Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. Article Swipe

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Lisa Pennells , Stephen Kaptoge , Angela Wood , Michael Sweeting , Xiaohui Zhao , Ian R. White , Stephen Burgess , Peter Willeit , Thomas Bolton , Karel G.M. Moons , Yvonne T. van der Schouw , Randi Selmer , Kay‐Tee Khaw , Vilmundur Guðnason , Gerd Assmann , Philippe Amouyel , Veikko Salomaa , Mika Kivimäki , Børge G. Nordestgaard , Michael J. Blaha , Lewis H. Kuller , Hermann Brenner , Richard F. Gillum , Christa Meisinger , Ian Ford , Matthew Knuiman , Annika Rosengren , Debbie A. Lawlor , Henry Völzke , Cyrus Cooper , Alejandro Marín Ibañez , Edoardo Casiglia , Jussi Kauhanen , Jackie A. Cooper , Beatriz L. Rodríguez , Johan Sundström , Elizabeth Barrett‐Connor , Rachel Dankner , Paul J. Nietert , Karina W. Davidson , Robert B. Wallace , Dan G. Blazer , Cecilia Björkelund , Chiara Donfrancesco , Harlan M. Krumholz , Aulikki Nissinen , Barry R. Davis , Sean Coady , Peter H. Whincup , Torben Jørgensen , Pierre Ducimetière , Maurizio Trevisan , Gunnar Engström , Carlos J. Crespo , T W Meade , Marjolein Visser , Daan Kromhout , Stefan Kiechl , Makoto Daimon , Jackie F. Price , Agustı́n Gómez de la Cámara , J. Wouter Jukema , Benoı̂t Lamarche , Altan Onat , Leon A. Simons , Maryam Kavousi , Yoav Ben‐Shlomo , John Gallacher , Joost Dekker , Hisatomi Arima , Nawar Shara , RobertW. Tipping , Ronan Roussel , Eric J. Brunner , Wolfgang Köenig , Masaru Sakurai , Jelena Pavlović , Ron T. Gansevoort , Dorothea Nagel , Uri Goldbourt , Elizabeth Barr , Luigi Palmieri , Inger Njølstad , Shinichi Sato , W. M. Monique Verschuren , Cherian Varghese , Ian Graham , Oyere Onuma , Philip Greenland , Mark Woodward , Majid Ezzati , Bruce M. Psaty , Naveed Sattar , Rod Jackson , Paul M. Ridker , Nancy R. Cook , Ralph B. D’Agostino , Simon G. Thompson , John Danesh , Emanuele Di Angelantonio ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.17863/cam.26008

AIMS: There is debate about the optimum algorithm for cardiovascular disease (CVD) risk estimation. We conducted head-to-head comparisons of four algorithms recommended by primary prevention guidelines, before and after 'recalibration', a method that adapts risk algorithms to take account of differences in the risk characteristics of the populations being studied. METHODS AND RESULTS: Using individual-participant data on 360 737 participants without CVD at baseline in 86 prospective studies from 22 countries, we compared the Framingham risk score (FRS), Systematic COronary Risk Evaluation (SCORE), pooled cohort equations (PCE), and Reynolds risk score (RRS). We calculated measures of risk discrimination and calibration, and modelled clinical implications of initiating statin therapy in people judged to be at 'high' 10 year CVD risk. Original risk algorithms were recalibrated using the risk factor profile and CVD incidence of target populations. The four algorithms had similar risk discrimination. Before recalibration, FRS, SCORE, and PCE over-predicted CVD risk on average by 10%, 52%, and 41%, respectively, whereas RRS under-predicted by 10%. Original versions of algorithms classified 29-39% of individuals aged ≥40 years as high risk. By contrast, recalibration reduced this proportion to 22-24% for every algorithm. We estimated that to prevent one CVD event, it would be necessary to initiate statin therapy in 44-51 such individuals using original algorithms, in contrast to 37-39 individuals with recalibrated algorithms. CONCLUSION: Before recalibration, the clinical performance of four widely used CVD risk algorithms varied substantially. By contrast, simple recalibration nearly equalized their performance and improved modelled targeting of preventive action to clinical need.

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Meta-analysis Computer science Algorithm Equalization (audio) Risk analysis (engineering) Medicine Internal medicine Decoding methods

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Type: article
Language: en
Landing Page: https://pdxscholar.library.pdx.edu/sph_facpub/452
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Related Works: 20
OpenAlex ID: https://openalex.org/W3191618931

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DOI: https://doi.org/10.17863/cam.26008

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Title: Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies.

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Language: en

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Publication year: 2018

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Publication date: 2018-12-12

