A comparison of three scoring systems for mortality risk among retrieved intensive care patients.

AIMS: To assess the impact of two paediatric intensive care unit retrieval teams on the performance of three mortality risk scoring systems: pre-ICU PRISM, PIM, and PRISM II. METHODS: A total of 928 critically ill children retrieved for intensive care from district general hospitals in the south east of England (crude mortality 7.8%) were studied. RESULTS: Risk stratification was similar between the two retrieval teams for scores utilising data primarily prior to ICU admission (pre-ICU PRISM, PIM), despite differences in case mix. The fewer variables required for calculation of PIM resulted in complete data collection in 88% of patients, compared to pre-ICU PRISM (24%) and PRISM II (60%). Overall, all scoring systems discriminated well between survival and non-survival (area under receiver operating characteristic curve 0.83-0.87), with no differences between the two hospitals. There was a tendency towards better discrimination in all scores for children compared to infants and neonates, and a poor discrimination for respiratory disease using pre-ICU PRISM and PRISM II but not PIM. All showed suboptimal calibration, primarily as a consequence of mortality over prediction among the medium (10-30%) mortality risk bands. CONCLUSIONS: PIM appears to offer advantages over the other two scores in terms of being less affected by the retrieval process and easier to collect. Recalibration of all scoring systems is needed.     

Reliability of PRISM and PIM scores in paediatric intensive care

Aims: To assess the reliability of mortality risk assessment using the Paediatric Risk of Mortality (PRISM) score and the Paediatric Index of Mortality (PIM) in daily practice. Methods: Twenty seven physicians from eight tertiary paediatric intensive care units (PICUs) were asked to assess the severity of illness of 10 representative patients using the PRISM and PIM scores. Physicians were divided into three levels of experience: intensivists (>3 years PICU experience, n = 12), PICU fellows (6–30 months of PICU experience, n = 6), and residents (<6 months PICU experience, n = 9). This represents all large PICUs and about half of the paediatric intensivists and PICU fellows working in the Netherlands. Results: Individual scores and predicted mortality risks for each patient varied widely. For PRISM scores the average intraclass correlation (ICC) was 0.51 (range 0.32–0.78), and the average kappa score 0.6 (range 0.28–0.87). For PIM scores the average ICC was 0.18 (range 0.08–0.46) and the average kappa score 0.53 (range 0.32–0.88). This variability occurred in both experienced and inexperienced physicians. The percentage of exact agreement ranged from 30% to 82% for PRISM scores and from 28 to 84% for PIM scores. Conclusion: In daily practice severity of illness scoring using the PRISM and PIM risk adjustment systems is associated with wide variability. These differences could not be explained by the physician''s level of experience. Reliable assessment of PRISM and PIM scores requires rigorous specific training and strict adherence to guidelines. Consequently, assessment should probably be performed by a limited number of well trained professionals.     

Early application of generic mortality risk scores in presumed meningococcal disease.

OBJECTIVE: Mortality from meningococcal disease typically occurs within 24 hrs of intensive care unit (ICU) admission. An early, accurate mortality-risk tool may aid in trial design for novel therapies. We assessed the performance of two generic scores that assign mortality risk within 1 hr of ICU admission: the Preintensive Care Pediatric Risk of Mortality (Pre-ICU PRISM) and Pediatric Index of Mortality (PIM). DESIGN: Prospective, observational study over 21 months. SETTING: Two tertiary pediatric ICUs accepting referrals from southeast England. PATIENTS: Patients were 165 consecutive children with meningococcal disease. Ages ranged from 0.1 to 17 yrs (median 2.3 yrs). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: PIM demonstrated greater sensibility, with complete data collected in 93% of cases, compared with 35% for the pre-ICU PRISM. Both scores discriminated well. The area under the receiver operating characteristic curve was 0.90 (95% confidence interval, 0.81-1.00) for PIM and 0.94 (95% confidence interval, 0.88-0.98) for Pre-ICU PRISM; this did not change when applied to the subgroup of patients with complete data. Both scores calibrated poorly, overestimating mortality in the medium-risk strata (and also in the high-risk stratum in the case of Pre-ICU PRISM). When used as a stratification tool for a hypothetical trial (60% reduction in mortality, 80% power), the scores allowed for a reduction in study size by 50% (PIM) and 43% (pre-ICU PRISM). CONCLUSIONS: Pre-ICU PRISM and PIM both discriminate well but calibrate poorly when applied to a cohort of children with meningococcal sepsis. Both scores provide an effective means of stratification for clinical trial purposes. The main advantage for PIM appears to be ease of data collection.     

