Advance Care Planning for Children With Rare Diseases: A Pilot RCT.

BACKGROUND AND OBJECTIVE: Pediatric rare diseases are often life-limiting conditions and/or require constant caregiving. Investigators assessed the initial efficacy of the FAmily CEntered (FACE) pediatric advance care planning (pACP), FACE-Rare, intervention on families' quality of life. METHODS: A pilot-phase, single-blinded, intent-to-treat, randomized controlled clinical trial enrolled families from 1 pediatric quaternary hospital between 2021 and 2023. Intervention families received 3 weekly 60-minute (FACE-Rare pACP) sessions: (1) Carer Support Needs Assessment Tool or Action Plan, (2) Carer Support Needs Assessment Tol Action Plan Review, and (3) Pediatric Next Steps: Respecting Choices pACP. Controls received treatment as usual (TAU). Outcome measures were Beck Anxiety Inventory, Family Appraisal of Caregiving, Functional Assessment of Chronic Illness Therapy (FACIT)-Spirituality, and health care utilization. Generalized mixed effect models with γ response assessed the intervention effect at 3-month follow-up. RESULTS: Children (n = 21) were aged 1 to 10 years, 48% male, 24% Black; and 100% technology dependent. Primary family caregivers (n = 21) were aged 30 to 43 years, 19% male, 19% Black; and 27% household income below the Federal poverty level. Dyads underwent 1:1 randomization: 9 to FACE-Rare and 12 to TAU. TAU caregivers reported statistically lower meaning and peace than FACE-Rare caregivers (0.9, P = .03, confidence interval [CI]: 0.75-0.99). Black caregivers reported significantly less caregiver distress (0.7, P = .04, CI: 0.47-0.98) than non-Black caregivers. Poor families reported more anxiety (3.5, P = .002, CI: 1.62-7.94), more caregiver strain (1.2, P = .006, CI: 1.07-1.42); and less family well-being (0.8, P = .02, CI: 0.64-0.95). CONCLUSIONS: FACE®-Rare was feasible, acceptable, safe, and demonstrated initial efficacy, providing greater feelings of meaning and peace to caregivers. Poverty impacted well-being. A multisite trial is needed to determine generalizability.

PedBotLab: A Novel Video Game-Based Robotic Ankle Platform Created for Therapeutic Exercise for Children With Neurological Impairments.

AIMS: Assess the potential benefits of using PedBotLab, a clinic based robotic ankle platform with integrated video game software, to improve ankle active and passive range of motion, strength, selective motor control, gait efficiency, and balance. METHODS: Ten participants with static neurological injuries and independent ambulation participated in a 10-week pilot study (Pro00013680) to assess feasibility and efficacy of PedBotLab as a therapeutic device twice weekly. Isometric ankle strength, passive and active ankle range of motion, plantarflexor spasticity, selective motor control of the lower extremity, balance, and gait speed were measured pre- and post-trial. RESULTS: Statistically significant improvements were seen in flexibility, active range of motion, and strength in multiple planes of ankle motion. Ankle dorsiflexion with knee flexion and knee extension demonstrated statistically significant results in all outcome measures. No significant changes were observed in gait speed outcomes. CONCLUSIONS: The use of PedbotLab can lead to improvements in ankle strength, flexibility, and active range of motion for children with static neurological injuries. Future studies aim to evaluate the effect on gait quality and work toward developing a home-based device.

External evaluation of the Dynamic Criticality Index: A machine learning model to predict future need for ICU care in hospitalized pediatric patients.

OBJECTIVE: To assess the single site performance of the Dynamic Criticality Index (CI-D) models developed from a multi-institutional database to predict future care. Secondarily, to assess future care-location predictions in a single institution when CI-D models are re-developed using single-site data with identical variables and modeling methods. Four CI-D models were assessed for predicting care locations >6-12 hours, >12-18 hours, >18-24 hours, and >24-30 hours in the future. DESIGN: Prognostic study comparing multi-institutional CI-D models' performance in a single-site electronic health record dataset to an institution-specific CI-D model developed using identical variables and modelling methods. The institution did not participate in the multi-institutional dataset. PARTICIPANTS: All pediatric inpatients admitted from January 1st 2018 -February 29th 2020 through the emergency department. MAIN OUTCOME(S) AND MEASURE(S): The main outcome was inpatient care in routine or ICU care locations. RESULTS: A total of 29,037 pediatric hospital admissions were included, with 5,563 (19.2%) admitted directly to the ICU, 869 (3.0%) transferred from routine to ICU care, and 5,023 (17.3%) transferred from ICU to routine care. Patients had a median [IQR] age 68 months (15-157), 47.5% were female and 43.4% were black. The area under the receiver operating characteristic curve (AUROC) for the multi-institutional CI-D models applied to a single-site test dataset was 0.493-0.545 and area under the precision-recall curve (AUPRC) was 0.262-0.299. The single-site CI-D models applied to an independent single-site test dataset had an AUROC 0.906-0.944 and AUPRC range from 0.754-0.824. Accuracy at 0.95 sensitivity for those transferred from routine to ICU care was 72.6%-81.0%. Accuracy at 0.95 specificity was 58.2%-76.4% for patients who transferred from ICU to routine care. CONCLUSION AND RELEVANCE: Models developed from multi-institutional datasets and intended for application to individual institutions should be assessed locally and may benefit from re-development with site-specific data prior to deployment.

Impact of the COVID-19 Pandemic on the Provision of Dialysis Service and Mortality in Veterans Receiving Maintenance Hemodialysis in the VA: An Interrupted Time-Series Analysis.

