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      OpenSAFELY: impact of national guidance on switching anticoagulant therapy during COVID-19 pandemic

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          Abstract

          Background

          Early in the COVID-19 pandemic, the National Health Service (NHS) recommended that appropriate patients anticoagulated with warfarin should be switched to direct-acting oral anticoagulants (DOACs), requiring less frequent blood testing. Subsequently, a national safety alert was issued regarding patients being inappropriately coprescribed two anticoagulants following a medication change and associated monitoring.

          Objective

          To describe which people were switched from warfarin to DOACs; identify potentially unsafe coprescribing of anticoagulants; and assess whether abnormal clotting results have become more frequent during the pandemic.

          Methods

          With the approval of NHS England, we conducted a cohort study using routine clinical data from 24 million NHS patients in England.

          Results

          20 000 of 164 000 warfarin patients (12.2%) switched to DOACs between March and May 2020, most commonly to edoxaban and apixaban. Factors associated with switching included: older age, recent renal function test, higher number of recent INR tests recorded, atrial fibrillation diagnosis and care home residency. There was a sharp rise in coprescribing of warfarin and DOACs from typically 50–100 per month to 246 in April 2020, 0.06% of all people receiving a DOAC or warfarin. International normalised ratio (INR) testing fell by 14% to 506.8 patients tested per 1000 warfarin patients each month. We observed a very small increase in elevated INRs (n=470) during April compared with January (n=420).

          Conclusions

          Increased switching of anticoagulants from warfarin to DOACs was observed at the outset of the COVID-19 pandemic in England following national guidance. There was a small but substantial number of people coprescribed warfarin and DOACs during this period. Despite a national safety alert on the issue, a widespread rise in elevated INR test results was not found. Primary care has responded rapidly to changes in patient care during the COVID-19 pandemic.

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          Variation in responsiveness to warranted behaviour change among NHS clinicians: novel implementation of change detection methods in longitudinal prescribing data

          Alex J. Walker, Felix Pretis, Anna Powell-Smith … (2019)
          Abstract Objectives To determine how clinicians vary in their response to new guidance on existing or new interventions, by measuring the timing and magnitude of change at healthcare institutions. Design Automated change detection in longitudinal prescribing data. Setting Prescribing data in English primary care. Participants English general practices. Main outcome measures In each practice the following were measured: the timing of the largest changes, steepness of the change slope (change in proportion per month), and magnitude of the change for two example time series (expiry of the Cerazette patent in 2012, leading to cheaper generic desogestrel alternatives becoming available; and a change in antibiotic prescribing guidelines after 2014, favouring nitrofurantoin over trimethoprim for uncomplicated urinary tract infection (UTI)). Results Substantial heterogeneity was found between institutions in both timing and steepness of change. The range of time delay before a change was implemented was large (interquartile range 2-14 months (median 8) for Cerazette, and 5-29 months (18) for UTI). Substantial heterogeneity was also seen in slope following a detected change (interquartile range 2-28% absolute reduction per month (median 9%) for Cerazette, and 1-8% (2%) for UTI). When changes were implemented, the magnitude of change showed substantially less heterogeneity (interquartile range 44-85% (median 66%) for Cerazette and 28-47% (38%) for UTI). Conclusions Substantial variation was observed in the speed with which individual NHS general practices responded to warranted changes in clinical practice. Changes in prescribing behaviour were detected automatically and robustly. Detection of structural breaks using indicator saturation methods opens up new opportunities to improve patient care through audit and feedback by moving away from cross sectional analyses, and automatically identifying institutions that respond rapidly, or slowly, to warranted changes in clinical practice.
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            Why did some practices not implement new antibiotic prescribing guidelines on urinary tract infection? A cohort study and survey in NHS England primary care

            Richard Croker, Alex J. Walker, Ben Goldacre (2018)
            To describe trends and geographical variation in prescribing of trimethoprim and nitrofurantoin to treat urinary tract infections, to describe variation in implementing guideline change and to compare actions taken to reduce trimethoprim use in high- and low-using Clinical Commissioning Groups (CCGs).
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              A perfect storm: Root cause analysis of supra-therapeutic anticoagulation with vitamin K antagonists during the COVID-19 pandemic

