Dataset Information

Depression and cardiovascular risk in primary care patients.

ABSTRACT:

Objective

This study assessed the relationship of both depression diagnosis and clinically significant depressive symptoms with individual cardiovascular risk factors and estimated total cardiovascular risk in primary care patients.

Methods

This study used a cross-sectional and retrospective design. Patients who had a primary care encounter between January 2016 and September 2018 and completed depression screening (PHQ-9) during the year prior to their appointment (N = 70,980) were included in this study. Data examining estimated total cardiovascular risk, specific cardiovascular risk factors, and relevant clinical diagnoses (including depression diagnosis) were extracted from the electronic health record. Patients were categorized into three groups: no depression (PHQ-9 < 10 and no depression diagnosis), controlled depression (PHQ-9 < 10 with previous depression diagnosis), and current depression (PHQ-9 ≥ 10). Groups were compared on estimated total risk and specific cardiovascular risk factors (e.g., body mass index [BMI], smoking status, lipids, blood pressure, and glucose).

Results

In adjusted analyses, patients with current depression (n = 18,267) demonstrated significantly higher 10-year and 30-year cardiovascular risk compared to patients with controlled depression (n = 33,383; 10-year: b = 0.59 [95% CI = 0.44,0.74]; 30-year: OR = 1.32 [95% CI = 1.26,1.39]) and patients without depression (n = 19,330; 10-year: b = 0.55 [95% CI = 0.37,0.73]; 30-year: OR = 1.56 [95% CI = 1.48,1.65]). Except for low-density lipoprotein (LDL), patients with current depression had the greatest cardiovascular risk across specific risk factors.

Conclusions

Individuals who had a depression diagnosis and clinically significant depressive symptoms had the greatest cardiovascular risk. Pathways to prevent cardiovascular disease in those with depression might focus on treating depressive symptoms as well as specific uncontrolled cardiovascular risk factors.

SUBMITTER: Hooker SA

PROVIDER: S-EPMC9237849 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Publications

Depression and cardiovascular risk in primary care patients.

Hooker Stephanie A SA O'Connor Patrick J PJ Sperl-Hillen JoAnn M JM Crain A Lauren AL Ohnsorg Kris K Kane Sheryl S Rossom Rebecca R

Journal of psychosomatic research 20220419

<h4>Objective</h4>This study assessed the relationship of both depression diagnosis and clinically significant depressive symptoms with individual cardiovascular risk factors and estimated total cardiovascular risk in primary care patients.<h4>Methods</h4>This study used a cross-sectional and retrospective design. Patients who had a primary care encounter between January 2016 and September 2018 and completed depression screening (PHQ-9) during the year prior to their appointment (N = 70,980) wer ...[more]

PMID: 35461074

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Project description:Objective Accurate cardiovascular risk estimations by patients and doctors are important as these affect health behaviour and medical decision making. We aimed to determine if doctors and patients were accurately estimating the absolute cardiovascular risk of patients in primary care. Methods A cross-sectional study was carried out in primary care clinics in Malaysia in 2014. Patients aged 35 years and above without known cardiovascular disease (CVDs) were included. Face-to-face interviews with a structured questionnaire were used to collect sociodemographic and clinical data as well as patients’ perception and doctors’ estimate of the patients’ CVD risk. Associations were tested using χ2, correlation and independent t-tests. Results We recruited 1094 patients and 57 doctors. Using the Framingham Risk Score (FRS) alone, 508 patients (46.4%) were in the high-risk group. When diabetes was included as high risk, the number increased to 776 (70.9%). Only 34.4% of patients and 55.7% of doctors correctly estimated the patient’s CVD risk in comparison with the reference FRS. Of the high-risk patients, 664 (85.6%) underestimated their CV risk. Factors associated with underestimation by patients included not having family history of CVD (adjusted OR (AOR): 2.705, 95% CI 1.538 to 4.757), smaller waist circumference (AOR: 0.979,95% CI 0.960 to 0.999) and ethnicity in comparison with the Malay as reference group (indigenous/others: AOR: 0.129, 95% CI 0.071 to 0.235). Doctors underestimated risk in 59.8% of the high-risk group. Factors associated with underestimation by doctors were patients factors such as being female (AOR: 2.232, 95% CI 1.460 to 3.410), younger age (AOR: 0.908, 95% CI 0.886 to 0.930), non-hypertensive (AOR: 1.731, 95% CI 1.067 to 2.808), non-diabetic (AOR: 1.931, 95% CI 1.114 to 3.348), higher high-density lipoprotein levels (AOR: 3.546, 95% CI 2.025 to 6.209), lower systolic blood pressure (AOR: 0.970, 95% CI 0.957 to 0.982), non-smoker (AOR: 2.246, 95% CI 1.354 to 3.726) and ethnicity in comparison with the Malay as reference group (Indian: AOR: 0.430, 95% CI 0.257 to 0.720; indigenous/others: AOR: 2.498, 95% CI 1.346 to 4.636). Conclusions The majority of consultations occurring between doctors and patients are being informed by inaccurate cardiovascular risk estimation.

Project description:BackgroundCardiovascular disease (CVD) preventive medications are recommended for patients at high short-term CVD risk. As most younger people with multiple raised CVD risk factors levels have low short-term risk, they could be falsely reassured to take no action. Heart age-the chronological age of a hypothetical person with the same short-term absolute CVD risk as the patient being assessed, but with an 'ideal' risk profile-is a complementary relative CVD risk metric developed to encourage these younger patients to make long-term lifestyle changes. However, clinicians sometimes use heart age to inform medication decisions. We assessed the appropriateness of this practice by comparing heart age and short-term CVD risk.MethodsNew Zealand primary care patients are recruited to the PREDICT cohort when their CVD risk is assessed. PREDICT is an ongoing prospective study in one-third of New Zealand general practices, designed to derive CVD risk prediction algorithms. Five-year CVD risk was calculated for 35-74-year-old PREDICT participants using published equations. Heart age was calculated using non-smoking, systolic blood pressure of 120 mm Hg and total cholesterol/high-density lipoprotein ratio of 3.5, as the 'ideal' risk profile. CVD risk and heart age gaps (difference between chronological age and heart age) were compared.ResultsAmong 371 676 PREDICT participants, 5-year CVD risk increased with age, approximately doubling every 10 years, whereas heart age gaps decreased with increasing age, approximately halving between 35 and 44-year olds and 65-74-year olds. There were 5-40-year heart age gap differences between groups with similar 5-year CVD risks, but different ages.ConclusionShort-term CVD risk and heart age are not interchangeable risk metrics. Short-term risk increases with increasing age whereas heart age gaps generally decline, with major differences between younger and older people with similar short-term risk. If heart age is used to inform medication decisions rather than encourage long-term lifestyle changes, older people at high short-term risk could be undertreated and younger people at low short-term risk could be unnecessarily medicated.

Dataset Information

Depression and cardiovascular risk in primary care patients.

Objective

Methods

Results

Conclusions

Publications

Depression and cardiovascular risk in primary care patients.

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