Complex patterns of multimorbidity associated with severe COVID-19 and long COVID.

BACKGROUND: Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses are scarce but may help to understand severe COVID-19 among patients at supposedly low risk. METHODS: We systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID. RESULTS: Here we identify 679 diseases associated with an increased risk for severe COVID-19 (n = 672) and/or Long COVID (n = 72) that span almost all clinical specialties and are strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we establish consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This includes a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observe partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis or rheumatoid arthritis, possibly indicating a segregation of disease mechanisms. CONCLUSIONS: Our results provide a unique reference that demonstrates how 1) complex co-occurrence of multiple - including non-fatal - conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.

Early in the COVID-19 pandemic it was clear that people with multiple chronic diseases were vulnerable and needed special protection, such as shielding. However, many people without such diseases required hospital care or died from COVID-19. Here, we investigated the importance of underlying diseases, including mild diseases not requiring hospitalization, for COVID-19 outcomes. Using information from electronic health records we find that many severe, but also less severe diseases increase the risk for severe COVID-19 and its impact on health even months after acute infection (Long COVID). This included an almost two-fold higher risk among people that reported poor well-being and fatigue. Our findings show the value of using primary care health records and the need to consider all the medical history of patients to identify those in need of special protection.

Complex patterns of multimorbidity associated with severe COVID-19 and Long COVID.

Early evidence that patients with (multiple) pre-existing diseases are at highest risk for severe COVID-19 has been instrumental in the pandemic to allocate critical care resources and later vaccination schemes. However, systematic studies exploring the breadth of medical diagnoses, including common, but non-fatal diseases are scarce, but may help to understand severe COVID-19 among patients at supposedly low risk. Here, we systematically harmonized >12 million primary care and hospitalisation health records from ~500,000 UK Biobank participants into 1448 collated disease terms to systematically identify diseases predisposing to severe COVID-19 (requiring hospitalisation or death) and its post-acute sequalae, Long COVID. We identified a total of 679 diseases associated with an increased risk for severe COVID-19 (n=672) and/or Long COVID (n=72) that spanned almost all clinical specialties and were strongly enriched in clusters of cardio-respiratory and endocrine-renal diseases. For 57 diseases, we established consistent evidence to predispose to severe COVID-19 based on survival and genetic susceptibility analyses. This included a possible role of symptoms of malaise and fatigue as a so far largely overlooked risk factor for severe COVID-19. We finally observed partially opposing risk estimates at known risk loci for severe COVID-19 for etiologically related diseases, such as post-inflammatory pulmonary fibrosis (e.g., MUC5B, NPNT, and PSMD3) or rheumatoid arthritis (e.g., TYK2), possibly indicating a segregation of disease mechanisms. Our results provide a unique reference that demonstrates how 1) complex co-occurrence of multiple - including non-fatal - conditions predispose to increased COVID-19 severity and 2) how incorporating the whole breadth of medical diagnosis can guide the interpretation of genetic risk loci.

Eligibility of Asian and European registry patients for phase III trials in heart failure with reduced ejection fraction.

AIMS: Traditional approaches to designing clinical trials for heart failure (HF) have historically relied on expertise and past practices. However, the evolving landscape of healthcare, marked by the advent of novel data science applications and increased data availability, offers a compelling opportunity to transition towards a data-driven paradigm in trial design. This research aims to evaluate the scope and determinants of disparities between clinical trials and registries by leveraging natural language processing for the analysis of trial eligibility criteria. The findings contribute to the establishment of a robust design framework for guiding future HF trials. METHODS AND RESULTS: Interventional phase III trials registered for HF on ClinicalTrials.gov as of the end of 2021 were identified. Natural language processing was used to extract and structure the eligibility criteria for quantitative analysis. The most common criteria for HF with reduced ejection fraction (HFrEF) were applied to estimate patient eligibility as a proportion of registry patients in the ASIAN-HF (N = 4868) and BIOSTAT-CHF registries (N = 2545). Of the 375 phase III trials for HF, 163 HFrEF trials were identified. In these trials, the most frequently encountered inclusion criteria were New York Heart Association (NYHA) functional class (69%), worsening HF (23%), and natriuretic peptides (18%), whereas the most frequent comorbidity-based exclusion criteria were acute coronary syndrome (64%), renal disease (55%), and valvular heart disease (47%). On average, 20% of registry patients were eligible for HFrEF trials. Eligibility distributions did not differ (P = 0.18) between Asian [median eligibility 0.20, interquartile range (IQR) 0.08-0.43] and European registry populations (median 0.17, IQR 0.06-0.39). With time, HFrEF trials became more restrictive, where patient eligibility declined from 0.40 in 1985-2005 to 0.19 in 2016-2022 (P = 0.03). When frequency among trials is taken into consideration, the eligibility criteria that were most restrictive were prior myocardial infarction, NYHA class, age, and prior HF hospitalization. CONCLUSIONS: Based on 14 trial criteria, only one-fifth of registry patients were eligible for phase III HFrEF trials. Overall eligibility rates did not differ between the Asian and European patient cohorts.

