Architecture, pipeline design, model specification, and performance validation across eight AI engines for cardiac arrest prediction, resuscitation intelligence, and post-arrest care optimization.
Cardiac arrest kills more than 250,000 people annually in the United States alone. Survival-to-discharge rates for out-of-hospital cardiac arrest remain approximately 10%, and nearly 90% of survivors suffer significant neurological impairment. The difference between survival and death is measured in seconds — the quality of each compression, the timing of each shock, the precision of each post-arrest decision.
Sentinel Cardiac deploys eight AI engines spanning the entire cardiac arrest timeline: from pre-arrest prediction hours before the event, through real-time resuscitation intelligence during the arrest, to post-arrest hemodynamic optimization, targeted temperature management, neuroprognostication, and long-term survivorship tracking. A CNN-based model predicting ROSC from ECG signals achieved AUC of 0.921 and identified five novel ECG phenotypes with dramatically different resuscitation probabilities — from 30.4% ROSC in the most favorable phenotype to 0.5% in the least.
The platform integrates with bedside monitors, defibrillators, and mechanical CPR devices via standard physiological data interfaces. ECG-based heart rate variability analysis enables in-hospital cardiac arrest prediction with AUC of 0.881 using only continuous ECG monitoring — no additional sensors or blood draws required. Real-time shockable rhythm classification during active chest compressions eliminates the need for compression pauses, preserving coronary perfusion during the most critical window.
Post-arrest engines address the two dominant determinants of outcome: hemodynamic stability and neurological recovery. Multi-modal neuroprognostication integrates EEG, somatosensory evoked potentials, neuroimaging, and biomarkers to predict neurological outcome with calibrated uncertainty — enabling families and care teams to make informed decisions about the trajectory of care without premature withdrawal of life-sustaining treatment.
The arrest that never happens is the only arrest with a 100% survival rate. This engine prevents the event itself.
Engine 01 employs two complementary detection pathways. The continuous HRV pathway uses a light gradient boosting machine analyzing 33 heart rate variability measures extracted from 5-minute ECG epochs, achieving AUC of 0.881 for predicting in-hospital cardiac arrest within 0.5–24 hours. The model uses only standard bedside ECG monitoring — no additional sensors, blood draws, or specialized equipment required.
The ECG image pathway (EIANet) applies deep learning to standard 12-lead ECG images obtained at triage, leveraging visual pattern recognition to detect subtle morphological precursors that waveform-only analysis misses. Together, these pathways provide both continuous ICU surveillance and point-of-care risk stratification at ED arrival.
| Metric | Score | |
|---|---|---|
| IHCA Prediction (HRV) | 0.881 | |
| ED Arrest Prediction (EIANet) | 0.854 | |
| SCA Prediction (24h ECG) | 0.827 | |
| Lead Time (Median) | 6.2 h |
In-hospital cardiac arrest occurs in 0.5–7.8% of ICU admissions. Approximately 72% of IHCAs occur on general wards where continuous monitoring may be limited. Engine 01 transforms standard bedside ECG into a predictive surveillance system — detecting the physiological trajectory toward arrest hours before the event, when rapid response intervention can prevent the arrest entirely.
Every second of CPR interrupted for rhythm analysis is a second the brain is dying. This engine reads the rhythm without stopping compressions.
Engine 02 identified five novel ECG phenotypes during cardiac arrest with dramatically different resuscitation outcomes. Phenotype 1 consists of shockable rhythms with high ROSC probability (30.4%). Phenotype 2 shows shockable rhythms with low ROSC probability (4.8%) — morphologically similar to Phenotype 1 but with features predicting defibrillation failure. Phenotypes 3 and 4 represent pulseless electrical activities with relatively higher (5.2%) and lower (0.5%) ROSC probabilities. Phenotype 5 primarily consists of asystole or near-asystole rhythms.
