Assessing the value of incorporating a polygenic risk score with non-genetic factors for predicting breast cancer diagnosis in the UK Biobank.

BACKGROUND: Previous studies have demonstrated that incorporating a polygenic risk score (PRS) to existing risk prediction models for breast cancer improves model fit, but to determine its clinical utility the impact on risk categorisation needs to be established. We add a PRS to two well-established models and quantify the difference in classification using the net reclassification improvement (NRI). METHODS: We analysed data from 126,490 post-menopausal women of "White British" ancestry, aged 40-69 years at baseline from the UK Biobank prospective cohort. The breast cancer outcome was derived from linked registry data and hospital records. We combined a PRS for breast cancer with 10-year risk scores from the Tyrer-Cuzick and Gail models, and compared these to the risk scores from the models using phenotypic variables alone. We report metrics of discrimination and classification, and consider the importance of the risk threshold selected. RESULTS: The Harrell's C statistic of the 10-year risk from the Tyrer-Cuzick and Gail models was 0.57 and 0.54, respectively, increasing to 0.67 when the PRS was included. Inclusion of the PRS gave a positive NRI for cases in both models (0.080 (95% confidence interval: 0.053, 0.104) and 0.051 (95% CI: 0.030, 0.073), respectively), with negligible impact on controls. CONCLUSIONS: The addition of a PRS for breast cancer to the well-established Tyrer-Cuzick and Gail models provides a substantial improvement in the prediction accuracy and risk stratification. IMPACT: These findings could have important implications for the ongoing discussion about the value of PRS in risk prediction models and screening.

Incorporating polygenic risk into the Leicester Risk Assessment score for 10-year risk prediction of type 2 diabetes.

AIMS: We evaluated whether incorporating information on ethnic background and polygenic risk enhanced the Leicester Risk Assessment (LRA) score for predicting 10-year risk of type 2 diabetes. METHODS: The sample included 202,529 UK Biobank participants aged 40-69 years. We computed the LRA score, and developed two new risk scores using training data (80% sample): LRArev, which incorporated additional information on ethnic background, and LRAprs, which incorporated polygenic risk for type 2 diabetes. We assessed discriminative and reclassification performance in a test set (20% sample). Type 2 diabetes was ascertained using primary care, hospital inpatient and death registry records. RESULTS: Over 10 years, 7,476 participants developed type 2 diabetes. The Harrell's C indexes were 0.796 (95% Confidence Interval [CI] 0.785, 0.806), 0.802 (95% CI 0.792, 0.813), and 0.829 (95% CI 0.820, 0.839) for the LRA, LRArev and LRAprs scores, respectively. The LRAprs score significantly improved the overall reclassification compared to the LRA (net reclassification index [NRI] = 0.033, 95% CI 0.015, 0.049) and LRArev (NRI = 0.040, 95% CI 0.024, 0.055) scores. CONCLUSIONS: Polygenic risk moderately improved the performance of the existing LRA score for 10-year risk prediction of type 2 diabetes.

AutoNet-Generated Deep Layer-Wise Convex Networks for ECG Classification.

The design of neural networks typically involves trial-and-error, a time-consuming process for obtaining an optimal architecture, even for experienced researchers. Additionally, it is widely accepted that loss functions of deep neural networks are generally non-convex with respect to the parameters to be optimised. We propose the Layer-wise Convex Theorem to ensure that the loss is convex with respect to the parameters of a given layer, achieved by constraining each layer to be an overdetermined system of non-linear equations. Based on this theorem, we developed an end-to-end algorithm (the AutoNet) to automatically generate layer-wise convex networks (LCNs) for any given training set. We then demonstrate the performance of the AutoNet-generated LCNs (AutoNet-LCNs) compared to state-of-the-art models on three electrocardiogram (ECG) classification benchmark datasets, with further validation on two non-ECG benchmark datasets for more general tasks. The AutoNet-LCN was able to find networks customised for each dataset without manual fine-tuning under 2 GPU-hours, and the resulting networks outperformed the state-of-the-art models with fewer than 5% parameters on all the above five benchmark datasets. The efficiency and robustness of the AutoNet-LCN markedly reduce model discovery costs and enable efficient training of deep learning models in resource-constrained settings.

Assessing the importance of primary care diagnoses in the UK Biobank.

