Can the use of Bayesian analysis methods correct for incompleteness in electronic health records diagnosis data? Development of a novel method using simulated and real-life clinical data

Background Patient health information is collected routinely in electronic health records (EHRs) and used for research purposes, however, many health conditions are known to be under-diagnosed or under-recorded in EHRs. In research, missing diagnoses result in under-ascertainment of true cases, which attenuates estimated associations between variables and results in a bias towards the null. Bayesian approaches allow the specification of prior information to the model, such as the likely rates of missingness in the data. This paper describes a Bayesian analysis approach which aimed to reduce attenuation of associations in EHR studies focussed on conditions characterised by under-diagnosis. Methods Study 1: We created synthetic data, produced to mimic structured EHR data where diagnoses were under-recorded. We fitted logistic regression (LR) models with and without Bayesian priors representing rates of misclassification in the data. We examined the LR parameters estimated by models with and without priors. Study 2: We used EHR data from UK primary care in a case-control design with dementia as the outcome. We fitted LR models examining risk factors for dementia, with and without generic prior information on misclassification rates. We examined LR parameters estimated by models with and without the priors, and estimated classification accuracy using Area Under the Receiver Operating Characteristic. Results Study 1: In synthetic data, estimates of LR parameters were much closer to the true parameter values when Bayesian priors were added to the model; with no priors, parameters were substantially attenuated by under-diagnosis. Study 2: The Bayesian approach ran well on real life clinic data from UK primary care, with the addition of prior information increasing LR parameter values in all cases. In multivariate regression models, Bayesian methods showed no improvement in classification accuracy over traditional LR. Conclusions The Bayesian approach showed promise but had implementation challenges in real clinical data: prior information on rates of misclassification was difficult to find. Our simple model made a number of assumptions, such as diagnoses being missing at random. Further development is needed to integrated the method into studies using real-life EHR data. Our findings nevertheless highlight the importance of developing methods to address missing diagnoses in EHR data

Identifying undetected dementia in UK primary care patients: a retrospective case-control study comparing machine-learning and standard epidemiological approaches

Background Identifying dementia early in time, using real world data, is a public health challenge. As only two-thirds of people with dementia now ultimately receive a formal diagnosis in United Kingdom health systems and many receive it late in the disease process, there is ample room for improvement. The policy of the UK government and National Health Service (NHS) is to increase rates of timely dementia diagnosis. We used data from general practice (GP) patient records to create a machine-learning model to identify patients who have or who are developing dementia, but are currently undetected as having the condition by the GP. Methods We used electronic patient records from Clinical Practice Research Datalink (CPRD). Using a case-control design, we selected patients aged >65y with a diagnosis of dementia (cases) and matched them 1:1 by sex and age to patients with no evidence of dementia (controls). We developed a list of 70 clinical entities related to the onset of dementia and recorded in the 5 years before diagnosis. After creating binary features, we trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, support vector machines, random forest and neural networks). We examined the most important features contributing to discrimination. Results The final analysis included data on 93,120 patients, with a median age of 82.6 years; 64.8% were female. The naïve Bayes model performed least well. The logistic regression, support vector machine, neural network and random forest performed very similarly with an AUROC of 0.74. The top features retained in the logistic regression model were disorientation and wandering, behaviour change, schizophrenia, self-neglect, and difficulty managing. Conclusions Our model could aid GPs or health service planners with the early detection of dementia. Future work could improve the model by exploring the longitudinal nature of patient data and modelling decline in function over time

Automated detection of patients with dementia whose symptoms have been identified in primary care but have no formal diagnosis: a retrospective case-control study using electronic primary care records

