Risk stratification scores for patients with acute heart failure in the Emergency Department: A systematic review

Miró, Òscar; Rosselló, Xavier; Platz, Elke; Masip, Josep; Gualandro, Danielle Menosi; Peacock, W. Frank; Price, Susanna; Cullen, Louise; DiSomma, Salvatore; Oliveira, Múcio Tavares de; McMurray, John J.V.; Martín‐Sánchez, Francisco Javier; Maisel, Alan S.; Vrints, Christiaan J.; Cowie, Martin R.; Bueno, Héctor; Mebazaa, Alexandre; Mueller, Christian

doi:10.1177/2048872620930889

Cited by 32 publications

(27 citation statements)

References 77 publications

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“…C‐statistics of 0.81, 95% CI: [0.77; 0.84] in the derivation and 0.74, 95% CI: [0.66; 0.81] in the validation cohorts, respectively, were reported. To date, these are the highest ranges of predictive scores published in studies on heart failure decompensation 7,8 …”

Section: Discussionmentioning

confidence: 99%

“…Most scores predicting heart failure decompensation events have been performed in hospitalized patients and included variables not available in routine primary care practice. The discrimination capacity of such models varies between 0.54 and 0.86 8 . Systematic reviews 7,8 have considered that, among the studies analysed, only three of the scores 13–15 were accurate and had been properly validated.…”

Section: Discussionmentioning

confidence: 99%

“…These heterogeneous models, however, present high variability and have not been widely implemented in routine clinical practice. Almost all of them included hospitalized patients, and the few that assessed patients attended in primary care or outpatient clinics were based on variables not accessible to the majority of primary healthcare professionals 7,8 . In addition, they showed a reduced predictive capacity in patients with low risk of death 9…”

Section: Introductionmentioning

confidence: 99%

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Risk stratification in heart failure decompensation in the community: HEFESTOS score

et al. 2021

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Aims Because evidence regarding risk stratification predicting prognosis of patients with heart failure (HF) decompensation attended in primary care is lacking, we developed and externally validated a model to forecast death/hospitalization during the first 30 days after an episode of decompensation. The predictive model is based on variables easily obtained in primary care settings. Methods and results HEFESTOS is a multinational study consisting of a derivation cohort of HF patients recruited in 14 primary healthcare centres in Barcelona and a validation cohort from primary healthcare in 9 other European countries. The derivation and validation cohorts included 561 and 250 patients, respectively. Percentages of women in the derivation and validation cohorts were 56.3% and 47.6% (P = 0.026), respectively. Mean age was 82.2 years (SD 8.03) in the derivation cohort, and 79.3 years (SD 10.3) in the validation one (P = 0.001). HF with preserved ejection fraction represented 72.1% in the derivation cohort and 58.8% in the validation one (P = 0.004). Mortality/hospitalization during the first 30 days after a decompensation episode was 30.5% and 26% (P = 0.225) for the derivation and validation cohorts, respectively. Multivariable logistic regression models were performed to develop a score of risk. The identified predictors were worsening of dyspnoea [odds ratio (OR): 2.5; P = 0.001], orthopnoea (OR: 2.16; P = 0.01), paroxysmal nocturnal dyspnoea (OR: 2.25; P = 0.01), crackles (OR: 2.35; P = 0.01), New York Heart Association functional class III/IV (OR: 2.11; P = 0.001), oxygen saturation ≤ 90% (OR: 4.98; P < 0.001), heart rate > 100 b.p.m. (OR: 2.72; P = 0.002), and previous hospitalization due to HF (OR: 2.45; P < 0.001). The model showed an area under the curve (AUC) of 0.807, 95% confidence interval (CI): [0.770; 0.845] in the derivation cohort and AUC 0.73, 95% CI: [0.660; 0.808] in the validation one. No significant differences between both cohorts were observed (P = 0.08). Regarding probability of hospitalization/death, three risk groups were defined: low <5%, medium 5–20%, and high >20%. Outcome incidence was 2.7% for the low‐risk group, 12.8% for medium risk, and 46.2% for high risk in the derivation cohort, and 9.1%, 12.9%, and 39.6% in the validation one. Conclusions The HEFESTOS score, based on variables easily accessible in a community setting and validated in an external European cohort, properly predicted the risk of death/hospitalization during the first 30 days after an HF decompensation episode.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Risk stratification in heart failure decompensation in the community: HEFESTOS score

