PCOS is associated with psychiatric morbidity and increased risk of ADHD and ASD in their children. Screening for mental health disorders should be considered during assessment.

Context The effects of Polycystic Ovary Syndrome (PCOS) on cardiovascular morbidity and mortality are unclear. Objective To establish the relative risk of myocardial infarction (MI), stroke, angina, revascularization and cardiovascular mortality for women with PCOS. Design Data were extracted from the Clinical Practice Research Datalink Aurum database. Patients with PCOS were matched to controls (1:1) by age, body mass index (BMI) category and primary care practice. The primary outcome was the time to major adverse cardiovascular event (MACE); a composite endpoint incorporating MI, stroke, angina, revascularization and cardiovascular mortality. Secondary outcomes were the individual MACE endpoints. Results Of 219,034 with a diagnosis of PCOS, 174,660 (79.7%) met the eligibility criteria and were matched. Crude rates of the composite endpoint, MI, stroke, angina, revascularization and cardiovascular mortality were respectively 82.7, 22.7, 27.4, 32.8, 10.5 and 6.97 per 100,000 patient-years for cases, and 64.3, 15.9, 25.7, 19.8, 7.13 and 7.75 per 100,000 patient-years for controls. In adjusted cox proportional hazard models (CPHM), the hazard ratios [HR] were 1.26 (95% confidence interval=1.13-1.41), 1.38 (1.11-1.72), 1.60 (1.32-1.94) and 1.50 (1.08-2.07) for the composite outcome, MI, angina and revascularization, respectively. In a time-dependent CPHM, weight gain (HR 1.01 [1.00-1.01]), prior type 2 diabetes (T2DM) (HR 2.40 [1.76-3.30]) and social deprivation (HR 1.53 [1.11-2.11]) increased risk of progression to the composite endpoint. Conclusions The risk of incident MI, angina and revascularization is increased in young women with PCOS. Weight and T2DM are potentially modifiable risk factors amenable to intervention.

Diabetes plays an important role in the complex relationship between chronic kidney disease (CKD) and cardiovascular disease. This retrospective observational study compared the influence of estimated glomerular filtration rate (eGFR) and proteinuria on the risk of major adverse cardiovascular event (MACE; myocardial infarction or stroke) in CKD patients with and without diabetes. Data were from a linked database of UK electronic health records. Individuals with CKD and no prior MACE were classified as type 1 diabetes (T1DM; n = 164), type 2 diabetes (T2DM; n = 9,711), and non-diabetes (non-DM; n = 75,789). Monthly updated time-dependent Cox proportional hazard models were constructed to calculate adjusted hazard ratios (aHRs) for progression to MACE from first record of abnormal eGFR or proteinuria (index date). In non-DM, aHRs (95% CIs) by baseline eGFR category (referent G2) were G1: 0.70 (0.55–0.90), G3a: 1.28 (1.20–1.35), G3b: 1.64 (1.52–1.76), G4: 2.19 (1.98–2.43), and G5: 3.12 (2.44–3.99), and by proteinuria category (referent A1) were A2: 1.13 (1.00–1.28), A2/3 (severity indeterminable): 1.58 (1.28–1.95), and A3: 1.64 (1.38–1.95). In T2DM, aHRs were G1: 0.98 (0.72–1.32), G3a: 1.18 (1.03–1.34), G3b: 1.31 (1.12–1.54), G4: 1.87 (1.53–2.29), G5: 2.87 (1.82–4.52), A2: 1.22 (1.04–1.42), A2/3: 1.45 (1.17–1.79), and A3: 1.82 (1.53–2.16). Low numbers in T1DM precluded analysis. Modelling T2DM and non-DM together, aHRs were, respectively, G1: 3.23 (2.38–4.40) and 0.70 (0.55–0.89); G2: 3.18 (2.73–3.70) and 1.00 (referent); G3a: 3.65 (3.13–4.25) and 1.28 (1.21–1.36); G3b: 4.01 (3.40–4.74) and 1.65 (1.54–1.77); G4: 5.78 (4.70–7.10) and 2.21 (2.00–2.45); G5: 9.00 (5.71–14.18) and 3.14 (2.46–4.00). In conclusion, reduced eGFR and proteinuria were independently associated with increased risk of MACE regardless of diabetes status. However, the risk of MACE in the same eGFR state was 4.6–2.4 times higher in T2DM than in non-DM.

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Incidence and prevalence are key epidemiological determinants characterizing the quantum of a disease. We compared incidence and prevalence estimates derived automatically from the first ever online, essentially real-time, healthcare analytics platform—Livingstone—against findings from comparable peer-reviewed studies in order to validate the descriptive epidemiology module. The source of routine NHS data for Livingstone was the Clinical Practice Research Datalink (CPRD). After applying a general search strategy looking for any disease or condition, 76 relevant studies were first retrieved, of which 10 met pre-specified inclusion and exclusion criteria. Findings reported in these studies were compared with estimates produced automatically by Livingstone. The published reports described elements of the epidemiology of 14 diseases or conditions. Lin’s concordance correlation coefficient (CCC) was used to evaluate the concordance between findings from Livingstone and those detailed in the published studies. The concordance of incidence values in the final year reported by each study versus Livingstone was 0.96 (95% CI: 0.89–0.98), whilst for all annual incidence values the concordance was 0.93 (0.91–0.94). For prevalence, concordance for the final annual prevalence reported in each study versus Livingstone was 1.00 (0.99–1.00) and for all reported annual prevalence values, the concordance was 0.93 (0.90–0.95). The concordance between Livingstone and the latest published findings was near perfect for prevalence and substantial for incidence. For the first time, it is now possible to automatically generate reliable descriptive epidemiology from routine health records, and in near-real time. Livingstone provides the first mechanism to rapidly generate standardised, descriptive epidemiology for all clinical events from real world data.

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