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Rapid community point-of-care testing for COVID-19 (RAPTOR-C19): protocol for a platform diagnostic study.
BACKGROUND: The aim of RApid community Point-of-care Testing fOR COVID-19 (RAPTOR-C19) is to assess the diagnostic accuracy of multiple current and emerging point-of-care tests (POCTs) for active and past SARS-CoV2 infection in the community setting. RAPTOR-C19 will provide the community testbed to the COVID-19 National DiagnOstic Research and Evaluation Platform (CONDOR). METHODS: RAPTOR-C19 incorporates a series of prospective observational parallel diagnostic accuracy studies of SARS-CoV2 POCTs against laboratory and composite reference standards in patients with suspected current or past SARS-CoV2 infection attending community settings. Adults and children with suspected current SARS-CoV2 infection who are having an oropharyngeal/nasopharyngeal (OP/NP) swab for laboratory SARS-CoV2 reverse transcriptase Digital/Real-Time Polymerase Chain Reaction (d/rRT-PCR) as part of clinical care or community-based testing will be invited to participate. Adults (≥ 16 years) with suspected past symptomatic infection will also be recruited. Asymptomatic individuals will not be eligible. At the baseline visit, all participants will be asked to submit samples for at least one candidate point-of-care test (POCT) being evaluated (index test/s) as well as an OP/NP swab for laboratory SARS-CoV2 RT-PCR performed by Public Health England (PHE) (reference standard for current infection). Adults will also be asked for a blood sample for laboratory SARS-CoV-2 antibody testing by PHE (reference standard for past infection), where feasible adults will be invited to attend a second visit at 28 days for repeat antibody testing. Additional study data (e.g. demographics, symptoms, observations, household contacts) will be captured electronically. Sensitivity, specificity, positive, and negative predictive values for each POCT will be calculated with exact 95% confidence intervals when compared to the reference standard. POCTs will also be compared to composite reference standards constructed using paired antibody test results, patient reported outcomes, linked electronic health records for outcomes related to COVID-19 such as hospitalisation or death, and other test results. DISCUSSION: High-performing POCTs for community use could be transformational. Real-time results could lead to personal and public health impacts such as reducing onward household transmission of SARS-CoV2 infection, improving surveillance of health and social care staff, contributing to accurate prevalence estimates, and understanding of SARS-CoV2 transmission dynamics in the population. In contrast, poorly performing POCTs could have negative effects, so it is necessary to undertake community-based diagnostic accuracy evaluations before rolling these out. TRIAL REGISTRATION: ISRCTN, ISRCTN14226970.
Consultations for clinical features of possible cancer and associated urgent referrals before and during the COVID-19 pandemic: an observational cohort study from English primary care
Background: It remains unclear to what extent reductions in urgent referrals for suspected cancer during the COVID-19 pandemic were the result of fewer patients attending primary care compared to GPs referring fewer patients. Methods: Cohort study including electronic health records data from 8,192,069 patients from 663 English practices. Weekly consultation rates, cumulative consultations and referrals were calculated for 28 clinical features from the NICE suspected cancer guidelines. Clinical feature consultation rate ratios (CRR) and urgent referral rate ratios (RRR) compared time periods in 2020 with 2019. Findings: Consultations for cancer clinical features decreased by 24.19% (95% CI: 24.04–24.34%) between 2019 and 2020, particularly in the 6–12 weeks following the first national lockdown. Urgent referrals for clinical features decreased by 10.47% (95% CI: 9.82–11.12%) between 2019 and 2020. Overall, once patients consulted with primary care, GPs urgently referred a similar or greater proportion of patients compared to previous years. Conclusion: Due to the significant fall in patients consulting with clinical features of cancer there was a lower than expected number of urgent referrals in 2020. Sustained efforts should be made throughout the pandemic to encourage the public to consult their GP with cancer clinical features.
