scholarly journals Does a prediction model for pregnancy of unknown location developed in the UK validate on a US population?

2010 ◽  
Vol 25 (10) ◽  
pp. 2434-2440 ◽  
Author(s):  
K. T. Barnhart ◽  
M. D. Sammel ◽  
D. Appleby ◽  
M. Rausch ◽  
T. Molinaro ◽  
...  
Keyword(s):  
Prediction Model ◽  
The Uk ◽  
Pregnancy Of Unknown Location

scholarly journals Development of a pest threshold decision support system for minimising damage to winter wheat from wheat bulb fly, Delia coarctata

2021 ◽  
Author(s):  
Daniel J Leybourne ◽  
Kate E Storer ◽  
Pete Berry ◽  
Steve Ellis
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Winter Wheat ◽  
Prediction Model ◽  
Support System ◽  
Predictive Accuracy ◽  
Control Strategies ◽  
Wheat Crop ◽  
Shoot Number ◽  
The Uk

Graphical AbstractIn this article we describe two predictive models that can be used for the integrated management of wheat bulb fly. Our first model is a pest level prediction model and our second model predicts the number of shoots a winter wheat crop will achieve by the terminal spikelet developmental stage. We revise and update current wheat bulb fly damage thresholds and combine this with our two models to devise a tolerance-based decision support system that can be used to minimise the risk of crop damage by wheat bulb fly. SummaryWheat bulb fly, Delia coarctata, is an important pest of winter wheat in the UK, causing significant damage of up to 4 t ha-1. Accepted population thresholds for D. coarctata are 250 eggs m-2 for crops sown up to the end of October and 100 eggs m-2 for crops sown from November. Fields with populations of D. coarctata that exceed the thresholds are at higher risk of experiencing economically damaging pest infestations. In the UK, recent withdrawal of insecticides means that only a seed treatment is available for chemical control of D. coarctata, however this is only effective for late-sown crops (November onwards) and accurate estimations of annual population levels are required to ensure a seed treatment is applied if needed. As a result of the lack of post-drilling control strategies, the management of D. coarctata is becoming increasingly reliant on non-chemical methods of control. Control strategies that are effective in managing similar stem-boring pests of wheat include sowing earlier and using higher seed rates to produce crops with more shoots and greater tolerance to shoot damage.In this study we develop two predictive models that can be used for integrated D. coarctata management. The first is an updated pest level prediction model that predicts D. coarctata populations from meteorological parameters with a predictive accuracy of 70%, which represents a significant improvement on the previous D. coarctata population prediction model. Our second model predicts the maximum number of shoots for a winter wheat crop that would be expected at the terminal spikelet development stage. This shoot number model uses information about the thermal time from plant emergence to terminal spikelet, leaf phyllochron length, plant population, and sowing date to predict the degree of tolerance a crop will have against D. coarctata. The shoot number model was calibrated against data collected from five field experiments and tested against data from four experiments. Model testing demonstrated that the shoot number model has a predictive accuracy of 70%. A decision support system using these two models for the sustainable management of D. coarcata risk is described.

A clinical prediction model to assess risk for pancreatic cancer among patients with new-onset diabetes.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15780-e15780
Author(s):  
Ben Boursi ◽  
Brian S. Finkelman ◽  
Bruce J. Giantonio ◽  
Kevin Haynes ◽  
Anil K. Rustgi ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Model ◽  
Ductal Adenocarcinoma ◽  
P Value ◽  
Diabetes Diagnosis ◽  
Roc Curve Analysis ◽  
Onset Diabetes ◽  
Definitive Screening ◽  
The Uk ◽  
New Onset

e15780 Background: Approximately 50% of all patients with pancreatic ductal adenocarcinoma (PDA) develop diabetes mellitus (DM) prior to cancer diagnosis. Targeted screening for PDA among those with new-onset diabetes may allow earlier diagnosis. We sought to develop and validate a PDA risk prediction model to identify high-risk individuals among those with new-onset diabetes. Methods: We conducted a retrospective cohort study in a population representative database from the UK. Individuals with incident diabetes after the age of 35 and ≥3 years of follow-up after DM diagnosis were eligible for inclusion. Candidate predictors consisted of epidemiological and clinical characteristics available at the time of diabetes diagnosis. Variables with p-value<0.25 in the univariable analyses were further evaluated using backward stepwise approach. Model discrimination was assessed using ROC curve analysis. Calibration was evaluated using the Hosmer–Lemeshow test. Results were internally validated using a bootstrapping procedure. Results: The study included 109,385 patients with new-onset diabetes. Among them, 390 (0.4%) were diagnosed with PDA within 3 years. The final model (AUC 0.82, 95% CI: 0.75-0.89) included age, BMI, BMI change, smoking, HbA1C, cholesterol, hemoglobin, creatinine and alkaline phosphatase, and use of PPI and anti-diabetic medication. Bootstrapping validation showed negligible optimism. If the predicted risk threshold for definitive PDA screening was set at 1% over 3 years, only 6.19% of the new-onset diabetes population would undergo definitive screening, and the corresponding sensitivity, specificity and positive predictive value would be 44.7%, 94.0%, and 2.6% respectively. Conclusions: A risk model based on widely available clinical parameters can help target PDA screening in patients with new-onset diabetes.

