scholarly journals Machine Learning-Based Approaches for Prediction of Patients’ Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage

2022 ◽  
Vol 12 (1) ◽  
pp. 112
Author(s):  
Rui Guo ◽  
Renjie Zhang ◽  
Ran Liu ◽  
Yi Liu ◽  
Hao Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Intracerebral Hemorrhage ◽  
Functional Outcome ◽  
Laboratory Data ◽  
Predictive Performance ◽  
High Morbidity ◽  
Spontaneous Intracerebral Hemorrhage ◽  
Receiver Operating Characteristic Curves ◽  
Ich Score

Spontaneous intracerebral hemorrhage (SICH) has been common in China with high morbidity and mortality rates. This study aims to develop a machine learning (ML)-based predictive model for the 90-day evaluation after SICH. We retrospectively reviewed 751 patients with SICH diagnosis and analyzed clinical, radiographic, and laboratory data. A modified Rankin scale (mRS) of 0–2 was defined as a favorable functional outcome, while an mRS of 3–6 was defined as an unfavorable functional outcome. We evaluated 90-day functional outcome and mortality to develop six ML-based predictive models and compared their efficacy with a traditional risk stratification scale, the intracerebral hemorrhage (ICH) score. The predictive performance was evaluated by the areas under the receiver operating characteristic curves (AUC). A total of 553 patients (73.6%) reached the functional outcome at the 3rd month, with the 90-day mortality rate of 10.2%. Logistic regression (LR) and logistic regression CV (LRCV) showed the best predictive performance for functional outcome (AUC = 0.890 and 0.887, respectively), and category boosting presented the best predictive performance for the mortality (AUC = 0.841). Therefore, ML might be of potential assistance in the prediction of the prognosis of SICH.

Irregular Shape as An Independent Predictor of Prognosis in Patients with Primary Intracerebral Hemorrhage

2021 ◽  
Author(s):  
Chunyang Liu ◽  
Haopeng Zhang ◽  
Lixiang Wang ◽  
Qiuyi Jiang ◽  
Enzhou Lu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Logistic Regression ◽  
Intracerebral Hemorrhage ◽  
Functional Outcome ◽  
Functional Outcomes ◽  
Irregular Shape ◽  
Spontaneous Intracerebral Hemorrhage ◽  
Poor Outcome ◽  
Irregular Shapes ◽  
D Dimer

Abstract BACKGROUND AND PURPOSE The utility of non-contrast computed tomography (NCCT) markers in the prognosis of spontaneous intracerebral hemorrhage (ICH) has been concerned. This study aimed to investigate the predictive value of the computed tomography irregularity shape for poor functional outcomes in patients with spontaneous intracerebral hemorrhage. PATIENTS AND Methods: We retrospectively reviewed all 782 patients with intracranial hemorrhage in our stroke emergency center from January 2018 to September 2019. Laboratory examination and CT examination were measured within 24 hours of admission. After three months, the patient's functional outcome was assessed using the modified Rankin Scale (mRS). Multinomial logistic regression analyses were applied to identify independent predictors of functional outcome in patients with intracerebral hemorrhage. RESULTS Out of the 627 patients included in this study, those with irregular shapes on CT imaging had a higher proportion of poor outcome and mortality 90 days after discharge (P<0.001). Irregular shapes were found to be significant independent predictors of poor outcome and mortality on multiple logistic regression analysis. Besides, the increase of plasma D-dimer was associated with the occurrence of irregular shape (P=0.0387). CONCLUSIONS Patients with irregular shape showed worse functional outcomes after intracerebral hemorrhage. The elevated expression level of plasma D-dimer may be directly related to the formation of irregular shapes.

scholarly journals Determining ICH Score: can we go beyond?

