Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of “high-risk” patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework.

Background: Emergency laparotomy (EL) is accompanied by high post‐operative morbidity and mortality which varies significantly between countries and populations. The aim of this study is to report outcomes of emergency laparotomy in Greece and to compare them with the results of the National Emergency Laparotomy Audit (NELA).Methods: This is a multicentre prospective cohort study undertaken between 01.2019 and 05.2020 including consecutive patients subjected to EL in 11 Greek hospitals. EL was defined according to NELA criteria. Demographics, clinical variables, and post‐operative outcomes were prospectively registered in an online database. Multivariable logistic regression analysis was used to identify independent predictors of post‐operative mortality.Results: There were 633 patients, 53.9% males, ASA class III/IV 43.6%, older than 65 years 58.6%. The most common operations were small bowel resection (20.5%), peptic ulcer repair (12.0%), adhesiolysis (11.8%) and Hartmann's procedure (11.5%). 30‐day post‐operative mortality reached 16.3% and serious complications occurred in 10.9%. Factors associated with post‐operative mortality were increasing age and ASA class, dependent functional status, ascites, severe sepsis, septic shock, and diabetes. HELAS cohort showed similarities with NELA patients in terms of demographics and preoperative risk. Post‐operative utilisation of ICU was significantly lower in the Greek cohort (25.8% vs 56.8%) whereas 30‐day post‐operative mortality was significantly higher (16.3% vs 8.7%).Conclusion: In this study, Greek patients experienced markedly worse mortality after emergency laparotomy compared with their British counterparts. This can be at least partly explained by underutilisation of critical care by surgical patients who are at high risk for death.

The optimal malnutrition screening tool in geriatric surgery has yet to be determined. Herein, we compare two main tools in older patients undergoing general surgery operations. Older patients (>65 years old) who underwent general surgery operations between 2012 and 2017 in a tertiary centre were included. The Malnutrition Universal Screening Tool (MUST) and the Mini Nutritional Assessment Short Form (MNA-SF) were used for nutritional risk assessment. Preoperative variables as well as postoperative outcomes were recorded prospectively. Agreement between tools was determined with the weighted kappa (κ) statistic. Multiple regression analysis was used to assess the association of the screening tools with postoperative outcomes. A total of 302 patients (median age 74 years, range: 65–92) were included. A similar number of patients were classified as medium/high risk for malnutrition with the MNA-SF and MUST (26% vs. 36%, p = 0.126). Agreement between the two tools was moderate (weighted κ: 0.474; 95%CI: 0.381–0.568). In the multivariate analysis, MNA-SF was associated significantly with postoperative mortality (p = 0.038) and with postoperative length of stay (p = 0.001). MUST was associated with postoperative length of stay (p = 0.048). The MNA-SF seems to be more consistently associated with postoperative outcomes in elderly patients undergoing general surgery compared with the MUST tool.

Risk prediction and stratification of short-term and long-term postoperative outcomes are growing in importance and scope of application in everyday clinical practice

Preoperative risk stratification in the elderly surgical patient is an essential part of contemporary perioperative care and can be done with the use of the American College of Surgeons Surgical Risk Calculator (ACS-SRC). However, data on the generalizability of the ACS-SRC in the elderly is scarce. In this study, we report an external validation of the ACS-RC in a geriatric cohort. A retrospective analysis of a prospectively maintained database was performed including patients aged > 65 who underwent general surgery procedures during 2012–2017 in a Greek academic centre. The predictive ability of the ACS-SRC for post-operative outcomes was tested with the use of Brier scores, discrimination, and calibration metrics. 471 patients were included in the analysis. 30-day postoperative mortality was 3.2%. Overall, Brier scores were lower than cut-off values for almost all outcomes. Discrimination was good for serious complications (c-statistic: 0.816; 95% CI: 0.762–0.869) and death (c-statistic: 0.824; 95% CI: 0.719–0.929). The Hosmer-Lemeshow test showed good calibration for all outcomes examined. Predicted and observed length of stay (LOS) presented significant differences for emergency and for elective cases. The ACS-SRC demonstrated good predictive performance in our sample and can aid preoperative estimation of multiple outcomes except for the prediction of post-operative LOS.

Purpose: Emergency laparotomy (EL) is a common operation with high risk for postoperative complications, thereby requiring accurate risk stratification to manage vulnerable patients optimally. We developed and internally validated a predictive model of serious complications after EL. Methods: Data for eleven carefully selected candidate predictors of 30-day postoperative complications (Clavien-Dindo grade > = 3) were extracted from the HELAS cohort of EL patients in 11 centres in Greece and Cyprus. Logistic regression with Least Absolute Shrinkage and Selection Operator (LASSO) was applied for model development. Discrimination and calibration measures were estimated and clinical utility was explored with decision curve analysis (DCA). Reproducibility and heterogeneity were examined with Bootstrap-based internal validation and Internal–External Cross-Validation. The American College of Surgeons National Surgical Quality Improvement Program’s (ACS-NSQIP) model was applied to the same cohort to establish a benchmark for the new model. Results: From data on 633 eligible patients (175 complication events), the SErious complications After Laparotomy (SEAL) model was developed with 6 predictors (preoperative albumin, blood urea nitrogen, American Society of Anaesthesiology score, sepsis or septic shock, dependent functional status, and ascites). SEAL had good discriminative ability (optimism-corrected c-statistic: 0.80, 95% confidence interval [CI] 0.79–0.81), calibration (optimism-corrected calibration slope: 1.01, 95% CI 0.99–1.03) and overall fit (scaled Brier score: 25.1%, 95% CI 24.1–26.1%). SEAL compared favourably with ACS-NSQIP in all metrics, including DCA across multiple risk thresholds. Conclusion: SEAL is a simple and promising model for individualized risk predictions of serious complications after EL. Future external validations should appraise SEAL’s transportability across diverse settings.