Acta neurochirurgica · 2025
Outcome prediction in aneurysmal subarachnoid hemorrhage with world federation of neurological societies grade V (OPAS-V).
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Yamada Shuhei, Nishida Takeo, Takenaka Tomofumi, Yamazaki Hiroki, Nakagawa Ryota, Takagaki Masatoshi, Yano Yoshihiro, Nakamura Hajime, Toyota Shingo, Fujinaka Toshiyuki, Taki Takuyu, Fujita Toshiaki, Kishima Haruhiko
Аннотация
BACKGROUND: Aneurysmal subarachnoid hemorrhage (aSAH) with World Federation of Neurological Societies (WFNS) grade V has a high mortality rate and poor prognosis. Some patients with WFNS grade V aSAH have had good outcomes after aggressive treatment; however, outcome predictions based on routine examinations and findings obtained at admission are yet to be reported. This study aimed to develop a decision tree model for predicting outcomes of patients with WFNS grade V aSAH to aid decision-making for treatment strategy.
METHOD: A multicenter study with retrospective and prospective data collected from 201 (derivation cohort) and 26 (validation cohort) patients with WFNS grade V aSAH, respectively, was conducted. Clinical outcomes were divided into good (Modified Rankin Scale [mRS] score at the time of discharge: 0-2) and poor (mRS score: 3-6) outcomes. A decision tree model was developed for the derivation cohort using the classification and regression tree method with clinical data including laboratory findings; it was named OPAS-V (Outcome Prediction in Aneurysmal Subarachnoid hemorrhage with WFNS grade V). The performance of the model was evaluated by area under the curve (AUC) and overall accuracy in both cohorts.
RESULTS: OPAS-V comprised 3 metrics; the percentage of lymphocytes (< 49.9% or not), age (> 50 yrs or not), and glucose to potassium ratio (≥ 3.2 or not). The model achieved an AUC of 0.828 (95% confidence interval: 0.712-0.944) and overall accuracy of 0.930. Moreover, the model performed well in the validation cohort with an AUC of 0.727 (95% confidence interval: 0.441-1) and overall accuracy of 0.885.
CONCLUSIONS: This study developed the first decision tree model for predicting outcomes of patients with WFNS grade V aSAH, based on simple findings obtained at admission. This may aid clinicians in determining treatment strategies for severe conditions such as WFNS grade V aSAH.
METHOD: A multicenter study with retrospective and prospective data collected from 201 (derivation cohort) and 26 (validation cohort) patients with WFNS grade V aSAH, respectively, was conducted. Clinical outcomes were divided into good (Modified Rankin Scale [mRS] score at the time of discharge: 0-2) and poor (mRS score: 3-6) outcomes. A decision tree model was developed for the derivation cohort using the classification and regression tree method with clinical data including laboratory findings; it was named OPAS-V (Outcome Prediction in Aneurysmal Subarachnoid hemorrhage with WFNS grade V). The performance of the model was evaluated by area under the curve (AUC) and overall accuracy in both cohorts.
RESULTS: OPAS-V comprised 3 metrics; the percentage of lymphocytes (< 49.9% or not), age (> 50 yrs or not), and glucose to potassium ratio (≥ 3.2 or not). The model achieved an AUC of 0.828 (95% confidence interval: 0.712-0.944) and overall accuracy of 0.930. Moreover, the model performed well in the validation cohort with an AUC of 0.727 (95% confidence interval: 0.441-1) and overall accuracy of 0.885.
CONCLUSIONS: This study developed the first decision tree model for predicting outcomes of patients with WFNS grade V aSAH, based on simple findings obtained at admission. This may aid clinicians in determining treatment strategies for severe conditions such as WFNS grade V aSAH.