The Modified Pediatric Early Warning Score Innovation Project (mPEWS-InPro) Mobile-Based Application Development

Another Way of Monitoring A Child's Clinical Deterioration

Lia Kartika, Ns, MKep., Sp.Kep.An; Dessie Wanda, PhD, MN, S.Kp; Nani Nurhaeni, Dr., MN, S.Kp

Disclosures

Pediatr Nurs. 2021;47(1):38-44. 

In This Article

Methods

Design

This study was an initial validation study of modified Pediatric Early Warning System (mPEWS)-InPro mobile-based application. To start, the first author conducted a literature search to identify the most suitable PEWS system that has the best sensitivity and specificity test. Second, the mPEWS was developed based on Fitzsimonz's (2017) PEWS due to the simplicity of the parameters measured as initial physiological parameters and the suitability of the parameters to mobile app. Third, the mPEWS system was applied to the pediatric patients, while at the same time, the comparison PEWS was also applied. After the implementation of mPEWS, the first author conducted a survey using an online platform that focused on exploring the advantages and disadvantages of mPEWS. To guide the method, the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) was used (this checklist can be downloaded at https://www.tripod-statement.org) (Moons et al., 2015).

Sample

Respondents involved were pediatric nurses caring for pediatric patients age one month to 17 years being treated in an infectious pediatric ward in a top referral hospital in the capital city of Indonesia. This study was an initial validation study; thus, the researchers chose the infectious pediatric ward as the research setting where the first author had her clinical practice at the time of data collection.

Instruments

In establishing the physiological parameters of age-appropriate applications, PEWS parameters from Fitzsimonz (2017) were adapted. However, one parameter was excluded from the original version: parents' concern regarding their patient's condition. This exclusion was due to two reasons: 1) the difficulty of maintaining the clinical effectiveness of family involvement (Albutt et al., 2017) and 2) the differences in research setting, where in the Irish context, parents' education is part of the process, while in this study, parents' education was not part of the process prior to the evaluation using the mPEWS mobile.

To see the effectiveness of the mPEWS-InPro scores (which was developed based on Fitzsimonz's PEWS), scores from mPEWS-InPro were compared to those from PEWS from Duncan and colleagues (2006). The researchers chose Duncan's PEWS for comparison because the PEWS scores had a higher sensitivity and specificity in assessing the clinical deterioration of children. Through the receiver operating characteristic (ROC) curve, the PEWS score from Duncan and colleagues (2006) was a stronger predictor of an impending or actual cardiopulmonary arrest than another PEWS tool by Haines and colleagues (2006) or the Bedside PEW System Score by Parshuram and colleagues (2009). The optimal trigger score of 5 for PEWS score by Duncan and colleagues (2006) demonstrated the best balance between sensitivity (86.6%) and specificity (72.2%) when compared to other tools. The PEWS score by Duncan and colleagues (2006) also demonstrated a significantly greater amount of accuracy (p < 0.05 with an AUROC of 0.85) (Robson et al., 2013). In this study, clinical deterioration is defined as a condition of patients who have a risk of cardiorespiratory arrest or a clinical manifestation of impending cardiorespiratory arrest that leads to the call of the RRT. A comparison of the parameters of three PEWS tools is presented in Table 1. This assessment quantitatively summed up the items of clinical conditions and physiological parameters on the paper-based PEWS (Duncan et al., 2006) and mPEWS-InPro-based mobile instrument.

The initial prototype application development was achieved with the assistance of technology and informatics experts. mPEWS-InPro was composed of eight parameters, including respiratory rate, respiratory effort, oxygen therapy, oxygen saturation, heart rate, systolic blood pressure, capillary refill time, and consciousness. Discussions on display, prototype flow, and notification flow occurred face-to-face to avoid misunderstandings. Random application testing on the physiological parameters of children according to age was performed three times per age category. Some mPEWS-InPro screen views are presented in Figure 1.

Figure 1.

mPEWS-InPro Screen Views

As described in Figure 1, the application login used a special username and password. Those who had login access consisted of the nurse, the nurse in charge, the head of the room, the duty doctor, and the doctor in charge. After completing the observation, the nurse then inputs the patient's eight physiological parameters. mPEWS-InPro then automatically calculates the total score and color code and indicates what interventions should be immediately implemented by the charge nurses. The escalated intervention and color codes appear through notification of the application according to the type of user. The stored data are secure within the server. A detailed patient data history can be traced in by anyone with access. A panic button can be used if the patient experienced seizures or bleeding.

Table 2 describes the specifications of mPEWS-InPro, which focus on device, minimum speed, and connectivity.

Procedure

This research was conducted with the approval of the Ethics Committee of Faculty of Nursing at Universitas Indonesia on February 7, 2018 (certificate number of the ethics approval letter – 24/UN2.F12.D/HKP.0204/2018). The research took place from March 2018 to April 2018. Clinical deterioration assessments were performed in 3x measurements on 135 treated children.

Data Analysis

The Statistical Package for the Social Sciences (SPSS) for Windows was used to analyze data. To test the validity of the mPEWS-InPro compared to the reference standard, sensitivity, specificity, receiver operating characteristic (ROC) curve, and the area under the ROC curve were calculated. Data were presented in the form of tables and graphs.

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