1. Construction of a risk prediction model for detecting postintensive care syndrome--mental disorders.
- Author
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Faying Wang, Jingshu Li, Yuying Fan, and Xiaona Qi
- Subjects
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MENTAL illness risk factors , *PSYCHIATRIC diagnosis , *RISK assessment , *POST-traumatic stress disorder , *WOUNDS & injuries , *PREDICTION models , *T-test (Statistics) , *RECEIVER operating characteristic curves , *ADULT respiratory distress syndrome , *MULTIPLE regression analysis , *QUESTIONNAIRES , *CATASTROPHIC illness , *DESCRIPTIVE statistics , *MANN Whitney U Test , *CHI-squared test , *ANXIETY , *LONGITUDINAL method , *INTENSIVE care units , *ANALYSIS of variance , *STATISTICS , *THEORY , *SOCIODEMOGRAPHIC factors , *MENTAL depression , *SLEEP disorders , *DIABETES , *DISEASE complications - Abstract
Background: Postintensive care syndrome (PICS) has adverse multidimensional effects on nearly half of the patients discharged from ICU. Mental disorders such as anxiety, depression and post-traumatic stress disorder (PTSD) are the most common psychological problems for patients with PICS with harmful complications. However, developing prediction models for mental disorders in post-ICU patients is an understudied problem. Aims: To explore the risk factors of PICS mental disorders, establish the prediction model and verify its prediction efficiency. Study Design: In this cohort study, data were collected from 393 patients hospitalized in the ICU of a tertiary hospital from April to September 2022. Participants were randomly assigned to modelling and validation groups using a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to select the predictors, multiple logistic regression analysis was used to establish the risk prediction model, and a dynamic nomogram was developed. The Hosmer-Lemeshow (HL) test was performed to determine the model's goodness of fit. The area under the receiver operating characteristic (ROC) curve was used to evaluate the model's prediction efficiency. Results: The risk factors of mental disorders were Sepsis-related organ failure assessment (SOFA) score, Charlson comorbidity index (CCI), delirium duration, ICU depression score and ICU sleep score. The HL test revealed that p = .249, the area under the ROC curve = 0.860, and the corresponding sensitivity and specificity were 84.8% and 71.0%, respectively. The area under the ROC curve of the verification group was 0.848. A mental disorders dynamic nomogram for post-ICU patients was developed based on the regression model. Conclusions: The prediction model provides a reference for clinically screening patients at high risk of developing post-ICU mental disorders, to enable the implementation of timely preventive management measures. Relevance to Clinical Practice: The dynamic nomogram can be used to systematically monitor various factors associated with mental disorders. Furthermore, nurses need to develop and apply accurate nursing interventions that consider all relevant variables. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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