Importance:Pediatric sepsis causes substantial morbidity and mortality, but population surveillance relies on administrative codes with limited and variable accuracy. Objective:To estimate US national incidence, mortality, and trends of sepsis in nonneonatal children using a Pediatric Sepsis Event (PSE) definition adapted from the 2024 Phoenix criteria for scalable electronic health record (EHR)-based surveillance using routinely captured clinical data. Design, Setting, and Participants:Retrospective cohort study of 3.9 million hospitalizations (age, >30 days to 17 years) in 2 EHR datasets: Epic Cosmos (245 health care systems, 2016-2023) and HCA Healthcare (146 hospitals, 2018-2023). Secondary datasets were analyzed to assess feasibility of implementation and face validity across heterogeneous settings. The PSE was validated through medical record reviews of 581 high-risk encounters at 3 geographically diverse hospitals. Exposures:A PSE required presumed infection with concurrent organ dysfunction using Phoenix-derived thresholds adapted for routine EHR data. Septic shock was defined as a PSE with cardiovascular dysfunction. Main Outcomes and Measures:Sepsis incidence, characteristics, and in-hospital mortality were calculated. Sensitivity and specificity of PSE for physician-adjudicated Phoenix sepsis were compared with administrative codes for severe sepsis/septic shock. National sepsis case counts and deaths in 2022 and temporal trends from 2016 to 2022 were estimated using regression models. Results:Among 3 925 809 pediatric hospitalizations from 2016 to 2023, 51 542 sepsis cases (mean age, 6.6 [SD, 6.0] years; 22 840 [44.3%] female) were identified (1.3% incidence); 37 405 (72.6%) were community onset and 31 744 (61.6%) had septic shock. In-hospital mortality was 10.1% and sepsis was present in 17.8% of hospitalizations that culminated in death. Incidence, characteristics, and mortality were broadly consistent across secondary datasets. On medical record review, the PSE definition had 69.9% sensitivity (95% CI, 58.1%-79.8%) and 93.1% specificity (95% CI, 89.6%-95.7%), with higher sensitivity than and comparable specificity with administrative codes. National estimates for 2022 were 18 231 sepsis cases (95% CI, 16 129-20 334) and 1877 deaths(95% CI, 1629-2126). Neither sepsis cases nor deaths changed significantly from 2016 to 2022 (annual change, 0.2% [95% CI, -2.2% to 2.7%] and 0.3% [95% CI, -3.1% to 3.8%], respectively). Conclusions and Relevance:An EHR-based definition for pediatric sepsis demonstrated strong validity compared with physician-adjudicated Phoenix sepsis and identified sepsis in 1.3% of pediatric hospitalizations with 10% mortality, corresponding to more than 18 000 cases and more than 1800 deaths annually in the US.

Clinicians currently make decisions about placing an intracranial pressure (ICP) monitor in children with traumatic brain injury (TBI) without the benefit of an accurate clinical decision support tool. The goal of this study was to develop and validate a model that predicts placement of an ICP monitor and updates as new information becomes available. A prospective observational cohort study was conducted from September 2014 to January 2024. The setting included one US hospital designated as an American College of Surgeons Level 1 Pediatric Trauma Center. Participants were 389 children with acute TBI admitted to the ICU who had at least one Glasgow Coma Scale (GCS) score ≤ 8 or intubation with at least one GCS-Motor ≤ 5. We excluded children who received ICP monitors prior to arrival, those with GCS = 3 and bilateral fixed, dilated pupils, and those with a do not resuscitate order. Of the 389 participants, 138 received ICP monitoring. Several machine learning models, including a recurrent neural network (RNN), were developed and validated using 4 combinations of input data. The best performing model, an RNN, achieved an F1 of 0.71 within 720 minutes of hospital arrival. The cumulative F1 of the RNN from minute 0 to 720 was 0.61. The best performing non-neural network model, standard logistic regression, achieved an F1 of 0.36 within 720 minutes of hospital arrival. These findings will contribute to design and implementation of a multidisciplinary clinical decision support tool for ICP monitor placement in children with TBI.

