Validation of the Pediatric Index of Mortality 3 in a Single Pediatric Intensive Care Unit in Korea

Incidence and Mortality Trends in Critically Ill Children: A Korean Population-Based Study

BACKGROUND

Monitoring mortality trends can help design ways to improve survival, but observation of national mortality trends in critically ill children is lacking for the Korean population.

METHODS

We analyzed the incidence and mortality trends of children younger than 18 years admitted to an intensive care unit (ICU) from 2012 to 2018 using the Korean National Health Insurance database. Neonates and neonatal ICU admissions were excluded. Multivariable logistic regression analyses were performed to estimate the odds ratio of in-hospital mortality according to admission year. Trends in incidence and in-hospital mortality of subgroups according to admission department, age, presence of intensivists, admissions to pediatric ICU, mechanical ventilation, and use of vasopressors were evaluated.

RESULTS

The overall mortality of critically ill children was 4.4%. There was a significant decrease in mortality from 5.5% in 2012 to 4.1% in 2018 (P for trend < 0.001). The incidence of ICU admission in children remained around 8.5/10,000 population years (P for trend = 0.069). In-hospital mortality decreased by 9.2% yearly in adjusted analysis (P < 0.001). The presence of dedicated intensivists (P for trend < 0.001, mortality decrease from 5.7% to 4.0%) and admission to pediatric ICU (P for trend < 0.001, mortality decrease from 5.0% to 3.2%) were associated with significant decreasing trends in mortality.

CONCLUSION

Mortality among critically ill children improved during the study period, and the improving trend was prominent in children with high treatment requirements. Varying mortality trends, according to ICU organizations, highlight that advances in medical knowledge should be supported structurally.

Clinical implications of discrepancies in predicting pediatric mortality between Pediatric Index of Mortality 3 and Pediatric Logistic Organ Dysfunction-2

Background

Pediatric Index of Mortality 3 (PIM 3) and Pediatric Logistic Organ Dysfunction-2 (PELOD-2) are validated tools for predicting mortality in children. Research suggests that these tools may have different predictive performance depending on patient group characteristics. Therefore, we designed this study to identify the factors that make the mortality rates predicted by the tools different.

Methods

This retrospective study included patients (<18 years) who were admitted to a pediatric intensive care unit from July 2017 to May 2019. After defining the predicted mortality of PIM 3 minus the predicted mortality rate of PELOD-2 as “difference in mortality prediction,” the clinical characteristics significantly related to this were analyzed using multivariable regression analysis. Predictive performance was analyzed through the Hosmer-Lemeshow test and area under the receiver operating characteristic curve (AUROC).

Results

In total, 945 patients (median [interquartile range] age, 3.0 [0.0–8.0] years; girls, 44.7%) were analyzed. The Hosmer-Lemeshow test revealed AUROCs of 0.889 (χ²=10.187, P=0.313) and 0.731 (χ²=6.220, P=0.183) of PIM 3 and PELOD-2, respectively. Multivariable linear regression analysis revealed that oxygen saturation, partial pressure of CO₂, base excess, platelet counts, and blood urea nitrogen levels were significant factors. Patient condition-related factors such as cardiac bypass surgery, seizures, cardiomyopathy or myocarditis, necrotizing enterocolitis, cardiac arrest, leukemia or lymphoma after the first induction, bone marrow transplantation, and liver failure were significantly related (P<0.001).

Conclusions

Both tools predicted observed mortality well; however, caution is needed in interpretation as they may show different prediction results in relation to specific clinical characteristics.

Evaluating the Ability of PRISM4 and PIM3 to Predict Mortality in Patients Admitted to Pediatric Intensive Care Unit; a Diagnostic Accuracy Study

Introduction

Limited resources and the large number of children in need of services in the pediatric intensive care unit (PICU) emphasize the need for effective allocation of resources for improving the outcome of at-risk patients. This study aimed to evaluate and compare the accuracy of PRISM4 and PIM3 systems in prediction of in-hospital mortality of patients admitted to PICU.

Methods

The present retrospective cross-sectional study was a diagnostic accuracy study performed on patients admitted to PICU of Qods Hospital, Qazvin, Iran, during one year. Scores of PRISM4 and PIM3 scales were calculated for each patient using the available calculators, and the outcome of patients regarding in-hospital mortality was recorded. Finally, screening performance characteristics of the mentioned scales in prediction of patients' mortality were calculated and reported.

