Hemodynamic Instability Prediction Through Continuous Multiparameter Monitoring in ICU

Cao, Hanqing; Eshelman, Larry J.; Nielsen, Larry; Gross, Brian D.; Saeed, Mohammed; Frassica, Joseph J.

doi:https://doi.org/10.1260/2040-2295.1.4.509

Journal of Healthcare Engineering

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Research Article | Open Access

Volume 1 | Article ID 490657 | https://doi.org/10.1260/2040-2295.1.4.509

Hemodynamic Instability Prediction Through Continuous Multiparameter Monitoring in ICU

Hanqing Cao,¹Larry J. Eshelman,¹Larry Nielsen,²Brian D. Gross,²Mohammed Saeed,²and Joseph J. Frassica²

Abstract

Current algorithms identifying hemodynamically unstable intensive care unit patients typically are limited to detecting existing dangerous conditions and suffer from high false alert rates. Our objective was to predict hemodynamic instability at least two hours before patient deterioration while maintaining a low false alert rate, using minute-by-minute heart rate (HR) and blood pressure (BP) data. We identified 66 stable and 104 unstable patients meeting our stability-instability criteria from the MIMIC II database, and developed multi-parameter measures using HR and BP. An instability index combining measures of BP, shock index, rate pressure product, and HR variation was developed from a multivariate regression model to predict hemodynamic instability (ROC of 0.82±0.03, sensitivity of 0.57±0.07 when the specificity was targeted at 0.90; the alert rate ratio of unstable to stable patients was 7.62). We conclude that these algorithms could form the basis for reliable predictive clinical alerts which identify patients likely to become hemodynamically unstable within the next few hours so that the clinicians can proactively manage these patients and provide necessary care.

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Copyright © 2010 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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