Heart-Failure Hospitalization and Worsening-Heart-Failure Endpoints

Definition and scope

Cardiovascular outcomes defined by clinical or validated administrative evidence of worsening heart failure, with separate rules for hospitalization, urgent outpatient care, recurrent events, and death. The target population, use, and analysis remain protocol decisions; this card explains the choices and their consequences rather than approving a study.

Operational specification

Specify whether the event requires admission, primary diagnosis, symptoms/signs, objective evidence, and intensification of therapy; distinguish trial-adjudicated definitions from a claims algorithm. Define transfers, same-episode readmissions, elective care, urgent outpatient events, and clinical onset versus service date. Name components if using cardiovascular death or a broader worsening-HF composite.

Analysis and diagnostics

Validate the operational algorithm in the source population/era as appropriate. Report first and recurrent events separately when both matter, consider terminal death in recurrent-event analysis, and assess changes in care setting and coding. An encounter classified as HF requires more evidence than the presence of a historical problem-list code.

When NOT to use and common errors

Do not equate every admission with an HF code to adjudicated HF hospitalization, combine inpatient and outpatient worsening events without naming the composite, count transfers as independent admissions, or treat death as ordinary missing follow-up in a recurrent-burden target.

Worked example and implementation boundary

A synthetic six-month analysis has three records for two true hospitalization episodes because one episode includes a hospital transfer. Calculate the episode count after the specified transfer collapse. Two episodes remain. This preparation step does not validate the HF diagnosis or estimate a recurrent-event treatment effect. The code performs that explicitly stated calculation with synthetic inputs. It is not a complete fitted analysis and does not demonstrate the identification assumptions, estimate sampling uncertainty, or certify application in another population.

Data-source and reporting requirements

Preserve the provenance, dates, units, and observation rule for each input. Claims capture services and transactions under observable benefits; absence of a record is not proof that an event or resource use did not occur. EHR and registry measurements depend on the collection process and may miss care outside the system. Linked sources add reconciliation and selection questions. Primary collection can improve a missing domain but still requires valid measurement and follow-up. Report which of these limitations affects the stated use, which evidence supports the chosen inputs, and what remains uncertain.

Worked example

Scenario

A synthetic six-month analysis has three records for two true hospitalization episodes because one episode includes a hospital transfer. Calculate the episode count after the specified transfer collapse.

Dataset

Synthetic inputs with units defined in the scenario; no patient data.

Steps

Result

Two episodes remain. This preparation step does not validate the HF diagnosis or estimate a recurrent-event treatment effect.

Trade-offs

Code example

admission_records = 3
transfer_duplicate = 1
result = admission_records-transfer_duplicate
assert abs(result - 2) < 1e-9
print(result)

admission_records <- 3
transfer_duplicate <- 1
result <- admission_records-transfer_duplicate
stopifnot(abs(result - 2) < 1e-9)
print(result)

data worked_example;
  admission_records = 3;
  transfer_duplicate = 1;
  result = admission_records-transfer_duplicate;
  put result=;
run;

Citations

1. [1]Hicks KA, et al. 2017 Cardiovascular and Stroke Endpoint Definitions for Clinical Trials. Journal of the American College of Cardiology. 2018.