Meta-Analysis for the Prediction of Mortality Rates in a Pediatric Intensive Care Unit Using Different Scores: PRISM-III/IV, PIM-3, and PELOD-2

Shen, Yulong; José, Juán

doi:10.3389/fped.2021.712276

Cited by 19 publications

(14 citation statements)

References 42 publications

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“…However, developing the optimal scoring method for critically ill children admitted to the PICU remains a challenging task [19]. Different methods are used in PICUs for the prediction of severity of disease and risk of mortality, including Pediatric Index of Mortality (PIM and PIM2), Pediatric Risk of Mortality (PRISM, PRISM III), Sequential Organ Failure Assessment (SOFA), Pediatric Sequential Organ Failure Assessment Score (p-SOFA), and the Paediatric Logistic Organ Dysfunction (PELOD) score [1,2,[5][6][7][8][9]. Although these scoring systems are effective in ICUs all over the world, it is important to evaluate them before using them in healthcare settings that are very different from where they were established [3,20,21].…”

Section: Discussionmentioning

confidence: 99%

“…Several prognostic scoring systems like Pediatric Index of Mortality (PIM and PIM2), Pediatric Risk of Mortality (PRISM, PRISM III), Sequential Organ Failure Assessment (SOFA), Pediatric Sequential Organ Failure Assessment Score (p-SOFA), and the Paediatric Logistic Organ Dysfunction (PELOD) score have been developed to predict PICU children's morbidity and death, which can be extremely helpful in treatment planning [1,2,[5][6][7][8][9]. The PRISM III 24 score is a commonly used system that is used to evaluate various scoring systems.…”

Section: Introductionmentioning

confidence: 99%

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

Baloch¹,

Shaikh²,

Gowa³

et al. 2022

Cureus

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Baloch¹,

Shaikh²,

Gowa³

et al. 2022

Cureus

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“…The last recorded temperature on a patient’s vital signs flowchart just before the REE IC measurement was documented. The severity of illness was assessed using the PRISM-III and the PELOD-2 scores [ 31 ], and the amount of care was assessed using the Therapeutic Intervention Scoring System (TISS) [ 32 ]. The ventilatory settings at the time of the measurement and the route of nutrition support, and the total calories received for the 24-h period before metabolic measurement were also recorded.…”

Section: Methodsmentioning

confidence: 99%

External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study

et al. 2022

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We evaluated the validity of sixteen predictive energy expenditure equations for resting energy expenditure estimation (eREE) against measured resting energy expenditure using indirect calorimetry (REEIC) in 153 critically ill children. Predictive equations were included based on weight, height, sex, and age. The agreement between eREE and REEIC was analyzed using the Bland–Altman method. Precision was defined by the 95% limits of the agreement; differences > ±10% from REEIC were considered clinically unacceptable. The reliability was assessed by the intraclass correlation coefficient (Cronbach’s alpha). The influence of anthropometric, nutritional, and clinical variables on REEIC was also assessed. Thirty (19.6%) of the 153 enrolled patients were malnourished (19.6%), and fifty-four were overweight (10.5%) or obese (24.8%). All patients received sedation and analgesia. Mortality was 3.9%. The calculated eREE either underestimated (median 606, IQR 512; 784 kcal/day) or overestimated (1126.6, 929; 1340 kcal/day) REEIC compared with indirect calorimetry (928.3, 651; 1239 kcal/day). These differences resulted in significant biases of −342 to 592 kcal (95% limits of agreement (precision)−1107 to 1380 kcal/day) and high coefficients of variation (up to 1242%). Although predicted equations exhibited moderate reliability, the clinically acceptable ±10% accuracy rate ranged from only 6.5% to a maximum of 24.2%, with the inaccuracy varying from −31% to +71.5% of the measured patient’s energy needs. REEIC (p = 0.017) and eREE (p < 0.001) were higher in the underweight compared to overweight and obese patients. Apart from a younger age, malnutrition, clinical characteristics, temperature, vasoactive drugs, neuromuscular blockade, and energy intake did not affect REEIC and thereby predictive equations’ accuracy. Commonly used predictive equations for calculating energy needs are inaccurate for individual patients, either underestimating or overestimating REEIC compared with indirect calorimetry. Altogether these findings underscore the urgency for measuring REEIC in clinical situations where accurate knowledge of energy needs is vital.

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“…A number of studies conducted in different countries, including India and Austria, compared the performance of different indices and showed variable results between the indices in the populations studied (16)(17)(18). In 2021, Shen and Jiang reported a good overall but variable performance for mortality prediction in the PICU across studies included in their meta-analysis (19). The variations in calibration and discrimination between the PRISM III and PIM III across disease groups could be due to the difference in the parameters used to calculate each score.…”

Section: Discussionmentioning

confidence: 99%

Performance of Pediatric Risk of Mortality III and Pediatric Index of Mortality III Scores in Tertiary Pediatric Intensive Unit in Saudi Arabia

2022

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ObjectiveTo assess the performance of the Pediatric Risk of Mortality III (PRISM III) and Pediatric Index of Mortality III (PIM III) indices in a tertiary pediatric intensive care unit (PICU) in Saudi Arabia and to identify the factors affecting the observed performance.DesignRetrospective, single-center study using data collected from the Virtual Pediatric Systems web-based database.SettingKing Fahad Medical City PICU, Saudi Arabia.PatientsAll pediatric patients <14 years of age admitted between 1 January 2015, and 31 December 2019.InterventionsComparison of PRISM III and PIM III performances in predicting mortality across different age groups, disease categories, and resuscitation decision statuses.MeasurementsNormality of distribution was assessed using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Patient characteristics were compared between survivors and non-survivors. The medians and ranges were calculated for continuous data, whereas frequencies and percentages were used for nominal data. The Mann–Whitney U test, Kruskal–Wallis test, and Chi-square test were used to compare the characteristics of survivors and non-survivors.Main ResultsThere was a significant difference between the predicted mortality and observed mortality in both the PRISM III and PIM III. Better discrimination was found after excluding do-not-resuscitate (DNR) patients. The worst calibration and discrimination were recorded for infants <12 months of age. The PRISM III performed significantly better in patients with metabolic/genetic and central nervous system illnesses. Non-DNR patients had a lower standardized mortality rate using the PRISM III and PIM III. The PRISM III and PIM III indices performed better in patients who died within the first week of admission.ConclusionThese models had sufficient discrimination ability and poor calibration. Since they were designed for particular patient characteristics and PICUs, further testing in different environments is necessary before utilization for planning and assessing performance. Alternatively, new models could be developed which are suitable for local PICUs.

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Meta-Analysis for the Prediction of Mortality Rates in a Pediatric Intensive Care Unit Using Different Scores: PRISM-III/IV, PIM-3, and PELOD-2

Cited by 19 publications

References 42 publications

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

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

External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study

Performance of Pediatric Risk of Mortality III and Pediatric Index of Mortality III Scores in Tertiary Pediatric Intensive Unit in Saudi Arabia

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