Neurodevelopmental assessment of normocephalic children born to Zika virus exposed and unexposed pregnant people.

BACKGROUND: Studies examining the association between in utero Zika virus (ZIKV) exposure and child neurodevelopmental outcomes have produced varied results. METHODS: We aimed to assess neurodevelopmental outcomes among normocephalic children born from pregnant people enrolled in the Zika in Pregnancy in Honduras (ZIPH) cohort study, July-December 2016. Enrollment occurred during the first prenatal visit. Exposure was defined as prenatal ZIKV IgM and/or ZIKV RNA result at enrollment. Normocephalic children, >6 months old, were selected for longitudinal follow-up using the Bayley Scales of Infant and Toddler Development (BSID-III) and the Ages & Stages Questionnaires: Social-Emotional (ASQ:SE-2). RESULTS: One hundred fifty-two children were assessed; after exclusion, 60 were exposed and 72 were unexposed to ZIKV during pregnancy. Twenty children in the exposed group and 21 children in the unexposed group had a composite score <85 in any of the BSID-III domains. Although exposed children had lower cognitive and language scores, differences were not statistically significant. For ASQ:SE-2 assessment, there were not statistically significant differences between groups. CONCLUSIONS: This study found no statistically significant differences in the neurodevelopment of normocephalic children between in utero ZIKV exposed and unexposed. Nevertheless, long-term monitoring of children with in utero ZIKV exposure is warranted. IMPACT: This study found no statistically significant differences in the neurodevelopment in normocephalic children with in utero Zika virus exposure compared to unexposed children, although the exposed group showed lower cognitive and language scores that persisted after adjustment by maternal age and education and after excluding children born preterm and low birth weight from the analysis. Children with prenatal Zika virus exposure, including those normocephalic and have no evidence of abnormalities at birth, should be monitored for neurodevelopmental delays. Follow-up is important to be able to detect developmental abnormalities that might not be detected earlier in life.

Microcephaly Outcomes among Zika Virus-Infected Pregnant Women in Honduras.

The impact of Zika virus (ZIKV) infection on pregnancies shows regional variation emphasizing the importance of studies in different geographical areas. We conducted a prospective study in Tegucigalpa, Honduras, recruiting 668 pregnant women between July 20, 2016, and December 31, 2016. We performed Trioplex real-time reverse transcriptase-PCR (rRT-PCR) in 357 serum samples taken at the first prenatal visit. The presence of ZIKV was confirmed in seven pregnancies (7/357, 2.0%). Nine babies (1.6%) had microcephaly (head circumference more than two SDs below the mean), including two (0.3%) with severe microcephaly (head circumference [HC] more than three SDs below the mean). The mothers of both babies with severe microcephaly had evidence of ZIKV infection. A positive ZIKV Trioplex rRT-PCR was associated with a 33.3% (95% CI: 4.3-77.7%) risk of HC more than three SDs below the mean.

Measurement of Head Circumference: Implications for Microcephaly Surveillance in Zika-Affected Areas.

Worldwide recognition of the Zika virus outbreak in the Americas was triggered by an unexplained increase in the frequency of microcephaly. While severe microcephaly is readily identifiable at birth, diagnosing less severe cases requires comparison of head circumference (HC) measurement to a growth chart. We examine measured values of HC and digit preference in those values, and, by extension, the prevalence of microcephaly at birth in two data sources: a research study in Honduras and routine surveillance data in Uruguay. The Zika in Pregnancy in Honduras study enrolled pregnant women prenatally and followed them until delivery. Head circumference was measured with insertion tapes (SECA 212), and instructions including consistent placement of the tape and a request to record HC to the millimeter were posted where newborns were examined. Three indicators of microcephaly were calculated: (1) HC more than 2 standard deviations (SD) below the mean, (2) HC more than 3 SD below the mean (referred to as "severe microcephaly") and (3) HC less than the 3rd percentile for sex and gestational age, using the INTERGROWTH-21st growth standards. We compared these results from those from a previous analysis of surveillance HC data from the Uruguay Perinatal Information System (Sistema Informático Perinatal (SIP). Valid data on HC were available on 579 infants, 578 with gestational age data. Nine babies (1.56%, 95% CI 0.71-2.93) had HC < 2SD, including two (0.35%, 95% CI 0.04-1.24) with HC < 3SD, and 11 (1.9%, 95% CI, 0.79-3.02) were below the 3rd percentile. The distribution of HC showed strong digit preference: 72% of measures were to the whole centimeter (cm) and 19% to the half-cm. Training and use of insertion tapes had little effect on digit preference, nor were overall HC curves sufficient to detect an increase in microcephaly during the Zika epidemic in Honduras. When microcephaly prevalence needs to be carefully analyzed, such as during the Zika epidemic, researchers may need to interpret HC data with caution.

