Pediatric Index Of Mortality Score Research Articles

Evaluation of non-thyroidal illness syndrome in shock patients admitted to pediatric intensive care unit in a developing country

During critical illness, children my experience various changes in their thyroid hormone levels. Such changes are termed non-thyroidal illness syndrome (NTI). The extent of change correlates with the severity of the illness and its outcomes in critically ill patients. This study aimed to investigate the correlation between the severity of shock and thyroid hormone derangement. This prospective observational study included forty patients aged one month to five years who were admitted to the pediatric intensive care unit (PICU) with shock. Thyroid function tests were conducted on admission, after shock reversal, and five days later. NTI patterns were observed in 70% of patients. The PIM2 score showed a significant negative correlation with T3 (r = − 0.353, p = 0.026) and FT3 levels on admission (r = − 0.417, p = 0.007). Furthermore, after shock reversal, the PIM2 score continued to exhibit significant negative correlations with T4 (r = − 0.444, p = 0.004), T3 (r = − 0.329, p = 0.038), FT3 (r = − 0.355, p = 0.025), and FT4 levels (r = − 0.379, p = 0.016). Conclusion: This study underscores the high prevalence of NTI in PICU shock patients and suggests monitoring thyroid hormone levels for outcome prediction and treatment guidance. Further research is needed to optimize NTI management in critically ill children.What is Known:• Non-thyroidal illness syndrome (NTIS) is a condition observed in critically ill patients.• There has been limited research on NTI in children, and existing studies have generated conflicting results regarding the relationship between thyroid hormones and clinical outcomes in cases of sepsis and septic shock.What is New:• The study has revealed dynamic changes in free triiodothyronine (FT3) levels during the process of shock reversal and recovery in children who experienced shock.• A significant negative correlation was found between the Pediatric Index of Mortality 2 (PIM2) score and several thyroid hormone levels, including FT3 on admission and T4, FT3, and FT4 on shock reversal.

Characteristics and Outcomes of Intrahospital Transfers from Neonatal Intensive Care to Pediatric Intensive Care Units.

Critically ill children may be transferred from the neonatal intensive care unit (NICU) to the pediatric intensive care unit (PICU) for further critical care, but the frequency and outcomes of this patient population are unknown. The aims of this study are to describe the characteristics and outcomes in patients transferred from NICU to PICUs. We hypothesized that a higher-than-expected mortality would be present for patients with respiratory or cardiovascular diagnoses that underwent a NICU to PICU transition and that specific factors (timing of transfer, illness severity, and critical care interventions) are associated with a higher risk of mortality in the cardiovascular group. Retrospective analysis of Virtual Pediatric Systems, LLC (2011-2019) deidentified cardiovascular and respiratory NICU to PICU subject data. We evaluated demographics, PICU length of stay, procedures, disposition, and mortality scores. Pediatric Index of Mortality 2 (PIM2) score was utilized to determine the standardized mortality ratio (SMR). SMR of 4,547 included subjects (3,607 [79.3%] cardiovascular and 940 [20.7%] respiratory) was 1.795 (95% confidence interval: 1.62-1.97, p < 0.0001). Multivariable logistic regression analysis demonstrated transfer age (cardiovascular: odds ratio, 1.246 [1.10-1.41], p = 0.0005; respiratory: 1.254 [1.07-1.47], p = 0.0046) and PIM2 scores (cardiovascular: 1.404 [1.25-1.58], p < 0.0001; respiratory: 1.353 [1.08-1.70], p = 0.0095) were significantly associated with increased odds of mortality. In this present study, we found that NICU to PICU observed deaths were high and various factors, particularly transfer age, were associated with increased odds of mortality. While the type of patients evaluated in this study likely influenced mortality, further investigation is warranted to determine if transfer timing is also a factor. · NICU patients may be transitioned to the PICU.. · NICU to PICU observed deaths were high.. · Transfer timing may be a factor..

Adults Are Not Just Large Kids: Caring for Adults in Pediatric Hospitals.

Adults Are Not Just Large Kids: Caring for Adults in Pediatric Hospitals.

