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Levels of Evidence according to the Oxford Center of Evidence Based medicine (https://www.cebm.net/2009/06/ oxford-centre-evidence-based-medicine-levels-evidence-march-2 009/)
Source publication
Background
Cancer treatment-related morbidity relevantly compromises health status in cancer survivors, and efforts to optimise health-related outcomes in this population are vital to maximising healthy survivorship. A pre-treatment assessment – and possibly preventive management strategies – of cancer patients at increased risk for cardiovascular...
Contexts in source publication
Context 1
... item is assigned a level of evidence (LOE) backing up its applicability, according to the system used by the Oxford Centre of Evidence-Based Medicine (Table 1). Level 1a evidence is viewed as definite, and level 5 as weak. ...Context 2
... prevention refers to delaying or preventing the onset of CVD, whereas secondary prevention aims to reduce the number of new or severe cases of CVD. Table 3 summarises the currently published studies in this area, where we have only included papers with the highest level of evidence (according to Table 1) within a specific class of medication. The role of intervention with angiotensin-converting enzyme (ACEiI) inhibitors, angiotensin-receptor blockers (ARB), beta-blockers, statins, aspirin/anticoagulants and other drugs will be discussed, respectively. ...Similar publications
The present research describes 25 years of cardiovascular mortality in a cohort of patients in Northern Italy. The study included patients with malignant cancer enrolled in the period of 1996–2019, and describes cardiovascular and cancer mortality in relation to sex, age, year of diagnosis, months of survivorship, tumor site, and standardized morta...
Citations
... Even though physical exercise, in general, reduces the risk of late-onset cardiovascular diseases, there is a lack of knowledge among health professionals, mainly due to the different approaches used. The recommendation is to perform 150 minutes of aerobic exercise per week, in addition to strength training 2 to 3 times a week [43]. ...
Objective: To review literature data demonstrating preservation of cardiac function in patients participating in exercise when initiating cardiotoxic anticancer therapy.
Methods: LILACS, MEDLINE/PubMed, SCOPUS and Web of Science, without filters for year of publication or languages. Study selection: Randomized and non-randomized clinical trials were selected that included outcomes assessment of left ventricular ejection fraction (LVEF) and global longitudinal strain index (GLS) comparing physical training concomitant with antineoplastic therapy to usual treatment.
Results: Four studies were included, totaling 137 individuals (51.5±8.5 years). Three studies combined training (resistance and aerobic) and exclusive aerobic training. All studies were performed on breast cancer patients. The data did not show protection from physical exercise on LVEF (MD: 1.37 [-0.84, 3.59]; p=0.23, I2:55%) and GLS (MD: 0.21 [-0.84, 1.26]; p=0.69, I2:49%). However, the reduction in VO2peak was greater in the control group (14%) compared to the intervention group (5%). Limitations: Clinical trials evaluating the cardioprotective effects of exercise on cardiac function are rare and heterogeneous.
Conclusion: Physical exercise performed at the start of cardiotoxic antineoplastic treatment does not alter echocardiographic markers of cardiac function. However, no dysfunctional changes were observed in this population in the time period investigated. The preventive role of exercise on functionality during this phase of treatment strengthens the evidence for peripheral benefits that have been widely recognized in the literature.
... In fact, it has been reported that approximately half of the patients at risk do not receive echocardiograms during cardiotoxic cancer treatment [14][15][16] . Although accurate risk assessment before systemic cancer treatment could facilitate physicians' detection of the occurrence of CTRCD, its prediction remains a major challenge due to the limited predictive accuracy and availability of current approaches 8,[17][18][19] . If a screening strategy utilizing a more accessible modality capable of accurate stratification of CTRCD risk is established to triage the patients to surveillance echocardiography under resource constraints, then fewer CTRCD patients would be missed with similar echocardiography resource utilization. ...
... However, the availability of GLS measurement is limited due to the requirement for proprietary specialized software. Several risk scores using clinical features and treatment factors have also been developed for predicting CTRCD, none of which has been established for clinical use due to limited performance or the lack of validation studies 18,33 . Therefore, establishing a more reliable, generalizable, and accessible risk stratification strategy for CTRCD is necessary to improve the management of patients who receive cardiotoxic chemotherapy 34 . ...
Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model’s insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73–4.10; log-rank p < 0.001). This remained consistent after adjusting for risk factors (adjusted HR, 2.57; 95% CI 1.62–4.10; p < 0.001). AI-CTRCD score enhanced prediction beyond known factors (time-dependent AUC for 2 years: 0.78 with AI-CTRCD score vs. 0.74 without; p = 0.005). In conclusion, the AI model robustly stratified CTRCD risk from baseline ECG.
