10
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      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

      systematic-review

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Introduction: The risk of mortality is higher in pediatric intensive care units (PICU). To prevent mortality in critically ill infants, optimal clinical management and risk stratification are required.

          Aims and Objectives: To assess the accuracy of PELOD-2, PIM-3, and PRISM-III/IV scores to predict outcomes in pediatric patients.

          Results: A total of 29 studies were included for quantitative synthesis in meta-analysis. PRISM-III/IV scoring showed pooled sensitivity of 0.78; 95% CI: 0.72–0.83 and pooled specificity of 0.75; 95% CI: 0.68–0.81 with 84% discrimination performance (SROC 0.84, 95% CI: 0.80–0.87). In the case of PIM-3, pooled sensivity 0.75; 95% CI 0.71–0.79 and pooled specificity 0.76; 95% CI 0.73–0.79 were observed with good discrimination power (SROC, 0.82, 95% CI 0.78–0.85). PELOD-2 scoring system had pooled sensitivity of 0.78 (95% CI: 0.71–0.83) and combined specificity of 0.75 (95% CI: 0.68–0.81), as well as good discriminating ability (SROC 0.83, 95% CI: 0.80–0.86) for mortality prediction in PICU patients.

          Conclusion: PRISM-III/IV, PIM-3, and PELOD-2 had good performance for mortality prediction in PICU but with low to moderate certainty of evidence. More well-designed studies are needed for the validation of the study results.

          Related collections

          Most cited references41

          • Record: found
          • Abstract: found
          • Article: not found

          PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration

          Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            PRISM III: an updated Pediatric Risk of Mortality score.

            The relationship between physiologic status and mortality risk should be reevaluated as new treatment protocols, therapeutic interventions, and monitoring strategies are introduced and as patient populations change. We developed and validated a third-generation pediatric physiology-based score for mortality risk, Pediatric Risk of Mortality III (PRISM III). Prospective cohort. There were 32 pediatric intensive care units (ICUs): 16 pediatric ICUs were randomly chosen and 16 volunteered. Consecutive admissions at each site were included until at least 11 deaths per site occurred. Physiologic data included the most abnormal values from the first 12 and the second 12 hrs of ICU stay. Outcomes and descriptive data were also collected. Physiologic variables where normal values change with age were stratified by age (neonate, infant, child, adolescent). The database was randomly split into development (90%) and validation (10%) sets. Variables and their ranges were chosen by computing the risk of death (odds ratios) relative to the midrange of survivors for each physiologic variable. Univariate and multivariate statistical procedures, including multiple logistic regression analysis, were used to develop the PRISM III score and mortality risk predictors. Data were collected on 11,165 admissions (543 deaths). The PRISM III score has 17 physiologic variables subdivided into 26 ranges. The variables most predictive of mortality were minimum systolic blood pressure, abnormal pupillary reflexes, and stupor/coma. Other risk factors, including two acute and two chronic diagnoses, and four additional risk factors, were used in the final predictors. The PRISM III score and the additional risk factors were applied to the first 12 hrs of stay (PRISM III-12) and the first 24 hours of stay (PRISM III-24). The Hosmer-Lemeshow chi-square goodness-of-fit evaluations demonstrated absence of significant calibration errors (p values: PRISM III-12 development = .2496; PRISM III-24 development = .1374; PRISM III-12 validation = .4168; PRISM III-24 validation = .5504). The area under the receiver operating curve and Flora's z-statistic indicated excellent discrimination and accuracy (area under the receiver operating curve - PRISM III-12 development 947 +/- 0.007; PRISM III-24 development 0.958 +/- 0.006; PRISM III-12 validation 0.941 +/- 0.021; PRISM III-24 validation 0.944 +/- 0.021; Flora's z-statistic - PRISM III-12 validation = .7479; PRISM III-24 validation = .9225), although generally, the PRISM III-24 performed better than the PRISM III-12 models. Excellent goodness-of-fit was also found for patient groups stratified by age (significance levels: PRISM III-12 = .1622; PRISM III-24 = .4137), and by diagnosis (significance levels: PRISM III-12 = .5992; PRISM III-24 = .7939). PRISM III resulted in several improvements over the original PRISM. Reassessment of physiologic variables and their ranges, better age adjustment for selected variables, and additional risk factors resulted in a mortality risk model that is more accurate and discriminates better. The large number of diverse ICUs in the database indicates PRISM III is more likely to be representative of United States units.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              PELOD-2: an update of the PEdiatric logistic organ dysfunction score.

              Multiple organ dysfunction syndrome is the main cause of death in adult ICUs and in PICUs. The PEdiatric Logistic Organ Dysfunction score developed in 1999 was primarily designed to describe the severity of organ dysfunction. This study was undertaken to update and improve the PEdiatric Logistic Organ Dysfunction score, using a larger and more recent dataset.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Pediatr
                Front Pediatr
                Front. Pediatr.
                Frontiers in Pediatrics
                Frontiers Media S.A.
                2296-2360
                24 August 2021
                2021
                : 9
                : 712276
                Affiliations
                [1] 1Department of Pediatrics, Shengzhou People's Hospital, the First Affiliated Hospital of Zhejiang University Shengzhou Branch , Shaoxing, China
                [2] 2NICU, Ningbo Women and Children's Hospital , Ningbo, China
                Author notes

                Edited by: Dincer Riza Yildizdas, Çukurova University, Turkey

                Reviewed by: Faruk Ekinci, Çukurova University, Turkey; Mutlu Uysal Yazici, Dr Sami Ulus Child Health and Diseases Training and Research Hospital, Turkey; Fulya Kamit, Yeni Yüzyil University, Turkey; Alper Koker, Akdeniz University, Turkey; Başak Nur Akyildiz, Erciyes University, Turkey; Mehmet Alakaya, Mersin University, Turkey

                *Correspondence: Juan Jiang qaws23546@ 123456163.com

                This article was submitted to Pediatric Critical Care, a section of the journal Frontiers in Pediatrics

                Article
                10.3389/fped.2021.712276
                8421854
                34504815
                e0bd351b-6a1f-43f7-9bc9-7dd06b2d999b
                Copyright © 2021 Shen and Jiang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 May 2021
                : 22 July 2021
                Page count
                Figures: 7, Tables: 1, Equations: 0, References: 41, Pages: 13, Words: 5952
                Categories
                Pediatrics
                Systematic Review

                meta-analysis,pediatric intensive care unit,pediatric risk of mortality,pediatric index of mortality,pediatric logistic organ dysfunction-2

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content389

                Cited by11

                Most referenced authors655