Coronary artery disease prediction in women and men using chest pain characteristics and risk factors: an observational study in outpatient clinics

Determining factors of service priority in the risk classification of patients with chest pain

OBJECTIVE

To verify the determining factors of high priority in the risk classification and the outcomes of the care provided to adult patients with chest pain.

METHOD

Retrospective study, conducted at an emergency referral service of a public hospital in the interior of São Paulo State, analyzing the records of care performed in the risk classification in 181 medical records of patients with the symptom of chest pain, between August and November 2020.

RESULTS

Individuals were most males 98 (54.1%), with moderate pain 133 (73.5), high priority for care 135 (74.5%) and who sought the service spontaneously 139 (76.8%). Of those classified as high priority, 47 (34.8%) were referred to the emergency room and, of these, 27 (17.0%) remained hospitalized. Female gender (p=0.0198; OR=0.40; CI=0.189-0.866) was independently associated with high priority of care.

CONCLUSION

Despite the priority classification, few participants were referred to the emergency room and required hospitalization. Female gender was a protective factor in the risk classification as high priority.

Moving from one state to another among patients experiencing unexplained chest pain during physical activity: A secondary qualitative analysis by Meleis transition theory

BACKGROUND

Unexplained chest pain is a common condition in medical settings. Nurses usually coordinate the rehabilitation of patients. Physical activity is recommended; however, it is one of the major avoidance behaviours in patients with coronary heart disease. There is a need for a deeper understanding of the transition that patients with unexplained chest pain undergo during physical activity.

AIM

To develop deeper understanding about experiences of transition in patients with unexplained chest pain during physical activity.

DESIGN

Secondary qualitative analysis of data from three exploratory studies.

METHOD

Meleis et al.'s transition theory was used as a framework for the secondary analysis.

FINDINGS

The transition was complex and multidimensional. The participants experienced personal processes of change toward health within the illness, corresponding to indicators of healthy transitions.

CONCLUSION

The process can be identified as a transition from an uncertain and often sick role to a healthy role. Knowledge regarding transition promotes a person-centred approach in which patients' perspectives are included. Nurses and other health professionals can better direct and plan the caring and rehabilitation of patients with unexplained chest pain by deepening their knowledge of the transition process based on physical activity.

Sex matters: the frequently overlooked importance of considering sex in computational models

Personalised medicine and the development of a virtual human or a digital twin comprises visions of the future of medicine. To realise these innovations, an understanding of the biology and physiology of all people are required if we wish to apply these technologies at a population level. Sex differences in health and biology is one aspect that has frequently been overlooked, with young white males being seen as the "average" human being. This has not been helped by the lack of inclusion of female cells and animals in biomedical research and preclinical studies or the historic exclusion, and still low in proportion, of women in clinical trials. However, there are many known differences in health between the sexes across all scales of biology which can manifest in differences in susceptibility to diseases, symptoms in a given disease, and outcomes to a given treatment. Neglecting these important differences in the development of any health technologies could lead to adverse outcomes for both males and females. Here we highlight just some of the sex differences in the cardio-respiratory systems with the goal of raising awareness that these differences exist. We discuss modelling studies that have considered sex differences and touch on how and when to create sex-specific models. Scientific studies should ensure sex differences are included right from the study planning phase and results reported using sex as a biological variable. Computational models must have sex-specific versions to ensure a movement towards personalised medicine is realised.

Improving the Prediction of Death from Cardiovascular Causes with Multiple Risk Markers

Background

Traditional risk factors including demographics, blood pressure, cholesterol, and diabetes status are successfully able to predict a proportion of cardiovascular disease (CVD) events. Whether including additional routinely measured factors improves CVD prediction is unclear. To determine whether a comprehensive risk factor list, including clinical blood measures, blood counts, anthropometric measures, and lifestyle factors, improves prediction of CVD deaths beyond traditional factors.