Results

218 patients with the mean age of 40.68 ± 37.92 (2-160) months were studied (57.8% female). There was a significant direct correlation between PIM3 score and duration of stay in PICU (p < 0.0001; r = 0.259), need for inotropic drug administration (p = 0.001), and mortality rate (p = 0.001). In addition, area under the receiver operating characteristic (ROC) curve of PIM3 and PRISM4 in prediction of mortality among patients admitted to the PICU was 0.939 (95%CI: 0.880 - 0.998) and 0.660 (95%CI: 0.371 - 0.950), respectively (p = 0.001). Based on the findings, the best cut-off point for PIM3 scale in prediction of mortality was the score of 4 and it was estimated to be the core of 8 for PRISM4 scale. Sensitivity and specificity of PIM3 scale in prediction of mortality in the cut-off of 4 points were 100.00 (95% CI: 56.09- 100.00) and 81.51 (95% CI: 75.47- 86.38), respectively. These measures were 42.85 (95%CI: 11.80- 79.76) and 98.10 (95%CI: 94.89- 99.39) for PRISM4 model, which indicates the higher sensitivity of PIM3 system in this regard.

Conclusion

based on the results of the present study, the accuracy of PIM3 is significantly higher than PRISM4 in prediction of in-hospital mortality among patients admitted to the PICU. It seems that considering the 100% sensitivity of PIM3 in prediction of outcome, this model is a better tool for screening patients who are at risk for in-hospital mortality in order to pay more attention and allocate more resources to improve their outcome.

Internal validation and evaluation of the predictive performance of models based on the PRISM-3 (Pediatric Risk of Mortality) and PIM-3 (Pediatric Index of Mortality) scoring systems for predicting mortality in Pediatric Intensive Care Units (PICUs)

PURPOSE

The study was aimed to assess the prognostic power The Pediatric Risk of Mortality-3 (PRISM-3) and the Pediatric Index of Mortality-3 (PIM-3) to predict in-hospital mortality in a sample of patients admitted to the PICUs.

DESIGN AND METHODS

The study was performed to include all children younger than 18 years of age admitted to receive critical care in two hospitals, Mashhad, northeast of Iran from December 2017 to November 2018. The predictive performance was quantified in terms of the overall performance by measuring the Brier Score (BS) and standardized mortality ratio (SMR), discrimination by assessing the AUC, and calibration by applying the Hosmer-Lemeshow test.

RESULTS

A total of 2446 patients with the median age of 4.2 months (56% male) were included in the study. The PICU and in-hospital mortality were 12.4 and 16.14%, respectively. The BS of the PRISM-3 and PIM-3 was 0.088 and 0.093 for PICU mortality and 0.108 and 0.113 for in-hospital mortality. For the entire sample, the SMR of the PRISM-3 and PIM-3 were 1.34 and 1.37 for PICU mortality and 1.73 and 1.78 for in-hospital mortality, respectively. The PRISM-3 demonstrated significantly higher discrimination power in comparison with the PIM-3 (AUC = 0.829 vs 0.745) for in-hospital mortality. (AUC = 0.779 vs 0.739) for in-hospital mortality. The HL test revealed poor calibration for both models in both outcomes.

CONCLUSIONS

The performance measures of PRISM-3 were better than PIM-3 in both PICU and in-hospital mortality. However, further recalibration and modification studies are required to improve the predictive power to a clinically acceptable level before daily clinical use.

PRACTICE IMPLICATIONS

The calibration of the PRISM-3 model is more satisfactory than PIM-3, however both models have fair discrimination power.

