Identifying individuals with multiple sclerosis in an electronic medical record

Krysko, Kristen M.; Ivers, Noah; Young, Jacqueline; O’Connor, Paul; Tu, Karen

doi:10.1177/1352458514538334

Cited by 35 publications

(26 citation statements)

References 18 publications

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“…Administrative data offer a unique opportunity to study the population-based prevalence of chronic diseases such as MS [19,21,22,23,24,25,26,27,28,29], or Parkinson's disease [30]. …”

Section: Introductionmentioning

confidence: 99%

“…However, administrative data have some limitations: they lack comprehensive clinical data, they do not include some population subgroups, nor those services uncovered by the National Health Service (NHS); moreover, the quality of data is not always perfect. For these reasons, it will be desirable to merge clinical registries and administrative data to obtain a complete view of MS, collecting both clinical and health care information [21]. …”

Section: Introductionmentioning

confidence: 99%

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Prevalence of Multiple Sclerosis in Tuscany (Central Italy): A Study Based on Validated Administrative Data

Battaglia

Policardo²,

Meucci³

et al. 2015

Neuroepidemiology

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Background: Multiple Sclerosis (MS) epidemiology in Italy is mainly based on population-based prevalence studies. Administrative data are an additional source of information, when available, in prevalence studies of chronic diseases such as MS. The aim of our study is to update the prevalence rate of MS in Tuscany (central Italy) as at 2011 using a validated case-finding algorithm based on administrative data. Methods: The prevalence was calculated using an algorithm based on the following administrative data: hospital discharge records, drug-dispensing records, disease-specific exemptions from copayment to health care, home and residential long-term care and inhabitant registry. To test algorithm sensitivity, we used a true-positive reference cohort of MS patients from the Tuscan MS register. To test algorithm specificity, we used another cohort of individuals who were presumably not affected by MS. Results: As at December 31, 2011, we identified 6,890 cases (4,738 females and 2,152 males) with a prevalence of 187.9 per 100,000. The sensitivity of algorithm was 98% and the specificity was 99.99%. Conclusions: We found a prevalence higher than the rates present in literature. Our algorithm, based on administrative data, can accurately identify MS patients; moreover, the resulting cohort is suitable to monitor disease care pathways.

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“…Administrative data offer a unique opportunity to study the population-based prevalence of chronic diseases such as MS [19,21,22,23,24,25,26,27,28,29], or Parkinson's disease [30]. …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prevalence of Multiple Sclerosis in Tuscany (Central Italy): A Study Based on Validated Administrative Data

Battaglia

Policardo²,

Meucci³

et al. 2015

Neuroepidemiology

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“…Krysko et al . 10 found an EMR-based algorithm to identify multiple sclerosis performed well (91.5% sensitivity and 100% specificity) and could be used as an accurate tool in primary-care settings. Ivers and colleagues found that an EMR-based algorithm accurately identified patients with Ischaemic Heart Disease (72.4% sensitivity and 99.3% specificity) while outperforming other methods of identification.…”

Section: Discussionmentioning

confidence: 94%

Development of a validated algorithm for the diagnosis of paediatric asthma in electronic medical records

Cave

Davey

Ahmadi

et al. 2016

npj Prim Care Resp Med

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An accurate estimation of the prevalence of paediatric asthma in Alberta and elsewhere is hampered by uncertainty regarding disease definition and diagnosis. Electronic medical records (EMRs) provide a rich source of clinical data from primary-care practices that can be used in better understanding the occurrence of the disease. The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) database includes cleaned data extracted from the EMRs of primary-care practitioners. The purpose of the study was to develop and validate a case definition of asthma in children 1–17 who consult family physicians, in order to provide primary-care estimates of childhood asthma in Alberta as accurately as possible. The validation involved the comparison of the application of a theoretical algorithm (to identify patients with asthma) to a physician review of records included in the CPCSSN database (to confirm an accurate diagnosis). The comparison yielded 87.4% sensitivity, 98.6% specificity and a positive and negative predictive value of 91.2% and 97.9%, respectively, in the age group 1–17 years. The algorithm was also run for ages 3–17 and 6–17 years, and was found to have comparable statistical values. Overall, the case definition and algorithm yielded strong sensitivity and specificity metrics and was found valid for use in research in CPCSSN primary-care practices. The use of the validated asthma algorithm may improve insight into the prevalence, diagnosis, and management of paediatric asthma in Alberta and Canada.

