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Posthuma LM, Breteler MJM, Lirk PB, Nieveen van Dijkum EJ, Visscher MJ, Breel JS, Wensing CAGL, Schenk J, Vlaskamp LB, van Rossum MC, Ruurda JP, Dijkgraaf MGW, Hollmann MW, Kalkman CJ, Preckel B. Surveillance of high-risk early postsurgical patients for real-time detection of complications using wireless monitoring (SHEPHERD study): results of a randomized multicenter stepped wedge cluster trial. Front Med (Lausanne) 2024; 10:1295499. [PMID: 38249988 PMCID: PMC10796990 DOI: 10.3389/fmed.2023.1295499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024] Open
Abstract
Background Vital signs measurements on the ward are performed intermittently. This could lead to failure to rapidly detect patients with deteriorating vital signs and worsens long-term outcome. The aim of this study was to test the hypothesis that continuous wireless monitoring of vital signs on the postsurgical ward improves patient outcome. Methods In this prospective, multicenter, stepped-wedge cluster randomized study, patients in the control group received standard monitoring. The intervention group received continuous wireless monitoring of heart rate, respiratory rate and temperature on top of standard care. Automated alerts indicating vital signs deviation from baseline were sent to ward nurses, triggering the calculation of a full early warning score followed. The primary outcome was the occurrence of new disability three months after surgery. Results The study was terminated early (at 57% inclusion) due to COVID-19 restrictions. Therefore, only descriptive statistics are presented. A total of 747 patients were enrolled in this study and eligible for statistical analyses, 517 patients in the control group and 230 patients in the intervention group, the latter only from one hospital. New disability at three months after surgery occurred in 43.7% in the control group and in 39.1% in the intervention group (absolute difference 4.6%). Conclusion This is the largest randomized controlled trial investigating continuous wireless monitoring in postoperative patients. While patients in the intervention group seemed to experience less (new) disability than patients in the control group, results remain inconclusive with regard to postoperative patient outcome due to premature study termination. Clinical trial registration ClinicalTrials.gov, ID: NCT02957825.
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Affiliation(s)
- Linda M. Posthuma
- Department of Anesthesiologie, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
| | | | - Philipp B. Lirk
- Department of Anesthesiologie, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
- Department of Anesthesiologie, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Els J. Nieveen van Dijkum
- Department of Surgery, Amsterdam University Medical Center, Location University of Amsterdam, Cancer Center Amsterdam, Amsterdam, Netherlands
| | - Maarten J. Visscher
- Department of Anesthesiologie, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
| | - Jennifer S. Breel
- Department of Anesthesiologie, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
| | - Carin A. G. L. Wensing
- Department of Anesthesiologie, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
| | - Jimmy Schenk
- Department of Anesthesiologie, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health, Quality of Care, Amsterdam, Netherlands
| | - Lyan B. Vlaskamp
- Department of Anesthesiologie, University Medical Center, Utrecht, Netherlands
| | | | - Jelle P. Ruurda
- Department of Gastro-Intestinal and Oncologic Surgery, University Medical Center Utrecht, Utrecht, Netherlands
| | - Marcel G. W. Dijkgraaf
- Department of Epidemiology and Data Science, Amsterdam University Medical Center, Location AMC, Amsterdam, Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, Netherlands
| | - Markus W. Hollmann
- Department of Anesthesiologie, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health, Quality of Care, Amsterdam, Netherlands
| | - Cor J. Kalkman
- Department of Anesthesiologie, University Medical Center, Utrecht, Netherlands
| | - Benedikt Preckel
- Department of Anesthesiologie, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, Netherlands
- Amsterdam Public Health, Quality of Care, Amsterdam, Netherlands
- Amsterdam Cardiovascular Science, Diabetes and Metabolism, Amsterdam, Netherlands
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2
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van Rossum MC, da Silva PMA, Wang Y, Kouwenhoven EA, Hermens HJ. Missing data imputation techniques for wireless continuous vital signs monitoring. J Clin Monit Comput 2023; 37:1387-1400. [PMID: 36729298 PMCID: PMC9893204 DOI: 10.1007/s10877-023-00975-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 01/16/2023] [Indexed: 02/03/2023]
Abstract
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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Affiliation(s)
- Mathilde C van Rossum
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands.
- Cardiovascular and Respiratory Physiology, University of Twente, Postbox 217, 7500 AE, Enschede, The Netherlands.
