Browsing by Author "Rogerson, Colin M."
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Item Characteristics and Outcomes of Critically Ill Children With Multisystem Inflammatory Syndrome(Wolters Kluwer, 2022-11) Snooks, Kellie; Scanlon, Matthew C.; Remy, Kenneth E.; Shein, Steven L.; Klein , Margaret J.; Zee-Cheng, Janine; Rogerson, Colin M.; Rotta, Alexandre T.; Lin, Anna; McCluskey, Casey K.; Carroll , Christopher L.; Pediatrics, School of MedicineObjectives: To characterize the prevalence of pediatric critical illness from multisystem inflammatory syndrome in children (MIS-C) and to assess the influence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strain on outcomes. Design: Retrospective cohort study. Setting: Database evaluation using the Virtual Pediatric Systems Database. Patients: All children with MIS-C admitted to the PICU in 115 contributing hospitals between January 1, 2020, and June 30, 2021. Measurements and Main Results: Of the 145,580 children admitted to the PICU during the study period, 1,338 children (0.9%) were admitted with MIS-C with the largest numbers of children admitted in quarter 1 (Q1) of 2021 (n = 626). The original SARS-CoV-2 viral strain and the D614G Strain were the predominant strains through 2020, with Alpha B.1.1.7 predominating in Q1 and quarter 2 (Q2) of 2021. Overall, the median PICU length of stay (LOS) was 2.7 days (25–75% interquartile range [IQR], 1.6–4.7 d) with a median hospital LOS of 6.6 days (25–75% IQR, 4.7–9.3 d); 15.2% received mechanical ventilation with a median duration of mechanical ventilation of 3.1 days (25–75% IQR, 1.9–5.8 d), and there were 11 hospital deaths. During the study period, there was a significant decrease in the median PICU and hospital LOS and a decrease in the frequency of mechanical ventilation, with the most significant decrease occurring between quarter 3 and quarter 4 (Q4) of 2020. Children admitted to a PICU from the general care floor or from another ICU/step-down unit had longer PICU LOS than those admitted directly from an emergency department. Conclusions: Overall mortality from MIS-C was low, but the disease burden was high. There was a peak in MIS-C cases during Q1 of 2021, following a shift in viral strains in Q1 of 2021. However, an improvement in MIS-C outcomes starting in Q4 of 2020 suggests that viral strain was not the driving factor for outcomes in this population.Item Critical Care Utilization in Children With Cancer: U.S. Pediatric Health Information System Database Cohort 2012-2021(Wolters Kluwer, 2024) Rogerson, Colin M.; Rowan, Courtney M.; Pediatrics, School of MedicineObjectives: To determine changes in pediatric oncology hospitalizations requiring intensive care over the period 2012-2021. Design: Retrospective study of hospital admission. Setting: Registry data from 36 children's hospitals in the U.S. Pediatric Health Information Systems database. Patients: Children 18 years or younger admitted to any of 36 hospitals with an oncology diagnosis. Interventions: None. Measurements and main results: There were a total of 55,827 unique patients accounted for 281,221 pediatric oncology hospitalizations over the 10-year period, and 16.6% of hospitalizations included admission to the PICU. Hospitalizations and PICU admissions steadily increased over this decade. Between 2012 and 2016, 15.1% of oncology hospitalizations were admitted to the PICU compared with 18.0% from 2017 to 2021 (difference 2.9% [95% CI, 2.6-3.2%] p ≤ 0.0001). Support with invasive mechanical ventilation also increased over time with 3.7% during 2012-2016 compared with 4.1% from 2017 to 2021 (difference 0.4% [95% CI, 0.2-0.5%] p ≤ 0.0001). Similar results were seen with cardiorespiratory life support using extracorporeal membrane oxygenation (difference 0.05% [95% CI, 0.02-0.07%] p = 0.0002), multiple vasoactive agent use (difference 0.3% [95% CI, 0.2-0.4%] p < 0.0001), central line placement (difference 5.3% [95% CI, 5.1-5.6%], p < 0.001), and arterial line placement (difference 0.4% [95% CI, 0.3-0.4%], p < 0.001). Year-on-year case fatality rate was unchanged over time (1.3%), but admission to the PICU during the second 5 years, compared with the first 5 years, was associated with