International Consensus Criteria for Pediatric Sepsis and Septic Shock.

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.

Development and Validation of the Phoenix Criteria for Pediatric Sepsis and Septic Shock.

Importance: The 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. Objective: To derive and validate novel criteria for pediatric sepsis and septic shock across differently resourced settings. Design, Setting, and Participants: Multicenter, 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. Exposure: Stacked 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 Measures: The 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. Results: Among 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 Relevance: The 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.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study.

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.

Radiographic assessment of acute vs chronic vertebral compression fractures.

PURPOSE: Distinguishing between acute and chronic vertebral compression fractures typically requires advanced imaging techniques such as magnetic resonance imaging (MRI). Recognizing specific radiographic findings associated with fracture acuity may improve the accuracy of radiographic assessment. METHODS: Patients with compression fractures that had both radiographic and MRI studies of the lumbar spine within a 30-day time frame were retrospectively reviewed. MRI studies were used to determine compression fracture acuity. Radiographs were interpreted by a separate group of radiologists blinded to the MRI results. Radiographic findings of endplate osteophyte, subendplate density, subendplate cleft, and subendplate cyst were recorded as was the overall impression of fracture acuity. RESULTS: Sensitivity and specificity for radiographic reporting of acute fracture were 0.52 (95% CI: 0.42, 0.61) and 0.95 (95% CI: 0.93, 0.97) respectively. For chronic fractures, the sensitivity and specificity were 0.52 (95% CI: 0.41, 0.63) and 0.94 (95% CI: 0.92, 0.96). The radiographic presence of a subendplate cleft increased the odds of a fracture being acute by a factor of 1.75 (95% CI: 1.09, 2.81; P = 0.0202). The radiographic presence of subendplate density increased the odds of a fracture being acute by a factor of 1.78 (95% CI: 1.21, 2.63; P = 0.0037). The presence of an endplate osteophyte or subendplate cyst was not significantly associated with fracture acuity. CONCLUSION: Radiographs are relatively insensitive in distinguishing between acute and chronic lumbar compression fractures but the presence of a subendplate cleft or subendplate density increases the likelihood that a given fracture is acute.

Accuracy of obstetric laceration diagnoses in the electronic medical record.

INTRODUCTION AND HYPOTHESIS: Patient safety data including rates of obstetric anal sphincter injury (OASI) are often derived from hospital discharge codes. With the transition to electronic medical records (EMRs), we hypothesized that electronic provider-entered delivery data would more accurately document obstetric perineal injury than traditional billing/diagnostic codes. METHODS: We evaluated the accuracy of perineal laceration diagnoses after singleton vaginal deliveries during one calendar year at an American tertiary academic medical center. We reviewed the entire hospital chart to determine the most likely laceration diagnosis and compared that expert review diagnosis (ExpRD) with documentation in the EMR delivery summary (EDS) and ICD-9 diagnostic codes (IDCs). RESULTS: We retrospectively selected 354 total delivery records. OASI complicated 56 of those. 303 records (86%) were coded identically by the EDS and IDCs. Diagnoses from the IDCs and the EDS were mostly correct compared with ExpRD (sensitivity = 96%, specificity = 100%). There was no systematic over- or under-diagnosis of OASI for either the EDS (p = 0.070) or the IDCs (p = 0.447). When considering all laceration types the EDS was correct for 21 (5.9%) lacerations that were incorrect according to the IDCs. Overall, the EDS was more accurate (p < 0.05) owing to errors in IDC minor laceration diagnoses. CONCLUSIONS: Electronic medical record delivery summary data and EMR-derived diagnostic codes similarly characterize OASI. The EDS does not improve OASI reporting, but may be more accurate when considering all perineal lacerations. This assumes that providers have correctly identified and categorized the lacerations that they record in the EMR.

Recent Trends in Clinical Setting and Provider Specialty for Endovascular Peripheral Artery Disease Interventions for the Medicare Population.