Adaptive calibration algorithm for plasma glucose estimation in continuous glucose monitoring.

Minimally or noninvasive continuous glucose monitors estimate plasma glucose from compartments alternative to blood, and may revolutionize the management of diabetes. However, the accuracy of current devices is still poor and it may partly depend on low performance of the implemented calibration algorithm. Here, a new adaptive calibration algorithm based on a population local-model-based intercompartmental glucose dynamic model is proposed. The novelty consists in the adaptation of data normalization parameters in real time to estimate and compensate patient's sensitivity variations. Adaptation is performed to minimize mean absolute relative deviation at the calibration points with a time window forgetting strategy. Four calibrations are used: preprandial and 1.5 h postprandial at two different meals. Two databases are used for validation: 1) a 9-h CGMS Gold (Medtronic, Northridge, USA) time series with paired reference glucose values from a clinical study in 17 subjects with type 1 diabetes; 2) data from 30 virtual patients (UVa simulator, Virginia, USA), where inter- and intrasubject variability of sensor's sensitivity were simulated. Results show how the adaptation of the normalization parameters improves the performance of the calibration algorithm since it counteracts sensor sensitivity variations. This improvement is more evident in one-week simulations.

A multiple local models approach to accuracy improvement in continuous glucose monitoring.

BACKGROUND: Continuous glucose monitoring (CGM) devices estimate plasma glucose (PG) from measurements in compartments alternative to blood. The accuracy of currently available CGM is yet unsatisfactory and may depend on the implemented calibration algorithms, which do not compensate adequately for the differences of glucose dynamics between the compartments. Here we propose and validate an innovative calibration algorithm for the improvement of CGM performance. METHODS: CGM data from GlucoDay(®) (A. Menarini, Florence, Italy) and paired reference PG have been obtained from eight subjects without diabetes during eu-, hypo-, and hyperglycemic hyperinsulinemic clamps. A calibration algorithm based on a dynamic global model (GM) of the relationship between PG and CGM in the interstitial space has been obtained. The GM is composed by independent local models (LMs) weighted and added. LMs are defined by a combination of inputs from the CGM and by a validity function, so that each LM represents to a variable extent a different metabolic condition and/or sensor-subject interaction. The inputs best suited for glucose estimation were the sensor current I and glucose estimations G, at different time instants [I(k), I(k)(-1), G(k)(-1)] (IIG). In addition to IIG, other inputs have been used to obtain the GM, achieving different configurations of the calibration algorithm. RESULTS: Even in its simplest configuration considering only IIG, the new calibration algorithm improved the accuracy of the estimations compared with the manufacturer's estimate: mean absolute relative difference (MARD)=10.8±1.5% versus 14.7±5.4%, respectively (P=0.012, by analysis of variance). When additional exogenous signals were considered, the MARD improved further (7.8±2.6%, P<0.05). CONCLUSIONS: The LM technique allows for the identification of intercompartmental glucose dynamics. Inclusion of these dynamics into the calibration algorithm improves the accuracy of PG estimations.