scholarly journals 35 Comparative Performance of Prediction Model, Non-Expert and Tele-Diagnosis of Common External and Middle Ear Disease Using A Patient Cohort from Cambodia That Included One Hundred and Thirty-Eight Ears

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
J Schuster-Bruce ◽  
P Shetty ◽  
J O'Donovan ◽  
R Mandavia ◽  
T Sokdavy ◽  
...  
Keyword(s):  
Prediction Model ◽  
Middle Ear ◽  
Comparative Performance ◽  
Suppurative Otitis Media ◽  
Middle Income ◽  
Middle Income Countries ◽  
Comparable Performance ◽  
Low Sensitivity ◽  
The Uk ◽  
Model Diagnosis

Abstract Introduction Globally 6% of the population suffers from disabling hearing loss and the majority resides in low- and middle-income countries, but diagnosis and treatment are hampered by poor availability of expert diagnosis. We compared the utility of tele-diagnosis, non-expert diagnosis, and prediction model diagnosis as a screening tool for common external and middle ear disorders. Method We recruited consecutive adult and paediatric patients presenting with ear or hearing symptoms to ENT outpatients at Children’s Surgical Centre, Cambodia. Each participant underwent sequential symptomatic and otoscopic assessment by a non-specialist and an ENT specialist. The non-specialist captured data using a novel automated symptom questionnaire loaded onto a smartphone otoscope. An ENT specialist in the UK subsequently reviewed these data. Results 138 ears were recruited. The prediction model performed poorly, but absence of otorrhoea was found to reliably exclude a diagnosis of chronic suppurative otitis media (negative predictive value=0.99). Both on-site non-expert and expert tele-diagnosis had high diagnostic specificity (90-99% and 86-99%), but low sensitivity (&lt;43% and 32-100%). Conclusions We report the first study to directly compare approaches for non-specialist diagnosis of disorders of the middle/external ear, which shows suboptimal but comparable performance using an automated questionnaire, on site non-expert diagnosis, or remote expert diagnosis

scholarly journals Development and Validation of a Prediction Model for 28-Day in-Hospital Mortality in Critically Ill Patients with COVID-19

2021 ◽  
Author(s):  
Paloma Ferrando-Vivas ◽  
Doug Gould ◽  
James Doidge ◽  
Karen Thomas ◽  
Paul Mouncey ◽  
...  
Keyword(s):  
Critical Care ◽  
Hospital Mortality ◽  
Prediction Model ◽  
Critically Ill ◽  
Eligible Patient ◽  
Calendar Time ◽  
Validation Data ◽  
Critical Care Units ◽  
Temporal Validation ◽  
The Uk

Objectives To develop and validate a prediction model for 28-day in-hospital mortality among adult patients critically ill with COVID-19 in the UK. Design Observational cohort study. Setting 287 adult critical care units in England, Wales and Northern Ireland, of which 260 admitted at least one eligible patient. Participants 10,933 patients with confirmed COVID-19 of whom 10,401 were eligible (excluding 532 patients with a duration of critical care less than 24 hours and 1 patient with unknown 28-day outcome): 8,666 development (March-April 2020) and 1,735 temporal validation (May-August 2020). Main outcome measures 28-day in-hospital mortality from start of critical care. Results Two models were developed using 14 patient level predictors selected from 30 candidate predictors, with and without adjustment for calendar time. In the temporal validation data, the model discrimination was maintained (c index 0.78) but calibration was poor, particularly for the model not adjusted for calendar time (ratio of observed to predicted mortality 0.74 versus 0.88 for the model adjusted for calendar time). Conclusions We developed and validated a prediction model for 28-day in-hospital mortality for patients critically ill with COVID-19. Although absolute predictions were inaccurate due to changing outcomes, the models will support risk-adjustment in analyses and monitoring changes in risk-adjusted outcome over time.

The diagnosis and management of factor VIII and IX inhibitors: a guideline from the UK Haemophilia Centre Doctors' Organization (UKHCDO)

2000 ◽  
Vol 111 (1) ◽  
pp. 78-90 ◽  
Author(s):  
C. R. M. Hay ◽  
T. P. Baglin ◽  
P. W. Collins ◽  
F. G. H. Hill ◽  
D. M. Keeling
Keyword(s):  
Factor Viii ◽  
Diagnosis And Management ◽  
Viii And Ix ◽  
The Uk

1476: Prostate Cancer Detection in Men Aged 45-49 Years in the UK Protect (Prostate Testing for Cancer and Treatment) Trial

2006 ◽  
Vol 175 (4S) ◽  
pp. 476-477
Author(s):  
Freddie C. Hamdy ◽  
Joanne Howson ◽  
Athene Lane ◽  
Jenny L. Donovan ◽  
David E. Neal
Keyword(s):  
Prostate Cancer ◽  
Cancer Detection ◽  
Treatment Trial ◽  
Prostate Cancer Detection ◽  
The Uk

653: The Protect (Prostate Testing for Cancer and Treatment) Study: Evaluating Screening and Treatment of Clinically Localised Prostate Cancer in the UK

2006 ◽  
Vol 175 (4S) ◽  
pp. 210-210
Author(s):  
◽  
Freddie C. Hamdy ◽  
Athene Lane ◽  
David E. Neal ◽  
Malcolm Mason ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
The Uk
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