Development and validation of a dynamic risk prediction system for constipation in patients with amyotrophic lateral...

Although constipation is a common non-motor symptom in patients with amyotrophic lateral sclerosis (ALS), it is poorly valued. Moreover, there is a bidirectional effect between constipation and neuropsychiatric and sleep disturbances. Thus, these symptoms are better treated simultaneously. Therefore, this study aimed to develop and validate a model for predicting the risk of constipation in ALS patients, to help clinicians identify and treat constipation early.

Data of 118 ALS admissions from an observational prospective cohort, registered between March 2017 and December 2021, were analyzed. Demographic data were obtained. Constipation was assessed using the Knowles-Eccersley-Scott Symptom Questionnaire. The severity of ALS was assessed using the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R). Anxiety and depressive symptoms were measured using the Hospital Anxiety and Depression Scale (HADS). The Pittsburgh Sleep Quality Index (PSQI) was used to assess patients' sleep status. The least absolute shrinkage and selection operator (LASSO) regression model was used to select factors and construct a nomogram. Nomogram model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). The model was internally validated using bootstrap validation in the current cohort.

Age, family history of constipation, total ALSFRS-R score, site of onset, total PSQI score, and depressed, were identified as significant predictors of the risk of constipation in ALS patients. The prediction model was validated to have good accuracy (Hosmer-Lemeshow test: χ² = 11.11, P > 0.05) and discrimination (AUC = 0.856, 95% confidence interval: 0.784-0.928). DCA and CIC showed that the nomogram model had excellent clinical performance.

A web-based ALS constipation risk calculator with good predictive performance was constructed to identify patients at high risk of constipation and to allow early intervention in a clinical context.