Performance of Three Mortality Prediction Scores and Evaluation of Important Determinants in Eight Pediatric Intensive Care Units in China

Nursing insights on the effectiveness of automated pupillometry in two distinct pediatric intensive care units

PURPOSE

Automated pupillometry (AP) facilitates objective pupillary assessment. In this study, we aimed at assessing nursing perspective about the utility of AP in neurocritically ill children to understand acceptance and usage barriers to guide development of a standardized use protocol.

METHODS

We conducted a web-based, cross-sectional, anonymous, Google™ survey of nurses at two independent pediatric ICUs which have been using AP over last four years. The survey included questions related to user-friendliness, barriers, acceptance, frequency of use, and method of documenting AP findings.

RESULTS

A total of 31 nurses responded to the survey. A total of 25 nurses (80.6%) used the automated pupillometer and 19 (61.3%) nurses preferred to use the automated pupillometer on critically ill intubated patients. Respondents rated the pupillometer a median [IQR] frequency of use of 7/10 [4-9] and a mean user-friendliness of 8/10 [7-10]. Barriers to pupillometer use included pupillometer unavailability, technical issues, lack of perceived clinical significance, and infection control.

CONCLUSION

Nurses have widely adopted the use of automated pupillometer in the PICU especially for critically ill intubated patients and rate it favorably for user-friendliness. Barriers against its use include limited resources, infection concerns, technical issues, and a lack of perceived clinical significance and training. Implementation of standardized PICU protocol for AP usage in critically ill children, may enhance the acceptance, increase usage and aid in objective assessments.

PRACTICE IMPLICATIONS

These findings can be used to create a standardized protocol on implementing automated pupillometry in the PICU for critically ill children.

Predicting functional and quality-of-life outcomes following pediatric sepsis: performance of PRISM-III and PELOD-2

BACKGROUND

Illness severity scores predict mortality following pediatric critical illness. Given declining PICU mortality, we assessed the ability of the Pediatric Risk of Mortality-III (PRISM) and Pediatric Logistic Organ Dysfunction-2 (PELOD) scores to predict morbidity outcomes.

METHODS

Among 359 survivors <18 years in the Life After Pediatric Sepsis Evaluation multicenter prospective cohort study, we assessed functional morbidity at hospital discharge (Functional Status Scale increase ≥3 points from baseline) and health-related quality of life (HRQL; Pediatric Quality of Life Inventory or Functional Status II-R) deterioration >25% from baseline at 1, 3, 6, and 12 months post-admission. We determined discrimination of admission PRISM and admission, maximum, and cumulative 28-day PELOD with functional and HRQL morbidity at each timepoint.

RESULTS

Cumulative PELOD provided the best discrimination of discharge functional morbidity (area under the receive operating characteristics curve [AUROC] 0.81, 95% CI 0.76-0.87) and 3-month HRQL deterioration (AUROC 0.71, 95% CI 0.61-0.81). Prediction was inferior for admission PRISM and PELOD and for 6- and 12-month HRQL assessments.

CONCLUSIONS

Illness severity scores have a good prediction of early functional morbidity but a more limited ability to predict longer-term HRQL. Identification of factors beyond illness severity that contribute to HRQL outcomes may offer opportunities for intervention to improve outcomes.

IMPACT

Illness severity scores are commonly used for mortality prediction and risk stratification in pediatric critical care research, quality improvement, and resource allocation algorithms. Prediction of morbidity rather than mortality may be beneficial given declining pediatric intensive care unit mortality. The PRISM and PELOD scores have moderate to good ability to predict new functional morbidity at hospital discharge following pediatric septic shock but limited ability to predict health-related quality of life outcomes in the year following PICU admission. Further research is needed to identify additional factors beyond illness severity that may impact post-discharge health-related quality of life.

