Arriola‐Montenegro, Jose

Immunotherapy has transformed oncology, yet kidney complications increasingly affect outcomes. This review summarizes nephrotoxicity from immune checkpoint inhibitors (ICIs), chimeric antigen receptor T-cell (CAR-T) therapies, and bispecific T-cell engagers (BiTEs). ICIs remain most used, with acute interstitial nephritis (AIN) as the hallmark lesion, while glomerular diseases and electrolyte abnormalities are less common. CAR-T therapy, though highly effective in hematologic cancers, carries risks of cytokine release syndrome, tumor lysis syndrome, and acute kidney injury (AKI). BiTEs, particularly teclistamab, show higher AKI rates than CAR-T, though mechanisms remain unclear. Early recognition, tailored management, and multidisciplinary collaboration are essential to ensure safe therapy and preserve kidney function.

Background: Early detection and prediction of arterial hypertension (HTN) are vital to reduce cardiovascular morbidity and mortality. AI has enhanced ECG diagnostic capabilities, enabling detection of latent cardiovascular abnormalities. Aims: This systematic review evaluates current evidence on AI-enhanced ECG for detection and prediction of HTN in in both clinical and subclinical populations. Methods: A search was conducted on April 28, 2025, in PubMed, Scopus, and Web of Science. Gray literature and study reference lists, plus the first 10 pages of Google Scholar, were screened. The review followed PRISMA 2020 guidelines. Bias risk was assessed using PROBAST (for prediction models) and QUADAS-2 (for diagnostic studies). Two reviewers independently performed study selection and data extraction after pilot training. Results: A total of 26 studies were included: 16 focused on detection and 10 on prediction of HTN using AI-enhanced ECG. Overall, 96% reported successful outcomes, underscoring the strong diagnostic and prognostic potential of this approach. Among detection studies, 94% successfully identified hypertensive individuals, classified blood pressure (BP) levels, or evaluated cardiovascular risk. Most were retrospective in design (75%), with internal validation in 44% and external validation in 19%. Sample sizes ranged from 100 to over 120,000, including healthy and cardiovascular patients. Commonly used algorithms included deep Learning, random forest, and ensemble methods, often using single-lead ECG data, highlighting the feasibility of wearable technologies. QUADAS-2 identified 56% of studies had low risk of bias. All prediction studies achieved accurate estimation of systolic/diastolic BP or predicted future HTN onset. Designs were 40% experimental and 50% retrospective. All studies reported internal validation, and 50% included external validation. AI models such as CNNs, ResNet-LSTM, and U-Net were used across clinical and community-based populations, with follow-up periods of up to 6.8 years. Populations were predominantly normotensive or mixed. PROBAST assessments showed 60% low and 40% moderate risk of bias. Conclusion: AI-enhanced ECG is a promising, non-invasive tool for early detection and prediction of HTN. However, external validation and clinical trials are needed to support integration and generalizability.

Heart failure (HF) is a complex clinical syndrome that represents one of the leading causes of morbidity and mortality in developed nations. It is well established that every HF-related hospital admission leads to worsened quality of life for the patient and their caregiver and also imposes a significant financial burden on society. Therefore, reducing hospital admissions for this population has emerged as a critical tactic over the past decades. Initial attempts at remote monitoring focused on self-reported vital signs and symptoms, yet these proved ineffective. Meanwhile, subsequent technological advancements have enabled the development of miniature sensors capable of detecting and monitoring a wide range of physiologically relevant parameters; some of these advancements have been integrated into implantable devices, such as pacemakers and defibrillators. However, noninvasive monitoring has recently emerged as an alternative option for patients with HF, offering early congestion detection without requiring an invasive procedure. This review aims to summarize implanted and noninvasive devices, their characteristics, monitored parameters, and potential limitations and challenges around their integration into routine clinical practices.

Cancer therapy-induced hypertension (HTN) is an increasingly recognized complication associated with a wide range of anticancer agents, including vascular endothelial growth factor (VEGF) inhibitors, proteasome inhibitors, tyrosine kinase inhibitors, and alkylating agents. The pathogenesis of HTN in this setting is multifactorial, involving mechanisms such as endothelial dysfunction, nitric oxide (NO) suppression, sympathetic nervous system activation, and vascular remodeling. Additional factors, including paraneoplastic syndromes, poorly controlled pain, mood disturbances, and overlapping cardiovascular risk factors like obesity and diabetes, further contribute to the complexity of diagnosis and management. Despite its prevalence and clinical implications, cancer therapy-induced HTN is often addressed using general population guidelines, with limited oncology-specific protocols available. Accurate blood pressure measurement and individualized treatment plans are critical to optimize outcomes and avoid interruptions to cancer therapy. Antihypertensive agents such as angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARB), and calcium channel blockers have shown efficacy in both blood pressure control and, in some cases, oncologic outcomes. A multidisciplinary approach involving oncologists, cardiologists, and primary care providers is essential to navigate the interplay between cancer treatment and cardiovascular health. Ongoing research is needed to develop targeted guidelines and improve the long-term care of cancer patients affected by treatment-induced HTN.

Abstract Background: Chronic kidney disease (CKD) has been associated with higher risk of gastrointestinal disorders, particularly Gastroesophageal reflux disease (GERD). However, the magnitude of this association and the underlying mechanisms remains unclear. Methods: A systematic search was conducted across major databases from inception to November 2024. We included cross-sectional and case-control studies evaluating the relationship between CKD and GERD. Data were extracted and analyzed using a random-effects model to calculate pooled odds ratios (ORs) and prevalence rates. Study quality was assessed using the Newcastle-Ottawa Scale, and heterogeneity was evaluated using the Cochran’s Q test and I² statistic. Results: Nine studies involving 4,650,709 participants were included. The pooled prevalence of GERD among CKD patients was 18% (95% CI: 0.10–0.26, I² =93.64%). The pooled crude OR for the association between CKD and GERD was 2.53 (95% CI: 1.30–4.92) and adjusted OR was 1.48 (95% CI: 1.05–2.08). Conclusion: This meta-analysis reveals a marginally significant association between CKD and GERD, highlighting higher prevalence of GERD among individuals with CKD. Furthers studies are needed to elucidate the underlying pathophysiological mechanisms and potential clinical implications.