Literature DB >> 35181762

Development and validation of hypertension prediction models: The Korean Genome and Epidemiology Study_Cardiovascular Disease Association Study (KoGES_CAVAS).

Hyun Kyung Namgung1,2, Hye Won Woo2,3, Jinho Shin4, Min-Ho Shin5, Sang Baek Koh6, Hyeon Chang Kim7, Yu-Mi Kim8,9,10, Mi Kyung Kim11,12,13.   

Abstract

This study aimed to develop and validate the hypertension risk prediction models of the CArdioVascular disease Association Study (CAVAS). Overall, 6,186 participants without hypertension at baseline were randomly divided into derivation and internal validation sets in a 6:4 ratio. We derived two prediction models: the first used the Framingham hypertension risk prediction factors (F-CAVAS-HTN); the second considered additional risk factors identified using stepwise Weibull regression analysis (CAVAS-HTN). These models were externally evaluated among Ansan and Ansung (A&A) participants, and the external validity of the Framingham and A&A prediction models (F-HTN and A&A-HTN) were assessed using the internal validation set of CAVAS. The discrimination, calibration, and net reclassification were determined. During the 4-year follow-up, 777 new cases of hypertension were diagnosed. All four models showed good discrimination (C-statistic ≥ 0.7). Internal calibrations were good for both the coefficient-based and the risk score-based F-CAVAS-HTN models, respectively (Hosmer-Lemeshow chi-square, H-L χ2 < 20, P ≥ 0.05). However, the two CAVAS models (H-L χ2 ≥ 20, P < 0.05, both) as well as the F-HTN and the A&A-HTN prediction models (H-L χ2 = 155.39, P < 0.0001; H-L χ2 = 209.72, P < 0.0001, respectively) were not externally calibrated. The F-CAVAS-HTN may be better than models with additional risk factors or derived for another population in the view of the findings of the internal validation in the present study, although future studies to improve the external validity of the F-CAVAS-HTN are needed.
© 2022. The Author(s), under exclusive licence to Springer Nature Limited.

Entities:  

Year:  2022        PMID: 35181762     DOI: 10.1038/s41371-021-00645-x

Source DB:  PubMed          Journal:  J Hum Hypertens        ISSN: 0950-9240            Impact factor:   3.012


  41 in total

1.  Development of a risk prediction model for incident hypertension in a working-age Japanese male population.

Authors:  Toshiaki Otsuka; Yuko Kachi; Hirotaka Takada; Katsuhito Kato; Eitaro Kodani; Chikao Ibuki; Yoshiki Kusama; Tomoyuki Kawada
Journal:  Hypertens Res       Date:  2014-11-13       Impact factor: 3.872

2.  Global Burden of Hypertension and Systolic Blood Pressure of at Least 110 to 115 mm Hg, 1990-2015.

Authors:  Mohammad H Forouzanfar; Patrick Liu; Gregory A Roth; Marie Ng; Stan Biryukov; Laurie Marczak; Lily Alexander; Kara Estep; Kalkidan Hassen Abate; Tomi F Akinyemiju; Raghib Ali; Nelson Alvis-Guzman; Peter Azzopardi; Amitava Banerjee; Till Bärnighausen; Arindam Basu; Tolesa Bekele; Derrick A Bennett; Sibhatu Biadgilign; Ferrán Catalá-López; Valery L Feigin; Joao C Fernandes; Florian Fischer; Alemseged Aregay Gebru; Philimon Gona; Rajeev Gupta; Graeme J Hankey; Jost B Jonas; Suzanne E Judd; Young-Ho Khang; Ardeshir Khosravi; Yun Jin Kim; Ruth W Kimokoti; Yoshihiro Kokubo; Dhaval Kolte; Alan Lopez; Paulo A Lotufo; Reza Malekzadeh; Yohannes Adama Melaku; George A Mensah; Awoke Misganaw; Ali H Mokdad; Andrew E Moran; Haseeb Nawaz; Bruce Neal; Frida Namnyak Ngalesoni; Takayoshi Ohkubo; Farshad Pourmalek; Anwar Rafay; Rajesh Kumar Rai; David Rojas-Rueda; Uchechukwu K Sampson; Itamar S Santos; Monika Sawhney; Aletta E Schutte; Sadaf G Sepanlou; Girma Temam Shifa; Ivy Shiue; Bemnet Amare Tedla; Amanda G Thrift; Marcello Tonelli; Thomas Truelsen; Nikolaos Tsilimparis; Kingsley Nnanna Ukwaja; Olalekan A Uthman; Tommi Vasankari; Narayanaswamy Venketasubramanian; Vasiliy Victorovich Vlassov; Theo Vos; Ronny Westerman; Lijing L Yan; Yuichiro Yano; Naohiro Yonemoto; Maysaa El Sayed Zaki; Christopher J L Murray
Journal:  JAMA       Date:  2017-01-10       Impact factor: 56.272

