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Clinical prediction models: from foundational concepts to practical application

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Veröffentlicht/Copyright: 2. März 2026
Diagnosis
Aus der Zeitschrift Diagnosis

Abstract

Objectives

Clinical prediction requires formalizing uncertainty into a statistical model. However, persistent confusion between prediction and inference, and between traditional (stepwise) and modern (penalized) development strategies, leads to unstable, poorly calibrated, and overfit models. A structured statistical framework is essential.

Methods

This article is a structured, didactic tutorial that explains the core concepts of clinical prediction models. It covers the definition of a prediction model, the fundamental strategies for its construction, and the essential framework for its evaluation, illustrated through an applied example using real-world clinical data.

Results

The tutorial illustrates model development using the GUSTO-I dataset (N = 40,830). Penalized methods (LASSO and Elastic Net) successfully identified clinical signals while eliminating engineered noise variables. The LASSO model (λ1se) achieved excellent discrimination (AUC 0.818; 95 % CI: 0.803–0.832) and overall accuracy (Brier score 0.058). Calibration analysis revealed a slope of 1.28 and intercept of 0.63, identifying conservative bias and systematic risk underestimation inherent to λ1se selection. Decision curve analysis confirmed significant clinical utility across relevant probability thresholds.

Conclusions

This guide equips clinicians with a rigorous methodological framework for the critical appraisal and interpretation of modern clinical prediction models.

Keywords: clinical prediction models; logistic regression; LASSO; overfitting; calibration; validation
Corresponding author: Javier Arredondo Montero, MD, PhD, Department of Pediatric Surgery, Complejo Asistencial Universitario de León, c/ Altos de Nava s/n, 24008, Castilla y León, León, Spain, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Javier Arredondo Montero (JAM): Conceptualization; Methodology; Software; Validation; Formal analysis; Investigation; Resources; Data Curation; Writing – Original Draft; Writing – Review & Editing; Visualization; Supervision; Project administration. The author has accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: Artificial intelligence (ChatGPT 4, OpenAI) was used for language editing and for generating a synthetic dataset under the author’s explicit instructions. It did not influence the scientific content, analysis, or interpretation.

  5. Conflict of interest: The author states no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/dx-2025-0152).

Received: 2025-10-25
Accepted: 2026-01-11
Published Online: 2026-03-02

© 2026 Walter de Gruyter GmbH, Berlin/Boston

https://doi.org/10.1515/dx-2025-0152
Schlagwörter für diesen Artikel
clinical prediction models; logistic regression; LASSO; overfitting; calibration; validation
Heruntergeladen am 4.5.2026 von https://www.degruyterbrill.com/document/doi/10.1515/dx-2025-0152/html?lang=de
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