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Related Concept Videos

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

51
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
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Actuarial Approach01:20

Actuarial Approach

36
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

81
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Related Experiment Video

Updated: May 8, 2025

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The eSAH score: a simple practical predictive model for SAH mortality and outcomes.

Rohan Sharma1, Daniel Mandl2, Fabian Föttinger2

  • 1Neurocritical Care Fellowship, Mayo Clinic School of Graduate Medical Education, Mayo Clinic College of Medicine and Science, Jacksonville, FL, USA.

Scientific Reports
|December 27, 2024
PubMed
Summary

A new scoring system, the eSAH score, accurately predicts mortality and outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH). This simple, quantifiable model uses computed tomography (CT) scan data, age, and Glasgow Coma Scale scores.

Keywords:
Computed axial tomographyDelayed cerebral ischemiaMortalityMultivariate analysisOutcomeSubarachnoid hemorrhageeSAH score

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Area of Science:

  • Neurology
  • Radiology
  • Critical Care Medicine

Background:

  • Aneurysmal subarachnoid hemorrhage (aSAH) is a critical condition with high rates of mortality and morbidity.
  • Predicting outcomes such as mortality, delayed cerebral ischemia (DCI), and functional status is crucial for patient management.

Purpose of the Study:

  • To develop and validate a simple, quantifiable scoring system to predict mortality, DCI, and modified Rankin Scale (mRS) outcomes in patients with aSAH.
  • To utilize readily available admission data, including subarachnoid hemorrhage volume (SAHV) from CT scans.

Main Methods:

  • Retrospective analysis of 277 aSAH patients.
  • Development of a mathematical radiographic model (SAHV) using the ABC/2 formula on noncontrast CT.
  • Multivariate logistic regression analysis to identify predictors and develop the enhanced SAH (eSAH) scoring system.

Main Results:

  • Age, Glasgow Coma Scale score, and SAHV were significant predictors of mRS outcomes, DCI, and in-hospital mortality.
  • The eSAH score, ranging from 0 to 5, demonstrated strong predictive power for mRS outcomes (AUC=0.89), DCI (AUC=0.75), and mortality (AUC=0.88).

Conclusions:

  • The proposed eSAH score is a simple, quantitative model that effectively predicts mortality and outcomes in aSAH patients.
  • The score integrates SAHV, Glasgow Coma Scale score, and age, offering a practical tool for clinical assessment.
  • Further validation in a larger cohort is planned to confirm these findings.