Heart failure with preserved ejection fraction (HFpEF) is currently affecting over half of heart failure patients.
The Centers for Medicare and Medicaid Services (CMS) and the National Center for Health Statistics (NCHS), two departments within the U.S. Federal Government’s Department of Health and Human Services (DHHS), use the International Classification of Diseases (ICD) for coding and reporting. Data retrieved through ICD provides critical knowledge on the extent, causes, and consequences of human disease and death worldwide. 
The 2023 edition of the ICD-10-CM codes for heart failure became effective on October 1, 2022.
These guidelines have been approved by the four organizations that make up the Cooperating Parties for the ICD-10-CM: the American Hospital Association (AHA), the American Health Information Management Association (AHIMA), CMS, and NCHS.
The ICD Code for HFpEF
The ICD code for HFpEF is 150.3.
This code for HFpEF, otherwise known as diastolic heart failure, has gone unchanged since its inception in 2016.
Code 150.3 is applicable to:
Diastolic left ventricular (LV) heart failure
Heart failure with normal ejection fraction
Heart failure with preserved ejection fraction (HFpEF)
ICD code 150.3 applies to HFpEF in general, and you shouldn’t use it for reimbursement purposes. Instead, you should use its sub-codes, which contain a greater level of detail.
I50.33: Acute on chronic diastolic (congestive) heart failure 
Note: This is the American ICD-10-CM version of 150.3. There may be international versions of ICD-10 150.3 that differ.
Coding for heart failure can be complex, which is why it’s important to know the series of steps and best practices involved during the process.
Documenting and coding heart failure can become complicated, especially when a diagnosis is uncertain. Medical professionals should adhere to current guidelines and code with as much detail as possible.
When coding for heart failure, the first step is to specify the type:
Systolic heart failure: Heart failure with reduced ejection fraction (HFrEF). This occurs when the left ventricle loses its ability to contract normally. The heart can’t pump with enough force to push enough blood into circulation. HFrEF is typically associated with a primary myocardial injury.
Diastolic heart failure: Heart failure with preserved ejection fraction (HFpEF). In this type, the muscle has become stiff, and the left ventricle can no longer relax normally. This results in the inability of the heart to fill with blood during the resting period between each beat. 
As evident, HFrEF and HFpEF are significantly different conditions, which is why HFpEF now has its own code. This allows medical professionals to properly record, risk stratify, and treat heart failure patients.
When you have determined the type of heart failure to code, you can then narrow it down with more detailed codes. For example, starting with HFpEF code 150.3, a coder will then specify the acuity of the diagnosis (see codes listed above).
Here are further details on these acuity codes:
Acute diastolic heart failure: heart failure that occurs as a sudden, life-threatening condition in which the heart has stopped functioning properly and is not able to deliver enough oxygen to meet the body’s needs.
Chronic diastolic heart failure: this is a long-term condition in which the heart struggles to pump enough oxygen throughout the body. It develops over a long period of time.
Acute on chronic diastolic heart failure: this is an acute flare-up of a chronic condition. Two commonly associated words include “exacerbated” or “decompensated” heart failure.
When coding heart failure, make sure to specify:
1. Type – systolic (HFrEF), diastolic (HFpEF), combined 2. Acuity – unspecified, acute, chronic, acute on chronic
Detecting HFpEF with precision
As many health practitioners know, diagnosing HFpEF patients can be challenging.
AI technology out of Oxford has been developed with Mayo Clinic to help health professionals more precisely detect HFpEF and support diagnosis. EchoGo® Heart Failureis a novel solution that applies AI to cardiovascular imaging to greatly simplify the identification of patients with HFpEF. With a 25% increase in diagnostic accuracy from current clinical practice, healthcare professionals will have increased confidence in diagnosis and help more patients get treatment sooner.