Selection of individuals for genetic testing for familial hypercholesterolaemia: development and external validation of a prediction model for the presence of a mutation causing familial hypercholesterolaemia

Abstract Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

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Prediction of meat quality traits in the abattoir using portable near-infrared spectrometers: heritability of predicted traits and genetic correlations with laboratory-measured traits

Journal of Animal Science and Biotechnology ◽

10.1186/s40104-021-00555-5 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Simone Savoia ◽

Andrea Albera ◽

Alberto Brugiapaglia ◽

Liliana Di Stasio ◽

Alessio Cecchinato ◽

...

Keyword(s):

Meat Quality ◽

Near Infrared ◽

External Validation ◽

Genetic Correlations ◽

Genetic Selection ◽

Quality Traits ◽

Eye Muscle ◽

Meat Quality Traits ◽

Color Traits ◽

Selection Of

Abstract Background The possibility of assessing meat quality traits over the meat chain is strongly limited, especially in the context of selective breeding which requires a large number of phenotypes. The main objective of this study was to investigate the suitability of portable infrared spectrometers for phenotyping beef cattle aiming to genetically improving the quality of their meat. Meat quality traits (pH, color, water holding capacity, tenderness) were appraised on rib eye muscle samples of 1,327 Piemontese young bulls using traditional (i.e., reference/gold standard) laboratory analyses; the same traits were also predicted from spectra acquired at the abattoir on the intact muscle surface of the same animals 1 d after slaughtering. Genetic parameters were estimated for both laboratory measures of meat quality traits and their spectra-based predictions. Results The prediction performances of the calibration equations, assessed through external validation, were satisfactory for color traits (R2 from 0.52 to 0.80), low for pH and purge losses (R2 around 0.30), and very poor for cooking losses and tenderness (R2 below 0.20). Except for lightness and purge losses, the heritability estimates of most of the predicted traits were lower than those of the measured traits while the genetic correlations between measured and predicted traits were high (average value 0.81). Conclusions Results showed that NIRS predictions of color traits, pH, and purge losses could be used as indicator traits for the indirect genetic selection of the reference quality phenotypes. Results for cooking losses were less effective, while the NIR predictions of tenderness were affected by a relatively high uncertainty of estimate. Overall, genetic selection of some meat quality traits, whose direct phenotyping is difficult, can benefit of the application of infrared spectrometers technology.

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

Statistics in Medicine ◽

10.1002/sim.9025 ◽

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

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Severe lymphopenia acquired during chemoradiotherapy for esophageal cancer: Incidence and external validation of a prediction model

Radiotherapy and Oncology ◽

10.1016/j.radonc.2021.08.009 ◽

2021 ◽

Vol 163 ◽

pp. 192-198

Author(s):

Tiuri E. Kroese ◽

Jasvir Jairam ◽

Jelle P. Ruurda ◽

Steven H. Lin ◽

Radhe Mohan ◽

...

Keyword(s):

Esophageal Cancer ◽

Prediction Model ◽

Cancer Incidence ◽

External Validation

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Is the crystal ball broken? Another external validation of the post‐withdrawal seizure‐relapse prediction model

Epilepsia ◽

10.1111/epi.17096 ◽

2021 ◽

Author(s):

Samuel W. Terman ◽

Herm J. Lamberink ◽

Geertruida Slinger ◽

Willem M. Otte ◽

James F. Burke ◽

...

Keyword(s):

Prediction Model ◽

External Validation ◽

Crystal Ball ◽

Relapse Prediction ◽

Seizure Relapse

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Update and external validation of a multivariable prediction model for tube feeding dependency for at least four weeks during chemoradiotherapy for head and neck cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.6040 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 6040-6040

Author(s):

Anna C. H. Willemsen ◽

Annemieke Kok ◽

Laura W.J. Baijens ◽

J. P. De Boer ◽

Remco de Bree ◽

...

Keyword(s):