Incidence and predictors of brain infarction in neonatal patients on extracorporeal membrane oxygenation: an observational cohort study

To determine the incidence and identify predictors of brain infarctions (BI) in neonatal patients treated with extracorporeal membrane oxygenation (ECMO). We performed a retrospective cohort study at ECMO Centre Karolinska, Stockholm, Sweden. Logistic regression models were used to identify BI predictors. Neonates (age 0–28 days) treated with veno-arterial (VA) or veno-venous (VV) ECMO between 2010 and 2018. The primary outcome was a computed tomography (CT) verified BI diagnosed during ECMO treatment. In total, 223 patients were included, 102 patients (46%) underwent at least one brain CT and 27 patients (12%) were diagnosed with a BI. BI diagnosis was associated with increased 30-day mortality (48% vs. 18%). High pre-ECMO Pediatric Index of Mortality score, sepsis as the indication for ECMO treatment, VA ECMO, conversion between ECMO modes, use of continuous renal replacement therapy, and extracranial thrombosis were identified as independent predictors of BI development. The incidence of BI in neonatal ECMO patients may be higher than previously understood. Risk factor identification may help initiate steps to lower the risk or facilitate earlier diagnosis of BI in neonates undergoing ECMO treatment.

Analysis of Fluid Balance as Predictor of Length of Assisted Mechanical Ventilation in Children Admitted to Pediatric Intensive Care Unit (PICU).

Background Ventilator-associated lung injury (VALI) is a devastating complication of assisted mechanical ventilation (AMV) and is one of the root causes of prolonged AMV. Many strategies were made to decrease the effect of the same. This study is conducted to determine the association of prolonged AMV with fluid balance and pediatric index of mortality 2 (PIM2) score. Methods This prospective observational study was carried out in a PICU of a tertiary care centre over a period of 12 months. Patient's fluid balance was calculated by tabulating fluid input-output over initial 48 hours of AMV. The PIM2 score on admission was documented. The association between qualitative variables was assessed by a chi-square test. Comparison of quantitative data measured between cases with duration of AMV ≥ 7 days and <7 days was done using the Mann–Whitney U test. Correlation between quantitative data was done by using the Pearson product moment correlation. Results Out of 40 patients, 27 patients who had ≥15% positive fluid balance required prolonged mechanical ventilation. Similarly, 27 patients with PIM2 score ≥ 5 required prolonged AMV. On applying the Pearson chi-square test, we found a significant association between positive fluid balance and prolonged mechanical ventilation (P value = 2.25 × 10−7 (<0.05)). Likewise, a statistically significant association was found between PIM2 score and prolonged ventilation (P value = 1.19 × 10−5 (<0.05)). Conclusion There is a significant association of prolonged AMV with positive fluid balance (>15%) and PIM2 score (>5). By strict maintenance of fluid balance with appropriate intervention, the length of AMV and PICU stay can be decreased.

Lactate, Base Excess, and the Pediatric Index of Mortality: Exploratory Study of an International, Multicenter Dataset.

To investigate the relationship between ICU admission blood lactate, base excess, and ICU mortality and to explore the effect of incorporating blood lactate into the Pediatric Index of Mortality. Retrospective cohort study based on data prospectively collected on every PICU admission submitted to the U.K. Pediatric Intensive Care Audit Network and to the Australia and New Zealand Pediatric Intensive Care Registry. Thirty-three PICUs in the United Kingdom/Republic of Ireland and nine PICUs and 20 general ICUs in Australia and New Zealand. All ICU admissions between January 1, 2012, and December 31, 2015. None. One hundred twenty-three thousand two hundred fifty-two admissions were recorded in both datasets; 81,576 (66.2%) in the United Kingdom/Republic of Ireland and 41,676 (33.8%) in Australia and New Zealand. Of these 75,070 (61%) had a base excess recorded, 63,316 (51%) had a lactate recorded, and 60,876 (49%) had both base excess and lactate recorded. Median lactate value was 1.5 mmol/L (interquartile range, 1-2.4 mmol/L) (United Kingdom/Republic of Ireland: 1.5 [1-2.5]; Australia and New Zealand: 1.4 [1-2.3]). Children with a lactate recorded had a higher illness severity, were more likely to be invasively ventilated, admitted after cardiac surgery, and had a higher mortality rate, compared with admissions with no lactate recorded (p < 0.001). The relationship between lactate and mortality was stronger (odds ratio, 1.32; 95% CI, 1.31-1.34) than between absolute base excess and mortality (odds ratio, 1.13; 95% CI, 1.12-1.14). Addition of lactate to the Pediatric Index of Mortality score led to a small improvement in performance over addition of absolute base excess, whereas adding both lactate and absolute base excess achieved the best performance. At PICU admission, blood lactate is more strongly associated with ICU mortality than absolute base excess. Adding lactate into the Pediatric Index of Mortality model may result in a small improvement in performance. Any improvement in Pediatric Index of Mortality performance must be balanced against the added burden of data capture when considering potential incorporation into the Pediatric Index of Mortality model.

