Discrepancies Between Physician-Perceived and Calculated Cardiovascular Risk in Primary Prevention: Implications for LDL-C Target Achievement and Appropriate Lipid-Lowering Therapy

Abstract

Introduction

Accurate risk assessment is critical in cardiovascular (CV) prevention, yet physicians often underestimate CV risk, leading to inadequate preventive measures.

Aim

This study evaluates the concordance between physician-perceived CV risk and calculated CV risk in a primary prevention setting.

Methods

This cross-sectional study included primary prevention patients from the Cardiology Outpatient Clinic of Caserta Hospital, Italy. Two independent cardiologists evaluated the physician-perceived risk, and a third resolved discrepancies. CV risk was calculated using SCORE2 for patients with 70 years or less and SCORE2-OP for those with more than 70 years. The concordance between perceived and calculated risks was assessed using Cohen’s kappa coefficient. Multivariate logistic regression analysis was performed to examine the influence of risk estimation on achieving low-density lipoprotein cholesterol (LDL-C) targets recommended by the ESC.

Results

389 patients had complete data for CV risk calculation. Physician-perceived risk categorized 8.7% of patients as low/moderate, 37.8% as high, and 53.5% as very-high risk. In contrast, calculated CV risk according to the SCORE2/SCORE2-OP classified 8% as low/moderate, 5.7% as high, and 86.4% as very-high risk. The concordance between perceived and calculated CV risk was poor (Cohen’s kappa 0.208, p < 0.001). Underestimated patients reached LDL-C targets in 16% of cases, well-estimated in 34.5%, and overestimated in 76.9%. Statin use was significantly lower in patients with underestimated CV risk (29.2%) compared to well-estimated (50%) and overestimated (76.9%) groups (p < 0.001). Multivariate analysis showed that patients with overestimated risk were more likely to achieve LDL-C targets (OR 5.33, CI 1.33–21.42, p = 0.018), whereas underestimated patients were 47% less likely (OR 0.53, CI 0.3–0.93, p = 0.027).

Conclusions

A significant discrepancy exists between physician-perceived and calculated CV risk, leading to risk underestimation in over one-third of patients. This underestimation is associated with lower LDL-C target achievement and reduced statin use.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40292-025-00705-0.

Introduction

Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality worldwide, despite advancements in medical science and public health initiatives. One significant concern in the management and prevention of CVD is the persistent underestimation of cardiovascular (CV) risk by physicians [1–4]This underestimation often means that patients at significant risk of CV events do not receive the appropriate preventive measures and interventions [1].

To accurately weigh individual’s risk of developing CVD, healthcare professionals utilize various risk assessment tools. Among the most widely recommended in practice are SCORE2 (Systematic COronary Risk Evaluation 2) and SCORE2-OP (SCORE2-Older Persons) [5, 6]. These tools consider multiple factors, including age, sex, blood pressure, cholesterol levels, and smoking status, to estimate the 10-year risk of a first fatal or non-fatal CV event. SCORE2 is tailored for individuals with 70 years of less, while SCORE2-OP is designed for those with more than 70, addressing the unique risk profiles of older adults. These models are more refined and accurate than previous versions, offering a nuanced approach to CV risk prediction [7]. Despite the availability of these validated tools, there may be a gap between the CV risk perceived by physicians and the risk calculated by SCORE2 and SCORE2-OP. Physicians often rely on clinical judgment, which can be influenced by factors such as patient appearance, subjective assessments, and anecdotal experiences. This reliance can lead to significant underestimation of true CV risk [8]. Research has highlighted that the CV risk calculated using these algorithms is often higher than what is perceived by physicians, indicating a significant gap in accurate risk assessment [9].

The objective of this study is to evaluate how physicians perceive patients’ CV risk and to determine the concordance between the perceived risk and the risk calculated using validated tools such as SCORE2 and SCORE2-OP.

