Object To develop and evaluate a risk predictive model for high level of self-reported symptom cluster distress in patients with hematologic malignancies.

Methods 354 patients who met the inclusion and exclusion criteria were selected at 5 tertiary hospitals in Beijing，Tianjin，Shandong，Jiangsu，and Anhui provinces in China from June to August 2023．Cases were randomly assigned to a modeling group and a validation group based on 5-old cross-validation method at a ratio of 8：2．The random forest algorithm was used to develop the risk predictive model in the modeling group．The receiver operating characteristic curve，Hosmer-Lemeshow goodness-of-fit test，calibration curve，and decision curve were used to comprehensively evaluate the prediction performance of the model in the validation group．Risk factors were identified based on the order of the importance of each influencing factors．

Results The incidence of high symptom cluster distress was 35.37％in the modeling group and 31.29％in the validation group．The area under the receiver operating characteristic curve of the prediction model was 0.91；the sensitivity was 68.6%，the specificity was 94.6％；Hosmer-Lemeshow goodness-of-fit test was insignificant(P=0.1375)；the decision curve was above the reference line．

Conclusion The risk predictive model based on random forest algorithm has good predictive performance，which is of great significance to help identify hematologic malignancies subgroups at high risk of symptom cluster distress，and will potentially promote symptom management．

Disclosures

No relevant conflicts of interest to declare.

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2024
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