Real-world use of ACEI/ARB in diabetic hypertensive patients before the initial diagnosis of obstructive coronary artery disease: patient characteristics and long-term follow-up outcome

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction

OBJECTIVES

Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications.

MATERIALS AND METHODS

A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions.

RESULTS

Leveraging EHR data from 11 627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032).

DISCUSSION

These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions.

CONCLUSIONS

Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

Cluster analysis of angiotensin biomarkers to identify antihypertensive drug treatment in population studies

BACKGROUND

The recent progress in molecular biology generates an increasing interest in investigating molecular biomarkers as markers of response to treatments. The present work is motivated by a study, where the objective was to explore the potential of the molecular biomarkers of renin-angiotensin-aldosterone system (RAAS) to identify the undertaken antihypertensive treatments in the general population. Population-based studies offer an opportunity to assess the effectiveness of treatments in real-world scenarios. However, lack of quality documentation, especially when electronic health record linkage is unavailable, leads to inaccurate reporting and classification bias.

METHOD

We present a machine learning clustering technique to determine the potential of measured RAAS biomarkers for the identification of undertaken treatments in the general population. The biomarkers were simultaneously determined through a novel mass-spectrometry analysis in 800 participants of the Cooperative Health Research In South Tyrol (CHRIS) study with documented antihypertensive treatments. We assessed the agreement, sensitivity and specificity of the resulting clusters against known treatment types. Through the lasso penalized regression, we identified clinical characteristics associated with the biomarkers, accounting for the effects of cluster and treatment classifications.

RESULTS

We identified three well-separated clusters: cluster 1 (n = 444) preferentially including individuals not receiving RAAS-targeting drugs; cluster 2 (n = 235) identifying angiotensin type 1 receptor blockers (ARB) users (weighted kappa κw = 74%; sensitivity = 73%; specificity = 83%); and cluster 3 (n = 121) well discriminating angiotensin-converting enzyme inhibitors (ACEi) users (κw = 81%; sensitivity = 55%; specificity = 90%). Individuals in clusters 2 and 3 had higher frequency of diabetes as well as higher fasting glucose and BMI levels. Age, sex and kidney function were strong predictors of the RAAS biomarkers independently of the cluster structure.

CONCLUSIONS

Unsupervised clustering of angiotensin-based biomarkers is a viable technique to identify individuals on specific antihypertensive treatments, pointing to a potential application of the biomarkers as useful clinical diagnostic tools even outside of a controlled clinical setting.

Small molecule angiotensin converting enzyme inhibitors: A medicinal chemistry perspective

Angiotensin-converting enzyme (ACE), a zinc metalloprotein, is a central component of the renin-angiotensin system (RAS). It degrades bradykinin and other vasoactive peptides. Angiotensin-converting-enzyme inhibitors (ACE inhibitors, ACEIs) decrease the formation of angiotensin II and increase the level of bradykinin, thus relaxing blood vessels as well as reducing blood volume, lowering blood pressure and reducing oxygen consumption by the heart, which can be used to prevent and treat cardiovascular diseases and kidney diseases. Nevertheless, ACEIs are associated with a range of adverse effects such as renal insufficiency, which limits their use. In recent years, researchers have attempted to reduce the adverse effects of ACEIs by improving the selectivity of ACEIs for structural domains based on conformational relationships, and have developed a series of novel ACEIs. In this review, we have summarized the research advances of ACE inhibitors, focusing on the development sources, design strategies and analysis of structure-activity relationships and the biological activities of ACE inhibitors.

