Jörg Haasenritter

Ruling out coronary artery disease in primary care: development and validation of a simple prediction rule

Background: Chest pain can be caused by various conditions, with life-threatening cardiac disease being of greatest concern. Prediction scores to rule out coronary artery disease have been developed for use in emergency settings. We developed and validated a simple prediction rule for use in primary care. Methods: We conducted a cross-sectional diagnostic study in 74 primary care practices in Germany. Primary care physicians recruited all consecutive patients who presented with chest pain (n = 1249) and recorded symptoms and findings for each patient (derivation cohort). An independent expert panel reviewed follow-up data obtained at six weeks and six months on symptoms, investigations, hospital admissions and medications to determine the presence or absence of coronary artery disease. Adjusted odds ratios of relevant variables were used to develop a prediction rule. We calculated measures of diagnostic accuracy for different cut-off values for the prediction scores using data derived from another prospective primary care study (validation cohort). Results: The prediction rule contained five determinants (age/sex, known vascular disease, patient assumes pain is of cardiac origin, pain is worse during exercise, and pain is not reproducible by palpation), with the score ranging from 0 to 5 points. The area under the curve (receiver operating characteristic curve) was 0.87 (95% confidence interval [CI] 0.83–0.91) for the derivation cohort and 0.90 (95% CI 0.87–0.93) for the validation cohort. The best overall discrimination was with a cut-off value of 3 (positive result 3–5 points; negative result ≤ 2 points), which had a sensitivity of 87.1% (95% CI 79.9%–94.2%) and a specificity of 80.8% (77.6%–83.9%). Interpretation: The prediction rule for coronary artery disease in primary care proved to be robust in the validation cohort. It can help to rule out coronary artery disease in patients presenting with chest pain in primary care.

Chest pain in primary care: Epidemiology and pre-work-up probabilities

Abstract Background/objective: Chest pain is a common complaint and reason for consultation. We aimed to study the epidemiology of chest pain with respect to underlying aetiologies and to establish pre-work-up probabilities for the primary care setting. Methods: 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 on the aetiology of chest pain at the time of patient recruitment. Results: The prevalence of chest pain among all attending patients was 0.7%. The majority (55.9%) of patients were women. Mean age was 59 (35–93) years. Of these patients, 53.2% had chest pains at the time of consultation and 29.6% presented with acute (<48 hours’ duration) chest pain. Pain originating from the chest wall was diagnosed in 46.6% of all patients, stable ischaemic heart disease (IHD) in 11.1%, and psychogenic disorders in 9.5%; 3.6% had acute coronary syndrome (ACS). Conclusion: The study adds important information about the epidemiology of chest pain as a frequent reason for consulting primary care practitioners. We provide updated pre-work-up probabilities for IHD for each age and sex category.

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

BackgroundChest 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.MethodsWe 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.ResultsWomen 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.ConclusionsWe 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 patients gender.

Accuracy of symptoms and signs for coronary heart disease assessed in primary care

BACKGROUND Diagnosing the aetiology of chest pain is challenging. There is still a lack of data on the diagnostic accuracy of signs and symptoms for acute coronary events in low-prevalence settings. AIM To evaluate the diagnostic accuracy of symptoms and signs in patients presenting to general practice with chest pain. DESIGN OF STUDY Cross-sectional diagnostic study with delayed-type reference standard. SETTING Seventy-four general practices in Germany. METHOD The study included 1249 consecutive patients presenting with chest pain. Data were reviewed by an independent reference panel, with coronary heart disease (CHD) and an indication for urgent hospital admission as reference conditions. Main outcome measures were sensitivity, specificity, likelihood ratio, predictive value, and odds ratio (OR) for non-trauma patients with a reference diagnosis. RESULTS Several signs and symptoms showed strong associations with CHD, including known vascular disease (OR = 5.13; 95% confidence interval [CI] = 2.83 to 9.30), pain worse on exercise (OR = 4.27; 95% CI = 2.31 to 7.88), patient assumes cardiac origin of pain (OR = 3.20; 95% CI = 1.53 to 6.60), cough present (OR = 0.08; 95% CI = 0.01 to 0.77), and pain reproducible on palpation (OR = 0.27; 95% CI = 0.13 to 0.56). For urgent hospital admission, effective criteria included pain radiating to the left arm (OR = 8.81; 95% CI = 2.58 to 30.05), known clinical vascular disease (OR = 7.50; 95% CI = 2.88 to 19.55), home visit requested (OR = 7.31; 95% CI = 2.27 to 23.57), and known heart failure (OR = 3.53; 95% CI = 1.14 to 10.96). CONCLUSION Although individual criteria were only moderately effective, in combination they can help to decide about further management of patients with chest pain in primary care.

