Frontiers in psychiatry · 2026
A clinical prediction model for delirium tremens: development and validation in alcohol-dependent patients using multivariable logistic regression.
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Zhong Jing, Huang Xiaoyu, Yao Xudong
Аннотация
BACKGROUND: Delirium tremens (DT) is a severe complication of alcohol withdrawal. This study aimed to develop and validate a prediction model for DT risk in hospitalized patients with alcohol dependence, using routine laboratory indicators.
METHODS: We retrospectively analyzed 347 patients with alcohol dependence admitted to the Addiction Medicine Department of a tertiary psychiatric hospital from 2020 to 2024. The primary outcome was DT occurrence. A prediction model was constructed using logistic regression, with data split into training (70%) and validation (30%) sets by random sampling. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).
RESULTS: Of 347 patients, 118 (34%) developed DT. LASSO regression identified 11 predictors: history of DT, ammonia, creatinine, uric acid, total bilirubin (Tbiliary), albumin (ALB), gamma-glutamyl transferase (GGT), chloride (Cl), free triiodothyronine (Free_T3), free thyroxine (Free_T4), neutrophil percentage (NEU%), and red blood cell (RBC) count. Logistic regression confirmed that history of DT, ammonia, creatinine, ALB, Free_T3, NEU%, and RBC were independent risk factors (P< 0.05). The model demonstrated robust performance: AUC = 0.9881 [95% CI: 0.9794-0.9967] in the training set and 0.9599 [95% CI: 0.9142-1.0000] in the validation set, with high net benefit in DCA.
CONCLUSIONS: This model, incorporating readily available biomarkers and clinical history, effectively predicts DT risk. Limitations include its retrospective design (potential selection bias) and exclusion of clinical scales (e.g., CIWA-Ar). Prospective multicenter studies are needed to validate its generalizability.
METHODS: We retrospectively analyzed 347 patients with alcohol dependence admitted to the Addiction Medicine Department of a tertiary psychiatric hospital from 2020 to 2024. The primary outcome was DT occurrence. A prediction model was constructed using logistic regression, with data split into training (70%) and validation (30%) sets by random sampling. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA).
RESULTS: Of 347 patients, 118 (34%) developed DT. LASSO regression identified 11 predictors: history of DT, ammonia, creatinine, uric acid, total bilirubin (Tbiliary), albumin (ALB), gamma-glutamyl transferase (GGT), chloride (Cl), free triiodothyronine (Free_T3), free thyroxine (Free_T4), neutrophil percentage (NEU%), and red blood cell (RBC) count. Logistic regression confirmed that history of DT, ammonia, creatinine, ALB, Free_T3, NEU%, and RBC were independent risk factors (P< 0.05). The model demonstrated robust performance: AUC = 0.9881 [95% CI: 0.9794-0.9967] in the training set and 0.9599 [95% CI: 0.9142-1.0000] in the validation set, with high net benefit in DCA.
CONCLUSIONS: This model, incorporating readily available biomarkers and clinical history, effectively predicts DT risk. Limitations include its retrospective design (potential selection bias) and exclusion of clinical scales (e.g., CIWA-Ar). Prospective multicenter studies are needed to validate its generalizability.