Timing of sodium-glucose cotransporter 2 inhibitor initiation and post-discharge outcomes in acute heart failure with diabetes: A population-based cohort study.

AIMS: Results from randomized trials suggest benefit of sodium-glucose cotransporter 2 (SGLT2) inhibitor initiation in clinically stable acute heart failure. We aim to examine the real-world effectiveness of early versus delayed post-discharge SGLT2 inhibitor initiation in people with acute heart failure and type 2 diabetes. METHODS AND RESULTS: Using linkable administrative databases in Ontario, Canada, individuals aged 66 years or older with type 2 diabetes who were discharged to the community from acute care hospitals for heart failure between 1 July 2016 and 31 March 2020 were included in this retrospective, population-based cohort study. The primary outcome was hospitalization for heart failure (HHF) or cardiovascular mortality as a composite. Follow-up started from discharge for maximum 1 year. We compared outcomes between post-discharge SGLT2 inhibitor initiation within 3 days, 4-90 days, or 91-180 days, versus delayed initiation for at least 180 days. The 'clone-censor-weight' approach with a target trial emulation framework was used to address time-related biases. There were 9641 eligible individuals. After cloning and artificial censoring, there were 38 564 clones, 12 439 person-years, and 7584 events. Compared to delayed initiation for at least 180 days, initiation within 3 days post-discharge was associated with a lower 1-year risk of HHF or cardiovascular mortality (risk ratio [RR] 0.65, 95% confidence interval [CI] 0.45-0.83), while initiation 4-90 days (RR 0.83, 95% CI 0.72-0.93) or 91-180 days (RR 0.89, 95% CI 0.79-0.97) showed smaller risk reduction. CONCLUSION: Real-world evidence supports early SGLT2 inhibitor initiation to reduce HHF or cardiovascular mortality in acute heart failure and type 2 diabetes.

Association of Lung Ultrasound Scores With Different Modes of Respiratory Support and Clinical Outcomes: An Observational Study in a Tertiary Care Neonatal Unit.

BACKGROUND: Lung ultrasound (LUS) is an evolving point-of-care tool in the neonatal intensive care unit. LUS score has been evaluated in adults as well as in neonates to characterize and diagnose various respiratory conditions. Recently, the LUS score has been evaluated for predicting clinical respiratory outcomes in neonates. OBJECTIVE: To assess the association between LUS score and various modes of respiratory support and clinical outcomes among neonates presenting with respiratory distress. METHODS: In this prospective, cross-sectional, observational study done in a tertiary care neonatal unit, the LUS score was calculated within three hours of receiving respiratory support. Subsequently, the LUS score was assigned with each escalation and de-escalation of respiratory support. Maximum LUS scores for each clinical outcome were also recorded. Inter-rater agreement was determined with the intraclass correlation coefficient. RESULT: A total of 162 LUS scans were performed in 65 babies with a mean gestation of 32.4 ± 3.7 weeks and median (IQR) birth weight of 1480 (1130-2000) grams. The LUS scores (median (IQR)) of babies on continuous positive airway pressure (CPAP), noninvasive positive pressure ventilation (NIPPV), and mechanical ventilation (MV) were 4 (3-6.5), 9 (8-11), and 12 (11-13.5), respectively (p-value < 0.001). The difference in maximum median LUS scores between different clinical outcomes was statistically significant, with a p-value < 0.001. LUS score had an excellent inter-rater agreement (intraclass correlation coefficient = 0.998; p-value < 0.001). CONCLUSION: There is an association between LUS score and different modes of respiratory support with scores increasing as the level of support increased. LUS score was also found to be related with clinical outcomes like death, extubation failure, and recovery, which could help in predicting the prognosis.

Medications for Lipid Control: Statins vs Newer Drugs.

