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
BACKGROUND: There is increasing evidence that maternal obesity is associated with several structural birth defects. Congenital abnormalities of the kidney and urinary tract (CAKUT) account for 30 to 50% of children starting kidney replacement therapy (KRT). We conducted a systematic review, meta-analysis and ecological study to explore the relationship between maternal obesity and CAKUT. METHODS: A systematic literature search was conducted in EMBASE, MEDLINE, Global Health, The Cochrane Library, Scopus and Web of Science. Study quality was assessed for bias and confounding. A meta-analysis using a random effect model was carried out to obtain a summary odds ratio (OR) and 95% confidence interval (CI). In the ecological study, country-level data were used to examine the correlation of secular trends in female obesity, CAKUT incidence and incidence of KRT. RESULTS: Eight epidemiological studies were included in the review-4 cohort studies and 4 case-control studies-7 of which were included in the meta-analysis. There was evidence of a positive association between obesity during pregnancy and the risk of CAKUT, with a summary OR = 1.14 (1.02-1.27). No association was seen with overweight, nor a dose response with increasing obesity. There was an increasing trend in countries' proportion of female obesity and an increasing trend in reported CAKUT incidence with specific rises seen in congenital hydronephrosis (CH) and multicystic kidney dysplasia (MCKD). CONCLUSIONS: Our findings suggest that pre-pregnancy obesity may be associated with increased risk of CAKUT at population level. Graphical abstract.