Comparison of PRISM III and PIM II Score in Predicting Mortality in Paediatric Intensive Care Unit: An Observational Study

Introduction: Prognostic scores play a vital role in predicting the outcome of children admitted in Paediatric Intensive Care Unit (PICU) thereby reducing the mortality. For paediatric populations Paediatric Risk of Mortality (PRISM) and Paediatric Index of Mortality (PIM) are the principal scores. As limited PICU beds are available in many tertiary care centres, PRISM score helps in predicting mortality risk and admission to PICU. Aim: To compare PRISM III and PIM II in predicting the mortality in sick children in a PICU and their relation between observed and predicted mortality. Materials and Methods: This was a prospective observational study, conducted in Chengalpattu Medical College Hospital, Chennai, Tamil Nadu, India, from July 2018 to June 2019 that enrolled 102 children who were admitted to PICU. At first hour of admission, PIM score was assessed and at 24 hours of admission PRISM score was assessed and the mortality was predicted. Children were followed-up until discharge or death, and the predicted mortality was compared with actual mortality and validation of scores was done using Statistical Package for the Social Sciences (SPSS) version 16. Results: Mean age of the population was 37.6 months, and majority of the children were aged less than 12 months. Male children were predominant (52%). Major system involvement was respiratory system 38 (37.3%) and mortality was 18 (17.6%). The mean score for death in PRISM and PIM were 11.8 and 19.9, respectively. The mean score for survival in PRISM and PIM were 4.4 and 9, respectively. Total PRISM and PIM score was lower in children who survived and mortality has been observed with higher scores. On comparison PRISM score was better to predict the mortality than PIM. The Area Under Curve (AUC) and sensitivity for PRISM score were 0.881 with 95% CI (0.769 to 0.992) and 94.44% respectively versus the AUC and sensitivity for PIM score were 0.768 with CI (0.628 to 0.908) and 61.11%, respectively. Using logistic regression, risk of mortality was analysed and found that increase in one score has 0.62 times the increased risk of death in PRISM score and thus it predicts the mortality better. Conclusion: The PRISM score was better than PIM score for risk stratification and to optimise available limited resources. Both scores underestimate the predicted mortality in comparison to observed mortality

Evaluation of Severity of Illness Scores in the Pediatric ECMO Population.

Though commonly used for adjustment of risk, severity of illness and mortality risk prediction scores, based on the first 24 h of intensive care unit (ICU) admission, have not been validated in the pediatric extracorporeal membrane oxygenation (ECMO) population. We aimed to determine the association of Pediatric Index of Mortality 2 (PIM2), Pediatric Risk of Mortality Score III (PRISM III) and Pediatric Logistic Organ Dysfunction (PELOD) scores with mortality in pediatric patients on ECMO. This was a retrospective cohort study of children ≤18 years of age included in the Pediatric ECMO Outcomes Registry (PEDECOR) from 2014 to 2018. Logistic regression and Receiver Operating Characteristics (ROC) curves were used to calculate the area under the curve (AUC) to evaluate association of mortality with the scores. Of the 655 cases, 289 (44.1%) did not survive until hospital discharge. AUCs for PIM2, PRISM III, and PELOD predicting mortality were 0.52, 0.52, and 0.51 respectively. PIM2, PRISM III, and PELOD scores are not associated with odds of mortality for pediatric patients receiving ECMO. These scores for a general pediatric ICU population should not be used for prognostication or risk stratification of a select population such as ECMO patients.

An Artificial Neural Network-Based Pediatric Mortality Risk Score: Development and Performance Evaluation Using Data From a Large North American Registry.