Objectives UK statistics suggest only two-thirds of patients with dementia get a diagnosis recorded in primary care. General practitioners (GPS) report barriers to formally diagnosing dementia, so some patients may be known by GPS to have dementia but may be missing a diagnosis in their patient record. We aimed to produce a method to identify these â known but unlabelled' patients with dementia using data from primary care patient records. Design Retrospective case-control study using routinely collected primary care patient records from Clinical Practice Research Datalink. Setting UK general practice. Participants English patients aged >65 years, with a coded diagnosis of dementia recorded in 2000-2012 (cases), matched 1:1 with patients with no diagnosis code for dementia (controls). Interventions Eight coded and nine keyword concepts indicating symptoms, screening tests, referrals and care for dementia recorded in the 5 years before diagnosis. We trialled machine learning classifiers to discriminate between cases and controls (logistic regression, naïve Bayes, random forest). Primary and secondary outcomes The outcome variable was dementia diagnosis code; the accuracy of classifiers was assessed using area under the receiver operating characteristic curve (AUC); the order of features contributing to discrimination was examined. Results 93 426 patients were included; the median age was 83 years (64.8% women). Three classifiers achieved high discrimination and performed very similarly. AUCs were 0.87-0.90 with coded variables, rising to 0.90-0.94 with keywords added. Feature prioritisation was different for each classifier; commonly prioritised features were Alzheimer's prescription, dementia annual review, memory loss and dementia keywords. Conclusions It is possible to detect patients with dementia who are known to GPS but unlabelled with a diagnostic code, with a high degree of accuracy in electronic primary care record data. Using keywords from clinic notes and letters improves accuracy compared with coded data alone. This approach could improve identification of dementia cases for record-keeping, service planning and delivery of good quality care

Could dementia be detected from UK primary care patients’ records by simple automated methods earlier than by the treating physician? A retrospective case-control study

Background: Timely diagnosis of dementia is a policy priority in the United Kingdom (UK). Primary care physicians receive incentives to diagnose dementia;, however, 33% of patients are still not receiving a diagnosis. We explored automating early detection of dementia using data from patients’ electronic health records (EHRs). We investigated: a) how early a machine-learning model could accurately identify dementia before the physician;, b) if models could be tuned for dementia subtype;, and c) what the best clinical features were for achieving detection. Methods: Using EHRs from Clinical Practice Research Datalink in a case-control design, we selected patients aged >65y with a diagnosis of dementia recorded 2000-2012 (cases) and matched them 1:1 to controls; we also identified subsets of Alzheimer’s and vVascular dementia patients. Using 77 coded concepts recorded in the 5 years before diagnosis, we trained random forest classifiers, and evaluated models using Area Under the Receiver Operating Characteristic Curve (AUC). We examined models by: year prior to diagnosis, subtype, and the most important features contributing to classification. Results: 95,202 patients (median age 83y; 64.8% female) were included (50% dementia cases). Classification of dementia cases and controls was poor 2-5 years prior to physician-recorded diagnosis (AUC range 0.55-0.65) but good in the year before (AUC: 0.84). Features indicating increasing cognitive and physical frailty dominated models 2-5 years before diagnosis; in the final year, initiation of the dementia diagnostic pathway (symptoms, screening and referral) explained the sudden increase in accuracy. No substantial differences were seen between all-cause dementia and subtypes. Conclusions: Automated detection of dementia earlier than the treating physician may be problematic, if using only primary care data. Future work should investigate more complex modelling, benefits of linking multiple sources of healthcare data and monitoring devices, or contextualising the algorithm to those cases that the GP would need to investigate

Shaping care home COVID-19 testing policy: a protocol for a pragmatic cluster randomised controlled trial of asymptomatic testing compared with standard care in care home staff (VIVALDI-CT)

