SCCM Resource Library-64-Investigating Correlations Between Risk Factors and Outcomes in Patients With COVID-19 in the PICU

Video Transcription

Hello, this is Dr. Ramesh Sachdeva. On behalf of my co-authors and myself, thank you for the opportunity to present our work today.  Identifying correlations between risk factors, severity of illness, and outcomes in patients with COVID-19 in the Pediatric Intensive Care Unit.  The disclosures related to the VPS and the authors of this work are summarized on this slide.  Factors associated with outcomes of children in the pediatric ICU with COVID-19 have not been fully studied, particularly after adjusting for the underlying severity of illness.  Severity of illness scoring systems, such as the PRISM-3 and Pediatric Index of Mortality  Scoring Systems, have previously been successfully used for severity of illness measurement and risk adjustment in the pediatric ICU setting.  The VPS represents a national and international network with over 1.5 million patient admission records from over 200 pediatric ICUs.  The VPS registry was used to identify patients admitted to the pediatric ICU with COVID-19.  The aim of the study was to investigate correlations and associations between specific clinical variables among specific subgroups of patients with COVID-19.  Correlations were studied between PRISM-3, probability of death, and the following variables, BMI, weight, age, medical and physical length of stay, and ventilator days.  The subgroups used in the analysis included the outcome measured by survival or death, factors such as obesity, and race.  Statistical analysis included Pearson's correlation coefficients for subgroups where the sample  size was greater than or equal to 30, or otherwise, the Spearman's correlation coefficients were used to investigate these associations.  Specific regression models were developed to evaluate the risk factors related to outcomes measured by survival or death.  The analysis used data from the Virtual Pediatric Systems Registry and included 1,528 cases with a diagnosis of COVID-19 admitted over a one-year time period ending in December  of 2020. These data presented today are the updated data and information as previously reported in the abstract related to this work.  Inclusion criteria for the analysis included age less than 18 years, and for correlations involving BMI, the age must be at least two years, and children must have height and weight  recorded. And for correlations involving PRISM-3, the records required to have PRISM-3 and probability of death recorded.  Obesity status was classified using the CDC growth chart, which specifies BMI and cutoffs  based upon age and gender. Descriptive statistics for patients who survived or died included 1,480 patients who survived and 37 deaths representing about 2.4% of the sample.  The average age of patients who survived was 9.8 years as compared to the average age of non-survivors being 11.4 years.  Of note is that there was a significant proportion of children admitted who were non-white with  Blacks, African-Americans, Hispanics representing the vast majority of non-white children.  Correlations for the entire data set included the relationship between PRISM-3 and the following variables, death, BMI, weight, length of stay, and age.  And as noted, survival and length of stay were significantly associated with PRISM-3 as would typically be expected using a severity of illness scoring system.  The data was subsequently analyzed using subgroups and the following correlations were  identified with respect to PRISM-3, probability of death, and the following variables, physical  and medical length of stay, ventilator days, age, weight, BMI, and overall length of stay stratified based upon obesity, non-obesity, and also survival or death.  As noted on the slide, BMI, weight, age was significantly correlated to severity of illness in the non-obese subgroup.  The PRISM-3 models were then developed using logistic regression analysis with the outcome being survival or death and the following independent variables, PRISM score, including  if PRISM score was high as defined by a probability of death greater than 0.006, which represented  the median cutoff of PRISM in this data set, gender, race, US census region, BMI, age, diagnosis for the patients admitted to the PICU.  The results showed that PRISM-3 score, probability of death, and male gender represented variables that were significantly associated with the outcome of survival or death.  Also of note, these logistic regression models had a high area under the receiver operating  curve and no significant lack of fit as measured by the Hossmer-Lemeshaw statistic in this logistic regression.  In conclusion, the overall mortality of children with COVID-19 in the pediatric intensive care  unit, about 2.4%, is similar to the average pediatric ICU mortality noted in the BPS data system.  The majority, over 70% of children admitted to the pediatric ICU with COVID-19 are non-white.  This is important to note because this is a higher proportion of children who are non-white in the pediatric ICU as compared to the general BPS registry data.  Also of note that the mortality rate of non-white and white children admitted with COVID-19 was similar.  Increasing weight and BMI were associated with increasing severity of illness for children with COVID-19 in the pediatric ICU.  And also increasing age was associated with increasing severity of illness for children with COVID-19 in the pediatric ICU.  Logistic regression models showed that PRISM-3 predicts outcome mortality for children with COVID-19 in the pediatric ICU.  And logistic regression models also suggest that male gender may be an independent risk factor associated with poor outcomes, mortality in these children.  Future directions should include the following key points. Results from these analysis should be tested further to better understand the clinical  implications of the correlations and associations identified in these analyses. The logistic regression models should also be further refined to further validate the  results observed in these analysis using additional data. And importantly, the initial trends noted in the study, particularly including the racial  disparities identified with a high proportion of non-white children admitted with COVID-19 to the pediatric intensive care unit should be further tested in the BPS registry as the  epidemiology of COVID-19 continues to evolve. Thank you for your attention.

