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Related Concept Videos

Actuarial Approach01:20

Actuarial Approach

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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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Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
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Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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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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Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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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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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Related Experiment Video

Updated: Jul 5, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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Pregnancy-adapted YEARS Algorithm: A Retrospective Analysis.

Alden Mileto1, Gina Rossi1, Benjamin Krouse1

  • 1Geisinger Commonwealth School of Medicine, Scranton, Pennsylvania.

The Western Journal of Emergency Medicine
|January 11, 2024
PubMed
Summary
This summary is machine-generated.

The pregnancy-adapted YEARS algorithm can safely reduce computed tomography pulmonary angiography (CTPA) use in pregnant patients with suspected pulmonary embolism (PE). This validation study in the US shows the algorithm aligns with clinical practice and reduces imaging needs.

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Area of Science:

  • Obstetrics and Gynecology
  • Radiology
  • Cardiology

Background:

  • Pulmonary embolism (PE) is a significant cause of morbidity in pregnant patients.
  • Current diagnostic workup for PE in pregnancy lacks a standardized approach.
  • European studies suggest a pregnancy-adapted YEARS algorithm can safely reduce imaging.

Purpose of the Study:

  • To evaluate the potential reduction in CTPA utilization using the pregnancy-adapted YEARS algorithm in pregnant patients in the US.
  • To externally validate the pregnancy-adapted YEARS algorithm in a US cohort.

Main Methods:

  • Retrospective chart analysis of 74 pregnant patients suspected of PE.
  • Application of the pregnancy-adapted YEARS algorithm (clinical signs of DVT, hemoptysis, PE as most likely diagnosis, D-dimer).
  • Analysis of CTPA use and 30-day follow-up for PE or DVT in patients not requiring imaging.

Main Results:

  • PE prevalence was 2.7% (2/74 patients).
  • If the algorithm was applied, 36 patients would not have required imaging; 7 CTPA were performed in this group.
  • No patients who did not receive initial CTPA were diagnosed with PE or DVT within 30 days.

Conclusions:

  • The pregnancy-adapted YEARS algorithm could decrease CTPA use in pregnant patients with suspected PE.
  • The algorithm demonstrated similar reductions in imaging compared to European studies.
  • The algorithm's alignment with clinical rationale suggests potential for widespread adoption.