Literature DB >> 23888045

Risk prediction models for mortality in ambulatory patients with heart failure: a systematic review.

Ana C Alba1, Thomas Agoritsas, Milosz Jankowski, Delphine Courvoisier, Stephen D Walter, Gordon H Guyatt, Heather J Ross.   

Abstract

BACKGROUND: Optimal management of heart failure requires accurate assessment of prognosis. Many prognostic models are available. Our objective was to identify studies that evaluate the use of risk prediction models for mortality in ambulatory patients with heart failure and describe their performance and clinical applicability. METHODS AND
RESULTS: We searched for studies in Medline, Embase, and CINAHL in May 2012. Two reviewers selected citations including patients with heart failure and reporting on model performance in derivation or validation cohorts. We abstracted data related to population, outcomes, study quality, model discrimination, and calibration. Of the 9952 studies reviewed, we included 34 studies testing 20 models. Only 5 models were validated in independent cohorts: the Heart Failure Survival Score, the Seattle Heart Failure Model, the PACE (incorporating peripheral vascular disease, age, creatinine, and ejection fraction) risk score, a model by Frankenstein et al, and the SHOCKED predictors. The Heart Failure Survival Score was validated in 8 cohorts (2240 patients), showing poor-to-modest discrimination (c-statistic, 0.56-0.79), being lower in more recent cohorts. The Seattle Heart Failure Model was validated in 14 cohorts (16 057 patients), describing poor-to-acceptable discrimination (0.63-0.81), remaining relatively stable over time. Both models reported adequate calibration, although overestimating survival in specific populations. The other 3 models were validated in a cohort each, reporting poor-to-modest discrimination (0.66-0.74). Among the remaining 15 models, 6 were validated by bootstrapping (c-statistic, 0.74-0.85); the rest were not validated.
CONCLUSIONS: Externally validated heart failure models showed inconsistent performance. The Heart Failure Survival Score and Seattle Heart Failure Model demonstrated modest discrimination and questionable calibration. A new model derived from contemporary patient cohorts may be required for improved prognostic performance.

Entities:  

Keywords:  heart failure; prediction models; prognosis; survival

Mesh:

Year:  2013        PMID: 23888045     DOI: 10.1161/CIRCHEARTFAILURE.112.000043

Source DB:  PubMed          Journal:  Circ Heart Fail        ISSN: 1941-3289            Impact factor:   8.790


  59 in total

1.  Recurrent Neural Networks for Early Detection of Heart Failure From Longitudinal Electronic Health Record Data: Implications for Temporal Modeling With Respect to Time Before Diagnosis, Data Density, Data Quantity, and Data Type.

Authors:  Robert Chen; Walter F Stewart; Jimeng Sun; Kenney Ng; Xiaowei Yan
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2019-10-15

2.  An Appraisal of Biomarker-Based Risk-Scoring Models in Chronic Heart Failure: Which One Is Best?

Authors:  Barbara S Doumouras; Douglas S Lee; Wayne C Levy; Ana C Alba
Journal:  Curr Heart Fail Rep       Date:  2018-02

3.  Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale.

Authors:  Eli Sherman; Hitinder Gurm; Ulysses Balis; Scott Owens; Jenna Wiens
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

4.  Baseline fragmented QRS is associated with increased all-cause mortality in heart failure with reduced ejection fraction: A systematic review and meta-analysis.

Authors:  Chanavuth Kanitsoraphan; Pattara Rattanawong; Poemlarp Mekraksakit; Pakawat Chongsathidkiet; Tanawan Riangwiwat; Napatt Kanjanahattakij; Wasawat Vutthikraivit; Saranapoom Klomjit; Subhanudh Thavaraputta
Journal:  Ann Noninvasive Electrocardiol       Date:  2018-10-17       Impact factor: 1.468

Review 5.  The American Heart Association Heart Failure Summit, Bethesda, April 12, 2017.

Authors:  Pamela N Peterson; Larry A Allen; Paul A Heidenreich; Nancy M Albert; Ileana L Piña
Journal:  Circ Heart Fail       Date:  2018-10       Impact factor: 8.790

6.  Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services.

Authors:  Suranga N Kasthurirathne; Joshua R Vest; Nir Menachemi; Paul K Halverson; Shaun J Grannis
Journal:  J Am Med Inform Assoc       Date:  2018-01-01       Impact factor: 4.497

Review 7.  Incremental predictive value of natriuretic peptides for prognosis in the chronic stable heart failure population: a systematic review.

Authors:  Andrew C Don-Wauchope; Pasqualina L Santaguida; Mark Oremus; Robert McKelvie; Usman Ali; Judy A Brown; Amy Bustamam; Nazmul Sohel; Stephen A Hill; Ronald A Booth; Cynthia Balion; Parminder Raina
Journal:  Heart Fail Rev       Date:  2014-08       Impact factor: 4.214

Review 8.  Clinical Prediction Models for Cardiovascular Disease: Tufts Predictive Analytics and Comparative Effectiveness Clinical Prediction Model Database.

Authors:  Benjamin S Wessler; Lana Lai Yh; Whitney Kramer; Michael Cangelosi; Gowri Raman; Jennifer S Lutz; David M Kent
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2015-07-07

Review 9.  Long-term management of end-stage heart failure.

Authors:  Marlena V Habal; A Reshad Garan
Journal:  Best Pract Res Clin Anaesthesiol       Date:  2017-07-18

10.  The utility of biomarker risk prediction score in patients with chronic heart failure.

Authors:  Alexander E Berezin; Alexander A Kremzer; Yulia V Martovitskaya; Tatyana A Berezina; Tatyana A Samura
Journal:  Clin Hypertens       Date:  2016-03-11
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