Combining Physiology-Based Modeling and Evolutionary Algorithms for Personalized, Noninvasive Cardiovascular Assessment Based on Electrocardiography and Ballistocardiography

Purpose: This study proposes a novel approach to obtain personalized estimates of cardiovascular parameters by combining (i) electrocardiography and ballistocardiography for noninvasive cardiovascular monitoring, (ii) a physiology-based mathematical model for predicting personalized cardiovascular variables, and (iii) an evolutionary algorithm (EA) for searching optimal model parameters.Methods: Electrocardiogram (ECG), ballistocardiogram (BCG), and a total of six blood pressure measurements are recorded on three healthy subjects. The R peaks in the ECG are used to segment the BCG signal into single BCG curves for each heart beat. The time distance between R peaks is used as an input for a validated physiology-based mathematical model that predicts distributions of pressures and volumes in the cardiovascular system, along with the associated BCG curve. An EA is designed to search the generation of parameter values of the cardiovascular model that optimizes the match between model-predicted and experimentally-measured BCG curves. The physiological relevance of the optimal EA solution is evaluated a posteriori by comparing the model-predicted blood pressure with a cuff placed on the arm of the subjects to measure the blood pressure.Results: The proposed approach successfully captures amplitudes and timings of the most prominent peak and valley in the BCG curve, also known as the J peak and K valley. The values of cardiovascular parameters pertaining to ventricular function can be estimated by the EA in a consistent manner when the search is performed over five different BCG curves corresponding to five different heart-beats of the same subject. Notably, the blood pressure predicted by the physiology-based model with the personalized parameter values provided by the EA search exhibits a very good agreement with the cuff-based blood pressure measurement.Conclusion: The combination of EA with physiology-based modeling proved capable of providing personalized estimates of cardiovascular parameters and physiological variables of great interest, such as blood pressure. This novel approach opens the possibility for developing quantitative devices for noninvasive cardiovascular monitoring based on BCG sensing.

Clinical Utility of KidneyIntelX in Early Stages of Diabetic Kidney Disease in the CANVAS Trial

<b><i>Introduction:</i></b> KidneyIntelX is a composite risk score, incorporating biomarkers and clinical variables for predicting progression of diabetic kidney disease (DKD). The utility of this score in the context of sodium glucose co-transporter 2 inhibitors and how changes in the risk score associate with future kidney outcomes are unknown. <b><i>Methods:</i></b> We measured soluble tumor necrosis factor receptor (TNFR)-1, soluble TNFR-2, and kidney injury molecule 1 on banked samples from CANagliflozin cardioVascular Assessment Study (CANVAS) trial participants with baseline DKD (estimated glomerular filtration rate [eGFR] 30–59 mL/min/1.73 m² or urine albumin-to-creatinine ratio [UACR] ≥30 mg/g) and generated KidneyIntelX risk scores at baseline and years 1, 3, and 6. We assessed the association of baseline and changes in KidneyIntelX with subsequent DKD progression (composite outcome of an eGFR decline of ≥5 mL/min/year [using the 6-week eGFR as the baseline in the canagliflozin group], ≥40% sustained decline in the eGFR, or kidney failure). <b><i>Results:</i></b> We included 1,325 CANVAS participants with concurrent DKD and available baseline plasma samples (mean eGFR 65 mL/min/1.73 m² and median UACR 56 mg/g). During a mean follow-up of 5.6 years, 131 participants (9.9%) experienced the composite kidney outcome. Using risk cutoffs from prior validation studies, KidneyIntelX stratified patients to low- (42%), intermediate- (44%), and high-risk (15%) strata with cumulative incidence for the outcome of 3%, 11%, and 26% (risk ratio 8.4; 95% confidence interval [CI]: 5.0, 14.2) for the high-risk versus low-risk groups. The differences in eGFR slopes for canagliflozin versus placebo were 0.66, 1.52, and 2.16 mL/min/1.73 m² in low, intermediate, and high KidneyIntelX risk strata, respectively. KidneyIntelX risk scores declined by 5.4% (95% CI: −6.9, −3.9) in the canagliflozin arm at year 1 versus an increase of 6.3% (95% CI: 3.8, 8.7) in the placebo arm (<i>p</i> &#x3c; 0.001). Changes in the KidneyIntelX score at year 1 were associated with future risk of the composite outcome (odds ratio per 10 unit decrease 0.80; 95% CI: 0.77, 0.83; <i>p</i> &#x3c; 0.001) after accounting for the treatment arm, without evidence of effect modification by the baseline KidneyIntelX risk stratum or by the treatment arm. <b><i>Conclusions:</i></b> KidneyIntelX successfully risk-stratified a large multinational external cohort for progression of DKD, and greater numerical differences in the eGFR slope for canagliflozin versus placebo were observed in those with higher baseline KidneyIntelX scores. Canagliflozin treatment reduced KidneyIntelX risk scores over time and changes in the KidneyIntelX score from baseline to 1 year associated with future risk of DKD progression, independent of the baseline risk score and treatment arm.