The UK Biobank has made general practitioner (GP) data (censoring date 2016-2017) available for approximately 45% of the cohort, whilst hospital inpatient and death registry (referred to as "HES/Death") data are available cohort-wide through 2018-2022 depending on whether the data comes from England, Wales or Scotland. We assessed the importance of case ascertainment via different data sources in UKB for three diseases that are usually first diagnosed in primary care: Parkinson's disease (PD), type 2 diabetes (T2D), and all-cause dementia. Including GP data at least doubled the number of incident cases in the subset of the cohort with primary care data (e.g. from 619 to 1390 for dementia). Among the 786 dementia cases that were only captured in the GP data before the GP censoring date, only 421 (54%) were subsequently recorded in HES. Therefore, estimates of the absolute incidence or risk-stratified incidence are misleadingly low when based only on the HES/Death data. For incident cases present in both HES/Death and GP data during the full follow-up period (i.e. until the HES censoring date), the median time difference between an incident diagnosis of dementia being recorded in GP and HES/Death was 2.25 years (i.e. recorded 2.25 years earlier in the GP records). Similar lag periods were also observed for PD (median 2.31 years earlier) and T2D (median 2.82 years earlier). For participants with an incident GP diagnosis, only 65.6% of dementia cases, 69.0% of PD cases, and 58.5% of T2D cases had their diagnosis recorded in HES/Death within 7 years since GP diagnosis. The effect estimates (hazard ratios, HR) of established risk factors for the three health outcomes mostly remain in the same direction and with a similar strength of association when cases are ascertained either using HES only or further adding GP data. The confidence intervals of the HR became narrower when adding GP data, due to the increased statistical power from the additional cases. In conclusion, it is desirable to extend both the coverage and follow-up period of GP data to allow researchers to maximise case ascertainment of chronic health conditions in the UK.

A medical multimodal large language model for future pandemics.

Deep neural networks have been integrated into the whole clinical decision procedure which can improve the efficiency of diagnosis and alleviate the heavy workload of physicians. Since most neural networks are supervised, their performance heavily depends on the volume and quality of available labels. However, few such labels exist for rare diseases (e.g., new pandemics). Here we report a medical multimodal large language model (Med-MLLM) for radiograph representation learning, which can learn broad medical knowledge (e.g., image understanding, text semantics, and clinical phenotypes) from unlabelled data. As a result, when encountering a rare disease, our Med-MLLM can be rapidly deployed and easily adapted to them with limited labels. Furthermore, our model supports medical data across visual modality (e.g., chest X-ray and CT) and textual modality (e.g., medical report and free-text clinical note); therefore, it can be used for clinical tasks that involve both visual and textual data. We demonstrate the effectiveness of our Med-MLLM by showing how it would perform using the COVID-19 pandemic "in replay". In the retrospective setting, we test the model on the early COVID-19 datasets; and in the prospective setting, we test the model on the new variant COVID-19-Omicron. The experiments are conducted on 1) three kinds of input data; 2) three kinds of downstream tasks, including disease reporting, diagnosis, and prognosis; 3) five COVID-19 datasets; and 4) three different languages, including English, Chinese, and Spanish. All experiments show that our model can make accurate and robust COVID-19 decision-support with little labelled data.

Kidney function, albuminuria, and their modification by genetic factors and risk of incident dementia in UK Biobank.

