A nomogram model for assessing predictors and prognosis of postoperative delirium in patients receiving acute type A aortic dissection surgery.

BACKGROUND: Postoperative delirium (POD) complicates the postoperative course. There is limited information on POD-related risk factors (RFs) and prognosis in patients with acute type A aortic dissection (ATAAD) after modified triple-branched stent graft implantation (MTBSG) surgery. METHODS: We retrospectively examined consecutive ATAAD patients who received MTBSG surgery in our hospital between January 2013 and December 2019. We employed univariate and multivariate analyses to identify stand-alone RFs for POD. A nomogram was next generated to estimate POD occurrence. The primary outcome was the development of POD, and the secondary outcomes were intensive care unit (ICU) and hospital stays, hospitalization costs, and in-hospital and follow-up mortality. RESULTS: We selected 692 patients, of whom 220 experienced POD (31.8%). Based on our analysis, the following factors enhanced the likelihood of POD development: alcohol consumption (p < 0.001), acute physiology and chronic health evaluation II score (p = 0.023), serum total bilirubin (p = 0.007), stage 3 acute kidney injury (p < 0.001), serum interleukin-6 (p = 0.031), post-operative analgesics usage (p = 0.015), and ventilation duration (p = 0.008). POD patients had significantly longer ventilator times (p = 0.003), ICU stays (p < 0.001), and hospital stays (p = 0.038), together with increased hospitalization costs (p < 0.001) and in-hospital mortality (p = 0.019). However, POD was not a RF for mortality during follow-up (log-rank p = 0.611). CONCLUSIONS: We demonstrated a strong link between POD and poor prognosis in ATAAD patients. We also constructed a prognosis estimator model which will benefit early management guidance to minimize the incidence of POD.

Altered dynamic brain activity and its association with memory decline after night shift-related sleep deprivation in nurses.

AIMS AND OBJECTIVES: To investigate, for the first time, aberrant time-varying local brain activity in nurses following night shift-related sleep deprivation (SD) and its association with memory decline. BACKGROUND: Prior studies have elucidated alterations in static local brain activity resulting from SD in the occupations outside medical profession. DESIGN: A longitudinal study followed the STROBE recommendations. METHODS: Twenty female nurses underwent resting-state functional magnetic resonance imaging and memory function assessment (by Complex Figure Test (CFT) and the California Verbal Learning Test, Second Edition (CVLT-II)) twice, once in a rested wakefulness (RW) state and another after SD. By combining the sliding-window approach and amplitude of low-frequency fluctuation (ALFF) analysis, the dynamic ALFF (dALFF) variability was calculated to reflect the characteristics of dynamic local brain activity. RESULTS: Poor performance on the CFT and CVLT-II was observed in nurses with night shift-related SD. Reduced dALFF variability was found in a set of cognition-related brain regions (including the medial/middle/superior frontal gyrus, anterior/posterior cingulate gyrus, precuneus, angular gyrus, orbitofrontal and subgenual areas, and posterior cerebellum lobe), while increased dALFF variability was observed in the somatosensory-related, visual and auditory regions. SD-related dALFF variability alterations correlated with changes in subjects' performance on the CFT and CVLT-II. CONCLUSIONS: Night shift-related SD disturbed dynamic brain activity in high cognitive regions and induced compensatory reactions in primary perceptual cortex. Identifying dALFF variability abnormalities may broaden our understanding of neural substrates underlying SD-related cognitive alterations, especially memory dysfunction. RELEVANCE TO CLINICAL PRACTICE: Night shift-related SD is as an important occupational hazard affecting brain function in nurses. The effective countermeasure addressing the adverse outcomes of SD should be advocated for nurses. PATIENT OR PUBLIC CONTRIBUTION: Patients or public were not involved in the design and implementation of the study or the analysis and interpretation of the data.

Novel model combining contrast-enhanced ultrasound with serology predicts hepatocellular carcinoma recurrence after hepatectomy.

BACKGROUND: Surgery is the primary curative option in patients with hepatocellular carcinoma (HCC). However, recurrence within 2 years is observed in 30%-50% of patients, being a major cause of mortality. AIM: To construct and verify a non-invasive prediction model combining contrast-enhanced ultrasound (CEUS) with serology biomarkers to predict the early recurrence of HCC. METHODS: Records of 744 consecutive patients undergoing first-line curative surgery for HCC in one institution from 2016-2018 were reviewed, and 292 local patients were selected for analysis. General characteristics including gender and age, CEUS liver imaging reporting and data system (LIRADS) parameters including wash-in time, wash-in type, wash-out time, and wash-out type, and serology biomarkers including alanine aminotransferase, aspartate aminotransferase, platelets, and alpha-fetoprotein (AFP) were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence. Then a nomogram called CEUS model was constructed. The CEUS model was then used to predict recurrence at 6 mo, 12 mo, and 24 mo, the cut-off value was calculate by X-tile, and each C-index was calculated. Then Kaplan-Meier curve was compared by log-rank test. The calibration curves of each time were depicted. RESULTS: A nomogram predicting early recurrence (ER), named CEUS model, was formulated based on the results of the multivariate Cox regression analysis. This nomogram incorporated tumor diameter, preoperative AFP level, and LIRADS, and the hazard ratio was 1.123 (95% confidence interval [CI]: 1.041-1.211), 1.547 (95%CI: 1.245-1.922), and 1.428 (95%CI: 1.059-1.925), respectively. The cut-off value at 6 mo, 12 mo, and 24 mo was 100, 80, and 50, and the C-index was 0.748 (95%CI: 0.683-0.813), 0.762 (95%CI: 0.704-0.820), and 0.762 (95%CI: 0.706-0.819), respectively. The model showed satisfactory results, and the calibration at 6 mo was desirable; however, the calibration at 12 and 24 mo should be improved. CONCLUSION: The CEUS model enables the well-calibrated individualized prediction of ER before surgery and may represent a novel tool for biomarker research and individual counseling.