The Association of a Frailty Index and Incident Delirium in Older Hospitalized Patients: An Observational Cohort Study
Bibliographic record
Abstract
Andrea Yevchak Sillner,1,2 Robert Owens McConeghy,1 Caroline Madrigal,1 Deborah J Culley,3 Rakesh C Arora,4,5 James L Rudolph1,6 1Center of Innovation in Long Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, RI, USA; 2College of Nursing, The Pennsylvania State University, University Park, PA, USA; 3Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 4Max Rady College of Medicine, Department of Surgery, University of Manitoba, Manitoba, ON, Canada; 5Cardiac Sciences Program, St. Boniface Hospital, Winnipeg, ON, Canada; 6Warren Alpert Medical School and School of Public Health, Brown University, Providence, RI, USACorrespondence: Andrea Yevchak Sillner Email amy139@psu.eduIntroduction/Background: Frailty identifies patients that have vulnerability to stress. Acute illness and hospitalization are stressors that may result in delirium and further accelerate the negative consequences of frailty.Purpose: The purpose of this study was to determine whether frailty, identified at hospital admission and as measured by a frailty index, is associated with incident delirium.Methods: A retrospective, observational, cohort study was done at a Veterans hospital between January 2013 and March 2014. English-speaking patients over 55 years were eligible. Exclusion criteria included inability to complete baseline assessments due to pre-existing cognitive impairment, emergent surgery; and/or admission from a nursing home, pre-existing delirium, and those with psychiatric disease or substance use disorder.Main Outcomes and Measures: Frailty index (FI) variables included cognitive screening, physical function and comorbidities. The FI was calculated as a proportion of possible deficits (range 0 to 1; higher scores indicate increased frailty). Incident delirium was measured daily by an expert clinician interview.Results: A total of 247 patients were admitted and 218 met inclusion/exclusion criteria, with a mean age of 71.54 years (SD = 9.53 years) and were predominantly white (92.7%) and male (91.7%). Participants were grouped using FI ranges as non-frail (FI < 0.25, n=56 (26%)), pre-frail (FI =0.25– 0.35, n=86 (39%)), and frail (FI > 0.35, n=76 (35%)). Pre-frailty and frailty were associated with incident delirium (non-frail: 3.6% vs pre-frail: 20.9% vs frail: 29.3%, p=0.001) and total delirium days (mean day =non-frail 0.04 vs pre-frail 0.35 vs frail 0.57, p=0.003). After adjustment for sociodemographic factors, pre-frail (adjusted OR=5.64, 95% CI: 1.23, 25.99) and frail status (adjusted OR=6.80, 95% CI: 1.38, 33.45) were independently associated with delirium.Conclusion: This study demonstrates that a frailty index is independently associated with incident delirium and suggests that admission assessments for frailty may identify patients at high risk of developing delirium.Keywords: delirium, frailty, frailty index, Veterans, hospital
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".