Personalized hypertension treatment recommendations by a data-driven model

Background Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. Methods Records from 1/1/2012 to 1/1/2020 at...

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Vydané v:	BMC medical informatics and decision making Ročník 23; číslo 1; s. 44 - 10
Hlavní autori:	Hu, Yang, Huerta, Jasmine, Cordella, Nicholas, Mishuris, Rebecca G., Paschalidis, Ioannis Ch
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 01.03.2023 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Antihypertensives Beta blockers Blood pressure Cardiovascular Diseases Clinical decision support Clinical practice guidelines Cluster Analysis Customization Decision support systems Diuretics Drug therapy Electronic health records Health care facilities Health Informatics Health services Hospitals Humans Hypertension Hypertension prescription Immunization Information Systems and Communication Service Least squares method Machine learning Management of Computing and Information Systems Medical Records Medicine Medicine & Public Health Outliers (statistics) Patients Precision medicine Predictions Prescription drugs Reduction Regression analysis Research Article United States Hypertension prescription Machine learning Clinical decision support
ISSN:	1472-6947, 1472-6947
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Popis
Shrnutí:	Background Hypertension is a prevalent cardiovascular disease with severe longer-term implications. Conventional management based on clinical guidelines does not facilitate personalized treatment that accounts for a richer set of patient characteristics. Methods Records from 1/1/2012 to 1/1/2020 at the Boston Medical Center were used, selecting patients with either a hypertension diagnosis or meeting diagnostic criteria (≥ 130 mmHg systolic or ≥ 90 mmHg diastolic, n = 42,752). Models were developed to recommend a class of antihypertensive medications for each patient based on their characteristics. Regression immunized against outliers was combined with a nearest neighbor approach to associate with each patient an affinity group of other patients. This group was then used to make predictions of future Systolic Blood Pressure (SBP) under each prescription type. For each patient, we leveraged these predictions to select the class of medication that minimized their future predicted SBP. Results The proposed model, built with a distributionally robust learning procedure, leads to a reduction of 14.28 mmHg in SBP, on average. This reduction is 70.30% larger than the reduction achieved by the standard-of-care and 7.08% better than the corresponding reduction achieved by the 2nd best model which uses ordinary least squares regression. All derived models outperform following the previous prescription or the current ground truth prescription in the record. We randomly sampled and manually reviewed 350 patient records; 87.71% of these model-generated prescription recommendations passed a sanity check by clinicians. Conclusion Our data-driven approach for personalized hypertension treatment yielded significant improvement compared to the standard-of-care. The model implied potential benefits of computationally deprescribing and can support situations with clinical equipoise.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1472-6947 1472-6947
DOI:	10.1186/s12911-023-02137-z