Changes in sick notes associated with COVID-19 from 2020 to 2022: a cohort study in 24 million primary care patients in OpenSAFELY-TPP.

OBJECTIVES: Long-term sickness absence from employment has negative consequences for the economy and can lead to widened health inequalities. Sick notes (also called 'fit notes') are issued by general practitioners when a person cannot work for health reasons for more than 7 days. We quantified the sick note rate in people with evidence of COVID-19 in 2020, 2021 and 2022, as an indication of the burden for people recovering from COVID-19. DESIGN: Cohort study. SETTING: With National Health Service (NHS) England approval, we used routine clinical data (primary care, hospital and COVID-19 testing records) within the OpenSAFELY-TPP database. PARTICIPANTS: People 18-64 years with a recorded positive test or diagnosis of COVID-19 in 2020 (n=365 421), 2021 (n=1 206 555) or 2022 (n=1 321 313); general population matched in age, sex and region in 2019 (n=3 140 326), 2020 (n=3 439 534), 2021 (n=4 571 469) and 2022 (n=4 818 870); people hospitalised with pneumonia in 2019 (n=29 673). PRIMARY OUTCOME MEASURE: Receipt of a sick note in primary care. RESULTS: Among people with a positive SARS-CoV-2 test or COVID-19 diagnosis, the sick note rate was 4.88 per 100 person-months (95% CI 4.83 to 4.93) in 2020, 2.66 (95% CI 2.64 to 2.67) in 2021 and 1.73 (95% CI 1.72 to 1.73) in 2022. Compared with the age, sex and region-matched general population, the adjusted HR for receipt of a sick note over the entire follow-up period (up to 10 months) was 4.07 (95% CI 4.02 to 4.12) in 2020 decreasing to 1.57 (95% CI 1.56 to 1.58) in 2022. The HR was highest in the first 30 days postdiagnosis in all years. Among people hospitalised with COVID-19, after adjustment, the sick note rate was lower than in people hospitalised with pneumonia. CONCLUSIONS: Given the under-recording of postacute COVID-19-related symptoms, these findings contribute a valuable perspective on the long-term effects of COVID-19. Despite likely underestimation of the sick note rate, sick notes were issued more frequently to people with COVID-19 compared with those without, even in an era when most people are vaccinated. Most sick notes occurred in the first 30 days postdiagnosis, but the increased risk several months postdiagnosis may provide further evidence of the long-term impact.

Creating a next-generation phenotype library: the health data research UK Phenotype Library.

Objective: To enable reproducible research at scale by creating a platform that enables health data users to find, access, curate, and re-use electronic health record phenotyping algorithms. Materials and Methods: We undertook a structured approach to identifying requirements for a phenotype algorithm platform by engaging with key stakeholders. User experience analysis was used to inform the design, which we implemented as a web application featuring a novel metadata standard for defining phenotyping algorithms, access via Application Programming Interface (API), support for computable data flows, and version control. The application has creation and editing functionality, enabling researchers to submit phenotypes directly. Results: We created and launched the Phenotype Library in October 2021. The platform currently hosts 1049 phenotype definitions defined against 40 health data sources and >200K terms across 16 medical ontologies. We present several case studies demonstrating its utility for supporting and enabling research: the library hosts curated phenotype collections for the BREATHE respiratory health research hub and the Adolescent Mental Health Data Platform, and it is supporting the development of an informatics tool to generate clinical evidence for clinical guideline development groups. Discussion: This platform makes an impact by being open to all health data users and accepting all appropriate content, as well as implementing key features that have not been widely available, including managing structured metadata, access via an API, and support for computable phenotypes. Conclusions: We have created the first openly available, programmatically accessible resource enabling the global health research community to store and manage phenotyping algorithms. Removing barriers to describing, sharing, and computing phenotypes will help unleash the potential benefit of health data for patients and the public.

Vaccinations, cardiovascular drugs, hospitalization, and mortality in COVID-19 and Long COVID.

