Prediction of cycle-one serious drug-related toxicity in phase I oncology t - Evidencio
Prediction of cycle-one serious drug-related toxicity in phase I oncology trials
All patients in phase I trials do not have equivalent susceptibility to serious drug-related toxicity (SDRT). This nomogram was developed to predict the risk of cycle-one SDRT to better select appropriate patients for phase I trials (c-index: 0.60-0.64).
Research authors: Hyman DM, Eaton AA, Gounder MM, Smith GL, Pamer EG, Hensley ML, Spriggs DR, Iasonos A.
Version: 1.26
  • Public
  • Clinical pharmacology
  • {{ modelType }}
  • Details
  • Validate algorithm
  • Save input
  • Load input
Display
Units

{{ section.title }}

{{ section.description }}

Calculate the result

Set more parameters to perform the calculation

Risk of cycle-one serious drug-related toxicity:

{{ resultSubheader }}
{{ $t('download_result_availability') }}
{{ chart.title }}
Result interval {{ additionalResult.min }} to {{ additionalResult.max }}

Conditional information


How this model should be used:
This nomogram could be used to further inform decision making and allow both patients and physicians to carefully weigh the risks of participation against the potential benefits of the experimental drug(s).

Model performance: 
The model was validated both internally (bootstrap-adjusted c-index: 0.60, N=3,104) and externally (c-index: 0.64, N=234). The median model-predicted probability of SDRT was 17.2% (range: 7.3% to 34.5%). Calibration curves for the nomogram in the derivation set are shown in Figure 1 and suggest excellent model calibration, with model estimates being close to observed rates.1

Source: 
1 Hyman DM, Eaton AA, Gounder MM et al. Nomogram to predict cycle-one serious drug-related toxicity in phase I oncology trials. J Clin Oncol. 2014;32(6):519-26.

{{ file.classification }}
PRO
Note
Notes are only visible in the result download and will not be saved by Evidencio

This algorithm is provided for educational, training and information purposes. It must not be used to support medical decision making, or to provide medical or diagnostic services. Read our full disclaimer.

Underlying algorithms Part of
Comments
Comment
Please enter a comment
Comments are visible to anyone

Algorithm feedback

No feedback yet 1 Comment {{ model.comments.length }} Comments
On {{ comment.created_at }} {{ comment.user.username }} a no longer registered author wrote:
{{ comment.content }}
logo

Please sign in to enable Evidencio print features

In order to use the Evidencio print features, you need to be logged in.
If you don't have an Evidencio Community Account you can create your free personal account at:

https://www.evidencio.com/registration

Printed results - Examples {{ new Date().toLocaleString() }}


Evidencio Community Account Benefits


With an Evidencio Community account you can:

  • Create and publish your own prediction algorithms.
  • Share your prediction algorithms with your colleagues, research group, organization or the world.
  • Review and provide feedback on algorithms that have been shared with you.
  • Validate your algorithms and validate algorithms from other users.
  • Find algorithms based on Title, Keyword, Author, Institute, or MeSH classification.
  • Use and save prediction algorithms and their data.
  • Use patient specific protocols and guidelines based on sequential algorithms and decision trees.
  • Stay up-to-date with new algorithms in your field as they are published.
  • Create your own lists of favorite algorithms and topics.

A personal Evidencio account is free, with no strings attached!
Join us and help create clarity, transparency, and efficiency in the creation, validation, and use of medical prediction algorithms.


Disclaimer: Calculations alone should never dictate patient care, and are no substitute for professional judgement.
Evidencio v3.38 © 2015 - 2025 Evidencio. All Rights Reserved