RSSDI Guidelines for the management of hypertension in patients with diabetes mellitus

Hypertension and diabetes mellitus (DM) are two of the leading lifestyle diseases in the Indian and South Asian populations that often co-exist due to overlapping pathophysiological factors. Obesity, insulin resistance, inflammation, and oxidative stress are thought to be some common pathways. Up to 50% of hypertensive cases in India are diagnosed with type 2 diabetes mellitus (T2DM), which defines the need for a comprehensive guideline for managing hypertension in diabetic patients. These RSSDI guidelines have been formulated based on consultation with expert endocrinologists in India and Southeast Asia, acknowledging the needs of the Indian population. Ambulatory blood pressure monitoring and office and home-based blood pressure (BP) monitoring are recommended for the early analysis of risks. Cardiovascular risks, end-organ damage, and renal disorders are the primary complications associated with diabetic hypertension that needs to be managed with the help of non-pharmacological and pharmacological interventions. The non-pharmacological interventions include the nutrition education of the patient to reduce the intake of salt, sodium, and trans fats and increase the consumption of nuts, fresh fruits, vegetables, and potassium-rich foods. It is also recommended to initiate 50 to 60 min of exercise three to four times a week since physical activity has shown to be more beneficial for hypertension control in Indian patients than dietary modulation. For the pharmacological management of hypertension in patients with T2DM, angiotensin II receptor blockers (ARBs) are recommended as the first line of therapy, demonstrating their superiority over other antihypertensive agents such as ACEi. However, most of the global hypertension guidelines recommend initiation with combination therapy to achieve better BP control in most patients and to reduce the risk of adverse events. For combination therapy, calcium channel blockers (CCBs) are recommended to be administered along with ARBs instead of beta-blockers or diuretics to avoid the risk of cardiovascular events and hyperglycaemia. Among the CCBs, novel molecules (e.g. cilnidipine) are recommended in combination with ARBs for better cardiovascular and reno-protection in diabetic hypertensive patients.

Associations between β-Blocker Therapy at Discharge and Long-Term Follow-Up Outcomes in Patients with Unstable Angina Pectoris

Background

The effects of β-blockers in patients with unstable angina pectoris (UAP) are unclear. We tried to evaluate associations between β-blockers in UAP and long-term outcomes.

Methods

We enrolled 5591 UAP patients and divided them into 2 groups based on β-blockers at discharge: 3790 did β-blockers and 1801 did not used them. Propensity score matching at 1 : 1 was performed to select 1786 patients from each group. The primary endpoint was major adverse cardiac and cerebral events (MACCE) during the long-term follow-up period.

Results

67.8% of patients were on β-blockers at discharge; these patients were more likely to have CHD risk factors, lower ejection fraction, and severity of the coronary artery lesions. Over a median of 25.0 years, the incidence of MACCE was 25.5%. The risk was not significantly different between those on and those not on β-blocker treatment. The multivariate Cox regression analysis showed that no β-blocker use at discharge was not an independent risk factor for MACCE and sequence secondary endpoints. After propensity score matching, the results were similar.

Conclusions

β-blocker use was not associated with lower MACCE and other secondary composite endpoints in long-term outcomes. This result adds to the increasing body of evidence that the routine prescription of β-blockers might not be indicated in patients with UAP. Trial registration had retrospectively registered.

ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage

Purpose

We develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH).

Method

We applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups – 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC).

Results

The incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739–0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694–0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762–0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774–0.794].

Conclusion

ICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03389-5.

Urine N-acetyl-Ser-Asp-Lys-Pro measurement as a versatile biomarker to assess adherence to angiotensin-converting enzyme inhibitors

BACKGROUND

Poor adherence to treatment is a major health issue in hypertension. The large number of drugs to be detected limits the implementation of chemical adherence testing by liquid chromatography/mass spectrometry (LC-MS/MS). AcSDKP, a peptide accumulating in the presence of angiotensin-converting-enzyme inhibitor (ACEI) treatment, has been validated as a proven marker of adherence by enzyme-linked immunosorbent assay. Our aim was to validate urine measurements of AcSDKP compared with active metabolites of various ACEI, measured simultaneously by LC-MS/MS.