Head And Neck Cancer ◽

Radiation Dose ◽

The Netherlands ◽

Prediction Model ◽

Head And Neck ◽

Neck Cancer ◽

Predictive Value ◽

Medical Center ◽

External Validation ◽

Tube Feeding

6040 Background: Patients who receive chemoradiation or bioradiation (CRT/BRT) for locally advanced head and neck squamous cell carcinoma (LAHNSCC) often experience high toxicity rates, which may interfere with oral intake, leading to (temporary) tube feeding (TF) dependency. International guidelines recommend gastrostomy insertion when the expected use of TF exceeds four weeks. In this study we aimed to update and externally validate a prediction model to identify patients in need for TF for at least four weeks, meeting the international criteria for prophylactic gastrostomy insertion. Methods: This retrospective multicenter cohort study was performed in four tertiary referral head and neck cancer centers in the Netherlands. The prediction model was developed using data from the University Medical Center Utrecht and the Netherlands Cancer Institute. The model was externally validated in patients from the Maastricht University Medical Center and Radboud University Medical Center. The primary endpoint was TF, initiated during or within 30 days after completion of CRT/BRT, and administered for at least four weeks. Potential predictors were retrieved from patient medical records and radiotherapy dose-volume parameters were calculated. Results: The developmental and validation cohort included 409 and 334 patients respectively. Multivariable analysis showed significant predictive value (p < 0.05) for adjusted diet at start of CRT/BRT, percentage weight change prior to treatment initiation, WHO performance status, tumor-site, nodal stage, mean radiation dose to the contralateral parotid gland, and mean radiation dose to the oral cavity. The area under the receiver operating characteristics curve for the updated model was 0.73 and after external validation 0.64. Positive and negative predictive value at 90% cut off were 80.0% and 48.2% respectively. Conclusions: This externally validated prediction model to estimate TF-dependency for at least four weeks in LAHNSCC patients performs well. This model, which will be presented, can be used in clinical practice to guide personalized decision making on prophylactic gastrostomy insertion.

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PO-1521 External validation of a prediction model for timely implementation of innovations in radiotherapy.

Radiotherapy and Oncology ◽

10.1016/s0167-8140(21)07972-x ◽

2021 ◽

Vol 161 ◽

pp. S1245-S1246

Author(s):

R. Swart ◽

M. Jacobs ◽

L. Boersma ◽

M. Behrendt ◽

M. Ketelaars ◽

...

Keyword(s):

Prediction Model ◽

External Validation ◽

Implementation Of Innovations

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Clinical prediction of thrombectomy eligibility: A systematic review and 4-item decision tree

International Journal of Stroke ◽

10.1177/1747493018801225 ◽

2018 ◽

Vol 14 (5) ◽

pp. 530-539 ◽

Cited By ~ 5

Author(s):

Gaia T Koster ◽

T Truc My Nguyen ◽

Erik W van Zwet ◽

Bjarty L Garcia ◽

Hannah R Rowling ◽

...

Keyword(s):

Decision Tree ◽

Prediction Model ◽

External Validation ◽

Facial Asymmetry ◽

Decision Support Tool ◽

Patient Characteristics ◽

Group Lasso ◽

Lasso Regression ◽

Vessel Occlusion ◽

Support Tool

Background A clinical large anterior vessel occlusion (LAVO)-prediction scale could reduce treatment delays by allocating intra-arterial thrombectomy (IAT)-eligible patients directly to a comprehensive stroke center. Aim To subtract, validate and compare existing LAVO-prediction scales, and develop a straightforward decision support tool to assess IAT-eligibility. Methods We performed a systematic literature search to identify LAVO-prediction scales. Performance was compared in a prospective, multicenter validation cohort of the Dutch acute Stroke study (DUST) by calculating area under the receiver operating curves (AUROC). With group lasso regression analysis, we constructed a prediction model, incorporating patient characteristics next to National Institutes of Health Stroke Scale (NIHSS) items. Finally, we developed a decision tree algorithm based on dichotomized NIHSS items. Results We identified seven LAVO-prediction scales. From DUST, 1316 patients (35.8% LAVO-rate) from 14 centers were available for validation. FAST-ED and RACE had the highest AUROC (both >0.81, p < 0.01 for comparison with other scales). Group lasso analysis revealed a LAVO-prediction model containing seven NIHSS items (AUROC 0.84). With the GACE (Gaze, facial Asymmetry, level of Consciousness, Extinction/inattention) decision tree, LAVO is predicted (AUROC 0.76) for 61% of patients with assessment of only two dichotomized NIHSS items, and for all patients with four items. Conclusion External validation of seven LAVO-prediction scales showed AUROCs between 0.75 and 0.83. Most scales, however, appear too complex for Emergency Medical Services use with prehospital validation generally lacking. GACE is the first LAVO-prediction scale using a simple decision tree as such increasing feasibility, while maintaining high accuracy. Prehospital prospective validation is planned.

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A comparison of Simon Broome and welsh criteria for selecting patients for familial hypercholesterolaemia (FH) genetic testing

Atherosclerosis ◽

10.1016/j.atherosclerosis.2018.06.552 ◽

2018 ◽

Vol 275 ◽

pp. e181

Author(s):

P. Ashfield-watt ◽

L. Gritzmacher ◽

I. McDowell ◽

G. Bayly ◽

K. Haralambos

Keyword(s):

Genetic Testing ◽

Familial Hypercholesterolaemia

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