Effects of Holding Beta-Blockers on the Vital Signs of Heart Failure Patients
Bibliographic record
Abstract
BACKGROUND: Heart failure with reduced ejection fraction (HFrEF) is associated with recurrent hospitalizations and high mortality. Guideline-directed medical therapy (GDMT) reduces morbidity, mortality and re-admission rates. Despite the evidence, less than 50% of patients with HFrEF are prescribed appropriate medical therapy. When hospitalized patients have these medications discontinued on admission or during hospitalization, they are less likely to have them restarted on discharge. The goal of this study was to determine the incidence of disruption of beta-blocker (BB) therapy during hospitalization for HFrEF patients admitted to an academic tertiary referral hospital. METHODS: We conducted a retrospective study in a single teaching hospital over the course of 1 year, and utilized data queried from the electronic medical record (EPIC) database. Inclusion criteria were met by patients with an ICD-10 code diagnosis of heart failure, left ventricular ejection fraction less than 40% and BB prescription prior to admission. Additional information noted included age, sex, vital signs throughout the admission and dates where BB was not given for a full 24-h period. Patients in the intensive care unit (ICU) were excluded due to uncertainty of their hemodynamics. Data were extracted from the electronic medical record database and analyzed through Python, Microsoft Excel and RStudio. The incidence of BB disruption during hospitalization was defined as a 24-h period where no BB was administered. Blood pressure (BP) and heart rate (HR) levels were compared between patients who received BB and patients who had a disruption in their BB. Measurements were also obtained to assess whether a correlation exists between holding BB therapy and time of the year, age, or sex. RESULTS: Between January 2018 and January 2019, 780 patient encounters met inclusion criteria for the study. Patients who were continued on BB therapy had an average BP of 120.8/68.7 mm Hg and an HR of 82.4 bpm on days they received their BB. Patients who had a disruption of BB therapy had an average BP of 117.7/67.6 mm Hg and an HR of 88.6 bpm on the days of the disruption (P < 0.001). There was no association between holding BB and age, sex, or time of year. CONCLUSIONS: This study showed that in an academic tertiary referral center, patients with HFrEF who are not in an ICU have a 23% chance of not receiving their recommended BB therapy for 24 h. While the differences measured for BP and HR are statistically significant, they are not clinically significant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".