In the primary and secondary prevention of atherosclerotic cardiovascular disease (ASCVD), statins are the primary pharmacologic intervention for ASCVD risk reduction. Statins have proven efficacy and safety in reducing cardiovascular events and total mortality in patients with and without clinically evident ASCVD. The purpose of this brief review is to provide a stepwise approach to lipid management, including lifestyle recommendations and medical therapy. We first review the main available approaches to lipid lowering and their mechanisms of action. We then summarise the findings of large randomised controlled trials investigating the benefit of statin therapy from 1994 to the present. The available statins are then reviewed, along with their main pharmacologic properties and potential adverse effects. Although statins are generally well tolerated, certain patients may require dose adjustments or alternative treatments because of side-effects. In patients not achieving adequate lipid control on a maximally tolerated statin, nonstatin medications, including ezetimibe and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, provide enhanced low-density lipoprotein cholesterol reduction and cardiovascular benefits, especially in high-risk patients inadequately managed with statins alone. We review the role of triglyceride-lowering agents, including fibric acid derivatives and icosapent ethyl. We then deal with special populations, including those with hepatic steatosis, chronic kidney disease, pregnancy, and heart failure. This field continues to progress, and novel therapies are under active investigation, including an oral PCSK9 inhibitor and molecular therapies targeting lipoprotein(a), angiopoietin-like protein 3, and apolipoprotein CIII. We can look forward to exciting developments that will have major impacts on patient health and management.

Empowering vertical farming through IoT and AI-Driven technologies: A comprehensive review.

The substantial increase in the human population dramatically strains food supplies. Farmers need healthy soil and natural minerals for traditional farming, and production takes a little longer time. The soil-free farming method known as vertical farming (VF) requires a small land and consumes a very small amount of water than conventional soil-dependent farming techniques. With modern technologies like hydroponics, aeroponics, and aquaponics, the notion of the VF appears to have a promising future in urban areas where farming land is very expensive and scarce. VF faces difficulty in the simultaneous monitoring of multiple indicators, nutrition advice, and plant diagnosis systems. However, these issues can be resolved by implementing current technical advancements like artificial intelligence (AI)-based control techniques such as machine learning (ML), deep learning (DL), the internet of things (IoT), image processing as well as computer vision. This article presents a thorough analysis of ML and IoT applications in VF system. The areas on which the attention is concentrated include disease detection, crop yield prediction, nutrition, and irrigation control management. In order to predict crop yield and crop diseases, the computer vision technique is investigated in view of the classification of distinct collections of crop images. This article also illustrates ML and IoT-based VF systems that can raise product quality and production over the long term. Assessment and evaluation of the knowledge-based VF system have also been outlined in the article with the potential outcomes, advantages, and limitations of ML and IoT in the VF system.

Validation of an AI-based solution for breast cancer risk stratification using routine digital histopathology images.

BACKGROUND: Stratipath Breast is a CE-IVD marked artificial intelligence-based solution for prognostic risk stratification of breast cancer patients into high- and low-risk groups, using haematoxylin and eosin (H&E)-stained histopathology whole slide images (WSIs). In this validation study, we assessed the prognostic performance of Stratipath Breast in two independent breast cancer cohorts. METHODS: This retrospective multi-site validation study included 2719 patients with primary breast cancer from two Swedish hospitals. The Stratipath Breast tool was applied to stratify patients based on digitised WSIs of the diagnostic H&E-stained tissue sections from surgically resected tumours. The prognostic performance was evaluated using time-to-event analysis by multivariable Cox Proportional Hazards analysis with progression-free survival (PFS) as the primary endpoint. RESULTS: In the clinically relevant oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative patient subgroup, the estimated hazard ratio (HR) associated with PFS between low- and high-risk groups was 2.76 (95% CI: 1.63-4.66, p-value < 0.001) after adjusting for established risk factors. In the ER+/HER2- Nottingham histological grade (NHG) 2 subgroup, the HR was 2.20 (95% CI: 1.22-3.98, p-value = 0.009) between low- and high-risk groups. CONCLUSION: The results indicate an independent prognostic value of Stratipath Breast among all breast cancer patients, as well as in the clinically relevant ER+/HER2- subgroup and the NHG2/ER+/HER2- subgroup. Improved risk stratification of intermediate-risk ER+/HER2- breast cancers provides information relevant for treatment decisions of adjuvant chemotherapy and has the potential to reduce both under- and overtreatment. Image-based risk stratification provides the added benefit of short lead times and substantially lower cost compared to molecular diagnostics and therefore has the potential to reach broader patient groups.

Artificial Intelligence-Based Digital Biomarkers for Type 2 Diabetes: A Review.

