Previously submitted to: JMIR Formative Research (no longer under consideration since Dec 05, 2022)
Date Submitted: Jul 14, 2022
Open Peer Review Period: Jul 14, 2022 - Sep 8, 2022
(closed for review but you can still tweet)
NOTE: This is an unreviewed Preprint
Warning: This is a unreviewed preprint (What is a preprint?). Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn (a note "no longer under consideration" will appear above).
Peer review me: Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period (in this case, a "Peer Review Me" button to sign up as reviewer is displayed above). All preprints currently open for review are listed here. Outside of the formal open peer-review period we encourage you to tweet about the preprint.
Citation: Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author).
Final version: If our system detects a final peer-reviewed "version of record" (VoR) published in any journal, a link to that VoR will appear below. Readers are then encourage to cite the VoR instead of this preprint.
Settings: If you are the author, you can login and change the preprint display settings, but the preprint URL/DOI is supposed to be stable and citable, so it should not be removed once posted.
Submit: To post your own preprint, simply submit to any JMIR journal, and choose the appropriate settings to expose your submitted version as preprint.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Semi-Automation of Keratopathy Visual Acuity Grading of Corneal Events in Belantamab Mafodotin Clinical Trials: Clinical Decision Support Software
ABSTRACT
Background:
Belantamab mafodotin (belamaf) has demonstrated clinically meaningful antimyeloma activity in patients with heavily pretreated multiple myeloma. However, the drug is highly active against dividing cells, which contributes to off-target adverse events, particularly ocular toxicity. Investigators or providers routinely use changes in best corrected visual acuity (BCVA) and corneal examination findings to determine Keratopathy Visual Acuity (KVA) grade to inform belamaf dose modification. We sought to model and translate the complexity of the ophthalmologic examination using the KVA scale into a mobile application that is easily applicable to patients presenting with ocular toxicities.
Objective:
We aimed to develop a semiautomated mobile app to facilitate the grading of ocular events in clinical trials involving belamaf.
Methods:
The paper process was semiautomated by creating a library of finite-state automaton (FSA) models to represent all permutations of KVA grade changes between baseline and current BCVA readings. The transition states in the FSA models operated independently of eye measurement units (eg, Snellen, logMAR, decimal) and provided a uniform approach to determining KVA grade changes. Together with the FSA, the complex decision tree for determining the grade change based on corneal examination findings was converted into logical statements that provide accurate and efficient computation of the overall KVA grade. First, a web-based user interface, conforming to clinical practice settings, was developed to simplify the input of key criteria for overall KVA grading. Subsequently, a mobile app was developed that included additional guided steps to assist in clinical decision-making.
Results:
The app underwent a robust Good Clinical Practice validation process in alignment with GSK standard operating procedures, and the outcomes were reviewed by key stakeholders, our medical lead for belamaf, and the systems integration team. The time to compute a patient’s overall KVA grade using the Belamaf Eye Exam (BEE) app was reduced from a 20- to 30-minute process to less than 1 to 2 minutes using our semiautomated KVA computational framework. The BEE app was well received, with most investigators surveyed selecting “satisfied” or “highly satisfied” for its accuracy and time efficiency. Based on current US Food and Drug Administration and EU standards, the BEE app is considered a nondevice clinical decision support software.
Conclusions:
We conclude that our semiautomated approach provides for an accurate, simplified method of assessment of patients’ corneal status that reduces the number of potential errors and more quickly delivers information critical for potential belamaf dose modifications related to ocular changes. The app is currently available on the Apple iOS and Android platforms for use by investigators of the DREAMM clinical trials. Moreover, this mobile app could extend to the clinic as a tool to support healthcare providers in making informed belamaf treatment decisions for their patients.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.