Predictive model of positive surgical margins after radical prostatectomy based on Bayesian network analysis.

Objective: This study aimed to analyze the independent risk factors for marginal positivity after radical prostatectomy and to evaluate the clinical value of the predictive model based on Bayesian network analysis. Methods: We retrospectively analyzed the clinical data from 238 patients who had undergone radical prostatectomy, between June 2018 and May 2022. The general clinical data, prostate specific antigen (PSA)-derived indicators, puncture factors, and magnetic resonance imaging (MRI) characteristics were included as predictive variables, and univariate and multivariate analyses were conducted. We established a nomogram model based on the independent predictors and adopted BayesiaLab software to generate tree-augmented naive (TAN) and naive Bayesian models based on 15 predictor variables. Results: Of the 238 patients included in the study, 103 exhibited positive surgical margins. Univariate analysis revealed that PSA density (PSAD) (P = 0.02), Gleason scores for biopsied tissue (P = 0.002) and the ratio of positive biopsy cores (P < 0.001), preoperative T staging (P < 0.001), and location of abnormal signals (P = 0.002) and the side of the abnormal signal (P = 0.009) were all statistically significant. The area under curve (AUC) of the established nomogram model based on independent predictors was 73.80%, the AUC of the naive Bayesian model based on 15 predictors was 82.71%, and the AUC of the TAN Bayesian model was 80.80%. Conclusion: The predictive model of positive resection margin after radical prostatectomy based on Bayesian network demonstrated high accuracy and usefulness.

Prediction model of gleason score upgrading after radical prostatectomy based on a bayesian network.

OBJECTIVE: To explore the clinical value of the Gleason score upgrading (GSU) prediction model after radical prostatectomy (RP) based on a Bayesian network. METHODS: The data of 356 patients who underwent prostate biopsy and RP in our hospital from January 2018 to May 2021 were retrospectively analysed. Fourteen risk factors, including age, body mass index (BMI), total prostate-specific antigen (tPSA), prostate volume, total prostate-specific antigen density (PSAD), the number and proportion of positive biopsy cores, PI-RADS score, clinical stage and postoperative pathological characteristics, were included in the analysis. Data were used to establish a prediction model for Gleason score elevation based on the tree augmented naive (TAN) Bayesian algorithm. Moreover, the Bayesia Lab validation function was used to calculate the importance of polymorphic Birnbaum according to the results of the posterior analysis and to obtain the importance of each risk factor. RESULTS: In the overall cohort, 110 patients (30.89%) had GSU. Based on all of the risk factors that were included in this study, the AUC of the model was 81.06%, and the accuracy was 76.64%. The importance ranking results showed that lymphatic metastasis, the number of positive biopsy cores, ISUP stage and PI-RADS score were the top four influencing factors for GSU after RP. CONCLUSIONS: The prediction model of GSU after RP based on a Bayesian network has high accuracy and can more accurately evaluate the Gleason score of prostate biopsy specimens and guide treatment decisions.

The Tp-e/QT ratio as a predictor of nocturnal premature ventricular contraction events in patients with obstructive sleep apnea.

BACKGROUND: Obstructive sleep apnea (OSA) is significantly associated with a higher risk of ventricular arrhythmia (VA). QT, the Tp-e/QT ratio, and QT dispersion (QTd) are used to evaluate myocardial repolarization and are highly correlated with VA. The aim was to evaluate the predictive value of the Tp-e/QT ratio and other electrocardiogram (ECG) parameters for nocturnal premature ventricular contractions (PVCs) in patients with OSA. METHODS: We retrospectively analyzed data from patients with OSA and conducted a 1:1 matched cohort study. Patients diagnosed with OSA who met our criteria for the PVC group, and sex- and age-matched patients with OSA who met our criteria for the control group were enrolled in the study. The Tp-e, Tp-e/QT ratio, corrected QT interval (QTc), corrected Tp-e interval (Tp-ec), and QTd were measured, calculated and analyzed. RESULTS: Patients in the PVC group (n = 31) showed a greater Tp-e, Tp-ec, QTc, Tp-e/QT ratio, and QTd than patients in the control group (n = 31). In the univariate binary logistic regression analysis, higher Tp-ec (OR: 1.025; P = 0.042), QTc (OR: 1.014; P = 0.036), Tp-e/QT ratio (OR: 1.675; P < 0.001), and QTd (OR: 1.052; P = 0.012) values were all significantly associated with nocturnal PVCs. In multivariate analysis and receiver operating characteristic analysis, a higher Tp-e/QT ratio (OR: 2.168; 95% CI: 0.762-0.952; P < 0.001) was an independent predictor of nocturnal PVCs. CONCLUSIONS: The QTc, Tp-e/QT ratio, and QTd in patients with OSA with nocturnal PVCs were significantly increased compared with those in patients without nocturnal PVCs. A prolonged Tp-e/QT ratio was an independent predictor of nocturnal PVCs in patients with OSA.

The Contribution of Psychological Distress to Resting Palpitations in Patients Who Recovered from Severe COVID-19.

BACKGROUND: Increasing numbers of patients have recovered from severe coronavirus disease 2019 (COVID-19) in Wuhan, China. This study aimed to evaluate the association of psychological distress with resting palpitations in recovered patients. METHODS: In this prospective cohort study, consecutive patients who recovered from severe COVID-19 and complained of resting palpitations were included. Dynamic electrocardiogram (ECG) was continuously monitored for 2 hours while patients were at rest. A survey using a palpitation frequency scale and the Hospital Anxiety and Depression Scale (HADS) was administered to all participants. RESULTS: Of the 289 consecutive patients who recovered from severe COVID-19, 24 patients (8.3%) suffered resting palpitation symptoms, and 22 patients were finally included. Two-hour Holter monitoring showed that 18 (81.8%) patients had tachyarrhythmias, of which the most common was sinus tachycardia (17/22, 77.3%). However, patients with sinus tachycardia showed a similar frequency of palpitation episodes compared to those without sinus tachycardia. Anxiety (68.2%) and depression (59.1%) were prevalent among these recovered patients. Patients with anxiety or depression symptoms had a higher frequency of palpitation episodes than those without anxiety or depression symptoms. In addition, both the HADS-anxiety score (r =0.609, P<0.01) and HADS-depression score (r =0.516, P=0.01) were positively related to the frequency of palpitation episodes. CONCLUSION: Symptoms of resting palpitations, manifested mainly by sinus tachycardia, are not uncommon in patients who recovered from severe COVID-19. Psychological distress (anxiety and depression) may be responsible, at least in part, for resting palpitation symptoms.

Application of the microwave digestion-vacuum filtration-automated scanning electron microscopy method for diatom detection in the diagnosis of drowning.

The cause of death for the decomposed corpses recovered from water is still a difficult issue in current forensic practice. In this article, we present two cases of bodies recovered from water with no positive findings to indicate the cause of their death. We apply both conventional acid digestion method as well as the microwave digestion-vacuum filtration-automated scanning electron microscopy method (MD-VF-Auto SEM) for diatom detection in different organs of both bodies. Our results indicate that MD-VF-Auto SEM method provide more accurate information and match further police investigation. This novel method would be a useful technique in assessing cause of death for body found in water.