Performance of PRISM III and PELOD-2 scores in a pediatric intensive care unit

Jm, Gonçalves; Severo, Mílton; Rocha, Cristina; Jardim, Joana; Mota, Teresa; Ribeiro, Augusto

doi:10.1007/s00431-015-2533-5

Cited by 68 publications

(63 citation statements)

References 22 publications

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“…To evaluate the independent contribution of A-RDW to the primary outcome, we apportioned our cohort by RDW quartiles and then performed an inter-quartile comparison of severity of illness, estimated mortality and actual mortality. We calculated standardized mortality ratios (SMR)(25) for the cohort by indexing the observed mortality to that predicted by PIM2(26). To evaluate the impact of A-RDW on estimation of mortality, we plotted receiver operating characteristic (ROC) curves for PIM2, A-RDW and then by combining A-RDW with PIM2.…”

Section: Methodsmentioning

confidence: 99%

RBC Distribution Width: Biomarker for Red Cell Dysfunction and Critical Illness Outcome?*

Said¹,

Spinella²,

Hartman³

et al. 2017

Pediatric Critical Care Medicine

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Objective Red blood cell distribution width (RDW) is reported to be an independent predictor of outcome in adults with a variety of conditions. We sought to determine if RDW is associated with morbidity or mortality in critically ill children. Design Retrospective observational study. Setting Tertiary pediatric intensive care unit. Patients All admissions to Saint Louis Children’s Hospital Pediatric Intensive Care Unit between 1/1/2005 and 12/31/2012. Interventions We collected demographics, laboratory values, hospitalization characteristics and outcomes. We calculated the relative change in RDW (R-RDW) from admission (A-RDW) to the highest RDW during the first 7 days of hospitalization. Our primary outcome was ICU mortality or use of ECMO as a composite. Secondary outcomes were ICU- and ventilator-free days. Measurements and main results We identified 3,913 eligible subjects with an estimated mortality (by PIM2) of 2.94±9.25% and an actual ICU mortality of 2.91%. For the study cohort, A-RDW was 14.12±1.89% and R-RDW was +2.63±6.23%. On univariate analysis, both A-RDW and R-RDW correlated with mortality or use of ECMO (OR=1.19 [95% CI: 1.12–1.27] and OR=1.06 [95% CI: 1.04–1.08], respectively, p<0.001). After adjusting for confounding variables, including severity of illness, both A-RDW (OR=1.13, 95% CI 1.03–1.24) and R-RDW (OR=1.04, 95% CI 1.01–1.07) remained independently associated with ICU mortality or use of ECMO. A-RDW and R-RDW both weakly correlated with fewer ICU-free (r2=0.038) and ventilator-free days, (r2=0.05), (p<0.001). Conclusions Independent of illness severity in critically ill children, A-RDW is associated with ICU mortality and morbidity. These data suggest that RDW may be a biomarker for RBC injury that is of sufficient magnitude to influence critical illness outcome, possibly via oxygen delivery impairment.

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Section: Methodsmentioning

confidence: 99%

RBC Distribution Width: Biomarker for Red Cell Dysfunction and Critical Illness Outcome?*

Said¹,

Spinella²,

Hartman³

et al. 2017

Pediatric Critical Care Medicine

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“…Several pediatric intensive care units (PICU) have adopted the Pediatric Risk of Mortality (PRISM), [9,10] Pediatric Index of Mortality (PIM), [11,12] or Acute Physiology and Chronic Health Evaluation II score (APACHE II) [13] to assess the severity of sepsis in pediatric patients. However, these indices involve multiple systematic parameters and complex computation; this might not be ideal for the clinical use in pediatric sepsis patients, as their care typically requires quick clinical judgment.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a mortality risk model for pediatric sepsis

Chen

et al. 2017

Medicine

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Pediatric sepsis is a burdensome public health problem. Assessing the mortality risk of pediatric sepsis patients, offering effective treatment guidance, and improving prognosis to reduce mortality rates, are crucial.We extracted data derived from electronic medical records of pediatric sepsis patients that were collected during the first 24 hours after admission to the pediatric intensive care unit (PICU) of the Hunan Children's hospital from January 2012 to June 2014. A total of 788 children were randomly divided into a training (592, 75%) and validation group (196, 25%). The risk factors for mortality among these patients were identified by conducting multivariate logistic regression in the training group. Based on the established logistic regression equation, the logit probabilities for all patients (in both groups) were calculated to verify the model's internal and external validities.According to the training group, 6 variables (brain natriuretic peptide, albumin, total bilirubin, D-dimer, lactate levels, and mechanical ventilation in 24 hours) were included in the final logistic regression model. The areas under the curves of the model were 0.854 (0.826, 0.881) and 0.844 (0.816, 0.873) in the training and validation groups, respectively.The Mortality Risk Model for Pediatric Sepsis we established in this study showed acceptable accuracy to predict the mortality risk in pediatric sepsis patients.