              V. Speed, R.K. Patel, R. Byrne … (2020)
              King's College Hospital hosts a busy anticoagulation clinic in south London within the epicentre of the COVID-19 outbreak in the UK [1]. After the UK entered a period of ‘lockdown’ from 23/03/2020, the Government advised shielding of elderly and vulnerable patients and limiting hospital visits. UK guidance for anticoagulation services was issued (Guidance for the safe switching of warfarin to direct oral anticoagulants (DOACs) for patients with non-valvular AF and venous thromboembolism (DVT / PE) during the coronavirus pandemic) with recommendations around maintaining safe anticoagulation whilst minimising exposure to COVID-19 infection [2]. These included switching to direct oral anticoagulants (DOACs) where appropriate, self-testing INRs, increasing the INR test interval for previously stable patients, and temporarily suspending anticoagulation with VKA therapy where this could no longer be safely continued. Despite implementation of these recommendations, we noted frequent high INR readings in clinical practice during this period. Routine practice at our institution is to perform root cause analysis on all INR results above >8.0. We report on the frequency of supra-therapeutic INR values during the COVID-19 pandemic compared to the previous year and during lockdown and describe the findings of the root cause analysis. Methods All INR results taken within our hospital and community anticoagulation service during a 6-week period were identified (01/03/2020–17/04/2020) centred around the lockdown date of 23/03/2020. We then compared these to the results from the same period in 2019. All cases with excessive elevation of the INR (>8.0) were selected. Data was collated from the anticoagulant clinic record and electronic patient records, which included patient characteristics, INR test frequency, drug compliance, co-medications, and changes in diet and alcohol intake as well as patient-reported events including bleeding. Bleeding events were defined by ISTH criteria [3,4]. Odds ratios (OR) with 95% confidence intervals (95% CI) were calculated to compare the incidence of high INRs during each time period. Root cause analysis was performed according to routine clinical practice, and the results reviewed by a panel of three in-house anticoagulation specialists. COVID-19 was defined as a risk factor for a high INR reading if the disease was possible or confirmed [5]. Results During the 2020 reporting period, 30/3214 (0.9%) INR samples received were > 8.0 (n = 30 patients), compared to 6/4079 (0.1%) (n = 6 patients) during the same period in the previous year (OR 6.3, 95% CI, 2.6–15.2; p   8 are described in Table 1 . Table 1 Patient characteristics for those with an INR >8 in 2019 and 2020.⁎ Table 1 Patient characteristics 2020n = 30 2019n = 6 n % n % Gender (male) 16 53 4 67 Age (years), mean [sd] 71 [14.5) – 70 [16.7] – 

 Ethnicity White 16 53 4 67 Black 8 27 2 33 Hispanic 4 13 – – Asian 2 7 – – Care home resident 3 10 1 17 

 Indication for anticoagulation AF 12 40 – – APS 2 7 – – VTE 8 27 6 100 LV Thrombus 2 7 – – Aortic valve replacement 2 7 – – Mitral valve replacement 1 3 – – CVA embolic 2 7 – – 

