Postoperative circadian patterns in wearable sensor measured heart rate: a prospective observational study.

PURPOSE: This study aimed to describe the 24-hour cycle of wearable sensor-obtained heart rate in patients with deterioration-free recovery and to compare it with patients experiencing postoperative deterioration. METHODS: A prospective observational trial was performed in patients following bariatric or major abdominal cancer surgery. A wireless accelerometer patch (Healthdot) continuously measured postoperative heart rate, both in the hospital and after discharge, for a period of 14 days. The circadian pattern, or diurnal rhythm, in the wearable sensor-obtained heart rate was described using peak, nadir and peak-nadir excursions. RESULTS: The study population consisted of 137 bariatric and 100 major abdominal cancer surgery patients. In the latter group, 39 experienced postoperative deterioration. Both surgery types showed disrupted diurnal rhythm on the first postoperative days. Thereafter, the bariatric group had significantly lower peak heart rates (days 4, 7-12, 14), lower nadir heart rates (days 3-14) and larger peak-nadir excursions (days 2, 4-14). In cancer surgery patients, significantly higher nadir (days 2-5) and peak heart rates (days 2-3) were observed prior to deterioration. CONCLUSIONS: The postoperative diurnal rhythm of heart rate is disturbed by different types of surgery. Both groups showed recovery of diurnal rhythm but in patients following cancer surgery, both peak and nadir heart rates were higher than in the bariatric surgery group. Especially nadir heart rate was identified as a potential prognostic marker for deterioration after cancer surgery.

Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors.

BACKGROUND AND OBJECTIVES: Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU admissions. METHODS: A retrospective observational cohort study was performed between January 2013 and December 2017 in a tertiary Dutch hospital. All patients undergoing surgery in the study period were selected. Cardiothoracic surgeries, obstetric surgeries, catheterization lab procedures, electroconvulsive therapy, day care procedures, intravenous line interventions and patients under the age of 18 years were excluded. The primary outcome was unanticipated ICU admission. RESULTS: An unanticipated ICU admission complicated the recovery of 223 (0.9%) patients. These patients had higher hospital mortality rates (13.9% versus 0.2%, p<0.001). Multivariable analysis resulted in predictors of unanticipated ICU admissions consisting of age, body mass index, general anesthesia in combination with epidural anesthesia, preoperative score, diabetes, administration of vasopressors, erythrocytes, duration of surgery and post anesthesia care unit stay, and vital parameters such as heart rate and oxygen saturation. The receiver operating characteristic curve of this model resulted in an area under the curve of 0.86 (95% CI 0.83-0.88). CONCLUSIONS: The prediction of unanticipated ICU admissions from electronic medical record data improved when the intra- and early postoperative factors were combined with preoperative patient factors. This emphasizes the need for clinical decision support tools in post anesthesia care units with regard to postoperative patient allocation.

Machine Learning for Postoperative Continuous Recovery Scores of Oncology Patients in Perioperative Care with Data from Wearables.

Assessing post-operative recovery is a significant component of perioperative care, since this assessment might facilitate detecting complications and determining an appropriate discharge date. However, recovery is difficult to assess and challenging to predict, as no universally accepted definition exists. Current solutions often contain a high level of subjectivity, measure recovery only at one moment in time, and only investigate recovery until the discharge moment. For these reasons, this research aims to create a model that predicts continuous recovery scores in perioperative care in the hospital and at home for objective decision making. This regression model utilized vital signs and activity metrics measured using wearable sensors and the XGBoost algorithm for training. The proposed model described continuous recovery profiles, obtained a high predictive performance, and provided outcomes that are interpretable due to the low number of features in the final model. Moreover, activity features, the circadian rhythm of the heart, and heart rate recovery showed the highest feature importance in the recovery model. Patients could be identified with fast and slow recovery trajectories by comparing patient-specific predicted profiles to the average fast- and slow-recovering populations. This identification may facilitate determining appropriate discharge dates, detecting complications, preventing readmission, and planning physical therapy. Hence, the model can provide an automatic and objective decision support tool.

