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Bidulka P, Scott J, Taylor DM, Udayaraj U, Caskey F, Teece L, Sweeting M, Deanfield J, de Belder M, Denaxas S, Weston C, Adlam D, Nitsch D. Impact of chronic kidney disease on case ascertainment for hospitalised acute myocardial infarction: an English cohort study. BMJ Open 2022; 12:e057909. [PMID: 35351727 PMCID: PMC8961119 DOI: 10.1136/bmjopen-2021-057909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 02/23/2022] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVES Acute myocardial infarction (AMI) case ascertainment improves for the UK general population using linked health data sets. Because care pathways for people with chronic kidney disease (CKD) change based on disease severity, AMI case ascertainment for these people may differ compared with the general population. We aimed to determine the association between CKD severity and AMI case ascertainment in two secondary care data sets, and the agreement in estimated glomerular filtration rate (eGFR) between the same data sets. METHODS We used a cohort study design. Primary care records for people with CKD or risk factors for CKD, identified using the National CKD Audit (2015-2017), were linked to the Myocardial Ischaemia National Audit Project (MINAP, 2007-2017) and Hospital Episode Statistics (HES, 2007-2017) secondary care registries. People with an AMI recorded in either MINAP, HES or both were included in the study cohort. CKD status was defined using eGFR, derived from the most recent serum creatinine value recorded in primary care. Moderate-severe CKD was defined as eGFR <60 mL/min/1.73 m2, and mild CKD or at risk of CKD was defined as eGFR ≥60 mL/min/1.73 m2 or eGFR missing. CKD stages were grouped as (1) At risk of CKD and Stages 1-2 (eGFR missing or ≥60 mL/min/1.73 m2), (2) Stage 3a (eGFR 45-59 mL/min/1.73 m2), (3) Stage 3b (eGFR 30-44 mL/min/1.73 m2) and (4) Stages 4-5 (eGFR <30 mL/min/1.73 m2). RESULTS We identified 6748 AMIs: 23% were recorded in both MINAP and HES, 66% in HES only and 11% in MINAP only. Compared with people at risk of CKD or with mild CKD, AMIs in people with moderate-severe CKD were more likely to be recorded in both MINAP and HES (42% vs 11%, respectively), or MINAP only (22% vs 5%), and less likely to be recorded in HES only (36% vs 84%). People with AMIs recorded in HES only or MINAP only had increased odds of death during hospitalisation compared with those recorded in both (adjusted OR 1.61, 95% CI 1.32 to 1.96 and OR 1.60, 95% CI 1.26 to 2.04, respectively). Agreement between eGFR at AMI admission (MINAP) and in primary care was poor (kappa (K) 0.42, SE 0.012). CONCLUSIONS AMI case ascertainment is incomplete in both MINAP and HES, and is associated with CKD severity.
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Affiliation(s)
- Patrick Bidulka
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Jemima Scott
- Population Health Sciences, University of Bristol, Bristol, UK
- Richard Bright Renal Service, North Bristol NHS Trust, Southmead Hospital, Bristol, UK
| | - Dominic M Taylor
- Population Health Sciences, University of Bristol, Bristol, UK
- Richard Bright Renal Service, North Bristol NHS Trust, Southmead Hospital, Bristol, UK
| | - Udaya Udayaraj
- Oxford Kidney Unit, Churchill Hospital, Oxford, Oxfordshire, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, Oxfordshire, UK
| | - Fergus Caskey
- Population Health Sciences, University of Bristol, Bristol, UK
- Richard Bright Renal Service, North Bristol NHS Trust, Southmead Hospital, Bristol, UK
| | - Lucy Teece
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, Leicestershire, UK
| | - Michael Sweeting
- Biostatistics Research Group, Department of Health Sciences, University of Leicester, Leicester, Leicestershire, UK
| | - John Deanfield
- National Institute for Cardiovascular Outcomes Research (NICOR), Barts Health NHS Trust, London, UK
- Institute of Cardiovascular Sciences, University College London, London, UK
| | - Mark de Belder
- National Institute for Cardiovascular Outcomes Research (NICOR), Barts Health NHS Trust, London, UK
| | - Spiros Denaxas
- Institute of Health Informatics, Faculty of Population Health Sciences, University College London, London, UK
- Health Data Research UK, London, UK
| | - Clive Weston
- Glangwili General Hospital, Carmarthen, Carmarthenshire, UK
| | - David Adlam
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Leicester, Leicestershire, UK
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
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Lee S, Doktorchik C, Martin EA, D'Souza AG, Eastwood C, Shaheen AA, Naugler C, Lee J, Quan H. Electronic Medical Record-Based Case Phenotyping for the Charlson Conditions: Scoping Review. JMIR Med Inform 2021; 9:e23934. [PMID: 33522976 PMCID: PMC7884219 DOI: 10.2196/23934] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 11/20/2020] [Accepted: 12/05/2020] [Indexed: 12/16/2022] Open
Abstract
Background Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.
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Affiliation(s)
- Seungwon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Chelsea Doktorchik
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Elliot Asher Martin
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Adam Giles D'Souza
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Alberta Health Services, Calgary, AB, Canada
| | - Cathy Eastwood
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Abdel Aziz Shaheen
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Christopher Naugler
- Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Pathology and Laboratory Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Joon Lee
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Data Intelligence for Health Lab, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Cardiac Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Hude Quan
- Centre for Health Informatics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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