 Target range 2–3 26 87 3 50 2.5–3.5 2 7 3 50 3–4 2 7 – – Abbreviations: AF, atrial fibrillation; APS, antiphospholipid syndrome; VTE, venous thromboembolism; LV, left ventricular thrombus; CVA, cerebral vascular accident; SD, standard deviation. ⁎ Included DVT, PE, renal vein thrombosis, cerebral sinus vein thrombosis, portal vein thrombosis. During the pandemic, 22/30 (73%) high INRs occurred during the 3-week lockdown period (OR 3.43, 95% CI, 1.52–7.73; p < .003). Risk factors for a high INR were identified during root cause analysis. These included COVID-19 infection (10 confirmed, 6 possible, totaling 16/30, 53%), antibiotic therapy (17/30, 57%), inpatient admission (12/30, 40%), recent hospital discharge within the previous 4 weeks (5/30, 17%), missed test date (3/30, 10%), entering an end of life treatment pathway (3/30, 10%), higher target INR (3/30, 10%), other interacting drugs (2/30, 7%) and prolonged test interval (1/30, 3%). The majority (13/16, 81%) of patients with possible or confirmed COVID-19 were prescribed antibiotics. In (2/30, 7%) patients the elevated INR was unexplained. (3/30, 10%) patients experienced bleeding: 2 minor bleeding, and 1 major bleed (spontaneous retroperitoneal haemorrhage), with no recorded deaths due to bleeding. Discussion Our data reveals a significant increase in high INR results during the COVID-19 pandemic, with the majority occurring after the introduction of a lockdown. The reasons for this are likely multifactorial, however more than half of our cases had COVID-19 (possible or confirmed) and/or antibiotic use. The use of antibiotics in patients with COVID-19 appears to be common. Given the high prevalence of COVID-19 in the community, it is important to reinforce the need for prescribers of antibiotics and patients to maintain good channels of communication with anticoagulation clinics regarding co-prescribing of interacting drugs. Subclinical derangements of coagulation and liver impairment have been reported in COVID-19 which might contribute to the problem [6,7]. Reduced vitamin K status has also been reported in patients with COVID-19 [8]; this could be associated with malabsorption due to small bowel COVID-19 involvement and/or reduced dietary intake. Other potential contributory factors include decreased access to green leafy vegetables due to stockpiling [9], increased alcohol consumption [10], and increased paracetamol prescribing may too have increased warfarin sensitivity during lockdown [11]. Furthermore, the psychological impact of social distancing and bereavement may have affected adherence to regular medications during this period [12]. Prior to increasing INR test intervals or switching patients from warfarin to a DOAC during the pandemic (in keeping with UK guidance), the data highlights the importance of careful individual risk assessment, given the high incidence of elevated INRs, frequent prescribing of co-medications and unforeseen consequences of a lockdown.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Open Heart
                Journal ID (iso-abbrev): Open Heart
                Journal ID (hwp): openhrt
                Journal ID (publisher-id): openheart
                Title: Open Heart
                Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                ISSN (Electronic): 2053-3624
                Publication date Collection: 2021
                Publication date (Electronic): 16 November 2021
                Publication date PMC-release: 16 November 2021
                Volume: 8
                Issue: 2
                Electronic Location Identifier: e001784
                Affiliations
                [1 ]departmentThe DataLab, Nuffield Department of Primary Care Health Sciences , University of Oxford , Oxford, UK
                [2 ]TPP , Leeds, UK
                [3 ]departmentDepartment of Non-communicable Disease Epidemiology , London School of Hygiene & Tropical Medicine , London, UK
                [4 ]departmentDepartment of Medical Statistics , London School of Hygiene & Tropical Medicine , London, UK
                [5 ]departmentDepartment of Infectious Disease Epidemiology , London School of Hygiene & Tropical Medicine , London, UK
                [6 ]departmentPopulation Health Sciences, Bristol Medical School , University of Bristol , Bristol, UK
                Author notes
                [Correspondence to ] Dr Ben Goldacre; ben.goldacre@ 123456phc.ox.ac.uk
                Author information
                http://orcid.org/0000-0003-3429-9576
                http://orcid.org/0000-0002-3786-9063
                http://orcid.org/0000-0003-4932-6135
                http://orcid.org/0000-0002-8114-9186
                http://orcid.org/0000-0002-2098-1278
                http://orcid.org/0000-0003-0783-0042
                http://orcid.org/0000-0002-6354-3454
                http://orcid.org/0000-0003-0113-2593
                http://orcid.org/0000-0002-1100-079X
                http://orcid.org/0000-0002-4681-4873
                http://orcid.org/0000-0001-5364-8757
                http://orcid.org/0000-0002-1637-837X
                http://orcid.org/0000-0002-1408-7907
                http://orcid.org/0000-0002-9162-4999
                http://orcid.org/0000-0001-8848-9493
                http://orcid.org/0000-0002-0362-6717
                http://orcid.org/0000-0002-8618-7333
                http://orcid.org/0000-0001-6888-2212
                http://orcid.org/0000-0002-8970-1406
                http://orcid.org/0000-0002-9168-6022
                http://orcid.org/0000-0002-5127-4728
                Article
                Publisher ID: openhrt-2021-001784
                DOI: 10.1136/openhrt-2021-001784
                PMC ID: 8595296
                PubMed ID: 34785588
                SO-VID: dc73ba8c-e72a-49af-af19-d3364a7641d5
                Copyright © © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.
                License:

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/.

                History
                Date received : 13 July 2021
                Date accepted : 08 October 2021
                Funding
                Funded by: Health Data Research UK;
                Funded by: FundRef http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Funded by: FundRef http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Funded by: FundRef http://dx.doi.org/10.13039/100014013, UK Research and Innovation;
                Categories
                Subject: Health Care Delivery, Economics and Global Health Care
                Subject: 1506
                Subject: 2474
                Series title: Original research
                Custom metadata
                special-feature unlocked

                Keywords: covid-19,healthcare economics and organisations,medication adherence,stroke
                Data availability:
                Keywords: covid-19, healthcare economics and organisations, medication adherence, stroke

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