Related Experiment Video
Updated: Feb 17, 2026
06:46
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
938
Development of Imminent Mortality Predictor for Advanced Cancer (IMPAC), a Tool to Predict Short-Term Mortality in
Kerin Adelson1, Donald K K Lee1, Salimah Velji1
1Yale Cancer Center and Smilow Cancer Hospital, Yale School of Management, and Yale New Haven Health, New Haven, CT; and Massachusetts General Hospital, Boston, MA.
Journal of Oncology Practice
|December 6, 2017
Summary
A new tool, Imminent Mortality Predictor in Advanced Cancer (IMPAC), accurately predicts short-term mortality in advanced cancer patients, aiding end-of-life care decisions and reducing costs.
Area of Science:
- Oncology
- Medical Informatics
- Health Economics
Background:
- End-of-life care for advanced cancer patients is often aggressive and costly.
- Current prognostication tools rely on subjective assessments, leading to inconsistent life expectancy estimations and goal-of-care discussions.
- An objective tool is needed to guide realistic end-of-life care planning.
Purpose of the Study:
- To develop and validate the Imminent Mortality Predictor in Advanced Cancer (IMPAC) model.
- To create an objective tool for predicting short-term mortality in hospitalized advanced cancer patients.
Main Methods:
- Utilized statistical learning techniques on electronic health record data from 669 advanced cancer patients.
- Developed a predictive model (IMPAC) to estimate survival probabilities.
- Validated the model's predictive performance for mortality at 30, 60, 90, and 180 days using a 70/30 training/validation split, repeated 20 times.
Main Results:
- IMPAC demonstrated a positive predictive value of nearly 60% for 90-day mortality at 40% sensitivity.
- Patients with >50% predicted 90-day mortality had a median survival of 47 days, versus 290 days for those with <50% predicted risk.
- The model achieved an average area under the receiver operating characteristic curve greater than 0.70 across all time horizons, with estimated cost savings of $15,413 per patient.
Conclusions:
- IMPAC is a novel, real-time prognostic tool that can assist oncologists in patient counseling regarding end-of-life care.
- The tool supports more realistic planning and has the potential for significant cost savings.
More Related Videos
Related Concept Videos
01:21Cancer Survival Analysis
784
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...
784
01:24Kaplan-Meier Approach
639
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,...
639
01:20Actuarial Approach
325
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,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
325
01:20Comparing the Survival Analysis of Two or More Groups
621
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...
621
01:18Introduction To Survival Analysis
855
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...
The primary goal of survival analysis is to estimate survival time—the time...
855
01:15Assumptions of Survival Analysis
447
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.
447