scholarly journals Optimizing Survival Predictions of Hypopharynx Cancer: Development of a Clinical Prediction Model

2019 ◽  
Vol 130 (9) ◽  
pp. 2166-2172 ◽  
Author(s):  
Coralie R. Arends ◽  
Japke F. Petersen ◽  
Vincent Noort ◽  
Adriana J. Timmermans ◽  
C. René Leemans ◽  
...  
Keyword(s):  
Prediction Model ◽  
Cancer Development ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Hypopharynx Cancer

scholarly journals Minimum sample size for external validation of a clinical prediction model with a binary outcome

2021 ◽  
Author(s):  
Richard D. Riley ◽  
Thomas P. A. Debray ◽  
Gary S. Collins ◽  
Lucinda Archer ◽  
Joie Ensor ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
External Validation ◽  
Minimum Sample Size ◽  
Binary Outcome ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Minimum Sample

Develop a Clinical Prediction Model for Postoperative Cognitive Dysfunction after Major Noncardiac Surgery in Elderly Patients: A Protocol for a Prospective Observational Study

Gerontology ◽  
2021 ◽  
pp. 1-8
Author(s):  
Yang Shen ◽  
Xianchen Li ◽  
Junyan Yao
Keyword(s):  
Observational Study ◽  
Prediction Model ◽  
Cognitive Decline ◽  
Cognitive Dysfunction ◽  
Prospective Observational Study ◽  
Postoperative Cognitive Dysfunction ◽  
Noncardiac Surgery ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Postoperative Cognitive Decline

Perioperative neurocognitive disorders (PNDs) refer to cognitive decline identified in the preoperative or postoperative period. It has been reported that the incidence of postoperative neurocognitive impairment after noncardiac surgery in patients older than 65 at 1 week was 25.8∼41.4%, and at 3 months 9.9∼12.7%. PNDs will last months or even develop to permanent dementia, leading to prolonged hospital stays, reduced quality of life, and increased mortality within 1 year. Despite the high incidence and poor prognosis of PNDs in the aged population, no effective clinical prediction model has been established to predict postoperative cognitive decline preoperatively. To develop a clinical prediction model for postoperative neurocognitive dysfunction, a prospective observational study (Clinical trial registration number: ChiCTR2000036304) will be performed in the Shanghai General Hospital during January 2021 to October 2022. A sample size of 675 patients aged &#x3e;65 years old, male or female, and scheduled for elective major noncardiac surgery will be recruited. A battery of neuropsychological tests will be used to test the cognitive function of patients at 1 week, 1 month, and 3 months postoperatively. We will evaluate the associations of PNDs with a bunch of candidate predictors including general characteristics of patients, blood biomarkers, indices associated with anesthesia and surgery, retinal nerve-fiber layer thickness, and frailty index to develop the clinical prediction model by using multiple logistic regression analysis and least absolute shrinkage and the selection operator (LASSO) method. The <i>k</i>-fold cross-validation method will be utilized to validate the clinical prediction model. In conclusion, this study was aimed to develop a clinical prediction model for postoperative cognitive dysfunction of old patients. It is anticipated that the knowledge gained from this study will facilitate clinical decision-making for anesthetists and surgeons managing the aged patients undergoing noncardiac surgery.

scholarly journals Predicting severe pneumonia in the emergency department: a global study of the Pediatric Emergency Research Networks (PERN)—study protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. e041093
Author(s):  
Todd Adam Florin ◽  
Daniel Joseph Tancredi ◽  
Lilliam Ambroggio ◽  
Franz E Babl ◽  
Stuart R Dalziel ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Severe Disease ◽  
Pediatric Emergency ◽  
Global Network ◽  
Clinical Prediction ◽  
Emergency Research ◽  
Research Networks ◽  
Clinical Prediction Model ◽  
Severity Scores