PURPOSE: To describe national trends in peripheral endovascular interventions by physician specialty, anatomic segment of disease, and clinical location of service. MATERIALS AND METHODS: Current Procedural Terminology codes were used to identify claims for peripheral vascular interventions (PVIs) in 2011-2017 Physician Supplier Procedure Summary master files, which contain 100% Part B Medicare billing. Market share was defined as enrollment-adjusted proportion of billed PVI services for each specialty. Annual volume of billed services was additionally evaluated by clinical location (inpatient, outpatient, office-based laboratories) and anatomic segment of disease (iliac, femoral/popliteal, infrapopliteal). RESULTS: Aggregate PVI claims increased 31.3%, from 227,091 in 2011 to 298,127 in 2017. Annual market share remained relatively stable for all specialties: surgery, 48.3%-49.6%; cardiology, 37.2%-35.1%; radiology, 12.8%-13.3%. Accounting for Medicare enrollment, the volume of iliac interventions decreased by 18% over the study period, while femoral/popliteal interventions increased modestly (+7.5%) and infrapopliteal interventions increased (+46%). The greatest proportional increase in infrapopliteal claims occurred among radiologists (surgeons +40.4%, cardiologists +32.1%, radiologists +106.6%). Adjusting for enrollment, claims from office-based laboratories increased substantially (+305.7%), while hospital-based billing decreased (inpatient -25.7%, outpatient -12.9%). Office-based laboratory utilization increased dramatically with all specialties (surgery +331.8%, cardiology +256.0%, radiology +475.7%). CONCLUSIONS: Utilization of PVIs continues to increase, while specialty market shares have stabilized since 2011, leaving surgeons and cardiologists as the major providers of endovascular peripheral artery disease care. The greatest relative increases are occurring in infrapopliteal interventions and office-based laboratory procedures, where radiologist involvement has increased dramatically.

Hospital Readmissions After Pediatric Trauma.

OBJECTIVES: To determine the rate, etiology, and timing of unplanned and planned hospital readmissions and to identify risk factors for unplanned readmission in children who survive a hospitalization for trauma. DESIGN: Multicenter retrospective cohort study of a probabilistically linked dataset from the National Trauma Data Bank and the Pediatric Health Information System database, 2007-2012. SETTING: Twenty-nine U.S. children's hospitals. PATIENTS: 51,591 children (< 18 yr at admission) who survived more than or equal to a 2-day hospitalization for trauma. MEASUREMENTS AND MAIN RESULTS: The primary outcome was unplanned readmission within 1 year of discharge from the injury hospitalization. Secondary outcomes included any readmission, reason for readmission, time to readmission, and number of readmissions within 1 year of discharge. The primary exposure groups were isolated traumatic brain injury, both traumatic brain injury and other injury, or nontraumatic brain injury only. We hypothesized a priori that any traumatic brain injury would be associated with both planned and unplanned hospital readmission. We used All Patient Refined Diagnosis Related Groups codes to categorize readmissions by etiology and planned or unplanned. Overall, 4,301/49,982 of the patients (8.6%) with more than or equal to 1 year of observation time were readmitted to the same hospital within 1 year. Many readmissions were unplanned: 2,704/49,982 (5.4%) experienced an unplanned readmission in the first year. The most common reason for unplanned readmission was infection (22%), primarily postoperative or posttraumatic infection (38% of readmissions for infection). Traumatic brain injury was associated with lower odds of unplanned readmission in multivariable analyses. Seizure or RBC transfusion during the index hospitalization were the strongest predictors of unplanned, earlier, and multiple readmissions. CONCLUSIONS: Many survivors of pediatric trauma experience unplanned, and potentially preventable, hospital readmissions in the year after discharge. Identification of those at highest risk of readmission can guide targeted in-hospital or postdischarge interventions.

Seizures in Children With Severe Traumatic Brain Injury.