BACKGROUND: Associations between kidney function and dementia risk are inconclusive. Chronic kidney disease (CKD) severity is determined by levels of both estimated glomerular filtration rate (eGFR) and the urine albumin to creatinine ratio (ACR). However, whether there is a graded increase in dementia risk for worse eGFR in each ACR category is unclear. Also, whether genetic risk for dementia impacts the associations is unknown. The current study aims to investigate the associations between eGFR and albuminuria with dementia risk both individually and jointly, whether the associations vary by different follow-up periods, and whether genetic factors modified the associations. METHODS: In 202,702 participants aged ≥ 60 years from the UK Biobank, Cox proportional-hazards models were used to examine the associations between eGFR and urine albumin creatinine ratio (ACR) with risk of incident dementia. GFR was estimated based on serum creatinine, cystatin C, or both. The models were restricted to different follow-up periods (< 5 years, 5-10 years, and ≥ 10 years) to investigate potential reverse causation. RESULTS: Over 15 years of follow-up, 6,042 participants developed dementia. Decreased kidney function (eGFR < 60 ml/min/1.73m2) was associated with an increased risk of dementia (Hazard Ratio [HR] = 1.42, 95% Confidence Interval [CI] 1.28-1.58), compared to normal kidney function (≥ 90 ml/min/1.73m2). The strength of the association remained consistent when the models were restricted to different periods of follow-up. The HRs for incident dementia were 1.16 (95% CI 1.07-1.26) and 2.24 (95% CI 1.79-2.80) for moderate (3-30 mg/mmol) and severely increased ACR (≥ 30 mg/mmol) compared to normal ACR (< 3 mg/mmol). Dose-response associations were observed when combining eGFR and ACR, with those in the severest eGFR and ACR group having the greatest risk of dementia (HR = 4.70, 95% CI 2.34-9.43). APOE status significantly modified the association (p = 0.04), with stronger associations observed among participants with a lower genetic risk of dementia. There was no evidence of an interaction between kidney function and non-APOE polygenic risk of dementia with dementia risk (p = 0.42). CONCLUSIONS: Kidney dysfunction and albuminuria were individually and jointly associated with higher dementia risk. The associations were greater amongst participants with a lower genetic risk of dementia based on APOE, but not non-APOE polygenic risk.

Independent external validation of the QRISK3 cardiovascular disease risk prediction model using UK Biobank.

OBJECTIVE: To externally evaluate the performance of QRISK3 for predicting 10 year risk of cardiovascular disease (CVD) in the UK Biobank cohort. METHODS: We used data from the UK Biobank, a large-scale prospective cohort study of 403 370 participants aged 40-69 years recruited between 2006 and 2010 in the UK. We included participants with no previous history of CVD or statin treatment and defined the outcome to be the first occurrence of coronary heart disease, ischaemic stroke or transient ischaemic attack, derived from linked hospital inpatient records and death registrations. RESULTS: Our study population included 233 233 women and 170 137 men, with 9295 and 13 028 incident CVD events, respectively. Overall, QRISK3 had moderate discrimination for UK Biobank participants (Harrell's C-statistic 0.722 in women and 0.697 in men) and discrimination declined by age (<0.62 in all participants aged 65 years or older). QRISK3 systematically overpredicted CVD risk in UK Biobank, particularly in older participants, by as much as 20%. CONCLUSIONS: QRISK3 had moderate overall discrimination in UK Biobank, which was best in younger participants. The observed CVD risk for UK Biobank participants was lower than that predicted by QRISK3, particularly for older participants. It may be necessary to recalibrate QRISK3 or use an alternate model in studies that require accurate CVD risk prediction in UK Biobank.

Combining machine learning with Cox models to identify predictors for incident post-menopausal breast cancer in the UK Biobank.

We aimed to identify potential novel predictors for breast cancer among post-menopausal women, with pre-specified interest in the role of polygenic risk scores (PRS) for risk prediction. We utilised an analysis pipeline where machine learning was used for feature selection, prior to risk prediction by classical statistical models. An "extreme gradient boosting" (XGBoost) machine with Shapley feature-importance measures were used for feature selection among [Formula: see text] 1.7 k features in 104,313 post-menopausal women from the UK Biobank. We constructed and compared the "augmented" Cox model (incorporating the two PRS, known and novel predictors) with a "baseline" Cox model (incorporating the two PRS and known predictors) for risk prediction. Both of the two PRS were significant in the augmented Cox model ([Formula: see text]). XGBoost identified 10 novel features, among which five showed significant associations with post-menopausal breast cancer: plasma urea (HR = 0.95, 95% CI 0.92-0.98, [Formula: see text]), plasma phosphate (HR = 0.68, 95% CI 0.53-0.88, [Formula: see text]), basal metabolic rate (HR = 1.17, 95% CI 1.11-1.24, [Formula: see text]), red blood cell count (HR = 1.21, 95% CI 1.08-1.35, [Formula: see text]), and creatinine in urine (HR = 1.05, 95% CI 1.01-1.09, [Formula: see text]). Risk discrimination was maintained in the augmented Cox model, yielding C-index 0.673 vs 0.667 (baseline Cox model) with the training data and 0.665 vs 0.664 with the test data. We identified blood/urine biomarkers as potential novel predictors for post-menopausal breast cancer. Our findings provide new insights to breast cancer risk. Future research should validate novel predictors, investigate using multiple PRS and more precise anthropometry measures for better breast cancer risk prediction.