IntroductionPneumonia is a frequent and costly cause of emergency department (ED) visits and hospitalisations in children. There are no evidence-based, validated tools to assist physicians in management and disposition decisions for children presenting to the ED with community-acquired pneumonia (CAP). The objective of this study is to develop a clinical prediction model to accurately stratify children with CAP who are at risk for low, moderate and severe disease across a global network of EDs.Methods and analysisThis study is a prospective cohort study enrolling up to 4700 children with CAP at EDs at ~80 member sites of the Pediatric Emergency Research Networks (PERN; https://pern-global.com/). We will include children aged 3 months to <14 years with a clinical diagnosis of CAP. We will exclude children with hospital admissions within 7 days prior to the study visit, hospital-acquired pneumonias or chronic complex conditions. Clinical, laboratory and imaging data from the ED visit and hospitalisations within 7 days will be collected. A follow-up telephone or text survey will be completed 7–14 days after the visit. The primary outcome is a three-tier composite of disease severity. Ordinal logistic regression, assuming a partial proportional odds specification, and recursive partitioning will be used to develop the risk stratification models.Ethics and disseminationThis study will result in a clinical prediction model to accurately identify risk of severe disease on presentation to the ED. Ethics approval was obtained for all sites included in the study. Cincinnati Children’s Hospital Institutional Review Board (IRB) serves as the central IRB for most US sites. Informed consent will be obtained from all participants. Results will be disseminated through international conferences and peer-reviewed publications. This study overcomes limitations of prior pneumonia severity scores by allowing for broad generalisability of findings, which can be actively implemented after model development and validation.

scholarly journals An IL28B Genotype-Based Clinical Prediction Model for Treatment of Chronic Hepatitis C

PLoS ONE ◽  
2011 ◽  
Vol 6 (7) ◽  
pp. e20904 ◽  
Author(s):  
Thomas R. O'Brien ◽  
James E. Everhart ◽  
Timothy R. Morgan ◽  
Anna S. Lok ◽  
Raymond T. Chung ◽  
...  
Keyword(s):  
Chronic Hepatitis ◽  
Hepatitis C ◽  
Prediction Model ◽  
Chronic Hepatitis C ◽  
Il28b Genotype ◽  
Clinical Prediction ◽  
Clinical Prediction Model

A clinical prediction model for adnexal torsion in pediatric and adolescent population

2021 ◽  
Author(s):  
Raanan Meyer ◽  
Nir Meller ◽  
Aya Mohr-Sasson ◽  
Shlomo Toussia-Cohen ◽  
Daphna Amitai Komem ◽  
...  
Keyword(s):  
Prediction Model ◽  
Clinical Prediction ◽  
Adnexal Torsion ◽  
Clinical Prediction Model ◽  
Adolescent Population

P10.03 Establishment and validation of clinical prediction model in WHO grade II glioma

2021 ◽  
Vol 23 (Supplement_2) ◽  
pp. ii28-ii28
Author(s):  
X Xue ◽  
Q Gao
Keyword(s):  
Prediction Model ◽  
Training Group ◽  
Clinical Prediction ◽  
Validation Group ◽  
Who Grade ◽  
Clinical Prediction Model ◽  
Cox Regression Analysis ◽  
Who Grade Ii Glioma ◽  
Grade Ii ◽  
Grade Ii Glioma

Abstract OBJECTIVE WHO grade II glioma has the characteristics of heterogeneity, and this disease progresses rapidly in some patients, in whom the malignant degree is equivalent to that of high-grade glioma. In order to accurately predict the prognosis of patients, an effective clinical prediction model based on relevant risk factors is needed which could provide a theoretical basis for optimization of clinical individualized treatment. METHODS According to the inclusion and exclusion criteria, eligible patients from January 2010 to December 2018 in our hospital were selected, and those who met the criteria were randomly assigned 4:1 to the training group and the validation group, respectively. The predictors were screened by univariate and multivariate Cox regression analysis, the prediction model was established, and the model was verified and evaluated. RESULTS A total of 258 patients with WHO grade II glioma were recruited, including 208 patients as the training group and 50 patients as the validation group. Six independent risk factors, including patient age, preoperative Karnofsky performance status (KPS) score, preoperative seizure symptoms, surgical resection range, tumor size and IDH status, were selected and included into the prediction model by univariate and multivariate Cox regression analysis, and were visualized in the form of Nomogram. The concordance index (C index) was used to evaluate the predictive ability of the model. Results showed that the C-index was 0.832 in the training group and 0.853 in the validation group, respectively, indicating good performance for the prediction model. The calibration charts were drawn in both groups respectively, which showed that the calibration lines were in good agreement with the standard lines, indicating good consistency between the two groups. CONCLUSIONS In this study, a clinical prediction model for WHO grade II glioma was established, and it was verified that the model has good predictive ability, which may be beneficial for clinical work.

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