CONTEXT:Adiponectin is a potent uterine tocolytic that decreases with gestational age, suggesting it could be a maternal metabolic quiescence factor. Maternal stress can influence preterm birth risk, and adiponectin levels may be stress responsive. OBJECTIVE:We characterized associations between adiponectin and glucocorticoids with preterm birth and modeled their predictive utility. We hypothesized maternal plasma adiponectin and cortisol are inversely related and lower adiponectin and higher cortisol associate with preterm birth. METHODS:We performed a nested case-control study using biobanked fasting maternal plasma. We included low-risk singleton pregnancies, and matched 1:3 (16 preterm, 46 term). We quantified high molecular weight (HMW), low molecular weight (LMW), and total adiponectin using an enzyme-linked immunosorbent assay. We validated a high-performance liquid chromatography-tandem mass spectrometry serum assay for use in plasma, to simultaneously measure cortisol, cortisone, and 5 related steroid hormones. We used linear/logistic regression to compare group means and machine learning for predictive modeling. RESULTS:The preterm group had lower mean LMW adiponectin (3.07 μg/mL vs 3.81 μg/mL at 15 weeks (w) 0 days (d), P = .045) and higher mean cortisone (34.4 ng/mL vs 29.0 ng/mL at 15w0d, P = .031). The preterm group had lower cortisol to cortisone and lower LMW adiponectin to cortisol ratios. We found HMW adiponectin, cortisol to cortisone ratio, cortisone, maternal height, age, and prepregnancy body mass index most strongly predicted preterm birth (area under the receiver operator curve = 0.8167). In secondary analyses, we assessed biomarker associations with maternal self-reported psychosocial stress. Lower perceived stress was associated with a steeper change in cortisone in the term group. CONCLUSION:Overall, metabolic and stress biomarkers are associated with preterm birth in this healthy cohort. We identify a possible mechanistic link between maternal stress and metabolism for pregnancy maintenance.

OBJECTIVE The medical community recently experienced a severe shortage of blood culture media bottles. Rates of blood stream infection (BSI) among critically ill children are low. We sought to design a machine learning (ML) model able to identify children at low risk for BSI to improve blood culture diagnostic stewardship. METHODS We developed and validated an extreme gradient-boosting ML classifier using an existing dataset of retrospective pediatric intensive care unit (PICU) patients from a single institution. Data from children aged 3 months to 18 years who had a blood culture collected within 24 hours of PICU admission were included. The first 80% of patients (1/1/2011-12/21/2018) were used for model training and the last 20% (12/22/2018-12/25/2020) for testing (temporal validation). All 121 variables from the original dataset (vital sign, laboratory, and other clinical data) were included as predictors. Negative predictive value (NPV) was the primary evaluation metric. RESULTS Of the 3121 blood cultures obtained during 2320 PICU admissions (2100 unique children), 205 (6.6%) were positive. Model NPV was 0.997 in the training set and identified 667/2321 (28.7%) of negative cultures. NPV was 0.993 in the test set and identified 151/595 (25.4%) of negative cultures. The number needed to harm was 151 (151 negative blood cultures could be avoided for each false negative prediction). Key predictors included central line presence, temperature rate of change, and mean platelet volume. CONCLUSIONS We trained and validated an ML model that accurately predicted >25% of subsequently negative blood cultures. If implemented prospectively, such models could help reduce unnecessary blood cultures among low-risk children.

