Objective:To analyze the risk factors for prolonged hospital stay in inpatients based on data from nutritionDay worldwide survey 2020 to 2022 conducted in China and to construct and validate a prediction model for clinical decision-making.Methods:This study was a retrospective study, the data source was the China's multi-centered nutritionalDay worldwide database for nutrition status in inpatients. A total of 2 335 cases registered in the database from 2020 to 2022 were selected for the study, comprising individuals aged 18 and above, with valid response for the 30-day prognosis questionnaires, and with complete clinical data. The demographic characteristics, nutrition-related indicators, disease information, and outcome indicators of the participants were collected. Based on the 75th percentile of hospitalization duration, the participants were divided into the prolonged length of stay group (570 cases) and the normal length of stay group (1 765 cases). A nomogram prediction model was constructed using the least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Logistic regression analysis. Area under the curve ( AUC), calibration curve, Hosmer-Lemeshow test, and clinical decision curve were used to verify discriminative ability, goodness-of-fit, and clinical effectiveness of the model. Results:Seven independent risk factors for prolonged length of stay were identified, namely body mass index (BMI), whether surgery occurred during hospitalization, intensive care unit admission during hospitalization, Nutrition Risk Screening 2002 score, ambulatory independence, previous hospitalization frequency, and weight loss in the past 3 months. A nomogram prediction model was established accordingly. The AUC of training set was 0.783 (95% CI: 0.759 - 0.807), and the AUC of validation set was 0.797 (95% CI: 0.746 - 0.849). Calibration curves and Hosmer-Lemeshow tests ( P=0.735 for training set, P=0.431 for validation set) indicated good model fitting. The clinical decision curve demonstrated the favorable clinical utility of the nomogram. Conclusions:BMI, whether they had surgery during hospitalization, whether they were admitted to the ICU during hospitalization, NRS 2002 score, whether they were able to walk independently, number of previous hospitalizations, and weight loss in the past 3 months were risk factors for longer stay in Chinese hospitalized patients. The nomogram prediction model developed in this study can forecast the risk of prolonged length of stay among Chinese inpatients, providing a basis for early identification and intervenion in high-risk patients.

Objective:To analyze the risk factors for prolonged hospital stay in inpatients based on data from nutritionDay worldwide survey 2020 to 2022 conducted in China and to construct and validate a prediction model for clinical decision-making.Methods:This study was a retrospective study, the data source was the China's multi-centered nutritionalDay worldwide database for nutrition status in inpatients. A total of 2 335 cases registered in the database from 2020 to 2022 were selected for the study, comprising individuals aged 18 and above, with valid response for the 30-day prognosis questionnaires, and with complete clinical data. The demographic characteristics, nutrition-related indicators, disease information, and outcome indicators of the participants were collected. Based on the 75th percentile of hospitalization duration, the participants were divided into the prolonged length of stay group (570 cases) and the normal length of stay group (1 765 cases). A nomogram prediction model was constructed using the least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Logistic regression analysis. Area under the curve ( AUC), calibration curve, Hosmer-Lemeshow test, and clinical decision curve were used to verify discriminative ability, goodness-of-fit, and clinical effectiveness of the model. Results:Seven independent risk factors for prolonged length of stay were identified, namely body mass index (BMI), whether surgery occurred during hospitalization, intensive care unit admission during hospitalization, Nutrition Risk Screening 2002 score, ambulatory independence, previous hospitalization frequency, and weight loss in the past 3 months. A nomogram prediction model was established accordingly. The AUC of training set was 0.783 (95% CI: 0.759 - 0.807), and the AUC of validation set was 0.797 (95% CI: 0.746 - 0.849). Calibration curves and Hosmer-Lemeshow tests ( P=0.735 for training set, P=0.431 for validation set) indicated good model fitting. The clinical decision curve demonstrated the favorable clinical utility of the nomogram. Conclusions:BMI, whether they had surgery during hospitalization, whether they were admitted to the ICU during hospitalization, NRS 2002 score, whether they were able to walk independently, number of previous hospitalizations, and weight loss in the past 3 months were risk factors for longer stay in Chinese hospitalized patients. The nomogram prediction model developed in this study can forecast the risk of prolonged length of stay among Chinese inpatients, providing a basis for early identification and intervenion in high-risk patients.