Neurological Pupillary Index (NPi) Measurement Using Pupillometry and Outcomes in Critically Ill Children

Aim/objective Neurological Pupil Index (NPi), measured by automated pupillometry (AP), allows the objective assessment of pupillary light reflex (PLR). NPi ranges from 0 (non-reactive) to 5 (normal). In this study, we aimed to compare neurologic and functional outcomes in children admitted for neurologic injury with normal (≥3) versus abnormal (<3) NPi measured during their pediatric intensive care unit (PICU) stay. Materials and methods We conducted a retrospective chart review of children between one month and 18 years admitted to our PICU with a diagnosis of neurologic injury between January 2019 and June 2022. We collected demographic, clinical, pupillometer, and outcome data, including mortality, Pediatric Cerebral Performance Category (PCPC), Pediatric Overall Performance Category (POPC), and Functional Status Score (FSS) at admission, at discharge, and at the three to six-month follow-up. We defined abnormal pupil response as any NPi <3 at any point during the PICU stay. Using the student's t-test and chi-square test, we compared the short-term and long-term outcomes of children with abnormal NPi (<3) versus those with normal NPi (≥3). Results There were 49 children who met the inclusion criteria and who had pupillometry data available for analysis. The mean (SD) Glasgow Coma Scale (GCS) in the study cohort was 5.6 (4.3), and 61% had low (<3) NPi during ICU stay. Mortality was significantly higher among patients with an abnormal NPi as compared to those with normal NPi. Children with abnormal NPi exhibited significant worsening of neurologic and functional status (ΔPCPC, ΔPOPC, and ΔFSS) from admission to discharge (mean (SD): 3.55(1.5), 3.45(1.43), 16.75(7.85), p<0.001) as compared to those with normal NPi (mean (SD): 1.45(0.93), 1.73(0.90), 3.55(2.07), p>0.05). The significant difference in neurologic and functional status persisted at the three to six-month follow-up between the two groups - children with abnormal NPi (mean (SD): 2.0(1.41), 2.08(1.38), 6.92(6.83), p<0.01) and children with normal NPi (mean (SD): 0.82(1.01), 0.94(1.03), 1.53(1.70), p>0.05). Conclusion In our retrospective cohort study, children admitted to the PICU for a neuro injury and with abnormal NPi (< 3) have higher mortality, and worse short-term and long-term neurologic and functional outcomes as compared to those with normal NPi (≥ 3) measured during the PICU course. AP provides an objective assessment of PLR and has potential applications for neuro-prognostication. More research needs to be done to elucidate the prognostic value of NPi in pediatrics.

Predicting Clinical Deterioration and Mortality at Differing Stages During Hospitalization: A Systematic Review of Risk Prediction Models in Children in Low- and Middle-Income Countries

OBJECTIVE

To determine which risk prediction model best predicts clinical deterioration in children at different stages of hospital admission in low- and middle-income countries.

METHODS

For this systematic review, Embase and MEDLINE databases were searched, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The key search terms were "development or validation study with risk-prediction model" AND "deterioration or mortality" AND "age 0-18 years" AND "hospital-setting: emergency department (ED), pediatric ward (PW), or pediatric intensive care unit (PICU)" AND "low- and middle-income countries." The Prediction Model Risk of Bias Assessment Tool was used by two independent authors. Forest plots were used to plot area under the curve according to hospital setting. Risk prediction models used in two or more studies were included in a meta-analysis.

RESULTS

We screened 9486 articles and selected 78 publications, including 67 unique predictive models comprising 1.5 million children. The best performing models individually were signs of inflammation in children that can kill (SICK) (ED), pediatric early warning signs resource limited settings (PEWS-RL) (PW), and Pediatric Index of Mortality (PIM) 3 as well as pediatric sequential organ failure assessment (pSOFA) (PICU). Best performing models after meta-analysis were SICK (ED), pSOFA and Pediatric Early Death Index for Africa (PEDIA)-immediate score (PW), and pediatric logistic organ dysfunction (PELOD) (PICU). There was a high risk of bias in all studies.

CONCLUSIONS

We identified risk prediction models that best estimate deterioration, although these risk prediction models are not routinely used in low- and middle-income countries. Future studies should focus on large scale external validation with strict methodological criteria of multiple risk prediction models as well as study the barriers in the way of implementation.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews: Prospero ID: CRD42021210489.

Clinical characteristics and outcomes of children with prolonged mechanical ventilation in PICUs in mainland China: A national survey

OBJECTIVE

The number of children on prolonged mechanical ventilation (PMV) in pediatric intensive care units (PICU) has increased markedly, but little is known about the situation in mainland China. We carried out a multicenter retrospective investigation to describe the clinical characteristics and prognosis of Chinese children receiving long-term ventilation in the PICU.

