Brain mass estimation by head circumference and body mass methods in neonatal glycaemic modelling and control

Interaction of maternal smoking and gestational diabetes mellitus on newborn head circumference and birthweight

INTRODUCTION

Maternal smoking during pregnancy and gestational diabetes mellitus (GDM) have opposite effects on fetal growth during pregnancy. The aim of the study was to evaluate the interaction of smoking during pregnancy and gestational diabetes mellitus on head circumference and birthweight of newborns.

MATERIAL AND METHODS

The study included all primiparous women with singleton pregnancies (n = 290 602) without previously diagnosed diabetes or hypertension in Finland between 2006 and 2018. The information on gestational diabetes mellitus, newborn birthweight and head circumference, and maternal smoking and backgrounds was derived from the Finnish Medical Birth Register. Linear regression models were used in the analyses.

RESULTS

In total 8.0% of parturients quit smoking during the first trimester and 9.9% continued smoking thereafter. The prevalence of GDM was 8.9% (n = 25 948). Newborns of women who continued smoking had a smaller head circumference (b = -0.24, SE = 0.01, p < 0.0001) and birthweight (b = -0.28, SE = 0.01, p < 0.0001) compared to newborns of women who did not smoke. Head circumference and birthweight were greater in newborns of women with GDM (b = 0.09, SE = 0.01, p < 0.0001 and b = 0.16, SE = 0.01, p < 0.0001, respectively) compared to newborns of women without GDM. In the interaction analyses, head circumference (b = -0.13, SE = 0.01, p < 0.0001) was smaller and birthweight (b = -0.13, SE = 0.02, p < 0.0001) was lower in newborns of women with GDM who continued smoking compared to newborns of women without GDM who did not smoke.

CONCLUSIONS

Although smoking and GDM have opposite effects on fetal growth, the negative effects of exposure to smoking are also seen in newborns of women with GDM. Compared to smoking after the first trimester of pregnancy, cessation of smoking during the first trimester was associated with greater head circumference and birthweight in newborns.

Model-based subcutaneous insulin for glycemic control of pre-term infants in the neonatal intensive care unit

Hyperglycaemia is a common problem in neonatal intensive care units (NICUs). Achieving good control can result in better outcomes for patients. However, good control is difficult, where poor control and resulting hypoglycaemia reduces outcomes and confounds results. Clinically validated models can provide good control, and subcutaneous insulin delivery can provide more options for insulin therapy for clinicians. However, this combination has only been significantly utilised in adult outpatient diabetes, but could hold benefit for treating NICU infants. This research combines a well-validated NICU metabolic model with subcutaneous insulin kinetics models to assess the feasibility of a model-based approach. Clinical data from 12 very/extremely pre-mature infants was collected for an average study duration of 10.1 days. Blood glucose, interstitial and plasma insulin, as well as subcutaneous and local insulin were modelled, and patient-specific insulin sensitivity profiles were identified for each patient. Modelling error was low, where the cohort median [IQR] mean percentage error was 0.8 [0.3 3.4] %. For external validation, insulin sensitivity was compared to previous NICU cohorts using the same metabolic model, where overall levels of insulin sensitivity were similar. Overall, the combined system model accurately captured observed glucose and insulin dynamics, showing the potential for a model-based approach to glycaemic control using subcutaneous insulin in this cohort. The results justify further model validation and clinical trial research to explore a model-based protocol.

Effects of maternal smoking on body size and proportions at birth: a register-based cohort study of 1.4 million births

OBJECTIVES

The aim of our work was to analyse the effect of maternal smoking on body size and body proportions of newborns when the mother had smoked only during the first trimester, in comparison with continued smoking after the first trimester. Furthermore, we have evaluated how growth restriction associated with maternal smoking contributes to changes in body proportions.

DESIGN

Register-based cohort study SETTING: Maternal Exposure (MATEX) cohort identified from the Finnish Medical Birth Register.

PARTICIPANTS

Singleton births without congenital anomalies and missing data (1.38 million) from 1 January 1991 to 31 December 2016.

METHODS

Logistic regression was used to quantify the effect of maternal smoking, stratified by the maternal smoking status.

