Validation of fragility fractures in primary care electronic medical records: A population-based study

Objectives. To investigate the efficacy and the safety of the three most commonly prescribed statins (rosuvastatin, atorvastatin, and pravastatin) for managing dyslipidemia among diabetic patients in Qatar. Subjects and Methods. This retrospective observational population-based study included 350 consecutive diabetes patients who were diagnosed with dyslipidemia and prescribed any of the indicated statins between September 2005 and September 2009. Data was collected by review of the Pharmacy Database, the Electronic Medical Records Database (EMR viewer), and the Patient's Medical Records. Comparisons of lipid profile measurements at baseline and at first- and second-year intervals were taken. Results. Rosuvastatin (10 mg) was the most effective at reducing LDL-C (29.03%). Atorvastatin reduced LDL-C the most at a dose of 40 mg (22.8%), and pravastatin reduced LDL-C the most at a dose of 20 mg (20.3%). All three statins were safe in relation to muscular and hepatic functions. In relation to renal function, atorvastatin was the safest statin as it resulted in the least number of patients at the end of 2 years of treatment with the new onset of microalbuminuria (10.9%) followed by rosuvastatin (14.3%) and then pravastatin (26.6%). Conclusion. In the Qatari context, the most effective statin at reducing LDL-C was rosuvastatin 10 mg. Atorvastatin was the safest statin in relation to renal function. Future large-scale prospective studies are needed to confirm these results.

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Can an alert in primary care electronic medical records increase participation in a population-based screening programme for colorectal cancer? COLO-ALERT, a randomised clinical trial

BMC Cancer ◽

10.1186/1471-2407-14-232 ◽

2014 ◽

Vol 14 (1) ◽

Cited By ~ 4

Author(s):

Carolina Guiriguet-Capdevila ◽

Laura Muñoz-Ortiz ◽

Irene Rivero-Franco ◽

Carme Vela-Vallespín ◽

Mercedes Vilarrubí-Estrella ◽

...

Keyword(s):

Colorectal Cancer ◽

Primary Care ◽

Clinical Trial ◽

Electronic Medical Records ◽

Medical Records ◽

Screening Programme ◽

Randomised Clinical Trial ◽

Population Based

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Risk Diagrams Based on Primary Care Electronic Medical Records and Linked Real-Time PCR Data to Monitor Local COVID-19 Outbreaks During the Summer 2020: A Prospective Study Including 7,671,862 People in Catalonia

Frontiers in Public Health ◽

10.3389/fpubh.2021.693956 ◽

2021 ◽

Vol 9 ◽

Author(s):

Marti Catala ◽

Ermengol Coma ◽

Sergio Alonso ◽

Enrique Álvarez-Lacalle ◽

Silvia Cordomi ◽

...

Keyword(s):

Primary Care ◽

Intensive Care ◽

Electronic Medical Records ◽

Early Identification ◽

Medical Records ◽

Hospital Admissions ◽

Nursing Home Residents ◽

Population Based ◽

Local Hospital ◽

Potential Growth

Monitoring transmission is a prerequisite for containing COVID-19. We report on effective potential growth (EPG) as a novel measure for the early identification of local outbreaks based on primary care electronic medical records (EMR) and PCR-confirmed cases. Secondly, we studied whether increasing EPG precedes local hospital and intensive care (ICU) admissions and mortality. Population-based cohort including all Catalan citizens' PCR tests, hospitalization, intensive care (ICU) and mortality between 1/07/2020 and 13/09/2020; linked EMR covering 88.6% of the Catalan population was obtained. Nursing home residents were excluded. COVID-19 counts were ascertained based on EMR and PCRs separately. Weekly empirical propagation (ρ7) and 14-day cumulative incidence (A14) and 95% confidence intervals were estimated at care management area (CMA) level, and combined as EPG = ρ7 × A14. Overall, 7,607,201 and 6,798,994 people in 43 CMAs were included for PCR and EMR measures, respectively. A14, ρ7, and EPG increased in numerous CMAs during summer 2020. EMR identified 2.70-fold more cases than PCRs, with similar trends, a median (interquartile range) 2 (1) days earlier, and better precision. Upticks in EPG preceded increases in local hospital admissions, ICU occupancy, and mortality. Increasing EPG identified localized outbreaks in Catalonia, and preceded local hospital and ICU admissions and subsequent mortality. EMRs provided similar estimates to PCR, but some days earlier and with better precision. EPG is a useful tool for the monitoring of community transmission and for the early identification of COVID-19 local outbreaks.

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Data Mining Approach to Identify Disease Cohorts from Primary Care Electronic Medical Records: A Case of Diabetes Mellitus

The Open Bioinformatics Journal ◽

10.2174/1875036201710010016 ◽

2017 ◽

Vol 10 (1) ◽

pp. 16-27 ◽

Cited By ~ 11

Author(s):

Ebenezer S. Owusu Adjah ◽

Olga Montvida ◽

Julius Agbeve ◽

Sanjoy K. Paul

Keyword(s):

Diabetes Mellitus ◽

Primary Care ◽

Electronic Medical Records ◽

Medical Records ◽

Characteristic Curve ◽

Large Population ◽

Classification Problem ◽

True Positive Rate ◽

Population Based ◽

True Negative

Background:Identification of diseased patients from primary care based electronic medical records (EMRs) has methodological challenges that may impact epidemiologic inferences.Objective:To compare deterministic clinically guided selection algorithms with probabilistic machine learning (ML) methodologies for their ability to identify patients with type 2 diabetes mellitus (T2DM) from large population based EMRs from nationally representative primary care database.Methods:Four cohorts of patients with T2DM were defined by deterministic approach based on disease codes. The database was mined for a set of best predictors of T2DM and the performance of six ML algorithms were compared based on cross-validated true positive rate, true negative rate, and area under receiver operating characteristic curve.Results:In the database of 11,018,025 research suitable individuals, 379 657 (3.4%) were coded to have T2DM. Logistic Regression classifier was selected as best ML algorithm and resulted in a cohort of 383,330 patients with potential T2DM. Eighty-three percent (83%) of this cohort had a T2DM code, and 16% of the patients with T2DM code were not included in this ML cohort. Of those in the ML cohort without disease code, 52% had at least one measure of elevated glucose level and 22% had received at least one prescription for antidiabetic medication.Conclusion:Deterministic cohort selection based on disease coding potentially introduces significant mis-classification problem. ML techniques allow testing for potential disease predictors, and under meaningful data input, are able to identify diseased cohorts in a holistic way.

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