(untitled) (Journal of Medical & Health Sciences Review, 2025)
Background: One of the clinical challenges in elderly patients with colorectal cancer (CRC) is the occurrence of postoperative complications because of age-related frailty, comorbidities, and frailty of age-related physiological decline. The statistical characteristics of the clinical variables that are pertinent to this study, together with their predictive powers, may help provide an initial intervention and may lead to the optimal outcome of the surgical procedure. Objective: To assess the internal consistency validity, and distribution properties of clinical and demographic variables in elderly patients with active CRC, and dwell upon the inter-variable correlations as well as postoperative hospital stay prediction through the Pearson correlation and multiple linear regression analysis. Methods: The retrospective quantitative study relied on the simulated data set of elderly patients (aged above 65) under colorectal cancer surgery. The first statistical tests were the Shapiro-Wilk test of normality, Cronbach’s alpha to test the reliability, the Kaiser-Meyer-Olkin (KMO), and Bartlett Test of the Bartlett Test of Sphericity to determine the construct validity. After validation, Pearson Correlation Matrix and Multiple Linear Regression were used to determine the predictive variables related to Hospital Days, which is taken to represent postoperative complication severity. Results: The level of internal consistency was very good, as Cronbach’s Alpha was 0.812, indicating that there was reliable measurement in all the clinical indicators. The KMO value was 0.501, and the Bartlett Test was significant (1632.83, p < 0.001); therefore, the sampling adequacy is acceptable, and inter-variable correlations are sufficient to apply multivariate analysis. The correlation matrix also indicated a weak positive connection between Duration of symptoms and Hospital Days, or that the longer the period of symptoms before operation, the longer the recovery time after it. The other variables, the Age and Time since the onset of symptoms, demonstrated lower correlation results. Hospital Days as the most significant predictor in the Multiple Linear Regression Analysis revealed the largest positive beta coefficient (beta = 1.0), whereas the other predictors i.e. Age (months), Duration of symptoms (Days) and Time since the onset of the symptoms (Days) showed low absolute values of beta, which implies poor independent predictor effects. However, the general model design justifies the worth of multivariable analysis in predicting surgical outcomes. Conclusion: The research proves that although separate variables might not be able to provide excellent independent predictions, the data structure as a whole is valid and reliable in modeling it further. In particular, the Duration of the symptoms shows emergence as an early predictor of a higher postoperative burden. These reasons point to the importance of the use of integrative preoperative risk assessment composed of demographic and clinical variables. The audited data will allow the next stage of the work (more sophisticated statistical modeling, such as logistic regression and factor analysis) and result in improving risk stratification models of CRC elderly patients.