Comparison of the pediatric risk of mortality, pediatric index of mortality, and pediatric index of mortality 2 models in a pediatric intensive care unit in China: A validation study

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.

Performance and analysis of four pediatric mortality prediction scores among critically ill children: A multicenter prospective observational study in four PICUs

OBJECTIVE

We aimed to evaluate and compare the prognostic performance of common pediatric mortality scoring systems (the Pediatric Index of Mortality 2 [PIM2], PIM3, Pediatric Risk of Mortality [PRISM], and PRISM4 scores) to determine which is the most applicable score in our pediatric study cohort.

METHODS

This prospective observational multicenter cohort study was conducted in four tertiary-care pediatric intensive care units (PICUs) in Turkey. All children, between 1 month and 16 years old, admitted to the participating PICUs between October 1, 2019, and March 31, 2020, were included in the study. Discrimination between death and survival was assessed by area under the receiver operating characteristic plot (AUC) for each model. The Hosmer-Lemeshow goodness-of-fit (GOF) test was used to assess the calibration of the models, RESULTS: A total of 570 patients (median age 35 months) were enrolled in the study. The observed mortality rate was 8.2% (47/570). The standardized mortality ratio (SMR) of PIM2, PIM3, PRISM, and PRISM4 with 95% confidence interval (CI) were 0.94 (0.68-1.23), 1.27 (0.93-1.68), 0.86 (0.63-1.13), and 1.5 (1.10-1.97), respectively. The AUC with 95% CI was 0.934 (0.91-0.96) for PIM2, 0.934 (0.91-0.96) for PIM3, 0.917 (0.88-0.95) for PRISM, and 0.926 (0.88-0.97) for PRISM4 models. The Hosmer-Lemeshow test showed that the difference between observed and predicted mortality by PIM3 (p = 0.003) and PRISM4 (p = 0.008) was statistically significant whereas PIM2 (p = 0.28) and PRISM (p = 0.62) showed good calibration.

CONCLUSION

The overall performance of (both discrimination and calibration) PRISM and PIM2 scoring systems in Turkish pediatric patients aged 1 month to 16 years was accurate and had the best fit for risk groups according to our study. Although PIM3 and PRISM4 have good discriminatory power, their calibration was very poor in our study cohort.

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.

Comparison of Pediatric Sequential Organ Failure Assessment and Pediatric Risk of Mortality III Score as Mortality Prediction in Pediatric Intensive Care Unit

Objective: To assess and compare the diagnostic accuracy of the Pediatric Risk of Mortality (PRISM) III score and Pediatric Sequential Organ Failure Assessment (p-SOFA) for the prediction of mortality in critically ill children.

Methodology: This was a cross-validation study conducted at the Pediatric Intensive Care Unit (PICU) of the National Institute of Child Health Karachi from February 2021 to July 2021. Two hundred eighty-six critically ill children of age one month to 15 years of either gender staying in PICU for more than 24 hours were included. Within 24 hours of admission, the p-SOFA and PRISM III 24 scores were calculated for all eligible children. The outcome of the study was mortality within 30 days of PICU admitted children. Data were analyzed using Statistical Package for the Social Sciences (SPSS) version 23.

Results: The median age was 24 months (range: 1-144 months). The 30-day mortality was estimated as 57%. The p-SOFA and PRISM scores were significantly greater in children who did not survive than survivors. The maximum p-SOFA score (area under the curve (AUC)=0.81, 95% CI=0.76-0.86, p=0.001) and PRISM III 24 score (AUC=0.75, 95% CI=0.69-0.81, p=0.001) had good discrimination for 30-day mortality. For the prediction of 30-day mortality at the cut-off value of p-SOFA>2, the sensitivity was 93.87%, specificity was 38.21%, and accuracy was 69.93%. Whereas at the cut-off value of PRISM III 24 score>8, the sensitivity was 55.83%, specificity was 77.24%, and accuracy was 65.03%.

Conclusion: The p-SOFA score is a good predictor for 30-day mortality in critically ill children and had better accuracy than the PRISM III 24 score.

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.

