Predicting short and long-term mortality after acute ischemic stroke using EHR

JOURNAL OF THE NEUROLOGICAL SCIENCES

卷:427

Article

Abedi, Vida^1,3  Avula, Venkatesh¹  Razavi, Seyed-Mostafa⁵  Bavishi, Shreya⁶  Chaudhary, Durgesh²  Shahjouei, Shima²  Wang, Ming⁴  Griessenauer, Christoph J.^2,7  Li, Jiang¹  Zand, Ramin² 

[1] Geisinger, Dept Mol & Funct Genom, Danville, PA USA

[2] Geisinger, Geisinger Neurosci Inst, Danville, PA USA

[3] Virginia Tech, Biocomplex Inst, Blacksburg, VA USA

[4] Penn State Canc Inst, Dept Publ Hlth Sci, Hershey, PA USA

[5] Heart & Rhythm Clin, San Jose, CA USA

[6] Gujarat Univ, AMC MET Med Coll, Ahmadabad, Gujarat, India

[7] Paracelsus Med Univ, Res Inst Neurointervent, Salzburg, Austria

关键词: Ischemic stroke;    Mortality;    Outcome prediction;    Machine learning;    Artificial intelligence;    EHR;    Electronic health record;   

DOI  :  10.1016/j.jns.2021.117560

来源: Elsevier

【 摘 要 】

Objective: Despite improvements in treatment, stroke remains a leading cause of mortality and long-term disability. In this study, we leveraged administrative data to build predictive models of short- and long-term post-stroke all-cause-mortality. Methods: The study was conducted and reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. We used patient-level data from electronic health records, three algorithms, and six prediction windows to develop models for post-stroke mortality. Results: We included 7144 patients from which 5347 had survived their ischemic stroke after two years. The proportion of mortality was between 8%(605/7144) within 1-month, to 25%(1797/7144) for the 2-years window. The three most common comorbidities were hypertension, dyslipidemia, and diabetes. The best Area Under the ROC curve(AUROC) was reached with the Random Forest model at 0.82 for the 1-month prediction window. The negative predictive value (NPV) was highest for the shorter prediction windows - 0.91 for the 1-month - and the best positive predictive value (PPV) was reached for the 6-months prediction window at 0.92. Age, hemoglobin levels, and body mass index were the top associated factors. Laboratory variables had higher importance when compared to past medical history and comorbidities. Hypercoagulation state, smoking, and end-stage renal disease were more strongly associated with long-term mortality. Conclusion: All the selected algorithms could be trained to predict the short and long-term mortality after stroke. The factors associated with mortality differed depending on the prediction window. Our classifier highlighted the importance of controlling risk factors, as indicated by laboratory measures.

【 授权许可】

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