OBJECTIVE: Traumatic brain injury causes substantial morbidity and mortality in children. Posttraumatic seizures may worsen outcomes after traumatic brain injury. Posttraumatic seizures risk factors are not completely understood. Our objective was to clarify posttraumatic seizures risk factors in a large cohort of children with severe traumatic brain injury. DESIGN: Retrospective cohort study of a probabilistically linked dataset from the National Trauma Data Bank and the Pediatric Health Information Systems database, 2007-2010. SETTING: Twenty-nine U.S. children's hospitals. PATIENTS: A total of 2,122 children (age, < 18 yr old at admission) with linked National Trauma Data Bank and Pediatric Health Information Systems records, severe (emergency department Glasgow Coma Scale, < 8) traumatic brain injury, hospital length of stay more than 24 hours, and nonmissing disposition. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The outcome was posttraumatic seizures, identified using validated International Classification of Diseases, 9th Revision, Clinical Modification diagnosis codes. Prespecified candidate predictors of posttraumatic seizures included age, injury mechanism, emergency department Glasgow Coma Scale, intracranial hemorrhage type, hypoxia, hypotension, and cardiac arrest. Posttraumatic seizures were diagnosed in 25.2% of children with severe traumatic brain injury. In those without abuse/assault or subdural hemorrhage, the posttraumatic seizures rate varied between 36.6% in those less than 2 years old and 16.4% in those 14-17 years old. Age, abusive mechanism, and subdural hemorrhage are each significant predictors of posttraumatic seizures. The risk of posttraumatic seizures has a complex relationship with these predictors. The estimated odds of posttraumatic seizures decrease with advancing age, odds ratio equal to 0.929 (0.905-0.954) per additional year of age with no abuse/assault and no subdural hemorrhage; odds ratio equal to 0.820 (0.730-0.922) per additional year of age when abuse and subdural hemorrhage are present. An infant with accidental traumatic brain injury and subdural hemorrhage has approximately the same estimated probability of posttraumatic seizures as an abused infant without subdural hemorrhage (47% [95% CI, 39-55%] vs 50% [95% CI, 41-58%]; p = 0.69). The triad of young age, injury by abuse/assault, and subdural hemorrhage confers the greatest estimated probability for posttraumatic seizures (60% [95% CI, 53-66%]). CONCLUSIONS: Posttraumatic seizures risk in children with severe traumatic brain injury is greatest with a triad of younger age, injury by abuse/assault, and subdural hemorrhage. However, posttraumatic seizures are common even in the absence of these factors.

Development and Prospective Validation of Tools to Accurately Identify Neurosurgical and Critical Care Events in Children With Traumatic Brain Injury.

OBJECTIVE: To develop and validate case definitions (computable phenotypes) to accurately identify neurosurgical and critical care events in children with traumatic brain injury. DESIGN: Prospective observational cohort study, May 2013 to September 2015. SETTING: Two large U.S. children's hospitals with level 1 Pediatric Trauma Centers. PATIENTS: One hundred seventy-four children less than 18 years old admitted to an ICU after traumatic brain injury. MEASUREMENTS AND MAIN RESULTS: Prospective data were linked to database codes for each patient. The outcomes were prospectively identified acute traumatic brain injury, intracranial pressure monitor placement, craniotomy or craniectomy, vascular catheter placement, invasive mechanical ventilation, and new gastrostomy tube or tracheostomy placement. Candidate predictors were database codes present in administrative, billing, or trauma registry data. For each clinical event, we developed and validated penalized regression and Boolean classifiers (models to identify clinical events that take database codes as predictors). We externally validated the best model for each clinical event. The primary model performance measure was accuracy, the percent of test patients correctly classified. The cohort included 174 children who required ICU admission after traumatic brain injury. Simple Boolean classifiers were greater than or equal to 94% accurate for seven of nine clinical diagnoses and events. For central venous catheter placement, no classifier achieved 90% accuracy. Classifier accuracy was dependent on available data fields. Five of nine classifiers were acceptably accurate using only administrative data but three required trauma registry fields and two required billing data. CONCLUSIONS: In children with traumatic brain injury, computable phenotypes based on simple Boolean classifiers were highly accurate for most neurosurgical and critical care diagnoses and events. The computable phenotypes we developed and validated can be used in any observational study of children with traumatic brain injury and can reasonably be applied in studies of these interventions in other patient populations.

