Hot Off the Press: Prospective and Explicit Clinical Validation of the Ottawa Heart Failure Risk Scale, With and Without Use of Quantitative <scp>NT</scp>‐pro<scp>BNP</scp>
Bibliographic record
Abstract
BACKGROUNDC ongestive heart failure admissions are common and expensive, and high percentages of patients are readmitted within 3 to 6 months.1-5 Canadian admission rates for heart failure are much lower than in America.6 There is a lack of evidence to guide physicians in disposition decisions-who can go home and who requires admissions?Many risk stratification scales discuss the risk of mortality, but do not include broader adverse events that may occur.7-16 The Ottawa Heart Failure Risk Score (OHFRS) was derived to predict significant adverse events (SAEs) at 14 days.17 The current study aims to validate the tool in a real-time clinical setting. ARTICLE SUMMARYThis prospective cohort study included patients ≥ 50 years old with dyspnea of <7 days' duration, due to acute heart failure.18 Patients too ill to be discharged were excluded.Treating physicians assessed the OHFRS approximately 2 to 8 hours after ED presentation.The primary outcome measured was SAE within 14 days, with SAEs including death from any cause within 30 days, admission to a monitored unit, any positive pressure ventilation, myocardial infarction, major cardiac procedure, new dialysis, or subsequent hospital admission if the patient was initially discharged from the ED.At an admission threshold of ≥1, the OHFRS would increase sensitivity from 71.8% to 91.8% for SAEs, but also increase admission rates.A threshold of ≥2 had a similar sensitivity, but decreased admissions (57.2% vs. 48.3%.)Addition of NT-proBNP levels did not substantially change the results. QUALITY ASSESSMENTThis was a multicenter, prospective, ED-based study, with explicitly specified predictor variables and outcomes.Some limitations were identified.First of all, clinicians were not blinded to the OHFRS score, and in fact the treating clinician calculated the score.Although they were instructed not to use the score when making decisions, this could result in incorporation bias.Patients were not enrolled consecutively, and patients presenting at night could be higher risk than those during the day.At lower scores (<1), the OHFRS increases sensitivity for SAEs, and as a result, admissions increase.However, it is unclear whether admitting patients would prevent any of the SAEs from occurring.The use of a composite outcome can
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".