Developing neural network models for early detection of cardiac arrest in emergency department

Jang, Dong-Hyun; Kim, Joonghee; Jo, You Hwan; Lee, Jae Hyuk; Hwang, Ji Eun; Park, Seung Min; Lee, Dong Hoon; Park, Inwon; Kim, Doyun; Chang, Hyunglan

doi:10.1016/j.ajem.2019.04.006

Cited by 47 publications

(75 citation statements)

References 16 publications

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“…In the above-mentioned systematic review [ 30 ], ANNs had a high level of accuracy and was statistically significant (odds ratio: 1.09). Further, similar results have been obtained in other previous reports [ 2 , 31 , 37 , 38 ]. Thus, clinical application of ANNs may enable more accurate prediction of ADRs than logistic regression model.…”

Section: Discussionsupporting

confidence: 92%

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

et al. 2020

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Artificial neural networks are the main tools for data mining and were inspired by the human brain and nervous system. Studies have demonstrated their usefulness in medicine. However, no studies have used artificial neural networks for the prediction of adverse drug reactions. We aimed to validate the usefulness of artificial neural networks for the prediction of adverse drug reactions and focused on vancomycin -induced nephrotoxicity. For constructing an artificial neural network, a multilayer perceptron algorithm was employed. A 10-fold cross validation method was adopted for evaluating the resultant artificial neural network. In total, 1141 patients who received vancomycin at Hokkaido University Hospital from November 2011 to February 2019 were enrolled. Among these patients, 179 (15.7%) developed vancomycin -induced nephrotoxicity. The top three risk factors of vancomycin -induced nephrotoxicity which are relatively important in the artificial neural networks were average vancomycin trough concentration ≥ 13.0 mg/L and concomitant use of piperacillin–tazobactam and vasopressor drugs. The predictive accuracy of the artificial neural network was 86.3% and that of the multiple logistic regression model (conventional statistical method) was 85.1%. Moreover, area under the receiver operating characteristic curve (AUROC) of the artificial neural network was 0.83. In the 10-fold cross-validation, the accuracy obtained was 86.0% and AUROC was 0.82. The artificial neural network model predicting the vancomycin -induced nephrotoxicity showed good predictive performance. This appears to be the first report of the usefulness of artificial neural networks for an adverse drug reactions risk prediction model.

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Section: Discussionsupporting

confidence: 92%

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

et al. 2020

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“…The outcomes being predicted in most studies focused on cardiorespiratory insufficiency-related events. Cardiac arrest was the primary outcome in 7 [24,26,35,36,38,42,45] studies, while general cardiorespiratory deterioration or decompensation was the primary outcome in 5 studies [25,39,41,43,44]. Another commonly predicted outcome was sepsis, which included the time of onset of sepsis [34,37,40], severe sepsis [33,34], septic shock [34], and sepsis-related mortality [23].…”

Section: Discussionmentioning

confidence: 99%

“…Outcomes were first identified, and baseline models were created using predefined parameter thresholds (ground truth) consistent with the MEWS [23,26,35] or NEWS [23,42,46] criteria for cardiorespiratory instability and general physiological deterioration, while the sepsis-related outcomes were identified based on the thresholds set within the systemic inflammatory response syndrome [34], quick Sequential Organ Failure Assessment (qSOFA) [23], and SOFA [37] criteria. Some studies [22,[27][28][29]43,44] also used thresholds and criteria based on the population served by their individual care setting.…”

Section: Discussionmentioning

confidence: 99%

“…While 3 studies [27][28][29] aimed to develop a remote home-based monitoring tool, the vital sign data used were obtained from the Medical Information Mart for Intensive Care (MIMIC and MIMIC-II) databases [30,31] consisting of data captured from patient monitors in different ICUs. Regarding location, 5 studies [24,26,[32][33][34] were conducted on general wards, 4 studies [11,23,35,36] were conducted in EDs, 7 studies [26,34,[37][38][39][40][41] were conducted in ICUs, 2 studies [25,42] were conducted in postoperative wards, and 4 studies [21,[43][44][45] in acute stay wards (medical admission unit, step-down units). Cohort sizes for the studies ranged from 12 patients [39] to 10,967,518 patient visits [11] (refer to Table 1).…”

