The impact of frailty on posttraumatic outcomes in older trauma patients: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Frailty is a risk factor for mortality among the elderly. However, evidence from longitudinal studies linking trauma and frailty is fragmented, and a comprehensive analysis of the relationship between frailty and adverse outcomes is lacking. Therefore, we conducted a systematic review and meta-analysis to examine whether frailty is predictive of posttraumatic results including mortality, adverse discharge, complications, and readmission in trauma patients. METHODS: This systematic review was registered with the PROSPERO international prospective register of systematic reviews. Articles in PubMed, Embase, and Web of Science databases from January 1, 1990, to October 31, 2019, were systematically searched. Articles in McDonald et al.'s study (J Trauma Acute Care Surg. 2016;80(5):824-834) and Cubitt et al.'s study (Injury 2019;50(11):1795-1808) were included for studies evaluating the association between frailty and outcomes in trauma patients. Cohort studies, both retrospective and prospective, were included. Study population was patients suffering trauma injuries with an average age of 50 years and older. Multivariate adjusted odds ratios (ORs) were calculated through a random-effects model, and the Newcastle-Ottawa Quality Assessment Scale was used to assess studies. RESULTS: We retrieved 11,313 entries. Thirteen studies including seven prospective and six retrospective cohort studies involving 50,348 patients were included in the meta-analysis. Frailty was a significant predictor of greater than 30-day mortality (OR, 2.41; 95% confidence interval [CI], 1.17-4.95; I = 88.1%), in-hospital and 30-day mortality (OR, 4.05; 95% CI, 2.02-8.11; I = 0%), postoperative complications (OR, 2.23; 95% CI, 1.34-3.73; I = 78.2%), Clavien-Dindo IV complications (OR, 4.16; 95% CI, 1.70-10.17; I = 0%), adverse discharge (OR, 1.80; 95% CI, 1.15-2.84; I = 78.6%), and readmission (OR, 2.16; 95% CI, 1.19-3.91; I = 21.5%) in elderly trauma patients. Subgroup analysis showed that prospective studies (OR, 3.06; 95% CI, 1.43-6.56) demonstrated a greater correlation between frailty and postoperative complications. CONCLUSION: Frailty has significant adverse impacts on the occurrence of posttraumatic outcomes. Further studies should focus on interventions for patients with frailty. Given the number of vulnerable elderly trauma patients grows, further studies are needed to determine the accuracy of these measures in terms of trauma outcomes. LEVEL OF EVIDENCE: Systematic review and meta-analysis, level IV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".