OUTCOME MEASURES

Body proportions indicated by low brain-to-body ratio (defined as <10th percentile); high ponderal index and high head-to-length ratio (defined as >90th percentile); small body size for gestational age at birth (defined as weight, length or head circumference <10th percentile) and preterm birth (<37 weeks) and low birth weight (2500 g).

RESULTS

Continued smoking after the first trimester was associated with high ponderal index (OR 1.26, 95% CI 1.23 to 1.28), low brain-to-body ratio (1.11, 1.07-1.15) and high head-to-length ratio (1.22, 1.19-1.26), corresponding with absolute risks of 22%, 10% and 19%, respectively). The effects were slightly lower when smoking had been quit during the first trimester. Similar effects were seen for the body size variables and low birth weight. Preterm birth was not associated with smoking only during first trimester.

CONCLUSIONS

Maternal smoking, independent of smoking duration during pregnancy, was associated with abnormal body proportions resulting from larger reduction of length and head circumference in comparison to weight. The effects of having quit smoking during the first trimester and having continued smoking after the first trimester were similar, suggesting the importance of early pregnancy as a sensitive exposure window.

Modelling intestinal glucose absorption in premature infants using continuous glucose monitoring data

BACKGROUND

Model-based glycaemic control protocols have shown promise in neonatal intensive care units (NICUs) for reducing both hyperglycaemia and insulin-therapy driven hypoglycaemia. However, current models for the appearance of glucose from enteral feeding are based on values from adult intensive care cohorts. This study aims to determine enteral glucose appearance model parameters more reflective of premature infant physiology.

METHODS

Peaks in CGM data associated with enteral milk feeds in preterm and term infants are used to fit a two compartment gut model. The first compartment describes glucose in the stomach, and the half life of gastric emptying is estimated as 20 min from literature. The second compartment describes glucose in the small intestine, and absorption of glucose into the blood is fit to CGM data. Two infant cohorts from two NICUs are used, and results are compared to appearances derived from data in highly controlled studies in literature.

RESULTS

The average half life across all infants for glucose absorption from the gut to the blood was 50 min. This result was slightly slower than, but of similar magnitude to, results derived from literature. No trends were found with gestational or postnatal age. Breast milk fed infants were found to have a higher absorption constant than formula fed infants, a result which may reflect known differences in gastric emptying for different feed types.

CONCLUSIONS

This paper presents a methodology for estimation of glucose appearance due to enteral feeding, and model parameters suitable for a NICU model-based glycaemic control context.

Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Insulin kinetics and the Neonatal Intensive Care Insulin-Nutrition-Glucose (NICING) model

BACKGROUND

Models of human glucose-insulin physiology have been developed for a range of uses, with similarly different levels of complexity and accuracy. STAR (Stochastic Targeted) is a model-based approach to glycaemic control. Elevated blood glucose concentrations (hyperglycaemia) are a common complication of stress and prematurity in very premature infants, and have been associated with worsened outcomes and higher mortality. This research identifies and validates the model parameters for model-based glycaemic control in neonatal intensive care.

METHODS

C-peptide, plasma insulin, and BG from a cohort of 41 extremely pre-term (median age 27.2 [26.2-28.7] weeks) and very low birth weight infants (median birth weight 839 [735-1000] g) are used alongside C-peptide kinetic models to identify model parameters associated with insulin kinetics in the NICING (Neonatal Intensive Care Insulin-Nutrition-Glucose) model. A literature analysis is used to determine models of kidney clearance and body fluid compartment volumes. The full, final NICING model is validated by fitting the model to a cohort of 160 glucose, insulin, and nutrition data records from extremely premature infants from two different NICUs (neonatal intensive care units).

RESULTS

Six model parameters related to insulin kinetics were identified. The resulting NICING model is more physiologically descriptive than prior model iterations, including clearance pathways of insulin via the liver and kidney, rather than a lumped parameter. In addition, insulin diffusion between plasma and interstitial spaces is evaluated, with differences in distribution volume taken into consideration for each of these spaces. The NICING model was shown to fit clinical data well, with a low model fit error similar to that of previous model iterations.

CONCLUSIONS

Insulin kinetic parameters have been identified, and the NICING model is presented for glycaemic control neonatal intensive care. The resulting NICING model is more complex and physiologically relevant, with no loss in bedside-identifiability or ability to capture and predict metabolic dynamics.