Effects of Sodium-Glucose Cotransporter 2 on Amputation Events: A Systematic Review and Meta-Analysis of Randomized-Controlled Trials

<b><i>Introduction:</i></b> Sodium-glucose cotransporter 2 (SGLT2) inhibitors are increasingly utilized in the treatment of diabetes mellitus as well as therapeutic extra-glycemic effects. However, there are still concerns over complications such as amputation events, given the results from the Canagliflozin Cardiovascular Assessment Study (CANVAS) trial. Hence, we conducted a systematic review and meta-analysis of randomized-controlled trials to investigate the effect of SGLT2 inhibitors on amputation events. <b><i>Methods:</i></b> Four electronic databases (PubMed, Embase, Cochrane, and SCOPUS) were searched on November 21, 2020, for articles published from January 1, 2000, up to November 21, 2020, for studies that examined the effect of SGLT2 inhibitors on amputation events. Random-effect pair-wise meta-analysis for hazard ratios and fixed-effect Peto odds ratio meta-analysis were utilized to summarize the studies. <b><i>Results:</i></b> A total of 15 randomized-controlled trials were included with a combined cohort of 63,716 patients. We demonstrated that there was no significant difference in amputation events across different types of SGLT2 inhibitors, different baseline populations, and different duration of SGLT2 inhibitor use. <b><i>Discussion/Conclusions:</i></b> In this meta-analysis, SGLT2 inhibitors were not associated with a significant difference in amputation events.

Association Between Circulating GDF‐15 and Cardio‐Renal Outcomes and Effect of Canagliflozin: Results From the CANVAS Trial

Background Studies have suggested that sodium glucose co‐transporter 2 inhibitors exert anti‐inflammatory effects. We examined the association of baseline growth differentiation factor‐15 (GDF‐15), a marker of inflammation and cellular injury, with cardiovascular events, hospitalization for heart failure (HF), and kidney outcomes in patients with type 2 diabetes in the CANVAS (Canagliflozin Cardiovascular Assessment Study) and determined the effect of the sodium glucose co‐transporter 2 inhibitor canagliflozin on circulating GDF‐15. Methods and Results The CANVAS trial randomized 4330 people with type 2 diabetes at high cardiovascular risk to canagliflozin or placebo. The association between baseline GDF‐15 and cardiovascular (non‐fatal myocardial infarction, non‐fatal stroke, cardiovascular death), HF, and kidney (40% estimated glomerular filtration rate decline, end‐stage kidney disease, renal death) outcomes was assessed using multivariable adjusted Cox regression models. During median follow‐up of 6.1 years (N=3549 participants with available samples), 555 cardiovascular, 129 HF, and 137 kidney outcomes occurred. Each doubling in baseline GDF‐15 was significantly associated with a higher risk of cardiovascular (hazard ratio [HR], 1.2; 95% CI, 1.0‒1.3), HF (HR, 1.5; 95% CI, 1.2‒2.0) and kidney (HR, 1.5; 95% CI, 1.2‒2.0) outcomes. Baseline GDF‐15 did not modify canagliflozin’s effect on cardiovascular, HF, and kidney outcomes. Canaglifozin treatment modestly lowered GDF‐15 compared with placebo; however, GDF‐15 did not mediate the protective effect of canagliflozin on cardiovascular, HF, or kidney outcomes. Conclusions In patients with type 2 diabetes at high cardiovascular risk, higher GDF‐15 levels were associated with a higher risk of cardiovascular, HF, and kidney outcomes. Canagliflozin modestly lowered GDF‐15, but GDF‐15 reduction did not mediate the protective effect of canagliflozin.

