Development and validation of the "FeiChangHeHu" WeChat mini-program for real-time management of chemotherapy-induced nausea and vomiting in lung cancer patients.

The study is to assess the accuracy and convenience of utilizing the "FeiChangHeHu" mini-program in predicting the risk of chemotherapy-induced nausea and vomiting (CINV), evaluating the prevalence and severity symptom related to CINV, and determining the temporal patterns of symptoms. A single-center, retrospective study was conducted at the Thoracic Oncology Department, TongJi Hospital, TongJi Medical College of Huazhong University of Science and Technology from April 24 to June 18, 2024. Eighty-seven adult lung cancer patients undergoing chemotherapy used the "FeiChangHeHu" mini-program to report symptoms daily. The platform integrated automated CINV risk stratification, symptom tracking, clinical alerts, and intervention for grade ≥2 symptoms. Data were collected prospectively and analyzed using descriptive statistics and inferential statistics, including chi-square tests and t tests by SPSS 26.0 (IBM Corporation, Armonk). The receiver operating characteristic curve and the area under the curve were used to quantify the ability of the model to distinguish risks. The moderate-to-high risk detection rate was 98.85% by mini-program. The model showed moderate performance with an area under the curve of 0.712 (95% confidence interval: 0.611-0.852), indicating acceptable classification accuracy. Nausea was the most prevalent toxicity, affecting 59.77% of patients, with 88% classified as grades 3 to 4. The temporal distribution of chemotherapy-induced adverse events exhibits distinct peaks across different symptoms. Nurses resolved 100% of alerts within 24 hours, and no severe CINV-related complications were reported during the study period. The "FeiChangHeHu" mini-program improves CINV risk detection and symptom monitoring in lung cancer patients. Future studies should explore scalability and artificial intelligence-driven predictive analytics.