Selection of Medical Diagnostic Codes for Analysis of Electronic Patient Records. Application to Stroke in a Primary Care Database

BACKGROUND: Electronic patient records from primary care databases are increasingly used in public health and health services research but methods used to identify cases with disease are not well described. This study aimed to evaluate the relevance of different codes for the identification of acute stroke in a primary care database, and to evaluate trends in the use of different codes over time.METHODS: Data were obtained from the General Practice Research Database from 1997 to 2006. All subjects had a minimum of 24 months of up-to-standard record before the first recorded stroke diagnosis. Initially, we identified stroke cases using a supplemented version of the set of codes for prevalent stroke used by the Office for National Statistics in Key health statistics from general practice 1998 (ONS codes). The ONS codes were then independently reviewed by four raters and a restricted set of 121 codes for 'acute stroke' was identified but the kappa statistic was low at 0.23.RESULTS: Initial extraction of data using the ONS codes gave 48,239 cases of stroke from 1997 to 2006. Application of the restricted set of codes reduced this to 39,424 cases. There were 2,288 cases whose index medical codes were for 'stroke annual review' and 3,112 for 'stroke monitoring'. The frequency of stroke review and monitoring codes as index codes increased from 9 per year in 1997 to 1,612 in 2004, 1,530 in 2005 and 1,424 in 2006. The one year mortality of cases with the restricted set of codes was 29.1% but for 'stroke annual review,' 4.6% and for 'stroke monitoring codes', 5.7%.CONCLUSION: In the analysis of electronic patient records, different medical codes for a single condition may have varying clinical and prognostic significance; utilisation of different medical codes may change over time; researchers with differing clinical or epidemiological experience may have differing interpretations of the relevance of particular codes. There is a need for greater transparency in the selection of sets of codes for different conditions, for the reporting of sensitivity analyses using different sets of codes, as well as sharing of code sets among researchers

Population based absolute and relative survival to 1 year of people with diabetes following a myocardial infarction: A cohort study using hospital admissions data

<p>Abstract</p> <p>Background</p> <p>People with diabetes who experience an acute myocardial infarction (AMI) have a higher risk of death and recurrence of AMI. This study was commissioned by the Department for Transport to develop survival tables for people with diabetes following an AMI in order to inform vehicle licensing.</p> <p>Methods</p> <p>A cohort study using data obtained from national hospital admission datasets for England and Wales was carried out selecting all patients attending hospital with an MI for 2003-2006 (inclusion criteria: aged 30+ years, hospital admission for MI (defined using ICD 10 code I21-I22). STATA was used to create survival tables and factors associated with survival were examined using Cox regression.</p> <p>Results</p> <p>Of 157,142 people with an MI in England and Wales between 2003-2006, the relative risk of death or recurrence of MI for those with diabetes (n = 30,407) in the first 90 days was 1.3 (95%CI: 1.26-1.33) crude rates and 1.16 (95%CI: 1.1-1.2) when controlling for age, gender, heart failure and surgery for MI) compared with those without diabetes (n = 129,960). At 91-365 days post AMI the risk was 1.7 (95% CI 1.6-1.8) crude and 1.50 (95%CI: 1.4-1.6) adjusted. The relative risk of death or re-infarction was higher at younger ages for those with diabetes and directly after the AMI (Relative risk; RR: 62.1 for those with diabetes and 28.2 for those without diabetes aged 40-49 [compared with population risk]).</p> <p>Conclusions</p> <p>This is the first study to provide population based tables of age stratified risk of re-infarction or death for people with diabetes compared with those without diabetes. These tables can be used for giving advice to patients, developing a baseline to compare intervention studies or developing license or health insurance guidelines.</p

