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Derivation and Validation of Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children

Derivation and Validation of Novel Phenotypes of Multiple Organ Dysfunction Syndrome in... Key Points Question Does data-driven IMPORTANCE Multiple organ dysfunction syndrome (MODS) is a dynamic and heterogeneous phenotyping based on the trajectories process associated with high morbidity and mortality in critically ill children. of organ dysfunction in the acute phase of critical illness among children with OBJECTIVE To determine whether data-driven phenotypes of MODS based on the trajectories of 6 multiple organ dysfunction syndrome organ dysfunctions have prognostic and therapeutic relevance in critically ill children. uncover phenotypes with prognostic and therapeutic relevance? DESIGN, SETTING, AND PARTICIPANTS This cohort study included 20 827 pediatric intensive care Findings In this 2-center cohort study encounters among 14 285 children admitted to 2 large academic pediatric intensive care units of 20 827 pediatric intensive care (PICUs) between January 2010 and August 2016. Patients were excluded if they were older than 21 encounters, a data-driven approach to years or had undergone cardiac surgery. The 6 subscores of the pediatric Sequential Organ Failure phenotyping patients with multiple Assessment (pSOFA) score were calculated for the first 3 days, including the subscores for organ dysfunction syndrome using the respiratory, cardiovascular, coagulation, hepatic, neurologic, and renal dysfunctions. MODS was trajectories of 6 organ dysfunctions defined as a pSOFA subscore of at least 2 in at least 2 organs. Encounters were split in a 80:20 ratio uncovered 4 reproducible and distinct for derivation and validation, respectively. The trajectories of the 6 subscores were used to derive a phenotypes with prognostic and set of data-driven phenotypes of MODS using subgraph-augmented nonnegative matrix potential therapeutic relevance. factorization in the derivation set. Data analysis was conducted from March to October 2019. Meaning In this study, data-driven EXPOSURES The primary exposure was phenotype membership. In the subset of patients with phenotyping based on the type, vasoactive-dependent shock, the interaction between hydrocortisone and phenotype membership severity, and trajectory of 6 organ and its association with outcomes were examined in a matched cohort. dysfunctions showed promising results in critically ill children with multiple MAIN OUTCOMES AND MEASURES The primary outcome was in-hospital mortality. Secondary organ dysfunction syndrome. outcomes included persistent MODS on day 7, and vasoactive-free, ventilator-free, and hospital-free days. Regression analysis was used to adjust for age, severity of illness, immunocompromised status, Supplemental content and study site. Author affiliations and article information are listed at the end of this article. RESULTS There were 14 285 patients with 20 827 encounters (median [interquartile range] age 5.2 years [1.5-12.7] years; 11 409 [54.8%; 95% CI, 54.1%-55.5%] male patients). Of these, 5297 encounters (25.4%; 95% CI, 24.8%-26.0%) were with patients who had MODS, of which 5054 (95.4%) met the subgraph count threshold and were included in the analysis. Subgraph augmented nonnegative matrix factorization uncovered 4 data-driven phenotypes of MODS, characterized by a combination of neurologic, respiratory, coagulation, and cardiovascular dysfunction, as follows: phenotype 1, severe, persistent encephalopathy (1019 patients [19.2%]); phenotype 2, moderate, resolving hypoxemia (1828 patients [34.5%]); phenotype 3, severe, persistent hypoxemia and shock (1012 patients [19.1%]); and phenotype 4, moderate, persistent thrombocytopenia and shock (1195 patients [22.6%]). These phenotypes were reproducible in a validation set of encounters, had distinct clinical characteristics, and were independently associated with outcomes. For example, using phenotype 2 as reference, the adjusted hazard ratios (aHRs) for death by 28 days were as follows: phenotype 1, aHR of 3.0 (IQR, 2.1-4.3); phenotype 3, aHR of 2.8 (IQR, 2.0-4.1); and phenotype (continued) Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 1/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children Abstract (continued) 4, aHR of 1.8 (IQR, 1.2-2.6). Interaction analysis in a matched cohort of patients with vasoactive- dependent shock revealed that hydrocortisone had differential treatment association with vasoactive-free days across phenotypes. For example, patients in phenotype 3 who received hydrocortisone had more vasoactive-free days than those who did not (23 days vs 18 days; P for interaction < .001), whereas patients in other phenotypes who received hydrocortisone either had no difference or had less vasoactive-free days. CONCLUSIONS AND RELEVANCE In this study, data-driven phenotyping in critically ill children with MODS uncovered 4 distinct and reproducible phenotypes with prognostic relevance and possible therapeutic relevance. Further validation and characterization of these phenotypes is warranted. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 Introduction The development of multiple organ dysfunction syndrome (MODS) is a common final pathway for death among critically ill children. Even among survivors, children with more severe organ dysfunction are at higher risk of developing long-term morbidity after critical illness. Approximately one-third of children admitted to a pediatric intensive care unit (PICU) have MODS on presentation or develop it during their illnesses, so moderating the burden of MODS could significantly affect the 1,3-5 outcomes of critically ill children. MODS can develop after many types of injury, but sepsis, trauma, and major surgery are the 1,6,7 most common etiologies in the PICU. The most frequently described shared mechanism of MODS pathophysiology is the development of dysregulated inflammation; however, decades of drug trials targeting mediators of inflammation have failed to show effectiveness in the reduction of the MODS 7-11 burden associated with sepsis and trauma. One of the most likely explanations for this is that MODS is a complex, dynamic, and heterogeneous process with many different phenotypes, and no single strategy will be effective in all phenotypes of MODS. In recent years, considerable emphasis has been placed on characterizing the phenotypes of 12-17 heterogenous syndromes in critically ill patients. These phenotypes may have prognostic and therapeutic implications and form the basis for precision medicine in the critical care setting. The derivation of these different phenotypes has included data-driven approaches using gene expression 12,17 14-16 13 data and clinical data as well as expert-based approaches using biomarkers and clinical data. In general, these approaches have used a single time (eg, initial presentation) to define the different phenotypes and have not investigated the dynamic patterns of illness, despite evidence that this 19-22 may be important in phenotyping. Furthermore, a single snapshot approach does not account for the fact that patients may present to a critical care setting at different points in their illness and that organ dysfunctions tend to peak between days 1 and 3 of admission. In this study we aimed to derive, validate, and characterize novel phenotypes of MODS in critically ill children using a data-driven approach based on the type, severity, and trajectory of organ dysfunctions in the acute phase of critical illness. Furthermore, we aimed to determine whether these phenotypes had prognostic and therapeutic relevance. Methods Study Design and Population This was a retrospective cohort study of critically ill children admitted to 2 large, academic PICUs in Chicago, Illinois, between January 1, 2010, and August 31, 2016. Patients were excluded if they were older than 21 years or were recovering from cardiac surgery. Data were extracted from the 2 JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 2/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children institutions’ data warehouses using structured queries and underwent quality checks for conformity, completeness, and plausibility. Data analysis was conducted from March to October 2019. The institutional review boards at the Ann and Robert H. Lurie Children’s Hospital of Chicago and The University of Chicago approved this study with a waiver of informed consent because of the retrospective nature of the analysis with minimal risk to patients. The reporting of this cohort study was performed using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Organ dysfunction severity was measured using the subscores of the pediatric Sequential Organ Failure Assessment (pSOFA) score, which measures dysfunction for the respiratory, cardiovascular, coagulation, hepatic, neurologic, and renal systems on a scale from 0 to 4 for each system. Patients had the 6 pSOFA subscores calculated for each 24-hour period between PICU admission and day 3, which was considered the acute phase of critical illness. Individual pSOFA subscores were carried forward for as long as 24 hours if they were not remeasured; otherwise, missing variables were assumed to be normal and the corresponding subscore was assigned a 0. Patients with MODS were defined as those with a pSOFA subscore of at least 2 in at least 2 organ systems within the first 3 days of admission. Severity of illness on admission was determined using the Pediatric Risk of Mortality (PRISM) III score using variables from the first 24 hours. Chronic comorbidities were based on the classification system developed by Feudtner et al. Patients with immunocompromised status were defined as those with an oncologic disease or transplant recipients. Patients who received antibiotics and microbiological cultures during the first 3 days were considered to have a confirmed or suspected infection. Each patient encounter was treated independently, but only the first PICU admission in a given hospitalization was included in the analysis. Primary and Secondary Outcomes The primary outcome was in-hospital mortality. The secondary outcomes included the presence of persistent MODS on day 7 after PICU admission (which included patients who died in the first week), and vasoactive-free, ventilator-free, and hospital-free days at 28 days after PICU admission. Data-Driven Phenotyping We used subgraph-augmented nonnegative matrix factorization to group patients with MODS into phenotypes based on the type, severity, and trajectory of each of the 6 pSOFA subscores in the first 3 days of PICU stay (Figure 1; eAppendix in the Supplement). Briefly, subgraph mining was used to extract the