Type 2 diabetes mellitus (T2DM), a complex metabolic disorder that burdens the health care system, requires early detection and treatment. Recent strides in digital health technologies, coupled with artificial intelligence (AI), may have the potential to revolutionize T2DM screening, diagnosis of complications, and management through the development of digital biomarkers. This review provides an overview of the potential applications of AI-driven biomarkers in the context of screening, diagnosing complications, and managing patients with T2DM. The benefits of using multisensor devices to develop digital biomarkers are discussed. The summary of these findings and patterns between model architecture and sensor type are presented. In addition, we highlight the pivotal role of AI techniques in clinical intervention and implementation, encompassing clinical decision support systems, telemedicine interventions, and population health initiatives. Challenges such as data privacy, algorithm interpretability, and regulatory considerations are also highlighted, alongside future research directions to explore the use of AI-driven digital biomarkers in T2DM screening and management.

Use of Neutrophil-to-Lymphocyte Ratio to Predict In-Hospital Mortality in Patients Admitted with Acute Decompensation of Atrial Fibrillation.

Background/Objectives: The prevalence of atrial fibrillation (AF) has been on the rise over the last 20 years. It is considered to be the most common cardiac arrhythmia and is associated with significant morbidity and mortality. The need for in-hospital management of patients having AF is increasing. Acute decompensation of cardiac rhythm is an indication for hospital admission. In the existing literature, several studies on different pathologies have observed that the risk of death was greater for patients with an increased neutrophil-to-lymphocyte ratio (NLR) and suggested that the NLR can be a useful biomarker to predict in-hospital mortality. This study aims to evaluate the link between the neutrophil-to-lymphocyte ratio at admission and death among the patients admitted to the medical ward for the acute manifestation of AF, and to gain a better understanding of how we can predict in-hospital all-cause death based on the NLR for these patients. Methods: A single-center retrospective study in an academic medical clinic was conducted. We analyzed if the NLR at in-hospital admission can be related to in-hospital mortality among the patients admitted for AF at the Medical Ward of Municipal Emergency University Hospital Timisoara between 2015 and 2016. After identifying a total of 1111 patients, we divided them into two groups: in-hospital death patients and surviving patients. We analyzed the NLR in both groups to determine if it is related to in-hospital mortality or not. One patient was excluded because of missing data. Results: Our analysis showed that patients who died during in-hospital admission had a significantly higher NLR compared to those who survived (p < 0.0001, 95% CI (1.54 to 3.48)). The NLR was found to be an independent predictor of in-hospital death among patients with AF, even for the patients with no raised level of blood leukocytes (p < 0.0001, 95% CI (0.6174 to 3.0440)). Additionally, there was a significant correlation between the NLR and the risk of in-hospital death for patients admitted with decompensated AF (p < 0.0001), with an area under the ROC curve of 0.745. Other factors can increase the risk of death for these patients (such as the personal history of stroke, HAS-BLED score, and age). Conclusions: The NLR is a useful biomarker to predict in-hospital mortality in patients with AF and can predict the risk of death with a sensitivity of 72.8% and a specificity of 70.4%. Further studies are needed to determine the clinical utility of the NLR in risk stratification and management of patients with AF.

Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab.

National Capital Territory of Delhi and its satellite cities suffer from poor air quality during the post-monsoon months of October-November. In this study, a novel attempt is made to estimate the contribution of different emission sources (industrial, residential, power generation, transportation, biomass burning, photochemical production, lateral transport, etc.) towards the criteria air pollutant carbon monoxide (CO) concentration over North India. Multiple simulations of the WRF-Chem model with a tagged tracer approach with different inputs (6 anthropogenic emission inventories and 3 biomass burning emission inventories) were used. The model performance was evaluated against the MOPITT retrieved CO surface concentration. Analysis of model simulated CO over North India suggests that anthropogenic emissions contribute around 32-49% to surface CO concentration while crop residue burning contributes 27-44% of which 80% originates from Punjab. For Delhi, the contribution from anthropogenic sources is dominant (53-77%) of which 10-28% is from the domestic sector and 14-55% is from the transport sector. Agricultural waste burning contributes about 15-30% to Delhi's surface CO concentration (of which 75% originates from Punjab). Crop residue burning emission is a chief source of CO over Punjab with a contribution of about 56-76%. The results suggest that industrial, transport, and domestic sector activities are more responsible for increased CO levels over New Delhi and surrounding regions than crop residue burning over Punjab. Furthermore, critical meteorological parameters like 10 m wind speed, boundary layer height, 2 m temperature, total precipitation, and relative humidity were evaluated against CO concentration to understand their impact on CO distribution. Results conclude that deteriorating air quality over the North Indian region is caused by a combination of prevailing meteorological factors (such as slow winds, shallow mixing layer, and cold temperatures) and man-made emissions.