Pediatric Risk of Mortality III Score in Predicting Mortality Among Diabetic Ketoacidosis Patients in a Pediatric Intensive Care Unit

Background

Diabetic ketoacidosis (DKA) is one of the most common complications of type 1 diabetes. Mortality is not uncommon in DKA, mostly in younger children with severe DKA and those complicated with cerebral edema. Early identification of high-risk patients can help in timely interventions to improve the outcome of DKA. Pediatric Risk of Mortality (PRISM III) is a standard scoring system to objectively predict the prognosis and outcome of pediatric intensive care unit (PICU) patients.

Objective

To predict the need for inotrope and mechanical ventilation and mortality rate using PRISM III in DKA patients admitted to PICU.

Methods

A prospective observational study was conducted in the PICU of the National Institute of Child Health, Karachi, from February 2020 to September 2021 involving 114 children. PRISM III scoring protocol was applied. A PRISM III score of >8 predicted higher mortality risk. 

Results

The mean PRISM III score was 6.56 ± 3.18 with 30 (26.3%) children having a score >8. Of the 30 (26.31%) patients with >8 PRISM III scores, 14 (46.67%) needed inotropic support, 6 (20%) needed mechanical ventilation, and there were eight (26.67%) mortalities. There was no reported mortality among patients with a PRISM III score ≤8. All differences were statistically significant (p < .05).

Conclusion

PRISM III is a highly sophisticated scoring system that can aid clinicians in the early prediction of adverse clinical outcomes in patients with DKA. Robust scientific evidence supporting its clinical application can help practically improve the outcome of DKA in young patients.

Impact of and risk factors for pediatric acute kidney injury defined by the pROCK criteria in a Chinese PICU population

BACKGROUND

The definition of pediatric AKI continues to evolve. We aimed to find a better AKI definition to predict outcomes and identify risk factors for AKI in a Chinese PICU.

METHODS

This study consisted of 3338 patients hospitalized in a Chinese PICU between 2016 and 2018. AKI was defined and staged using pROCK criteria, which were compared with KDIGO criteria. AKI outcomes, including mortality, daily cost and length of stay (LOS), were assessed. Risk factors for AKI were also estimated.

RESULTS

The incidence of AKI in the PICU was 7.7% according to pROCK criteria. The characteristics of patients with KDIGO-defined AKI who did not meet the pROCK were similar to those without AKI. pROCK outperformed KDIGO in predicting mortality with a higher c index in the Cox models (0.81 versus 0.79, P = 0.013). AKI, as well as AKI stages, were associated with higher mortality (HR: 10.5, 95%CI: 6.66-19.5), daily cost (β = 2064, P < 0.01) and LOS (β = 2.30, P < 0.01). Age, comorbidities, mechanical ventilation (MV), pediatric critical illness score (PCIS) and exposure to drugs had significant influence on AKI occurrence.

CONCLUSIONS

The mortality predictability of pROCK was slightly greater than that of KDIGO. Older age, underlying comorbidities, MV, decreased PCIS and exposure to drugs were potential risk factors for AKI.

IMPACT

Two AKI criteria, pROCK and KDIGO, were significantly associated with an increased risk of mortality and pROCK was slightly greater than that of KDIGO. Older age, comorbidities, mechanical ventilation, decreased PCIS and exposure to drugs were potential risk factors for AKI. This study first used the pROCK criteria to provide an epidemiologic description of pediatric AKI in Chinese PICU. This study compared the AKI outcomes across the pROCK and KDIGO AKI criteria, indicating the prior utility for AKI classification in Chinese children. This study indicated that the potential risk factors for AKI were older age, comorbidities, mechanical ventilation, decreased PCIS and exposure to drugs.

