A novel scoring system to estimate chemotherapy-induced myocardial toxicity: Risk assessment prior to non-anthracycline chemotherapy regimens

A Systematic Review of the Cardiotoxic Effects of Targeted Therapies in Oncology

Cancer therapy advancements have improved survival rates but also introduced significant cardiotoxic risks. Cardiotoxicity, a critical adverse effect of cancer treatments such as doxorubicin, trastuzumab, and radiotherapy, poses substantial challenges. This systematic review synthesizes findings from studies on cardiotoxicity induced by cancer therapies, focusing on detection and management. Key predictors of chemotherapy-induced myocardial toxicity (CIMT) include advanced age, hypertension, hyperlipidemia, diabetes, and elevated N-terminal pro-B-type natriuretic peptide levels. Regular echocardiographic assessments, particularly of the left ventricular global longitudinal strain (LVGLS) and left ventricular ejection fraction (LVEF), are essential for early detection. The CardTox-Score, incorporating these risk factors, shows high sensitivity and specificity in predicting CIMT. Advanced imaging techniques and biomarkers play crucial roles in identifying at-risk patients before functional decline. Early biomarkers and imaging techniques such as LVGLS and LVEF are effective in diagnosing and managing cardiotoxicity, allowing timely interventions. Cardiology involvement in patient care significantly enhances adherence to cardiac monitoring guidelines and reduces cardiotoxicity risks. Management strategies emphasize regular cardiac monitoring, patient education, and the use of cardioprotective agents. A collaborative approach between cardiologists and oncologists is vital to assess cardiovascular risks, minimize vascular toxicity, and manage long-term adverse effects, ensuring the safety and efficacy of cancer therapies. This review underscores the importance of early detection and proactive management of cardiotoxicity in cancer patients to optimize treatment outcomes and improve quality of life.

Development and Validation of a Nomogram Model for the Risk of Cardiac Death in Patients Treated with Chemotherapy for Esophageal Cancer

The primary cause of mortality in esophageal cancer survivors is cardiac death. Early identification of cardiac mortality risk during chemotherapy for esophageal cancer is crucial for improving the prognosis. We developed and validated a nomogram model to identify patients with high cardiac mortality risk after chemotherapy for esophageal cancer for early screening and clinical decision-making. We randomly allocated 37,994 patients with chemotherapy-treated esophageal cancer into two groups using a 7:3 split ratio: model training (n = 26,598) and validation (n = 11,396). 5- and 10-year survival rates were used as endpoints for model training and validation. Decision curve analysis and the consistency index (C-index) were used to evaluate the model's net clinical advantage. Model performance was evaluated using receiver operating characteristic curves and computing the area under the curve (AUC). Kaplan-Meier survival analysis based on the prognostic index was performed. Patient risk was stratified according to the death probability. Age, surgery, sex, and year were most closely related to cardiac death and used to plot the nomograms. The C-index for the training and validation datasets were 0.669 and 0.698, respectively, indicating the nomogram's net clinical advantage in predicting cardiac death risk at 5 and 10 years. The 5- and 10-year AUCs were 0.753 and 0.772 for the training dataset and 0.778 and 0.789 for the validation dataset, respectively. The accuracy of the model in predicting cardiac death risk was moderate. This nomogram can identify patients at risk of cardiac death after chemotherapy for esophageal cancer at an early stage.

A prediction model for severe hematological toxicity of BTK inhibitors

Bruton's tyrosine kinase inhibitor (BTKi) has revolutionized the treatment of B-cell lymphomas. However, BTKi-related hematological toxicity hinders treatment continuity and may further affect clinical efficacy. To identify risk factors and predict the likelihood of BTKi-related hematological toxicities, we constructed and validated a prediction model for severe hematological toxicity of BTKi. Approved by the hospital medical science research ethics committee (No. M2022427), we collected real-world data in patients treated with BTKi from a Lymphoma Research Center in China. The outcome of interest was severe hematological toxicity caused by BTKi. 36 candidate variables were categorized into demographics, diagnostic and treatment information, laboratory data, and medical history. The study sample was randomly divided into training (70%) and validation (30%) sets. We developed and compared the performance of various modelling methods, including decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and logistic regression (LR). Finally, we constructed a Web-calculator of the optimal model to estimate the risk of hematological toxicity. This study was designed, conducted and reported strictly in compliance with the TRIPOD checklist. Data from a total 121 patients were included [median age, 65 years (range, 56-73 years); 74 (61.15%) men; 47 (38.84%) severe hematological toxicity]. The XGBoost model demonstrated better overall properties than other models, achieving high discrimination (AUC: 0.671; accuracy: 0.730; specificity: 0.913) and clinical benefit. The following 10 variables were used to develop the XGBoost model: white blood cell count (WBC), neutrophil count (Neut), red blood cell count (RBC), platelet count (PLT), fibrinogen (Fib), total protein (TP), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gender and type of BTKi. SHAP values demonstrated insightful associations between these variables and hematological toxicity. Finally, to facilitate clinical and research use, we also deploy the XGBoost model on a web-calculator for free access. The XGBoost model with promising accuracy was developed to predict the severe hematological toxicity of BTKi. It helps to strengthen the proactive monitoring and management of patients with hematological toxicity, and thus achieve long-term continuous BTKi treatment.

Markers of Neuroinflammation in the Serum of Prepubertal Children with Fetal Alcohol Spectrum Disorders

BACKGROUND

Fetal Alcohol Spectrum Disorders (FASD) are the manifestation of the damage caused by alcohol consumption during pregnancy. Children with Fetal Alcohol Syndrome (FAS), the extreme FASD manifestation, show both facial dysmorphology and mental retardation. Alcohol consumed during gestational age prejudices brain development by reducing, among others, the synthesis and release of neurotrophic factors and neuroinflammatory markers. Alcohol drinking also induces oxidative stress.

HYPOTHESIS/OBJECTIVE

The present study aimed to investigate the potential association between neurotrophins, neuroinflammation, and oxidative stress in 12 prepubertal male and female FASD children diagnosed as FAS or partial FAS (pFAS).

METHODS

Accordingly, we analyzed, in the serum, the level of BDNF and NGF and the oxidative stress, as Free Oxygen Radicals Test (FORT) and Free Oxygen Radicals Defense (FORD). Moreover, serum levels of inflammatory mediators (IL-1α, IL-2, IL-6, IL-10, IL-12, MCP-1, TGF-β, and TNF-α) involved in neuroinflammatory and oxidative processes have been investigated.

RESULTS

We demonstrated low serum levels of NGF and BDNF in pre-pubertal FASD children with respect to healthy controls. These changes were associated with higher serum presence of TNF- α and IL-1α. Quite interestingly, an elevation in the FORD was also found despite normal FORT levels. Moreover, we found a potentiation of IL-1α, IL-2, IL-10, and IL-1α1 in the analyzed female compared to male children.

CONCLUSION

The present investigation shows an imbalance in the peripheral neuroimmune pathways that could be used in children as early biomarkers of the deficits observed in FASD.