Scientific
publications

BACKGROUND: Research into diabetes mellitus (DM) often requires a reproducible method for identifying and distinguishing individuals with type 1 DM (T1DM) and type 2 DM (T2DM). OBJECTIVES: To develop a method to identify individuals with T1DM and T2DM using UK primary care electronic health records. METHODS: Using data from The Health Improvement Network primary care database, we developed a two-step algorithm. The first algorithm step identified individuals with potential T1DM or T2DM based on diagnostic records, treatment, and clinical test results. We excluded individuals with records for rarer DM subtypes only. For individuals to be considered diabetic, they needed to have at least two records indicative of DM; one of which was required to be a diagnostic record. We then classified individuals with T1DM and T2DM using the second algorithm step. A combination of diagnostic codes, medication prescribed, age at diagnosis, and whether the case was incident or prevalent were used in this process. We internally validated this classification algorithm through comparison against an independent clinical examination of The Health Improvement Network electronic health records for a random sample of 500 DM individuals. RESULTS: Out of 9,161,866 individuals aged 0-99 years from 2000 to 2014, we classified 37,693 individuals with T1DM and 418,433 with T2DM, while 1,792 individuals remained unclassified. A small proportion were classified with some uncertainty (1,155 [3.1%] of all individuals with T1DM and 6,139 [1.5%] with T2DM) due to unclear health records. During validation, manual assignment of DM type based on clinical assessment of the entire electronic record and algorithmic assignment led to equivalent classification in all instances. CONCLUSION: The majority of individuals with T1DM and T2DM can be readily identified from UK primary care electronic health records. Our approach can be adapted for use in other health care settings.

BACKGROUND: Few studies have evaluated incidence rates and risk factors for heart failure hospitalization (HFH) and mortality starting at initial heart failure (HF) diagnosis. METHODS: Patients with a first ever recorded diagnosis of HF between January 2000 and December 2005 (N=3516) were identified from The Health Improvement Network primary care database and followed until April 2011 to identify HFHs (through linked hospitalization data) and deaths. HF patients were stratified by hospitalization status at HF diagnosis (start date), and hazard ratios (HRs) for HFH and death were estimated using Cox regression. Predictors of HF re-admission were identified by nested case-control analysis among patients hospitalized at start date, using patients with a new HFH during follow-up as cases and a set of age and sex matched controls. RESULTS: During a mean follow-up of 4.5years, 32% of incident HF patients (n=1119) had a HFH and 58.5% died (n=2056). Compared with patients not hospitalized at initial HF diagnosis (N=2759), a two-fold increased risk of HFH during follow-up, consistent over time, was seen in patients hospitalized at initial HF diagnosis (N=757); adjusted HR 2.14 (95% CI: (1.88-2.44). Major predictors of HFH were renal impairment, diabetes and prior HFH. Mortality rates were higher in HF patients hospitalized at initial HF diagnosis, but the major determinant of mortality was HFH during follow-up: HR 3.88 (95% CI: 3.53-4.26) irrespective of HFH status at initial diagnosis. CONCLUSION: Patients hospitalized at initial HF diagnosis or with a HFH thereafter are at high risk of worse outcomes.

OBJECTIVES: Previous, large, prescription-event monitoring studies in patients receiving PPI therapy recorded instances of convulsion or seizure. The objective of this study was to quantify the relative risk of seizure associated with the use of proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) in a general population, overall and stratified by epilepsy status, and to determine the effects of demographics and comorbidities. METHODS: In this observational study (NCT01744301), patients aged 20-84years in the study period from 1 January 2005 to 31 December 2011 were identified from The Health Improvement Network. In a nested case-control analysis, 8605 patients with seizure were matched to 40000 controls. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated using logistic regression. RESULTS: After adjustment, there were no associations between current PPI use and seizure risk in the overall population (OR: 1.05; 95% CI: 0.87-1.27), the subcohort with epilepsy (OR: 0.87; 95% CI: 0.49-1.53), and the subcohort without epilepsy (OR: 1.05; 95% CI: 0.87-1.28). There were no associations between current H2RA use and seizure risk in the overall population (OR: 1.16; 95% CI: 0.62-2.18) and the subcohort without epilepsy (OR: 1.02; 95% CI: 0.51-2.01). Seizures were less frequent in women than in men. Dementia/psychosis, anxiety, depression, and use of anxiolytics, antidepressants, and paracetamol were associated with an increased seizure risk. CONCLUSIONS: Our study revealed that the use of PPIs and the use of H2RAs were not associated with an increased risk of seizures in the overall population or in the cohorts stratified by epilepsy status.

