scholarly journals Application of clinical prediction modeling in pediatric neurosurgery: a case study

2021 ◽  
Author(s):  
Hendrik-Jan Mijderwijk ◽  
Thomas Beez ◽  
Daniel Hänggi ◽  
Daan Nieboer
Keyword(s):  
Decision Making ◽  
Prediction Models ◽  
Pediatric Neurosurgery ◽  
Medical Decision ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Current Form ◽  
Related Risk ◽  
Decision Making Processes

AbstractThere has been an increasing interest in articles reporting on clinical prediction models in pediatric neurosurgery. Clinical prediction models are mathematical equations that combine patient-related risk factors for the estimation of an individual’s risk of an outcome. If used sensibly, these evidence-based tools may help pediatric neurosurgeons in medical decision-making processes. Furthermore, they may help to communicate anticipated future events of diseases to children and their parents and facilitate shared decision-making accordingly. A basic understanding of this methodology is incumbent when developing or applying a prediction model. This paper addresses this methodology tailored to pediatric neurosurgery. For illustration, we use original pediatric data from our institution to illustrate this methodology with a case study. The developed model is however not externally validated, and clinical impact has not been assessed; therefore, the model cannot be recommended for clinical use in its current form.

scholarly journals Clinical Prediction Models for Primary Prevention of Cardiovascular Disease: Validity in Independent Cohorts

2021 ◽  
Author(s):  
Gaurav Gulati ◽  
Riley J Brazil ◽  
Jason Nelson ◽  
David van Klaveren ◽  
Christine M. Lundquist ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Decision Making ◽  
Primary Prevention ◽  
Prediction Models ◽  
Model Updating ◽  
Clinical Prediction ◽  
Net Benefit ◽  
Clinical Prediction Models ◽  
C Statistic ◽  
Prevention Of Cardiovascular Disease

AbstractBackgroundClinical prediction models (CPMs) are used to inform treatment decisions for the primary prevention of cardiovascular disease. We aimed to assess the performance of such CPMs in fully independent cohorts.Methods and Results63 models predicting outcomes for patients at risk of cardiovascular disease from the Tufts PACE CPM Registry were selected for external validation on publicly available data from up to 4 broadly inclusive primary prevention clinical trials. For each CPM-trial pair, we assessed model discrimination, calibration, and net benefit. Results were stratified based on the relatedness of derivation and validation cohorts, and net benefit was reassessed after updating model intercept, slope, or complete re-estimation. The median c statistic of the CPMs decreased from 0.77 (IQR 0.72-0.78) in the derivation cohorts to 0.63 (IQR 0.58-0.66) when externally validated. The validation c-statistic was higher when derivation and validation cohorts were considered related than when they were distantly related (0.67 vs 0.60, p < 0.001). The calibration slope was also higher in related cohorts than distantly related cohorts (0.69 vs 0.58, p < 0.001). Net benefit analysis suggested substantial likelihood of harm when models were externally applied, but this likelihood decreased after model updating.ConclusionsDiscrimination and calibration decrease significantly when CPMs for primary prevention of cardiovascular disease are tested in external populations, particularly when the population is only distantly related to the derivation population. Poorly calibrated predictions lead to poor decision making. Model updating can reduce the likelihood of harmful decision making, and is needed to realize the full potential of risk-based decision making in new settings.

scholarly journals A nonparametric updating method to correct clinical prediction model drift

2019 ◽  
Vol 26 (12) ◽  
pp. 1448-1457 ◽  
Author(s):  
Sharon E Davis ◽  
Robert A Greevy ◽  
Christopher Fonnesbeck ◽  
Thomas A Lasko ◽  
Colin G Walsh ◽  
...  
Keyword(s):  
Prediction Models ◽  
Testing Procedure ◽  
Learning Approaches ◽  
Department Of Veterans Affairs ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Subsequent Performance ◽  
Nonparametric Models ◽  
Population Shift

Abstract Objective Clinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models. Materials and Methods We describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy. We illustrate the test’s properties on simulated scenarios of population shift and 2 models based on Department of Veterans Affairs inpatient admissions. Results In simulations, the test generally recommended no update under no population shift, no update or modest recalibration under case mix shifts, intercept correction under changing outcome rates, and refitting under shifted predictor-outcome associations. The recommended updates provided superior or similar calibration to that achieved with more complex updating. In the case study, however, small update sets lead the test to recommend simpler updates than may have been ideal based on subsequent performance. Discussion Our test’s recommendations highlighted the benefits of simple updating as opposed to systematic refitting in response to performance drift. The complexity of recommended updating methods reflected sample size and magnitude of performance drift, as anticipated. The case study highlights the conservative nature of our test. Conclusions This new test supports data-driven updating of models developed with both biostatistical and machine learning approaches, promoting the transportability and maintenance of a wide array of clinical prediction models and, in turn, a variety of applications relying on modern prediction tools.