... Cancer treatment can result in the development of physical health conditions (i.e., late effects), which can have varying impacts on the quality and length of life of survivors [2]. Examples of late effects include subsequent malignant neoplasms (SMNs) and cardiovascular complications [3][4][5]. In addition to physical impacts, late effects can alter the life trajectories of cancer survivors through impacts on psychological, educational, functional, and financial outcomes during adulthood [6]. ...
Physical late effects of treatment are well-documented among childhood cancer survivors in high-income countries, but whether prevalence and risk factors are comparable in low- and middle-income countries (LMICs) is unclear. We conducted a systematic review to assess physical late effect outcomes among childhood cancer survivors in LMICs.
Five health sciences databases were searched from inception to November 2022 in all languages. We included observational studies conducted in LMICs that evaluated physical late effects of treatment in childhood cancer survivors. Mean or median cohort follow-up must have been ≥ 5 years from original cancer diagnosis.
Sixteen full articles and five conference abstracts were included. Studies were conducted in lower-middle (n = 12, 57%) or upper-middle income (n = 9, 43%) countries; nearly half (n = 9, 43%) were conducted in India. Five cohorts (24%) were comprised entirely of 5-year survivors. Subsequent malignant neoplasms were reported in 0–11% of survivors (n = 10 studies). Hypothyroidism and metabolic syndrome prevalence ranged from 2–49% (n = 4 studies) and 4–17% (n = 5 studies), respectively. Gonadal dysfunction ranged from 3–47% (n = 4 studies). Cardiac dysfunction ranged from 1–16% (n = 3 studies). Late effects of the musculoskeletal and urinary systems were least investigated.
Substantial knowledge gaps exist in LMIC childhood cancer survivorship. No low-income country data were found. In middle-income countries, late effects were defined and assessed variably and limited by selection bias and small sample sizes.
Survivors in LMICs can experience physical late effects of treatment, though additionally systematically collected data from survivor cohorts are needed to fill knowledge gaps.
... При этом необходимо отметить, что также во всем мире лидерами в структуре смертности являются онкологические заболевания [4], динамическое распространение которых однозначно свидетельствует о нарастании проблемы [5]. Так, по данным ВОЗ, в мире количество новых случаев заболеваемостью раком в 2020 году составило более 19 млн человек, а смертность -9,96 млн. ...
Objective: to present data on available modern methods of diagnosis of subclinical cardiovascular toxicity of chemotherapy. Modern strategies of treatment of cancer allow to stable remission and increase life expectancy of patients, the success of which is balanced by increased mortality of cancer patients due to the development of side effects of chemotherapy, the most prognostically significant of which are cardiovascular complications. Early diagnosis of cardiovascular toxicity in cancer patients is a “new” priority for collaboration of oncologists and cardiologists, as the development or progression of an already existing heart disease has a significant impact on the duration and quality of life of cancer patients. This review, at the current level of development of medical science, presents methods of diagnosis and monitoring of subclinical cardiovascular toxicity against the background of chemotherapy. Use for the diagnosis of subclinical cardiotoxicity such modern methods of visualization as two- and three-dimensional echocardiography, echocardiography with the definition of global longitudinal deformation of the myocardium - spectrum-tracking echocardiography, myocardial work, magnetic resonance tomography, definition of diastolic dysfunction allows to identify manifestations of cardiovascular toxicity in earlier terms, thereby affecting the prognosis and quality of life in cancer patients receiving chemotherapy treatment.
... Machine learning (ML) techniques have achieved remarkable results in predicting CVDs in cancer patients [4]. Unfortunately, they are still not part of the clinical practice: cardio-oncologists rely on older, less accurate cardiovascular risk stratification tools such as the Framingham score [5]. ...
Over the last decades, many prognostic models based on artificial intelligence techniques have been used to provide detailed predictions in healthcare. Unfortunately, the real-world observational data used to train and validate these models are almost always affected by biases that can strongly impact the outcomes validity: two examples are values missing not-at-random and selection bias. Addressing them is a key element in achieving transportability and in studying the causal relationships that are critical in clinical decision making, going beyond simpler statistical approaches based on probabilistic association. In this context, we propose a novel approach that combines selection diagrams, missingness graphs, causal discovery and prior knowledge into a single graphical model to estimate the cardiovascular risk of adolescent and young females who survived breast cancer. We learn this model from data comprising two different cohorts of patients. The resulting causal network model is validated by expert clinicians in terms of risk assessment, accuracy and explainability, and provides a prognostic model that outperforms competing machine learning methods.
