AbstractBackgroundNon-pharmacological interventions were introduced based on modelling studies which suggested that the English National Health Service (NHS) would be overwhelmed by the COVID-19 pandemic. In this study, we describe the pattern of bed occupancy across England during the first wave of the pandemic, January 31st to June 5th 2020.MethodsBed availability and occupancy data was extracted from daily reports submitted by all English secondary care providers, between 27-Mar and 5-June. Two thresholds for ‘safe occupancy’ were utilized (85% as per Royal College of Emergency Medicine and 92% as per NHS Improvement).FindingsAt peak availability, there were 2711 additional beds compatible with mechanical ventilation across England, reflecting a 53% increase in capacity, and occupancy never exceeded 62%. A consequence of the repurposing of beds meant that at the trough, there were 8·7% (8,508) fewer general and acute (G&A) beds across England, but occupancy never exceeded 72%. The closest to (surge) capacity that any trust in England reached was 99·8% for general and acute beds. For beds compatible with mechanical ventilation there were 326 trust-days (3·7%) spent above 85% of surge capacity, and 154 trust-days (1·8%) spent above 92%. 23 trusts spent a cumulative 81 days at 100% saturation of their surge ventilator bed capacity (median number of days per trust = 1 [range: 1 to 17]). However, only 3 STPs (aggregates of geographically co-located trusts) reached 100% saturation of their mechanical ventilation beds.InterpretationThroughout the first wave of the pandemic, an adequate supply of all bed-types existed at a national level. Due to an unequal distribution of bed utilization, many trusts spent a significant period operating above ‘safe-occupancy’ thresholds, despite substantial capacity in geographically co-located trusts; a key operational issue to address in preparing for a potential second wave.FundingThis study received no funding.Research in ContextEvidence Before This StudyWe identified information sources describing COVID-19 related bed and mechanical ventilator demand modelling, as well as bed occupancy during the first wave of the pandemic by performing regular searches of MedRxiv, PubMed and Google, using the terms ‘COVID-19’, ‘mechanical ventilators’, ‘bed occupancy’, ‘England’, ‘UK’, ‘demand’, and ‘non-pharmacological interventions (NPIs)’, until June 20th, 2020. Two UK-specific studies were found that modelled the demand for mechanical ventilators, one of which incorporated sensitivity analysis based on the introduction of NPIs and found that their effects might prevent the healthcare system being overwhelmed. Separately, several news reports were found pertaining to a single hospital that reached ventilator capacity in England during the first wave of the pandemic, however, no single authoritative source was identified detailing impact across all hospital sites in England.Added Value of This StudyThis national study of hospital-level bed occupancy in England provides unique and timely insight into bed-specific resource utilization during the first wave of the COVID-19 pandemic, nationally, and by specific (geographically defined) health footprints. We found evidence of an unequal distribution of resource utilization across England. Although occupancy of beds compatible with mechanical ventilation never exceeded 62% at the national level, 52 (30%) hospitals across England reached 100% saturation at some point during the first wave of the pandemic. Close examination of the geospatial data revealed that in the vast majority of circumstances there was relief capacity in geographically co-located hospitals. Over the first wave it was theoretically possible to markedly reduce (by 95.1%) the number of hospitals at 100% saturation of their mechanical ventilator bed capacity by redistributing patients to nearby hospitals.Implications of all the Available EvidenceNow-casting using routinely collected administrative data presents a robust approach to rapidly evaluate the effectiveness of national policies introduced to prevent a healthcare system being overwhelmed in the context of a pandemic illness. Early investment in operational field hospital and an independent sector network may yield more overtly positive results in the winter, when G&A occupancy-levels regularly exceed 92% in England, however, during the first wave of the pandemic they were under-utilized. Moreover, in the context of the non-pharmacological interventions utilized during the first wave of COVID-19, demand for beds and mechanical ventilators was much lower than initially predicted, but despite this many trust spent a significant period of time operating above ‘safe-occupancy’ thresholds. This finding demonstrates that it is vital that future demand (prediction) models reflect the nuances of local variation within a healthcare system. Failure to incorporate such geographical variation can misrepresent the likelihood of surpassing availability thresholds by averaging out over regions with relatively lower demand, and presents a key operational issue for policymakers to address in preparing for a potential second wave.

