Background: Patients undergoing major surgery are at risk of complications, so‐called serious adverse events (SAE). Continuous monitoring may detect deteriorating patients by recording abnormal vital signs. We aimed to assess the association between abnormal vital signs inspired by Early Warning Score thresholds and subsequent SAEs in patients undergoing major abdominal surgery.Methods: Prospective observational cohort study continuously monitoring heart rate, respiratory rate, peripheral oxygen saturation, and blood pressure for up to 96 h in 500 postoperative patients admitted to the general ward. Exposure variables were vital sign abnormalities, primary outcome was any serious adverse event occurring within 30 postoperative days. The primary analysis investigated the association between exposure variables per 24 h and subsequent serious adverse events.Results: Serious adverse events occurred in 37% of patients, with 38% occurring during monitoring. Among patients with SAE during monitoring, the median duration of vital sign abnormalities was 272 min (IQR 110–447), compared to 259 min (IQR 153–394) in patients with SAE after monitoring and 261 min (IQR 132–468) in the patients without any SAE (p = .62 for all three group comparisons). Episodes of heart rate ≥110 bpm occurred in 16%, 7.1%, and 3.9% of patients in the time before SAE during monitoring, after monitoring, and without SAE, respectively (p < .002). Patients with SAE after monitoring experienced more episodes of hypotension ≤90 mm Hg/24 h (p = .001).Conclusion: Overall duration of vital sign abnormalities at current thresholds were not significantly associated with subsequent serious adverse events, but more patients with tachycardia and hypotension had subsequent serious adverse events.Trial registration: Clinicaltrials.gov, identifier NCT03491137.

Objective. Wireless sensors for continuous monitoring of vital signs have potential to improve patient care by earlier detection of deterioration in general ward patients. We aimed to assess agreement between wireless and standard (wired) monitoring devices in patients hospitalized with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Approach. Paired measurements of vital signs were recorded with 15 min intervals for two hours. The primary outcome was agreement between wireless and standard monitor measurements using the Bland and Altman method to calculate bias with 95% limits of agreement (LoA). We considered LoA of less than ±5 beats min ⁻¹ (bpm) acceptable for heart rate (HR), whereas agreement of peripheral oxygen saturation (SpO ₂ ), respiratory rate (RR), and blood pressure (BP) were acceptable if within ±3%-points, ±3 breaths min ⁻¹ (brpm), and ±10 mmHg, respectively. Main results. 180 sample-pairs of vital signs from 20 with AECOPD patients were recorded for comparison. The wireless versus standard monitor bias was 0.03 (LoA −3.2 to 3.3) bpm for HR measurements, 1.4% (LoA −0.7% to 3.6%) for SpO ₂ , −7.8 (LoA −22.3 to 6.8) mmHg for systolic BP and −6.2 (LoA −16.8 to 4.5) mmHg for diastolic BP. The wireless versus standard monitor bias for RR measurements was 0.75 (LoA −6.1 to 7.6) brpm. Significance. Commercially available wireless monitors could accurately measure HR in patients admitted with AECOPD compared to standard wired monitoring. Agreement for SpO ₂ were borderline acceptable while agreement for RR and BP should be interpreted with caution.

Background: Patients admitted to the emergency care setting with COVID‐19‐infection can suffer from sudden clinical deterioration, but the extent of deviating vital signs in this group is still unclear. Wireless technology monitors patient vital signs continuously and might detect deviations earlier than intermittent measurements. The aim of this study was to determine frequency and duration of vital sign deviations using continuous monitoring compared to manual measurements. A secondary analysis was to compare deviations in patients admitted to ICU or having fatal outcome vs. those that were not.Methods: Two wireless sensors continuously monitored (CM) respiratory rate (RR), heart rate (HR), and peripheral arterial oxygen saturation (SpO₂). Frequency and duration of vital sign deviations were compared with point measurements performed by clinical staff according to regional guidelines, the National Early Warning Score (NEWS).Results: SpO₂ < 92% for more than 60 min was detected in 92% of the patients with CM vs. 40% with NEWS (p < .00001). RR > 24 breaths per minute for more than 5 min were detected in 70% with CM vs. 33% using NEWS (p = .0001). HR ≥ 111 for more than 60 min was seen in 51% with CM and 22% with NEWS (p = .0002). Patients admitted to ICU or having fatal outcome had longer durations of RR > 24 brpm (p = .01), RR > 21 brpm (p = .01), SpO₂ < 80% (p = .01), and SpO₂ < 85% (p = .02) compared to patients that were not.Conclusion: Episodes of desaturation and tachypnea in hospitalized patients with COVID‐19 infection are common and often not detected by routine measurements.

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.