Peripheral Nucleated Red Blood Cells and Mortality in Critically Ill Children

The present retrospective cohort study examines whether there is an association between circulating nucleated red blood cells (nRBCs) and mortality in critically ill children. nRBCs are erythropoietic progenitor cells not found in peripheral blood of healthy adults and children beyond the neonatal period. The presence of circulating nRBCs is associated with poor prognosis in adults and neonates, though little is known about their significance in children. Admissions to both the general and cardiac pediatric intensive care unit at the Stollery Children's Hospital in Edmonton, Alberta between January 1, 2015 and December 31, 2017 were examined, and logistic regression was performed to ascertain the association between the peak absolute nRBC counts and in-hospital mortality in critically ill children. A total of 2065 admissions were included. The number of admissions with detectable nRBCs was 386 (prevalence: 13.9%), and the number of deaths was 93 (mortality: 4.5%). A statistically significant association was found between the absolute value of nRBC peak and intensive care unit mortality (odds ratio=1.37; 95% confidence interval: 1.13-1.67; P=0.002) as well as hospital mortality (odds ratio=1.38; 95% confidence interval: 1.12-1.70; P=0.003) independent of the Pediatric Index of Mortality 3 score (PIM3). This result warrants more attention to nRBC values and their potential clinical use.

Multicenter validation of PIM3 and PIM2 in Brazilian pediatric intensive care units

OBJECTIVE

To validate the PIM3 score in Brazilian PICUs and compare its performance with the PIM2.

METHODS

Observational, retrospective, multicenter study, including patients younger than 16 years old admitted consecutively from October 2013 to September 2019. We assessed the Standardized Mortality Ratio (SMR), the discrimination capability (using the area under the receiver operating characteristic curve - AUROC), and the calibration. To assess the calibration, we used the calibration belt, which is a curve that represents the correlation of predicted and observed values and their 95% Confidence Interval (CI) through all the risk ranges. We also analyzed the performance of both scores in three periods: 2013-2015, 2015-2017, and 2017-2019.

RESULTS

41,541 patients from 22 PICUs were included. Most patients aged less than 24 months (58.4%) and were admitted for medical conditions (88.6%) (respiratory conditions = 53.8%). Invasive mechanical ventilation was used in 5.8%. The median PICU length of stay was three days (IQR, 2-5), and the observed mortality was 1.8% (763 deaths). The predicted mortality by PIM3 was 1.8% (SMR 1.00; 95% CI 0.94-1.08) and by PIM2 was 2.1% (SMR 0.90; 95% CI 0.83-0.96). Both scores had good discrimination (PIM3 AUROC = 0.88 and PIM2 AUROC = 0.89). In calibration analysis, both scores overestimated mortality in the 0%-3% risk range, PIM3 tended to underestimate mortality in medium-risk patients (9%-46% risk range), and PIM2 also overestimated mortality in high-risk patients (70%-100% mortality risk).

CONCLUSIONS

Both scores had a good discrimination ability but poor calibration in different ranges, which deteriorated over time in the population studied.

Administrative data in pediatric critical care research-Potential, challenges, and future directions

Heterogenous patient populations with small case numbers constitute a relevant barrier to research in pediatric critical care. Prospective studies bring along logistic barriers and-if interventional-ethical concerns. Therefore, retrospective observational investigations, mainly multicenter studies or analyses of registry data, prevail in the field of pediatric critical care research. Administrative health care data represent a possible alternative to overcome small case numbers and logistic barriers. However, their current use is limited by a lack of knowledge among clinicians about the availability and characteristics of these data sets, along with required expertise in the handling of large data sets. Specifically in the field of critical care research, difficulties to assess the severity of the acute disease and estimate organ dysfunction and outcomes pose additional challenges. In contrast, trauma research has shown that classification of injury severity from administrative data can be achieved and chronic disease scores have been developed for pediatric patients, nurturing confidence that the remaining obstacles can be overcome. Despite the undoubted challenges, interdisciplinary collaboration between clinicians and methodologic experts have resulted in impactful publications from across the world. Efforts to enable the estimation of organ dysfunction and measure outcomes after critical illness are the most urgent tasks to promote the use of administrative data in critical care. Clever analysis and linking of different administrative health care data sets carry the potential to advance observational research in pediatric critical care and ultimately improve clinical care for critically ill children.