BACKGROUND:When coronavirus disease 2019 (COVID-19) mitigation efforts waned, viral respiratory infections (VRIs) surged, potentially increasing the risk of postviral invasive bacterial infections (IBIs). We sought to evaluate the change in epidemiology and relationships between specific VRIs and IBIs [complicated pneumonia, complicated sinusitis and invasive group A streptococcus (iGAS)] over time using the National COVID Cohort Collaborative (N3C) dataset. METHODS:We performed a secondary analysis of all prospectively collected pediatric (<19 years old) and adult encounters at 58 N3C institutions, stratified by era: pre-pandemic (January 1, 2018, to February 28, 2020) versus pandemic (March 1, 2020, to June 1, 2023). We compared the characteristics and outcomes of patients with prespecified VRIs and IBIs, including correlation between VRI cases and subsequent IBI cases. RESULTS:We identified 965,777 pediatric and 9,336,737 adult hospitalizations. Compared with pre-pandemic, pandemic-era children demonstrated higher mean monthly cases of adenovirus (121 vs. 79.1), iGAS (5.8 vs. 3.3), complicated pneumonia (282 vs. 178) and complicated sinusitis (29.8 vs. 16.3), P < 0.005 for all. Among pandemic-era children, peak correlation between RSV cases and subsequent complicated sinusitis cases occurred with a 60-day lag (correlation coefficient 0.56, 95% confidence interval: 0.52-0.59, P < 0.001) while peak correlation between influenza and complicated sinusitis occurred with a 33-day lag (0.55, 0.51-0.58, P < 0.001). Correlation among other VRI-IBI pairs was modest during the pandemic and often lower than during the pre-pandemic era. CONCLUSIONS:Since COVID-19 emerged, mean monthly cases of iGAS, complicated pneumonia, and complicated sinusitis have been higher. Pandemic-era RSV and influenza cases were correlated with subsequent cases of complicated sinusitis in children. However, many other VRI-IBI correlations decreased during the pandemic.

OBJECTIVE:. To determine the accuracy of a custom version of the generative pretrained transformer (GPT)-4o large language model (LLM) in identifying PICU admissions with vs. without bacterial pneumonia using clinical notes. DESIGN:. In this retrospective cohort study, the GPT-4o model was provided guidance on our institution’s pneumonia diagnosis practices through a custom prompt and instructed to analyze PICU provider notes from the first 2 calendar days of PICU admission to identify bacterial pneumonia diagnoses. Diagnoses from the manually curated Virtual Pediatric Systems (VPS) Registry were used as the gold standard. SETTING:. A 48-bed, academic, quaternary care PICU. PATIENTS:. Children 3 months old to 18 years old admitted to the PICU from January 1, 2023, to December 31, 2023. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. GPT-4o analyzed 10,081 notes from 3,317 PICU admissions over 5.0 minutes (mean 0.03 s per note). Of the 3317 study encounters, 481(14.5%) had a VPS admission pneumonia diagnosis. GPT-4o accurately classified 3143 of 3317 (94.8%) encounters. In a post hoc adjudication analysis, a blinded PICU attending reviewed patient charts with VPS-GPT discordant classifications. The GPT-4o classification matched that of the blinded PICU attending in 125 of 174 (71.8%) of such encounters. The most common reason for incorrect classification by GPT-4o was that a pneumonia diagnosis was listed in the initial notes but later rescinded when a different diagnosis was identified. CONCLUSIONS:. The GPT-4o LLM was able to accurately and rapidly identify critically ill children with vs. without bacterial pneumonia. This study suggests similar tools could be developed to automate and accelerate processes typically requiring manual chart review.

Data sharing is necessary to maximize the actionable knowledge generated from research data. Data challenges can encourage secondary analyses of datasets. Data challenges in biomedicine often rely on advanced cloud-based computing infrastructure and expensive industry partnerships. Examples include challenges that use Google Cloud virtual machines and the Sage Bionetworks Dream Challenges platform. Such robust infrastructures can be financially prohibitive for investigators without substantial resources. Given the potential to develop scientific and clinical knowledge and the NIH emphasis on data sharing and reuse, there is a need for inexpensive and computationally lightweight methods for data sharing and hosting data challenges. To fill that gap, we developed a workflow that allows for reproducible model training, testing, and evaluation. We leveraged public GitHub repositories, open-source computational languages, and Docker technology. In addition, we conducted a data challenge using the infrastructure we developed. In this manuscript, we report on the infrastructure, workflow, and data challenge results. The infrastructure and workflow are likely to be useful for data challenges and education.

