scholarly journals Minimum sample size calculations for external validation of a clinical prediction model with a time‐to‐event outcome

2021 ◽  
Author(s):  
Richard D. Riley ◽  
Gary S. Collins ◽  
Joie Ensor ◽  
Lucinda Archer ◽  
Sarah Booth ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
External Validation ◽  
Minimum Sample Size ◽  
Clinical Prediction ◽  
Time To Event ◽  
Clinical Prediction Model ◽  
Sample Size Calculations ◽  
Minimum Sample

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

2020 ◽  
Vol 40 (1) ◽  
pp. 133-146 ◽  
Author(s):  
Lucinda Archer ◽  
Kym I. E. Snell ◽  
Joie Ensor ◽  
Mohammed T. Hudda ◽  
Gary S. Collins ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
External Validation ◽  
Minimum Sample Size ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
Continuous Outcome ◽  
Minimum Sample

scholarly journals Minimum sample size for developing a multivariable prediction model: PART II - binary and time-to-event outcomes

2018 ◽  
Vol 38 (7) ◽  
pp. 1276-1296 ◽  
Author(s):  
Richard D Riley ◽  
Kym IE Snell ◽  
Joie Ensor ◽  
Danielle L Burke ◽  
Frank E Harrell Jr ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
Minimum Sample Size ◽  
Time To Event ◽  
Minimum Sample

Development and external validation of an online clinical prediction model for augmented renal clearance in adult mixed critically ill patients: the ARC predictor

2019 ◽  
Author(s):  
Matthias Gijsen ◽  
Chao-yuan Huang ◽  
Marine Flechet ◽  
Ruth Van Daele ◽  
Peter Declercq ◽  
...  
Keyword(s):  
Prediction Model ◽  
Renal Clearance ◽  
Tertiary Care ◽  
External Validation ◽  
Clinical Information ◽  
Clinical Prediction ◽  
Augmented Renal Clearance ◽  
Threshold Probability ◽  
Clinical Prediction Model ◽  
Icu Stay

Abstract BackgroundAugmented renal clearance (ARC) might lead to subtherapeutic plasma levels of drugs with predominant renal clearance. Early identification of ARC remains challenging for the intensive care unit (ICU) physician. We developed and validated the ARC predictor, a clinical prediction model for ARC on the next day during ICU stay, and made it available via an online calculator. Its predictive performance was compared with that of two existing models for ARC.MethodsA large multicenter database including medical, surgical and cardiac ICU patients (n = 33258 ICU days) from three Belgian tertiary care academic hospitals was used for the development of the prediction model. Development was based on clinical information available during ICU stay. We assessed performance by measuring discrimination, calibration and net benefit. The final model was externally validated (n = 10259 ICU days) in a single-center population.ResultsARC was found on 19.6% of all ICU days in the development cohort. Six clinical variables were retained in the ARC predictor: day from ICU admission, age, sex, serum creatinine, trauma and cardiac surgery. External validation confirmed good performance with an area under the curve of 0.88 (95% CI 0.87 – 0.88), and a sensitivity and specificity of 84.1 (95% CI 82.5 – 85.7) and 76.3 (95% CI 75.4 – 77.2) at the default threshold probability of 0.2, respectively.ConclusionARC on the next day can be predicted with good performance during ICU stay, using routinely collected clinical information that is readily available at bedside. The ARC predictor is available at www.arcpredictor.com.

scholarly journals Development and external validation of an online clinical prediction model for augmented renal clearance in adult mixed critically ill patients: the ARC predictor

2020 ◽  
Author(s):  
Matthias Gijsen ◽  
Chao-yuan Huang ◽  
Marine Flechet ◽  
Ruth Van Daele ◽  
Peter Declercq ◽  
...  
Keyword(s):  
Cardiac Surgery ◽  
Prediction Model ◽  
Renal Clearance ◽  
Tertiary Care ◽  
External Validation ◽  
Clinical Information ◽  
Clinical Prediction ◽  
Augmented Renal Clearance ◽  
Clinical Prediction Model ◽  
Icu Stay

Abstract Background Augmented renal clearance (ARC) might lead to subtherapeutic plasma levels of drugs with predominant renal clearance. Early identification of ARC remains challenging for the intensive care unit (ICU) physician. We developed and validated the ARC predictor, a clinical prediction model for ARC on the next day during ICU stay, and made it available via an online calculator. Its predictive performance was compared with that of two existing models for ARC. Methods A large multicenter database including medical, surgical and cardiac surgery ICU patients (n = 33258 ICU days) from three Belgian tertiary care academic hospitals was used for the development of the prediction model. Development was based on clinical information available during ICU stay. We assessed performance by measuring discrimination, calibration and net benefit. The final model was externally validated (n = 10259 ICU days) in a single-center population. Results ARC was found on 19.6% of all ICU days in the development cohort. Six clinical variables were retained in the ARC predictor: day from ICU admission, age, sex, serum creatinine, trauma and cardiac surgery. External validation confirmed good performance with an area under the curve of 0.88 (95% CI 0.87 – 0.88), and a sensitivity and specificity of 84.1 (95% CI 82.5 – 85.7) and 76.3 (95% CI 75.4 – 77.2) at the default threshold probability of 0.2, respectively. Conclusion ARC on the next day can be predicted with good performance during ICU stay, using routinely collected clinical information that is readily available at bedside. The ARC predictor is available at www.arcpredictor.com .

scholarly journals Minimum sample size for developing a multivariable prediction model: Part I - Continuous outcomes

2018 ◽  
Vol 38 (7) ◽  
pp. 1262-1275 ◽  
Author(s):  
Richard D. Riley ◽  
Kym I.E. Snell ◽  
Joie Ensor ◽  
Danielle L. Burke ◽  
Frank E. Harrell ◽  
...  
Keyword(s):  
Prediction Model ◽  
Sample Size ◽  
Minimum Sample Size ◽  
Continuous Outcomes ◽  
Minimum Sample
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