METHOD

We first studied the time-dependent relationships between urinary perindoprilat and AcSDKP in a pharmacokinetic/pharmacodynamic study in healthy volunteers. We then compared the sensitivity and specificity of urinary AcSDKP vs. three ACEI active metabolites (enalaprilat, perindoprilat, ramiprilat) taken as reference to detect nonadherence in spot urine samples from a prospective cohort of hypertensive outpatients.

RESULTS

The urinary excretion profiles of AcSDKP and perindoprilat were similar, exhibited a significant correlation, and showed excellent agreement in healthy volunteers. In patients, we found a similar agreement between AcSDKP and the three ACEI metabolites urinary concentrations. The sensitivity and specificity for adherence assessment of urine AcSDKP was 92.2 and 100%, respectively. We observed a difference in the evaluation of good adherence between ACEI metabolites (85.7%) and AcSDKP (79.0%) because of discrepancies in samples where AcSDKP reached undetectability quicker than ACEI metabolites. This characteristic of AcSDKP is of particular interest and could better reflect the true adherence status of patients.

CONCLUSION

Overall, spot urine AcSDKP measurement by LC-MS/MS is a reliable marker of the intake of ACEI treatment and could substitute ACEI metabolites detection.

Vitamin D levels condition the outcome benefits of renin-angiotensin system inhibitors (RASI) among patients undergoing percutaneous coronary intervention

BACKGROUND

Vitamin D deficiency is estimated as the most common medical condition worldwide, with severe implications on survival and on several inflammatory, immune-mediated and thrombotic disorders, and especially for cardiovascular disease. Recent studies have suggested that vitamin D could directly regulate the Renin-Angiotensin System (RAS) activity, therefore potentially interfering with the pharmacological effects of RAS Inhibitors (RASI), an issue that has seldom been explored. Therefore, the aim of the present study was to evaluate the prognostic impact of the use of RASI according to vitamin D levels among patients with coronary artery disease (CAD) undergoing percutaneous coronary intervention (PCI).

METHODS

Consecutive patients undergoing PCI were included. Main clinical features and chemistry parameters were assessed at admission. Vitamin D levels were measured by chemiluminescence immunoassay kit LIAISON® Vitamin D assay (Diasorin Inc). Severe deficiency was defined for 25(OH)D < 10 ng/mL. The primary study endpoint was defined as the occurrence of major cardiovascular events (MACE, a composite of death, recurrent Myocardial Infarction (MI) and target vessel revascularization) at the longest available follow-up.

RESULTS

We included a total of 705 patients, that were divided according to vitamin D tertiles (< 12.7 ng/mL; 12.7-21.59 ng/mL; ≥21.6 ng/mL) and use of RASI. RASI therapy was significantly associated to arterial hypertension, creatinine, lower 25(OH)D, use of statins, diuretics, ASA and ticagrelor across vitamin D tertiles. At a median follow-up of 996 [377-1552] days, MACE occurred in 174 (24.7 %) patients. Severe hypovitaminosis D was significantly associated with a higher rate of MACE (HR[95 %CI] = 0.75[0.62-0.91], p = 0.004). The use of RASI significantly lowered the rate of MACE in patients with lower vitamin D (I tertile: 41.3 % vs 25.9 %, adjusted HR[95 %CI] = 0.43[0.26-0.73], p = 0.002); whilst a non-significant effect was observed for II and III tertiles values (18.6 %vs 29.5 %, adjusted HR[95 %CI] = 1.16[0.57-2.34], p = 0.69, and 21.2 % vs 12.6 %, adjusted HR[95 %CI] = 1.1[0.46-2.62], p = 0.83) (p int = 0.04). A similar prognostic interaction for RASI and vitamin D was observed for cardiovascular mortality and MI (p int = 0.03).

CONCLUSION

Among patients undergoing PCI, the use of RASI was associated with lower risk of MACE only among patients with lower levels of vitamin D. Future larger studies are certainly warranted in order to define the prognostic implications of vitamin D supplementation on the RAS system modulation, especially among patients treated with RASI.