The ACROBAT 2022 challenge: Automatic registration of breast cancer tissue.

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.

Unilateral C1-C2 Posterior Fusion in a Patient With Right Vertebral Artery Anomaly With Intracanal Trajectory: A Case Report.

CASE: A 59-year-old woman presented with progressively worsening neck pain and radicular symptoms. Cervical radiographs revealed C1-C2 dynamic instability. Magnetic resonance imaging and computed tomographic angiogram revealed an anomalous right vertebral artery with intracanal trajectory at C1. A unilateral left C1-C2 fusion with a C1 lateral mass screw and C2 transarticular screw placement was performed due to the anomalous artery. At 14-month follow-up, the patient's cervical symptoms had resolved. CONCLUSION: In this patient with an aberrant vertebral artery who was indicated for C1-C2 fusion, a unilateral contralateral fusion with a C1 lateral mass screw and C2 transarticular screw was a satisfactory treatment option.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images.

BACKGROUND: Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens. METHODS: A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis. RESULTS: Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79). CONCLUSIONS: DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Maximising Large Language Model Utility in Cardiovascular Care: A Practical Guide.

Large language models (LLMs) have emerged as powerful tools in artificial intelligence, demonstrating remarkable capabilities in natural language processing and generation. In this article, we explore the potential applications of LLMs in enhancing cardiovascular care and research. We discuss how LLMs can be used to simplify complex medical information, improve patient-physician communication, and automate tasks such as summarising medical articles and extracting key information. In addition, we highlight the role of LLMs in categorising and analysing unstructured data, such as medical notes and test results, which could revolutionise data handling and interpretation in cardiovascular research. However, we also emphasise the limitations and challenges associated with LLMs, including potential biases, reasoning opacity, and the need for rigourous validation in medical contexts. This review provides a practical guide for cardiovascular professionals to understand and harness the power of LLMs while navigating their limitations. We conclude by discussing the future directions and implications of LLMs in transforming cardiovascular care and research.

Utilizing synchronous care to improve cardiovascular and renal health among patients with type 2 diabetes: Proof-of-concept results from the DECIDE-CV clinical programme.

AIM: The management of patients with type 2 diabetes is asynchronous, i.e. not coordinated in time, resulting in delayed access to care and low use of guideline-directed medical therapy (GDMT). METHODS: We retrospectively analysed consecutive patients assessed in the 'synchronized' DECIDE-CV clinic. In this outpatient clinic, patients with type 2 diabetes and cardiovascular or chronic kidney disease are simultaneously assessed by an endocrinologist, cardiologist and nephrologist in the same visit. The primary outcome was use of GDMT before and after the assessment in the clinic, including sodium-glucose cotransporter 2 inhibitors, glucagon-like peptide 1 receptor agonists, renin-angiotensin system blockers and mineralocorticoid receptor antagonists. Secondary outcomes included the baseline-to-last-visit change in surrogate laboratory biomarkers. RESULTS: The first 232 patients evaluated in the clinic were included. The mean age was 67 ± 12 years, 69% were men and 92% had diabetes. In total, 73% of patients had atherosclerotic cardiovascular disease, 65% heart failure, 56% chronic kidney disease and 59% had a urinary albumin-to-creatinine ratio ≥30 mg/g. There was a significant increase in the use of GDMT:sodium-glucose cotransporter 2 inhibitors (from 44% to 87% of patients), glucagon-like peptide 1 receptor agonists (from 8% to 45%), renin-angiotensin system blockers (from 77% to 91%) and mineralocorticoid receptor antagonists (from 25% to 45%) (p < .01 for all). Among patients with paired laboratory data, glycated haemoglobin, urinary albumin-to-creatinine ratio and N-terminal proB-type natriuretic peptide levels significantly dropped from baseline (p < .05 for all). CONCLUSIONS: Joint assessment of patients with diabetes in a synchronized cardiometabolic clinic holds promise for enhancing GDMT use and has led to significant reductions in surrogate cardiovascular and renal laboratory biomarkers.