AbstractObjectivesTo determine the trend in mortality risk over time in people with severe COVID-19 requiring critical care (high intensive unit [HDU] or intensive care unit [ICU]) management.MethodsWe accessed national English data on all adult COVID-19 specific critical care admissions from the COVID-19 Hospitalisation in England Surveillance System (CHESS), up to the 29th June 2020 (n=14,958). The study period was 1st March until 30th May, meaning every patient had 30 days of potential follow-up available. The primary outcome was in-hospital 30-day all-cause mortality. Hazard ratios for mortality were estimated for those admitted each week using a Cox proportional hazards models, adjusting for age (non-linear restricted cubic spline), sex, ethnicity, comorbidities, and geographical region.Results30-day mortality peaked for people admitted to critical care in early April (peak 29.1% for HDU, 41.5% for ICU). There was subsequently a sustained decrease in mortality risk until the end of the study period. As a linear trend from the first week of April, adjusted mortality risk decreased by 11.2% (adjusted HR 0.89 [95% CI 0.87 - 0.91]) per week in HDU, and 9.0% (adjusted HR 0.91 [95% CI 0.88 - 0.94]) in ICU.ConclusionsThere has been a substantial mortality improvement in people admitted to critical care with COVID-19 in England, with markedly lower mortality in people admitted in mid-April and May compared to earlier in the pandemic. This trend remains after adjustment for patient demographics and comorbidities suggesting this improvement is not due to changing patient characteristics. Possible causes include the introduction of effective treatments as part of clinical trials and a falling critical care burden.

OBJECTIVES: to measure temporal trends in survival over time in people with severe coronavirus disease 2019 requiring critical care (high dependency unit or ICU) management, and to assess whether temporal variation in mortality was explained by changes in patient demographics and comorbidity burden over time. DESIGN: Retrospective observational cohort; based on data reported to the COVID-19 Hospitalisation in England Surveillance System. The primary outcome was in-hospital 30-day all-cause mortality. Unadjusted survival was estimated by calendar week of admission, and Cox proportional hazards models were used to estimate adjusted survival, controlling for age, sex, ethnicity, major comorbidities, and geographical region. SETTING: One hundred eight English critical care units. PATIENTS: all adult (18 yr +) coronavirus disease 2019 specific critical care admissions between March 1, 2020, and June 27, 2020. INTERVENTIONS: Not applicable. MEASUREMENTS AND MAIN RESULTS: Twenty-one thousand eighty-two critical care patients (high dependency unit n = 15,367; ICU n = 5,715) were included. Unadjusted survival at 30 days was lowest for people admitted in late March in both high dependency unit (71.6% survival) and ICU (58.0% survival). By the end of June, survival had improved to 92.7% in high dependency unit and 80.4% in ICU. Improvements in survival remained after adjustment for patient characteristics (age, sex, ethnicity, and major comorbidities) and geographical region. CONCLUSIONS: There has been a substantial improvement in survival amongst people admitted to critical care with coronavirus disease 2019 in England, with markedly higher survival rates in people admitted in May and June compared with those admitted in March and April. Our analysis suggests this improvement is not due to temporal changes in the age, sex, ethnicity, or major comorbidity burden of admitted patients.

