Di Sun, Tingting Huang, Jiaojiao Li, Meishuo Liu, Xu Zhang, Mengyao Cui
{"title":"基于LASSO-Logistic回归模型的癌症家属照顾者预期悲伤预测模型的建立与验证。","authors":"Di Sun, Tingting Huang, Jiaojiao Li, Meishuo Liu, Xu Zhang, Mengyao Cui","doi":"10.1002/pon.70236","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Anticipatory grief is a significant emotional challenge for family caregivers of cancer patients, yet its early identification remains limited by subjective assessments and a lack of predictive tools. This study aimed to develop and validate a predictive model for anticipatory grief among family caregivers of cancer patients in China.</p><p><strong>Methods: </strong>A multicenter cross-sectional study was conducted from February to October 2023, involving 642 family caregivers of lung and breast cancer patients from two tertiary hospitals in Liaoning Province, China. Latent Profile Analysis (LPA) classified caregivers into anticipatory grief risk categories based on the Anticipatory Grief Scale. LASSO-logistic regression was used to identify predictors and construct a predictive model, which was validated using discrimination (AUC), calibration (Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis). A web-based nomogram was developed for practical application.</p><p><strong>Results: </strong>The mean anticipatory grief score was 72.44 ± 18.49, with LPA identifying three profiles: low (54.52%), moderate (30.53%), and high (14.95%) anticipatory grief. Seven predictors were identified: caregiver education level, monthly income, physical condition, caregiving duration, and patient cancer type, employment status, and time since diagnosis. The model showed good discrimination (AUC: 0.769 training, 0.671 validation), calibration (P = 0.095 training, P = 0.801 validation), and clinical utility (net benefit at 34%-62% threshold). The web-based tool is accessible at https://nomogrameofag.shinyapps.io/dynnomapp/.</p><p><strong>Conclusions: </strong>This study developed a predictive model for anticipatory grief, identifying key risk factors and providing a practical tool for healthcare providers to identify high-risk caregivers. The findings support targeted interventions to enhance caregiver well-being and patient care quality, though future research should expand cancer types and incorporate qualitative insights for broader applicability.</p>","PeriodicalId":20779,"journal":{"name":"Psycho‐Oncology","volume":"34 7","pages":"e70236"},"PeriodicalIF":3.5000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and Validation of a Predictive Model for Anticipatory Grief in Family Caregivers of Cancer Patients: Based on LASSO-Logistic Regression Model.\",\"authors\":\"Di Sun, Tingting Huang, Jiaojiao Li, Meishuo Liu, Xu Zhang, Mengyao Cui\",\"doi\":\"10.1002/pon.70236\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Anticipatory grief is a significant emotional challenge for family caregivers of cancer patients, yet its early identification remains limited by subjective assessments and a lack of predictive tools. This study aimed to develop and validate a predictive model for anticipatory grief among family caregivers of cancer patients in China.</p><p><strong>Methods: </strong>A multicenter cross-sectional study was conducted from February to October 2023, involving 642 family caregivers of lung and breast cancer patients from two tertiary hospitals in Liaoning Province, China. Latent Profile Analysis (LPA) classified caregivers into anticipatory grief risk categories based on the Anticipatory Grief Scale. LASSO-logistic regression was used to identify predictors and construct a predictive model, which was validated using discrimination (AUC), calibration (Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis). A web-based nomogram was developed for practical application.</p><p><strong>Results: </strong>The mean anticipatory grief score was 72.44 ± 18.49, with LPA identifying three profiles: low (54.52%), moderate (30.53%), and high (14.95%) anticipatory grief. Seven predictors were identified: caregiver education level, monthly income, physical condition, caregiving duration, and patient cancer type, employment status, and time since diagnosis. The model showed good discrimination (AUC: 0.769 training, 0.671 validation), calibration (P = 0.095 training, P = 0.801 validation), and clinical utility (net benefit at 34%-62% threshold). The web-based tool is accessible at https://nomogrameofag.shinyapps.io/dynnomapp/.</p><p><strong>Conclusions: </strong>This study developed a predictive model for anticipatory grief, identifying key risk factors and providing a practical tool for healthcare providers to identify high-risk caregivers. The findings support targeted interventions to enhance caregiver well-being and patient care quality, though future research should expand cancer types and incorporate qualitative insights for broader applicability.</p>\",\"PeriodicalId\":20779,\"journal\":{\"name\":\"Psycho‐Oncology\",\"volume\":\"34 7\",\"pages\":\"e70236\"},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Psycho‐Oncology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1002/pon.70236\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Psycho‐Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1002/pon.70236","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
Development and Validation of a Predictive Model for Anticipatory Grief in Family Caregivers of Cancer Patients: Based on LASSO-Logistic Regression Model.
Background: Anticipatory grief is a significant emotional challenge for family caregivers of cancer patients, yet its early identification remains limited by subjective assessments and a lack of predictive tools. This study aimed to develop and validate a predictive model for anticipatory grief among family caregivers of cancer patients in China.
Methods: A multicenter cross-sectional study was conducted from February to October 2023, involving 642 family caregivers of lung and breast cancer patients from two tertiary hospitals in Liaoning Province, China. Latent Profile Analysis (LPA) classified caregivers into anticipatory grief risk categories based on the Anticipatory Grief Scale. LASSO-logistic regression was used to identify predictors and construct a predictive model, which was validated using discrimination (AUC), calibration (Hosmer-Lemeshow test), and clinical utility (Decision Curve Analysis). A web-based nomogram was developed for practical application.
Results: The mean anticipatory grief score was 72.44 ± 18.49, with LPA identifying three profiles: low (54.52%), moderate (30.53%), and high (14.95%) anticipatory grief. Seven predictors were identified: caregiver education level, monthly income, physical condition, caregiving duration, and patient cancer type, employment status, and time since diagnosis. The model showed good discrimination (AUC: 0.769 training, 0.671 validation), calibration (P = 0.095 training, P = 0.801 validation), and clinical utility (net benefit at 34%-62% threshold). The web-based tool is accessible at https://nomogrameofag.shinyapps.io/dynnomapp/.
Conclusions: This study developed a predictive model for anticipatory grief, identifying key risk factors and providing a practical tool for healthcare providers to identify high-risk caregivers. The findings support targeted interventions to enhance caregiver well-being and patient care quality, though future research should expand cancer types and incorporate qualitative insights for broader applicability.
期刊介绍：
Psycho-Oncology is concerned with the psychological, social, behavioral, and ethical aspects of cancer. This subspeciality addresses the two major psychological dimensions of cancer: the psychological responses of patients to cancer at all stages of the disease, and that of their families and caretakers; and the psychological, behavioral and social factors that may influence the disease process. Psycho-oncology is an area of multi-disciplinary interest and has boundaries with the major specialities in oncology: the clinical disciplines (surgery, medicine, pediatrics, radiotherapy), epidemiology, immunology, endocrinology, biology, pathology, bioethics, palliative care, rehabilitation medicine, clinical trials research and decision making, as well as psychiatry and psychology.
This international journal is published twelve times a year and will consider contributions to research of clinical and theoretical interest. Topics covered are wide-ranging and relate to the psychosocial aspects of cancer and AIDS-related tumors, including: epidemiology, quality of life, palliative and supportive care, psychiatry, psychology, sociology, social work, nursing and educational issues.
Special reviews are offered from time to time. There is a section reviewing recently published books. A society news section is available for the dissemination of information relating to meetings, conferences and other society-related topics. Summary proceedings of important national and international symposia falling within the aims of the journal are presented.
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