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The multinomial mixed-effect regression model for predicting PCOC phases in hospice patients.

I-Ting Liu1,2, Jui-Hung Tsai1,2, Peng-Chan Lin1,2

  • 1Department of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.

Supportive Care in Cancer : Official Journal of the Multinational Association of Supportive Care in Cancer
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PubMed
Summary

This study identifies key Palliative Care Outcomes Collaboration (PCOC) items to accurately classify patient phases. A new model improves hospice care by enabling timely interventions for terminal patients.

Keywords:
Clinical assessmentHospice carePCOC phasePalliative Care Outcomes Collaboration (PCOC)

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Area of Science:

  • Palliative Care
  • Health Services Research
  • Clinical Informatics

Background:

  • The Palliative Care Outcomes Collaboration (PCOC) aims to systematically improve patient outcomes.
  • Accurate identification of PCOC phases and crucial data items is challenging.
  • Classifying PCOC phases is essential for tailoring end-of-life care.

Purpose of the Study:

  • To identify essential PCOC data items for phase classification.
  • To develop a predictive model for accurately classifying PCOC phases in terminal patients.
  • To enhance the quality of hospice care through improved phase identification.

Main Methods:

  • Retrospective cohort study analyzing PCOC data across four phases: stable, unstable, deteriorating, and terminal.
  • Inclusion of terminal patients from July 2020 to March 2023.
  • Application of a multinomial mixed-effect regression model for repeated measurement data analysis.

Main Results:

  • Analysis of 13,219 care phases from 1933 terminally ill patients.
  • Significant differences observed in symptom assessment, problem severity, performance status, and daily living activities across PCOC phases.
  • A robust prediction model demonstrated high accuracy (AUCs 0.920-0.96) for classifying PCOC phases.

Conclusions:

  • Key PCOC items distinguishing care phases were identified.
  • An accurate prediction model was developed to classify PCOC phases.
  • The model supports enhanced hospice quality by enabling timely interventions and care adjustments.