A time series analysis of the ecologic relationship between acute and intermediate PM2.5 exposure duration on neonatal intensive care unit admissions in Florida

Introduction and Objective: The skin and respiratory system of premature neonates are in permanent contact with indoor room air. We longitudinally analyzed the room air climate and quality in neonatal intensive care inside and outside an incubator. Methods: Sampling was performed in 2 patient rooms and inside a neonatal incubator (Caleo, Draeger Medical, Lübeck, Germany) over 6 weeks with 5-min resolution resulting in 12,090 samples (U-Monitor, U-Earth Biotech, London, UK). Temperature, humidity, and air pollutants, including particulate matter (<1 μm [PM1] and <2.5 μm [PM2.5]), volatile organic compounds (VOC), and odorous gases (OG), were recorded. Room air parameters were analyzed using time series analysis. A linear regression model was used to check for statistically significant linear trends. Statistical analysis was performed using decompensation of time series analysis and spectral analysis by fast Fourier transformation. Results: The indoor climate target values of the ward’s central ventilation system for temperature and humidity were not always met. Room air parameters (PM, VOC, and OG) showed significant daytime-dependent fluctuations with different oscillation frequencies per day. The daily mean (first quartile – third quartile) concentrations of PM2.5 were significantly higher inside the incubator compared to the surrounding ambient air (2,158 [1,948–2,298] pcs/L vs. 2,018 [1,852–2,058] pcs/L; p < 0.001). OG were significantly lower inside the incubator compared to ambient air. VOC levels inside the incubator were substantially higher during the first 5 days of the observation period compared to VOC levels in the surrounding ambient air. Conclusions: The indoor climate of neonatal intensive care units should be monitored in real time to detect deviations from target parameters quickly. In our neonatal intensive care unit, indoor air quality fluctuated significantly depending on the time of day. We highly suspect that air pollutants are carried into the direct patient environment by visitors and medical staff. The incubator does not protect against PM and VOC exposure but reduces exposure to OG. Cleaning procedures may lead to substantially higher concentrations of VOC inside the incubator and may represent a potentially harmful factor for premature infants.

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Effect of nocturnal sound reduction on the incidence of delirium in intensive care unit patients: An interrupted time series analysis

Intensive and Critical Care Nursing ◽

10.1016/j.iccn.2017.01.008 ◽

2017 ◽

Vol 41 ◽

pp. 18-25 ◽

Cited By ~ 19

Author(s):

Ineke van de Pol ◽

Mat van Iterson ◽

Jolanda Maaskant

Keyword(s):

Intensive Care Unit ◽

Time Series ◽

Intensive Care ◽

Time Series Analysis ◽

Interrupted Time Series ◽

Interrupted Time Series Analysis ◽

Series Analysis ◽

Sound Reduction

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Sustained multimodal antimicrobial stewardship in an Australian tertiary intensive care unit from 2008–2015: an interrupted time-series analysis

International Journal of Antimicrobial Agents ◽

10.1016/j.ijantimicag.2018.01.017 ◽

2018 ◽

Vol 51 (4) ◽

pp. 620-628 ◽

Cited By ~ 4

Author(s):

Suman Adhikari ◽

Michael Piza ◽

Peter Taylor ◽

Kush Deshpande ◽

Doris Lam ◽

...

Keyword(s):

Intensive Care Unit ◽

Time Series ◽

Intensive Care ◽

Time Series Analysis ◽

Antimicrobial Stewardship ◽

Interrupted Time Series ◽

Interrupted Time Series Analysis ◽

Series Analysis

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Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit

Applied Clinical Informatics ◽

10.4338/aci-2015-09-ra-0127 ◽

2016 ◽

Vol 07 (02) ◽

pp. 275-289 ◽

Cited By ~ 6

Author(s):

Stephen Hoover ◽

Eric Jackson ◽

David Paul ◽

Robert Locke ◽

Muge Capan

Keyword(s):

Intensive Care Unit ◽

Time Series ◽

Intensive Care ◽

Clinical Practice ◽

Linear Regression ◽

Neonatal Intensive Care Unit ◽

Neonatal Intensive Care ◽

Resource Planning ◽

Forecasting Models ◽

Best Fitting

SummaryAccurate prediction of future patient census in hospital units is essential for patient safety, health outcomes, and resource planning. Forecasting census in the Neonatal Intensive Care Unit (NICU) is particularly challenging due to limited ability to control the census and clinical trajectories. The fixed average census approach, using average census from previous year, is a forecasting alternative used in clinical practice, but has limitations due to census variations.Our objectives are to: (i) analyze the daily NICU census at a single health care facility and develop census forecasting models, (ii) explore models with and without patient data characteristics obtained at the time of admission, and (iii) evaluate accuracy of the models compared with the fixed average census approach.We used five years of retrospective daily NICU census data for model development (January 2008 - December 2012, N=1827 observations) and one year of data for validation (January - December 2013, N=365 observations). Best-fitting models of ARIMA and linear regression were applied to various 7-day prediction periods and compared using error statistics.The census showed a slightly increasing linear trend. Best fitting models included a nonseasonal model, ARIMA(1,0,0), seasonal ARIMA models, ARIMA(1,0,0)×(1,1,2)7 and ARIMA(2,1,4)×(1,1,2)14, as well as a seasonal linear regression model. Proposed forecasting models resulted on average in 36.49% improvement in forecasting accuracy compared with the fixed average census approach.Time series models provide higher prediction accuracy under different census conditions compared with the fixed average census approach. Presented methodology is easily applicable in clinical practice, can be generalized to other care settings, support shortand long-term census forecasting, and inform staff resource planning.

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Predicting hospital mortality for intensive care unit patients: Time-series analysis

Health Informatics Journal ◽

10.1177/1460458219850323 ◽

2019 ◽

Vol 26 (2) ◽

pp. 1043-1059 ◽

Cited By ~ 4

Author(s):

Aya Awad ◽

Mohamed Bader-El-Den ◽

James McNicholas ◽

Jim Briggs ◽

Yasser El-Sonbaty

Keyword(s):

Intensive Care Unit ◽

Time Series ◽

Intensive Care ◽

Time Series Analysis ◽

Intensive Care Unit Admission ◽

Hospital Mortality ◽

Prediction Models ◽

Scoring Systems ◽

Machine Learning Classification ◽

Series Analysis

Current mortality prediction models and scoring systems for intensive care unit patients are generally usable only after at least 24 or 48 h of admission, as some parameters are unclear at admission. However, some of the most relevant measurements are available shortly following admission. It is hypothesized that outcome prediction may be made using information available in the earliest phase of intensive care unit admission. This study aims to investigate how early hospital mortality can be predicted for intensive care unit patients. We conducted a thorough time-series analysis on the performance of different data mining methods during the first 48 h of intensive care unit admission. The results showed that the discrimination power of the machine-learning classification methods after 6 h of admission outperformed the main scoring systems used in intensive care medicine (Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score and Sequential Organ Failure Assessment) after 48 h of admission.

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