Integrating monitor alarms with laboratory test results to enhance patient deterioration prediction

Bai, Yang; Do, Duc; Harris, P.A.; Schindler, Daniel; Boyle, Noel G.; Drew, Barbara J.; Hu, Xiao

doi:10.1016/j.jbi.2014.09.006

Cited by 22 publications

(30 citation statements)

References 36 publications

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“…Sensitivity (number of warnings generated prior to an event divided by the total number of events [true positives/(true positives + false negatives)]) and a combination of metrics such as false-alarm rate (number of warnings not associated with an event over some unit of time [false positives/monitoring time]) and positive predictive value (number of warnings generated prior to an event divided by the total number of warnings [true positives/(true positives + false positives)]) may be reported to characterize the performance of this type of detection system. As these metrics alone fail to incorporate the timeliness of the detection [19] and patterns leading up to the event into the performance metrics (see below in II. Potential Warning Index Patterns Leading up to an Event ), they may not always provide a complete characterization of the performance of a continuous warning index to predict a change in patient conditions.…”

Section: Introductionmentioning

confidence: 99%

Evaluating performance of early warning indices to predict physiological instabilities

Scully

Daluwatte

2017

Journal of Biomedical Informatics

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Patient monitoring algorithms that analyze multiple features from physiological signals can produce an index that serves as a predictive or prognostic measure for a specific critical health event or physiological instability. Classical detection metrics such as sensitivity and positive predictive value are often used to evaluate new patient monitoring indices for such purposes, but since these metrics do not take into account the continuous nature of monitoring, the assessment of a warning system to notify a user of a critical health event remains incomplete. In this article, we present challenges of assessing the performance of new warning indices and propose a framework that provides a more complete characterization of warning index performance predicting a critical event that includes the timeliness of the warning. The framework considers 1) an assessment of the sensitivity to provide a notification within a meaningful time window, 2) the cumulative sensitivity leading up to an event, 3) characteristics on if the warning stays on until the event occurs once a warning has been activated, and 4) the distribution of warning times and the burden of additional warnings (e.g., false-alarm rate) throughout monitoring that may or may not be associated with the event of interest. Using an example from an experimental study of hemorrhage, we examine how this characterization can differentiate two warning systems in terms of timeliness of warnings and warning burden.

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Section: Introductionmentioning

confidence: 99%

Evaluating performance of early warning indices to predict physiological instabilities

Scully

Daluwatte

2017

Journal of Biomedical Informatics

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“…Rather, we expect to integrate the new alarms into the SuperAlarm framework because we anticipate the combination of metrics to identify patients at risk for cardiac arrest has the potential to reduce false positive alarms. A recent study that integrates laboratory test results with existing monitor alarms in the SuperAlarm framework improved the accuracy of code blue event prediction (Bai et al, 2014). This study indicates that SuperAlarm has the potential to incorporate multi-domain clinical data for improved patient monitoring and possibly reduction of false alarms.…”

Section: Discussionmentioning

confidence: 99%

“…With less emphasis on individual alarms, the framework is less sensitive to false alarms. Bai et al further integrated laboratory test results with existing monitor alarms in the SuperAlarm framework to improve prediction performance (Bai et al, 2014). Hu et al also derived additional alarms based on ECG metrics currently not available on patient monitors (Hu et al, 2013), which could be further incorporated into the SuperAlarm framework as a possible solution to improve cardiac patient monitoring.…”

Section: Introductionmentioning

confidence: 99%

Developing new predictive alarms based on ECG metrics for bradyasystolic cardiac arrest

