Chronic neuropathic pain (CNP) is a complex condition often arising from neural maladaptation after nerve injury. Understanding CNP complications involves the intricate interplay between brain-heart dynamics, assessed through quantitative electroencephalogram (qEEG) and heart rate variability (HRV). However, insights into their interaction in chronic pain are limited. Resting EEG and simultaneous electrocardiogram (lead II) of the participants were recorded for qEEG and HRV analysis.Correlations between HRV and qEEG parameters were calculated and compared with age, sex, and body mass index (BMI)-matched controls. CNP patients showed reduced HRV and significant increases in qEEG power spectral densities within delta, theta, and beta frequency ranges. A positive correlation was found between low frequency/high frequency (LF/HF) ratio in HRV analysis and theta, alpha, and beta frequency bands in qEEG among CNP patients. However, no significant correlation was observed between parasympathetic indices and theta, beta bands in qEEG within CNP group, unlike age, sex, and BMI-matched healthy controls. CNP patients display significant HRV reductions and distinctive qEEG patterns. While healthy controls exhibit significant correlations between parasympathetic HRV parameters and qEEG spectral densities, these relationships are diminished or absent in CNP individuals.LF/HF ratio, reflecting sympathovagal balance, correlates significantly with qEEG frequency bands (theta, alpha, beta), illuminating autonomic dysregulation in CNP.These findings emphasize the intricate brain-heart interplay in chronic pain, warranting further exploration.

Purpose: Prostate cancer (PCa) screening can lead to potential over-diagnosis/over-treatment of indolent cancers. There is a need to optimize practices to better risk-stratify patients. We examined initial longitudinal outcomes of mid-life men with an elevated baseline prostate-specific antigen (PSA) following initiation of a novel screening program within a system-wide network. Materials and Methods: We assessed our primary care network patients ages 40 to 49 years with a PSA measured following implementation of an electronic health record screening algorithm from 2/2/2017–2/21/2018. The multidisciplinary algorithm was developed taking factors including age, race, family history, and PSA into consideration to provide a personalized approach to urology referral to be used with shared decision-making. Outcomes of men with PSA ≥1.5 ng/mL were evaluated through 7/2021. Statistical analyses identified factors associated with PCa detection. Clinically significant PCa (csPCa) was defined as Gleason Grade Group (GGG) ≥2 or GGG1 with PSA ≥10 ng/mL. Results: The study cohort contained 564 patients, with 330 (58.5%) referred to urology for elevated PSA. Forty-nine (8.7%) underwent biopsy; of these, 20 (40.8%) returned with PCa. Eleven (2.0% of total cohort and 55% of PCa diagnoses) had csPCa. Early referral timing (odds ratio [OR], 4.58) and higher PSA (OR, 1.07) were significantly associated with PCa at biopsy on multivariable analysis (both p<0.05), while other risk factors were not. Referred patients had higher mean PSAs (2.97 vs. 1.98, p=0.001). Conclusions Preliminary outcomes following implementation of a multidisciplinary screening algorithm identified PCa in a small, important percentage of men in their forties. These results provide insight into baseline PSA measurement to provide early risk stratification and detection of csPCa in patients with otherwise extended life expectancy. Further follow-up is needed to possibly determine the prognostic significance of such mid-life screening and optimize primary care physician-urologist coordination.

Background and aims Noninvasive predictors of choledocholithiasis have generally exhibited marginal performance characteristics. We aimed to identify noninvasive independent predictors of endoscopic retrograde cholangiopancreatography (ERCP)-confirmed choledocholithiasis and accordingly developed predictive machine learning models (MLMs). Methods Clinical data of consecutive patients undergoing first-ever ERCP for suspected choledocholithiasis from 2015 to 2019 were abstracted from a prospectively-maintained database. Multiple logistic regression was used to identify predictors of ERCP-confirmed choledocholithiasis. MLMs were then trained to predict ERCP-confirmed choledocholithiasis using pre-ERCP ultrasound (US) imaging only as well as using all available noninvasive imaging (US, computed tomography, and/or magnetic resonance cholangiopancreatography). The diagnostic performance of American Society for Gastrointestinal Endoscopy (ASGE) “high-likelihood” criteria was compared to MLMs. Results We identified 270 patients (mean age 46 years, 62.2% female, 73.7% Hispanic/Latino, 59% with noninvasive imaging positive for choledocholithiasis) with native papilla who underwent ERCP for suspected choledocholithiasis, of whom 230 (85.2%) were found to have ERCP-confirmed choledocholithiasis. Logistic regression identified choledocholithiasis on noninvasive imaging (odds ratio (OR) = 3.045, P = 0.004) and common bile duct (CBD) diameter on noninvasive imaging (OR = 1.157, P = 0.011) as predictors of ERCP-confirmed choledocholithiasis. Among the various MLMs trained, the random forest-based MLM performed best; sensitivity was 61.4% and 77.3% and specificity was 100% and 75.0%, using US-only and using all available imaging, respectively. ASGE high-likelihood criteria demonstrated sensitivity of 90.9% and specificity of 25.0%; using cut-points achieving this specificity, MLMs achieved sensitivity up to 97.7%. Conclusions MLMs using age, sex, race/ethnicity, presence of diabetes, fever, body mass index (BMI), total bilirubin, maximum CBD diameter, and choledocholithiasis on pre-ERCP noninvasive imaging predict ERCP-confirmed choledocholithiasis with good sensitivity and specificity and outperform the ASGE criteria for patients with suspected choledocholithiasis.