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Background Traditional statistical approaches to prediction of outcomes have drawbacks when applied to large clinical databases. It is hypothesized that machine learning methodologies might overcome these limitations by considering higher-dimensional and nonlinear relationships among patient variables. Methods and Results The Unified Network for Organ Sharing (UNOS) database was queried from 1987 to 2014 for adult patients undergoing cardiac transplantation. The dataset was divided into 3 time periods corresponding to major allocation adjustments and based on geographic regions. For our outcome of 1-year survival, we used the standard statistical methods logistic regression, ridge regression, and regressions with LASSO (least absolute shrinkage and selection operator) and compared them with the machine learning methodologies neural networks, naive-Bayes, tree-augmented naive-Bayes, support vector machines, random forest, and stochastic gradient boosting. Receiver operating characteristic curves and C-statistics were calculated for each model. C-Statistics were used for comparison of discriminatory capacity across models in the validation sample. After identifying 56,477 patients, the major univariate predictors of 1-year survival after heart transplantation were consistent with earlier reports and included age, renal function, body mass index, liver function tests, and hemodynamics. Advanced analytic models demonstrated similarly modest discrimination capabilities compared with traditional models (C-statistic ≤0.66, all). The neural network model demonstrated the highest C-statistic (0.66) but this was only slightly superior to the simple logistic regression, ridge regression, and regression with LASSO models (C-statistic = 0.65, all). Discrimination did not vary significantly across the 3 historically important time periods. Conclusions The use of advanced analytic algorithms did not improve prediction of 1-year survival from heart transplant compared with more traditional prediction models. The prognostic abilities of machine learning techniques may be limited by quality of the clinical dataset.

Summary Background Non-contrast head CT scan is the current standard for initial imaging of patients with head trauma or stroke symptoms. We aimed to develop and validate a set of deep learning algorithms for automated detection of the following key findings from these scans: intracranial haemorrhage and its types (ie, intraparenchymal, intraventricular, subdural, extradural, and subarachnoid); calvarial fractures; midline shift; and mass effect. Methods We retrospectively collected a dataset containing 313 318 head CT scans together with their clinical reports from around 20 centres in India between Jan 1, 2011, and June 1, 2017. A randomly selected part of this dataset (Qure25k dataset) was used for validation and the rest was used to develop algorithms. An additional validation dataset (CQ500 dataset) was collected in two batches from centres that were different from those used for the development and Qure25k datasets. We excluded postoperative scans and scans of patients younger than 7 years. The original clinical radiology report and consensus of three independent radiologists were considered as gold standard for the Qure25k and CQ500 datasets, respectively. Areas under the receiver operating characteristic curves (AUCs) were primarily used to assess the algorithms. Findings The Qure25k dataset contained 21 095 scans (mean age 43 years; 9030 [43%] female patients), and the CQ500 dataset consisted of 214 scans in the first batch (mean age 43 years; 94 [44%] female patients) and 277 scans in the second batch (mean age 52 years; 84 [30%] female patients). On the Qure25k dataset, the algorithms achieved an AUC of 0·92 (95% CI 0·91–0·93) for detecting intracranial haemorrhage (0·90 [0·89–0·91] for intraparenchymal, 0·96 [0·94–0·97] for intraventricular, 0·92 [0·90–0·93] for subdural, 0·93 [0·91–0·95] for extradural, and 0·90 [0·89–0·92] for subarachnoid). On the CQ500 dataset, AUC was 0·94 (0·92–0·97) for intracranial haemorrhage (0·95 [0·93–0·98], 0·93 [0·87–1·00], 0·95 [0·91–0·99], 0·97 [0·91–1·00], and 0·96 [0·92–0·99], respectively). AUCs on the Qure25k dataset were 0·92 (0·91–0·94) for calvarial fractures, 0·93 (0·91–0·94) for midline shift, and 0·86 (0·85–0·87) for mass effect, while AUCs on the CQ500 dataset were 0·96 (0·92–1·00), 0·97 (0·94–1·00), and 0·92 (0·89–0·95), respectively. Interpretation Our results show that deep learning algorithms can accurately identify head CT scan abnormalities requiring urgent attention, opening up the possibility to use these algorithms to automate the triage process. Funding Qure.ai.

Background Whereas heart failure ( HF ) is a complex clinical syndrome, conventional approaches to its management have treated it as a singular disease, leading to inadequate patient care and inefficient clinical trials. We hypothesized that applying advanced analytics to a large cohort of HF patients would improve prognostication of outcomes, identify distinct patient phenotypes, and detect heterogeneity in treatment response. Methods and Results The Swedish Heart Failure Registry is a nationwide registry collecting detailed demographic, clinical, laboratory, and medication data and linked to databases with outcome information. We applied random forest modeling to identify predictors of 1‐year survival. Cluster analysis was performed and validated using serial bootstrapping. Association between clusters and survival was assessed with Cox proportional hazards modeling and interaction testing was performed to assess for heterogeneity in response to HF pharmacotherapy across propensity‐matched clusters. Our study included 44 886 HF patients enrolled in the Swedish Heart Failure Registry between 2000 and 2012. Random forest modeling demonstrated excellent calibration and discrimination for survival (C‐statistic=0.83) whereas left ventricular ejection fraction did not (C‐statistic=0.52): there were no meaningful differences per strata of left ventricular ejection fraction (1‐year survival: 80%, 81%, 83%, and 84%). Cluster analysis using the 8 highest predictive variables identified 4 clinically relevant subgroups of HF with marked differences in 1‐year survival. There were significant interactions between propensity‐matched clusters (across age, sex, and left ventricular ejection fraction and the following medications: diuretics, angiotensin‐converting enzyme inhibitors, β‐blockers, and nitrates, P Conclusions Machine learning algorithms accurately predicted outcomes in a large data set of HF patients. Cluster analysis identified 4 distinct phenotypes that differed significantly in outcomes and in response to therapeutics. Use of these novel analytic approaches has the potential to enhance effectiveness of current therapies and transform future HF clinical trials.

BACKGROUND: The number of heart transplants performed is limited by organ availability and is managed by the United Network for Organ Sharing (UNOS). Efforts are underway to make organ disbursement more equitable as demand increases. HYPOTHESIS: Significant variation exists in contemporary patterns of care, wait times, and outcomes among patients undergoing heart transplantation across UNOS regions. METHODS: We identified adult patients undergoing first, single‐organ heart transplantation between January 2006 and December 2014 in the UNOS dataset and compared sociodemographic and clinical profiles, wait times, use of mechanical circulatory support (MCS), status at time of transplantation, and 1‐year survival across UNOS regions. RESULTS: We analyzed 17 096 patients undergoing heart transplantation. There were no differences in age, sex, renal function, and peripheral vascular resistance across regions; however, there was 3‐fold variation in median wait time (range, 48–166 days) across UNOS regions. Proportion of patients undergoing transplantation with status 1A ranged from 36% to 79% across regions (P

Tracking the progression of brain tumors is a challenging task, due to the slow growth rate and the combination of different tumor components, such as cysts, enhancing patterns, edema and necrosis. In this paper, we propose a Deep Neural Network based architecture that does automatic segmentation of brain tumor, and focuses on improving accuracy at the edges of these different classes. We show that enhancing the loss function to give more weight to the edge pixels significantly improves the neural network’s accuracy at classifying the boundaries. In the BRATS 2016 challenge, our submission placed third on the task of predicting progression for the complete tumor region.