EEG Monitoring and Antiepileptic Drugs in Children with Severe TBI.

BACKGROUND: Traumatic brain injury (TBI) causes substantial morbidity and mortality in US children. Post-traumatic seizures (PTS) occur in 11-42% of children with severe TBI and are associated with unfavorable outcome. Electroencephalographic (EEG) monitoring may be used to detect PTS and antiepileptic drugs (AEDs) may be used to treat PTS, but national rates of EEG and AED use are not known. The purpose of this study was to describe the frequency and timing of EEG and AED use in children hospitalized after severe TBI. METHODS: Retrospective cohort study of 2165 children at 30 hospitals in a probabilistically linked dataset from the National Trauma Data Bank (NTDB) and the Pediatric Health Information Systems (PHIS) database, 2007-2010. We included children (age <18 years old at admission) with linked NTDB and PHIS records, severe (Emergency Department [ED] Glasgow Coma Scale [GCS] <8) TBI, hospital length of stay >24 h, and non-missing disposition. The primary outcomes were EEG and AED use. RESULTS: Overall, 31.8% of the cohort had EEG monitoring. Of those, 21.8% were monitored on the first hospital day. The median duration of EEG monitoring was 2.0 (IQR 1.0, 4.0) days. AEDs were prescribed to 52.0% of the cohort, of whom 61.8% received an AED on the first hospital day. The median duration of AED use was 8.0 (IQR 4.0, 17.0) days. EEG monitoring and AED use were more frequent in children with known risk factors for PTS. EEG monitoring and AED use were not related to hospital TBI volume. CONCLUSION: EEG use is relatively uncommon in children with severe TBI, but AEDs are frequently prescribed. EEG monitoring and AED use are more common in children with known risk factors for PTS.

Effect of obesity on neonatal outcomes in pregnancies with preterm premature rupture of membranes.

BACKGROUND: Maternal obesity is associated with increased systemic inflammation and an increased risk of preterm premature rupture of membranes. There is an established association between an inflammatory intrauterine environment and adverse neonatal outcomes that is independent of gestational age and mediated by the fetal inflammatory response. It is unknown whether the maternal systemic inflammation that is present in obese women influences the intrauterine environment and predisposes the fetus to adverse neonatal outcomes after preterm premature rupture of membranes. OBJECTIVE: The purpose of this study was to determine whether maternal obesity is associated with adverse neonatal outcomes in pregnancies that are complicated by preterm premature rupture of membranes. STUDY DESIGN: This was a secondary analysis of the Maternal-Fetal Medicine Units Network Randomized Clinical Trial on the Beneficial Effects of Antenatal Magnesium Sulfate. Women with singleton pregnancies that were affected by preterm premature rupture of membranes who delivered live-born infants between 24 + 0 and 33 + 6 weeks of gestation were included. An adverse neonatal outcome was defined as a composite outcome of neonatal death, severe necrotizing enterocolitis, respiratory distress syndrome, sepsis, or severe intraventricular hemorrhage. The rates of the composite outcome were compared between obese (body mass index, ≥30 kg/m(2)) and nonobese women. Multivariable logistic regression was used to evaluate the independent effect of obesity on neonatal outcomes. Magnesium sulfate administration, steroid administration, maternal diabetes mellitus, gestational age at delivery, indomethacin exposure, birthweight, and chorioamnionitis were all considered as possible covariates in the multivariable regression models. RESULTS: Three hundred twenty-five of the 1288 women (25.2%) who were included were obese, and 202 of these women (62.2%) had neonates with adverse outcomes. In univariable analysis, maternal prepregnancy obesity was associated with increased odds of an adverse neonatal outcome (odds ratio, 0.30, 95% confidence interval, 1.00-1.68). However, in our multivariable logistic regression model, gestational age at delivery (odds ratio, 0.93, 95% confidence interval, 0.92-0.94 per day), but not maternal obesity (odds ratio, 1.02, 95% confidence interval, 0.75-1.38), was associated with adverse neonatal outcomes. Obese African American women experienced preterm premature rupture of membranes (189 vs 196 days; p < .001) and delivery (199 vs 205 days; p < .001) earlier than nonobese African American women. This difference was not seen in non-African American women. CONCLUSIONS: Maternal obesity was not associated independently with adverse neonatal outcomes in pregnancies that were affected by preterm premature rupture of membranes after adjustment for gestational age at birth. However, obese African American women rupture and deliver earlier than other women, which causes increased neonatal morbidity.

