The truth Hertz-synchronization of electroencephalogram signals with physiological waveforms recorded in an intensive care unit.

Objective.The ability to synchronize continuous electroencephalogram (cEEG) signals with physiological waveforms such as electrocardiogram (ECG), invasive pressures, photoplethysmography and other signals can provide meaningful insights regarding coupling between brain activity and other physiological subsystems. Aligning these datasets is a particularly challenging problem because device clocks handle time differently and synchronization protocols may be undocumented or proprietary.Approach.We used an ensemble-based model to detect the timestamps of heartbeat artefacts from ECG waveforms recorded from inpatient bedside monitors and from cEEG signals acquired using a different device. Vectors of inter-beat intervals were matched between both datasets and robust linear regression was applied to measure the relative time offset between the two datasets as a function of time.Main Results.The timing error between the two unsynchronized datasets ranged between -84 s and +33 s (mean 0.77 s, median 4.31 s, IQR25-4.79 s, IQR75 11.38s). Application of our method improved the relative alignment to within ± 5ms for more than 61% of the dataset. The mean clock drift between the two datasets was 418.3 parts per million (ppm) (median 414.6 ppm, IQR25 411.0 ppm, IQR75 425.6 ppm). A signal quality index was generated that described the quality of alignment for each cEEG study as a function of time.Significance.We developed and tested a method to retrospectively time-align two clinical waveform datasets acquired from different devices using a common signal. The method was applied to 33,911h of signals collected in a paediatric critical care unit over six years, demonstrating that the method can be applied to long-term recordings collected under clinical conditions. The method can account for unknown clock drift rates and the presence of discontinuities caused by clock resynchronization events.

Making machine learning matter to clinicians: model actionability in medical decision-making.

Machine learning (ML) has the potential to transform patient care and outcomes. However, there are important differences between measuring the performance of ML models in silico and usefulness at the point of care. One lens to use to evaluate models during early development is actionability, which is currently undervalued. We propose a metric for actionability intended to be used before the evaluation of calibration and ultimately decision curve analysis and calculation of net benefit. Our metric should be viewed as part of an overarching effort to increase the number of pragmatic tools that identify a model's possible clinical impacts.

Timing errors and temporal uncertainty in clinical databases-A narrative review.

A firm concept of time is essential for establishing causality in a clinical setting. Review of critical incidents and generation of study hypotheses require a robust understanding of the sequence of events but conducting such work can be problematic when timestamps are recorded by independent and unsynchronized clocks. Most clinical models implicitly assume that timestamps have been measured accurately and precisely, but this custom will need to be re-evaluated if our algorithms and models are to make meaningful use of higher frequency physiological data sources. In this narrative review we explore factors that can result in timestamps being erroneously recorded in a clinical setting, with particular focus on systems that may be present in a critical care unit. We discuss how clocks, medical devices, data storage systems, algorithmic effects, human factors, and other external systems may affect the accuracy and precision of recorded timestamps. The concept of temporal uncertainty is introduced, and a holistic approach to timing accuracy, precision, and uncertainty is proposed. This quantitative approach to modeling temporal uncertainty provides a basis to achieve enhanced model generalizability and improved analytical outcomes.

Symptom subtypes and risk of incident cardiovascular and cerebrovascular disease in a clinic-based obstructive sleep apnea cohort.

STUDY OBJECTIVES: Patients with obstructive sleep apnea (OSA) are at increased risk of cardiovascular and cerebrovascular disease, but predicting those at greatest risk is challenging. Using latent class analysis, patients with OSA can be placed into discrete symptom subtypes. The aim of this study was to determine whether symptom subtypes are associated with future cerebrovascular disease in patients with OSA in a clinic-based cohort. METHODS: Patients with suspected OSA referred for a polysomnogram at an academic sleep center completed a comprehensive symptom survey. Patients with OSA (apnea-hypopnea index ≥ 5 events/h) were then placed into symptom subtypes based on responses to survey questions using latent class analysis. Cardiovascular events (stroke, myocardial infarction, unstable angina, bypass grafting, percutaneous coronary intervention, cardiac resynchronization therapy, defibrillation) occurring within 8 years of polysomnogram were identified by linkage to provincial health databases. RESULTS: 1,607 patients were studied, of whom 1,292 had OSA. One hundred forty first events occurred within 8 years of polysomnogram. Patients in the excessively sleepy with disturbed sleep subtype had a significantly increased rate of events compared to the minimally symptomatic subtype (hazard ratio = 2.25, 95% confidence interval: 1.02-4.94; P = .04). Two symptoms (restless legs and dozing off or sleeping while talking to someone) were significantly associated with future risk of cerebrovascular disease (hazard ratio = 1.68, 1.12-2.49 and 4.23, 1.61-11.16, respectively). CONCLUSIONS: Patients with OSA in the clinic who are in the excessively sleepy with disturbed sleep subtype are significantly more likely to have a future cardiovascular event. This underscores the importance of understanding clinical heterogeneity and incorporating symptom subtype definitions into routine clinical care. CITATION: Allen AJH, Jen R, Mazzotti DR, et al. Symptom subtypes and risk of incident cardiovascular and cerebrovascular disease in a clinic-based obstructive sleep apnea cohort. J Clin Sleep Med. 2022;18(9):2093-2102.

