Objective To assess low-density lipoprotein cholesterol (LDL-C) response in patients after initiation of statins, and future risk of cardiovascular disease (CVD). Methods Prospective cohort study of 165 411 primary care patients, from the UK Clinical Practice Research Datalink, who were free of CVD before statin initiation, and had at least one pre-treatment LDL-C within 12 months before, and one post-treatment LDL-C within 24 months after, statin initiation. Based on current national guidelines, <40% reduction in baseline LDL-C within 24 months was classified as a sub-optimal statin response. Cox proportional regression and competing-risks survival regression models were used to determine adjusted hazard ratios (HRs) and sub-HRs for incident CVD outcomes for LDL-C response to statins. Results 84 609 (51.2%) patients had a sub-optimal LDL-C response to initiated statin therapy within 24 months. During 1 077 299 person-years of follow-up (median follow-up 6.2 years), there were 22 798 CVD events (12 142 in sub-optimal responders and 10 656 in optimal responders). In sub-optimal responders, compared with optimal responders, the HR for incident CVD was 1.17 (95% CI 1.13 to 1.20) and 1.22 (95% CI 1.19 to 1.25) after adjusting for age and baseline untreated LDL-C. Considering competing risks resulted in lower but similar sub-HRs for both unadjusted (1.13, 95% CI 1.10 to 1.16) and adjusted (1.19, 95% CI 1.16 to 1.23) cumulative incidence function of CVD. Conclusions Optimal lowering of LDL-C is not achieved within 2 years in over half of patients in the general population initiated on statin therapy, and these patients will experience significantly increased risk of future CVD.

Background and Purpose: Data about variations in stroke incidence and subsequent major adverse outcomes are essential to inform secondary prevention and prioritizing resources to those at the greatest risk of major adverse end points. We aimed to describe the age, sex, and socioeconomic differences in the rates of first nonfatal stroke and subsequent major adverse outcomes. Methods: The cohort study used linked Clinical Practice Research Datalink and Hospital Episode Statistics data from the United Kingdom. The incidence rate (IR) ratio of first nonfatal stroke and subsequent major adverse outcomes (composite major adverse cardiovascular events, recurrent stroke, cardiovascular disease-related, and all-cause mortality) were calculated and presented by year, sex, age group, and socioeconomic status based on an individual’s location of residence, in adults with incident nonfatal stroke diagnosis between 1998 and 2017. Results: A total of 82 774 first nonfatal stroke events were recorded in either primary care or hospital data—an IR of 109.20 per 100 000 person-years (95% CI, 108.46–109.95). Incidence was significantly higher in women compared with men (IR ratio, 1.13 [95% CI, 1.12–1.15]; P <0.001). Rates adjusted for age and sex were higher in the lowest compared with the highest socioeconomic status group (IR ratio, 1.10 [95% CI, 1.08–1.13]; P <0.001). For subsequent major adverse outcomes, the overall incidence for major adverse cardiovascular event was 38.05 per 100 person-years (95% CI, 37.71–38.39) with a slightly higher incidence in women compared with men (38.42 versus 37.62; IR ratio, 1.02 [95% CI, 1.00–1.04]; P =0.0229). Age and socioeconomic status largely accounted for the observed higher incidence of adverse outcomes in women. Conclusions: In the United Kingdom, incidence of initial stroke and subsequent major adverse outcomes are higher in women, older populations, and people living in socially deprived areas.

Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches improve detection of FH in electronic health records, though clinical impact is under-explored. We assessed performance of an array of machine-learning approaches for enhancing detection of FH, and their clinical utility, within a large primary care population. A retrospective cohort study was done using routine primary care clinical records of 4,027,775 individuals from the United Kingdom with total cholesterol measured from 1 January 1999 to 25 June 2019. Predictive accuracy of five common machine-learning algorithms (logistic regression, random forest, gradient boosting machines, neural networks and ensemble learning) were assessed for detecting FH. Predictive accuracy was assessed by area under the receiver operating curves (AUC) and expected vs observed calibration slope; with clinical utility assessed by expected case-review workload and likelihood ratios. There were 7928 incident diagnoses of FH. In addition to known clinical features of FH (raised total cholesterol or LDL-C and family history of premature coronary heart disease), machine-learning (ML) algorithms identified features such as raised triglycerides which reduced the likelihood of FH. Apart from logistic regression (AUC, 0.81), all four other ML approaches had similarly high predictive accuracy (AUC > 0.89). Calibration slope ranged from 0.997 for gradient boosting machines to 1.857 for logistic regression. Among those screened, high probability cases requiring clinical review varied from 0.73% using ensemble learning to 10.16% using deep learning, but with positive predictive values of 15.5% and 2.8% respectively. Ensemble learning exhibited a dominant positive likelihood ratio (45.5) compared to all other ML models (7.0–14.4). Machine-learning models show similar high accuracy in detecting FH, offering opportunities to increase diagnosis. However, the clinical case-finding workload required for yield of cases will differ substantially between models.

