1
|
Bidulka P, Fu EL, Leyrat C, Kalogirou F, McAllister KSL, Kingdon EJ, Mansfield KE, Iwagami M, Smeeth L, Clase CM, Bhaskaran K, van Diepen M, Carrero JJ, Nitsch D, Tomlinson LA. Stopping renin-angiotensin system blockers after acute kidney injury and risk of adverse outcomes: parallel population-based cohort studies in English and Swedish routine care. BMC Med 2020; 18:195. [PMID: 32723383 PMCID: PMC7389346 DOI: 10.1186/s12916-020-01659-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 06/08/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The safety of restarting angiotensin-converting enzyme inhibitors (ACEI) or angiotensin II receptor blockers (ARB) after acute kidney injury (AKI) is unclear. There is concern that previous users do not restart ACEI/ARB despite ongoing indications. We sought to determine the risk of adverse events after an episode of AKI, comparing prior ACEI/ARB users who stop treatment to those who continue. METHODS We conducted two parallel cohort studies in English and Swedish primary and secondary care, 2006-2016. We used multivariable Cox regression to estimate hazard ratios (HR) for hospital admission with heart failure (primary analysis), AKI, stroke, or death within 2 years after hospital discharge following a first AKI episode. We compared risks of admission between people who stopped ACEI/ARB treatment to those who were prescribed ACEI/ARB within 30 days of AKI discharge. We undertook sensitivity analyses, including propensity score-matched samples, to explore the robustness of our results. RESULTS In England, we included 7303 people with AKI hospitalisation following recent ACEI/ARB therapy for the primary analysis. Four thousand three (55%) were classified as stopping ACEI/ARB based on no prescription within 30 days of discharge. In Sweden, we included 1790 people, of whom 1235 (69%) stopped treatment. In England, no differences were seen in subsequent risk of heart failure (HR 1.10; 95% confidence intervals (CI) 0.93-1.30), AKI (HR 0.90; 95% CI 0.77-1.05), or stroke (HR 0.99; 95% CI 0.71-1.38), but there was an increased risk of death (HR 1.27; 95% CI 1.15-1.41) in those who stopped ACEI/ARB compared to those who continued. Results were similar in Sweden: no differences were seen in risk of heart failure (HR 0.91; 95% CI 0.73-1.13) or AKI (HR 0.81; 95% CI 0.54-1.21). However, no increased risk of death was seen (HR 0.94; 95% CI 0.78-1.13) and stroke was less common in people who stopped ACEI/ARB (HR 0.56; 95% CI 0.34-0.93). Results were similar across all sensitivity analyses. CONCLUSIONS Previous ACEI/ARB users who continued treatment after an episode of AKI did not have an increased risk of heart failure or subsequent AKI compared to those who stopped the drugs.
Collapse
Affiliation(s)
- Patrick Bidulka
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
| | - Edouard L Fu
- Department of Clinical Epidemiology, Leiden University Medical Center, Albinusdreef, Leiden, 2333ZA, The Netherlands
| | - Clémence Leyrat
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Fotini Kalogirou
- Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Hills Road, Cambridge, CB2 0QQ, UK
| | - Katherine S L McAllister
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Edward J Kingdon
- Sussex Kidney Unit, Royal Sussex County Hospital, Brighton, BN2 5BE, UK
| | - Kathryn E Mansfield
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Masao Iwagami
- Department of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
| | - Liam Smeeth
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Catherine M Clase
- Department of Medicine, Department of Health Research, Evidence and Impact, McMaster University, Hamilton, ON, Canada
| | - Krishnan Bhaskaran
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Merel van Diepen
- Department of Clinical Epidemiology, Leiden University Medical Center, Albinusdreef, Leiden, 2333ZA, The Netherlands
| | - Juan-Jesus Carrero
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Nobels väg 12, Stockholm, Sweden
| | - Dorothea Nitsch
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Laurie A Tomlinson
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| |
Collapse
|
2
|
Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, Cumbers S, Jonas A, McAllister KSL, Myles P, Granger D, Birse M, Branson R, Moons KGM, Collins GS, Ioannidis JPA, Holmes C, Hemingway H. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ 2020; 368:l6927. [PMID: 32198138 DOI: 10.1136/bmj.l6927] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Sebastian Vollmer
- Alan Turing Institute, Kings Cross, London, UK
- Departments of Mathematics and Statistics, University of Warwick, Coventry, UK
| | - Bilal A Mateen
- Alan Turing Institute, Kings Cross, London, UK
- Warwick Medical School, University of Warwick, Coventry, UK
- Kings College Hospital, Denmark Hill, London, UK
| | - Gergo Bohner
- Alan Turing Institute, Kings Cross, London, UK
- Departments of Mathematics and Statistics, University of Warwick, Coventry, UK
| | - Franz J Király
- Alan Turing Institute, Kings Cross, London, UK
- Department of Statistical Science, University College London, London, UK
| | | | - Pall Jonsson
- Science Policy and Research, National Institute for Health and Care Excellence, Manchester, UK
| | - Sarah Cumbers
- Health and Social Care Directorate, National Institute for Health and Care Excellence, London, UK
| | - Adrian Jonas
- Data and Analytics Group, National Institute for Health and Care Excellence, London, UK
| | | | - Puja Myles
- Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK
| | - David Granger
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Mark Birse
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Richard Branson
- Medicines and Healthcare products Regulatory Agency, London, UK
| | - Karel G M Moons
- Julius Centre for Health Sciences and Primary Care, UMC Utrecht, Utrecht University, Utrecht, Netherlands
| | - Gary S Collins
- UK EQUATOR Centre, Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, UK
| | - John P A Ioannidis
- Meta-Research Innovation Centre at Stanford, Stanford University, Stanford, CA, USA
| | - Chris Holmes
- Alan Turing Institute, Kings Cross, London, UK
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK
| | - Harry Hemingway
- Health Data Research UK London, University College London, London, UK
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, University College London, London, UK
| |
Collapse
|
3
|
Kwok CS, Anderson SG, McAllister KSL, Sperrin M, O'Kane PD, Keavney B, Nolan J, Myint PK, Zaman A, Buchan I, Ludman PF, de Belder MA, Mamas MA. Impact of age on the prognostic value of left ventricular function in relation to procedural outcomes following percutaneous coronary intervention: insights from the British Cardiovascular Intervention Society. Catheter Cardiovasc Interv 2014; 85:944-51. [PMID: 25408308 DOI: 10.1002/ccd.25732] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Accepted: 11/02/2014] [Indexed: 12/30/2022]
Abstract
BACKGROUND Around one third of patients undergoing percutaneous coronary intervention (PCI) have left ventricular (LV) dysfunction. Whilst the prevalence of LV dysfunction is known to increase with age, the prevalence of LV dysfunction in different age groups in the PCI setting is not known and the effect of age on the prognostic value of LV function in the PCI setting has not been examined. METHODS The relationship between LV function and 30-day mortality in patients undergoing PCI in different age groups (<60 years, 60 to <70 years, 70 to <80 years and ≥80 years) was studied in 246,840 patients in the UK between 2006 and 2011. RESULTS Prevalent LV dysfunction in patients undergoing PCI increased with age; 25,106/83,161 (30.2%: <60 years), 24,114/76,895 (31.4%: 60 to <70 years), 23,580/64,711 36.4% (70 to <80 years) and 9,851/22,073 (44.6%) in patients aged 80 or over (P < 0.0001). Poor LV function was independently associated with increased risk of 30-day mortality outcomes in all age groups (OR 5.65:95% CI 4.21-7.58, age <60 years; OR 5.07: 95% CI 3.91-6.57, age 60 to <70 years; OR 4.50: 95% CI 3.64-5.57, 70 to <80 years and OR 4.83:95% CI 3.79-6.15, age ≥80 years). CONCLUSIONS Our analysis suggests that worsening LV function is an important independent predictor of worse 30-day mortality outcomes across all age groups and underscores the need for a measure of LV function in all patients for accurate risk stratification prior to PCI.
Collapse
Affiliation(s)
- Chun Shing Kwok
- Cardiovascular Research Group, Institute of Cardiovascular Sciences, University of Manchester, Manchester, United Kingdom
| | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|