scholarly journals Protocol for the derivation and validation of a clinical prediction model to support the diagnosis of asthma in children and young people in primary care

2020 ◽  
Vol 5 ◽  
pp. 50
Author(s):  
Luke Daines ◽  
Laura J. Bonnett ◽  
Andy Boyd ◽  
Steve Turner ◽  
Steff Lewis ◽  
...  
Keyword(s):  
Primary Care ◽  
Missing Data ◽  
Young People ◽  
Prediction Model ◽  
Research Database ◽  
Asthma Diagnosis ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Children And Young People ◽  
Asthma In Children

Background: Accurately diagnosing asthma can be challenging. Uncertainty about the best combination of clinical features and investigations for asthma diagnosis is reflected in conflicting recommendations from international guidelines. One solution could be a clinical prediction model to support health professionals estimate the probability of an asthma diagnosis. However, systematic review evidence identifies that existing models for asthma diagnosis are at high risk of bias and unsuitable for clinical use. Being mindful of previous limitations, this protocol describes plans to derive and validate a prediction model for use by healthcare professionals to aid diagnostic decision making during assessment of a child or young person with symptoms suggestive of asthma in primary care. Methods: A prediction model will be derived using data from the Avon Longitudinal Study of Parents and Children (ALSPAC) and linked primary care electronic health records (EHR). Data will be included from study participants up to 25 years of age where permissions exist to use their linked EHR. Participants will be identified as having asthma if they received at least three prescriptions for an inhaled corticosteroid within a one-year period and have an asthma code in their EHR. To deal with missing data we will consider conducting a complete case analysis. However, if the exclusion of cases with missing data substantially reduces the total sample size, multiple imputation will be used. A multivariable logistic regression model will be fitted with backward stepwise selection of candidate predictors.  Apparent model performance will be assessed before internal validation using bootstrapping techniques. The model will be adjusted for optimism before external validation in a dataset created from the Optimum Patient Care Research Database. Discussion: This protocol describes a robust strategy for the derivation and validation of a prediction model to support the diagnosis of asthma in children and young people in primary care.

scholarly journals Predicting risk of pancreatic cancer in individuals with new-onset type-2 diabetes in primary care: protocol for the development and validation of a clinical prediction model (QPancreasD)

2021 ◽  
Author(s):  
Pui San Tan ◽  
Ashley Clift ◽  
Weiqi Liao ◽  
Martina Patone ◽  
Carol Coupland ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Pancreatic Cancer ◽  
Prediction Model ◽  
Individual Risk ◽  
Diabetes Diagnosis ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
New Onset

Background Pancreatic cancer continues to have an extremely poor prognosis in part due to late diagnosis. 25% of pancreatic cancer patients have a prior diagnosis of diabetes, and hence identifying individuals at risk of pancreatic cancer in those with recently diagnosed type 2 diabetes may be a useful opportunity to identify candidates for screening and early detection. In this study, we will comparatively evaluate regression and machine learning-based clinical prediction models for estimating individual risk of developing pancreatic cancer two years after type 2 diabetes diagnosis. Methods In the development dataset, we will include adults aged 30-84 years with incident type-2 diabetes registered with QResearch primary care database. Patients will be followed up from type-2 diabetes diagnosis to first diagnosis of pancreatic cancer as recorded in any one of primary care records, hospital episode statistics, cancer registry data, or death records. Cox-proportional hazards models will be used to develop a risk prediction model for estimating individual risk of developing pancreatic cancer during up to 2 years of follow-up. We will perform variable selection using a combination of clinical and statistical significance approach i.e. HR <0.9 or >1.1 and p<0.01. Linear predictors and baseline survivor function at 2 years will be used to compute absolute risk predictions. Internal-external cross-validation (IECV) framework across geographical regions within England will be used to assess performance and pooled using random effects meta-analysis using: (i) model fit in terms of variation explained by the model Royston & Sauerbrei's R2D, (ii) calibration slope and calibration-in-the-large, and (iii) discrimination measured in terms of Harrell's C and Royston & Sauerbrei's D-statistic. Further, we will evaluate machine learning (ML) approaches for the clinical prediction model using neural networks (NN) and XGBoost. The model predictors and performance of these will be compared with the results of those derived from the regression-based strategy. Discussion The proposed study will develop and validate a novel risk prediction model to aid early diagnosis of pancreatic cancer in patients with new-onset diabetes in primary care. With an enhanced decision-risk tool for use at point-of care by general practitioners to assess pancreatic cancer risk, it may improve decision-making so that at-risk patients are rapidly prioritised to aid early diagnosis of pancreatic cancer in patients with newly diagnosed diabetes.

Impact of incorporating echocardiographic screening into a clinical prediction model to optimise utilisation of echocardiography in primary care

2020 ◽  
Author(s):  
Adriana C. Diamantino ◽  
Bruno R. Nascimento ◽  
Maria Carmo P. Nunes ◽  
Craig A. Sable ◽  
Kaciane K. B. Oliveira ◽  
...  
Keyword(s):  
Primary Care ◽  
Prediction Model ◽  
Clinical Prediction ◽  
Clinical Prediction Model

scholarly journals Impact of Incorporating Echocardiographic Screening into a Clinical Prediction Model to Optimize Utilization of Echocardiography in Primary Care

2020 ◽  
Author(s):  
Adriana Diamantino ◽  
Bruno Nascimento ◽  
Maria Carmo Nunes ◽  
Craig Sable ◽  
Kaciane Oliveira ◽  
...  
Keyword(s):  
Primary Care ◽  
Prediction Model ◽  
Clinical Prediction ◽  
Clinical Prediction Model

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
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