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.

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.

Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer.

BACKGROUND: Histological grade is a well-known prognostic factor that is routinely assessed in breast tumours. However, manual assessment of Nottingham Histological Grade (NHG) has high inter-assessor and inter-laboratory variability, causing uncertainty in grade assignments. To address this challenge, we developed and validated a three-level NHG-like deep learning-based histological grade model (predGrade). The primary performance evaluation focuses on prognostic performance. METHODS: This observational study is based on two patient cohorts (SöS-BC-4, N = 2421 (training and internal test); SCAN-B-Lund, N = 1262 (test)) that include routine histological whole-slide images (WSIs) together with patient outcomes. A deep convolutional neural network (CNN) model with an attention mechanism was optimised for the classification of the three-level histological grading (NHG) from haematoxylin and eosin-stained WSIs. The prognostic performance was evaluated by time-to-event analysis of recurrence-free survival and compared to clinical NHG grade assignments in the internal test set as well as in the fully independent external test cohort. RESULTS: We observed effect sizes (hazard ratio) for grade 3 versus 1, for the conventional NHG method (HR = 2.60 (1.18-5.70 95%CI, p-value = 0.017)) and the deep learning model (HR = 2.27, 95%CI 1.07-4.82, p-value = 0.033) on the internal test set after adjusting for established clinicopathological risk factors. In the external test set, the unadjusted HR for clinical NHG 2 versus 1 was estimated to be 2.59 (p-value = 0.004) and clinical NHG 3 versus 1 was estimated to be 3.58 (p-value < 0.001). For predGrade, the unadjusted HR for predGrade 2 versus 1 HR = 2.52 (p-value = 0.030), and 4.07 (p-value = 0.001) for preGrade 3 versus 1 was observed in the independent external test set. In multivariable analysis, HR estimates for neither clinical NHG nor predGrade were found to be significant (p-value > 0.05). We tested for differences in HR estimates between NHG and predGrade in the independent test set and found no significant difference between the two classification models (p-value > 0.05), confirming similar prognostic performance between conventional NHG and predGrade. CONCLUSION: Routine histopathology assessment of NHG has a high degree of inter-assessor variability, motivating the development of model-based decision support to improve reproducibility in histological grading. We found that the proposed model (predGrade) provides a similar prognostic performance as clinical NHG. The results indicate that deep CNN-based models can be applied for breast cancer histological grading.

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.

Spatiotemporal signal space separation for regions of interest: Application for extracting neuromagnetic responses evoked by deep brain stimulation.

Magnetoencephalography (MEG) recordings are often contaminated by interference that can exceed the amplitude of physiological brain activity by several orders of magnitude. Furthermore, the activity of interference sources may spatially extend (known as source leakage) into the activity of brain signals of interest, resulting in source estimation inaccuracies. This problem is particularly apparent when using MEG to interrogate the effects of brain stimulation on large-scale cortical networks. In this technical report, we develop a novel denoising approach for suppressing the leakage of interference source activity into the activity representing a brain region of interest. This approach leverages spatial and temporal domain projectors for signal arising from prespecified anatomical regions of interest. We apply this denoising approach to reconstruct simulated evoked response topographies to deep brain stimulation (DBS) in a phantom recording. We highlight the advantages of our approach compared to the benchmark-spatiotemporal signal space separation-and show that it can more accurately reveal brain stimulation-evoked response topographies. Finally, we apply our method to MEG recordings from a single patient with Parkinson's disease, to reveal early cortical-evoked responses to DBS of the subthalamic nucleus.

Regional variation in cause of death in patients with type 2 diabetes: Insights from EXSCEL.

