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Predicting the Risk of Psychological Distress among Lung Cancer Patients: Development and Validation of a Predictive Algorithm Based on Sociodemographic and Clinical Factors

  • Dades identificatives

    Identificador: imarina:9218780
    Autors:
    Tian XJin YTang LPi YPChen WQJimenez-Herrera M
    Resum:
    Objective: Lung cancer patients reported the highest incidence of psychological distress. It is extremely important to identify which patients at high risk for psychological distress. The study aims to develop and validate a predictive algorithm to identify lung cancer patients at high risk for psychological distress. Methods: This cross-sectional study identified the risk factors of psychological distress in lung cancer patients. Data on sociodemographic and clinical variables were collected from September 2018 to August 2019. Structural equation model (SEM) was conducted to determine the associations between all factors and psychological distress, and then construct a predictive algorithm. Coincidence rate was also calculated to validate this predictive algorithm. Results: Total 441 participants sent back validated questionnaires. After performing SEM analysis, educational level (β = 0.151, P = 0.004), residence (β = 0.146, P = 0.016), metastasis (β = 0.136, P = 0.023), pain degree (β = 0.133, P = 0.005), family history (β = -0.107, P = 0.021), and tumor, node, and metastasis stage (β = -0.236, P < 0.001) were independent predictors for psychological distress. The model built with these predictors showed an area under the curve of 0.693. A cutoff of 66 predicted clinically significant psychological distress with a sensitivity, specificity, positive predictive value, and negative predictive value of 65.41%, 66.90%, 28.33%, and 89.67%, respectively. The coincidence rate between predictive algorithm and distress thermometer was 64.63%. Conclusions: A validated, easy-to-use predictive algorithm was developed in this study, which can be used to identify patients at high risk of psychological distress with moderate accuracy.
  • Altres:

    Autor segons l'article: Tian X; Jin Y; Tang L; Pi YP; Chen WQ; Jimenez-Herrera M
    Departament: Infermeria
    Autor/s de la URV: Jiménez Herrera, María Francisca / Jin, Yan Fei / Tian, Xu
    Paraules clau: Structural equation model Quality-of-life Psychological distress Prediction model Lung neoplasm women thermometer structural equation model stress service use psychological distress prevalence prediction model model depression care anxiety
    Resum: Objective: Lung cancer patients reported the highest incidence of psychological distress. It is extremely important to identify which patients at high risk for psychological distress. The study aims to develop and validate a predictive algorithm to identify lung cancer patients at high risk for psychological distress. Methods: This cross-sectional study identified the risk factors of psychological distress in lung cancer patients. Data on sociodemographic and clinical variables were collected from September 2018 to August 2019. Structural equation model (SEM) was conducted to determine the associations between all factors and psychological distress, and then construct a predictive algorithm. Coincidence rate was also calculated to validate this predictive algorithm. Results: Total 441 participants sent back validated questionnaires. After performing SEM analysis, educational level (β = 0.151, P = 0.004), residence (β = 0.146, P = 0.016), metastasis (β = 0.136, P = 0.023), pain degree (β = 0.133, P = 0.005), family history (β = -0.107, P = 0.021), and tumor, node, and metastasis stage (β = -0.236, P < 0.001) were independent predictors for psychological distress. The model built with these predictors showed an area under the curve of 0.693. A cutoff of 66 predicted clinically significant psychological distress with a sensitivity, specificity, positive predictive value, and negative predictive value of 65.41%, 66.90%, 28.33%, and 89.67%, respectively. The coincidence rate between predictive algorithm and distress thermometer was 64.63%. Conclusions: A validated, easy-to-use predictive algorithm was developed in this study, which can be used to identify patients at high risk of psychological distress with moderate accuracy.
    Àrees temàtiques: Oncology (nursing) Oncology Nursing
    Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
    Adreça de correu electrònic de l'autor: xu.tian@estudiants.urv.cat maria.jimenez@urv.cat
    Identificador de l'autor: 0000-0003-2599-3742
    Data d'alta del registre: 2024-07-27
    Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
    Enllaç font original: https://apjon.org/article/S2347-5625(21)00064-0/fulltext
    URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referència a l'article segons font original: Asia-Pacific Journal Of Oncology Nursing. 8 (4): 403-412
    Referència de l'ítem segons les normes APA: Tian X; Jin Y; Tang L; Pi YP; Chen WQ; Jimenez-Herrera M (2021). Predicting the Risk of Psychological Distress among Lung Cancer Patients: Development and Validation of a Predictive Algorithm Based on Sociodemographic and Clinical Factors. Asia-Pacific Journal Of Oncology Nursing, 8(4), 403-412. DOI: 10.4103/apjon.apjon-2114
    DOI de l'article: 10.4103/apjon.apjon-2114
    Entitat: Universitat Rovira i Virgili
    Any de publicació de la revista: 2021
    Tipus de publicació: Journal Publications
  • Paraules clau:

    Nursing,Oncology,Oncology (Nursing)
    Structural equation model
    Quality-of-life
    Psychological distress
    Prediction model
    Lung neoplasm
    women
    thermometer
    structural equation model
    stress
    service use
    psychological distress
    prevalence
    prediction model
    model
    depression
    care
    anxiety
    Oncology (nursing)
    Oncology
    Nursing
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