2009 ◽  
Vol 67 (3a) ◽  
pp. 605-608 ◽  
Author(s):  
Gustavo Cartaxo Patriota ◽  
João Manoel da Silva-Júnior ◽  
Alécio Cristino Evangelista Santos Barcellos ◽  
Joaquim Barbosa de Sousa Silva Júnior ◽  
Diogo Oliveira Toledo ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Functional Outcome ◽  
Auxiliary Variable ◽  
Accurate Information ◽  
Spontaneous Intracerebral Hemorrhage ◽  
Great Heterogeneity ◽  
One Year ◽  
The One ◽  
Ich Score ◽  
Reliable Scale

Spontaneous intracerebral hemorrhage (SICH) still presents a great heterogeneity in its clinical evaluation, demonstrating differences in the enrollment criteria used for the study of intracerebral hemorrhage (ICH) treatment. The aim of the current study was to assess the ICH Score, a simple and reliable scale, determining the 30-day mortality and the one-year functional outcome. Consecutive patients admitted with acute SICH were prospectively included in the study. ICH Scores ranged from 0 to 4, and each increase in the ICH Score was associated with an increase in the 30-day mortality and with a progressive decrease in good functional outcome rates. However, the occurrence of a pyramidal pathway injury was better related to worse functional outcome than the ICH Score. The ICH Score is a good predictor of 30-day mortality and functional outcome, confirming its validity in a different socioeconomic populations. The association of the pyramidal pathway injury as an auxiliary variable provides more accurate information about the prognostic evolution.

scholarly journals Imaging-Based Outcome Prediction of Acute Intracerebral Hemorrhage

2021 ◽  
Author(s):  
Jawed Nawabi ◽  
Helge Kniep ◽  
Sarah Elsayed ◽  
Constanze Friedrich ◽  
Peter Sporns ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intracerebral Hemorrhage ◽  
Functional Outcome ◽  
Image Features ◽  
Ct Scans ◽  
Youden Index ◽  
Survival Prediction ◽  
Enhanced Ct ◽  
Acute Intracerebral Hemorrhage ◽  
Ich Score

AbstractWe hypothesized that imaging-only-based machine learning algorithms can analyze non-enhanced CT scans of patients with acute intracerebral hemorrhage (ICH). This retrospective multicenter cohort study analyzed 520 non-enhanced CT scans and clinical data of patients with acute spontaneous ICH. Clinical outcome at hospital discharge was dichotomized into good outcome and poor outcome using different modified Rankin Scale (mRS) cut-off values. Predictive performance of a random forest machine learning approach based on filter- and texture-derived high-end image features was evaluated for differentiation of functional outcome at mRS 2, 3, and 4. Prediction of survival (mRS ≤ 5) was compared to results of the ICH Score. All models were tuned, validated, and tested in a nested 5-fold cross-validation approach. Receiver-operating-characteristic area under the curve (ROC AUC) of the machine learning classifier using image features only was 0.80 (95% CI [0.77; 0.82]) for predicting mRS ≤ 2, 0.80 (95% CI [0.78; 0.81]) for mRS ≤ 3, and 0.79 (95% CI [0.77; 0.80]) for mRS ≤ 4. Trained on survival prediction (mRS ≤ 5), the classifier reached an AUC of 0.80 (95% CI [0.78; 0.82]) which was equivalent to results of the ICH Score. If combined, the integrated model showed a significantly higher AUC of 0.84 (95% CI [0.83; 0.86], P value <0.05). Accordingly, sensitivities were significantly higher at Youden Index maximum cut-offs (77% vs. 74% sensitivity at 76% specificity, P value <0.05). Machine learning–based evaluation of quantitative high-end image features provided the same discriminatory power in predicting functional outcome as multidimensional clinical scoring systems. The integration of conventional scores and image features had synergistic effects with a statistically significant increase in AUC.

Abstract 3022: Image-Guided Endoscopic Evacuation of Spontaneous Intracerebral Hemorrhage

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Joshua R Dusick ◽  
Justin Dye ◽  
Nestor Gonzalez ◽  
Jennifer Varma ◽  
John Frazee ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
High Morbidity ◽  
Spontaneous Intracerebral Hemorrhage ◽  
Hematoma Volume ◽  
Invasive Approach ◽  
Image Guided ◽  
Aspiration Technique ◽  
Endoscopic Evacuation ◽  
Ich Score ◽  
The Impact