Ruling out coronary heart disease in primary care: external validation of a clinical prediction rule

BACKGROUND The Marburg Heart Score (MHS) aims to assist GPs in safely ruling out coronary heart disease (CHD) in patients presenting with chest pain, and to guide management decisions. AIM To investigate the diagnostic accuracy of the MHS in an independent sample and to evaluate the generalisability to new patients. DESIGN AND SETTING Cross-sectional diagnostic study with delayed-type reference standard in general practice in Hesse, Germany. METHOD Fifty-six German GPs recruited 844 males and females aged ≥ 35 years, presenting between July 2009 and February 2010 with chest pain. Baseline data included the items of the MHS. Data on the subsequent course of chest pain, investigations, hospitalisations, and medication were collected over 6 months and were reviewed by an independent expert panel. CHD was the reference condition. Measures of diagnostic accuracy included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, likelihood ratios, and predictive values. RESULTS The AUC was 0.84 (95% confidence interval [CI] = 0.80 to 0.88). For a cut-off value of 3, the MHS showed a sensitivity of 89.1% (95% CI = 81.1% to 94.0%), a specificity of 63.5% (95% CI = 60.0% to 66.9%), a positive predictive value of 23.3% (95% CI = 19.2% to 28.0%), and a negative predictive value of 97.9% (95% CI = 96.2% to 98.9%). CONCLUSION Considering the diagnostic accuracy of the MHS, its generalisability, and ease of application, its use in clinical practice is recommended.

Chest wall syndrome in primary care patients with chest pain: presentation, associated features and diagnosis

BACKGROUND Chest wall syndrome (CWS) is the most frequent aetiology of chest pain in a primary care setting. OBJECTIVE The aims of the study are to describe the epidemiology, clinical characteristics and prognosis of CWS and to provide a simple decision rule for diagnosis. METHODS We included 1212 consecutive patients with chest pain aged 35 years and older attending 74 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 the diagnosis of CWS. RESULTS GPs diagnosed pain originating from the chest wall in 46.6% of all patients. In most patients, pain was localized retrosternal (52.0%) and/or on the left side (69.2%). In total, 28.0% of CWS patients showed persistent pain and most patients reported no temporal association of pain (72.3%). In total, 55.4% of patients still had chest pain after 6 months. A simple score containing four determinants (localized muscle tension, stinging pain, pain reproducible by palpation and absence of cough) shows an area under the receiver operating characteristic curve of 0.78 (95% confidence interval: 0.75-0.81). CONCLUSIONS This study broadens the knowledge about pain characteristics and the diagnostic accuracy of selected signs and symptoms for CWS. A simple four-point score can help the GP in the diagnostic workup of chest pain patients.

Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty

A proper representation of the uncertainty involved in a prediction is an important prerequisite for the acceptance of machine learning and decision support technology in safety-critical application domains such as medical diagnosis. Despite the existence of various probabilistic approaches in these fields, there is arguably no method that is able to distinguish between two very different sources of uncertainty: aleatoric uncertainty, which is due to statistical variability and effects that are inherently random, and epistemic uncertainty which is caused by a lack of knowledge. In this paper, we propose a method for binary classification that does not only produce a prediction of the class of a query instance but also a quantification of the two aforementioned sources of uncertainty. Despite being grounded in probability and statistics, the method is formalized within the framework of fuzzy preference relations. The usefulness and reasonableness of our approach is confirmed on a suitable data set with information about patients suffering from chest pain.

In-vivo-validation of a cardiovascular risk prediction tool: the arriba-pro study

BackgroundCalculation of individual risk is the cornerstone of effective cardiovascular prevention. arriba is a software to estimate the individual risk to suffer a cardiovascular event in 10 years. Prognosis and the absolute effects of pharmacological and lifestyle interventions help the patient make a well-informed decision. The risk calculation algorithm currently used in arriba is based on the Framingham risk algorithm calibrated to the German setting. The objective of this study is to evaluate and adapt the algorithm for the target population in primary care in Germany.Methods/designarriba-pro will be conducted within the primary care scheme provided by a large health care insurer in Baden-Württemberg, Germany. Patients who are counseled with arriba by their general practitioners (GPs) will be included in the arriba-pro cohort. Exposure data from the consultation with arriba such as demographic data and risk factors will be recorded automatically by the practice software and transferred to the study centre. Information on relevant prescription drugs (effect modifiers) and cardiovascular events (outcomes) will be derived from administrative sources.DiscussionThe study is unique in simulating a therapy naïve cohort, matching exactly research and application setting, using a robust administrative data base, and, finally, including patients with known cardiovascular disease who have been excluded from previous studies.Trial registrationThe study is registered with Deutsches Register Klinischer Studien (DRKS00004633).