Polygenic Risk of Prediabetes, Undiagnosed Diabetes, and Incident Type 2 Diabetes Stratified by Diabetes Risk Factors.

Context: Early diagnosis of type 2 diabetes is crucial to reduce severe comorbidities and complications. Current screening recommendations for type 2 diabetes include traditional risk factors, primarily body mass index (BMI) and family history, however genetics also plays a key role in type 2 diabetes risk. It is important to understand whether genetic predisposition to type 2 diabetes modifies the effect of these traditional factors on type 2 diabetes risk. Objective: This work aimed to investigate whether genetic risk of type 2 diabetes modifies associations between BMI and first-degree family history of diabetes with 1) prevalent prediabetes or undiagnosed diabetes; and 2) incident confirmed type 2 diabetes. Methods: We included 431 658 individuals aged 40 to 69 years at baseline of multiethnic ancestry from the UK Biobank. We used a multiethnic polygenic risk score for type 2 diabetes (PRST2D) developed by Genomics PLC. Prediabetes or undiagnosed diabetes was defined as baseline glycated hemoglobin greater than or equal to 42 mmol/mol (6.0%), and incident type 2 diabetes was derived from medical records. Results: At baseline, 43 472 participants had prediabetes or undiagnosed diabetes, and 17 259 developed type 2 diabetes over 15 years follow-up. Dose-response associations were observed for PRST2D with each outcome in each category of BMI or first-degree family history of diabetes. Those in the highest quintile of PRST2D with a normal BMI were at a similar risk as those in the middle quintile who were overweight. Participants who were in the highest quintile of PRST2D and did not have a first-degree family history of diabetes were at a similar risk as those with a family history who were in the middle category of PRST2D. Conclusion: Genetic risk of type 2 diabetes remains strongly associated with risk of prediabetes, undiagnosed diabetes, and future type 2 diabetes within categories of nongenetic risk factors. This could have important implications for identifying individuals at risk of type 2 diabetes for prevention and early diagnosis programs.

Hypertension, a dementia polygenic risk score, APOE genotype, and incident dementia.

INTRODUCTION: There is inconsistent evidence on whether genetic risk for dementia modifies the association between hypertension and dementia. METHODS: In 198,965 dementia-free participants aged ≥60 years, Cox proportional-hazards models were used to investigate the association between hypertension and incident dementia. A polygenic risk score (PRS) based on 38 non-apolipoprotein E (APOE) single nucleotide polymorphisms and APOE ε4 status were used to determine genetic risk for dementia. RESULTS: Over 15 years follow-up, 6270 participants developed dementia. Hypertension was associated with a 19% increased risk of dementia (hazard ratio = 1.19, 95% confidence interval 1.11-1.27). The associations remained similar when stratifying by genetic risk, with no evidence for multiplicative interaction by dementia PRS (P = 0.20) or APOE ε4 status (P = 0.16). However, the risk difference between those with and without hypertension was larger among those at higher genetic risk. DISCUSSION: Hypertension was associated with an increased risk of dementia regardless of genetic risk for dementia.

Incidence of fractures in people with intellectual disabilities over the life course: a retrospective matched cohort study.

