Development and Initial Validation of a Risk Score for Predicting In‐Hospital and 1‐Year Mortality in Patients With Hip Fractures
Bibliographic record
Abstract
UNLABELLED: Our objectives were to better define the rates and determinants of in-hospital and 1-year mortality after hip fracture. We studied a population-based cohort of 3981 hip fracture patients. Using multivariable regression methods, we identified risk factors for mortality (older age, male sex, long-term care residence, 10 prefracture co-morbidities) and calculated a hip fracture-specific score that could accurately predict or risk-adjust in-hospital and 1-year mortality. Our methods, after further validation, may be useful for comparing outcomes across hospitals or regions. INTRODUCTION: Hip fractures in the elderly are common and associated with significant mortality and variations in outcome. The rates and determinants of mortality after hip fracture are not well defined. Our objectives were (1) to define the rate of in-hospital and 1-year mortality in hip fracture patients, (2) to describe co-morbidities at the time of fracture, and (3) to develop and validate a multivariable risk-adjustment model for mortality. MATERIALS AND METHODS: We studied a population-based cohort of 3981 hip fracture patients > or =60 years of age admitted to hospitals in a large Canadian health region from 1994 to 2000. We collected sociodemographic and prefracture co-morbidity data. Main outcomes were in-hospital and 1-year mortality. We used multivariable regression methods to first derive a risk-adjustment model for mortality in 2187 patients treated at one hospital and then validated it in 1794 patients treated at another hospital. These models were used to calculate a score that could predict or risk-adjust in-hospital and 1-year mortality after hip fracture. RESULTS AND CONCLUSIONS: The median age of the cohort was 82 years, 71% were female, and 26% had more than four prefracture co-morbidities. In-hospital mortality was 6.3%; 10.2% for men and 4.7% for women (adjusted odds ratio, 1.8; 95% CI, 1.3-2.4). Mortality at 1 year was 30.8%; 37.5% for men and 28.2% for women (adjusted p < 0.001). Older age, male sex, long-term care residence, and 10 different co-morbidities were independently associated with mortality. Risk-adjustment models based on these variables had excellent accuracy for predicting mortality in-hospital (c-statistic = 0.82) and at 1 year (c-statistic = 0.74). We conclude that 1 in 15 elderly patients with hip fracture will die during hospitalization, and almost one-third of those who survive to discharge will die within the year. The determinants of mortality were primarily older age, male sex, and prefracture co-morbidities. Our hip fracture-specific risk-adjustment tool is pragmatic and reliable, and after further validation, may be useful for comparing outcomes across different hospitals or regions.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".