Methods

Study Design and Population

This cross-sectional study aims to evaluate physicians’ perceptions of CV risk and compare this perceived CV risk with the CV risk calculated using the SCORE2 and SCORE2-OP tools. Patients were recruited from the Cardiology Outpatient Clinic of the S. Anna and S. Sebastiano Hospital in Caserta. Only patients without atherosclerotic cardiovascular disease were included in the study. Patients with known atherosclerotic cardiovascular disease, heterozygous familial hypercholesterolemia, diabetes, or chronic kidney disease were excluded from the study because these conditions already categorize and stratify patients based on their clinical information.

Data were collected from patients attending Cardiology Outpatient Clinic for any reason between January 2023 and December 2023. Patients were enrolled consecutively and provided informed consent. Data collected included demographic information such as age, sex, and smoking status, as well as clinical parameters such as blood pressure and cholesterol levels [total, low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) for both SCORE2 and SCORE2-OP]; the presence of diabetes, chronic kidney disease, and atherosclerotic disease. All clinical measurements were conducted following standardized procedures.

Risk Assessment

For perceived risk, two independent cardiologists conducted the initial risk perception assessment blindly, without access to the calculated risk scores or the final consensus classification, to ensure unbiased clinical judgment. To estimate each patient’s CV risk, they based on clinical, laboratory, and instrumental information before using risk calculators. Each cardiologist’s estimation was recorded on a scale of low-moderate, high, and very-high risk. In cases where there was a discrepancy between the two cardiologists’ assessments, a third cardiologist was consulted to review the case and provide a final consensus judgment. For the calculated risk, SCORE2 and SCORE2-OP were used. The cardiovascular risk calculation using SCORE2 and SCORE2-OP was subsequently performed by a third cardiologist who was not involved in the initial risk perception assessment. This approach ensured independence between the subjective assessment and the objective calculation of CV risk. To maintain objectivity and minimize potential biases, the roles of initial risk assessment and risk calculation were clearly separated, with distinct cardiologists assigned to each task. SCORE2 was used for patients with 70 years or less. This tool estimates the 10-year risk of fatal or non-fatal CV events, categorizing patients into low-moderate, high, and very-high risk groups. SCORE2-OP was used for patients with more than70 years. Similar to SCORE2, it estimates the 10-year risk but is specifically tailored for older adults. The risk categories were determined based on the prevention guidelines provided by the European Society of Cardiology (ESC). The categories were defined as follows: low-to-moderate CV risk includes patients with a calculated risk of < 2.5% (< 50 years), < 5% (50–69 years), and < 7.5% (≥ 70 years). High CV risk includes patients with a calculated risk of 2.5 to < 7.5% (< 50 years), 5 to < 10% (50–69 years), and 7.5 to < 15% (≥ 70 years). Very-high CV risk includes patients with a calculated risk of ≥ 7.5% (< 50 years), ≥ 10% (50–69 years), and ≥ 15% (≥ 70 years) [1].

The calculated and perceived risks may coincide, and these patients will be categorized as “well-estimated.” If the perceived risk is lower than the calculated risk, patients will be categorized as “underestimated,” and if the perceived risk is higher than the calculated risk, patients will be categorized as “overestimated.”

Additionally, LDL-C values will be assessed to determine the percentage of patients achieving target levels recommended by the current ESC guidelines according to their respective calculated risk profiles. The LDL-C targets were defined according to the 2019 ESC/EAS guidelines for dyslipidemia management, with thresholds of < 116 mg/dL (3.0 mmol/L) for low risk, < 100 mg/dL (2.6 mmol/L) for moderate risk, < 70 mg/dL (1.8 mmol/L) for high risk, and < 55 mg/dL (1.4 mmol/L) for very high risk patients. The percentage of patients at target LDL-C levels will be compared between the “well-estimated” group, the “overestimated”, and the “underestimated” group to evaluate any significant differences.

