Should We Perform a Heart Failure Risk Score?

Howlett, Jonathan G.

doi:10.1161/circheartfailure.112.973172

Cited by 20 publications

(23 citation statements)

References 9 publications

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“…Of the 9 model outcomes that predicted CV mortality, including sudden cardiac death and pump failure, 3 also modeled all-cause mortality and 1 HF hospitalization. The median [range] of final candidate variables was 6 , with 4 [1][2][3][4][5][6][7][8][9][10] retained within the final model, similar to the number retained for all-cause mortality (Table 3). Of the 9 CV mortality model outcomes, 3 reported C-statistic for model discrimination, 3 reported AUC-ROC, 1 used Kaplan-Meier assessment, and 2 used the Therneau's survival concordance index.…”

Section: Risk Prediction Model Outcomesmentioning

confidence: 95%

“…Of the 17 model outcomes that predicted all-cause mortality, 3 assessed CV mortality as a co-primary endpoint, 3 HF hospitalization, and 5 composite outcomes ( Table 3). The median [range] of final candidate variables entered for selection during model development was 10 [2-48], and following candidate variable selection through multivariable modeling, 5 [1][2][3][4][5][6][7][8][9][10][11][12][13][14] variables were retained.…”

Section: Risk Prediction Model Outcomesmentioning

confidence: 99%

“…Composite endpoints. Fourteen model outcomes assessed composite outcomes, with median [range] candidate variables of 7 , of which the same median number (7 [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]) were retained in the final model. The majority of model outcomes (n = 13/14) reported methods of discrimination, most commonly using C-statistic (n = 7) or reporting as AUC-ROC (n = 5); 1 used Kaplan-Meier assessment.…”

Section: Risk Prediction Model Outcomesmentioning

confidence: 99%

“…A number of risk prediction models have been published to statistically predict the risk of future outcomes associated with HF. Despite these models, clinicians seem reluctant to adopt them in daily practice [10], possibly due to their reliability at the patient level being poor, the variety of approaches to choose from, and/or the complexity of statistical methodologies [11]. Clinicians are aware that HF increases a patient's cardiovascular (CV) risk, and this complexity may mean that clinicians are reluctant to employ a risk-specific model when they see all patients as high risk.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating risk prediction models for adults with heart failure: A systematic literature review

Tanna

Wirtz

Burrows³

2020

PLoS ONE

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Background The ability to predict risk allows healthcare providers to propose which patients might benefit most from certain therapies, and is relevant to payers' demands to justify clinical and economic value. To understand the robustness of risk prediction models for heart failure (HF), we conducted a systematic literature review to (1) identify HF risk-prediction models, (2) assess statistical approach and extent of validation, (3) identify common variables, and (4) assess risk of bias (ROB). Methods Literature databases were searched from March 2013 to May 2018 to identify risk prediction models conducted in an out-of-hospital setting in adults with HF. Distinct risk prediction variables were ranked according to outcomes assessed and incorporation into the studies. ROB was assessed using Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Of 4720 non-duplicated citations, 40 risk-prediction publications were deemed relevant. Within the 40 publications, 58 models assessed 55 (co)primary outcomes, including allcause mortality (n = 17), cardiovascular death (n = 9), HF hospitalizations (n = 15), and composite endpoints (n = 14). Few publications reported detail on handling missing data (n = 11; 28%). The discriminatory ability for predicting all-cause mortality, cardiovascular death, and composite endpoints was generally better than for HF hospitalization. 105 distinct predictor variables were identified. Predictors included in >5 publications were: N-terminal prohormone brain-natriuretic peptide, creatinine, blood urea nitrogen, systolic blood pressure, sodium, NYHA class, left ventricular ejection fraction, heart rate, and characteristics including male sex, diabetes, age, and BMI. Only 11/58 (19%) models had overall low ROB, based on our application of PROBAST. In total, 26/58 (45%) models discussed internal validation, and 14/58 (24%) external validation.

