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510(k) Data Aggregation

    K Number
    K243729
    Device Name
    Prismira
    Manufacturer
    Date Cleared
    2025-06-13

    (192 days)

    Product Code
    Regulation Number
    882.5803
    Reference & Predicate Devices
    Predicate For
    N/A
    Why did this record match?
    Reference Devices :

    K231337, K233496

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    Prismira is a digital therapeutic indicated to improve attention function in adults ages 22-55 years old with primarily inattentive or combined-type Attention Deficit and Hyperactivity Disorder (ADHD). Patients who engage with Prismira demonstrate improvements in a digitally assessed measure of sustained and selective attention, Test of Variables of Attention (TOVA), and may not display benefits in typical behavioral symptoms, such as hyperactivity.

    Prismira should be considered for use as part of a therapeutic program that may include clinician directed therapy, medication, and/or educational programs, which further address symptoms of the disorder.

    Device Description

    Prismira is software-as-a-medical device (SaMD) that resides on the user's mobile device and can be executed at home.

    Prismira is a prescription digital therapeutic indicated to improve attention function in patients 22 and older with primarily inattentive or combined type ADHD. Patients who engage with Prismira demonstrate improvements in attention functioning and may not display benefits in other behavioral symptoms such as hyperactivity. Prismira is intended to be used as part of a therapeutic program that may include clinician-directed therapy, medication, and/or educational programs, which further address symptoms of the disorder.

    The Prismira App also includes an Engagement System to promote compliance with the treatment. The Engagement System gives the patient encouragement and feedback on progress at multiple timescales: performance on the most recent gameplay and historical game performance, completion of the daily assignment, progress toward the weekly target in the current week, and historical assignment completion.

    Each game includes visual and optional auditory stimulus presentation. The basic input from the patient includes physical interaction with a touch screen accomplished by using the mobile device. The device's feedback within each game includes visual and optional auditory responses to the inputs. Output from each game includes a score earned by playing the game. Each gameplay is brief, typically between 1 and 5 minutes in duration, depending on the game. The treatment program guides patients through an ordered list of games (the "script") that enables subjects to engage in the recommended daily regimen (approximately 15 minutes per day).

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and study details for the Prismira device, based on the provided FDA 510(k) clearance letter:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (as implied by study success and substantial equivalence claims):

    • Primary Endpoint: Statistically significant improvement in TOVA ACS in the LLM-001 therapy arm compared to the control arm.
    • Safety: No new or increased safety risks compared to the predicate device, and a favorable safety profile with minimal adverse events.
    Metric / EndpointAcceptance Criteria (Implied)Reported Device Performance (Prismira / LLM-001)
    Primary Endpoint: TOVA ACS Change from Baseline at Day 63Statistically significant difference between active and control arms.- LLM-001 Change from Baseline Mean: 1.1 points (SD=3.6)
    • Control Change from Baseline Mean: 0.3 points (SD=3.6)
    • LSM Difference (LLM-001 - Control): 0.78 points (SE=0.32)
    • LSM Difference p-value: 0.0149 (statistically significant)
    • LSM improvement of 1.09 points was less than the recognized MCID of 1.4 points, but notably greater than the predicate device (0.93 points). |
      | Secondary Endpoint: CGI-I | Improvement over control (desirable for clinical benefit). | Superior clinical benefit for the LLM-001 arm over control (p=0.0087). (For LLM-001 subjects, 32.1% were assessed as "very much improved" on the CGI-I). |
      | Secondary Endpoint: ADHD-RS Total Score | Improvement over control (desirable for clinical benefit). | 30.4% of LLM-001 subjects experienced greater than 30% reduction in their ADHD symptoms based on ADHD-RS total score. (Numerically favored LLM-001, but no statistically meaningful differences between groups for the continuous measure). |
      | Adverse Events (AEs) | Minimal; no serious AEs; low treatment-related AE rate. | - No serious adverse events reported.
    • Only two treatment-related AEs (frustration) out of 275 LLM-001 treated subjects (AE rate of 0.7%).
    • No subjects exited early for medical or gameplay-related complaints. |
      | Treatment Compliance | High, demonstrating feasibility of use. | Mean of 874.7 minutes (97.2%) of 900 expected minutes. |

    2. Sample Size and Data Provenance for the Test Set (Clinical Study)

    • Sample Size: 560 subjects were enrolled in The GAMES Study.
      • Safety Population: All 560 subjects.
      • Intent-to-Treat (ITT) Population: 456 subjects (224 randomized to LLM-001 active therapy, 232 to control therapy) who successfully completed gameplay treatment and Day 63 follow-up assessments.
    • Data Provenance: Not explicitly stated (e.g., country of origin). However, it was a "multi-center" clinical trial, suggesting diverse locations. It was a prospective, randomized, double-blind, parallel-group, sham-controlled clinical trial.