OBJECTIVES: To investigate the cardiovascular safety of non-steroidal anti-inflammatory drugs (NSAIDs) and estimate the risk of hospital admission for heart failure with use of individual NSAIDs. DESIGN: Nested case-control study. SETTING: Five population based healthcare databases from four European countries (the Netherlands, Italy, Germany, and the United Kingdom). PARTICIPANTS: Adult individuals (age ≥18 years) who started NSAID treatment in 2000-10. Overall, 92 163 hospital admissions for heart failure were identified and matched with 8 246 403 controls (matched via risk set sampling according to age, sex, year of cohort entry). MAIN OUTCOME MEASURE: Association between risk of hospital admission for heart failure and use of 27 individual NSAIDs, including 23 traditional NSAIDs and four selective COX 2 inhibitors. Associations were assessed by multivariable conditional logistic regression models. The dose-response relation between NSAID use and heart failure risk was also assessed. RESULTS: Current use of any NSAID (use in preceding 14 days) was found to be associated with a 19% increase of risk of hospital admission for heart failure (adjusted odds ratio 1.19; 95% confidence interval 1.17 to 1.22), compared with past use of any NSAIDs (use >183 days in the past). Risk of admission for heart failure increased for seven traditional NSAIDs (diclofenac, ibuprofen, indomethacin, ketorolac, naproxen, nimesulide, and piroxicam) and two COX 2 inhibitors (etoricoxib and rofecoxib). Odds ratios ranged from 1.16 (95% confidence interval 1.07 to 1.27) for naproxen to 1.83 (1.66 to 2.02) for ketorolac. Risk of heart failure doubled for diclofenac, etoricoxib, indomethacin, piroxicam, and rofecoxib used at very high doses (≥2 defined daily dose equivalents), although some confidence intervals were wide. Even medium doses (0.9-1.2 defined daily dose equivalents) of indomethacin and etoricoxib were associated with increased risk. There was no evidence that celecoxib increased the risk of admission for heart failure at commonly used doses. CONCLUSIONS: The risk of hospital admission for heart failure associated with current use of NSAIDs appears to vary between individual NSAIDs, and this effect is dose dependent. This risk is associated with the use of a large number of individual NSAIDs reported by this study, which could help to inform both clinicians and health regulators.

BACKGROUND: Electronic medical records (EMR) can be a cost-effective source for hypertension surveillance. However, diagnosis of hypertension in EMR is commonly under-coded and warrants the needs to review blood pressure and antihypertensive drugs for hypertension case identification. METHODS: We included all the patients actively registered in The Health Improvement Network (THIN) database, UK, on 31 December 2011. Three case definitions using diagnosis code, antihypertensive drug prescriptions and abnormal blood pressure, respectively, were used to identify hypertension patients. We compared the prevalence and treatment rate of hypertension in THIN with results from Health Survey for England (HSE) in 2011. RESULTS: Compared with prevalence reported by HSE (29.7%), the use of diagnosis code alone (14.0%) underestimated hypertension prevalence. The use of any of the definitions (38.4%) or combination of antihypertensive drug prescriptions and abnormal blood pressure (38.4%) had higher prevalence than HSE. The use of diagnosis code or two abnormal blood pressure records with a 2-year period (31.1%) had similar prevalence and treatment rate of hypertension with HSE. CONCLUSIONS: Different definitions should be used for different study purposes. The definition of 'diagnosis code or two abnormal blood pressure records with a 2-year period' could be used for hypertension surveillance in THIN.

OBJECTIVE: The use of risk prediction models grows as electronic medical records become widely available. Here, we develop and validate a model to identify individuals at increased risk for colorectal cancer (CRC) by analyzing blood counts, age, and sex, then determine the model's value when used to supplement conventional screening. MATERIALS AND METHODS: Primary care data were collected from a cohort of 606 403 Israelis (of whom 3135 were diagnosed with CRC) and a case control UK dataset of 5061 CRC cases and 25 613 controls. The model was developed on 80% of the Israeli dataset and validated using the remaining Israeli and UK datasets. Performance was evaluated according to the area under the curve, specificity, and odds ratio at several working points. RESULTS: Using blood counts obtained 3-6 months before diagnosis, the area under the curve for detecting CRC was 0.82 ± 0.01 for the Israeli validation set. The specificity was 88 ± 2% in the Israeli validation set and 94 ± 1% in the UK dataset. Detecting 50% of CRC cases, the odds ratio was 26 ± 5 and 40 ± 6, respectively, for a false-positive rate of 0.5%. Specificity for 50% detection was 87 ± 2% a year before diagnosis and 85 ± 2% for localized cancers. When used in addition to the fecal occult blood test, our model enabled more than a 2-fold increase in CRC detection. DISCUSSION: Comparable results in 2 unrelated populations suggest that the model should generally apply to the detection of CRC in other groups. The model's performance is superior to current iron deficiency anemia management guidelines, and may help physicians to identify individuals requiring additional clinical evaluation. CONCLUSIONS: Our model may help to detect CRC earlier in clinical practice.