Diagnostic Accuracy of Imaging Findings in Pleural Empyema: Systematic Review and Meta-Analysis

Endogenous endophthalmitis due to Serratia marcescens secondary to late-onset empyema Post-Cardiac surgery in an End-Stage renal disease patient on peritoneal dialysis

Endogenous bacterial endophthalmitis results from bacterial seeding of the eye during bacteremia. A diagnosis of endogenous bacterial endophthalmitis requires clinical findings such as vitritis or hypopyon along with positive blood cultures. Serratia marcescens is the second most common pathogen causing hospital-acquired ocular infections. This report describes a case of endogenous bacterial endophthalmitis caused by S. marcescens in an older adult with end-stage renal disease (ESRD) on peritoneal dialysis, who had late-onset pleural empyema secondary to coronary artery bypass grafting (CABG). A 61-year-old gentleman presented with a two-day history of cloudy vision, black floaters, pain, swelling, and gradual vision loss in his right eye. There was no history of trauma, ocular surgeries, or previous similar episodes. He had myocardial infarction treated with CABG 3 months back. Examination showed a 3 mm hypopyon in the anterior chamber. He had classic signs of endophthalmitis with positive blood cultures for S. marcescens. He was treated with high-dose intravenous meropenem and intravitreal ceftazidime without vitrectomy. Endophthalmitis progressed to complete vision loss in his right eye, requiring evisceration. Endophthalmitis caused by S. marcescens is rare, but long-term outcomes can be severe, causing complete vision loss in about 60% of the patients. It is usually hospital-acquired, and the source can be late-onset empyema several months after cardiac surgery, in an immunocompromised patient. Systemic antibiotics should be supplemented with intravitreal agents with or without pars plana vitrectomy.

Impact of photon counting detector CT derived virtual monoenergetic images and iodine maps on the diagnosis of pleural empyema

PURPOSE

The purpose of this study was to evaluate the impact of virtual monoenergetic image (VMI) energies and iodine maps on the diagnosis of pleural empyema with photon counting detector computed tomography (PCD-CT).

MATERIALS AND METHODS

In this IRB-approved retrospective study, consecutive patients with non-infectious pleural effusion or histopathology-proven empyema were included. PCD-CT examinations were performed on a dual-source PCD-CT in the multi-energy (QuantumPlus) mode at 120 kV with weight-adjusted intravenous contrast-agent. VMIs from 40-70 keV obtained in 10 keV intervals and an iodine map was reconstructed for each scan. CT attenuation was measured in the aorta, the pleura and the peripleural fat (between autochthonous dorsal muscles and dorsal ribs). Contrast-to-noise (CNR) and signal-to-noise (SNR) ratios were calculated. Two blinded radiologists evaluated if empyema was present (yes/no), and rated diagnostic confidence (1 to 4; not confident to fully confident, respectively) with and without using the iodine map. Sensitivity, specificity and diagnostic confidence were estimated. Interobserver agreement was estimated using an unweighted Cohen kappa test. A one-way ANOVA was used to compare variables. Differences in sensitivity and specificity between the different levels of energy were searched using McNemar test.

RESULTS

Sixty patients (median age, 60 years; 26 women) were included. A strong negative correlation was found between image noise and VMI energies (r = -0.98; P = 0.001) and CNR increased with lower VMI energies (r = -0.98; P = 0.002). Diagnostic accuracy (96%; 95% CI: 82-100) as well as diagnostic confidence (3.4 ± 0.75 [SD]) were highest at 40 keV. Diagnostic accuracy and confidence at higher VMI energies improved with the addition of iodine maps (P ≤0.001). Overall, no difference in CT attenuation of peripleural fat between patients with empyema and those with pleural effusion was found (P = 0.07).

CONCLUSION

Low VMI energies lead to a higher diagnostic accuracy and diagnostic confidence in the diagnosis of pleural empyema. Iodine maps help in diagnosing empyema only at high VMI energies.

Considerations on Baseline Generation for Imaging AI Studies Illustrated on the CT-Based Prediction of Empyema and Outcome Assessment

For AI-based classification tasks in computed tomography (CT), a reference standard for evaluating the clinical diagnostic accuracy of individual classes is essential. To enable the implementation of an AI tool in clinical practice, the raw data should be drawn from clinical routine data using state-of-the-art scanners, evaluated in a blinded manner and verified with a reference test. Three hundred and thirty-five consecutive CTs, performed between 1 January 2016 and 1 January 2021 with reported pleural effusion and pathology reports from thoracocentesis or biopsy within 7 days of the CT were retrospectively included. Two radiologists (4 and 10 PGY) blindly assessed the chest CTs for pleural CT features. If needed, consensus was achieved using an experienced radiologist’s opinion (29 PGY). In addition, diagnoses were extracted from written radiological reports. We analyzed these findings for a possible correlation with the following patient outcomes: mortality and median hospital stay. For AI prediction, we used an approach consisting of nnU-Net segmentation, PyRadiomics features and a random forest model. Specificity and sensitivity for CT-based detection of empyema (n = 81 of n = 335 patients) were 90.94 (95%-CI: 86.55–94.05) and 72.84 (95%-CI: 61.63–81.85%) in all effusions, with moderate to almost perfect interrater agreement for all pleural findings associated with empyema (Cohen’s kappa = 0.41–0.82). Highest accuracies were found for pleural enhancement or thickening with 87.02% and 81.49%, respectively. For empyema prediction, AI achieved a specificity and sensitivity of 74.41% (95% CI: 68.50–79.57) and 77.78% (95% CI: 66.91–85.96), respectively. Empyema was associated with a longer hospital stay (median = 20 versus 14 days), and findings consistent with pleural carcinomatosis impacted mortality.