Background Cellulitis can be a difficult diagnosis to make. 31% of patients admitted from the emergency department with suspected lower limb cellulitis are misdiagnoses, with incorrect treatment potentially resulting in avoidable hospital admission and antibiotic prescribing. Objective We sought to identify diagnostic criteria or tools that have been developed for lower limb cellulitis. Methods We conducted a systematic review using Ovid MEDLINE and Embase databases in May 2018, with the aim of describing diagnostic criteria and tools developed for lower limb cellulitis, and assessing the quality of the studies identified, using the QUADAS‐2 tool. We included all types of study describing diagnostic criteria or tools. Results Eight observational studies were included. Five studies examined biochemical markers, two studies assessed imaging and one study developed a diagnostic decision model. All eight studies were high risk in at least one domain for bias. Limitations The quantity and quality of available data was low. Results could not be pooled due to the heterogeneity in findings. Conclusion There is a lack of high quality publications describing criteria or tools for diagnosing lower limb cellulitis. Future studies using prospective designs and validated in both primary and secondary care settings are needed.

Background: The association between obesity, major adverse cardiovascular events (MACE), and mortality in patients with incident stroke is not well established. We assessed the relationship between body mass index (BMI) and MACE in patients with incident stroke.Methods: The population‐based cohort study identified 30 702 individuals from the Clinical Practice Research Datalink (CPRD GOLD) and Hospital Episode Statistics (HES) databases from the United Kingdom. Individuals were aged ≥18 years with incident stroke between 1‐1‐1998 and 31‐12‐2017, a BMI recorded within 24 months before incident stroke, and no prior history of MACE. BMI was categorized as underweight (<18.5 kg/m²), normal (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), obesity class I (30.0–34.9 kg/m²), class II (35.0–39.9 kg/m²) and class III (≥40 kg/m2). MACE was defined as a composite of incident coronary heart disease, recurrent stroke, peripheral vascular disease (PVD), heart failure, and cardiovascular‐related mortality. Multivariable Cox regression was used to assess differences in MACE risk between BMI categories.Results: At baseline, 1217 (4.0%) were underweight, 10 783 (35.1%) had a normal BMI, 10 979 (35.8%) had overweight, 5206 (17.0%) had obesity Class I, 1749 (5.7%) Class II, and 768 (2.5%) Class III. In multivariable analysis, higher BMI were associated with lower risk of subsequent MACE [overweight: HR 0.96, 95% CI 0.93–0.99)]; PVD [overweight: 0.65 (0.49–0.85); obesity Class III: 0.19 (0.50–0.77)]; cardiovascular‐related death [overweight: 0.80 (0.74–0.86); obesity Class I: 0.79 (0.71–0.88); Class II: 0.80 (0.67–0.96)]; and all‐cause mortality [overweight: 0.75 (0.71–0.79); obesity Class I: 0.75 (0.70–0.81); Class II: 0.77 (0.68–0.86)] when compared to those with normal BMI. The results were similar irrespective of sex, diabetes mellitus, smoking or cancer at time of incident stroke.Conclusions: In patients with incident stroke, overweight or obesity were associated with a more favourable prognosis for subsequent MACE, PVD, and mortality, irrespective of sex, diabetes mellitus, smoking, or cancer at baseline. As with other cohort studies, our study demonstrates an association. Randomized control trials should be considered to robustly evaluate the impact of weight management recommendations on subsequent cardiovascular outcomes in stroke survivors.