Identifying Patterns of Medical Intervention in Acute Respiratory Failure: A Retrospective Observational Study.

IMPORTANCE: Characterizing medical interventions delivered to ICU patients over time and their relationship to outcomes can help set expectations and inform decisions made by patients, clinicians, and health systems. OBJECTIVES: To determine whether distinct and clinically relevant pathways of medical intervention can be identified among adult ICU patients with acute respiratory failure. DESIGN SETTING AND PARTICIPANTS: Retrospective observational study using all-payer administrative claims data from 2012 to 2014. Patients were identified from the Healthcare Cost and Utilization Project State Inpatient Databases from Maryland, Massachusetts, Nevada, and Washington. MAIN OUTCOMES AND MEASURES: Patterns of cumulative medical intervention delivery, over time, using temporal k-means clustering of interventions delivered up to hospital days 0, 5, 10, 20, and up to discharge. RESULTS: A total of 12,175 admissions were identified and divided into training (75%; n = 9,130) and validation sets (25%; n = 3,045). Without applying a priori classification and using only medical interventions to cluster, we identified three distinct pathways of intervention accounting for 93.5% of training set admissions. We found 45.9% of admissions followed a "cardiac" intervention pathway (e.g., cardiac catheterization, cardioversion); 36.7% followed a "general" pathway (e.g., diagnostic interventions); and 17.4% followed a "prolonged" pathway (e.g., tracheostomy, gastrostomy). Prolonged pathway admissions had longer median hospital length of stay (13 d; interquartile range [IQR], 7.5-18.5 d) compared with cardiac (5; IQR, 2.5-7.5) and general (5; IQR, 3-7). In-hospital death occurred in 24.6% of prolonged pathway admissions compared with 17.9% of cardiac and 6.9% of general. Findings were confirmed in the validation set. CONCLUSIONS AND RELEVANCE: Most ICU admissions for acute respiratory failure follow one of three clinically relevant pathways of medical intervention which are associated with hospitalization outcomes. This study helps define the longitudinal nature of critical care delivery, which can inform efforts to predict patient outcomes, communicate with patients and their families, and organize critical care resources.

Toward Sharing Brain Images: Differentially Private TOF-MRA Images With Segmentation Labels Using Generative Adversarial Networks.

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging.