Monitoring of High- and Intermediate-Risk Surgical Patients

Posthuma, Linda M.; Visscher, Maarten J.; Hollmann, Markus W.; Preckel, Benedikt

doi:10.1213/ane.0000000000004345

Cited by 14 publications

(9 citation statements)

References 30 publications

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“…A further meta-analysis focusing on the early detection of sepsis concluded that even mortality is reduced (risk ratio 0.56) by automated alerts when comparing artificial intelligence–based continuous vital sign monitoring systems to standard care [ 4 ]. However, wearables provide the possibility of early diagnosis and therefore of initiating timely therapies, but obviously do not alone constitute a therapeutic tool [ 35 ]. Furthermore, the compliance of patients using such wearables is of fundamental importance.…”

Section: Discussionmentioning

confidence: 99%

Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial

et al. 2022

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Background Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. Objective However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. Methods We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). Results All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). Conclusions Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate. Trial Registration ClinicalTrials.gov NCT05418881; https://www.clinicaltrials.gov/ct2/show/NCT05418881

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Section: Discussionmentioning

confidence: 99%

Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial

et al. 2022

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“…The benefits of patient monitoring with wireless sensors have been shown [18][19][20][21]. To be useful for patient management after major surgical interventions, reliable wireless sensors must be connected to a central system with alarm notification.…”

Section: Principal Findingsmentioning

confidence: 99%

Heart Rate and Oxygen Saturation Monitoring With a New Wearable Wireless Device in the Intensive Care Unit: Pilot Comparison Trial

Murali¹,

Rincon²,

Cassina³

et al. 2020

JMIR Biomed Eng

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Background Continuous cardiac monitoring with wireless sensors is an attractive option for early detection of arrhythmia and conduction disturbances and the prevention of adverse events leading to patient deterioration. We present a new sensor design (SmartCardia), a wearable wireless biosensor patch, for continuous cardiac and oxygen saturation (SpO2) monitoring. Objective This study aimed to test the clinical value of a new wireless sensor device (SmartCardia) and its usefulness in monitoring the heart rate (HR) and SpO2 of patients. Methods We performed an observational study and monitored the HR and SpO2 of patients admitted to the intensive care unit (ICU). We compared the device under test (SmartCardia) with the ICU-grade monitoring system (Dräger-Healthcare). We defined optimal correlation between the gold standard and the wireless system as <10% difference for HR and <4% difference for SpO2. Data loss and discrepancy between the two systems were critically analyzed. Results A total of 58 ICU patients (42 men and 16 women), with a mean age of 71 years (SD 11), were included in this study. A total of 13.49 (SD 5.53) hours per patient were recorded. This represents a total recorded period of 782.3 hours. The mean difference between the HR detected by the SmartCardia patch and the ICU monitor was 5.87 (SD 16.01) beats per minute (bias=–5.66, SD 16.09). For SpO2, the average difference was 3.54% (SD 3.86; bias=2.9, SD 4.36) for interpretable values. SmartCardia’s patch measures SpO2 only under low-to-no activity conditions and otherwise does not report a value. Data loss and noninterpretable values of SpO2 represented 26% (SD 24) of total measurements. Conclusions The SmartCardia device demonstrated clinically acceptable accuracy for HR and SpO2 monitoring in ICU patients.

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“…In a hospital ward setting, wireless monitoring provides the opportunity to measure vital signs continuously, which allows active notification of vital signs abnormalities and evaluation of trends [ 1 , 2 ]. Accordingly, remote technologies have been deployed to assist early identification of patient deterioration in high-risk surgical or general ward patients [ 3 , 4 ], and were proposed for monitoring of isolated patients during the COVID-19 pandemic [ 5 ]. Furthermore, the continuous data can be used for automated analysis and risk modelling, aiming to support patient monitoring and clinical decision-making.…”

Section: Introductionmentioning

confidence: 99%

Missing data imputation techniques for wireless continuous vital signs monitoring

Rossum

Silva

Wang

et al. 2023

J Clin Monit Comput

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Wireless vital signs sensors are increasingly used for remote patient monitoring, but data analysis is often challenged by missing data periods. This study explored the performance of various imputation techniques for continuous vital signs measurements. Wireless vital signs measurements (heart rate, respiratory rate, blood oxygen saturation, axillary temperature) from surgical ward patients were used for repeated random simulation of missing data periods (gaps) of 5–60 min in two-hour windows. Gaps were imputed using linear interpolation, spline interpolation, last observation- and mean carried forwards technique, and cluster-based prognosis. Imputation performance was evaluated using the mean absolute error (MAE) between original and imputed gap samples. Besides, effects on signal features (window’s slope, mean) and early warning scores (EWS) were explored. Gaps were simulated in 1743 data windows, obtained from 52 patients. Although MAE ranges overlapped, median MAE was structurally lowest for linear interpolation (heart rate: 0.9–2.6 beats/min, respiratory rate: 0.8–1.8 breaths/min, temperature: 0.04–0.17 °C, oxygen saturation: 0.3–0.7% for 5–60 min gaps) but up to twice as high for other techniques. Three techniques resulted in larger ranges of signal feature bias compared to no imputation. Imputation led to EWS misclassification in 1–8% of all simulations. Imputation error ranges vary between imputation techniques and increase with gap length. Imputation may result in larger signal feature bias compared to performing no imputation, and can affect patient risk assessment as illustrated by the EWS. Accordingly, careful implementation and selection of imputation techniques is warranted.

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Monitoring of High- and Intermediate-Risk Surgical Patients

Cited by 14 publications

References 30 publications

Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial

Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial

Heart Rate and Oxygen Saturation Monitoring With a New Wearable Wireless Device in the Intensive Care Unit: Pilot Comparison Trial

Missing data imputation techniques for wireless continuous vital signs monitoring

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