The Accuracy of Wrist-Worn Photoplethysmogram-Measured Heart and Respiratory Rates in Abdominal Surgery Patients: Observational Prospective Clinical Validation Study.

BACKGROUND: Postoperative deterioration is often preceded by abnormal vital parameters. Therefore, vital parameters of postoperative patients are routinely measured by nursing staff. Wrist-worn sensors could potentially provide an alternative tool for the measurement of vital parameters in low-acuity settings. These devices would allow more frequent or even continuous measurements of vital parameters without relying on time-consuming manual measurements, provided their accuracy in this clinical population is established. OBJECTIVE: This study aimed to assess the accuracy of heart rate (HR) and respiratory rate (RR) measures obtained via a wearable photoplethysmography (PPG) wristband in a cohort of postoperative patients. METHODS: The accuracy of the wrist-worn PPG sensor was assessed in 62 post-abdominal surgery patients (mean age 55, SD 15 years; median BMI 34, IQR 25-40 kg/m2). The wearable obtained HR and RR measurements were compared to those of the reference monitor in the postanesthesia or intensive care unit. Bland-Altman and Clarke error grid analyses were performed to determine agreement and clinical accuracy. RESULTS: Data were collected for a median of 1.2 hours per patient. With a coverage of 94% for HR and 34% for RR, the device was able to provide accurate measurements for the large majority of the measurements as 98% and 93% of the measurements were within 5 bpm or 3 rpm of the reference signal. Additionally, 100% of the HR and 98% of the RR measurements were clinically acceptable on Clarke error grid analysis. CONCLUSIONS: The wrist-worn PPG device is able to provide measurements of HR and RR that can be seen as sufficiently accurate for clinical applications. Considering the coverage, the device was able to continuously monitor HR and report RR when measurements of sufficient quality were obtained. TRIAL REGISTRATION: ClinicalTrials.gov NCT03923127; https://www.clinicaltrials.gov/ct2/show/NCT03923127.

A wearable patch based remote early warning score (REWS) in major abdominal cancer surgery patients.

INTRODUCTION: The shift toward remote patient monitoring methods to detect clinical deterioration requires testing of wearable devices in real-life clinical settings. This study aimed to develop a remote early warning scoring (REWS) system based on continuous measurements using a wearable device, and compare its diagnostic performance for the detection of deterioration to the diagnostic performance of the conventional modified early warning score (MEWS). MATERIALS AND METHODS: The study population of this prospective, single center trial consisted of patients who underwent major abdominal cancer surgery and were monitored using routine in-hospital spotcheck measurements of the vital parameters. Heart and respiratory rates were measured continuously using a wireless accelerometer patch (HealthDot). The prediction by MEWS of deterioration toward a complication graded Clavien-Dindo of 2 or higher was compared to the REWS derived from continuous measurements by the wearable patch. MAIN RESULTS: A total of 103 patients and 1909 spot-check measurements were included in the analysis. Postoperative deterioration was observed in 29 patients. For both EWS systems, the sensitivity (MEWS: 0.20 95% CI: [0.13-0.29], REWS: 0.20 95% CI: [0.13-0.29]) and specificity (MEWS: 0.96 95% CI: [0.95-0.97], REWS: 0.96 95% CI: [0.95-0.97]) were assessed. CONCLUSIONS: The diagnostic value of the REWS method, based on continuous measurements of the heart and respiratory rates, is comparable to that of the MEWS in patients following major abdominal cancer surgery. The wearable patch could detect the same amount of deteriorations, without requiring manual spot check measurements.

Quality Improvement in the Preoperative Evaluation: Accuracy of an Automated Clinical Decision Support System to Calculate CHA2DS2-VASc Scores.

