Dataset Information

Statin use and the risk of colorectal cancer in a population-based electronic health records study.

ABSTRACT: There is extensive debate regarding the protective effect of 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins) on colorectal cancer (CRC). We aimed to assess the association between CRC risk and exposure to statins using a large cohort with prescription data. We carried out a case-control study in Catalonia using the System for Development of Primary Care Research (SIDIAP) database that recorded patient diseases history and linked data on reimbursed medication. The study included 25 811 cases with an incident diagnosis of CRC between 2010 and 2015 and 129 117 frequency-matched controls. Subjects were classified as exposed to statins if they had ever been dispensed statins. Analysis considering mean daily defined dose, cumulative duration and type of statin were performed. Overall, 66 372 subjects (43%) were exposed to statins. There was no significant decrease of CRC risk associated to any statin exposure (OR?=?0.98; 95% CI: 0.95-1.01). Only in the stratified analysis by location a reduction of risk for rectal cancer was observed associated to statin exposure (OR?=?0.87; 95% CI: 0.81-0.92). This study does not support an overall protective effect of statins in CRC, but a protective association with rectal cancer merits further research.

SUBMITTER: Ibanez-Sanz G

PROVIDER: S-EPMC6753123 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Statin use and the risk of colorectal cancer in a population-based electronic health records study.

Ibáñez-Sanz Gemma G Guinó Elisabet E Pontes Caridad C Quijada-Manuitt Mª Ángeles MÁ de la Peña-Negro Luisa C LC Aragón María M Domínguez Marga M Rodríguez-Alonso Lorena L Blasco Alex A García-Rodríguez Ana A Morros Rosa R Moreno Victor V

Scientific reports 20190919 1

There is extensive debate regarding the protective effect of 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors (statins) on colorectal cancer (CRC). We aimed to assess the association between CRC risk and exposure to statins using a large cohort with prescription data. We carried out a case-control study in Catalonia using the System for Development of Primary Care Research (SIDIAP) database that recorded patient diseases history and linked data on reimbursed medication. The study inclu ...[more]

PMID: 31537841

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Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Project description:BackgroundThe database used for the NHS Bowel Cancer Screening Programme (BCSP) derives participant information from primary care records. Combining predictors with FOBTs has shown to improve referral decisions and accuracy. The richer data available from GP databases could be used to complement screening referral decisions by identifying those at greatest risk of colorectal cancer. We determined the availability of data for key predictors and whether this information could be used to inform more accurate screening referral decisions.MethodsAn English BCSP cohort was derived using the electronic notifications received from the BCSP database to GP records. The cohort covered a period between 13th May 2009 to 17th January 2017. Completeness of variables and univariable associations were assessed. Risk prediction models were developed using Cox regression and multivariable fractional polynomials with backwards elimination. Optimism adjusted performance metrics were reported. The sensitivity and specificity of a combined approach using the negative FOBT model plus FOBT positive patients was determined using a probability equivalent to a 3% PPV NICE guidelines level.Results292,059 participants aged 60-74 were derived for the BCSP screening cohort. A model including the screening test result had a C-statistic of 0.860, c-slope of 0.997, and R2 of 0.597. A model developed for negative screening results only had a C-statistic of 0.597, c-slope of 0.940, and R2 of 0.062. Risk predictors included in the models included; age, sex, alcohol consumption, IBS diagnosis, family history of gastrointestinal cancer, smoking status, previous negatives and whether a GP had ordered a blood test. For the combined screening approach, sensitivity increased slightly from 53.90% (FOBT only) to 58.82% but at the expense of an increased referral rate.ConclusionsThis research has identified several potential predictors for CRC in a BCSP population. A risk prediction model developed for BCSP FOBT negative patients was not clinically useful due to a low sensitivity and increased referral rate. The predictors identified in this study should be investigated in a refined algorithm combining the quantitative FIT result. Combining data from multiple sources enables fuller patient profiles using the primary care and screening database interface.

Project description:ObjectiveSubstance use screening in adolescence is unstandardized and often documented in clinical notes, rather than in structured electronic health records (EHRs). The objective of this study was to integrate logic rules with state-of-the-art natural language processing (NLP) and machine learning technologies to detect substance use information from both structured and unstructured EHR data.Materials and methodsPediatric patients (10-20 years of age) with any encounter between July 1, 2012, and October 31, 2017, were included (n = 3890 patients; 19 478 encounters). EHR data were extracted at each encounter, manually reviewed for substance use (alcohol, tobacco, marijuana, opiate, any use), and coded as lifetime use, current use, or family use. Logic rules mapped structured EHR indicators to screening results. A knowledge-based NLP system and a deep learning model detected substance use information from unstructured clinical narratives. System performance was evaluated using positive predictive value, sensitivity, negative predictive value, specificity, and area under the receiver-operating characteristic curve (AUC).ResultsThe dataset included 17 235 structured indicators and 27 141 clinical narratives. Manual review of clinical narratives captured 94.0% of positive screening results, while structured EHR data captured 22.0%. Logic rules detected screening results from structured data with 1.0 and 0.99 for sensitivity and specificity, respectively. The knowledge-based system detected substance use information from clinical narratives with 0.86, 0.79, and 0.88 for AUC, sensitivity, and specificity, respectively. The deep learning model further improved detection capacity, achieving 0.88, 0.81, and 0.85 for AUC, sensitivity, and specificity, respectively. Finally, integrating predictions from structured and unstructured data achieved high detection capacity across all cases (0.96, 0.85, and 0.87 for AUC, sensitivity, and specificity, respectively).ConclusionsIt is feasible to detect substance use screening and results among pediatric patients using logic rules, NLP, and machine learning technologies.