The influence of glucose-lowering therapies on cancer risk in type 2 diabetes

AIMS/HYPOTHESIS: The risk of developing a range of solid tumours is increased in type 2 diabetes, and may be influenced by glucose-lowering therapies. We examined the risk of development of solid tumours in relation to treatment with oral agents, human insulin and insulin analogues. METHODS: This was a retrospective cohort study of people treated in UK general practices. Those included in the analysis developed diabetes >40 years of age, and started treatment with oral agents or insulin after 2000. A total of 62,809 patients were divided into four groups according to whether they received monotherapy with metformin or sulfonylurea, combined therapy (metformin plus sulfonylurea), or insulin. Insulin users were grouped according to treatment with insulin glargine, long-acting human insulin, biphasic analogue and human biphasic insulin. The outcome measures were progression to any solid tumour, or cancer of the breast, colon, pancreas or prostate. Confounding factors were accounted for using Cox proportional hazards models. RESULTS: Metformin monotherapy carried the lowest risk of cancer. In comparison, the adjusted HR was 1.08 (95% CI 0.96-1.21) for metformin plus sulfonylurea, 1.36 (95% CI 1.19-1.54) for sulfonylurea monotherapy, and 1.42 (95% CI 1.27-1.60) for insulin-based regimens. Adding metformin to insulin reduced progression to cancer (HR 0.54, 95% CI 0.43-0.66). The risk for those on basal human insulin alone vs insulin glargine alone was 1.24 (95% CI 0.90-1.70). Compared with metformin, insulin therapy increased the risk of colorectal (HR 1.69, 95% CI 1.23-2.33) or pancreatic cancer (HR 4.63, 95% CI 2.64-8.10), but did not influence the risk of breast or prostate cancer. Sulfonylureas were associated with a similar pattern of risk as insulin. CONCLUSIONS/INTERPRETATION: Those on insulin or insulin secretagogues were more likely to develop solid cancers than those on metformin, and combination with metformin abolished most of this excess risk. Metformin use was associated with lower risk of cancer of the colon or pancreas, but did not affect the risk of breast or prostate cancer. Use of insulin analogues was not associated with increased cancer risk as compared with human insulin

Type 2 diabetes, socioeconomic status and life expectancy in Scotland (2012-2014):a population-based observational study

Aims/hypothesis: The aim of this study was to assess the role of socioeconomic status (SES) in the associations between type 2 diabetes and life expectancy in a complete national population. Methods: An observational population-based cohort study was performed using the Scottish Care Information – Diabetes database. Age-specific life expectancy (stratified by SES) was calculated for all individuals with type 2 diabetes in the age range 40–89 during the period 2012–2014, and for the remaining population of Scotland aged 40–89 without type 2 diabetes. Differences in life expectancy between the two groups were calculated. Results: Results were based on 272,597 individuals with type 2 diabetes and 2.75 million people without type 2 diabetes (total for 2013, the middle calendar year of the study period). With the exception of deprived men aged 80–89, life expectancy in people with type 2 diabetes was significantly reduced (relative to the type 2 diabetes-free population) at all ages and levels of SES. Differences in life expectancy ranged from −5.5 years (95% CI −6.2, −4.8) for women aged 40–44 in the second most-deprived quintile of SES, to 0.1 years (95% CI −0.2, 0.4) for men aged 85–89 in the most-deprived quintile of SES. Observed life-expectancy deficits in those with type 2 diabetes were generally greater in women than in men. Conclusions/interpretation: Type 2 diabetes is associated with reduced life expectancy at almost all ages and levels of SES. Elimination of life-expectancy deficits in individuals with type 2 diabetes will require prevention and management strategies targeted at all social strata (not just deprived groups)

A study about the relevance of adding acetylsalicylic acid in primary prevention in subjects with type 2 diabetes mellitus: effects on some new emerging biomarkers of cardiovascular risk

AIM: To evaluate the relevance of adding acetylsalicylic acid (ASA) in primary prevention in subjects with type 2 diabetes mellitus. METHODS: 213 patients with type 2 diabetes mellitus and hypertension were randomized to amlodipine 5 mg, or amlodipine 5 mg + ASA 100 mg for 3 months (Phase A); then, if adequate blood pressure control was reached patients terminated the study; otherwise, amlodipine was up-titrated to 10 mg/day for further 3 months and compared to amlodipine 10 mg + ASA 100 mg (Phase B). We assessed at baseline, at the end of Phase A, and at the end of Phase B the levels of some new emerging biomarkers of cardiovascular risk including: high sensitivity C-reactive protein (Hs-CRP), adiponectin (ADN), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), myeloperoxidase (MPO), soluble CD40 ligand (sCDL40). RESULTS: Compared to baseline, at the end of Phase A, patients treated with amlodipine 5 mg + ASA 100 mg showed a statistically significant reduction of Hs-CRP (−15.0%), TNF-α (−21.7%), MPO (−9.7%), and sCDL40 (−15.7%), and a statistically significant increase of ADN (+15.0%). These values were significantly better than the ones obtained with amlodipine alone. Similarly, at the end of Phase B, amlodipine 10 mg + ASA significantly lowered Hs-CRP (−18.8%), TNF-α (−15.0%), MPO (−9.2%), and sCDL40 (−20.0%) and increased ADN (+11.8%), with a better effect compared to amlodipine alone. CONCLUSION: All biomarkers considered were significantly improved by ASA addition. These data suggest that the use of ASA in primary prevention could be useful in patients with type 2 diabetes mellitus and hypertension. Trial registration: ClinicalTrials.gov: NCT0206421

