A developmental-descriptive study carried out in the NICU of Yas Women Hospital affiliated to Tehran University of Medical Sciences in collaboration with School of Allied Medical Science and Saadat CO. (Tehran-Iran) from October 2015 to July 2017.Twenty preterm infants with gestational age 24-28 weeks entered the study. Congenital diaphragmatic hernia, cyanotic heart disease, severe apnea, perfusion index (PI) < 0.4 and any medical conditions with deviation from the usual SPO2 target range was considered as exclusion criteria.
Firstly, a checklist was prepared according to literature review to define some important demographic and physiologic parameters related maintaining of PaO2 in a target range (gestational age, sex, temperature, heart rate, results of arterial blood gas test, FIO2, SPO2, PI, PH, pCO2, pO2, Hct,…). In the next step, eight expert neonatologists and pediatric intensivists were asked to prioritizethese parametersby score 1 to 10. Of 19 variables, 4criteria had the greatest scores. The efficiency index(from 1 to 5) was calculated for each important criterion(Table 1).Hence, alterations of these important variables should be constantly considered and all decisions should be made based on them. After determining the significant variables associated arterial oxygen status, physicians were asked to design a medical algorithm (Figure 1).
Development and implementation of devices
After algorithm design, database software was prepared based on pulse oximeter parameters. A Wi-Fi module (designed by Saadat CO.) was implemented to receive data from pulse oximeter and send inputs to user'smobile. Moreover, a mobile application was also developedwitha particular service for reading input data from the module. Differentmobile alerting approachesincluding text message, sound alarm and vibrating alarm detected different variables; vibrate alarm was implemented for high or low FIO2 and sound-vibrate alarm was designed for demonstrating heart rate, temperature, PI, sudden fluctuations or device disconnection from patient. It should be noted that all of these alarms type were optional for staff to set for each patient's events based on the importance of the events.
Mobile phone was connected through Wi-Fi network. User (neonatologist or NICU nurse) could receive information related patient's situation to perform or order the necessary cares. Using this new algorithm could provide a controlling system to change FIO2 under the direct supervision of medical specialist. After receiving notifications associated increase or decrease of FIO2 levels and user's confirmation; the alterations were applied on the ventilator. If this alarm was not confirmed by user, the alterations were notapplied, as well (Figure 2, 3).
First, a pilot experimental study was carried out on 10 participants to compare FIO2 between RMC and RCLAC groups. A significant difference was observed between groups regarding to FIO2 (in RMC group= 97.4 ±3.2 vs. in RCLAC group= 74.1±19; p= 0.0001). Then, based on using Compare Means formula; with the proposed sample size of 8, the study had a power of 90% and an alpha error of 0.05. Finally, according an investigation by Hallenberger et al. (2), 18 subjects entered the study for better data analysis with appropriate power.
In the present study, two approaches (RMC and RCLAC) in twenty-four–hour periods were compared. Nurses were asked to pay special attention to signs and warnings. With software alerts, the nurse should increase or decrease the amount of FIO2. After nurse confirmation, the alerts were applied on ventilator. If the alarm was not confirmed by nurse, alerts would not be applied on ventilator. Similarly, the software did not change the amount of FIO2. The nurse-patient ratio at NICU was 1:2 to 1:3.
Primary/ Secondary outcomes:
The FIO2 algorithm was tested and compared among premature neonates with RMC and RCLAC to keep SPO2 in the target range as the primary outcomes. Heart rate alterations following decrease or increase of SPO2 and FIO2 were also sassed as our secondary outcomes.
The present study was taken from a medical student thesis with ID; IR290-441. Ethics approval was obtained from the institutional review board of Tehran University of Medical Sciences according to Helsinki declaration (IR.TUMS.SPH.REC.1395.1537). All participants' parents gave written consent before enrollment. All gathered data were considered confidential and no extra cost was imposed on our participants.
Data Acquisition and Analysis
RCLAC software had the ability to record and save data correlated demographic characteristics, respiratory or heart co-morbidities, FIO2, SPO2, PI, temperature, and ABG results on the mobile phone. Moreover, this database had the potential to be connected to central database. Local database from the mobile phone were transferred to a PC-Computer. Then information was extracted from database with SQL query. All data were classified by patients file numbers. Recorded data were analyzed to compare the FIO2 between RMC and RCLAC groups. All statistical analyses were conducted using SPSS 19. Data were presented as mean±standard deviation for continuous variables and n (%) for categorical variables. Kolmogorov-Smirnov Test and T-Test were used for analyzing the relationships between variables.