Statistical Analysis

Continuous variables were presented as mean ± standard deviation (SD) or median with interquartile range (IQR). Comparisons between groups were conducted using analysis of variance (ANOVA) for normally distributed data or the Kruskal–Wallis test for non-normally distributed data. Categorical variables were expressed as frequencies and percentages, with comparisons made using the Pearson chi-square test. The concordance between perceived and calculated risks was evaluated using Cohen’s kappa coefficient. Missing data were managed with a complete case analysis approach, as the proportion of missing values was less than 1%.

Multivariate logistic regression analysis, adjusted for confounding variables, was performed to examine the influence of overestimation of CV risk and underestimation of CV risk on the variable LDL-C target to predict the achievement of the LDL-C target.

All statistical tests were two-sided, and a p-value of less than 0.05 was considered statistically significant. Statistical analyses were performed using Statistical Package for the Social Sciences software version 25 (SPSS, IBM^®, Armonk, New York).

Results

The study included 421 patients. Comprehensive data necessary for calculating risk scores was available for 389 patients, who constitute the study population. The baseline characteristics of patients are shown in Table 1. The study cohort had a mean age of 51.3 years (±12.59), with participants ranging from 40 to 89 years old, indicating a middle-aged to elderly cohort. The male sex accounted for 52.2% of the cases; the mean body mass index was 29.2 Kg/m², classifying the average participant as overweight, thus including individuals from normal weight to obese categories.

Table 1.

Baseline characteristics of patients

			Overall population (n = 389)
Age - yrs., mean (SD)			51.35 (12.59)
Male sex, n (%)			197 (50.6)
Body Mass Index, mean (SD)			29.3 (6.15)
Hypertension, n (%)			205 (52.7)
Smoking habit, n (%)			159 (40.8)
Familial history of ASCVD, n (%)			97 (24.9)
Total Cholesterol - mg/dL, mean (SD)			189.76 (42.08)
HDL-C - mg/dL, mean (SD)			49.63 (12.83)
LDL-C - mg/dL, mean (SD)			104.5(52.4)
Triglycerides - mg/dL, mean (SD)			153.16 (56.16)
Systolic Blood Pressure mmHg, mean (SD)			134.26 (23.39)
eGFR (CKD-EPI), mean (SD)			90.24 (15.3)
SCORE2, mean (SD)			13.77 (2.11)
SCORE2-OP, mean (SD)			16.1 (1.76)
Medication
Beta-blockers, n (%)			107 (27.5)
Ace-inhibitors/ARB, n (%)			151 (38.8)
Calcium channel blockers, n (%)			95 (24.4)
Antiplatelet therapy, n (%)			162 (41.6)
Statins, n (%)			168 (43.19)
Ezetimibe, n (%)			128 (32.9)
Bempedoic acid, n (%)			24 (6.17)

ARB = angiotensin receptor blockers; ASCVD = atherosclerotic cardiovascular disease; CKD-EPI = chronic kidney disease epidemiology collaboration; eGFR = estimated glomerular filtration rate; HDL-C = high density lipoprotein cholesterol; LDL-C = low density lipoprotein cholesterol; SCORE2 = systematic coronary risk evaluation 2; SCORE2-OP = systematic coronary risk evaluation 2 older persons; SD = standard deviation

The mean LDL-C was 104.5 mg/dL (±52.4 mg/dL). The estimated glomerular filtration rate, was 90.2 mL/min/1.73 m², indicating ed generally preserved kidney function. The CV risk scores, SCORE2 and SCORE2 OP had means of 13.77 (±2.11) and 16.1 (±1.76), respectively, highlighting a moderate to high risk of CV events.

Using stratification based on clinical judgment, physician-perceived risk classified patients into low/moderate CV risk in 8.7% of cases, high CV risk in 37.8%, and very-high CV risk in 53.5% of cases, while calculating risk with scores, this was low/moderate in 8% of cases, high in 5.7%, and very high in 86.4% of cases; with a statistically significant difference and an underestimation of very-high risk patients who were classified as high (p < 0.001) (Fig. 1). In Table 2, we report the prevalences of patients transitioning between cardiovascular risk categories when comparing physician-perceived risk and calculated risk using the two methods analyzed, providing a detailed understanding of the reclassification patterns in the study population.

