Related Experiment Video
Updated: Jan 30, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Risk scores for predicting dysphagia in critically ill patients after cardiac surgery
Xiao-Dong Zhou1, Wei-Hua Dong2, Chu-Huan Zhao2
1Department of Cardiovascular Medicine, The Heart Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Background:
This study aimed at developing and validating a scoring model to stratify critically ill patients after cardiac surgery based on risk for dysphagia, a common but often neglected complication.
Methods:
Data were prospectively collected and analyzed from January 2016 to June 2017 from 395 consecutive post cardiac surgery patients at the cardiac care unit (CCU) at a single center; 103 (26.1%) developed dysphagia. Univariate and multivariate logistic analyses were used to identify independent predictors for dysphagia. The survival nomogram was developed on the basis of a multivariable Cox model, which allowed us to obtain survival probability estimations. The predictive performance of the nomogram was verified for discrimination and calibration. Areas under receiver operating characteristic curve analysis were used to illustrate and evaluate the diagnostic performance of the novel model.
Results:
The final novel scoring model, named SSG-OD, consists of three independent factors: gastric intubation (OR = 1.024, 95% CI 1.015-1.033), sedative drug use duration (OR = 1.031, 95% CI 1.001-1.063) and stroke or not (OR = 6.182, 95% CI 3.028-12.617). SSG-OD identified patients at risk for dysphagia with sensitivity of 68.5% and specificity of 89.0% (OR = 0.833, 95% CI: 0.782-0.884). The positive and negative likelihood ratios were 6.22 and 0.35.
Conclusions:
The novel SSG-OD scoring system to risk stratify CCU patients for dysphagia is an easy-to-use bedside prognostication aid with good predictive performance and the potential to reduce aspiration incidence and accelerate recovery.
More Related Videos
09:44Manual Muscle Testing: A Method of Measuring Extremity Muscle Strength Applied to Critically Ill Patients
Published on: April 12, 2011
07:25Predicting Amputation using Local Circulating Mononuclear Progenitor Cells in Angioplasty-treated Patients with Critical Limb Ischemia
Published on: September 22, 2020
Related Concept Videos
Introduction to z Scores
z scores...
Introduction to z Scores
z scores...
z Scores and Area Under the Curve
Predicting Molecular Geometry
Critical Region, Critical Values and Significance Level
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
Critical Values