Development and Validation of a Clinical-Based Signature to Predict the 90-Day Functional Outcome for Spontaneous Intracerebral Hemorrhage

Huang, Xiaoyu; Wang, Dan; Zhang, Qiaoying; Ma, Yaqiong; Li, Shenglin; Zhao, Hui; Deng, Juan; Yang, Jingjing; Ren, Jialiang; Xu, Minhui; Xi, Huaze; Li, Fukai; Zhang, Hongyu; Xie, Yijing; Yuan, Long; Hai, Yucheng; Yue, Mengying; Qin, Zhou; Zhang, Junlin

doi:10.3389/fnagi.2022.904085

Cited by 5 publications

(5 citation statements)

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“…The comparative analysis of clinical data derived from patients exhibiting good and poor outcomes has unveiled a noteworthy dissimilarity in terms of age distribution between the two cohorts. Aging stands as a widely acknowledged and pivotal risk determinant linked to a less favorable prognosis among individuals afflicted with SAH, as substantiated by previous studies [ 24 , 25 ]. This discovery holds noteworthy implications for clinical practice.…”

Section: Discussionmentioning

confidence: 73%

Non-Contrasted CT Radiomics for SAH Prognosis Prediction

Shan

Wang

et al. 2023

Bioengineering

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Subarachnoid hemorrhage (SAH) denotes a serious type of hemorrhagic stroke that often leads to a poor prognosis and poses a significant socioeconomic burden. Timely assessment of the prognosis of SAH patients is of paramount clinical importance for medical decision making. Currently, clinical prognosis evaluation heavily relies on patients’ clinical information, which suffers from limited accuracy. Non-contrast computed tomography (NCCT) is the primary diagnostic tool for SAH. Radiomics, an emerging technology, involves extracting quantitative radiomics features from medical images to serve as diagnostic markers. However, there is a scarcity of studies exploring the prognostic prediction of SAH using NCCT radiomics features. The objective of this study is to utilize machine learning (ML) algorithms that leverage NCCT radiomics features for the prognostic prediction of SAH. Retrospectively, we collected NCCT and clinical data of SAH patients treated at Beijing Hospital between May 2012 and November 2022. The modified Rankin Scale (mRS) was utilized to assess the prognosis of patients with SAH at the 3-month mark after the SAH event. Based on follow-up data, patients were classified into two groups: good outcome (mRS ≤ 2) and poor outcome (mRS > 2) groups. The region of interest in NCCT images was delineated using 3D Slicer software, and radiomic features were extracted. The most stable and significant radiomic features were identified using the intraclass correlation coefficient, t-test, and least absolute shrinkage and selection operator (LASSO) regression. The data were randomly divided into training and testing cohorts in a 7:3 ratio. Various ML algorithms were utilized to construct predictive models, encompassing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perceptron (MLP). Seven prediction models based on radiomic features related to the outcome of SAH patients were constructed using the training cohort. Internal validation was performed using five-fold cross-validation in the entire training cohort. The receiver operating characteristic curve, accuracy, precision, recall, and f-1 score evaluation metrics were employed to assess the performance of the classifier in the overall dataset. Furthermore, decision curve analysis was conducted to evaluate model effectiveness. The study included 105 SAH patients. A comprehensive set of 1316 radiomics characteristics were initially derived, from which 13 distinct features were chosen for the construction of the ML model. Significant differences in age were observed between patients with good and poor outcomes. Among the seven constructed models, model_SVM exhibited optimal outcomes during a five-fold cross-validation assessment, with an average area under the curve (AUC) of 0.98 (standard deviation: 0.01) and 0.88 (standard deviation: 0.08) on the training and testing cohorts, respectively. In the overall dataset, model_SVM achieved an accuracy, precision, recall, f-1 score, and AUC of 0.88, 0.84, 0.87, 0.84, and 0.82, respectively, in the testing cohort. Radiomics features associated with the outcome of SAH patients were successfully obtained, and seven ML models were constructed. Model_SVM exhibited the best predictive performance. The radiomics model has the potential to provide guidance for SAH prognosis prediction and treatment guidance.

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Section: Discussionmentioning

confidence: 73%

Non-Contrasted CT Radiomics for SAH Prognosis Prediction

Shan

Wang

et al. 2023

Bioengineering

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“…Additionally, among the 28 eligible studies, 15 studies were multi-centered ( 27 – 29 , 39 , 45 , 47 , 52 , 55 , 58 – 64 ); five studies extracted their original data from databases ( 24 , 25 , 32 , 37 , 57 ); and the remaining 24 studies were single-centered. Most eligible studies focused on IS and 7 studies ( 23 , 32 , 39 , 40 , 48 , 60 , 63 ) on HS. A study ( 32 ) used the same four models to predict the motor function in patients with IS and HS respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Due to distinct purposes, these included studies defined poor motor function in a different manner. Specifically, it was defined as Modified Rankin Scale >1 in 4 studies ( 26 , 31 , 45 , 58 ), Modified Rankin Scale >3 in 4 ( 39 , 53 , 60 , 63 ) studies, Modified Rankin Scale >4 in two ( 25 , 42 ) studies, and Modified Rankin Scale >2 in other 34 studies. In this study, a total of 13 primary studies using radiomics as predictive factors were identified ( 27 , 30 , 36 , 37 , 49 , 51 , 52 , 54 – 57 , 61 , 62 ), from which 33 prediction models were extracted.…”

Section: Resultsmentioning

confidence: 99%

Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis

Chi

Zhao

et al. 2023

Front. Neurol.