INTRODUCTION: Care home residents have experienced significant morbidity, mortality and disruption following outbreaks of SARS-CoV-2. Regular SARS-CoV-2 testing of care home staff was introduced to reduce transmission of infection, but it is unclear whether this remains beneficial. This trial aims to investigate whether use of regular asymptomatic staff testing, alongside funding to reimburse sick pay for those who test positive and meet costs of employing agency staff, is a feasible and effective strategy to reduce COVID-19 impact in care homes. METHODS AND ANALYSIS: The VIVALDI-Clinical Trial is a multicentre, open-label, cluster randomised controlled, phase III/IV superiority trial in up to 280 residential and/or nursing homes in England providing care to adults aged >65 years. All regular and agency staff will be enrolled, excepting those who opt out. Homes will be randomised to the intervention arm (twice weekly asymptomatic staff testing for SARS-CoV-2) or the control arm (current national testing guidance). Staff who test positive for SARS-CoV-2 will self-isolate and receive sick pay. Care providers will be reimbursed for costs associated with employing temporary staff to backfill for absence arising directly from the trial.The trial will be delivered by a multidisciplinary research team through a series of five work packages.The primary outcome is the incidence of COVID-19-related hospital admissions in residents. Secondary outcomes include the number and duration of outbreaks and home closures. Health economic and modelling analyses will investigate the cost-effectiveness and cost consequences of the testing intervention. A process evaluation using qualitative interviews will be conducted to understand intervention roll out and identify areas for optimisation to inform future intervention scale-up, should the testing approach prove effective and cost-effective. Stakeholder engagement will be undertaken to enable the sector to plan for results and their implications and to coproduce recommendations on the use of testing for policy-makers. ETHICS AND DISSEMINATION: The study has been approved by the London-Bromley Research Ethics Committee (reference number 22/LO/0846) and the Health Research Authority (22/CAG/0165). The results of the trial will be disseminated regardless of the direction of effect. The publication of the results will comply with a trial-specific publication policy and will include submission to open access journals. A lay summary of the results will also be produced to disseminate the results to participants. TRIAL REGISTRATION NUMBER: ISRCTN13296529

Shaping care home COVID-19 testing policy: a protocol for a pragmatic cluster randomised controlled trial of asymptomatic testing compared with standard care in care home staff (VIVALDI-CT)

INTRODUCTION: Care home residents have experienced significant morbidity, mortality and disruption following outbreaks of SARS-CoV-2. Regular SARS-CoV-2 testing of care home staff was introduced to reduce transmission of infection, but it is unclear whether this remains beneficial. This trial aims to investigate whether use of regular asymptomatic staff testing, alongside funding to reimburse sick pay for those who test positive and meet costs of employing agency staff, is a feasible and effective strategy to reduce COVID-19 impact in care homes. METHODS AND ANALYSIS: The VIVALDI-Clinical Trial is a multicentre, open-label, cluster randomised controlled, phase III/IV superiority trial in up to 280 residential and/or nursing homes in England providing care to adults aged >65 years. All regular and agency staff will be enrolled, excepting those who opt out. Homes will be randomised to the intervention arm (twice weekly asymptomatic staff testing for SARS-CoV-2) or the control arm (current national testing guidance). Staff who test positive for SARS-CoV-2 will self-isolate and receive sick pay. Care providers will be reimbursed for costs associated with employing temporary staff to backfill for absence arising directly from the trial.The trial will be delivered by a multidisciplinary research team through a series of five work packages.The primary outcome is the incidence of COVID-19-related hospital admissions in residents. Secondary outcomes include the number and duration of outbreaks and home closures. Health economic and modelling analyses will investigate the cost-effectiveness and cost consequences of the testing intervention. A process evaluation using qualitative interviews will be conducted to understand intervention roll out and identify areas for optimisation to inform future intervention scale-up, should the testing approach prove effective and cost-effective. Stakeholder engagement will be undertaken to enable the sector to plan for results and their implications and to coproduce recommendations on the use of testing for policy-makers. ETHICS AND DISSEMINATION: The study has been approved by the London-Bromley Research Ethics Committee (reference number 22/LO/0846) and the Health Research Authority (22/CAG/0165). The results of the trial will be disseminated regardless of the direction of effect. The publication of the results will comply with a trial-specific publication policy and will include submission to open access journals. A lay summary of the results will also be produced to disseminate the results to participants. TRIAL REGISTRATION NUMBER: ISRCTN13296529