METHODS

A retrospective study was performed in 11 PICUs. All participating patients with prolonged mechanical ventilation in the study were retrospectively identified and included from cases admitted to PICUs between January 1, 2017 and December 31, 2019.

RESULTS

A total of 346 children diagnosed with prolonged mechanical ventilation were included in the study. Overall, 240 survived and were discharged from PICU, 55 died in hospital, and 51 withdrew from mechanical ventilation support with 41 died after discharge. Lower airway diseases were the most common underlying causes (41.6%), followed by central nervous system diseases (29.5%), and neuromuscular diseases (13.3%). Most children (327, 94.5%) received invasive mechanical ventilation (IMV) and only 19 (5.5%) children received noninvasive ventilation (NIV). The median time of tracheostomy after ventilation was 21 days (15-35). Children with tracheostomy had lower mortality with longer PICU stay compared with patients without tracheostomy. Children who underwent tracheostomy were more likely to have central nervous system diseases and neuromuscular diseases.

CONCLUSION

This study showed a steady increase in the number of children receiving prolonged mechanical ventilation during the study period in Chinese PICUs with distinct clinical characteristics and outcomes. A better community-based care for PMV children is needed in mainland China.

Performance of the PRISM I, PIM2, PELOD-2 and PRISM IV scoring systems in western China: a multicenter prospective study

BACKGROUND

The aim of this study was to evaluate the performance of the four scoring tools in predicting mortality in pediatric intensive care units (PICUs) in western China.

METHODS

This was a multicenter, prospective, cohort study conducted in six PICUs in western China. The performances of the scoring systems were evaluated based on both discrimination and calibration. Discrimination was assessed by calculating the area under the receiver operating characteristic curve (AUC) for each model. Calibration was measured across defined groups based on mortality risk using the Hosmer-Lemeshow goodness-of-fit test.

RESULTS

A total of 2034 patients were included in this study, of whom 127 (6.2%) died. For the entire cohort, AUCs for Pediatric Risk of Mortality Score (PRISM) I, Pediatric Index of Mortality 2 (PIM2), Pediatric Logistic Organ Dysfunction Score-2 (PELOD-2) and PRISM IV were 0.88 [95% confidence interval (CI) 0.85-0.92], 0.84 (95% CI 0.80-0.88), 0.80 (95% CI 0.75-0.85), and 0.91 (95% CI 0.88-0.94), respectively. The Hosmer-Lemeshow goodness-of-fit Chi-square value was 12.71 (P = 0.12) for PRISM I, 4.70 (P = 0.79) for PIM2, 205.98 (P < 0.001) for PELOD-2, and 7.50 (P = 0.48) for PRISM IV [degree of freedom (df) = 8]. The standardized mortality ratios obtained with the PRISM I, PIM2, PELOD-2, and PRISM IV models were 0.87 (95% CI, 0.75-1.01), 0.97 (95% CI, 0.85-1.12), 1.74 (95% CI, 1.58-1.92), and 1.05 (95% CI, 0.92-1.21), respectively.

CONCLUSIONS

PRISM IV performed best and can be used as a prediction tool in PICUs in Western China. However, PRISM IV needs to be further validated in NICUs.

Risk factors for air leakage during invasive mechanical ventilation in pediatric intensive care units

Purpose

This study aimed to investigate air leakage during invasive mechanical ventilation (IMV) in a pediatric intensive care unit (PICU) and explore potential risk factors.

Methods

We conducted a retrospective cohort study of children who underwent IMV in a single-center PICU in a tertiary referral hospital. Air leakage risk factors and factors associated with an improved outcome were assessed.

Results

A total of 548 children who underwent IMV were enrolled in this study. Air leakage occurred in 7.5% (41/548) of the cases in the PICU. Air leakage increased the duration of IMV and hospitalization time. Multivariate logistic regression analysis showed a higher risk of air leakage during IMV for PICU patients with acute respiratory dyspnea syndrome (ARDS) (OR = 4.38), a higher pediatric critical illness score (PCIS) (OR = 1.08), or a higher peak inspiratory pressure (PIP) (OR = 1.08), whereas the risk was lower for patients with central respiratory failure (OR = 0.14). The logistic model had excellent predictive power for air leakage, with an area under the curve of 0.883 and tenfold cross-validation. Patients aged between 1 and 6 years who were diagnosed with measles or pneumonia and had a low positive end-expiratory pressure (PEEP) or high PaO2/FiO2 ratio were associated with improved outcomes. Patients diagnosed with central respiratory failure or congenital heart diseases were associated with less desirable outcomes.