The Comparison of PRISM and PIM scoring systems for mortality risk in infantile intensive care

Scoring systems that predict the risk of mortality for children in an intensive care unit (ICU) are needed for the evaluation of the effectiveness of pediatric intensive care. The Pediatric Risk of Mortality (PRISM) and the Pediatric Index of Mortality (PIM) scores have been developed to predict mortality among children in the ICU. The purpose of this study was to evaluate whether these systems are effective and population-independent. PRISM and PIM scores were calculated prospectively during a 1-year period solely on 105 non-surgical infants admitted to the ICU. Statistical analysis was performed to assess the performance of the scoring systems. There were 29 (27.6 per cent) deaths and 76 (72.4 per cent) survivors. SMR and Z scores for PIM and PRISM signified higher mortality and poor performance. Prediction of mortality by the scoring systems appeared to be underestimated in almost all risk groups. The Hosmer and Lemeshow test showed a satisfactory overall calibration of both scoring systems. Although ROC analysis showed a poor discriminatory function of both scores, a marginally acceptable performance for PIM was observed. The ROC curve also showed an acceptable performance for PIM, for patients with pre-existent chronic disorder. Although care must be taken not to overstate the importance of our results, we believe that when revised according to the characteristics of the population, PIM may perform well in predicting the mortality risk for infants in the ICU, especially in countries where the mortality rate is relatively high and pre-existent chronic disorders are more common.     

Reliability of PRISM and PIM scores in paediatric intensive care.

AIMS: To assess the reliability of mortality risk assessment using the Paediatric Risk of Mortality (PRISM) score and the Paediatric Index of Mortality (PIM) in daily practice. METHODS: Twenty seven physicians from eight tertiary paediatric intensive care units (PICUs) were asked to assess the severity of illness of 10 representative patients using the PRISM and PIM scores. Physicians were divided into three levels of experience: intensivists (>3 years PICU experience, n = 12), PICU fellows (6-30 months of PICU experience, n = 6), and residents (<6 months PICU experience, n = 9). This represents all large PICUs and about half of the paediatric intensivists and PICU fellows working in the Netherlands. RESULTS: Individual scores and predicted mortality risks for each patient varied widely. For PRISM scores the average intraclass correlation (ICC) was 0.51 (range 0.32-0.78), and the average kappa score 0.6 (range 0.28-0.87). For PIM scores the average ICC was 0.18 (range 0.08-0.46) and the average kappa score 0.53 (range 0.32-0.88). This variability occurred in both experienced and inexperienced physicians. The percentage of exact agreement ranged from 30% to 82% for PRISM scores and from 28 to 84% for PIM scores. CONCLUSION: In daily practice severity of illness scoring using the PRISM and PIM risk adjustment systems is associated with wide variability. These differences could not be explained by the physician's level of experience. Reliable assessment of PRISM and PIM scores requires rigorous specific training and strict adherence to guidelines. Consequently, assessment should probably be performed by a limited number of well trained professionals.     

The suitability of the Pediatric Index of Mortality (PIM), PIM2, the Pediatric Risk of Mortality (PRISM), and PRISM III for monitoring the quality of pediatric intensive care in Australia and New Zealand.

