Literature DB >> 35224666

Clinical value of the HATCH score for predicting adverse outcomes in patients with heart failure.

Naoki Shibata1, Toru Kondo2, Ryota Morimoto2, Shingo Kazama2, Akinori Sawamura3, Itsumure Nishiyama4, Toshiaki Kato4, Tasuku Kuwayama2, Hiroaki Hiraiwa2, Norio Umemoto3, Toru Asai3, Takahiro Okumura2, Toyoaki Murohara2.   

Abstract

The HATCH score is employed as a risk assessment tool for atrial fibrillation (AF) development. However, the impact of the HATCH score on the long-term adverse outcomes in patients with acute heart failure (AHF) remains unknown. We investigated the clinical value of the HATCH score in patients with AHF. From a multicenter AHF registry, we retrospectively evaluated 1543 consecutive patients who required hospitalization owing to AHF (median age, 78 [69-85] years; 42.3% women) from January 2012 to December 2019. These patients were divided into five risk groups based on their HATCH score at admission (scores 0, 1, 2, 3, and 4-7). The correlation between the HATCH score and the composite outcome, including all-cause mortality and re-hospitalization due to HF, was analyzed using Kaplan-Meier and Cox proportional-hazard analyses. The median HATCH score was 2 [1-3], and the median age was 78 years (69-85 years). During the follow-up period (median, 16.8 months), the composite endpoint occurred in 691 patients (44.8%), including 416 (27%) patients who died (with 65 [4.2%] in-hospitalization deaths) and 455 (29.5%) patients requiring re-hospitalizations due to HF. The Kaplan-Meier analysis showed a significant increase in the composite endpoint with an increasing HATCH score (log-rank, p < 0.001). The multivariate Cox regression model revealed that the HATCH score was an independent predictor of the composite endpoint (hazard ratio [HR] 1.181; 95% confidence interval [CI]: 1.111-1.255; p < 0.001) with all-cause mortality (HR 1.153, 95% CI 1.065-1.249; p < 0.001) and re-hospitalizations due to HF (HR 1.21; 95% CI 1.124-1.303; p < 0.001) in patients with AHF, regardless of the presence or absence of AF, ejection fraction, and etiology. The HATCH score is an independent predictor of adverse outcomes in patients with AHF.
© 2022. Springer Japan KK, part of Springer Nature.

Entities:  

Keywords:  Acute heart failure; HATCH score; Prognosis

Mesh:

Year:  2022        PMID: 35224666     DOI: 10.1007/s00380-022-02035-w

Source DB:  PubMed          Journal:  Heart Vessels        ISSN: 0910-8327            Impact factor:   2.037


  39 in total

Review 1.  2017 ACC/AHA/HFSA Focused Update of the 2013 ACCF/AHA Guideline for the Management of Heart Failure: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines and the Heart Failure Society of America.

Authors:  Clyde W Yancy; Mariell Jessup; Biykem Bozkurt; Javed Butler; Donald E Casey; Monica M Colvin; Mark H Drazner; Gerasimos S Filippatos; Gregg C Fonarow; Michael M Givertz; Steven M Hollenberg; JoAnn Lindenfeld; Frederick A Masoudi; Patrick E McBride; Pamela N Peterson; Lynne Warner Stevenson; Cheryl Westlake
Journal:  Circulation       Date:  2017-04-28       Impact factor: 29.690

2.  Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies.

Authors:  Stuart J Pocock; Cono A Ariti; John J V McMurray; Aldo Maggioni; Lars Køber; Iain B Squire; Karl Swedberg; Joanna Dobson; Katrina K Poppe; Gillian A Whalley; Rob N Doughty
Journal:  Eur Heart J       Date:  2012-10-24       Impact factor: 29.983

3.  Predictors of mortality and morbidity in patients with chronic heart failure.

Authors:  Stuart J Pocock; Duolao Wang; Marc A Pfeffer; Salim Yusuf; John J V McMurray; Karl B Swedberg; Jan Ostergren; Eric L Michelson; Karen S Pieper; Christopher B Granger
Journal:  Eur Heart J       Date:  2005-10-11       Impact factor: 29.983

4.  JCS 2017/JHFS 2017 Guideline on Diagnosis and Treatment of Acute and Chronic Heart Failure - Digest Version.

Authors:  Hiroyuki Tsutsui; Mitsuaki Isobe; Hiroshi Ito; Hiroshi Ito; Ken Okumura; Minoru Ono; Masafumi Kitakaze; Koichiro Kinugawa; Yasuki Kihara; Yoichi Goto; Issei Komuro; Yoshikatsu Saiki; Yoshihiko Saito; Yasushi Sakata; Naoki Sato; Yoshiki Sawa; Akira Shiose; Wataru Shimizu; Hiroaki Shimokawa; Yoshihiko Seino; Koichi Node; Taiki Higo; Atsushi Hirayama; Miyuki Makaya; Tohru Masuyama; Toyoaki Murohara; Shin-Ichi Momomura; Masafumi Yano; Kenji Yamazaki; Kazuhiro Yamamoto; Tsutomu Yoshikawa; Michihiro Yoshimura; Masatoshi Akiyama; Toshihisa Anzai; Shiro Ishihara; Takayuki Inomata; Teruhiko Imamura; Yu-Ki Iwasaki; Tomohito Ohtani; Katsuya Onishi; Takatoshi Kasai; Mahoto Kato; Makoto Kawai; Yoshiharu Kinugasa; Shintaro Kinugawa; Toru Kuratani; Shigeki Kobayashi; Yasuhiko Sakata; Atsushi Tanaka; Koichi Toda; Takashi Noda; Kotaro Nochioka; Masaru Hatano; Takayuki Hidaka; Takeo Fujino; Shigeru Makita; Osamu Yamaguchi; Uichi Ikeda; Takeshi Kimura; Shun Kohsaka; Masami Kosuge; Masakazu Yamagishi; Akira Yamashina
Journal:  Circ J       Date:  2019-09-10       Impact factor: 2.993

5.  Triage after hospitalization with advanced heart failure: the ESCAPE (Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness) risk model and discharge score.

Authors:  Christopher M O'Connor; Vic Hasselblad; Rajendra H Mehta; Gudaye Tasissa; Robert M Califf; Mona Fiuzat; Joseph G Rogers; Carl V Leier; Lynne W Stevenson
Journal:  J Am Coll Cardiol       Date:  2010-03-02       Impact factor: 24.094

6.  The Seattle Heart Failure Model: prediction of survival in heart failure.

Authors:  Wayne C Levy; Dariush Mozaffarian; David T Linker; Santosh C Sutradhar; Stefan D Anker; Anne B Cropp; Inder Anand; Aldo Maggioni; Paul Burton; Mark D Sullivan; Bertram Pitt; Philip A Poole-Wilson; Douglas L Mann; Milton Packer
Journal:  Circulation       Date:  2006-03-13       Impact factor: 29.690

Review 7.  Predicting mortality in patients with acute heart failure: Role of risk scores.

Authors:  Andrea Passantino; Francesco Monitillo; Massimo Iacoviello; Domenico Scrutinio
Journal:  World J Cardiol       Date:  2015-12-26

8.  Predictors of mortality after discharge in patients hospitalized with heart failure: an analysis from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF).

Authors:  Christopher M O'Connor; William T Abraham; Nancy M Albert; Robert Clare; Wendy Gattis Stough; Mihai Gheorghiade; Barry H Greenberg; Clyde W Yancy; James B Young; Gregg C Fonarow
Journal:  Am Heart J       Date:  2008-10       Impact factor: 4.749

9.  Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model.

Authors:  Douglas S Lee; Peter C Austin; Jean L Rouleau; Peter P Liu; David Naimark; Jack V Tu
Journal:  JAMA       Date:  2003-11-19       Impact factor: 56.272

10.  Impact of predictive value of Fibrosis-4 index in patients hospitalized for acute heart failure.

Authors:  Naoki Shibata; Toru Kondo; Shingo Kazama; Yuki Kimura; Hideo Oishi; Yoshihito Arao; Hiroo Kato; Shogo Yamaguchi; Tasuku Kuwayama; Hiroaki Hiraiwa; Ryota Morimoto; Takahiro Okumura; Takuya Sumi; Akinori Sawamura; Kiyokazu Shimizu; Toyoaki Murohara
Journal:  Int J Cardiol       Date:  2020-09-30       Impact factor: 4.164

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