BackgroundIn the pediatric intensive care unit (PICU), quantifying illness severity can be guided by risk models to enable timely identification and appropriate intervention. Logistic regression models, including the pediatric index of mortality 2 (PIM-2) and pediatric risk of mortality III (PRISM-III), produce a mortality risk score using data that are routinely available at PICU admission. Artificial neural networks (ANNs) outperform regression models in some medical fields.ObjectiveIn light of this potential, we aim to examine ANN performance, compared to that of logistic regression, for mortality risk estimation in the PICU.MethodsThe analyzed data set included patients from North American PICUs whose discharge diagnostic codes indicated evidence of infection and included the data used for the PIM-2 and PRISM-III calculations and their corresponding scores. We stratified the data set into training and test sets, with approximately equal mortality rates, in an effort to replicate real-world data. Data preprocessing included imputing missing data through simple substitution and normalizing data into binary variables using PRISM-III thresholds. A 2-layer ANN model was built to predict pediatric mortality, along with a simple logistic regression model for comparison. Both models used the same features required by PIM-2 and PRISM-III. Alternative ANN models using single-layer or unnormalized data were also evaluated. Model performance was compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision recall curve (AUPRC) and their empirical 95% CIs.ResultsData from 102,945 patients (including 4068 deaths) were included in the analysis. The highest performing ANN (AUROC 0.871, 95% CI 0.862-0.880; AUPRC 0.372, 95% CI 0.345-0.396) that used normalized data performed better than PIM-2 (AUROC 0.805, 95% CI 0.801-0.816; AUPRC 0.234, 95% CI 0.213-0.255) and PRISM-III (AUROC 0.844, 95% CI 0.841-0.855; AUPRC 0.348, 95% CI 0.322-0.367). The performance of this ANN was also significantly better than that of the logistic regression model (AUROC 0.862, 95% CI 0.852-0.872; AUPRC 0.329, 95% CI 0.304-0.351). The performance of the ANN that used unnormalized data (AUROC 0.865, 95% CI 0.856-0.874) was slightly inferior to our highest performing ANN; the single-layer ANN architecture performed poorly and was not investigated further.ConclusionsA simple ANN model performed slightly better than the benchmark PIM-2 and PRISM-III scores and a traditional logistic regression model trained on the same data set. The small performance gains achieved by this two-layer ANN model may not offer clinically significant improvement; however, further research with other or more sophisticated model designs and better imputation of missing data may be warranted.

P0148 / #1909: COMPARISON OF SCORING SYSTEMS IN TERTIARY PEDIATRIC INTENSIVE CARE UNIT

Aims & Objectives: To compare the scoring systems in tertiary pediatric intensive care unit (PICU) in Turkey. Methods: Patients, admitted to the PICU from November 2018 to October 2019, were included. Demographic findings, lactate levels and the or pediatric risk of mortality (PRISM) 3, PRISM 4, pSOFA (pediatric sequential organ failure assessment), pediatric index of mortality 2 (PIM2), PELOD-2 (pediatric logistic organ dysfunction-2) in the first 24 hours at admission were recorded. Results: 326 patients (159 girls and 167 boys; mean age 55.5 months; IQR 1-213) were included and the mortality rate was 10.4%. Mean PRISM 3 score (3.2± 0.4 vs. 30.7 ± 6.0), PRISM 4 score (1.04 ± 0.08 vs. 12.2±4), pSOFA score (1.30 ± 0.11 vs. 7.02 ± 0.84), PIM2 score (2.83 ± 0.35/ vs. 26.6 ± 6.1) and PELOD-2 score (0.67 ± 0.18 vs. 22.4 ± 6.5) were higher among patients who died than the survivors (p<0.001, p<0.01, p<0.001, p<0.001 and p<0.01; respectively). Although all scoring systems were higher in patients who died; pSOFA had higher sensitivity comparing the other scoring systems, while lower specificity. Conclusions: The maximum pSOFA score in the first 24 hours at admission had better performance than other scoring systems for PICU mortality without classifying different disease groups. It would be useful to evaluate scoring systems in different disease groups and larger patient series.

Validity of Pediatric Index of Mortality 2 score as an Outcome Predictor in Pediatric ICU of a Public Sector Tertiary Care Hospital in Pakistan.