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BackgroundRecent studies have reported that machine learning (ML), with a relatively strong capacity for processing non-linear data and adaptive ability, could improve the accuracy and efficiency of prediction. The article summarizes the published studies on ML models that predict motor function 3–6 months post-stroke.MethodsA systematic literature search was conducted in PubMed, Embase, Cochorane and Web of Science as of April 3, 2023 for studies on ML prediction of motor function in stroke patients. The quality of the literature was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). A random-effects model was preferred for meta-analysis using R4.2.0 because of the different variables and parameters.ResultsA total of 44 studies were included in this meta-analysis, involving 72,368 patients and 136 models. Models were categorized into subgroups according to the predicted outcome Modified Rankin Scale cut-off value and whether they were constructed based on radiomics. C-statistics, sensitivity, and specificity were calculated. The random-effects model showed that the C-statistics of all models were 0.81 (95% CI: 0.79; 0.83) in the training set and 0.82 (95% CI: 0.80; 0.85) in the validation set. According to different Modified Rankin Scale cut-off values, C-statistics of ML models predicting Modified Rankin Scale>2(used most widely) in stroke patients were 0.81 (95% CI: 0.78; 0.84) in the training set, and 0.84 (95% CI: 0.81; 0.87) in the validation set. C-statistics of radiomics-based ML models in the training set and validation set were 0.81 (95% CI: 0.78; 0.84) and 0.87 (95% CI: 0.83; 0.90), respectively.ConclusionML can be used as an assessment tool for predicting the motor function in patients with 3–6 months of post-stroke. Additionally, the study found that ML models with radiomics as a predictive variable were also demonstrated to have good predictive capabilities. This systematic review provides valuable guidance for the future optimization of ML prediction systems that predict poor motor outcomes in stroke patients.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022335260, identifier: CRD42022335260.

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“…The amount of bleeding after a cerebral hemorrhage is an important indicator of its severity. Hematoma size has been reported to be closely related to prognosis in patients with SICH (19)(20)(21). In these patients, intracranial hematoma is involved in secondary brain injury as well as mechanical injury.…”

Section: Discussionmentioning

confidence: 99%

Development and validation of a novel clinical prediction model to predict the 90-day functional outcome of spontaneous intracerebral hemorrhage

Geng,

Guo,

Cao

et al. 2023

Front. Neurol.

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BackgroundSpontaneous intracerebral hemorrhage (SICH) is associated with high mortality and disability. Accurately predicting adverse prognostic risks of SICH is helpful in developing risk stratification and precision medicine strategies for this phenomenon.MethodsWe analyzed 413 patients with SICH admitted to Hefei Second People's Hospital as a training cohort, considering 74 patients from the First Affiliated Hospital of Anhui Medical University for external validation. Univariate and multivariate logistic regression analyses were used to select risk factors for 90-day functional outcomes, and a nomogram was developed to predict their incidence in patients. Discrimination, fitting performance, and clinical utility of the resulting nomogram were evaluated through receiver operating characteristic (ROC) curves, accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), calibration plots, and decision curves analysis (DCA), respectively.ResultsOf the 413 patients, 180 had a poor prognosis. Univariate analysis showed significant variance of age, systolic pressure, intraventricular hemorrhage (IVH), Glasgow Coma Scale (GCS) scores, National Institute of Health Stroke Scale (NIHSS) scores, and hematoma volume between the groups (p < 0.05). Logistic multivariate regression analysis showed that age, IVH, NIHSS, and hematoma volume were associated with unfavorable outcomes. Based on the results, a nomogram model was developed with an area under the ROC curve of 0.91 (95% CI; 0.88–0.94) and 0.89 (95% CI; 0.80–0.95) in the training and validation sets, respectively. In the validation set, the accuracy, sensitivity, specificity, PPV, and NPV of the model were 0.851, 0.923, 0.812, 0.727, and 0.951, respectively. The calibration plot demonstrates the goodness of fit between the nomogram predictions and actual observations. Finally, DCA indicated significant clinical adaptability.ConclusionWe developed and validated a short-term prognostic nomogram model for patients with SICH including NIHSS scores, age, hematoma volume, and IVH. This model has valuable potential in predicting the prognosis of patients with SICH.

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Development and Validation of a Clinical-Based Signature to Predict the 90-Day Functional Outcome for Spontaneous Intracerebral Hemorrhage

Cited by 5 publications

References 55 publications

Non-Contrasted CT Radiomics for SAH Prognosis Prediction

Non-Contrasted CT Radiomics for SAH Prognosis Prediction

Machine learning prediction of motor function in chronic stroke patients: a systematic review and meta-analysis

Development and validation of a novel clinical prediction model to predict the 90-day functional outcome of spontaneous intracerebral hemorrhage

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