---
title: "Time-Varying Predictors for Clinical Prediction in Pediatric Hospital Medicine"
id: "pubmed-42764170"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42764170"
content_type: "clinical_feed_article"
specialty: "Pediatrics"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42764170/"
doi: "10.1542/hpeds.2026-009395"
published_at: "2026-09-21T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Time-Varying Predictors for Clinical Prediction in Pediatric Hospital Medicine
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42764170
- **Specialty:** [Pediatrics](https://medichelpline.com/clinical-feed/pediatrics.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42764170/)
- **DOI:** [10.1542/hpeds.2026-009395](https://doi.org/10.1542%2Fhpeds.2026-009395)
- **Published At:** 2026-09-21T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- Clinical prediction models often use data from a single time point, but hospitalized children’s clinical status commonly evolves during admission. - **Time-varying predictors** are serial measurements recorded over the course of hospitalization that can capture evolving clinical trajectories. - Incorporating longitudinal, patient-specific data into prediction models can produce more accurate and dynamic risk estimates that better reflect changing clinical status. - The article reviews key steps in developing time-varying prediction models for **pediatric hospital medicine**, including selecting appropriate clinical settings and ensuring reliable data recording. - Data structuring for time-varying models requires reformatting longitudinal measurements so they can be used in analytic approaches designed for repeated measures. - The review summarizes analytic strategies suitable for time-varying predictors and contrasts them with single time point methods. - Advantages of time-varying models include improved predictive performance and alignment with clinical trajectories; limitations include increased data and analytic complexity and potential challenges in implementation. - A published example is discussed in which a time-varying model predicting **in-hospital mortality** among severely malnourished children used daily clinical signs; incorporating daily signs improved predictive accuracy versus a model using admission-only data. - The article positions time-varying prediction modelling as an important methodological opportunity for pediatric hospitalists who want to leverage longitudinal electronic health data in practice.
## Clinical Analysis & Structured Key Points
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Online ahead of print. # Clinical Prediction With Time-Varying Predictors in Pediatric Hospital Medicine [Alastair Fung](https://pubmed.ncbi.nlm.nih.gov/?term=Fung+A&cauthor_id=42764170)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42764170/#full-view-affiliation-1 "Division of Paediatric Medicine, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada."), [Joseph Beyene](https://pubmed.ncbi.nlm.nih.gov/?term=Beyene+J&cauthor_id=42764170)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42764170/#full-view-affiliation-2 "Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada."), [David D'Arienzo](https://pubmed.ncbi.nlm.nih.gov/?term=D%27Arienzo+D&cauthor_id=42764170)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42764170/#full-view-affiliation-3 "Department of Pediatrics, The Montreal Children's Hospital, McGill University, Montreal, Quebec, Canada."), [Rishi P Mediratta](https://pubmed.ncbi.nlm.nih.gov/?term=Mediratta+RP&cauthor_id=42764170)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42764170/#full-view-affiliation-4 "Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California.") Affiliations Expand ### Affiliations * 1 Division of Paediatric Medicine, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. * 2 Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada. * 3 Department of Pediatrics, The Montreal Children's Hospital, McGill University, Montreal, Quebec, Canada. * 4 Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California. * PMID: **42764170** * DOI: [ 10.1542/hpeds.2026-009395 ](https://doi.org/10.1542/hpeds.2026-009395) Item in Clipboard # Clinical Prediction With Time-Varying Predictors in Pediatric Hospital Medicine Alastair Fung et al. Hosp Pediatr. 2026. Show details Display options Display options Format Abstract PubMed PMID Hosp Pediatr Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Hosp+Pediatr%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Hosp+Pediatr%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42764170/) . 2026 Sep 21:e2026009395. doi: 10.1542/hpeds.2026-009395. Online ahead of print. ### Authors [Alastair Fung](https://pubmed.ncbi.nlm.nih.gov/?term=Fung+A&cauthor_id=42764170)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42764170/#short-view-affiliation-1 "Division of Paediatric Medicine, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada."), [Joseph Beyene](https://pubmed.ncbi.nlm.nih.gov/?term=Beyene+J&cauthor_id=42764170)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42764170/#short-view-affiliation-2 "Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada."), [David D'Arienzo](https://pubmed.ncbi.nlm.nih.gov/?term=D%27Arienzo+D&cauthor_id=42764170)[ 3 ](https://pubmed.ncbi.nlm.nih.gov/42764170/#short-view-affiliation-3 "Department of Pediatrics, The Montreal Children's Hospital, McGill University, Montreal, Quebec, Canada."), [Rishi P Mediratta](https://pubmed.ncbi.nlm.nih.gov/?term=Mediratta+RP&cauthor_id=42764170)[ 4 ](https://pubmed.ncbi.nlm.nih.gov/42764170/#short-view-affiliation-4 "Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California.") ### Affiliations * 1 Division of Paediatric Medicine, Department of Paediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada. * 2 Department of Health Research Methods, Evidence and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada. * 3 Department of Pediatrics, The Montreal Children's Hospital, McGill University, Montreal, Quebec, Canada. * 4 Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California. * PMID: **42764170** * DOI: [ 10.1542/hpeds.2026-009395 ](https://doi.org/10.1542/hpeds.2026-009395) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract Clinical prediction models traditionally rely on measurements obtained at a single time point to estimate risk and guide management. However, the clinical status of hospitalized children often evolves during admission, and time-varying measurements obtained over the course of hospitalization may provide more accurate, dynamic estimates of risk that better reflect clinical trajectories. Time-varying prediction models are therefore an increasingly important methodological opportunity for pediatric hospitalists seeking to leverage patient-specific longitudinal data in clinical practice. In this article, we review the development of clinical prediction models using time-varying predictors in pediatric hospital medicine, including appropriate clinical settings and data recording, data structuring, analytic approaches, and advantages and limitations. We illustrate these principles using a published example of a time-varying model developed to predict in-hospital mortality among severely malnourished children, highlighting how incorporating daily clinical signs improved predictive accuracy compared with a single time point model using admission data alone. Copyright © 2027 by the American Academy of Pediatrics. 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