OpenFDA Medical Device Adverse Event & Safety Verification Benchmark

This guide documents the OpenFDA-Grounded Medical Device Adverse Event Safety Suite in IINTS-AF, explaining how real-world Class I and Class II medical device recall records from the US Food and Drug Administration (FDA) are modeled into adversarial stress-test scenarios to evaluate controller safety.

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1. Grounding Artificial Pancreas Safety in Real FDA Recalls

Traditional in-silico testing often relies on idealized scenarios (e.g., standard meal bolus, announced exercise). However, real-world commercial Automated Insulin Delivery (AID) systems frequently experience critical hardware and software failures in the field.

The US FDA Medical Device Databases (MAUDE & Recalls) document thousands of verified device malfunctions resulting in severe hypoglycemia, diabetic ketoacidosis (DKA), hospitalization, and coma.

flowchart TD
    subgraph OpenFDA_Registry["FDA Real-World Recall Registry"]
        A["Tandem Control-IQ+ Software Defect<br>(Class I Recall: Spurious Auto-Bolus)"]
        B["MiniMed 600/700 Battery Circuit Failure<br>(Class I Recall: Truncated Alert & Power Loss)"]
        C["Infusion Set Tubing Occlusion<br>(Class II Recall: Pressure Build & Burst Dumping)"]
        D["Omnipod PDM Packet-Loss Desync<br>(Class II Recall: IOB Corruption & Stacked Bolus)"]
        E["Dexcom Mobile OS Background Crash<br>(Class II Recall: Silenced Hypo Alarms)"]
    end

    subgraph IINTS_AF_Engine["IINTS-AF Digital Twin Platform"]
        F["Physiological ODE Simulation<br>(Multi-Compartment Bergman + Hovorka)"]
        G["Deterministic Fault Injection Engine"]
    end

    subgraph Evaluation["Comparative Safety Evaluation"]
        H["Unmitigated Standard Control<br>(100% Adverse Event Failure Rate)"]
        I["IINTS-AF Dual-Guard Supervisor<br>(0.0% Adverse Event Rate, 100% Protection)"]
    end

    A & B & C & D & E --> G
    G --> F
    F --> H & I

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2. The 5 Verified FDA Recall Cases in IINTS-AF

Located in src/iints/safety/openfda_safety.py:

FDA Recall Case ID	Manufacturer & Brand	FDA Recall Class	Real-World Failure Mechanism	Modeled Clinical Hazard
FDA-2024-TANDEM-AUTOBOLUS	Tandem Diabetes Care (t:slim X2 Control-IQ+ / Mobi)	Class I	Algorithm software defect paired with G7 sensor delivers unexpected automatic correction bolus (auto-bolus).	Severe Hypoglycemia (\(<54\text{ mg/dL}\))
FDA-2024-MINIMED-BATTERY-DEPLETION	Medtronic MiniMed (600 & 700 Series)	Class I	Physical impact shortens battery alert threshold from 10h to \(<2\text{h}\), followed by sudden unannounced basal cessation.	Diabetic Ketoacidosis (DKA) / Hyperglycemia
FDA-2023-INFUSION-OCCLUSION-BURST	Medtronic / Roche / Unomedical (Quick-set / Sure-T)	Class II	Tubing kink causes silent fluid occlusion, followed by sudden reservoir pressure release and bolus dumping.	Severe Glycemic Shock (Hyper \(\to\) Rebound Hypo)
FDA-2023-OMNIPOD-IOB-DESYNC	Insulet Corporation (Omnipod DASH / 5 PDM)	Class II	Bluetooth packet drop corrupts Insulin on Board (IOB) tracking, causing algorithm to recommend duplicate stacked boluses.	Insulin Stacking & Severe Hypoglycemia
FDA-2024-DEXCOM-SILENT-CRASH	Dexcom, Inc. (G6 / G7 Mobile App)	Class II	Mobile OS background task termination silences CGM alarms during nocturnal glucose descent.	Undetected Nocturnal Hypoglycemic Shock

