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Purpose: To describe an approach wherein high-dimensional hospital data can be used to identify generalizable risk factors for surgical complications for which there may be limited prior knowledge, as illustrated in the context of hemostasis-related complications (HRC). Patients and Methods: This was a retrospective study of the Premier Healthcare Database. Patients included for the study underwent video-assisted thoracoscopic lobectomy (VATL), laparoscopic right colectomy (LRC), or laparoscopic sleeve gastrectomy (LSG) on an inpatient setting between Oct-2015 and Feb-2020 (first = index). The outcome, HRC, comprised hemorrhage, control of bleeding, and acute posthemorrhagic anemia. For each cohort, a high-dimensional dataset (ie, comprising 1000s of candidate risk factors) was constructed using taxonomies from the Clinical Classification Software Refined (CCSR). Candidate risk factors were fed into logistic regression models with a 70%/30% train/test split for each cohort; clinically plausible risk factors that were consistently significant predictors of HRC across the 3 training models were then used in a final parsimonious model including sex, age, race, and payor; finally, the parsimonious model was applied to the test data to compare predicted risk with observed incidence of HRSC. Results: The study included 11,141 VATL, 20,156 LRC, and 121,547 LSG patients, in whom 7.5%, 7.8%, and 1.2% experienced HRSC, respectively. Ultimately, 6 clinically plausible CCSR categories were identified as being statistically significant predictors across all 3 cohorts (eg, coagulation and hemorrhagic disorders, malnutrition, alcohol-related disorders, among others). In the parsimonious model applied to the test data, the observed incidence of HRSC was substantially higher in the top quintile vs bottom quintile of predicted risk: LSG 2.05% vs 0.53%, LRC 13.30% vs 4.11%, VATS 12.49% vs 5.04%. Conclusion: High-dimensional real-world data can be useful to identify risk factors for outcomes that generalize across multiple cohorts. The risk factors identified herein should be considered for inclusion in future studies of hemostasis-related complications.
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RATIONALE AND OBJECTIVES: Develop a deep learning-based algorithm using the U-Net architecture to measure abdominal fat on computed tomography (CT) images. MATERIALS AND METHODS: Sequential CT images spanning the abdominal region of seven subjects were manually segmented to calculate subcutaneous fat (SAT) and visceral fat (VAT). The resulting segmentation maps of SAT and VAT were augmented using a template-based data augmentation approach to create a large dataset for neural network training. Neural network performance was evaluated on both sequential CT slices from three subjects and randomly selected CT images from the upper, central, and lower abdominal regions of 100 subjects. RESULTS: Both subcutaneous and abdominal cavity segmentation images created by the two methods were highly comparable with an overall Dice similarity coefficient of 0.94. Pearson's correlation coefficients between the subcutaneous and visceral fat volumes quantified using the two methods were 0.99 and 0.99 and the overall percent residual squared error were 5.5% and 8.5%. Manual segmentation of SAT and VAT on the 555 CT slices used for testing took approximately 46 hours while automated segmentation took approximately 1 minute. CONCLUSION: Our data demonstrates that deep learning methods utilizing a template-based data augmentation strategy can be employed to accurately and rapidly quantify total abdominal SAT and VAT with a small number of training images.