Sarcoma of the heart: survival after surgery

Lars Niclauss1, Michael Montemurro2, Matthias Kirsch1

  • 1Department of Cardiovascular Surgery, University Hospital of Lausanne (CHUV), Lausanne, Switzerland.

Insights

Malignant cardiac sarcomas are rare but operable. Complete resection, no metastases, and specific sarcoma types improve survival, though the 1-year mortality rate remains high at 44%.

Area of Science:

  • Cardiovascular Surgery
  • Surgical Oncology
  • Cardiac Pathology

Background:

  • Malignant intracardiac tumours, particularly sarcomas, are exceptionally rare.
  • Optimal therapeutic strategies for cardiac sarcomas lack established consensus.
  • This study retrospectively analyzes outcomes for patients undergoing surgery for cardiac sarcomas.

Observation:

  • Nine patients with cardiac sarcomas treated between 2000 and 2015 were reviewed.
  • All patients presented with cardiac symptoms and survived the initial surgery.
  • Symptom relief or improvement was observed in all patients post-operation.

Findings:

  • Cardiac sarcomas represent 0.14% of resected malignant cardiac tumours.
  • The 1-year mortality rate was 44%, indicating an unfavorable prognosis.
  • Complete tumor resection, absence of metastasis, and specific histological types significantly impacted long-term survival.

Implications:

  • Surgery is a viable and safe option for selected patients with cardiac sarcomas, managing symptoms and preventing early mortality.
  • Further research is needed to optimize treatment protocols for this rare malignancy.
  • Understanding prognostic factors is crucial for guiding therapeutic decisions and improving patient outcomes.
Abstract

Related Concept Videos

Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
745
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
437
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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...
840
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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...
618
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
639
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
435