lower odds of mortality (difference 0.7% [95% CI, 0.3-1.1%]) (odds ratio 0.82 [95% CI, 0.75-0.90%] p < 0.001). Conclusions: The percentage of pediatric oncology hospitalizations resulting in PICU admission has increased over the past 10 years. Despite the increasing use of PICU admission and markers of acuity, and on comparing 2017-2021 with 2012-2016, there are lower odds of mortality.Item Epidemiology and Outcomes of SARS-CoV-2 Infection or Multisystem Inflammatory Syndrome in Children vs Influenza Among Critically Ill Children(American Medical Association, 2022-06-01) Shein, Steven L.; Carroll, Christopher L.; Remy, Kenneth E.; Rogerson, Colin M.; McCluskey, Casey K.; Lin, Anna; Rotta, Alexandre T.; Pediatrics, School of MedicineThis cohort study compares the epidemiology and outcomes of patients in the pediatric intensive care unit with SARS-CoV-2–related disease during the first 15 months of the COVID-19 pandemic vs children with critical influenza prior to the pandemic.Item External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by persistent hypoxemia, encephalopathy, and shock(Research Square, 2023-08-02) Atreya, Mihir R.; Bennett, Tellen D.; Geva, Alon; Faustino, E. Vincent S.; Rogerson, Colin M.; Lutfi, Riad; Cvijanovich, Natalie Z.; Bigham, Michael T.; Nowak, Jeffrey; Schwarz, Adam J.; Baines, Torrey; Haileselassie, Bereketeab; Thomas, Neal J.; Luo, Yuan; Sanchez-Pinto, L. Nelson; Novel Data-Driven Sepsis Phenotypes in Children Study and the Genomics of Pediatric Septic Shock Investigators; Pediatrics, School of MedicineObjective: Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. Data-driven phenotyping approaches that leverage electronic health record (EHR) data hold promise given the widespread availability of EHRs. We sought to externally validate the data-driven 'persistent hypoxemia, encephalopathy, and shock' (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk-strata. Design: We trained and validated a random forest classifier using organ dysfunction subscores in the EHR dataset used to derive the PHES phenotype. We used the classifier to assign phenotype membership in a test set consisting of prospectively enrolled pediatric septic shock patients. We compared biomarker profiles of those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk-strata. Setting: 25 pediatric intensive care units (PICU) across the U.S. Patients: EHR data from 15,246 critically ill patients sepsis-associated MODS and 1,270 pediatric septic shock patients in the test cohort of whom 615 had biomarker data. Interventions: None. Measurements and main results: The area under the receiver operator characteristic curve (AUROC) of the new classifier to predict PHES phenotype membership was 0.91(95%CI, 0.90-0.92) in the EHR validation set. In the test set, patients with the PHES phenotype were independently associated with both increased odds of complicated course (adjusted odds ratio [aOR] of 4.1, 95%CI: 3.2-5.4) and 28-day mortality (aOR of 4.8, 95%CI: 3.11-7.25) after controlling for age, severity of illness, and immuno-compromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and overlapped with high risk-strata based on PERSEVERE biomarkers predictive of death and persistent MODS. Conclusions: The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlap with higher risk-strata based on validated biomarker approaches.Item Frequency and Correlates of Pediatric High-Flow Nasal Cannula Use for Bronchiolitis, Asthma, and Pneumonia(Daedalus Enterprises, 2022) Rogerson, Colin M.; Carroll, Aaron E.; Tu, Wanzhu; He, Tian; Schleyer, Titus K.; Rowan, Courtney M.; Owora, Arthur H.; Mendonca, Eneida A.; Pediatrics, School of MedicineBackground: Heated humidified high-flow nasal cannula (HFNC) is a respiratory support device historically used in pediatrics for infants with bronchiolitis. No large-scale analysis has determined the current frequency or demographic distribution of HFNC use in children. The objective of this study was to determine the frequency and correlates of HFNC use in children presenting to the hospital for asthma, bronchiolitis, or pneumonia. Methods: This longitudinal observational study was based on electronic health record data from a large