Performance of Pediatric Index of Mortality in a Tertiary Care PICU in India

2021 ◽  
Author(s):  
Nisha Toteja ◽  
Bharat Choudhary ◽  
Daisy Khera ◽  
Rohit Sasidharan ◽  
Prem Prakash Sharma ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Resource Constraints ◽  
Tertiary Care ◽  
Pediatric Intensive Care ◽  
Scoring Systems ◽  
Standardized Mortality Ratio ◽  
Operating Characteristics ◽  
Resource Limited ◽  
Mortality Ratio ◽  
Pediatric Index Of Mortality

AbstractPediatric index of mortality-3 (PIM-3) is the latest update of one of the commonly used scoring systems in pediatric intensive care. It has free accessibility and is easy to use. However, there are some skepticisms regarding its practical usefulness in resource-limited settings. Hence, there is a need to generate region-specific data to evaluate its performance in different case mixes and resource constraints. The aim of the study is to evaluate the performance of the PIM-3 score in predicting mortality in a tertiary care PICU of a developing country. This was a retrospective cohort study. All children aged 1 month to 18 years admitted to the PICU during the study period from July 2016 to December 2018 were included. We reviewed the patient admission details and the case records of the enrolled. patients. Patient demographics, disease profile, co-morbidities, and PIM-3 scores were recorded along with the outcome. Area under receiver operating characteristics (AUROC) curves was used to determine discrimination. Standardized mortality ratio (SMR) and Hosmer Lemeshow goodness of fit were used to assess the calibration. Out of 282 children enrolled, 62 (21.9%) died. 58.5% of the patients were males, and 60% were less than 5 years of age. The principal diagnoses included respiratory and neurological conditions. The AUROC for PIM-3 was 0.961 (95% CI [0.93, 0.98]) and overall SMR was 1.28 (95% CI [0.96, 1.59]). Hosmer-Lemeshow goodness-of-fit was suggestive of poor calibration (χ 2 = 11.7, p < 0.05). We concluded that PIM-3 had good discrimination but poor calibration in our PICU setting.

scholarly journals Assessment of suitability of pediatric index of mortality 2 score for monitoring the outcome of pediatric intensive care unit patients and associated risk factors of mortality in a tertiary care center in South India

2021 ◽  
Vol 8 (8) ◽  
pp. 1379
Author(s):  
Sreekrishna Y. ◽  
Adarsh E. ◽  
Lavanya T. S.
Keyword(s):  
Goodness Of Fit ◽  
Care Center ◽  
Tertiary Care ◽  
Scoring Systems ◽  
Standardized Mortality Ratio ◽  
Tertiary Care Center ◽  
Goodness Of Fit Test ◽  
Death Rates ◽  
Mortality Ratio ◽  
Pediatric Index Of Mortality

Background: Pediatric index of mortality 2 (PIM 2) score is an illness severity and scoring systems used for predicting outcome of children admitted to PICU. The objective was to evaluate the usefulness of PIM 2 score in predicting mortality in our PICU, assess whether the model is calibrated to our case mix and to compare the observed and expected death rates by calculating standardised mortality ratio. Methods: It was a prospective observational study done in a tertiary care center from January 2019 to June 2020. Consecutive 120 patients admitted to PICU aged from 1 month to 18 years were enrolled in study. PIM 2 scoring was calculated for the data obtained within 1 hour of admission to PICU. The outcome was recorded as death or discharge. PIM 2 logit score is calculated using software.Results: PIM2 can discriminate between death and survival with area under curve (AUC) of 0.867 with 95% CI (0.729,0.980). PIM 2 predicted death rate was significant (p<0.001). The model is well calibrated with Hosmer- Lemeshow Goodness-of-fit test p=0.961 (p>0.05). The observed death rates are equal to predicted death rates and standardized mortality ratio (SMR) is equal to 1. Conclusions: PIM 2 score predicted mortality correlated well with observed mortality in PICU patients. The model is well calibrated for use in our set up and discriminate well between survivors and   non-survivors.

