Interpretation of continuously measured vital signs data of COVID-19 patients by nurses and physicians at the general ward: A mixed methods study

Background Continuous monitoring of vital signs is introduced at general hospital wards to detect patient deterioration. Interpretation and response currently rely on experience and expert opinion. This study aims to determine whether consensus exist among hospital professionals regarding the interpretation of vital signs of COVID-19 patients. In addition, we assessed the ability to recognise respiratory insufficiency and evaluated the interpretation process. Methods We performed a mixed methods study including 24 hospital professionals (6 nurses, 6 junior physicians, 6 internal medicine specialists, 6 ICU nurses). Each participant was presented with 20 cases of COVID-19 patients, including 4 or 8 hours of continuously measured vital signs data. Participants estimated the patient's situation ('improving', 'stable', or 'deteriorating') and the possibility of developing respiratory insufficiency. Subsequently, a semi-structured interview was held focussing on the interpretation process. Consensus was assessed using Krippendorff's alpha. For the estimation of respiratory insufficiency, we calculated the mean positive/negative predictive value. Interviews were analysed using inductive thematic analysis. Results We found no consensus regarding the patient's situation (α 0.41, 95%CI 0.29-0.52). The mean positive predictive value for respiratory insufficiency was high (0.91, 95%CI 0.86- 0.97), but the negative predictive value was 0.66 (95%CI 0.44-0.88). In the interviews, two themes regarding the interpretation process emerged. "Interpretation of deviations"included the strategies participants use to determine stability, focused on finding deviations in data. "Inability to see the patient"entailed the need of hospital professionals to perform a patient evaluation when estimating a patient's situation. Conclusion The interpretation of continuously measured vital signs by hospital professionals, and recognition of respiratory insufficiency using these data, is variable, which might be the result of different interpretation strategies, uncertainty regarding deviations, and not being able to see the patient. Protocols and training could help to uniform interpretation, but decision support systems might be necessary to find signs of deterioration that might otherwise go unnoticed

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

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.</p

Measuring free-living physical activity with three commercially available activity monitors for telemonitoring purposes : Validation study

Background: Remote monitoring of physical activity in patients with chronic conditions could be useful to offer care professionals real-time assessment of their patient's daily activity pattern to adjust appropriate treatment. However, the validity of commercially available activity trackers that can be used for telemonitoring purposes is limited. Objective: The purpose of this study was to test usability and determine the validity of 3 consumer-level activity trackers as a measure of free-living activity. Methods: A usability evaluation (study 1) and validation study (study 2) were conducted. In study 1, 10 individuals wore one activity tracker for a period of 30 days and filled in a questionnaire on ease of use and wearability. In study 2, we validated three selected activity trackers (Apple Watch, Misfit Shine, and iHealth Edge) and a fourth pedometer (Yamax Digiwalker) against the reference standard (Actigraph GT3X) in 30 healthy participants for 72 hours. Outcome measures were 95% limits of agreement (LoA) and bias (Bland-Altman analysis). Furthermore, median absolute differences (MAD) were calculated. Correction for bias was estimated and validated using leave-one-out cross validation. Results: Usability evaluation of study 1 showed that iHealth Edge and Apple Watch were more comfortable to wear as compared with the Misfit Flash. Therefore, the Misfit Flash was replaced by Misfit Shine in study 2. During study 2, the total number of steps of the reference standard was 21,527 (interquartile range, IQR 17,475-24,809). Bias and LoA for number of steps from the Apple Watch and iHealth Edge were 968 (IQR -5478 to 7414) and 2021 (IQR -4994 to 9036) steps. For Misfit Shine and Yamax Digiwalker, bias was -1874 and 2004, both with wide LoA of (13,869 to 10,121) and (-10,932 to 14,940) steps, respectively. The Apple Watch noted the smallest MAD of 7.7% with the Actigraph, whereas the Yamax Digiwalker noted the highest MAD (20.3%). After leave-one-out cross validation, accuracy estimates of MAD of the iHealth Edge and Misfit Shine were within acceptable limits with 10.7% and 11.3%, respectively. Conclusions: Overall, the Apple Watch and iHealth Edge were positively evaluated after wearing. Validity varied widely between devices, with the Apple Watch being the most accurate and Yamax Digiwalker the least accurate for step count in free-living conditions. The iHealth Edge underestimates number of steps but can be considered reliable for activity monitoring after correction for bias. Misfit Shine overestimated number of steps and cannot be considered suitable for step count because of the low agreement. Future studies should focus on the added value of remotely monitoring activity patterns over time in chronic patients