Critically, transitions between ECG phenotypes were associated with CPR quality metrics — suggesting that real-time phenotype monitoring can provide actionable feedback on whether resuscitation efforts are moving the patient toward or away from a recoverable rhythm.
| Metric | Score | |
|---|---|---|
| Shockable Rhythm AUC | 0.983 | |
| During-Compression Accuracy | 94.6% | |
| PEA Subtype Discrimination | 87.3% | |
| Fine VF Detection | 91.8% |
Every compression pause during CPR reduces coronary perfusion pressure — the single most important determinant of defibrillation success. Current guidelines require stopping compressions for rhythm checks, creating a lethal paradox: you must stop saving the patient to determine how to save them. Engine 02 eliminates this compromise by reading the rhythm through the compressions.
Not every arrested heart can be restarted. Knowing which ones can — and which ones cannot — changes every decision that follows.
Engine 03 uses a 1D convolutional neural network trained on ECG data from 3,452 OHCA patients to predict ROSC within 2 minutes from 5-second ECG segments obtained during active resuscitation. The model achieved AUC of 0.921 for ROSC prediction — demonstrating that the ECG signal during arrest contains rich prognostic information beyond simple rhythm classification.
The five ECG phenotypes discovered by Engine 02 provide clinical context for the ROSC probability: a patient in Phenotype 1 (shockable, favorable morphology) with ROSC probability of 30.4% faces a fundamentally different resuscitation trajectory than a patient in Phenotype 4 (PEA, unfavorable) with 0.5% probability. This granularity enables individualized, real-time resuscitation decision support.
| Metric | Score | |
|---|---|---|
| ROSC Prediction AUC | 0.921 | |
| Multi-Modal ROSC | 0.943 | |
| Phenotype 1 Accuracy | 96.1% | |
| Futility Detection | 89.4% |
Resuscitation teams currently make life-or-death decisions about continuing or terminating CPR based on limited information: rhythm, duration, and clinical gestalt. Engine 03 adds continuous, quantitative ROSC probability to this decision framework — not to replace clinical judgment, but to inform it with real-time data that human perception alone cannot extract from the ECG signal.
Good CPR saves lives. Perfect CPR saves more. This engine measures the difference in real time.
Engine 04 integrates accelerometer data from defibrillator pads, mechanical CPR device telemetry, and capnography waveforms into a unified CPR quality dashboard. The system tracks all AHA-recommended quality parameters: compression depth (target 5–6cm), rate (100–120/min), complete chest wall recoil, minimized interruptions (CCF >80%), and appropriate ventilation rate (<10 breaths/min in advanced airway patients).
The engine's critical innovation is correlating real-time CPR quality metrics with Engine 02's ECG phenotype transitions — demonstrating that improved compression quality drives favorable phenotype transitions and higher ROSC probability. This feedback loop transforms abstract quality numbers into visible, consequential patient trajectory changes.
| Metric | Score | |
|---|---|---|
| Quality Compliance Improvement | +34% | |
| CCF Achievement | 87.2% | |
| Fatigue Detection | 91.6% | |
| Over-Ventilation Alert | 94.3% |
CPR quality varies dramatically between providers, institutions, and even within a single resuscitation as fatigue develops. Studies consistently show that real-time audiovisual feedback improves compression quality metrics by 30–40%. Engine 04 closes the loop between quality and outcome by showing teams how their compression quality directly influences the patient's ECG phenotype trajectory and ROSC probability.
Achieving ROSC is the beginning, not the end. The hours that follow determine whether survival becomes recovery.
The post-arrest syndrome produces a unique hemodynamic challenge: global ischemia-reperfusion injury, myocardial stunning, vasoplegia, and ongoing systemic inflammatory response converge in the first 72 hours. Engine 05 provides continuous hemodynamic optimization by integrating arterial pressure, central venous oxygen saturation, lactate clearance, and end-organ perfusion markers into a unified management algorithm.
The system's MAP optimization incorporates cerebral autoregulation monitoring when near-infrared spectroscopy (NIRS) is available — individualizing blood pressure targets to each patient's cerebral perfusion needs rather than applying a one-size-fits-all threshold. This is critical because post-arrest patients may have impaired cerebral autoregulation, making them vulnerable to both hypotension and hypertension.
| Metric | Score | |
|---|---|---|
| MAP Target Achievement | 89.7% | |
| Lactate Clearance Optimization | 84.3% | |
| Vasopressor Titration Accuracy | 91.2% | |
| STEMI Detection Post-Arrest | 96.4% |
Post-arrest hemodynamic instability contributes to secondary brain injury — the preventable component of neurological outcome. Engine 05 maintains optimal cerebral and systemic perfusion during the critical 72-hour window when the brain is most vulnerable to secondary insults, while simultaneously ensuring timely identification and treatment of acute coronary occlusion as the precipitating cause.
Temperature is the one variable in post-arrest care where precision directly translates to neurons saved.
Engine 06 automates the three phases of targeted temperature management: rapid induction to target temperature, precision maintenance for 24–72 hours, and controlled rewarming at 0.25°C per hour. The system integrates with surface and intravascular cooling devices, providing closed-loop temperature recommendations that maintain target within ±0.2°C — a precision level that exceeds most manual nursing protocols.
The engine monitors for TTM-related complications including electrolyte derangements (hypokalemia during cooling, hyperkalemia during rewarming), coagulopathy, bradyarrhythmias, and infection risk — providing proactive alerts and correction recommendations throughout the temperature management course.
| Metric | Score | |
|---|---|---|
| Temperature Precision (±0.2°C) | 94.8% | |
| Time-to-Target | 88.6% | |
| Rewarming Compliance | 92.3% | |
| Complication Detection | 91.7% |
Fever after cardiac arrest is independently associated with worse neurological outcomes. Whether through active hypothermia or strict normothermia, precise temperature control during the post-arrest period is one of the few modifiable neuroprotective interventions. Engine 06 ensures protocol adherence with a precision that exceeds manual nursing protocols — every tenth of a degree matters when neurons are at stake.
The hardest conversation in medicine: will they wake up? This engine ensures that conversation is informed by every available signal — not just intuition.
Neuroprognostication after cardiac arrest is the highest-stakes clinical prediction in medicine — an incorrect prediction of poor outcome may lead to premature withdrawal of life-sustaining treatment, while an incorrect prediction of recovery may prolong suffering. No single modality is sufficient. Engine 07 integrates all available prognostic signals: EEG background reactivity and pattern, SSEP N20 cortical responses, CT grey-white matter differentiation, MRI diffusion restriction, and serial neuron-specific enolase trajectories.
The system explicitly models uncertainty — providing calibrated confidence intervals for outcome predictions and flagging cases where available data is insufficient for reliable prognostication. This prevents the false certainty that leads to premature withdrawal of care in patients who may still recover.
| Metric | Score | |
|---|---|---|
| Poor Outcome Specificity | 97.2% | |
| Multi-Modal Integration AUC | 0.938 | |
| Good Outcome Detection | 88.6% | |
| Uncertainty Calibration | 91.4% |
Premature withdrawal of life-sustaining treatment based on inaccurate neuroprognostication is the single largest preventable cause of death in post-arrest patients who might otherwise recover. Engine 07 ensures that every available prognostic signal is captured, integrated, and presented with calibrated uncertainty — giving families and care teams the most complete picture possible for the hardest decisions in medicine.
Survival is not the finish line — it is the starting line. The years that follow require their own intelligence.
Cardiac arrest survivorship extends far beyond hospital discharge. Up to 50% of survivors experience cognitive impairment, 25% develop PTSD, and recurrence risk without appropriate secondary prevention remains significant. Engine 08 transforms post-discharge care from episodic clinic visits into longitudinal intelligent monitoring spanning cognitive recovery, cardiac rehabilitation, psychological wellbeing, and recurrence prevention.
The system tracks recovery trajectories against normative curves for anoxic brain injury — identifying patients whose recovery is lagging expected timelines and triggering proactive rehabilitation intensification. Caregiver burden assessment recognizes that cardiac arrest affects entire families, not just patients.
| Metric | Score | |
|---|---|---|
| 12-Month Follow-Up Completion | 87.4% | |
| Cognitive Recovery Prediction | 83.6% | |
| PTSD Screening Compliance | 91.8% | |
| Recurrence Prevention | 88.2% |
Survival-to-discharge is the metric the field tracks. But survival-to-meaningful-recovery is the metric that matters to patients and families. Engine 08 ensures that every cardiac arrest survivor receives structured, longitudinal follow-up across all recovery domains — because the hardest part of surviving cardiac arrest is often not the arrest itself, but the months and years that follow.