RX-1 Sleep

K253148

Vivaquant, Inc. Dba Rhythm Express · cleared 2026-06-22 · product code MNR · Anesthesiology

FDA 510(k) summary for RX-1 Sleep (K253148)

Constat organizes this clearance into predicate context, validation evidence, reported performance, postmarket signals, and reimbursement links. Each extracted claim stays tied to the public FDA source; missing fields remain explicitly unstated.

Validation studies
2
Cited predicates
2
Performance observations
8
Product code
MNR

Premarket evidence — what FDA accepted

Device typesamd
source quote (p.4)
“RX-1 Sleep is a Software as a Medical Device (SaMD) to aid in the evaluation of sleep-related breathing disorders in adults suspected of having sleep apnea.”
Algorithmtwo cloud-based static/locked AI deep learning models for AHI and for sleep/wake
source quote (p.5)
“RX-1 Sleep employs two cloud-based static/locked AI deep learning models for AHI and for sleep/wake that receive inputs from two FDA-cleared devices used in the clinical and home environment: a) the Rhythm Express RX-1 mini Remote Cardiac Monitoring System (RCMS) (K241179), and b) a Viatom pulse oximeter”
Adaptive (vs locked)No
source quote (p.5)
“RX-1 Sleep employs two cloud-based static/locked AI deep learning models for AHI and for sleep/wake”
PCCPNo
Cybersecurity addressedYes
source quote (p.11)
“AAMI TIR 57:2016 Principles for medical device security – Risk management, AAMI TIR 97:2019 Principles for medical device security – Postmarket risk management for device manufacturers, FDA guidance “Cybersecurity in Medical Devices: Quality Management System Considerations and Content of Premarket Submissions Guidance for Industry and Food and Drug Administration”, issued February 3, 2026”

Validation studies (2)

Prospective clinical

n=476 patients

endpoints: agreement between RX-1 Sleep predicted AHI and PSG-scored AHI under AASM Rule 1B using Bland-Altman analysis; sensitivity and specificity of RX-1 Sleep for detecting AHI ">= 5" under Rule 1B; sensitivity and specificity of RX-1 Sleep for detecting AHI ">= 15" under Rule 1A and 1B and AHI ">= 5" under Rule 1A; sensitivity and specificity at the severe OSA threshold (AHI ">= 30") under Rule 1A and 1B; sleep/wake classification

standards: FDA guidance “Applying Human Factors and Usability Engineering to Medical Devices” issued February 3, 2016, ANSI AMMI ISO 14971:2019 Medical devices – Applications of risk management to medical devices, AAMI TIR 57:2016 Principles for medical device security – Risk management, AAMI TIR 97:2019 Principles for medical device security – Postmarket risk management for device manufacturers, AAMI CR 34971:2022 Guidance on the Application of ISO 14971 to Artificial Intelligence and Machine Learning, ANSI AAMI IEC 62366-1:2015+AMD1:2020 Medical devices Part 1: Application of usability engineering to medical devices including Amendment 1, ISO 15223-1 Fourth edition 2021-07 Medical devices – Symbols to be used with information to be supplied by the manufacturer: Part 1: General requirements, IEC 82304-1 Edition 1.0 2016-10 Health software – Part 1: General requirements for product safety, ANSI AAMI IEC 62304:2006/A1:2106 Medical device software – Software life cycle processes [including Amendment 1 (2016)], AAMI TIR 36:2007 Validation of software for regulated processes, FDA guidance “Content of Premarket Submissions for Device Software Functions" issued June 14, 2023, FDA draft guidance “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations” issued January 7, 2025, FDA guidance “Cybersecurity in Medical Devices: Quality Management System Considerations and Content of Premarket Submissions Guidance for Industry and Food and Drug Administration”, issued February 3, 2026, FDA guidance “Off-the-Shelf Software Use in Medical Devices”, issued August 11, 2023

