Factors associated with enrolment into a national COVID-19 pulse oximetry remote monitoring programme in England: a retrospective observational study.

BACKGROUND: Hypoxaemia is an important predictor of severity in individuals with COVID-19 and can present without symptoms. The COVID Oximetry @home (CO@h) programme was implemented across England in November, 2020, providing pulse oximeters to higher-risk people with COVID-19 to enable early detection of deterioration and the need for escalation of care. We aimed to describe the clinical and demographic characteristics of individuals enrolled onto the programme and to assess whether there were any inequalities in enrolment. METHODS: This retrospective observational study was based on data from a cohort of people resident in England recorded as having a positive COVID-19 test between Oct 1, 2020, and May 3, 2021. The proportion of participants enrolled onto the CO@h programmes in the 7 days before and 28 days after a positive COVID-19 test was calculated for each clinical commissioning group (CCG) in England. Two-level hierarchical multivariable logistic regression with random intercepts for each CCG was run to identify factors predictive of being enrolled onto the CO@h programme. FINDINGS: CO@h programme sites were reported by NHS England as becoming operational between Nov 21 and Dec 31, 2020. 1 227 405 people resident in 72 CCGs had a positive COVID-19 test between the date of programme implementation and May 3, 2021, of whom 19 932 (1·6%) were enrolled onto the CO@h programme. Of those enrolled, 14 441 (72·5%) were aged 50 years or older or were identified as clinically extremely vulnerable (ie, having a high-risk medical condition). Higher odds of enrolment onto the CO@h programme were found in older individuals (adjusted odds ratio 2·21 [95% CI 2·19-2·23], p<0·001, for those aged 50-64 years; 3·48 [3·33-3·63], p<0·001, for those aged 65-79 years; and 2·50 [2·34-2·68], p<0·001, for those aged ≥80 years), in individuals of non-White ethnicity (1·35 [1·28-1·43], p<0·001, for Asian individuals; 1·13 [1·04-1·22], p=0·005, for Black individuals; and 1·17 [1·03-1·32], p=0·015, for those of mixed ethnicity), in those who were overweight (1·31 [1·26-1·37], p<0·001) or obese (1·69 [1·63-1·77], p<0·001), or in those identified as clinically extremely vulnerable (1·58 [1·51-1·65], p<0·001), and lower odds were reported in those from the least socioeconomically deprived areas compared with those from the most socioeconomically deprived areas (0·75 [0·69-0·81]; p<0·001). INTERPRETATION: Nationally, uptake of the CO@h programme was low, with clinical judgment used to determine eligibility. Preferential enrolment onto the pulse oximetry monitoring programme was observed in people known to be at the highest risk of developing severe COVID-19. FUNDING: NHS England, National Institute for Health Research, and The Wellcome Trust.

Evaluating the impact of a pulse oximetry remote monitoring programme on mortality and healthcare utilisation in patients with COVID-19 assessed in emergency departments in England: a retrospective matched cohort study.

BACKGROUND: To identify the impact of enrolment onto a national pulse oximetry remote monitoring programme for COVID-19 (COVID-19 Oximetry @home; CO@h) on health service use and mortality in patients attending Emergency Departments (EDs). METHODS: We conducted a retrospective matched cohort study of patients enrolled onto the CO@h pathway from EDs in England. We included all patients with a positive COVID-19 test from 1 October 2020 to 3 May 2021 who attended ED from 3 days before to 10 days after the date of the test. All patients who were admitted or died on the same or following day to the first ED attendance within the time window were excluded. In the primary analysis, participants enrolled onto CO@h were matched using demographic and clinical criteria to participants who were not enrolled. Five outcome measures were examined within 28 days of first ED attendance: (1) Death from any cause; (2) Any subsequent ED attendance; (3) Any emergency hospital admission; (4) Critical care admission; and (5) Length of stay. RESULTS: 15 621 participants were included in the primary analysis, of whom 639 were enrolled onto CO@h and 14 982 were controls. Odds of death were 52% lower in those enrolled (95% CI 7% to 75%) compared with those not enrolled onto CO@h. Odds of any ED attendance or admission were 37% (95% CI 16% to 63%) and 59% (95% CI 32% to 91%) higher, respectively, in those enrolled. Of those admitted, those enrolled had 53% (95% CI 7% to 76%) lower odds of critical care admission. There was no significant impact on length of stay. CONCLUSIONS: These findings indicate that for patients assessed in ED, pulse oximetry remote monitoring may be a clinically effective and safe model for early detection of hypoxia and escalation. However, possible selection biases might limit the generalisability to other populations.