scholarly journals Pediatric Index of Mortality and PIM2 Scores Have Good Calibration in a Large Cohort of Children from a Developing Country

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jhuma Sankar ◽  
Archana Singh ◽  
M. Jeeva Sankar ◽  
Sunil Joghee ◽  
Shashikant Dewangan ◽  
...  
Keyword(s):  
Developing Country ◽  
Goodness Of Fit ◽  
Tertiary Care ◽  
Roc Curves ◽  
Large Cohort ◽  
Standardized Mortality Ratio ◽  
Goodness Of Fit Test ◽  
Discriminative Ability ◽  
Pediatric Index Of Mortality ◽  
Good Calibration

Objective. Our objective was to validate the Pediatric Index of Mortality (PIM) and PIM2 scores in a large cohort of children from a developing country.Design. Prospective observational study.Setting. Pediatric intensive care unit of a tertiary care teaching hospital.Patients. All children aged <18 years admitted between June 2011 and July 2013.Measurements and Main Results. We evaluated the discriminative ability and calibration as measured by the area under the receiver operating characteristic (ROC) curves, the Hosmer-Lemeshow goodness-of-fit (GOF), and standardized mortality ratio (SMR), respectively. Of the 819 children enrolled, 232 (28%) died. The median (IQR) age of the study subjects was 4 years (0.8, 10). The major reasons for ICU admission as well as mortality were sepsis/severe sepsis. The area under ROC curves for PIM and PIM2 was 0.72 (95% CI: 0.67–0.75) and 0.74 (95% CI: 0.70–0.78), respectively. The goodness-of-fit test showed a good calibration across deciles of risk for the two scores withPvalues being >0.05. The SMR (95% CI) was 0.99 (0.85–1.15) and 1 (0.85–1.16) for PIM and PIM2, respectively. The calibration across different age and diagnostic subgroups was also good.Conclusion. PIM and PIM2 scores had good calibration in our setup.

scholarly journals The pediatric index of mortality 3 score to predict mortality in a pediatric intensive care unit in Palembang, South Sumatera, Indonesia

2017 ◽  
Vol 57 (3) ◽  
pp. 164 ◽  
Author(s):  
Destiana Sera Puspita Sari ◽  
Indra Saputra ◽  
Silvia Triratna ◽  
Mgs. Irsan Saleh
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Pediatric Intensive Care Unit ◽  
Goodness Of Fit ◽  
Pediatric Intensive Care ◽  
Standardized Mortality Ratio ◽  
Goodness Of Fit Test ◽  
Advanced Therapies ◽  
Probability Of Death ◽  
Pediatric Index Of Mortality

Background For critically ill patients in the pediatric intensive care unit (PICU), a scoring system is helpful for assessing the severity of morbidity and predicting the risk of mortality. The Pediatric Index of Mortality (PIM) 3 score consists of ten easy simple variables, so that the probability of death can be assessed prior to undergoing advanced therapies. The PIM 3 score in inexpensive and comprised of routine laboratory variables performed in PICU patients. In Indonesia, studies to validate the PIM 3 score have been limited.Objective To evaluate the PIM 3 score for predicting the probability of death in the PICU, Dr. Mohammad Hoesin Hospital (MHH), Palembang.Methods A prospective, cohort study was performed in the PICU, MHH, Palembang, from February to April 2016. The PIM 3 score was calculated within 2 hours of patients admission to the PICU by an  android calculator application. PIM3 score and mortality were analyzed by Mann-Whitney test; calibration was performed by Hosmer-Lameshow goodness of fit test, discrimination was done by receiver operating characteristic (ROC) curve analysis; and standardized mortality ratio (SMR) was calculated.Results During the study period there were 81 PICU patients, 69 children were included, ranging in age from 1,5 to 187 months. The overall mortality rate was 40,58%. The most common illnesses in our subjects were malignancy (17,4%), post non-thoracic surgery (14,5%), dengue shock syndrome (14,5%), respiratory disease (13%), and neurological disease (11,6%). Subjects’ PIM3 scores ranged from 1,02% to 58,84%, with means of 26,08% in non-survivors and 13,05% in survivors. The SMR was 2,24, indicating that death was underpredicted. The AUC of 0,771 (95% CI of 0,651 to 0,891) indicated that the PIM3 score had good discrimination.Conclusion In Mohammad Hoesin Hospital, Palembang, South Sumatera, the PIM 3 can be used to predict mortality in PICU patients, but the score should be multiplied by a factor of 2.24. This recalibration is needed due to the presumed lower standard of care at this hospital compared to that of the originating PIM 3 institutions in developed countries.

