Machine Learning-Based Approaches for Prediction of Patients' Functional Outcome and Mortality after Spontaneous Intracerebral Hemorrhage.
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SUBMITTER: Guo R
PROVIDER: S-EPMC8778760 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Spontaneous intracerebral hemorrhage (ICH) has an increasing incidence and a worse outcome in elderly patients. The ability to predict the functional outcome in these patients can be helpful in supporting treatment decisions and establishing prognostic expectations. We evaluated the performance of a machine learning (ML) model to predict the 6-month functional status in elderly patients with ICH leveraging the predictive value of the clinical characteristics at hospital admission. Data were extracted by a retrospective multicentric database of patients ≥ 70 years of age consecutively admitted for the management of spontaneous ICH between January 1, 2014 and December 31, 2019. Relevant demographic, clinical, and radiological variables were selected by a feature selection algorithm (Boruta) and used to build a ML model. Outcome was determined according to the Glasgow Outcome Scale (GOS) at 6 months from ICH: dead (GOS 1), poor outcome (GOS 2-3: vegetative status/severe disability), and good outcome (GOS 4-5: moderate disability/good recovery). Ten features were selected by Boruta with the following relative importance order in the ML model: Glasgow Coma Scale, Charlson Comorbidity Index, ICH score, ICH volume, pupillary status, brainstem location, age, anticoagulant/antiplatelet agents, intraventricular hemorrhage, and cerebellar location. Random forest prediction model, evaluated on the hold-out test set, achieved an AUC of 0.96 (0.94-0.98), 0.89 (0.86-0.93), and 0.93 (0.90-0.95) for dead, poor, and good outcome classes, respectively, demonstrating high discriminative ability. A random forest classifier was successfully trained and internally validated to stratify elderly patients with spontaneous ICH into prognostic subclasses. The predictive value is enhanced by the ability of ML model to identify synergy among variables.
| S-EPMC9349060 | biostudies-literature
Project description:BackgroundThe elevation of plasma D-dimer levels may predict a higher risk of thrombosis and play a role in the pathological process of patients after spontaneous intracerebral hemorrhage (ICH). However, its function in predicting the prognosis of ICH has not been verified on large cases.Patients and methodsRetrospective cohort study of 1,332 consecutive patients with spontaneous ICH at an academic medical center was conducted. Functional outcome at three months after ICH was dichotomized using the modified Rankin Scale (0-2 versus 3-6). D-dimer level in blood was analyzed within 1 hr of admission. An ICH outcome score combining D-dimer level for evaluating poor functional outcome and mortality was tested.ResultsThe proportion of patients with poor functional outcome and mortality at three months was significantly higher in patients with elevated D-dimer level (p < .001). Multivariable analysis demonstrated that elevated D-dimer level was an independent predictor of poor functional outcome (odds ratio 1.486, 95% confidence interval 1.086-2.060, p = .014) and mortality (odds ratio 2.015, 95% confidence interval 1.186-3.423, p = .01). An increasing ICH outcome score combining D-dimer level was associated with increased poor functional outcome and mortality.ConclusionsElevated plasma D-dimer level after spontaneous ICH is associated with poor functional outcome and mortality. The study suggests that elevated D-dimer level has a predictive value for outcome and mortality in patients with spontaneous ICH.
| S-EPMC7821563 | biostudies-literature
Project description:ObjectivesTo test the hypothesis that admission hemoglobin levels are associated with outcome in primary, nontraumatic intracerebral hemorrhage.DesignIndividual patient data meta-analysis of three studies of intracerebral hemorrhage.SettingTwo randomized clinical trials and one multiethnic observational study.PatientsPatients with spontaneous, nontraumatic intracerebral hemorrhage.InterventionsNone.Measurements and main resultsOur exposure of interest was admission hemoglobin levels and the primary outcome was 3-month postintracerebral hemorrhage-dichotomized modified Rankin Scale (0-3 vs 4-6). Intermediate outcomes were admission hematoma volume and hematoma expansion defined as 6 mL or 33% increase in hemorrhage size on repeat CT. A total of 4,172 intracerebral hemorrhage patients were included in the study (mean age 63 [sd = 14]; female sex 1,668 [40%]). Each additional g/dL of admission hemoglobin was associated with 14% (odds ratio, 0.86; 95% CI, 0.82-0.91) and 7% (odds ratio, 0.93; 95% CI, 0.88-0.98) reductions in the risk of poor outcome in unadjusted and adjusted analyses, respectively. Dose-response analyses indicated a linear relationship between admission hemoglobin levels and poor outcome across the entire evaluated range (test-for-trend p < 0.001). No consistent associations were found between the admission hemoglobin levels and hematoma volume or hematoma expansion.ConclusionsHigher hemoglobin levels are associated with better outcome in intracerebral hemorrhage. Further research is needed to evaluate admission hemoglobin levels as both a therapeutic target and predictor of outcome.
