دوره 13، شماره 3 - ( 6-1404 )
جلد 13 شماره 3 صفحات 0-0 |
برگشت به فهرست نسخه ها
Ethics code: IR.MAZUMS.IMAMHOSPITAL.REC.1396.10
Clinical trials code: NA
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Moosazadeh M, Nadi Ghara A A, Gheibi M, Janbabaei G, Hedayatizadeh Omran A, Alizadeh-Navaei R, et al . Advanced Survival Analysis of Iranian Lung Cancer Patients: Prospective Cohort Study of Prognostic Factors. Iran J Health Sci 2025; 13 (3)
URL: http://jhs.mazums.ac.ir/article-1-1059-fa.html
URL: http://jhs.mazums.ac.ir/article-1-1059-fa.html
Advanced Survival Analysis of Iranian Lung Cancer Patients: Prospective Cohort Study of Prognostic Factors. علوم بهداشتی ایران. 1404; 13 (3)
چکیده: (51 مشاهده)
Background and purpose: Addressing survival in lung cancer patients is crucial because it directly informs prognosis, guides personalized treatment strategies, and highlights disparities in healthcare access. Understanding survival patterns and their determinants is key to improving patient outcomes, allocating resources effectively, and advancing oncological research for better therapies and early detection methods. Thus, given the existing gaps in literature regarding different survival rates and disparities in the impact of demographical and clinical factors such as age, gender, residence, and tumor type on survival, the present study aimed to assess up-to-date and comprehensive data from lung cancer (LC) patients in Mazandaran province by employing a diverse set of more sophisticated survival models.
Materials and Methods: This prospective cohort study consisted of 708 LC patients diagnosed between 2017 and 2019 and followed up until February 2023 from the Cancer Registry Center of Mazandaran University of Medical Sciences using census-based sampling. For survival analysis, non-parametric, semi-parametric, and parametric models such as Kaplan-Meier survival curves, log-rank test, Univariate, multivariate cox proportional-hazards regression, multivariate cox regression with time-varying covariates, and Exponential PH model with Gamma Frailty Distribution models were used on variables including age, gender, residential area, and tumor type.
Results: Out of 708 LC patients, 431 (61.02%) patients died during the follow up period. Mean age of LC patients was 64 ± 12.42 years. Majority of patients were male (75%), 198 (27.97%) patients were older than 70 years old, and 31 (4.38%), 24 (3.39%), and 53 (7.49%) patients had well differentiated, moderately differentiated, and poorly differentiated tumors, respectively. The Exponential PH model with Gamma Frailty Distribution was selected and presented as the best fitting parametric model. The overall survival rate was 69% at 6 month, 54% at 1 year, 44% at 2 years, and 39% at 3 years. Tumor type was the most significant predictor of survival (HR: 1.98, 95%CI: 1.32-2.95 for small cell LC in comparison to non-small LC). However, age, gender, and residential location had no significant association with survival. Also, time-varying analysis uncovered that the influence of tumor type diminishes over the course of follow-up (HR: 0.998, 95%CI: 0.997-0.998).
Conclusion: This study highlights the importance of utilizing advanced models and time-varying analyses to identify factors influencing the survival of lung cancer patients.
Materials and Methods: This prospective cohort study consisted of 708 LC patients diagnosed between 2017 and 2019 and followed up until February 2023 from the Cancer Registry Center of Mazandaran University of Medical Sciences using census-based sampling. For survival analysis, non-parametric, semi-parametric, and parametric models such as Kaplan-Meier survival curves, log-rank test, Univariate, multivariate cox proportional-hazards regression, multivariate cox regression with time-varying covariates, and Exponential PH model with Gamma Frailty Distribution models were used on variables including age, gender, residential area, and tumor type.
Results: Out of 708 LC patients, 431 (61.02%) patients died during the follow up period. Mean age of LC patients was 64 ± 12.42 years. Majority of patients were male (75%), 198 (27.97%) patients were older than 70 years old, and 31 (4.38%), 24 (3.39%), and 53 (7.49%) patients had well differentiated, moderately differentiated, and poorly differentiated tumors, respectively. The Exponential PH model with Gamma Frailty Distribution was selected and presented as the best fitting parametric model. The overall survival rate was 69% at 6 month, 54% at 1 year, 44% at 2 years, and 39% at 3 years. Tumor type was the most significant predictor of survival (HR: 1.98, 95%CI: 1.32-2.95 for small cell LC in comparison to non-small LC). However, age, gender, and residential location had no significant association with survival. Also, time-varying analysis uncovered that the influence of tumor type diminishes over the course of follow-up (HR: 0.998, 95%CI: 0.997-0.998).
Conclusion: This study highlights the importance of utilizing advanced models and time-varying analyses to identify factors influencing the survival of lung cancer patients.
نوع مطالعه: پژوهشي |
موضوع مقاله:
اپيدميولوژي
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