Smoking habit change after cancer diagnosis: effect on cardiovascular risk

Smoking Habits Following Cancer Diagnosis and Heart Failure

This cohort study examines the risk of heart failure in patients by smoking status after diagnosis of cancer.

Post-diagnosis smoking habit change and incident dementia in cancer survivors

INTRODUCTION

Many individuals change their smoking habits after cancer diagnosis. We aimed to evaluate the association of post-diagnosis smoking habit change with incident dementia in cancer survivors.

METHODS

We identified 558,127 individuals who were diagnosed with cancer at age ≥ 20 and survived for ≥ 3 years. Participants were classified into four groups: (1) sustained non-smokers, (2) initiators/relapsers, (3) quitters, and (4) continuing smokers. Dementia risk in each group was assessed using a cause-specific Cox model.

RESULTS

After cancer diagnosis, 2.3% of pre-diagnosis non-smokers initiated/relapsed into smoking, while 51.7% of pre-diagnosis smokers quit smoking. Compared to sustained non-smokers, multivariable-adjusted risk of dementia was 29% higher among initiators/relapsers, 11% higher among quitters, and 31% higher among continuing smokers. Compared to continuing smokers, the risk was 15% lower among quitters.

DISCUSSION

In cancer survivors, smoking initiation/relapse was associated with increased risk of dementia, whereas smoking cessation was associated with decreased risk of dementia.

HIGHLIGHTS

Approximately half of pre-diagnosis smokers quit smoking after a cancer diagnosis. Smoking cessation was associated with a 15% reduced risk of dementia. More than 2% of pre-diagnosis non-smokers initiated or relapsed into smoking after a cancer diagnosis. Smoking initiation/relapse was associated with a 29% elevated risk of dementia.

Association of lifestyle modification with the development of cardiovascular disease in gastric cancer patients who underwent gastrectomy: A nationwide population-based study

BACKGROUND

While cancer patients are at an increased risk of cardiovascular disease (CVD), the role of modifiable risk factors remains poorly understood. This study investigated whether lifestyle modifications affect CVD development in gastric cancer patients who undergo surgery.

METHODS

Using data from the Korean National Health Insurance Service (NHIS), gastric cancer patients who underwent surgery from 2010 to 2017 were identified. Lifestyle behaviours, surveyed within 2 years before and after surgery were analysed. Incident CVD, defined as a composite of myocardial infarction and stroke, was compared among subgroups of lifestyle behaviour changes.

RESULTS

Among 22,211 gastrectomy patients, 628 (2.8%) developed CVD (5.68/1000 person-years). Persistent smokers (HR: 1.72, 95% CI: 1.33-2.22) and new smokers (HR: 1.85, 95% CI: 1.04-3.30) faced higher CVD risks than non-smokers, with an especially pronounced risk in persistent-smoking females (HR: 3.89, 95% CI: 1.20-12.62). Smoking cessation showed no significant risk difference compared to non-smokers (HR: 1.16, 95% CI: 0.93-1.43). Female new drinkers had a higher CVD risk than non-drinking females (HR: 2.89, 95% CI: 1.06-7.88), while men did not show such association. Changes in physical activity, when compared to physical inactivity, were not associated with CVD risk.

CONCLUSION

Gastric cancer patients who smoked after surgery were more likely to develop CVD irrespective of their prior smoking status, with a notable vulnerability in persistent female smokers. Smoking cessation could potentially mitigate CVD risk to levels observed in non-smokers. Alcohol intake should be avoided following surgery, especially for female gastric cancer patients.

Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model

Introduction

Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge.

Methods

This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures.

Results

Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set.

Discussion

We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

Smoking patterns and the intention to quit in German cancer patients: a cross-sectional study

BACKGROUND

Continued smoking after a cancer diagnosis can be associated with lower treatment tolerance, poorer outcomes, and reduced quality of life compared to non-smoking cancer patients or to those who have quit. Yet about 60% of patients continue to smoke after being diagnosed and find it difficult to quit. To address this problem, it is necessary to identify current and past smoking patterns (e.g., frequency of use, types of tobacco products) and determine whether there is motivation to quit. Similarly, factors associated with continued smoking should be identified. These data will provide the basis for the development of smoking cessation programs tailored to the needs of cancer patients.

METHODS

A questionnaire was distributed to cancer patients older than 18 years in a German Comprehensive Cancer Center. Participating cancer patients were divided into three main groups: 1) patients who stopped smoking before being diagnosed with cancer (Ex-before); 2) patients who stopped smoking after a cancer diagnosis (Ex-after); and 3) patients who currently smoke cigarettes (CS). Sociodemographic, medical, and psychosocial data were collected, as well as smoking patterns and the motivation to quit smoking.

RESULTS

About half of patients (51%) who smoked before diagnosis continue to smoke after a cancer diagnosis. Being diagnosed with a tobacco-related cancer type was associated with a decreased probability of continued smoking. Patients with tobacco-related tumors and receiving positive support in burdensome situations were more likely to have a higher cigarette dependence. Of all CS, 59.1% had intention to quit, and 22.7% reported having taken action to quit. The support by a smoking cessation program was considered important. CS were willing to spend up to €100 for support and were open to multiple sessions per week, group sessions, one-on-one sessions and/or online support.

CONCLUSION

These findings underscore the importance of educating cancer patients about the consequences of smoking and to provide them with support to quit. Identified risk factors may further help to recognize cancer patients with high risk of continued smoking after diagnosis.

TRIAL REGISTRATION

The study was registered at OSF ( https://osf.io/3c9km ) and published as a study protocol at " https://bmjopen.bmj.com/content/13/4/e069570 ".

Dose-response association between cardiovascular health and mortality in cancer survivors

BACKGROUND

There is little knowledge on the dose-response association between cardiovascular health (CVH) and risk of all-cause, cardiovascular disease (CVD) and cancer deaths among cancer survivors.

AIMS

We aimed to examine the dose-response association of CVH with all-cause, CVD, and cancer mortality.

METHODS

A total of 1701 US adult cancer survivors were followed-up during a median of 7.3 (IQR 4.0-10.2) years from 2007 to 2018 through the National Health and Nutrition Examination Survey (NHANES). We used the American Heart Association´s (AHA) Life´s Essential 8 (LE8) as a proxy for CVH.

RESULTS

Restricted cubic spline models indicated a close to inverse linear shape for the dose-response association between LE8 score and all-cause mortality with significant risk reductions within the range between 61.25 (Hazard ratio [HR]: 0.76, 95% CI, 0.59-0.98) and 100 points (HR: 0.28, 95%CI, 0.12-0.62), and a curvilinear shape for the dose-response association between LE8 score and CVD deaths with significant risk reductions within the range between 50.25 (HR: 0.72, 95% CI, 0.52-0.99) and 90.25 points (HR: 0.15, 95%CI, 0.02-0.98). No significant dose-response association was observed between LE8 and cancer deaths.

CONCLUSIONS

Our study showed a close to inverse relationship between higher LE8 and risk of death from all cause, an inverse curvilinear relationship between higher LE8 and the risk for CVD death, and a non-significant association between higher LE8 and the risk of cancer death among US adult cancer survivors, which may translate to a substantial number of annual averted deaths and thus important public health implications.