220 Electrocardiographic findings and mortality in COVID-19 patients hospitalized in different clinical settings

Aims 12-lead electrocardiogram (ECG) still represents the first line approach for cardiovascular assessment even in patients with COVID-19. We therefore sought to describe and compare ECG findings in three different hospital settings: intensive care unit (ICU) (invasive ventilatory support), respiratory care unit (RCU) (non-invasive ventilatory support) and Covid-19 dedicated internal medicine unit (IMU) (oxygen supplement with or without high flow). Methods and results We retrospectively analysed the 12-lead ECGs of 1124 consecutive patients hospitalized for respiratory distress and COVID-19 in a single III level hospital. Age, gender, main clinical data and in-hospital survival were recorded. 548 patients were hospitalized in IMU, 361 in RCU, 215 in ICU. Arrhythmias in general were less frequently found in RCU (16% vs. 26%, P &lt; 0.001). Deaths occurred more frequently in ICU patients (43% vs. 20–21%, P &lt; 0.001). After pooling predictors of mortality (age, intensity of care setting, heart rate, ST-elevation, QTc prolongation, Q-waves, right bundle branch block, and atrial fibrillation), the risk of in-hospital death can be estimated by using a derived score. Three zones of mortality risk can be thus identified: &lt;5%, score &lt;5 points; 5–50% score 5–10, and &gt;50%, score &gt;10 points. The accuracy of the score assessed at ROC curve analysis was 0.791. Conclusions ECG differences at admission con be found in COVID-19 patients according to different clinical settings and intensity of care. A simplified score derived from few clinical and ECG variables may predict in-hospital mortality with a good accuracy.

Cardiovascular safety profile of taxanes and vinca alkaloids: 30 years FDA registry experience

ObjectiveAntimicrotubular agents are among the most commonly used classes of chemotherapeutic agents, but the risk of cardiovascular adverse events (CVAEs) remains unclear. Our objective was to study the CVAEs associated with antimicrotubular agents.MethodsThe Food and Drug Administration’s Adverse Event Reporting System was used to study CVAEs in adults from 1990 to 2020. Reported single-agent (only taxane or vinca alkaloid) CVAEs were compared with combination therapy (with at least one of the four major cardiotoxic drugs: anthracycline, HER2Neu inhibitors, tyrosine kinase inhibitors and checkpoint inhibitors) using adjusted polytomous logistic regression.ResultsOver 30 years, 134 398 adverse events were reported, of which 18 426 (13.4%) were CVAEs, with 74.1% due to taxanes and 25.9% due to vinca alkaloids. In 30 years, there has been a reduction in the proportion of reported CVAEs for taxanes from 15% to 11.8% (Cochran-Armitage P-trends <0.001) with no significant change in the proportion of reported CVAEs for vinca alkaloids (9.2%–11.7%; P-trends=0.06). The proportion of reported CVAEs was lower in both taxane and vinca alkaloid monotherapy versus combination therapy (reporting OR=0.50 and 0.55, respectively). Anthracyclines and HER2Neu inhibitor combinations with taxanes or vinca alkaloids primarily drove the higher burden of combination CVAEs. Hypertension requiring hospitalisation and heart failure was significantly lower in monotherapy versus combination antimicrotubular agent therapy.ConclusionsAntimicrotubular agents are associated with CVAEs, especially in combination chemotherapy regimens. Based on this study, we suggest routine cardiovascular assessment of patients with cancer before initiating antimicrotubular agents in combination therapy.