OBJECTIVE: To compare the performance of the Pediatric Index of Mortality (PIM), PIM2, the Pediatric Risk of Mortality (PRISM), and PRISM III in Australia and New Zealand. DESIGN: A two-phase prospective observational study. Phase 1 assessed the performance of PIM, PRISM, and PRISM III between 1997 and 1999. Phase 2 assessed PIM2 in 2000 and 2001. SETTING: Ten intensive care units in Australia and New Zealand. PATIENTS: Included in the study were 26,966 patients aged <16 yrs; 1,147 patients died in the intensive care unit. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Discrimination between death and survival was assessed by calculating the area under the receiver operating characteristic plot for each model. The areas (95% confidence interval) for PIM, PIM2, PRISM, and PRISM III were 0.89 (0.88-0.90), 0.90 (0.88-0.91), 0.90 (0.89-0.91), and 0.93 (0.92-0.94). The calibration of the models was assessed by comparing the number of observed to predicted deaths in different diagnostic and risk groups. Prediction was best using PIM2 with no difference between observed and expected mortality (standardized mortality ratio [95% confidence interval] 0.97 [0.86-1.05]). PIM, PRISM III, and PRISM all overpredicted death, predicting 116%, 130%, and 189% of observed deaths, respectively. The performance of individual units was compared during phase 1, using PIM, PRISM, and PRISM III. There was agreement between the models in the identification of outlying units; two units performed better than expected and one unit worse than expected for each model. CONCLUSIONS: Of the models tested, PIM2 was the most accurate and had the best fit in different diagnostic and risk groups; therefore, it is the most suitable mortality prediction model to use for monitoring the quality of pediatric intensive care in Australia and New Zealand. More information about the performance of the models in other regions is required before these results can be generalized.     

Correlation between severity of disease and reimbursement of costs in neonatal and paediatric intensive care patients

Abstract Aim: The aim of the present study was to investigate the correlation between neonatal, paediatric and adult disease severity scores and reimbursement by health insurances. Methods: The setting was a university hospital's neonatal intensive care unit (NICU) and paediatric intensive care unit (PICU). We performed a prospective study of all patients admitted over the 3-month study period. Data collected included five scoring systems to predict mortality or to quantify disease severity (Paediatric Index of Mortality [PIM], Paediatric Risk of Mortality [PRISM], Simplified Acute Physiological Score [SAPS], Score for Neonatal Acute Physiology [SNAP], Therapeutic Intervention Scoring System [TISS]) on a daily basis, the total reimbursement as calculated by the grouper according to the German diagnosis-related groups (DRG) system, age of the patient, length of stay (LOS), International Classification of Diseases (ICD)-10 and DRG diagnosis. Our intention was to determine the correlation between different neonatal, paediatric and adult scores (PIM, PRISM III, SAPS-II, SNAP, Core-10-TISS), and reimbursement by the health insurance on the basis of the German DRG system in its 2005 and 2007 version. Results: No positive correlation between any score applied and reimbursement by the health insurance could be identified. Reimbursement was positively correlated to the length of hospital stay. Positive correlations could also be shown for some of the scores among each other. Conclusion: We conclude that other scoring systems or measures of disease severity urgently need to be established to terminate the chronic underfunding of paediatric intensive care medicine in the developed countries.     

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

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 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.     

Application of different scoring systems and their value in pediatric intensive care unit

BackgroundLittle is known on the impact of risk factors that may complicate the course of critical illness. Scoring systems in ICUs allow assessment of the severity of diseases and predicting mortality.ObjectivesApply commonly used scores for assessment of illness severity and identify the combination of factors predicting patient’s outcome.MethodsWe included 231 patients admitted to PICU of Cairo University, Pediatric Hospital. PRISM III, PIM2, PEMOD, PELOD, TISS and SOFA scores were applied on the day of admission. Follow up was done using SOFA score and TISS.ResultsThere were positive correlations between PRISM III, PIM2, PELOD, PEMOD, SOFA and TISS on the day of admission, and the mortality rate (p < 0.0001). TISS and SOFA score had the highest discrimination ability (AUC: 0.81, 0.765, respectively). Significant positive correlations were found between SOFA score and TISS scores on days 1, 3 and 7 and PICU mortality rate (p < 0.0001). TISS had more ability of discrimination than SOFA score on day 1 (AUC: 0.843, 0.787, respectively).ConclusionScoring systems applied in PICU had good discrimination ability. TISS was a good tool for follow up. LOS, mechanical ventilation and inotropes were risk factors of mortality.     

Evaluation of Pediatric Risk of Mortality (PRISM) scoring in African children with falciparum malaria.