Video Summary

Dr. Ramesh Sachdeva presents a study on identifying correlations between risk factors, severity of illness, and outcomes in children with COVID-19 in the Pediatric Intensive Care Unit (PICU). The study uses data from the Virtual Pediatric Systems Registry (VPS) and includes 1,528 cases of COVID-19 admitted between 2019 and 2020. The study finds that increasing weight, body mass index (BMI), and age are associated with higher severity of illness in children with COVID-19. Logistic regression models show that PRISM-3 (severity of illness score) predicts mortality outcomes, and male gender may be an independent risk factor. The study highlights the need for further analysis and validation of these findings.

Asset Subtitle

Pediatrics, Crisis Management, 2021

Asset Caption

The Society of Critical Care Medicine's Critical Care Congress features internationally renowned faculty and content sessions highlighting the most up-to-date, evidence-based developments in critical care medicine. This is a presentation from the 2021 Critical Care Congress held virtually from January 31-February 12, 2021.

Ramesh C. Sachdeva, BS, MB, FCCM

Introduction/Hypothesis: With the ongoing COVID-19 pandemic and the evolving epidemiology in children, it is important to identify factors associated with outcomes in the pediatric ICU (PICU). Although emerging research suggests that patient weight and race are associated with outcome, this has not been fully evaluated after adjusting for severity of illness (SOI). This analysis investigates the relation between SOI, clinical risk factors such as weight, race, and clinical course of patients and outcomes (survival/mortality) of patients with COVID-19 in the PICU.

Methods: 432 COVID-19 patients under 18 years old distributed among 64 PICUs in various geographic regions across the US were included in the analyses. Data from the national Virtual Pediatric Systems registry was used to investigate correlations between factors associated with mortality and Pediatric Risk of Mortality (PRISM III) probability of death (POD) to reflect SOI. Pearson and Spearman correlations were calculated for different subgroups of the cohort between SOI and the following: BMI, weight, age, medical and physical length of stay (MLOS/PLOS), and length of invasive ventilation (LOV). BMI was adjusted for pediatric males and females using the CDC growth charts. Preliminary logistic regressions were developed using PRISM III for predicting outcomes.

Results: In the non-African American/non-Hispanic subgroup, significant positive correlations were found between weight and SOI (R=0.168/p=0.033) and between age and SOI (R=0.168/p=0.034). However, BMI was not significantly correlated with SOI in the subgroups studied. Significant positive correlation (p <0.001) was found between MLOS and SOI and between PLOS and SOI for the survivor (R=0.404; R=0.401), obese (R=0.497; R=0.489), and combined African American/Hispanic (R=0.438; R=0.433) subgroups. The survivor (R=0.242/p=0.018), obese (R=0.535/p=0.049), and Hispanic (R=0.512/p=0.006) subgroups had significant positive correlations between LOV and SOI. The logistic regression predicting outcomes using PRISM III POD was tested to determine the feasibility of developing such a model (N=8 mortalities), and resulted in an AROC=75.1% and Hosmer-Lemeshow goodness of fit test with p=0.81.

Conclusions: Race, obesity are correlated with outcomes; PRISM III appears to predict mortality in PICU COVID-19 patients.

Meta Tag

Content Type Presentation

Knowledge Area Pediatrics

Knowledge Area Crisis Management

Knowledge Level Intermediate

Knowledge Level Advanced

Membership Level Select

Tag COVID-19

Year 2021

Keywords

correlations

severity of illness

outcomes

children

COVID-19

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