- Department of Surgery, Hospital Group Twente, Almelo, The Netherlands.
| | - Pedro M Alves da Silva
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
- NOVA School of Science and Technology, NOVA University of Lisbon, Lisbon, Portugal
| | - Ying Wang
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
- ZGT Academy, Hospital group Twente, Almelo, The Netherlands
| | | | - Hermie J Hermens
- Biomedical Signals and Systems, University of Twente, Enschede, The Netherlands
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3
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van Rossum MC, Bekhuis REM, Wang Y, Hegeman JH, Folbert EC, Vollenbroek-Hutten MMR, Kalkman CJ, Kouwenhoven EA, Hermens HJ. Early Warning Scores to Support Continuous Wireless Vital Sign Monitoring for Complication Prediction in Patients on Surgical Wards: Retrospective Observational Study. JMIR Perioper Med 2023; 6:e44483. [PMID: 37647104 PMCID: PMC10500362 DOI: 10.2196/44483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 06/16/2023] [Accepted: 07/07/2023] [Indexed: 09/01/2023] Open
Abstract
BACKGROUND Wireless vital sign sensors are increasingly being used to monitor patients on surgical wards. Although early warning scores (EWSs) are the current standard for the identification of patient deterioration in a ward setting, their usefulness for continuous monitoring is unknown. OBJECTIVE This study aimed to explore the usability and predictive value of high-rate EWSs obtained from continuous vital sign recordings for early identification of postoperative complications and compares the performance of a sensor-based EWS alarm system with manual intermittent EWS measurements and threshold alarms applied to individual vital sign recordings (single-parameter alarms). METHODS Continuous vital sign measurements (heart rate, respiratory rate, blood oxygen saturation, and axillary temperature) collected with wireless sensors in patients on surgical wards were used for retrospective simulation of EWSs (sensor EWSs) for different time windows (1-240 min), adopting criteria similar to EWSs based on manual vital signs measurements (nurse EWSs). Hourly sensor EWS measurements were compared between patients with (event group: 14/46, 30%) and without (control group: 32/46, 70%) postoperative complications. In addition, alarms were simulated for the sensor EWSs using a range of alarm thresholds (1-9) and compared with alarms based on nurse EWSs and single-parameter alarms. Alarm performance was evaluated using the sensitivity to predict complications within 24 hours, daily alarm rate, and false discovery rate (FDR). RESULTS The hourly sensor EWSs of the event group (median 3.4, IQR 3.1-4.1) was significantly higher (P<.004) compared with the control group (median 2.8, IQR 2.4-3.2). The alarm sensitivity of the hourly sensor EWSs was the highest (80%-67%) for thresholds of 3 to 5, which was associated with alarm rates of 2 (FDR=85%) to 1.2 (FDR=83%) alarms per patient per day respectively. The sensitivity of sensor EWS-based alarms was higher than that of nurse EWS-based alarms (maximum=40%) but lower than that of single-parameter alarms (87%) for all thresholds. In contrast, the (false) alarm rates of sensor EWS-based alarms were higher than that of nurse EWS-based alarms (maximum=0.6 alarm/patient/d; FDR=80%) but lower than that of single-parameter alarms (2 alarms/patient/d; FDR=84%) for most thresholds. Alarm rates for sensor EWSs increased for shorter time windows, reaching 70 alarms per patient per day when calculated every minute. CONCLUSIONS EWSs obtained using wireless vital sign sensors may contribute to the early recognition of postoperative complications in a ward setting, with higher alarm sensitivity compared with manual EWS measurements. Although hourly sensor EWSs provide fewer alarms compared with single-parameter alarms, high false alarm rates can be expected when calculated over shorter time spans. Further studies are recommended to optimize care escalation criteria for continuous monitoring of vital signs in a ward setting and to evaluate the effects on patient outcomes.