Impact of age and comorbid heart failure on the utility of smart voice-assistant devices.

Aims: The accuracy of voice-assisted technologies, such as Amazon Alexa, to collect data in patients who are older or have heart failure (HF) is unknown. The aim of this study is to analyse the impact of increasing age and comorbid HF, when compared with younger participants and caregivers, and how these different subgroups classify their experience using a voice-assistant device, for screening purposes. Methods and results: Subgroup analysis (HF vs. caregivers and younger vs. older participants) of the VOICE-COVID-II trial, a randomized controlled study where participants were assigned with subsequent crossover to receive a SARS-CoV2 screening questionnaire by Amazon Alexa or a healthcare personnel. Overall concordance between the two methods was compared using unweighted kappa scores and percentage of agreement. From the 52 participants included, the median age was 51 (34-65) years and 21 (40%) were HF patients. The HF subgroup showed a significantly lower percentage of agreement compared with caregivers (95% vs. 99%, P = 0.03), and both the HF and older subgroups tended to have lower unweighted kappa scores than their counterparts. In a post-screening survey, both the HF and older subgroups were less acquainted and found the voice-assistant device more difficult to use compared with caregivers and younger individuals. Conclusion: This subgroup analysis highlights important differences in the performance of a voice-assistant-based technology in an older and comorbid HF population. Younger individuals and caregivers, serving as facilitators, have the potential to bridge the gap and enhance the integration of these technologies into clinical practice. Study Registration: ClinicalTrials.gov Identifier: NCT04508972.

The Role of Perioperative Nutritional Status and Supplementation in Orthopaedic Surgery: A Review of Postoperative Outcomes.

¼ Identification of malnourished and at-risk patients should be a standardized part of the preoperative evaluation process for every patient.¼ Malnourishment is defined as a disorder of energy, protein, and nutrients based on the presence of insufficient energy intake, weight loss, muscle atrophy, loss of subcutaneous fat, localized or generalized fluid accumulation, or diminished functional status.¼ Malnutrition has been associated with worse outcomes postoperatively across a variety of orthopaedic procedures because malnourished patients do not have a robust metabolic reserve available for recovery after surgery.¼ Screening assessment and basic laboratory studies may indicate patients' nutritional risk; however, laboratory values are often not specific for malnutrition, necessitating the use of prognostic screening tools.¼ Nutrition consultation and perioperative supplementation with amino acids and micronutrients are 2 readily available interventions that orthopaedic surgeons can select for malnourished patients.

Decreased Instability in High-Risk (Hip-Spine 2B) Patients After Modifications of Surgical Planning and Technique.

BACKGROUND: Patients undergoing primary total hip arthroplasty (THA) who have spinal deformity and a stiff spine are the highest-risk group for instability. Despite the increasing use of dual-mobility cups and large femoral heads, dislocation remains a major complication after THA. Preoperative planning becomes a critical aspect of ensuring precise component positioning within a safe zone. The purpose of this study was to investigate dislocation rates over a 9-year period. METHODS: A retrospective review of 4,731 THAs performed by 3 orthopaedic surgeons between January 2014 and March 2023 was performed. Spinopelvic measurements were conducted to determine the hip-spine classification group for each patient. Only patients classified as 2B (pelvic incidence-lumbar lordosis > 10° and Δsacral slope < 10°) were eligible. Both absolute and relative dislocation frequencies were then analyzed using time-series analysis techniques and Fisher's exact tests. RESULTS: A total of 281 hip-spine 2B patients undergoing primary THA were eligible for analysis (57% women; mean age, range: 66 years, 23 to 87; mean body mass index, range: 28, 16 to 45). The overall dislocation rate was 4.3%. Use of femoral head sizes ≥ 40 mm increased from 4% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001), while the use of dual-mobility cups decreased from 100% in 2014 to 2019 to 37% in 2020 to 2023 (P < .001). Acetabular component planning was changed from the supine plane to the standing plane in February 2020. Those changes in surgical practice were notably correlated with a significant decrease in dislocation rates from 6.8% in 2014 to 2019 to 1.5% in 2020 to 2023 (P = .03). CONCLUSIONS: Our study demonstrates that the introduction of advanced preoperative THA planning to the standing plane, coupled with precise intraoperative technology for implant placement, can significantly reduce the risk of instability in high-risk THA patients. Notably, we observed a significant decrease in dislocation rates, which aligned with the shift in surgical practice. LEVEL OF EVIDENCE: IV.

