Citations

Introduction: Heart failure (HF) is a leading cause of hospitalization, morbidity and mortality. Deep learning (DL) techniques appear to show promising results in risk stratification and prognosis in several conditions in medicine. However, few methods using DL exist to help quantitatively estimate prognosis of HF. We hypothesized that deep learning (DL) techniques could prognosis of HF using simple variables. We propose application of a custom-built deep-neural-network model to identify mortality in HF patients. Methods: Custom-built deep-neural-networks were assessed using survey data from 42,147 participants from the National Health and Nutrition Examination Survey 1999-2016 (NHANES). Variables were selected using clinical judgment and stepwise backward regressions to develop prediction models. We partitioned the data into training and testing sets and repetitive experiments. We then evaluated model performance based on discrimination and calibration including the area under the receiver-operator characteristics curve (C-statistics), balanced accuracy, probability calibration with sigmoid, and the Brier score, respectively. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We validated models’ performance using Mount Sinai database. Results: Of 42,147 participants with 4,060 variables, 1,491 (3.5%) had HF and HF mortality was 51.8%. In validation cohort, of 26,333 HF patients, the mortality in HF patients was 405 (1.5%). Final model using only 20 variables (age, race, gender, BMI, smoking, alcohol consumption, HTN, COPD, SBP, DBP, HR, HDL, LDL, CRP, A1C, BUN, creatinine, hemoglobin, sodium level, on statin) was tested. A state-of-the-art deep learning models achieved high accuracy for predicting mortality in HF patients with an AUC of 0.96 (95% CI: 0.95-0.99) in the first cohort and AUC of 0.93 (95% CI: 0.91-0.96) in validation cohort. Conclusions: A deep neural network model has shown to have high predictive accuracy and discriminative and calibrative power for prediction of HF mortality. Further research can delineate the clinical implications of DL in predicting HF mortality.

335 Background: Systematic reviews are outdated quickly when the evidence is rapidly evolving as the process is laborious and there is little incentive for primary author team of an index SRMA to update the evidence. Consequently, there is an epidemic of redundant SRMAs performed by different teams—sometimes with conflicted results—for treatment of first line mRCC. Methods: We have created a living, interactive systematic review (LISR)and network meta-analysis(LINMA) for the treatment of first line mRCC using an Artificial intelligence (AI) assisted framework for evidence synthesis (Living, Interactive evidence synthesis framework) (LIvE) . The framework is implemented in five-layered architecture (application layer, shared module layer, core service layer, middleware layer, and storage layer) which work together to automate the identification of new studies and analysis and semi-automate the screening and data extraction. Dynamic features such as interactive tables, figures and evidence maps are enabled using Python and JavaScript programming languages. Results: We have maintained a living, interactive evidence profile for the first line treatment mRCC since September 2019 ( LIVING WEBSITE) . Living search strategy identifies new studies as they become available. As of October 13, 2020 LISR, includes data 14 clinical trials ( PRISMA ). Baseline characteristics are summarized in an interactive table ( TABLE) . Cabozantinib& Nivolumab (Cabo-Nivo) is the highest ranked drug for improving Overall Response (OR), Progression Free Survival (PFS) and Overall Survival (OS) whereas Ipilimumab in combination with Nivolumab (Ipi-Nivo) is highest ranked drug for achieving complete response (CR). Ipi-Nivo and Atezolizumab & Bevacizumab (Ate-Bev) ranked highest and Cabo-Nivo ranked lowest for treatment related Adverse events (TRAEs). Results of network meta-analysis are summarized as interactive tables and plots ( NMA ), summary of findings tables ( MULTIPLE COMAPRISONS ) and evidence maps ( MAP ). Conclusions: LISRs can potentially reduce redundancy, increase transparency, reproducibility, enable shared-decision making (at a guideline level, or in a patient-clinician dyad) and support living guidelines.

AbstractBackground & AimsGlycogenic hepatopathy (GH) in type 1 diabetes‐mellitus (T1DM) is characterized by hepatomegaly and perturbations of liver chemistries (LC) that have not been well studied. Furthermore, misdiagnosis with other hepatic complications of T1DM, such as nonalcoholic fatty liver disease, has been described. We perform a systematic review of biopsy‐proven GH reports in T1DM patients to identify LC patterns.MethodsA systematic review identified reports of biopsy‐proven GH in patients with T1DM. We excluded GH with other liver diseases, Mauriac syndrome, or GH without T1DM. Two reviewers screened and extracted studies and assessed their methodological quality. LC elevation magnitude, AST‐to‐ALT ratio, R‐ratio to designate hepatocellular, cholestatic or mixed pattern of hepatic injury, and evolution of transaminases after glycemic control were analyzed.ResultsA total of 192 patients were included, with median age of 20 years, 73% adults, 66% females, median duration of T1DM before diagnosis 10 years, median adult body mass index 21 kg/m2, median HbA1c 12%, at least one episode of diabetic ketoacidosis 70%, and hepatomegaly 92%. ALT and AST showed moderate‐to‐severe elevation in 78% and 76%, respectively, AST/ALT >1 in 71% and hepatocellular to mixed pattern of hepatic injury in 81%. Transaminase improvement with glycemic control was the rule, regardless of other factors in multilinear regression analysis.ConclusionGH tends to have AST‐predominant elevation with a median of 13 times the upper normal limit and R‐ratio >2, which may distinguish it from other etiologies of AST‐predominant LC elevation, and in the appropriate clinical context, may obviate invasive tests.

