Development and internal validation of a risk scoring system for gastrointestinal events requiring surgery in gastrointestinal lymphoma patients

2018 ◽  
Vol 34 (4) ◽  
pp. 693-699 ◽  
Author(s):  
Tomonori Aoki ◽  
Atsuo Yamada ◽  
Miwako Takahashi ◽  
Ryota Niikura ◽  
Kazuhiro Toyama ◽  
...  
Keyword(s):  
Scoring System ◽  
Gastrointestinal Lymphoma ◽  
Internal Validation ◽  
Gastrointestinal Events ◽  
Risk Scoring ◽  
Risk Scoring System

scholarly journals Development of a Genetic and Clinical Data-Based (GC) Risk Score for Predicting Survival of Hepatocellular Carcinoma Patients After Tumor Resection

2018 ◽  
Vol 48 (2) ◽  
pp. 491-502 ◽  
Author(s):  
Shengsen Chen ◽  
Chao Wang ◽  
An Cui ◽  
Kangkang Yu ◽  
Chong Huang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Clinical Data ◽  
Scoring System ◽  
Cancer Tissue ◽  
Predictive Tool ◽  
Internal Validation ◽  
Scoring Model ◽  
Risk Scoring ◽  
Risk Scoring System

Background/Aims: Carnitine palmitoyltransferase 1A (CPT1A) is a rate-limiting enzyme in the transport of long-chain fatty acids for β-oxidation. Increasing evidence has indicated that CPT1A plays an important role in carcinogenesis. However, the expression and prognostic value of CPT1A in hepatocellular carcinoma (HCC) have not been extensively studied. Methods: Here, we collected 66 post-operative liver cancer tissue samples. Gene profile expression was tested by RT-PCR. Receiver operating characteristic (ROC) analysis was performed and multivariate analysis with Cox’s Proportional Hazard Model was used for confirming the selected markers’ predictive efficiency for HCC patients’ survival. A simple risk scoring system was created based on Cox’s regression modeling and bootstrap internal validation. Results: Cox multivariate regression analysis demonstrated that CPT1A, tumor size, intrahepatic metastasis, TNM stage and histological grade were independent risk factors for the prognosis of HCC patients after surgery. Our genetic and clinical data-based (GC) risk scoring system revealed that HCC patients whose total score≥3 are more likely to relapse and die than patients whose total score < 3. Finally, the good discriminatory power of our risk scoring model was validated by bootstrap internal validation. Conclusions: The genetic and clinical data-based risk scoring model can be a promising predictive tool for liver cancer patients’ prognosis after operation.

A Risk Scoring System for Predicting Overall Survival in Patients with Liver Cancer

2020 ◽  
Author(s):  
Haibei Xin ◽  
Guanxiong Zhang ◽  
Wei Zhou ◽  
Shanshan Li ◽  
Minfeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Liver Cancer ◽  
Scoring System ◽  
Risk Scoring ◽  
Risk Scoring System

A Novel Risk Scoring System for Predicting Primary Graft Dysfunction after Orthotopic Heart Transplantation

2020 ◽  
Vol 26 (10) ◽  
pp. S136-S137
Author(s):  
Syed Adeel Ahsan ◽  
Jasjit Bhinder ◽  
Syed Zaid ◽  
Parija Sharedalal ◽  
Chhaya Aggarwal-Gupta ◽  
...  
Keyword(s):  
Heart Transplantation ◽  
Scoring System ◽  
Orthotopic Heart Transplantation ◽  
Primary Graft Dysfunction ◽  
Graft Dysfunction ◽  
Risk Scoring ◽  
Risk Scoring System

scholarly journals Novel risk scoring system for metastatic renal cell carcinoma patients treated with cabozantinib

2021 ◽  
pp. 100393
Author(s):  
Dylan J. Martini ◽  
Meredith R. Kline ◽  
Yuan Liu ◽  
Julie M. Shabto ◽  
Bradley C. Carthon ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Scoring System ◽  
Risk Scoring ◽  
Risk Scoring System

scholarly journals Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 853
Author(s):  
Jee-Yun Kim ◽  
Jeong Yee ◽  
Tae-Im Park ◽  
So-Youn Shin ◽  
Man-Ho Ha ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Linear Regression ◽  
Parenteral Nutrition ◽  
Prediction Model ◽  
Scoring System ◽  
Prediction Equation ◽  
Icu Patients ◽  
Risk Scoring ◽  
Risk Scoring System

Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15–29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2–4, and 5–6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) − 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.

A machine learning‐based risk scoring system for infertility considering different age groups

2020 ◽  
Author(s):  
ShuJie Liao ◽  
Lei Jin ◽  
Wan‐Qiang Dai ◽  
Ge Huang ◽  
Wulin Pan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scoring System ◽  
Age Groups ◽  
Risk Scoring ◽  
Risk Scoring System
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