Lifestyle variables and the risk of myocardial infarction in the General Practice Research Database

<p>Abstract</p> <p>Background</p> <p>The primary objective of this study is to estimate the association between body mass index (BMI) and the risk of first acute myocardial infarction (AMI). As a secondary objective, we considered the association between other lifestyle variables, smoking and heavy alcohol use, and AMI risk.</p> <p>Methods</p> <p>This study was conducted in the general practice research database (GPRD) which is a database based on general practitioner records and is a representative sample of the United Kingdom population. We matched cases of first AMI as identified by diagnostic codes with up to 10 controls between January 1^st, 2001 and December 31^st, 2005 using incidence density sampling. We used multiple imputation to account for missing data.</p> <p>Results</p> <p>We identified 19,353 cases of first AMI which were matched on index date, GPRD practice and age to 192,821 controls. There was a modest amount of missing data in the database, and the patients with missing data had different risks than those with recorded values. We adjusted our analysis for each lifestyle variable jointly and also for age, sex, and number of hospitalizations in the past year. Although a record of underweight (BMI <18.0 kg/m²) did not alter the risk for AMI (adjusted odds ratio (OR): 1.00; 95% confidence interval (CI): 0.87–1.11) when compared with normal BMI (18.0–24.9 kg/m²), obesity (BMI ≥30 kg/m²) predicted an increased risk (adjusted OR: 1.41; 95% CI: 1.35–1.47). A history of smoking also predicted an increased risk of AMI (adjusted OR: 1.81; 95% CI: 1.75–1.87) as did heavy alcohol use (adjusted OR: 1.15; 95% CI: 1.06–1.26).</p> <p>Conclusion</p> <p>This study illustrates that obesity, smoking and heavy alcohol use, as recorded during routine care by a general practitioner, are important predictors of an increased risk of a first AMI. In contrast, low BMI does not increase the risk of a first AMI.</p

The Diabetes Pearl: Diabetes biobanking in The Netherlands

Contains fulltext : 109720.pdf (publisher's version ) (Open Access)ABSTRACT: BACKGROUND: Type 2 diabetes is associated with considerable comorbidity and severe complications, which reduce quality of life of the patients and require high levels of healthcare. The Diabetes Pearl is a large cohort of patients diagnosed with type 2 diabetes, covering different geographical areas in the Netherlands. The aim of the study is to create a research infrastructure that will allow the study of risk factors, including biomarkers and genetic determinants for severe diabetes complications. METHODS/DESIGN: Baseline examinations began November 2009 and will continue through 2012. By the end of 2012, it is expected that 7000 patients with type 2 diabetes will be included in the Diabetes Pearl cohort. To ensure quality of the data collected, standard operation procedures were developed and used in all 8 recruitment centers. From all patients who provide informed consent, the following information is collected: personal information, medication use, physical examination (antropometry, blood pressure, electrocardiography (ECG), retina photographs, ankle-brachial index, peripheral vibration perception), self-report questionnaire (socio-economic status, lifestyle, (family) history of disease, and psychosocial well-being), laboratory measurements (glucose, A1c, lipid profile, kidney function), biobank material (storage of urine and blood samples and isolated DNA). All gathered clinical data and biobank information is uploaded to a database for storage on a national level. Biobanks are maintained locally at all recruitment centers. DISCUSSION: The Diabetes Pearl is large-scale cohort of type 2 diabetes patients in the Netherlands aiming to study risk factors, including biomarkers and genetic markers, for disease deterioration and the development of severe diabetes complications. As a result of the well-designed research design and the national coverage, the Diabetes Pearl data can be of great value to national and international researchers with an interest in diabetes related research

Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting

<p>Abstract</p> <p>Background</p> <p>The World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.</p> <p>Methods</p> <p>We conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.</p> <p>Results</p> <p>Thirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).</p> <p>Conclusions</p> <p>We found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.</p