AIMS: Type 2 diabetes (T2D) is a risk factor for cardiovascular and non-cardiovascular mortality. However, global distribution of cause-specific deaths in T2D is poorly understood. We characterized cause-specific deaths by geographic region among individuals with T2D at risk for cardiovascular disease (CVD). METHODS AND RESULTS: The international EXSCEL trial included 14,752 participants with T2D (73% with established CVD). We identified the proportion of deaths over 5-year follow-up attributed to cardiovascular and non-cardiovascular causes, and associated risk factors. During median 3.2-year follow-up, 1,091 (7.4%) participants died. Adjudicated causes of death were 723 cardiovascular (66.3% of deaths), including 252 unknown, and 368 non-cardiovascular (33.7%). Most deaths occurred in North America (N = 356/9.6% across region) and Eastern Europe (N = 326/8.1%), with fewest in Asia/Pacific (N = 68/4.4%). The highest proportional cause-specific deaths by region were sudden cardiac in Asia/Pacific (23/34% of regional deaths) and North America (86/24%); unknown in Eastern Europe (90/28%) and Western Europe (39/21%); and non-malignant non-cardiovascular in Latin America (48/31%). Cox proportional hazards model for adjudicated causes of death showed prognostic risk factors (hazard ratio [95% CI]) for cardiovascular and non-cardiovascular deaths, respectively: heart failure 2.04 (1.72-2.42) and 1.86 (1.46-2.39); peripheral artery disease 1.83 (1.54-2.18) and 1.78 (1.40-2.26); and current smoking status 1.61 (1.29-2.01) and 1.77 (1.31-2.40). CONCLUSIONS: In a contemporary T2D trial population, with and without established CVD, leading causes of death varied by geographic region. Underlying mechanisms leading to variability in cause of death across geographic regions and its impact on clinical trial endpoints warrant future research.

Migration and Separation of Polymers in Nonuniform Active Baths.

Polymerlike structures are ubiquitous in nature and synthetic materials. Their configurational and migration properties are often affected by crowded environments leading to nonthermal fluctuations. Here, we study an ideal Rouse chain in contact with a nonhomogeneous active bath, characterized by the presence of active self-propelled agents which exert time-correlated forces on the chain. By means of a coarse-graining procedure, we derive an effective evolution for the center of mass of the chain and show its tendency to migrate toward and preferentially localize in regions of high and low bath activity depending on the model parameters. In particular, we demonstrate that an active bath with nonuniform activity can be used to separate efficiently polymeric species with different lengths and/or connectivity.

Oscillatory Force Autocorrelations in Equilibrium Odd-Diffusive Systems.

The force autocorrelation function (FACF), a concept of fundamental interest in statistical mechanics, encodes the effect of interactions on the dynamics of a tagged particle. In equilibrium, the FACF is believed to decay monotonically in time, which is a signature of slowing down of the dynamics of the tagged particle due to interactions. Here, we analytically show that in odd-diffusive systems, which are characterized by a diffusion tensor with antisymmetric elements, the FACF can become negative and even exhibit temporal oscillations. We also demonstrate that, despite the isotropy, the knowledge of FACF alone is not sufficient to describe the dynamics: the full autocorrelation tensor is required and contains an antisymmetric part. These unusual properties translate into enhanced dynamics of the tagged particle quantified via the self-diffusion coefficient that, remarkably, increases due to particle interactions.

The MAGMA pipeline for comprehensive genomic analyses of clinical Mycobacterium tuberculosis samples.

BACKGROUND: Whole genome sequencing (WGS) holds great potential for the management and control of tuberculosis. Accurate analysis of samples with low mycobacterial burden, which are characterized by low (<20x) coverage and high (>40%) levels of contamination, is challenging. We created the MAGMA (Maximum Accessible Genome for Mtb Analysis) bioinformatics pipeline for analysis of clinical Mtb samples. METHODS AND RESULTS: High accuracy variant calling is achieved by using a long seedlength during read mapping to filter out contaminants, variant quality score recalibration with machine learning to identify genuine genomic variants, and joint variant calling for low Mtb coverage genomes. MAGMA automatically generates a standardized and comprehensive output of drug resistance information and resistance classification based on the WHO catalogue of Mtb mutations. MAGMA automatically generates phylogenetic trees with drug resistance annotations and trees that visualize the presence of clusters. Drug resistance and phylogeny outputs from sequencing data of 79 primary liquid cultures were compared between the MAGMA and MTBseq pipelines. The MTBseq pipeline reported only a proportion of the variants in candidate drug resistance genes that were reported by MAGMA. Notable differences were in structural variants, variants in highly conserved rrs and rrl genes, and variants in candidate resistance genes for bedaquiline, clofazmine, and delamanid. Phylogeny results were similar between pipelines but only MAGMA visualized clusters. CONCLUSION: The MAGMA pipeline could facilitate the integration of WGS into clinical care as it generates clinically relevant data on drug resistance and phylogeny in an automated, standardized, and reproducible manner.

