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Background The relationship between neutrophils and outcomes post-MI is not completely characterized. We examined the associations of neutrophil count with mortality and post-MI heart failure (HF) and their incremental value for risk discrimination in the community. Methods and Results MI was diagnosed with cardiac pain, biomarkers, and Minnesota coding of the electrocardiogram. Neutrophil count at presentation, reported as counts ×109/L, was categorized by tertiles (lower tertile <5.7; middle tertile 5.7–8.5, and upper tertile >8.5). From 1979 to 2002, 2,047 incident MIs occurred in Olmsted County, MN (mean age 68±14 years, 44% women). Median (25th–75th percentile) neutrophil count was 7.0 (5.1–9.5). Within 3 years post-MI, 577 patients died and 770 developed HF. Overall survival and survival free of HF decreased with increased neutrophil tertile (p<0.001). Compared to the lower tertile, the age and sex adjusted hazard ratio (HR) for death was 1.44 (95% CI: 1.14–1.81) for the middle tertile and 2.60 (95% CI: 2.10–3.22) for the upper tertile (p<0.001). Similarly, for HF the HR was 1.32 (95%CI: 1.09–1.59) for the middle and 2.12 (95% CI: 1.77–2.53) for the upper tertile (p<0.001). These associations persisted after adjustment for risk factors, comorbidities, Killip class, revascularization, and ejection fraction. Neutrophil count improved risk discrimination as indicated by increases in the area under the receiver operating characteristic curves (all p<0.05) and by the integrated discrimination improvement analysis (all p<0.001). Conclusions In the community, the neutrophil count was strongly and independently associated with death and HF post-MI and improved risk discrimination over traditional predictors.

Although cardiac events occurred more frequently in men, the incremental value of exercise echocardiography was comparable in both genders. Of all exercise electrocardiographic and exercise echocardiographic variables, workload and exercise wall motion score index had the strongest association with outcome. The results of exercise echocardiography have comparable implications in both men and women.

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ObjectiveTo construct and validate billing code algorithms for identifying patients with peripheral arterial disease (PAD).MethodsWe extracted all encounters and line item details including PAD-related billing codes at Mayo Clinic Rochester, Minnesota, between July 1, 1997 and June 30, 2008; 22 712 patients evaluated in the vascular laboratory were divided into training and validation sets. Multiple logistic regression analysis was used to create an integer code score from the training dataset, and this was tested in the validation set. We applied a model-based code algorithm to patients evaluated in the vascular laboratory and compared this with a simpler algorithm (presence of at least one of the ICD-9 PAD codes 440.20–440.29). We also applied both algorithms to a community-based sample (n=4420), followed by a manual review.ResultsThe logistic regression model performed well in both training and validation datasets (c statistic=0.91). In patients evaluated in the vascular laboratory, the model-based code algorithm provided better negative predictive value. The simpler algorithm was reasonably accurate for identification of PAD status, with lesser sensitivity and greater specificity. In the community-based sample, the sensitivity (38.7% vs 68.0%) of the simpler algorithm was much lower, whereas the specificity (92.0% vs 87.6%) was higher than the model-based algorithm.ConclusionsA model-based billing code algorithm had reasonable accuracy in identifying PAD cases from the community, and in patients referred to the non-invasive vascular laboratory. The simpler algorithm had reasonable accuracy for identification of PAD in patients referred to the vascular laboratory but was significantly less sensitive in a community-based sample.

In men with stable CAD, sildenafil had no effect on symptoms, exercise duration, or presence or extent of exercise-induced ischemia, as assessed by exercise echocardiography.

Objective Lower extremity peripheral arterial disease (PAD) is highly prevalent and affects millions of individuals worldwide. We developed a natural language processing (NLP) system for automated ascertainment of PAD cases from clinical narrative notes and compared the performance of the NLP algorithm to billing code algorithms, using ankle-brachial index (ABI) test results as the gold standard. Methods We compared the performance of the NLP algorithm to 1) results of gold standard ABI; 2) previously validated algorithms based on relevant ICD-9 diagnostic codes (simple model) and 3) a combination of ICD-9 codes with procedural codes (full model). A dataset of 1,569 PAD patients and controls was randomly divided into training (n= 935) and testing (n= 634) subsets. Results We iteratively refined the NLP algorithm in the training set including narrative note sections, note types and service types, to maximize its accuracy. In the testing dataset, when compared with both simple and full models, the NLP algorithm had better accuracy (NLP: 91.8%, full model: 81.8%, simple model: 83%, P<.001), PPV (NLP: 92.9%, full model: 74.3%, simple model: 79.9%, P<.001), and specificity (NLP: 92.5%, full model: 64.2%, simple model: 75.9%, P<.001). Conclusions A knowledge-driven NLP algorithm for automatic ascertainment of PAD cases from clinical notes had greater accuracy than billing code algorithms. Our findings highlight the potential of NLP tools for rapid and efficient ascertainment of PAD cases from electronic health records to facilitate clinical investigation and eventually improve care by clinical decision support.