Population-level impact of a pulse oximetry remote monitoring programme on mortality and healthcare utilisation in the people with COVID-19 in England: a national analysis using a stepped wedge design.

BACKGROUND: To identify the population-level impact of a national pulse oximetry remote monitoring programme for COVID-19 (COVID Oximetry @home (CO@h)) in England on mortality and health service use. METHODS: We conducted a retrospective cohort study using a stepped wedge pre-implementation and post-implementation design, including all 106 Clinical Commissioning Groups (CCGs) in England implementing a local CO@h programme. All symptomatic people with a positive COVID-19 PCR test result from 1 October 2020 to 3 May 2021, and who were aged ≥65 years or identified as clinically extremely vulnerable were included. Care home residents were excluded. A pre-intervention period before implementation of the CO@h programme in each CCG was compared with a post-intervention period after implementation. Five outcome measures within 28 days of a positive COVID-19 test: (i) death from any cause; (ii) any ED attendance; (iii) any emergency hospital admission; (iv) critical care admission and (v) total length of hospital stay. RESULTS: 217 650 people were eligible and included in the analysis. Total enrolment onto the programme was low, with enrolment data received for only 5527 (2.5%) of the eligible population. The period of implementation of the programme was not associated with mortality or length of hospital stay. The period of implementation was associated with increased health service utilisation with a 12% increase in the odds of ED attendance (95% CI: 6% to 18%) and emergency hospital admission (95% CI: 5% to 20%) and a 24% increase in the odds of critical care admission in those admitted (95% CI: 5% to 47%). In a secondary analysis of CO@h sites with at least 10% or 20% of eligible people enrolled, there was no significant association with any outcome measure. CONCLUSION: At a population level, there was no association with mortality before and after the implementation period of the CO@h programme, and small increases in health service utilisation were observed. However, lower than expected enrolment is likely to have diluted the effects of the programme at a population level.

Assessing the safety of home oximetry for COVID-19: a multisite retrospective observational study.

OBJECTIVES: To determine the safety and effectiveness of home oximetry monitoring pathways for patients with COVID-19 in the English National Health Service. DESIGN: Retrospective, multisite, observational study of home oximetry monitoring for patients with suspected or proven COVID-19. SETTING: This study analysed patient data from four COVID-19 home oximetry pilot sites in England across primary and secondary care settings. PARTICIPANTS: A total of 1338 participants were enrolled in a home oximetry programme across four pilot sites. Participants were excluded if primary care data and oxygen saturations at rest at enrolment were not available. Data from 908 participants were included in the analysis. INTERVENTIONS: Home oximetry monitoring was provided to participants with a known or suspected diagnosis of COVID-19. Participants were enrolled following attendance to emergency departments, hospital admission or referral through primary care services. RESULTS: Of 908 patients enrolled into four different COVID-19 home oximetry programmes in England, 771 (84.9%) had oxygen saturations at rest of 95% or more, and 320 (35.2%) were under 65 years of age and without comorbidities. 52 (5.7%) presented to hospital and 28 (3.1%) died following enrolment, of which 14 (50%) had COVID-19 as a named cause of death. All-cause mortality was significantly higher in patients enrolled after admission to hospital (OR 8.70 (2.53-29.89)), compared with those enrolled in primary care. Patients enrolled after hospital discharge (OR 0.31 (0.15-0.68)) or emergency department presentation (OR 0.42 (0.20-0.89)) were significantly less likely to present to hospital than those enrolled in primary care. CONCLUSIONS: This study finds that home oximetry monitoring can be a safe pathway for patients with COVID-19; and indicates increases in risk to vulnerable groups and patients with oxygen saturations <95% at enrolment, and in those enrolled on discharge from hospital. Findings from this evaluation have contributed to the national implementation of home oximetry across England.

