Reply to ‘Adding non-tumor radiomic features to the prognostic model is bothersome but useful’

2022 ◽  
pp. jnumed.121.263730
Author(s):  
Tahir Yusufaly ◽  
Loren Mell
Keyword(s):  
Prognostic Model

PROGNOSTIC MODEL OF SEASONAL RHINOCONJUNCTIVITIS CLINICAL SYMPTOMS PROBABILITY

2018 ◽  
Vol 97 (2) ◽  
pp. 44-49
Author(s):  
D.M. Shiryaeva ◽  
◽  
N.V. Minaeva ◽  
I.P. Korukina ◽  
V.V. Chichagov ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Clinical Symptoms

scholarly journals Development of a Prognostic Model to Predict Mortality after Traumatic Brain Injury in Intensive Care Setting in a Developing Country

2021 ◽  
Vol 12 (02) ◽  
pp. 368-375
Author(s):  
Mini Jayan ◽  
Dhaval Shukla ◽  
Bhagavatula Indira Devi ◽  
Dhananjaya I. Bhat ◽  
Subhas K. Konar
Keyword(s):  
Traumatic Brain Injury ◽  
Intensive Care ◽  
Brain Injury ◽  
Hospital Mortality ◽  
Prognostic Model ◽  
Third Ventricle ◽  
The Third ◽  
Scan Data ◽  
Gcs Score ◽  
Basal Cisterns

Abstract Objectives We aimed to develop a prognostic model for the prediction of in-hospital mortality in patients with traumatic brain injury (TBI) admitted to the neurosurgery intensive care unit (ICU) of our institute. Materials and Methods The clinical and computed tomography scan data of consecutive patients admitted after a diagnosis TBI in ICU were reviewed. Construction of the model was done by using all the variables of Corticosteroid Randomization after Significant Head Injury and International Mission on Prognosis and Analysis of Clinical Trials in TBI models. The endpoint was in-hospital mortality. Results A total of 243 patients with TBI were admitted to ICU during the study period. The in-hospital mortality was 15.3%. On multivariate analysis, the Glasgow coma scale (GCS) at admission, hypoxia, hypotension, and obliteration of the third ventricle/basal cisterns were significantly associated with mortality. Patients with hypoxia had eight times, with hypotensions 22 times, and with obliteration of the third ventricle/basal cisterns three times more chance of death. The TBI score was developed as a sum of individual points assigned as follows: GCS score 3 to 4 (+2 points), 5 to 12 (+1), hypoxia (+1), hypotension (+1), and obliteration third ventricle/basal cistern (+1). The mortality was 0% for a score of “0” and 85% for a score of “4.” Conclusion The outcome of patients treated in ICU was based on common admission variables. A simple clinical grading score allows risk stratification of patients with TBI admitted in ICU.

scholarly journals Discovery of predictors of sudden cardiac arrest in diabetes: rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001554
Author(s):  
Laura H van Dongen ◽  
Peter P Harms ◽  
Mark Hoogendoorn ◽  
Dominic S Zimmerman ◽  
Elisabeth M Lodder ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Prognostic Model ◽  
Early Recognition ◽  
Sudden Cardiac Arrest ◽  
Clinical Genetic ◽  
Innovative Strategy ◽  
Rare Variant Analysis ◽  
Increased Risk ◽  
Gp Care

IntroductionEarly recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information.AimTo describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA.MethodsThe RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA.ConclusionThe RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses.

Sign in / Sign up

Export Citation Format

Share Document