Literature DB >> 16738502

Performance of Pediatric Risk of Mortality (PRISM), Pediatric Index of Mortality (PIM), and PIM2 in a pediatric intensive care unit in a developing country.

Anu Thukral1, Rakesh Lodha, Mohammad Irshad, Narendra K Arora.   

Abstract

OBJECTIVE: To determine the discriminative ability and calibration of existing scoring systems in predicting the outcome (mortality) in children admitted to an Indian pediatric intensive care unit (PICU).
DESIGN: Prospective cohort study.
SETTING: Pediatric Intensive Care Unit, Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, from July 1, 2002, to July 31, 2003. PATIENTS: A total of 246 patients were admitted. After exclusion of 29 neonates and two patients who stayed in the PICU for <or=2 hrs, 215 patients were enrolled in the study. Of these 215 patients, 139 patients survived at the end of the PICU stay.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: Discrimination between death and survival was assessed by calculating the area under the receiver operating characteristic curve for each model. The areas under the curve (95% confidence intervals) for Pediatric Risk of Mortality (PRISM), Pediatric Index of Mortality (PIM), and PIM2 were 0.80 (0.74-0.86), 0.82 (0.76-0.88), and 0.81 (0.75-0.87), respectively. The area under the receiver operating characteristic curves was significantly greater for older children compared with infants. The existing scores underpredicted the mortality; the standardized mortality ratios (SMRs) (95% confidence interval) using PRISM, PIM, and PIM2 models were 1.20 (0.94-1.50), 1.57 (1.24-1.96), and 1.57 (1.24-1.59), respectively. The SMRs were higher in children with severe malnutrition, those with underlying illness, and those with serum albumin <or=2.5 g/dL.
CONCLUSIONS: The area under the receiver operating characteristic curve for all the models evaluated was >0.8. However, all the models underpredicted mortality. The likely reasons for this could be differences in the patient profile and greater load of severity of illness being managed with lesser resources--both physical and human--and differences in the quality of care.

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Year:  2006        PMID: 16738502     DOI: 10.1097/01.PCC.0000227105.20897.89

Source DB:  PubMed          Journal:  Pediatr Crit Care Med        ISSN: 1529-7535            Impact factor:   3.624


  32 in total

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2.  Validation of pediatric index of mortality 2 (PIM2) in a single pediatric intensive care unit in Japan.

Authors:  Toshihiro Imamura; Satoshi Nakagawa; Ran D Goldman; Takeo Fujiwara
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3.  Predicting Outcome in Mechanically Ventilated Pediatric Patients.

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4.  Performance of PRISM (Pediatric Risk of Mortality) score and PIM (Pediatric Index of Mortality) score in a tertiary care pediatric ICU.

Authors:  Roshani N Taori; Keya R Lahiri; Milind S Tullu
Journal:  Indian J Pediatr       Date:  2010-02-22       Impact factor: 1.967

5.  Management of septic shock: where do we stand?

Authors:  J Sankar; R Lodha; S K Kabra
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6.  Mortality prediction models for pediatric intensive care: comparison of overall and subgroup specific performance.

Authors:  Idse H E Visser; Jan A Hazelzet; Marcel J I J Albers; Carin W M Verlaat; Karin Hogenbirk; Job B van Woensel; Marc van Heerde; Dick A van Waardenburg; Nicolaas J G Jansen; Ewout W Steyerberg
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7.  Performance of PRISM III and PELOD-2 scores in a pediatric intensive care unit.

Authors:  Jean-Pierre Gonçalves; Milton Severo; Carla Rocha; Joana Jardim; Teresa Mota; Augusto Ribeiro
Journal:  Eur J Pediatr       Date:  2015-04-15       Impact factor: 3.183

8.  Comparison of Severity Scoring Systems in a Pediatric Intensive Care Unit in India: A Single-Center Prospective, Observational Cohort Study.

Authors:  Vinayak K Patki; Sandeep Raina; Jennifer V Antin
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Review 9.  Use of risk stratification indices to predict mortality in critically ill children.

Authors:  Maria Grazia Sacco Casamassima; Jose H Salazar; Dominic Papandria; James Fackler; Kristin Chrouser; Emily F Boss; Fizan Abdullah
Journal:  Eur J Pediatr       Date:  2013-03-23       Impact factor: 3.183

10.  How is mechanical ventilation employed in a pediatric intensive care unit in Brazil?

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