A novel classification method for predicting acute hypotensive episodes in critical care

Bhattacharya, Sakyajit; Rajan, Vaibhav; Huddar, Vijay

doi:10.1145/2649387.2649400

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…* email: pmousavi@cs.queensu.ca 90% of mean arterial blood pressure (MAP) readings were below 60 mmHg 6 . This definition of AHE has been commonly used in the literature when developing predictive models for the ICU since the initial challenge [9][10][11][12][13] . However, as discussed by Moody et al 6 , common false positive predictions of an AHE in many of these studies may have been annotated as an actual episode by a less restrictive definition and considered a true positive in practice.…”

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confidence: 99%

Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach

Chan

Chen

Sedghi

et al. 2020

Sci Rep

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the vast quantities of data generated and collected in the intensive care Unit (icU) have given rise to large retrospective datasets that are frequently used for observational studies. the temporal nature and fine granularity of much of the data collected in the ICU enable the pursuit of predictive modeling. in particular, forecasting acute hypotensive episodes (AHe) in intensive care patients has been of interest to researchers in critical care medicine. Given an advance warning of an AHe, care providers may be prompted to search for evolving disease processes and help mitigate negative clinical outcomes. However, the conventionally adopted definition of an AHE does not account for interpatient variability and is restrictive. To reflect the wider trend of global clinical and research efforts in precision medicine, we introduce a patient-specific definition of AHE in this study and propose deep learning based models to predict this novel definition of AHE in data from multiple independent institutions. We provide extensive evaluation of the models by studying their accuracies in detecting patient-specific AHEs with lead-times ranging from 10 min to 1 hour before the onset of the event. The resulting models achieve AUROC values ranging from 0.57-0.87 depending on the lead time of the prediction. We demonstrate the generalizability and robustness of our approach through the use of independent multi-institutional data.

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confidence: 99%

Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach

Chan

Chen

Sedghi

et al. 2020

Sci Rep

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“…This subgroup includes ( 36 , 38 , 48 , 53 , 103 ). These publications describe approaches for predicting patient deterioration, which can reduce alarm fatigue by scheduling an action in the near future, preempting further critical alarms.…”

Section: Resultsmentioning

confidence: 99%

“…First, Bhattacharya et al ( 48 ) investigate the prediction of an acute hypotensive episode (AHE) up to 120 minutes before its start. The used data are mean arterial pressure (MAP) measurements (

systolic ABP

diastolic ABP) from the MIMIC-II database ( 18 ), measured every minute.…”

Section: Resultsmentioning

confidence: 99%

“…This is the case in (43)(44)(45)(46)(47) Furthermore, the MIMIC-II database is an important material in alarm fatigue research. MIMIC-II is used by (36,(48)(49)(50)(51)(52)(53). It is, however, noteworthy that specifically MIMIC-II is used while its successor MIMIC-III (54) and other ICU databases such as HiRID (55) and eICU CRD (56) are not even mentioned in the publications under review.…”

Section: Datamentioning

confidence: 99%

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Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Chromik

Klopfenstein

Pfitzner

et al. 2022

Front. Digit. Health

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Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233461, identifier: CRD42021233461.

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Event Prediction in Healthcare Analytics: Beyond Prediction Accuracy

Zhou

et al. 2016

Lecture Notes in Computer Science

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A novel classification method for predicting acute hypotensive episodes in critical care

Cited by 8 publications

References 13 publications

Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach

Generalizable deep temporal models for predicting episodes of sudden hypotension in critically ill patients: a personalized approach

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Event Prediction in Healthcare Analytics: Beyond Prediction Accuracy

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