External validation of a novel signature of illness in continuous cardiorespiratory monitoring to detect early respiratory deterioration of ICU patients.

OBJECTIVE: The goal of predictive analytics monitoring is the early detection of patients at high risk of subacute potentially catastrophic illnesses. An excellent example of a targeted illness is respiratory failure leading to urgent unplanned intubation, where early detection might lead to interventions that improve patient outcomes. Previously, we identified signatures of this illness in the continuous cardiorespiratory monitoring data of intensive care unit (ICU) patients and devised algorithms to identify patients at rising risk. Here, we externally validated three logistic regression models to estimate the risk of emergency intubation developed in Medical and Surgical ICUs at the University of Virginia. APPROACH: We calculated the model outputs for more than 8000 patients in the University of California-San Francisco ICUs, 240 of whom underwent emergency intubation as determined by individual chart review. MAIN RESULTS: We found that the AUC of the models exceeded 0.75 in this external population, and that the risk rose appreciably over the 12 h before the event. SIGNIFICANCE: We conclude that there are generalizable physiological signatures of impending respiratory failure in the continuous cardiorespiratory monitoring data.

Predict In-Hospital Code Blue Events using Monitor Alarms through Deep Learning Approach.

Bedside monitors in hospital intensive care units (ICUs) are known to produce excessive false alarms that could desensitize caregivers, resulting in delayed or even missed clinical interventions to life-threatening events. Our previous studies proposed a framework aggregating information in monitor alarm data by mining frequent alarm combinations (i.e., SuperAlarm) that are predictive to clinical endpoints, such as code blue events, in an effort to address this critical issue. In the present pilot study, we hypothesize that sequential deep learning models, specifically long-short term memory (LSTM), could capture time-depend features in continuous alarm sequences preceding code blue events and these features may be predictive of these endpoints. LSTM models are trained from continuous alarm sequences in various window lengths preceding code blue events, and the preliminary results showed the best performance reached an AUC of 0.8549. With the selection of optimal cutoff threshold, the 2-hour window model achieved 85.75% sensitivity and 72.61% specificity, respectively.

Electrocardiogram Signal Quality Assessment Based on Structural Image Similarity Metric.

OBJECTIVE: We developed an image-based electrocardiographic (ECG) quality assessment technique that mimics how clinicians annotate ECG signal quality. METHODS: We adopted the structural similarity measure (SSIM) to compare images of two ECG records that are obtained from displaying ECGs in a standard scale. Then, a subset of representative ECG images from the training set was selected as templates through a clustering method. SSIM between each image and all the templates were used as the feature vector for the linear discriminant analysis classifier. We also employed three commonly used ECG signal quality index (SQI) measures: baseSQI, kSQI, and sSQI to compare with the proposed image quality index (IQI) approach. We used 1926 annotated ECGs, recorded from patient monitors, and associated with six different ECG arrhythmia alarm types which were obtained previously from an ECG alarm study at the University of California, San Francisco (UCSF). In addition, we applied the templates from the UCSF database to test the SSIM approach on the publicly available PhysioNet Challenge 2011 data. RESULTS: For the UCSF database, the proposed IQI algorithm achieved an accuracy of 93.1% and outperformed all the SQI metrics, baseSQI, kSQI, and sSQI, with accuracies of 85.7%, 63.7%, and 73.8% respectively. Moreover, evaluation of our algorithm on the PhysioNet data showed an accuracy of 82.5%. CONCLUSION: The proposed algorithm showed better performance for assessing ECG signal quality than traditional signal processing methods. SIGNIFICANCE: A more accurate assessment of ECG signal quality can lead to a more robust ECG-based diagnosis of cardiovascular conditions.