Functional Status Scale in Children With Traumatic Brain Injury: A Prospective Cohort Study.

OBJECTIVES: In children with traumatic brain injury, 1) to describe the hospital discharge functional outcome and change from baseline function using the Functional Status Scale and 2) to determine any associations between discharge Functional Status Scale and age, injury mechanism, neurologic examination, imaging, and other predictors of outcome. DESIGN: Prospective observational cohort study, May 2013 to November 2015. SETTING: Two U.S. children's hospitals designated as American College of Surgeons level 1 pediatric trauma centers. PATIENTS: Children less than 18 years old admitted to an ICU with acute traumatic brain injury and either a surgical or critical care intervention within the first 24 hours or in-hospital mortality. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome was hospital discharge Functional Status Scale. Most, 133 of 196 (68%), had severe traumatic brain injury (admission Glasgow Coma Scale, 3-8). Overall hospital mortality was 14%; 20% among those with severe traumatic brain injury. Hospital discharge Functional Status Scale had an inverse relationship with Glasgow Coma Scale: for each increase in admission Glasgow Coma Scale by 1, the discharge Functional Status Scale decreased by 0.5 (95% CI, 0.7-0.3). Baseline Functional Status Scale was collected at one site (n = 75). At that site, nearly all (61/62) of the survivors had normal or near-normal (≤ 7) preinjury Functional Status Scale. More than one-third, 23 of 62 (37%), of survivors had new morbidity at hospital discharge (increase in Functional Status Scale, ≥ 3). Among children with severe traumatic brain injury who had baseline Functional Status Scale collected, 21 of 41 survivors (51%) had new morbidity at hospital discharge. The mean change in Functional Status Scale from baseline to hospital discharge was 3.9 ± 4.9 overall and 5.2 ± 5.4 in children with severe traumatic brain injury. CONCLUSIONS: More than one-third of survivors, and approximately half of survivors with severe traumatic brain injury, will have new morbidity. Hospital discharge Functional Status Scale, change from baseline Functional Status Scale, and new morbidity acquisition can be used as outcome measures for hospital-based care process improvement initiatives and interventional studies of children with traumatic brain injury.

Should we customize PTV expansions for BMI? Daily cone beam computerized tomography to assess organ motion in postoperative endometrial and cervical cancer patients.

AIM: A single-institution review assessing patient characteristics contributing to daily organ motion in postoperative endometrial and cervical cancer patients treated with intensity-modulated radiotherapy (IMRT). BACKGROUND: The Radiation Therapy Oncology Group has established consensus guidelines for postoperative pelvic IMRT, recommending a 7 mm margin on all three axes of the target volume. MATERIALS AND METHODS: Daily shifts on 457 radiation setups for 18 patients were recorded in the x axis (lateral), y axis (superior-inferior) and z axis (anterior-posterior); daily positions of the planning tumor volume were referenced with the initial planning scan to quantify variations. RESULTS: Of the 457 sessions, 85 (18.6%) had plan shifts of at least 7 mm in one of the three dimensions. For obese patients (body mass index [BMI] ≥ 30), 75/306 (24.5%) sessions had plan shifts ≥7 mm. Odds of having a shift ≥7 mm in any direction was greater for obese patients under both univariate (OR 4.227, 95% CI 1.235-14.466, p = 0.021) and multivariate (OR 5.000, 95% CI 1.341-18.646, p = 0.016) analyses (MVA). Under MVA, having a BMI ≥ 30 was associated with increased odds of shifts in the anterior-posterior (1.173 mm, 95% CI 0.281-2.065, p = 0.001) and lateral (2.074 mm, 95% CI 1.284-2.864, p < 0.000) directions but not in the superior-inferior axis (0.298 mm, 95% CI -0.880 to 1.475, p = 0.619) exceeding 7 mm. CONCLUSIONS: Based on these findings, the standard planned tumor volume expansion of 7 mm is less likely to account for daily treatment changes in obese patients.