Background: Current osteoporosis guidelines do not identify individuals with intellectual disabilities (ID) as at risk of fracture, potentially missing opportunities for prevention. We aimed to assess the incidence of fractures in people with ID over the life course. Methods: Descriptive analysis of open cohort study using anonymised electronic health records from the UK Clinical Practice Research Datalink, linked to the Hospital Episode Statistics database (Jan 1, 1998-Dec 31, 2017). All individuals with ID were matched on age and sex to five individuals without ID.  We calculated the incidence rate (95% CI) per 10000 person-years (py) and incidence rate ratio (IRR, 95% CI) to compare fractures between individuals with and without ID (age 1-17 and ≥18 years) for any fracture, and in those aged 18-49 and ≥ 50 years for major osteoporotic fracture (vertebra, shoulder, wrist, hip), and for hip fracture. Findings: 43176 individuals with ID (15470 children aged 1-17 years; 27706 adults aged ≥ 18 years) were identified and included (40.4% females) along with 215733 matched control individuals.  The median age at study entry was 24 (10th-90th centiles 3-54) years. Over a median (10th-90th centile) follow-up of 7.1 (0.9-17.6) and 6.5 (0.8-17.6) years, there were 5941 and 24363 incident fractures in the ID and non ID groups respectively. Incidence of any fracture was 143.5 (131.8-156.3) vs 120.7 (115.4-126.4)/10000 py (children), 174.2 (166.4-182.4)/10000 py vs 118.2 (115.3-121.2)/10000 py (adults) in females. In males it was 192.5 (182.4-203.2) vs 228.5 (223.0-234.1)/10000 py (children), 155.6 (149.3-162.1)/10000 py vs 128.4 (125.9-131.0)/10000 py (adults). IRR for major osteoporotic fracture was 1.81 (1.50-2.18) age 18-49 years, 1.69 (1.53-1.87) age ≥ 50 years in women. In men it was 1.56 (1.36-1.79) age 18-49 years, 2.45 (2.13-2.81) age ≥ 50 years. IRR for hip fracture was 7.79 (4.14-14.65) age 18-49 years, 2.28 (1.91-2.71) age ≥ 50 years in women. In men it was 6.04 (4.18-8.73) age 18-49 years, 3.91 (3.17-4.82) age ≥ 50 years. Comparable rates of major osteoporotic fracture and of hip fracture occurred approximately 15 and 20 years earlier respectively in women and 20 and 30 years earlier respectively in men with ID than without ID. Fracture distribution differed profoundly, hip fracture 9.9% vs 5.0% of any fracture in adults with ID vs without ID. Interpretation: The incidence, type, and distribution of fractures in people with intellectual disabilities suggest early onset osteoporosis. Prevention and management strategies are urgently required, particularly to reduce the incidence of hip fracture. Funding: National Institute for Health and Care Research.

Assessing agreement between different polygenic risk scores in the UK Biobank.

Polygenic risk scores (PRS) are proposed for use in clinical and research settings for risk stratification. However, there are limited investigations on how different PRS diverge from each other in risk prediction of individuals. We compared two recently published PRS for each of three conditions, breast cancer, hypertension and dementia, to assess the stability of using these algorithms for risk prediction in a single large population. We used imputed genotyping data from the UK Biobank prospective cohort, limited to the White British subset. We found that: (1) 20% or more of SNPs in the first PRS were not represented in the more recent PRS for all three diseases, by the same SNP or a surrogate with R2 > 0.8 by linkage disequilibrium (LD). (2) Although the difference in the area under the receiver operating characteristic curve (AUC) obtained using the two PRS is hardly appreciable for all three diseases, there were large differences in individual risk prediction between the two PRS. For instance, for each disease, of those classified in the top 5% of risk by the first PRS, over 60% were not so classified by the second PRS. We found substantial discordance between different PRS for the same disease, indicating that individuals could receive different medical advice depending on which PRS is used to assess their genetic susceptibility. It is desirable to resolve this uncertainty before using PRS for risk stratification in clinical settings.

Calculating Polygenic Risk Scores (PRS) in UK Biobank: A Practical Guide for Epidemiologists.

A polygenic risk score estimates the genetic risk of an individual for some disease or trait, calculated by aggregating the effect of many common variants associated with the condition. With the increasing availability of genetic data in large cohort studies such as the UK Biobank, inclusion of this genetic risk as a covariate in statistical analyses is becoming more widespread. Previously this required specialist knowledge, but as tooling and data availability have improved it has become more feasible for statisticians and epidemiologists to calculate existing scores themselves for use in analyses. While tutorial resources exist for conducting genome-wide association studies and generating of new polygenic risk scores, fewer guides exist for the simple calculation and application of existing genetic scores. This guide outlines the key steps of this process: selection of suitable polygenic risk scores from the literature, extraction of relevant genetic variants and verification of their quality, calculation of the risk score and key considerations of its inclusion in statistical models, using the UK Biobank imputed data as a model data set. Many of the techniques in this guide will generalize to other datasets, however we also focus on some of the specific techniques required for using data in the formats UK Biobank have selected. This includes some of the challenges faced when working with large numbers of variants, where the computation time required by some tools is impractical. While we have focused on only a couple of tools, which may not be the best ones for every given aspect of the process, one barrier to working with genetic data is the sheer volume of tools available, and the difficulty for a novice to assess their viability. By discussing in depth a couple of tools that are adequate for the calculation even at large scale, we hope to make polygenic risk scores more accessible to a wider range of researchers.

Speech-in-noise hearing impairment is associated with an increased risk of incident dementia in 82,039 UK Biobank participants.

INTRODUCTION: Little is known about the association between speech-in-noise (SiN) hearing impairment and dementia. METHODS: In 82,039 dementia-free participants aged ≥60 years were selected from the UK Biobank. Cox proportional-hazards models were used to investigate whether SiN hearing impairment is associated with an increased risk of incident dementia. RESULTS: Over 11 years of follow-up (median = 10.1), 1285 participants developed dementia. Insufficient and poor SiN hearing were associated with a 61% (hazard ratio [HR] = 1.61, 95% confidence [CI] 1.41-1.84) and 91% (HR = 1.91, 95% CI 1.55-2.36) increased risk of developing dementia, respectively, compared to normal SiN hearing. The association remained similar when restricting to follow-up intervals of ≤3, >3 to  <6, >6 to <9, and >9 years. There was limited evidence for mediation through depressive symptoms and social isolation. DISCUSSION: SiN hearing impairment is independently associated with incident dementia, providing further evidence for hearing impairment as a potential modifiable dementia risk factor.

Are polygenic risk scores for systolic blood pressure and LDL-cholesterol associated with treatment effectiveness, and clinical outcomes among those on treatment?

AIMS: Many studies have investigated associations between polygenic risk scores (PRS) and the incidence of cardiovascular disease (CVD); few have examined whether risk factor-related PRS predict CVD outcomes among adults treated with risk-modifying therapies. We assessed whether PRS for systolic blood pressure (PRSSBP) and for low-density lipoprotein cholesterol (PRSLDL-C) were associated with achieving SBP and LDL-C-related targets, and with major adverse cardiovascular events (MACE: non-fatal stroke or myocardial infarction, CVD death, and revascularization procedures). METHODS AND RESULTS: Using observational data from the UK Biobank (UKB), we calculated PRSSBP and PRSLDL-C and constructed two sub-cohorts of unrelated adults of White British ancestry aged 40-69 years and with no history of CVD, who reported taking medications used in the treatment of hypertension or hypercholesterolaemia. Treatment effectiveness in achieving adequate risk factor control was ascertained using on-treatment blood pressure (BP) or LDL-C levels measured at enrolment (uncontrolled hypertension: BP ≥ 140/90 mmHg; uncontrolled hypercholesterolaemia: LDL-C ≥ 3 mmol/L). We conducted multivariable logistic and Cox regression modelling for incident events, adjusting for socioeconomic characteristics, and CVD risk factors. There were 55 439 participants using BP lowering therapies (51.0% male, mean age 61.0 years, median follow-up 11.5 years) and 33 787 using LDL-C lowering therapies (58.5% male, mean age 61.7 years, median follow-up 11.4 years). PRSSBP was associated with uncontrolled hypertension (odds ratio 1.70; 95% confidence interval: 1.60-1.80) top vs. bottom quintile, equivalent to a 5.4 mmHg difference in SBP, and with MACE [hazard ratio (HR) 1.13; 1.04-1.23]. PRSLDL-C was associated with uncontrolled hypercholesterolaemia (HR 2.78; 2.58-3.00) but was not associated with subsequent MACE. CONCLUSION: We extend previous findings in the UKB cohort to examine PRSSBP and PRSLDL-C with treatment effectiveness. Our results indicate that both PRSSBP and PRSLDL-C can help identify individuals who, despite being on treatment, have inadequately controlled SBP and LDL-C, and for SBP are at higher risk for CVD events. This extends the potential role of PRS in clinical practice from identifying patients who may need these interventions to identifying patients who may need more intensive intervention.

Prevalence and determinants of hypertension control among almost 100 000 treated adults in the UK.