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.

Cardiovascular phenotypes in type 2 diabetes: Latent class analysis of the CANVAS Program and CREDENCE trial.

AIM: To identify unique clinical phenotypes in type 2 diabetes (T2D) and investigate their treatment response to canagliflozin using latent class analysis. METHODS: This was a pooled latent class analysis of the individuals in the CANVAS Program and CREDENCE trial. The co-primary endpoints were hospitalization for heart failure (HHF) and the composite of cardiovascular death (CVD) or HHF. Secondary endpoints included three-point major adverse CV events, its individual components, and all-cause mortality. We completed Cox proportional hazards models to evaluate the effect of canagliflozin across phenotypes. RESULTS: Four distinct phenotypes were identified: Phenotype 1 (n = 966, 6.6%), with the lowest prevalence of heart failure, kidney dysfunction and hypertension; Phenotype 2 (n = 4169, 28.7%), primarily comprising females with a high prevalence of atherosclerotic vascular disease (ASCVD); Phenotype 3 (n = 7108, 48.9%), predominately males with a high prevalence of ASCVD; and Phenotype 4 (n = 2300, 15.8%), possessing the highest prevalences of HF and renal dysfunction. A hierarchical increase in the risk of the primary endpoint was observed across the phenotypes, with the highest CV risk observed for Phenotype 4 (hazard ratio for HHF: 7.57 [95% CI: 4.19-13.69]). Canagliflozin significantly reduced HHF and the composite CVD or HHF across phenotypes (all P values for interaction > .05). CONCLUSION: We identified four clinically distinct T2D phenotypes with differential CV risks. Canagliflozin reduced the risk of CV events, irrespective of the phenotype, emphasizing its broad therapeutic acceptability.

A supercritical fluid co-extract of turmeric powder and dried coconut shreds shows neuroprotection against AlCl3-induced Alzheimer's disease in rats through nose to brain delivery.

This study was aimed at investigating the neuroprotective potential of a co-extract obtained by supercritical fluid extraction (SFE) of turmeric powder and dried coconut shreds against aluminium chloride (AlCl3)-induced Alzheimer's disease (AD) in male Wistar rats. Fifty animals were allocated to five groups, which received saline (vehicle control, group 1), a combination of saline and aluminium chloride (AlCl3) (disease control, group 2), coconut oil (COO) (SFE extracted, treatment group 3), turmeric oleoresin (Cur) (SFE extracted, treatment group 4) and SFE co-extract of turmeric powder and coconut shreds (CurCOO) (treatment group 5). Animals were subjected to behavioural evaluation. In addition, the hippocampal section of the brain from all groups was subjected to biochemical, molecular and histopathological evaluations. The results showed CurCOO administered intranasally improved cognitive abilities, reversed histological alterations in the brain, reduced hippocampus inflammation studied through proinflammatory cytokine markers like TNF-α and IL-6 as compared to the disease control group. The impact of CurCOO on preventive neurodegeneration was also observed through a reduction in protein transcription factor NF-kB in the treated group 5 as compared to a disease control group. The effect of intranasal delivery of CurCOO on the neurons responsible for memory consolidation was evident from low acetylcholinesterase (AChE) enzyme activity in the treated groups with respect to AlCl3 induced group. Summarily, the results demonstrated intranasal delivery of CurCOO to show better efficacy than Cur and COO in preventing neurodegeneration associated with AlCl3 induced Alzheimer's disease.

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.

Pathophysiological Sex Differences in Heart Failure Progression After Acute Coronary Syndrome: Insights From the EXAMINE Trial.