Open source and reproducible and inexpensive infrastructure for data challenges and education.

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.

phoenix: an R package and Python module for calculating the Phoenix pediatric sepsis score and 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.

Hyponatremia in children with bronchiolitis admitted to the pediatric intensive care unit is associated with worse outcomes.

OBJECTIVE: To characterize the relationship between hyponatremia (serum sodium <135 mEq/L) and clinical outcomes in children ages 1 month to 2 years admitted to the pediatric intensive care unit (PICU) with bronchiolitis. STUDY DESIGN: Single-center retrospective cohort study comprising children who were admitted to the PICU between January 2009 and April 2011. Serum sodium concentrations, collected within the first 2 hours after admission to the PICU, were recorded and associations with clinical outcomes were calculated. Quantitative data are presented as mean ± SD or percentage. Student t-test, Fisher exact test, and χ(2) analyses were performed as appropriate. Subjects were excluded if they were previously diagnosed with chronic disease that would affect initial serum sodium concentration. RESULTS: Children with bronchiolitis were enrolled (n = 102; age = 10.7 ± 6.7 months). Twenty-three patients (22%) were diagnosed with hyponatremia within 2 hours of admission. Mortality (13% vs 0%; P = .011), ventilator time (8.41 ± 2 days vs 4.11 ± 2 days; P = .001), duration of stay in the PICU (10.63 ± 2.5 days vs 5.82 ± 2.09 days; P = .007), and noninvasive ventilator support (65% vs 24%; P = .007) were significantly different between subjects with hyponatremia vs those without. There were no differences in the number of patients with seizures, bronchodilator use, steroid use, intubation requirement, oxygen use at discharge, or hospital readmission. CONCLUSIONS: Pediatric patients diagnosed with bronchiolitis who present with a serum sodium concentration less than 135 mEq/L within 2 hours of admission to the PICU fare worse than their cohorts with normonatremia. A prospective study to evaluate the effects of hyponatremia appears justified.

Development and validation of a Bayesian network predicting neurosurgical intervention after injury in children and adolescents.

BACKGROUND: Timely 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. METHODS: We 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). RESULTS: The 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). CONCLUSION: We 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 EVIDENCE: Prognostic and Epidemiological; Level III.

Machine Learning Approach to Predicting Absence of Serious Bacterial Infection at PICU Admission.

BACKGROUND AND OBJECTIVES: Serious bacterial infection (SBI) is common in the PICU. Antibiotics can mitigate associated morbidity and mortality but have associated adverse effects. Our objective is to develop machine learning models able to identify SBI-negative children and reduce unnecessary antibiotics. METHODS: We developed models to predict SBI-negative status at PICU admission using vital sign, laboratory, and demographic variables. Children 3-months to 18-years-old admitted to our PICU, between 2011 and 2020, were included if evaluated for infection within 24-hours, stratified by documented antibiotic exposure in the 48-hours prior. Area under the receiver operating characteristic curve (AUROC) was the primary model accuracy measure; secondarily, we calculated the number of SBI-negative children subsequently provided antibiotics in the PICU identified as low-risk by each model. RESULTS: A total of 15 074 children met inclusion criteria; 4788 (32%) received antibiotics before PICU admission. Of these antibiotic-exposed patients, 2325 of 4788 (49%) had an SBI. Of the 10 286 antibiotic-unexposed patients, 2356 of 10 286 (23%) had an SBI. In antibiotic-exposed children, a radial support vector machine model had the highest AUROC (0.80) for evaluating SBI, identifying 48 of 442 (11%) SBI-negative children provided antibiotics in the PICU who could have been spared a median 3.7 (interquartile range 0.9-9.0) antibiotic-days per patient. In antibiotic-unexposed children, a random forest model performed best, but was less accurate overall (AUROC 0.76), identifying 33 of 469 (7%) SBI-negative children provided antibiotics in the PICU who could have been spared 1.1 (interquartile range 0.9-3.7) antibiotic-days per patient. CONCLUSIONS: Among children who received antibiotics before PICU admission, machine learning models can identify children at low risk of SBI and potentially reduce antibiotic exposure.

