Evidence map›Paper›PMID 42488608›Full record

ArticleFrontiers in physiology2026

Refining humane endpoint detection by time-series forecasting and threshold definition using a multivariate severity score.

Sara Lutscher, Lisa Goral, Christine Häger, Anna Munk, Miriam Heider, Nora Weegh, Dietmar Zechner, Heidrun Potschka, Andreas von Knethen, André Bleich and 1 more

Abstract read
In one paragraph

Article in Frontiers in physiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Sara LutscherInstitute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany.
Lisa GoralInstitute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany.
Christine HägerInstitute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany.
Anna MunkInstitute of Pharmacology, Toxicology, and Pharmacy, Ludwig-Maximilians-Universität München, Munich, Germany.
Miriam HeiderInstitute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany.
Nora WeeghInstitute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany.
Dietmar ZechnerRudolf-Zenker-Institute for Experimental Surgery, Rostock University Medical Center, Rostock, Germany.
Heidrun PotschkaInstitute of Pharmacology, Toxicology, and Pharmacy, Ludwig-Maximilians-Universität München, Munich, Germany.
Andreas von KnethenDepartment of Anesthesiology, Intensive Care Medicine and Pain and Therapy, University Hospital Frankfurt, Frankfurt, Germany.
André BleichInstitute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany.
Steven R TalbotInstitute for Laboratory Animal Science, Hannover Medical School, Hannover, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Severity assessment in animal-based research is not only mandatory but also requires the identification of robust, objective, and model-specific parameters. The RELative Severity Assessment (RELSA) procedure enabled multivariate severity assessment, allowing interpretation of outcomes in the context of a predefined severity grading. This study expands the application of the RELSA score by combining it with an Autoregressive Integrated Moving Average (ARIMA)-based prediction model, which we call foRcast. To facilitate interpretation of severity, a kernel density estimate (KDE) was applied to define exploratory thresholds on the RELSA scale. Data of mice reaching humane endpoints across models representing a broad range of severity profiles were reanalyzed using transmitter-derived parameters (e.g., heart rate) and behavioral parameters (e.g., voluntary wheel running), depending on the model. The primary focus was predicting RELSA scores at the humane endpoint using foRcast. With an overall root mean square error of 0.069 and 96% of actual RELSA scores falling within the 95% prediction interval of the forecasts, this tool showed promising preliminary performance. KDE was performed on sepsis data, yielding candidate thresholds at RELSA = 0.337 and RELSA = 0.647, which could be used to define severity zones for classifying the animals' burden, although these should not be confused with regulatory severity gradings. This proof-of-concept study showed the potential of the foRcast tool to accurately predict RELSA scores across various animal models and interventions, demonstrating the possibility to identify individual animals at risk of reaching the humane endpoint. The data also emphasize the intrinsic limitation of ARIMA models: drastic changes in the course cannot be reliably predicted. This underscores the need for higher-resolution measurements to detect early changes in animal well-being. Overall, the definition of candidate severity thresholds on the RELSA scale and the foRcast model represent valuable additions to the range of severity tools for refining severity assessment in animal-based research.

Indexed as

3Rsforecastinghumane endpoint predictionpreclinical researchrefinementseverity assessment

Identifiers

PMID42488608
PMCPMC13388068

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.