key: cord-1040837-06k2qz0d authors: Boniface, M.; Burns, D.; Duckworth, C.; Duruiheoma, F.; Armitage, H.; Ratcliffe, N.; Duffy, J.; O'Keeffe, C.; Inada-Kim, M. title: COVID Oximetry @home: evaluation of patient outcomes date: 2021-06-02 journal: nan DOI: 10.1101/2021.05.29.21257899 sha: 89344b9714b98b123212a322467b23c1db648c19 doc_id: 1040837 cord_uid: 06k2qz0d Background: COVID-19 has placed unprecedented demands on hospitals. A clinical service, COVID Oximetry @home (CO@h) was launched in November 2020 to support remote monitoring of COVID-19 patients in the community. Remote monitoring through CO@h aims to identify early patient deterioration and provide timely escalation for cases of silent hypoxia, while reducing the burden on secondary care. Methods: We conducted a retrospective service evaluation of COVID-19 patients onboarded to CO@h from November 2020 to March 2021 in the North Hampshire (UK) community led service (a collaboration of 15 GP practices, covering a population of 230,000 people). We have compared outcomes for patients admitted to Basingstoke & North Hampshire Hospital who were CO@h patients (COVID-19 patients with monitoring of SpO2 (n=137)), with non CO@h patients (those directly admitted without being monitored by CO@h (n=633)). Odds Ratio analysis was performed to contrast the likelihood of patient outcomes resulting in 30 day mortality, ICU admission and length of stay greater than 3, 7, 14, and 28 days. Results: Hospital length of stay was reduced by an average of 6.3 days for CO@h patients (6.9 95% CI [5.6 - 8.1]) in comparison to Non-CO@h (13.2 95% CI [12.2 - 14.1]). The most significant odds ratio effect was on mortality (0.23 95%CI [0.11 - 0.49]), followed by length of stay > 14 days (OR 0.23 95%CI [0.13 - 0.42]), length of stay > 28 days (OR 0.23 95%CI [0.08 - 0.65]), length of stay > 7 days (OR 0.35 95%CI [0.24 - 0.52]), and length of stay > 3 days (OR 0.52 95%CI [0.35 - 0.78]). Mortality and length of stay outcomes were statistically significant. Only 5/137 (3.6%) where admitted to ICU compared with 52/633 (8.2%) for Non-CO@h. Conclusions: CO@h has demonstrated considerably improved patient outcomes reducing the odds of longer length hospital stays and mortality. The rapidly evolving COVID-19 pandemic has been responsible for 3.4 million deaths worldwide (World Health Organization, 2021) and has placed unprecedented strain on healthcare systems. A significant proportion of patients hospitalised with acute COVID-19 have severe hypoxia (very low blood oxygen saturation) frequently presenting 'silently' (i.e. without breathlessness). Silent hypoxia is an independent indicator of worse patient outcomes (O'Carroll, et al., 2020) (Brouqui, et al., 2021) , and delayed presentations of severe COVID-19; often leading to extended hospital stays, higher risk of ICU admission, and higher mortality rates (Vindrola-Padros, et al., 2020) . UK guidelines recommend that patient acuity should therefore be assessed with the use of pulse oximetry (i.e. monitoring oxygen saturation) when diagnosed with COVID-19 (NHS England, 2020) (Inada-Kim, et al., 2020) . A clinical service COVID Oximetry @home (CO@h) was launched November 2020 within a COVID-19 Integrated Care Pathway to support remote monitoring of COVID-19 patients by primary care and timely escalation to secondary care. Remote home monitoring through CO@h have been implemented to 1) maintain NHS capacity, 2) decrease nosocomial COVID-19 transmission, and 3) identify early patient deterioration and provide timely escalation to reduce hospital length of stay, and mortality from silent hypoxia (Stockly, 2020) (Boniface, Zlatev, Guerrero-Luduena, & Armitage, 2020) . In its original definition, a CO@h Service consists of two fundamental components: (1) using a predictive model to identify individual patients in a population who are at high risk of future unplanned hospital admission; and (2) offering these people a period of intensive, multidisciplinary, case management at home using the systems, staffing and daily routine (Lewis et al., 2006) (Lewis, et al., 2013) . Patients are referred by clinical services responsible for operating CO@h services then triaged prior to onboarding for remote monitoring to ensure that the CO@h offers an appropriate level of care. Even though the CO@h service is virtual, it is integrated into a care partnership with physical care for patient assessment (e.g. COVID-19 testing and initial observations) when deemed clinically appropriate. A schematic of the Integrated Care Partnership for North Hampshire is shown in Figure 1 , highlighting the important relationship between physical and virtual services in the overall delivery of care. This report outlines a quality improvement (QI) initiative to implement CO@h service in North Hampshire and retrospectively evaluate their efficacy. Through adoption of the Plan-Do-Study-Act (PDSA) framework and using evidence-based practice, the CO@h services were continually improved as a rapid response to the evolving pandemic. Evidence-based practice was enabled by close communication between professional analysts, who provided data insight, and healthcare professionals, who provided operational insight. CO@h went live in November 2020 although it should be noted that some pilot services were operational in North Hampshire during the 1st wave of the pandemic from April 2020 to June 2020. The data relating to CO@h pilots during the 1st wave period was not available for analysis. In this report we aim to disseminate the effectiveness of the CO@h service implemented in the Integrated Care Partnership of North Hampshire. To our knowledge, this is the first QI initiative directly reporting outcomes for COVID-19 patients treated virtually for an NHS Trust. Our findings will be of interest to healthcare organisations looking to implement further CO@h services as a response to the ongoing pandemic. All patients with suspected COVID-19 admitted to North Hants CO@h or Basingstoke and North Hants Hospital between 1 November 2020 to 31 March 2021 were eligible for inclusion. CO@h patients were then linked to their subsequent hospital admissions. Confirmed COVID-19 cases were then identified from these suspected cases by requiring at least one SARS-CoV-2 positive test associated with the admission. We separated our cohort into an intervention group, where patients had at least one referral to the CO@h service, and a control group, where patients have not had such a referral. There was a total of 1496 patients, with 783 patients in the intervention group and 713 patients in the control group. To evaluate the outcomes between comparable groups, we required that each patient in the intervention group had at least one hospital admission via the emergency department. We therefore excluded patients in the intervention group who returned a negative COVID test result following referral to CO@h (n=35) and those whose outcomes did not result in escalation to hospital (n=611), leaving 137 CO@h patients that were escalated to hospital. For the control group we excluded COVID-19 patients (n=80) admitted from hospital locations other than the emergency department to reduce confounding factors between the intervention and control groups: patients in this group may have already been admitted to hospital with complex ongoing acute All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 2, 2021. ; https://doi.org/10. 1101 care needs in addition to COVID-19 resulting in an increased likelihood for negative outcomes, such as longer length of stay and mortality. This left 633 patients in the Non-CO@h control group, of which 55 patients were readmitted to hospital within 30 days of first admission. For readmissions an episode of care was created aggregating length of stay over multiple admissions and where the patient outcome was deemed to be the outcome from the last admission event. We evaluated the CO@h service using a comparison of increasingly acute outcomes associated with COVID-19 patient trajectory for those with a positive COVID-19 test who required admission to hospital. We considered the following outcomes: 30 day mortality, ICU admission, and whether the length of stay in hospital was above 3 days, 7 days, 14 days, or 28 days. We identified a death through hospital medical records. We identified ICU admissions through a specific electronic patient record flag. The length of stay was computed from the point of hospital admission to discharge. We have linked data from primary care systems operated by CO@h with secondary care systems operated by Hampshire Hospitals NHS Foundation Trust (HHFT) to create a database supporting analysis of the full trajectory of COVID episodes. The linking included CO@h service and hospital admission records. To ensure that the data used is of a satisfactory quality, we subsequently excluded admissions that did not meet the following criteria: for each admission record, we required that admission date must be equal to or before the discharge date; the discharge date and patient outcome must be recorded; and that any duplicate records were removed. There were 2 hospital admission records with a discharge date before the corresponding admission date, and 11 admissions without a discharge date or outcome. There were 2 instances of duplicates: 1 duplicate set of CO@h referral records, and 1 duplicate set of hospital admission records. The dataset also contains some hospital admission records for which the date of admission is prior to discharge from a CO@h service. In some cases, patients admitted to hospital were not discharged from CO@h immediately. If the hospital admission was likely to only be less than 24hrs (i.e., Same Day Emergency Care), patients would continue intervention by