Michael Czihal

Intra-abdominal organ infarctions require prompt imaging for diagnosis. Contrast-enhanced computed tomography (CECT) is considered the gold standard but involves radiation, iodinated contrast, and logistical challenges in critically ill patients. Contrast-enhanced ultrasound (CEUS) enables real-time assessment of microvascular perfusion without ionizing radiation. The aim of this study was to evaluate the diagnostic performance of CEUS compared to CECT for detecting intra-abdominal organ infarctions. This retrospective observational study included patients treated at a tertiary care center (2010–2024) who underwent both CEUS and CECT for suspected intra-abdominal organ infarction. With CECT as reference standard, diagnostic performance parameters and agreement (Cohen’s kappa) were calculated. 24 patients (median age 60 years; 12 female) were included. CECT confirmed organ infarction in 16/24 patients (66.7%). CEUS correctly identified 15 of these cases (Sensitivity 93.8%; 95% confidence interval [CI]: 71.7–98.9%). Specificity was 75.0% (95% CI: 40.9–92.9%), with two false-positive CEUS findings. Positive and negative predictive values were 88.2% (95% CI: 65.7–96.7%) and 85.7% (95% CI: 48.7–97.4%), respectively. Agreement of CECT and CEUS was substantial (Cohen’s κ = 0.71). Wedge-shaped perfusion defects were identified in 93.8% of CT-confirmed infarctions on both modalities. CEUS demonstrates high sensitivity and good agreement with CECT for the detection of intra-abdominal organ infarctions. CEUS represents a valuable imaging modality, particularly when CECT is contraindicated or not available. Prospective studies are warranted to further define its role in clinical practice.

Background: The potential role of structured clinical pretest probability assessment in the diagnostic workup of giant cell arteritis (GCA) is not well defined. Patients and methods: We applied four clinical prediction rules to patients enrolled in the ongoing prospective PREDICT GCA study. All patients underwent temporal artery biopsy in addition to the detailed clinical and sonographic workup. The final diagnosis was based on the 6 months follow-up and ambiguous cases were judged by an independent expert panel. The diagnostic accuracy of the four models was determined using 2×2 contingency tables and receiver operating characteristic analysis. Patients with and without discordant results in different scoring systems were compared using univariate significance tests. Results: Seventy patients were analysed. The highest diagnostic accuracy (area under the curve (AUC) 0.84) and the highest negative predictive value (NPV, 86.7%) were provided by the Ing et al model. The other scores performed worse, with AUC/NPV of 0.74/78.6% (Southend GCA Probability Score), 0.75/73.7% (Bhavsar-Khalidi Score) and 0.70/77.8% (PREDICT score). In 37.5% of patients, clinically significant discordances in categorisation (classification as both low and non-low risk by different scoring systems) were evident. These patients less frequently presented with clinical symptoms and signs of cranial GCA (all p<0.01), but the rate of visual impairment did not differ from that in patients with consistent risk estimation results. Conclusion: Clinical prediction models for the diagnosis of GCA differ in their diagnostic accuracy. Clinically relevant discordances of individual risk prediction are common. Trial registration number: DRKS00031293.

Full Papers; Authors: T. Öz, L.-C. Thielmann, C. Lottspeich, L.J.U. Reik, T. Wirthmiller, I. Prearo, M.J. Mackert, C. Gebhardt, H. Schulze-Koops, M. Czihal