Development and validation of a prediction model for postoperative intensive care unit admission in patients with non-cardiac surgery.

BACKGROUND: Accurately forecasting patients admitted to the intensive care units (ICUs) after surgery may improve clinical outcomes and guide the allocation of expensive and limited ICU resources. However, studies on predicting postoperative ICU admission in non-cardiac surgery have been limited. OBJECTIVE: To develop and validate a prediction model combining pre- and intraoperative variables to predict ICU admission after non-cardiac surgery. METHODS: This study is based on data from the Vital Signs DataBase (VitalDB) database. Predictors were selected using the least absolute shrinkage and selection operator regression method and logistic regression to develop a nomogram and an online web calculator. The model was internally verified by 1000-Bootstrap resampling. Performance of model was evaluated using area under the receiver operating characteristic curve (AUC), calibration curve and Brier score. The Youden's index was used to find the optimal nomogram's probability threshold. Clinical utility was assessed by decision curve analysis. RESULTS: This study included 5216 non-cardiac surgery patients; of these, 812 (15.6%) required postoperative ICU admission. Potential predictors included age, ASA classification, surgical department, emergency surgery, preoperative albumin level, preoperative urea nitrogen level, intraoperative crystalloid, intraoperative transfusion, intraoperative catheterization, and surgical time. A nomogram was constructed with an AUC of 0.917 (95% CI: 0.907-0.926) and a Brier score of 0.077. The Bootstrap-adjusted AUC was 0.914; the adjusted Brier score was 0.078. The calibration curve showed good agreement between predicted and actual probabilities; and the decision curve indicated clinical usefulness. Finally, we established an online web calculator for clinical application (https://xuzhikun.shinyapps.io/postopICUadmission1/). CONCLUSION: We developed and internally validated an easy-to-use nomogram for predicting ICU admission after non-cardiac surgery.

A comprehensive investigation discovered the novel methyltransferase METTL24 as one presumably prognostic gene for kidney renal clear cell carcinoma potentially modulating tumor immune microenvironment.

Background: Recently, an increasing number of studies have uncovered the aberrant expression of methyltransferase-like family (METTL) plays an important role in tumorigenesis, such as METTL3 (an m6A writer). In our recent work, we discovered METTL24 expression was highly associated with the hazard ratio (HR) of kidney renal clear cell carcinoma (KIRC) compared to other tumors, implying a special function of METTL24 in KIRC carcinogenesis. Until now, the functions and mechanisms of METTL24 in KIRC have remained mostly unknown. Methods: The mRNA expression of METTL24 in KIRC was analyzed using the TIMER 2.0, GEPIA, and UALCAN databases. The immunohistochemical assay was performed to validate METTL24 expression in our self-built Chinese cohort (n tumor = 88, n normal = 85). The gene set enrichment analysis (GSEA) was used to investigate the biological processes in which METTL24 might be engaged. The Spearman analysis was used to evaluate the expression correlations between METTL24 and a range of immunological variables, and the effects of METTL24 on the infiltration levels of multiple immune cells were explored using TCGA data. The upstream transcription factors of METTL24 were screened through a multi-omics analysis. Results: METTL24 expression in KIRC tissues was significantly decreased compared to normal adjacent kidney tissues, which was associated with the lower survival rate of KIRC patients. METTL24 potentially participated in the immune-relevant biological processes such as cytokine binding, NF-kappa B binding, MHC protein complex, and interleukin-12 action. Besides, METTL24 expression was linked to a number of immune checkpoints, cytokines, chemokines, and chemokine receptors, and also correlated with the infiltration levels of 10 types of immune cells in KIRC. Meanwhile, METTL24 expression differently affected the overall survival rates (OS) of KIRC patients with high or low levels of immune infiltration. Finally, CTCF and EP300 were discovered to be the probable transcription factors of METTL24 in KIRC. Conclusion: This study revealed that METTL24 might serve as a prognostic marker in KIRC and as one immune-relevant target for clinical treatment.