Association between Frequency Domain Heart Rate Variability and Unplanned Readmission to Hospital in Geriatric Patients
Bibliographic record
Abstract
BACKGROUND: An accurate prediction of unplanned readmission (UR) after discharge from hospital can facilitate physician's decision making processes for providing better quality of care in geriatric patients. The objective of this study was to explore the association of cardiac autonomic functions as measured by frequency domain heart rate variability (HRV) and 14-day UR in geriatric patients. METHODS: Patients admitted to the geriatric ward of a regional hospital in Chiayi county in Taiwan were followed prospectively from July 2006 to June 2007. Those with invasive tubes and those who were heavy smokers, heavy alcohol drinkers, on medications that might influence HRV, or previously admitted to the hospital within 30 days were excluded. Cardiac autonomic functions were evaluated by frequency domain indices of HRV. Multiple logistic regression was used to assess the association between UR and HRV indices adjusted for age and length of hospitalization. RESULTS: A total of 78 patients met the inclusion criteria and 15 of them were readmitted within 14 days after discharge. The risk of UR was significantly higher in patients with lower levels of total power (OR = 1.39; 95% CI = 1.04-2.00), low frequency power (LF) (OR = 1.22; 95% CI = 1.03-1.49), high frequency power (HF) (OR = 1.27; 95% CI = 1.02-1.64), and lower ratios of low frequency power to high frequency power (LF/HF ratio) (OR = 1.96; 95% CI = 1.07-3.84). CONCLUSION: This is the first study to evaluate the association between frequency domain heart rate variability and the risk of UR in geriatric patients. Frequency domain heart rate variability indices measured on admission were significantly associated with increased risk of UR in geriatric patients. Additional studies are required to confirm the value and feasibility of using HRV indices on admission as a non-invasive tool to assist the prediction of UR in geriatric patients.
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 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 | 0.000 | 0.002 |
| 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) | 0.001 | 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 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".