Pediatric Index of Mortality 2 (PIM-2) is one of the leading mortality scores used in intensive care units all around the world. We assessed its validity as an outcome predictor in a pediatric intensive care unit (PICU) of Mayo Hospital/King Edward Medical University Lahore, Pakistan. We enrolled 154 consecutive admissions, aged 1 month to 13 years, requiring intensive care from January to June of 2019. Patient demographics along with PIM-2 data were collected; PIM-2 score and mortality risk was calculated; and the outcome recorded as death or survival. The median age at admission was 0.50 years (interquartile range [IQR]: 0.24-1.78) and the median weight was 5.0 kg (IQR: 3.08-10.0) with females constituting 54%; malnutrition was also common (66%). Observed mortality was 29.9% (46 out of 154) and expected mortality (cut-off ≥ 99.8%) was 27.9% with a standardized mortality ratio of 1.07 (95% confidence interval [CI]: 0.79-1.41). Sepsis was the most common diagnosis at admission (27.9%) with the highest mortality (52.2%). Chi-square analysis revealed a sensitivity of 54.3% and a specificity of 83.3% ( p -value 0.00). PIM-2 score showed acceptable discrimination between survivors and nonsurvivors with an area under the receiver operating characteristic curve of 0.75 (95% CI: 0.67-0.84) ( p -value = 0.00); however, poor calibration according to Hosmer-Lemeshow goodness of fit test (Chi-square = 15.80, df = 7, and p -value of 0.027 [< 0.1]), thus requiring recalibration according to local population characteristics.

A description of premature and ex-premature infants admitted to the paediatric intensive care unit in the first six months of life

Background. Prematurity is a major risk factor for paediatric morbidity and mortality. Rehospitalisation with paediatric intensive care unit (PICU) admission constitutes significant morbidity; however, the extent of this problem in South Africa (SA) is not known. Objective. To describe the outcomes, clinical course and characteristics of premature and ex-premature infants admitted to a SA PICU, and to determine predictors of mortality. Methods. This prospective observational study analysed unplanned PICU admissions of infants in the first six months of life, over a six-month period. The primary outcome was mortality. Data were analysed using standard descriptive and inferential statistics. Results. Included in the study were 29 infants (65% male; median (interquartile range) birthweight and gestational age 1 715 (1 130 - 2 340) g and 32 (29 - 34) weeks, respectively) in 33 admissions. Five (17.2%) infants died in the PICU. Apnoea (39.4%), respiratory failure (24.2%) and shock (24.2%) were the most common reasons for PICU admission, secondary to pneumonia (33.3%), sepsis (27.3%) and meningitis (12.1%); 72.4% of infants were mechanically ventilated; and 48.3% received blood transfusions. Higher revised paediatric index of mortality score ( p =0.03), inotrope use ( p <0.0001), longer duration of mechanical ventilation ( p =0.03), and cardiac arrest in the PICU ( p <0.0001) were associated with mortality on univariate analysis with no independent predictors of mortality. Conclusion. Infections leading to apnoea, respiratory failure and shock are common indications for PICU readmission in premature infants. Mechanical ventilation and blood transfusion were frequently required.

Pediatric Index of Mortality 3 score as a predictor for the outcomes of critically ill patients

Background The ability to predict mortality in critically ill patients is important for assessing patient prognosis, evaluating therapy, and assessing intensive care unit quality. The Pediatric Index of Mortality (PIM) 3 is a scoring system to predict outcomes in order to assist clinical decision-making. Objective To assess the ability of PIM 3 to predict outcomes of critically ill PICU patients.Methods This prospective cohort study included 150 children aged 1 month to 18 years who were admitted to the pediatric intensive care unit (PICU), Sanglah Hospital, Denpasar, Bali. Subjects were grouped into two based on ROC curve PIM score ≥48 and &lt;48. The PIM 3 score was consisted of 10 variables, with a re-diagnosis classification of the PIM 2 score. Bivariate analysis was conducted to both groups to find the distribution of mortality in both groups, followed by homogenity test on variables gender, age, nutritional status, lenght of stay and mechanical ventilation. Variables which made the cut on bivariate test were included in multivariate analysis.Results The optimal PIM 3 score limit in predicting mortality was ≥48, with area under the curve (AUC) 76% (95%CI 0.69 to 0.85). Multivariate analysis revealed a 2.48 times increased risk to mortality in patients with PIM 3 score ≥48 (95%CI 1.6 to 3.7). In addition, PICU length of stay ≤7 days was a significant risk factor for mortality. Conclusion The PIM 3 has a good ability to predict the outcome of critically ill PICU patients. Critically ill patients with PIM 3 score ≥48 have a higher risk of mortality compared to those with PIM 3 &lt; 48.

