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. 2019 Jan 8;6:410. doi: 10.3389/fped.2018.00410

Table 1.

Characteristics of studies of pediatric early warning systems in resource limited settings.

References Country Setting No. of subjects Age range System Study design Purpose Trigger & response Key results
Olson et al. (12) Malawi Large referral hospital with high dependency unit 54 Cases; 161 Controls < 15 years old Inpatient Triage, Assessment and Treatment (ITAT) Prospective nested case-control study Develop ITAT score to identify clinical deterioration in inpatients and prompt physician evaluation ITAT score >3 triggers physician assessment ITAT score >3: OR for death 4.8, predicts mortality with AUROC 0.76
Olson et al. (14) Malawi Large referral hospital with high dependency unit 1,642 Assessments < 15 years old Inpatient Triage, Assessment and Treatment (ITAT) Prospective quality improvement project Improve pediatric inpatient surveillance by provision of vital sign equipment implementation of vital sign assistant program ITAT score >3 triggers physician assessment Vital signs assistants associated with increased frequency and accuracy of ITAT scores & increased clinician notiifcations; Mortality reduced from 9.3% to 5.7%
Chaiyakusil and Pandee (15) Thailand Emergency Department in tertiary university hospital with ICU 1,136 < 15 years old Pediatric Early Warning Score (PEWS) Prospective descriptive study Validation of PEWS on arrival in Emergency Department to predict inpatient admission disposition None Predicts Admission: AUROC 0.73, PEWS 1 or above, Sensitivity 78%, Specificity 59.6%; Predicts ICU admission: AUC 0.98, PEWS 3 or above, Sensitivity 100%; Specificity 90.5%
George et al. (16) Uganda, Tanzania & Kenya Mix of 6 large and small hospitals 3,125*;
1,053**;
5,098**
2 months - 12 years Bedside PEWS Retrospective data used to develop and validate PET score Develop an easy to use, bedside score for risk of mortality based on clinical signs None PET score associated with mortality w/in 48 hrs w AUROC 0.77–0.86; Predicts mortality better than PEWS, PRISMIII & Aquamat
Miranda et al. (17) Brazil Pediatric referral hospital without ICU 271 0–10 years Brighton PEWS (BPEWS) Integrative Review Literature review, translation and adaptation of BPEWS for Brazilian Portuguese and Pilot test None BPEWS was successfully translated and adapted for Brazilian Portuguese and in the pilot study, 26.6% of children were considered at risk of clinical deterioration based on PEWS score >2
Miranda et al. (18) Brazil Pediatric referral hospital without ICU 271 0–10 years Brighton PEWS adapted for Brazilian Portuguese (BPEWS-Br) Prospective evaluation of diagnostic acuracy Evaluation of diagnostic accuracy of BPEWS-Br compared to provder assessment of clinical deterioration None BPEWS-Br associated with clinical signs of deterioration, AUROC 0.919; BPEWS-Br score >2, sensitivity 73.9% and specificity 95.5%
Agulnik et al. (13) Guatemala Pediatric oncology hospital with ICU 129 Cases; 129 Controls < 18 years Pediatric Early Warning Score (PEWS) Retrospective matched case-control study Validation of the ability of PEWS to predict the need for unplanned ICU transfer PEWS algorithm followed, high PEWS prompted physician and/or ICU evaluation PEWS correlated with unplanned ICU transfer: AUROC 0.94; PEWS >3, sensitivity 88% and specificity 93%; Higher PEWS prior to PICU transfer associated with increased morbidity & mortality, higher PIM2 and increased support requirements in ICU
Agulnik et al. (19) Guatemala Pediatric oncology hospital with ICU 287 < 18 years Pediatric Early Warning Score (PEWS) Retrospective cohort study the year before and year after implementation of PEWS. Describe the effect of implementation of a PEWS system on the frequency of clinical deterioration events PEWS algorithm followed, high PEWS prompted physician and/or ICU evaluation Abnormal PEWS in 93% of unplanned PICU transfer; Reduced frequency of clinical deterioration events, severe sepsis, septic shock and organ dysfunction; Reduced ICU utilization; No change in mortality
*

Development data set;

**

Validation data set.