Background and Objectives: Clinical decision support systems are advocated to improve the quality and efficiency in healthcare. However, before implementation, validation of these systems needs to be performed. In this evaluation we tested our hypothesis that a computerized clinical decision support system can calculate the CHA2DS2-VASc score just as well compared to manual calculation, or even better and more efficiently than manual calculation in patients with atrial rhythm disturbances. Materials and Methods: In n = 224 patents, we calculated the total CHA2DS2-VASc score manually and by an automated clinical decision support system. We compared the automated clinical decision support system with manually calculation by physicians. Results: The interclass correlation between the automated clinical decision support system and manual calculation showed was 0.859 (0.611 and 0.931 95%-CI). Bland-Altman plot and linear regression analysis shows us a bias of -0.79 with limit of agreement (95%-CI) between 1.37 and -2.95 of the mean between our 2 measurements. The Cohen's kappa was 0.42. Retrospective analysis showed more human errors than algorithmic errors. Time it took to calculate the CHA2DS2-VASc score was 11 s per patient in the automated clinical decision support system compared to 48 s per patient with the physician. Conclusions: Our automated clinical decision support system is at least as good as manual calculation, may be more accurate and is more time efficient.

Advanced practice providers versus medical residents as leaders of rapid response teams: A 12-month retrospective analysis.

PURPOSE: In a time of worldwide physician shortages, the advanced practice providers (APPs) might be a good alternative for physicians as the leaders of a rapid response team. This retrospective analysis aimed to establish whether the performance of APP-led rapid response teams is comparable to the performance of rapid response teams led by a medical resident of the ICU. MATERIAL AND METHODS: In a retrospective single-center cohort study, the electronic medical record of a tertiary hospital was queried during a 12-months period to identify patients who had been visited by our rapid response team. Patient- and process-related outcomes of interventions of rapid response teams led by an APP were compared with those of teams led by a medical resident using various parameters, including the MAELOR tool, which measures the performance of a rapid response team. RESULTS: In total, 179 responses of the APP-led teams were analyzed, versus 275 responses of the teams led by a resident. Per APP, twice as many calls were handled than per resident. Interventions of teams led by APPs, and residents did not differ in number of admissions (p = 0.87), mortality (p = 0.8), early warning scores (p = 0.2) or MAELOR tool triggering (p = 0.19). Both groups scored equally on time to admission (p = 0.67) or time until any performed intervention. CONCLUSION: This retrospective analysis showed that the quality of APP-led rapid response teams was similar to the quality of teams led by a resident. These findings need to be confirmed by prospective studies with balanced outcome parameters.

Accuracy of vital parameters measured by a wearable patch following major abdominal cancer surgery.

INTRODUCTION: Recent advances in wearable technology allow for the development of wirelessly connected sensors to continuously measure vital parameters in the general ward or even at home. The present study assesses the accuracy of a wearable patch (Healthdot) for continuous monitoring of heartrate (HR) and respiration rate (RR). MATERIALS AND METHODS: The Healthdot measures HR and RR by means of chest accelerometry. The study population consisted of patients following major abdominal oncological surgery. The analysis focused on the agreement between HR and RR measured by the Healthdot and the gold standard patient monitor in the intensive and post-anesthesia care unit. RESULTS: For HR, a total of 112 h of measurements was collected in 26 patients. For RR, a total of 102 h of measurements was collected in 21 patients. On second to second analysis, 97% of the HR and 87% of the RR measurements were within 5 bpm and 3 rpm of the reference monitor. Assessment of 5-min averaged data resulted in 96% of the HR and 95% of the RR measurements within 5 bpm and 3 rpm of the reference monitor. A Clarke error grid analysis showed that 100% of the HR and 99.4% of the 5-min averaged data was clinically acceptable. CONCLUSION: The Healthdot accurately measured HR and RR in a cohort of patients recovering from major abdominal surgery, provided that good quality data was obtained. These results push the Healthdot forward as a clinically acceptable tool in low acuity settings for unobtrusive, automatic, wireless and continuous monitoring.

Validation of Heart Rate Extracted From Wrist-Based Photoplethysmography in the Perioperative Setting: Prospective Observational Study.