3.  A point-score system superior to blood pressure measures alone for predicting incident hypertension: Tehran Lipid and Glucose Study.

Authors:  Mohammadreza Bozorgmanesh; Farzad Hadaegh; Yadollah Mehrabi; Fereidoun Azizi
Journal:  J Hypertens       Date:  2011-08       Impact factor: 4.844

4.  Prediction of blood pressure changes over time and incidence of hypertension by a genetic risk score in Swedes.

Authors:  Cristiano Fava; Marketa Sjögren; Martina Montagnana; Elisa Danese; Peter Almgren; Gunnar Engström; Peter Nilsson; Bo Hedblad; Gian Cesare Guidi; Pietro Minuz; Olle Melander
Journal:  Hypertension       Date:  2012-12-10       Impact factor: 10.190

Review 5.  2017 ACC/AHA/AAPA/ABC/ACPM/AGS/APhA/ASH/ASPC/NMA/PCNA Guideline for the Prevention, Detection, Evaluation, and Management of High Blood Pressure in Adults: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Practice Guidelines.

Authors:  Paul K Whelton; Robert M Carey; Wilbert S Aronow; Donald E Casey; Karen J Collins; Cheryl Dennison Himmelfarb; Sondra M DePalma; Samuel Gidding; Kenneth A Jamerson; Daniel W Jones; Eric J MacLaughlin; Paul Muntner; Bruce Ovbiagele; Sidney C Smith; Crystal C Spencer; Randall S Stafford; Sandra J Taler; Randal J Thomas; Kim A Williams; Jeff D Williamson; Jackson T Wright
Journal:  J Am Coll Cardiol       Date:  2017-11-13       Impact factor: 24.094

6.  Thrombosis: Pharmacomechanical thrombolysis in DVT.

Authors:  Gregory B Lim
Journal:  Nat Rev Cardiol       Date:  2017-12-21       Impact factor: 32.419

7.  Prediction models for the risk of new-onset hypertension in ethnic Chinese in Taiwan.

Authors:  K-L Chien; H-C Hsu; T-C Su; W-T Chang; F-C Sung; M-F Chen; Y-T Lee
Journal:  J Hum Hypertens       Date:  2010-07-08       Impact factor: 3.012

8.  Prediction of incident hypertension risk in women with currently normal blood pressure.

Authors:  Nina P Paynter; Nancy R Cook; Brendan M Everett; Howard D Sesso; Julie E Buring; Paul M Ridker
Journal:  Am J Med       Date:  2009-05       Impact factor: 4.965

9.  A risk score for predicting near-term incidence of hypertension: the Framingham Heart Study.

Authors:  Nisha I Parikh; Michael J Pencina; Thomas J Wang; Emelia J Benjamin; Katherine J Lanier; Daniel Levy; Ralph B D'Agostino; William B Kannel; Ramachandran S Vasan
Journal:  Ann Intern Med       Date:  2008-01-15       Impact factor: 25.391

10.  Incident hypertension and its prediction model in a prospective northern urban Han Chinese cohort study.

Authors:  Y Chen; C Wang; Y Liu; Z Yuan; W Zhang; X Li; Y Yang; X Sun; F Xue; C Zhang
Journal:  J Hum Hypertens       Date:  2016-06-02       Impact factor: 3.012

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  1 in total

1.  Predicting South Korea adolescents vulnerable to depressive disorder using Bayesian nomogram: A community-based cross-sectional study.

Authors:  Haewon Byeon
Journal:  World J Psychiatry       Date:  2022-07-19
  1 in total

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