Evidence map›Paper›PMID 39222130›Full record

ArticleSupportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer2024

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

I-Ting Liu, Jui-Hung Tsai, Peng-Chan Lin, Pei-Fang Su, Yi-Chia Liu, Ying-Tzu Huang, Ge-Lin Chiu, Yu-Yeh Chen, Wei-Shu Lai

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

I-Ting LiuDepartment of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Jui-Hung TsaiDepartment of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Peng-Chan LinDepartment of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Pei-Fang SuDepartment of Statistics, National Cheng Kung University, Tainan, Taiwan.
Yi-Chia LiuThe Center for Quantitative Sciences, Clinical Medicine Research Center, National Cheng Kung University Hospital, Tainan, Taiwan.
Ying-Tzu HuangDepartment of Oncology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Ge-Lin ChiuDepartment of Nursing, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Yu-Yeh ChenDepartment of Nursing, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Wei-Shu LaiDepartment of Nursing, College of Medicine, National Cheng Kung University, University Rd, No. 1, Tainan, 70101, Taiwan. weisue@mail.ncku.edu.tw.

Funding

Ministry of Health and Welfare MOHW111-TDU-B-221-114005Ministry of Science and Technology MOST 111-2634-F-006-002National Cheng Kung University Hospital NCKUH-11102061
6 · The paper itself

Abstract

purposeThe Palliative Care Outcomes Collaboration (PCOC) aims to enhance patient outcomes systematically. However, identifying crucial items and accurately determining PCOC phases remain challenging. This study aims to identify essential PCOC data items and construct a prediction model to accurately classify PCOC phases in terminal patients.

methodsA retrospective cohort study assessed PCOC data items across four PCOC phases: stable, unstable, deteriorating, and terminal. From July 2020 to March 2023, terminal patients were enrolled. A multinomial mixed-effect regression model was used for the analysis of multivariate PCOC repeated measurement data.

resultsThe dataset comprised 1933 terminally ill patients from 4 different hospice service settings. A total of 13,219 phases of care were analyzed. There were significant differences in the symptom assessment scale, palliative care problem severity score, Australia-modified Karnofsky performance status, and resource utilization groups-activities of daily living among the four PCOC phases of care. Clinical needs, including pain and other symptoms, declined from unstable to terminal phases, while psychological/spiritual and functional status for bed mobility, eating, and transfers increased. A robust prediction model achieved areas under the curves (AUCs) of 0.94, 0.94, 0.920, and 0.96 for stable, unstable, deteriorating, and terminal phases, respectively.

conclusionsCritical PCOC items distinguishing between PCOC phases were identified, enabling the development of an accurate prediction model. This model enhances hospice care quality by facilitating timely interventions and adjustments based on patients' PCOC phases.

Indexed as

Hospice CarePalliative CareActivities of Daily LivingAdultAgedAged, 80 and overCohort StudiesFemaleHumansKarnofsky Performance StatusMaleMiddle AgedRegression AnalysisRetrospective StudiesClinical assessmentHospice carePalliative Care Outcomes Collaboration (PCOC)PCOC phase

Identifiers

PMID39222130
PMCPMC11369001

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.