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

[This corrects the article DOI: 10.1371/journal.pone.0224135.].

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

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 all-cause 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. CONCLUSIONS: The majority of the 58 identified risk-prediction models for HF present particular concerns according to ROB assessment, mainly due to lack of validation and calibration. The potential utility of novel approaches such as machine learning tools is yet to be determined. REGISTRATION NUMBER: The SLR was registered in Prospero (ID: CRD42018100709).

Economic Evaluations of Pharmacological Treatments in Heart Failure Patients: A Methodological Review with a Focus on Key Model Drivers.

Various decision analytic models exist for evaluating the cost-effectiveness of pharmacological interventions for heart failure (HF). Despite this, studies that explore drivers influencing these modeling approaches remain scarce. Through a systematic review of the literature, the present study sought to identify model drivers that emerge from economic evaluations of HF pharmacological interventions. Among the 72 cost effectiveness papers evaluating HF drug interventions, the most frequently identified, top 5 ranked model drivers impacting the incremental cost-effectiveness ratio (ICER) were cost of treatment and utility, identified in 10% of studies, respectively. Other drivers that emerged as top 5 ranked drivers in > 5% of studies included treatment effect on mortality (or cardiovascular mortality), duration of treatment, and baseline cardiovascular mortality. Model drivers reported at the top of tornado diagrams were treatment effect on mortality or on cardiovascular mortality. Collectively, these observations highlight the key importance of treatment effect in driving cost-effectiveness models for HF.