Evaluation of Contrast-Enhanced Ultrasound in Diagnosis of Acute Kidney Injury of Patients in Intensive Care Unit.

Background: Ultrasound can assess renal perfusion, but its role in the evaluation of acute kidney injury (AKI) is still unclear. This prospective cohort study was to investigate the value of contrast-enhanced ultrasound (CEUS) in the evaluation of AKI in intensive care unit (ICU) patients. Methods: Fifty-eight patients were recruited from ICU between October 2019 and October 2020, and CEUS was used to monitor the renal microcirculation perfusion within 24h after admission. Parameters included rise time (RT), time to peak intensity (TTP), amplitude of peak intensity (PI), area under the curve (AUC), time from peak to one half (TP1/2) of renal cortex and medulla. Ultrasonographical findings, demographics, laboratory, etc were collected for further analysis. Results: There were 30 patients in the AKI group and 28 patients in the non-AKI group. The TTP, PI, TP1/2 of the cortex and the RT, TTP, TP1/2 of the medulla in the AKI group were significantly longer than in the non-AKI group (P < 0.05);. The TTP (OR = 1.261, 95% CI: 1.083-1.468, P = 0.003) (AUCs 0.733, Sen% 83.3, Spe%57.1), TP1/2 (OR = 1.079, 95% CI: 1.009-1.155, P = 0.027) (AUCs 0.658, Sen% 76.7, Spe%50.0) of the cortex and RT (OR = 1.453, 95% CI: 1.051-2.011, P = 0.024) (AUCs 0.686, Sen% 43.3, Spe%92.9) of the medulla were related to the AKI. Eight new-onset AKI cases occurred in the non-AKI group within 7 days, the RT, TTP, TP1/2 of the cortex and medulla were significantly longer in the new-onset AKI group than in the non-AKI group (P < 0.05), but serum creatinine and blood urea nitrogen were no differences between groups (P > 0.05). Conclusion: This study indicates CEUS can assess the renal perfusion in AKI. TTP and TP1/2 of the cortex and RT of the medulla can aid the diagnosis of AKI in ICU patients.

Optimizing the prediction of sepsis-associated encephalopathy with cerebral circulation time utilizing a nomogram: a pilot study in the intensive care unit.

Background: Sepsis-associated encephalopathy (SAE) is prevalent in intensive care unit (ICU) environments but lacks established treatment protocols, necessitating prompt diagnostic methods for early intervention. Traditional symptom-based diagnostics are non-specific and confounded by sedatives, while emerging biomarkers like neuron-specific enolase (NSE) and S100 calcium-binding protein B (S100B) have limited specificity. Transcranial Doppler (TCD) indicators, although is particularly relevant for SAE, requires high operator expertise, limiting its clinical utility. Objective: This pilot study aims to utilize cerebral circulation time (CCT) assessed via contrast-enhanced ultrasound (CEUS) as an innovative approach to investigate the accuracy of SAE prediction. Further, these CCT measurements are integrated into a nomogram to optimize the predictive performance. Methods: This study employed a prospective, observational design, enrolling 67 ICU patients diagnosed with sepsis within the initial 24 h. Receiver operating characteristic (ROC) curve analyses were conducted to assess the predictive accuracy of potential markers including NSE, S100B, TCD parameters, and CCT for SAE. A nomogram was constructed via multivariate Logistic Regression to further explore the combined predictive potential of these variables. The model's predictive performance was evaluated through discrimination, calibration, and decision curve analysis (DCA). Results: SAE manifested at a median of 2 days post-admission in 32 of 67 patients (47.8%), with the remaining 35 sepsis patients constituting the non-SAE group. ROC curves revealed substantial predictive utility for CCT, pulsatility index (PI), and S100B, with CCT emerging as the most efficacious predictor, evidenced by an area under the curve (AUC) of 0.846. Multivariate Logistic Regression identified these markers as independent predictors for SAE, leading to the construction of a nomogram with excellent discrimination, substantiated by an AUC of 0.924 through bootstrap resampling. The model exhibited satisfactory concordance between observed and predicted probabilities, and DCA confirmed its clinical utility for the prompt identification of SAE. Conclusion: This study highlighted the enhanced predictive value of CCT in SAE detection within ICU settings. A novel nomogram incorporating CCT, PI, and S100B demonstrated robust discrimination, calibration, and clinical utility, solidifying it as a valuable tool for early SAE intervention.

Associations Between Vertebral Marrow Proton Density Fat Fraction and Risk of Prostate Cancer.