OBJECTIVE: To identify factors associated with hypertension control among treated middle-aged UK adults. METHODS: A cross-sectional population-based study including 99 468 previously diagnosed, treated hypertensives enrolled in the UK Biobank. Hypertension control was defined as systolic blood pressure <140 mm Hg and diastolic blood pressure <90 mm Hg. RESULTS: Median age was 62.3 years (IQR 57.3 to 66.0), 45.9% female, 92.0% white, 40.1% obese, 9.3% current smokers and 19.4% had prior cardiovascular disease. 38.1% (95% CI 37.8% to 38.4%) were controlled. In multivariable logistic regression, associations with lack of hypertension control included: older age (OR 0.61, 95% CI 0.58 to 0.64 for 60-69 years compared with age 40-50 years), higher alcohol use (OR 0.61, 95% CI 0.58 to 0.64, for consuming >30 units per week compared with none), black ethnicity (OR 0.73, 95% CI 0.65 to 0.82 compared with white), obesity (OR 0.73, 95% CI 0.71 to 0.76 compared with normal body mass index). The strongest positive association with control was having ≥3 comorbidities (OR 2.09, 95% CI 1.95 to 2.23). Comorbidities associated with control included cardiovascular disease (OR 2.11, 95% CI 2.04 to 2.19), migraines (OR 1.68, 95% CI 1.56 to 1.81), diabetes (OR 1.32, 95% CI 1.27 to 1.36) and depression (OR 1.27, 95% CI 1.20 to 1.34). CONCLUSIONS: In one of the largest population-based analyses of middle-aged adults with measured blood pressure, the majority of treated hypertensives were uncontrolled. Risk factors for hypertension were associated with a lower probability of control. Having a comorbidity was associated with higher probability of control, possibly due to more frequent interaction with the healthcare system and/or appropriate management of those at greater cardiovascular risk.

Prevalence and Determinants of Hypertension Awareness, Treatment, and Control in Botswana: A Nationally Representative Population-Based Survey.

INTRODUCTION: Hypertension is a leading risk factor for cardiovascular mortality and an emerging public health concern in sub-Saharan Africa. Few studies have examined performance on the management of hypertension in this region, where the context may be distinct from other developing regions. OBJECTIVES: We aimed to determine the prevalence and correlates of hypertension, awareness, treatment, and control among adults in Botswana, a middle-income African country undergoing rapid demographic transition and with high HIV burden. METHODS: In this 2014 cross-sectional survey of adults aged 15-69 years, information on sociodemographic characteristics, lifestyle behavior, and medical history was collected through in-person interviews and physical measurements (body mass index and triplicate blood pressure (BP)). Hypertension was defined as self-report of use of antihypertensives in the previous two weeks and/or having elevated BP (≥140/90 mmHg). Multivariable logistic regression was employed to explore factors associated with hypertension, awareness (report of previous diagnosis), treatment (antihypertensives), and control (BP < 140/90). RESULTS: Our analysis (N = 4,007) yielded an age-standardized hypertension prevalence of 30% (95% CI: 28%-32%, N = 1,393). Among hypertensives, 54% (50-58%) were unaware of their condition, 45% (40-50%) of those aware were untreated, and 63% (55-70%) of those on medications were suboptimally treated (BP ≥ 140/90 mmHg). A fifth of hypertensives who were diagnosed but not on medications had BP ≥ 180/110 mmHg. Diabetes was the strongest correlate of hypertension and awareness (aOR 4.00, 1.86-8.59; aOR 3.30, 1.44-7.55, respectively). Males were less likely to be aware (aOR 0.62, 0.41-0.94) or controlled (aOR 0.36, 0.16-0.83). Obese individuals were more likely to be treated (aOR 2.17, 1.12-4.22), yet less likely to be controlled (aOR 0.32, 0.15-0.66). CONCLUSIONS: We report the first nationally representative estimates of the hypertension care cascade performance in Botswana, which will support planning and future policy evaluations. Findings contribute to the relatively sparse evidence on this subject and may inform development of innovations that improve quality of hypertension management and adherence support in similar settings.

Early warning score adjusted for age to predict the composite outcome of mortality, cardiac arrest or unplanned intensive care unit admission using observational vital-sign data: a multicentre development and validation.

