Promotion of Physical Activity Among University Students With Social Media Or Text Messaging: A Systematic Review

Regular physical exercise lowers the risk of all-cause mortality and various chronic diseases. New technologies, such as smartphones and social media, have been used successfully as health promotion tools in college populations. The purpose of this study was to conduct a systematic review of studies examining the effectiveness of interventions that used modern technologies, as with social media or text messaging, to promote physical activity or reducing sedentary behavior in college students. The systematic review was conducted on the PubMed and SCOPUS databases, considering studies published from 2012 to 2022. For a total of 19 articles selected, an evidence table was drawn up, and the quality of the studies was assessed using the PRISMA checklist. The interventions differed enormously in design, from the strategies implemented to the types of outcome considered. Fifteen of the 19 studies demonstrated an improvement in participants' physical activity levels, 3 studies found no such improvement, and 1 reported a worsening of baseline activity levels. Interventions to improve college students' physical activity levels through the use of social media and/or text messaging tend to be effective. However, many factors can influence the effectiveness of such interventions. For example, a gender-related difference emerged in student participation, and the interventions proved more effective if they were accompanied by the creation of social groups.

Cost items in melanoma patients by clinical characteristics and time from diagnosis

Background

Costs related to the care of melanoma patients have been rising over the past few years due to increased disease incidence as well as the introduction of innovative treatments. The aim of this study is to analyse CMM cost items based on stage at diagnosis, together with other diagnostic and prognostic characteristics of the melanoma.

Methods

Analyses were performed on 2,647 incident cases of invasive CMM that were registered in 2015 and 2017 in the Veneto Cancer Registry (RTV). Direct melanoma-related costs per patient were calculated for each year ranging from 2 years before diagnosis to 4 years after, and were stratified by cost items such as outpatient services, inpatient drug prescriptions, hospital admissions, hospice admissions, and emergency room treatment. Average yearly costs per patient were compared according to available clinical-pathological characteristics. Lastly, log-linear multivariable analysis was performed to investigate potential cost drivers among these clinical-pathological characteristics.

Findings

Overall, the average direct costs related to melanoma are highest in the first year after diagnosis (€2,903) and then decrease over time. Hospitalization costs are 8 to 16 times higher in the first year than in subsequent years, while the costs of outpatient services and inpatient drugs decrease gradually over time. When stratified by stage it is observed that the higher expenditure associated with more advanced stages of CMM is mainly due to inpatient drug use.

Conclusion

The results of the present study show that grouping patients according to tumour characteristics can improve our understanding of the different cost items associated with cutaneous malignant melanoma. CMM patients experience higher costs in the first year after diagnosis due to higher hospitalization and outpatient services. Policy makers should consider overall and stage-specific annual costs when allocating resources for the management of CMM patients.

The Prevalent Comorbidome at the Onset of Psoriasis Diagnosis

INTRODUCTION

Psoriasis (PsO) is currently regarded as a systemic inflammatory disease with a growing burden of post-diagnosis associated comorbidities. To determine the initial burden of comorbiditis we evaluated the comorbidome at PsO onset.

METHODS

In a matched case-control study, we extracted data on 57,228 patients and 125 morbidities from the Clalit Health Services Israeli insurance database. PsO cases were matched with control individuals by sex and age at enrolment. As pre-existing comorbidities, we considered all conditions already present in controls at the same age as the matched PsO case at the time of their diagnosis. To test for differences in the odds of comorbidities between the case and control groups, logistic regression analyses were run to calculate the odds ratio (OR) for each comorbidity, after which the comorbidome was graphically represented.

RESULTS

In this study we enrolled 28,614 PsO patients and 28,614 controls with an average age of 45.3 ± 19.6 years. At the time of diagnosis, PsO patients were more likely to be diagnosed with 2-4 comorbidities (28.8% vs 23.8%) and > 5 (19.6% vs 12.9%,). PsO patients' specific comorbidomes evidenced several pathological cores: autoimmune and inflammatory systemic diseases [i.e., hidradenitis suppurativa (OR 3.55, 95% CI 1.88-7.28) or polymyalgia rheumatica (OR 3.01 95% CI 1.96-4.77)], inflammatory bowel diseases [i.e., Crohn's disease (OR 2.99 95% CI 2.20-4.13)], pulmonary inflammatory diseases [i.e., chronic obstructive pulmonary disease (OR 1.81 95% CI 1.61-2.04)], hepatological diseases [i.e., cirrhosis (OR 2.00 95% CI 1.36-3.00)], endocrine diseases [dysthyroidisms (OR 1.82 95% CI 1.30-2.59)], mental disorders [i.e., depression (OR 1.72 95% CI 1.57-1.87)], and cardiovascular diseases (i.e., hypertension (OR 1.47 95% CI 1.41-1.53)].

CONCLUSION

The PsO-onset comorbidome may help health professionals plan more comprehensive patient management. By screening for these common PsO-linked conditions, early diagnosis and treatment may become more frequent, thus greatly benefiting patients on their medical journey.