PREDICTING INDIVIDUAL RISK OF MUSCLE DISORDERS IN PATIENTS ELIGIBLE FOR STATIN TREATMENT: STRATIFY-STATINMD MODEL DERIVATION USING DATA FROM ELECTRONIC HEALTH RECORDS
OBJECTIVE: Concerns about muscle-related adverse events have posed a dilemma when considering statin prescription for prevention of cardiovascular disease (CVD). This study aimed to develop a prediction model for an individual's risk of muscle disorders to support clinical decision making in primary care. DESIGN AND METHOD: A prospective cohort design was adopted, using electronic health records from the Clinical Practice Research Datalink in the UK. Males aged over 50 and females aged over 60, who were potentially eligible for statin treatment based on their underlying CVD risk, were followed-up for ten years. The primary outcome was hospitalisation or death in those with a diagnosis of muscle disorders. The Fine-Gray proportional sub-distribution hazards model was fitted to address competing risk of death from other causes. Statin prescriptions within the 12 months before follow up and other predictors were included in the model based on a literature review. RESULTS: The cohort included 1,785,207 patients, with a mean age of 64 and 44% females. Patients prescribed statins were predicted to have a higher risk of muscle disorders (atorvastatin: hazard ratio = 1.77 [95% confidence interval: 1.58 - 1.97]; rosuvastatin: 2.04 [1.58 - 2.63]; simvastatin: 1.58 [1.45 - 1.71]; other statins (fluvastatin/pravastatin): 1.38 [1.14 - 1.68]). Female sex, deprivation, smoking, obesity, frailty, liver or kidney disease, rheumatic arthritis, previous muscle problems, degenerative joint disorders, hypothyroidism, vitamin D or B12 deficiency, and the use of drugs that are potentially myotoxic or interact with statins also increased an individual's risk (Table). An automated risk calculator was developed based on the model (Figure). CONCLUSIONS: This model uses routinely available patient characteristics and medical history to predict an individual's risk of muscle disorders. The calculator may help clinicians and patients communicate the safety concerns and make shared decisions or monitoring strategies on statin treatment. External validation of this model is ongoing to support general application of the risk calculator in clinical practice.
PREDICTING THE RISK OF FALLS IN PATIENTS WITH AN INDICATION FOR ANTIHYPERTENSIVE TREATMENT: DEVELOPMENT AND VALIDATION OF THE STRATIFY-FALLS PREDICTION MODEL
OBJECTIVE: Falls are one of the most common side effects associated with antihypertensive medication. In patients at high risk of falls, the additional risk of harm from medication may outweigh the potential benefits in terms of cardiovascular risk reduction. However, it is currently unclear which patients are at high risk of falls. This study aimed to develop and validate a clinical prediction model for risk of hospitalisation or death from falls in adults eligible for antihypertensive treatment. DESIGN AND METHOD: Eligible patients were aged 40 and above, with at least one blood pressure measurement between 130-179 mm Hg. The outcome was a fall resulting in hospitalisation or death within 10 years of entering the study. Model development was conducted in data from CPRD GOLD using a Fine-Gray approach, accounting for the competing risk of death from other causes, with subsequent recalibration at specific time-points using pseudo-values. External validation was conducted using data from CPRD Aurum, with performance assessed through calibration curves, the Observed/Expected (O/E) ratio, C-statistic, and D-statistic, each pooled across GP practices. RESULTS: Analysis included 1,773,224 patients (62,691 events) for model development, and 3,805,322 (206,956 events) for external validation. Conditional on other variables, increasing age, being female, of white ethnicity, being a heavy drinker and living in an area of high social deprivation were associated with an increased risk of falls. Upon external validation, the re-calibrated model showed good discrimination, with pooled C-statistics of 0.843 (95%CI: 0.841 to 0.844) and 0.833 (95%CI: 0.831 to 0.835) at 5 and 10 years respectively. Original model calibration was poor on visual inspection and whilst this improved with re-calibration, under-prediction of risk remained (Observed/Expected ratio 1.839 at 10 years, 95%CI: 1.811 to 1.865) (Figure 1). CONCLUSIONS: STRATIFY-Falls uses commonly recorded clinical characteristics and shows good discrimination on external validation, accurately distinguishing between patients at high and low risk of falls in the next 5-10 years. This model may be used in routine clinical practice to help identify those at high risk of falls, for whom the benefits of antihypertensive medication may be outweighed by the harms.
