Purpose: Acute pyelonephritis (AP), a complication of urolithiasis, can be fatal if it progresses to septic shock. We aimed to evaluate the performance of excretory phase computed tomography (CT) in predicting bacteremia among AP patients with upper urinary tract calculi.
Methods: We reviewed medical records of 250 patients diagnosed with AP and upper urinary tract calculi and who were admitted to our institute. We analyzed 132 patients who underwent excretory phase CT. Excretory phase CT images were obtained 7 min after injection with the contrast agent. Obstruction was classified either as high or low grade. Univariate and multivariate analyses were performed to identify the risk factors of bacteremia.
Results: Of 132 patients, 73 (55.3%) had bacteremia. Escherichia coli was the most frequently identified pathogen in blood cultures. Univariate analysis demonstrated that high-grade obstruction on excretory phase CT and quick Sepsis-related Organ Failure Assessment (qSOFA) score ≥ 2 were correlated with bacteremia. In addition, the administration of vasopressors was significantly associated with bacteremia (31.5% vs. 6.8%; p < 0.001). Multivariate analysis identified high-grade obstruction on excretory phase CT [odds ratio (OR) 6.68; p < 0.001] and qSOFA score ≥ 2 (OR 3.59, p = 0.03) as independent risk factors for bacteremia.
Conclusions: Excretory phase CT images can be used to predict bacteremia by evaluating the degree of ureteral obstruction. The evaluation of the passage of urine shown by excretory phase CT is critical in patients with AP associated with upper urinary tract calculi.
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http://dx.doi.org/10.1007/s11255-018-1999-2 | DOI Listing |
Acad Radiol
December 2024
Department of Radiology, the First Medical Center of the Chinese PLA General Hospital, 28 Fuxing Road, Haidian District, Beijing 100853, China (X.W., H.K., X.B., X.N., C.L., S.Y., H.W.). Electronic address:
Rationale And Objectives: To improve the diagnostic recognition of papillary renal neoplasm with reverse polarity (PRNRP) through comprehensive analysis of computed tomography (CT) and magnetic resonance imaging (MRI) findings.
Materials And Methods: A retrospective multi-center study was conducted on patients with pathologically confirmed PRNRPs from 2019 to 2024, encompassing six institutions. Clinical and pathological data were meticulously documented.
BMC Cancer
December 2024
Department of Urology, The 2nd Affiliated Hospital of Kunming Medical University, No. 374 Dianmian Road, Kunming, Yunnan, 650101, People's Republic of China.
Background: Upper urinary tract urothelial carcinoma (UTUC) is a rare and highly aggressive malignancy characterized by poor prognosis, making the accurate identification of high-grade (HG) UTUC essential for subsequent treatment strategies. This study aims to develop and validate a nomogram model using computed tomography urography (CTU) images to predict HG UTUC.
Methods: A retrospective cohort study was conducted to include patients with UTUC who underwent radical nephroureterectomy and received a CTU examination prior to surgery.
Urol Int
December 2024
College of Medicine, University of Sulaimani, Sulaymaniyah, Iraq.
Introduction: A comprehensive understanding of the renal vascular anatomy variations is paramount to a successful kidney transplant. This reduces vascular injury risks and minimizes ischemia duration, optimizing surgical outcomes. The current study aims to assess the accuracy of renal computed tomography angiography (CTA) findings of live renal donors by comparing them with intraoperative findings.
View Article and Find Full Text PDFCureus
October 2024
Radiodiagnosis, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research (DMIHER), Wardha, IND.
Introduction: This study aims to evaluate the role of multidetector computed tomography (MDCT) urography in cases of obstructive uropathy to determine the cause, side, site, and level of obstruction and to differentiate between acute and chronic cases of obstructive uropathy based on imaging features.
Methods: Using Cochran's formula, a sample size of 121 patients was calculated. The patients underwent computed tomography (CT) urography to assess the obstructing agents causing obstructive uropathy.
Eur J Radiol Open
December 2024
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 200025, China.
Purpose: To compare image quality and detection accuracy of renal stones between deep learning image reconstruction (DLIR) and Adaptive Statistical Iterative Reconstruction-Veo (ASIR-V) reconstructed virtual non-contrast (VNC) images and true non-contrast (TNC) images in spectral CT Urography (CTU).
Methods: A retrospective analysis was conducted on images of 70 patients who underwent abdominal-pelvic CTU in TNC phase using non-contrast scan and contrast-enhanced corticomedullary phase (CP) and excretory phase (EP) using spectral scan. The TNC scan was reconstructed using ASIR-V70 % (TNC-AR70), contrast-enhanced scans were reconstructed using AR70, DLIR medium-level (DM), and high-level (DH) to obtain CP-VNC-AR70/DM/DH and EP-VNC-AR70/DM/DH image groups, respectively.
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