Search Engines to Capture Missing Deaths From Institutional Data Warehouse.

INTRODUCTION: Clinical publications use mortality as a hard end point. It is unknown how many patient deaths are under-reported in institutional databases. The objective of this study was to query mortality in our patient cohort from our data warehouse and compare these deaths to those identified in different databases. METHODS: We passed the first/last name and date of birth of 134 patients through online mortality search engines (Find a Grave Index, US Cemetery and Funeral Home Collection, etc.) to assess their ability to capture patient deaths and compared that to deaths recorded from our institutional data warehouse. RESULTS: Our institutional data warehouse found approximately one-third of the total patient mortalities. After the Social Security Death Index, we found that the Find a Grave Index captured the most mortalities missed by the institutional data warehouse. These results highlight the advantages of incorporating readily available search engines into institutional data warehouses for the accurate collection of patient mortalities, particularly those that occur outside of index operative admission. CONCLUSIONS: The incorporation of the mortality search engines significantly augmented the capture of patient deaths. Our approach may be useful for tailored patient outreach and reporting mortalities with institutional data.

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or 'equivocal' images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the 'equivocal' class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems.

Reconstruction options and outcomes for breast sarcoma patients.

Breast sarcomas constitute a rare and heterogeneous group of tumors. Given their aggressive nature and the potential for extensive resections, rates of reconstruction have been low. We retrospectively reviewed subjects derived from our institutional registry presented between 2003 and 2015. Thirty-four patients with primary breast sarcoma were identified. The average age was 51.9 years and the average follow-up was 58 months. The most common histological type was malignant phyllodes (61.8%). Two patients suffered cancer recurrence. Twelve patients (35.3%) underwent reconstruction. Four underwent implant-based reconstruction, seven had autologous-based reconstruction, and one had combined reconstruction. Major complications were one flap loss and one implant removal. Our relatively high rates of breast reconstruction suggest a newly increased willingness to offer reconstruction to this rarer patient population.