Attainment of NICE blood pressure targets among older people with newly diagnosed hypertension: nationwide linked electronic health records cohort study
Background: it is not known if clinical practice reflects guideline recommendations for the management of hypertension in older people and whether guideline adherence varies according to overall health status. Aims: to describe the proportion of older people attaining National Institute for Health and Care Excellence (NICE) guideline blood pressure targets within 1 year of hypertension diagnosis and determine predictors of target attainment. Methods: a nationwide cohort study of Welsh primary care data from the Secure Anonymised Information Linkage databank including patients aged ≥65 years newly diagnosed with hypertension between 1st June 2011 and 1st June 2016. The primary outcome was attainment of NICE guideline blood pressure targets as measured by the latest blood pressure recording up to 1 year after diagnosis. Predictors of target attainment were investigated using logistic regression. Results: there were 26,392 patients (55% women, median age 71 [IQR 68-77] years) included, of which 13,939 (52.8%) attained a target blood pressure within a median follow-up of 9 months. Success in attaining target blood pressure was associated with a history of atrial fibrillation (OR 1.26, 95% CI 1.11, 1.43), heart failure (OR 1.25, 95% CI 1.06, 1.49) and myocardial infarction (OR 1.20, 95% CI 1.10, 1.32), all compared to no history of each, respectively. Care home residence, the severity of frailty, and increasing co-morbidity were not associated with target attainment following adjustment for confounder variables. Conclusions: blood pressure remains insufficiently controlled 1 year after diagnosis in nearly half of older people with newly diagnosed hypertension, but target attainment appears unrelated to baseline frailty, multi-morbidity or care home residence.
Antihypertensive Deprescribing in Older Adults: a Practical Guide
Purpose of Review: To summarise evidence on both appropriate and inappropriate antihypertensive drug withdrawal. Recent Findings: Deprescribing should be attempted in the following steps: (1) identify patients with several comorbidities and significant functional decline, i.e. people at higher risk for negative outcomes related to polypharmacy and lower blood pressure; (2) check blood pressure; (3) identify candidate drugs for deprescribing; (4) withdraw medications at 4-week intervals; (5) monitor blood pressure and check for adverse events. Although evidence is accumulating regarding short-term outcomes of antihypertensive deprescribing, long-term effects remain unclear. Summary: The limited evidence for antihypertensive deprescribing means that it should not be routinely attempted, unless in response to specific adverse events or following discussions between physicians and patients about the uncertain benefits and harms of the treatment. Perspectives: Clinical controlled trials are needed to examine the long-term effects of deprescribing in older subjects, especially in those with comorbidities, and significant functional decline.
PREDICTING THE RISK OF SYNCOPE IN PATIENTS WITH AN INDICATION FOR ANTIHYPERTENSIVE TREATMENT: DEVELOPMENT OF THE STRATIFY-SYNCOPE PREDICTION MODEL
OBJECTIVE: Syncope is one of the most common side effects associated with antihypertensive medication. In patients at increased of syncope, the additional risk of harm from antihypertensive medication may outweigh the potential benefits of treatment in terms of cardiovascular risk reduction. However, it is unclear how to identify patients at most risk of syncope events. This study aimed to develop a clinical prediction model for risk of hospitalisation or death from syncope. DESIGN AND METHOD: This was a cohort study using data from the Clinical Practice Research Datalink (CPRD) in the UK. The electronic health records of patients aged greater than 40, with at least one blood pressure measurement between 130-179 mmHg were included. Outcomes were defined as a syncope event resulting in hospitalisation or death within 10 years of baseline. Predictors of syncope were based on the literature and expert opinion and included patient characteristics, past medical history and prescribed treatment (including antihypertensive prescription). A Fine-Gray model was used to adjust for competing risk of mortality and results are reported as subdistribution hazard ratios (SHR). RESULTS: A total of 1,772,617 patients were eligible for the study, with mean age of 59 and 48% males. Median follow up was 6.2 years with 39898 syncope events (2.3%). The antihypertensive medications most strongly associated with syncope were alpha blockers (SHR: 1.21, 95%CI 1.15 to 1.28) and ACE inhibitors (SHR: 1.19, 95%CI 1.16 to 1.22). Other important predictors included age (Figure 1), male sex, high social deprivation, heavy alcohol consumption, previous syncope, diabetes, dementia, structural cardiac problems, arrhythmias, spinal cord injuries, parkinsonism, cardiopulmonary disease and prescription of antidepressants, antipsychotics and opioids (table 1). CONCLUSIONS: This prediction model identified a number of strong predictors of syncope which are routinely available in an individual's electronic health records. The accuracy of this model will examined in a further ∼3,000,000 patients from a different electronic health record database. If it is found to perform well, such a model could be used to provide personalised estimates of an individual's risk of harm from antihypertensive treatment, thus facilitating more informed treatment choices.
