External validation of a risk score in the prediction of the mortality after esophagectomy for cancer

2016 ◽  
pp. n/a-n/a ◽  
Author(s):  
X. B. D'Journo ◽  
J. Berbis ◽  
J. Jougon ◽  
P.-Y. Brichon ◽  
J. Mouroux ◽  
...  
Keyword(s):  
Risk Score ◽  
External Validation ◽  
Esophagectomy For Cancer

Abstract # OR-20: External Validation of the Leicester Self-Assessment Diabetes Risk Score in a Population with Intellectual Disability

2016 ◽  
Vol 22 ◽  
pp. 12
Author(s):  
Laura Gray ◽  
Yogini Chudasama ◽  
Alison Dunkley ◽  
Freya Tyrer ◽  
Rebecca Spong ◽  
...  
Keyword(s):  
Intellectual Disability ◽  
Risk Score ◽  
External Validation ◽  
Diabetes Risk ◽  
Self Assessment ◽  
Diabetes Risk Score

External Validation of the VOCAL‐Penn Cirrhosis Surgical Risk Score in Two Large, Independent Health Systems

2021 ◽  
Author(s):  
Nadim Mahmud ◽  
Zachary Fricker ◽  
Sarjukumar Panchal ◽  
James D. Lewis ◽  
David S. Goldberg ◽  
...  
Keyword(s):  
Health Systems ◽  
Risk Score ◽  
External Validation ◽  
Surgical Risk

Risk of Conversion to Arthroplasty After Hip Arthroscopy: Validation of a Published Risk Score Using an Independent, Prospectively Collected Database

2021 ◽  
pp. 036354652199382
Author(s):  
Mario Hevesi ◽  
Devin P. Leland ◽  
Philip J. Rosinsky ◽  
Ajay C. Lall ◽  
Benjamin G. Domb ◽  
...  
Keyword(s):  
Risk Score ◽  
Roc Curve ◽  
Hip Arthroscopy ◽  
External Validation ◽  
Area Under The Curve ◽  
Harris Hip Score ◽  
Brier Score ◽  
Level Of Evidence ◽  
Center Edge ◽  
Modified Harris Hip Score

Background: Hip arthroscopy is rapidly advancing and increasingly commonly performed. The most common surgery after arthroscopy is total hip arthroplasty (THA), which unfortunately occurs within 2 years of arthroscopy in up to 10% of patients. Predictive models for conversion to THA, such as that proposed by Redmond et al, have potentially substantial value in perioperative counseling and decreasing early arthroscopy failures; however, these models need to be externally validated to demonstrate broad applicability. Purpose: To utilize an independent, prospectively collected database to externally validate a previously published risk calculator by determining its accuracy in predicting conversion of hip arthroscopy to THA at a minimum 2-year follow-up. Study Design: Cohort study (diagnosis); Level of evidence, 1. Methods: Hip arthroscopies performed at a single center between November 2015 and March 2017 were reviewed. Patients were assessed pre- and intraoperatively for components of the THA risk score studied—namely, age, modified Harris Hip Score, lateral center-edge angle, revision procedure, femoral version, and femoral and acetabular Outerbridge scores—and followed for a minimum of 2 years. Conversion to THA was determined along with the risk score’s receiver operating characteristic (ROC) curve and Brier score calibration characteristics. Results: A total of 187 patients (43 men, 144 women, mean age, 36.0 ± 12.4 years) underwent hip arthroscopy and were followed for a mean of 2.9 ± 0.85 years (range, 2.0-5.5 years), with 13 patients (7%) converting to THA at a mean of 1.6 ± 0.9 years. Patients who converted to THA had a mean predicted arthroplasty risk of 22.6% ± 12.0%, compared with patients who remained arthroplasty-free with a predicted risk of 4.6% ± 5.3% ( P < .01). The Brier score for the calculator was 0.04 ( P = .53), which was not statistically different from ideal calibration, and the calculator demonstrated a satisfactory area under the curve of 0.894 ( P < .001). Conclusion: This external validation study supported our hypothesis in that the THA risk score described by Redmond et al was found to accurately predict which patients undergoing hip arthroscopy were at risk for converting to subsequent arthroplasty, with satisfactory discriminatory, ROC curve, and Brier score calibration characteristics. These findings are important in that they provide surgeons with validated tools to identify the patients at greatest risk for failure after hip arthroscopy and assist in perioperative counseling and decision making.

