Fundamental Statistical Concepts in Clinical Trials and Diagnostic Testing

The Value of Exercise Electrocardiography in Outpatients with Stable Chest Pain and Low Pre-Test Probability of Significant Coronary Artery Disease

The role of exercise electrocardiography (ECG) in the investigation of stable chest pain has been questioned. The American Heart Association guidelines suggest the use of exercise ECG in patients with stable chest pain and low pre-test probability (PTP) of significant coronary artery disease, while the European Society of Cardiology Guidelines does not. This retrospective observational study aimed to assess the usefulness of exercise ECG in the low-PTP population with stable chest pain. We reviewed the medical records for all outpatient exercise ECGs conducted because of stable chest pain at the Department of Medicine and Emergency, Sahlgrenska University Hospital, Mölndal, Sweden, during 2016-2018. The identified patients were categorized in low-, intermediate-, or high-risk pre-test probability of significant coronary artery disease. All low-PTP patients were followed for one year post investigation for the incidence of acute coronary syndrome and all-cause mortality. Thus, 505 patients (mean age 60 years, 56% women) with low PTP were included in the study. Only four patients (0.6%) experienced incident myocardial infarction (three patients) or all-cause mortality (one patient). The negative predictive value of exercise ECG was 99.7%, and the positive predictive value was 28.6%. In this low-PTP population, exercise ECG yields a good negative predictive value and a poor positive predictive value.

Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population

Background

Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE.

Methods

The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model.

Results

A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736–0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA.

Conclusion

Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram.