• Universal point-of-care screening via AI-driven pattern recognition
  • No new tests or blood-work
  • Uses routine data (EHR) already in patient file
  • No specific data demands
  • Generalizable in future to other targets beyond PF, ILAs

CKD

ILD

ZeBRA

ICD

Enable early diagnosis

Target PF/IPF or ILDs broadly

Seamless background integration with Epic workflows

Primary care

*Onishchenko, Dmytro, Robert J. Marlowe, Che G. Ngufor, Louis J. Faust, Andrew H. Limper, Gary M. Hunninghake, Fernando J. Martinez, and Ishanu Chattopadhyay. "Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records." Nature Medicine 28, no. 10 (2022): 2107-2116.

Raising Flags before patient or their doctor notice symptoms

downstream care modulation

model published, retrospectively validated*

TimestampedDiagnostic procedural codes & prescriptions

MASH

Rx

Px

ZeBRA: Test-free Zero-contact Point-of-Care Universal Screening of Interstitial Lung Disease in UKHC

ZeBRA Publications

 

AI-driven Test-Free Prediction of ICU Admission, Insulin Dependence, and Exocrine Dysfunction after Acute Pancreatitis

Under Review

Predictive Performance (PF)*

High AUC across high and low risk sub-cohorts

Highlights:

  • 1 yr out AUC ~88%
  • Positive Likelihood ration ~40

*Onishchenko, D., Marlowe, R.J., Ngufor, C.G. et al. Screening for idiopathic pulmonary fibrosis using comorbidity signatures in electronic health records. Nat Med 28, 2107–2116 (2022). https://doi.org/10.1038/s41591-022-02010-y

Model

Interstitial Lung Disease (ILD)

US prevalence: 1 in 500 (higher by 1.5X-2X in KY)

IPF prevalence:  10-25% of ILD

ILD

Age group: 50-85 years old  

Observation window:  

1+ years of records   

Prediction window: 1 year  

Used dataset size:   

Case: 25.4k, Control: 15.1M 

 

Performance (95% Specificity):  

Males:  

AUC 82.2% (82.0%, 82.5%)  

Sensitivity 39.7% (39.3%, 40.1%)  

Positive LR: 7.81 (7.85, 8.01)  

Negative LR: 0.64 (0.64, 0.63)  

  

Females:  

AUC 82.1% (81.8%, 82.3%)  

Sensitivity 39.1% (38.7%, 39.5%)  

Positive LR: 7.77 (7.74, 7.90)  

Negative LR: 0.64 (0.65, 0.64) 

Current validation results (MarketScan)

Interstitial Lung Disease (ILD)

US prevalence: 1 in 500

95% specificity/39% sensitivity99% specificity/17% sensitivity99.5% specificity/12.5% sensitivity
Additional ILD diagnoses from ZeBRA546238175
Total ILD diagnoses per year with ZeBRA746438375
Additional transplant-eligible patients with ZeBRA1647153
Expected False Positives29,9305,9862,993
Net annual contribution margin*$46,613,500$12,706,700$7,703,350

Case Study: University of Kentucky Heath-care (UKHC)

Patient population: 600K unique patients per year
Current ILD diagnoses: 200 per year

* diagnostic workup margin (CT+PFT): $950, lunng transplant contribution margin: $120,000, incremental program operating cost: -$1.5M

Target AUC
Frailty / Physical Debility 96.2%
Alzheimer's Disease and Related Dementia (ADRD) 93.4%
Chronic Fatigue Syndrome / ME 93.2%
Acute Pancreatitis: ICU Visit 92.3%
Chronic Pancreatitis: Exocrine Pancreatic Insufficiency 92.1%
Idiopathic Pulmonary Fibrosis (IPF) 91.6%
Sarcopenia 91.0%
Parkinson's Disease 87.9%
Dementia / Degenerative Neurologic Disease 87.8%
Acute Pancreatitis: Insulin Dependence 87.2%
Suicide Attempts / Suicidal Ideations (Males 50--75) 86.0%
Chronic Inflammation 85.9%
Heart Failure with Preserved Ejection Fraction (HFpEF) 84.9%
Suicide Attempts / Suicidal Ideations (Males 25--50) 84.0%
Interstitial Lung Diseases (ILD) 82.2%
Age-related Macular Degeneration 82.1%
Autism Spectrum Disorder (ASD) 81.8%
Chronic Kidney Disease (CKD) 81.8%
Cerebral Infarction 81.1%
Chronic Obstructive Pulmonary Disease (COPD) 81.0%
Major Depressive Disorder 80.5%
Myocardial Infarction / Cardiac Arrest post-arthroplasty 80.1%
CKD Progression to Stage 4+ 80.1%
Prostate Cancer 80.0%
Osteoporosis 79.5%
Post-Traumatic Stress Disorder (PTSD) 78.1%
Hearing Loss 72.7%
Osteoarthritis 72.5%
Systemic Connective Tissue Disorders 72.0%

ZeBRA Model Family (expanding list)

[
    {
        "patient_id": "P000038",
        "sex": "F",
        "birth_date": "01-01-2006",
        "DX_record": [
            {"date": "07-31-2006", "code": "Z38.00"},
            {"date": "08-07-2006", "code": "P59.9"},
            {"date": "08-29-2016", "code": "J01.90"},
            {"date": "09-10-2016", "code": "J01.90"},
            {"date": "11-14-2016", "code": "J01.91"}
        ],
        "RX_record": [
            {"date": "10-29-2011", "code": "rxLDA017"},
            {"date": "05-16-2015", "code": "rxIDG004"},
            {"date": "08-08-2015", "code": "rxIDG004"},
            {"date": "06-04-2016", "code": "rxIDD013"}
        ],
        "PROC_record": [
            {"date": "02-05-2007", "code": "90723"},
            {"date": "11-05-2007", "code": "J1100"}
        ]
    }
]
{
  "predictions": [
    {
      "error_code": "",
      "patient_id": "P000012",
      "predicted_risk": 0.005794344620009157,
      "probability": 0.8253881317184486
    }
  ],
  "target": "TARGET"
}

Data Out

Data In

Current state: Fully functional API *

  • user-specific API
  • Custom models targeting range of disorders
  • Scalable

*Documentation:  https://github.com/zeroknowledgediscovery/paraknowledgedoc

Model ready to deploy behind UK firewall

ZeBRA_UKHC

By Ishanu Chattopadhyay

ZeBRA_UKHC

Brief talk on the ZeBRA Platform

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