OBJECTIVE: To identify highest-risk subgroups for COVID-19 and Long COVID(LC), particularly in contexts of influenza and cardiovascular disease(CVD). METHODS: Using national, linked electronic health records for England (NHS England Secure Data Environment via CVD-COVID-UK/COVID-IMPACT Consortium), we studied individuals (of all ages) with COVID-19 and LC (2020-2023). We compared all-cause hospitalization and mortality by prior CVD, high CV risk, vaccination status (COVID-19/influenza), and CVD drugs, investigating impact of vaccination and CVD prevention using population preventable fractions. RESULTS: Hospitalization and mortality were 15.3% and 2.0% among 17,373,850 individuals with COVID-19 (LC rate 1.3%), and 16.8% and 1.4% among 301,115 with LC. Adjusted risk of mortality and hospitalization were reduced with COVID-19 vaccination ≥ 2 doses(COVID-19:HR 0.36 and 0.69; LC:0.44 and 0.90). With influenza vaccination, mortality was reduced, but not hospitalization (COVID-19:0.86 and 1.01, and LC:0.72 and 1.05). Mortality and hospitalization were reduced by CVD prevention in those with CVD, e.g., anticoagulants- COVID:19:0.69 and 0.92; LC:0.59 and 0.88; lipid lowering- COVID-19:0.69 and 0.86; LC:0.68 and 0.90. COVID-19 vaccination averted 245044 of 321383 and 7586 of 8738 preventable deaths after COVID-19 and LC, respectively. INTERPRETATION: Prior CVD and high CV risk are associated with increased hospitalization and mortality in COVID-19 and LC. Targeted COVID-19 vaccination and CVD prevention are priority interventions. FUNDING: NIHR. HDR UK.

Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats.

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

Disagreement concerning atopic dermatitis subtypes between an English prospective cohort (ALSPAC) and linked electronic health records.

BACKGROUND: Subtypes of atopic dermatitis (AD) have been derived from the Avon Longitudinal Study of Parents and Children (ALSPAC) based on presence and severity of symptoms reported in questionnaires (Severe-Frequent, Moderate-Frequent, Moderate-Declining, Mild-Intermittent, Unaffected/Rare). Good agreement between ALSPAC and linked electronic health records (EHRs) would increase trust in the clinical validity of these subtypes and allow inferring subtypes from EHRs alone, which would enable their study in large primary care databases. OBJECTIVES: 1. Explore if presence and number of AD records in EHRs agrees with AD symptom and severity reports from ALSPAC; 2. Explore if EHRs agree with ALSPAC-derived AD subtypes; 3. Construct models to classify ALSPAC-derived AD subtype using EHRs. METHODS: We used data from the ALSPAC prospective cohort study from 11 timepoints until age 14 years (1991-2008), linked to local general practice EHRs. We assessed how far ALSPAC questionnaire responses and derived subtypes agreed with AD as established in EHRs using different AD definitions (e.g., diagnosis and/or prescription) and other AD-related records. We classified AD subtypes using EHRs, fitting multinomial logistic regression models tuning hyperparameters and evaluating performance in the testing set (ROC AUC, accuracy, sensitivity, and specificity). RESULTS: 8,828 individuals out of a total 13,898 had both been assigned an AD subtype and had linked EHRs. The number of AD-related codes in EHRs generally increased with severity of AD subtype, however not all with the Severe-Frequent subtypes had AD in EHRs, and many with the Unaffected/Rare subtype did have AD in EHRs. When predicting ALSPAC AD subtype using EHRs, the best tuned model had ROC AUC of 0.65, sensitivity of 0.29 and specificity of 0.83 (both macro averaged); when different sets of predictors were used, individuals with missing EHR coverage excluded, and subtypes combined, sensitivity was not considerably improved. CONCLUSIONS: ALSPAC and EHRs disagreed not just on AD subtypes, but also on whether children had AD or not. Researchers should be aware that individuals considered as having AD in one source may not be considered as having AD in another.

Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity.

Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond "White", "Black", "Asian", "Mixed" and "Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.

Sexual and reproductive health and rights of migrant women attending primary care in England: A population-based cohort study of 1.2 million individuals of reproductive age (2009-2018).