    3. Number of Experts and Qualifications for Ground Truth (Test Set)

    • The study used blinded clinicians to assess the CGI-I (Clinical Global Impression-Improvement), a global measure of clinical improvement.
    • Qualifications of Experts: Not explicitly stated beyond "blinded clinicians." Their expertise would be in diagnosing and assessing ADHD.

    4. Adjudication Method for the Test Set

    • The primary effectiveness measure (TOVA ACS) was a digitally assessed measure, so it wouldn't typically require human adjudication in the same way imaging studies might.
    • For the CGI-I, it was assessed by "blinded clinicians." This implies independent assessment rather than a consensus/adjudication process among multiple experts for each individual case's outcome on this specific measure. The "blinded" nature is the key control here.
    • There's no mention of a 2+1, 3+1, or other specific adjudication method for the clinical endpoints.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

    • No, an MRMC comparative effectiveness study was not done. This type of study typically applies to diagnostic imaging devices where human readers interpret medical images with and without AI assistance.
    • This study was a randomized controlled clinical trial evaluating a digital therapeutic, focusing on its direct effect on patient outcomes (attention function) compared to a control.

    6. Standalone Performance Study

    • Yes, a standalone (algorithm only) performance study was done in the context of the clinical trial.
    • The "LLM-001 active therapy" arm represents the device's performance when used by the patient as intended, without direct human-in-the-loop interaction from the algorithm's perspective. The clinicians were involved in diagnosis and overall patient care, but the daily therapeutic delivery was directly from the device to the patient.
    • The primary endpoint, "change from baseline in the digitally assessed measure of sustained and selective attention, the Test of Variables of Attention (TOVA®)," is a direct measure of the algorithm's effect on attention function.

    7. Type of Ground Truth Used (Clinical Study)

    • The primary ground truth for effectiveness was derived from the Test of Variables of Attention (TOVA®) Attention Comparison Score (ACS), a digitally assessed, objective measure of sustained and selective attention. This is a standardized, quantitative neuropsychological assessment tool.
    • Secondary ground truths included:
      • ADHD-RS: ADHD Rating Scale, likely a clinician-rated symptom scale or patient-reported outcome.
      • CGI-I: Clinical Global Impression-Improvement, a clinician-assessed global measure of improvement.
      • BRIEF-A: Behavior Rating Inventory of Executive Function – Adult, likely a patient-reported or clinician-rated scale.
      • WFIRS-S: Weiss Functional Impairment Rating Scale – Self Report, a patient-reported outcome.
      • AAQoL: Adult ADHD Quality of Life Questionnaire, a patient-reported outcome.
    • Diagnosis of ADHD (combined or inattentive type) was confirmed using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria and confirmed by the Mini-International Neuropsychiatric Interview (MINI) for ADHD adult version.

    8. Sample Size for the Training Set

    • The document does not explicitly state the sample size for the training set used to develop the Prismira algorithm. The provided clinical study (The GAMES Study) is the pivotal validation study designed to test the already developed device.
    • The device description mentions an "Adaptive algorithm and Engagement System." Adaptive algorithms are often trained on large datasets, but the details of that training process and dataset size are not included in this 510(k) summary. References ¹ and ² hint at large-scale cognitive training data and online trials, which might relate to the algorithm's development.

    9. How Ground Truth for the Training Set Was Established

    • Similar to the training set sample size, the document does not explicitly describe how ground truth for the training set was established.
    • Given that it's an "adaptive algorithm," training data would likely involve cognitive performance metrics and potentially diagnostic information from previous patient populations. This could involve using standardized cognitive tests (like TOVA or similar measures) as the "ground truth" to train the algorithm to improve performance on those measures.
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