Introduction: Spontaneous intracerebral hemorrhage (ICH) is associated with a high morbidity and mortality rate despite current medical management. The benefits of open surgical evacuation for ICH remain controversial. Here we present initial results of the effectiveness of stereotactic image-guided endoscopic evacuation of ICH. Methods: Over 9-years, 41 patients with ICH (age 65+−14 years, 66% male, average admission GCS 10 & ICH Score 2, 46% with concurrent intraventricular hemorrhage) were treated. The current technique, which evolved from using direct endoscopic visualization, uses frameless stereotactic guidance alone to aspirate at two specified locations within the hematoma. An endoscope sheath is introduced through a bur hole into the hematoma along its long axis. Suction is applied to the sheath, without endoscopic viewing, at two locations, 1/3 and 2/3 of the way through the long-axis of the ICH. Endoscopic visualization of the cavity is then performed to ensure hemostasis. ICH volume was calculated using pre- and postoperative CT measurements ((length x width x height)/2). Results: Pre- and postoperative ICH volumes averaged 56.5 and 15.9cc, respectively, a reduction of 67.6+−41.9% (p<0.0001) with greater than 50% reduction in 78% of patients. Within 30 days, two patients (5%) developed rebleeding, one with acutely increased hematoma volume on postop CT. Average preop modified Rankin Score (mRS) decreased from 4.4 to 4.1 at follow-up (p=0.17). Seven-day and 30-day mortality occurred in 5 (12%) and 1 (2.4%) patients, respectively. This 30-day mortality compares favorably with the predicted rate of 26% based on average ICH score of 2 for the series. There were 2 surgical complications including ipsilateral ischemic stroke and subdural hematoma. Comparing the current aspiration technique to the previous technique, there were trends towards greater average reduction in hematoma volume (81.7% versus 58.5%, respectively, p=0.08) and greater improvement in clinical outcome (average mRS improvement 0.75 points versus 0 points, respectively, p=0.08). Conclusions: Image-guided endoscopic evacuation is a minimally-invasive approach to reduce ICH volume. Greater than 50% reduction in hematoma volume was achieved in the large majority of patients. Further study is required to evaluate the impact of endoscopic ICH evacuation on clinical outcomes compared to other treatments.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Individual Risk ◽  
European Project ◽  
Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

Admission Hemoglobin Levels Are Associated With Functional Outcome in Spontaneous Intracerebral Hemorrhage

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Julián N. Acosta ◽  
Audrey C. Leasure ◽  
Lindsey R. Kuohn ◽  
Cameron P. Both ◽  
Nils H. Petersen ◽  
...  
Keyword(s):  
Intracerebral Hemorrhage ◽  
Functional Outcome ◽  
Spontaneous Intracerebral Hemorrhage

Abstract 16588: Using Machine Learning to Improve Survival Prediction After Heart Transplantation

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Brian Ayers ◽  
Toumas Sandhold ◽  
Igor Gosev ◽  
Sunil Prasad ◽  
Arman Kilic
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Heart Transplantation ◽  
Risk Prediction ◽  
Predictive Analytics ◽  
Predictive Performance ◽  
Orthotopic Heart Transplantation ◽  
Survival Prediction ◽  
One Year ◽  
Testing Set

Introduction: Prior risk models for predicting survival after orthotopic heart transplantation (OHT) have displayed only modest discriminatory capability. With increasing interest in the application of machine learning (ML) to predictive analytics in clinical medicine, this study aimed to evaluate whether modern ML techniques could improve risk prediction in OHT. Methods: Data from the United Network for Organ Sharing registry was collected for all adult patients that underwent OHT from 2000 through 2019. The primary outcome was one-year post-transplant mortality. Dimensionality reduction and data re-sampling were employed during training. The final ensemble model was created from 100 different models of each algorithm: deep neural network, logistic regression, adaboost, and random forest. Discriminatory capability was assessed using area under receiver-operating-characteristic curve (AUROC), net reclassification index (NRI), and decision curve analysis (DCA). Results: Of the 33,657 study patients, 26,926 (80%) were randomly selected for the training set and 6,731 (20%) as a separate testing set. One-year mortality was balanced between cohorts (11.0% vs 11.3%). The optimal model performance was a final ensemble ML model. This model demonstrated an improved AUROC of 0.764 (95% CI, 0.745-0.782) in the testing set as compared to the other models (Figure). Additionally, the final model demonstrated an improvement of 72.9% ±3.8% (p<0.001) in predictive performance as assessed by NRI compared to logistic regression. The DCA showed the final ensemble method improved risk prediction across the entire spectrum of predicted risk as compared to all other models (p<0.001). Conclusions: An ensemble ML model was able to achieve greater predictive performance as compared to individual ML models as well as logistic regression for predicting survival after OHT. This analysis demonstrates the promise of ML techniques in risk prediction in OHT.

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