ABC | Volume 114, Nº6, June 2020

Editorial Why We Build Models – From Clinical Cardiology Practice to Infectious Disease Epidemics Marcio Sommer Bittencourt 1,2, 3 Centro de Medicina Preventiva, Hospital Israelita Albert Einstein e Faculdade Israelita de Ciência da Saúde Albert Einstein, 1 São Paulo, SP - Brazil Centro de Pesquisa Clínica e Epidemiológica, Hospital Universitário, Universidade de São Paulo, 2 São Paulo, SP - Brazil Diagnósticos da América (DASA), 3 São Paulo, SP - Brazil Mailing Address: Marcio Sommer Bittencourt • Av. Lineu Prestes, 2565. Postal Code 05508-000, São Paulo, SP - Brazil E-mail: msbittencourt@mail.harvard.edu Keywords Cardiovascular Diseases; Biomarkers; Risk Factors; Risk Assessment; Risk Reduction Behavior; Prevention and Control; Coronavirus; COVID-19; Pandemics. Francisco, 64 years old, comes to your office for a preventive health evaluation. He has a history of well- controlled hypertension and is otherwise well. His past medical history is unremarkable. No family history of cardiovascular disease or smoking and LDL-cholesterol (LDL-C) of 90 mg/dL. After discussing with the patient, you are unsure if this patient’s risk benefit profile would favor the use of statins. Instead of trusting your personal feelings, you decide to use the Framingham risk score (FRS) to decide if statins would be recommended. 1 The calculated Framingham score is 8.1% and you decide not to initiate statins at this point. One month later, Francisco returns to your office with typical angina on major exertion but has no signs of unstable disease. Once again, to avoid overconfidence on your initial impression, you decide to use the Diamond and Forrester (DF) chest pain prediction rule, which estimates the pretest probability of obstructive coronary artery disease (CAD). 2 For a male at his age, the rule suggests a pretest probability of 94% (high probability), so you decide to request an invasive angiography in the outpatient setting. A couple of days before the test, Francisco calls you complaining of worsening chest pain similar to the previous presentation, but now at rest. You tell the patient to go to the emergency room, where troponins are normal and resting ECG has 1 mm ST-segment depression in the inferior leads. Calculated TIMI risk score is 1, indicating low risk. 3 The patient is admitted for 48 hours, undergoes a negative treadmill test limited by poor physical performance and is discharged home with appropriate medication. One week later, he returns to the hospital with ST-segment elevation myocardial infarction. He is rushed to the Cath lab and a severe lesion in the mid right coronary artery is documented. He undergoes a percutaneous coronary intervention. After three days in the hospital, he is discharged home. Were the FRS, the DF or the TIMI risk scores correct or incorrect? Were the scores able to predict what happened to the patient? The FRS stratified the patient as low risk, meaning less than 10% risk of a major cardiovascular event in 10 years. The DF suggested an almost certain presence of obstructive CAD, whereas the TIMI risk score suggested low (5%) risk of death, recurrent myocardial infarction (MI) or severe ischemia requiring urgent revascularization in two weeks. Yet, the patient presented MI less than one week later. Cardiologists are used to risk scores derived from risk prediction models. Models are simplifications of real life, and such simplifications make models generalizable to a broader population and externally valid to other individuals beyond the initial cohort of patients where they were developed. The models select a limited number of variables considered of higher importance to predict the desired outcome, but several assumptions are made based on each of those variables and the population they apply to. If such assumptions change, the model may no longer be valid or it might need to be recalculated or recalibrated to fit the new environment. For example, the FRS considers smoking as present or absent. Thus, past smokers are considered of similar risk to non-smokers. Also, individuals smoking two cigarettes a day are considered just like those smoking three packs a day. While those aspects may lead to imprecision when estimating the risk of an individual, they can have much broader implications when such changes in the value of each parameter occur at the population level. For example, when the FRS was derived, an average smoker would smoke one to two packs a day. Currently, most smokers smoke less than a quarter than that. Thus, applying the old version of the FRS in the current reality might result in an incorrect risk estimation. This issues with models are known to cardiologists, and most scores are, at some point, updated to recalibrate and improve precision using new or recalibrated variables. Using the example case above, one might suggest the use of the atherosclerotic cardiovascular disease (ASCVD) score instead of the FRS. Using the ASCVD score, the 10-year risk of major cardiovascular events would be 10.6% and, according to newer guidelines, statins would be recommended to reduce this patient’s cardiovascular risk. 4 This updated risk stratification score is expected to be more accurate. In other situations, we may even consider the model as no longer useful and the entire approach should be different. For example, one could say 1 mm ST-segment depression at rest would characterize high risk irrespective DOI: https://doi.org/10.36660/abc.20200527 992