Background: Evidence on the sexual and reproductive health and rights (SRHR) of migrants is lacking globally. We describe SRHR healthcare resource use and long-acting reversible contraceptives (LARCs) prescriptions for migrant versus non-migrant women attending primary care in England (2009-2018). Methods: This population-based observational cohort study, using Clinical Practice Research Datalink (CPRD) GOLD, included females living in England aged 15 to 49. Migration was defined using a validated codelist. Rates per 100 person years at risk (pyar) and adjusted rate ratios (RRs) were measured in migrants versus non-migrants for consultations related to all-causes, six exemplar SRHR outcomes, and LARC prescriptions. Proportions of migrants and non-migrants ever prescribed LARC were calculated. Findings: There were 25,112,116 consultations across 1,246,353 eligible individuals. 98,214 (7.9 %) individuals were migrants. All-cause consultation rates were lower in migrants versus non-migrants (509 vs 583/100pyar;RR 0.9;95 %CI 0.9-0.9), as were consultations rates for emergency contraception (RR 0.7;95 %CI 0.7-0.7) and cervical screening (RR 0.96;95 %CI 0.95-0.97). Higher rates of consultations were found in migrants for abortion (RR 1.2;95 %CI 1.1-1.2) and management of fertility problems (RR 1.39;95 %CI 1.08-1.79). No significant difference was observed for chlamydia testing and domestic violence. Of 1,205,258 individuals eligible for contraception, the proportion of non-migrants ever prescribed LARC (12.2 %;135,047/1,107,894) was almost double that of migrants (6.91 %;6,728/97,364). Higher copper intrauterine devices prescription rates were found in migrants (RR 1.53;95 %CI 1.45-1.61), whilst hormonal LARC rates were lower for migrants: levonorgestrel intrauterine device (RR 0.63;95 %CI 0.60-0.66), subdermal implant (RR 0.72;95 %CI 0.69-0.75), and progesterone-only injection (RR 0.35;95 %CI 0.34-0.36). Interpretation: Healthcare resource use differs between migrant and non-migrant women of reproductive age. Opportunities identified for tailored interventions include access to primary care, LARCs, emergency contraception and cervical screening. An inclusive approach to examining health needs is essential to actualise sexual and reproductive health as a human right.

Improving reporting standards for phenotyping algorithm in biomedical research: 5 fundamental dimensions.

INTRODUCTION: Phenotyping algorithms enable the interpretation of complex health data and definition of clinically relevant phenotypes; they have become crucial in biomedical research. However, the lack of standardization and transparency inhibits the cross-comparison of findings among different studies, limits large scale meta-analyses, confuses the research community, and prevents the reuse of algorithms, which results in duplication of efforts and the waste of valuable resources. RECOMMENDATIONS: Here, we propose five independent fundamental dimensions of phenotyping algorithms-complexity, performance, efficiency, implementability, and maintenance-through which researchers can describe, measure, and deploy any algorithms efficiently and effectively. These dimensions must be considered in the context of explicit use cases and transparent methods to ensure that they do not reflect unexpected biases or exacerbate inequities.

Phenotypes and rates of cancer-relevant symptoms and tests in the year before cancer diagnosis in UK Biobank and CPRD Gold.

Early diagnosis of cancer relies on accurate assessment of cancer risk in patients presenting with symptoms, when screening is not appropriate. But recorded symptoms in cancer patients pre-diagnosis may vary between different sources of electronic health records (EHRs), either genuinely or due to differential completeness of symptom recording. To assess possible differences, we analysed primary care EHRs in the year pre-diagnosis of cancer in UK Biobank and Clinical Practice Research Datalink (CPRD) populations linked to cancer registry data. We developed harmonised phenotypes in Read v2 and CTV3 coding systems for 21 symptoms and eight blood tests relevant to cancer diagnosis. Among 22,601 CPRD and 11,594 UK Biobank cancer patients, 54% and 36%, respectively, had at least one consultation for possible cancer symptoms recorded in the year before their diagnosis. Adjusted comparisons between datasets were made using multivariable Poisson models, comparing rates of symptoms/tests in CPRD against expected rates if cancer site-age-sex-deprivation associations were the same as in UK Biobank. UK Biobank cancer patients compared with those in CPRD had lower rates of consultation for possible cancer symptoms [RR: 0.61 (0.59-0.63)], and lower rates for any primary care consultation [RR: 0.86 (95%CI 0.85-0.87)]. Differences were larger for 'non-alarm' symptoms [RR: 0.54 (0.52-0.56)], and smaller for 'alarm' symptoms [RR: 0.80 (0.76-0.84)] and blood tests [RR: 0.93 (0.90-0.95)]. In the CPRD cohort, approximately representative of the UK population, half of cancer patients had recorded symptoms in the year before diagnosis. The frequency of non-specific presenting symptoms recorded in the year pre-diagnosis of cancer was substantially lower among UK Biobank participants. The degree to which results based on highly selected biobank cohorts are generalisable needs to be examined in disease-specific contexts.

