The modified 5-item frailty index in total hip arthroplasty patients: a retrospective cohort from a low-middle income country
Bibliographic record
Abstract
Total hip arthroplasty (THA) is increasing in low- and middle-income countries (LMICs) due to rising rates of hip fractures and an aging population. Identifying frail patients at risk for postoperative complications is vital for improving outcomes. This study examines the utility of the Modified 5-Item Frailty Index (mFI-5) in predicting 30-day morbidity and mortality in THA patients in resource-limited settings, where other models like the Elixhauser Comorbidity Measure (ECM) and Charlson Comorbidity Index (CCI) may be impractical due to data constraints. This retrospective cohort study included 498 patients undergoing THA at tertiary-care hospital between January 2014 and December 2019. Patients were stratified based on their mFI-5 scores (≤ 1 vs. > 1). Postoperative complications, length of stay, and mortality were compared between groups. Multivariable logistic regression was used to assess outcomes. Of the 498 patients, 62.8% had an mFI-5 score ≤ 1, and 37.2% had a score > 1. Complication rates were higher in the mFI-5 > 1 group (17.8%) versus the ≤ 1 group (9.6%). After adjusting for covariates, patients with mFI-5 > 1 had a 97% higher likelihood of complications (aOR = 1.97, 95% CI 1.06–3.70). Each additional hospital day increased complication risk by 13% (aOR = 1.13, 95% CI: 1.05–1.21). The mFI-5 is a practical, efficient tool for predicting postoperative complications in THA patients, particularly in resource-limited environments. Its use in LMICs could improve preoperative planning, reduce complications, and provide better outcome estimates for patients and healthcare providers. Given the growing geriatric population, integrating the mFI-5 into routine THA planning could enhance patient care and resource allocation. Further research is needed to validate its use across larger datasets. Aim This study investigates the utility of the mFI-5 as a reliable predictive tool for post-operative outcomes in patients undergoing THA in resource-limited settings. We hypothesize that mFI-5 can effectively stratify surgical risks, allowing for better perioperative planning in environments with constrained resources. Findings The mFI-5 proved to be a reliable predictor of postoperative complications and length of stay (LOS) in patients undergoing THA. Higher mFI-5 scores were significantly associated with increased risks of infections, dislocations, and extended hospital stays, indicating its usefulness in identifying high-risk patients. Message Implementing mFI-5 in resource limited settings can enhance perioperative decision-making, improve patient outcomes, and reduce the financial burden on patients and healthcare systems in low- and middle-income countries (LMICs).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".