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The PhySoft Anemia Management System® (PhySoftAMS®) is a software application used to obtain, track and trend patient data pertaining to the management of anemia, and to provide a schedule of erythropoiesis-stimulating agent (ESA) dosage recommendations to help achieve and maintain target hemoglobin levels in dialysis patients. PhySoftAMS® is intended to help physicians. nurses, clinicians, and anemia managers manage anemia in adult stage 5 chronic kidney disease (CKD) patients.

PhySoftAMS® is not a substitute for, but is rather intended to assist, clinical judgment. The erythropoiesis-stimulating agent (ESA) dosing regimen options calculated by this device are intended to be used by qualified and trained medical personnel to inform the optimization of the dosage of ESAs in accordance with their approved labeling in conjunction with clinical history, symptoms, and other diagnostic measurements, as well as the medical professional's clinical judgment. No medical decision should be based solely on the patient Hgb response to dosing regimen options calculated by this device.

PhySoftAMS® is a software application used to obtain, track and trend patient data pertaining to the management of anemia and to provide a schedule of erythropoiesis-stimulating agent (ESA) dosage recommendations to help achieve and maintain target hemoglobin levels in dialysis patients. PhySoftAMS® is intended to help physicians, nurses, clinicians and anemia managers manage anemia in adult stage 5 chronic kidney disease (CKD) patients.

PhySoftAMS® is intended for use by medical personnel such as clinicians, nurses, and physicians in dialysis clinics or other settings where anemia management for hemodialysis patients is conducted.

Healthcare professionals access PhySoftAMS® directly using a web application graphical user interface (GUI) or indirectly using the drug dosing-related screens of a third party's electronic health record (EHR) system via an application programming interface (API) provided by the PhySoftAMS® application server.

PhySoftAMS® evaluates whether adequate historical data is available to model patient ESA dose-Hgb response dynamics and project future ESA dose-Hgb response. If adequate data is available, PhySoftAMS® enables a physician to model a patient and select from one or more dosing schedule options most likely to result in achieving target Hgb levels or, at the physician's discretion. override the presented dosing schedule options.

The provided text describes a 510(k) premarket notification for a medical device called PhySoftAMS®, a software application for managing anemia in dialysis patients. However, the document does not contain the detailed information necessary to answer all the questions regarding acceptance criteria and the study proving the device meets those criteria.

Specifically, the document states:

• "Bench testing results demonstrate the modified device performance is computationally equivalent to the performance of the predicate device."
• "Software verification and validation of the device modifications that are the subject of this submission demonstrated that the enhancements for the subject (modified) device perform as intended and have no effect on the modeling process or other device functions of PhySoftAMS®."

This indicates that internal testing (bench testing, software V&V) was performed to show computational equivalence and proper function of the modifications, but it does not detail a clinical study or performance study with acceptance criteria, sample sizes, ground truth establishment, or multi-reader multi-case studies as requested.

Therefore, many of the requested details cannot be extracted from this document. I can only provide information directly inferable from the given text.

Here's an attempt to answer based only on the provided text, highlighting what is not available:

Acceptance Criteria and Study for PhySoftAMS®

The provided 510(k) summary (K232283) focuses on demonstrating substantial equivalence of a modified PhySoftAMS® to its predicate device (PhySoft AMS™, K130579), primarily regarding the new integration with EHR systems via an API. The performance data presented relates to computational equivalence and software functionality, rather than a clinical performance study with defined acceptance criteria for diagnostic or clinical accuracy.

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided text, formal acceptance criteria for a clinical performance study (e.g., sensitivity, specificity, accuracy targets) and corresponding reported performance metrics are not explicitly stated. The "Performance" section focuses on computational and functional equivalence to the predicate.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a sample size for a "test set" in the context of a clinical or retrospective/prospective performance study. The evaluations described (bench testing, software V&V) typically do not involve patient-level test sets in the same manner as a diagnostic study. The data provenance (country, retrospective/prospective) is also not mentioned.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not mention using experts to establish ground truth for a test set, as no such clinical or performance test set is described. The device provides "dosing recommendations," which would typically be compared against clinical outcomes or expert judgment in a performance study, but this is not detailed here.

4. Adjudication Method for the Test Set

Since no test set adjudicated by experts is described, the adjudication method is not applicable/not mentioned.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No MRMC study is described in the provided text. The device offers dosing recommendations and assists human users, but there is no information on a study comparing human readers with and without AI assistance, or an effect size.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

The text indicates the device "provides a schedule of erythropoiesis-stimulating agent (ESA) dosage recommendations to help achieve and maintain target hemoglobin levels" and is "intended to help physicians, nurses, clinicians, and anemia managers manage anemia." It explicitly states, "PhySoftAMS® is not a substitute for, but is rather intended to assist, clinical judgment." This implies a human-in-the-loop design. No standalone algorithmic performance study results are detailed in the document. The "computational equivalence" refers to the underlying modeling process itself.

7. The Type of Ground Truth Used

For the software verification and validation, the "ground truth" would be the expected behavior and output of the algorithms and software functions, based on predefined specifications and the known performance of the predicate device. For potential clinical application, the ultimate ground truth would relate to actual patient hemoglobin levels and clinical outcomes, but the studies described do not detail the collection or establishment of such ground truth.

8. The Sample Size for the Training Set

The document does not mention a training set size. This device appears to use PK/PD modeling of patient response rather than a deep learning model that typically requires a large training set of annotated data. The system tracks and trends past Hgb and ESA dosages for individual patient modeling, not a general training dataset for an AI model.

9. How the Ground Truth for the Training Set Was Established

As no training set (in the context of machine learning) is explicitly mentioned, the method for establishing its ground truth is not detailed. The modeling is described as "PK/PD modeling of patient response to ESAs," which is typically based on physiological and pharmacological principles, not a data-driven training set with established ground truth labels in the same way an image classification AI might be trained.

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