Literature DB >> 23607443

Derivation and validation of a prediction score for acute kidney injury in patients hospitalized with acute heart failure in a Chinese cohort.

Yin-Na Wang1, Hong Cheng, Tong Yue, Yi-Pu Chen.   

Abstract

AIMS: Acute kidney injury (AKI) is a common complication among patients hospitalized for acute heart failure (AHF), and is associated with increased mortality. The goal of this study was to derive and validate a prediction score for AKI in AHF patients.
METHODS: The hospital medical records of 1709 patients with AHF were reviewed. AKI was defined as an increase in serum creatinine (SCr) of ≥26.4 μmol/L or ≥50% within 48 h. A multivariate logistic regression analysis was undertaken to develop a new prediction score. The area under the receiver operating characteristic (ROC) curve and the Hosmer-Lemeshow goodness-of-fit statistic test were calculated to assess the discrimination and calibration of the prediction score, respectively.
RESULTS: Acute kidney injury developed in 32.2% of patients with AHF. Factors independently associated with the risk of AKI included: ≥70 years of age, ≥3 previous hospital admissions for AHF, systolic blood pressure <90 mmHg, serum sodium <130 mmol/L, heart functional class IV, proteinuria, SCr ≥104 μmol/L and intravenous furosemide dose ≥80 mg/day. A prediction score for AKI was derived based on the β coefficients of each risk factor. Patients with ≥8 points would be considered at high risk for development of AKI (55.1% incidence vs 18% in those with <8 points, P < 0.001). Both the derived and validated datasets showed adequate discrimination (area under ROC curve was 0.76 in both datasets) and calibration (Hosmer-Lemeshow statistic test, P = 0.98 and 0.13, respectively).
CONCLUSION: The newly derived and validated clinical prediction score may effectively predict AKI in the patients hospitalized with AHF.
© 2013 The Authors. Nephrology © 2013 Asian Pacific Society of Nephrology.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23607443     DOI: 10.1111/nep.12092

Source DB:  PubMed          Journal:  Nephrology (Carlton)        ISSN: 1320-5358            Impact factor:   2.506


  12 in total

1.  Clinical prediction scores for type 1 cardiorenal syndrome derived and validated in chinese cohorts.

Authors:  Hong Cheng; Yi-Pu Chen
Journal:  Cardiorenal Med       Date:  2014-12-13       Impact factor: 2.041

2.  The Japanese Clinical Practice Guideline for acute kidney injury 2016.

Authors:  Kent Doi; Osamu Nishida; Takashi Shigematsu; Tomohito Sadahiro; Noritomo Itami; Kunitoshi Iseki; Yukio Yuzawa; Hirokazu Okada; Daisuke Koya; Hideyasu Kiyomoto; Yugo Shibagaki; Kenichi Matsuda; Akihiko Kato; Terumasa Hayashi; Tomonari Ogawa; Tatsuo Tsukamoto; Eisei Noiri; Shigeo Negi; Koichi Kamei; Hirotsugu Kitayama; Naoki Kashihara; Toshiki Moriyama; Yoshio Terada
Journal:  J Intensive Care       Date:  2018-08-13

Review 3.  The Japanese clinical practice guideline for acute kidney injury 2016.

Authors:  Kent Doi; Osamu Nishida; Takashi Shigematsu; Tomohito Sadahiro; Noritomo Itami; Kunitoshi Iseki; Yukio Yuzawa; Hirokazu Okada; Daisuke Koya; Hideyasu Kiyomoto; Yugo Shibagaki; Kenichi Matsuda; Akihiko Kato; Terumasa Hayashi; Tomonari Ogawa; Tatsuo Tsukamoto; Eisei Noiri; Shigeo Negi; Koichi Kamei; Hirotsugu Kitayama; Naoki Kashihara; Toshiki Moriyama; Yoshio Terada
Journal:  Clin Exp Nephrol       Date:  2018-10       Impact factor: 2.801

Review 4.  Nephrology in china.

Authors:  Zhi-Hong Liu
Journal:  Nat Rev Nephrol       Date:  2013-07-23       Impact factor: 28.314

Review 5.  Utilizing electronic health records to predict acute kidney injury risk and outcomes: workgroup statements from the 15(th) ADQI Consensus Conference.

Authors:  Scott M Sutherland; Lakhmir S Chawla; Sandra L Kane-Gill; Raymond K Hsu; Andrew A Kramer; Stuart L Goldstein; John A Kellum; Claudio Ronco; Sean M Bagshaw
Journal:  Can J Kidney Health Dis       Date:  2016-02-26

6.  Predicting AKI in emergency admissions: an external validation study of the acute kidney injury prediction score (APS).

Authors:  L E Hodgson; B D Dimitrov; P J Roderick; R Venn; L G Forni
Journal:  BMJ Open       Date:  2017-03-08       Impact factor: 2.692

7.  Derivation and validation of a prediction score for acute kidney injury secondary to acute myocardial infarction in Chinese patients.

Authors:  Feng-Bo Xu; Hong Cheng; Tong Yue; Nan Ye; He-Jia Zhang; Yi-Pu Chen
Journal:  BMC Nephrol       Date:  2019-05-30       Impact factor: 2.388

8.  Multi-perspective predictive modeling for acute kidney injury in general hospital populations using electronic medical records.

Authors:  Jianqin He; Yong Hu; Xiangzhou Zhang; Lijuan Wu; Lemuel R Waitman; Mei Liu
Journal:  JAMIA Open       Date:  2018-11-15

9.  Calibration drift in regression and machine learning models for acute kidney injury.

Authors:  Sharon E Davis; Thomas A Lasko; Guanhua Chen; Edward D Siew; Michael E Matheny
Journal:  J Am Med Inform Assoc       Date:  2017-11-01       Impact factor: 4.497

Review 10.  Systematic review of prognostic prediction models for acute kidney injury (AKI) in general hospital populations.

Authors:  Luke Eliot Hodgson; Alexander Sarnowski; Paul J Roderick; Borislav D Dimitrov; Richard M Venn; Lui G Forni
Journal:  BMJ Open       Date:  2017-09-27       Impact factor: 2.692

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.