The Drug Titration Paradox Updated and Reinterpreted: With Perfect Titration, Dose and Effect Will Be Uncorrelated

The drug titration paradox is a clinical observation at a population level whereby patients receiving a high dose of medication appear to have a lower-than-average clinical effect, and patients receiving a low dose of medication appear to have a higher-than-average clinical effect. Consequently, there would be a negative correlation between dose and effect. The paradox was previously described as present in all titratable medications, particularly anesthetics. The underlying assumption of the paradox is that titration is gradual, that is, consisting of a sequence of relatively minor adjustments with the intent to avoid target over-shoot. This assumption may not hold true with technological advancements and automated drug delivery. The methodology of the paradox was re-explored without that assumption, both mathematically and with computer simulation of different control schemes that may be used to assist with drug titration. In these cases, achieving a positive correlation between dose and effect and reversing the paradox is possible. Recognizing that a perfectly titrated medication will have zero correlation coefficient at a population level, measuring the correlation coefficient over time can be a useful quality metric in evaluating automation technology in drug delivery.

Authors

Institutions

Publication Details

Journal
Clinical Pharmacology & Therapeutics
Published
2025-04-22
DOI
https://doi.org/10.1002/cpt.3673
Primary Topic
Anesthesia and Sedative Agents
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Drug Titration Paradox Updated and Reinterpreted: With Perfect Titration, Dose and Effect Will Be Uncorrelated

Klaske van Heusden, Élie Sarraf
Clinical Pharmacology & Therapeutics
Anesthesia and Sedative Agents
article

The Drug Titration Paradox Updated and Reinterpreted: With Perfect Titration, Dose and Effect Will Be Uncorrelated

Klaske van Heusden, Élie Sarraf
article en

Abstract

The drug titration paradox is a clinical observation at a population level whereby patients receiving a high dose of medication appear to have a lower-than-average clinical effect, and patients receiving a low dose of medication appear to have a higher-than-average clinical effect. Consequently, there would be a negative correlation between dose and effect. The paradox was previously described as present in all titratable medications, particularly anesthetics. The underlying assumption of the paradox is that titration is gradual, that is, consisting of a sequence of relatively minor adjustments with the intent to avoid target over-shoot. This assumption may not hold true with technological advancements and automated drug delivery. The methodology of the paradox was re-explored without that assumption, both mathematically and with computer simulation of different control schemes that may be used to assist with drug titration. In these cases, achieving a positive correlation between dose and effect and reversing the paradox is possible. Recognizing that a perfectly titrated medication will have zero correlation coefficient at a population level, measuring the correlation coefficient over time can be a useful quality metric in evaluating automation technology in drug delivery.

Clinical Pharmacology & TherapeuticsVol. 118(1)
University of British Columbia (CA), University of British Columbia, Okanagan Campus (CA), Penn State Milton S. Hershey Medical Center (US)
Good health and well-being
Openalex Percentile: Top 85%
Anesthesia and Sedative Agents
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.