The take home naloxone intervention multicentre emergency setting feasibility (TIME) trial: an early perspective from one UK ambulance service

BackgroundDrug poisoning deaths in England and Wales have increased by 52% since 2011 with over half involving opioids. Deaths are preventable if naloxone is administered in time. Take Home Naloxone (THN) kits have been distributed through drug services;however, uptake is low and effectiveness unproven. The TIME trial tests the feasibility of conducting a full randomised controlled trial of providing THN administration and basic life support training to high-risk opioid-users in emergency care settings.MethodsA multi-site feasibility trial commenced in June 2019 with two hospitals and their surrounding ambulance services (Bristol Royal Infirmary (BRI) with South Western Ambulance NHS Foundation Trust (SWASFT) and Hull Royal Infirmary with Yorkshire Ambulance Service) randomly allocated to intervention arms;and sites in Wrexham and Sheffield allocated as ‘usual care’ controls. SWASFT began recruiting in October 2019 with the aim of recruiting and training 50% (n=111) of paramedics working within the BRI’s catchment area, to supply THN to at least 100 eligible patients during a 12-month period.ResultsThe trial was suspended between 17.03.2020-06.08.2020 and extended to 01.03.2021 (COVID-19). Despite this, 121 SWASFT paramedics undertook TIME training. TIME trained paramedics attended 30% (n=57) of the n=190 opioid-related emergency calls requiring naloxone administration during the study period. A total of n=29 potentially eligible patients were identified before and n=28 after the COVID-19 suspension. Two patients were supplied with THN during each period. During the COVID-19 suspension, twenty-two potentially eligible patients were missed. The majority of eligible patients presented with a reduced consciousness level, preventing recruitment (73%;n=42/48). These patients were transported to hospital for further treatment (n=39) or died on scene following advanced life support (n=3).ConclusionsThe lowered consciousness levels of prehospital emergency ambulance patients who present with opioid poisoning, often prevent the delivery of training required to enable the supply of THN.

Retrospective cohort study of admission timing and mortality following COVID-19 infection in England.

OBJECTIVES: We investigated whether the timing of hospital admission is associated with the risk of mortality for patients with COVID-19 in England, and the factors associated with a longer interval between symptom onset and hospital admission. DESIGN: Retrospective observational cohort study of data collected by the COVID-19 Hospitalisation in England Surveillance System (CHESS). Data were analysed using multivariate regression analysis. SETTING: Acute hospital trusts in England that submit data to CHESS routinely. PARTICIPANTS: Of 14 150 patients included in CHESS until 13 May 2020, 401 lacked a confirmed diagnosis of COVID-19 and 7666 lacked a recorded date of symptom onset. This left 6083 individuals, of whom 15 were excluded because the time between symptom onset and hospital admission exceeded 3 months. The study cohort therefore comprised 6068 unique individuals. MAIN OUTCOME MEASURES: All-cause mortality during the study period. RESULTS: Timing of hospital admission was an independent predictor of mortality following adjustment for age, sex, comorbidities, ethnicity and obesity. Each additional day between symptom onset and hospital admission was associated with a 1% increase in mortality risk (HR 1.01; p<0.005). Healthcare workers were most likely to have an increased interval between symptom onset and hospital admission, as were people from Black, Asian and minority ethnic (BAME) backgrounds, and patients with obesity. CONCLUSION: The timing of hospital admission is associated with mortality in patients with COVID-19. Healthcare workers and individuals from a BAME background are at greater risk of later admission, which may contribute to reports of poorer outcomes in these groups. Strategies to identify and admit patients with high-risk and those showing signs of deterioration in a timely way may reduce the consequent mortality from COVID-19, and should be explored.

Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study.

OBJECTIVE: To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. DESIGN: Population based cohort study. SETTING AND PARTICIPANTS: QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. MAIN OUTCOME MEASURES: The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. RESULTS: 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell's C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. CONCLUSION: The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.