Management and outcomes of myocardial infarction in people with impaired kidney function in England.

BACKGROUND: Acute myocardial infarction (AMI) causes significant mortality and morbidity in people with impaired kidney function. Previous observational research has demonstrated reduced use of invasive management strategies and inferior outcomes in this population. Studies from the USA have suggested that disparities in care have reduced over time. It is unclear whether these findings extend to Europe and the UK. METHODS: Linked data from four national healthcare datasets were used to investigate management and outcomes of AMI by estimated glomerular filtration rate (eGFR) category in England. Multivariable logistic and Cox regression models compared management strategies and outcomes by eGFR category among people with kidney impairment hospitalised for AMI between 2015-2017. RESULTS: In a cohort of 5 835 people, we found reduced odds of invasive management in people with eGFR < 60mls/min/1.73m2 compared with people with eGFR ≥ 60 when hospitalised for non-ST segment elevation MI (NSTEMI). The association between eGFR and odds of invasive management for ST-elevation MI (STEMI) varied depending on the availability of percutaneous coronary intervention. A graded association between mortality and eGFR category was demonstrated both in-hospital and after discharge for all people. CONCLUSIONS: In England, patients with reduced eGFR are less likely to receive invasive management compared to those with preserved eGFR. Disparities in care may however be decreasing over time, with the least difference seen in patients with STEMI managed via the primary percutaneous coronary intervention pathway. Reduced eGFR continues to be associated with worse outcomes after AMI.

Clustering long-term health conditions among 67728 people with multimorbidity using electronic health records in Scotland.

There is still limited understanding of how chronic conditions co-occur in patients with multimorbidity and what are the consequences for patients and the health care system. Most reported clusters of conditions have not considered the demographic characteristics of these patients during the clustering process. The study used data for all registered patients that were resident in Fife or Tayside, Scotland and aged 25 years or more on 1st January 2000 and who were followed up until 31st December 2018. We used linked demographic information, and secondary care electronic health records from 1st January 2000. Individuals with at least two of the 31 Elixhauser Comorbidity Index conditions were identified as having multimorbidity. Market basket analysis was used to cluster the conditions for the whole population and then repeatedly stratified by age, sex and deprivation. 318,235 individuals were included in the analysis, with 67,728 (21·3%) having multimorbidity. We identified five distinct clusters of conditions in the population with multimorbidity: alcohol misuse, cancer, obesity, renal failure, and heart failure. Clusters of long-term conditions differed by age, sex and socioeconomic deprivation, with some clusters not present for specific strata and others including additional conditions. These findings highlight the importance of considering demographic factors during both clustering analysis and intervention planning for individuals with multiple long-term conditions. By taking these factors into account, the healthcare system may be better equipped to develop tailored interventions that address the needs of complex patients.

Evaluating the impact of alternative phenotype definitions on incidence rates across a global data network.

Objective: Developing accurate phenotype definitions is critical in obtaining reliable and reproducible background rates in safety research. This study aims to illustrate the differences in background incidence rates by comparing definitions for a given outcome. Materials and Methods: We used 16 data sources to systematically generate and evaluate outcomes for 13 adverse events and their overall background rates. We examined the effect of different modifications (inpatient setting, standardization of code set, and code set changes) to the computable phenotype on background incidence rates. Results: Rate ratios (RRs) of the incidence rates from each computable phenotype definition varied across outcomes, with inpatient restriction showing the highest variation from 1 to 11.93. Standardization of code set RRs ranges from 1 to 1.64, and code set changes range from 1 to 2.52. Discussion: The modification that has the highest impact is requiring inpatient place of service, leading to at least a 2-fold higher incidence rate in the base definition. Standardization showed almost no change when using source code variations. The strength of the effect in the inpatient restriction is highly dependent on the outcome. Changing definitions from broad to narrow showed the most variability by age/gender/database across phenotypes and less than a 2-fold increase in rate compared to the base definition. Conclusion: Characterization of outcomes across a network of databases yields insights into sensitivity and specificity trade-offs when definitions are altered. Outcomes should be thoroughly evaluated prior to use for background rates for their plausibility for use across a global network.

A taxonomy of early diagnosis research to guide study design and funding prioritisation.

