Home About us Editorial board Search Ahead of print Current issue Archives Submit article Instructions Subscribe Contacts Login 
  • Users Online: 111
  • Home
  • Print this page
  • Email this page
ORIGINAL ARTICLE
Year : 2021  |  Volume : 8  |  Issue : 4  |  Page : 403-412

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


1 Department of Nursing, Rovira I Virgili University, Tarragona, Spain; Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China
2 Department of Nursing, Rovira I Virgili University, Tarragona, Spain
3 Department of Gastroenterology, Chongqing University Cancer Hospital, Chongqing, China

Correspondence Address:
PhD, RN Maria F Jimenez-Herrera
Department of Nursing, Rovira I Virgili University, Tarragona
Spain
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/apjon.apjon-2114

Rights and Permissions

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.


[FULL TEXT] [PDF]*
Print this article     Email this article
 Next article
 Previous article
 Table of Contents

 Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
 Citation Manager
 Access Statistics
 Reader Comments
 Email Alert *
 Add to My List *
 * Requires registration (Free)
 

 Article Access Statistics
    Viewed366    
    Printed0    
    Emailed0    
    PDF Downloaded32    
    Comments [Add]    

Recommend this journal