Comparison of Scoring Systems for Mortality Prediction in Pediatric Multitrauma Patients

2021 ◽  
Author(s):  
Mehmet Çelegen ◽  
Kübra Çelegen
Keyword(s):  
Injury Severity ◽  
Pediatric Trauma ◽  
Pediatric Intensive Care ◽  
Scoring Systems ◽  
International Normalized Ratio ◽  
Mortality Prediction ◽  
Risk Scores ◽  
Mortality Ratio ◽  
Risk Predictors ◽  
Trauma Score

AbstractThe aim of this study was to compare scoring systems for mortality prediction and determine the threshold values of this scoring systems in pediatric multitrauma patients. A total of 57 multitrauma patients referred to the pediatric intensive care unit from January 2020 to August 2021 were included. The pediatric trauma score (PTS), injury severity score (ISS), base deficit (B), international normalized ratio (I), Glasgow coma scale (G) (BIG) score, and pediatric risk of mortality 3 (PRISM 3) score were analyzed for all patients. Of the study group, 35% were females and 65% were males with a mean age of 72 months (interquartile range: 140). All groups' mortality ratio was 12.2%. All risk scores based on mortality prediction were statistically significant. Cutoff value for PTS was 3.5 with 96% sensitivity and 62% specificity; for the ISS, it was 20.5 with 92% sensitivity and 43% specificity; threshold of the BIG score was 17.75 with 85.7% sensitivity and 34% specificity; and 12.5 for PRISM 3 score with 87.6% sensitivity and 28% specificity. PTS, ISS, BIG score, and PRISM 3 score were accurate risk predictors for mortality in pediatric multitrauma patients. ISS was superior to PTS, PRISM 3 score, and BIG score for discrimination between survivors and nonsurvivors.

scholarly journals Predicting PICU patient survival prognosis: Pediatric Logistic Organ Dysfunction vs Pediatric Index of Mortality scores

2012 ◽  
Vol 52 (3) ◽  
pp. 165 ◽  
Author(s):  
Edwina Winiarti ◽  
Muhammad Sholeh Kosim ◽  
Mohammad Supriatna
Keyword(s):  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Pediatric Intensive Care ◽  
Scoring Systems ◽  
Survival Prognosis ◽  
Logistic Organ Dysfunction ◽  
Risk Of Mortality ◽  
Sensitivity Specificity ◽  
Pediatric Index Of Mortality