Intracranial meningioma surveillance using volumetrics from T2-weighted MRI.

BACKGROUND AND PURPOSE: The gold standard for imaging of meningiomas is MRI with gadolinium-based contrast agent. Due to increased costs, time, and uncertain chronic effects of gadolinium exposure, use of noncontrast T2-weighted imaging (T2WI) in lieu of contrast-enhanced MRI has been an increasing focus of research across various diagnostic scenarios. The purpose of this study was to evaluate the diagnostic accuracy of T2WI in detecting changes in meningioma tumor volume. METHODS: Imaging and clinical data were reviewed for 82 consecutive patients undergoing MR-surveillance of intracranial meningioma. Using volumetric-T2WI, two neuroradiologists independently calculated tumor volumes. Measurements were compared to a baseline study contrast-enhanced T1 tumor volume. Using contrast-enhanced sequences as the reference standard, statistical analysis was performed to determine the accuracy of T2WI in detecting changes of meningioma volume. RESULTS: Using only T2WI, readers detected meningioma volume change ≥ 20% in 19/82 patients and volume change <20% in 63/82 patients. Reader accuracy for detecting change in tumor volume on T2WI ≥ 20% was 0.85, sensitivity 0.65, specificity 0.93, positive predictive value (PPV) 0.79, and negative predictive value (NPV) 0.87. For meningiomas >1 ml, reader accuracy for detecting change in tumor volume on T2WI ≥20% was 0.90, sensitivity 0.78, specificity 0.95, PPV 0.88, and NPV 0.91. Change in tumor volume on T2WI ≥20% was detected with 100% accuracy for posterior fossa meningiomas. Inter-reader agreement for all meningiomas was moderate (κ = 0.45) improving to substantial agreement (κ = 0.77) with tumor volumes >1 ml. CONCLUSION: Volumetric-T2WI detects changes in meningioma volume with comparable accuracy to gold standard T1 postcontrast imaging, particularly with higher tumor volumes and posterior fossa locations.

A Nested Case-Control Study of Allopregnanolone and Preterm Birth in the Healthy Start Cohort.

Context: Chronic stress is a risk factor for preterm birth; however, objective measures of stress in pregnancy are limited. Maternal stress biomarkers may fill this gap. Steroid hormones and neurosteroids such as allopregnanolone (ALLO) play important roles in stress physiology and pregnancy maintenance and therefore may be promising for preterm birth prediction. Objective: We evaluated maternal serum ALLO, progesterone, cortisol, cortisone, pregnanolone, and epipregnanolone twice in gestation to evaluate associations with preterm birth. Methods: We performed a nested case-control study using biobanked fasting serum samples from the Healthy Start prebirth cohort. We included healthy women with a singleton pregnancy and matched preterm cases with term controls (1:1; N = 27 per group). We used a new HPLC-tandem mass spectrometry assay to quantify ALLO and five related steroids. We used ANOVA, Fisher exact, χ2, t test, and linear and logistic regression as statistical tests. Results: Maternal serum ALLO did not associate with preterm birth nor differ between groups. Mean cortisol levels were significantly higher in the preterm group early in pregnancy (13w0d-18w0d; P < 0.05) and higher early pregnancy cortisol associated with increased odds of preterm birth (at 13w0d; odds ratio, 1.007; 95% CI, 1.0002-1.014). Progesterone, cortisone, pregnanolone, and epipregnanolone did not associate with preterm birth. Conclusion: The findings from our pilot study suggest potential utility of cortisol as a maternal serum biomarker for preterm birth risk assessment in early pregnancy. Further evaluation using larger cohorts and additional gestational timepoints for ALLO and the other analytes may be informative.