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(1) Background: Evidence on the clinical accuracy of wireless photoplethysmography (PPG)-based vital sign monitoring is limited. This study evaluated the accuracy, technical performance, and patient comfort of a novel PPG-based earsensor for measuring oxygen saturation (SpO2), pulse rate (PR), and respiratory rate (RR) in postoperative patients. (2) Methods: In this observational method comparison study, SpO2, PR, and RR were simultaneously recorded using the earsensor and compared with continuous monitoring in patients admitted overnight to the post-anesthesia care unit. Outcome measures were bias, 95% limits of agreement (LoA), and average root mean square (ARMS). Technical performance was evaluated by data loss and data gap duration. Patient comfort was assessed using a questionnaire. (3) Results: Twenty-one patients contributed to 264 h of data. Bias was 1.7% for SpO2 (ARMS 2.4%; LoA −1.8% to 5.1%), 1.2 bpm for PR (ARMS 3.9 bpm; LoA –6.1 to 8.4 bpm), and 0.3 brpm for RR (ARMS 4.4 brpm; LoA –8.4 to 8.9 brpm). Overall, data loss was 42% for SpO2, 33% for RR, and 29% for PR; most data gaps were under 30 min. Patient-reported comfort was high (77%). (4) Conclusions: The earsensor accurately measured SpO2 and PR. RR accuracy was outside the predefined criteria. Despite substantial data loss, patient comfort was high, supporting the potential of PPG-based sensors for unobtrusive vital sign trend monitoring in low-acuity settings.

ObjectivesLow cardiac output syndrome (LCOS) is a serious complication after cardiac surgery. Despite scientific interest in LCOS, there is no uniform definition used in current research and clinicians cannot properly compare different study findings. We aimed to collect the LCOS definitions used in literature and subsequently applied the definitions obtained to existing data to estimate their effect on the intraoperative LCOS incidences in adults, children and infants.DesignThis is a literature review, followed by a retrospective cohort study.SettingThis is a single-institutional study from a university hospital in the Netherlands.ParticipantsPatients from all ages undergoing cardiac surgery with cardiopulmonary bypass between June 2011 and August 2018.InterventionsWe obtained different definitions of LCOS used in the literature and applied these to data obtained from an anesthesia information management system to estimate intraoperative incidences of LCOS. We compared intraoperative incidences of LCOS in different populations based on age (infants, children and adults).Measurements and main resultsThe literature search identified 262 LCOS definitions, that were applied to intraoperative data from 7,366 patients. Using the 10 most frequently published LCOS definitions, the obtained incidence estimates ranged from 0.4 to 82% in infants, from 0.6 to 56% in children and from 1.5 to 91% in adults.ConclusionThere is an important variety in definitions used to describe LCOS. When applied to data obtained from clinical care, these different definitions resulted in large distribution of intraoperative LCOS incidence rates. We therefore advocate for standardization of the LCOS definition to improve clinical understanding and enable adequate comparison of outcomes and treatment effects both in daily care and in research.

Vital signs follow circadian patterns in both healthy volunteers and critically ill patients, which seem to be influenced by disease severity in the latter. In this study we explored the existence of circadian patterns in heart rate, respiratory rate and skin temperature of hospitalized COVID-19 patients, and aimed to explore differences in circadian rhythm amplitude during patient deterioration. We performed a retrospective study of COVID-19 patients admitted to the general ward of a tertiary hospital between April 2020 and March 2021. Patients were continuously monitored using a wireless sensor and fingertip pulse oximeter. Data was divided into three cohorts: patients who recovered, patients who developed respiratory insufficiency and patients who died. For each cohort, a population mean cosinor model was fitted to detect rhythmicity. To assess changes in amplitude, a mixed-effect cosinor model was fitted. A total of 429 patients were monitored. Rhythmicity was observed in heartrate for the recovery cohort (p<0.001), respiratory insufficiency cohort (p<0.001 and mortality cohort (p = 0.002). Respiratory rate showed rhythmicity in the recovery cohort (p<0.001), but not in the other cohorts (p = 0.18 and p = 0.51). Skin temperature also showed rhythmicity in the recovery cohort (p<0.001), but not in the other cohorts (p = 0.22 and p = 0.12). For respiratory insufficiency, only the amplitude of heart rate circadian pattern increased slightly the day before (1.2 (99%CI 0.16-2.2, p = 0.002)). In the mortality cohort, the amplitude of heart rate decreased (-1.5 (99%CI -2.6- -0.42, p<0.001)) and respiratory rate amplitude increased (0.72 (99%CI 0.27-1.3, p = 0.002) the days before death. A circadian rhythm is present in heart rate of COVID-19 patients admitted to the general ward. For respiratory rate and skin temperature, rhythmicity was only found in patients who recover, but not in patients developing respiratory insufficiency or death. We found no consistent changes in circadian rhythm amplitude accompanying patient deterioration.