Background Determining prognosis of patients using scoringsystems have been done in many pediatric intensive care units(PICU). The scoring systems frequently used are pediatric logisticorgan dy sfunction (PELOD), pediatric index of mortality (PIM)and pediatric risk of mortality (PRISM).Objective To compare the performance of PELOD and PIM scoresin predicting the prognosis of survival vs death in PICU patients.Methods A prognostic test in this prospective, cohort study wasconducted in the PICU of the Kariadi General Hospital, Semarang.PELOD and PIM calculations were performed using formulae frompreviously published articles. Statistical analyses included receiveroperating curve (ROC) characteristics to describe discriminationcapacity, sensitivity, specificity, positive predictive value, negativepredictive value and accuracy.Results Thirty-three patients fulfilling the inclusion criteria wereenrolled in the study. PELOD score for area under the ROCwas 0.87 (95% CI 0.73 to 1.0; P=0.003), while that for PIMwas 0.65 (95% CI 0.39 to 0.90; P=0.2). PELOD scores showedsensitivity 85.7% (95% CI 59.8 to 100), specificity 84.6% (95%CI 70.7 to 98.5), positive predictive value 60.0% (95% CI 29.6to 90.4) negative predictive value 95.6% (95% CI 87.3 to 100)and accuracy 84.8%. PIM scores showed sensitivity 85.7% (95%CI 59.8 to 100), specificity 50.0% (95% CI 30.8 to 69.2), positivepredictive value 31.6% (95% CI 10,7 to 52.5), negative predictivevalue 92.9% (95% CI 79.4 to 100) and accuracy 57.6%.Conclusion PELOD scoring had better specificity, positive predictivevalue, negative predictive value, accuracy and discrimination capacitythan PIM scoring for predicting the survival prognosis of patients inthe PICU. [Paediatr Indones. 2012;52:165-9].

scholarly journals Comparison of Pediatric Risk of Mortality III, Pediatric Index of Mortality 2, and Pediatric Index of Mortality 3 in Predicting Mortality in a Pediatric Intensive Care Unit

2018 ◽  
Vol 07 (04) ◽  
pp. 201-206 ◽  
Author(s):  
Priyamvada Tyagi ◽  
Mukesh Agrawal ◽  
Milind Tullu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Cardiopulmonary Resuscitation ◽  
Roc Curve ◽  
Goodness Of Fit ◽  
Pediatric Intensive Care ◽  
Goodness Of Fit Test ◽  
Risk Of Mortality ◽  
Pediatric Index Of Mortality ◽  
Good Calibration

Aims To compare and validate the Pediatric Risk of Mortality (PRISM) III, Pediatric Index of Mortality (PIM) 2, and PIM 3 scores in a tertiary care pediatric intensive care unit (PICU) (Indian setting). Materials and Methods All consecutively admitted patients in the PICU of a public hospital (excluding those with unstable vital signs or cardiopulmonary resuscitation within 2 hours of admission, cardiopulmonary resuscitation before admission, and discharge or death in less than 24 hours after admission) were included. PRISM III, PIM 2, and PIM 3 scores were calculated. Mortality discrimination for the three scores was calculated using the receiver operating characteristic (ROC) curve, and calibration was performed using the Hosmer–Lemeshow goodness-of-fit test. Results A total of 350 patients were included (male:female = 1.3:1) over the study duration of 18 months (median age: 12 months [interquartile range: 4–60 months]). Nearly half were infants (47.4%). Patients with central nervous system disease were the highest (22.8%) followed by cardiovascular system (20.6%). Mortality rate was 39.4% (138 deaths). The area under the ROC curve for the PRISM III score was 0.667, and goodness-of-fit test showed no significant difference between the observed and expected mortalities in any of these categories (p > 0.5), showing good calibration. Areas under the ROC curve for the PIM 2 and PIM 3 scores were 0.728 and 0.726, respectively. For both the scores, the goodness-of-fit test showed good calibration. Conclusions Although all the three scores demonstrate good calibration, the PIM 2 and PIM 3 scores have an advantage regarding the better discrimination ability, ease of data collection, simplicity of computation, and inherent capacity of not being affected by treatment in PICU.

