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Record W4306290981 · doi:10.1093/eurheartj/ehac544.480

Mortality and 30-day readmission rate following leadless pacemaker implantation: insights from the Nationwide Readmissions Database

2022· article· en· W4306290981 on OpenAlexaboutno aff
R Tonegawa-Kuji, Koshiro Kanaoka, Makoto Mori, Masa Tsugu Nakai, Yoshitaka Iwanaga

Bibliographic record

VenueEuropean Heart Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac pacing and defibrillation studies
Canadian institutionsnot available
FundersNational Cerebral and Cardiovascular Center
KeywordsMedicineLogistic regressionComplicationEmergency medicineMortality rateQuarter (Canadian coin)PopulationDatabaseInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Clinical trials and registry data showed encouraging outcomes for leadless pacemaker (LP) implantation. However, reports of patient characteristics, trends, and clinical outcomes in a real-world population are limited. Purpose To provide real-world evidence of the rates, trends, and patient characteristics associated with in-hospital complications and 30-days readmission after LP implantations. Methods Using the all-payer, nationally representative Nationwide Readmissions Database between 2017 and 2019, we analyzed leadless or conventional pacemaker implantations. The national trends of in-hospital mortality, in-hospital complication rates and 30-day readmission rates after pacemaker implantation and their national trends were analyzed. Mixed-effects multivariable logistic regression analysis was performed to identify factors associated with in-hospital death and 30-days readmission in LP patients. Results A total of 137,732 admissions (age: 78 [70–85], 5,986 LP implantations) were analyzed (Figure 1). The in-hospital mortality, overall in-hospital complication rate, and 30-days readmission rates after LP implantations were 5.0%, 16%, and 16%, respectively. In LP recipients, the national estimate of in-hospital mortality declined from 10.9% in the second quarter of 2017 to 4.3% in the fourth quarter of 2019 (P<0.001) (Figure 2a). Furthermore, the national estimate of overall complication rate declined from 20.6% in the second quarter of 2017 to 13.0% in the fourth quarter of 2019 (P<0.001) (Figure 2b). Conversely, non-elective 30-day readmission rate did not show any increasing or decreasing trends in LP recipients (P=0.74) (Figure 2c). In LP recipients, female sex (odds ratio [OR] 1.42, 95% confidence interval [CI] 1.04–1.93), history of heart failure (OR 2.02, 95% CI 1.46–2.79), and first quartile annual pacemaker implantation hospital volume (OR 2.49, 95% CI 1.56–3.97, fourth quartile annual pacemaker implantation hospital volume used as a reference standard) were factors associated with in-hospital death. Conclusions Analysis of the nationally representative claims database in the US showed in-hospital mortality, complication rates, and non-elective 30-day readmission rate of 5.0%, 16%, and 16% respectively, for LP implantation performed during hospitalization. Although in-hospital mortality and complication rates showed a decreasing trend over time, ongoing surveillance is needed for the safety of LP implantation. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Intramural research fund (21-6-9)for cardiovascular diseases of national cerebral and cardiovascular center.

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)0.007
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.100
GPT teacher head0.357
Teacher spread0.257 · 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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Citations0
Published2022
Admission routes1
Has abstractyes

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