Variation in Arterial and Central Venous Catheter Use in Pediatric Intensive Care Units

Describe patient and hospital characteristics associated with Arterial Catheter (AC) or Central Venous Catheter (CVC) use among pediatric intensive care units (ICUs). Hierarchical mixed effects analyses were used to identify patient and hospital characteristics associated with AC or CVC placement. The ICU adjusted median odds ratios (ICU-AMOR) for the admission ICU, marginal R2, and conditional intraclass correlation coefficient were reported. 166 PICUs in the Virtual PICU Systems (VPS, LLC) Database. 682,791 patients with unscheduled admissions to the PICU. None. ACs were placed in (median, [interquartile range]) 8.2% [4.9%-11.3%] of admissions, and CVCs were placed in 14.9% [10.4%-19.3%] of admissions across cohort ICUs. Measured patient characteristics explained about 25% of the variability in AC and CVC placement. Higher Pediatric Index of Mortality 2 (PIM2) illness severity scores were associated with increased odds of placement (Odds Ratio (95th% Confidence Interval)) AC: 1.88 (1.87-1.89) and CVC: 1.82 (1.81-1.83) per 1 unit increase in PIM2 score. Primary diagnoses of cardiovascular, gastrointestinal, hematology/oncology, infectious, renal/genitourinary, rheumatology, and transplant were associated with increased odds of AC or CVC placement compared to a primary respiratory diagnosis. Presence of in-house attendings 24/7 was associated with increased odds of AC placement 1.32 (1.11-1.57). Admission ICU explained 4.9% and 3.5% of the variability in AC or CVC placement, respectively. The ICU-AMOR showed a patient would have a median increase in odds of 55% and 43% for AC or CVC placement, respectively, if the same patient moved from an ICU with lower odds of placement to an ICU with higher odds of placement. Variation in AC or CVC use exists among PICUs. The admission ICU was more strongly associated with AC than with CVC placement. Further study is needed to understand unexplained variation in AC and CVC use.

Prediction of Pediatric Critical Care Resource Utilization for Disaster Triage.

Pediatric protocols to guide allocation of limited resources during a disaster lack data to validate their use. The 2011 Pediatric Emergency Mass Critical Care Task Force recommended that expected duration of critical care be incorporated into resource allocation algorithms. We aimed to determine whether currently available pediatric illness severity scores can predict duration of critical care resource use. Retrospective cohort study. Seattle Children's Hospital. PICU patients admitted 2016-2018 for greater than or equal to 12 hours (n = 3,206). None. We developed logistic and linear regression models in two-thirds of the cohort to predict need for and duration of PICU resources based on Pediatric Risk of Mortality-III, Pediatric Index of Mortality-3, and serial Pediatric Logistic Organ Dysfunction-2 scores. We tested the predictive accuracy of the models with the highest area under the receiver operating characteristic curve (need for each resource) and R (duration of use) in a validation cohort of the remaining one of three of the sample and among patients admitted during one-third of the sample and among patients admitted during surges of respiratory illness. Pediatric Logistic Organ Dysfunction score calculated 12 hours postadmission had higher predictive accuracy than either Pediatric Risk of Mortality or Pediatric Index of Mortality scores. Models incorporating 12-hour Pediatric Logistic Organ Dysfunction score, age, Pediatric Overall Performance Category, Pediatric Cerebral Performance Category, chronic mechanical ventilation, and postoperative status had an area under the receiver operating characteristic curve = 0.8831 for need for any PICU resource (positive predictive value 80.2%, negative predictive value 85.9%) and area under the receiver operating characteristic curve = 0.9157 for mechanical ventilation (positive predictive value 85.7%, negative predictive value 89.2%) within 7 days of admission. Models accurately predicted greater than or equal to 24 hours of any resource use for 78.9% of patients and greater than or equal to 24 hours of ventilation for 83.1%. Model fit and accuracy improved for prediction of resource use within 3 days of admission, and was lower for noninvasive positive pressure ventilation, vasoactive infusions, continuous renal replacement therapy, extracorporeal membrane oxygenation, and length of stay. A model incorporating 12-hour Pediatric Logistic Organ Dysfunction score performed well in estimating how long patients may require PICU resources, especially mechanical ventilation. A pediatric disaster triage algorithm that includes both likelihood for survival and for requiring critical care resources could minimize subjectivity in resource allocation decision-making.