Symptom subtypes and cognitive function in a clinic-based OSA cohort: a multi-centre Canadian study.

BACKGROUND: Distinct symptom subtypes are found in patients with OSA. The association between these subtypes and neurocognitive function is unclear. OBJECTIVE: The purposes of this study were to assess whether OSA symptom subtypes are present in a cohort of Canadian patients with suspected OSA and evaluate the relationship between subtypes and neurocognitive function. METHODS: Patients with suspected OSA who completed a symptom questionnaire and underwent testing for OSA were included. Symptom subtypes were identified using latent class analysis. Associations between subtypes and neurocognitive outcomes (Montreal Cognitive Assessment [MoCA], Rey Auditory Verbal Learning Test [RAVLT], Wechsler Adult Intelligence Scale [WAIS-IV], Digit-Symbol Coding subtest [DSC]) were assessed using analysis of covariance (ANCOVA), controlling for relevant covariates. RESULTS: Four symptom subtypes were identified in patients with OSA (oxygen desaturation index ≥5 events/hour). Three were similar to prior studies, including the Excessively Sleepy (N=405), Disturbed Sleep (N=382) and Minimally Symptomatic (N=280), and one was a novel subtype in our sample defined as Excessively Sleepy with Disturbed Sleep (N=247). After covariate adjustment, statistically significant differences among subtypes (p=0.037) and among subtypes and patients without OSA (p=0.044) were observed in DSC scores; the Minimally Symptomatic subtype had evidence of higher DSC scores than all other groups, including non-OSA patients. No differences were seen in MoCA or RAVLT. CONCLUSIONS: Results support the existence of previously identified OSA symptom subtypes of excessively sleepy, disturbed sleep and minimally symptomatic in a clinical sample from Canada. Subtypes were not consistently associated with neurocognitive function across multiple instruments.

A practical approach to storage and retrieval of high-frequency physiological signals.

OBJECTIVE: Storage of physiological waveform data for retrospective analysis presents significant challenges. Resultant data can be very large, and therefore becomes expensive to store and complicated to manage. Traditional database approaches are not appropriate for large scale storage of physiological waveforms. Our goal was to apply modern time series compression and indexing techniques to the problem of physiological waveform storage and retrieval. APPROACH: We deployed a vendor-agnostic data collection system and developed domain-specific compression approaches that allowed long term storage of physiological waveform data and other associated clinical and medical device data. The database (called AtriumDB) also facilitates rapid retrieval of retrospective data for high-performance computing and machine learning applications. MAIN RESULTS: A prototype system has been recording data in a 42-bed pediatric critical care unit at The Hospital for Sick Children in Toronto, Ontario since February 2016. As of December 2019, the database contains over 720,000 patient-hours of data collected from over 5300 patients, all with complete waveform capture. One year of full resolution physiological waveform storage from this 42-bed unit can be losslessly compressed and stored in less than 300 GB of disk space. Retrospective data can be delivered to analytical applications at a rate of up to 50 million time-value pairs per second. SIGNIFICANCE: Stored data are not pre-processed or filtered. Having access to a large retrospective dataset with realistic artefacts lends itself to the process of anomaly discovery and understanding. Retrospective data can be replayed to simulate a realistic streaming data environment where analytical tools can be rapidly tested at scale.

Temporal Variability in the Sampling of Vital Sign Data Limits the Accuracy of Patient State Estimation.

OBJECTIVES: Physiologic signals are typically measured continuously in the critical care unit, but only recorded at intermittent time intervals in the patient health record. Low frequency data collection may not accurately reflect the variability and complexity of these signals or the patient's clinical state. We aimed to characterize how increasing the temporal window size of observation from seconds to hours modifies the measured variability and complexity of basic vital signs. DESIGN: Retrospective analysis of signal data acquired between April 1, 2013, and September 30, 2015. SETTING: Critical care unit at The Hospital for Sick Children, Toronto. PATIENTS: Seven hundred forty-seven patients less than or equal to 18 years old (63,814,869 data values), within seven diagnostic/surgical groups. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Measures of variability (SD and the absolute differences) and signal complexity (multiscale sample entropy and detrended fluctuation analysis [expressed as the scaling component α]) were calculated for systolic blood pressure, heart rate, and oxygen saturation. The variability of all vital signs increases as the window size increases from seconds to hours at the patient and diagnostic/surgical group level. Significant differences in the magnitude of variability for all time scales within and between groups was demonstrated (p < 0.0001). Variability correlated negatively with patient age for heart rate and oxygen saturation, but positively with systolic blood pressure. Changes in variability and complexity of heart rate and systolic blood pressure from time of admission to discharge were found. CONCLUSIONS: In critically ill children, the temporal variability of physiologic signals supports higher frequency data capture, and this variability should be accounted for in models of patient state estimation.