Wireless continuous single-lead ST-segment monitoring to detect new-onset myocardial injury at the general ward-An exploratory subanalysis.

Patients admitted for acute medical conditions and major noncardiac surgery are at risk of myocardial injury. This is frequently asymptomatic, especially in the context of concomitant pain and analgesics, and detection thus relies on cardiac biomarkers. Continuous single-lead ST-segment monitoring from wireless electrocardiogram (ECG) may enable more timely intervention, but criteria for alerts need to be defined to reduce false alerts. This study aimed to determine optimal ST-deviation thresholds from wireless single-lead ECG for detection of myocardial injury following major abdominal cancer surgery and during acute exacerbation of chronic obstructive pulmonary disease. Patients were monitored with a wireless single-lead ECG patch for up to 4 days and had daily troponin measurements. Single-lead ST-segment deviations of <0.255 mV and/or >0.245 mV (based on previous study comparison with 0.1 mV 12-lead ECG and variation in single-lead ECG) were analyzed for relation to myocardial injury defined as hsTnT elevation of 20-64 ng/L with an absolute change of ≥5 ng/L, or a hsTnT level ≥ 65 ng/L. In total, 528 patients were included for analysis, of which 15.5% had myocardial injury. For corrected ST-thresholds lasting ≥10 and ≥ 20 min, we found specificities of 91% and 94% and sensitivities of 17% and 13% with odds ratios of 2.0 (95% CI: 1.1; 3.9) and 2.4 (95% CI: 1.1; 5.1) for myocardial injury. In conclusion, wireless single-lead ECG monitoring with corrected ST thresholds detected patients developing myocardial injury with specificities >90% and sensitivities <20%, suggesting increased focus on sensitivity improvement.

Quantifying physiological stability in the general ward using continuous vital signs monitoring: the circadian kernel density estimator.

Technological advances seen in recent years have introduced the possibility of changing the way hospitalized patients are monitored by abolishing the traditional track-and-trigger systems and implementing continuous monitoring using wearable biosensors. However, this new monitoring paradigm raise demand for novel ways of analyzing the data streams in real time. The aim of this study was to design a stability index using kernel density estimation (KDE) fitted to observations of physiological stability incorporating the patients' circadian rhythm. Continuous vital sign data was obtained from two observational studies with 491 postoperative patients and 200 patients with acute exacerbation of chronic obstructive pulmonary disease. We defined physiological stability as the last 24 h prior to discharge. We evaluated the model against periods of eight hours prior to events defined either as severe adverse events (SAE) or as a total score in the early warning score (EWS) protocol of ≥ 6, ≥ 8, or ≥ 10. The results found good discriminative properties between stable physiology and EWS-events (area under the receiver operating characteristics curve (AUROC): 0.772-0.993), but lower for the SAEs (AUROC: 0.594-0.611). The time of early warning for the EWS events were 2.8-5.5 h and 2.5 h for the SAEs. The results showed that for severe deviations in the vital signs, the circadian KDE model can alert multiple hours prior to deviations being noticed by the staff. Furthermore, the model shows good generalizability to another cohort and could be a simple way of continuously assessing patient deterioration in the general ward.