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Record W2977249696 · doi:10.1177/2054358119879777

Facility Variation and Predictors of Do Not Resuscitate Orders of Hemodialysis Patients in Canada: DOPPS

2019· article· en· W2977249696 on OpenAlexafffundabout
Danielle Moorman, Ranjeeta Mallick, Emily Rhodes, Brian Bieber, Gihad Nesrallah, Jan Maree Davis, Rita S. Suri, Jeffery Perl, Peter Tanuseputro, Ronald L. Pisoni, Bruce Robinson, Manish M. Sood

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2019
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsSt. Michael's HospitalUniversité de MontréalHumber River Regional HospitalOttawa Hospital
FundersCancer Care Ontario
KeywordsMedicineHemodialysisDo Not Resuscitate OrderLife expectancyQuality of life (healthcare)CohortPopulationDialysisKidney diseaseDo not resuscitateRetrospective cohort studyEmergency medicineInternal medicineIntensive care medicineEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Life expectancy in patients with end-stage kidney disease treated with hemodialysis (HD) is limited, and as such, the presence of an advanced care directive (ACD) may improve the quality of death as experienced for patients and families. Strategies to discuss and implement ACDs are limited with little being known about the status of Do Not Resuscitate (DNR) orders in the Canadian HD population. OBJECTIVES: Using data from the Dialysis Outcomes and Practice Patterns Study (DOPPS), we set out to (1) examine the variability in DNR orders across Canada and its largest province, Ontario and (2) identify clinical and functional status measures associated with a DNR order. DESIGN: We conducted a retrospective cohort study using data from the DOPPS Canada Phase 4 to 6 from 2009 to 2017. SETTING: DOPPS facilities in Canada. PATIENTS: All adults (>18 years) who initiated chronic HD with a documented ACD were included. MEASUREMENTS: ACD and DNR orders. METHODS: Descriptive statistics were compared for baseline characteristics (demographics, comorbidities, medications, facility characteristics, and patient functional status) and DNR status. The crude proportion of patients per facility with a DNR order was calculated across Canada and Ontario. Functional status was determined by activities of daily living and components of the Kidney Disease Quality of Life (KDQOL)-validated questionnaire. We used generalized estimating equations (GEEs) to create sequential multivariable models (demographics, comorbidities, and functional status) of variables associated with DNR status. RESULTS: A total of 1556 (96% of total) patients treated with HD had a documented ACD and were included. A total of 10% of patients had a DNR order. The crude variation of DNR status differed considerably across facilities within Canada, between Ontario and non-Ontario, and within Ontario (interprovince variation = 6.3%-17.1%, Ontario vs non-Ontario = 8.2% vs 11.7%, intraprovincial variation [Ontario] = 1%-26%). Patients with a DNR order were more commonly older, white, with cardiac comorbidities, with less or shorter predialysis care compared with those without a DNR order. Patients with a DNR order reported lower energy, more difficulty with transfers, meal preparation, household tasks, and financial management. In a multivariate model, age, cardiac disease, stroke, dialysis duration, and intradialytic weight gain were associated with DNR status. LIMITATIONS: Relatively small number of events or measures in certain categories. CONCLUSIONS: A large inter- and intraprovincial (Ontario) variation was observed regarding DNR orders across Canada highlighting areas for potential quality improvement. While functional status did not appear to have a bearing on the presence of a DNR order, the presence of various comorbidities was associated with the presence of a DNR order.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.005
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.212
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2019
Admission routes3
Has abstractyes

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