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.

Rationale: 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.Methods: 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.Results: 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.Conclusion: 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: Virtual hospital-at-home care might be an alternative to standard hospital care for patients with infectious diseases. In this study, we explore the potential for virtual hospital-at-home care and a potential design for this population. Methods: This was a retrospective cohort study of internal medicine patients suspected of infectious diseases, admitted between 1 January and 31 December 2019. We collected information on delivered care during emergency department visits, the first 24 h, between 24 and 72 h, and after 72 h of admission. Care components that could be delivered at home were combined into care packages, and the potential number of eligible patients per package was described. The most feasible package was described in detail. Results: 763 patients were included, mostly referred for general internal medicine (35%), and the most common diagnosis was lower respiratory tract infection (27%). The most frequently administered care components were laboratory tests, non-oral medication, and intercollegiate consultation. With a combination of telemonitoring, video consultation, non-oral medication administration, laboratory tests, oxygen therapy, and radiological diagnostics, 48% of patients were eligible for hospital-at-home care, with 35% already eligible directly after emergency department visits. Conclusion: While the potential for virtual hospital-at-home care is high, it depends greatly on which care can be arranged.

A malfunction of the innate immune response in COVID-19 is associated with eosinopenia, particularly in more severe cases. This study tested the hypothesis that this eosinopenia is COVID-19 specific and is associated with systemic activation of eosinophils. Blood of 15 healthy controls and 75 adult patients with suspected COVID-19 at the ER were included before PCR testing and analyzed by point-of-care automated flow cytometry (CD10, CD11b, CD16, and CD62L) in the absence or presence of a formyl peptide (fNLF). Forty-five SARS-CoV-2 PCR positive patients were grouped based on disease severity. PCR negative patients with proven bacterial (n = 20) or other viral (n = 10) infections were used as disease controls. Eosinophils were identified with the use of the FlowSOM algorithm. Low blood eosinophil numbers (<100 cells/μL; p < 0.005) were found both in patients with COVID-19 and with other infectious diseases, albeit less pronounced. Two discrete eosinophil populations were identified in healthy controls both before and after activation with fNLF based on the expression of CD11b. Before activation, the CD11b^bright population consisted of 5.4% (CI_95% = 3.8, 13.4) of total eosinophils. After activation, this population of CD11b^bright cells comprised nearly half the population (42.21%, CI_95% = 35.9, 54.1). Eosinophils in COVID-19 had a similar percentage of CD11b^bright cells before activation (7.6%, CI_95% = 4.5, 13.6), but were clearly refractory to activation with fNLF as a much lower percentage of cells end up in the CD11b^bright fraction after activation (23.7%, CI_95% = 18.5, 27.6; p < 0.001). Low eosinophil numbers in COVID-19 are associated with refractoriness in responsiveness to fNLF. This might be caused by migration of fully functional cells to the tissue.

Continuous remote monitoring of vital signs on the hospital ward gained popularity during the Severe Acute Respiratory Syndrome coronavirus 2 pandemic due to its ability to support early detection of respiratory failure, and the possibility to do so without physical contact between patient and clinician. The effect of continuous monitoring on patient room visits has not been established yet. To assess the impact of continuous monitoring on the number of patient room visits for patients suspected of Corona Virus Disease 2019 (COVID-19) and the use of personal protection equipment. We performed a before-after study at a ward with private rooms for patients suspected of COVID-19 at a tertiary hospital in Nijmegen, The Netherlands. Non-participant observers observed hospital staff during day, evening and night shifts to record patient room visits and personal protection equipment usage. After eleven days, wearable continuous vital sign monitoring was introduced. An interrupted time series analysis was applied to evaluate the effect of continuous monitoring on the number of patient room visits, visits for obtaining vital signs (Modified Early Warning Score visits) and the amount of personal protection equipment used. During the 45 day study period, 86 shifts were observed. During each shift, approximately six rooms were included. A total of 2347 patient room visits were recorded. The slope coefficient for the number of patient room visits did not change after introducing continuous vital sign monitoring (B -0.003, 95% confidence interval -0.022/0.016). The slope coefficients of the number of Modified Early Warning Score visits and the amount of personal protection equipment used did not change either (B -0.002, 95% confidence interval -0.021/0.017 and B 0.046, 95% confidence interval -0.008/0.099). The number of Modified Early Warning Score visits did show a decline over the entire study period, however this decline was not influenced by the intervention. Evening and night shifts were associated with fewer patient room visits compared to day shifts. Introduction of continuous vital sign monitoring at a general ward for patients with suspected COVID-19 did not reduce the number of patient room visits or the usage of personal protection equipment by hospital staff. The number of Modified Early Warning Score visits declined over time, but this was not related to the introduction of continuous monitoring. Detailed analysis of the influence of continuous monitoring on the workflow of hospital staff reveals key points to increase efficacy of this intervention.

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.