Methods

The analysis comprised of 21,982 participants aged 40 years and older (mean age=59.4 years at baseline) from the National Health and Nutrition Examination Survey (NHANES) from 2001 to 2016 survey cycles. Data were linked with the National Death Index mortality data through 2019 and split into 80:20 training and testing sets. Relative to the traditional risk factors (age, sex, race/ethnicity, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, antihypertensive medications, and diabetes), we compared models with an additional 22 clinical blood biomarkers, 20 complete blood counts, 7 anthropometric measures, 51 dietary factors, 13 cardiovascular health-related questions, and all 113 predictors together. To build prediction models for CVD mortality, we performed Cox proportional hazards regression, elastic-net (ENET) penalized Cox regression, and random survival forest, and compared classification using C-index and net reclassification improvement.

Results

During follow-up (median, 9.3 years), 3,075 participants died; 30.9% (1,372/3,075) deaths were from cardiovascular causes. In Cox proportional hazards models with traditional risk factors (C-index=0.850), CVD mortality classification improved with incorporation of clinical blood biomarkers (C-index=0.867), blood counts (C-index=0.861), and all predictors (C-index=0.871). Net CVD mortality reclassification improved 13.2% by adding clinical blood biomarkers and 12.2% by adding all predictors. Results for ENET-penalized Cox regression and random survival forest were similar. No improvement was observed in separate models for anthropometric measures, dietary nutrient intake, or cardiovascular health-related questions.

Conclusions

The addition of clinical blood biomarkers and blood counts substantially improves CVD mortality prediction, beyond traditional risk factors. These biomarkers may serve as an important clinical and public health screening tool for the prevention of CVD deaths.

Addressing persistent evidence gaps in cardiovascular sex differences research - the potential of clinical care data

Women have historically been underrepresented in cardiovascular clinical trials, resulting in a lack of sex-specific data. This is especially problematic in two situations, namely those where diseases manifest differently in women and men and those where biological differences between the sexes might affect the efficacy and/or safety of medication. There is therefore a pressing need for datasets with proper representation of women to address questions related to these situations. Clinical care data could fit this bill nicely because of their unique broad scope across both patient groups and clinical measures. This perspective piece presents the potential of clinical care data in sex differences research and discusses current challenges clinical care data-based research faces. It also suggests strategies to reduce the effect of these limitations, and explores whether clinical care data alone will be sufficient to close evidence gaps or whether a more comprehensive approach is needed.

Routine clinical care data from thirteen cardiac outpatient clinics: design of the Cardiology Centers of the Netherlands (CCN) database

BACKGROUND

Despite the increasing availability of clinical data due to the digitalisation of healthcare systems, data often remain inaccessible due to the diversity of data collection systems. In the Netherlands, Cardiology Centers of the Netherlands (CCN) introduced "one-stop shop" diagnostic clinics for patients suspected of cardiac disease by their general practitioner. All CCN clinics use the same data collection system and standardised protocol, creating a large regular care database. This database can be used to describe referral practices, evaluate risk factors for cardiovascular disease (CVD) in important patient subgroups, and develop prediction models for use in daily care.

CONSTRUCTION AND CONTENT

The current database contains data on all patients who underwent a cardiac workup in one of the 13 CCN clinics between 2007 and February 2018 (n = 109,151, 51.9% women). Data were pseudonymised and contain information on anthropometrics, cardiac symptoms, risk factors, comorbidities, cardiovascular and family history, standard blood laboratory measurements, transthoracic echocardiography, electrocardiography in rest and during exercise, and medication use. Clinical follow-up is based on medical need and consisted of either a repeat visit at CCN (43.8%) or referral for an external procedure in a hospital (16.5%). Passive follow-up via linkage to national mortality registers is available for 95% of the database.

UTILITY AND DISCUSSION

The CCN database provides a strong base for research into historically underrepresented patient groups due to the large number of patients and the lack of in- and exclusion criteria. It also enables the development of artificial intelligence-based decision support tools. Its contemporary nature allows for comparison of daily care with the current guidelines and protocols. Missing data is an inherent limitation, as the cardiologist could deviate from standardised protocols when clinically indicated.

CONCLUSION

The CCN database offers the opportunity to conduct research in a unique population referred from the general practitioner to the cardiologist for diagnostic workup. This, in combination with its large size, the representation of historically underrepresented patient groups and contemporary nature makes it a valuable tool for expanding our knowledge of cardiovascular diseases.

TRIAL REGISTRATION

Not applicable.