Applying the Seattle Heart Failure Model in the Office Setting in the Era of Electronic Medical Records

Abstract

The Seattle Heart Failure Model (SHFM) is arguably the most popular and well-validated HF prognostic model. 4,8,9,12,13 Although developed in a small, clinical trial cohort limited to HF patients with an ejection fraction (EF) <35% and New York Heart Association (NYHA) Class IIIB or IV, the SHFM has been externally validated in both more and less healthy HF cohorts with variable predictive performance. 12,14-19 However, to our knowledge, the SHFM has not been validated among HF patients in the context of the office visit. The transportability of the SHFM to the office setting is questionable given large discrepancies in disease severity between the SHFM derivation cohort and HF patients in the office environment. Accordingly, in the present study we assessed several predictive properties of the SHFM in the office environment to evaluate its potential utility in this common setting. In addition to common metrics of predictive performance, such as discrimination and calibration, we also sought to determine the "warranty period" of a baseline risk estimate from the SHFM. Because several of the SHFM elements reflect transient, modifiable states, we hypothesized that the predictive value of a single baseline risk estimate would wane over time. Finally, risk thresholds are proposed for labeling HF patients as low and high risk in the office setting. Despite therapeutic advances, heart failure (HF) remains an extremely morbid condition associated with frequent distressing symptoms and a 5-year mortality rate approaching 50% following diagnosis. 1-4 Although aggregate experience informs the expected clinical trajectory, the subsequent clinical course of any single HF patient is difficult to predict, particularly in the office setting, where patients are more likely to be relatively stable and have no obvious signals serving as impetus to alter care. 1,5-8 Prognosis estimation can help guide the management of such patients, yet relying solely on clinical intuition for such a task can be challenging. 4-6,9,10 To make prognosis estimation more objective, several risk prediction models have been developed that transform a collection of prognostic determinants into a single numeric estimate of absolute mortality risk over a specified time frame. 4,9-11 Risk prediction models enable better placement of individuals along the risk continuum, allowing, in theory, more judicious and cost-efficient application of HF therapies. 6,11 Furthermore, electronic medical record (EMR) systems should make such models easier to apply in the future by serving as a repository for necessary data elements, providing an environment for behind-the-scenes calculations of quantitative risk estimates, and displaying quantitative model output with accompanying management recommendations as a form of clinical decision support. Background: Prediction models such as the Seattle Heart Failure Model (SHFM) can help guide management of heart failure (HF) patients, but the SHFM has not been validated in the office environment. This retrospective cohort study...

Predicting Survival From Large Echocardiography and Electronic Health Record Datasets

“…We subsequently evaluated 5-year survival prediction performance of 3 additional clinical risk scoring systems: 1) the Seattle Heart Failure Model (11), 2) the Charlson Comorbidity Index (CCI) (10), and 3) the ACC/AHA cardiovascular risk guideline score (9,18). The Seattle Heart Failure Model was applied only to the subgroup of heart failure patients obtained from a previous study at our institution (19).…”

Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system

“…NYHA was only available in free text in a small subset of notes, so we assumed all patients had a mean value of 2.5 as previously done. 11…”

“…In this study, we compared the ease of implementation and the performance of two of the most widely used and tested HF risk models, Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score, [11][12][13][14][15][16][17][18][19] with MARKER-HF in ambulatory patients with HF treated at a large, integrated health system using EHR data. We hypothesized that MARKER-HF would be easier to implement and require less data engineering than SHFM and MAGGIC while having similar overall performance for predicting 1-year risk of mortality in a larger, more representative population of patients with HF.…”

A 5-Year Survival Prediction Model for Chronic Heart Failure Patients Induced by Coronary Heart Disease with Traditional Chinese Medicine Intervention

“…To date, a variety of CHF prognostic prediction models have been established clinically [ 4 , 5 ], of which the Seattle Heart Failure Model has the greatest impact. This model incorporates multiple risk factors and can accurately predict the survival rate up to 3 years and has received prospective verification [ 6 ]. However, owing to the inclusion of complex or unconventional clinical indicators, many models have not been fully utilized [ 7 ].…”

“…Most models predict survival time less than 3 years and lack of attention to the long-term prognosis of patients with CHF. More importantly, Seattle Heart Failure Model also faces the dilemma of failure in long-term prediction [ 6 ].…”