Plasma lactate can improve the accuracy of the Pediatric Sequential Organ Failure Assessment Score for prediction of mortality in critically ill children: A pilot study

BackgroundPlasma lactate has been used to predict the prognosis of critically ill children, but mortality risk scores appear to be more appealing, particularly in resource-limited countries. ObjectiveTo assess the prognostic utility of lactate compared with the pediatric Sequential Organ Failure Assessment (pSOFA) score among the general pediatric intensive care unit (PICU) population. MethodsThis was a prospective observational study including 78 children admitted to a tertiary-level PICU. Plasma lactate was measured upon admission and repeated 24h later. pSOFA score, Pediatric Risk of Mortality, and Pediatric Index of Mortality-2 (PIM2) were calculated. The primary outcome was 30-day mortality. ResultsIn total, 47.4% of patients had hyperlactatemia at admission. Among these, 20.5% had persistent hyperlactatemia. No significant difference in admission lactate level was found between survivors and nonsurvivors. The 24-h, peak, and average lactate levels were higher among nonsurvivors (P=0.005, 0.035, and 0.019, respectively). The 24-h lactate level and pSOFA score were independent predictors of mortality (adjusted odds ratio and 95% confidence interval=1.12 [1.02–1.23] and 1.80 [1.23–2.64], respectively]. The 24-h lactate level showed positive correlations with pSOFA, PRISM, and PIM2 (Spearman correlation coefficient=0.31, 0.23, 0.43; P=0.006, P=0.047, P<0.001, respectively). The 24-h lactate level had an area under the receiver operating characteristic curve (AUC) of 0.77 (P=0.013) for mortality prediction, while admission, peak, and average lactate level had an AUC of 0.69, 0.69, 0.71 (P=0.086, P=0.035, P=0.019), respectively. PIM2, PRISM, and pSOFA score had an AUC of 0.80, 0.78, 0.82 (P=0.001, P=0.001, and P<0.001), respectively. Combining 24-h lactate level with pSOFA demonstrated superior performance (AUC=0.88). ConclusionBoth 24-h lactate level and pSOAF are useful for prediction of mortality. Incorporating the 24-h lactate level into the pSOFA Score achieved superior prognostic utility.

Association Between Resilience and Psychological Morbidity in Parents of Critically Ill Children.

To determine whether parental resilience, measured at ICU admission, is associated with parent-reported symptoms of depression, anxiety, posttraumatic stress, and satisfaction with ICU care 3-5 weeks following ICU discharge. Planned prospective, observational study nested in a randomized comparative trial. PICUs and cardiac ICUs in two, free-standing metropolitan area children's hospitals. English- and Spanish-speaking parents whose children were younger than 18 years old and had anticipated ICU stay of greater than 24 hours or Pediatric Index of Mortality score of greater than or equal to 4 at the time of consent. All ICU admissions were screened for inclusion. Of 4,251 admissions reviewed, 1,360 were eligible. Five hundred families were approached and 382 enrolled. Two hundred thirty-two parents from 210 families with complete data were included in analysis. All participating parents completed the Connor-Davidson Resilience Scale at the time of consent and outcome measures 3-5 weeks after ICU discharge. All parents completed the Patient-Reported Outcome Measurement Information System Short Forms 8a for Depression and Anxiety, Impact of Event Scale-Revised for posttraumatic stress, and Pediatric Family Satisfaction-ICU 24 for parental satisfaction 3-5 weeks after ICU discharge. Higher parental resilience was associated with fewer symptoms of depression, anxiety, and posttraumatic stress in the final model (all p < 0.0001). Shorter length of stay, early mechanical ventilation, Latino ethnicity, and lower illness severity (both objective and parental perceptions) were associated with less morbidity in some or all measured mental health outcomes. Higher parental resilience is associated with fewer reported symptoms of anxiety, depression, and posttraumatic stress 3-5 weeks after ICU discharge. Parental resilience may impact parental post-ICU psychological morbidity. Measuring parental resilience could be one approach to identify parents at risk for post-ICU psychological morbidity. Future research into the impact of interventions designed to boost parental resilience is warranted.

Impact of Patient Census and Admission Mortality on Pediatric Intensive Care Unit Attending Electronic Health Record Activity: A Preliminary Study.

