ABSTRACT
Background: Currently, surgical resection is the mainstay for colorectal liver metastases (CRLM) management and the only potentially curative treatment modality. Prognostication tools can support patient selection for surgical resection to maximize therapeutic benefit. This study aimed to develop a survival prediction model using machine learning based on a multicenter patient sample in Hong Kong. Methods: Patients who underwent hepatectomy for CRLM between 1 January 2009 and 31 December 2018 in four hospitals in Hong Kong were included in the study. Survival analysis was performed using Cox proportional hazards (CPH). A stepwise selection on Cox multivariable models with Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to a multiply-imputed dataset to build a prediction model. The model was validated in the validation set, and its performance was compared with that of Fong Clinical Risk Score (CRS) using concordance index. Results: A total of 572 patients were included with a median follow-up of 3.6 years. The full models for overall survival (OS) and recurrence-free survival (RFS) consist of the same 8 established and novel variables, namely colorectal cancer nodal stage, CRLM neoadjuvant treatment, Charlson Comorbidity Score, pre-hepatectomy bilirubin and carcinoembryonic antigen (CEA) levels, CRLM largest tumor diameter, extrahepatic metastasis detected on positron emission-tomography (PET)-scan as well as KRAS status. Our CRLM Machine-learning Algorithm Prognostication model (CMAP) demonstrated better ability to predict OS (C-index =0.651), compared with the Fong CRS for 1-year (C-index =0.571) and 5-year OS (C-index =0.574). It also achieved a C-index of 0.651 for RFS. Conclusions: We present a promising machine learning algorithm to individualize prognostications for patients following resection of CRLM with good discriminative ability.
ABSTRACT
INTRODUCTION: COVID-19 pandemic has resulted in significant changes in pedagogy for undergraduate medical curriculum. Many physical clinical teachings have been replaced by online pedagogy. This study aims to evaluate the relation between medical students' stress during COVID-19 pandemic and their academic performance at the final examination. METHODS: This is a cross-sectional questionnaire-based study. Student's stress level were evaluated by the COVID-19 Student Stress Questionnaire (CSSQ). Correlation of stress level and students' performance at the final examination was performed. RESULTS: 110 out of 221 (49.8%) final-year medical students responded to the questionnaire, 13 students failed in the final examination (case) while 97 students passed in the final MBBS examination (control).Baseline demographic data between case and control were comparable. The median age for both cases and controls were 24 years.Compared to controls, cases reported higher levels of stress in all domains, namely in relation to risk of contagion, social isolation, interpersonal relationships with relatives, university colleagues and professors, academic life, and sexual life. Notably, a significantly higher proportion of cases reported academic-related stress compared to controls (p < 0.01), with 100% of cases perceiving their academic studying experience during the COVID-19 pandemic to be "very" or "extremely" stressful, compared to 35.1% of controls. CONCLUSION: Increased stress to academic and study during COVID-19 was associated with worse examination outcome at the final examination. Extra academic support will be needed to cater students' need during the pandemic.
