A risk prediction model for xerostomia: a retrospective cohort study

Gerodontology ◽  
2015 ◽  
Vol 33 (4) ◽  
pp. 562-568 ◽  
Author(s):  
Alessandro Villa ◽  
Francesco Nordio ◽  
Anita Gohel
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Risk Prediction Model

scholarly journals Development and Validation of a Prognostic Nomogram for Cerebral Infarction Patients in Intensive Care Units: a Retrospective Cohort Study

2021 ◽  
Author(s):  
Mei Yu ◽  
Jun Chen ◽  
Guoping Xu ◽  
Rui Zeng ◽  
Qiang Liu
Keyword(s):  
Cohort Study ◽  
Cerebral Infarction ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Risk Prediction ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Clinical Utility ◽  
Risk Prediction Model ◽  
Lasso Regression

Abstract OBJECTIVES: Our study aimed to establish a utility risk prediction model for the prognosis of patients with cerebral infarction.BACKGROUND: Despite large number of studies focus on the prognosis risk factors of patients with cerebral infarction, there were still lack of utility and visual risk prediction model for predicting the in-hospital mortality of patients with cerebral infarction.METHODS: The study is a retrospective cohort study. The lasso regression model was used for data dimension reduction and feature selection. Model of hospital mortality of cerebral infarction patients was developed by multivariable logistic regression analysis. Calibration and discrimination were used to assess the performance of the nomogram. Decision curve analysis (DCA) was used to evaluate the clinical utility of the model. RESULTS: Overall, 1,564 patients (1315 survivals and 249 deaths) with cerebral infarction included in our research from MIMIC-IV database. The incident of in-hospital mortality is 15.9%. Lasso regression model verified that age, white blood cell count, anion gap (AG), SOFA score were significantly correlated with hospital mortality. The risk prediction model demonstrated a good discrimination with an AUC of ROC 0.789 (95% CI 0.752–0.826) in training set and 0.829 (95% CI 0.791–0.867) in test set. The calibration plot of risk prediction model showed predicted probabilities against observed death rates indicated excellent concordance. DCA showed that this model has good clinical benefits.Conclusion: We developed a nomogram that predicts hospital mortality in patients with cerebral infarction according to the real world’s data. The nomogram exhibited excellent discrimination and calibration capacity, favoring its clinical utility.

scholarly journals Prediction Model for Hospital-Acquired Pressure Ulcer Development: Retrospective Cohort Study

10.2196/13785 ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. e13785 ◽  
Author(s):  
Sookyung Hyun ◽  
Susan Moffatt-Bruce ◽  
Cheryl Cooper ◽  
Brenda Hixon ◽  
Pacharmon Kaewprag
Keyword(s):  
Cohort Study ◽  
Prediction Model ◽  
Pressure Ulcer ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Pressure Ulcer Development ◽  
Hospital Acquired

Development of a Risk Prediction Model for Carbapenem-Resistant Enterobacteriaceae Infection after Liver Transplantation: A Multinational Cohort Study

2021 ◽  
Author(s):  
Maddalena Giannella ◽  
Maristela Freire ◽  
Matteo Rinaldi ◽  
Edson Abdala ◽  
Arianna Rubin ◽  
...  
Keyword(s):  
Liver Transplantation ◽  
Cohort Study ◽  
Prediction Model ◽  
Risk Prediction ◽  
Univariate Analysis ◽  
Risk Prediction Model ◽  
Validation Dataset ◽  
High Morbidity ◽  
Carbapenem Resistant ◽  
Carbapenem Resistant Enterobacteriaceae

Abstract Background Patients colonized with carbapenem resistant Enterobacteriaceae (CRE) are at higher risk of developing CRE infection after liver transplantation (LT) with associated high morbidity and mortality. Prediction model for CRE infection after LT among carriers could be useful to target preventive strategies. Methods Multinational multicenter cohort study of consecutive adult patients underwent LT and colonized with CRE before or after LT, from January 2010 to December 2017. Risk factors for CRE infection were analyzed by univariate analysis and by Fine-Gray sub-distribution hazard model, with death as competing event. A nomogram to predict 30- and 60-day CRE infection risk was created. Results 840 LT recipients found to be colonized with CRE before (n=203) or after (n=637) LT were enrolled. CRE infection was diagnosed in 250 (29.7%) patients within 19 (IQR 9-42) days after LT. Pre-and post-LT colonization, multisite post-LT colonization, prolonged mechanical ventilation, acute renal injury, and surgical re-intervention were retained in the prediction model. Median 30 and 60-day predicted risk was 15% (IQR 11-24%) and 21% (IQR 15-33%), respectively. Discrimination and prediction accuracy for CRE infection was acceptable on derivation (AUC 74.6, Brier index 16.3) and bootstrapped validation dataset (AUC 73.9, Brier index 16.6). Decision-curve analysis suggested net benefit of model-directed intervention over default strategies (treat all, treat none) when CRE infection probability exceeded 10%. The risk prediction model is freely available as mobile application at https://idbologna.shinyapps.io/CREPostOLTPredictionModel/. Conclusions Our clinical prediction tool could enable better targeting interventions for CRE infection after transplant.

Sign in / Sign up

Export Citation Format

Share Document