Importance:Sepsis is a leading cause of death among children worldwide. Current pediatric-specific criteria for sepsis were published in 2005 based on expert opinion. In 2016, the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) defined sepsis as life-threatening organ dysfunction caused by a dysregulated host response to infection, but it excluded children.Objective:To update and evaluate criteria for sepsis and septic shock in children.Evidence Review:The Society of Critical Care Medicine (SCCM) convened a task force of 35 pediatric experts in critical care, emergency medicine, infectious diseases, general pediatrics, nursing, public health, and neonatology from 6 continents. Using evidence from an international survey, systematic review and meta-analysis, and a new organ dysfunction score developed based on more than 3 million electronic health record encounters from 10 sites on 4 continents, a modified Delphi consensus process was employed to develop criteria.Findings:Based on survey data, most pediatric clinicians used sepsis to refer to infection with life-threatening organ dysfunction, which differed from prior pediatric sepsis criteria that used systemic inflammatory response syndrome (SIRS) criteria, which have poor predictive properties, and included the redundant term, severe sepsis. The SCCM task force recommends that sepsis in children be identified by a Phoenix Sepsis Score of at least 2 points in children with suspected infection, which indicates potentially life-threatening dysfunction of the respiratory, cardiovascular, coagulation, and/or neurological systems. Children with a Phoenix Sepsis Score of at least 2 points had in-hospital mortality of 7.1% in higher-resource settings and 28.5% in lower-resource settings, more than 8 times that of children with suspected infection not meeting these criteria. Mortality was higher in children who had organ dysfunction in at least 1 of 4-respiratory, cardiovascular, coagulation, and/or neurological-organ systems that was not the primary site of infection. Septic shock was defined as children with sepsis who had cardiovascular dysfunction, indicated by at least 1 cardiovascular point in the Phoenix Sepsis Score, which included severe hypotension for age, blood lactate exceeding 5 mmol/L, or need for vasoactive medication. Children with septic shock had an in-hospital mortality rate of 10.8% and 33.5% in higher- and lower-resource settings, respectively.Conclusions and Relevance:The Phoenix sepsis criteria for sepsis and septic shock in children were derived and validated by the international SCCM Pediatric Sepsis Definition Task Force using a large international database and survey, systematic review and meta-analysis, and modified Delphi consensus approach. A Phoenix Sepsis Score of at least 2 identified potentially life-threatening organ dysfunction in children younger than 18 years with infection, and its use has the potential to improve clinical care, epidemiological assessment, and research in pediatric sepsis and septic shock around the world.

ImportanceThe Society of Critical Care Medicine Pediatric Sepsis Definition Task Force sought to develop and validate new clinical criteria for pediatric sepsis and septic shock using measures of organ dysfunction through a data-driven approach.ObjectiveTo derive and validate novel criteria for pediatric sepsis and septic shock across differently resourced settings.Design, Setting, and ParticipantsMulticenter, international, retrospective cohort study in 10 health systems in the US, Colombia, Bangladesh, China, and Kenya, 3 of which were used as external validation sites. Data were collected from emergency and inpatient encounters for children (aged <18 years) from 2010 to 2019: 3 049 699 in the development (including derivation and internal validation) set and 581 317 in the external validation set.ExposureStacked regression models to predict mortality in children with suspected infection were derived and validated using the best-performing organ dysfunction subscores from 8 existing scores. The final model was then translated into an integer-based score used to establish binary criteria for sepsis and septic shock.Main Outcomes and MeasuresThe primary outcome for all analyses was in-hospital mortality. Model- and integer-based score performance measures included the area under the precision recall curve (AUPRC; primary) and area under the receiver operating characteristic curve (AUROC; secondary). For binary criteria, primary performance measures were positive predictive value and sensitivity.ResultsAmong the 172 984 children with suspected infection in the first 24 hours (development set; 1.2% mortality), a 4-organ-system model performed best. The integer version of that model, the Phoenix Sepsis Score, had AUPRCs of 0.23 to 0.38 (95% CI range, 0.20-0.39) and AUROCs of 0.71 to 0.92 (95% CI range, 0.70-0.92) to predict mortality in the validation sets. Using a Phoenix Sepsis Score of 2 points or higher in children with suspected infection as criteria for sepsis and sepsis plus 1 or more cardiovascular point as criteria for septic shock resulted in a higher positive predictive value and higher or similar sensitivity compared with the 2005 International Pediatric Sepsis Consensus Conference (IPSCC) criteria across differently resourced settings.Conclusions and RelevanceThe novel Phoenix sepsis criteria, which were derived and validated using data from higher- and lower-resource settings, had improved performance for the diagnosis of pediatric sepsis and septic shock compared with the existing IPSCC criteria.