Fig. 1.

Difference in cardiovascular risk stratification using physician perception and SCORE2/SCORE2-OP tools. CV = Cardiovascular; SCORE2 = Systematic COronary Risk Evaluation 2); SCORE2-OP = SCORE2-Older Persons

Table 2.

Agreement of CV risk stratification according to the perceived and calculated risk

	Cardiovascular Risk	Calculated risk
		Low to Moderate (n = 31)	High (n = 22)	Very-High (n = 336)
Perceived Rrisk	Low to Moderate (n = 34)	27 (87.1%)	7 (31.8%)	0 (0%)
	High (n = 147)	4 (12.9%)	6 (27.3%)	137 (40.8%)
	Very-High (n = 208)	0 (0%)	9 (40.9%)	199 (59.2%)

A total of 232 patients (59.7%) received correct risk stratification comparing perceived and calculated CV risk and was defined as well-estimated; at variance, 3.3% were overestimated, and 144 patients (37%) were underestimated.

Underestimated patients reach the LDL-C target in 16% of cases, 34.5% in the well-estimated group, and 76.9% in the overestimated group (Fig. 2). Patients whose risk was underestimated received statins in 29.2% of cases, well-estimated patients in 50%, and overestimated patients in 76.9% (p < 0.001); they received statin/ezetimibe association in 11.8%, 43.5% and 76.9%, respectively (p > 0.001). Among those reaching the LDL-C target, ezetimibe alone or in combination with statins was taken in 61.1% and 49.6%, respectively (p < 0.001) (Fig. 3).

Fig. 2.

LDL-C target achievement according to cardiovascular risk stratification. CV = Cardiovascular; LDL-C = Low-density lipoprotein cholesterol; LLT = lipid-lowering therapy

Fig. 3.

LLT according to estimated CV risk. LLT = Lipid lowering therapy; CV = Cardiovascular

The agreement between perceived and calculated CV risk using Cohen’s Kappa statistic was 0.208 (p < 0.001). These results suggest that while the agreement is statistically significant, the level of agreement is only slight.

The multivariate logistic regression analysis indicated that the use of statins, statin/ezetimibe association, and bempedoic acid were significant positive predictors of achieving the LDL-C target. Using well-estimated patients as a reference, patients whose risk was overestimated were more likely to reach the recommended LDL-C target [odds ratio (OR) 5.33; CI 1.33–21.42, p = 0.018], while patients whose risk was underestimated were 47% less likely to reach the LDL-C target (OR 0.53; CI 0.3–0.93, p = 0.027) (Table 3).

Table 3.

Multiple linear regression to predict achievement of the LDL-C target

					Odds Ratio		95% C. I.		p-value
Age					0.98		0.95–1.02		0.062
Sex, male					0.78		0.48–1.26		0.307
Statins therapy					2.48		1.5–4.1		< 0.001
Ezetimibe therapy					1.84		1.1–3.08		0.019
Bempedoic Acid therapy					3.81		1.49–9.77		0.005
Well-estimation					1		Ref.		Ref.
Overestimation					5.33		1.33–21.42		< 0.018
Underestimation					0.53		0.3–0.93		0.027

LDL-C = low-density lipoprotein cholesterol

Discussion

The main findings of the current analysis can be summarized as follows (Graphical Abstract). First, most patients with very-high CV risk in the primary prevention setting are misclassified and their risk is underestimated. In fact, in 37% of cases, CV physician-perceived CV risk was underestimated, and only in 59.6% of cases there was proper risk stratification based on clinical judgment. This was confirmed by Cohen’s Kappa statistic that indicated poor agreement between perceived risk and calculated CV risk using validated tools. Second, patients whose risk is underestimated achieved less often the recommended LDL-C target and were less treated with lipid-lowering therapy. Third, in the multivariate analysis, underestimation of CV risk emerged as a significant negative predictor of achieving the LDL-C target, resulting in a half chance of achieving it.

