Development and validation of a predictive model for patients with post-extubation dysphagia

BACKGROUND:Swallowing disorder is a common clinical symptom that can lead to a series of complications,including aspiration,aspiration pneumonia,and malnutrition.This study aimed to investigate risk factors of post-extubation dysphagia(PED)in intensive care unit(ICU)patients with endotracheal intubation,and to develop a risk-predictive model for PED,which could serve as an assessment tool for the prevention and control of PED.METHODS:Patients retrospectively selected from June to December 2021 in a tertiary hospital served as the derivation cohort.Patients recruited from the same hospital from March to June 2022served as the external validation cohort for the predictive model.We used a combination of variable screening and least absolute shrinkage and selection operator(LASSO)regression to select the most useful candidate predictors and checked the multicollinearity of independent variables using the variance inflation factor method.Multivariate logistic regression analysis was performed to calculate the odds ratio(OR;95%confidence interval[95%CI])and P-value for each variable to predict diagnosis.The screened risk factors were introduced into R software to build a nomogram model.The performance of the model,including discrimination ability,calibration,and clinical benefit,was evaluated by plotting the receiver operating characteristic(ROC),calibration,and decision curves.RESULTS:A total of 305 patients were included in this study.Among them,235 patients(53PED vs.182 non-PED)were enrolled in the derivation cohort,while 70 patients(17 PED vs.53 nonPED)were enrolled in the validation cohort.The independent predictors included age,pause of sedatives,level of consciousness,activities of daily living(ADL)score,nasogastric tube,sore throat,and voice disorder.These predictors were used to establish the predictive nomogram model.The model demonstrated good discriminative ability,and the area under the ROC curve(AUC)was 0.945(95%CI 0.904-0.970).Applying the predictive model to the validation cohort demonstrated good discrimination with an AUC of 0.907(95%CI 0.831-0.983)and good calibration.The decision-curve analysis of this nomogram showed a net benefit of the model.CONCLUSION:A predictive model that incorporates age,pause of sedatives,level of consciousness,ADL score,nasogastric tube,sore throat,and voice disorder may have the potential to predict PED in ICU patients.

Prediction of motor recovery after ischemic stroke using diffusion tensor imaging： A meta-analysis

BACKGROUND: This systematic review aims to investigate the prediction value of diffusion tensor imaging for motor function recovery of ischemic stroke patients.METHODS: Cochrane Central Register of Controlled Trials(CENTRAL)(the Cochrane Library 2016, Issue 9), Pub Med, Embase, Clarivate Analytics, Scopus, CINAHL, Chinese Biomedical Literature Database, China National Knowledge Infrastructure and Google Scholar were searched for either motor recovery or corticospinal tract integrity by diffusion tensor imaging in different stroke phase from January 1, 1970, to October 31, 2016. The study design and participants were subjected to metrological analysis. Correlation coeffi cient(r) was used for evaluating the relationship between fractional anisotropy(FA) and motor function outcome. Correlation coeffi cient values were extracted from each study, and 95% confidence intervals(CIs) were calculated by Fisher's z transformation. Meta-analysis was conducted by STATA software.RESULTS: Fifteen studies with a total of 414 patients were included. Meta-analysis showed that FA in the subacute phase had the signifi cant correlation with motor function outcome(ES=0.75, 95%CI 0.62–0.87), which showed moderate quality based on GRADE system. The weight correlation coeffi cient revealed that an effect size(ES) of FA in acute phase and chronic phase was 0.51(95%CI 0.33–0.68) and 0.62(95%CI 0.47–0.77) respectively.CONCLUSION: This meta-analysis reveals that FA in the subacute phase after ischemic stroke is a good predictor for functional motor recovery, which shows moderate quality based on the GRADE system.