§ 868.2200 — Adjunctive pain measurement device for anesthesiology.

§ 868.2200 Adjunctive pain measurement device for anesthesiology.

(a) Identification. An adjunctive pain measurement device for anesthesiology is a prescription device that includes software algorithms to analyze physiological sensor data and measure response to painful stimuli in patients under general anesthesia. The device may be software-only or it may include hardware such as physiological sensors. This device type is intended for adjunctive use to tailor analgesic administration to a patient's actual response to painful stimuli and is not intended to independently direct decision-making.

(b) Classification. Class II (special controls). The special controls for this device are:

(1) Clinical data must be provided to validate the algorithm in support of the intended use and include the following:

(i) Comparison of output measure(s) to a reference method to demonstrate the required accuracy and/or sensitivity and specificity of the output measure(s);

(ii) Demonstration of the consistency of the output and representativeness of the range of data sources and data quality likely to be encountered in the intended use population and relevant use conditions in the intended use environment;

(iii) Evaluation of the type of pain (e.g., nociceptive, somatic, visceral, neuropathic) that is within the scope of the indicated use; and

(iv) For devices using algorithms based on machine learning, the clinical validation must be completed using a dataset that is separate from the training dataset.

(2) Software description, verification, and validation based on comprehensive hazard analysis must be performed. Software documentation must include:

(i) Full characterization of technical parameters of the software, including any algorithm(s);

(ii) Description of mechanisms for handling of noisy or missing data and poor signal quality under expected conditions of use;

(iii) Specification of acceptable incoming sensor data quality control measures;

(iv) Mitigation of impact of user error or failure of any subsystem components (signal detection and analysis, data display, and storage) on output accuracy; and

(v) Justification for the validity of the algorithm(s) (e.g., clinical relevance of decision threshold).

(3) Non-clinical performance data must demonstrate that the device performs as intended under anticipated conditions of use. Performance testing under anticipated conditions of use must demonstrate the ability of the device software/algorithm to detect adequate input signal quality and handle noisy or missing data and poor signal quality.

(4) Usability assessment must be provided to mitigate the risk of misinterpretation of device output.

(5) The patient contacting components of the device must be demonstrated to be biocompatible.

(6) Performance testing must demonstrate the electromagnetic compatibility and electrical safety of any hardware components of the device.

(7) Labeling must include the following:

(i) A summary of the clinical validation data, including demographics and other relevant characteristics of the clinical study participants (including age, sex, race or ethnicity, and patient condition), the anesthetic regimen (including types (e.g., morphine, hydromorphone, fentanyl) and doses of pain medication used), a summary of results, and information on subpopulations (age, sex, race, or ethnicity) that may experience disparate performance.

(ii) A description of what the device measures and outputs to the user.

(iii) The type of sensor data used, including specification of compatible sensors for data acquisition.

(iv) Warnings identifying sensor signal-acquisition factors that may impact output.

(v) Warnings to identify and avoid specific patient conditions or concomitant medical therapies that could mask pain or negatively impact device performance leading to inaccurate measurements.

(8) Recommendations for clinical interpretation of the output, including warning(s) emphasizing the adjunctive use of the output measure(s).

[91 FR 32342, June 1, 2026]