Validity of Pediatric Index of Mortality 2 score as an Outcome Predictor in Pediatric ICU of a Public Sector Tertiary Care Hospital in Pakistan

Pediatric Index of Mortality 2 (PIM-2) is one of the leading mortality scores used in intensive care units all around the world. We assessed its validity as an outcome predictor in a pediatric intensive care unit (PICU) of Mayo Hospital/King Edward Medical University Lahore, Pakistan. We enrolled 154 consecutive admissions, aged 1 month to 13 years, requiring intensive care from January to June of 2019. Patient demographics along with PIM-2 data were collected; PIM-2 score and mortality risk was calculated; and the outcome recorded as death or survival. The median age at admission was 0.50 years (interquartile range [IQR]: 0.24–1.78) and the median weight was 5.0 kg (IQR: 3.08–10.0) with females constituting 54%; malnutrition was also common (66%). Observed mortality was 29.9% (46 out of 154) and expected mortality (cut-off ≥ 99.8%) was 27.9% with a standardized mortality ratio of 1.07 (95% confidence interval [CI]: 0.79–1.41). Sepsis was the most common diagnosis at admission (27.9%) with the highest mortality (52.2%). Chi-square analysis revealed a sensitivity of 54.3% and a specificity of 83.3% (p-value 0.00). PIM-2 score showed acceptable discrimination between survivors and nonsurvivors with an area under the receiver operating characteristic curve of 0.75 (95% CI: 0.67–0.84) (p-value = 0.00); however, poor calibration according to Hosmer–Lemeshow goodness of fit test (Chi-square = 15.80, df = 7, and p-value of 0.027 [< 0.1]), thus requiring recalibration according to local population characteristics.

Comparison of pediatric scoring systems for mortality in septic patients and the impact of missing information on their predictive power: a retrospective analysis

Background

Scores can assess the severity and course of disease and predict outcome in an objective manner. This information is needed for proper risk assessment and stratification. Furthermore, scoring systems support optimal patient care, resource management and are gaining in importance in terms of artificial intelligence.

Objective

This study evaluated and compared the prognostic ability of various common pediatric scoring systems (PRISM, PRISM III, PRISM IV, PIM, PIM2, PIM3, PELOD, PELOD 2) in order to determine which is the most applicable score for pediatric sepsis patients in terms of timing of disease survey and insensitivity to missing data.

Methods

We retrospectively examined data from 398 patients under 18 years of age, who were diagnosed with sepsis. Scores were assessed at ICU admission and re-evaluated on the day of peak C-reactive protein. The scores were compared for their ability to predict mortality in this specific patient population and for their impairment due to missing data.

Results

PIM (AUC 0.76 (0.68-0.76)), PIM2 (AUC 0.78 (0.72-0.78)) and PIM3 (AUC 0.76 (0.68-0.76)) scores together with PRSIM III (AUC 0.75 (0.68-0.75)) and PELOD 2 (AUC 0.75 (0.66-0.75)) are the most suitable scores for determining patient prognosis at ICU admission. Once sepsis is pronounced, PELOD 2 (AUC 0.84 (0.77-0.91)) and PRISM IV (AUC 0.8 (0.72-0.88)) become significantly better in their performance and count among the best prognostic scores for use at this time together with PRISM III (AUC 0.81 (0.73-0.89)). PELOD 2 is good for monitoring and, like the PIM scores, is also largely insensitive to missing values.

Conclusion

Overall, PIM scores show comparatively good performance, are stable as far as timing of the disease survey is concerned, and they are also relatively stable in terms of missing parameters. PELOD 2 is best suitable for monitoring clinical course.

Performance of Three Mortality Prediction Scores and Evaluation of Important Determinants in Eight Pediatric Intensive Care Units in China

