Predicting Performance Status 1 Year After Critical Illness in Patients 80 Years or Older: Development of a Multivariable Clinical Prediction Model
Bibliographic record
Abstract
OBJECTIVE: We sought to develop and internally validate a clinical prediction model to estimate the outcome of very elderly patients 12 months after being admitted to the ICU. DESIGN: Prospective, longitudinal cohort study. SETTING: Twenty-two Canadian ICUs. PATIENTS: We recruited 527 patients 80 years or older who had a medical or urgent surgical diagnosis and were admitted to an ICU for at least 24 hours. MEASUREMENTS AND MAIN RESULTS: At baseline, we completed a comprehensive geriatric assessment of enrolled patients; survival and functional status was determined 12 months later. We defined recovery from critical illness as Palliative Performance Scale score of greater than or equal to 60. We used logistic regression analysis to examine factors associated with this outcome. Of the 434 patients (82%) whose Palliative Performance Scale was known at 12 months, 50% had died and 29% (126/434) had a score of greater than or equal to 60. In the multivariable model, we found that being married, having a primary diagnosis of emergency coronary artery bypass grafting or valve replacement, and higher baseline Palliative Performance Scale were independently predictive of a 12-month Palliative Performance Scale score of greater than or equal to 60. Male sex, primary diagnosis of stroke, and higher Acute Physiology and Chronic Health Evaluation II score, Charlson comorbidity index, or clinical frailty scale were independently predictive of Palliative Performance Scale score of less than 60. CONCLUSION: Approximately one-quarter of very old ICU patients achieve a reasonable level of function 1 year after admission. This prediction model applied to individual patients may be helpful in decision making about the utility of life support for very elderly patients who are admitted to the ICU.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".