Objective:To explore the factors influencing diarrhea after nasal feeding in patients with severe neurological illness, construct a nomogram model and verify its predictive performance.Methods:A retrospective study was conducted to select 797 nasal feeding patients admitted to the Neurological Intensive Care Unit of the Tianjin First Central Hospital from May 2020 to November 2023 as study subjects. Patients were divided into a modeling set ( n=558) and a validation set ( n=239) in a 7∶3 ratio. Influencing factors were screened based on univariate analysis, correlations between variables were detected by multicollinearity diagnostics, and then risk factors were identified using binomial Logistic regression, and a nomogram model was created. The predictive capacity of the model was assessed using the area under the receiver operating characteristic curve ( AUC), the degree of fit of the model was evaluated using the Hosmer-Lemeshow test and calibration curves were plotted, and the practical application value of the model in clinical practice was appraised by means of clinical decision curve analysis. Results:Binomial Logistic regression analysis showed that Glasgow Coma Scale score ( OR=0.891), Acute Physiology and Chronic Health Evaluation Ⅱ score ( OR=1.063), sedative-analgesic ( OR=0.326), albumin level ( OR=0.856), days of antibiotic use ( OR=3.338), and acid suppressants ( OR=3.260 ) were the factors influencing diarrhea after nasal feeding in patients with severe neurological illness ( P<0.05). In the validation set, the AUC for the nomogram model of the risk of diarrhea after nasal feeding in patients with severe neurological illness was 0.82 [95% CI (0.77, 0.87) ], with a sensitivity of 0.72 and a specificity of 0.72. Clinical decision curve showed nomogram model had high application value. Conclusions:The constructed nomogram model has good predictive performance and can help healthcare professionals in neurocritical care specialties to identify patients at high risk of diarrhea at an early stage, thus informing the development of individualized prevention strategies in clinical practice.

Objective:To explore the factors influencing diarrhea after nasal feeding in patients with severe neurological illness, construct a nomogram model and verify its predictive performance.Methods:A retrospective study was conducted to select 797 nasal feeding patients admitted to the Neurological Intensive Care Unit of the Tianjin First Central Hospital from May 2020 to November 2023 as study subjects. Patients were divided into a modeling set ( n=558) and a validation set ( n=239) in a 7∶3 ratio. Influencing factors were screened based on univariate analysis, correlations between variables were detected by multicollinearity diagnostics, and then risk factors were identified using binomial Logistic regression, and a nomogram model was created. The predictive capacity of the model was assessed using the area under the receiver operating characteristic curve ( AUC), the degree of fit of the model was evaluated using the Hosmer-Lemeshow test and calibration curves were plotted, and the practical application value of the model in clinical practice was appraised by means of clinical decision curve analysis. Results:Binomial Logistic regression analysis showed that Glasgow Coma Scale score ( OR=0.891), Acute Physiology and Chronic Health Evaluation Ⅱ score ( OR=1.063), sedative-analgesic ( OR=0.326), albumin level ( OR=0.856), days of antibiotic use ( OR=3.338), and acid suppressants ( OR=3.260 ) were the factors influencing diarrhea after nasal feeding in patients with severe neurological illness ( P<0.05). In the validation set, the AUC for the nomogram model of the risk of diarrhea after nasal feeding in patients with severe neurological illness was 0.82 [95% CI (0.77, 0.87) ], with a sensitivity of 0.72 and a specificity of 0.72. Clinical decision curve showed nomogram model had high application value. Conclusions:The constructed nomogram model has good predictive performance and can help healthcare professionals in neurocritical care specialties to identify patients at high risk of diarrhea at an early stage, thus informing the development of individualized prevention strategies in clinical practice.