The present study demonstrates a significant discrepancy between physician-perceived CV risk and the risk calculated using standardized scoring systems. Our findings reveal a significant underestimation of very-high risk patients, often categorized as high-risk based on clinical judgment. This misclassification is evident in the low measure of agreement between perceived and calculated risk, as indicated by a Cohen’s Kappa statistic of 0.208, suggesting only slight agreement. In primary prevention practice, physicians may rely too often on their own experience and intuition rather than formal risk assessment tools. This can lead to a subjective bias, where patients who do not outwardly exhibit severe symptoms may be perceived as low risk [10, 11]. In busy clinical settings, the time required to perform detailed risk assessments using tools like SCORE2 or SCORE2 OP may be seen as impractical. Consequently, quick judgments based on superficial assessments can prevail. Studies have shown that time pressure significantly influences clinical decision-making, leading to a reliance on heuristics and rapid assessments rather than thorough evaluations. This can compromise adherence to clinical guidelines and the thoroughness of patient care​ [12]. Most of the studies have focused on the gap in patients’ perception of their CV risk, rather than on misclassification by the physician. Grauman et al.[13] showed that individuals with very good general health (OR 2.60, 95% CI 1.10–6.16) and no family history of myocardial infarction (OR 2.27, 95% CI 1.24–4.18) are significantly more likely to underestimate their CV risk, with an especially pronounced effect among those with a high calculated risk (without family history OR 22.57, 95% CI 6.17–82.54; with very good/excellent health OR 15.78, 95% CI 3.73–66.87)[13]. This underestimation, influenced by a false sense of security due to perceived good health, can hinder the effective prevention and management of CVD. Navar et al.[14] examined the gap between patient-perceived and actual 10-year atherosclerotic CVD risk and its effect on willingness for preventive therapy in 4187 patients. There was no correlation between perceived and calculated risk (ρ=−0.01, p = 0.46), with 72.2% of patients overestimating their risk (mean perceived 33.3% vs. mean calculated 17.1%, p < 0.01). Patients had an overly optimistic view of their risk relative to peers. This misperception influenced willingness for future prevention therapy (p < 0.01) but not current statin use (p = 0.18). The perceived relative risk increased statin use (risk ratio 1.04 [95% CI, 1.02–1.06]) and willingness for prevention therapy (risk ratio 1.11, 95% CI 1.07–1.16) [14].

The implications of misalignment between physicians’ perception and calculated risk are profound. Underestimation of CV risk can lead to insufficient preventive measures and therapeutic interventions, potentially worsening patient prognosis. Conversely, overestimation may lead to unnecessary treatments, exposing patients to unwarranted side effects and increased healthcare costs. The underestimation of CV risk by clinicians may have critical implications for patient management, particularly in the implementation of aggressive lipid-lowering strategies. Our data indicate that 37% of patients were underestimated regarding their CV risk, while only 3.3% were overestimated. This discrepancy underscores a potential gap in clinical practice, where reliance on clinical judgment alone may not suffice for accurate risk stratification. Notably, only 16% of patients whose CV risk was underestimated reached their LDL-C target, compared to 34.5% in the well-estimated group and 76.9% in the overestimated group. This underachievement among underestimated patients correlates with a lower prescription rate of statins (29.2%) and statin/ezetimibe combination therapy (11.8%), compared to well-estimated patients (50% and 43.5%, respectively) and overestimated patients (76.9% for both therapies). Given that underestimation of risk was independently associated with a 47% lower likelihood of reaching LDL-C targets, it is imperative to enhance the accuracy of risk assessment tools utilized in clinical settings. Multivariate logistic regression analysis identified the use of statins, statin/ezetimibe combination, and bempedoic acid as significant positive predictors for achieving LDL-C targets. This finding aligns with existing literature that emphasizes the efficacy of these agents in lipid management and CV risk reduction [15]. Patients whose CV risk was overestimated were more likely to achieve the LDL-C target (OR 5.33, CI 1.33–21.42, p = 0.018), suggesting that a conservative approach to risk estimation may inadvertently benefit patient outcomes by prompting more aggressive treatment measures.