Background and objectivesIntermittent vital signs measurements are the current standard on hospital wards, typically recorded once every 8 hours. Early signs of deterioration may therefore be missed. Recent innovations have resulted in ‘wearable’ sensors, which may capture patient deterioration at an earlier stage. The objective of this study was to determine whether a wireless ‘patch’ sensor is able to reliably measure respiratory and heart rate continuously in high-risk surgical patients. The secondary objective was to explore the potential of the wireless sensor to serve as a safety monitor.DesignIn an observational methods comparisons study, patients were measured with both the wireless sensor and bedside routine standard for at least 24 hours.SettingUniversity teaching hospital, single centre.ParticipantsTwenty-five postoperative surgical patients admitted to a step-down unit.Outcome measuresPrimary outcome measures were limits of agreement and bias of heart rate and respiratory rate. Secondary outcome measures were sensor reliability, defined as time until first occurrence of data loss.Results1568 hours of vital signs data were analysed. Bias and 95% limits of agreement for heart rate were −1.1 (−8.8 to 6.5) beats per minute. For respiration rate, bias was −2.3 breaths per minute with wide limits of agreement (−15.8 to 11.2 breaths per minute). Median filtering over a 15 min period improved limits of agreement of both respiration and heart rate. 63% of the measurements were performed without data loss greater than 2 min. Overall data loss was limited (6% of time).ConclusionsThe wireless sensor is capable of accurately measuring heart rate, but accuracy for respiratory rate was outside acceptable limits. Remote monitoring has the potential to contribute to early recognition of physiological decline in high-risk patients. Future studies should focus on the ability to detect patient deterioration on low care environments and at home after discharge.

Prediction models may facilitate risk-based management of health care conditions. In a large cluster-randomized trial, presenting calculated risks of postoperative nausea and vomiting (PONV) to physicians (assistive approach) increased risk-based management of PONV. This increase did not improve patient outcome—that is, PONV incidence. This prompted us to explore how prediction tools guide the decision-making process of physicians. Using mixed methods, we interviewed eight physicians to understand how predicted risks were perceived by the physicians and how they influenced decision making. Subsequently, all 57 physicians of the trial were surveyed for how the presented risks influenced their perceptions. Although the prediction tool made physicians more aware of PONV prevention, the physicians reported three barriers to use predicted risks in their decision making. PONV was not considered an outcome of utmost importance; decision making on PONV prophylaxis was mostly intuitive rather than risk based; prediction models do not weigh benefits and risks of prophylactic drugs. Combining probabilistic output of the model with their clinical experience may be difficult for physicians, especially when their decision-making process is mostly intuitive. Adding recommendations to predicted risks (directive approach) was considered an important step to facilitate the uptake of a prediction tool.

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. 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. 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. 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.

PurposeAltered respiratory rate (RR) has been identified as an important predictor of serious adverse events during hospitalization. Introduction of a well-tolerated continuous RR monitor could potentially reduce serious adverse events such as opioid-induced respiratory depression. The purpose of this study was to investigate the ability of different monitor devices to detect RR in low care clinical settings.MethodsThis was a prospective method-comparison study with a cross-sectional design. Thoracic impedance pneumography (IPG), frequency modulated continuous wave radar, and an acoustic breath sounds monitor were compared with the gold standard of capnography for their ability to detect RR in breaths per minute (breaths·min−1) in awake postoperative patients in the postanesthesia care unit. The Bland and Altman method for repeated measurements and mixed effect modelling was used to obtain bias and limits of agreement (LoA). Furthermore, the ability of the three devices to assist with correct treatment decisions was evaluated in Clarke Error Grids.ResultsTwenty patients were monitored for 1,203 min, with a median [interquartile range] of 61 [60-63] min per patient. The bias (98.9% LoA) were 0.1 (−7.9 to 7.9) breaths·min−1 for the acoustic monitor, −1.6 (−10.8 to 7.6) for the radar, and −1.9 (−13.1 to 9.2) for the IPG. The extent to which the monitors guided adequate or led to inadequate treatment decisions (determined by Clarke Error Grid analysis) differed significantly between the monitors (P = 0.011). Decisions were correct 96% of the time for acoustic, 95% of the time for radar, and 94% of the time for IPG monitoring devices.ConclusionsNone of the studied devices (acoustic, IPG, and radar monitor) had LoA that were within our predefined (based on clinical judgement) limits of ± 2 breaths·min−1. The acoustic breath sound monitor predicted the correct treatment more often than the IPG and the radar device.