Predicting 90-day mortality after bariatric surgery: an independent, external validation of the OS-MRS prognostic risk score

2014 ◽  
Vol 10 (5) ◽  
pp. 774-779 ◽  
Author(s):  
David Arterburn ◽  
Eric S. Johnson ◽  
Melissa G. Butler ◽  
David Fisher ◽  
Elizabeth A. Bayliss
Keyword(s):  
Bariatric Surgery ◽  
Risk Score ◽  
External Validation

External validation and comparison of risk score models in pediatric heart transplants

2021 ◽  
Author(s):  
Alia Dani ◽  
Justin S. Heidel ◽  
Tingting Qiu ◽  
Yin Zhang ◽  
Yizhao Ni ◽  
...  
Keyword(s):  
Risk Score ◽  
External Validation ◽  
Heart Transplants

A Risk Score Model Associated with Tumor Microenvironment Predicts Disease-Free Survival After Radical Prostatectomy

2020 ◽  
Author(s):  
Hao Zhao ◽  
Xuening Zhang ◽  
Zhan Shi ◽  
Songhe Shi
Keyword(s):  
Tumor Microenvironment ◽  
Gleason Score ◽  
Risk Score ◽  
Regression Models ◽  
Cox Regression ◽  
External Validation ◽  
Disease Free Survival ◽  
Data Set ◽  
Free Survival ◽  
Disease Free

Abstract Background Tumor microenvironment (TME) and immune checkpoint inhibitors has been shown to promote active immune responses through different mechanisms. We aimed to identify the important prognostic genes and prognostic characteristics related to TME in prostate cancer (PCa).Methods The gene transcriptome profiles and clinical information of PCa patients were obtained from the TCGA database, and the immune, stromal and estimate scores were calculated by the ESTIMATE algorithm. We evaluated the prognostic value of risk score (RS) model based on univariate Cox and LASSO Cox regression models analysis, and established a nomogram to predict disease-free survival (DFS) in PCa patients. The GSE70768 data set was used for external validation. Finally, 22 subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the Cibersort algorithm.Results In this study, the patients with higher immune, stromal, and estimate scores were associated with poorer DFS, higher Gleason score, and higher AJCC T stage. Based on the immune and stromal scores, the Venny diagram screened out 515 cross DEGs. The univariate COX and Lasso Cox regression models were used to select 18 DEGs from 515 DEGs, and constructed a RS model. The DFS of the high-RS group was significantly lower than that of the low-RS group (P<0.001). The AUC of 1-year, 3-year and 5-year DFS rates in RS model were 0.778, 0.754 and 0.750, respectively. In addition, the RS model constructed from 18 genes was found to be more sensitive than Gleason score (1, 3, 5 year AUC= 0.704, 0.677 and 0.682). The nomograms of DFS were established based on RS and Gleason scores. The AUC of the nomograms in the first, third, and fifth years were 0.802, 0.808, and 0.796, respectively. These results have been further validated in GEO. In addition, the proportion of Tregs was higher in high-RS patients (P<0.05), and the expression of five immune checkpoints (CTLA-4, PD-1, LAG-3, TIM-3 and TIGIT) was higher in high-RS patients (P<0.05).Conclusion We identified 18 TME-related genes from the TCGA database, which were significantly related to DFS in PCa patients.

A Risk Score for Predicting the Incidence of Hemorrhage in Critically Ill Neonates: Development and Validation Study

2020 ◽  
Author(s):  
Rozeta Sokou ◽  
Daniele Piovani ◽  
Aikaterini Konstantinidi ◽  
Andreas G. Tsantes ◽  
Stavroula Parastatidou ◽  
...  
Keyword(s):  
Prediction Model ◽  
Critically Ill ◽  
Risk Score ◽  
External Validation ◽  
Prognostic Index ◽  
Bleeding Risk ◽  
Outcome Variable ◽  
Calibration Plot ◽  
Characteristic Analysis ◽  
Critically Ill Neonates

AbstractThe aim of the study was to develop and validate a prediction model for hemorrhage in critically ill neonates which combines rotational thromboelastometry (ROTEM) parameters and clinical variables. This cohort study included 332 consecutive full-term and preterm critically ill neonates. We performed ROTEM and used the neonatal bleeding assessment tool (NeoBAT) to record bleeding events. We fitted double selection least absolute shrinkage and selection operator logit regression to build our prediction model. Bleeding within 24 hours of the ROTEM testing was the outcome variable, while patient characteristics, biochemical, hematological, and thromboelastometry parameters were the candidate predictors of bleeding. We used both cross-validation and bootstrap as internal validation techniques. Then, we built a prognostic index of bleeding by converting the coefficients from the final multivariable model of relevant prognostic variables into a risk score. A receiver operating characteristic analysis was used to calculate the area under curve (AUC) of our prediction index. EXTEM A10 and LI60, platelet counts, and creatinine levels were identified as the most robust predictors of bleeding and included them into a Neonatal Bleeding Risk (NeoBRis) index. The NeoBRis index demonstrated excellent model performance with an AUC of 0.908 (95% confidence interval [CI]: 0.870–0.946). Calibration plot displayed optimal calibration and discrimination of the index, while bootstrap resampling ensured internal validity by showing an AUC of 0.907 (95% CI: 0.868–0.947). We developed and internally validated an easy-to-apply prediction model of hemorrhage in critically ill neonates. After external validation, this model will enable clinicians to quantify the 24-hour bleeding risk.

Development and external validation of a novel risk score to predict long-term clinical recurrence after radical prostatectomy

2019 ◽  
Vol 18 (1) ◽  
pp. e225
Author(s):  
E. Mazzone ◽  
G. Gandaglia ◽  
S. Knipper ◽  
M. Graefen ◽  
D. Tilki ◽  
...  
Keyword(s):  
Radical Prostatectomy ◽  
Risk Score ◽  
External Validation ◽  
Clinical Recurrence
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