Validation of the ‘paediatric extracorporeal membrane oxygenation prediction’ model in a UK extracorporeal membrane oxygenation centre

Perfusion ◽  
2020 ◽  
Vol 35 (8) ◽  
pp. 802-805
Author(s):  
Hari Krishnan Kanthimathinathan ◽  
Sarah Webb ◽  
David Ellis ◽  
Margaret Farley ◽  
Timothy J Jones
Keyword(s):  
Extracorporeal Membrane Oxygenation ◽  
Prediction Model ◽  
Risk Adjustment ◽  
Goodness Of Fit ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Standardized Mortality Ratio ◽  
Membrane Oxygenation ◽  
Mortality Ratio ◽  
Operating Characteristic Curve

Introduction: There is a need for a universal risk-adjustment model that may be used regardless of the indication and nature of neonatal or paediatric extracorporeal membrane oxygenation support. The ‘paediatric extracorporeal membrane oxygenation prediction’ model appeared to be a promising candidate but required external validation. Methods: We performed a validation study using institutional database of extracorporeal membrane oxygenation patients (2008-2019). We used the published paediatric extracorporeal membrane oxygenation prediction score calculator to derive estimated mortality based on the model in this cohort of patients in our institutional database. We used standardized mortality ratio, area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test in 10 deciles to assess model performance. Results: We analysed 154 extracorporeal membrane oxygenation episodes in 150 patients. About 53% of the patients were full term (age ⩽30 days and gestation at birth ⩾37 weeks) neonates. The commonest category of extracorporeal membrane oxygenation support was cardiac (42%). The overall in-paediatric intensive care unit mortality was 37% (57/154) and the in-hospital mortality was 42% (64/154). Distribution of estimated mortality risk was similar to the derivation study. The calculated standardized mortality ratio was 0.81 based on the paediatric extracorporeal membrane oxygenation prediction model of risk-adjustment. The area under the receiver operating characteristic curve was 0.55 (0.45-0.64) and Hosmer-Lemeshow-test p value <0.001 was unable to support goodness-of-fit. Conclusion: This small single-centre study with a small number of events was unable to validate the paediatric extracorporeal membrane oxygenation prediction-model of risk-adjustment. Although this remains the most promising of all the available models, further validation in larger data sets and/or refinement may be required before widespread use.

scholarly journals Impact of Operator Expertise on Collection of the APACHE II Score and on the Derived Risk of Death and Standardized Mortality Ratio

2005 ◽  
Vol 33 (5) ◽  
pp. 585-590 ◽  
Author(s):  
D. Ledoux ◽  
S. Finfer ◽  
S. Mckinley
Keyword(s):  
Goodness Of Fit ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Clinical Staff ◽  
Apache Ii ◽  
Apache Ii Score ◽  
Standardized Mortality Ratio ◽  
Risk Of Death ◽  
Mortality Ratio ◽  
The Impact

We assessed the impact of operator expertise on collection of the APACHE II score, the derived risk of death and standardized mortality ratio in 465 consecutive patients admitted to a multi-disciplinary tertiary hospital ICU. Research coordinators and junior clinical staff independently collected the APACHE II variables; experts (senior clinical staff) rescored 20 % of the records. Agreement was moderate between junior clinical staff and research coordinators or senior clinical staff for most variables of the acute physiology score (weighted κ<0.6); agreement between research coordinators and senior clinical staff data collectors was good (weighted κ >0.75). The APACHE II score and its derived risk of death (ROD) were significantly lower using the junior clinical staff dataset compared to research coordinators and senior clinical staff (APACHE II score: 13.4±9.2 vs 16.8±8.5 vs 17.1±7.7, P<0.001; ROD: 14.7%±22.4% vs 21.6%±22.6% vs 20.8%±22.4%, P<0.01 respectively). The discriminative capacity was not altered by the lack of agreement (area under Receiver Operator Characteristic curve >0.8) but calibration of ROD from the junior clinical staff dataset was poor (Goodness-of-fit: P=0.001). The standardized mortality ratio (SMR) was higher with the junior clinical staff dataset (SMR: 1.22, 95% CI: 0.96-1.52 vs 0.87, 95% CI: 0.70-1.06 vs 0.76, 95% CI: 0.40-1.3 calculated from junior clinical staff, research coordinators and senior clinical staff data-sets respectively). We conclude that the expertise of data collectors significantly influences the APACHE II score, the derived risk of death and the standardized mortality ratio. Given the importance of such scores, ICUs should be provided with sufficient resources to train and employ dedicated data collectors.

