Utility of clinical risk predictors for preoperative cardiovascular risk prediction

被引:37
作者
Biccard, B. M. [1 ]
Rodseth, R. N. [1 ]
机构
[1] Univ KwaZulu Natal, Perioperat Res Unit, Inkosi Albert Luthuli Cent Hosp, Dept Anaesthet,Nelson R Mandela Sch Med, ZA-4013 Congella, South Africa
关键词
clinical risk factors; complications; myocardial infarction; modelling; risk; MAJOR NONCARDIAC SURGERY; EUROSCORE MULTINATIONAL DATABASE; PERIOPERATIVE CARDIAC RISK; IN-HOSPITAL MORTALITY; VASCULAR-SURGERY; HEART-FAILURE; MYOCARDIAL-INFARCTION; ADVERSE EVENTS; APGAR SCORE; ROC CURVE;
D O I
10.1093/bja/aer194
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Cardiovascular risk prediction using clinical risk factors is integral to both the European and the American algorithms for preoperative cardiac risk assessment and perioperative management for non-cardiac surgery. We have reviewed these risk factors and their ability to guide clinical decision making. We examine their limitations and attempt to identify factors which may improve their performance when used for clinical risk stratification. To improve the performance of the clinical risk factors, it is necessary to create uniformity in the definitions of both cardiovascular outcomes and the clinical risk factors. The risk factors selected should reflect the degree of organ dysfunction rather than a historical diagnosis. Parsimonious model design should be applied, making use of a minimal number of continuous variables rather than creating overfitted models. The inclusion of age in the model may assist partly in controlling for the duration of risk factor exposure. Risk assignment should occur throughout the perioperative period and the risk factors chosen for model inclusion should vary depending on when the assignment occurs (before operation, intraoperatively, or after operation).
引用
收藏
页码:133 / 143
页数:11
相关论文
共 50 条
[31]   NSAIDs and Cardiovascular Risk [J].
Gislason, Gunnar H. .
AMERICAN FAMILY PHYSICIAN, 2009, 80 (12) :1366-+
[32]   Cardiovascular Risk Prediction in Patients With Human Immunodeficiency Virus [J].
Raggi, Paolo ;
De Francesco, Davide ;
Guaraldi, Giovanni .
JAMA CARDIOLOGY, 2017, 2 (09) :1048-1048
[33]   Investigation on Cardiovascular Risk Prediction Using Genetic Information [J].
Pu, Li-Na ;
Zhao, Ze ;
Zhang, Yuan-Ting .
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2012, 16 (05) :795-808
[34]   Cardiovascular Disease Risk Prediction in the HIV Outpatient Study [J].
Thompson-Paul, Angela M. ;
Lichtenstein, Kenneth A. ;
Armon, Carl ;
Palella, Frank J., Jr. ;
Skarbinski, Jacek ;
Chmiel, Joan S. ;
Hart, Rachel ;
Wei, Stanley C. ;
Loustalot, Fleetwood ;
Brooks, John T. ;
Buchacz, Kate .
CLINICAL INFECTIOUS DISEASES, 2016, 63 (11) :1508-1516
[35]   Coronary calcification improves cardiovascular risk prediction in the elderly [J].
Vliegenthart, R ;
Oudkerk, M ;
Hofman, A ;
Oei, HHS ;
van Dijck, W ;
van Rooij, FJA ;
Witteman, JCM .
CIRCULATION, 2005, 112 (04) :572-577
[36]   Circulating, Imaging, and Genetic Biomarkers in Cardiovascular Risk Prediction [J].
Ge, Yin ;
Wang, Thomas J. .
TRENDS IN CARDIOVASCULAR MEDICINE, 2011, 21 (04) :105-112
[37]   Risk Reduction to Disease Management: Clinical Pharmacists as Cardiovascular Care Providers [J].
Di Palo, Katherine E. ;
Patel, Khusbu ;
Kish, Troy .
CURRENT PROBLEMS IN CARDIOLOGY, 2019, 44 (09) :276-292
[38]   Risk Prediction Using Polygenic Risk Scores for Prevention of Stroke and Other Cardiovascular Diseases [J].
Abraham, Gad ;
Rutten-Jacobs, Loes ;
Inouye, Michael .
STROKE, 2021, 52 (09) :2983-2991
[39]   Obesity and Cardiovascular Disease: a Risk Factor or a Risk Marker? [J].
Mandviwala, Taher ;
Khalid, Umair ;
Deswal, Anita .
CURRENT ATHEROSCLEROSIS REPORTS, 2016, 18 (05)
[40]   Guidelines for preoperative cardiac risk assessment [J].
Foex, P. ;
Sear, J. W. .
BRITISH JOURNAL OF ANAESTHESIA, 2012, 108 (03) :525-525