Machine learning analysis of metabolomic biomarkers for diagnosis of heart failure

Background There is emerging evidence that the pathophysiological mechanisms of heart failure are associated with alterations in serum metabolites. Such metabolomic signatures may be useful for heart failure diagnosis, stratification and prognosis. Purpose To evaluate the utility of including metabolomic biomarkers in addition to traditional cardiac biomarkers in a machine learning prediction model of heart failure diagnosis in the well-characterised Canagliflozin Cardiovascular Assessment Study (CANVAS) cohort. Methods A subgroup of the CANVAS/CANVAS-R study cohort was analysed. 101 metabolites in plasma were measured by HPLC (HILIC)-mass spectrometry. A 10-times 5-fold cross-validated support vector machine model with radial basis kernel function was constructed to predict heart failure diagnosis using traditional biomarkers alone and using the combination of traditional biomarkers and metabolomic biomarkers. Model performance and variable importance were both evaluated by area under the curve (AUC) of the receiver operating characteristics (ROC) curve. Results are shown as mean ± standard deviation. Results 967 patients (of which 402 patients had heart failure) were included in the analysis with 341 females, mean age 63±8 years and mean body mass index (BMI) 33±5 kg/m2. All patients had diabetes mellitus with mean HbA1c 8.2±0.9%. The prediction model based on only traditional biomarkers had mean AUC 72±3% and the prediction model based on both traditional biomarkers and metabolomic biomarkers had mean AUC 80±3%. The top metabolomic biomarkers for predicting heart failure were threonine, L-homoserine, creatine and deoxyadenosine. Conclusion Metabolomic biomarkers improved diagnostic performance of a heart failure prediction model and captured variation not encompassed by traditional cardiac biomarkers. FUNDunding Acknowledgement Type of funding sources: Private company. Main funding source(s): Janssen Research and Development

Cardiovascular risk in patients with Cushing’s disease – an interdisciplinary problem

Cushing’s disease is a chronic endogenous hypercortisolaemia associated with overproduction of adrenocorticotropic hormone by a pituitary adenoma, leading to multiple systemic complications that significantly increase morbidity and mortality, as well as reduce the quality of life as a result of prolonged tissue exposure to excess cortisol. Hypercortisolaemia in Cushing’s disease is associated with significant functional and constitutional disorders of the entire body. The consequences of chronic hypercortisolaemia include haemodynamic disorders associated with excessive vascular contraction and increased blood pressure, obesity, carbohydrate metabolism disorders, dyslipidaemia, and coagulopathies, which may contribute to significant cardiovascular remodelling. Cardiovascular disorders have a particular impact on long-term prognosis and quality of life in Cushing’s disease. If left untreated, Cushing’s disease significantly increases the cardiovascular risk and limits the treatment options for secondary organ complications. Cardiovascular mortality (myocardial infarction, heart failure, stroke) is several times higher in patients with Cushing’s disease than in the general population. Early diagnosis of the corticotropic pituitary tumour, as well as a thorough morphological and functional cardiovascular assessment seem essential in risk stratification. Normalisation of cortisol levels after combined neurosurgical and/or pharmacological treatment reduces mortality and the risk of cardiovascular and respiratory complications. The aim of this study is to present the complexity of clinical problems in patients with Cushing’s disease, who are in a particular need of interdisciplinary care.

Effects of canagliflozin compared with placebo on major adverse cardiovascular and kidney events in patient groups with different baseline levels of HbA1c, disease duration and treatment intensity: results from the CANVAS Program

Aims/hypothesis Type 2 diabetes mellitus can manifest over a broad clinical range, although there is no clear consensus on the categorisation of disease complexity. We assessed the effects of canagliflozin, compared with placebo, on cardiovascular and kidney outcomes in the CANagliflozin cardioVascular Assessment Study (CANVAS) Program over a range of type 2 diabetes mellitus complexity, defined separately by baseline intensity of treatment, duration of diabetes and glycaemic control. Methods We performed a post hoc analysis of the effects of canagliflozin on major adverse cardiovascular events (MACE) according to baseline glucose-lowering treatments (0 or 1, 2 or 3+ non-insulin glucose-lowering treatments, or insulin-based treatment), duration of diabetes (<10, 10 to 16, >16 years) and HbA1c (≤53.0 mmol/mol [<7.0%], >53.0 to 58.5 mmol/mol [>7.0% to 7.5%], >58.5 to 63.9 mmol/mol [>7.5 to 8.0%], >63.9 to 69.4 mmol/mol [8.0% to 8.5%], >69.4 to 74.9 mmol/mol [>8.5 to 9.0%] or >74.9 mmol/mol [>9.0%]). We analysed additional secondary endpoints for cardiovascular and kidney outcomes, including a combined kidney outcome of sustained 40% decline in eGFR, end-stage kidney disease or death due to kidney disease. We used Cox regression analyses and compared the constancy of HRs across subgroups by fitting an interaction term (p value for significance <0.05). Results At study initiation, 5095 (50%) CANVAS Program participants were treated with insulin, 2100 (21%) had an HbA1c > 74.9 mmol/mol (9.0%) and the median duration of diabetes was 12.6 years (interquartile interval 8.0–18 years). Canagliflozin reduced MACE (HR 0.86 [95% CI 0.75, 0.97]) with no evidence that the benefit differed between subgroups defined by the number of glucose-lowering treatments, the duration of diabetes or baseline HbA1c (all p-heterogeneity >0.17). Canagliflozin reduced MACE in participants receiving insulin with no evidence that the benefit differed from other participants in the trial (HR 0.85 [95% CI 0.72, 1.00]). Similar results were observed for other cardiovascular outcomes and for the combined kidney outcome (HR for combined kidney outcome 0.60 [95% CI 0.47, 0.77]), with all p-heterogeneity >0.37. Conclusions/interpretation In people with type 2 diabetes mellitus at high cardiovascular risk, there was no evidence that cardiovascular and renal protection with canagliflozin differed across subgroups defined by baseline treatment intensity, duration of diabetes or HbA1c. Graphical abstract

