Fengping Zhu1,2,3,4, Zhiguang Pan1,2,3,4, Ying Tang5, Pengfei Fu1, Sijie Cheng6, Wenzhong Hou6, Qi Zhang6, Hong Huang6, Yirui Sun1,2,3,4. 1. Department of Neurosurgery, Huahsan Hospital, Fudan University, Shanghai, China. 2. Neurosurgical Institute of Fudan University, Shanghai, China. 3. Shanghai Clinical Medical Center of Neurosurgery, Shanghai, China. 4. Shanghai Key Laboratory of Brain Function and Restoration and Neural Regeneration, Shanghai, China. 5. Department of Nursing, Huahsan Hospital, Fudan University, Shanghai, China. 6. Information Center, Huahsan Hospital, Fudan University, Shanghai, China.
Abstract
AIMS: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms. METHODS: A retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy-related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four-fold cross-validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score. RESULTS: Albumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913-0.950), precision (92.4%, 95% CI: 0.897-0.951), F1 score (91.5%, 95% CI: 0.889-0.964), and recall score (93.6%, 95% CI: 0.909-0.964), and yielded higher area under the receiver operating characteristic curve (AU-ROC) (0.962, 95% CI: 0.942-0.982). CONCLUSION: The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage.
AIMS: Coagulation abnormality is one of the primary concerns for patients with spontaneous intracerebral hemorrhage admitted to ER. Conventional laboratory indicators require hours for coagulopathy diagnosis, which brings difficulties for appropriate intervention within the optimal window. This study evaluates the possibility of building efficient coagulopathy prediction models using data mining and machine learning algorithms. METHODS: A retrospective cohort enrolled 1668 cases with acute spontaneous intracerebral hemorrhage from three medical centers, excluding those under antithrombotic therapies. Coagulopathy-related clinical parameters were initially screened by univariate analysis. Two machine learning algorithms, the random forest and the support vector machine, were deployed via an approach of four-fold cross-validation to screen out the most important parameters contributing to the occurrence of coagulopathy. Model discrimination was assessed using metrics, including accuracy, precision, recall, and F1 score. RESULTS: Albumin/globulin ratio, neutrophil count, lymphocyte percentage, aspartate transaminase, alanine transaminase, hemoglobin, platelet count, white blood cell count, neutrophil percentage, systolic and diastolic pressure were identified as major predictors to the occurrence of acute coagulopathy. Compared to support vector machine, the model based on the random forest algorithm showed better accuracy (93.1%, 95% confidence interval [CI]: 0.913-0.950), precision (92.4%, 95% CI: 0.897-0.951), F1 score (91.5%, 95% CI: 0.889-0.964), and recall score (93.6%, 95% CI: 0.909-0.964), and yielded higher area under the receiver operating characteristic curve (AU-ROC) (0.962, 95% CI: 0.942-0.982). CONCLUSION: The constructed models exhibit good prediction accuracy and efficiency. It might be used in clinical practice to facilitate target intervention for acute coagulopathy in patients with spontaneous intracerebral hemorrhage.
Authors: Jennifer A Frontera; John J Lewin; Alejandro A Rabinstein; Imo P Aisiku; Anne W Alexandrov; Aaron M Cook; Gregory J del Zoppo; Monisha A Kumar; Ellinor I B Peerschke; Michael F Stiefel; Jeanne S Teitelbaum; Katja E Wartenberg; Cindy L Zerfoss Journal: Neurocrit Care Date: 2016-02 Impact factor: 3.210
Authors: Brent Whittaker; Sarah C Christiaans; Jessica L Altice; Mike K Chen; Alfred A Bartolucci; Charity J Morgan; Jeffrey D Kerby; Jean-François Pittet Journal: Shock Date: 2013-05 Impact factor: 3.454
Authors: Lynne Uhl; Susan F Assmann; Taye H Hamza; Ryan W Harrison; Terry Gernsheimer; Sherrill J Slichter Journal: Blood Date: 2017-07-05 Impact factor: 22.113
Authors: Spencer L Waddle; Meher R Juttukonda; Sarah K Lants; Larry T Davis; Rohan Chitale; Matthew R Fusco; Lori C Jordan; Manus J Donahue Journal: J Cereb Blood Flow Metab Date: 2019-05-08 Impact factor: 6.200