The categorical variables will be expressed as n and percentage and continuous variables as mean ± standard deviation. The frailty markers will be compared between groups of frail and non-frail . Categorical variables will be analysed using Chi-square or Fisher Exact test.
The primary analysis of metabolomics data will involve a range of pre-processing normalisation procedures to help scale the differences in the magnitude of metabolite concentrations. Normalised features will then be compared for analytical reproducibility by using multivariate principal component analysis to determine QC clustering and outlier identification.
The association between frailty markers and mortality will be analysed using regression model and will be adjusted for confounding factors such as presence of sepsis and type of surgery. Sensitivity and specificity analyses will be performed using the Receiver Operating Characteristic curve . A p-value of < 0.05 is considered statistically significant.
Result from EQ-5D-5L questionnaire will be reported and analysed quantitatively using the analysis tool provided by the EuroQol Office [This study hypothesises that patient’s assessed frailty changes following EmLAP in frail and non- frail patients, irrespective of age. We propose that non- frail patients will have better survival rates and recover faster compared to the frail.
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