Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Actuarial Approach01:20

Actuarial Approach

98
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,...
98
Peripheral Artery Disease V: Postoperative Nursing Management01:23

Peripheral Artery Disease V: Postoperative Nursing Management

12
During the postoperative period, it is crucial to focus on maintaining circulation, identifying and managing potential complications, and planning for discharge.Nursing AssessmentVital signs monitoring: Regularly monitor vital signs, including blood pressure, heart rate, respiratory rate, and temperature, to detect early signs of complications such as bleeding and infection.Circulation assessment: Monitor pulses, perform Doppler assessments, and check capillary refill, color, temperature, and...
12
Cancer Survival Analysis01:21

Cancer Survival Analysis

394
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...
394
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

157
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.
157
Cardiomyopathy VII: Pre and Post Operative Nursing Management01:28

Cardiomyopathy VII: Pre and Post Operative Nursing Management

16
Patients with hypertrophic cardiomyopathy (HCM) and left ventricular outflow tract (LVOT) obstruction who remain symptomatic despite optimal medical therapy may undergo a septal myectomy (Morrow procedure). This procedure involves excising a portion of the hypertrophied septum below the aortic valve using a heart-lung machine to improve blood flow through the LVOT. Effective preoperative and postoperative nursing management ensures successful patient outcomes, minimizes complications, and...
16
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

226
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...
226

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Integrating machine learning and pathway modelling to explore factors associated with chronic post-surgical pain and quality of life: a secondary observational analysis of the ENIGMA-II trial.

BJA open·2026
Same author

Small numbers of clusters in cluster randomised trials: a scoping review of problems and proposed solutions.

Journal of clinical epidemiology·2026
Same author

Prognostic value of the Duke Activity Status Index for preoperative cardiac risk stratification: an international pooled cohort study.

EClinicalMedicine·2026
Same author

Perioperative intravenous fluid and chronic kidney disease: long-term follow-up of the Restrictive versus Liberal Fluid Therapy in Major Abdominal Surgery (RELIEF) randomised trial.

British journal of anaesthesia·2026
Same author

Reducing Bias in Cluster Randomized Trials in Nephrology.

Journal of the American Society of Nephrology : JASN·2026
Same author

The effect of intraoperative dexamethasone on glycaemic responses in people with diabetes mellitus: a preplanned analysis of the Perioperative ADministration of Dexamethasone and Infection trial.

British journal of anaesthesia·2026

Related Experiment Video

Updated: Jul 23, 2025

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles
04:00

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles

Published on: July 26, 2024

580

Predicting Death or Disability after Surgery in the Older Adult.

Mark A Shulman1, Sophie Wallace1, Annie Gilbert2

  • 1Department of Anaesthesiology and Perioperative Medicine, Alfred Hospital and Monash University, Melbourne, Australia.

Anesthesiology
|July 11, 2023
PubMed
Summary

Older patients face risks of disability after surgery. A new point-score model predicts 6-month death or disability, aiding surgical risk assessment for elderly individuals.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K
Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

9.8K

Related Experiment Videos

Last Updated: Jul 23, 2025

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles
04:00

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles

Published on: July 26, 2024

580
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.2K
Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit
06:52

Assessment of Dependence in Activities of Daily Living Among Older Patients in an Acute Care Unit

Published on: September 30, 2020

9.8K

Area of Science:

  • Geriatric Surgery
  • Surgical Outcomes Research
  • Predictive Modeling in Medicine

Background:

  • Older adults are susceptible to new or worsening disability post-surgery.
  • Predictors of postoperative disability in this demographic are not well-defined.
  • This study addresses the need for better risk stratification in elderly surgical patients.

Purpose of the Study:

  • To develop and validate a predictive model for 6-month mortality or disability in older surgical patients.
  • To transform the model into a practical point-score for clinical use.
  • To improve preoperative assessment and patient counseling.

Main Methods:

  • A prospective, single-center registry included patients aged 70+ undergoing various surgeries.
  • Data integrated electronic health records, administrative data (ICD-10-AM), and World Health Organization Disability Assessment Schedule (WHODAS) scores.
  • Patients were randomly assigned to model development (70%) and internal validation (30%) cohorts, with external validation using a separate trial dataset.

Main Results:

  • Preoperative disability was common, with 43% disabled and 19% significantly disabled.
  • At 6 months, 12% of patients died and 42% were dead or disabled.
  • The validated point-score model, incorporating WHODAS score, age, dementia, and chronic kidney disease, showed good predictive discrimination (AUC 0.74-0.77).

Conclusions:

  • A robust point-score model was developed and validated to predict death or disability in older patients undergoing surgery.
  • This tool can aid clinicians in identifying high-risk elderly patients.
  • The model's inclusion of functional status (WHODAS) highlights its importance in surgical risk assessment.