Dataset Information

Concentrations and Sources of Airborne Particles in a Neonatal Intensive Care Unit.

ABSTRACT: Premature infants in neonatal intensive care units (NICUs) have underdeveloped immune systems, making them susceptible to adverse health consequences from air pollutant exposure. Little is known about the sources of indoor airborne particles that contribute to the exposure of premature infants in the NICU environment. In this study, we monitored the spatial and temporal variations of airborne particulate matter concentrations along with other indoor environmental parameters and human occupancy. The experiments were conducted over one year in a private-style NICU. The NICU was served by a central heating, ventilation and air-conditioning (HVAC) system equipped with an economizer and a high-efficiency particle filtration system. The following parameters were measured continuously during weekdays with 1-min resolution: particles larger than 0.3 ?m resolved into 6 size groups, CO2 level, dry-bulb temperature and relative humidity, and presence or absence of occupants. Altogether, over sixteen periods of a few weeks each, measurements were conducted in rooms occupied with premature infants. In parallel, a second monitoring station was operated in a nearby hallway or at the local nurses' station. The monitoring data suggest a strong link between indoor particle concentrations and human occupancy. Detected particle peaks from occupancy were clearly discernible among larger particles and imperceptible for submicron (0.3-1 ?m) particles. The mean indoor particle mass concentrations averaged across the size range 0.3-10 ?m during occupied periods was 1.9 ?g/m(3), approximately 2.5 times the concentration during unoccupied periods (0.8 ?g/m(3)). Contributions of within-room emissions to total PM10 mass in the baby rooms averaged 37-81%. Near-room indoor emissions and outdoor sources contributed 18-59% and 1-5%, respectively. Airborne particle levels in the size range 1-10 ?m showed strong dependence on human activities, indicating the importance of indoor-generated particles for infant's exposure to airborne particulate matter in the NICU.

SUBMITTER: Licina D

PROVIDER: S-EPMC4866781 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Concentrations and Sources of Airborne Particles in a Neonatal Intensive Care Unit.

Licina Dusan D Bhangar Seema S Brooks Brandon B Baker Robyn R Firek Brian B Tang Xiaochen X Tang Xiaochen X Morowitz Michael J MJ Banfield Jillian F JF Nazaroff William W WW

PloS one 20160513 5

Premature infants in neonatal intensive care units (NICUs) have underdeveloped immune systems, making them susceptible to adverse health consequences from air pollutant exposure. Little is known about the sources of indoor airborne particles that contribute to the exposure of premature infants in the NICU environment. In this study, we monitored the spatial and temporal variations of airborne particulate matter concentrations along with other indoor environmental parameters and human occupancy. ...[more]

PMID: 27175913

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Project description:BACKGROUND:Given its narrow therapeutic range, digoxin's pharmacokinetic parameters in infants are difficult to predict due to variation in birth weight and gestational age, especially for critically ill newborns. There is limited evidence to support the safety and dosage requirements of digoxin, let alone to predict its concentrations in infants. This study aimed to compare the concentrations of digoxin predicted by traditional regression modeling and artificial neural network (ANN) modeling for newborn infants given digoxin for clinically significant patent ductus arteriosus (PDA). METHODS:A retrospective chart review was conducted to obtain data on digoxin use for clinically significant PDA in a neonatal intensive care unit. Newborn infants who were given digoxin and had digoxin concentration(s) within the acceptable range were identified as subjects in the training model and validation datasets, accordingly. Their demographics, disease, and medication information, which were potentially associated with heart failure, were used for model training and analysis of digoxin concentration prediction. The models were generated using backward standard multivariable linear regressions (MLRs) and a standard backpropagation algorithm of ANN, respectively. The common goodness-of-fit estimates, receiver operating characteristic curves, and classification of sensitivity and specificity of the toxic concentrations in the validation dataset obtained from MLR or ANN models were compared to identify the final better predictive model. RESULTS:Given the weakness of correlations between actual observed digoxin concentrations and pre-specified variables in newborn infants, the performance of all ANN models was better than that of MLR models for digoxin concentration prediction. In particular, the nine-parameter ANN model has better forecasting accuracy and differentiation ability for toxic concentrations. CONCLUSION:The nine-parameter ANN model is the best alternative than the other models to predict serum digoxin concentrations whenever therapeutic drug monitoring is not available. Further cross-validations using diverse samples from different hospitals for newborn infants are needed.

Dataset Information

Concentrations and Sources of Airborne Particles in a Neonatal Intensive Care Unit.

Publications

Concentrations and Sources of Airborne Particles in a Neonatal Intensive Care Unit.

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