Ishanu Chattopadhyay

Assistant Professor of Medicine

UChicago

Dmytro Onishchenko

UChicago

Rapid Universal Point-of-care Screening for ILD/IPF Using Comorbidity Signatures in Electronic Health Records

University of Chicago Medicine

NHLBI IPF Stakeholder Summit

Nov 2022

Fernando Martinez, Weill Cornell

Gary Hunninghake

Harvard Med School

Andrew Limper Mayo Clinic

shortness of breath

dry cough

doctor can hear velcro crackles

Common Symptoms

>50 years old

more men than women

IPF

Rare disease

~5 in 10,000

Post-Dx

Survival

~4 years

At least one misdiagnosis

~55%

Two or more misdiagnosis

38%

Initially attributed to age related symptoms:

72%

Cannot always be seen on CXR

Non-specific symptoms

PCP workflow demands

~ 4yrs

current survival ~4yrs

~ 4yrs

current clinical DX

ZCoR screening

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

n=~3M

AUC~90%

Likelihood ratio ~30

Conventional AI/ML attempts to model the physician

AI in IPF Research

Co-morbidity Patterns
No data demands
Use whatever data is already on patient file

Co-morbidity Patterns
No data demands
Use whatever data is already on patient file

Primary Care

Pulmonologist

ZCoR Flag

No blood tests
No imaging
No pulmonary function tests

ICD administrative codes

IPF

ILD

target codes appear

Past medical history

No target codes appear

case

control

2yrs

target codes appear

Past medical history

No target codes appear

case

control

2yrs

IPF drugs prescribed

Signature of IPF diagnostic sequence

pirfenidone or nintedanib

age > 50 years
at least two IPF target codes identified at least 1 month apart
chest CT procedure (ICD-9-CM 87.41 and Current Procedural Terminology, 4th Edition, codes 71250, 71260 and 71270) before the first diagnostic claim for IPF
no claims for alternative ILD codes occurring on or after the first IPF claim

Truven MarketScan (IBM)
Commerical Claims & Encounters Database
2003-2018

>100M patients visible

>7B individual claims

>87K unique diagnostic codes

>7% Medicare data present

2,053,277 patients included in study

Univesity of Chicago Medicam Center 
2012-2021

68,658 patients

Random sample from Optumlabs Data Warehouse courtsey Mayo Clinic

861,280 patients

2,983,215 patients

performance tables

Marketscan Out-of-sample Results

specificty~99%

NPV>99.9%

IPF

ILD

performance tables

UCM Out-of-sample Results

specificty~99%

NPV>99.9%

IPF

ILD

False Positives:

Heathcare Capacity

Ethics:

Risk from Imaging Tests

For every 20-30 flags,

1 is positive

General likelihood ratio 60-80
PPV 3.5-5%

Notifying patients 4 years early?

No cure, why screen

minimal

acceptable?

Better outcomes

early anti-fibrotic therapy seems increasingly promising

better shot at lung transplant

early dx reduces hospital-izations by a factor of 1-3

Collard, Harold R., Alex J. Ward, Stephan Lanes, D. Cortney Hayflinger, Daniel M. Rosenberg, and Elke Hunsche. "Burden of illness in idiopathic pulmonary fibrosis." Journal of medical economics 15, no. 5 (2012): 829-835.

Future

Seamless Embedding in EHR system (EPIC App)
Pragmatic Clinical Trial in Primary Care Setting
Track impact from initial flag to diagnosis with regards to patient outcome and healthcare utilization
Develop extended ZCoR version which uses data available after initial flag, such as CXR outcomes, and clinical notes

The Team

Gary Hunninghake, Pulmonary C, Harvard

Fernando Martinez, Pulmonary Critical Care, Weill Cornell

Director, Thoracic Research Unit, Mayo Clinic

Backup Slides

Method Details

Longitudinal history is important, cannot simply process snapshots

* For IPF screening

Conventional AI/ML has made incremental progress

Challenges

Non-generalizable

Start-over for each application

Expensive

Limited applicability at point of care

No true discovery

Rely on experts to leverage known risks

Leveraging Longitudinal Patterns

Specialized HMM models from code sequences

Model control and case cohorts seprately

given a new test case, compute likelihood of sample arising from case models vs control models

sequence likelihood defect

ZeD Lab: Predictive Screening from Comorbidity Footprints

Nature Medicine

JAHA

CELL Reports

Science Adv.

ZeD Lab: Predictive Screening from Comorbidity Footprints

	ZED performance	Competition
Autism*	>80% AUC at 2 yrs	Double false positives
Alzheimer's Disease	~90% AUC	60-70% AUC
Idiopathic Pulmonary Fibrosis	~90% AUC	NA
MACE	~80% AUC	~70% AUC
Bipolar Disorder	~85% AUC	NA
CKD	~85% AUC	NA
Cancers	~75% AUC	NA