Coronary heart disease in primary care: accuracy of medical history and physical findings in patients with chest pain – a study protocol for a systematic review with individual patient data

BackgroundChest pain is a common complaint in primary care, with coronary heart disease (CHD) being the most concerning of many potential causes. Systematic reviews on the sensitivity and specificity of symptoms and signs summarize the evidence about which of them are most useful in making a diagnosis. Previous meta-analyses are dominated by studies of patients referred to specialists. Moreover, as the analysis is typically based on study-level data, the statistical analyses in these reviews are limited while meta-analyses based on individual patient data can provide additional information. Our patient-level meta-analysis has three unique aims. First, we strive to determine the diagnostic accuracy of symptoms and signs for myocardial ischemia in primary care. Second, we investigate associations between study- or patient-level characteristics and measures of diagnostic accuracy. Third, we aim to validate existing clinical prediction rules for diagnosing myocardial ischemia in primary care. This article describes the methods of our study and six prospective studies of primary care patients with chest pain. Later articles will describe the main results.Methods/DesignWe will conduct a systematic review and IPD meta-analysis of studies evaluating the diagnostic accuracy of symptoms and signs for diagnosing coronary heart disease in primary care. We will perform bivariate analyses to determine the sensitivity, specificity and likelihood ratios of individual symptoms and signs and multivariate analyses to explore the diagnostic value of an optimal combination of all symptoms and signs based on all data of all studies. We will validate existing clinical prediction rules from each of the included studies by calculating measures of diagnostic accuracy separately by study.DiscussionOur study will face several methodological challenges. First, the number of studies will be limited. Second, the investigators of original studies defined some outcomes and predictors differently. Third, the studies did not collect the same standard clinical data set. Fourth, missing data, varying from partly missing to fully missing, will have to be dealt with.Despite these limitations, we aim to summarize the available evidence regarding the diagnostic accuracy of symptoms and signs for diagnosing CHD in patients presenting with chest pain in primary care.Review registrationCentre for Reviews and Dissemination (University of York): CRD42011001170

Development and validation of a clinical prediction rule for chest wall syndrome in primary care

BackgroundChest wall syndrome (CWS), the main cause of chest pain in primary care practice, is most often an exclusion diagnosis. We developed and evaluated a clinical prediction rule for CWS.MethodsData from a multicenter clinical cohort of consecutive primary care patients with chest pain were used (59 general practitioners, 672 patients). A final diagnosis was determined after 12 months of follow-up. We used the literature and bivariate analyses to identify candidate predictors, and multivariate logistic regression was used to develop a clinical prediction rule for CWS. We used data from a German cohort (n = 1212) for external validation.ResultsFrom bivariate analyses, we identified six variables characterizing CWS: thoracic pain (neither retrosternal nor oppressive), stabbing, well localized pain, no history of coronary heart disease, absence of general practitioner’s concern, and pain reproducible by palpation. This last variable accounted for 2 points in the clinical prediction rule, the others for 1 point each; the total score ranged from 0 to 7 points. The area under the receiver operating characteristic (ROC) curve was 0.80 (95% confidence interval 0.76-0.83) in the derivation cohort (specificity: 89%; sensitivity: 45%; cut-off set at 6 points). Among all patients presenting CWS (n = 284), 71% (n = 201) had a pain reproducible by palpation and 45% (n = 127) were correctly diagnosed. For a subset (n = 43) of these correctly classified CWS patients, 65 additional investigations (30 electrocardiograms, 16 thoracic radiographies, 10 laboratory tests, eight specialist referrals, one thoracic computed tomography) had been performed to achieve diagnosis. False positives (n = 41) included three patients with stable angina (1.8% of all positives). External validation revealed the ROC curve to be 0.76 (95% confidence interval 0.73-0.79) with a sensitivity of 22% and a specificity of 93%.ConclusionsThis CWS score offers a useful complement to the usual CWS exclusion diagnosing process. Indeed, for the 127 patients presenting CWS and correctly classified by our clinical prediction rule, 65 additional tests and exams could have been avoided. However, the reproduction of chest pain by palpation, the most important characteristic to diagnose CWS, is not pathognomonic.