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Affiliation(s)
- Mathilde C van Rossum
- Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
- Department of Cardiovascular and Respiratory Physiology, University of Twente, Enschede, Netherlands
| | - Robin E M Bekhuis
- Department of Surgery, Hospital Group Twente, Almelo, Netherlands
- Hospital Group Twente Academy, Hospital Group Twente, Almelo, Netherlands
| | - Ying Wang
- Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
- Hospital Group Twente Academy, Hospital Group Twente, Almelo, Netherlands
| | | | - Ellis C Folbert
- Department of Surgery, Hospital Group Twente, Almelo, Netherlands
| | | | - Cornelis J Kalkman
- Department of Anesthesiology, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Hermie J Hermens
- Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
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van Wijk RJ, Quinten VM, van Rossum MC, Bouma HR, Ter Maaten JC. Predicting deterioration of patients with early sepsis at the emergency department using continuous heart rate variability analysis: a model-based approach. Scand J Trauma Resusc Emerg Med 2023; 31:15. [PMID: 37005664 PMCID: PMC10067229 DOI: 10.1186/s13049-023-01078-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 03/18/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND Sepsis is a life-threatening disease with an in-hospital mortality rate of approximately 20%. Physicians at the emergency department (ED) have to estimate the risk of deterioration in the coming hours or days and decide whether the patient should be admitted to the general ward, ICU or can be discharged. Current risk stratification tools are based on measurements of vital parameters at a single timepoint. Here, we performed a time, frequency, and trend analysis on continuous electrocardiograms (ECG) at the ED to try and predict deterioration of septic patients. METHODS Patients were connected to a mobile bedside monitor that continuously recorded ECG waveforms from triage at the ED up to 48 h. Patients were post-hoc stratified into three groups depending on the development of organ dysfunction: no organ dysfunction, stable organ dysfunction or progressive organ dysfunction (i.e., deterioration). Patients with de novo organ dysfunction and those admitted to the ICU or died were also stratified to the group of progressive organ dysfunction. Heart rate variability (HRV) features over time were compared between the three groups. RESULTS In total 171 unique ED visits with suspected sepsis were included between January 2017 and December 2018. HRV features were calculated over 5-min time windows and summarized into 3-h intervals for analysis. For each interval, the mean and slope of each feature was calculated. Of all analyzed features, the average of the NN-interval, ultra-low frequency, very low frequency, low frequency and total power were different between the groups at multiple points in time. CONCLUSIONS We showed that continuous ECG recordings can be automatically analyzed and used to extract HRV features associated with clinical deterioration in sepsis. The predictive accuracy of our current model based on HRV features derived from the ECG only shows the potential of HRV measurements at the ED. Unlike other risk stratification tools employing multiple vital parameters this does not require manual calculation of the score and can be used on continuous data over time. Trial registration The protocol of this study is published by Quinten et al., 2017.
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Affiliation(s)
- Raymond J van Wijk
- Emergency Department, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands.
| | - Vincent M Quinten
- Emergency Department, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands
| | - Mathilde C van Rossum
- Biomedical Signals and Systems, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands
- Cardiovascular and Respiratory Physiology, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands
| | - Hjalmar R Bouma
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
- Department Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Jan C Ter Maaten
- Emergency Department, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, the Netherlands
- Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
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5
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Haveman ME, van Rossum MC, Vaseur RME, van der Riet C, Schuurmann RCL, Hermens HJ, de Vries JPPM, Tabak M. Continuous Monitoring of Vital Signs With Wearable Sensors During Daily Life Activities: Validation Study. JMIR Form Res 2022; 6:e30863. [PMID: 34994703 PMCID: PMC8783291 DOI: 10.2196/30863] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/17/2021] [Accepted: 10/05/2021] [Indexed: 01/19/2023] Open
Abstract
Background Continuous telemonitoring of vital signs in a clinical or home setting may lead to improved knowledge of patients’ baseline vital signs and earlier detection of patient deterioration, and it may also facilitate the migration of care toward home. Little is known about the performance of available wearable sensors, especially during daily life activities, although accurate technology is critical for clinical decision-making. Objective The aim of this study is to assess the data availability, accuracy, and concurrent validity of vital sign data measured with wearable sensors in volunteers during various daily life activities in a simulated free-living environment. Methods Volunteers were equipped with 4 wearable sensors (Everion placed on the left and right arms, VitalPatch, and Fitbit Charge 3) and 2 reference devices (Oxycon Mobile and iButton) to obtain continuous measurements of heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and temperature. Participants performed standardized activities, including resting, walking, metronome breathing, chores, stationary cycling, and recovery afterward. Data availability was measured as the percentage of missing data. Accuracy was evaluated by the median absolute percentage error (MAPE) and concurrent validity using the Bland-Altman plot with mean difference and 95% limits of agreement (LoA). Results A total of 20 volunteers (median age 64 years, range 20-74 years) were included. Data availability was high for all vital signs measured by VitalPatch and for HR and temperature measured by Everion. Data availability for HR was the lowest for Fitbit (4807/13,680, 35.14% missing data points). For SpO2 measured by Everion, median percentages of missing data of up to 100% were noted. The overall accuracy of HR was high for all wearable sensors, except during walking. For RR, an overall MAPE of 8.6% was noted for VitalPatch and that of 18.9% for Everion, with a higher MAPE noted during physical activity (up to 27.1%) for both sensors. The accuracy of temperature was high for VitalPatch (MAPE up to 1.7%), and it decreased for Everion (MAPE from 6.3% to 9%). Bland-Altman analyses showed small mean differences of VitalPatch for HR (0.1 beats/min [bpm]), RR (−0.1 breaths/min), and temperature (0.5 °C). Everion and Fitbit underestimated HR up to 5.3 (LoA of −39.0 to 28.3) bpm and 11.4 (LoA of −53.8 to 30.9) bpm, respectively. Everion had a small mean difference with large LoA (−10.8 to 10.4 breaths/min) for RR, underestimated SpO2 (>1%), and overestimated temperature up to 2.9 °C. Conclusions Data availability, accuracy, and concurrent validity of the studied wearable sensors varied and differed according to activity. In this study, the accuracy of all sensors decreased with physical activity. Of the tested sensors, VitalPatch was found to be the most accurate and valid for vital signs monitoring.