| S-EPMC8611893 | biostudies-literature
Project description:Spontaneous intracerebral hemorrhage (ICH) results in high rates of morbidity and mortality, with intraventricular hemorrhage (IVH) being associated with even worse outcomes. Therapeutic interventions in acute ICH have continued to emerge with focus on arresting hemorrhage expansion, clot volume reduction of both intraventricular and parenchymal hematomas, and targeting perihematomal edema and inflammation. Large randomized controlled trials addressing the effectiveness of rapid blood pressure lowering, hemostatic therapy with platelet transfusion, and other clotting complexes and hematoma volume reduction using minimally invasive techniques have impacted clinical guidelines. We review the recent evolution in the management of acute spontaneous ICH, discussing which interventions have been shown to be safe and which may potentially improve outcomes.
| S-EPMC7851203 | biostudies-literature
Project description:Purpose of reviewSpontaneous intracerebral hemorrhage (ICH) is the deadliest stroke subtype. Acute treatments necessitate rapid hemorrhage control to minimize secondary brain injury. Here, we discuss the overlap of transfusion medicine and acute ICH care relating to diagnostic testing and therapies relevant for coagulopathy reversal and secondary brain injury prevention.Recent findingsHematoma expansion (HE) is the largest contributor to poor outcomes after ICH. Conventional coagulation assays to diagnose coagulopathy after ICH does not predict HE. Given the testing limitations, empiric pragmatic hemorrhage control therapies have been trialed but have not improved ICH outcomes, with some therapies even causing harm. It is still unknown whether faster administration of these therapies will improve outcomes. Alternative coagulation tests (e.g., viscoelastic hemostatic assays, amongst others) may identify coagulopathies relevant for HE, currently not diagnosed using conventional assays. This provides opportunities for rapid, targeted therapies. In parallel, ongoing work is investigating alternative treatments using transfusion-based or transfusion-sparing pharmacotherapies that can be implemented in hemorrhage control strategies after ICH.SummaryFurther work is needed to identify improved laboratory diagnostic approaches and transfusion medicine treatment strategies to prevent HE and optimize hemorrhage control in ICH patients, who appear particularly vulnerable to the impacts of transfusion medicine practices.
| S-EPMC9996464 | biostudies-literature
Project description:We 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.
| S-EPMC8557152 | biostudies-literature
Project description:Background and Purpose- Observational data suggest that antiplatelet therapy after intracerebral hemorrhage (ICH) alleviates thromboembolic risk without increasing the risk of recurrent ICH. Given the paucity of data on the relationship between antiplatelet therapy after ICH and functional outcomes, we aimed to study this association in a multicenter cohort. Methods- We meta-analyzed data from (1) the Massachusetts General Hospital ICH registry (n=1854), (2) the Virtual International Stroke Trials Archive database (n=762), and (3) the Yale stroke registry (n=185). Our exposure was antiplatelet therapy after ICH, which was modeled as a time-varying covariate. Our primary outcomes were all-cause mortality and a composite of major disability or death (modified Rankin Scale score 4-6). We used Cox proportional regression analyses to estimate the hazard ratio of death or poor functional outcome as a function of antiplatelet therapy and random-effects meta-analysis to pool the estimated HRs across studies. Additional analyses stratified by hematoma location (lobar and deep ICH) were performed. Results- We included a total of 2801 ICH patients, of whom 288 (10.3%) were started on antiplatelet medications after ICH. Median times to antiplatelet therapy ranged from 7 to 39 days. Antiplatelet therapy after ICH was not associated with mortality (hazard ratio, 0.85; 95% CI, 0.66-1.09), or death or major disability (hazard ratio, 0.83; 95% CI, 0.59-1.16) compared with patients not started on antiplatelet therapy. Similar results were obtained in additional analyses stratified by hematoma location. Conclusions- Antiplatelet therapy after ICH appeared safe and was not associated with all-cause mortality or functional outcome, regardless of hematoma location. Randomized clinical trials are needed to determine the effects and harms of antiplatelet therapy after ICH.
