Relationships between orthostatic hypotension, frailty, falling and mortality in elderly care home residents
Bibliographic record
Abstract
BACKGROUND: Orthostatic hypotension (OH; profound falls in blood pressure when upright) is a common deficit that increases in incidence with age, and may be associated with falling risk. Deficit accumulation results in frailty, regarded as enhanced vulnerability to adverse outcomes. We aimed to evaluate the relationships between OH, frailty, falling and mortality in elderly care home residents. METHODS: From the Minimum Data Set (MDS) document, a frailty index (FI-MDS) was generated from a list of 58 deficits, ranging from 0 (no deficits) to 1.0 (58 deficits). OH was evaluated from beat-to-beat blood pressure and heart rate (finger plethysmography) collected during a 15-min supine-seated orthostatic stress test. Retrospective and prospective falling rates (falls/year) were extracted from facility falls incident reports. All-cause 3-year mortality was determined. Data are reported as mean ± standard error. RESULTS: Data were obtained from 116 older adults (aged 84.2 ± 0.9 years; 44% males) living in two long term care facilities. The mean FI-MDS was 0.36 ± 0.01; FI-MDS was correlated with age (r = 0.277; p = 0.003). Those who were frail (FI ≥ 0.27) had larger Initial (- 17.8 ± 4.2 vs - 6.1 ± 3.3 mmHg, p = 0.03) and Consensus (- 22.7 ± 4.3 vs - 11.5 ± 3.3 mmHg, p = 0.04) orthostatic reductions in systolic arterial pressure. Frail individuals had higher prospective and retrospective falling rates and higher 3-year mortality. Receiver operating characteristic curves evaluated the ability of FI-MDS alone to predict prospective falls (sensitivity 72%, specificity 36%), Consensus OH (sensitivity 68%, specificity 60%) and 3-year mortality (sensitivity 77%, specificity 49%). Kaplan Meier survival analyses showed significantly higher 3-year mortality in those who were frail compared to the non-frail (p = 0.005). CONCLUSIONS: Frailty can be captured using a frailty index based on MDS data in elderly individuals living in long term care, and is related to susceptibility to orthostatic hypotension, falling risk and 3-year mortality. Use of the MDS to generate a frailty index may represent a simple and convenient risk assessment tool for older adults living in long term care. Older adults who are both frail and have impaired orthostatic blood pressure control have a particularly high risk of falling and should receive tailored management to mitigate this risk.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".