IMPORTANCE Guidelines for patients with atherosclerotic cardiovascular disease (ASCVD) recommend intensive statin therapy and adding nonstatin therapy if low-density lipoprotein cholesterol (LDL-C) levels are 70 mg/dL or more. Compliance with guidelines is often low.OBJECTIVE To track LDL-C treatment patterns in the US over 2 years. DESIGN, SETTING, AND PARTICIPANTS GOULD is a prospective observational registry study involving multiple centers. Patients with ASCVD receiving any lipid-lowering therapy (LLT) were eligible. Between December 2016 and July 2018, patients were enrolled in 1 of 3 cohorts: (1) those currently receiving proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i) and 2 groups not receiving PCSK9i drugs, with (2) LDL-C levels of 100 mg/dL or more or (3) LDL-C levels of 70 to 99 mg/dL. Patients had medical record reviews and telephone interviews every 6 months. Analysis was done on data collected as of October 5, 2020. MAIN OUTCOMES AND MEASURESThe primary outcome was the change in LLT use in 2 years. Secondary outcomes included the number of LDL-C measurements, LDL-C levels, and responses to structured physician and patient questionnaires over 2 years.RESULTS A total of 5006 patients were enrolled (mean [SD] age, 67.8 [9.9] years; 1985 women [39.7%]; 4312 White individuals [86.1%]). At 2 years, 885 (17.1%) had LLT intensification. In the cohorts with LDL-C levels of 100 mg/dL or more and 70 to 99 mg/dL, LLT intensification occurred in 403 (22.4%) and 383 (14.4%), respectively; statins were intensified in 115 (6.4%) and 168 (6.3%), ezetimibe added in 123 (6.8%) and 118 (4.5%), and PCSK9i added in 114 (6.3%) and 58 (2.2%), respectively. In the PCSK9i cohort, 508 of 554 (91.7%) were still taking PCSK9i at 2 years. Lipid panels were measured at least once over 2 years in 3768 patients (88.5%; PCSK9i cohort, 492 [96.1%]; LDL-C levels Ն100 mg/dL or more, 1294 [85.9%]; 70-99 mg/dL, 1982 [88.6%]). Levels of LDL-C fell from medians (interquartile ranges) of 120 (108-141) mg/dL to 95 (73-118) mg/dL in the cohort with LDL-C levels of 100 mg/dL or more, 82 (75-89) to 77 (65-90) mg/dL in the cohort with LDL-C levels of 70 to 99 mg/dL, and 67 (42-104) mg/dL to 67 (42-96) mg/dL in the PCSK9i cohort. Levels of LDL-C less than 70 mg/dL at 2 years were achieved by 308 patients (21.0%) and 758 patients (33.9%) in the cohorts with LDL-C levels of 100 mg/dL or more and 70 to 99 mg/dL, respectively, and 272 patients (52.4%) in the PCSK9i cohort. At 2 years, practice characteristics were associated with more LLT intensification (teaching vs nonteaching hospitals, 148 of 589 [25.1%] vs 600 of 3607 [16.6%]; lipid protocols or none, 359 of 1612 [22.3%] vs 389 of 2584 [15.1%]; cardiology, 452 of 2087 [21.7%] vs internal or family medicine, 204 of 1745 [11.7%] and other, 92 of 364 [25.3%]; all P < .001) and achievement of LDL-C less than 70 mg/dL (teaching vs nonteaching hospitals, 173 of 488 [35.5%] vs 823 of 2986 [27.6%]; lipid protocols vs none, 451 of 1411 [32.0%] vs 545 of 2063 [26.4%]; both P < .001; cardi...