Performance of Pediatric Index of Mortality in a Tertiary Care PICU in India

Pediatric index of mortality-3 (PIM-3) is the latest update of one of the commonly used scoring systems in pediatric intensive care. It has free accessibility and is easy to use. However, there are some skepticisms regarding its practical usefulness in resource-limited settings. Hence, there is a need to generate region-specific data to evaluate its performance in different case mixes and resource constraints. The aim of the study is to evaluate the performance of the PIM-3 score in predicting mortality in a tertiary care PICU of a developing country. This was a retrospective cohort study. All children aged 1 month to 18 years admitted to the PICU during the study period from July 2016 to December 2018 were included. We reviewed the patient admission details and the case records of the enrolled. patients. Patient demographics, disease profile, co-morbidities, and PIM-3 scores were recorded along with the outcome. Area under receiver operating characteristics (AUROC) curves was used to determine discrimination. Standardized mortality ratio (SMR) and Hosmer Lemeshow goodness of fit were used to assess the calibration. Out of 282 children enrolled, 62 (21.9%) died. 58.5% of the patients were males, and 60% were less than 5 years of age. The principal diagnoses included respiratory and neurological conditions. The AUROC for PIM-3 was 0.961 (95% CI [0.93, 0.98]) and overall SMR was 1.28 (95% CI [0.96, 1.59]). Hosmer-Lemeshow goodness-of-fit was suggestive of poor calibration (χ 2 = 11.7, p < 0.05). We concluded that PIM-3 had good discrimination but poor calibration in our PICU setting.

COVID-19 and Co-infection in Children: The Indian Perspectives

BACKGROUND AND OBJECTIVES

Assessing the co-infections with COVID-19 is crucial to delineate its true clinical impact. Pediatric information in this aspect is limited. Our study aims to analyze the spectrum of co-infections in pediatric COVID-19 patients and determine the clinical as well as laboratory parameters predicting co-infection.

METHODOLOGY

In this prospective observational study conducted from June to December 2020 in a single tertiary care institution, data pertaining to demographic, illness and treatment-related variables were analyzed among two subsets of pediatric patients of age 1 month-12 years with RT-PCR-confirmed COVID-19 infection-Group A: those with confirmed co-infection and Group B: moderate to severe disease without co-infection. Among Group A, etiology of co-infection was characterized through relevant microbiological examination within 48 h admission.

RESULT

Among our study population, 15.03% and 20.6% had co-infections and moderate to severe disease respectively. Among those with confirmed co-infection, 32.5%, 11.6% and 6.97% recorded blood culture, respiratory secretion and CSF growth, respectively, the picture being dominated by Methicillin resistant and sensitive Staphylococcus aureus. Serum serology demonstrated Scrub typhus infection to be most prevalent. Concurrent respiratory viral infections were seen in 11.6%. Children with co-infection had significantly higher morbidity and need for supportive therapy. Predictors of co-infection were localization of infection, Neutrophil count ≥10×109, age-specific lymphopenia, CRP > 100 mg/dl and hyperferritinemia.

CONCLUSION

Co-infections are an important factor prognosticating pediatric COVID infection. Their early detection, prompt and appropriate treatment is of paramount importance.

Pediatric Index of Mortality 3-An Evaluation of Function Among ICUs In South Africa

OBJECTIVES

To evaluate the performance of the Pediatric Index of Mortality 3 as mortality risk assessment model.

DESIGN

This prospective study included all admissions 30 days to 18 years old for 12 months during 2016 and 2017. Data gathered included the following: age and gender, diagnosis and reason for PICU admission, data specific for the Pediatric Index of Mortality 3 calculation, PICU outcomes (death or survival), and length of PICU stay.

SETTING

Nine units that care for children within tertiary or quaternary academic hospitals in South Africa.

PATIENTS

All admissions 30 days to 18 years old, excluding premature infants, children who died within 2 hours of admission, or children transferred to other PICUs, and those older than 18 years old.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

There were 3,681 admissions of which 2,253 (61.3%) were male. The median age was 18 months (interquartile range, 6-59.5 mo). There were 354 deaths (9.6%). The Pediatric Index of Mortality 3 predicted 277.47 deaths (7.5%). The overall standardized mortality ratio was 1.28. The area under the receiver operating characteristic curve was 0.81 (95% CI 0.79-0.83). The Hosmer-Lemeshow goodness-of-fit test statistic was 174.4 (p < 0.001). Standardized mortality ratio for all age groups was greater than 1. Standardized mortality ratio for diagnostic subgroups was mostly greater than 1 except for those whose reason for PICU admission was classified as accident, toxin and envenomation, and metabolic which had an standardized mortality ratio less than 1. There were similar proportions of respiratory patients, but significantly greater proportions of neurologic and cardiac (including postoperative) patients in the Pediatric Index of Mortality 3 derivation cohort than the South African cohort. In contrast, the South African cohort contained a significantly greater proportion of miscellaneous (including injury/accident victims) and postoperative noncardiac patients.

