Toward personalized medicine for pharmacological interventions in neonates using vital signs

Abstract

Vital signs, such as heart rate and oxygen saturation, are continuously monitored for infants in neonatal care units. Pharmacological interventions can alter an infant's vital signs, either as an intended effect or as a side effect, and consequently could provide an approach to explore the wide variability in pharmacodynamics across infants and could be used to develop models to predict outcome (efficacy or adverse effects) in an individual infant. This will enable doses to be tailored according to the individual, shifting the balance toward efficacy and away from the adverse effects of a drug. Pharmacological analgesics are frequently not given in part due to the risk of adverse effects, yet this exposes infants to the short‐ and long‐term effects of painful procedures. Personalized analgesic dosing will be an important step forward in providing safer effective pain relief in infants. The aim of this paper was to describe a framework to develop predictive models of drug outcome from analysis of vital signs data, focusing on analgesics as a representative example. This framework investigates changes in vital signs in response to the analgesic (prior to the painful procedure) and proposes using machine learning to examine if these changes are predictive of outcome—either efficacy (with pain response measured using a multimodal approach, as changes in vital signs alone have limited sensitivity and specificity) or adverse effects. The framework could be applied to both preterm and term infants in neonatal care units, as well as older children. Sharing vital signs data are proposed as a means to achieve this aim and bring personalized medicine rapidly to the forefront in neonatology.

From Prediction to Practice: A Rapid Review on the Opportunities and Challenges of using Artificial Intelligence in the Neonatal Intensive Care Unit (NICU) (Preprint)

“…They encompass predictive modelling, spanning length of stay, disease severity, and mortality risk, contributing to a deeper understanding of neonatal conditions. These studies deepen the comprehension of neonatal conditions and their determinants, aligning with the literature's focus on advancing scientific understanding and medical care in the NICU (43,(61)(62)(63)(64)(65)(66).…”

Early prediction of severe retinopathy of prematurity requiring laser treatment using physiological data

“…34 This highlights the need for sharing physiological and neonatal outcome data; developing a consensus of a standardized format for data sharing is important to facilitate these aims. 13 The NEDROP2 study and a large Swiss study reported that, respectively, 39 out of 1085 (3.6%) and 94 out of 7817 (1.2%) fully screened infants received treatment for developing ROP. 35,36 This is lower compared to the 14% of infants in our study who required laser treatment, probably because our study population is not fully comparable to these studies which included infants born at both high-care and regional centers and highlights the likely need to include infants from lower-care settings to create a model that will be accurate in all centers.…”

“…Although the obtained physiological monitor data are used to detect trends and identify severe episodes of physiological instability, this snapshot method means that these data are often not used to their full potential and opportunities to improve clinical management may be missed. [13][14][15] Analyzing physiological data could potentially lead to the identification of meaningful patterns that would otherwise be difficult or even impossible to identify, and may enable the prediction of individual risk for ROP. Previously Sullivan et al investigated whether measures of heart rate and SpO 2 in the first 7 days of life can improve predictions of mortality and morbidity in very low birth weight infants.…”

The effect of acute respiratory events and respiratory stimulants on EEG-recorded brain activity in neonates: A systematic review

“…All four studies showed changes in the EEG with the use of respiratory stimulants, but further work is needed to ascertain whether and how these changes are related to other factors, such as age, dose, and comorbidities, and to understand how these changes in EEG might affect brain development. Understanding the effect of respiratory stimulants on brain activity may help drive individualised drug dosing in infants ( Hartley, 2021 , Hartley et al, 2021 , Kumar et al, 2020 ).…”