Critical Care 2017, 21(Suppl 1):P349 Introduction Imbalance in cellular energetics has been suggested to be an important mechanism for organ failure in sepsis and septic shock. We hypothesized that such energy imbalance would either be caused by metabolic changes leading to decreased energy production or by increased energy consumption. Thus, we set out to investigate if mitochondrial dysfunction or decreased energy consumption alters cellular metabolism in muscle tissue in experimental sepsis. Methods We submitted anesthetized piglets to sepsis (n = 12) or placebo (n = 4) and monitored them for 3 hours. Plasma lactate and markers of organ failure were measured hourly, as was muscle metabolism by microdialysis. Energy consumption was intervened locally by infusing ouabain through one microdialysis catheter to block major energy expenditure of the cells, by inhibiting the major energy consuming enzyme, N+/K + -ATPase. Similarly, energy production was blocked infusing sodium cyanide (NaCN), in a different region, to block the cytochrome oxidase in muscle tissue mitochondria. Results All animals submitted to sepsis fulfilled sepsis criteria as defined in Sepsis-3, whereas no animals in the placebo group did. Muscle glucose decreased during sepsis independently of N+/K + -ATPase or cytochrome oxidase blockade. Muscle lactate did not increase during sepsis in naïve metabolism. However, during cytochrome oxidase blockade, there was an increase in muscle lactate that was further accentuated during sepsis. Muscle pyruvate did not decrease during sepsis in naïve metabolism. During cytochrome oxidase blockade, there was a decrease in muscle pyruvate, independently of sepsis. Lactate to pyruvate ratio increased during sepsis and was further accentuated during cytochrome oxidase blockade. Muscle glycerol increased during sepsis and decreased slightly without sepsis regardless of N+/K + -ATPase or cytochrome oxidase blocking. There were no significant changes in muscle glutamate or urea during sepsis in absence/presence of N+/K + -ATPase or cytochrome oxidase blockade. ConclusionsThese results indicate increased metabolism of energy substrates in muscle tissue in experimental sepsis. Our results do not indicate presence of energy depletion or mitochondrial dysfunction in muscle and should similar physiologic situation be present in other tissues, other mechanisms of organ failure must be considered. , and long-term follow up has shown increased fracture risk [2]. It is unclear if these changes are a consequence of acute critical illness, or reduced activity afterwards. Bone health assessment during critical illness is challenging, and direct bone strength measurement is not possible. We used a rodent sepsis model to test the hypothesis that critical illness causes early reduction in bone strength and changes in bone architecture. Methods 20 Sprague-Dawley rats (350 ± 15.8g) were anesthetised and randomised to receive cecal ligation and puncture (CLP) (50% cecum length, 18G needle single pass through anterior and posterior wa...

Objectives In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model’s results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals. Methods In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image. Results Our model achieved accuracy of 90.3%, (95% CI: 86.3–93.7%) specificity of 90% (95% CI: 84.3–94%), and sensitivity of 90.5% (95% CI: 85–94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93–0.97). Conclusion We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19. Key Points • A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. • A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model’s image embeddings. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-08050-1.

In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in the diagnosis and monitoring of patients with COVID-19. Machine learning solutions have been shown to be useful for X-ray analysis and classification in a range of medical contexts. The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans. In this retrospective study, a classifier was built using a pre-trained deep learning model (ReNet50) and enhanced by data augmentation and lung segmentation to detect COVID-19 in frontal CXR images collected between January 2018 and July 2020 in four hospitals in Israel. A nearest-neighbors algorithm was implemented based on the network results that identifies the images most similar to a given image. The model was evaluated using accuracy, sensitivity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and of the precision-recall (P-R) curve. The dataset sourced for this study includes 2362 CXRs, balanced for positive and negative COVID-19, from 1384 patients (63 +/- 18 years, 552 men). Our model achieved 89.7% (314/350) accuracy and 87.1% (156/179) sensitivity in classification of COVID-19 on a test dataset comprising 15% (350 of 2326) of the original data, with AUC of ROC 0.95 and AUC of the P-R curve 0.94. For each image we retrieve images with the most similar DNN-based image embeddings; these can be used to compare with previous cases.

<b><i>Background:</i></b> Severe acute respiratory syndrome due to coronavirus 2 (SARS CoV-2) is a novel infectious disease, which has quickly developed into a pandemic. The spectrum of COVID-19 symptoms is broad, ranging from a mild, self-limiting respiratory tract illness to severe progressive pneumonia, multi-organ failure and possible death. Despite much effort and multiple clinical trials, there are, to date, no specific therapeutic agents to treat or cure the coronavirus infection. <b><i>Case Reports:</i></b> The present paper presents 5 cases of patients with moderate to severe COVID-19 infections, 2 of them hospitalized in the intensive care unit, who were successfully treated with homeopathy. <b><i>Results:</i></b> All 5 patients responded to homeopathic treatment in an unexpectedly short time span, improving both physically and mentally. <b><i>Conclusion:</i></b> The present case series emphasizes the rapidity of response among moderate to severely ill patients to homeopathic treatment, when conventional medical options have been unable to relieve or shorten the disease. The observations described should encourage use of homeopathy in treating patients with COVID-19 during the acute phase of the disease.