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Record W4386303671 · doi:10.1053/j.ajkd.2023.05.011

Outcomes of Integrated Home Dialysis Care: Results From the Canadian Organ Replacement Register

2023· article· en· W4386303671 on OpenAlexafffundabout
Louis‐Charles Desbiens, Karthik Tennankore, Rémi Goupil, Jeffrey Perl, Emilie Trinh, Christopher T. Chan, Annie‐Claire Nadeau‐Fredette

Bibliographic record

VenueAmerican Journal of Kidney Diseases · 2023
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsMcGill UniversityMcGill University Health CentreToronto General HospitalUniversity Health NetworkDalhousie UniversityHôpital Maisonneuve-RosemontSt. Michael's HospitalHôpital du Sacré-Cœur de MontréalUniversité de Montréal
FundersFonds de Recherche du Québec - Santé
KeywordsMedicineHome hemodialysisHemodialysisDialysisHazard ratioRenal replacement therapyPeritoneal dialysisProportional hazards modelInternal medicineConfidence interval

Abstract

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RATIONALE & OBJECTIVE: The integrated home dialysis model proposes the initiation of kidney replacement therapy (KRT) with peritoneal dialysis (PD) and a timely transition to home hemodialysis (HHD) after PD ends. We compared the outcomes of patients transitioning from PD to HHD with those initiating KRT with HHD. STUDY DESIGN: Observational analysis of the Canadian Organ Replacement Register (CORR). SETTINGS & PARTICIPANTS: All patients who initiated PD or HHD within the first 90 days of KRT between 2005 and 2018. EXPOSURE: Patients transitioning from PD to HHD (PD+HHD group) versus patients initiating KRT with HHD (HHD group). OUTCOME: (1) A composite of all-cause mortality and modality transfer (to in-center hemodialysis or PD for 90 days) and (2) all hospitalizations (considered as recurrent events). ANALYTICAL APPROACH: A propensity score analysis for which PD+HHD patients were matched 1:1 to (1) incident HHD patients ("incident-match" analysis) or (2) HHD patients with a KRT vintage at least equivalent to the vintage of PD+HHD patients at the transition time ("vintage-matched" analysis). Cause-specific hazards models (composite outcome) and shared frailty models (hospitalization) were used to compare groups. RESULTS: Among 63,327 individuals in the CORR, 163 PD+HHD patients (median of 1.9 years in PD) and 711 HHD patients were identified. In the incident-match analysis, compared to the HHD patients, the PD+HHD group had a similar risk of the composite outcome (HR, 0.88 [95% CI, 0.58-1.32]) and hospitalizations (HR, 1.04 [95% CI, 0.76-1.41]). In the vintage-match analysis, PD+HHD patients had a lower hazard for the composite outcome (HR, 0.61 [95% CI, 0.40-0.94]) but a similar hospitalization risk (HR, 0.85 [95% CI, 0.59-1.24]). LIMITATIONS: Risk of survivor bias in the PD+HHD cohort and residual confounding. CONCLUSIONS: Controlling for KRT vintage, the patients transitioning from PD to HHD had better clinical outcomes than the incident HHD patients. These data support the use of integrated home dialysis for patients initiating home-based KRT. PLAIN-LANGUAGE SUMMARY: The integrated home dialysis model proposes the initiation of dialysis with peritoneal dialysis (PD) and subsequent transition to home hemodialysis (HHD) once PD is no longer feasible. It allows patients to benefit from initial lifestyle advantages of PD and to continue home-based treatments after its termination. However, some patients may prefer to initiate dialysis with HHD from the outset. In this study, we compared the long-term clinical outcomes of both approaches using a large Canadian dialysis register. We found that both options led to a similar risk of hospitalization. In contrast, the PD-to-HHD model led to improved survival when controlling for the duration of kidney failure.

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)0.002
metaresearch head score (Gemma)0.010
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 score0.033
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.012
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.000
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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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".

Quick stats

Citations11
Published2023
Admission routes3
Has abstractyes

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Same venueAmerican Journal of Kidney DiseasesSame topicDialysis and Renal Disease ManagementFrench-language works237,207