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Affiliation(s)
- Marjolein E Haveman
- Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Mathilde C van Rossum
- Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands.,Department of Cardiovascular and Respiratory Physiology, University of Twente, Enschede, Netherlands
| | - Roswita M E Vaseur
- Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands
| | - Claire van der Riet
- Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Richte C L Schuurmann
- Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Hermie J Hermens
- Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands.,eHealth group, Roessingh Research and Development, Enschede, Netherlands
| | - Jean-Paul P M de Vries
- Department of Surgery, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Monique Tabak
- Department of Biomedical Signals and Systems, University of Twente, Enschede, Netherlands.,eHealth group, Roessingh Research and Development, Enschede, Netherlands
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Breteler MJM, Numan L, Ruurda JP, van Hillegersberg R, van der Horst S, Dohmen DAJ, van Rossum MC, Kalkman CJ. Wireless Remote Home Monitoring of Vital Signs in Patients Discharged Early After Esophagectomy: Observational Feasibility Study. JMIR Perioper Med 2020; 3:e21705. [PMID: 33393923 PMCID: PMC7728408 DOI: 10.2196/21705] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 07/22/2020] [Accepted: 10/28/2020] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Hospital stays after major surgery are shorter than ever before. Although enhanced recovery and early discharge have many benefits, some complications will now first manifest themselves in home settings. Remote patient monitoring with wearable sensors in the first days after hospital discharge may capture clinical deterioration earlier but is largely uncharted territory. OBJECTIVE This study aimed to assess the technical feasibility of patients, discharged after esophagectomy, being remotely monitored at home with a wireless patch sensor and the experiences of these patients. In addition, we determined whether observing vital signs with a wireless patch sensor influences clinical decision making. METHODS In an observational feasibility study, vital signs of patients were monitored with a wearable patch sensor (VitalPatch, VitalConnect Inc) during the first 7 days at home after esophagectomy and discharge from hospital. Vital signs trends were shared with the surgical team once a day, and they were asked to check the patient's condition by phone each morning. Patient experiences were evaluated with a questionnaire, and technical feasibility was analyzed on a daily basis as the percentage of data loss and gap durations. In addition, the number of patients for whom a change in clinical decision was made based on the results of remote vital signs monitoring at home was assessed. RESULTS Patients (N=20) completed 7 days each of home monitoring with the wearable patch sensor. Each of the patients had good recovery at home, and remotely observed vital signs trends did not alter clinical decision making. Patients appreciated that surgeons checked their vital signs daily (mean 4.4/5) and were happy to be called by the surgical team each day (mean 4.5/5). Wearability of the patch was high (mean 4.4/5), and no reports of skin irritation were mentioned. Overall data loss of vital signs measurements at home was 25%; both data loss and gap duration varied considerably among patients. CONCLUSIONS Remote monitoring of vital signs combined with telephone support from the surgical team was feasible and well perceived by all patients. Future studies need to evaluate the impact of home monitoring on patient outcome as well as the cost-effectiveness of this new approach.
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Affiliation(s)
- Martine J M Breteler
- Department of Anesthesiology, University Medical Center Utrecht, Utrecht, Netherlands.,Luscii Healthtech BV, Amsterdam, Netherlands
| | - Lieke Numan
- Department of Anesthesiology, University Medical Center Utrecht, Utrecht, Netherlands.,Department of Technical Medicine, University of Twente, Enschede, Netherlands
| | - Jelle P Ruurda
- Department of Surgery, University Medical Center Utrecht, Utrecht, Netherlands
| | | | | | | | - Mathilde C van Rossum
- Department of Technical Medicine, University of Twente, Enschede, Netherlands.,Biomedical Signals and Systems Group, University of Twente, Enschede, Netherlands
| | - Cor J Kalkman
- Department of Anesthesiology, University Medical Center Utrecht, Utrecht, Netherlands
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