BACKGROUND: Measurement of heart rate (HR) through an unobtrusive, wrist-worn optical HR monitor (OHRM) could enable earlier recognition of patient deterioration in low acuity settings and enable timely intervention. OBJECTIVE: The goal of this study was to assess the agreement between the HR extracted from the OHRM and the gold standard 5-lead electrocardiogram (ECG) connected to a patient monitor during surgery and in the recovery period. METHODS: In patients undergoing surgery requiring anesthesia, the HR reported by the patient monitor's ECG module was recorded and stored simultaneously with the photopletysmography (PPG) from the OHRM attached to the patient's wrist. The agreement between the HR reported by the patient's monitor and the HR extracted from the OHRM's PPG signal was assessed using Bland-Altman analysis during the surgical and recovery phase. RESULTS: A total of 271.8 hours of data in 99 patients was recorded simultaneously by the OHRM and patient monitor. The median coverage was 86% (IQR 65%-95%) and did not differ significantly between surgery and recovery (Wilcoxon paired difference test P=.17). Agreement analysis showed the limits of agreement (LoA) of the difference between the OHRM and the ECG HR were within the range of 5 beats per minute (bpm). The mean bias was -0.14 bpm (LoA between -3.08 bpm and 2.79 bpm) and -0.19% (LoA between -5 bpm to 5 bpm) for the PPG- measured HR compared to the ECG-measured HR during surgery; during recovery, it was -0.11 bpm (LoA between -2.79 bpm and 2.59 bpm) and -0.15% (LoA between -3.92% and 3.64%). CONCLUSIONS: This study shows that an OHRM equipped with a PPG sensor can measure HR within the ECG reference standard of -5 bpm to 5 bpm or -10% to 10% in the perioperative setting when the PPG signal is of sufficient quality. This implies that an OHRM can be considered clinically acceptable for HR monitoring in low acuity hospitalized patients.

Reliability of heart rate and respiration rate measurements with a wireless accelerometer in postbariatric recovery.

Recognition of early signs of deterioration in postoperative course could be improved by continuous monitoring of vital parameters. Wearable sensors could enable this by wireless transmission of vital signs. A novel accelerometer-based device, called Healthdot, has been designed to be worn on the skin to measure the two key vital parameters respiration rate (RespR) and heart rate (HeartR). The goal of this study is to assess the reliability of heart rate and respiration rate measured by the Healthdot in comparison to the gold standard, the bedside patient monitor, during the postoperative period in bariatric patients. Data were collected in a consecutive group of 30 patients who agreed to wear the device after their primary bariatric procedure. Directly after surgery, a Healthdot was attached on the patients' left lower rib. Vital signs measured by the accelerometer based Healthdot were compared to vital signs collected with the gold standard patient monitor for the period that the patient stayed at the post-anesthesia care unit. Over all patients, a total of 22 hours of vital signs obtained by the Healthdot were recorded simultaneously with the bedside patient monitor data. 87.5% of the data met the pre-defined bias of 5 beats per minute for HeartR and 92.3% of the data met the pre-defined bias of 5 respirations per minute for RespR. The Healthdot can be used to accurately derive heart rate and respiration rate in postbariatric patients. Wireless continuous monitoring of key vital signs has the potential to contribute to earlier recognition of complications in postoperative patients. Future studies should focus on the ability to detect patient deterioration in low-care environments and at home after discharge from the hospital.

Intelligent checklists improve checklist compliance in the intensive care unit: a prospective before-and-after mixed-method study.

BACKGROUND: We examined whether a context and process-sensitive 'intelligent' checklist increases compliance with best practice compared with a paper checklist during intensive care ward rounds. METHODS: We conducted a single-centre prospective before-and-after mixed-method trial in a 35 bed medical and surgical ICU. Daily ICU ward rounds were observed during two periods of 8 weeks. We compared paper checklists (control) with a dynamic (digital) clinical checklist (DCC, intervention). The primary outcome was compliance with best clinical practice, measured as the percentages of checked items and unchecked critical items. Secondary outcomes included ICU stay and the usability of digital checklists. Data are presented as median (interquartile range). RESULTS: Clinical characteristics and severity of critical illness were similar during both control and intervention periods of study. A total of 36 clinicians visited 197 patients during 352 ward rounds using the paper checklist, compared with 211 patients during 366 ward rounds using the DCC. Per ICU round, a median of 100% of items (94.4-100.0) were completed by DCC, compared with 75.1% (66.7-86.4) by paper checklist (P=0.03). No critical items remained unchecked by the DCC, compared with 15.4% (8.3-27.3) by the paper checklist (P=0.01). The DCC was associated with reduced ICU stay (1 day [1-3]), compared with the paper checklist (2 days [1-4]; P=0.05). Usability of the DCC was judged by clinicians to require further improvement. CONCLUSIONS: A digital checklist improved compliance with best clinical practice, compared with a paper checklist, during ward rounds on a mixed ICU. CLINICAL TRIAL REGISTRATION: NCT03599856.