Objectives:The publication of the Phoenix criteria for pediatric sepsis and septic shock initiates a new era in clinical care and research of pediatric sepsis. Tools to consistently and accurately apply the Phoenix criteria to electronic health records (EHRs) is one part of building a robust and internally consistent body of research across multiple research groups and datasets. Materials and Methods:We developed the phoenix R package and Python module to provide researchers with intuitive and simple functions to apply the Phoenix criteria to EHR data. Results:The phoenix R package and Python module enable researchers to apply the Phoenix criteria to EHR datasets and derive the relevant indicators, total scores, and sub-scores. Discussion:The transition to the Phoenix criteria marks a major change in the conceptual definition of pediatric sepsis. Applicable across differentially resourced settings, the Phoenix criteria should help improve clinical care and research. Conclusion:The phoenix R package and Python model are freely available on CRAN, PyPi, and GitHub. These tools enable the consistent and accurate application of the Phoenix criteria to EHR datasets.

Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. These approaches, however, either do not accomodate a more general association between community structure and an outcome or cannot accommodate additional covariates that may confound the association of interest. We propose a semiparametric kernel machine regression model for either a continuous or binary outcome, where covariate effects are modeled parametrically and brain connectivity measures are measured nonparametrically. By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. We evaluate our proposed methodology on both simulated and real datasets.

Abstract Disclosure: G. Mayne: None. P.E. DeWitt: None. J. Wen: None. U. Christians: None. D. Dabelea: None. J. Schulkin: None. K. Hurt: None. Background: Adiponectin is a potent uterine tocolytic and decreases with gestational age, so it could be a maternal metabolic quiescence factor during pregnancy. Maternal stress can influence risk for preterm birth, and adiponectin may be inversely related to cortisol. Therefore, we characterized a potential mechanistic link between preterm birth, adiponectin, and cortisol. In this pilot study, we hypothesized maternal plasma adiponectin and cortisol concentrations are inversely related and that lower adiponectin and higher cortisol associate with spontaneous preterm birth. Methods: We performed a nested case-control study using biobank morning fasting plasma samples, taken twice in pregnancy for the Healthy Start pre-birth cohort. We included low-risk women with singleton pregnancies and excluded mothers with major medical illness, preeclampsia, chronic hypertension, or prior preterm birth. We matched preterm cases with term controls (1:3) by gestational age at first blood sample and least variation in time between blood samples (16 preterm, 46 term). We quantified high (HMW), low molecular weight (LMW), and total adiponectin using an ELISA assay. We also quantified cortisol and five related steroid hormones from the same specimens using HPLC/mass spectrometry. We used generalized estimating equations and additive models to compare group means and associations between adiponectin and cortisol. We used machine learning with gradient boosting for predictive modeling. Results: Contrary to our hypothesis, total, HMW, and LMW adiponectin positively associated with cortisol across gestation (P<0.001 for all), however the association was reduced in the preterm compared with term group and significantly so for LMW (P=0.044). In predictive modelling, HMW adiponectin, maternal pre-pregnancy BMI, maternal age, and cortisol were the four most highly relevant predictors for preterm birth (AUROC=0.704). Conclusion: In healthy low-risk patients, we found associations between adiponectin and cortisol differ across gestation for pregnancies ending with term or preterm delivery and have some predictive value in this cohort. This suggests a possible mechanistic link between maternal metabolism and stress for pregnancy maintenance. These biomarkers may prove useful as predictors in larger studies and might indicate possible maternal metabolic therapies for preterm birth prevention. Presentation: Friday, June 16, 2023