Project description:BackgroundThe mission of medical schools is a sustainable commitment to orient education, research, and services based on the priorities and expectations of society. The most common complaints of patients from comprehensive health service centers (CHSCs) based on the data from electronic health records were assessed in order to determine primary health care (PHC) priorities for the educational planning of medical students in Iran.MethodsA population-based national study was designed to assess clinical complaints of patients in all age groups who were referred to CHSCs at least once to be visited by physicians. All the data in the census were extracted from electronic health records in PHC system during 2015-2020, classified by the International Classification of Primary Care 2nd edition (ICPC-2e-English), and statistically analyzed. The total number of complaints that were recorded in the system was 17,430,139.Results59% of the referring patients were women. The highest number of referrals was related to the age group of 18-59 years (56.9%), while the lowest belonged to the elderly people (13.3%). In all age and sex groups, the first ten complaints of patients with three top priorities in each category included process (follow-up, consultation, and results exam), digestive (toothache and gum complaint, abdominal pain, and diarrhea), respiratory (cough, sore throat, and runny nose), general (fever, pain, and weakness and fatigue), musculoskeletal (back pain, leg complaint, and knee injuries), endocrine and nutritional (weight gain, Feeding problem, and weight loss), cardiovascular (hypertension, palpitations, and Postural hypotension), neurological (headache, dizziness, and paralysis), sexual dysfunction (vaginal complaint, discharge, and irregular menstruation), and dermatological (pruritus, rash, and inflammation) problems.ConclusionHigh priorities in referring to PHC had a key role in assessing the country's health needs. Since this study was in line with the national pattern of complaints and patients' profile, the present findings can be helpful to amend policy-making, educational planning and curricula development in medical schools.

Project description:ImportanceStatin use prior to hospitalization for Coronavirus Disease 2019 (COVID-19) is hypothesized to improve inpatient outcomes including mortality, but prior findings from large observational studies have been inconsistent, due in part to confounding. Recent advances in statistics, including incorporation of machine learning techniques into augmented inverse probability weighting with targeted maximum likelihood estimation, address baseline covariate imbalance while maximizing statistical efficiency.ObjectiveTo estimate the association of antecedent statin use with progression to severe inpatient outcomes among patients admitted for COVD-19.Design, setting and participantsWe retrospectively analyzed electronic health records (EHR) from individuals ≥ 40-years-old who were admitted between March 2020 and September 2022 for ≥ 24 h and tested positive for SARS-CoV-2 infection in the 30 days before to 7 days after admission.ExposureAntecedent statin use-statin prescription ≥ 30 days prior to COVID-19 admission.Main outcomeComposite end point of in-hospital death, intubation, and intensive care unit (ICU) admission.ResultsOf 15,524 eligible COVID-19 patients, 4412 (20%) were antecedent statin users. Compared with non-users, statin users were older (72.9 (SD: 12.6) versus 65.6 (SD: 14.5) years) and more likely to be male (54% vs. 51%), White (76% vs. 71%), and have ≥ 1 medical comorbidity (99% vs. 86%). Unadjusted analysis demonstrated that a lower proportion of antecedent users experienced the composite outcome (14.8% vs 19.3%), ICU admission (13.9% vs 18.3%), intubation (5.1% vs 8.3%) and inpatient deaths (4.4% vs 5.2%) compared with non-users. Risk differences adjusted for labs and demographics were estimated using augmented inverse probability weighting with targeted maximum likelihood estimation using Super Learner. Statin users still had lower rates of the composite outcome (adjusted risk difference: - 3.4%; 95% CI: - 4.6% to - 2.1%), ICU admissions (- 3.3%; - 4.5% to - 2.1%), and intubation (- 1.9%; - 2.8% to - 1.0%) but comparable inpatient deaths (0.6%; - 1.3% to 0.1%).Conclusions and relevanceAfter controlling for confounding using doubly robust methods, antecedent statin use was associated with minimally lower risk of severe COVID-19-related outcomes, ICU admission and intubation, however, we were not able to corroborate a statin-associated mortality benefit.

Dataset Information

Statin use and the risk of colorectal cancer in a population-based electronic health records study.

Publications

Statin use and the risk of colorectal cancer in a population-based electronic health records study.

Similar Datasets

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