Predicting Patient-ventilator Asynchronies with Hidden Markov Models

Yaroslav Marchuk,Robert M Kacmarek,Lluis Blanch,Bernat Sales,Gemma Gomà,Rudys Magrans,Carles Subirà,Jaume Montanya,Josefina López-Aguilar,Rafael Fernández,Candelaria De Haro

doi:10.1038/s41598-018-36011-0

Abstract

In mechanical ventilation, it is paramount to ensure the patient’s ventilatory demand is met while minimizing asynchronies. We aimed to develop a model to predict the likelihood of asynchronies occurring. We analyzed 10,409,357 breaths from 51 critically ill patients who underwent mechanical ventilation >24 h. Patients were continuously monitored and common asynchronies were identified and regularly indexed. Based on discrete time-series data representing the total count of asynchronies, we defined four states or levels of risk of asynchronies, z1 (very-low-risk) – z4 (very-high-risk). A Poisson hidden Markov model was used to predict the probability of each level of risk occurring in the next period. Long periods with very few asynchronous events, and consequently very-low-risk, were more likely than periods with many events (state z4). States were persistent; large shifts of states were uncommon and most switches were to neighbouring states. Thus, patients entering states with a high number of asynchronies were very likely to continue in that state, which may have serious implications. This novel approach to dealing with patient-ventilator asynchrony is a first step in developing smart alarms to alert professionals to patients entering high-risk states so they can consider actions to improve patient-ventilator interaction.

Highlights

Personalized or precision medicine is an emerging concept that will change clinical practice in ICUs in the short-to-mid term, helping physicians choose the right therapy at the right time[12,13]
To take into account the wide heterogeneity among ICU patients and high complexity of critical illness, we considered time series covering the entire period of mechanical ventilation in patients with different conditions
The heart rate and bradycardia and tachycardia episodes were similar in the different states. This proof-of-concept study shows that it is feasible to use hidden Markov model (HMM) to predict patient-ventilator asynchronies in critically ill patients and to infer the probability that the number of asynchrony events will be above a given threshold

Summary

Introduction

Personalized or precision medicine is an emerging concept that will change clinical practice in ICUs in the short-to-mid term, helping physicians choose the right therapy at the right time[12,13]. ICU patients are intensely and continuously monitored, generating extremely large datasets All this data is readily available and can be exploited with big data tools and automated learning systems, providing a unique opportunity to improve decision-making in this demanding environment. We aimed to obtain proof of concept that regularly indexing the most common types of patient-ventilator asynchronies along time can generate a discrete time series that can be used to predict the probability of asynchronies occurring in future periods. Not a specific aim of this study, we conducted a subanalysis of the probable effects of asynchronies on some cardiovascular parameters

Objectives

Results

Conclusion