Dataset Information

Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models.

ABSTRACT: At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as inputs. Included were consecutive AIS patients with MCA M1 and proximal M2 occlusions. The XGBoost, LightGBM, CatBoost, and Random Forest were used to predict the outcome. Minimum redundancy maximum relevancy was used for selecting features. The primary outcomes were the National Institutes of Health Stroke Scale (NIHSS) shift and the modified Rankin Score (mRS) at 90 days. The algorithm with the highest area under the receiver operating characteristic curve (AUROC) for predicting the favorable and unfavorable outcome groups at 90 days was LightGBM. Random Forest had the highest AUROC when predicting the favorable and unfavorable groups based on the NIHSS shift. Using clinical, laboratory, and imaging parameters in conjunction with machine learning, we accurately predicted the functional outcome of AIS patients with proximal MCA occlusions.

SUBMITTER: Ozkara BB

PROVIDER: S-EPMC9918022 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models.

Ozkara Burak B BB Karabacak Mert M Hamam Omar O Wang Richard R Kotha Apoorva A Khalili Neda N Hoseinyazdi Meisam M Chen Melissa M MM Wintermark Max M Yedavalli Vivek S VS

Journal of clinical medicine 20230120 3

At present, clinicians are expected to manage a large volume of complex clinical, laboratory, and imaging data, necessitating sophisticated analytic approaches. Machine learning-based models can use this vast amount of data to create forecasting models. We aimed to predict short- and medium-term functional outcomes in acute ischemic stroke (AIS) patients with proximal middle cerebral artery (MCA) occlusions using machine learning models with clinical, laboratory, and quantitative imaging data as ...[more]

PMID: 36769491

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Project description:BackgroundStriatocapsular infarcts (SCIs) are defined as large subcortical infarcts involving the territory of more than one lenticulostriate artery. SCI without concomitant ischemia in the more distal middle cerebral artery (MCA) territory [isolated SCI (iSCI)] has been described as a rare infarct pattern. The purpose of this study was to assess the prevalence of iSCI in patients treated with endovascular thrombectomy (ET), to evaluate baseline and procedural parameters associated with this condition, and to describe the clinical course of iSCI patients.MethodsA retrospective analysis of 206 consecutive patients with an isolated MCA occlusion involving the lenticulostriate arteries and treated with ET was performed. Baseline patient and procedural characteristics and ischemic involvement of the striatocapsular and distal MCA territory [iSCI, as opposed to non-isolated SCI (niSCI)] were analyzed using multivariate logistic regression models. Prevalence of iSCI was assessed, and clinical course was determined with the rates of substantial neurological improvement and good functional short- and mid-term outcome (discharge/day 90 Modified Rankin Scale ≤2).ResultsiSCI was detected in 53 patients (25.7%), and niSCI was detected in 153 patients (74.3%). Successful reperfusion [thrombolysis in cerebral infarction (TICI) 2b/3] [adjusted odds ration (aOR) 8.730, 95% confidence interval (95% CI) 1.069-71.308] and good collaterals (aOR 2.100, 95% CI 1.119-3.944) were associated with iSCI. In successfully reperfused patients, TICI 3 was found to be an additional factor associated with iSCI (aOR 5.282, 1.759-15.859). Patients with iSCI had higher rates of substantial neurological improvement (71.7 vs. 37.9%, p < 0.001) and higher rates of good functional short- and mid-term outcome (58.3 vs. 23.7%, p < 0.001 and 71.4 vs. 41.7%, p < 0.001). However, while iSCI patients, in general, had a more favorable outcome, considerable heterogeneity in outcome was observed.ConclusionHigh rates of successful reperfusion (TICI 2b/3) and in particular, complete reperfusion (TICI 3) are associated with iSCIs. The high prevalence of iSCI in successfully reperfused patients with good collaterals corroborates previous concepts of iSCI pathogenesis. iSCI, once considered a rare pattern of cerebral ischemia, is likely to become more prevalent with increases in endovascular stroke therapy. This may have implications for patient rehabilitation and pathophysiological analyses of ischemic damage confined to subcortical regions of the MCA territory.

Project description:Malignant cerebral edema occurs when brain swelling displaces and compresses vital midline structures within the first week of a large middle cerebral artery stroke. Early interventions such as hyperosmolar therapy or surgical decompression may reverse secondary injury but must be administered judiciously. To optimize treatment and reduce secondary damage, clinicians need strategies to frequently and quantitatively assess the trajectory of edema using updated, relevant information. However, existing risk assessment tools are limited by the absence of structured records capturing the evolution of edema and typically estimate risk at a single time point early in the admission, therefore failing to account for changes in variables over the following hours or days. To address this, we developed and validated dynamic machine learning models capable of accurately predicting the severity of midline structure displacement, an established indicator of malignant edema, in real-time. Our models can provide updated estimations as frequently as every hour, using data from structured time-varying patient records, radiographic text, and human-curated neurological characteristics. Our work resulted in two novel multi-class classification models, collectively named Hybrid Ensemble Learning Models for Edema Trajectory (HELMET), predicting the progression of midline shift over 8-hour (HELMET-8) and 24-hour windows (HELMET-24), respectively. HELMET combines transformer-based large language models with supervised ensemble learning, demonstrating the value of merging human expertise and multimodal health records in developing clinical risk scores. Both models were trained on a retrospective cohort of 15,696 observations from 623 patients hospitalized with large middle cerebral artery ischemic stroke and were externally validated using 3,713 observations from 60 patients at a separate hospital system. Our HELMET models are accurate and generalize effectively to diverse populations, achieving a cross-validated mean area under the receiver operating characteristic score of 96.6% in the derivation cohort and 92.5% in the external validation cohort. Moreover, our approach provides a framework for developing hybrid risk prediction models that integrate both human-extracted and algorithm-derived multi-modal inputs. Our work enables accurate estimation of complex, dynamic, and highly specific clinical targets, such as midline shift, in real-time, even when relevant structured information is limited in electronic health record databases.

Dataset Information

Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models.

Publications

Prediction of Functional Outcome in Stroke Patients with Proximal Middle Cerebral Artery Occlusions Using Machine Learning Models.

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