Failure to recognize acute deterioration in hospitalized patients may contribute to cardiopulmonary arrest, unscheduled intensive care unit admission and increased mortality. In this systematic review we aimed to determine whether continuous non-invasive respiratory monitoring improves early diagnosis of patient deterioration and reduces critical incidents on hospital wards. Studies were retrieved from Medline, Embase, CINAHL, and the Cochrane library, searched from 1970 till October 25, 2014. Electronic databases were searched using keywords and corresponding synonyms 'ward', 'continuous', 'monitoring' and 'respiration'. Pediatric, fetal and animal studies were excluded. Since no validated tool is currently available for diagnostic or intervention studies with continuous monitoring, methodological quality was assessed with a modified tool based on modified STARD, CONSORT, and TREND statements. Six intervention and five diagnostic studies were included, evaluating the use of eight different devices for continuous respiratory monitoring. Quantitative data synthesis was not possible because intervention, study design and outcomes differed considerably between studies. Outcomes estimates for the intervention studies ranged from RR 0.14 (0.03, 0.64) for cardiopulmonary resuscitation to RR 1.00 (0.41, 2.35) for unplanned ICU admission after introduction of continuous respiratory monitoring. The methodological quality of most studies was moderate, e.g. 'before-after' designs, incomplete reporting of primary outcomes, and incomplete clinical implementation of the monitoring system. Based on the findings of this systematic review, implementation of routine continuous non-invasive respiratory monitoring on general hospital wards cannot yet be advocated as results are inconclusive, and methodological quality of the studies needs improvement. Future research in this area should focus on technology explicitly suitable for low care settings and tailored alarm and treatment algorithms.

Background: To ensure availability of hospital beds and improve COVID-19 patients’ well-being during the ongoing pandemic, hospital care could be offered at home. Retrospective studies show promising results of deploying remote hospital care to reduce the number of days spent in the hospital, but the beneficial effect has yet to be established. Methods: We conducted a single centre, randomised trial from January to June 2021, including hospitalised COVID-19 patients who were in the recovery stage of the disease. Hospital care for the intervention group was transitioned to the patient’s home, including oxygen therapy, medication and remote monitoring. The control group received in-hospital care as usual. The primary endpoint was the number of hospital-free days during the 30 days following randomisation. Secondary endpoints included health care consumption during the follow-up period and mortality. Results: A total of 62 patients were randomised (31 control, 31 intervention). The mean difference in hospital-free days was 1.7 (26.7 control vs. 28.4 intervention, 95% CI of difference −0.5 to 4.2, p = 0.112). In the intervention group, the index hospital length of stay was 1.6 days shorter (95% CI −2.4 to −0.8, p < 0.001), but the total duration of care under hospital responsibility was 4.1 days longer (95% CI 0.5 to 7.7, p = 0.028). Conclusion: Remote hospital care for recovering COVID-19 patients is feasible. However, we could not demonstrate an increase in hospital-free days in the 30 days following randomisation. Optimising the intervention, timing, and identification of patients who will benefit most from remote hospital care could improve the impact of this intervention.

IntroductionCongenital heart disease (CHD) is a major cause of infant mortality. Many infants with CHD require corrective surgery with most operations requiring cardiopulmonary bypass (CPB). CPB triggers a systemic inflammatory response which is associated with low cardiac output syndrome (LCOS), postoperative morbidity and mortality. Delivery of nitric oxide (NO) into CPB circuits can provide myocardial protection and reduce bypass-induced inflammation, leading to less LCOS and improved recovery. We hypothesised that using NO during CPB increases ventilator-free days (VFD) (the number of days patients spend alive and free from invasive mechanical ventilation up until day 28) compared with standard care. Here, we describe the NITRIC trial protocol.Methods and analysisThe NITRIC trial is a randomised, double-blind, controlled, parallel-group, two-sided superiority trial to be conducted in six paediatric cardiac surgical centres. One thousand three-hundred and twenty infants <2 years of age undergoing cardiac surgery with CPB will be randomly assigned to NO at 20 ppm administered into the CPB oxygenator for the duration of CPB or standard care (no NO) in a 1:1 ratio with stratification by age (<6 and ≥6 weeks), single ventricle physiology (Y/N) and study centre. The primary outcome will be VFD to day 28. Secondary outcomes include a composite of LCOS, need for extracorporeal membrane oxygenation or death within 28 days of surgery; length of stay in intensive care and in hospital; and, healthcare costs. Analyses will be conducted on an intention-to-treat basis. Preplanned secondary analyses will investigate the impact of NO on host inflammatory profiles postsurgery.Ethics and disseminationThe study has ethical approval (HREC/17/QRCH/43, dated 26 April 2017), is registered in the Australian New Zealand Clinical Trials Registry (ACTRN12617000821392) and commenced recruitment in July 2017. The primary manuscript will be submitted for publication in a peer-reviewed journal.Trial registration numberACTRN12617000821392