Objective To construct and validate a precise predictive model for supportive care needs within 24 hours after surgical anesthesia recovery in breast cancer patients.
Methods A retrospective analysis was conducted on data of 156 breast cancer patients who underwent surgical treatment in the hospital from June 2022 to June 2024. Based on their supportive care needs within 24 hours after surgical anesthesia recovery, the patients were divided into no and low demand group (n=41) and moderate and high demand group (n=115). Clinical data of the two groups were compared using one-way analysis of variance. Binary Logistic regression analysis was employed to identify factors influencing supportive care needs within 24 hours after surgical anesthesia recovery in breast cancer patients, and a predictive model was constructed accordingly.
Results Binary Logistic regression analysis revealed that sources of medical expenses (non-urban medical insurance), occupation (worker), primary caregiver (spouse), the M. D. Anderson Symptom Inventory (MDASI) score, and Quality of Recovery (QoR) score were all influencing factors for supportive care needs within 24 hours after surgical anesthesia recovery in breast cancer patients (P < 0.05). Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) for predicting supportive care needs within 24 hours after surgical anesthesia recovery in breast cancer patients was 0.635 for sources of medical expenses, 0.723 for occupation, 0.618 for primary caregiver, 0.742 for MDASI score, and 0.749 for QoR score, respectively. The AUC of the predictive model for supportive care needs within 24 hours after surgical anesthesia recovery in breast cancer patients was 0.965, with a sensitivity of 93.0% and a specificity of 90.2%. Internal validation of the model using the Bootstrap method with B=1, 000 self-sampling times demonstrated an overall predictive accuracy of 88.5%, indicating good predictive performance.
Conclusion Sources of medical expenses (non-urban medical insurance), occupation (worker), primary caregiver (spouse), MDASI score, and QoR score are all influencing factors for supportive care needs within 24 hours after surgical anesthesia recovery in breast cancer patients. The predictive model constructed based on these factors exhibits good predictive value and can serve as a quantitative decision-making tool for optimizing postoperative nursing pathways.
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