regional health information exchange, the Indiana Network for Patient Care (INPC). Subjects were age 0-18 y with recorded hospital encounters at an INPC hospital between 2010-2019 with International Classification of Diseases codes for bronchiolitis, asthma, or pneumonia. Annual proportions of HFNC use among all hospital encounters were assessed using generalized additive models. Log-binomial regression models were used to identify correlates of incident HFNC use and determine risk ratios of specific subjects receiving HFNC. Results: The study sample included 242,381 unique subjects with 412,712 hospital encounters between 2010-2019. The 10-y period prevalence of HFNC use was 2.54% (6,155/242,381) involving 7,974 encounters. Hospital encounters utilizing HFNC increased by 400%, from 326 in 2010 to 1,310 in 2019. This increase was evenly distributed across all 3 diagnostic categories (bronchiolitis, asthma, and pneumonia). Sex, race, age, and ethnicity all significantly influenced the risk of HFNC use. Over the 10-y period, the percentage of all hospital encounters using HFNC increased from 1.11% in 2010 to 3.15% in 2018. Subjects with multiple diagnoses had significantly higher risk of receiving HFNC. Conclusions: The use of HFNC in children presenting to the hospital with common respiratory diseases has increased substantially over the past decade and is no longer confined to treating infants with bronchiolitis. Demographic and diagnostic factors significantly influenced the frequency of HFNC use.Item Institutional Variability in Respiratory Support Use for Pediatric Critical Asthma: A Multicenter Retrospective Study(American Thoracic Society, 2024) Rogerson, Colin M.; White, Benjamin R.; Smith, Michele; Hogan, Alexander H.; Abu-Sultaneh, Samer; Carroll, Christopher L.; Shein, Steven L.; Pediatrics, School of MedicineRationale: Over 20,000 children are hospitalized in the United States for asthma every year. Although initial treatment guidelines are well established, there is a lack of high-quality evidence regarding the optimal respiratory support devices for these patients. Objectives: The objective of this study was to evaluate institutional and temporal variability in the use of respiratory support modalities for pediatric critical asthma. Methods: We conducted a retrospective cohort study using data from the Virtual Pediatrics Systems database. Our study population included children older than 2 years old admitted to a VPS contributing pediatric intensive care unit from January 2012 to December 2021 with a primary diagnosis of asthma or status asthmaticus. We evaluated the percentage of encounters using a high-flow nasal cannula (HFNC), continuous positive airway pressure (CPAP), noninvasive bilevel positive pressure ventilation (NIV), and invasive mechanical ventilation (IMV) for all institutions, then divided institutions into quintiles based on the volume of patients. We created logistic regression models to determine the influence of institutional volume and year of admission on respiratory support modality use. We also conducted time-series analyses using Kendall’s tau. Results: Our population included 77,115 patient encounters from 163 separate institutions. Institutional use of respiratory modalities had significant variation in HFNC (28.3%, interquartile range [IQR], 11.0–49.0%; P < 0.01), CPAP (1.4%; IQR, 0.3–4.3%; P < 0.01), NIV (8.6%; IQR, 3.5–16.1%; P < 0.01), and IMV (5.1%; IQR, 3.1–8.2%; P < 0.01). Increased institutional patient volume was associated with significantly increased use of NIV (odds ratio [OR], 1.33; 1.29–1.36; P < 0.01) and CPAP (OR, 1.20; 1.15–1.25; P < 0.01), and significantly decreased use of HFNC (OR, 0.80; 0.79–0.81; P < 0.01) and IMV (OR, 0.82; 0.79–0.86; P < 0.01). Time was also associated with a significant increase in the use of HFNC (11.0–52.3%; P < 0.01), CPAP (1.6–5.4%; P < 0.01), and NIV (3.7–21.2%; P < 0.01), whereas there was no significant change in IMV use (6.1–4.0%; P = 0.11). Conclusions: Higher-volume centers are using noninvasive positive pressure ventilation more frequently for pediatric critical asthma and lower frequencies of HFNC and IMV. Treatment with HFNC, CPAP, and NIV for this population