OBJECTIVE: Little is known about the use of generic severity scores in severe childhood infectious diseases. The purpose of this prospective study was to evaluate the performance of the Pediatric Risk of Mortality (PRISM) scoring system in predicting the outcome of falciparum malaria in African children. DESIGN, SETTING, PATIENTS: All children admitted to a 120-bed pediatric ward in a tertiary care hospital in Dakar, Senegal, with a primary diagnosis of acute malaria were assigned a PRISM score after 24 hrs or at time of death. INTERVENTIONS: None. RESULTS: PRISM discrimination, evaluated by areas under receiver operating characteristic curves (AUC), was good both for all acute malaria cases (n = 311; lethality, 9%; AUC, 0.89; 95% confidence interval [CI], 0.85-0.92) and for severe malaria cases (n = 233; lethality, 12%; AUC, 0.86; 95% CI, 0.81-0.90). However, the number of children who died was greater than the number of deaths predicted by PRISM (standardized mortality ratio, 2.16; 95% CI, 1.46-2.87). CONCLUSION: This discrepancy observed in five classes of expected mortality (Hosmer-Lemeshow chi-square test, p < .001) may have been due to chance (sample size too small for a valid test), to a lower standard of care in Dakar than in the American hospitals where PRISM was designed, or to a failure of PRISM to classify risk in severe malaria.     

Calibration of the paediatric index of mortality in UK paediatric intensive care units

AIM—To test a paediatric intensive care mortality prediction model for UK use.
METHOD—Prospective collection of data from consecutive admissions to five UK paediatric intensive care units (PICUs), representing a broad cross section of paediatric intensive care activity. A total of 7253 admissions were analysed using tests of the discrimination and calibration of the logistic regression equation.
RESULTS—The model discriminated and calibrated well. The area under the ROC plot was 0.84 (95% CI 0.819 to 0.853). The standardised mortality ratio was 0.87 (95% CI 0.81 to 0.94). There was remarkable concordance in the performance of the paediatric index of mortality (PIM) within each PICU, and in the performance of the PICUs as assessed by PIM. Variation in the proportion of admissions that were ventilated or transported from another hospital did not affect the results.
CONCLUSION—We recommend that UK PICUs use PIM for their routine audit needs. PIM is not affected by the standard of therapy after admission to PICU, the information needed to calculate PIM is easy to collect, and the model is free.

     

Risk adjusted mortality of critical illness in a defined geographical region.

AIMS: To evaluate the performance of the Paediatric Risk of Mortality (PRISM) score in a population of UK children and to use this score to examine severity of illness adjusted mortality of critically ill children <16 years old in a defined geographical region. METHODS: Observational study of a defined population of critically ill children (<16 years old) admitted to hospitals in the South West Region between 1 December 1996 and 30 November 1998. RESULTS: Data were collected from 1148 eligible admissions. PRISM was found to perform acceptably in this population. There was no significant difference between the overall number of observed deaths and those predicted by PRISM. Admissions with mortality risk 30% or greater had significantly greater odds ratio for death in general intensive care units compared with the tertiary paediatric intensive care unit. CONCLUSIONS: Children with a high initial risk of mortality based on PRISM score were significantly more likely to survive in a tertiary paediatric intensive care unit than in general intensive care units in this region. However, there was no evidence from this study that admissions with lower mortality risk than 30% had significantly worse mortality in non-tertiary general units than in tertiary paediatric intensive care units.     

Risk adjusted mortality of critical illness in a defined geographical region

Aims: To evaluate the performance of the Paediatric Risk of Mortality (PRISM) score in a population of UK children and to use this score to examine severity of illness adjusted mortality of critically ill children <16 years old in a defined geographical region. Methods: Observational study of a defined population of critically ill children (<16 years old) admitted to hospitals in the South West Region between 1 December 1996 and 30 November 1998. Results: Data were collected from 1148 eligible admissions. PRISM was found to perform acceptably in this population. There was no significant difference between the overall number of observed deaths and those predicted by PRISM. Admissions with mortality risk 30% or greater had significantly greater odds ratio for death in general intensive care units compared with the tertiary paediatric intensive care unit. Conclusions: Children with a high initial risk of mortality based on PRISM score were significantly more likely to survive in a tertiary paediatric intensive care unit than in general intensive care units in this region. However, there was no evidence from this study that admissions with lower mortality risk than 30% had significantly worse mortality in non-tertiary general units than in tertiary paediatric intensive care units.     

Calibration of the paediatric index of mortality in UK paediatric intensive care units.

