Elevated serum globulin gap as a highly reliable marker of elevated erythrocyte sedimentation rate in patients with systemic rheumatic diseases.

OBJECTIVE: Serum C-reactive protein (CRP) level and erythrocyte sedimentation rate (ESR) are the two most commonly used markers of inflammation in clinical practice. Reducing the need for these tests could lead to considerable cost savings without sacrificing the quality of patient care. METHODS: The electronic medical records of patients with systemic rheumatic diseases seen between May 2015 and June 2017 in the rheumatology clinics at a single academic medical center were retrospectively reviewed. Correlations and receiver operator characteristic (ROC) curves between serum CRP level and ESR vs serum globulin gap (the difference between levels of total protein and albumin) and albumin-to-globulin (A:G) ratio were determined. RESULTS: In two independent cohorts (discovery: 263 subjects, 446 entries; validation: 438 subjects, 1959 entries), the globulin gap and A:G ratio correlated (p < 0.001) with CRP level and ESR, with correlation coefficients being greater for ESR than for CRP level. ROC curve analyses demonstrated better area-under-curve for ESR than for CRP level. The percentages of entries with elevated globulin gap (≥4.0 g/dl) and low A:G ratio (<0.8) were ∼8.4% and ∼2.6%, respectively, and each had a positive predictive value of ≥0.960 for elevated ESR. Among patients with high globulin gap, the change in globulin gap over time faithfully reflected changes in ESR. CONCLUSION: In the subset of systemic rheumatic disease patients who harbor an elevated globulin gap, the ESR is almost always elevated. This novel observation sets the conceptual foundation and rationale for subsequent prospective studies that assess whether ESR testing in this subset of rheumatic disease patients could be reduced without sacrificing patient care. Ultimately, ordering an ESR test may often be unnecessary, thereby resulting in cost savings.

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.