CO@h seamlessly as they left hospital. If hospital admission was longer than 24 hours, patients were discharged from CO@h, and in some cases referred back to CO@h following hospital discharge. Patients were also given advice to contact 999, or another service based on their self-submitted readings. In some cases, the patient would then be conveyed and admitted to hospital ahead of discharge from the CO@h service. In all cases, we include those patients within our intervention group as they have received the CO@h intervention. For each outcome, we have produced contingency tables and calculated the odds ratio with 95% confidence intervals. We have calculated the empirical cumulative distribution function for length of stay in the intervention and control groups, in addition to their smoothed distribution functions (calculated by kernel density estimate with a Gaussian kernel restricted to positive values). Analysis was performed in Python v3.9.4 (using pandas, seaborn, statsmodels). All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 2, 2021. This service evaluation did not require ethics approval. The study was however evaluated by the University of Southampton Ethics Committee (REF ERGO/61242) and approved as a service evaluation following Data Protection Impact Assessment and establishment of Data Sharing Agreements. NHS England and NHS Improvement have been given legal notice by the Secretary of State for Health and Social Care to support the processing and sharing of information to help the COVID-19 response under Health Service Control of Patient Information Regulations 2002 (COPI). This is to ensure that confidential patient information can be used and shared appropriately and lawfully for purposes related to the COVID-19 response. Data were extracted from medical records by clinicians providing care for the patients and an anonymised extract of the data were provided to the team at the University of Southampton. Due to information governance concerns, the data will not be made public. However, it may be made accessible via reasonable request to the corresponding author. The North Hants CO@h service treated 783 patients of which 137 patients were subsequently escalated and admitted to hospital. The Basingstoke and North Hants hospital had a further 692 admissions (633 individual patients) directly via the emergency department who were not treated by the CO@h service. The uptake of the CO@h service is shown in Figure 3 . The ratio of CO@h referrals to the number of hospital admissions increased consistently throughout the period, with a ratio of 30/113 (1:4) during November 2020, to 85/21 (4:1) during March 2021. The uptake reached a maximum of 363 referrals All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted June 2, 2021. ; https://doi. org/10.1101 org/10. /2021 during January 2021 coinciding with the increase in COVID-19 prevalence during that period. Table 1 shows the age and gender demographics for patients in the intervention and control groups as ascertained from by medical records. Distributions representing the length of stay for both the intervention and control groups are shown in Figure 3 . The 80 th percentile length of stay for the intervention group was 9 days, 10 days shorter than the 21 days for the control group. The mean length of stay was 6.9 days (95% CI 5.6 -8.1 days) in the intervention group, and 13.2 days (95% CI 12.2 -14.1 days) in the control group. The CO@h initiative was implemented nationally to protect patients by improving early recognition of deterioration in COVID-19 and to protect the healthcare system from being overwhelmed with inappropriate admissions. The CO@h service was universally implemented across England (by Feb 2021) over a period of 3 months. In this QI initiative we focussed on evaluating the efficacy of CO@h by retrospectively evaluating COVID-19 patient outcomes for those admitted to CO@h in North Hants Primary Care Network and subsequently being admitted to hospital. The initiative achieved its aim with statistically significant reductions in length of stay, ICU admission, and mortality rate for patients admitted to hospital via the CO@h pathway relative to direct hospital admission. Patients hospitalised with acute COVID-19 are at severe risk of hypoxia, which can commonly present without breathlessness (Greenhalgh, et al., 2021) . The benefits of identifying hypoxia early are clear (Knight & al., 2020) , however to our knowledge, this QI service evaluation is the first to demonstrate that CO@h has resulted in improved patient outcomes for an NHS trust (i.e., mortality, ICU admission and length of stay). This CO@h service was implemented as part of the national framework (NHS England, 2020) and therefore these findings should be of interest to future CO@h operations in response to the pandemic. CO@h services have now been provisioned