Diagnosis Of Urinary Stone Research Articles

Relationship among CT-based abdominal adipose tissue areas and pancreatic ductal adenocarcinoma in male

Purpose It is known that obesity can be a risk factor for many types of cancer, including the pancreas. Visceral obesity rather than overall obesity is held more responsible for this relationship. This study aimed to evaluate the relationship of adipose tissue areas and their distribution (subcutaneous and visceral) with pancreatic ductal adenocarcinoma (PDAC) in male patients. Materials and-Method The medical data and abdominopelvic computed tomography (CT) examinations of male patients diagnosed with PDAC who underwent surgery or a biopsy in our hospital between January 2015 and January 2020 were retrospectively evaluated. An age-matched control group was formed from 49 male patients who underwent CT with a preliminary diagnosis of urinary stone without a history of malignancy and weight loss and no malignancy on CT at the time of presentation. Adipose tissue areas (total [TAT], visceral [VAT] and subcutaneous [SAT]) were measured in both groups, their VAT/TAT, VAT/SAT and SAT/TAT ratios were calculated, and the data were compared between the two groups. Results Patients with PDAC had significantly greater TAT, VAT and SAT areas than the control group (p = 0.002, p = 0.01, and p = 0.003, respectively). However, there was no significant differences in the VAT/TAT, VAT/SAT and SAT/TAT ratios between the two groups (p = 0.60, p = 0.60, and p = 0.73, respectively). Conclusion In this study, all adipose tissue areas (VAT, SAT, and TAT) were shown to be increased in male patients with PDAC. Both visceral obesity and overall obesity present as risk factors for PDAC in male patients.

MP34-03 CAN STATIN USE REDUCE THE RISK OF UPPER URINARY TRACT STONE IN PATIENTS WITH DYSLIPIDEMIA? A NATION-WIDE POPULATION-BASED AND WITH AN 8-YEAR FOLLOW-UP STUDY

INTRODUCTION AND OBJECTIVES: Cumulative evidences indicate a tight association between urinary stone and dyslipidemia. Statins are widely used in clinical treatment of dyslipidemia. It is interesting and also important to know whether statin use can reduce the risk of upper urinary tract stone in patients with dyslipidemia. METHODS: We used data sourced from Longitudinal Health Insurance Database, which consists of one million randomly selected subjects from the National Health Insurance Research Database of Taiwan. Health Insurance System of Taiwan covers approximately 23 million people (98% of population). From 1997 to 2001, a total of 11785 subjects, 5926 males and 5859 females, with age 18-year-old or older were identified as dyslipidemia. All subjects did not have previous diagnosis of urinary stone. A cohort of 23570 (2 for each subject with dyslipidemia) age and gender matched subjects without the diagnosis of dyslipidemia and urinary stone were enrolled as the control group. All subjects were followed up to the end of 2009 with a minimal follow-up of 8 years. Multi-variables, including statin use, hypertension, and diabetes mellitus (DM), were taken into account. A Cox proportional hazard regression model was used to calculate the risk of upper urinary tract stone in study and control groups. RESULTS: At the end of follow-up, 1817 (15.4%) of the 11785 dyslipidemia subjects and 2445 (10.4%) of the 23570 control subjects developed upper urinary tract stones. After adjusting for age, gender, hypertension and DM, dyslipidemia was associated with a significantly increased risk of urinary stone (hazard ratio 2.086, 95% confidence interval, 1.929 2.256, p< 0.0001). The dyslipidemia subjects were further stratified into statin user group and non-statin user group. Nine hundred and sixty eight (15.1%) of the 6401 statin users and 849 (15.8%) of the 5384non-statin users developedupper urinary tract stones.Dyslipidemia patients who received statin treatment were not less likely to develop upper urinary tract stone than the patients who never used statin (hazard ratio 1.074, 95% confidence interval, 0.976 1.181, p 1⁄4 0.1430). CONCLUSIONS: Dyslipidemia is an independent risk factor for upper urinary tract stone. The findings of this study suggest that the use of statin cannot reduce the risk of upper urinary tract stone in patients with dyslipidemia.

Artificial neural network, genetic algorithm, and logistic regression applications for predicting renal colic in emergency settings

BackgroundLogistic regression is the most common statistical model for processing multivariate data in the medical literature. Artificial intelligence models like an artificial neural network (ANN) and genetic algorithm (GA) may also be useful to interpret medical data.AimsThe purpose of this study was to perform artificial intelligence models on a medical data sheet and compare to logistic regression.MethodsANN, GA, and logistic regression analysis were carried out on a data sheet of a previously published article regarding patients presenting to an emergency department with flank pain suspicious for renal colic.ResultsThe study population was composed of 227 patients: 176 patients had a diagnosis of urinary stone, while 51 ultimately had no calculus. The GA found two decision rules in predicting urinary stones. Rule 1 consisted of being male, pain not spreading to back, and no fever. In rule 2, pelvicaliceal dilatation on bedside ultrasonography replaced no fever. ANN, GA rule 1, GA rule 2, and logistic regression had a sensitivity of 94.9, 67.6, 56.8, and 95.5%, a specificity of 78.4, 76.47, 86.3, and 47.1%, a positive likelihood ratio of 4.4, 2.9, 4.1, and 1.8, and a negative likelihood ratio of 0.06, 0.42, 0.5, and 0.09, respectively. The area under the curve was found to be 0.867, 0.720, 0.715, and 0.713 for all applications, respectively.ConclusionData mining techniques such as ANN and GA can be used for predicting renal colic in emergency settings and to constitute clinical decision rules. They may be an alternative to conventional multivariate analysis applications used in biostatistics.

The symptoms & diagnosis of urinary stone

The symptoms & diagnosis of urinary stone