Literature DB >> 30137498

Identification of validated case definitions for medical conditions used in primary care electronic medical record databases: a systematic review.

Kerry A McBrien1,2, Sepideh Souri2, Nicola E Symonds3, Azin Rouhi4, Brendan C Lethebe2, Tyler S Williamson2, Stephanie Garies1,2, Richard Birtwhistle5, Hude Quan2, Gabriel E Fabreau2,6, Paul E Ronksley2.   

Abstract

Objectives: Data derived from primary care electronic medical records (EMRs) are being used for research and surveillance. Case definitions are required to identify patients with specific conditions in EMR data with a degree of accuracy. The purpose of this study is to identify and provide a summary of case definitions that have been validated in primary care EMR data. Materials and
Methods: We searched MEDLINE and Embase (from inception to June 2016) to identify studies that describe case definitions for clinical conditions in EMR data and report on the performance metrics of these definitions.
Results: We identified 40 studies reporting on case definitions for 47 unique clinical conditions. The studies used combinations of International Classification of Disease version 9 (ICD-9) codes, Read codes, laboratory values, and medications in their algorithms. The most common validation metric reported was positive predictive value, with inconsistent reporting of sensitivity and specificity. Discussion: This review describes validated case definitions derived in primary care EMR data, which can be used to understand disease patterns and prevalence among primary care populations. Limitations include incomplete reporting of performance metrics and uncertainty regarding performance of case definitions across different EMR databases and countries.
Conclusion: Our review found a significant number of validated case definitions with good performance for use in primary care EMR data. These could be applied to other EMR databases in similar contexts and may enable better disease surveillance when using clinical EMR data. Consistent reporting across validation studies using EMR data would facilitate comparison across studies. Systematic review registration: PROSPERO CRD42016040020 (submitted June 8, 2016, and last revised June 14, 2016).

Entities:  

Year:  2018        PMID: 30137498     DOI: 10.1093/jamia/ocy094

Source DB:  PubMed          Journal:  J Am Med Inform Assoc        ISSN: 1067-5027            Impact factor:   4.497


  9 in total

Review 1.  A primer on quantitative bias analysis with positive predictive values in research using electronic health data.

Authors:  Sophia R Newcomer; Stan Xu; Martin Kulldorff; Matthew F Daley; Bruce Fireman; Jason M Glanz
Journal:  J Am Med Inform Assoc       Date:  2019-12-01       Impact factor: 4.497

2.  Adults with diabetes mellitus in Newfoundland and Labrador: a population-based, cross-sectional analysis.

Authors:  Julia Lukewich; Richard Buote; Shabnam Asghari; Kris Aubrey-Bassler; John Knight; Maria Mathews
Journal:  CMAJ Open       Date:  2020-12-18

3.  Comparing ascertainment of chronic condition status with problem lists versus encounter diagnoses from electronic health records.

Authors:  Robert W Voss; Teresa D Schmidt; Nicole Weiskopf; Miguel Marino; David A Dorr; Nathalie Huguet; Nate Warren; Steele Valenzuela; Jean O'Malley; Ana R Quiñones
Journal:  J Am Med Inform Assoc       Date:  2022-04-13       Impact factor: 4.497

4.  Long term impact of prophylactic antibiotic use before incision versus after cord clamping on children born by caesarean section: longitudinal study of UK electronic health records.

Authors:  Dana Šumilo; Krishnarajah Nirantharakumar; Brian H Willis; Gavin M Rudge; James Martin; Krishna Gokhale; Rasiah Thayakaran; Nicola J Adderley; Joht Singh Chandan; Kelvin Okoth; Isobel M Harris; Ruth Hewston; Magdalena Skrybant; Jonathan J Deeks; Peter Brocklehurst
Journal:  BMJ       Date:  2022-05-17

5.  Identifying cases of spinal cord injury or disease in a primary care electronic medical record database.

Authors:  John Shepherd; Karen Tu; Jacqueline Young; Jawad Chishtie; B Catharine Craven; Rahim Moineddin; Susan Jaglal
Journal:  J Spinal Cord Med       Date:  2021       Impact factor: 1.985

6.  Burden of chronic diseases associated with periodontal diseases: a retrospective cohort study using UK primary care data.

Authors:  Dawit T Zemedikun; Joht Singh Chandan; Devan Raindi; Amarkumar Dhirajlal Rajgor; Krishna Margadhmane Gokhale; Tom Thomas; Marie Falahee; Paola De Pablo; Janet M Lord; Karim Raza; Krishnarajah Nirantharakumar
Journal:  BMJ Open       Date:  2021-12-19       Impact factor: 2.692

7.  The data-collection on adverse effects of anti-HIV drugs (D:A:D) model for predicting cardiovascular events: External validation in a diverse cohort of people living with HIV.

Authors:  Ifedioranma Anikpo; Afiba Manza-A Agovi; Matthew J Cvitanovich; Frank Lonergan; Marc Johnson; Rohit P Ojha
Journal:  HIV Med       Date:  2021-08-19       Impact factor: 3.094

8.  Increased Cardiometabolic and Mortality Risk Following Childhood Maltreatment in the United Kingdom.

Authors:  Joht Singh Chandan; Kelvin Okoth; Krishna Margadhamane Gokhale; Siddhartha Bandyopadhyay; Julie Taylor; Krishnarajah Nirantharakumar
Journal:  J Am Heart Assoc       Date:  2020-05-15       Impact factor: 5.501

9.  Type 2 diabetes mellitus, glycaemic control, associated therapies and risk of rheumatoid arthritis: a retrospective cohort study.

Authors:  Dawit T Zemedikun; Krishna Gokhale; Joht Singh Chandan; Jennifer Cooper; Janet M Lord; Andrew Filer; Marie Falahee; Krishnarajah Nirantharakumar; Karim Raza
Journal:  Rheumatology (Oxford)       Date:  2021-12-01       Impact factor: 7.580

  9 in total

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