The association between prehospital care and in-hospital treatment decisions in acute stroke: A cohort study
Background: Hospital prealerting in acute stroke improves the timeliness of subsequent treatment, but little is known about the impact of prehospital assessments on in-hospital care. Objective: Examine the association between prehospital assessments and notification by emergency medical service staff on the subsequent acute stroke care pathway. Methods: This was a cohort study of linked patient medical records. Consenting patients with a diagnosis of stroke were recruited from two urban hospitals. Data from patient medical records were extracted and entered into a Cox regression analysis to investigate the association between time to CT request and recording of onset time, stroke recognition (using the Face Arm Speech Test (FAST)) and sending of a prealert message. Results: 151 patients (aged 71±15 years) travelled to hospital via ambulance and were eligible for this analysis. Time of symptom onset was recorded in 61 (40%) cases, the FAST test was positive in 114 (75%) and a prealert message was sent in 65 (44%). Following adjustment for confounding, patients who had time of onset recorded (HR 0.73, 95% CI 0.52 to 1.03), were FAST-positive (HR 0.54, 95% CI 0.37 to 0.80) or were prealerted (HR 0.26, 95% CI 0.18 to 0.38), were more likely to receive a timely CT request in hospital. Conclusions: This study highlights the importance of hospital prealerting, accurate stroke recognition, and recording of onset time. Those not recognised with stroke in a prehospital setting appear to be excluded from the possibility of rapid treatment in hospital, even before they have been seen by a specialist.
Measuring adherence to antihypertensive medication using an objective test in older adults attending primary care: cross-sectional study
Analysis of urine samples using liquid chromatography-tandem mass spectrometry (LC-MS/MS) has previously revealed high rates of non-adherence to antihypertensive medication. It is unclear whether these rates represent those in the general population. This study aimed to investigate whether it is feasible to collect urine samples in a primary care setting and analyse them using LC-MS/MS to detect non-adherence to antihypertensive medication. This study used a prospective, observational cohort design. Consecutive patients were recruited opportunistically from five general practices in UK primary care. They were aged ≥65 years with hypertension and had at least one antihypertensive prescription. Participants were asked to provide a urine sample for analysis of medication adherence. Samples were sent to a laboratory via post and analysed using LC-MS/MS. Predictors of adherence to medication were explored with multivariable logistic regression. Of 349 consecutive patients approached for the study, 214 (61.3%) gave informed consent and 191 (54.7%) provided a valid urine sample for analysis. Participants were aged 76.2 ± 6.6 years and taking a median of 2 antihypertensive medications (IQR 1–3). A total of 27/191 participants (14.2%) reported not taking all of their medications on the day of urine sample collection. However, LC-MS/MS analysis of samples revealed only 4/27 (9/191 in total; 4.7%) were non-adherent to some of their medications. Patients prescribed more antihypertensive medications were less likely to be adherent (OR 0.24, 95%CI 0.09–0.65). Biochemical testing for antihypertensive medication adherence is feasible in routine primary care, although non-adherence to medication is generally low, and therefore widespread testing is not indicated.
Impact of self-monitoring of blood pressure on processes of hypertension care and long-term blood pressure control
BACKGROUND: Self-monitoring of blood pressure (SMBP) improves blood pressure (BP) outcomes at 12-months, but information is lacking on how SMBP affects hypertension care processes and longer-term BP outcomes. METHODS AND RESULTS: We pooled individual participant data from 4 randomized clinical trials of SMBP in the United Kingdom (combined n=2590) with varying intensities of support. Multivariable random effects regression was used to estimate the probability of antihypertensive intensification at 12 months for usual care versus SMBP. Using these data, we simulated 5-year BP control rates using a validated mathematical model. Trial participants were mostly older adults (mean age 66.6 years, SD 9.5), male (53.9%), and predominantly white (95.6%); mean baseline BP was 151.8/85.0 mm Hg. Compared with usual care, the likelihood of antihypertensive intensification increased with both SMBP with feedback to patient or provider alone (odds ratio 1.8, 95% CI 1.2–2.6) and with telemonitoring or self-management (3.3, 2.5–4.2). Over 5 years, we estimated 33.4% BP control (<140/90 mm Hg) with usual care (95% uncertainty interval 27.7%–39.4%). One year of SMBP with feedback to patient or provider alone achieved 33.9% (28.3%–40.3%) BP control and SMBP with telemonitoring or self-management 39.0% (33.1%–45.2%) over 5 years. If SMBP interventions and associated BP control processes were extended to 5 years, BP control increased to 52.4% (45.4%–59.8 %) and 72.1% (66.5%–77.6%), respectively. CONCLUSIONS: One year of SMBP plus telemonitoring or self-management increases the likelihood of antihypertensive intensification and could improve BP control rates at 5 years; continuing SMBP for 5 years could further improve BP control.