AbstractBackground & AimsAlthough transient elastography (TE) is used to determine liver stiffness as a surrogate to hepatic fibrosis, the normal range in children is not well defined. We performed a systematic review and individual participant data (IPD) meta‐analysis to determine the range of liver stiffness in healthy children and evaluate the influence of important biological parameters.MethodsWe pooled data from 10 studies that examined healthy children using TE. We divided 1702 children into two groups: ≥3 years (older group) and < 3 years of age (younger group). Univariate and multivariate linear regression models predicting liver stiffness were conducted.ResultsAfter excluding children with obesity, diabetes, or abnormal liver tests, 652 children were analysed. Among older children, mean liver stiffness was 4.45 kPa (95% confidence interval 4.34‐4.56), and increased liver stiffness was associated with age, sedation status, and S probe use. In the younger group, the mean liver stiffness was 4.79 kPa (95% confidence interval 4.46‐5.12), and increased liver stiffness was associated with sedation status and Caucasian race. In a subgroup analysis, hepatic steatosis on ultrasound was significantly associated with increased liver stiffness. We define a reference range for normal liver stiffness in healthy children as 2.45‐5.56 kPa.ConclusionsWe have established TE‐derived liver stiffness ranges for healthy children and propose an upper limit of liver stiffness in healthy children to be 5.56 kPa. We have identified increasing age, use of sedation, probe size, and presence of steatosis on ultrasound as factors that can significantly increase liver stiffness.

Abstract Background Excess adipose tissue is associated with an abnormal lipid profile that may improve with weight reduction. In this meta-analysis, we aimed to estimate the magnitude of change in lipid parameters associated with weight loss in adults who are overweight or obese. Methods We searched MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, and Scopus from 2013 to September, 2018. We included randomized controlled trials (RCTs) that evaluated interventions to treat adult obesity (lifestyle, pharmacologic and surgical) with follow-up of 6 months or more. Results We included 73 RCTs with moderate-to-low risk of bias, enrolling 32 496 patients (mean age, 48.1 years; weight, 101.6 kg; and body mass index [BMI], 36.3 kg/m2). Lifestyle interventions (diet, exercise, or both), pharmacotherapy, and bariatric surgery were associated with reduced triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) concentrations and increased high-density lipoprotein cholesterol (HDL-C) at 6 and 12 months. The following data are for changes in lipid parameters after 12 months of the intervention with 95% CI. Following lifestyle interventions, per 1 kg of weight lost, TGs were reduced by –4.0 mg/dL (95% CI, –5.24 to –2.77 mg/dL), LDL-C was reduced by –1.28 mg/dL (95% CI, –2.19 to –0.37 mg/dL), and HDL-C increased by 0.46 mg/dL (95% CI, 0.20 to 0.71 mg/dL). Following pharmacologic interventions, per 1 kg of weight lost, TGs were reduced by –1.25 mg/dL (95% CI, –2.94 to 0.43 mg/dL), LDL-C was reduced by –1.67 mg/dL (95% CI, –2.28 to –1.06 mg/dL), and HDL-C increased by 0.37 mg/dL (95% CI, 0.23 to 0.52 mg/dL). Following bariatric surgery, per 1 kg of weight lost, TGs were reduced by –2.47 mg/dL (95% CI, –3.14 to –1.80 mg/dL), LDL-C was reduced by –0.33 mg/dL (95% CI, –0.77 to 0.10 mg/dL), and HDL-C increased by 0.42 mg/dL (95% CI, 0.37 to 0.47 mg/dL). Low-carbohydrate diets resulted in reductions in TGs and increases in HDL-C, whereas low-fat diets resulted in reductions in TGs and LDL-C and increases in HDL-C. Results were consistent across malabsorptive and restrictive surgery. Conclusions Weight loss in adults is associated with statistically significant changes in serum lipids. The reported magnitude of improvement can help in setting expectations, inform shared decision making, and facilitate counseling.

AbstractSevere cases of COVID-19 infection, often leading to death, have been associated with variants of acute respiratory distress syndrome (ARDS). Cell therapy with mesenchymal stromal cells (MSCs) is a potential treatment for COVID-19 ARDS based on preclinical and clinical studies supporting the concept that MSCs modulate the inflammatory and remodeling processes and restore alveolo-capillary barriers. The authors performed a systematic literature review and random-effects meta-analysis to determine the potential value of MSC therapy for treating COVID-19-infected patients with ARDS. Publications in all languages from 1990 to March 31, 2020 were reviewed, yielding 2691 studies, of which nine were included. MSCs were intravenously or intratracheally administered in 117 participants, who were followed for 14 days to 5 years. All MSCs were allogeneic from bone marrow, umbilical cord, menstrual blood, adipose tissue, or unreported sources. Combined mortality showed a favorable trend but did not reach statistical significance. No related serious adverse events were reported and mild adverse events resolved spontaneously. A trend was found of improved radiographic findings, pulmonary function (lung compliance, tidal volumes, PaO2/FiO2 ratio, alveolo-capillary injury), and inflammatory biomarker levels. No comparisons were made between MSCs of different sources.Significance statement The potential benefits of mesenchymal stromal cell (MSC) therapy for patients with COVID-19 acute respiratory distress syndrome support the rapid commencement of clinical trials, as well as the compassionate use of MSCs that already have documented safety profiles from FDA-approved studies.