Medical calculators derived synthetic cohorts: a novel method for generating synthetic patient data.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
20 May 2024
20 May 2024
Historique:
received:
11
10
2023
accepted:
08
05
2024
medline:
20
5
2024
pubmed:
20
5
2024
entrez:
19
5
2024
Statut:
epublish
Résumé
This study shows that we can use synthetic cohorts created from medical risk calculators to gain insights into how risk estimations, clinical reasoning, data-driven subgrouping, and the confidence in risk calculator scores are connected. When prediction variables aren't evenly distributed in these synthetic cohorts, they can be used to group similar cases together, revealing new insights about how cohorts behave. We also found that the confidence in predictions made by these calculators can vary depending on patient characteristics. This suggests that it might be beneficial to include a "normalized confidence" score in future versions of these calculators for healthcare professionals. We plan to explore this idea further in our upcoming research.
Identifiants
pubmed: 38763934
doi: 10.1038/s41598-024-61721-z
pii: 10.1038/s41598-024-61721-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
11437Informations de copyright
© 2024. The Author(s).
Références
Gonzales, A., Guruswamy, G. & Smith, S. R. Synthetic data in health care: A narrative review. PLOS Digit. Health 2, e0000082. https://doi.org/10.1371/journal.pdig.0000082 (2023).
doi: 10.1371/journal.pdig.0000082
pubmed: 36812604
pmcid: 9931305
Giuffrè, M. & Shung, D. L. Harnessing the power of synthetic data in healthcare: Innovation, application, and privacy. NPJ Digit. Med. 6, 186. https://doi.org/10.1038/s41746-023-00927-3 (2023).
doi: 10.1038/s41746-023-00927-3
pubmed: 37813960
pmcid: 10562365
Goncalves, A. et al. Generation and evaluation of synthetic patient data. BMC Med. Res. Methodol. 20, 108. https://doi.org/10.1186/s12874-020-00977-1 (2020).
doi: 10.1186/s12874-020-00977-1
pubmed: 32381039
pmcid: 7204018
Endres M., Mannarapotta Venugopal A. & Tran T. S. Synthetic data generation: A comparative study. in Proceeding of 26th International Database Engineering Application Symposium ACM, 94–102. https://doi.org/10.1145/3548785.3548793 (2022).
Green, T. A., Whitt, S., Belden, J. L., Erdelez, S. & Shyu, C. R. Medical calculators: Prevalence, and barriers to use. Comp. Meth. Prog. Biomed. 179, 105002. https://doi.org/10.1016/j.cmpb.2019.105002 (2019).
doi: 10.1016/j.cmpb.2019.105002
MDCalc. Frequently Asked Questions. https://www.mdcalc.com/faq . Accessed 12 Feb 2024.
Soleimanpour, N. & Bann, M. Clinical risk calculators informing the decision to admit: A methodologic evaluation and assessment of applicability. PLoS ONE 17, 12. https://doi.org/10.1371/journal.pone.0279294 (2022).
doi: 10.1371/journal.pone.0279294
Challener, D. W., Prokop, L. J. & Abu-Saleh, O. The proliferation of reports on clinical scoring Systems: Issues about uptake and clinical utility. JAMA 321(24), 2405–2406. https://doi.org/10.1001/jama.2019.5284 (2019).
doi: 10.1001/jama.2019.5284
pubmed: 31125046
Cowley, L. E. et al. Methodological standards for the development and evaluation of clinical prediction rules: A review of the literature. Diagn. Progn. Res. 3, 16. https://doi.org/10.1186/s41512-019-0060-y (2019).
doi: 10.1186/s41512-019-0060-y
pubmed: 31463368
pmcid: 6704664
Marcus, G., Godoy, L., Jeanson, F. & Farkouh, M. E. Secondary prevention efficacy variability in MI survivors: Introduction of the unmet risk index. Preprint https://doi.org/10.5281/zenodo.10729886 (2024).
