Predicting adverse hemodynamic events in critically ill patients

Yoon, Joo Heung; Pinsky, Michael R.

doi:10.1097/mcc.0000000000000496

Cited by 16 publications

(16 citation statements)

References 41 publications

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“…A major challenge in predicting CRI from complex physiologic time series data is the ability to transform those data into a reliable risk model. Recently, data-driven classification methods with parsimonious use of multi-granular features show promise in understanding embedded patterns from complex vital sign trends preceding overt CRI [17]. Our group has previously shown the utility of a composite early warning signature vital sign index in the early prediction of CRI in step-down patients, wherein upcoming CRI events were predicted with an accuracy of 80% at 9.7 min prior to overt instability [18].…”

Section: Introductionmentioning

confidence: 99%

Predicting tachycardia as a surrogate for instability in the intensive care unit

Yoon

Chen

et al. 2019

J Clin Monit Comput

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Tachycardia is a strong though non-specific marker of cardiovascular stress that proceeds hemodynamic instability. We designed a predictive model of tachycardia using multi-granular intensive care unit (ICU) data by creating a risk score and dynamic trajectory. A subset of clinical and numerical signals were extracted from the Multiparameter Intelligent Monitoring in Intensive Care II database. A tachycardia episode was defined as heart rate ≥ 130/min lasting for ≥ 5 min, with ≥ 10% density. Regularized logistic regression (LR) and random forest (RF) classifiers were trained to create a risk score for upcoming tachycardia. Three different risk score models were compared for tachycardia and control (non-tachycardia) groups. Risk trajectory was generated from time windows moving away at 1 min increments from the tachycardia episode. Trajectories were computed over 3 hours leading up to the episode for three different models. From 2809 subjects, 787 tachycardia episodes and 707 control periods were identified. Patients with tachycardia had increased vasopressor support, longer ICU stay, and increased ICU mortality than controls. In model evaluation, RF was slightly superior to LR, which accuracy ranged from 0.847 to 0.782, with area under the curve from 0.921 to 0.842. Risk trajectory analysis showed average risks for tachycardia group evolved to 0.78 prior to the tachycardia episodes, while control group risks remained < 0.3. Among the three models, the internal control model demonstrated evolving trajectory approximately 75 min before tachycardia episode. Clinically relevant tachycardia episodes can be predicted from vital sign time series using machine learning algorithms.

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Section: Introductionmentioning

confidence: 99%

Predicting tachycardia as a surrogate for instability in the intensive care unit

Yoon

Chen

et al. 2019

J Clin Monit Comput

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“…Building risk score trajectories provides conceptual and practical advantages. Instantaneous scores may be subject to stochastic variations, erroneous entries, and artifacts, while trajectories provide historical context to a risk, which translate to the clinical concept of worsening or improving health state, perhaps in response to an intervention [29][30][31]. Second, studies suggest that the prognosis of critically ill patients is associated with early recognition and timely intervention of abrupt changes [32].…”

Section: Discussionmentioning

confidence: 99%

Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit

et al. 2020

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Background Even brief hypotension is associated with increased morbidity and mortality. We developed a machine learning model to predict the initial hypotension event among intensive care unit (ICU) patients and designed an alert system for bedside implementation. Materials and methods From the Medical Information Mart for Intensive Care III (MIMIC-3) dataset, minute-by-minute vital signs were extracted. A hypotension event was defined as at least five measurements within a 10-min period of systolic blood pressure ≤ 90 mmHg and mean arterial pressure ≤ 60 mmHg. Using time series data from 30-min overlapping time windows, a random forest (RF) classifier was used to predict risk of hypotension every minute. Chronologically, the first half of extracted data was used to train the model, and the second half was used to validate the trained model. The model’s performance was measured with area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Hypotension alerts were generated using risk score time series, a stacked RF model. A lockout time were applied for real-life implementation. Results We identified 1307 subjects (1580 ICU stays) as the hypotension group and 1619 subjects (2279 ICU stays) as the non-hypotension group. The RF model showed AUROC of 0.93 and 0.88 at 15 and 60 min, respectively, before hypotension, and AUPRC of 0.77 at 60 min before. Risk score trajectories revealed 80% and > 60% of hypotension predicted at 15 and 60 min before the hypotension, respectively. The stacked model with 15-min lockout produced on average 0.79 alerts/subject/hour (sensitivity 92.4%). Conclusion Clinically significant hypotension events in the ICU can be predicted at least 1 h before the initial hypotension episode. With a highly sensitive and reliable practical alert system, a vast majority of future hypotension could be captured, suggesting potential real-life utility.

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“…This could only be done in limited fields, including physics or astronomy. However, with recent exponential growth in computing power and portability, the power of AI became available to many fields, including critical care medicine where data are vast, abundant, and complex [ 2 ]. More and more clinical investigations are being performed using AI-driven models to leverage the data in the intensive care unit (ICU), but our understanding of the power and utility of AI in critical care medicine is still quite rudimentary.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Critical Care Medicine

2022

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This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2022. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2022 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from https://link.springer.com/bookseries/8901 .

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Predicting adverse hemodynamic events in critically ill patients

Abstract: Using advanced analytic tools to glean knowledge from clinical data streams is rapidly becoming a reality whose clinical impact potential is great.

Cited by 16 publications

References 41 publications

Predicting tachycardia as a surrogate for instability in the intensive care unit

Predicting tachycardia as a surrogate for instability in the intensive care unit

Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit

Artificial Intelligence in Critical Care Medicine

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