BACKGROUND: Therapies can reduce the risk of heart failure (HF) development and progression in type 2 diabetes; nevertheless, the risk of these outcomes is greater in females than in males. METHODS AND RESULTS: To investigate sex differences in HF development and progression, we compared baseline circulating proteins (Olink Cardiovascular II panel) in males and females with type 2 diabetes and recent acute coronary syndrome for the outcome of HF hospitalization. Data were from the placebo-controlled Examination of Cardiovascular Outcomes with Alogliptin vs Standard of Care (EXAMINE) trial. Pathophysiological sex-differences were interpreted with network and pathway over-representation analyses. The EXAMINE trial enrolled 5380 participants (32.1% females) with biomarker data available for 95.4% of individuals. Analyses revealed 43 biomarkers were differentially expressed in HF hospitalization, of which 18 were sex specific. Among these 43 biomarkers, interleukin-6 was identified as a central node for the pathogenesis of HF hospitalization in both females and in males. Additional pathway over-representation analyses demonstrated that biomarkers associated with inflammatory pathways related to endothelial dysfunction and cardiac fibrosis were more up-regulated in females than males with HF hospitalization. Differential expression of 3 biomarkers (pentraxin-related protein 3, hydroxyacid oxidase 1, and carbonic anhydrase 5A) was independently associated with an increased risk of HF hospitalization in females but not in males (interaction P < .05). CONCLUSIONS: In males and females with type 2 diabetes and acute coronary syndrome, interleukin-6 seems to be central in the pathogenesis of HF. Females exhibit higher levels of circulating proteins related to immunological pathways, reflecting sex-specific differences underlying HF development and progression.

Voice-assisted Artificial Intelligence-enabled Screening for Severe Acute Respiratory Syndrome Coronavirus 2 Exposure in Cardiovascular Clinics: Primary Results of the VOICE-COVID-19-II Randomized Trial.

BACKGROUND: Voice-assisted artificial intelligence-based systems may streamline clinical care among patients with heart failure (HF) and caregivers; however, randomized clinical trials are needed. We evaluated the potential for Amazon Alexa (Alexa), a voice-assisted artificial intelligence-based system, to conduct screening for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a HF clinic. METHODS AND RESULTS: We enrolled 52 participants (patients and caregivers) from a HF clinic who were randomly assigned with a subsequent cross-over to receive a SARS-CoV-2 screening questionnaire via Alexa or health care personnel. The primary outcome was overall response concordance, as measured by the percentage of agreement and unweighted kappa scores between groups. A postscreening survey evaluated comfort with using the artificial intelligence-based device. In total, 36 participants (69%) were male, the median age was 51 years (range 34-65 years) years and 36 (69%) were English speaking. Twenty-one participants (40%) were patients with HF. For the primary outcome, there were no statistical differences between the groups: Alexa-research coordinator group 96.9% agreement and unweighted kappa score of 0.92 (95% confidence interval 0.84-1.00) vs research coordinator-Alexa group 98.5% agreement and unweighted kappa score of 0.95 (95% confidence interval 0.88-1.00) (P value for all comparisons > .05). Overall, 87% of participants rated their screening experience as good or outstanding. CONCLUSIONS: Alexa demonstrated comparable performance to a health care professional for SARS-CoV-2 screening in a group of patients with HF and caregivers and may represent an attractive approach to symptom screening in this population. Future studies evaluating such technologies for other uses among patients with HF and caregivers are warranted. NCT04508972.

Strain-Controlled Critical Slowing Down in the Rheology of Disordered Networks.

Networks and dense suspensions frequently reside near a boundary between soft (or fluidlike) and rigid (or solidlike) regimes. Transitions between these regimes can be driven by changes in structure, density, or applied stress or strain. In general, near the onset or loss of rigidity in these systems, dissipation-limiting heterogeneous nonaffine rearrangements dominate the macroscopic viscoelastic response, giving rise to diverging relaxation times and power-law rheology. Here, we describe a simple quantitative relationship between nonaffinity and the excess viscosity. We test this nonaffinity-viscosity relationship computationally and demonstrate its rheological consequences in simulations of strained filament networks and dense suspensions. We also predict critical signatures in the rheology of semiflexible and stiff biopolymer networks near the strain stiffening transition.

External validation and extension of the TIMI risk score for heart failure in diabetes for patients with recent acute coronary syndrome: An analysis of the EXAMINE trial.