subgraphs representing the trajectory of individual organ dysfunctions. We then split the cohort of 5054 encounters with MODS and adequate subgraph counts into a derivation set (4044 encounters [80.0%]) and a validation set (1010 encounters [20.0%]). Nonnegative matrix factorization (NMF) was implemented on the patient-subgraph count matrix in the derivation set, which resulted in a patient-phenotype distribution matrix and a phenotype-subgraph mixture 21,28 coefficient matrix. Patients in the derivation and validation set were then assigned phenotype membership based on the highest probability group in the mixture coefficient matrix. Patients assigned to each phenotype were then compared across the derivation and validation sets. As a sensitivity analysis to determine external validity of the phenotypes, the same procedures were conducted using patients with MODS in the second hospital (PICU B) as the external validation set. Additional details can be found in the eAppendix in the Supplement. Phenotype Characteristics and Clinical Relevance Evaluation Clinical Characteristics of the MODS Phenotypes The clinical characteristics of patients in each of the MODS phenotypes were compared across phenotypes. These included demographic characteristics, severity of illness, organ support, comorbidities, microbiologic and laboratory test results by day 3, and associated outcomes. The JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 3/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children laboratory tests and summary measures included were based on those frequently used in prior 3,25,29,30 pediatric organ dysfunction and severity of illness scores. Prognostic Relevance of the MODS Phenotypes The prognostic relevance of the MODS phenotypes was tested by comparing the association of each phenotype with the outcomes of interest after adjusting for common confounders of PICU-related morbidity and mortality, including age, immunocompromised state, severity of illness on admission 4,31 using the PRISM-III score, and study site. The phenotype with the lowest overall in-hospital mortality was used as reference. Additionally, the association of noncharacteristic organ dysfunctions and in-hospital mortality was analyzed in each of the phenotypes. Noncharacteristic organ dysfunctions were defined as those organ systems that were not used to characterize a specific phenotype and had a pSOFA subscore of at least 2 by day 3 of PICU stay. Therapeutic Relevance of the MODS Phenotypes in Patients With Vasoactive-Dependent Shock There are few organ dysfunction–specific treatments routinely used in patients with MODS. One exception is the use of intravenous (IV) hydrocortisone in patients with vasoactive-dependent shock. Hydrocortisone may contribute to hemodynamic stability through pleiotropic effects; however, mixed results in large randomized clinical trials among adults with septic shock have led to variable 32-36 use among practitioners. Given that shock is a common feature of MODS, we tested the hypothesis that the heterogeneity of treatment effect of IV hydrocortisone in patients with vasoactive-dependent shock is in part explained by their phenotype. Vasoactive-dependent shock was defined as the use of dopamine greater than 5 μg/kg/min or any dose of epinephrine or norepinephrine infusions in patients with MODS. To balance the confounders across treatment groups, we first performed propensity score matching of patients based on the propensity to receive IV hydrocortisone, using age, immunocompromised state, PRISM-III score, study site, and phenotype membership as covariates. We then performed an interaction effect analysis for in-hospital mortality and vasoactive-free days, using phenotype membership, treatment group (ie, receiving2mg/kg/d of IV hydrocortisone or not), and their interaction as covariates to determine whether there was a differential treatment effect associated with phenotype membership. Additional details can be found in the eAppendix in the Supplement. Figure 1. Data-Driven Phenotyping Using Subgraph Augmented Nonnegative Matrix Factorization Subgraph Nonnegative Phenotype mining matrix factorization characterization “Progressive hypoxemia and persistent shock” CV “Early thrombocytopenia, M F with new hepatobiliary ≈× Resp dysfunction and shock” Subgraphs Renal coefficients “...” Phenotypes Subgraphs ... counts Time Subgraph mining extracts representative subgraphs from the trajectories of the 6 nonnegative matrix factorization with sparseness in the left factor algorithm to gradually pediatric Sequential Organ Failure Assessment subscores in patients with MODS, which reduce the error between M and F × C. Once the nonnegative matrix factorization is results in a matrix, M, of patient-subgraph counts. Nonnegative matrix factorization is completed, the final phenotypes can be characterized given the highly interpretable used to derive a matrix, F, of hidden features (in this case the phenotypes), and a matrix nature of the algorithm. Of note, the size of the matrices, number of groups, and the of mixture coefficient C with the coefficients that compose the subgraph-based phenotype characterization examples here are used for illustration purposes only. CV phenotypes. The factorization is done by iteratively updating F and C using the sparse indicates cardiovascular; resp, respiratory. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 4/14 Patients Organ systems Patients Patients Phenotypes JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children Statistical Analysis Data were analyzed using R version 3.6.1 (R Project for Statistical Computing). Categorical variables were compared using the χ test and continuous variables using the Kruskal-Wallis test. Regression analysis was used to adjust for confounders when comparing outcomes. Logistic regression was used to compare binary outcomes, and Poisson regression was used to compare count outcomes. Survival analysis to 28 days was performed using Kaplan-Meier curves and adjusted Cox regression analysis. Statistical significance was set at P < .05, and all tests were 2-tailed. Results Study Population There were 20 827 patient encounters among 14 285 unique patients in the 2 PICUs during the study period. The median (interquartile range [IQR]) age was 5.2 (1.5-12.7) years, and 11 409 (54.8%; 95% CI, 54.1%-55.5%) were male patients. A total of 5297 patients (25.4%; 95% CI, 24.8%-26.0%) met MODS criteria, and these patients had an associated in-hospital mortality of 449 (8.5%; 95% CI, 7.7%-9.3%). The clinical characteristics and outcomes of patients with and without MODS are presented in Table 1. Data-Driven Phenotyping After performing subgraph mining of the pSOFA subscore trajectories during the first 3 days among patients with MODS and eliminating redundant, rare, and nonrepresentative subgraphs based on the criteria described in the eAppendix in the Supplement, there were 145 representative subgraphs uncovered. A total of 243 patient encounters (4.6%) with MODS (with an in-hospital mortality of 3 [1.2%]) had none of these representative subgraphs and were therefore excluded from the remainder of the analysis, resulting in 5054 encounters (95.4%) with patients with MODS in the final cohort. Of the 145 subgraphs representing pSOFA subscore trajectories in these 5054 encounters, Table 1. Demographic and Clinical Characteristics of Patients With and Without MODS Patients, No. (%) With MODS Without MODS Clinical variables (n = 5297) (n = 15 530) P value Age, median (IQR), y 4.7 (1.0-12.6) 5.4 (1.6-12.7) <.001 Male patients 2898 (54.7) 8511 (54.8) .90 Race/ethnicity Non-Hispanic White 1700 (32.1) 4966 (32) Black 1726 (32.6) 5515 (35.5) <.001 Hispanic 1342 (25.3) 3650 (23.5) Other 529 (10) 1399 (9) PRISM-III score on admission, median (IQR) 7 (4-13) 0 (0-4) <.001 Vasoactives by day 3 1222 (23.1) 84 (0.5) <.001 Mechanical ventilation by day 3 3883 (73.3) 3108 (20) <.001 Immunocompromised state 922 (17.4) 1422 (9.2) <.001 Multiple chronic comorbidities 3153 (59.5) 5806 (37.3) <.001 Postoperative 1286 (24.3) 3679 (23.7) .40 Trauma 200 (3.8) 827 (5.3) <.001 Infection by day 3 3145 (59.4) 4665 (30) <.001 Maximum pSOFA score by day 3, median (IQR) 7 (6-9) 2 (1-3) <.001 Maximum PELOD-2 score by day 3, median (IQR) 7 (5-10) 2 (2-4) <.001 Abbreviations: IQR, interquartile range; MODS, multiple organ dysfunction syndrome; PRISM-III, MODS on day 7 1418 (26.8) 124 (0.8) <.001 Pediatric Risk of Mortality III; pSOFA, pediatric Length of stay, median (IQR), d 9.1 (4.8-17.9) 3.2 (1.8-5.9) <.001 Sequential Organ Failure Assessment; PELOD-2, In-hospital mortality 449 (8.5) 73 (0.5) <.001 Pediatric Logistic Organ Dysfunction, version 2. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 5/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children 31 (21.4%) represented worsening organ dysfunction, 24 (16.6%) represented moderate-to-severe organ dysfunction with no change, and 90 (62.1%) represented improvement in organ dysfunction. Sparse NMF was performed in the derivation set (4044 encounters [80.0%]) using the counts of the 145 subgraphs as sole input. This uncovered 4 distinct phenotypes based on the changes in pSOFA subscore in the first 3 days. Phenotype membership was then assigned to patients in the entire cohort based on the highest probability group membership according to the mixture coefficient matrix. Patients in each phenotype were then compared across the derivation and validation sets. Within each of the 4 phenotypes, patients had similar distributions of pSOFA subscores on day 1 to day 3 (eTable 1 in the Supplement), laboratory test characteristics by day 3 (eTable 2 in the Supplement), and outcomes (eTable 3 in the Supplement). In the sensitivity analysis using 3013 patient encounters (59.6%) in PICU A as derivation and 2041 patient encounters (40.4%) in PICU B as validation, again sparse NMF uncovered 4 phenotypes, which were consistent with those in the original analysis. The distributions of pSOFA subscores, laboratory test results, and outcomes were similar within phenotypes across the derivation patient encounters in PICU A and the external validation patient encounters in PICU B (eTable 4 in the Supplement). Patients within each phenotype from the original NMF were merged and used for the remainder of the analyses. Characterization of the MODS Phenotypes The most representative subgraphs for each phenotype were chosen from the mixture coefficient matrix of the NMF model, such that the selected subgraphs accounted for at least 75% of the phenotype group coefficients. The 10 most representative subgraphs for each of the 4 MODS phenotypes and the relative weight of each organ dysfunction subscore in the first 3 days, stratified by phenotype, appear