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Authors: Lisa Pennells, Stephen Kaptoge, Angela Wood, Michael Sweeting, Xiaohui Zhao, Ian R. White, Stephen Burgess, Peter Willeit, Thomas Bolton, Karel G.M. Moons, Yvonne T. van der Schouw, Randi Selmer, Kay‐Tee Khaw, Vilmundur Guðnason, Gerd Assmann, Philippe Amouyel, Veikko Salomaa, Mika Kivimäki, Børge G. Nordestgaard, Michael J. Blaha, Lewis H. Kuller, Hermann Brenner, Richard F. Gillum, Christa Meisinger, Ian Ford, Matthew Knuiman, Annika Rosengren, Debbie A. Lawlor, Henry Völzke, Cyrus Cooper, Alejandro Marín Ibañez, Edoardo Casiglia, Jussi Kauhanen, Jackie A. Cooper, Beatriz L. Rodríguez, Johan Sundström, Elizabeth Barrett‐Connor, Rachel Dankner, Paul J. Nietert, Karina W. Davidson, Robert B. Wallace, Dan G. Blazer, Cecilia Björkelund, Chiara Donfrancesco, Harlan M. Krumholz, Aulikki Nissinen, Barry R. Davis, Sean Coady, Peter H. Whincup, Torben Jørgensen, Pierre Ducimetière, Maurizio Trevisan, Gunnar Engström, Carlos J. Crespo, T W Meade, Marjolein Visser, Daan Kromhout, Stefan Kiechl, Makoto Daimon, Jackie F. Price, Agustı́n Gómez de la Cámara, J. Wouter Jukema, Benoı̂t Lamarche, Altan Onat, Leon A. Simons, Maryam Kavousi, Yoav Ben‐Shlomo, John Gallacher, Joost Dekker, Hisatomi Arima, Nawar Shara, RobertW. Tipping, Ronan Roussel, Eric J. Brunner, Wolfgang Köenig, Masaru Sakurai, Jelena Pavlović, Ron T. Gansevoort, Dorothea Nagel, Uri Goldbourt, Elizabeth Barr, Luigi Palmieri, Inger Njølstad, Shinichi Sato, W. M. Monique Verschuren, Cherian Varghese, Ian Graham, Oyere Onuma, Philip Greenland, Mark Woodward, Majid Ezzati, Bruce M. Psaty, Naveed Sattar, Rod Jackson, Paul M. Ridker, Nancy R. Cook, Ralph B. D’Agostino, Simon G. Thompson, John Danesh, Emanuele Di Angelantonio

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Concepts: Meta-analysis, Computer science, Algorithm, Equalization (audio), Risk analysis (engineering), Medicine, Internal medicine, Decoding methods

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abstract_inverted_index.SCORE,	145
abstract_inverted_index.action	249
abstract_inverted_index.adapts	33
abstract_inverted_index.before	26
abstract_inverted_index.cohort	84
abstract_inverted_index.debate	3
abstract_inverted_index.event,	196
abstract_inverted_index.factor	127
abstract_inverted_index.judged	110
abstract_inverted_index.method	31
abstract_inverted_index.nearly	239
abstract_inverted_index.people	109
abstract_inverted_index.pooled	83
abstract_inverted_index.simple	237
abstract_inverted_index.statin	106, 203
abstract_inverted_index.target	133
abstract_inverted_index.varied	233
abstract_inverted_index.widely	228
abstract_inverted_index.METHODS	50
abstract_inverted_index.account	38
abstract_inverted_index.average	152
abstract_inverted_index.disease	10
abstract_inverted_index.optimum	6
abstract_inverted_index.prevent	193
abstract_inverted_index.primary	23
abstract_inverted_index.profile	128
abstract_inverted_index.reduced	181
abstract_inverted_index.similar	139
abstract_inverted_index.studies	67
abstract_inverted_index.therapy	107, 204
abstract_inverted_index.whereas	159
abstract_inverted_index.without	60
abstract_inverted_index.(SCORE),	82
abstract_inverted_index.COronary	79
abstract_inverted_index.Original	119, 164
abstract_inverted_index.RESULTS:	52
abstract_inverted_index.Reynolds	88
abstract_inverted_index.baseline	63
abstract_inverted_index.clinical	102, 224, 251
abstract_inverted_index.compared	72
abstract_inverted_index.contrast	213
abstract_inverted_index.improved	244
abstract_inverted_index.initiate	202
abstract_inverted_index.measures	94
abstract_inverted_index.modelled	101, 245
abstract_inverted_index.original	210
abstract_inverted_index.studied.	49
abstract_inverted_index.versions	165
abstract_inverted_index.algorithm	7
abstract_inverted_index.conducted	15
abstract_inverted_index.contrast,	179, 236
abstract_inverted_index.equalized	240
abstract_inverted_index.equations	85
abstract_inverted_index.estimated	190
abstract_inverted_index.incidence	131
abstract_inverted_index.necessary	200
abstract_inverted_index.targeting	246
abstract_inverted_index.Evaluation	81
abstract_inverted_index.Framingham	74
abstract_inverted_index.Systematic	78
abstract_inverted_index.algorithm.	188
abstract_inverted_index.algorithms	20, 35, 121, 137, 167, 232
abstract_inverted_index.calculated	93
abstract_inverted_index.classified	168
abstract_inverted_index.countries,	70
abstract_inverted_index.initiating	105
abstract_inverted_index.prevention	24
abstract_inverted_index.preventive	248
abstract_inverted_index.proportion	183
abstract_inverted_index.CONCLUSION:	220
abstract_inverted_index.algorithms,	211
abstract_inverted_index.algorithms.	219
abstract_inverted_index.comparisons	17
abstract_inverted_index.differences	40
abstract_inverted_index.estimation.	13
abstract_inverted_index.guidelines,	25
abstract_inverted_index.individuals	171, 208, 216
abstract_inverted_index.performance	225, 242
abstract_inverted_index.populations	47
abstract_inverted_index.prospective	66
abstract_inverted_index.recommended	21
abstract_inverted_index.calibration,	99
abstract_inverted_index.head-to-head	16
abstract_inverted_index.implications	103
abstract_inverted_index.participants	59
abstract_inverted_index.populations.	134
abstract_inverted_index.recalibrated	123, 218
abstract_inverted_index.recalibration	180, 238
abstract_inverted_index.respectively,	158
abstract_inverted_index.cardiovascular	9
abstract_inverted_index.discrimination	97
abstract_inverted_index.over-predicted	148
abstract_inverted_index.recalibration,	143, 222
abstract_inverted_index.substantially.	234
abstract_inverted_index.characteristics	44
abstract_inverted_index.discrimination.	141
abstract_inverted_index.under-predicted	161
abstract_inverted_index.'recalibration',	29
abstract_inverted_index.individual-participant	54
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	219
citation_normalized_percentile.value	0.40213744
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False