Implementation of an automated early warning scoring system in a surgical ward: Practical use and effects on patient outcomes.

INTRODUCTION: Early warning scores (EWS) are being increasingly embedded in hospitals over the world due to their promise to reduce adverse events and improve the outcomes of clinical patients. The aim of this study was to evaluate the clinical use of an automated modified EWS (MEWS) for patients after surgery. METHODS: This study conducted retrospective before-and-after comparative analysis of non-automated and automated MEWS for patients admitted to the surgical high-dependency unit in a tertiary hospital. Operational outcomes included number of recorded assessments of the individual MEWS elements, number of complete MEWS assessments, as well as adherence rate to related protocols. Clinical outcomes included hospital length of stay, in-hospital and 28-day mortality, and ICU readmission rate. RESULTS: Recordings in the electronic medical record from the control period contained 7929 assessments of MEWS elements and were performed in 320 patients. Recordings from the intervention period contained 8781 assessments of MEWS elements in 273 patients, of which 3418 were performed with the automated EWS system. During the control period, 199 (2.5%) complete MEWS were recorded versus 3991 (45.5%) during intervention period. With the automated MEWS systems, the percentage of missing assessments and the time until the next assessment for patients with a MEWS of ≥2 decreased significantly. The protocol adherence improved from 1.1% during the control period to 25.4% when the automated MEWS system was involved. There were no significant differences in clinical outcomes. CONCLUSION: Implementation of an automated EWS system on a surgical high dependency unit improves the number of complete MEWS assessments, registered vital signs, and adherence to the EWS hospital protocol. However, this positive effect did not translate into a significant decrease in mortality, hospital length of stay, or ICU readmissions. Future research and development on automated EWS systems should focus on data management and technology interoperability.

RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis.

We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients.

Ribavirin-induced externalization of phosphatidylserine in erythrocytes is predominantly caused by inhibition of aminophospholipid translocase activity.

Ribavirin in combination with interferon-α is the standard treatment for chronic hepatitis C, but often induces severe anemia forcing discontinuation of the therapy. Whereas suppression of bone marrow by interferon may impact on the production of erythrocytes, it has been suggested that accumulation of ribavirin in erythrocytes induces alterations causing an early removal of these cells by the mononuclear phagocytic system. Externalization of phosphatidylserine, which is exclusively present in the cytoplasmic leaflet of the plasma membrane, is a recognition signal for phagocytosis in particular of apoptotic cells. Here, we demonstrate that surface exposure of phosphatidylserine upon prolonged treatment of erythrocytes with ribavirin results mainly from inactivation of the aminophospholipid translocase, an ATP-dependent lipid pump, which specifically transports phosphatidylserine from the outer to the inner leaflet of the plasma membrane. Inactivation is due to severe ATP depletion, although competitive inhibition by ribavirin or its phosphorylated derivatives cannot be excluded. Phospholipid scramblase, responsible for collapse of lipid asymmetry, appears to be of minor importance as erythrocytes of patients with the Scott syndrome, lacking Ca(2+)-induced lipid scrambling, are equally sensitive to ribavirin treatment. Neither the antioxidant N-acetylcysteine nor the pan-caspase inhibitor Q-VD-OPH did affect ribavirin-induced phosphatidylserine exposure, suggesting that oxidative stress or apoptotic-related mechanisms are not involved in this process. In conclusion, we propose that spontaneous loss of lipid asymmetry, not corrected by aminophospholipid translocase activity, is the mechanism for ribavirin-induced phosphatidylserine exposure that may contribute to ribavirin-induced anemia.