Reference Values for Chronotropic Index from 1280 Incremental Cycle Ergometry Tests.

PURPOSE: Clinical cardiopulmonary exercise testing can determine causes of exercise limitation. The slope of heart rate (fC) versus oxygen uptake (V˙O2), which we call the chronotropic index (CI), can help identify cardiovascular impairment. We aimed to develop a reference equation for CI based on a large number of subjects considered to have normal exercise responses. METHODS: From a database of 13,728 incremental cycle ergometry exercise tests, we identified 1280 normal tests based on the absence of a clinical diagnosis, normal body mass index, and normal aerobic performance plus absence of cardiovascular disease, medications, or ventilatory limitation. A linear mixed-model approach was used to analyze the relationship between CI and other variables. RESULTS: Subjects were age 18-84 yr, and 693 (54.1%) were men. Mean ± SD CI in men was lower than in women, 41.2 ± 9.3 beats per liter versus 63.4 ± 15.7 L. Age (in years), sex (0, male; 1, female), height (in centimeters), and weight (in kilograms) were significant predictors for CI:CIi = 106.9 + 0.16 × agei + 14.3 × sexi - 0.31 × heighti - 0.24 × weighti. The SE of estimates ranged from 10.6 to 11.2 L (median of 10.7 L). CONCLUSIONS: We report a reference equation for CI derived from normal subjects. The CI can be used in conjunction with V˙O2max to interpret maximal cardiopulmonary exercise tests. We consider a high CI to be cardiovascular impairment and a low CI plus low V˙O2max to be chronotropic insufficiency.

Prediction of Clinical Deterioration in Hospitalized Adult Patients with Hematologic Malignancies Using a Neural Network Model.

INTRODUCTION: Clinical deterioration (ICU transfer and cardiac arrest) occurs during approximately 5-10% of hospital admissions. Existing prediction models have a high false positive rate, leading to multiple false alarms and alarm fatigue. We used routine vital signs and laboratory values obtained from the electronic medical record (EMR) along with a machine learning algorithm called a neural network to develop a prediction model that would increase the predictive accuracy and decrease false alarm rates. DESIGN: Retrospective cohort study. SETTING: The hematologic malignancy unit in an academic medical center in the United States. PATIENT POPULATION: Adult patients admitted to the hematologic malignancy unit from 2009 to 2010. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: Vital signs and laboratory values were obtained from the electronic medical record system and then used as predictors (features). A neural network was used to build a model to predict clinical deterioration events (ICU transfer and cardiac arrest). The performance of the neural network model was compared to the VitalPac Early Warning Score (ViEWS). Five hundred sixty five consecutive total admissions were available with 43 admissions resulting in clinical deterioration. Using simulation, the neural network outperformed the ViEWS model with a positive predictive value of 82% compared to 24%, respectively. CONCLUSION: We developed and tested a neural network-based prediction model for clinical deterioration in patients hospitalized in the hematologic malignancy unit. Our neural network model outperformed an existing model, substantially increasing the positive predictive value, allowing the clinician to be confident in the alarm raised. This system can be readily implemented in a real-time fashion in existing EMR systems.

When host defense goes awry: Modeling sepsis-induced immunosuppression.

Sepsis is associated with an initial hyperinflammatory state; however, therapeutic trials targeting the inflammatory response have yielded disappointing results. It is now appreciated that septic patients often undergo a period of relative immunosuppression, rendering them susceptible to secondary infections. Interest in this phenomenon has led to the development of animal models to study the immune dysfunction of sepsis. In this review, we analyze the available models of sepsis-induced immunosuppression.