Prospective clinical

n=30 patients

endpoints: posture performance; respiratory effort performance

standards: FDA guidance “Applying Human Factors and Usability Engineering to Medical Devices” issued February 3, 2016, ANSI AMMI ISO 14971:2019 Medical devices – Applications of risk management to medical devices, AAMI TIR 57:2016 Principles for medical device security – Risk management, AAMI TIR 97:2019 Principles for medical device security – Postmarket risk management for device manufacturers, AAMI CR 34971:2022 Guidance on the Application of ISO 14971 to Artificial Intelligence and Machine Learning, ANSI AAMI IEC 62366-1:2015+AMD1:2020 Medical devices Part 1: Application of usability engineering to medical devices including Amendment 1, ISO 15223-1 Fourth edition 2021-07 Medical devices – Symbols to be used with information to be supplied by the manufacturer: Part 1: General requirements, IEC 82304-1 Edition 1.0 2016-10 Health software – Part 1: General requirements for product safety, ANSI AAMI IEC 62304:2006/A1:2106 Medical device software – Software life cycle processes [including Amendment 1 (2016)], AAMI TIR 36:2007 Validation of software for regulated processes, FDA guidance “Content of Premarket Submissions for Device Software Functions" issued June 14, 2023, FDA draft guidance “Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations” issued January 7, 2025, FDA guidance “Cybersecurity in Medical Devices: Quality Management System Considerations and Content of Premarket Submissions Guidance for Industry and Food and Drug Administration”, issued February 3, 2026, FDA guidance “Off-the-Shelf Software Use in Medical Devices”, issued August 11, 2023

Reported performance (8 observations)

sensitivity0.878CI [83.05, 91.62%]
source quote (p.13)
“87.80% (83.05%, 91.62%)”
specificity0.9391CI [90.00, 96.63%]
source quote (p.13)
“93.91% (90.00%, 96.63%)”
sensitivityas written: “Sleep/wake detection sensitivity”0.9163CI (91.54%, 91.72%)
source quote (p.13)
“91.63% (91.54%, 91.72%)”
specificityas written: “Sleep/wake detection specificity”0.8334CI (83.12%, 83.56%)
source quote (p.13)
“83.34% (83.12%, 83.56%)”
accuracyas written: “Posture Accuracy (ITT)”0.947CI (94.6-94.8)
source quote (p.13)
“94.7% (94.6-94.8)”
accuracyas written: “Posture Accuracy (AT)”0.942CI (94.2-94.3)
source quote (p.13)
“94.2% (94.2-94.3)”
accuracyas written: “Respiratory Effort Accuracy (ITT)”0.891CI (88.5-89.8)
source quote (p.13)
“89.1% (88.5-89.8)”
accuracyas written: “Respiratory Effort Accuracy (AT)”0.894CI (88.7-90.1)
source quote (p.13)
“89.4% (88.7-90.1)”

Each value carries its own analysis unit and task — never compare or pool across devices. Source: 510(k) summary PDF.

Predicate network

Postmarket — what happened after clearance

Not yet tracked — the weekly postmarket refresh hasn't snapshotted this device.

Reimbursement — how devices like this got paid

Not yet tracked — no payment pathway indexed for this clearance (the reimbursement corpus is a growing seed set).

Applicable FDA guidance — what the submission is measured against

FDA guidance documents and guiding principles applicable to 510(k) AI/ML devices in the Anesthesiology panel. A curated reference index, not legal or regulatory advice — each item states its own status, and a draft is never binding.

Applicability is derived from the device's FDA advisory panel and pathway — cross-cutting guidances apply to every AI/ML device; panel-specific ones are flagged. Titles, dates, and links verified against fda.gov as of July 2026.

Related FDA clearances

These are recent clearances in the same FDA product-code category. Shared classification does not by itself make a device an appropriate predicate.

Constat Precedent · public FDA/CMS data · descriptive decision-support, not regulatory or reimbursement advice. Share this page: constat.dev/precedent/device/K253148