Physicians may spend a significant amount of time using the electronic health record (EHR), but this is understudied in the pediatric intensive care unit (PICU). The objective of this study is to quantify PICU attending physician EHR usage and determine its association with patient census and mortality scores. During the year 2016, total EHR, chart review, and documentation times of 7 PICU physicians were collected retrospectively utilizing an EHR-embedded time tracking software package. We examined associations between documentation times and patient census and maximum admission mortality scores. Odds ratios (ORs) are reported per 1-unit increase in patient census and mortality scores. Overall, total daily EHR usage time (median time [hh:mm] [25th, 75th percentile]) was 2:10 (1:31, 3:08). For all hours (8 a.m.-8 a.m.), no strong association was noted between total EHR time, chart review, and documentation times and patient census, Pediatric Index of Mortality 2 (PIM2), or Pediatric Risk of Mortality 3 (PRISM3) scores. For regular hours (8 a.m.-7 p.m.), no strong association was noted between total EHR, chart review, and documentation times and patient census, PIM2, or PRISM3 scores. When patient census was higher, the odds of EHR after-hour usage (7 p.m.-8 a.m.) was higher (OR 1.262 [1.135, 1.403], p < 0.0001), but there were no increased odds with PIM2 (OR 1.090 [0.956, 1.242], p = 0.20) and PRISM3 (OR 1.010 [0.984, 1.036], p = 0.47) scores. A subset of physicians spent less time performing EHR-related tasks when patient census and admission mortality scores were elevated. We performed a novel evaluation of physician EHR workflow in our PICU. Our pediatric critical care physicians spend approximately 2 hours (out of an expected 10-hour shift) each service day using the EHR, but there was no strong or consistent association between EHR usage and patient census or mortality scores. Future larger scale studies are needed to ensure validity of these results.

Paediatric sequential organ failure assessment (pSOFA) score: a new mortality prediction score in the paediatric intensive care unit

ObjectivesTo assess performance of the age-adapted SOFA score in children admitted into Paediatric Intensive Care Units (PICUs) and whether the SOFA score can compete with the systemic inflammatory response syndrome (SIRS) in diagnosing sepsis, as recommended in the Sepsis-3 consensus definitions. MethodsTwo-centre prospective observational study in 281 children admitted to the PICU. We calculated the SOFA, Pediatric Risk of Mortality (PRISM), and Pediatric Index of Mortality-2 (PIM2) scores and assessed for the presence of SIRS at admission. The primary outcome was 30-day mortality. ResultsThe SOFA score was higher in nonsurvivors (P<.001) and mortality increased progressively across patient subgroups from lower to higher SOFA scores. The receiver operating characteristic (ROC) curve analysis revealed that the area under the curve (AUC) of the SOFA score for predicting 30-day mortality was 0.89, compared to AUCs of 0.84 and 0.79 for the PRISM and PIM2 scores, respectively. The AUC of the SOFA score for predicting a prolonged stay in the PICU was 0.67. The SOFA score was correlated to the PRISM score (rs = 0.59) and the PIM2 score (rs = 0.51). In children with infection, the AUC of the SOFA score for predicting mortality was 0.87 compared to an AUC of 0.60 using SIRS. The diagnosis of sepsis applying a SOFA cutoff of 3 points predicted mortality better than both the SIRS and the SOFA cutoff of 2 points recommended by the Sepsis-3 consensus. ConclusionsThe SOFA score at admission is useful for predicting outcomes in the general PICU population and is more accurate than SIRS for definition of paediatric sepsis.

A Study of Mortality in Cardiac Patients in a Pediatric Intensive Care Unit.

Objective: One of the major causes of mortality in the pediatric intensive care unit (PICU) is heart disease. This study aimed to determine the causes of mortality in children with pre-existing cardiac abnormalities who were admitted into the PICU.Methods: Data were collected through patient profile assessment and outcome and heart diseases affecting prognosis were analyzed. Medical records of children were reviewed retrospectively. The updated Pediatric Index of Mortality 2 (PIM2) scores were used. Exploratory data analysis was performed using descriptive measures. Kolmogorov-Smirnov tests were used to test the normality of data distribution.Results: Out of 566 admissions into PICU, 76 (13.4%) had cardiac abnormalities. Median and range of PICU stay were 5.50 and 417.88 days. The mean PIM2 score on admission was found to be 31.05. The most common admission was due to atrioventricular septal defect (AVSD) (15.7%), cardiomyopathy (13.1%), ventricular septal defect (VSD) (11.8%), tetralogy of Fallot (10.5%) and others (48.9%). There were multiple cardiac anomalies in 3.9% of patients. The most important cause of cardiac mortality in PICU was septic shock (26.0%) followed by cardiogenic shock (20.6%), and cardiac failure (13.7%). The nosocomial infection rate of cardiac patients in PICU was 10.5%.Conclusions: Our study reconfirmed that the PIM2 score is a good indicator of cardiac diseases. Infections, nosocomial infections, pneumonia, and septic shock were the leading causes of mortality in cardiac patients. Better infection control in the PICU may have a significant impact on decreasing mortality rates.