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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

  • Identification data

    Identifier: imarina:9218780
    Authors:
    Tian XJin YTang LPi YPChen WQJimenez-Herrera M
    Abstract:
    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.
  • Others:

    Author, as appears in the article.: Tian X; Jin Y; Tang L; Pi YP; Chen WQ; Jimenez-Herrera M
    Department: Infermeria
    URV's Author/s: Jiménez Herrera, María Francisca / Jin, Yan Fei / Tian, Xu
    Keywords: 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
    Abstract: 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.
    Thematic Areas: Oncology (nursing) Oncology Nursing
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: xu.tian@estudiants.urv.cat maria.jimenez@urv.cat
    Author identifier: 0000-0003-2599-3742
    Record's date: 2024-07-27
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://apjon.org/article/S2347-5625(21)00064-0/fulltext
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Asia-Pacific Journal Of Oncology Nursing. 8 (4): 403-412
    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
    Article's DOI: 10.4103/apjon.apjon-2114
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2021
    Publication Type: Journal Publications
  • Keywords:

    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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