in Figure 2 and eFigure 1 in the Supplement. These representative subgraphs were used to characterize the phenotypes based on the most distinctive characteristics, using the organ dysfunction type, severity, and trajectory in the first 3 days of PICU stay. Based on this, the 4 MODS phenotypes were characterized as follows: phenotype 1, severe, persistent encephalopathy (1019 patients [19.2%]); phenotype 2, moderate, resolving hypoxemia (1828 patients [34.5%]); phenotype 3: severe, persistent hypoxemia and shock (1012 patients [19.1%]); and phenotype 4, moderate, persistent thrombocytopenia and shock (1195 patients [22.6%]) The clinical characteristics and associated outcomes of patients in each of the 4 MODS phenotypes are presented in Table 2. When compared across age groups, the distribution and outcomes associated with the 4 MODS phenotypes remained consistent (eTable 5 in the Supplement). Prognostic Relevance of the MODS Phenotypes The outcomes associated with each of the 4 phenotypes after adjusting for confounders including age, immunocompromised state, PRISM-III score, and study site are presented in eTable 6 in the Supplement. After adjusting for the same confounders in Cox regression analysis and using phenotype 2, which had the lowest mortality as reference, the adjusted hazard ratios (aHRs) for death by 28 days were as follows: phenotype 1, aHR of 3.0 (IQR, 2.1-4.3); phenotype 3, aHR of 2.8 (IQR, 2.0-4.1); and phenotype 4, aHR of 1.8 (IQR, 1.2-2.6). The Kaplan-Meier survival curves for the 4 phenotypes are presented in eFigure 2 in the Supplement. The multivariable logistic regression analyses of the association of noncharacteristic organ dysfunctions and in-hospital mortality showed significant differences between phenotypes and are presented in eTable 7 in the Supplement. Therapeutic Relevance of the MODS Phenotypes in Patients With Vasoactive-Dependent Shock A total of 1220 of 5054 patient encounters (24.1%; 95% CI, 23.0%-25.3%) had vasoactive- dependent shock by day 3, with an associated in-hospital mortality of 299 patients (24.5% [95% CI, 22.1%-27%]). Of these, 342 (28.0%; 95% CI, 25.5%-30.6%) received IV hydrocortisone. Using JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 6/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children propensity score matching, 330 treated patients were successfully matched 1:1 with 330 untreated patients using age, immunocompromised state, PRISM-III score, study site, and phenotype membership as covariates. The matched cohort demonstrated a balanced distribution of covariates, with absolute standardized mean difference of less than 0.1 (eFigure 3 in the Supplement). Interaction analysis in the matched cohort revealed that IV hydrocortisone had a differential treatment effect on vasoactive-free days across phenotypes, most notably with phenotype 3 having most benefit associated with treatment (23 vasoactive-free days in treated patients vs 18 vasoactive-free days in treated patients; P for interaction < .001). The interaction was not significant for in-hospital mortality (Table 3). Discussion We derived and validated 4 novel, data-driven phenotypes of MODS in critically ill children based on organ dysfunction trajectories. These phenotypes have distinct clinical characteristics, are independently associated with outcomes, and have different sets of organ dysfunction–based risk factors for death. In a subset of patients with vasoactive-dependent shock who were matched based on the propensity to receive IV hydrocortisone, treatment was associated with a difference in Figure 2. Common Subgraphs in Each of the 4 Data-Driven MODS Phenotypes Subscore Cardiovascular Coagulation Hepatic Neurologic Renal Respiratory A Phenotype 1 B Phenotype 2 80% 53.2% 9.9% 9.8% 8.5% 49.3% 22.8% 22% 18% 14.9% 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Days Days 5.9% 5.8% 5.1% 4.4% 3.3% 14.1% 13.7% 8.2% 4% 3.8% 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Days Days C Phenotype 3 D Phenotype 4 81.9% 37% 30.7% 27.7% 27.6% 46.4% 39.2% 16.7% 13.7% 11.9% 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Days Days 16.9% 13.6% 13.5% 9.7% 8.7% 8.9% 6% 5.9% 5.4% 5.4% 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Days Days The most common subgraphs in each phenotype and the proportion of patients who had each of them. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 7/14 Subscore Subscore Subscore Subscore Subscore Subscore Subscore Subscore JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children Table 2. Demographic and Clinical Characteristics and Outcomes of the 4 MODS Phenotypes No. (%) Phenotype 1, Phenotype 2, Phenotype 3, Phenotype 4, severe, persistent moderate, resolving severe, persistent moderate, persistent encephalopathy hypoxemia hypoxemia and thrombocytopenia and Clinical variable (n = 1019) (n = 1828) shock (n = 1012) shock (n = 1195) P value Age, median (IQR), y 4.3 (1.0-12.0) 3.9 (0.9-11.3) 3.5 (0.8-11.3) 8.1 (1.8-15.2) <.001 Male patients 555 (54.5) 989 (54.1) 557 (55.0) 671 (56.2) .73 Race/ethnicity Non-Hispanic White 311 (30.5) 557 (30.5) 319 (31.5) 447 (37.4) Black 410 (40.2) 636 (34.8) 318 (31.4) 285 (23.8) <.001 Hispanic 197 (19.3) 453 (24.8) 265 (26.2) 353 (29.5) Other 101 (9.9) 182 (10) 110 (10.9) 110 (9.2) PRISM-III score on admission, median (IQR) 8 (5-15) 6 (2-10) 10 (5-19) 9 (5-13) <.001 Vasoactives by day 3 179 (17.6) 107 (6) 392 (38.7) 542 (45.3) <.001 Mechanical ventilation by day 3 926 (91) 1409 (77.1) 880 (87) 553 (46.3) <.001 Immunocompromised state 105 (10.3) 240 (13.1) 154 (15.2) 382 (32) <.001 Multiple chronic comorbidities 607 (59.6) 1043 (57.1) 608 (60) 760 (63.4) .004 Postoperative 223 (21.9) 527 (28.8) 164 (16.2) 307 (25.7) <.001 Trauma 72 (7.1) 55 (3) 41 (4.1) 30 (2.5) <.001 Infection by day 3 595 (58.4) 953 (52.1) 724 (71.5) 755 (63.2) <.001 Primary site Bloodstream 59 (5.8) 117 (6.4) 119 (11.2) 193 (16.2) <.001 Respiratory 334 (32.8) 510 (27.9) 385 (38) 193 (16.2) <.001 Urinary 66 (6.5) 96 (5.3) 62 (6.1) 81 (6.8) .32 Other sites 36 (3.5) 60 (3.3) 35 (3.5) 45 (3.8) .92 Culture negative 194 (19) 308 (16.8) 252 (24.9) 328 (27.4) <.001 Microorganism Bacteria Gram positive 133 (13.1) 184 (10.1) 159 (15.7) 127 (10.6) <.001 Gram negative 165 (16.2) 224 (12.3) 178 (17.6) 137 (11.5) <.001 Other bacteria 32 (3.1) 50 (2.7) 22 (2.2) 39 (3.3) .42 Fungi 12 (1.2) 17 (0.9) 18 (1.8) 21 (1.8) .14 Viruses 170 (16.7) 333 (18.2) 230 (22.7) 184 (15.4) <.001 Laboratory results by day 3, median (IQR) Minimum white blood cell count, /μL 7900 (5200-11 000) 8300 (5600-11 600) 7000 (3800-10 900) 5900 (2500-9400) <.001 Minimum lymphocyte count, /μL 1300 (700-2200) 1300 (700-2300) 1100 (500-2000) 800 (300-1500) <.001 Maximum blood urea nitrogen, mg/dL 12 (8-18) 12 (8-18) 14 (9-23) 15 (10-28) <.001 Maximum creatinine, mg/dL 0.4 (0.3-0.6) 0.4 (0.2-0.6) 0.4 (0.3-0.8) 0.5 (0.3-0.9) <.001 Minimum albumin, g/dL 3.1 (2.5-3.6) 3.1 (2.6-3.7) 2.7 (2.2-3.3) 2.8 (2.3-3.4) <.001 Maximum aminotransferase, U/L Aspartate 53 (31-147) 49 (30-111) 61 (34-201) 62 (32-141) <.001 Alanine 28 (18-17) 27 (17-71) 36 (19-122) 37 (18-97) <.001 Maximum total bilirubin, mg/dL 0.5 (0.3-1.2) 0.5 (0.2-2.2) 0.6 (0.3-1.5) 1.2 (0.5-3.2) <.001 Minimum hemoglobin, g/dL 9 (7.4-11) 9.6 (7.8-11.3) 8.8 (7.2-10.5) 8.3 (7-10) <.001 Minimum platelet count, /μL 185 000 188 000 156 000 82 000 (27 000-179 000) <.001 (104 000-268 000) (94 000-270 000) (68 000-249 000) Maximum international normalized ratio 1.4 (1.2-1.7) 1.3 (1.2-1.6) 1.5 (1.2-2.1) 1.4 (1.2-1.7) <.001 Maximum partial thromboplastin time, s 35 (30-45) 35 (30-44) 40 (32-59) 39 (31-54) <.001 Maximum lactate, mg/dL 18.9 (10.8-35.1) 14.4 (9.9-26.1) 22.5 (12.6-52.2) 18.0 (11.7-34.2) <.001 (continued) JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 8/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children Table 2. Demographic and Clinical Characteristics and Outcomes of the 4 MODS Phenotypes (continued) No. (%) Phenotype 1, Phenotype 2, Phenotype 3, Phenotype 4, severe, persistent moderate, resolving severe, persistent moderate, persistent encephalopathy hypoxemia hypoxemia and thrombocytopenia and Clinical variable (n = 1019) (n = 1828) shock (n = 1012) shock (n = 1195) P value Outcomes Vasoactive-free days at 28 d, median (IQR) 28 (27-28) 28 (28-28) 28 (24-28) 27 (26-28) <.001 Ventilator-free days at 28 d, median (IQR) 23 (13-26) 26 (21-27) 20 (6-26) 27 (22-28) <.001 Hospital-free days at 28 d, median (IQR) 16 (1-23) 20 (12-24) 12 (1-21) 17 (4-22) <.001 Persistent MODS on day 7 320 (31.4) 319 (17.5) 448 (44.3) 307 (25.7) <.001 In-hospital mortality 116 (11.3) 60 (3.3) 176 (17.4) 94 (7.9) <.001 Abbreviations: IQR, interquartile range; MODS, multiple organ dysfunction syndrome; to grams per liter, multiply by 10; blood urea nitrogen to millimoles per liter, multiply by PRISM-III, Pediatric Risk of Mortality III. 0.357; creatinine to micromoles per liter, multiply by 88.4; lymphocyte, platelet, and white blood cell count to ×10 per microliter, multiply by 0.001; and total bilirubin to SI conversion factors: To convert alanine aminotransferase and aspartate millimoles per liter, multiply by 17.104. aminotransferase to microkatals per liter, multiply by 0.0167; albumin and hemoglobin Table 3. Differences in Response to IV Hydrocortisone by Phenotype in a Propensity Score Matched Cohort of Patients With Vasoactive-Dependent Shock Phenotype 1 (n = 72) Phenotype 2 (n = 71) Phenotype 3 (n = 287) Phenotype 4 (n = 230) Treated Untreated Treated Untreated Treated Untreated Treated Untreated Outcome (n = 39) (n = 33) (n = 36) (n = 35) (n = 146) (n = 141) (n = 109) (n = 121) P value Vasoactive-free days, median (IQR) 15 (0-25) 17 (1-24) 25 (23-26) 24 (21-27) 23 (1-26) 18 (0-26) 26 (23-27) 26 (24-27) <.001 In-hospital mortality, No.