... Supportive care is composed of four components: communication and decision-making; education; symptom management; and psychological and spiritual issues [28,29]. The study of Fradley et al. (2021) providing inter-disciplinary approach with motivational interviewing and empathy via telephone calls, showed a positive impact on the mental health of 33 women with breast cancer undergoing chemo-therapy [30]. Furthermore, with scarce evidence on interventional studies including cardio-oncology patients, cohort studies reveal the importance of lifestyle modification and exercise on minimizing cardiovascular events in cancer survivors [31][32][33][34][35]. ...
Purpose of Review
The current review describes the role of the cardio-oncology nurse and the need for personalized heart failure care for the patient with cancer.
Recent Findings
It is a new role whereby cardiology or heart failure nurses care for patients with cancer who develop cardiotoxicity or cardiovascular diseases, either during the cancer therapy or in a later stage. Inter-disciplinary approach is important for individualized early treatment, shortened interruptions to cancer therapy, and irreversible cardiovascular injury prevention. Nurses have a key role in early evaluation and quality control of the care provided.
Summary
This is a quite new clinical area and not much evidence exists for the development of clinical guidelines and pathways to support clinicians. More trials are needed for the development of clinical recommendations.
Predicting outcomes for cancer patients initiating chemotherapy is essential for care planning and offers potential to support clinical and health policy decision-making. Existing models leveraging deep learning with longitudinal healthcare data have demonstrated the benefits of Transformer-based approaches to learning temporal relationships among medical codes (e.g., diagnoses, medications, procedures). Recent applications have also recognised the benefit of including patient information such as demographics to improve predictions. However, much of the existing work has focused on Electronic Health Record (EHR) data, and applications to administrative claims data, which has a differing temporal structure to EHR, are limited. Furthermore, it is still unclear how to best encode medical data from both EHR and claims data and model it collectively in Transformer models. Motivated by the above, this work proposes a Multi-Level Transformer specifically designed for claims data (Claims-MLT) to enhance cancer outcome prediction. The model uses a dual-level structure to learn effective patient representations by considering the low-level claims item relationships and sequential patterns in patient claim histories. We also integrate patient demographic and clinical features to provide additional information to the model. We evaluate our approach on two tasks from a real-world cancer dataset containing breast and colorectal cancer patients, and demonstrate the proposed model outperforms comparative baselines.
Background:
People with cancer have an increased risk of cardiovascular disease. Risk prediction equations developed in New Zealand accurately predict 5-year cardiovascular disease risk in a general primary care population in the country. We assessed the performance of these equations for survivors of cancer in New Zealand.
Methods:
For this validation study, patients aged 30-74 years from the PREDICT open cohort study, which was used to develop the New Zealand cardiovascular disease risk prediction equations, were included in the analysis if they had a primary diagnosis of invasive cancer at least 2 years before the date of the first cardiovascular disease risk assessment. The risk prediction equations are sex-specific and include the following predictors: age, ethnicity, socioeconomic deprivation index, family history of cardiovascular disease, smoking status, history of atrial fibrillation and diabetes, systolic blood pressure, total cholesterol to HDL cholesterol ratio, and preventive pharmacotherapy (blood-pressure-lowering, lipid-lowering, and antithrombotic drugs). Calibration was assessed by comparing the mean predicted 5-year cardiovascular disease risk, estimated using the risk prediction equations, with the observed risk across deciles of risk, for men and women, and according to the three clinical 5-year cardiovascular disease risk groups in New Zealand guidelines (<5%, 5% to <15%, and ≥15%). Discrimination was assessed by Harrell's C statistic.
Findings:
14 263 patients were included in the study. The mean age was 61 years (SD 9) for men and 60 years (SD 8) for women, with a median follow-up of 5·8 years for men and 5·7 years for women. The observed cardiovascular disease risk was underpredicted by a maximum of 2·5% in male and 3·2% in female decile groups. When patients were grouped according to clinical risk groups, observed cardiovascular disease risk was underpredicted by less than 2% in the lower risk groups and overpredicted by 2·2% for men and 3·3% for women in the highest risk group. Harrell's C statistics were 0·67 (SE 0·01) for men and 0·73 (0·01) for women.
Interpretation:
The New Zealand cardiovascular disease risk prediction equations reasonably predicted the observed 5-year cardiovascular disease risk in survivors of cancer in the country, in whom risk prediction was considered clinically appropriate. Prediction could be improved by adding cancer-specific variables and considering competing risks. Our findings suggest that the equations are reasonable clinical tools for use in survivors of cancer in New Zealand.
Funding:
Auckland Medical Research Foundation, Health Research Council of New Zealand.