Agniel, D., Kohane, I. S. & Weber, G. M. Biases in electronic health record data due to processes within the healthcare system: Retrospective observational study. BMJ 361, k1479. https://doi.org/10.1136/bmj.k1479 (2018).
doi: 10.1136/bmj.k1479
pubmed: 29712648
pmcid: 5925441
Bansilal, S., Castellano, J. M. & Fuster, V. Global burden of CVD: Focus on secondary prevention of cardiovascular disease. Int. J. Cardiol. 201(Suppl 1), S1-7. https://doi.org/10.1016/S0167-5273(15)31026-3 (2015).
doi: 10.1016/S0167-5273(15)31026-3
pubmed: 26747389
Hammer, Y. et al. Guideline-recommended therapies and clinical outcomes according to the risk for recurrent cardiovascular events after an acute coronary syndrome. J. Am. Heart Assoc. 7, e009885. https://doi.org/10.1161/jaha.118.009885 (2018).
doi: 10.1161/jaha.118.009885
pubmed: 30371188
pmcid: 6222928
Lloyd-Jones, D. M. et al. Estimating longitudinal risks and benefits from cardiovascular preventive therapies among medicare patients: The million hearts longitudinal ASCVD risk assessment tool: A special report from the American Heart Association and American College of Cardiology. J. Am. Coll. Cardiol. 69(12), 1617–1636. https://doi.org/10.1161/cir.0000000000000467 (2017).
doi: 10.1161/cir.0000000000000467
pubmed: 27825770
Dorresteijn, J. A. N. et al. Development and validation of a prediction rule for recurrent vascular events based on a cohort study of patients with arterial disease: The SMART risk score. Heart 99(12), 866–867. https://doi.org/10.1136/heartjnl-2013-303640 (2013).
doi: 10.1136/heartjnl-2013-303640
pubmed: 23574971
Kononenko, I. Machine learning for medical diagnosis: History, state of the art and perspective. Artif. Intell. Med. 23(1), 89–109. https://doi.org/10.1016/S0933-3657(01)00077-X (2001).
doi: 10.1016/S0933-3657(01)00077-X
pubmed: 11470218
Gerlinger, C., Wessel, J., Kallischnigg, G. & Endrikat, J. Pattern recognition in menstrual bleeding diaries by statistical cluster analysis. BMC Womens Health 9, 21. https://doi.org/10.1186/1472-6874-9-21 (2009).
doi: 10.1186/1472-6874-9-21
pubmed: 19607665
pmcid: 2717079
Alashwal, H., El Halaby, M., Crouse, J. J., Abdalla, A. & Moustafa, A. A. The application of unsupervised clustering methods to Alzheimer’s disease. Front. Comp. Neurosci. 13, 31 (2019).
doi: 10.3389/fncom.2019.00031
Woldeamanuel, Y. W., Sanjanwala, B. M., Peretz, A. M. & Cowan, R. P. Exploring natural clusters of chronic migraine phenotypes: A cross-sectional clinical study. Sci. Rep. 10, 2804. https://doi.org/10.1038/s41598-020-59738-1 (2020).
doi: 10.1038/s41598-020-59738-1
pubmed: 32071349
pmcid: 7028739
Panlilio, L. V. et al. Beyond abstinence and relapse: Cluster analysis of drug-use patterns during treatment as an outcome measure for clinical trials. Psychopharmacology 237, 3369–3381. https://doi.org/10.1007/s00213-020-05618-5 (2020).
doi: 10.1007/s00213-020-05618-5
pubmed: 32990768
pmcid: 7579498
Bonner, C., Fajardo, M. A., Hui, S., Stubbs, R. & Trevena, L. Clinical validity, understandability, and actionability of online cardiovascular disease risk calculators: Systematic review. J. Med. Internet Res. 20, e29. https://doi.org/10.2196/jmir.8538 (2018).
doi: 10.2196/jmir.8538
pubmed: 29391344
pmcid: 5814602
Rousseeuw, P. J. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comp. Appl. Math. 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7 (1987).
doi: 10.1016/0377-0427(87)90125-7
Bentley, C. et al. Conducting clinical trials-costs, impacts, and the value of clinical trials networks: A scoping review. Clin. Trials 16(2), 183–193. https://doi.org/10.1177/1740774518820060 (2019).
doi: 10.1177/1740774518820060
pubmed: 30628466