| S-EPMC6941441 | biostudies-literature
Project description:ObjectiveSubarachnoid hemorrhage (SAH) is often devastating with increased early mortality, particularly in those with presumed delayed cerebral ischemia (DCI). The ability to accurately predict survival for SAH patients during the hospital course would provide valuable information for healthcare providers, patients, and families. This study aims to utilize electronic health record (EHR) data and machine learning approaches to predict the adverse outcome for nontraumatic SAH adult patients.MethodsThe cohort included nontraumatic SAH patients treated with vasopressors for presumed DCI from a large EHR database, the Cerner Health Facts® EMR database (2000-2014). The outcome of interest was the adverse outcome, defined as death in hospital or discharged to hospice. Machine learning-based models were developed and primarily assessed by area under the receiver operating characteristic curve (AUC).ResultsA total of 2467 nontraumatic SAH patients (64% female; median age [interquartile range]: 56 [47-66]) who were treated with vasopressors for presumed DCI were included in the study. 934 (38%) patients died or were discharged to hospice. The model achieved an AUC of 0.88 (95% CI, 0.84-0.92) with only the initial 24 h EHR data, and 0.94 (95% CI, 0.92-0.96) after the next 24 h.InterpretationEHR data and machine learning models can accurately predict the risk of the adverse outcome for critically ill nontraumatic SAH patients. It is possible to use EHR data and machine learning techniques to help with clinical decision-making.
| S-EPMC7664270 | biostudies-literature
Project description:ObjectiveAccurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP).MethodsA prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis.ResultsAmong the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions.ConclusionsThis study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.
| S-EPMC10968679 | biostudies-literature
Project description:PurposeTo describe the association between factors routinely available in hyperacute care of spontaneous intracerebral haemorrhage (ICH) patients and functional outcome.MethodsWe searched Medline, Embase and CINAHL in February 2020 for original studies reporting associations between markers available within six hours of arrival in hospital and modified Rankin Scale (mRS) at least 6 weeks post-ICH. A random-effects meta-analysis was performed where three or more studies were included.FindingsThirty studies were included describing 40 markers. Ten markers underwent meta-analysis and age (OR = 1.06; 95%CI = 1.05 to 1.06; p < 0.001), pre-morbid dependence (mRS, OR = 1.73; 95%CI = 1.52 to 1.96; p < 0.001), level of consciousness (Glasgow Coma Scale, OR = 0.82; 95%CI = 0.76 to 0.88; p < 0.001), stroke severity (National Institutes of Health Stroke Scale, OR=1.19; 95%CI = 1.13 to 1.25; p < 0.001), haematoma volume (OR = 1.12; 95%CI=1.07 to 1.16; p < 0.001), intraventricular haemorrhage (OR = 2.05; 95%CI = 1.68 to 2.51; p < 0.001) and deep (vs. lobar) location (OR = 2.64; 95%CI = 1.65 to 4.24; p < 0.001) were predictive of outcome but systolic blood pressure, CT hypodensities and infratentorial location were not. Of the remaining markers, sex, medical history (diabetes, hypertension, prior stroke), prior statin, prior antiplatelet, admission blood results (glucose, cholesterol, estimated glomerular filtration rate) and other imaging features (midline shift, spot sign, sedimentation level, irregular haematoma shape, ultraearly haematoma growth, Graeb score and onset to CT time) were associated with outcome.ConclusionMultiple demographic, pre-morbid, clinical, imaging and laboratory factors should all be considered when prognosticating in hyperacute ICH. Incorporating these in to accurate and precise models will help to ensure appropriate levels of care for individual patients.
| S-EPMC8921779 | biostudies-literature