Conclusions

Patients with ARDS, a higher PCIS at admission or a higher PIP were at higher risk of air leakage.

Performance and analysis of four pediatric mortality prediction scores among critically ill children: A multicenter prospective observational study in four PICUs

OBJECTIVE

We aimed to evaluate and compare the prognostic performance of common pediatric mortality scoring systems (the Pediatric Index of Mortality 2 [PIM2], PIM3, Pediatric Risk of Mortality [PRISM], and PRISM4 scores) to determine which is the most applicable score in our pediatric study cohort.

METHODS

This prospective observational multicenter cohort study was conducted in four tertiary-care pediatric intensive care units (PICUs) in Turkey. All children, between 1 month and 16 years old, admitted to the participating PICUs between October 1, 2019, and March 31, 2020, were included in the study. Discrimination between death and survival was assessed by area under the receiver operating characteristic plot (AUC) for each model. The Hosmer-Lemeshow goodness-of-fit (GOF) test was used to assess the calibration of the models, RESULTS: A total of 570 patients (median age 35 months) were enrolled in the study. The observed mortality rate was 8.2% (47/570). The standardized mortality ratio (SMR) of PIM2, PIM3, PRISM, and PRISM4 with 95% confidence interval (CI) were 0.94 (0.68-1.23), 1.27 (0.93-1.68), 0.86 (0.63-1.13), and 1.5 (1.10-1.97), respectively. The AUC with 95% CI was 0.934 (0.91-0.96) for PIM2, 0.934 (0.91-0.96) for PIM3, 0.917 (0.88-0.95) for PRISM, and 0.926 (0.88-0.97) for PRISM4 models. The Hosmer-Lemeshow test showed that the difference between observed and predicted mortality by PIM3 (p = 0.003) and PRISM4 (p = 0.008) was statistically significant whereas PIM2 (p = 0.28) and PRISM (p = 0.62) showed good calibration.

CONCLUSION

The overall performance of (both discrimination and calibration) PRISM and PIM2 scoring systems in Turkish pediatric patients aged 1 month to 16 years was accurate and had the best fit for risk groups according to our study. Although PIM3 and PRISM4 have good discriminatory power, their calibration was very poor in our study cohort.

Risk factors and optimal predictive scoring system of mortality for children with acute paraquat poisoning

BACKGROUND

There is no suitable scoring system that can be used to predict mortality in children with acute paraquat intoxication (APP).

AIM

To optimize a predictive scoring system for mortality in children with APP.

METHODS

A total of 113 children with APP from January 1, 2010 to January 1, 2020 were enrolled in this study. These patients were divided into survivors and non-survivors. We compared the clinical characteristics between the two groups and analyzed the independent prognostic risk factors. The survival rates of patients with different values of the pediatric critical illness score (PCIS) were assessed using kaplan-meier survival analysis. The best scoring system was established by using the area under the receiver operating characteristic curve analysis.

RESULTS

The overall mortality rate was 23.4%. All non-survivors died within 20 days; 48.1% (13/27) died within 3 days, and 70.3% (19/27) died within 7 days. Compared to survivors, the non-survivors were older, had higher white blood cell count, alanine aminotransferase (ALT), aspartate aminotransferase, serum creatinine, blood urea nitrogen, glucose, and pediatric early warning score, and had lower platelet count, albumin, Serum sodium (Na⁺) and PCIS. ALT and PCIS were the independent prognostic risk factors for children with APP. The survival rate of children classified as extremely critical patients (100%) was lower than that of children classified as critical (60%) or noncritical (6.7%) patients. The specificity of ALT was high (96.51%), but the sensitivity was low (59.26%). The sensitivity and specificity of ALT combined with PCIS were high, 92.59% and 87.21%, respectively. The difference in mortality was significantly higher for ALT combined with PCIS (area under the receiver operating characteristic: 0.937; 95%CI: 0.875-0.974; P < 0.05).

