Comparing the Hospital Frailty Risk Score and the Clinical Frailty Scale Among Older Adults With Chronic Obstructive Pulmonary Disease Exacerbation
Bibliographic record
Abstract
Importance: Frailty is associated with severe morbidity and mortality among people with chronic obstructive pulmonary disease (COPD). Interventions such as pulmonary rehabilitation can treat and reverse frailty, yet frailty is not routinely measured in pulmonary clinical practice. It is unclear how population-based administrative data tools to screen for frailty compare with standard bedside assessments in this population. Objective: To determine the agreement between the Hospital Frailty Risk Score (HFRS) and the Clinical Frailty Scale (CFS) among hospitalized individuals with COPD and to determine the sensitivity and specificity of the HFRS (vs CFS) to detect frailty. Design, Setting, and Participants: A cross-sectional study was conducted among hospitalized patients with COPD exacerbation. The study was conducted in the respiratory ward of a single tertiary care academic hospital (The Ottawa Hospital, Ottawa, Ontario, Canada). Participants included consenting adult inpatients who were admitted with a diagnosis of acute COPD exacerbation from December 2016 to June 2019 and who used a clinical care pathway for COPD. There were no specific exclusion criteria. Data analysis was performed in March 2022. Exposure: Degree of frailty measured by the CFS. Main Outcomes and Measures: The HFRS was calculated using hospital administrative data. Primary outcomes were the sensitivity and specificity of the HFRS to detect frail and nonfrail individuals according to CFS assessments of frailty, and the secondary outcome was the optimal probability threshold of the HFRS to discriminate frail and nonfrail individuals. Results: Among 99 patients with COPD exacerbation (mean [SD] age, 70.6 [9.5] years; 56 women [57%]), 14 (14%) were not frail, 33 (33%) were vulnerable, 18 (18%) were mildly frail, and 34 (34%) were moderately to severely frail by the CFS. The HFRS (vs CFS) had a sensitivity of 27% and specificity of 93% to detect frail vs nonfrail individuals. The optimal probability threshold for the HFRS was 1.4 points or higher. The corresponding sensitivity to detect frailty was 69%, and the specificity was 57%. Conclusions and Relevance: In this cross-sectional study, using the population-based HFRS to screen for frailty yielded poor detection of frailty among hospitalized patients with COPD compared with the bedside CFS. These findings suggest that use of the HFRS in this population may result in important missed opportunities to identify and provide early intervention for frailty, such as pulmonary rehabilitation.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".