TY - JOUR
T1 - Predicting short and long-term mortality after acute ischemic stroke using EHR
AU - Abedi, Vida
AU - Avula, Venkatesh
AU - Razavi, Seyed Mostafa
AU - Bavishi, Shreya
AU - Chaudhary, Durgesh
AU - Shahjouei, Shima
AU - Wang, Ming
AU - Griessenauer, Christoph J.
AU - Li, Jiang
AU - Zand, Ramin
N1 - Funding Information:
Vida Abedi has financial research support from the NIH grant no. R56HL116832 subawarded to Geisinger during the study period. Ramin Zand has financial research support from Bucknell University Initiative Program, Roche—Genentech Biotechnology Company, the Geisinger Health Plan Quality fund, and receives institutional support from the Geisinger Health System during the study period. This study was in part supported by the NIH grant no. R56HL116832 . Funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2021
PY - 2021/8/15
Y1 - 2021/8/15
N2 - 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.
AB - 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.
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U2 - 10.1016/j.jns.2021.117560
DO - 10.1016/j.jns.2021.117560
M3 - Article
C2 - 34218182
AN - SCOPUS:85109456641
VL - 427
JO - Journal of the Neurological Sciences
JF - Journal of the Neurological Sciences
SN - 0022-510X
M1 - 117560
ER -