(%) 20 (51.3) 14 (42.4) 4 (11.1) 4 (11.4) 50 (34.2) 65 (46.1) 14 (12.8) 9 (7) .11 Abbreviations: IQR, interquartile range; IV, intravenous. P value for interaction between IV hydrocortisone and phenotype membership using phenotype 2 as reference group (ie, 3 interaction terms) and analysis of deviance to test global significance. vasoactive-free days across phenotypes, suggesting that these phenotypes may be associated with differences in response to therapy. Our findings share similarities with previously described data-driven phenotypes of sepsis and sepsis-induced MODS, particularly phenotypes 3 and 4, which were the MODS phenotypes most frequently associated with infections in our cohort. Phenotype 3 shares similarities with the “shock with hypoxemia and altered mental status” phenotype described by Knox et al and the γ phenotype described by Seymour et al in adult patients, especially in terms of hypoxemia, vasoactive- dependence, low albumin levels, and high mortality. Phenotype 4 shares similarities with the hepatic 15 14 disease phenotype described by Knox et al and the δ phenotype described by Seymour et al, particularly in terms of the thrombocytopenia, hepatobiliary dysfunction, and shock as well as the relatively preserved pulmonary function. To our knowledge, our study is the first to use a data-driven approach to derive MODS phenotypes in critically ill children. Further biomarker and molecular endotyping analyses will be needed to compare our phenotypes with other previously described 12,13,17 clinical phenotypes and endotypes in pediatric and adult patients. The association of IV hydrocortisone administration with differential treatment effect on vasoactive-free days in patients with vasoactive-dependent shock, particularly in phenotype 3, could partially be explained by the enrichment with patients suffering severe hypoxemia, severe shock, and infections. Prior randomized clinical trials and secondary analyses of trial data have found that the use of hydrocortisone was associated with improved outcomes in patients with sepsis-associated 38,39 acute respiratory distress syndrome. However, our results should be interpreted with caution because they are part of an interaction effect analysis on a propensity score matched cohort. The standard approach is to perform an interaction effects analysis across subgroups with a treatment assignment that has been randomized to ensure a better balance of all possible confounders. Given the selection bias inherent in observational data, we used propensity score matching to balance JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 9/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children observed covariates, but it is likely that we did not account for all possible confounders. Therefore, these results require further validation and are only hypothesis generating at this point. Our data-driven phenotyping approach to MODS has several important implications. We describe a broad stratification of patients that can be readily performed using widely available electronic health record data and that appears to have prognostic relevance and, potentially, therapeutic value. It is likely that many of the patients within each phenotype share many of the same underlying pathophysiological disturbances and dysregulated host responses, as evidenced by the shared clinical characteristics and laboratory test results. Understanding the common features of these underlying pathophysiologies could help determine which interventions may be most beneficial to individual patients, regardless of the etiology of MODS and beyond the traditional boundaries of syndromes like sepsis or acute respiratory distress syndrome. For example, cardiovascular dysfunction is associated with the most detrimental outcomes in patients with severe encephalopathy in phenotype 1, suggesting that targeted blood pressure management and close cerebral perfusion monitoring, as recommended for patients with traumatic brain injury, may be warranted in patients with this phenotype even if most do not have traumatic brain injury. Similarly, renal dysfunction seems to most negatively affect patients in phenotype 3 who suffer severe hypoxemia and shock, indicating that careful fluid management and nephrotoxin avoidance might be most beneficial in this group of patients. In addition, further endotyping and molecular characterization of these broad phenotypes may uncover relevant MODS subphenotypes that may 41-43 benefit from novel targeted interventions, including immunomodulatory strategies. Furthermore, the molecular characterization of these phenotypes may help in the task of assigning phenotype membership shortly after patients are admitted, which would make this phenotyping schema more clinically actionable in the critical care setting. In short, data-driven phenotyping may help develop precision medicine strategies that can reduce the mortality and morbidity associated with MODS, but further investigation into the value of this phenotyping approach in research and clinical care is warranted. Limitations Our findings are subject to several limitations. First, the data for this study were collected retrospectively from 2 academic PICUs in the same city, limiting the generalizability of our findings. Second, our data-driven phenotyping approach was intentionally sparse, using only organ dysfunction trajectories to define broad phenotypes and determine their prognostic and therapeutic relevance, but performing deeper phenotyping using other clinical features and biomarkers may result in phenotypes with potentially higher resolution and greater clinical utility. Third, the therapeutic relevance of IV hydrocortisone was studied using an interaction effect analysis on a propensity score matched cohort, and the results must be interpreted with caution, as previously discussed. Fourth, although we described the MODS phenotypes as distinct entities, it is likely that many patients share features of more than 1 phenotype, and future analyses to better understand the implications of these overlaps are warranted. Conclusions In this study, we derived and validated 4 data-driven phenotypes of MODS in critically ill children that have distinct clinical characteristics, are independently associated with outcomes, and may be therapeutically relevant in the subset of patients with vasoactive-dependent shock. Our data-driven phenotyping approach in MODS shows promising results, but further characterization of these broad phenotypes and validation in different settings is warranted. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 10/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children ARTICLE INFORMATION Accepted for Publication: April 22, 2020. Published: August 11, 2020. doi:10.1001/jamanetworkopen.2020.9271 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Sanchez-Pinto LN et al. JAMA Network Open. Corresponding Author: L. Nelson Sanchez-Pinto, MD, MBI, Division of Critical Care Medicine, Ann and Robert H. Lurie Children’s Hospital of Chicago, 225 E Chicago Ave, PO Box 73, Chicago, IL 60611 (lsanchezpinto@ luriechildrens.org). Author Affiliations: Critical Care, Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Sanchez-Pinto); Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Sanchez-Pinto, Luo); Division of Critical Care Medicine, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois (Sanchez-Pinto, Pendergrast); Driskill Graduate Program, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Stroup); Section of Critical Care, Department of Pediatrics, The University of Chicago, Chicago, Illinois (Pinto). Author Contributions: Dr Sanchez-Pinto and Ms Stroup had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Sanchez-Pinto, Luo. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Sanchez-Pinto. Critical revision of the manuscript for important intellectual content: Stroup, Pendergrast, Pinto, Luo. Statistical analysis: Sanchez-Pinto, Stroup, Luo. Obtained funding: Sanchez-Pinto. Administrative, technical, or material support: Pendergrast, Pinto, Luo. Supervision: Sanchez-Pinto, Luo. Conflict of Interest Disclosures: None reported. Funding/Support: This work was supported by grant R21HD096402 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development to Dr Sanchez-Pinto and grant R21LM012618 from the National Library of Medicine to Dr Luo. Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Additional Contributions: The authors would like to thank Adin-Cristian Andrei, PhD (Department of Preventive Medicine, Northwestern University Feinberg School of Medicine), for his biostatistical expertise and advice related to the propensity score matching and interaction effects analysis in patients with vasoactive-dependent shock. He was not compensated for his time. The authors would also like to thank the Data Analytics and Reporting team at Ann and Robert H. Lurie Children’s Hospital of Chicago and the Center for Research Informatics at The University of Chicago for providing the raw electronic health record data for the study. REFERENCES 1. Tamburro RF, Jenkins TL. Multiple organ dysfunction syndrome: a challenge for the pediatric critical care community. Pediatr Crit Care Med. 2017;18(3_suppl)(suppl 1):S1-S3. 2. Matics TJ, Pinto NP, Sanchez-Pinto LN. Association of organ dysfunction scores and functional outcomes following pediatric critical illness. 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Fitzgerald JC, Weiss SL, Maude SL, et al. Cytokine release syndrome after chimeric antigen receptor T cell therapy for acute lymphoblastic leukemia. Crit Care Med. 2017;45(2):e124-e131. doi:10.1097/CCM. 42. Hall MW, Knatz NL, Vetterly C, et al. Immunoparalysis and nosocomial infection in children with multiple organ dysfunction syndrome. Intensive Care Med. 2011;37(3):525-532. doi:10.1007/s00134-010-2088-x 43. Shakoory B, Carcillo JA, Chatham WW, et al. Interleukin-1 receptor blockade is associated with reduced mortality in sepsis patients with features of the macrophage activation syndrome: re-analysis of a prior phase III trial. Crit Care Med. 2016;44(2):275-281. doi:10.1097/CCM.0000000000001402 SUPPLEMENT. eAppendix. Supplementary Methods eFigure 1. Alluvial Plots of Organ Dysfunction Burden by Day in the 4 MODS Phenotypes eFigure 2. Kaplan-Meier Survival Curves for the First 28 Days for the 4 MODS Phenotypes eFigure 3. Covariate Balance in the Propensity Score Matched Cohort of Patients With Vasoactive-Dependent Shock Who Received and Did Not Receive Intravenous Hydrocortisone eTable 1. Maximum pSOFA Subscores Achieved by Day in Patients Within Each of the 4 MODS Phenotypes in the Derivation and Validation Sets eTable 2. Laboratory Test Results by Day 3 of PICU Stay in the 4 MODS Phenotypes Across the Derivation and Validation Sets JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 13/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children eTable 3. Outcomes Associated With the 4 MODS Phenotypes Across the Derivation and Validation Sets eTable 4. Laboratory Test Results and Outcomes Associated With the MODS Phenotypes When Using Patients in Hospital A as the Derivation Set and Patients in Hospital B as an External Validation Set eTable 5. Distribution and Outcomes Associated With the 4 MODS Phenotypes Across Age Groups eTable 6. Unadjusted and Adjusted Outcomes Associated With the 4 MODS Phenotypes eTable 7. Multivariable Association Between Noncharacteristic Organ Dysfunctions and In-Hospital Mortality eReferences. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 14/14 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA Network Open American Medical Association

Derivation and Validation of Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children