CONCLUSIONS

The Pediatric Index of Mortality 3 discrimination between death and survival among South African units was good. Case-mix differences between these units and the Pediatric Index of Mortality 3 derivation cohort may partly explain the poor calibration. We need to recalibrate Pediatric Index of Mortality 3 to the local setting.

Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

The aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

Comparison of Performance of the Pediatric Index of Mortality (PIM)-2 and PIM-3 Scores in the Pediatric Intensive Care Unit of a High Complexity Institution

OBJECTIVE

To determine the performance of each of the available pediatric index of mortality (PIM) scores, by assessing the capability for discrimination and calibration in patients admitted to a pediatric intensive care unit in Bogotá.

DESIGN AND SETTING

We designed a retrospective, observational cohort study, which included all patients aged between a month and 17 years and 364 days, admitted to the pediatric intensive care unit of a high complexity university hospital between April 1, 2016 and December 31, 2018. We analyzed the standardized mortality ratio, discrimination, calibration, and net reclassification index (NRI) for each model.

RESULTS

A total of 722 patients were included, the mortality rate was 3.74%, and for PIM-3, the ratio between expected and observed mortality was 0.66 [confidence interval (CI) 0.40-1.05] for PIM-2 and 1.00 (CI 0.59-1.68) for PIM-3. The Hosmer-Lemeshow (HL) test suggests inadequate calibration for PIM-2 (HL = 13.18, p = 0.11) and adequate calibration for PIM-3 (HL = 28.08, p < 0.01). The area under the diagnostic performance curves for PIM-2 and PIM-3 were 0.87 (95% CI 0.80-0.94) and 0.89 (95% CI 0.82-0.95), respectively. The NRI was -27.1%. PIM-3 classified survivors better than PIM-2, but inadequately classified nonsurvivors.

CONCLUSION

Although both models show adequate discrimination ability, PIM-3 shows a better correlation between predicted risk score and observed mortality. Thus, it may be a useful tool for measuring the internal processes of intensive care units in Colombia and for making comparisons between groups of similar characteristics.

HOW TO CITE THIS ARTICLE

Quiñónez-López D, Patino-Hernandez D, Zuluaga CA, García ÁA, Muñoz-Velandia OM. Comparison of Performance of the Pediatric Index of Mortality (PIM)-2 and PIM-3 Scores in the Pediatric Intensive Care Unit of a High Complexity Institution. Indian J Crit Care Med 2020;24(11):1095-1102.

Are the Pediatric Index of Mortality 2 and 3 equal predictors of mortality? An intensive care unit-based concordance study

Objective

To determine the concordance of mortality risk classification through the use of the Pediatric Index of Mortality (PIM) 2 and 3.

Methods

Through a retrospective cohort, we evaluated patients admitted to the pediatric intensive care unit between April 2016 and December 2018. We calculated the mortality risk with the PIM 2 and 3. Analyses were carried out to determine the concordance between the risk classification obtained with both scales using unweighted and linearly weighted kappa.

Results

A total of 722 subjects were included, and 66.6% had a chronic condition. The overall mortality was 3.7%. The global kappa concordance coefficient for classifying patients according to risk with the PIM 2 and 3 was moderate at 0.48 (95%CI 0.43 - 0.53). After linear weighting, concordance was substantial at 0.64 (95%CI 0.59 - 0.69). For cardiac surgery patients, concordance for risk classification was fair at 0.30 (95%CI 0.21 - 0.39), and after linear weighting, concordance was only moderate at 0.49 (95%CI 0.39 - 0.59). The PIM 3 assigned a lower risk than the PIM 2 in 44.8% of patients in this subgroup.

Conclusion

Our study proves that the PIM 2 and 3 are not clinically equivalent and should not be used interchangeably for quality evaluation across pediatric intensive care units. Validation studies must be performed before using the PIM 2 or PIM 3 in specific settings.