internationally, for example, StepOne have applied the CO@h model to support intervention of COVID patients across 16 states in India (George Institute, 2021). COVID-19 is endemic worldwide, and hence, there is an urgent need for optimised early identification of patient deterioration for patients at home. As healthcare systems aim to restore elective activity, the backlog of non-COVID patients requiring intervention is stark. In England, the British Medical Association estimates there were 3.37 million fewer elective procedures and 21.4 million fewer outpatient attendances between April 2020 and March 2021 (Association, 2021). The COVID-19 pandemic has generated research into effective and streamlined patient care which has ramifications beyond the context of the pandemic. CO@h is one aspect of the nationally led programme NHS @home; which aims to maximise the use of technology to support more people to better self-manage their health and care at home (NHS England, NHS @home, 2021). With access to more timely preventative care, patient burden on both primary and emergency care can be reduced while providing patients more personalised intervention. In particular, home pulse oximetry has long been used in primary care settings as a cost-effective approach to monitor chronic lung conditions and heart disease (Plüddemann, Thompson, Heneghan, & Price, 2011) , and there is a growing evidence base for the model's effectiveness and safety in COVID-19 (Vindrola-Padros, et al., 2020 ) (Inada-Kim, et al., 2020 (Boniface, Zlatev, Guerrero-Luduena, & Armitage, 2020 ) (Greenhalgh, et al., 2021 ) (Nunan, et al., 2020) . Further prospective studies are required to understand how remote monitoring can be implemented in wider contexts, potentially focussed on high risk patients with significant comorbidities. These findings must be understood in light of their limitations. CO@h was rapidly developed in response to the pandemic, and as a result, the improvement cycles were conducted at pace. PDSA quality improvement was conducted using evidence-based practice, where insights were provided by data professionals to clinicians. A multi-disciplinary team of healthcare professionals, QI personnel, and data scientists met frequently to discuss patient care, and CO@h efficacy. Operational improvements were implemented through these discussions to deliver continual improvement especially procedures relating to integrated services between conveyance, CO@h and hospitals. Formally, the distinct improvement cycles were as follows: (1) CO@h service pilots (1 st wave of the pandemic: March 2020 to July 2020) including community COVID-19 assessment centres implemented without remote monitoring beyond paper diaries and phone, treating n=1600 suspected-COVID patients and escalating n=105 to hospital; and support by hospital Same Day Emergency Care and care homes telemedicine services (2) NHS Trust-wide implementation of CO@h (2 nd wave of the pandemic: November 2020 to March 2021) with efficacy evaluation presented here. Finally, this service evaluation is for an integrated community care pathway and a single hospital trust, therefore generalisation is limited by population size and clinical setting. COVID Oximetry @home (CO@h) has demonstrated significantly improved patient survival, while increasing hospital capacity. To our knowledge, this is the first QI report concerning the efficacy of CO@h service and evaluation of mortality, ICU admission, and length of stay benefits for an NHS Hospital Trust. COVID-19 patients admitted to the CO@h service have been found to have significantly reduced odds of longer length hospital stays, and mortality. These findings demonstrate that, despite the studies limitations, CO@h should be considered nationally and internationally in response to the ongoing pandemic and that larger evaluations of efficacy and quality should be undertaken. 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The British journal of general practice : the journal of the RCGP paper on Virtual Wards, Silent Hypoxia and improving COVID Remote home monitoring (virtual wards) during the COVID-19 pandemic: a systematic review WHO Coronavirus (COVID-19) Dashboard We thank Claire Parker from Hampshire, Southampton & Isle of Wight Clinical Commissioning Group for their support during the specification, delivery, and evaluation of CO@h. We extend our special thanks to the CO@h Clinical Team who cared for patients and collated data including Alison Hullah (Lead Advanced Nurse Practitioner), Roisin Howell (Lead Advanced Nurse Practitioner) and Giselle Beaumont (Care Coordinator). This report received funding from the NHSX RECOxCARE (Remote oximetry in community care for COVID-19 patients) project and from NHS England to support data collection. The views expressed in this publication are those of the author(s) and not necessarily those of the funders.