et al. 2021

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“…Importantly, the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) [21] and the Prediction model Risk Of Bias Assessment Tool (PROBAST) [23] were published in 2015 and 2019 to improve reporting transparency and methodological quality of prediction. A 2020 systematic review regarding risk prediction models in patients with AHF have focused on the models utilized in the ED setting; however, models exclusively applied in hospitalized patients were excluded from the review [25].…”

Section: Reporting and Disseminationmentioning

confidence: 99%

“…Embase, Pubmed, Web of Science, and the Cochrane Library will be searched for results from their inception onwards. Detailed search strategies are presented in Supplementary le in Appendix 2, and the search terms cover expressions for acute heart failure, prediction model, and mortality/readmission [25]. We will focus on studies published in English.…”

Section: Information Sources and Search Strategymentioning

confidence: 99%

Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

Zhang¹,

Zhou²,

Zhang³

et al. 2021

Preprint

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Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

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Clinical profile and 90 day outcomes of 10 851 heart failure patients across India: National Heart Failure Registry

et al. 2022

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Aims Limited data on the uptake of guideline-directed medical therapies (GDMTs) and the mortality of acute decompensated HF (ADHF) patients are available from India. The National Heart Failure Registry (NHFR) aimed to assess clinical presentation, practice patterns, and the mortality of ADHF patients in India. Methods and resultsThe NHFR is a facility-based, multi-centre clinical registry of consecutive ADHF patients with prospective follow-up. Fifty three tertiary care hospitals in 21 states in India participated in the NHFR. All consecutive ADHF patients who satisfied the European Society of Cardiology criteria were enrolled in the registry. All-cause mortality at 90 days was the main outcome measure. In total, 10 851 consecutive patients were recruited (mean age: 59.9 years, 31% women). Ischaemic heart disease was the predominant aetiology for HF (72%), followed by dilated cardiomyopathy (18%). Isolated right HF was noted in 62 (0.6%) participants. In eligible HF patients, 47.5% received GDMT. The 90 day mortality was 14.2% (14.9% and 13.9% in women and men, respectively) with a re-admission rate of 8.4%. An inverse relationship between educational class based on years of education and 90 day mortality (high mortality in the lowest educational class) was observed in the study population. Patients with HF with reduced ejection fraction and HF with mildly reduced ejection fraction who did not receive GDMT experienced higher mortality (log-rank P < 0.001) than those who received GDMT. Baseline educational class, body mass index, New York Heart Association functional class, ejection fraction, dependent oedema, serum creatinine, QRS > 120 ms, atrial fibrillation, mitral regurgitation, haemoglobin levels, serum sodium, and GDMT independently predicted 90 day mortality. Conclusion One of seven ADHF patients in the NHFR died during the first 90 days of follow-up. One of two patients received GDMT. Adherence to GDMT improved survival in HF patients with reduced and mildly reduced ejection fractions. Our findings call for innovative quality improvement initiatives to improve the uptake of GDMT among HF patients in India.

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Risk stratification scores for patients with acute heart failure in the Emergency Department: A systematic review

Cited by 32 publications

References 77 publications

Risk stratification in heart failure decompensation in the community: HEFESTOS score

Risk stratification in heart failure decompensation in the community: HEFESTOS score

Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

Clinical profile and 90 day outcomes of 10 851 heart failure patients across India: National Heart Failure Registry

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