Unnecessary harm is avoided by reliable paediatric index of mortality2 scores without arterial gas sampling

AIM

To investigate whether unnecessary harm could be avoided in children admitted to paediatric intensive care (PICU), we analysed the impact of arterial blood gas on the paediatric index of mortality score2 (PIM2) and the derived predicted death rate (PDR).

METHODS

From January 1, 2008 to December 31, 2010, 1793 consecutive admissions, newborn infants to 16 years of age (median 0.71 years) from a single, tertiary PICU in Gothenburg Sweden, were collected. Admission information on arterial oxygen tension (PaO₂ ) and fraction of inspired oxygen (FiO₂ ) was extracted from 990 admissions.

RESULTS

There was close agreement between PIM2 score and PDR regardless of whether the PaO₂ /FiO₂ ratio was omitted or not. In the subgroup of admissions with a respiratory admission diagnosis, the inclusion of the PaO₂ /FiO₂ ratio increased the accuracy of the PIM2 score as well as the PDR. The standard mortality ratio was slightly but not significantly overestimated by excluding the PaO₂ /FiO₂ ratio.

CONCLUSION

To avoid unnecessary harm to children admitted to PICU, an arterial blood gas analysis should only be performed if clinically indicated or if the child has a respiratory admission diagnosis. Estimation of the PIM2 score and PDR will not be less accurate by this approach.

Performance of the Pediatric Index of Mortality 3 Score in PICUs in Argentina: A Prospective, National Multicenter Study

OBJECTIVE

To assess the performance of the Pediatric Index of Mortality 3 score in a population of children admitted to PICUs in Argentina.

DESIGN

Prospective, national, multicenter study.

SETTING

Forty-nine PICUs located in Argentina belonging to public and private institutions.

PATIENTS

All children between 1 month and 16 years old admitted to the participating PICUs between May 15, 2016, and February 15, 2017.

INTERVENTIONS

None.

MEASUREMENT AND MAIN RESULTS

A total of 6,602 patients were enrolled in the study. The observed mortality was 8% (531/6,602), whereas mortality predicted by Pediatric Index of Mortality 3 was 6.16% (407 deaths). The standardized mortality rate was 1.3 (95% CI, 1.20-1.42). The area under the receiver operating characteristic curve was 0.83 (95% CI, 0.82-0.85). The Hosmer-Lemeshow test showed that the difference between the mortality observed and the mortality predicted by Pediatric Index of Mortality 3 was statistically significant (χ, 135.63; p < 0.001).

CONCLUSIONS

The Pediatric Index of Mortality 3 score adequately discriminated patients who died from those who survived in our population. However, the observed mortality was higher than predicted by the score. The use of an updated instrument such as Pediatric Index of Mortality 3 will allow an actual comparison between pediatric intensive care provided in the country and care provided internationally. This might also allow future planning of pediatric intensive care services in Argentina.

Validation of Pediatric Index of Mortality 3 for Predicting Mortality among Patients Admitted to a Pediatric Intensive Care Unit

Background

The objective of this study was to evaluate the usefulness of the newest version of the pediatric index of mortality (PIM) 3 for predicting mortality and validating PIM 3 in Korean children admitted to a single intensive care unit (ICU).

Methods

We enrolled children at least 1 month old but less than 18 years of age who were admitted to the medical ICU between March 2009 and February 2015. Performances of the pediatric risk of mortality (PRISM) III, PIM 2, and PIM 3 were evaluated by assessing the area under the receiver operating characteristic (ROC) curve, conducting the Hosmer-Lemeshow test, and calculating the standardized mortality ratio (SMR).

Results

In total, 503 children were enrolled; the areas under the ROC curve for PRISM III, PIM 2, and PIM 3 were 0.775, 0.796, and 0.826, respectively. The area under the ROC curve was significantly greater for PIM 3 than for PIM 2 (P<0.001) and PRISM III (P=0.016). There were no significant differences in the Hosmer-Lemeshow test results for PRISM III (P=0.498), PIM 2 (P=0.249), and PIM 3 (P=0.337). The SMR calculated using PIM 3 (1.11) was closer to 1 than PIM 2 (0.84).

Conclusions

PIM 3 showed better prediction of the risk of mortality than PIM 2 for the Korean pediatric population admitted in the ICU.