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Abstract

Key Points Question Does data-driven IMPORTANCE Multiple organ dysfunction syndrome (MODS) is a dynamic and heterogeneous phenotyping based on the trajectories process associated with high morbidity and mortality in critically ill children. of organ dysfunction in the acute phase of critical illness among children with OBJECTIVE To determine whether data-driven phenotypes of MODS based on the trajectories of 6 multiple organ dysfunction syndrome organ dysfunctions have prognostic and therapeutic relevance in critically ill children. uncover phenotypes with prognostic and therapeutic relevance? DESIGN, SETTING, AND PARTICIPANTS This cohort study included 20 827 pediatric intensive care Findings In this 2-center cohort study encounters among 14 285 children admitted to 2 large academic pediatric intensive care units of 20 827 pediatric intensive care (PICUs) between January 2010 and August 2016. Patients were excluded if they were older than 21 encounters, a data-driven approach to years or had undergone cardiac surgery. The 6 subscores of the pediatric Sequential Organ Failure phenotyping patients with multiple Assessment (pSOFA) score were calculated for the first 3 days, including the subscores for organ dysfunction syndrome using the respiratory, cardiovascular, coagulation, hepatic, neurologic, and renal dysfunctions. MODS was trajectories of 6 organ dysfunctions defined as a pSOFA subscore of at least 2 in at least 2 organs. Encounters were split in a 80:20 ratio uncovered 4 reproducible and distinct for derivation and validation, respectively. The trajectories of the 6 subscores were used to derive a phenotypes with prognostic and set of data-driven phenotypes of MODS using subgraph-augmented nonnegative matrix potential therapeutic relevance. factorization in the derivation set. Data analysis was conducted from March to October 2019. Meaning In this study, data-driven EXPOSURES The primary exposure was phenotype membership. In the subset of patients with phenotyping based on the type, vasoactive-dependent shock, the interaction between hydrocortisone and phenotype membership severity, and trajectory of 6 organ and its association with outcomes were examined in a matched cohort. dysfunctions showed promising results in critically ill children with multiple MAIN OUTCOMES AND MEASURES The primary outcome was in-hospital mortality. Secondary organ dysfunction syndrome. outcomes included persistent MODS on day 7, and vasoactive-free, ventilator-free, and hospital-free days. Regression analysis was used to adjust for age, severity of illness, immunocompromised status, Supplemental content and study site. Author affiliations and article information are listed at the end of this article. RESULTS There were 14 285 patients with 20 827 encounters (median [interquartile range] age 5.2 years [1.5-12.7] years; 11 409 [54.8%; 95% CI, 54.1%-55.5%] male patients). Of these, 5297 encounters (25.4%; 95% CI, 24.8%-26.0%) were with patients who had MODS, of which 5054 (95.4%) met the subgraph count threshold and were included in the analysis. Subgraph augmented nonnegative matrix factorization uncovered 4 data-driven phenotypes of MODS, characterized by a combination of neurologic, respiratory, coagulation, and cardiovascular dysfunction, as follows: phenotype 1, severe, persistent encephalopathy (1019 patients [19.2%]); phenotype 2, moderate, resolving hypoxemia (1828 patients [34.5%]); phenotype 3, severe, persistent hypoxemia and shock (1012 patients [19.1%]); and phenotype 4, moderate, persistent thrombocytopenia and shock (1195 patients [22.6%]). These phenotypes were reproducible in a validation set of encounters, had distinct clinical characteristics, and were independently associated with outcomes. For example, using phenotype 2 as reference, the adjusted hazard ratios (aHRs) for death by 28 days were as follows: phenotype 1, aHR of 3.0 (IQR, 2.1-4.3); phenotype 3, aHR of 2.8 (IQR, 2.0-4.1); and phenotype (continued) Open Access. This is an open access article distributed under the terms of the CC-BY License. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 1/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children Abstract (continued) 4, aHR of 1.8 (IQR, 1.2-2.6). Interaction analysis in a matched cohort of patients with vasoactive- dependent shock revealed that hydrocortisone had differential treatment association with vasoactive-free days across phenotypes. For example, patients in phenotype 3 who received hydrocortisone had more vasoactive-free days than those who did not (23 days vs 18 days; P for interaction < .001), whereas patients in other phenotypes who received hydrocortisone either had no difference or had less vasoactive-free days. CONCLUSIONS AND RELEVANCE In this study, data-driven phenotyping in critically ill children with MODS uncovered 4 distinct and reproducible phenotypes with prognostic relevance and possible therapeutic relevance. Further validation and characterization of these phenotypes is warranted. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 Introduction The development of multiple organ dysfunction syndrome (MODS) is a common final pathway for death among critically ill children. Even among survivors, children with more severe organ dysfunction are at higher risk of developing long-term morbidity after critical illness. Approximately one-third of children admitted to a pediatric intensive care unit (PICU) have MODS on presentation or develop it during their illnesses, so moderating the burden of MODS could significantly affect the 1,3-5 outcomes of critically ill children. MODS can develop after many types of injury, but sepsis, trauma, and major surgery are the 1,6,7 most common etiologies in the PICU. The most frequently described shared mechanism of MODS pathophysiology is the development of dysregulated inflammation; however, decades of drug trials targeting mediators of inflammation have failed to show effectiveness in the reduction of the MODS 7-11 burden associated with sepsis and trauma. One of the most likely explanations for this is that MODS is a complex, dynamic, and heterogeneous process with many different phenotypes, and no single strategy will be effective in all phenotypes of MODS. In recent years, considerable emphasis has been placed on characterizing the phenotypes of 12-17 heterogenous syndromes in critically ill patients. These phenotypes may have prognostic and therapeutic implications and form the basis for precision medicine in the critical care setting. The derivation of these different phenotypes has included data-driven approaches using gene expression 12,17 14-16 13 data and clinical data as well as expert-based approaches using biomarkers and clinical data. In general, these approaches have used a single time (eg, initial presentation) to define the different phenotypes and have not investigated the dynamic patterns of illness, despite evidence that this 19-22 may be important in phenotyping. Furthermore, a single snapshot approach does not account for the fact that patients may present to a critical care setting at different points in their illness and that organ dysfunctions tend to peak between days 1 and 3 of admission. In this study we aimed to derive, validate, and characterize novel phenotypes of MODS in critically ill children using a data-driven approach based on the type, severity, and trajectory of organ dysfunctions in the acute phase of critical illness. Furthermore, we aimed to determine whether these phenotypes had prognostic and therapeutic relevance. Methods Study Design and Population This was a retrospective cohort study of critically ill children admitted to 2 large, academic PICUs in Chicago, Illinois, between January 1, 2010, and August 31, 2016. Patients were excluded if they were older than 21 years or were recovering from cardiac surgery. Data were extracted from the 2 JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 2/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children institutions’ data warehouses using structured queries and underwent quality checks for conformity, completeness, and plausibility. Data analysis was conducted from March to October 2019. The institutional review boards at the Ann and Robert H. Lurie Children’s Hospital of Chicago and The University of Chicago approved this study with a waiver of informed consent because of the retrospective nature of the analysis with minimal risk to patients. The reporting of this cohort study was performed using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. Organ dysfunction severity was measured using the subscores of the pediatric Sequential Organ Failure Assessment (pSOFA) score, which measures dysfunction for the respiratory, cardiovascular, coagulation, hepatic, neurologic, and renal systems on a scale from 0 to 4 for each system. Patients had the 6 pSOFA subscores calculated for each 24-hour period between PICU admission and day 3, which was considered the acute phase of critical illness. Individual pSOFA subscores were carried forward for as long as 24 hours if they were not remeasured; otherwise, missing variables were assumed to be normal and the corresponding subscore was assigned a 0. Patients with MODS were defined as those with a pSOFA subscore of at least 2 in at least 2 organ systems within the first 3 days of admission. Severity of illness on admission was determined using the Pediatric Risk of Mortality (PRISM) III score using variables from the first 24 hours. Chronic comorbidities were based on the classification system developed by Feudtner et al. Patients with immunocompromised status were defined as those with an oncologic disease or transplant recipients. Patients who received antibiotics and microbiological cultures during the first 3 days were considered to have a confirmed or suspected infection. Each patient encounter was treated independently, but only the first PICU admission in a given hospitalization was included in the analysis. Primary and Secondary Outcomes The primary outcome was in-hospital mortality. The secondary outcomes included the presence of persistent MODS on day 7 after PICU admission (which included patients who died in the first week), and vasoactive-free, ventilator-free, and hospital-free days at 28 days after PICU admission. Data-Driven Phenotyping We used subgraph-augmented nonnegative matrix factorization to group patients with MODS into phenotypes based on the type, severity, and trajectory of each of the 6 pSOFA subscores in the first 3 days of PICU stay (Figure 1; eAppendix in the Supplement). Briefly, subgraph mining was used to extract the subgraphs representing the trajectory of individual organ dysfunctions. We then split the cohort of 5054 encounters with MODS and adequate subgraph counts into a derivation set (4044 encounters [80.0%]) and a validation set (1010 encounters [20.0%]). Nonnegative matrix factorization (NMF) was implemented on the patient-subgraph count matrix in the derivation set, which resulted in a patient-phenotype distribution matrix and a phenotype-subgraph mixture 21,28 coefficient matrix. Patients in the derivation and validation set were then assigned phenotype membership based on the highest probability group in the mixture coefficient matrix. Patients assigned to each phenotype were then compared across the derivation and validation sets. As a sensitivity analysis to determine external validity of the phenotypes, the same procedures were conducted using patients with MODS in the second hospital (PICU B) as the external validation set. Additional details can be found in the eAppendix in the Supplement. Phenotype Characteristics and Clinical Relevance Evaluation Clinical Characteristics of the MODS Phenotypes The clinical characteristics of patients in each of the MODS phenotypes were compared across phenotypes. These included demographic characteristics, severity of illness, organ support, comorbidities, microbiologic and laboratory test results by day 3, and associated outcomes. The JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 3/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children laboratory tests and summary measures included were based on those frequently used in prior 3,25,29,30 pediatric organ dysfunction and severity of illness scores. Prognostic Relevance of the MODS Phenotypes The prognostic relevance of the MODS phenotypes was tested by comparing the association of each phenotype with the outcomes of interest after adjusting for common confounders of PICU-related morbidity and mortality, including age, immunocompromised state, severity of illness on admission 4,31 using the PRISM-III score, and study site. The phenotype with the lowest overall in-hospital mortality was used as reference. Additionally, the association of noncharacteristic organ dysfunctions and in-hospital mortality was analyzed in each of the phenotypes. Noncharacteristic organ dysfunctions were defined as those organ systems that were not used to characterize a specific phenotype and had a pSOFA subscore of at least 2 by day 3 of PICU stay. Therapeutic Relevance of the MODS Phenotypes in Patients With Vasoactive-Dependent Shock There are few organ dysfunction–specific treatments routinely used in patients with MODS. One exception is the use of intravenous (IV) hydrocortisone in patients with vasoactive-dependent shock. Hydrocortisone may contribute to hemodynamic stability through pleiotropic effects; however, mixed results in large randomized clinical trials among adults with septic shock have led to variable 32-36 use among practitioners. Given that shock is a common feature of MODS, we tested the hypothesis that the heterogeneity of treatment effect of IV hydrocortisone in patients with vasoactive-dependent shock is in part explained by their phenotype. Vasoactive-dependent shock was defined as the use of dopamine greater than 5 μg/kg/min or any dose of epinephrine or norepinephrine infusions in patients with MODS. To balance the confounders across treatment groups, we first performed propensity score matching of patients based on the propensity to receive IV hydrocortisone, using age, immunocompromised state, PRISM-III score, study site, and phenotype membership as covariates. We then performed an interaction effect analysis for in-hospital mortality and vasoactive-free days, using phenotype membership, treatment group (ie, receiving2mg/kg/d of IV hydrocortisone or not), and their interaction as covariates to determine whether there was a differential treatment effect associated with phenotype membership. Additional details can be found in the eAppendix in the Supplement. Figure 1. Data-Driven Phenotyping Using Subgraph Augmented Nonnegative Matrix Factorization Subgraph Nonnegative Phenotype mining matrix factorization characterization “Progressive hypoxemia and persistent shock” CV “Early thrombocytopenia, M F with new hepatobiliary ≈× Resp dysfunction and shock” Subgraphs Renal coefficients “...” Phenotypes Subgraphs ... counts Time Subgraph mining extracts representative subgraphs from the trajectories of the 6 nonnegative matrix factorization with sparseness in the left factor algorithm to gradually pediatric Sequential Organ Failure Assessment subscores in patients with MODS, which reduce the error between M and F × C. Once the nonnegative matrix factorization is results in a matrix, M, of patient-subgraph counts. Nonnegative matrix factorization is completed, the final phenotypes can be characterized given the highly interpretable used to derive a matrix, F, of hidden features (in this case the phenotypes), and a matrix nature of the algorithm. Of note, the size of the matrices, number of groups, and the of mixture coefficient C with the coefficients that compose the subgraph-based phenotype characterization examples here are used for illustration purposes only. CV phenotypes. The factorization is done by iteratively updating F and C using the sparse indicates cardiovascular; resp, respiratory. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 4/14 Patients Organ systems Patients Patients Phenotypes JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children Statistical Analysis Data were analyzed using R version 3.6.1 (R Project for Statistical Computing). Categorical variables were compared using the χ test and continuous variables using the Kruskal-Wallis test. Regression analysis was used to adjust for confounders when comparing outcomes. Logistic regression was used to compare binary outcomes, and Poisson regression was used to compare count outcomes. Survival analysis to 28 days was performed using Kaplan-Meier curves and adjusted Cox regression analysis. Statistical significance was set at P < .05, and all tests were 2-tailed. Results Study Population There were 20 827 patient encounters among 14 285 unique patients in the 2 PICUs during the study period. The median (interquartile range [IQR]) age was 5.2 (1.5-12.7) years, and 11 409 (54.8%; 95% CI, 54.1%-55.5%) were male patients. A total of 5297 patients (25.4%; 95% CI, 24.8%-26.0%) met MODS criteria, and these patients had an associated in-hospital mortality of 449 (8.5%; 95% CI, 7.7%-9.3%). The clinical characteristics and outcomes of patients with and without MODS are presented in Table 1. Data-Driven Phenotyping After performing subgraph mining of the pSOFA subscore trajectories during the first 3 days among patients with MODS and eliminating redundant, rare, and nonrepresentative subgraphs based on the criteria described in the eAppendix in the Supplement, there were 145 representative subgraphs uncovered. A total of 243 patient encounters (4.6%) with MODS (with an in-hospital mortality of 3 [1.2%]) had none of these representative subgraphs and were therefore excluded from the remainder of the analysis, resulting in 5054 encounters (95.4%) with patients with MODS in the final cohort. Of the 145 subgraphs representing pSOFA subscore trajectories in these 5054 encounters, Table 1. Demographic and Clinical Characteristics of Patients With and Without MODS Patients, No. (%) With MODS Without MODS Clinical variables (n = 5297) (n = 15 530) P value Age, median (IQR), y 4.7 (1.0-12.6) 5.4 (1.6-12.7) <.001 Male patients 2898 (54.7) 8511 (54.8) .90 Race/ethnicity Non-Hispanic White 1700 (32.1) 4966 (32) Black 1726 (32.6) 5515 (35.5) <.001 Hispanic 1342 (25.3) 3650 (23.5) Other 529 (10) 1399 (9) PRISM-III score on admission, median (IQR) 7 (4-13) 0 (0-4) <.001 Vasoactives by day 3 1222 (23.1) 84 (0.5) <.001 Mechanical ventilation by day 3 3883 (73.3) 3108 (20) <.001 Immunocompromised state 922 (17.4) 1422 (9.2) <.001 Multiple chronic comorbidities 3153 (59.5) 5806 (37.3) <.001 Postoperative 1286 (24.3) 3679 (23.7) .40 Trauma 200 (3.8) 827 (5.3) <.001 Infection by day 3 3145 (59.4) 4665 (30) <.001 Maximum pSOFA score by day 3, median (IQR) 7 (6-9) 2 (1-3) <.001 Maximum PELOD-2 score by day 3, median (IQR) 7 (5-10) 2 (2-4) <.001 Abbreviations: IQR, interquartile range; MODS, multiple organ dysfunction syndrome; PRISM-III, MODS on day 7 1418 (26.8) 124 (0.8) <.001 Pediatric Risk of Mortality III; pSOFA, pediatric Length of stay, median (IQR), d 9.1 (4.8-17.9) 3.2 (1.8-5.9) <.001 Sequential Organ Failure Assessment; PELOD-2, In-hospital mortality 449 (8.5) 73 (0.5) <.001 Pediatric Logistic Organ Dysfunction, version 2. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 5/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children 31 (21.4%) represented worsening organ dysfunction, 24 (16.6%) represented moderate-to-severe organ dysfunction with no change, and 90 (62.1%) represented improvement in organ dysfunction. Sparse NMF was performed in the derivation set (4044 encounters [80.0%]) using the counts of the 145 subgraphs as sole input. This uncovered 4 distinct phenotypes based on the changes in pSOFA subscore in the first 3 days. Phenotype membership was then assigned to patients in the entire cohort based on the highest probability group membership according to the mixture coefficient matrix. Patients in each phenotype were then compared across the derivation and validation sets. Within each of the 4 phenotypes, patients had similar distributions of pSOFA subscores on day 1 to day 3 (eTable 1 in the Supplement), laboratory test characteristics by day 3 (eTable 2 in the Supplement), and outcomes (eTable 3 in the Supplement). In the sensitivity analysis using 3013 patient encounters (59.6%) in PICU A as derivation and 2041 patient encounters (40.4%) in PICU B as validation, again sparse NMF uncovered 4 phenotypes, which were consistent with those in the original analysis. The distributions of pSOFA subscores, laboratory test results, and outcomes were similar within phenotypes across the derivation patient encounters in PICU A and the external validation patient encounters in PICU B (eTable 4 in the Supplement). Patients within each phenotype from the original NMF were merged and used for the remainder of the analyses. Characterization of the MODS Phenotypes The most representative subgraphs for each phenotype were chosen from the mixture coefficient matrix of the NMF model, such that the selected subgraphs accounted for at least 75% of the phenotype group coefficients. The 10 most representative subgraphs for each of the 4 MODS phenotypes and the relative weight of each organ dysfunction subscore in the first 3 days, stratified by phenotype, appear in Figure 2 and eFigure 1 in the Supplement. These