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Cancer Survival Analysis01:21

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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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Machine Learning Model Development and Validation for Predicting Outcome in Stage 4 Solid Cancer Patients with Septic

Byuk Sung Ko1, Sanghoon Jeon1, Donghee Son2

  • 1Department of Emergency Medicine, College of Medicine, Hanyang University, Seoul 04763, Republic of Korea.

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|December 11, 2022
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Summary
This summary is machine-generated.

A new machine learning model accurately predicts 28-day mortality in advanced cancer patients with septic shock, outperforming existing scores. This prognostic tool aids in minimizing futile treatments for better patient care.

Keywords:
cancer patientmachine learningprognosisseptic shock

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Area of Science:

  • Oncology
  • Critical Care Medicine
  • Data Science

Background:

  • Prognostic scores for advanced cancer patients with septic shock are limited, impacting treatment decisions.
  • Minimizing futile treatments is crucial for improving end-of-life care in this vulnerable population.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) model for predicting 28-day mortality in advanced cancer patients experiencing septic shock.
  • To compare the performance of the ML model against established scoring systems like SOFA and APACHE II.

Main Methods:

  • A multi-center, retrospective observational study utilized a septic shock registry of stage 4 cancer patients.
  • Data was split into training (70%) and testing (30%) sets; a balanced random forest (BRF) model was optimized using 10-fold cross-validation.
  • The primary outcome measured was 28-day mortality.

Main Results:

  • The study included 897 patients, with a 28-day mortality rate of 26.4%.
  • The optimized BRF model achieved an Area Under the Curve (AUC) of 0.821 in the training set and 0.859 in the test set for predicting 28-day mortality.
  • The BRF model demonstrated significantly superior predictive performance compared to SOFA and APACHE II scores (p < 0.001).

Conclusions:

  • The developed ML model, specifically BRF, significantly outperforms existing scoring systems in predicting mortality for advanced cancer patients with septic shock.
  • This advanced prognostic tool has the potential to support real-time clinical decision-making regarding appropriate levels of care.
  • Further validation in diverse international settings is recommended to enhance the algorithm's generalizability.