Multivariate modeling to identify patterns in clinical data: the example of chest pain

<p>Abstract</p> <p>Background</p> <p>In chest pain, physicians are confronted with numerous interrelationships between symptoms and with evidence for or against classifying a patient into different diagnostic categories. The aim of our study was to find natural groups of patients on the basis of risk factors, history and clinical examination data which should then be validated with patients' final diagnoses.</p> <p>Methods</p> <p>We conducted a cross-sectional diagnostic study in 74 primary care practices to establish the validity of symptoms and findings for the diagnosis of coronary heart disease. A total of 1199 patients above age 35 presenting with chest pain were included in the study. General practitioners took a standardized history and performed a physical examination. They also recorded their preliminary diagnoses, investigations and management related to the patient's chest pain. We used multiple correspondence analysis (MCA) to examine associations on variable level, and multidimensional scaling (MDS), k-means and fuzzy cluster analyses to search for subgroups on patient level. We further used heatmaps to graphically illustrate the results.</p> <p>Results</p> <p>A multiple correspondence analysis supported our data collection strategy on variable level. Six factors emerged from this analysis: „chest wall syndrome“, „vital threat“, „stomach and bowel pain“, „angina pectoris“, „chest infection syndrome“, and „ self-limiting chest pain“. MDS, k-means and fuzzy cluster analysis on patient level were not able to find distinct groups. The resulting cluster solutions were not interpretable and had insufficient statistical quality criteria.</p> <p>Conclusions</p> <p>Chest pain is a heterogeneous clinical category with no coherent associations between signs and symptoms on patient level.</p

Gender bias revisited: new insights on the differential management of chest pain

<p>Abstract</p> <p>Background</p> <p>Chest pain is a common complaint and reason for consultation in primary care. Few data exist from a primary care setting whether male patients are treated differently than female patients. We examined whether there are gender differences in general physicians' (GPs) initial assessment and subsequent management of patients with chest pain, and how these differences can be explained</p> <p>Methods</p> <p>We conducted a prospective study with 1212 consecutive chest pain patients. The study was conducted in 74 primary care offices in Germany from October 2005 to July 2006. After a follow up period of 6 months, an independent interdisciplinary reference panel reviewed clinical data of every patient and decided about the etiology of chest pain at the time of patient recruitment (delayed type-reference standard). We adjusted gender differences of six process indicators for different models.</p> <p>Results</p> <p>GPs tended to assume that CHD is the cause of chest pain more often in male patients and referred more men for an exercise test (women 4.1%, men 7.3%, p = 0.02) and to the hospital (women 2.9%, men 6.6%, p < 0.01). These differences remained when adjusting for age and cardiac risk factors but ceased to exist after adjusting for the typicality of chest pain.</p> <p>Conclusions</p> <p>While observed gender differences can not be explained by differences in age, CHD prevalence, and underlying risk factors, the less typical symptom presentation in women might be an underlying factor. However this does not seem to result in suboptimal management in women but rather in overuse of services for men. We consider our conclusions rather hypothesis generating and larger studies will be necessary to prove our proposed model.</p

Ruling out coronary heart disease in primary care patients with chest pain: a clinical prediction score

Chest pain raises concern for the possibility of coronary heart disease. Scoring methods have been developed to identify coronary heart disease in emergency settings, but not in primary care. Data were collected from a multicenter Swiss clinical cohort study including 672 consecutive patients with chest pain, who had visited one of 59 family practitioners' offices. Using delayed diagnosis we derived a prediction rule to rule out coronary heart disease by means of a logistic regression model. Known cardiovascular risk factors, pain characteristics, and physical signs associated with coronary heart disease were explored to develop a clinical score. Patients diagnosed with angina or acute myocardial infarction within the year following their initial visit comprised the coronary heart disease group. The coronary heart disease score was derived from eight variables: age, gender, duration of chest pain from 1 to 60 minutes, substernal chest pain location, pain increasing with exertion, absence of tenderness point at palpation, cardiovascular risks factors, and personal history of cardiovascular disease. Area under the receiver operating characteristics curve was of 0.95 with a 95% confidence interval of 0.92; 0.97. From this score, 413 patients were considered as low risk for values of percentile 5 of the coronary heart disease patients. Internal validity was confirmed by bootstrapping. External validation using data from a German cohort (Marburg, n = 774) revealed a receiver operating characteristics curve of 0.75 (95% confidence interval, 0.72; 0.81) with a sensitivity of 85.6% and a specificity of 47.2%. This score, based only on history and physical examination, is a complementary tool for ruling out coronary heart disease in primary care patients complaining of chest pain

