SynthesisBMC medical research methodology2018
Prognostic models for intracerebral hemorrhage: systematic review and meta-analysis.
Synthesis in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05424614 (Capital's Funds for Health Improvement and Research), which is not on this map. Cited by 39 papers, 5 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Capital's Funds for Health Improvement and Research
Who cites it
39 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Prediction models for CAR-T Cell therapy-related adverse events in hematologic malignancies: a systematic review and meta-analysis.BMC cancer · 2026Pooled it
- Accuracy of Home Blood Pressure Monitoring in Youth: A Systematic Review and Meta-Analysis.The Journal of pediatrics · 2025Pooled it
- Guidelines for Neuroprognostication in Critically Ill Adults with Intracerebral Hemorrhage.Neurocritical care · 2024Guideline
- Methodological quality of multivariate prognostic models for intracranial haemorrhages in intensive care units: a systematic review.BMJ open · 2021Pooled it
- Intracranial hypertension in patients with aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis.Neurosurgical review · 2021Pooled it
- Data-driven upper limb sensorimotor recovery prediction in patients with stroke: A prospective cohort study.MethodsX · 2026Article
- Development and Validation of an Interpretable Machine Learning-Based Clinical Prediction Model for Short-Term Mortality in Intracerebral Hemorrhage With Thrombocytopenia: A Multicenter Study.CNS neuroscience & therapeutics · 2026Article
- Prompt-Sensitive Decision Behavior of Large Language Models in Intensive Care Unit Mortality Prediction for Spontaneous Intracerebral Hemorrhage: Comparative Benchmarking Study.Journal of medical Internet research · 2026Article
- Deep Learning for Dynamic Prognostic Prediction in Minimally Invasive Surgery for Intracerebral Hemorrhage: Model Development and Validation Study.JMIR medical informatics · 2026Article
- Hematoma expansion in intracerebral hemorrhage: neuroimaging biomarkers, radiomics-based prediction models, and clinical translation.Frontiers in human neuroscience · 2026Review
- Explainable models for predicting long-term outcomes in patients with spontaneous intracerebral haemorrhage: a retrospective cohort study.Stroke and vascular neurology · 2025Article
- Online Dynamic Nomogram for Predicting 90-Day Prognosis of Patients With Primary Basal Ganglia Cerebral Hemorrhage After Microscopic Keyhole Craniotomy for Hematoma Removal.Brain and behavior · 2025Article
- Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.Journal of the American Heart Association · 2024Article
- How much of the variance in functional outcome related to intracerebral hemorrhage volume is already apparent in neurological status at admission?Journal of neurology · 2024Article
- Prognostic biomarkers of intracerebral hemorrhage identified using targeted proteomics and machine learning algorithms.PloS one · 2024Article
- Incorporating platelet-to-white blood cell ratio into survival prediction models for intracerebral hemorrhage: a nomogram approach.Frontiers in neurology · 2024Article
- The National Institutes of Health Stroke Scale is comparable to the ICH score in predicting outcomes in spontaneous acute intracerebral hemorrhage.Frontiers in neurology · 2024Article
- Unveiling the Crystal Ball: Predictors of Adverse Outcomes in Intracerebral Hemorrhage Patients.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2023Article
- Integrative transcriptomic and network pharmacology analysis reveals the neuroprotective role of BYHWD through enhancing autophagy by inhibiting Ctsb in intracerebral hemorrhage mice.Chinese medicine · 2023Article
- Development and validation of a random forest model to predict functional outcome in patients with intracerebral hemorrhage.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrognostic tools for intracerebral hemorrhage (ICH) patients are potentially useful for ascertaining prognosis and recommended in guidelines to facilitate streamline assessment and communication between providers. In this systematic review with meta-analysis we identified and characterized all existing prognostic tools for this population, performed a methodological evaluation of the conducting and reporting of such studies and compared different methods of prognostic tool derivation in terms of discrimination for mortality and functional outcome prediction.
methodsPubMed, ISI, Scopus and CENTRAL were searched up to 15th September 2016, with additional studies identified using reference check. Two reviewers independently extracted data regarding the population studied, process of tool derivation, included predictors and discrimination (c statistic) using a predesignated spreadsheet based in the CHARMS checklist. Disagreements were solved by consensus. C statistics were pooled using robust variance estimation and meta-regression was applied for group comparisons using random effect models.
resultsFifty nine studies were retrieved, including 48,133 patients and reporting on the derivation of 72 prognostic tools. Data on discrimination (c statistic) was available for 53 tools, 38 focusing on mortality and 15 focusing on functional outcome. Discrimination was high for both outcomes, with a pooled c statistic of 0.88 for mortality and 0.87 for functional outcome. Forty three tools were regression based and nine tools were derived using machine learning algorithms, with no differences found between the two methods in terms of discrimination (p = 0.490). Several methodological issues however were identified, relating to handling of missing data, low number of events per variable, insufficient length of follow-up, absence of blinding, infrequent use of internal validation, and underreporting of important model performance measures.
conclusionsPrognostic tools for ICH discriminated well for mortality and functional outcome in derivation studies but methodological issues require confirmation of these findings in validation studies. Logistic regression based risk scores are particularly promising given their good performance and ease of application.
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