et al. 2015

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Objectives We investigated 17 metrics derived from four leads of electrocardiographic (ECG) signals from hospital patient monitors to develop new ECG alarms for predicting adult bradyasystolic cardiac arrest events. Methods A retrospective case-control study was designed to analyze 17 ECG metrics from 27 adult bradyasystolic and 304 control patients. The 17 metrics consisted of PR interval (PR), P-wave duration (Pdur), QRS duration (QRSdur), RR interval (RR), QT interval (QT), estimate of serum K+ using only frontal leads (SerumK2), T-wave complexity (T Complex), ST segment levels for leads I, II, V (ST I, ST II, ST V), and 7 heart rate variability (HRV) metrics. These 7 HRV metrics were standard deviation of normal to normal intervals (SDNN), total power, very low frequency power, low frequency power, high frequency power, normalized low frequency power, and normalized high frequency power. Controls were matched by gender, age (± 5 years), admission to the same hospital unit within the same month, and the same major diagnostic category. A research ECG analysis software program developed by co-author Mortara D was used to automatically extract the metrics. The absolute value for each ECG metric, and the duration, terminal value, and slope of the dominant trend for each ECG metric, were derived and tested as the alarm conditions. The maximal true positive rate (TPR) of detecting cardiac arrest at a prescribed maximal false positive rate (FPR) based on the trending conditions was reported. Lead time was also recorded as the time between the first time alarm condition was triggered and the event of cardiac arrest. Results While conditions based on the absolute values of ECG metrics do not provide discriminative information to predict bradyasystolic cardiac arrest, the trending conditions can be useful. For example, with a max FPR = 5.0%, some derived alarms conditions are: trend duration of PR > 2.8 hours (TPR = 48.2%, lead time = 10.0 ± 6.6 hours), trend duration of QRSdur > 2.7 hours (TPR = 40.7%, lead time = 8.8 ± 6.2 hours), trend duration of RR > 3.5 hours (TPR = 51.9%, lead time = 6.4 ± 5.5 hours), trend duration of T Complex > 2.9 hours (TPR = 40.7%, lead time = 6.8 ± 5.5 hours), trend duration of ST I > 3.0 hours (TPR of 51.9%, lead time = 8.4 ± 8.0 hours), trend duration of SDNN > 3.6 hours (TPR of 40.7%, lead time = 11.0 ± 8.6 hours), trend duration of HRV total power > 3.0 hours (TPR of 25.9%, lead time = 7.5 ± 8.1 hours), terminal value of ST I < −56 μV (TPR = 22.2%, lead time = 12.8 ± 8.3 hours), and slope of QR > 19.4 ms/hour (TPR = 25.9%, lead time = 6.7 ± 6.9 hours). Eleven trend duration alarms, eight terminal value alarms, and ten slope alarms, achieved a positive TPR with zero FPR. Furthermore, these alarms conditions with zero PFR can be combined by the “OR” logic could further improve the TPR without increasing the FPR. Conclusions The trend duration, terminal value, and slope of the dominant trend of the ECG metrics considered in this study are able to predict a subset of patients with ...

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“…[31] combined lab tests with monitor alarms to build "SuperAlarm" patterns, which provided fair accuracy at detecting code blue events while maintaining low rates of false alarm. There have been more significant efforts to filter out alarms from existing systems.…”

Section: Related Workmentioning

confidence: 99%

Clinician-in-the-Loop Annotation of ICU Bedside Alarm Data

Roederer

Dimartino

Gutsche

et al. 2016

2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

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In this work, we describe the state of clinical monitoring in the intensive care unit and operating room, where patients are at their most fragile and thus monitoring is most heightened. We describe how large amounts of data generated by monitoring patients' physiologic signals, along with the ubiquitous aspecific threshold alarms in use today, cause dangerous alarm fatigue for medical caregivers. In order to build more specific, more useful alarms, we gathered a novel data set that would allow us to assess the number, types, and utility of alarms currently in use in the intensive care unit. To do this, we developed a system to collect physiologic monitor data, alarms, and annotations of those alarms provided electronically by clinicians. We describe the collection process for this novel data set and provide a preliminary description of the data. Abstract-In this work, we describe the state of clinical monitoring in the intensive care unit and operating room, where patients are at their most fragile and thus monitoring is most heightened. We describe how large amounts of data generated by monitoring patients' physiologic signals, along with the ubiquitous aspecific threshold alarms in use today, cause dangerous alarm fatigue for medical caregivers. In order to build more specific, more useful alarms, we gathered a novel data set that would allow us to assess the number, types, and utility of alarms currently in use in the intensive care unit. To do this, we developed a system to collect physiologic monitor data, alarms, and annotations of those alarms provided electronically by clinicians. We describe the collection process for this novel data set and provide a preliminary description of the data.

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Integrating monitor alarms with laboratory test results to enhance patient deterioration prediction

Cited by 22 publications

References 36 publications

Evaluating performance of early warning indices to predict physiological instabilities

Evaluating performance of early warning indices to predict physiological instabilities

Developing new predictive alarms based on ECG metrics for bradyasystolic cardiac arrest

Clinician-in-the-Loop Annotation of ICU Bedside Alarm Data

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