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

Epilepsia ◽  
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

Response: Brightening the crystal ball: A constructive reappraisal of the postwithdrawal seizure relapse prediction model

Epilepsia ◽  
2021 ◽  
Author(s):  
Margherita Contento ◽  
Bruno Bertaccini ◽  
Martina Biggi ◽  
Matteo Magliani ◽  
Ylenia Failli ◽  
...  
Keyword(s):  
Prediction Model ◽  
Crystal Ball ◽  
Relapse Prediction ◽  
Seizure Relapse

1575: External Validation of Outcome Prediction Model for Ureteral/Renal Calculi

2005 ◽  
Vol 173 (4S) ◽  
pp. 427-427
Author(s):  
Sijo J. Parekattil ◽  
Udaya Kumar ◽  
Nicholas J. Hegarty ◽  
Clay Williams ◽  
Tara Allen ◽  
...  
Keyword(s):  
Prediction Model ◽  
Outcome Prediction ◽  
External Validation ◽  
Renal Calculi

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

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

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

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.

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.

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

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

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

2018 ◽  
Vol 14 (5) ◽  
pp. 530-539 ◽  
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.

External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients

2021 ◽  
Author(s):  
Andrew Wong ◽  
Erkin Otles ◽  
John P. Donnelly ◽  
Andrew Krumm ◽  
Jeffrey McCullough ◽  
...  
Keyword(s):  
Prediction Model ◽  
External Validation ◽  
Hospitalized Patients
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