Effects of the SGLT2 inhibitor canagliflozin on plasma biomarkers TNFR-1, TNFR-2 and KIM-1 in the CANVAS trial

Aims/hypothesis Higher plasma concentrations of tumour necrosis factor receptor (TNFR)-1, TNFR-2 and kidney injury molecule-1 (KIM-1) have been found to be associated with higher risk of kidney failure in individuals with type 2 diabetes in previous studies. Whether drugs can reduce these biomarkers is not well established. We measured these biomarkers in samples of the CANVAS study and examined the effect of the sodium–glucose cotransporter 2 inhibitor canagliflozin on these biomarkers and assessed whether the early change in these biomarkers predict cardiovascular and kidney outcomes in individuals with type 2 diabetes in the CANagliflozin cardioVascular Assessment Study (CANVAS). Methods Biomarkers were measured with immunoassays (proprietary multiplex assay performed by RenalytixAI, New York, NY, USA) at baseline and years 1, 3 and 6. Mixed-effects models for repeated measures assessed the effect of canagliflozin vs placebo on the biomarkers. Associations of baseline levels and the early change (baseline to year 1) for each biomarker with the kidney outcome were assessed using multivariable-adjusted Cox regression. Results In total, 3523/4330 (81.4%) of the CANVAS participants had available samples at baseline. Each doubling in baseline TNFR-1, TNFR-2 and KIM-1 was associated with a higher risk of kidney outcomes, with corresponding HRs of 3.7 (95% CI 2.3, 6.1; p < 0.01), 2.7 (95% CI 2.0, 3.6; p < 0.01) and 1.5 (95% CI 1.2, 1.8; p < 0.01), respectively. Canagliflozin reduced the level of the plasma biomarkers with differences in TNFR-1, TNFR-2 and KIM-1 between canagliflozin and placebo during follow-up of 2.8% (95% CI 3.4%, 1.3%; p < 0.01), 1.9% (95% CI 3.5%, 0.2%; p = 0.03) and 26.7% (95% CI 30.7%, 22.7%; p < 0.01), respectively. Within the canagliflozin treatment group, each 10% reduction in TNFR-1 and TNFR-2 at year 1 was associated with a lower risk of the kidney outcome (HR 0.8 [95% CI 0.7, 1.0; p = 0.02] and 0.9 [95% CI 0.9, 1.0; p < 0.01] respectively), independent of other patient characteristics. The baseline and 1 year change in biomarkers did not associate with cardiovascular or heart failure outcomes. Conclusions/interpretation Canagliflozin decreased KIM-1 and modestly reduced TNFR-1 and TNFR-2 compared with placebo in individuals with type 2 diabetes in CANVAS. Early decreases in TNFR-1 and TNFR-2 during canagliflozin treatment were independently associated with a lower risk of kidney disease progression, suggesting that TNFR-1 and TNFR-2 have the potential to be pharmacodynamic markers of response to canagliflozin. Graphical abstract