AIMS: The Thrombolysis in Myocardial Infarction Risk Score for Heart Failure (HF) in Diabetes (TRS-HFDM ) prognosticates HF hospitalization in people with type 2 diabetes (T2D). This study aimed to externally validate and extend its use for those with recent acute coronary syndrome (ACS). MATERIALS AND METHODS: The TRS-HFDM was externally validated in the Examination of Cardiovascular Outcomes with Alogliptin versus Standard of Care (EXAMINE) trial (n = 5380) and extended with natriuretic biomarkers. Missing data were multiply imputed. Initial TRS-HFDM variables were previous HF (2 points), atrial fibrillation (1 point), coronary artery disease (1 point), estimated glomerular filtration rate <60 ml/min/1.73 m2 (1 point), and urine albumin-to-creatinine ratio 30-300 mg/g (1 point) and >300 mg/g (2 points). RESULTS: In total, HF hospitalization occurred in 193 (3.6%) patients. Based on the TRS-HFDM , 25% of patients were classified as intermediate risk (1 point), 30% were classified as high risk (2 points), 19% were classified as very-high risk (3 points) and 26% were classified as severe risk (≥4 points). Before model extension, discrimination (C-index 0.76, 95%·CI 0.73-0.80) and calibration (calibration slope 0.82, 95%·CI 0.65-1.0; calibration-in-the-large -0.15, 95%·CI -0.37-0.64) were moderate-to-good in individuals with T2D and recent ACS. The extension of TRS-HFDM with the addition of N-terminal pro-B-type natriuretic peptide (NT-ProBNP) improved discrimination (C-index 0.82, 95%·CI 0.79-0.85) and calibration (calibration slope 0.84, 95%·CI 0.66-1.02; calibration-in-the-large -0.12, 95%·CI -0.33-0.081) for this higher-risk population. CONCLUSION: The TRS-HFDM with the extension of NT-ProBNP improves risk stratification and generalizes the use of the risk score for patients with T2D and ACS. Future validation studies in ACS populations may be warranted.

Glucagon-like peptide 1 receptor agonists in patients with type 2 diabetes with and without chronic heart failure: A meta-analysis of randomized placebo-controlled outcome trials.

AIM: Glucagon-like peptide 1 receptor agonists (GLP1-RA) reduce atherosclerotic events in patients with type 2 diabetes (T2D) and a high cardiovascular risk. The effect of GLP1-RA to reduce heart failure (HF) has been inconsistent across T2D trials, and individual trials were underpowered to assess the effect of GLP1-RA according to HF history. In this meta-analysis we aim to assess the effect of GLP1-RA in patients with and without HF history in stable ambulatory patients with T2D. METHODS: Random-effects meta-analysis of placebo-controlled trials. The hazard ratio (HR) and 95% confidence intervals (95% CI) were extracted from the treatment effect estimates of HF subgroup analyses reported in each individual study. The primary outcome was a composite of HF hospitalization or cardiovascular death. RESULTS: In total, 54 092 patients with T2D from seven randomized controlled trials were included, of whom 8460 (16%) had HF history. Compared with placebo, GLP1-RA did not reduce the composite of HF hospitalization or cardiovascular death in patients with HF history: HR 0.96, 95% CI: 0.84-1.08, but reduced this outcome in patients without HF history: HR 0.84, 95% CI: 0.76-0.92. GLP1-RA did not reduce all-cause death in patients with HF history: HR 0.98, 95% CI: 0.86-1.11, but reduced mortality in patients without HF history: HR 0.85, 95% CI: 0.79-0.92. GLP1-RA reduced atherosclerotic events regardless of HF history: HR 0.85, 95% CI: 0.75-0.97 with HF, and HR 0.88, 95% CI: 0.83-0.93 without HF. CONCLUSIONS: Treatment with GLP1-RA did not reduce HF hospitalizations and mortality in patients with concomitant T2D and HF, but may prevent new-onset HF and mortality in patients with T2D without HF. The reduction of atherosclerotic events with GLP1-RA was not influenced by HF history status.

Risk of adverse events with liraglutide in heart failure with reduced ejection fraction: A post hoc analysis of the FIGHT trial.