Section: Study Characteristicsmentioning

confidence: 99%

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Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review (Preprint)

Muralitharan¹,

Nelson²,

Di³

et al. 2020

Preprint

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BACKGROUND Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively and preventing adverse outcomes. Vital signs-based aggregate-weighted Early Warning Systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results. OBJECTIVE To identify, summarize, and evaluate the available research, current state of utility and challenges with machine learning based early warning systems using vital signs to predict the risk of physiological deterioration in acutely ill patients, across acute and ambulatory care settings. METHODS PubMed, CINAHL, Cochrane Library, Web of Science, Embase, and Google Scholar were searched for peer-reviewed, original studies with keywords related to “vital signs”, “clinical deterioration”, and “machine learning”. Included studies used patient vital signs along with demographics and described a machine learning model for predicting an outcome in acute and ambulatory care settings. Data were extracted following PRISMA, TRIPOD, and Cochrane Collaboration guidelines. RESULTS 24 peer-reviewed studies were identified for inclusion from 417 articles. 23 studies were retrospective, while 1 was prospective in nature. Care settings included general wards, ICUs, emergency departments, step-down units, medical assessment units, post-anesthetic wards, and home care. Machine learning models including logistic regression, tree-based methods, kernel-based methods and neural networks were most commonly used to predict the risk of deterioration. The area under the curve for models ranged from 0.57 to 0.97. CONCLUSIONS In studies that compared performance, reported results suggest that machine learning based early warning systems can achieve greater accuracy than aggregate weighted early warning systems but several areas for further research were identified. While these models have the potential to provide clinical decision support, there is a need for standardized outcome measures to allow for rigorous evaluation of performance across models. Further research needs to address the interpretability of model outputs by clinicians, clinical efficacy of these systems through prospective study design, and their potential impact in different clinical settings.

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“…During the last decade, increased computational power and improved algorithms have led to a renaissance for machine learning as an alternative to traditional regression models to analyse large data sets. Machine learning has been found valuable in various clinical settings such as interpretation of ECG (electrocardiography) patterns and detection of cardiac arrest in emergency calls or in the emergency department, to predict outcome in traumatic brain injury and to predict the need for critical care as an alternative to conventional triage and early warning scores [1][2][3][4][5]. It has also been suggested for mortality prediction in patients admitted to intensive care units (ICUs) [6].…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care

et al. 2020

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Background: Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM). Methods: Using the cohort of the TTM trial, we performed a post hoc analysis of 932 unconscious patients from 36 centres with OHCA of a presumed cardiac cause. The patient outcome was the functional outcome, including survival at 180 days follow-up using a dichotomised Cerebral Performance Category (CPC) scale with good functional outcome defined as CPC 1-2 and poor functional outcome defined as CPC 3-5. Outcome prediction and severity class assignment were performed using a supervised machine learning model based on ANN. Results: The outcome was predicted with an area under the receiver operating characteristic curve (AUC) of 0.891 using 54 clinical variables available on admission to hospital, categorised as background, pre-hospital and admission data. Corresponding models using background, pre-hospital or admission variables separately had inferior prediction performance. When comparing the ANN model with a logistic regression-based model on the same cohort, the ANN model performed significantly better (p = 0.029). A simplified ANN model showed promising performance with an AUC above 0.852 when using three variables only: age, time to ROSC and first monitored rhythm. The ANN-stratified analyses showed similar intervention effect of TTM to 33°C or 36°C in predefined classes with different risk of a poor outcome.

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Developing neural network models for early detection of cardiac arrest in emergency department

Cited by 47 publications

References 16 publications

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

Validation of the usefulness of artificial neural networks for risk prediction of adverse drug reactions used for individual patients in clinical practice

Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review (Preprint)

Artificial neural networks improve early outcome prediction and risk classification in out-of-hospital cardiac arrest patients admitted to intensive care

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