AIM: To test a paediatric intensive care mortality prediction model for UK use. METHOD: Prospective collection of data from consecutive admissions to five UK paediatric intensive care units (PICUs), representing a broad cross section of paediatric intensive care activity. A total of 7253 admissions were analysed using tests of the discrimination and calibration of the logistic regression equation. RESULTS: The model discriminated and calibrated well. The area under the ROC plot was 0.84 (95% CI 0.819 to 0.853). The standardised mortality ratio was 0.87 (95% CI 0.81 to 0.94). There was remarkable concordance in the performance of the paediatric index of mortality (PIM) within each PICU, and in the performance of the PICUs as assessed by PIM. Variation in the proportion of admissions that were ventilated or transported from another hospital did not affect the results. CONCLUSION: We recommend that UK PICUs use PIM for their routine audit needs. PIM is not affected by the standard of therapy after admission to PICU, the information needed to calculate PIM is easy to collect, and the model is free.     

Excess morbidity associated with interhospital transport.

A prospective study was performed to determine whether excess morbidity occurred in critically ill and injured pediatric patients during interhospital transport compared with morbidity in a control group. Control observations were made during the first 2 hours of pediatric intensive care unit (PICU) care of patients emergently admitted from within the same institution and not requiring interhospital transport. The first 2 PICU hours of control patients corresponded to the interval of transport in those who required interhospital transfer. Transport care was provided by nonspecialized teams from referring hospitals. Morbidity occurred in 20.9% of 177 transported patients, exceeding the morbidity rate of 11.3% in 195 control patients (P < .05). The difference in morbidity was due to intensive care-related adverse events (eg, plugged or dislodged endotracheal tubes, loss of intravenous access) in 15.3% and 3.6% of transported and control patients, respectively (P < .05). Physiologic deterioration occurred at similar rates of 7.9% and 8.7% in transported and control patients, respectively (P > .05). Slightly greater pre-ICU severity of illness in transported than control patients (median Pediatric Risk of Mortality Score = 10 and 7, respectively, P < .05) and greater pre-ICU therapy relative to severity (P < .05) in control patients are potential confounding sources of the morbidity differences. If patients are stratified into subgroups of similar pre-ICU severity, an excess of intensive care-related adverse events in transported patients remains evident in the severe subgroup (P < .05). Further investigation is warranted to determine whether specialized transport teams can reduce the excess morbidity associated with interhospital transport of critically ill and injured pediatric patients.     

Pediatric risk of mortality (PRISM) score in meningococcal disease

To assess the pediatric risk of mortality (PRISM) score as a prognostic scoring system in severe meningococcal disease, the files of 53 consecutive patients admitted to a tertiary pediatric intensive care with a clinical diagnosis of meningococcal disease and positive cultures from blood and/or cerebrospinal fluid were analysed. PRISM-score-based expected mortality was compared with observed mortality. Expected mortality in the whole study population was 29% while observed mortality was 19% (P < 0.05). The highest expected and observed mortality was found in septicaemic patients without (documented) meningitis, while meningitis patients without septicaemia had the lowest mortality. All patients with a mortality risk below 18.3% (n = 29) survived whereas all those with a mortality risk of 65% or higher (n = 7) died. Of the 17 patients with a mortality risk between 18.3% and 63.9%, 14 survived and 3 died. The area under the receiver-operating characteristic (ROC) curve was 0.94, which is at least comparable with the best-performing meningococcal-disease-specific scoring systems. Conclusion The PRISM score is a useful generic measure of severity of illness in meningococcal disease and can be used to determine the effectiveness of different treatment strategies. Received: 5 May 1999 / Accepted: 11 January 2000     