Regarding the control of LDL-C in primary prevention, the EURIKA study, which included patients with at least one major CV risk factor, found that only 41.2% of dyslipidemic patients achieved LDL-C levels below 115 mg/dL [16]. Similarly, the primary care arm of EUROASPIRE V reported that only 46.9% of dyslipidemic patients reached adequate LDL-C control [17].

These results are consistent with previous studies that have highlighted the limitations of clinical judgment in risk stratification. Rossello et al. [18], in a report from the ESC Prevention of CVD Programme, emphasized the superior predictive accuracy of scoring systems compared to clinical judgment alone. Despite European guidelines recommending their use, these tools are underutilized in clinical practice. Different risk algorithms are available for various patient populations, and U-prevent.com is the only tool providing comprehensive algorithms for all categories, endorsed by the European Association of Preventive Cardiology. A recent study, by Landolfo et al.[19], have demonstrated the substantial reclassification of cardiovascular risk when employing the updated SCORE2/SCORE2-OP algorithms, particularly in populations referred for hypertension management. In their cohort of 1,539 patients, the proportion classified as low-to-moderate risk decreased from 55 to 23%, while high-risk patients increased from 20 to 38%, and very high-risk patients rose from 25 to 37%. This reclassification was accompanied by a significant reduction in the proportion of patients achieving LDL-C targets, with control rates dropping from 26 to 20% when using the Friedewald formula and to as low as 18% with the Martin and Sampson equations [19]. These findings underscore the importance of using contemporary CV risk assessment tools to refine LDL-C targets and optimize preventive strategies. Our results align with these observations, reinforcing the need for accurate CV risk stratification to guide therapeutic decisions and improve patient outcomes.

It should also be emphasized that in our study, we referred to SCORE2 and SCORE2-OP, while the risk categories referenced in the LDL-C targets of the 2019 European dyslipidemia guidelines used SCORE. However, there seems to be a slight difference in risk stratification between previous and current tools. A cross-sectional study included 85,802 patients in Hungary, and CV risk levels were determined using the SCORE and SCORE2 risk estimation methods. Using the SCORE2 method instead of SCORE, 43.91% of the population was classified at a higher risk level, significantly increasing the number of patients identified as having high or very high CV risk [9].

The slight agreement observed between perceived and calculated risk in our study emphasizes the need for enhanced integration of validated risk assessment tools in routine clinical practice. While clinical judgment remains invaluable, its complementarity with evidence-based risk scores can provide a more robust framework for managing patients at risk of cardiovascular events. Future research should focus on strategies to bridge this gap, including continued education for healthcare professionals on the utility of risk scores and the potential benefits of more nuanced risk stratification.

Traditional risk factors such as hypertension and hyperlipidemia are well recognized. However, emerging risk factors like chronic inflammation, psychosocial stress, and socioeconomic status might be underappreciated, leading to an incomplete risk assessment. In addition, current stratification systems do not capture factors such as obesity, now a recognized CV risk factor, and Lp(a) levels and familiar history of CV disease [20–22].

The use of tools such as SCORE2 and SCORE2-OP in clinical practice presents several challenges for clinicians. These calculators require not only a solid understanding of their methodology but also efficient integration into daily workflows. However, time constraints during consultations, a lack of familiarity with the tools, or even limited access to these resources can significantly hinder their use.