BACKGROUNDTimely surgical decompression improves functional outcomes and survival among children with traumatic brain injury and increased intracranial pressure. Previous scoring systems for identifying the need for surgical decompression after traumatic brain injury in children and adults have had several barriers to use. These barriers include the inability to generate a score with missing data, a requirement for radiographic imaging that may not be immediately available, and limited accuracy. To address these limitations, we developed a Bayesian network to predict the probability of neurosurgical intervention among injured children and adolescents (aged 1-18 years) using physical examination findings and injury characteristics observable at hospital arrival.METHODSWe obtained patient, injury, transportation, resuscitation, and procedure characteristics from the 2017 to 2019 Trauma Quality Improvement Project database. We trained and validated a Bayesian network to predict the probability of a neurosurgical intervention, defined as undergoing a craniotomy, craniectomy, or intracranial pressure monitor placement. We evaluated model performance using the area under the receiver operating characteristic and calibration curves. We evaluated the percentage of contribution of each input for predicting neurosurgical intervention using relative mutual information (RMI).RESULTSThe final model included four predictor variables, including the Glasgow Coma Scale score (RMI, 31.9%), pupillary response (RMI, 11.6%), mechanism of injury (RMI, 5.8%), and presence of prehospital cardiopulmonary resuscitation (RMI, 0.8%). The model achieved an area under the receiver operating characteristic curve of 0.90 (95% confidence interval [CI], 0.89-0.91) and had a calibration slope of 0.77 (95% CI, 0.29-1.26) with a y intercept of 0.05 (95% CI, -0.14 to 0.25).CONCLUSIONWe developed a Bayesian network that predicts neurosurgical intervention for all injured children using four factors immediately available on arrival. Compared with a binary threshold model, this probabilistic model may allow clinicians to stratify management strategies based on risk.LEVEL OF EVIDENCEPrognostic and Epidemiological; Level III.

Introduction: Childhood systolic and diastolic blood pressure (BP) norms vary by age, sex, and stature making interpretation of individual BP measurements challenging. Conversion to a blood pressure percentile (BPP) can assist in diagnosis and prognostication for many conditions including shock and hypertension. However, many published BPPs are only available for children >1 year, often require a stature measurement (missing at admission in up to 33% of electronic health records per prior reports), and only provide percentiles above the median. To fill these gaps, we developed an R package (‘pedbp’) and web application using BP data from prior publications to facilitate rapid calculation of BPP estimates. Methods: We performed a literature search for pediatric population BP measurements published 1981-2022. We extracted published BP means and standard deviations and percentiles for systolic and diastolic BP measurements for each cohort: age, sex, and stature (if applicable). We used this data to define a Gaussian for systolic and diastolic BPPs for each cohort (with separate distributions for cohorts of unknown stature). Results: The pedbp package (http://cran.r-project.org/package=pedbp) in the R programming language makes obtaining a BPP for a single BP measurement possible with a single function call. For example, the BPP for a BP of 100/60 in a 44-month-old male with length 105 cm is found via: “p_bp(q_sbp = 100, q_dbp = 60, age = 44, male = 1, height = 105)” which returns a value of 0.7086 (70.86th percentile) for SBP, and 0.8486 (84.86th percentile) for DBP. Batch processing (conversion to BPPs for >1 observation) is also supported. We created an intuitive web version of this method for individual and batch BPP processing at https://dewittpe.shinyapps.io/pedbp/. A BPP for an individual BP measurement can be obtained by entering patient data (BP, sex, and stature [if known]) into corresponding text boxes. Batch processing is accomplished by uploading a csv file with results then provided as a downloadable csv file. Conclusions: The pedbp package and web tool provide BPPs for children of all ages regardless of stature measurement availability. Such tools could facilitate identification of children with shock or hypertension. A potential limitation is assumption of Gaussian BP distributions.

Background More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. Methods This was a retrospective case–control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. Results Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33–1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05–4.73), long (8–30 days, OR 1.69, 95% CI 1.31–2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45–4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18–1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40–1.60), chronic lung disease (OR 1.63, 95% CI 1.53–1.74), and obesity (OR 1.23, 95% CI 1.16–1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. Conclusions This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

This diagnostic study assesses the ability of a pediatric blood pressure percentile tool to accelerate identification of children with hypertension and hypotension by clinicians and researchers.