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“…Gelişmiş merkezlerde bu oran %5.2-10 iken gelişmemiş ülkelerde %67'ye çıkabilmektedir (7,8 Bizim çalışmamızda gözlenen ve beklenen mortalite arasındaki farkın istatistiksel olarak anlamlı bulunması merkezimiz için bu skorlamarın kalibrasyonunun uygun olmadığını düşündürmek-tedir. Benzer şekilde PIM2, PRISM skorlamalarının uygulandığı, 26.966 hastanın katıldığı Avustralya ve Yeni Zelanda merkezli çalışmada ROC eğrisi altında kalan alanlar her skorlama için de 0.90 bulunmasına karşın skorlamaların kalibrasyonlarının iyi Çalışmada hastaların önemli bir kısmı (%38) süt çocuğu yaş grubundaydı.…”

Section: Discussionunclassified

The Suitability of Pediatric Index of Mortality 2 (Pım2) and Pediatric Risk of Mortality (Prısm) for Pediatric Intensive Care in Turkey

Oymak¹,

Bayrakçı²

2017

Turkish J Pediatr Dis

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ÖZAmaç: Çocuk yoğun bakım ünitelerinde (ÇYBÜ) son derece teknolojik kurulumlara ihtiyaç bulunmaktadır. Gelişmiş teknolojiye ek olarak bu birimlerdeki hizmeti standart hale getirmek üzere mortalite skorlama sistemlerine ihtiyaç olmaktadır. Pediatric Index of Mortality (PIM) ve Pediatric Risk of Mortality (PRISM) skorlamaları en iyi mortalite tahmin skorlarıdır.Çalışmanın amacı, ileri merkezlerde geliştirilmiş olan skorlamaların etkinliklerini kıyaslayarak aralarından ünitemiz için en uygun olanını seçmek ve bu skorlama sistemini yoğun bakım ünitemize yerleştirmektir. ABSTRACTObjective: There is a need for a highly technological setting in pediatric intensive care units. In addition to advanced technology, mortality scoring systems are necessary to standardize the care given in these units. Pediatric Risk of Mortality (PRISM) and Pediatric Index of Mortality (PIM) are the best known mortality prediction scores.The aim of this study was to determine and validate the scoring systems' effectiveness in order to choose the most suitable one and put it in regular practice in our unit. We included the three hundred eighty nine admissions to the PICU within one year in the study. Material and Methods:Mortality scores for PIM2 and PRISM were calculated prospectively. Discriminatory performance was assessed with the Receiver Operating Curve (ROC). Statistical analysis for calibration of the scoring systems was carried out using the Hosmer and Lemeshow goodness-of-fit test. P values were < 0.05 for all scoring systems. Standardized mortality rates (SMR) were also calculated.Results: All three scoring systems exhibited good discrimination with area under the curve values of 0.84, 0.86 and 0.89 for PIM2, PRISM12 and PRISM24 respectively. SMRs were less than 1.0 for all scoring systems. Conclusion:This result showed that mortality was lower than that expected for our unit indicating that these scoring systems have good discriminatory performance, but their calibrations were poor for our unit.

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Performance of PRISM III and PELOD-2 scores in a pediatric intensive care unit

Cited by 68 publications

References 22 publications

RBC Distribution Width: Biomarker for Red Cell Dysfunction and Critical Illness Outcome?*

RBC Distribution Width: Biomarker for Red Cell Dysfunction and Critical Illness Outcome?*

Development and validation of a mortality risk model for pediatric sepsis

The Suitability of Pediatric Index of Mortality 2 (Pım2) and Pediatric Risk of Mortality (Prısm) for Pediatric Intensive Care in Turkey

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