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

OBJECTIVE: Pediatric Index of Mortality 2 (PIM2) is an up-to-date mortality prediction model in the public domain that has not yet been widely validated. We aimed to evaluate this score in the population of patients admitted to our pediatric intensive care unit. DESIGN: Prospective cohort study. SETTING: Multidisciplinary pediatric intensive care unit in a general university hospital in Buenos Aires, Argentina. PATIENTS: All consecutive patients admitted between January 1, 2004, and December 31, 2005. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 1,574 patients included in the study. We observed 41 (2.6%) deaths, and PIM2 estimated 48.1 (3.06) deaths. Discrimination assessed by the area under the receiver operating characteristic curve was 0.9 (95% confidence interval, 0.89-0.92). Calibration across five conventional mortality risk intervals assessed by the Hosmer-Lemeshow goodness-of-fit test showed chi5 = 12.2 (p = .0348). The standardized mortality ratio for the whole population was 0.85 (95% confidence interval, 0.6-1.1). CONCLUSIONS: PIM2 showed an adequate discrimination between death and survival and a poor calibration assessed by the Hosmer-Lemeshow goodness-of-fit test. The standardized mortality ratio and clinical analysis of the Hosmer-Lemeshow table make us consider that PIM2 reasonably predicted the outcome of our patients.     

Mortality in severe meningococcal disease.

AIM: To evaluate mortality of critically ill children admitted with meningococcal disease. METHODS: Prospective study of all children admitted to a regional paediatric intensive care unit (PICU) between January 1995 and March 1998 with meningococcal disease. Outcome measures were actual overall mortality, predicted mortality (by PRISM), and standardised mortality ratio. RESULTS: A total of 123 children were admitted with meningococcal disease. There was an overall PICU mortality of 11 children (8.9%). The total mortality predicted by PRISM was 24.9. The standardised mortality ratio (SMR) was 0.44. Results were compared with those from four previously published meningococcal PICU studies (USA, Australia, UK, Netherlands) in which PRISM scores were calculated. The overall PICU mortality and SMR were lower than those in the previously published studies. CONCLUSION: Compared with older studies and calibrating for disease severity, this study found a decrease in the mortality of critically ill children with meningococcal disease.     

The pediatric risk of mortality score in infants and children with fulminant liver failure

The pediatric risk of mortality (PRISM) score as a severity scoring system has never been assessed in infants and children with fulminant liver failure (FLF). A retrospective case study of 109 infants and children admitted in a 22-bed pediatric and neonatal intensive care unit of a tertiary university hospital, National Referral Center for Pediatric Liver Transplantation, from March 1986 to August 1997 was carried out. PRISM score was not significantly different within etiologic FLF categories, or between infants and children. However, PRISM score (mean +/- SD) showed significant difference (p = 0.001) between the 27 patients who spontaneously recovered with supportive care (8.8 +/- 5.0) and 82 patients who underwent emergency liver transplantation (ELT) or those who died before (14.9 +/- 7.7). PRISM score-based probability of mortality was underestimated when compared with observed mortality. A death probability higher than 20% had a 24% sensitivity and 95% specificity for severe outcome. Reciever operating characteristic curve for PRISM score showed elevated discriminative power (Az = 0.91) for discerning children with severe outcome from those who spontaneously recovered with supportive care. A PRISM score more than 10 showed an odds ratio of 2.69 for predicting severe outcome (95% CI: 1.11-6.55; p = 0.038). In conclusion, the PRISM score is an accurate means of severity assessment in pediatric FLF. However, PRISM score-based mortality was of low predictive value.     

Comparison of two different severity scores (Paediatric Risk of Mortality [PRISM] and the Glasgow Meningococcal Sepsis Prognostic Score [GMSPS]) in meningococcal disease: preliminary analysis

Two different illness severity scores, Pediatric Risk of Mortality (PRISM) and the Glasgow Meningococcal Sepsis Prognostic Score (GMSPS), were evaluated and compared in meningococcal disease in two paediatric intensive care units. Forty-nine children with a median age of 36 months who had meningococcal sepsis confirmed by laboratory data were evaluated. Overall mortality was 18%. The median GMSPS was 3 in survivors and 8 in non-survivors. A GMSPS > or = 8 was significantly associated with death (p = 0.0001) with a mortality predictivity and specificity of 70% and 92.5%, respectively. The median PRISM score in survivors was 5.5 and 23 in non-survivors. A PRISM score of > or = 11 was significantly related to death (p < 0.0001). The Kendal correlation co-efficient between GMSPS and PRISM showed tau = 0.6859 (p = 0.0000). It is concluded that GMSPS and PRISM are useful methods for identifying and classifying children into low and high risk categories. GMSPS > or = 8 or a PRISM score > or = 11 are significantly predictive of mortality.