MétaCan
Menu
Back to cohort

Reexamination of the UN10 Rule to Discontinue Resuscitation During In-Hospital Cardiac Arrest

2019· article· en· W2947925625 on OpenAlexaff
Bradley J. Petek, Daniel Bennett, Christian Ngô, Paul S. Chan, Brahmajee K. Nallamothu, Steven M. Bradley, Yuanyuan Tang, Rodney A. Hayward, Carl van Walraven, Zachary D. Goldberger

Bibliographic record

VenueJAMA Network Open · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsUniversity of Ottawa
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Heart, Lung, and Blood InstituteNational Institutes of HealthAmerican Heart AssociationHealth Services Research and DevelopmentU.S. Department of Veterans Affairs
KeywordsResuscitationMedicineCardiopulmonary resuscitationCardiac resuscitationClinical deathEmergency medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Importance: Several clinical decision rules (CDRs) have been developed to help practitioners know when to safely terminate resuscitative efforts after in-hospital cardiac arrest (IHCA). The UN10 rule, a CDR that uses 3 intra-arrest variables, has been shown to predict a poor chance of survival to discharge. However, its large-scale applicability in clinical settings remains unknown. Objective: To assess the performance of a parsimonious CDR in a national cohort of individuals with IHCA. Design, Setting, and Participants: This retrospective cohort study used a nationwide cohort from the American Heart Association Get With the Guidelines-Resuscitation IHCA registry to derive a sample of 96 509 patients from 716 US hospitals who experienced IHCA from January 1, 2000, to January 26, 2016. Data analysis began in January 2018 and concluded in June 2018. Exposures: The UN10 rule uses 3 variables: (1) unwitnessed arrest, (2) nonshockable rhythm, and (3) no return of spontaneous circulation within 10 minutes of resuscitative efforts. The CDR indicates futility if all 3 criteria are met. This CDR was analyzed according to the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) reporting guideline. Main Outcomes and Measures: The primary outcome was survival to hospital discharge following resuscitation. Favorable neurologic status at discharge was also assessed. Overall rates of survival and survival with favorable neurologic status (cerebral performance category score, 1 or 2) were compared with predicted values by the UN10 rule using 2 × 2 contingency tables. Results: Of 96 509 patients, 55 761 (57.8%) were men, and the mean (SD) age was 67.1 (15.3) years. In total, 18 713 patients (19.4%) survived to discharge, and 16 134 patients (16.7%) were discharged with a favorable neurologic status. Overall, 15 838 patients (16.4%) met all 3 criteria for futility in the UN10 rule. A total of 1005 patients (6.3%) who met the UN10 rule survived to discharge, and 754 (4.8%) survived with favorable neurologic status. The percentage of patients meeting the UN10 rule (ie, predicting futile resuscitation) who actually survived in our study cohort was substantially higher than the initial derivation cohort (0%) and single-center validation cohort (1.1%). The positive predictive value of the UN10 rule was 93.7% (95% CI, 93.3%-94.0%), which was lower than the initial derivation cohort (100%; 95% CI, 97.5%-100%) and validation cohort (98.9%; 95% CI, 96.5%-99.7%). Conclusions and Relevance: Patients who met the UN10 rule were associated with unfavorable neurologic status and low rates of survival after IHCA. Yet their survival rates are higher than reported in the initial validation study, raising the question of whether the UN10 rule may have limited utility as a definitive measure of futility during resuscitations in real-world clinical settings.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.020
Threshold uncertainty score0.308

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.006
GPT teacher head0.254
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 teacher head, 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

Citations16
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJAMA Network OpenSame topicCardiac Arrest and ResuscitationFrench-language works237,207