Brain health scores to predict neurological outcomes from electronic health records.


Journal

International journal of medical informatics
ISSN: 1872-8243
Titre abrégé: Int J Med Inform
Pays: Ireland
ID NLM: 9711057

Informations de publication

Date de publication:
Dec 2023
Historique:
received: 07 09 2022
revised: 30 03 2023
accepted: 21 10 2023
medline: 20 11 2023
pubmed: 28 10 2023
entrez: 27 10 2023
Statut: ppublish

Résumé

Preserving brain health is a critical priority in primary care, yet screening for these risk factors in face-to-face primary care visits is challenging to scale to large populations. We aimed to develop automated brain health risk scores calculated from data in the electronic health record (EHR) enabling population-wide brain health screening in advance of patient care visits. This retrospective cohort study included patients with visits to an outpatient neurology clinic at Massachusetts General Hospital, between January 2010 and March 2021. Survival analysis with an 11-year follow-up period was performed to predict the risk of intracranial hemorrhage, ischemic stroke, depression, death and composite outcome of dementia, Alzheimer's disease, and mild cognitive impairment. Variables included age, sex, vital signs, laboratory values, employment status and social covariates pertaining to marital, tobacco and alcohol status. Random sampling was performed to create a training (70%) set for hyperparameter tuning in internal 5-fold cross validation and an external hold-out testing (30%) set of patients, both stratified by age. Risk ratios for high and low risk groups were evaluated in the hold-out test set, using 1000 bootstrapping iterations to calculate 95% confidence intervals (CI). The cohort comprised 17,040 patients with an average age of 49 ± 15.6 years; majority were males (57 %), White (78 %) and non-Hispanic (80 %). The low and high groups average risk ratios [95 % CI] were: intracranial hemorrhage 0.46 [0.45-0.48] and 2.07 [1.95-2.20], ischemic stroke 0.57 [0.57-0.59] and 1.64 [1.52-1.69], depression 0.68 [0.39-0.74] and 1.29 [0.78-1.38], composite of dementia 0.27 [0.26-0.28] and 3.52 [3.18-3.81] and death 0.24 [0.24-0.24] and 3.96 [3.91-4.00]. Simple risk scores derived from routinely collected EHR accurately quantify the risk of developing common neurologic and psychiatric diseases. These scores can be computed automatically, prior to medical care visits, and may thus be useful for large-scale brain health screening.

Sections du résumé

BACKGROUND BACKGROUND
Preserving brain health is a critical priority in primary care, yet screening for these risk factors in face-to-face primary care visits is challenging to scale to large populations. We aimed to develop automated brain health risk scores calculated from data in the electronic health record (EHR) enabling population-wide brain health screening in advance of patient care visits.
METHODS METHODS
This retrospective cohort study included patients with visits to an outpatient neurology clinic at Massachusetts General Hospital, between January 2010 and March 2021. Survival analysis with an 11-year follow-up period was performed to predict the risk of intracranial hemorrhage, ischemic stroke, depression, death and composite outcome of dementia, Alzheimer's disease, and mild cognitive impairment. Variables included age, sex, vital signs, laboratory values, employment status and social covariates pertaining to marital, tobacco and alcohol status. Random sampling was performed to create a training (70%) set for hyperparameter tuning in internal 5-fold cross validation and an external hold-out testing (30%) set of patients, both stratified by age. Risk ratios for high and low risk groups were evaluated in the hold-out test set, using 1000 bootstrapping iterations to calculate 95% confidence intervals (CI).
RESULTS RESULTS
The cohort comprised 17,040 patients with an average age of 49 ± 15.6 years; majority were males (57 %), White (78 %) and non-Hispanic (80 %). The low and high groups average risk ratios [95 % CI] were: intracranial hemorrhage 0.46 [0.45-0.48] and 2.07 [1.95-2.20], ischemic stroke 0.57 [0.57-0.59] and 1.64 [1.52-1.69], depression 0.68 [0.39-0.74] and 1.29 [0.78-1.38], composite of dementia 0.27 [0.26-0.28] and 3.52 [3.18-3.81] and death 0.24 [0.24-0.24] and 3.96 [3.91-4.00].
CONCLUSIONS CONCLUSIONS
Simple risk scores derived from routinely collected EHR accurately quantify the risk of developing common neurologic and psychiatric diseases. These scores can be computed automatically, prior to medical care visits, and may thus be useful for large-scale brain health screening.

Identifiants

pubmed: 37890202
pii: S1386-5056(23)00288-5
doi: 10.1016/j.ijmedinf.2023.105270
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105270

Subventions

Organisme : NINDS NIH HHS
ID : R01 NS102574
Pays : United States

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Marta Fernandes (M)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States. Electronic address: mbentofernandes@mgh.harvard.edu.

Haoqi Sun (H)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States. Electronic address: hsun8@mgh.harvard.edu.

Zeina Chemali (Z)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States. Electronic address: zelchemali@mgh.harvard.edu.

Shibani S Mukerji (SS)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States. Electronic address: smukerji@partners.org.

Lidia M V R Moura (L)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States. Electronic address: lidia.moura@mgh.harvard.edu.

Sahar F Zafar (SF)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States. Electronic address: sfzafar@mgh.harvard.edu.

Akshata Sonni (A)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States. Electronic address: asonni@partners.org.

Alessandro Biffi (A)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States. Electronic address: abiffi@mgh.harvard.edu.

Jonathan Rosand (J)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States. Electronic address: jrosand@partners.org.

M Brandon Westover (M)

Department of Neurology, Massachusetts General Hospital (MGH), Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Data Animation Center (CDAC), MGH, Boston, MA, United States; Henry and Allison McCance Center for Brain Health, MGH, Boston, MA, United States. Electronic address: mwestover@mgh.harvard.edu.

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Classifications MeSH