CONCLUSION

In our study, ALT and PCIS were independent prognostic risk factors for children with APP. ALT combined with PCIS is an optimal predictive mortality scoring system for children with APP.

Accuracy of SIRS, age-adapted pSOFA, and quick SOFA scoring systems for predicting outcomes in paediatric patients with sepsis: a meta-analysis

BACKGROUND

Sepsis is the leading cause of mortality in children. Several scoring systems are used to predict outcome and mortality for pediatric patients with sepsis, but how they compare to each other in terms of sensitivity and specificity is unclear.

METHODS

The systematic literature review was performed following PRISMA guidelines. Publically accessible search engines and study databases such as PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), and Google Scholar were scanned for articles published from January 1990 to March 2021 using relevant key words. All relevant studies were analyzed separately by two reviewers. A random-effects model was used to calculate the pooled sensitivity and pooled specificity with a 95% confidence interval (CI). Heterogeneity was evaluated using I², which estimates the percentage of variation between study results due to heterogeneity rather than sampling error.

RESULTS

Eleven studies met inclusion criteria and evaluated the SOFA scoring system. The pooled sensitivity, specificity, and SROC for prediction of mortality were 83% (95% CI: 76%-88%), 72% (95% CI: 60%-81%), and 85% (95% CI: 82%-88%), respectively. Six studies examined the SIRS system. Pooled sensitivity, specificity, and SROC were 80% (95% CI: 64%-90%), 36% (95% CI: 23%-51%), and 59% (95% CI: 55%-63%), respectively.

CONCLUSION

This meta-analysis shows that SOFA was superior to SIRS for predicting mortality in PICU patients with sepsis. Additional prospective multi-centric studies are needed to better evaluate and validate this finding.

Performance of PRISM III, PELOD-2, and P-MODS Scores in Two Pediatric Intensive Care Units in China

Objective: The performances of the pediatric risk of mortality score III (PRISM III), pediatric logistic organ dysfunction score-2 (PELOD-2), and pediatric multiple organ dysfunction score (P-MODS) in Chinese patients are unclear. This study aimed to assess the performances of these scores in predicting mortality in critically ill pediatric patients. Methods: This retrospective observational study was conducted at two tertiary-care PICUs of teaching hospitals in China. A total of 1,253 critically ill pediatric patients admitted to the two Pediatric Intensive Care Units (PICUs) of the First Affiliated Hospital, Sun Yat-Sen University from August 2014 to December 2019 and Shen-Zhen Children's Hospital from January 2019 to December 2019 were analyzed. The indexes of discrimination and calibration were applied to evaluate score performance for the three models (PRISM III, PELOD-2, and P-MODS scores). The receiver operating characteristic (ROC) curve was plotted, and the efficiency of PRISM III, PELOD-2, and P-MODS in predicting death were evaluated by the area under ROC curve (AUC). Hosmer-Lemeshow goodness-of-fit test was used to evaluate the degree of fitting between the mortality predictions of each scoring system and the actual mortality. Results: A total of 1,253 pediatric patients were eventually enrolled in this study (median age, 38 months; overall mortality rate, 8.9%; median length of PICU stay, 8 days). Compared to the survival group, the non-survival group showed significantly higher PRISM III, PELOD-2, and P-MODS scores [PRISM III: 18 (12, 23) vs. 11 (0, 16); PELOD-2, 8 (4, 10) vs. 4 (0, 6); and P-MODS: 5 (4, 9) vs. 3 (0, 4), all P < 0.001]. ROC curve analysis showed that the AUCs of PRISM III, PELOD-2, and P-MODS for predicting the death of critically ill children were 0.858, 0.721, and 0.596, respectively. Furthermore, in the Hosmer-Lemeshow goodness-of-fit test, PRISM III and PELOD-2 showed the better calibration between predicted mortality and observed mortality (PRISM III: χ² = 5.667, P = 0.368; PELOD-2: χ² = 9.582, P = 0.276; P-MODS: χ² = 12.449, P = 0.015). Conclusions: PRISM III and PELOD-2 can discriminate well between survivors and non-survivors. PRISM III and PELOD-2 showed the better calibration between predicted and observed mortality, while P-MODS showed poor calibration.