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Hung LC, Su YY, Sun JM, Huang WT, Sung SF. Clinical narratives as a predictor for prognosticating functional outcomes after intracerebral hemorrhage. J Neurol Sci 2023; 453:120807. [PMID: 37717279 DOI: 10.1016/j.jns.2023.120807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 08/20/2023] [Accepted: 09/11/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Intracerebral hemorrhage (ICH) is a devastating stroke type that causes high mortality rates and severe disability among survivors. Many prognostic models are available for prognosticating patients with ICH. This study aimed to investigate whether clinical narratives can improve the performance for predicting functional outcomes after ICH. METHODS This study used data from the hospital stroke registry and electronic health records. The study population (n = 1363) was randomly divided into a training set (75%, n = 1023) and a holdout test set (25%, n = 340). Five risk scores for ICH were used as baseline prognostic models. Using natural language processing (NLP), text-based markers were generated from the clinical narratives of the training set through machine learning (ML) and deep learning (DL) approaches. The primary outcome was a poor functional outcome (modified Rankin Scale score of 3 to 6) at hospital discharge. The predictive performance was compared between the baseline models and models enhanced by incorporating the text-based markers using the holdout test set. RESULTS The enhanced prognostic models outperformed the baseline models, regardless of whether ML or DL approaches were used. The areas under the receiver operating characteristic curve (AUCs) of the baseline models were between 0.760 and 0.892. Adding the text-based marker to the baseline models significantly increased the model discrimination, with AUCs ranging from 0.861 to 0.914. The net reclassification improvement and integrated discrimination improvement indices also showed significant improvements. CONCLUSIONS Using NLP to extract textual information from clinical narratives could improve the predictive performance of all baseline prognostic models for ICH.
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Affiliation(s)
- Ling-Chien Hung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Ying-Ying Su
- Department of Radiology, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Jui-Ming Sun
- Section of Neurosurgery, Department of Surgery, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Wan-Ting Huang
- Clinical Medicine Research Center, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan; Department of Beauty & Health Care, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan.
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Kent DM, Leung LY, Puttock EJ, Wang AY, Luetmer PH, Kallmes DF, Nelson J, Fu S, Zheng C, Vickery EM, Liu H, Noyce AJ, Chen W. Development of Parkinson Disease and Its Relationship with Incidentally Discovered White Matter Disease and Covert Brain Infarction in a Real-World Cohort. Ann Neurol 2022; 92:620-630. [PMID: 35866711 PMCID: PMC9489676 DOI: 10.1002/ana.26458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 11/08/2022]
Abstract
OBJECTIVE This study aimed to examine the relationship between covert cerebrovascular disease, comprised of covert brain infarction and white matter disease, discovered incidentally in routine care, and subsequent Parkinson disease. METHODS Patients were ≥50 years and received neuroimaging for non-stroke indications in the Kaiser Permanente Southern California system from 2009 to 2019. Natural language processing identified incidentally discovered covert brain infarction and white matter disease and classified white matter disease severity. The Parkinson disease outcome was defined as 2 ICD diagnosis codes. RESULTS 230,062 patients were included (median follow-up 3.72 years). A total of 1,941 Parkinson disease cases were identified (median time-to-event 2.35 years). Natural language processing identified covert cerebrovascular disease in 70,592 (30.7%) patients, 10,622 (4.6%) with covert brain infarction and 65,814 (28.6%) with white matter disease. After adjustment for known risk factors, white matter disease was associated with Parkinson disease (hazard ratio 1.67 [95%CI, 1.44, 1.93] for patients <70 years and 1.33 [1.18, 1.50] for those ≥70 years). Greater severity of white matter disease was associated with increased incidence of Parkinson disease(/1,000 person-years), from 1.52 (1.43, 1.61) in patients without white matter disease to 4.90 (3.86, 6.13) in those with severe disease. Findings were robust when more specific definitions of Parkinson disease were used. Covert brain infarction was not associated with Parkinson disease (adjusted hazard ratio = 1.05 [0.88, 1.24]). INTERPRETATION Incidentally discovered white matter disease was associated with subsequent Parkinson disease, an association strengthened with younger age and increased white matter disease severity. Incidentally discovered covert brain infarction did not appear to be associated with subsequent Parkinson disease. ANN NEUROL 2022;92:620-630.
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Affiliation(s)
- David M. Kent
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Lester Y. Leung
- Department of Neurology, Tufts Medical Center, Boston, MA,
USA
| | - Eric J. Puttock
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
| | - Andy Y. Wang
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | | | | | - Jason Nelson
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Sunyang Fu
- Department of AI and Informatics, Mayo Clinic, Rochester,
MN, USA
| | - Chengyi Zheng
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
| | - Ellen M. Vickery
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Hongfang Liu
- Department of AI and Informatics, Mayo Clinic, Rochester,
MN, USA
| | - Alastair J. Noyce
- Preventive Neurology Unit, Wolfson Institute of Population
Health, Queen Mary University of London, UK
- Department of Clinical and Movement Neuroscience, UCL
Institute of Neurology, London, UK
| | - Wansu Chen
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
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