Background: The mortality prediction scores were widely used in pediatric intensive care units. However, their performances were unclear in Chinese patients and there were also no reports based on large sample sizes in China. This study aims to evaluate the performances of three existing severity assessment scores in predicting PICU mortality and to identify important determinants. Methods: This prospective observational cohort study was carried out in eight multidisciplinary, tertiary-care PICUs of teaching hospitals in China. All eligible patients admitted to the PICUs between Aug 1, 2016, and Jul 31, 2017, were consecutively enrolled, among whom 3,957 were included for analysis. We calculated PCIS, PRISM IV, and PELOD-2 scores based on patient data collected in the first 24 h after PICU admission. The in-hospital mortality was defined as all-cause death within 3 months after admission. The discrimination of mortality was assessed using the area under the receiver-operating characteristics curve (AUC) and calibrated using the Hosmer-Lemeshow goodness-of-fit test. Results: A total of 4,770 eligible patients were recruited (median age 18.2 months, overall mortality rate 4.7%, median length of PICU stay 6 days), and 3,957 participants were included in the analysis. The AUC (95% confidence intervals, CI) were 0.74 (0.71-0.78), 0.76 (0.73-0.80), and 0.80 (0.77-0.83) for PCIS, PRISM IV, and PELOD-2, respectively. The Hosmer-Lemeshow test gave a chi-square of 3.16 for PCIS, 2.16 for PRISM IV and 4.81 for PELOD-2 (p ≥ 0.19). Cox regression identified five predictors from the items of scores better associated with higher death risk, with a C-index of 0.83 (95%CI 0.79-0.86), including higher platelet (HR = 1.85, 95% CI 1.59-2.16), invasive ventilation (HR = 1.40, 1.26-1.55), pupillary light reflex (HR = 1.31, 95% CI 1.22-1.42) scores, lower pH (HR 0.89, 0.84-0.94), and extreme PaO₂ (HR 2.60, 95% CI 1.61-4.19 for the 1st quantile vs. 4th quantile) scores. Conclusions: Performances of the three scores in predicting PICU mortality are comparable, and five predictors were identified with better prediction to PICU mortality in Chinese patients.

Etiologies and Clinical Outcomes of Patients With Secondary Hemophagocytic Lymphohistiocytosis at a Tertiary PICU

OBJECTIVES

To assess the etiologies and outcomes of patients with secondary hemophagocytic lymphohistiocytosis in the PICU.

DESIGN

Prospective observational cohort study.

SETTING

A single PICU at a pediatric tertiary hospital in Hanoi, Vietnam.

PATIENTS

Pediatric patients meeting the criteria for secondary hemophagocytic lymphohistiocytosis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Between June 2017 and May 2018, 25 consecutive patients with a mean (SD) age of 23.3 months (21.6 mo) were included. Collected variables included etiologies of hemophagocytic lymphohistiocytosis and clinical and laboratory findings at admission. The Pediatric Index of Mortality 2 score at admission was calculated. Outcomes were death and multiple organ dysfunction. The severity of multiple organ dysfunction was assessed by the Pediatric Logistic Organ Dysfunction 2 score. The mean (SD) Pediatric Index of Mortality 2 predicted mortality rate was 5.6% (7.6%). Cytomegalovirus and Epstein-Barr virus coinfections (60%) were the most common suspected etiology of hemophagocytic lymphohistiocytosis. Other etiologies included Epstein-Barr virus sole infections (20%), cytomegalovirus sole infections (16%), and one unknown cause (4%). Multiple organ dysfunction (excluding hematologic failure) was found in 22 patients (88%) with death occurring in 14 patients (56%). The mean (SD) Pediatric Logistic Organ Dysfunction 2 predicted mortality rate among patients with multiple organ dysfunction was 11.9% (11.2%). Despite having lower Pediatric Index of Mortality 2 predicted mortality rates at admission, Epstein-Barr virus-cytomegalovirus coinfection cases with multiple organ dysfunction had slightly greater Pediatric Logistic Organ Dysfunction 2 predicted mortality rates than Epstein-Barr virus sole infection cases with multiple organ dysfunction: 12.2% (10.5%) versus 11.3% (11.0%). However, these rates were lower than cytomegalovirus sole infection cases with multiple organ dysfunction (14.4% [16.3%]). Area under the curve values for Pediatric Index of Mortality 2 and Pediatric Logistic Organ Dysfunction 2 were 0.74 (95% CI, 0.52-0.95) and 0.78 (95% CI, 0.52-1.00), respectively, suggesting that both scales were fair to good at predicting mortality.

CONCLUSIONS

Viral infections, particularly Epstein-Barr virus-cytomegalovirus coinfections, were a common cause of secondary hemophagocytic lymphohistiocytosis. The implication of these coinfections on the clinical course of hemophagocytic lymphohistiocytosis needs to be delineated.