Purpose

Silicone oil (SO) is often used as an intraocular tamponade in repairs of retinal detachments. It may be associated with complications such as cataract, glaucoma, keratopathy, subretinal migration of oil, fibrous epiretinal and sub retinal proliferations, and oil emulsification. The purpose of this report is to describe a rare phenomenon of intraocular silicone oil migration into the cerebral ventricles, which may later be mistaken for intraventricular hemorrhages on neuroimaging.

Methods

Case report with literature review.

Results

A patient with a history of retinal detachment repair with intraocular SO presented with headaches. Neuroimaging revealed SO migration to the cerebral ventricles. The patient was treated conservatively with symptom management and headaches resolved.

Conclusions

We present a case of intraocular SO migration to the cerebral ventricles and review the current literature. We also propose two mechanisms for this phenomenon.

Purpose

To report a case of uveitic glaucoma with a congested optic disc where optical coherence tomography angiography (OCT-A) provided diagnostic utility in assessing glaucomatous damage but optical coherence tomography (OCT) alone had limited utility.

Observations

We report a case of a 33-year-old Caucasian female referred to the USC Roski Eye Institute for uncontrolled intraocular pressure (IOP) in the left eye. She was managed by an outside provider for 6 months, where her IOP ranged from 28 to 42 mm Hg in the left eye on maximally tolerated medical therapy. Her clinical exam was consistent with Herpes family trabeculitis, optic nerve congestion, and possible glaucomatous damage. Initial evaluation of the optic nerve by standard modalities (fundus exam and OCT) was limited by optic nerve congestion; however, OCT-A showed peripapillary hypoperfusion, as commonly observed in glaucomatous eyes. She underwent aqueous shunt implantation for elevated IOPs poorly controlled by medications.

Conclusions and importance

OCT-A can be a useful tool in the evaluation of glaucoma in instances where disc congestion masks both nerve excavation and retinal nerve fiber thinning normally seen on exam and on standard OCT of the optic nerve.

Background

Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. We sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions.

Methods

We conducted a prospective survey of internal medicine clinicians in an urban safety-net hospital. Clinicians prospectively predicted patients' 30-day readmission risk on 5-point Likert scales, subsequently dichotomized into low- vs. high-risk. We compared human with machine predictions using discrimination, net reclassification, and diagnostic test characteristics. Observed readmissions were ascertained from a regional hospitalization database. We also developed and assessed a "human-plus-machine" logistic regression model incorporating both human and machine predictions.

Results

We included 1183 hospitalizations from 106 clinicians, with a readmission rate of 20.8%. Both clinicians and the EHR model had similar discrimination (C-statistic 0.66 vs. 0.66, p = 0.91). Clinicians had higher specificity (79.0% vs. 48.9%, p < 0.001) but lower sensitivity (43.9 vs. 75.2%, p < 0.001) than EHR model predictions. Compared with machine, human was better at reclassifying non-readmissions (non-event NRI + 30.1%) but worse at reclassifying readmissions (event NRI - 31.3%). A human-plus-machine approach best optimized discrimination (C-statistic 0.70, 95% CI 0.67-0.74), sensitivity (65.5%), and specificity (66.7%).

Conclusion

Clinicians had similar discrimination but higher specificity and lower sensitivity than EHR model predictions. Human-plus-machine was better than either alone. Readmission risk prediction strategies should incorporate clinician assessments to optimize the accuracy of readmission predictions.

OBJECTIVE: Design a predictive risk model for minimizing iliofemoral vascular complications (IVC) in a contemporary era of transfemoral-transcatheter aortic valve replacement (TF-TAVR). BACKGROUND: IVC remains a common complication of TF-TAVR despite the technological improvement in the new-generation transcatheter systems (NGTS) and enclosed poor outcomes and quality of life. Currently, there is no accepted tool to assess the IVC risk for calcified and tortuous vessels. METHODS: We reconstructed CT images of 516 propensity-matched TF-TAVR patients using the NGTS to design a predictive anatomical model for IVC and validated it on a new cohort of 609 patients. Age, sex, peripheral artery disease, valve size, and type were used to balance the matched cohort. RESULTS: IVC occurred in 214 (7.2%) patients. Sheath size (p = 0.02), the sum of angles (SOA) (p < .0001), number of curves (NOC) (p < .0001), minimal lumen diameter (MLD) (p < .001), and sheath-to-femoral artery diameter ratio (SFAR) (p = 0.012) were significant predictors for IVC. An indexed risk score (CSI) consisting of multiplying the SOA and NOC divided by the MLD showed 84.3% sensitivity and 96.8% specificity, when set to >100, in predicting IVC (C-stat 0.936, 95% CI 0.911-0.959, p < 0.001). Adding SFAR > 1.00 in a tree model increased the overall accuracy to 97.7%. In the validation cohort, the model predicted 89.5% of the IVC cases with an overall 89.5% sensitivity, 98.9% specificity, and 94.2% accuracy (C-stat 0.842, 95% CI 0.904-0.980, p < .0001). CONCLUSION: Our CT-based validated-model is the most accurate and easy-to-use tool assessing IVC risk and should be used for calcified and tortuous vessels in preprocedural planning.