Tutorial

Offline Critical Care Mechanical Ventilation & ARDS Trajectory Predictor

Difficulty: Advanced Time: 25 min read

Introduction

Managing mechanical ventilation in intensive care units (ICUs) for patients with Acute Respiratory Distress Syndrome (ARDS) requires continuously balancing PEEP (Positive End-Expiratory Pressure), FiO2, tidal volume, and airway driving pressures to prevent ventilator-induced lung injury (VILI). In this cookbook, we introduce VentGuard-Transformer-v1, an offline temporal transformer model that ingests continuous bedside ventilator waveform telemetry and arterial blood gas (ABG) panels to forecast ARDS severity trajectories and recommend lung-protective ventilation parameters on edge ICU hardware.

Prerequisites

  • Python 3.9+
  • PyTorch & pandas
  • openphr-cli
  • Continuous bedside ventilator wave (flow, pressure, volume) telemetry & serial ABG logs

Step 1: Ingest Bedside Telemetry & ABG Panels

Load high-frequency respiratory waveforms along with PaO2/FiO2 (P/F ratio) trends, compliance metrics, and arterial blood gas parameters.

import json
import torch

# Load ventilator waveform sample and serial ABG measurements
with open('ventilator_telemetry.json', 'r') as f:
    vent_data = json.load(f)

# Extract P/F ratio, driving pressure (P_plat - PEEP), and compliance
pf_ratio = vent_data['PaO2'] / vent_data['FiO2']
driving_pressure = vent_data['P_plateau'] - vent_data['PEEP']

features = torch.tensor([pf_ratio, driving_pressure, vent_data['tidal_volume_ibw']], dtype=torch.float32).unsqueeze(0)
print("ICU Ventilator feature vector initialized:", features.shape)

Step 2: Execute Local ARDS Prediction Pipeline

Run the openphr-cli pipeline to evaluate 24-hour ARDS progression risks and lung-protective ventilation targets:

openphr-cli run ventguard-transformer-v1 \
    --input-telemetry ./ventilator_telemetry.json \
    --abg ./abg_panel.json \
    --output-ards-risk ./ards_trajectory.json

Expected Outcome

A continuous 24-hour forecast of ARDS severity (Berlin definition: Mild, Moderate, Severe), weaning readiness indices, and VILI risk warnings generated completely offline without cloud latency or external network dependency.