AIM: To perform a post hoc analysis of the FIGHT trial, evaluating the effect of liraglutide (vs. placebo) on the totality of events in patients with heart failure with reduced ejection fraction (HFrEF). MATERIALS AND METHODS: FIGHT was a double-blind randomized controlled trial (RCT) that studied liraglutide versus placebo in 300 recently hospitalized patients with HFrEF followed for 180 days. The main outcome of the present analysis was total events of hospitalizations for heart failure (HF) or all-cause death. Secondary outcomes included total arrhythmic events and prespecified total events of interest (arrhythmias, sudden cardiac death, acute coronary syndrome, worsening HF, cerebrovascular event, venous thromboembolism, lightheadedness, presyncope/syncope or worsening renal function). Treatment effect was evaluated with negative binomial regression. RESULTS: Compared to placebo, there was a trend towards increased risk with liraglutide of total HF hospitalizations or all-cause deaths (96 vs. 143 events, incidence rate ratio [IRR] 1.41, 95% confidence interval [CI] 0.98-2.04; P = 0.064) and total arrhythmias (21 vs. 39, IRR 1.76, 95% CI 0.92-3.37; P = 0.088). Total prespecified events of interest were increased with liraglutide compared to placebo (196 vs. 295, IRR 1.43, 95% CI 1.06-1.92; P = 0.018). The risk of HF hospitalizations or all-cause deaths with liraglutide was higher among patients in New York Heart Association (NYHA) Class III to IV (IRR 1.86, 95% CI 1.21-2.85) than in those in NYHA Class I to II (IRR 0.62, 95% CI 0.31-1.23; interaction P = 0.008), and among patients with diabetes (interaction P = 0.051). The risk of arrhythmic events was higher among those without an implanted cardiac device (interaction P = 0.047). CONCLUSIONS: In patients with HFrEF, liraglutide might increase the risk of cardiovascular adverse effects, an effect possibly driven by excess risk of arrhythmias and worsening HF events. As this was a post hoc analysis, these results should be interpreted as exploratory and hypothesis-generating. Further RCTs must be conducted before drawing definitive conclusions.

Sacral Slope Change From Standing to Relaxed-Seated Grossly Overpredicts the Presence of a Stiff Spine.

BACKGROUND: Several authors propose that a change in sacral slope of ≤10° between the standing and relaxed-seated positions (ΔSSstanding→relaxed-seated) identifies a patient with a stiff lumbar spine and has suggested the use of dual-mobility bearings for such patients undergoing a total hip arthroplasty (THA). The aim of this study was to assess how accurately ΔSSstanding→relaxed-seated can identify patients with a stiff spine. METHODS: A prospective, multicentre, consecutive cohort series of 312 patients had standing, relaxed-seated, and flexed-seated lateral radiographs prior to THA. ΔSSstanding→relaxed-seated was determined by the change in sacral slope between the standing and relaxed-seated positions. Lumbar flexion (LF) was defined as the difference in lumbar lordotic angle between standing and flexed-seated. LF ≤20° was considered a stiff spine. The predictive value of ΔSSstanding→relaxed-seated for characterizing a stiff spine was assessed. RESULTS: A weak correlation between ΔSSstanding→relaxed-seated and LF was identified (r2 = 0.13). Eighty six patients (28%) had ΔSSstanding→relaxed-seated ≤10° and 19 patients (6%) had a stiff spine. Of the 86 patients with ΔSSstanding→relaxed-seated ≤10°, 13 had a stiff spine. The positive predictive value of ΔSSstanding→relaxed-seated ≤10° for identifying a stiff spine was 15%. CONCLUSION: In this cohort, ΔSSstanding→relaxed-seated ≤10° was not correlated with a stiff spine. Using this simplified approach could lead to a 7-fold overprediction of patients with a stiff lumbar spine and abnormal spinopelvic mobility, unnecessary use of dual-mobility bearings, and incorrect component alignment targets. Referring to patients with ΔSSstanding→relaxed-seated ≤10° as being stiff is misleading. The flexed-seated position should be used to effectively assess a patient's spine mobility prior to THA.