Predicting post-stroke cognitive impairment using electronic health record data
Importance: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification.
Objective: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records.
Design: Cohort study with patients enrolled between 2003-2016 with follow-up through 2022.
Setting: Primary care practices affiliated with two academic medical centers.
Participants: Individuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort).
Exposures: Predictors of PSCI were ascertained from the electronic health record.
Main Outcome: The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included.
Results: The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts.
Conclusions and Relevance: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.
| Errataetall: |
UpdateIn: Int J Stroke. 2024 Mar 28;:17474930241246156. - PMID 38546170 |
|---|---|
| Medienart: |
E-Artikel |
| Erscheinungsjahr: |
2024 |
|---|---|
| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
|---|---|
| Enthalten in: |
medRxiv : the preprint server for health sciences - (2024) vom: 04. Feb. |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Ashburner, Jeffrey M [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Post-stroke cognitive impairment |
|---|
| Anmerkungen: |
Date Revised 18.04.2024 published: Electronic UpdateIn: Int J Stroke. 2024 Mar 28;:17474930241246156. - PMID 38546170 Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1101/2024.02.02.24302240 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM36842670X |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM36842670X | ||
| 003 | DE-627 | ||
| 005 | 20240418232558.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 240214s2024 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1101/2024.02.02.24302240 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1379.xml |
| 035 | |a (DE-627)NLM36842670X | ||
| 035 | |a (NLM)38352557 | ||
| 035 | |a (PII)2024.02.02.24302240 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Ashburner, Jeffrey M |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Predicting post-stroke cognitive impairment using electronic health record data |
| 264 | 1 | |c 2024 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 18.04.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a UpdateIn: Int J Stroke. 2024 Mar 28;:17474930241246156. - PMID 38546170 | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a Importance: Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification | ||
| 520 | |a Objective: To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records | ||
| 520 | |a Design: Cohort study with patients enrolled between 2003-2016 with follow-up through 2022 | ||
| 520 | |a Setting: Primary care practices affiliated with two academic medical centers | ||
| 520 | |a Participants: Individuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort) | ||
| 520 | |a Exposures: Predictors of PSCI were ascertained from the electronic health record | ||
| 520 | |a Main Outcome: The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included | ||
| 520 | |a Results: The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts | ||
| 520 | |a Conclusions and Relevance: Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts | ||
| 650 | 4 | |a Preprint | |
| 650 | 4 | |a post-stroke cognitive impairment | |
| 650 | 4 | |a risk prediction | |
| 650 | 4 | |a risk stratification | |
| 700 | 1 | |a Chang, Yuchiao |e verfasserin |4 aut | |
| 700 | 1 | |a Porneala, Bianca |e verfasserin |4 aut | |
| 700 | 1 | |a Singh, Sanjula D |e verfasserin |4 aut | |
| 700 | 1 | |a Yechoor, Nirupama |e verfasserin |4 aut | |
| 700 | 1 | |a Rosand, Jonathan M |e verfasserin |4 aut | |
| 700 | 1 | |a Singer, Daniel E |e verfasserin |4 aut | |
| 700 | 1 | |a Anderson, Christopher D |e verfasserin |4 aut | |
| 700 | 1 | |a Atlas, Steven J |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t medRxiv : the preprint server for health sciences |d 2020 |g (2024) vom: 04. Feb. |w (DE-627)NLM310900166 |7 nnns |
| 773 | 1 | 8 | |g year:2024 |g day:04 |g month:02 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1101/2024.02.02.24302240 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |j 2024 |b 04 |c 02 | ||