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Demographic, clinical and laboratory data of iCCA patients enrolled in the study. Data are presented as median (interquartile range: IQR) or number (%).

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BackgroundArtificial intelligence (AI) has been widely adopted for the prediction of latent shock occurrence in critically ill patients in intensive care units (ICUs). However, the usefulness of an ICU-based model to predict latent shock risk in an emergency department (ED) setting remains unclear. This study aimed to develop an AI model to predict latent shock risk in patients admitted to EDs.MethodsMultiple regression analysis was used to compare the difference between Medical Information Mart for Intensive Care (MIMIC)-IV-ICU and MIMIC-IV-ED datasets. An adult noninvasive model was constructed based on the MIMIC-IV-ICU v3.0 database and was externally validated in populations admitted to an ED. Its efficiency was compared with efficiency of testing with noninvasive systolic blood pressure (nSBP) and shock index.ResultsA total of 50,636 patients from the MIMIC-IV-ICU database was used to develop the model, and a total of 2,142 patients from the Philips IntelliSpace Critical Care and Anesthesia (ICCA)-ED and 425,087 patients from the MIMIC-IV-ED were used for external validation. The modeling and validation data revealed similar non-invasive feature distributions. Multiple regression analysis of the MIMIC-IV-ICU and MIMIC-IV-ED datasets showed mostly similar characteristics. The area under the receiver operating characteristic curve (AUROC) of the noninvasive model 10 min before the intervention was 0.90 (95% CI: 0.84–0.96), and the diagnosis accordance rate (DAR) was above 80%. More than 80% of latent shock patients were identified more than 70 min earlier using the noninvasive model; thus, it performed better than evaluating shock index and nSBP.ConclusionThe adult noninvasive model can effectively predict latent shock occurrence in EDs, which is better than using shock index and nSBP.

Currently, there are still no definitive consensus in the treatment of intrahepatic cholangiocarcinoma (iCCA). This study aimed to build a clinical decision support tool based on machine learning using the Surveillance, Epidemiology, and End Results (SEER) database and the data from the Fifth Medical Center of the PLA General Hospital in China. 4,398 eligible patients from the SEER database and 504 eligible patients from the hospital data, who presented with histologically proven iCCA, were enrolled for modeling by cross-validation based on machine learning. All the models were trained using the open-source Python library scikit-survival version 0.16.0. Shapley additive explanations method was used to help clinicians better understand the obtained results. Permutation importance was calculated using library ELI5. All involved treatment modalities could contribute to a better prognosis. Three models were derived and tested using different data sources, with concordance indices of 0.67, 0.69, and 0.73, respectively. The prediction results were consistent with those under actual situations involving randomly selected patients. Model 2, trained using the hospital data, was selected to develop an online tool, due to its advantage in predicting short-term prognosis. The prediction model and tool established in this study can be applied to predict the prognosis of iCCA after treatment by inputting the patient’s clinical parameters or TNM stages and treatment options, thus contributing to optimal clinical decisions.KEY MESSAGESA prognostic model related to disease staging and treatment mode was conducted using the method of machine learning, based on the big data of multi centers.The online calculator can predict the short-term survival prognosis of intrahepatic cholangiocarcinoma, thus, help to make the best clinical decision.The online calculator built to calculate the mortality risk and overall survival can be easily obtained and applied. A prognostic model related to disease staging and treatment mode was conducted using the method of machine learning, based on the big data of multi centers. The online calculator can predict the short-term survival prognosis of intrahepatic cholangiocarcinoma, thus, help to make the best clinical decision. The online calculator built to calculate the mortality risk and overall survival can be easily obtained and applied.

Estudo 3722 Gráficos divulgados pela RIOTUR , com informações prestadas pela ICCA - International Congress and Conference Association.

Background
Multifocal intrahepatic cholangiocarcinoma (m-ICC) is an aggressive form of primary liver cancer, often associated with poor outcomes. Although surgical resection is considered the only curative treatment for ICC, multifocality is frequently regarded as a contraindication due to the high risk of recurrence and limited survival benefits. Aim To perform a systematic literature review on the outcomes of surgical treatment of m-iCCA. Methods This systematic review was performed according to PRISMA statement. A study protocol for the review was registered in the International Prospective Register of Systematic Reviews database. Databases were systematically searched for studies analysing surgical treatment outcomes for m-iCCA. Results Ten articles with 2392 patients who had m-ICCA were included in our review. The reviewed studies reported extensive surgical procedures with median survival ranging from 18.9 to 27 months. Recurrence rates were higher in m-iCCA patients (67.8–74.3%) compared to solitary ICC cases (52.4–60.5%), with recurrence-free survival as short as 4.5 months. One study reported a 5-year survival rate of 12.9% for surgical patients compared to 0% for non-operated patients. Survival outcomes were influenced by adverse prognostic indicators. Conclusions Surgical resection for multifocal intrahepatic cholangiocarcinoma is a challenging treatment option due to the high likelihood of recurrence and the aggressive nature of the disease. Despite these challenges, surgery may offer survival benefits for carefully selected patients.

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