OBJECTIVES: Early warning scores (EWS) alerting for in-hospital deterioration are commonly developed using routinely collected vital-sign data from the whole in-hospital population. As these in-hospital populations are dominated by those over the age of 45 years, resultant scores may perform less well in younger age groups. We developed and validated an age-specific early warning score (ASEWS) derived from statistical distributions of vital signs. DESIGN: Observational cohort study. SETTING: Oxford University Hospitals (OUH) July 2013 to March 2018 and Portsmouth Hospitals (PH) NHS Trust January 2010 to March 2017 within the Hospital Alerting Via Electronic Noticeboard database. PARTICIPANTS: Hospitalised patients with electronically documented vital-sign observations OUTCOME: Composite outcome of unplanned intensive care unit admission, mortality and cardiac arrest. METHODS AND RESULTS: Statistical distributions of vital signs were used to develop an ASEWS to predict the composite outcome within 24 hours. The OUH development set consisted of 2 538 099 vital-sign observation sets from 142 806 admissions (mean age (SD): 59.8 (20.3)). We compared the performance of ASEWS to the National Early Warning Score (NEWS) and our previous EWS (MCEWS) on an OUH validation set consisting of 581 571 observation sets from 25 407 emergency admissions (mean age (SD): 63.0 (21.4)) and a PH validation set consisting of 5 865 997 observation sets from 233 632 emergency admissions (mean age (SD): 64.3 (21.1)). ASEWS performed better in the 16-45 years age group in the OUH validation set (AUROC 0.820 (95% CI 0.815 to 0.824)) and PH validation set (AUROC 0.840 (95% CI 0.839 to 0.841)) than NEWS (AUROC 0.763 (95% CI 0.758 to 0.768) and AUROC 0.836 (95% CI 0.835 to 0.838) respectively) and MCEWS (AUROC 0.808 (95% CI 0.803 to 0.812) and AUROC 0.833 (95% CI 0.831 to 0.834) respectively). Differences in performance were not consistent in the elder age group. CONCLUSIONS: Accounting for age-related vital sign changes can more accurately detect deterioration in younger patients.

How to maintain the maximal level of blinding in randomisation for a placebo-controlled drug trial.

We illustrate the approach of randomising treatments and compare it with the traditional approach of randomising patients, using a case study drawn from the authors' experience in clinical trials. The setting is a double-blind parallel two-arm randomised controlled trial (RCT), but the method in this paper can be extended to single-blind, cross-over, or multi-arm RCTs. We propose the concept of two different levels of blinding: full blinding and partial blinding. We subsequently show how to maintain the maximal level of blinding. Using an example, we show that a pharmacist can be fully blinded if the investigational medical products (IMPs) that they prescribe (instead of patients) are randomised, and they can be partially blinded if they need to dispense replacement (i.e., surplus) IMPs. A small number of surplus IMPs is commonly required in a clinical trial to replace lost or damaged IMPs. We note that the concept of full blinding and partial blinding is different from double-blind trial, and the level of blinding is relevant in both single-blind and double-blind trials. A trial statistician needs to work closely with all parties in the design of the randomisation, including the pharmacist, the trial manager, and the manufacturer. We detail what should and should not be shown in the various documents that the trial statistician need to provide to the pharmacist and to the manufacturer. We provide template tables for these documents.

The correlation between baseline score and post-intervention score, and its implications for statistical analysis.

BACKGROUND: When using a continuous outcome measure in a randomised controlled trial (RCT), the baseline score should be measured in addition to the post-intervention score, and it should be analysed using the appropriate statistical analysis. METHODS: We derive the correlation between the change score and baseline score and show that there is always a correlation (usually negative) between the change score and baseline score. We discuss the following correlations and provide the mathematical derivations in the Appendix: Correlation between change score and baseline score Correlation between change score and post score Correlation between change score and average score. The setting here is a parallel, two-arm RCT, but the method discussed in this paper is applicable for any studies or trials that have a continuous outcome measure; it is not restricted to RCTs. RESULTS: We show that using the change score as the outcome measure does not address the problem of regression to the mean, nor does it take account of the baseline imbalance. Whether the outcome is change score or post score, one should always adjust for baseline using analysis of covariance (ANCOVA); otherwise, the estimated treat effect may be biased. We show that these correlations also apply when comparing two measurement methods using Bland-Altman plots. CONCLUSIONS: The correlation between baseline and post-intervention scores can be derived using the variance sum law. We can then use the derived correlation to calculate the required sample size in the design stage. Baseline imbalance may occur in RCTs, and ANCOVA should be used to adjust for baseline in the analysis stage.