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1.
Bone Jt Open ; 4(11): 865-872, 2023 Nov 15.
Article in English | MEDLINE | ID: mdl-37963491

ABSTRACT

Aims: The Ponseti method is the gold standard treatment for congenital talipes equinovarus (CTEV), with the British Consensus Statement providing a benchmark for standard of care. Meeting these standards and providing expert care while maintaining geographical accessibility can pose a service delivery challenge. A novel 'Hub and Spoke' Shared Care model was initiated to deliver Ponseti treatment for CTEV, while addressing standard of care and resource allocation. The aim of this study was to assess feasibility and outcomes of the corrective phase of Ponseti service delivery using this model. Methods: Patients with idiopathic CTEV were seen in their local hospitals ('Spokes') for initial diagnosis and casting, followed by referral to the tertiary hospital ('Hub') for tenotomy. Non-idiopathic CTEV was managed solely by the Hub. Primary and secondary outcomes were achieving primary correction, and complication rates resulting in early transfer to the Hub, respectively. Consecutive data were prospectively collected and compared between patients allocated to Hub or Spokes. Mann-Whitney U test, Wilcoxon signed-rank test, or chi-squared tests were used for analysis (alpha-priori = 0.05, two-tailed significance). Results: Between 1 March 2020 and 31 March 2023, 92 patients (139 feet) were treated at the service (Hub 50%, n = 46; Spokes 50%, n = 46), of whom nine were non-idiopathic. All patients (n = 92), regardless of allocation, ultimately achieved primary correction, with idiopathic patients at the Hub requiring fewer casts than the Spokes (mean 4.0 (SD 1.4) vs 6.9 (SD 4.4); p < 0.001). Overall, 60.9% of Spokes' patients (n = 28/46) required transfer to the Hub due to complications (cast slips Hub n = 2; Spokes n = 17; p < 0.001). These patients ultimately achieved full correction at the Hub. Conclusion: The Shared Care model was found to be feasible in terms of providing primary correction to all patients, with results comparable to other published services. Complication rates were higher at the Spokes, although these were correctable. Future research is needed to assess long-term outcomes, parents' satisfaction, and cost-effectiveness.

2.
Ann Hepatobiliary Pancreat Surg ; 26(1): 17-30, 2022 Feb 28.
Article in English | MEDLINE | ID: mdl-35220286

ABSTRACT

Oncological scoring systems in surgery are used as evidence-based decision aids to best support management through assessing prognosis, effectiveness and recurrence. Currently, the use of scoring systems in the hepato-pancreato-biliary (HPB) field is limited as concerns over precision and applicability prevent their widespread clinical implementation. The aim of this review was to discuss clinically useful oncological scoring systems for surgical management of HPB patients. A narrative review was conducted to appraise oncological HPB scoring systems. Original research articles of established and novel scoring systems were searched using Google Scholar, PubMed, Cochrane, and Ovid Medline. Selected models were determined by authors. This review discusses nine scoring systems in cancers of the liver (CLIP, BCLC, ALBI Grade, RETREAT, Fong's score), pancreas (Genç's score, mGPS), and biliary tract (TMHSS, MEGNA). Eight models used exclusively objective measurements to compute their scores while one used a mixture of both subjective and objective inputs. Seven models evaluated their scoring performance in external populations, with reported discriminatory c-statistic ranging from 0.58 to 0.82. Selection of model variables was most frequently determined using a combination of univariate and multivariate analysis. Calibration, another determinant of model accuracy, was poorly reported amongst nine scoring systems. A diverse range of HPB surgical scoring systems may facilitate evidence-based decisions on patient management and treatment. Future scoring systems need to be developed using heterogenous patient cohorts with improved stratification, with future trends integrating machine learning and genetics to improve outcome prediction.

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