Researchers and research funders aiming to improve diagnosis seek to identify if, when, where, and how earlier diagnosis is possible. This has led to the propagation of research studies using a wide range of methodologies and data sources to explore diagnostic processes. Many such studies use electronic health record data and focus on cancer diagnosis. Based on this literature, we propose a taxonomy to guide the design and support the synthesis of early diagnosis research, focusing on five key questions: Do healthcare use patterns suggest earlier diagnosis could be possible? How does the diagnostic process begin? How do patients progress from presentation to diagnosis? How long does the diagnostic process take? Could anything have been done differently to reach the correct diagnosis sooner? We define families of diagnostic research study designs addressing each of these questions and appraise their unique or complementary contributions and limitations. We identify three further questions on relationships between the families and their relevance for examining patient group inequalities, supported with examples from the cancer literature. Although exemplified through cancer as a disease model, we recognise the framework is also applicable to non-neoplastic disease. The proposed framework can guide future study design and research funding prioritisation.

ADRA2A and IRX1 are putative risk genes for Raynaud's phenomenon.

Raynaud's phenomenon (RP) is a common vasospastic disorder that causes severe pain and ulcers, but despite its high reported heritability, no causal genes have been robustly identified. We conducted a genome-wide association study including 5,147 RP cases and 439,294 controls, based on diagnoses from electronic health records, and identified three unreported genomic regions associated with the risk of RP (p < 5 × 10-8). We prioritized ADRA2A (rs7090046, odds ratio (OR) per allele: 1.26; 95%-CI: 1.20-1.31; p < 9.6 × 10-27) and IRX1 (rs12653958, OR: 1.17; 95%-CI: 1.12-1.22, p < 4.8 × 10-13) as candidate causal genes through integration of gene expression in disease relevant tissues. We further identified a likely causal detrimental effect of low fasting glucose levels on RP risk (rG = -0.21; p-value = 2.3 × 10-3), and systematically highlighted drug repurposing opportunities, like the antidepressant mirtazapine. Our results provide the first robust evidence for a strong genetic contribution to RP and highlight a so far underrated role of α2A-adrenoreceptor signalling, encoded at ADRA2A, as a possible mechanism for hypersensitivity to catecholamine-induced vasospasms.

Genome-wide association studies and cross-population meta-analyses investigating short and long sleep duration.

Sleep duration has been linked to a wide range of negative health outcomes and to reduced life expectancy. We present genome-wide association studies of short ( ≤ 5 h) and long ( ≥ 10 h) sleep duration in adults of European (N = 445,966), African (N = 27,785), East Asian (N = 3141), and admixed-American (N = 16,250) ancestry from UK Biobank and the Million Veteran Programme. In a cross-population meta-analysis, we identify 84 independent loci for short sleep and 1 for long sleep. We estimate SNP-based heritability for both sleep traits in each ancestry based on population derived linkage disequilibrium (LD) scores using cov-LDSC. We identify positive genetic correlation between short and long sleep traits (rg = 0.16 ± 0.04; p = 0.0002), as well as similar patterns of genetic correlation with other psychiatric and cardiometabolic phenotypes. Mendelian randomisation reveals a directional causal relationship between short sleep and depression, and a bidirectional causal relationship between long sleep and depression.

Determining prescriptions in electronic healthcare record data: methods for development of standardized, reproducible drug codelists.

Objective: To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases. Materials and Methods: We developed methods to generate drug codelists and tested this using the Clinical Practice Research Datalink (CPRD) Aurum database, accounting for missing data in the database. We generated codelists for: (1) cardiovascular disease and (2) inhaled Chronic Obstructive Pulmonary Disease (COPD) therapies, applying them to a sample cohort of 335 931 COPD patients. We compared searching all drug dictionary variables (A) against searching only (B) chemical or (C) ontological variables. Results: In Search A, we identified 165 150 patients prescribed cardiovascular drugs (49.2% of cohort), and 317 963 prescribed COPD inhalers (94.7% of cohort). Evaluating output per search strategy, Search C missed numerous prescriptions, including vasodilator anti-hypertensives (A and B:19 696 prescriptions; C:1145) and SAMA inhalers (A and B:35 310; C:564). Discussion: We recommend the full search (A) for comprehensiveness. There are special considerations when generating adaptable and generalizable drug codelists, including fluctuating status, cohort-specific drug indications, underlying hierarchical ontology, and statistical analyses. Conclusions: Methods must have end-to-end clinical input, and be standardizable, reproducible, and understandable to all researchers across data contexts.