A Novel Technique for Identifying Patients with ICU Needs Using Hemodynamic Features

A Jalali,P Ghorbanian,A Ghaffari,C Nataraj

doi:10.1155/2012/696194

A Jalali, P Ghorbanian + Show 2 more

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https://doi.org/10.1155/2012/696194

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Abstract

Identification of patients requiring intensive care is a critical issue in clinical treatment. The objective of this study is to develop a novel methodology using hemodynamic features for distinguishing such patients requiring intensive care from a group of healthy subjects. In this study, based on the hemodynamic features, subjects are divided into three groups: healthy, risky and patient. For each of the healthy and patient subjects, the evaluated features are based on the analysis of existing differences between hemodynamic variables: Blood Pressure and Heart Rate. Further, four criteria from the hemodynamic variables are introduced: circle criterion, estimation error criterion, Poincare plot deviation, and autonomic response delay criterion. For each of these criteria, three fuzzy membership functions are defined to distinguish patients from healthy subjects. Furthermore, based on the evaluated criteria, a scoring method is developed. In this scoring method membership degree of each subject is evaluated for the three classifying groups. Then, for each subject, the cumulative sum of membership degree of all four criteria is calculated. Finally, a given subject is classified with the group which has the largest cumulative sum. In summary, the scoring method results in 86% sensitivity, 94.8% positive predictive accuracy and 82.2% total accuracy.

Highlights

Hemodynamic instability is most commonly associated with abnormal or unstable blood pressure (BP), especially hypotension, or more broadly associated with inadequate global or regional perfusion
From a total of seventy subject data which were collected from MIMIC II database, the algorithm was first trained with twenty-five subjects including ten healthy and fifteen patients
The proposed method was applied to 45 cases from Physionet database, containing 12 healthy subjects and 33 patients

Summary

Introduction

Hemodynamic instability is most commonly associated with abnormal or unstable blood pressure (BP), especially hypotension, or more broadly associated with inadequate global or regional perfusion. Inadequate perfusion may compromise important organs, such as the heart and the brain, due to limits on coronary and cerebral autoregulation, and may cause life-threatening illnesses or even death. It is crucial to identify patients who are likely to become hemodynamically unstable for early detection and treatment of these life-threatening conditions [1]. The modern intensive care units (ICUs) typically employ continuous hemodynamic monitoring (e.g., heart rate (HR) and invasive arterial BP measurements) to track the state of patients. Clinicians in a busy ICU are overwhelmed with the task of assimilating and interpreting the tremendous volumes of data into working hypotheses. It is important to have automated algorithms that can accurately process and classify the large amount of monitoring data and identify patients who are on the verge of becoming unstable [1]

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