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Construction and Verification of Prediction Model of Frailty Risk in Elderly
Patients with Hematological Neoplasms

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1
J. Zhao, Y. Liu, Q. Zhang, and W. Xie 2
1
Myelodysplastic Syndromes (MDS) Center, State Key Laboratory of Experimental Hematology, National
1
Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology
& Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin
2
Institutes of Health Science. CAMS&PUMC, China, and Nursing Department, State Key Laboratory of
Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell
Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences &
Peking Union Medical College, Tianjin Institutes of Health Science. CAMS&PUMC, China

Background: Frailty manifests as a non-specific state where the a decline in multi-system capacity
and an increase in vulnerability. Research has shown that the prevalence of frailty in hematological
neoplasms(HN) patients was between 49.6% to 66%. Additionally, over 60% of them are prone to be
frail due to diseases and side effects, leading to adverse outcomes. Therefore, Gobbens’ holistic
conceptual model of frailty was employed to guide the selection of predictor variables.

Objective: Develop and validate a nomogram for the prediction of frailty among elderly patients with
Oral Presentation Abstracts
HN.

Methods: A total of 505 elderly patients with HN from 17 hospitals in China from April 2023 to November 2023
were included in the study. Characteristic features and disease-related information were collected, and
geriatric assessments were conducted. Patients were divided by the Geriatric 8 questionnaire into frail and
non-frail. The Chi-square test and Mann-Whitney U test were used to compare the characteristics of the
two groups. Univariate analysis and binary logistic regression were used to explore factors affecting frailty,
and a risk prediction model was established in the form of a nomogram. Internal validation was carried out
by the bootstrap using receiver operating characteristics, calibration curve, and decision analysis curve.

Results: The incidence rate of frailty in elderly patients with HN was 56.6% (286/505). The nomogram
model (including cycles of chemotherapy, self-care ability, nutritional status, and hemoglobin level)
showed good predicting performance in both training and validation sets. In the validation cohort, the
AUC was 0.867 (95%CI 0.833~0.897), Youden index was 0.557, and the sensitivity and specificity were
85.71% and 69.95%, respectively. The average absolute error of the coincidence degree between the
predicted value and the real value was 0.01.

Conclusion: The nomogram showed good predicting value on elderly patients with HN, which can
provide a reference for clinical doctors and nurses to identify the frailty risk.

Keywords: elderly, frailty, hematological neoplasms, prediction model, risk assessment

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Correspondence: Wenjun Xie, Nursing Department, State Key Laboratory of Experimental Hematology,
National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of
Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical
College, Tianjin Institutes of Health Science. CAMS&PUMC, China
E-mail: xiewenjun@ihcams.ac.cn

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