Medical and Personal Characteristics Can Predict the Risk of Lung Metastasis.

AIMS: Understanding the correlations between underlying medical and personal characteristics of a patient with cancer and the risk of lung metastasis may improve clinical management and outcomes. We used machine learning methodologies to predict the risk of lung metastasis using readily available predictors. MATERIALS AND METHODS: We retrospectively analysed a cohort of 11 164 oncological patients, with clinical records gathered between 2000 and 2020. The input data consisted of 94 parameters, including age, body mass index (BMI), sex, social history, 81 primary cancer types, underlying lung disease and diabetes mellitus. The strongest underlying predictors were discovered with the analysis of the highest performing method among four distinct machine learning methods. RESULTS: Lung metastasis was present in 958 of 11 164 oncological patients. The median age and BMI of the study population were 63 (±19) and 25.12 (±5.66), respectively. The random forest method had the most robust performance among the machine learning methods. Feature importance analysis revealed high BMI as the strongest predictor. Advanced age, smoking, male gender, alcohol dependence, chronic obstructive pulmonary disease and diabetes were also strongly associated with lung metastasis. Among primary cancers, melanoma and renal cancer had the strongest correlation. CONCLUSIONS: Using a machine learning-based approach, we revealed new correlations between personal and medical characteristics of patients with cancer and lung metastasis. This study highlights the previously unknown impact of predictors such as obesity, advanced age and underlying lung disease on the occurrence of lung metastasis. This prediction model can assist physicians with preventive risk factor control and treatment strategies.

Community capacity assessment in preventing substance abuse: a participatory approach.

BACKGROUND: Community-based participatory research (CBPR) increasingly is being used to address health issues. Few evidence exist to indicate how builds the capacity of communities to function as health promoter and what resources are required to promote successful efforts. This article presents the result of a capacity assessment for preventing drug abuse through CBPR, which working with rather than in communities, to strengthen a community's problem-solving capacity. For exploring the perception of stakeholders, a dynamic model of the dimensions of community and partnership capacity served as the theoretical framework. METHODS: In this descriptive research, stakeholder analysis helps us to identify appropriate of stakeholders (Key stakeholders). Data were collected using a topic guide concerned with capacity for preventing drug abuse. Interviews were audiotape and transcribed. Data were analyzed thematically. RESULTS: CBPR has been undertaken to involve local people in making decisions about the kind of change they want in their community and the allocation of resources to reduce substance abuse. We identified key stakeholders and examining their interests, resources and constraints of different stakeholders. CONCLUSION: The current study has shown the benefits of community-based participatory approach in assessing capacity. Through CBPR process people who affected by Drug issue engaged in analysis of their own situation and helps identity innovative solutions for their complex problem. This participatory approach to a capacity assessment resulted in a synergistic effort that provided a more accurate picture of community issues and concerns.