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“…An EMRALD team physician verified the charts to remove false positives from the study cohort, such as those with only brief runs of AF or those with only transient AF following surgery and follow-up testing that was normal. The methods for developing the search are similar to those used in previous studies [77] and based on previously described patient populations [16]. This search algorithm identified AF patients with one or more of the following components: (1) Recording of AF, including paroxysmal AF, “fibrillation”, and “flutter” in the problem list or history of past health fields of the EMR record [Additional file 3]; (2) electrocardiogram (ECG) and Holter monitor reporting AF; or (3) OAC prescription without clot, thrombosis, embolism, DVT, PE, valve replacement in the problem list, or history of past health fields, in combination with calcium channel blockers (CCB), ß-blockers, or digoxin prescription.…”

Section: Methodsmentioning

confidence: 99%

Improving stroke prevention therapy for patients with atrial fibrillation in primary care: protocol for a pragmatic, cluster-randomized trial

et al. 2016

Self Cite

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BackgroundThe prevalence of atrial fibrillation (AF) is growing as the population ages, and at least 15% of ischemic strokes are attributed to AF. However, many high-risk AF patients are not offered guideline-recommended stroke prevention therapy due to a variety of system, provider, and patient-level barriers.MethodsWe will conduct a pragmatic, cluster-randomized controlled trial randomizing primary care clinics to test a “toolkit” of quality improvement interventions in primary care. In keeping with the recommendations of the chronic care model to simultaneously activate patients and facilitate proactive care by providers, the toolkit includes provider-focused strategies (education, audit and feedback, electronic decision support, and reminders) plus patient-directed strategies (educational letters and reminders). The trial will include two feedback cycles at baseline and approximately 6 months and a final data collection at approximately 12 months. The study will be powered to show a difference of 10% in the primary outcome of proportion of patients receiving guideline-recommended stroke prevention therapy. Analysis will follow the intention-to-treat principle and will be blind to treatment allocation. Unit of analysis will be the patient; models will use generalized estimating equations to account for clustering at the clinical level.DiscussionStroke prevention therapy using anticoagulation in patients with AF is known to reduce strokes by two thirds or more in clinical trials, but most studies indicate under-use of this treatment in real-world practice. If the toolkit successfully improves care for patients with AF, stakeholders will be engaged to facilitate broader application to maximize the potential to improve patient outcomes. The intervention toolkit tested in this project could also provide a model to improve quality of care for other chronic cardiovascular conditions managed in primary care.Trial registrationClinicalTrials.gov (NCT01927445). Registered August 14, 2014 at https://clinicaltrials.gov/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13012-016-0523-2) contains supplementary material, which is available to authorized users.

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Identifying individuals with multiple sclerosis in an electronic medical record

Abstract: Data within an EMR can be used to accurately identify patients with MS. This study has positive implications for clinicians, researchers and policy makers as it provides the potential to identify cohorts of MS patients in the primary care setting to examine quality of care.

Cited by 35 publications

References 18 publications

Prevalence of Multiple Sclerosis in Tuscany (Central Italy): A Study Based on Validated Administrative Data

Prevalence of Multiple Sclerosis in Tuscany (Central Italy): A Study Based on Validated Administrative Data

Development of a validated algorithm for the diagnosis of paediatric asthma in electronic medical records

Improving stroke prevention therapy for patients with atrial fibrillation in primary care: protocol for a pragmatic, cluster-randomized trial

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