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Section: Risk Prediction Model Outcomesmentioning

confidence: 95%

Section: Risk Prediction Model Outcomesmentioning

confidence: 99%

Section: Risk Prediction Model Outcomesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluating risk prediction models for adults with heart failure: A systematic literature review

Tanna

Wirtz

Burrows³

2020

PLoS ONE

View full text Add to dashboard Cite

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“…Likewise, a "low-risk" threshold was also Advance Publication by-J-STAGE models in clinical practice does not appear widely prevalent, despite some evidence suggesting that objective mathematical prediction models may allow better predictions in certain scenarios than physician intuition. 33 Furthermore, going through the calculation process may draw attention to an aberrant value of a lesser appreciated prognostic factor that may be amenable to modification (e.g., low cholesterol). Finally, as discussed above, the SHFM can be applied repeatedly at future time points whenever new information on model elements becomes available as a means to track risk, monitor disease progression, and assess therapeutic effectiveness.…”

Section: Using the Shfmmentioning

confidence: 99%

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

Williams

Agarwal

2018

Circ J

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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...

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Heart failure ‘the cancer of the heart’: the prognostic role of the HLM score

Severino,

Mancone,

D'Amato

et al. 2023

ESC Heart Failure

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AimsThe multi‐systemic effects of heart failure (HF) resemble the spread observed during cancer. We propose a new score, named HLM, analogous to the TNM classification used in oncology, to assess the prognosis of HF. HLM refers to H: heart damage, L: lung involvement, and M: systemic multiorgan involvement. The aim was to compare the HLM score to the conventional New York Heart Association (NYHA) classification, American College of Cardiology/American Heart Association (ACC/AHA) stages, and left ventricular ejection fraction (LVEF), to assess the most accurate prognostic tool for HF patients.Methods and resultsWe performed a multicentre, observational, prospective study of consecutive patients admitted for HF. Heart, lung, and other organ function parameters were collected. Each patient was classified according to the HLM score, NYHA classification, ACC/AHA stages, and LVEF assessed by transthoracic echocardiography. The follow‐up period was 12 months. The primary endpoint was a composite of all‐cause death and rehospitalization due to HF. A total of 1720 patients who completed the 12 month follow‐up period have been enrolled in the study. 520 (30.2%) patients experienced the composite endpoint of all‐cause death and rehospitalization due to HF. 540 (31.4%) patients were female. The mean age of the study population was 70.5 ± 12.9. The mean LVEF at admission was 42.5 ± 13%. Regarding the population distribution across the spectrum of HLM score stages, 373 (21.7%) patients were included in the HLM‐1, 507 (29.5%) in the HLM‐2, 587 (34.1%) in the HLM‐3, and 253 (14.7%) in the HLM‐4. HLM was the most accurate score to predict the primary endpoint at 12 months. The area under the receiver operating characteristic curve (AUC) was greater for the HLM score compared with the NYHA classification, ACC/AHA stages, or LVEF, regarding the composite endpoint (HLM = 0.645; NYHA = 0.580; ACC/AHA = 0.589; LVEF = 0.572). The AUC of the HLM score was significantly better compared with the LVEF (P = 0.002), ACC/AHA (P = 0.029), and NYHA (P = 0.009) AUC.ConclusionsThe HLM score has a greater prognostic power compared with the NYHA classification, ACC/AHA stages, and LVEF assessed by transthoracic echocardiography in terms of the composite endpoint of all‐cause death and rehospitalization due to HF at 12 months of follow‐up.

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Should We Perform a Heart Failure Risk Score?

Cited by 20 publications

References 9 publications

Evaluating risk prediction models for adults with heart failure: A systematic literature review

Evaluating risk prediction models for adults with heart failure: A systematic literature review

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

Heart failure ‘the cancer of the heart’: the prognostic role of the HLM score

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