Early identification of first-year students at risk of dropping out of high-school entry medical school: the usefulness of teachers' ratings of class participation

Dropping out from undergraduate medical education is costly for students, medical schools, and society in general. Therefore, the early identification of potential dropout students is important. The contribution of personal features to dropout rates has merited exploration. However, there is a paucity of research on aspects of student experience that may lead to dropping out. In this study, underpinned by theoretical models of student commitment, involvement, and engagement, we explored the hypothesis of using inferior participation as an indicator of a higher probability of dropping out in year 1. Class participation was calculated as an aggregate score based on teachers' daily observations in class. The study used a longitudinal dataset of six cohorts of high-school entry students (N = 709, 67% females) in one medical school with an annual intake of 120 students. The findings confirmed the initial hypothesis and showed that lower scores of class participation in year 1 added predictive ability to pre-entry characteristics (Pseudo-R2 raised from 0.22 to 0.28). Even though the inclusion of course failure in year 1 resulted in higher explanatory power than participation in class (Pseudo-R2 raised from 0.28 to 0.63), ratings of class participation may be advantageous to anticipate dropout identification, as those can be collected prior to course failure. The implications for practice are that teachers' ratings of class participation can play a role in indicating medical students who may eventually drop out. We conclude that the scores of class participation can contribute to flagging systems for the early detection of student dropouts.(undefined)info:eu-repo/semantics/acceptedVersio

Gender differences in presentation and diagnosis of chest pain in primary care

<p>Abstract</p> <p>Background</p> <p>Chest pain is a common complaint and reason for consultation in primary care. Research related to gender differences in regard to Coronary Heart Disease (CHD) has been mainly conducted in hospital but not in primary care settings. We aimed to analyse gender differences in aetiology and clinical characteristics of chest pain and to provide gender related symptoms and signs associated with CHD.</p> <p>Methods</p> <p>We included 1212 consecutive patients with chest pain aged 35 years and older attending 74 general practitioners (GPs). GPs recorded symptoms and findings of each patient and provided follow up information. An independent interdisciplinary reference panel reviewed clinical data of every patient and decided about the aetiology of chest pain at the time of patient recruitment. Multivariable regression analysis was performed to identify clinical predictors that help to rule in or out CHD in women and men.</p> <p>Results</p> <p>Women showed more psychogenic disorders (women 11,2%, men 7.3%, p = 0.02), men suffered more from CHD (women 13.0%, men 17.2%, p = 0.04), trauma (women 1.8%, men 5.1%, p < 0.001) and pneumonia/pleurisy (women 1.3%, men 3.0%, p = 0.04) Men showed significantly more often chest pain localised on the right side of the chest (women 9.1%, men 25.0%, p = 0.01). For both genders known clinical vascular disease, pain worse with exercise and age were associated positively with CHD. In women pain duration above one hour was associated positively with CHD, while shorter pain durations showed an association with CHD in men. In women negative associations were found for stinging pain and in men for pain depending on inspiration and localised muscle tension.</p> <p>Conclusions</p> <p>We found gender differences in regard to aetiology, selected clinical characteristics and association of symptoms and signs with CHD in patients presenting with chest pain in a primary care setting. Further research is necessary to elucidate whether these differences would support recommendations for different diagnostic approaches for CHD according to a patient's gender.</p