Mortality outcomes and survival patterns of patients with myeloproliferative neoplasms in Malaysia

Yee Yee Yap1, Jameela Sathar2, Kian Boon Law3

  • 1Department of Haematology, Ampang Hospital, Jalan Mewah Utara, Pandan Mewah, 68000, Ampang, Selangor, Malaysia. yapyeeyee09@gmail.com.

Abstract

Insights

Myeloproliferative neoplasm (MPN) impacts survival, with outcomes varying by subtype, sex, and hemoglobin levels. The JAK2 V617F mutation did not significantly affect survival in this Malaysian study.

Area of Science:

  • Hematology
  • Oncology
  • Epidemiology

Background:

  • Prognosis for myeloproliferative neoplasms (MPN) remains challenging, even with JAK2 V617F mutation analysis.
  • Survival patterns for MPN patients in developing nations are not well-established.

Purpose of the Study:

  • To analyze survival patterns and mortality outcomes in a nationwide Malaysian cohort of MPN patients.
  • To identify risk factors influencing mortality in different MPN subtypes.

Main Methods:

  • Utilized data from a national MPN registry (2009-2015) in Malaysia.
  • Included 671 patients with essential thrombocythaemia (ET), polycythaemia vera (PV), primary myelofibrosis (PMF), and unclassified MPN (MPN-U).
  • Traced mortality status until December 2018, applying right censoring.

Main Results:

  • The cohort comprised 283 ET, 269 PV, 62 PMF, and 57 MPN-U cases.
  • Female patients showed better overall survival (OS) in ET and MPN-U.
  • Increased age, male sex, lower hemoglobin, and diagnoses of PMF or MPN-U were associated with worse mortality outcomes.

Conclusions:

  • MPN significantly reduces patient survival, with variations based on subtype, sex, fibrosis, and hemoglobin levels.
  • The JAK2 V617F mutation was not a significant predictor of survival or mortality in this cohort.
  • Findings highlight the importance of specific clinical factors in MPN prognostication.

Related Concept Videos

Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
235
Actuarial Approach01:20

Actuarial Approach

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,...
150
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

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,...
299
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...
339
Cancer Survival Analysis01:21

Cancer Survival Analysis

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...
478