Hillingdon Hospital is an NHS hospital in Pield Heath Road, Hillingdon, Greater London. It is one of two hospitals run by the Hillingdon Hospitals NHS Foundation Trust, the other being Mount Vernon Hospital.
Postoperative adverse events (AEs) significantly impact patient outcomes and healthcare resources. The Clavien-Dindo Classification (CDC) is widely used to grade surgical complications, but manual grading is labor-intensive and subject to inter-observer variability. Automated algorithms, including rule-based, machine learning (ML), and large language model (LLM)-based natural language processing (NLP) tools, offer scalable solutions for consistent complication grading. A systematic review was conducted following PRISMA 2020 guidelines. Databases searched included PubMed, Embase, Scopus, and Cochrane Library. Studies reporting automated grading of surgery-related AEs using the CDC as a reference, with human validation, were included. Data extraction covered algorithm type, sample size, surgical population, comparator, data source, performance metrics, and outcomes. Three studies met the inclusion criteria, encompassing a total of 1,661 surgical cases. Automated algorithms for Clavien-Dindo Classification (CDC) grading including rule-based systems, machine-learning (ML) models, and large language model (LLM)/natural language processing (NLP) approaches demonstrate high agreement with expert reviewers, with rule-based algorithms achieving Cohen's κ up to 0.89, ML prediction models reporting discrimination up to an AUC of 0.863 for severe (CDC ≥ III) complications, and LLM/NLP approaches reaching accuracy of approximately 97% and Cohen's κ up to 0.92. Together, these methods show potential for scalable and, in some settings, near-real-time postoperative complication monitoring. These tools may support clinical decision-making, research, and quality improvement with promising but preliminary applicability across surgical domains. However, conclusions are limited by the small number of available studies and heterogeneity in surgical settings.
This systematic review synthesizes evidence on biomechanical fracture thresholds of the tibia and fibula under axial and multi-axial loading. A comprehensive search of PubMed, Embase, Scopus, and the Cochrane Library identified six studies, including experimental cadaveric, computational, and material testing investigations, comprising 72 postmortem specimens and validated finite element models. Outcomes assessed included axial force, bending moments, failure load, stress distribution, and fracture patterns. Results indicate that tibial fracture thresholds range from ~7.5 kN in female specimens to 11.3 kN under combined axial and bending loads, with fibula contribution increasing axial tolerance by ~10%. Variance and confidence interval measures were not reported in the included biomechanical studies; therefore, findings are presented descriptively. Multi-axial loading consistently reduced fracture tolerance compared with isolated axial loading, and fracture resistance was influenced by specimen gender, load duration, and biomechanical methodology. Risk of bias ranged from low to moderate across studies. These findings provide clinically relevant benchmarks for injury prediction, preclinical testing, and orthopedic device design, emphasizing the importance of multi-axial assessment in understanding lower-limb fracture mechanics.
BACKGROUND:Total knee arthroplasty (TKA) is increasingly performed in elderly patients with osteoarthritis, yet preoperative frailty is associated with adverse outcomes. Limited data exist from South Asian regions like Pakistan, where resource constraints and high osteoarthritis prevalence may influence these associations. AIM:To evaluate the association between preoperative frailty, assessed by the modified Frailty Index, and postoperative complications, hospital length of stay (LOS), 90-day readmission rates, and 1-year functional recovery in elderly Pakistani patients undergoing primary TKA. METHODS:This retrospective cohort study analyzed de-identified records from Bahawal Victoria Hospital, Bahawalpur, Pakistan (from January 2015 to September 2025). Patients aged ≥ 65 years with primary unilateral TKA for osteoarthritis were included. Frail patients (n = 512) were propensity score-matched 1:4 to non-frail controls (n = 2048) using nearest-neighbor matching with a caliper of 0.1. Propensity score estimation followed established methodological standards. Logistic regression, t-tests, χ 2 tests, and Kaplan-Meier analyses were used, with P < 0.05 denoting significance. Sensitivity analyses addressed alternative modified Frailty Index thresholds, matching overlap and missing data. RESULTS:Post-matching, groups were balanced. Frail patients had higher composite complications [21.1% vs 9.2%; adjusted odds ratio (OR) = 2.61, 95%CI: 2.05-3.32; P < 0.001], including surgical site infection (OR = 3.12, 95%CI: 2.18-4.46; P < 0.001), deep vein thrombosis (OR = 2.85, 95%CI: 1.92-4.23; P < 0.001), and pulmonary embolism (OR = 4.02, 95%CI: 2.45-6.59; P < 0.001). LOS was prolonged (5.6 ± 1.9 days vs 3.9 ± 1.5 days; P < 0.001), readmissions increased (17% vs 4%; OR = 4.74, 95%CI: 3.56-6.31; P < 0.001), and 1-year Knee Society Score was lower (75.5 ± 10.2 vs 85.1 ± 8.1; P < 0.001), with smaller delta Knee Society Score (30.3 ± 11.4 vs 39.0 ± 10.2; P < 0.001), approaching the minimal clinically important difference of approximately 9 points. Sensitivity analyses confirmed robustness. CONCLUSION:Preoperative frailty is associated with increased complications, extended LOS, higher readmissions, and impaired functional recovery in elderly TKA patients in this Pakistani cohort. These findings are most directly applicable to tertiary care centers in Pakistan with comparable patient complexity and resource availability. Extrapolation to primary care or markedly different healthcare systems requires further validation. Routine frailty screening may aid risk stratification and perioperative management in similar settings, considering regional challenges such as high tuberculosis prevalence.