Body mass index and cardiorenal outcomes in the EMPEROR-Preserved trial: Principal findings and meta-analysis with the DELIVER trial.

AIMS: Both low and high body mass index (BMI) are associated with poor heart failure outcomes. Whether BMI modifies benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2i) in heart failure with preserved ejection fraction (HFpEF) requires further investigation. METHODS AND RESULTS: Using EMPEROR-Preserved data, the effects of empagliflozin versus placebo on the risks for the primary outcome (hospitalization for heart failure [HHF] or cardiovascular [CV] death), change in estimated glomerular filtration rate (eGFR) slopes, change in Kansas City Cardiomyopathy Questionnaire clinical summary score (KCCQ-CSS), and secondary outcomes across baseline BMI categories (<25 kg/m2, 25 to <30 kg/m2, 30 to <35 kg/m2, 35 to <40 kg/m2 and ≥40 kg/m2) were examined, and a meta-analysis conducted with DELIVER. Forty-five percent had a BMI of ≥30 kg/m2. For the primary outcome, there was a consistent treatment effect of empagliflozin versus placebo across the BMI categories with no formal interaction (p trend = 0.19) by BMI categories. There was also no difference in the effects on secondary outcomes including total HHF (p trend = 0.19), CV death (p trend = 0.20), or eGFR slope with slower declines with empagliflozin regardless of BMI (range 1.12-1.71 ml/min/1.73 m2 relative to placebo, p trend = 0.85 for interaction), though there was no overall impact on the composite renal endpoint. The difference in weight change between empagliflozin and placebo was -0.59, -1.48, -1.54, -0.87, and - 2.67 kg in the lowest to highest BMI categories (p trend = 0.016 for interaction). A meta-analysis of data from EMPEROR-Preserved and DELIVER showed a consistent effect of SGLT2i versus placebo across BMI categories for the outcome of HHF or CV death. There was a trend toward greater absolute KCCQ-CSS benefit at 32 weeks with empagliflozin at higher BMIs (p = 0.08). CONCLUSIONS: Empagliflozin treatment resulted in broadly consistent cardiac effects across the range of BMI in patients with HFpEF. SGLT2i treatment yields benefit in patients with HFpEF regardless of baseline BMI.

Data-driven MEG analysis to extract fMRI resting-state networks.

The electrophysiological basis of resting-state networks (RSN) is still under debate. In particular, no principled mechanism has been determined that is capable of explaining all RSN equally well. While magnetoencephalography (MEG) and electroencephalography are the methods of choice to determine the electrophysiological basis of RSN, no standard analysis pipeline of RSN yet exists. In this article, we compare the two main existing data-driven analysis strategies for extracting RSNs from MEG data and introduce a third approach. The first approach uses phase-amplitude coupling to determine the RSN. The second approach extracts RSN through an independent component analysis of the Hilbert envelope in different frequency bands, while the third new approach uses a singular value decomposition instead. To evaluate these approaches, we compare the MEG-RSN to the functional magnetic resonance imaging (fMRI)-RSN from the same subjects. Overall, it was possible to extract RSN with MEG using all three techniques, which matched the group-specific fMRI-RSN. Interestingly the new approach based on SVD yielded significantly higher correspondence to five out of seven fMRI-RSN than the two existing approaches. Importantly, with this approach, all networks-except for the visual network-had the highest correspondence to the fMRI networks within one frequency band. Thereby we provide further insights into the electrophysiological underpinnings of the fMRI-RSNs. This knowledge will be important for the analysis of the electrophysiological connectome.