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3. The IINTS-AF Dual-Guard Safety Supervisor Architecture

To protect virtual patients from catastrophic device faults, IINTS-AF deploys a multi-guard deterministic supervisor between the automated dosing algorithm and the delivery actuator:

flowchart LR
    A["Dosing Algorithm / Controller"] --> B{"IINTS-AF Dual-Guard Supervisor"}

    subgraph Guards["Supervisory Safety Interventions"]
        G1["1. Single-Step Bolus Guard<br>Clamps uncommanded boluses > 1.8 U"]
        G2["2. Predictive Low Glucose Suspend (PLGS)<br>Suspends delivery when G < 100 mg/dL"]
        G3["3. Occlusion & Burst Dumping Limiter<br>Absorbs sudden reservoir discharge"]
        G4["4. Telemetry Watchdog<br>Falls back to safe basal during sensor dropout"]
    end

    B --> Guards
    Guards --> C["Pump Actuator / Virtual Patient"]

1. Single-Step Bolus Guard: Intercepts runaway auto-boluses and duplicate stacked dosing (\(>1.8\text{ U}\)), clamping them to a safe rate (\(\le 0.3\text{ U}\)).
2. Predictive Low Glucose Suspend (PLGS): Suspends insulin delivery completely whenever glucose falls below \(100\text{ mg/dL}\) or rapid negative velocity is detected.
3. Burst Dumping Limiter: Prevents sudden multi-unit delivery following occlusion release.
4. Telemetry Watchdog: Detects missing CGM packets (\(>20\text{ min}\)) and gracefully transitions into a safe attenuated basal rate (\(0.8\times\)).

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4. Empirical Safety Benchmark Results

Execution results from results/fda_safety_study/FDA_ADVERSE_EVENTS_SAFETY_REPORT.md:

Metric	Unmitigated Standard Controller	IINTS-AF Supervised Platform	Clinical Significance
Adverse Event Rate (\(<54\) or \(>250\text{ mg/dL}\))	100.0% (5/5 fail)	0.0% (5/5 protected)	100.0% Absolute Risk Reduction
Minimum Glucose Observed	35.0 mg/dL (Severe Coma Risk)	69.8 mg/dL (Safe Range)	Prevents neuroglycopenic hypoglycemia
Hazard Detection Rate	0.0% (Blind to failure)	100.0% (5/5 detected)	Complete real-time hazard observability
Mean Fault Mitigation Latency	N/A	5.0 minutes	Rapid automated containment

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5. CLI Usage

List Registered FDA Device Cases

iints safety fda-list

Run the Full Safety Benchmark

iints safety fda-benchmark \
  --output-dir results/fda_safety_study

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6. Python API Integration

from iints.safety.openfda_safety import FDA_RECALL_REGISTRY, run_fda_safety_benchmark, simulate_fda_failure_scenario

# 1. Run individual scenario
tandem_case = FDA_RECALL_REGISTRY[0]
df_unmit, metrics_unmit = simulate_fda_failure_scenario(tandem_case, enable_supervisor=False)
df_sup, metrics_sup = simulate_fda_failure_scenario(tandem_case, enable_supervisor=True)

print(f"Unmitigated Min Glucose: {metrics_unmit.min_glucose_mgdl} mg/dL (Adverse Event: {not metrics_unmit.adverse_event_prevented})")
print(f"Supervised Min Glucose: {metrics_sup.min_glucose_mgdl} mg/dL (Protected: {metrics_sup.adverse_event_prevented})")

# 2. Run aggregate benchmark suite
report = run_fda_safety_benchmark(output_dir="results/fda_safety_study")
print(f"Adverse Event Reduction: {report.unmitigated_adverse_event_rate_pct - report.supervised_adverse_event_rate_pct:.1f}%")