scholarly journals Clinical prediction models to inform individualized decision-making in subfertile couples: a stratified medicine approach

2014 ◽  
Vol 29 (9) ◽  
pp. 1851-1858 ◽  
Author(s):  
D. J. McLernon ◽  
E. R. te Velde ◽  
E. W. Steyerberg ◽  
B. W. J. Mol ◽  
S. Bhattacharya
Keyword(s):  
Decision Making ◽  
Prediction Models ◽  
Stratified Medicine ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Individualized Decision

scholarly journals A genetic programming approach to development of clinical prediction models: A case study in symptomatic cardiovascular disease

PLoS ONE ◽  
2018 ◽  
Vol 13 (9) ◽  
pp. e0202685 ◽  
Author(s):  
Christian A. Bannister ◽  
Julian P. Halcox ◽  
Craig J. Currie ◽  
Alun Preece ◽  
Irena Spasić
Keyword(s):  
Cardiovascular Disease ◽  
Genetic Programming ◽  
Prediction Models ◽  
Programming Approach ◽  
Clinical Prediction ◽  
Clinical Prediction Models

scholarly journals Use Machine Learning Methods to Explore the Associated Risk Factors of Osteoporosis or Bone Loss in Chinese People With Type 2 Diabetes: A Clinical Prediction Model

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A250-A251
Author(s):  
Yaqian Mao ◽  
Jixing Liang ◽  
Wei Lin ◽  
Junping Wen ◽  
Gang Chen
Keyword(s):  
Risk Factors ◽  
Bone Loss ◽  
Prediction Models ◽  
Clinical Prediction ◽  
Clinical Prediction Models ◽  
Prediction Ability ◽  
Related Risk ◽  
Associated Risk Factors ◽  
Index Value

Abstract Objective: This study aimed to use machine learning (ML) methods to explore the risk factors associated with OP and bone loss in the Chinese T2DM population, so as to construct useful clinical prediction models. Methods: This was a two-center, retrospective study. The data came from a chronic disease epidemiological investigation database conducted in Ningde City and Wuyishan City, Fujian Province, China from March 2011 to December 2014. Finally, 798 T2DM patients who met the enrollment criteria were included in the final analysis. In order to control gender as a confounding factor that affects the results, we constructed two clinical prediction models based on different genders. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter relevant feature variables. The selected characteristic variables were modeled by logistic regression (LR), and clinical nomograms were used for more intuitive expression. The stability, clinical applicability and recognition of the model were evaluated by C-index, receiver operating characteristic (ROC) curve, calibration chart and decision curve analysis (DCA). Internal verification was achieved through bootstrapping validation. Results: In exploring the related risk factors of OP or bone loss in female T2DM patients. There were a total of 9 related predictors, namely age, marital status, glutamyl transpeptidase, fracture, coronary heart disease, fruit-flavored drinks, moderate-intensity exercise, menopause and nap time were determined by LASSO analysis from a total of 69 variables. The model we constructed using these 9 related predictors showed medium prediction ability (C-index value: 0.738, 95%CI[0.692, 0.784]), the C-index in bootstrapping validation was 0.714, and the area under the ROC curve (AUC) was 0.738. The DCA showed that if the risk threshold was between 4% and 100%, the nomogram could be used clinically. In exploring the related risk factors of OP and bone loss in male T2DM patients. A total of 12 related predictors were identified from 65 variables through LASSO analysis, including age, marital status, fasting serum insulin, alanine aminotransferase, coronary heart disease, respiratory diseases, diabetic retinopathy, seafood, desserts, fruit-flavored beverages, coffee, high-intensity exercise. The model we constructed using these 12 related predictors showed medium prediction ability (C-index value: 0.751, 95%CI[0.694–0.808]), the C-index in bootstrapping validation was 0.704, and the AUC value was 0.751. The DCA showed that if the risk threshold was between 3% and 68%, the nomogram could be used clinically. Conclusion: We explored the associated risk factors of osteoporosis or bone Loss in Chinese people with type 2 diabetes, and developed a risk nomogram with moderate predictive power. The nomogram can help clinicians and patients make joint decisions before treatment.

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