Bone marrow adipocytes may be responsible for cancer progression. Although marrow adipogenesis is suspected to be involved in prostate carcinogenesis, an association between marrow adiposity and prostate cancer risk has not been clearly established in vivo. This work included 115 newly diagnosed cases of histologically confirmed prostate cancer (range, 48-79 years) and 87 age-matched healthy controls. Marrow proton density fat fraction (PDFF) was measured by 3.0-T MR spectroscopy at the spine lumbar. Associations between marrow PDFF and risk of prostate cancer by stage of disease and grade sub-types were performed using multivariable polytomous logistic regression. There were no significant group differences in the vertebral marrow PDFF, despite prostate cancer patients having 6.6% higher marrow PDFF compared to the healthy controls (61.7 ± 9.8% vs. 57.9 ± 6.5%; t = 1.429, p = 0.161). After adjusting for various clinical and demographic characteristics, we found that elevated marrow PDFF was related to an increased risk of high-grade prostate cancer [odds ratios (OR) = 1.31; 95% confidence interval (CI), 1.08-1.57; p = 0.003]. Likewise, increased marrow PDFF had a significantly positive correlation with aggressive prostate cancer risk (OR = 1.54; 95% CI, 1.13-1.92; p <0.001). There were no associations between marrow PDFF and low-grade (p = 0.314) or non-aggressive (p = 0.435) prostate cancer risk. The data support the hypothesis that marrow adiposity was correlated with increased risk of aggressive prostate cancer, supporting a link between adipogenesis and prostate cancer risk.

Nomogram Incorporating Contrast-Enhanced Ultrasonography Predicting Time to the Development of Castration-Resistant Prostate Cancer.

BACKGROUND: It is valuable to predict the time to the development of castration-resistant prostate cancer (CRPC) in patients with advanced prostate cancer (PCa). This study aimed to build and validate a nomogram incorporating the clinicopathologic characteristics and the parameters of contrast-enhanced ultrasonography (CEUS) to predict the time to CRPC after androgen deprivation therapy (ADT). METHODS: Patients with PCa were divided into the training (n = 183) and validation cohorts (n = 37) for nomogram construction and validation. The clinicopathologic characteristics and CEUS parameters were analyzed to determine the independent prognosis factors and serve as the basis of the nomogram to estimate the risk of 1-, 2-, and 3-year progress to CRPC. RESULTS: T stage, distant metastasis, Gleason score, area under the curve (AUC), prostate-specific antigen (PSA) nadir, and time to PSA nadir were the independent predictors of CRPC (all P < 0.05). Three nomograms were built to predict the time to CRPC. Owing to the inclusion of CEUS parameter, the discrimination of the established nomogram (C-index: 0.825 and 0.797 for training and validation datasets) was improved compared with the traditional prediction model (C-index: 0.825 and 0.797), and when it excluded posttreatment PSA, it still obtained an acceptable discrimination (C-index: 0.825 and 0.797). CONCLUSIONS: The established nomogram including regular prognostic indicators and CEUS obtained an improved accuracy for the prediction of the time to CRPC. It was also applicable for early prediction of CRPC when it excluded posttreatment PSA, which might be helpful for individualized diagnosis and treatment.

Association Between Contrast-Enhanced Ultrasound Indicators and Prostate Cancer Biochemical Recurrence After Treatment.

BACKGROUND: Some patients with prostate cancer (PCa) will experience biochemical recurrence (BCR) after treatment. Current researches have identified the influencing factors of BCR, but these factors are difficult to quantify and hence unable to accurately predict the BCR in PCa patients. OBJECTIVE: To explore the value of contrast-enhanced ultrasound (CEUS) indicators in predicting the BCR after treatment by evaluating the association between them. PATIENTS AND METHODS: In a retrospective cohort study, 157 PCa patients were recruited and received prostate specific antigen (PSA) measurement, CEUS, pathological classification, and immunohistochemistry after puncture biopsy. PCa patients with BCR were included in the recurrence group, while the remaining patients were included in the non-recurrence group after a 5-year follow-up. The clinical characteristics and CEUS indicators were compared between the two groups, and the multivariable COX regression was used for screening the influencing factors of BCR. Receiver operating characteristic (ROC) curves were used to analyze the value of potential factors in predicting BCR. The effect of the combined prediction model was explored to improve the accuracy of the prediction. RESULTS: Twelve patients are lost during the follow-up period and the final analysis included 145 patients. The 5-year BCR rate of PCa patients was 27%, with 43 patients in the recurrence group and 102 patients in the non-recurrence group. Multivariate analysis showed that lymph node metastasis (P<0.001), distant metastasis (P<0.001), Gleason score (P<0.001), pretreatment PSA (P<0.001), treatment method (P<0.001), peak intensity (PI) (P=0.001), and time to peak (TTP) (P=0.003) were independent influencing factors for BCR after treatment. ROC analysis showed that the AUCs of all indicators in predicting BCR were not high (all <0.9). The combination of lymph node metastasis, Gleason score, pretreatment PSA, and treatment method can improve the predictive accuracy (AUC = 0.85), but the AUC was still under 0.9. The combined prediction model including CEUS time-intensity curve (TIC) indicators (PI and TTP) could accurately predict the BCR after treatment (AUC=0.953). The sensitivity and specificity were 93.02% and 88.24%, respectively. CONCLUSION: The prediction model including TIC indicators and common influencing factors can more accurately predict the BCR in PCa patients.