To overcome these barriers and improve adherence to CV risk calculators, several measures can be implemented: (i) continuous medical education programs should emphasize the importance of using validated risk assessment tools like SCORE2 and SCORE2-OP, training on the integration and interpretation of these tools within clinical practice is crucial; (ii) embedding risk calculators within electronic health records systems can streamline the process of risk assessment, making it more accessible and time-efficient for physicians; (iii) engaging a team of healthcare professionals, including nurses, dietitians, and pharmacists, can provide a comprehensive approach to risk assessment and management; (iv) increasing awareness of the impact of non-traditional risk factors on CV health can lead to more comprehensive risk evaluations; (v) educating patients about their own CV risk and involving them in the decision-making process can enhance adherence to preventive measures and interventions. Another promising approach involves leveraging automation and artificial intelligence (AI). By integrating AI systems into clinical workflows, these tools could automatically analyze patient data and provide accurate, personalized risk stratification. This would significantly reduce the cognitive and time burden on clinicians while ensuring consistency in risk assessment. Adapting risk calculators to better reflect local populations and contexts is another area worth exploring. While tools like SCORE2 are designed for European populations, they may not accurately capture CV risk in specific ethnic or regional groups. Tailoring these algorithms to the demographic and clinical characteristics of local populations could improve their applicability and reliability. The inclusion of emerging risk factors in future iterations of risk calculators could also make these tools more comprehensive. Factors such as chronic inflammation, psychosocial stress, and glycemic variability are increasingly recognized as important contributors to CV risk but are not currently accounted for in many tools. Finally, scientific societies could play a pivotal role by actively promoting and standardizing the use of CV risk calculators. Through awareness campaigns, development of accessible guidelines, and regular updates to tools, these organizations can help bridge the gap between evidence and practice.

By addressing these challenges through systemic changes and fostering both clinician and patient engagement, the adoption of CV risk calculators in routine practice could become more widespread, ultimately improving the precision of CV risk stratification and the effectiveness of preventive measures.

Limitations

A limitation of our study is its monocentric design, which may restrict the generalizability of the findings. While this design ensured standardized data collection and assessment procedures, a multicentric approach would have provided greater robustness and external validity by incorporating diverse patient populations and clinical practices. Additionally, the cross-sectional nature of the study limits our ability to establish causal relationships between physician-perceived cardiovascular (CV) risk and clinical outcomes, such as LDL-C target achievement. Furthermore, potential unmeasured confounders, such as socioeconomic status, lifestyle factors, and adherence to prescribed therapies, may have influenced the results but were not fully accounted for in our analysis. A notable proportion of the study population (41.6%) was on antiplatelet therapy, which may reflect local prescribing practices rather than adherence to current guideline recommendations for primary prevention. This could be due to historical prescribing patterns and influenced by a reluctance to discontinue existing therapies initiated prior to referral. While these practices may introduce a potential bias, they underscore the importance of aligning treatment strategies with evidence-based guidelines. Future multicentric and prospective studies are already being planned to address these limitations, enabling the inclusion of a broader spectrum of clinical settings and patient characteristics while exploring causal relationships in greater depth.

Conclusions

This study highlights a significant discrepancy between physician-perceived and calculated CV risk using SCORE2 and SCORE2-OP tools, with more than one-third of patients who are underestimated. Underestimation of CV risk is associated with a significantly lower likelihood of achieving LDL-C targets, emphasizing the importance of accurate risk stratification. Accurate CV risk assessment is a cornerstone of effective CVD prevention and management. The use of standardized risk scores should be encouraged to complement clinical evaluation, potentially improving patient outcomes through better-targeted therapies. Further investigation into the causes of these discrepancies and interventions to mitigate them will be crucial in optimizing CV care.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

Author contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Arturo Cesaro; Vincenzo Acerbo; Francesco Scialla; Enrica Golia; Claudia Concilio; Gianmaria Scherillo; Gianantonio De Michele. The first draft of the manuscript was written by Arturo Cesaro and Paolo Calabrò and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

Fund

Open access funding provided by Università degli Studi della Campania Luigi Vanvitelli within the CRUI-CARE Agreement.

No funds, grants, or other support was received.

Declarations

Conflict of interest

All authors declare no conflict of interest. The authors have no relevant financial or non-financial interests to disclose.

Human and animal rights

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The figures are created in https://BioRender.com