is increasing in the last decade.Item Learning competing risks across multiple hospitals: one-shot distributed algorithms(Oxford University Press, 2024) Zhang, Dazheng; Tong, Jiayi; Jing, Naimin; Yang, Yuchen; Luo, Chongliang; Lu, Yiwen; Christakis, Dimitri A.; Güthe, Diana; Hornig, Mady; Kelleher, Kelly J.; Morse, Keith E.; Rogerson, Colin M.; Divers, Jasmin; Carroll, Raymond J.; Forrest, Christopher B.; Chen, Yong; Pediatrics, School of MedicineObjectives: To characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children's hospitals, we quantified the impacts of a wide range of risk factors on the risk of post-acute sequelae of SARS-COV-2 (PASC) among children and adolescents. Materials and methods: Our ODACoR algorithms are effectively executed due to their devised simplicity and communication efficiency. We evaluated our algorithms via extensive simulation studies as applications to quantification of the impacts of risk factors for PASC among children and adolescents using data from eight children's hospitals including the Children's Hospital of Philadelphia, Cincinnati Children's Hospital Medical Center, Children's Hospital of Colorado covering over 6.5 million pediatric patients. The accuracy of the estimation was assessed by comparing the results from our ODACoR algorithms with the estimators derived from the meta-analysis and the pooled data. Results: The meta-analysis estimator showed a high relative bias (∼40%) when the clinical condition is relatively rare (∼0.5%), whereas ODACoR algorithms exhibited a substantially lower relative bias (∼0.2%). The estimated effects from our ODACoR algorithms were identical on par with the estimates from the pooled data, suggesting the high reliability of our federated learning algorithms. In contrast, the meta-analysis estimate failed to identify risk factors such as age, gender, chronic conditions history, and obesity, compared to the pooled data. Discussion: Our proposed ODACoR algorithms are communication-efficient, highly accurate, and suitable to characterize the complex interplay between multiple clinical conditions. Conclusion: Our study demonstrates that our ODACoR algorithms are communication-efficient and can be widely applicable for analyzing multiple clinical conditions in a time-to-event analysis framework.Item Machine learning models to predict and benchmark PICU length of stay with application to children with critical bronchiolitis(Wiley, 2023-06) Rogerson, Colin M.; Heneghan, Julia A.; Kohne, Joseph G.; Goodman, Denise M.; Slain, Katherine N.; Cecil, Cara A.; Kane, Jason M.; Hall, Matt; Pediatrics, School of MedicineObjective To create models for prediction and benchmarking of pediatric intensive care unit (PICU) length of stay (LOS) for patients with critical bronchiolitis. Hypothesis We hypothesize that machine learning models applied to an administrative database will be able to accurately predict and benchmark the PICU LOS for critical bronchiolitis. Design Retrospective cohort study. Patients All patients less than 24-month-old admitted to the PICU with a diagnosis of bronchiolitis in the Pediatric Health Information Systems (PHIS) Database from 2016 to 2019. Methodology Two random forest models were developed to predict the PICU LOS. Model 1 was developed for benchmarking using all data available in the PHIS database for the hospitalization. Model 2 was developed for prediction using only data available on hospital admission. Models were evaluated using R2 values, mean standard error (MSE), and the observed to expected ratio (O/E), which is the total observed LOS divided by the total predicted LOS from the model. Results The models were trained on 13,838 patients admitted from 2016 to 2018 and validated on 5254 patients admitted in 2019. While Model 1 had superior R2 (0.51 vs. 0.10) and (MSE) (0.21 vs. 0.37) values compared to Model 2, the O/E ratios were similar (1.18 vs. 1.20). Institutional median O/E (LOS) ratio was 1.01 (IQR 0.90–1.09) with wide variability present between institutions. Conclusions Machine learning models developed using an administrative database were able to predict and benchmark the length of PICU stay for patients with critical bronchiolitis.Item Outcomes