ward observation with an AUC-ROC of 0.842 ± 0.00 with the most important features being age and chief complaint. For admission to intensive care, the models had an AUC-ROC of 0.819 ± 0.002 with the most important features being the Manchester Triage category and heart rate, and for the outcome 30 day-mortality an AUC-ROC of 0.925 ± 0.001. The most important features for the prediction of 30 day-mortality were age and general ward admission. Conclusion Machine learning can provide prediction on discharge versus admission to general wards and intensive care and inform about risk on 30 day-mortality for patients in the emergency department. Institutional review board Ethics Committee of the Medical Faculty at the Johannes Kepler University, Linz, Austria, Study Number 1233/2020.

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Machine learning-based prediction of massive perioperative allogeneic blood transfusion in cardiac surgery

Cited by 11 publications

References 29 publications

Machine learning in transfusion medicine: A scoping review

Machine learning in transfusion medicine: A scoping review

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools

Using emergency department triage for machine learning-based admission and mortality prediction

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