A data-driven concept schema for defining clinical research data needs

ObjectivesThe Patient, Intervention, Control/Comparison, and Outcome (PICO) framework is an effective technique for framing a clinical question. We aim to develop the counterpart of PICO to structure clinical research data needs.MethodsWe use a data-driven approach to abstracting key concepts representing clinical research data needs by adapting and extending an expert-derived framework originally developed for defining cancer research data needs. We annotated clinical trial eligibility criteria, EHR data request logs, and data queries to electronic health records (EHR), to extract and harmonize concept classes representing clinical research data needs. We evaluated the class coverage, class preservation from the original framework, schema generalizability, schema understandability, and schema structural correctness through a semi-structured interview with eight multidisciplinary domain experts. We iteratively refined the schema based on the evaluations.ResultsOur data-driven schema preserved 68% of the 63 classes from the original framework and covered 88% (73/82) of the classes proposed by evaluators. Class coverage for participants of different backgrounds ranged from 60% to 100% with a median value of 95% agreement among the individual evaluators. The schema was found understandable and structurally sound.ConclusionsOur proposed schema may serve as the counterpart to PICO for improving the research data needs communication between researchers and informaticians.     

A simple risk score using routine data for predicting cardiovascular disease in primary care

Background

Population-based screening for cardiovascular disease (CVD) risk, incorporating blood tests, is proposed in several countries.

Aim

The aim of this study was to evaluate whether a simple approach to identifying individuals at high risk of CVD using routine data might be effective.

Design of study

Prospective cohort study (EPIC-Norfolk).

Setting

Norfolk area, UK.

Method

A total of 21 867 men and women aged 40–74 years, who were free from CVD and diabetes at baseline, participated in the study. The discrimination (the area under the receiver operating characteristic curve [aROC]), calibration, sensitivity/specificity, and positive/negative predictive value were evaluated for different risk thresholds of the Framingham risk equations and the Cambridge diabetes risk score (as an example of a simple risk score using routine data from electronic general practice records).

Results

During 203 664 person-years of follow-up, 2213 participants developed a first CVD event (10.9 per 1000 person-years). The Cambridge diabetes risk score predicted CVD events reasonably well (aROC 0.72; 95% confidence interval [CI] = 0.71 to 0.73), while the Framingham risk score had the best predictive ability (aROC 0.77; 95% CI = 0.76 to 0.78). The Framingham risk score overestimated risk of developing CVD in this representative British population by 60%.

Conclusion

A risk score incorporating routinely available data from GP records performed reasonably well at predicting CVD events. This suggests that it might be more efficient to use routine data as the first stage in a stepwise population screening programme to identify people at high risk of developing CVD before more time- and resource-consuming tests are used.     

Exploring preferences for symptom management in primary care: a discrete choice experiment using a questionnaire survey

BackgroundSymptoms are important drivers for the use of primary care services. Strategies aimed at shifting the focus away from the GP have broadened the range of primary healthcare available.AimTo explore preferences for managing symptoms and investigate trade-offs that the public are willing to make when deciding between different primary care services.MethodA discrete choice experiment examined management preferences for three symptoms of differing seriousness (diarrhoea, dizziness, and chest pain). Willingness-to-pay estimates compared preferences between symptoms, and by sex, age, and income.ResultsPreferences differed significantly between symptoms. ‘Self-care’ was the preferred action for diarrhoea and ‘consulting a GP’ for dizziness and chest pain. ‘Waiting time’ and ‘chance of a satisfactory outcome’ were important factors for all three symptoms, although their relative importance differed. Broadly, people were more prepared to wait longer and less prepared to trade a good chance of a satisfactory outcome for symptoms rated as more serious. Generally, preferences within subgroups followed similar patterns as for the whole sample, although there were differences in the relative strength of preferences.ConclusionDespite increased choices in primary care, ‘traditional’ actions of ‘self-care’ for minor symptoms and ‘GP consultation’ for more serious symptoms were preferred. The present findings suggest, however, that people may be willing to trade between different health services, particularly for less serious symptoms. Understanding the relative importance of different factors may help inform interventions aimed at changing management behaviour or improving services.     

A clinical prediction rule for meniscal tears in primary care: development and internal validation using a multicentre study

Background

In primary care, meniscal tears are difficult to detect. A quick and easy clinical prediction rule based on patient history and a single meniscal test may help physicians to identify high-risk patients for referral for magnetic resonance imaging (MRI).

Aim

The study objective was to develop and internally validate a clinical prediction rule (CPR) for the detection of meniscal tears in primary care.

Design and setting

In a cross-sectional multicentre study, 121 participants from primary care were included if they were aged 18–65 years with knee complaints that existed for <6 months, and who were suspected to suffer from a meniscal tear.

Method

One diagnostic physical meniscal test and 14 clinical variables were considered to be predictors of MRI outcome. Using known predictors for the presence of meniscal tears, a ‘quick and easy’ CPR was derived.

Results

The final CPR included the variables sex, age, weight-bearing during trauma, performing sports, effusion, warmth, discolouration, and Deep Squat test. The final model had an AUC of 0.76 (95% CI = 0.72 to 0.80). A cut-point of 150 points yielded an overall sensitivity of 86.1% and a specificity of 45.5%. For this cut-point, the positive predictive value was 55.0%, and the negative predictive value was 81.1%. A scoring system was provided including the corresponding predicted probabilities for a meniscal tear.

Conclusion

The CPR improved the detection of meniscal tears in primary care. Further evaluation of the CPR in new primary care patients is needed, however, to assess its usefulness.