Does Microalbuminuria Predict Mortality in Pediatric Intensive Care Unit?

2021 ◽  
Author(s):  
Baris Akbas ◽  
Asena A. Ozdemir ◽  
Ali E. Arslankoylu
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Pediatric Intensive Care Unit ◽  
Predictive Value ◽  
Pediatric Intensive Care ◽  
Scoring Systems ◽  
Multiple Organ ◽  
Operating Characteristics ◽  
Spot Urine ◽  
Risk Of Mortality

AbstractThe aim of this study is to assess the accuracy of microalbuminuria (MA) to predict the mortality in pediatric intensive care unit (PICU). Between December 2014 and November 2015, 250 patients who were 1 month to 18 years old monitored at least 24 hours in PICU and met study criteria were included. Spot urine samples were measured for microalbuminuria. Pediatric Risk of Mortality III-24 and Pediatric Multiple Organ Dysfunction scores were calculated by using the worst parameters in first 24 hours. The collected data were analyzed with statistical methods and compared with mortality scoring systems and observed mortality. MA values were significantly higher in nonsurvivors than the average of the survivors (18 vs. 48 mg/g, p < 0.05). The receiver operating characteristics curve analysis showed that the areas under the curves for MA was 0.81 at a cut-off value of 32 mg/g, MA measured in 24 hours of admission to PICU may be able to discriminate between patients a with sensitivity of 85.2, specificity of 70.8%, positive predictive value of 31.5%, and negative predictive value of 96.8%. MA is a useful tool to predict mortality in PICU.

scholarly journals Feasibility of Routine Data Collection on Intensive Care Unit Performance and Activity in Resource Limited Settings

2019 ◽  
Author(s):  
Syed Muhammad Muneeb Ali ◽  
Muhammad Iqbal Memon ◽  
Shahzad Hussain Waqar ◽  
Salman Shafi koul ◽  
Vincent Ioos ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Intensive Care ◽  
Demographic Data ◽  
Routine Data ◽  
Population Characteristics ◽  
Standardized Mortality Ratio ◽  
Resource Limited Settings ◽  
Resource Limited ◽  
Number Of Patients ◽  
Mortality Ratio

Background: Routine collection and analysis of data allows a critical care department to highlight the outcomes of the interventions done and to identify the grounds for improvement. Data on characteristic and outcomes of patients admitted in intensive care units (ICUs) are lacking. Methods: A software (ICU e-monitoring®) was designed to enter for each patient demographic data, SAPS3 on admission, Nine Equivalent Manpower Use Score, presence of medical devices and episodes of hospital acquired infections. We report data collected during 2014 with comparison to data collected with the same methodology in 2008 [1]. Objective: To determine the standardized mortality ratio, the mean length of ICU stay, mean length of mechanical ventilation and ICU acquired infection incidence rate. Study design: Descriptive Place of study: Medical ICU, Pakistan Institute of Medical Sciences Islamabad Results: A total of 196 admissions were recorded during the year 2014 vs 354 in 2008. 47.2% were males and 52.8% were females. Mean age was 32.1 years ± 15.3 SD (37.7 ± 18.9 SD in 2008). A total of 65 (33%) deaths were recorded during the year and standardized mortality ratio was found to be 0.71 vs 1.09 in 2008. Mean Length of stay was 15.9 Days ± 12.9 SD (9.3 days ± 8.9 in 2008) and mean duration of mechanical ventilation was found to be 12.04 Days (8.7 in 2008). Overall ventilator associated pneumonia (VAP) rate was 42.3 cases per 1000 ventilator days. Rate of Catheter Related Blood Stream Infections (CRBSI) was found to be 17.2 cases per 1000 CVC days. Conclusion: Major changes in our patient population characteristics were seen between 2008 and 2013: number of patients and standardized mortality was decreased while incidence of VAP and CRBSI was increased. It is possible to collect meaningful data on ICU performance and activity in resource limited settings.

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