Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data – CORRIGENDUM

Bose, Sanjukta N.; Verigan, Adam; Hanson, Jade; Ahumada, Luis M.; Ghazarian, Sharon R.; Goldenberg, Neil A.; Stock, Arabela; Jacobs, Jeffrey P.

doi:10.1017/s1047951119002452

Cited by 4 publications

(3 citation statements)

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“…20,21 Bose and colleagues were successful in performing a single-institutional retrospective cohort study where their model was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75%. 22,23 Also, Barker et al proposed a hybrid neural network for the prediction of mortality risk in neonatal ICUs. 24 As is the case with these examples, machine learning can efficiently model a variety of patient measurements, variables, and even model the passage of time.…”

Section: Discussionmentioning

confidence: 99%

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

et al. 2022

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Background: Although serum lactate levels are widely accepted markers of haemodynamic instability, an alternative method to evaluate haemodynamic stability/instability continuously and non-invasively may assist in improving the standard of patient care. We hypothesise that blood lactate in paediatric ICU patients can be predicted using machine learning applied to arterial waveforms and perioperative characteristics. Methods: Forty-eight post-operative children, median age 4 months (2.9–11.8 interquartile range), mean baseline heart rate of 131 beats per minute (range 33–197), mean lactate level at admission of 22.3 mg/dL (range 6.3–71.1), were included. Morphological arterial waveform characteristics were acquired and analysed. Predicting lactate levels was accomplished using regression-based supervised learning algorithms, evaluated with hold-out cross-validation, including, basing prediction on the currently acquired physiological measurements along with those acquired at admission, as well as adding the most recent lactate measurement and the time since that measurement as prediction parameters. Algorithms were assessed with mean absolute error, the average of the absolute differences between actual and predicted lactate concentrations. Low values represent superior model performance. Results: The best performing algorithm was the tuned random forest, which yielded a mean absolute error of 3.38 mg/dL when predicting blood lactate with updated ground truth from the most recent blood draw. Conclusions: The random forest is capable of predicting serum lactate levels by analysing perioperative variables, including the arterial pressure waveform. Thus, machine learning can predict patient blood lactate levels, a proxy for haemodynamic instability, non-invasively, continuously and with accuracy that may demonstrate clinical utility.

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Section: Discussionmentioning

confidence: 99%

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

et al. 2022

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“…With the available patient data from the electronic heath records (EHR), our group has previously demonstrated a successful prediction model development for IHCA within the pediatric cardiac population, through the use of a multivariable logistic regression model [ 4 ]. While the literature using such algorithms to predict IHCAs is growing [ 5 ], there are limited studies looking at predicting IHCA in the broader pediatric population [ 6 , 7 ], as well as those with heart disease [ 8 , 9 , 10 , 11 ]. Where the majority of studies using machine learning algorithms to predict IHCA involve using continuous (high frequency) physiologic monitoring, one study has shown that routinely collected data from the EHR (i.e., vital signs and laboratory values) can detect adverse events up to 8 h prior to an IHCA in patients with critical CHD (i.e., single ventricle physiology) [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm

Skinner

Esangbedo

et al. 2023

JCM

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Background: Children with congenital and acquired heart disease are at a higher risk of cardiac arrest compared to those without heart disease. Although the monitoring of cardiopulmonary resuscitation quality and extracorporeal resuscitation technologies have advanced, survival after cardiac arrest in this population has not improved. Cardiac arrest prevention, using predictive algorithms with machine learning, has the potential to reduce cardiac arrest rates. However, few studies have evaluated the use of these algorithms in predicting cardiac arrest in children with heart disease. Methods: We collected demographic, laboratory, and vital sign information from the electronic health records (EHR) of all the patients that were admitted to a single-center pediatric cardiac intensive care unit (CICU), between 2010 and 2019, who had a cardiac arrest during their CICU admission, as well as a comparator group of randomly selected non-cardiac-arrest controls. We compared traditional logistic regression modeling against a novel adaptation of a machine learning algorithm (functional gradient boosting), using time series data to predict the risk of cardiac arrest. Results: A total of 160 unique cardiac arrest events were matched to non-cardiac-arrest time periods. Using 11 different variables (vital signs and laboratory values) from the EHR, our algorithm’s peak performance for the prediction of cardiac arrest was at one hour prior to the cardiac arrest (AUROC of 0.85 [0.79,0.90]), a performance that was similar to our previously published multivariable logistic regression model. Conclusions: Our novel machine learning predictive algorithm, which was developed using retrospective data that were collected from the EHR and predicted cardiac arrest in the children that were admitted to a single-center pediatric cardiac intensive care unit, demonstrated a performance that was similar to that of a traditional logistic regression model. While these results are encouraging, future research, including prospective validations with multicenter data, is warranted prior to the implementation of this algorithm as a real-time clinical decision support tool.

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“…At its core, the notion is very attractive: use available data, rather than add new and expensive technology, such as advanced monitoring systems, which continue to grow in popularity and cost. While other groups have published their work in creating models to detect the risk of deterioration in children with heart disease, [4][5][6] these models have relied on the use of high-frequency continuous physiologic data. The idea of using discrete, rather high-fidelity and waveform data is very provocative.…”

mentioning

confidence: 99%

Commentary: What's in the secret sauce? With so many ingredients, who knows…but maybe, who cares?

Yu¹,

Wolovits²

2022

The Journal of Thoracic and Cardiovascular Surgery

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Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data – CORRIGENDUM

Cited by 4 publications

References 0 publications

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

Machine learning predicts blood lactate levels in children after cardiac surgery in paediatric ICU

Predicting Cardiac Arrest in Children with Heart Disease: A Novel Machine Learning Algorithm

Commentary: What's in the secret sauce? With so many ingredients, who knows…but maybe, who cares?

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