Dataset Information

Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.

ABSTRACT: While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the training cohort, and 378 (30%) were used as the test cohort. The XGBoost model predicted stroke requiring surgical intervention with high accuracy in the test cohort, with an area under the receiver operating characteristic curve of 0.802 (sensitivity 0.748, specificity 0.853). We found that simple survey items, such as the level of consciousness, vital signs, sudden headache, and speech abnormalities were the most significant variables for accurate prediction. This algorithm can be useful for prehospital stroke management, which is crucial for better patient outcomes.

SUBMITTER: Yoshida Y

PROVIDER: S-EPMC10241931 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.

Yoshida Yoichi Y Hayashi Yosuke Y Shimada Tadanaga T Hattori Noriyuki N Tomita Keisuke K Miura Rie E RE Yamao Yasuo Y Tateishi Shino S Iwadate Yasuo Y Nakada Taka-Aki TA

Scientific reports 20230605 1

While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and ...[more]

PMID: 37277424

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Project description:Time-limited acute stroke treatments are underused, primarily due to prehospital delay. One approach to decreasing prehospital delay is to increase stroke preparedness, the ability to recognize stroke, and the intention to immediately call emergency medical services, through community engagement with high-risk communities. Our community-academic partnership developed and tested "Stroke Ready," a peer-led, workshop-based, health behavior intervention to increase stroke preparedness among African American youth and adults in Flint, Michigan. Outcomes were measured with a series of 9 stroke and nonstroke 1-minute video vignettes; after each video, participants selected their intended response (primary outcome) and symptom recognition (secondary outcome), receiving 1 point for each appropriate stroke response and recognition. We assessed differences between baseline and posttest appropriate stroke response, which was defined as intent to call 911 for stroke vignettes and not calling 911 for nonstroke, nonemergent vignettes and recognition of stroke. Outcomes assessments were performed before workshop 1 (baseline), at the conclusion of workshop 2 (immediate post-test), and 1 month later (delayed post-test). A total of 101 participants completed the baseline assessment (73 adults and 28 youths), 64 completed the immediate post-test, and 68 the delayed post-test. All participants were African American. The median age of adults was 56 (interquartile range 35-65) and of youth was 14 (interquartile range 11-16), 65% of adults were women, and 50% of youths were women. Compared to baseline, appropriate stroke response was improved in the immediate post-test (4.4 versus 5.2, P<0.01) and was sustained in the delayed post-test (4.4 versus 5.2, P<0.01). Stroke recognition did not change in the immediate post-test (5.9 versus 6.0, P=0.34), but increased in the delayed post-test (5.9 versus 6.2, P=0.04). Stroke Ready increased stroke preparedness, a necessary step toward increasing acute stroke treatment rates. URL: https://www.clinicaltrials.gov/. Unique identifier: NCT01499173.

Project description:Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Project description:BackgroundAlthough multiple prediction models have been developed to predict hospital admission to emergency departments (EDs) to address overcrowding and patient safety, only a few studies have examined prediction models for prehospital use. Development of institution-specific prediction models is feasible in this age of data science, provided that predictor-related information is readily collectable.ObjectiveWe aimed to develop a hospital admission prediction model based on patient information that is commonly available during ambulance transport before hospitalization.MethodsPatients transported by ambulance to our ED from April 2018 through March 2019 were enrolled. Candidate predictors were age, sex, chief complaint, vital signs, and patient medical history, all of which were recorded by emergency medical teams during ambulance transport. Patients were divided into two cohorts for derivation (3601/5145, 70.0%) and validation (1544/5145, 30.0%). For statistical models, logistic regression, logistic lasso, random forest, and gradient boosting machine were used. Prediction models were developed in the derivation cohort. Model performance was assessed by area under the receiver operating characteristic curve (AUROC) and association measures in the validation cohort.ResultsOf 5145 patients transported by ambulance, including deaths in the ED and hospital transfers, 2699 (52.5%) required hospital admission. Prediction performance was higher with the addition of predictive factors, attaining the best performance with an AUROC of 0.818 (95% CI 0.792-0.839) with a machine learning model and predictive factors of age, sex, chief complaint, and vital signs. Sensitivity and specificity of this model were 0.744 (95% CI 0.716-0.773) and 0.745 (95% CI 0.709-0.776), respectively.ConclusionsFor patients transferred to EDs, we developed a well-performing hospital admission prediction model based on routinely collected prehospital information including chief complaints.

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Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.

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Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.

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