Epidemiology, pathological characteristics and survival of retroperitoneal soft‑tissue sarcomas compared with non‑retroperitoneal soft tissue sarcomas

Retroperitoneal soft-tissue sarcomas (RPS) are rare forms of mesenchymal tumors that account for ~0.15% of all malignancies. The purpose of the present study was to determine the differences between RPS and non-RPS anatomopathological and clinical features and to analyze whether the hazard ratio for short-term mortality differs between patients with RPS and non-RPS, after adjusting for differences in baseline anatomopathological and clinical features. The Veneto Cancer Registry, a high-resolution population-based dataset spanning the regional population, was used as a data source for the analysis. The current analysis focuses on all incident cases of soft-tissue sarcoma recorded by the Registry from January 1, 2017 to December 31, 2018. A bivariate analysis was carried out to compare demographic and clinical characteristics in RPS and non-RPS. Short-term mortality risk was analyzed by primary tumor site. The significance of variations in survival by site group was determined using Kaplan-Meier curves and the Log-rank test. Finally, Cox regression was used to assess the hazard ratio for survival by sarcoma group. RPS accounted for 22.8% of the total sample (92 out of 404 cases). The mean age at diagnosis was 67.6 years for RPS vs. 63.4 for non-RPS; 41.3% of RPS were >150 mm vs. 5.5% for non-RPS. Stages III and IV were more prevalent in RPS (53.2 vs. 35.6%), despite the fact that, in both groups, advanced stages are the most common onset at diagnosis. Regarding surgical margins, the present study showed that R0 is the most prevalent in non-RPS (48.7%), while R1-R2 is the most frequent in patients with RPS (39.1%). The 3-year mortality rate for retroperitoneum was 42.9 vs. 25.7%. Comparing RPS and non-RPS, the multivariable Cox model showed a hazard ratio of 1.58 after adjusting for all other prognostic factors. RPS clinical and anatomopathological characteristics differ from those of non-RPS. Overall, despite adjusting for other prognostic factors, the retroperitoneum site was an independent prognostic factor associated with a worse overall survival in sarcoma patients compared with other sites.

Hospital readmissions and mortality following discharge against medical advice: a five-year retrospective, population-based cohort study in Veneto region, Northeast Italy

OBJECTIVES

The aim of this study was to examine the odds of readmission and mortality after discharge against medical advice (DAMA) in the Veneto region of Northeast Italy, drawing on data from the regional archives of emergency department records and hospital discharge records.

DESIGN

A retrospective cohort study.

SETTING

Hospital discharges, Veneto region, Italy.

PARTICIPANTS

All patients discharged after being admitted to a public or accredited private hospital between January 2016 and 31 January 2021 in the Veneto region were considered. A total of 3 574 124 index discharges were examined for inclusion in the analysis.

PRIMARY AND SECONDARY OUTCOME MEASURES

Readmission and overall mortality at 30 days after the index discharge against admission.

RESULTS

In our cohort, 7.6‰ of patients left hospital against their doctor's advice (n=19 272). These DAMA patients were more likely to be younger (mean age: 45.5 vs 55.0), foreign (22.1% vs 9.1%). The adjusted odds of readmission after DAMA was 2.76 (CI 95% 2.62-2.90) at 30 days (9.5% DAMA vs 4.6% not-DAMA), and the highest readmission rate was recorded in the first 24 hours after the index discharge. Mortality was higher for DAMA patients after adjusting for patient-level and hospital-level characteristics (with adjusted ORs of 1.40 for in-hospital mortality and 1.48 for overall mortality).

CONCLUSIONS

The present study shows that DAMA patients are more likely to die and to need hospital readmission than patients discharged by their doctors. DAMA patients should be more committed to a proactive and diligent postdischarge care.

Machine learning to predict overall short-term mortality in cutaneous melanoma

BACKGROUND

Cutaneous malignant melanoma (CMM) ranks among the ten most frequent malignancies, clinicopathological staging being of key importance to predict prognosis. Artificial intelligence (AI) has been recently applied to develop prognostically reliable staging systems for CMM. This study aims to provide a useful machine learning based tool to predict the overall CMM short-term survival.

METHODS

CMM records as collected at the Veneto Cancer Registry (RTV) and at the Veneto regional health service were considered. A univariate Cox regression validated the strength and direction of each independent variable with overall mortality. A range of machine learning models (Logistic Regression classifier, Support-Vector Machine, Random Forest, Gradient Boosting, and k-Nearest Neighbors) and a Deep Neural Network were then trained to predict the 3-years mortality probability. Five-fold cross-validation and Grid Search were performed to test the best data preprocessing procedures, features selection, and to optimize models hyperparameters. A final evaluation was carried out on a separate test set in terms of balanced accuracy, precision, recall and F1 score. The best model was deployed as online tool.

RESULTS

The univariate analysis confirmed the significant prognostic value of TNM staging. Adjunctive clinicopathological variables not included in the AJCC 8th melanoma staging system, i.e., sex, tumor site, histotype, growth phase, and age, were significantly linked to overall survival. Among the models, the Neural Network and the Random Forest models featured the best prognostic performance, achieving a balanced accuracy of 91% and 88%, respectively. According to the Gini importance score, age, T and M stages, mitotic count, and ulceration appeared to be the variables with the greatest impact on survival prediction.

CONCLUSIONS

Using data from patients with CMM, we developed an AI algorithm with high staging reliability, on top of which a web tool was implemented ( unipd.link/melanomaprediction ). Being essentially based on routinely recorded clinicopathological variables, it can already be implemented with minimal effort and further tested in the current clinical practice, an essential phase for validating the model's accuracy beyond the original research context.