representative subgraphs were used to characterize the phenotypes based on the most distinctive characteristics, using the organ dysfunction type, severity, and trajectory in the first 3 days of PICU stay. Based on this, the 4 MODS phenotypes were characterized as follows: phenotype 1, severe, persistent encephalopathy (1019 patients [19.2%]); phenotype 2, moderate, resolving hypoxemia (1828 patients [34.5%]); phenotype 3: severe, persistent hypoxemia and shock (1012 patients [19.1%]); and phenotype 4, moderate, persistent thrombocytopenia and shock (1195 patients [22.6%]) The clinical characteristics and associated outcomes of patients in each of the 4 MODS phenotypes are presented in Table 2. When compared across age groups, the distribution and outcomes associated with the 4 MODS phenotypes remained consistent (eTable 5 in the Supplement). Prognostic Relevance of the MODS Phenotypes The outcomes associated with each of the 4 phenotypes after adjusting for confounders including age, immunocompromised state, PRISM-III score, and study site are presented in eTable 6 in the Supplement. After adjusting for the same confounders in Cox regression analysis and using phenotype 2, which had the lowest mortality as reference, the adjusted hazard ratios (aHRs) for death by 28 days were as follows: phenotype 1, aHR of 3.0 (IQR, 2.1-4.3); phenotype 3, aHR of 2.8 (IQR, 2.0-4.1); and phenotype 4, aHR of 1.8 (IQR, 1.2-2.6). The Kaplan-Meier survival curves for the 4 phenotypes are presented in eFigure 2 in the Supplement. The multivariable logistic regression analyses of the association of noncharacteristic organ dysfunctions and in-hospital mortality showed significant differences between phenotypes and are presented in eTable 7 in the Supplement. Therapeutic Relevance of the MODS Phenotypes in Patients With Vasoactive-Dependent Shock A total of 1220 of 5054 patient encounters (24.1%; 95% CI, 23.0%-25.3%) had vasoactive- dependent shock by day 3, with an associated in-hospital mortality of 299 patients (24.5% [95% CI, 22.1%-27%]). Of these, 342 (28.0%; 95% CI, 25.5%-30.6%) received IV hydrocortisone. Using JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 6/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children propensity score matching, 330 treated patients were successfully matched 1:1 with 330 untreated patients using age, immunocompromised state, PRISM-III score, study site, and phenotype membership as covariates. The matched cohort demonstrated a balanced distribution of covariates, with absolute standardized mean difference of less than 0.1 (eFigure 3 in the Supplement). Interaction analysis in the matched cohort revealed that IV hydrocortisone had a differential treatment effect on vasoactive-free days across phenotypes, most notably with phenotype 3 having most benefit associated with treatment (23 vasoactive-free days in treated patients vs 18 vasoactive-free days in treated patients; P for interaction < .001). The interaction was not significant for in-hospital mortality (Table 3). Discussion We derived and validated 4 novel, data-driven phenotypes of MODS in critically ill children based on organ dysfunction trajectories. These phenotypes have distinct clinical characteristics, are independently associated with outcomes, and have different sets of organ dysfunction–based risk factors for death. In a subset of patients with vasoactive-dependent shock who were matched based on the propensity to receive IV hydrocortisone, treatment was associated with a difference in Figure 2. Common Subgraphs in Each of the 4 Data-Driven MODS Phenotypes Subscore Cardiovascular Coagulation Hepatic Neurologic Renal Respiratory A Phenotype 1 B Phenotype 2 80% 53.2% 9.9% 9.8% 8.5% 49.3% 22.8% 22% 18% 14.9% 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Days Days 5.9% 5.8% 5.1% 4.4% 3.3% 14.1% 13.7% 8.2% 4% 3.8% 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Days Days C Phenotype 3 D Phenotype 4 81.9% 37% 30.7% 27.7% 27.6% 46.4% 39.2% 16.7% 13.7% 11.9% 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Days Days 16.9% 13.6% 13.5% 9.7% 8.7% 8.9% 6% 5.9% 5.4% 5.4% 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 Days Days The most common subgraphs in each phenotype and the proportion of patients who had each of them. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 7/14 Subscore Subscore Subscore Subscore Subscore Subscore Subscore Subscore JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children Table 2. Demographic and Clinical Characteristics and Outcomes of the 4 MODS Phenotypes No. (%) Phenotype 1, Phenotype 2, Phenotype 3, Phenotype 4, severe, persistent moderate, resolving severe, persistent moderate, persistent encephalopathy hypoxemia hypoxemia and thrombocytopenia and Clinical variable (n = 1019) (n = 1828) shock (n = 1012) shock (n = 1195) P value Age, median (IQR), y 4.3 (1.0-12.0) 3.9 (0.9-11.3) 3.5 (0.8-11.3) 8.1 (1.8-15.2) <.001 Male patients 555 (54.5) 989 (54.1) 557 (55.0) 671 (56.2) .73 Race/ethnicity Non-Hispanic White 311 (30.5) 557 (30.5) 319 (31.5) 447 (37.4) Black 410 (40.2) 636 (34.8) 318 (31.4) 285 (23.8) <.001 Hispanic 197 (19.3) 453 (24.8) 265 (26.2) 353 (29.5) Other 101 (9.9) 182 (10) 110 (10.9) 110 (9.2) PRISM-III score on admission, median (IQR) 8 (5-15) 6 (2-10) 10 (5-19) 9 (5-13) <.001 Vasoactives by day 3 179 (17.6) 107 (6) 392 (38.7) 542 (45.3) <.001 Mechanical ventilation by day 3 926 (91) 1409 (77.1) 880 (87) 553 (46.3) <.001 Immunocompromised state 105 (10.3) 240 (13.1) 154 (15.2) 382 (32) <.001 Multiple chronic comorbidities 607 (59.6) 1043 (57.1) 608 (60) 760 (63.4) .004 Postoperative 223 (21.9) 527 (28.8) 164 (16.2) 307 (25.7) <.001 Trauma 72 (7.1) 55 (3) 41 (4.1) 30 (2.5) <.001 Infection by day 3 595 (58.4) 953 (52.1) 724 (71.5) 755 (63.2) <.001 Primary site Bloodstream 59 (5.8) 117 (6.4) 119 (11.2) 193 (16.2) <.001 Respiratory 334 (32.8) 510 (27.9) 385 (38) 193 (16.2) <.001 Urinary 66 (6.5) 96 (5.3) 62 (6.1) 81 (6.8) .32 Other sites 36 (3.5) 60 (3.3) 35 (3.5) 45 (3.8) .92 Culture negative 194 (19) 308 (16.8) 252 (24.9) 328 (27.4) <.001 Microorganism Bacteria Gram positive 133 (13.1) 184 (10.1) 159 (15.7) 127 (10.6) <.001 Gram negative 165 (16.2) 224 (12.3) 178 (17.6) 137 (11.5) <.001 Other bacteria 32 (3.1) 50 (2.7) 22 (2.2) 39 (3.3) .42 Fungi 12 (1.2) 17 (0.9) 18 (1.8) 21 (1.8) .14 Viruses 170 (16.7) 333 (18.2) 230 (22.7) 184 (15.4) <.001 Laboratory results by day 3, median (IQR) Minimum white blood cell count, /μL 7900 (5200-11 000) 8300 (5600-11 600) 7000 (3800-10 900) 5900 (2500-9400) <.001 Minimum lymphocyte count, /μL 1300 (700-2200) 1300 (700-2300) 1100 (500-2000) 800 (300-1500) <.001 Maximum blood urea nitrogen, mg/dL 12 (8-18) 12 (8-18) 14 (9-23) 15 (10-28) <.001 Maximum creatinine, mg/dL 0.4 (0.3-0.6) 0.4 (0.2-0.6) 0.4 (0.3-0.8) 0.5 (0.3-0.9) <.001 Minimum albumin, g/dL 3.1 (2.5-3.6) 3.1 (2.6-3.7) 2.7 (2.2-3.3) 2.8 (2.3-3.4) <.001 Maximum aminotransferase, U/L Aspartate 53 (31-147) 49 (30-111) 61 (34-201) 62 (32-141) <.001 Alanine 28 (18-17) 27 (17-71) 36 (19-122) 37 (18-97) <.001 Maximum total bilirubin, mg/dL 0.5 (0.3-1.2) 0.5 (0.2-2.2) 0.6 (0.3-1.5) 1.2 (0.5-3.2) <.001 Minimum hemoglobin, g/dL 9 (7.4-11) 9.6 (7.8-11.3) 8.8 (7.2-10.5) 8.3 (7-10) <.001 Minimum platelet count, /μL 185 000 188 000 156 000 82 000 (27 000-179 000) <.001 (104 000-268 000) (94 000-270 000) (68 000-249 000) Maximum international normalized ratio 1.4 (1.2-1.7) 1.3 (1.2-1.6) 1.5 (1.2-2.1) 1.4 (1.2-1.7) <.001 Maximum partial thromboplastin time, s 35 (30-45) 35 (30-44) 40 (32-59) 39 (31-54) <.001 Maximum lactate, mg/dL 18.9 (10.8-35.1) 14.4 (9.9-26.1) 22.5 (12.6-52.2) 18.0 (11.7-34.2) <.001 (continued) JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 8/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children Table 2. Demographic and Clinical Characteristics and Outcomes of the 4 MODS Phenotypes (continued) No. (%) Phenotype 1, Phenotype 2, Phenotype 3, Phenotype 4, severe, persistent moderate, resolving severe, persistent moderate, persistent encephalopathy hypoxemia hypoxemia and thrombocytopenia and Clinical variable (n = 1019) (n = 1828) shock (n = 1012) shock (n = 1195) P value Outcomes Vasoactive-free days at 28 d, median (IQR) 28 (27-28) 28 (28-28) 28 (24-28) 27 (26-28) <.001 Ventilator-free days at 28 d, median (IQR) 23 (13-26) 26 (21-27) 20 (6-26) 27 (22-28) <.001 Hospital-free days at 28 d, median (IQR) 16 (1-23) 20 (12-24) 12 (1-21) 17 (4-22) <.001 Persistent MODS on day 7 320 (31.4) 319 (17.5) 448 (44.3) 307 (25.7) <.001 In-hospital mortality 116 (11.3) 60 (3.3) 176 (17.4) 94 (7.9) <.001 Abbreviations: IQR, interquartile range; MODS, multiple organ dysfunction syndrome; to grams per liter, multiply by 10; blood urea nitrogen to millimoles per liter, multiply by PRISM-III, Pediatric Risk of Mortality III. 0.357; creatinine to micromoles per liter, multiply by 88.4; lymphocyte, platelet, and white blood cell count to ×10 per microliter, multiply by 0.001; and total bilirubin to SI conversion factors: To convert alanine aminotransferase and aspartate millimoles per liter, multiply by 17.104. aminotransferase to microkatals per liter, multiply by 0.0167; albumin and hemoglobin Table 3. Differences in Response to IV Hydrocortisone by Phenotype in a Propensity Score Matched Cohort of Patients With Vasoactive-Dependent Shock Phenotype 1 (n = 72) Phenotype 2 (n = 71) Phenotype 3 (n = 287) Phenotype 4 (n = 230) Treated Untreated Treated Untreated Treated Untreated Treated Untreated Outcome (n = 39) (n = 33) (n = 36) (n = 35) (n = 146) (n = 141) (n = 109) (n = 121) P value Vasoactive-free days, median (IQR) 15 (0-25) 17 (1-24) 25 (23-26) 24 (21-27) 23 (1-26) 18 (0-26) 26 (23-27) 26 (24-27) <.001 In-hospital mortality, No.