1 Versus 2-cm Excision Margins for pT2-pT4 Primary Cutaneous Melanoma (MelMarT): A Feasibility Study

Abstract Background There is a lack of consensus regarding optimal surgical excision margins for primary cutaneous melanoma &gt; 1 mm in Breslow thickness (BT). A narrower surgical margin is expected to be associated with lower morbidity, improved quality of life (QoL), and reduced cost. We report the results of a pilot international study (MelMarT) comparing a 1 versus 2-cm surgical margin for patients with primary melanoma &gt; 1 mm in BT. Methods This phase III, multicentre trial [NCT02385214] administered by the Australia &amp; New Zealand Medical Trials Group (ANZMTG 03.12) randomised patients with a primary cutaneous melanoma &gt; 1 mm in BT to a 1 versus 2-cm wide excision margin to be performed with sentinel lymph node biopsy. Surgical closure technique was at the discretion of the treating surgeon. Patients’ QoL was measured (FACT-M questionnaire) at baseline, 3, 6, and 12 months after randomisation. Results Between January 2015 and June 2016, 400 patients were randomised from 17 centres in 5 countries. A total of 377 patients were available for analysis. Primary melanomas were located on the trunk (56.9%), extremities (35.6%), and head and neck (7.4%). More patients in the 2-cm margin group required reconstruction (34.9 vs. 13.6%; p &lt; 0.0001). There was an increased wound necrosis rate in the 2-cm arm (0.5 vs. 3.6%; p = 0.036). After 12 months’ follow-up, no differences were noted in QoL between groups. Discussion This pilot study demonstrates the feasibility of a large international RCT to provide a definitive answer to the optimal excision margin for patients with intermediate- to high-risk primary cutaneous melanoma. </jats:sec

Accelerated partner therapy contact tracing for people with chlamydia (LUSTRUM): a crossover cluster-randomised controlled trial.

BACKGROUND Accelerated partner therapy has shown promise in improving contact tracing. We aimed to evaluate the effectiveness of accelerated partner therapy in addition to usual contact tracing compared with usual practice alone in heterosexual people with chlamydia, using a biological primary outcome measure. METHODS We did a crossover cluster-randomised controlled trial in 17 sexual health clinics (clusters) across England and Scotland. Participants were heterosexual people aged 16 years or older with a positive Chlamydia trachomatis test result, or a clinical diagnosis of conditions for which presumptive chlamydia treatment and contact tracing are initially provided, and their sexual partners. We allocated phase order for clinics through random permutation within strata. In the control phase, participants received usual care (health-care professional advised the index patient to tell their sexual partner[s] to attend clinic for sexually transmitted infection screening and treatment). In the intervention phase, participants received usual care plus an offer of accelerated partner therapy (health-care professional assessed sexual partner[s] by telephone, then sent or gave the index patient antibiotics and sexually transmitted infection self-sampling kits for their sexual partner[s]). Each phase lasted 6 months, with a 2-week washout at crossover. The primary outcome was the proportion of index patients with a positive C trachomatis test result at 12-24 weeks after contact tracing consultation. Secondary outcomes included proportions and types of sexual partners treated. Analysis was done by intention-to-treat, fitting random effects logistic regression models. This trial is registered with the ISRCTN registry, 15996256. FINDINGS Between Oct 24, 2018, and Nov 17, 2019, 1536 patients were enrolled in the intervention phase and 1724 were enrolled in the control phase. All clinics completed both phases. In total, 4807 sexual partners were reported, of whom 1636 (34%) were steady established partners. Overall, 293 (19%) of 1536 index patients chose accelerated partner therapy for a total of 305 partners, of whom 248 (81%) accepted. 666 (43%) of 1536 index patients in the intervention phase and 800 (46%) of 1724 in the control phase were tested for C trachomatis at 12-24 weeks after contact tracing consultation; 31 (4·7%) in the intervention phase and 53 (6·6%) in the control phase had a positive C trachomatis test result (adjusted odds ratio [OR] 0·66 [95% CI 0·41 to 1·04]; p=0·071; marginal absolute difference -2·2% [95% CI -4·7 to 0·3]). Among index patients with treatment status recorded, 775 (88·0%) of 881 patients in the intervention phase and 760 (84·6%) of 898 in the control phase had at least one treated sexual partner at 2-4 weeks after contact tracing consultation (adjusted OR 1·27 [95% CI 0·96 to 1·68]; p=0·10; marginal absolute difference 2·7% [95% CI -0·5 to 6·0]). No clinically significant harms were reported. INTERPRETATION Although the evidence that the intervention reduces repeat infection was not conclusive, the trial results suggest that accelerated partner therapy can be safely offered as a contact tracing option and is also likely to be cost saving. Future research should find ways to increase uptake of accelerated partner therapy and develop alternative interventions for one-off sexual partners. FUNDING National Institute for Health Research