IntroductionTo identify risk factors, absolute risk, and impact on treatment discontinuation of genital infections with sodium-glucose co-transporter-2 inhibitors (SGLT2i).Research design and methodsWe assessed the relationship between baseline characteristics and genital infection in 21 004 people with type 2 diabetes initiating SGLT2i and 55 471 controls initiating dipeptidyl peptidase-4 inhibitors (DPP4i) in a UK primary care database. We assessed absolute risk of infection in those with key risk factors and the association between early genital infection and treatment discontinuation.ResultsGenital infection was substantially more common in those treated with SGLT2i (8.1% within 1 year) than DPP4i (1.8%). Key predictors of infection with SGLT2i were female sex (HR 3.64; 95% CI 3.23 to 4.11) and history of genital infection; <1 year before initiation (HR 4.38; 3.73 to 5.13), 1–5 years (HR 3.04; 2.64 to 3.51), and >5 years (HR 1.79; 1.55 to 2.07). Baseline HbA1c was not associated with infection risk for SGLT2i, in contrast to DPP4i where risk increased with higher HbA1c. One-year absolute risk of genital infection with SGLT2i was highest for those with a history of prior infection (females 23.7%, males 12.1%), compared with those without (females 10.8%, males 2.7%). Early genital infection was associated with a similar discontinuation risk for SGLT2i (HR 1.48; 1.21–1.80) and DPP4i (HR 1.58; 1.21–2.07).ConclusionsFemale sex and history of prior infection are simple features that can identify subgroups at greatly increased risk of genital infections with SGLT2i therapy. These data can be used to risk-stratify patients. High HbA1c is not a risk factor for genital infections with SGLT2i.

AIMS: to measure the variation in prescribing of second-line non-insulin diabetes drugs. MATERIALS AND METHODS: We evaluated time trends for the period 1998 to 2016, using England's publicly available prescribing datasets, and stratified these by the order in which they were prescribed to patients using the Clinical Practice Research Datalink. We calculated the proportion of each class of diabetes drug as a percentage of the total per year. We evaluated geographical variation in prescribing using general practice-level data for the latest 12 months (to August 2017), with aggregation to Clinical Commissioning Groups. We calculated percentiles and ranges, and plotted maps. RESULTS: Prescribing of therapy after metformin is changing rapidly. Dipeptidyl peptidase-4 (DPP-4) inhibitor use has increased markedly, with DPP-4 inhibitors now the most common second-line drug (43% prescriptions in 2016). The use of sodium-glucose co-transporter-2 (SGLT-2) inhibitors also increased rapidly (14% new second-line, 27% new third-line prescriptions in 2016). There was wide geographical variation in choice of therapies and average spend per patient. In contrast, metformin was consistently used as a first-line treatment in accordance with guidelines. CONCLUSIONS: in England there is extensive geographical variation in the prescribing of diabetes drugs after metformin, and increasing use of higher-cost DPP-4 inhibitors and SGLT-2 inhibitors compared with low-cost sulphonylureas. Our findings strongly support the case for comparative effectiveness trials of current diabetes drugs.

OBJECTIVES: to describe the measurement of heart rate turbulence (HRT) after ventricular premature beats and compare HRT in healthy Doberman pinschers and those with dilated cardiomyopathy (DCM), with and without congestive heart failure (CHF). ANIMALS: Sixty-five client-owned Dobermans: 20 healthy (NORMAL), 31 with preclinical DCM and 14 with DCM and CHF (DCM + CHF). METHODS: a retrospective study of data retrieved from clinical records and ambulatory ECG (Holter) archives, including data collected previously for a large-scale prospective study of Dobermans with preclinical DCM. Holter data were reanalysed quantitatively, including conventional time-domain heart rate variability and the HRT parameters turbulence onset and turbulence slope. RESULTS: Heart rate turbulence could be measured in 58/65 dogs. Six Holter recordings had inadequate ventricular premature contractions (VPCs) and one exhibited VPCs too similar to sinus morphology. Heart rate turbulence parameter, turbulence onset, was significantly reduced in DCM dogs, whereas conventional heart rate variability measures were not. Heart rate variability and HRT markers were reduced in DCM + CHF dogs as expected. CONCLUSIONS: Heart rate turbulence can be measured from the majority of good quality standard canine 24-hour Holter recordings with >5 VPCs. Turbulence onset is significantly reduced in Dobermans with preclinical DCM which indicates vagal withdrawal early in the course of disease. Heart rate turbulence is a powerful prognostic indicator in human cardiac disease which can be measured from standard 24-hour ambulatory ECG (Holter) recordings using appropriate computer software. Further studies are warranted to assess whether HRT may be of prognostic value in dogs with preclinical DCM and in other canine cardiac disease.