(%) 20 (51.3) 14 (42.4) 4 (11.1) 4 (11.4) 50 (34.2) 65 (46.1) 14 (12.8) 9 (7) .11 Abbreviations: IQR, interquartile range; IV, intravenous. P value for interaction between IV hydrocortisone and phenotype membership using phenotype 2 as reference group (ie, 3 interaction terms) and analysis of deviance to test global significance. vasoactive-free days across phenotypes, suggesting that these phenotypes may be associated with differences in response to therapy. Our findings share similarities with previously described data-driven phenotypes of sepsis and sepsis-induced MODS, particularly phenotypes 3 and 4, which were the MODS phenotypes most frequently associated with infections in our cohort. Phenotype 3 shares similarities with the “shock with hypoxemia and altered mental status” phenotype described by Knox et al and the γ phenotype described by Seymour et al in adult patients, especially in terms of hypoxemia, vasoactive- dependence, low albumin levels, and high mortality. Phenotype 4 shares similarities with the hepatic 15 14 disease phenotype described by Knox et al and the δ phenotype described by Seymour et al, particularly in terms of the thrombocytopenia, hepatobiliary dysfunction, and shock as well as the relatively preserved pulmonary function. To our knowledge, our study is the first to use a data-driven approach to derive MODS phenotypes in critically ill children. Further biomarker and molecular endotyping analyses will be needed to compare our phenotypes with other previously described 12,13,17 clinical phenotypes and endotypes in pediatric and adult patients. The association of IV hydrocortisone administration with differential treatment effect on vasoactive-free days in patients with vasoactive-dependent shock, particularly in phenotype 3, could partially be explained by the enrichment with patients suffering severe hypoxemia, severe shock, and infections. Prior randomized clinical trials and secondary analyses of trial data have found that the use of hydrocortisone was associated with improved outcomes in patients with sepsis-associated 38,39 acute respiratory distress syndrome. However, our results should be interpreted with caution because they are part of an interaction effect analysis on a propensity score matched cohort. The standard approach is to perform an interaction effects analysis across subgroups with a treatment assignment that has been randomized to ensure a better balance of all possible confounders. Given the selection bias inherent in observational data, we used propensity score matching to balance JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 9/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children observed covariates, but it is likely that we did not account for all possible confounders. Therefore, these results require further validation and are only hypothesis generating at this point. Our data-driven phenotyping approach to MODS has several important implications. We describe a broad stratification of patients that can be readily performed using widely available electronic health record data and that appears to have prognostic relevance and, potentially, therapeutic value. It is likely that many of the patients within each phenotype share many of the same underlying pathophysiological disturbances and dysregulated host responses, as evidenced by the shared clinical characteristics and laboratory test results. Understanding the common features of these underlying pathophysiologies could help determine which interventions may be most beneficial to individual patients, regardless of the etiology of MODS and beyond the traditional boundaries of syndromes like sepsis or acute respiratory distress syndrome. For example, cardiovascular dysfunction is associated with the most detrimental outcomes in patients with severe encephalopathy in phenotype 1, suggesting that targeted blood pressure management and close cerebral perfusion monitoring, as recommended for patients with traumatic brain injury, may be warranted in patients with this phenotype even if most do not have traumatic brain injury. Similarly, renal dysfunction seems to most negatively affect patients in phenotype 3 who suffer severe hypoxemia and shock, indicating that careful fluid management and nephrotoxin avoidance might be most beneficial in this group of patients. In addition, further endotyping and molecular characterization of these broad phenotypes may uncover relevant MODS subphenotypes that may 41-43 benefit from novel targeted interventions, including immunomodulatory strategies. Furthermore, the molecular characterization of these phenotypes may help in the task of assigning phenotype membership shortly after patients are admitted, which would make this phenotyping schema more clinically actionable in the critical care setting. In short, data-driven phenotyping may help develop precision medicine strategies that can reduce the mortality and morbidity associated with MODS, but further investigation into the value of this phenotyping approach in research and clinical care is warranted. Limitations Our findings are subject to several limitations. First, the data for this study were collected retrospectively from 2 academic PICUs in the same city, limiting the generalizability of our findings. Second, our data-driven phenotyping approach was intentionally sparse, using only organ dysfunction trajectories to define broad phenotypes and determine their prognostic and therapeutic relevance, but performing deeper phenotyping using other clinical features and biomarkers may result in phenotypes with potentially higher resolution and greater clinical utility. Third, the therapeutic relevance of IV hydrocortisone was studied using an interaction effect analysis on a propensity score matched cohort, and the results must be interpreted with caution, as previously discussed. Fourth, although we described the MODS phenotypes as distinct entities, it is likely that many patients share features of more than 1 phenotype, and future analyses to better understand the implications of these overlaps are warranted. Conclusions In this study, we derived and validated 4 data-driven phenotypes of MODS in critically ill children that have distinct clinical characteristics, are independently associated with outcomes, and may be therapeutically relevant in the subset of patients with vasoactive-dependent shock. Our data-driven phenotyping approach in MODS shows promising results, but further characterization of these broad phenotypes and validation in different settings is warranted. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 10/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children ARTICLE INFORMATION Accepted for Publication: April 22, 2020. Published: August 11, 2020. doi:10.1001/jamanetworkopen.2020.9271 Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Sanchez-Pinto LN et al. JAMA Network Open. Corresponding Author: L. Nelson Sanchez-Pinto, MD, MBI, Division of Critical Care Medicine, Ann and Robert H. Lurie Children’s Hospital of Chicago, 225 E Chicago Ave, PO Box 73, Chicago, IL 60611 (lsanchezpinto@ luriechildrens.org). Author Affiliations: Critical Care, Department of Pediatrics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Sanchez-Pinto); Health and Biomedical Informatics, Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Sanchez-Pinto, Luo); Division of Critical Care Medicine, Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois (Sanchez-Pinto, Pendergrast); Driskill Graduate Program, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (Stroup); Section of Critical Care, Department of Pediatrics, The University of Chicago, Chicago, Illinois (Pinto). Author Contributions: Dr Sanchez-Pinto and Ms Stroup had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: Sanchez-Pinto, Luo. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Sanchez-Pinto. Critical revision of the manuscript for important intellectual content: Stroup, Pendergrast, Pinto, Luo. Statistical analysis: Sanchez-Pinto, Stroup, Luo. Obtained funding: Sanchez-Pinto. Administrative, technical, or material support: Pendergrast, Pinto, Luo. Supervision: Sanchez-Pinto, Luo. Conflict of Interest Disclosures: None reported. Funding/Support: This work was supported by grant R21HD096402 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development to Dr Sanchez-Pinto and grant R21LM012618 from the National Library of Medicine to Dr Luo. Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Additional Contributions: The authors would like to thank Adin-Cristian Andrei, PhD (Department of Preventive Medicine, Northwestern University Feinberg School of Medicine), for his biostatistical expertise and advice related to the propensity score matching and interaction effects analysis in patients with vasoactive-dependent shock. He was not compensated for his time. The authors would also like to thank the Data Analytics and Reporting team at Ann and Robert H. Lurie Children’s Hospital of Chicago and the Center for Research Informatics at The University of Chicago for providing the raw electronic health record data for the study. REFERENCES 1. Tamburro RF, Jenkins TL. Multiple organ dysfunction syndrome: a challenge for the pediatric critical care community. Pediatr Crit Care Med. 2017;18(3_suppl)(suppl 1):S1-S3. 2. Matics TJ, Pinto NP, Sanchez-Pinto LN. Association of organ dysfunction scores and functional outcomes following pediatric critical illness. 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Fitzgerald JC, Weiss SL, Maude SL, et al. Cytokine release syndrome after chimeric antigen receptor T cell therapy for acute lymphoblastic leukemia. Crit Care Med. 2017;45(2):e124-e131. doi:10.1097/CCM. 42. Hall MW, Knatz NL, Vetterly C, et al. Immunoparalysis and nosocomial infection in children with multiple organ dysfunction syndrome. Intensive Care Med. 2011;37(3):525-532. doi:10.1007/s00134-010-2088-x 43. Shakoory B, Carcillo JA, Chatham WW, et al. Interleukin-1 receptor blockade is associated with reduced mortality in sepsis patients with features of the macrophage activation syndrome: re-analysis of a prior phase III trial. Crit Care Med. 2016;44(2):275-281. doi:10.1097/CCM.0000000000001402 SUPPLEMENT. eAppendix. Supplementary Methods eFigure 1. Alluvial Plots of Organ Dysfunction Burden by Day in the 4 MODS Phenotypes eFigure 2. Kaplan-Meier Survival Curves for the First 28 Days for the 4 MODS Phenotypes eFigure 3. Covariate Balance in the Propensity Score Matched Cohort of Patients With Vasoactive-Dependent Shock Who Received and Did Not Receive Intravenous Hydrocortisone eTable 1. Maximum pSOFA Subscores Achieved by Day in Patients Within Each of the 4 MODS Phenotypes in the Derivation and Validation Sets eTable 2. Laboratory Test Results by Day 3 of PICU Stay in the 4 MODS Phenotypes Across the Derivation and Validation Sets JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 13/14 JAMA Network Open | Critical Care Medicine Novel Phenotypes of Multiple Organ Dysfunction Syndrome in Critically Ill Children eTable 3. Outcomes Associated With the 4 MODS Phenotypes Across the Derivation and Validation Sets eTable 4. Laboratory Test Results and Outcomes Associated With the MODS Phenotypes When Using Patients in Hospital A as the Derivation Set and Patients in Hospital B as an External Validation Set eTable 5. Distribution and Outcomes Associated With the 4 MODS Phenotypes Across Age Groups eTable 6. Unadjusted and Adjusted Outcomes Associated With the 4 MODS Phenotypes eTable 7. Multivariable Association Between Noncharacteristic Organ Dysfunctions and In-Hospital Mortality eReferences. JAMA Network Open. 2020;3(8):e209271. doi:10.1001/jamanetworkopen.2020.9271 (Reprinted) August 11, 2020 14/14

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