of a respiratory therapist driven high flow nasal cannula management protocol for pediatric critical asthma patients(Wiley, 2023-10) Maue, Danielle K.; Cater, Daniel T.; Rogerson, Colin M.; Ealy, Aimee; Tori, Alvaro J.; Abu-Sultaneh, Samer; Pediatrics, School of MedicineIntroduction This study aimed to determine if a respiratory therapist (RT)-driven high flow nasal cannula (HFNC) protocol could decrease duration of HFNC use, pediatric intensive care unit (PICU) and hospital length of stay (LOS), and duration of continuous albuterol use in pediatric patients with critical asthma. Methods This was a quality improvement project performed at a quaternary academic PICU. Patients admitted to the PICU between 2 and 18 years of age with a diagnosis of asthma requiring continuous albuterol and HFNC were included. Implementation of an RT-driven HFNC protocol [Plan-Do-Study-Act (PDSA) 1] occurred in October 2017. Additional interventions included weaning continuous albuterol and HFNC simultaneously (PDSA 2; March 2019), adjusting HFNC wean rate (PDSA 3; July 2020), and a HFNC holiday (PDSA 4; October 2021). HFNC duration was the primary outcome. Secondary outcomes included LOS data and continuous albuterol duration. Noninvasive ventilation (NIV), invasive mechanical ventilation (IMV), and 7-day PICU and hospital readmission rates were used as balancing measures. Results A total of 410 patients were included. Patient demographics and adjunct therapy use did not differ among the groups. After PDSA 2, mean HFNC duration decreased (26.8–18.1 h). Mean PICU LOS decreased (41–31.8 h). Mean hospital LOS also decreased (86.5–68 h). These outcomes remained stable during PDSA 3 and 4. Continuous albuterol duration and NIV use were unchanged, while IMV use decreased. Conclusions An RT-driven HFNC protocol led to decreased length of HFNC and PICU and hospital LOS for pediatric patients with critical asthma without an increase in adverse events.Item Predicting Duration of Invasive Mechanical Ventilation in the Pediatric ICU(Mary Ann Liebert, 2023-11-25) Rogerson, Colin M.; Abu-Sultaneh, Samer; Loberger, Jeremy M.; Ross, Patrick; Khemani, Robinder G.; Sanchez-Pinto, L. Nelson; Pediatrics, School of MedicineBackground: Timely ventilator liberation can prevent morbidities associated with invasive mechanical ventilation in the pediatric ICU (PICU). There currently exists no standard benchmark for duration of invasive mechanical ventilation in the PICU. This study sought to develop and validate a multi-center prediction model of invasive mechanical ventilation duration to determine a standardized duration of invasive mechanical ventilation ratio. Methods: This was a retrospective cohort study using registry data from 157 institutions in the Virtual Pediatric Systems database. The study population included encounters in the PICU between 2012-2021 involving endotracheal intubation and invasive mechanical ventilation in the first day of PICU admission who received invasive mechanical ventilation for > 24 h. Subjects were stratified into a training cohort (2012-2017) and 2 validation cohorts (2018-2019/2020-2021). Four models to predict the duration of invasive mechanical ventilation were trained using data from the first 24 h, validated, and compared. Results: The study included 112,353 unique encounters. All models had observed-to-expected (O/E) ratios close to one but low mean squared error and R2 values. The random forest model was the best performing model and achieved an O/E ratio of 1.043 (95% CI 1.030-1.056) and 1.004 (95% CI 0.990-1.019) in the validation cohorts and 1.009 (95% CI 1.004-1.016) in the full cohort. There was a high degree of institutional variation, with single-unit O/E ratios ranging between 0.49-1.91. When stratified by time period, there were observable changes in O/E ratios at the individual PICU level over time. Conclusions: We derived and validated a model to predict the duration of invasive mechanical ventilation that performed well in aggregated predictions at the PICU and the cohort level. This model could be beneficial in quality improvement and institutional benchmarking initiatives for use at the PICU level and for tracking of performance over time.