INTRODUCTION: According to the US Renal Data System (USRDS), patients with end-stage kidney disease (ESKD) on maintenance dialysis had higher mortality during early COVID-19 pandemic. Less is known about the effect of the pandemic on the delivery of outpatient maintenance hemodialysis and its impact on death. We examined the effect of pandemic-related disruption on the delivery of dialysis treatment and mortality in patients with ESKD receiving maintenance hemodialysis in the Veterans Health Administration (VHA) facilities, the largest integrated national healthcare system in the USA. METHODS: Using national VHA electronic health records data, we identified 7,302 Veterans with ESKD who received outpatient maintenance hemodialysis in VHA healthcare facilities during the COVID-19 pandemic (February 1, 2020, to December 31, 2021). We estimated the average change in the number of hemodialysis treatments received and deaths per 1,000 patients per month during the pandemic by conducting interrupted time-series analyses. We used seasonal autoregressive moving average (SARMA) models, in which February 2020 was used as the conditional intercept and months thereafter as conditional slope. The models were adjusted for seasonal variations and trends in rates during the pre-pandemic period (January 1, 2007, to January 31, 2020). RESULTS: The number (95% CI) of hemodialysis treatments received per 1,000 patients per month during the pre-pandemic and pandemic periods were 12,670 (12,525-12,796) and 12,865 (12,729-13,002), respectively. Respective all-cause mortality rates (95% CI) were 17.1 (16.7-17.5) and 19.6 (18.5-20.7) per 1,000 patients per month. Findings from SARMA models demonstrate that there was no reduction in the dialysis treatments delivered during the pandemic (rate ratio: 0.999; 95% CI: 0.998-1.001), but there was a 2.3% (95% CI: 1.5-3.1%) increase in mortality. During the pandemic, the non-COVID hospitalization rate was 146 (95% CI: 143-149) per 1,000 patients per month, which was lower than the pre-pandemic rate of 175 (95% CI: 173-176). In contrast, there was evidence of higher use of telephone encounters during the pandemic (3,023; 95% CI: 2,957-3,089), compared with the pre-pandemic rate (1,282; 95% CI: 1,241-1,324). CONCLUSIONS: We found no evidence that there was a disruption in the delivery of outpatient maintenance hemodialysis treatment in VHA facilities during the COVID-19 pandemic and that the modest rise in deaths during the pandemic is unlikely to be due to missed dialysis.

Oncogenic Proteomics Approaches for Translational Research and HIV-Associated Malignancy Mechanisms.

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.

Clinical Instability Is a Sign of Severity of Illness: A Cohort Study.

OBJECTIVES: Test the hypothesis that within patient clinical instability measured by deterioration and improvement in mortality risk over 3-, 6-, 9-, and 12-hour time intervals is indicative of increasing severity of illness. DESIGN: Analysis of electronic health data from January 1, 2018, to February 29, 2020. SETTING: PICU and cardiac ICU at an academic children's hospital. PATIENTS: All PICU patients. Data included descriptive information, outcome, and independent variables used in the Criticality Index-Mortality. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 8,399 admissions with 312 deaths (3.7%). Mortality risk determined every three hours using the Criticality Index-Mortality, a machine learning algorithm calibrated to this hospital. Since the sample sizes were sufficiently large to expect statical differences, we also used two measures of effect size, the proportion of time deaths had greater instability than survivors, and the rank-biserial correlation, to assess the magnitude of the effect and complement our hypothesis tests. Within patient changes were compared for survivors and deaths. All comparisons of survivors versus deaths were less than 0.001. For all time intervals, two measures of effect size indicated that the differences between deaths and survivors were not clinically important. However, the within-patient maximum risk increase (clinical deterioration) and maximum risk decrease (clinical improvement) were both substantially greater in deaths than survivors for all time intervals. For deaths, the maximum risk increase ranged from 11.1% to 16.1% and the maximum decrease ranged from -7.3% to -10.0%, while the median maximum increases and decreases for survivors were all less than ± 0.1%. Both measures of effect size indicated moderate to high clinical importance. The within-patient volatility was greater than 4.5-fold greater in deaths than survivors during the first ICU day, plateauing at ICU days 4-5 at 2.5 greater volatility. CONCLUSIONS: Episodic clinical instability measured with mortality risk is a reliable sign of increasing severity of illness. Mortality risk changes during four time intervals demonstrated deaths have greater maximum and within-patient clinical instability than survivors. This observation confirms the clinical teaching that clinical instability is a sign of severity of illness.

The Antiviral Compound PSP Inhibits HIV-1 Entry via PKR-Dependent Activation in Monocytic Cells.

Actin depolymerization factor (ADF) cofilin-1 is a key cytoskeleton component that serves to lessen cortical actin. HIV-1 manipulates cofilin-1 regulation as a pre- and post-entry requisite. Disruption of ADF signaling is associated with denial of entry. The unfolded protein response (UPR) marker Inositol-Requiring Enzyme-1α (IRE1α) and interferon-induced protein (IFN-IP) double-stranded RNA- activated protein kinase (PKR) are reported to overlap with actin components. In our published findings, Coriolus versicolor bioactive extract polysaccharide peptide (PSP) has demonstrated anti-HIV replicative properties in THP1 monocytic cells. However, its involvement towards viral infectivity has not been elucidated before. In the present study, we examined the roles of PKR and IRE1α in cofilin-1 phosphorylation and its HIV-1 restrictive roles in THP1. HIV-1 p24 antigen was measured through infected supernatant to determine PSP's restrictive potential. Quantitative proteomics was performed to analyze cytoskeletal and UPR regulators. PKR, IRE1α, and cofilin-1 biomarkers were measured through immunoblots. Validation of key proteome markers was done through RT-qPCR. PKR/IRE1α inhibitors were used to validate viral entry and cofilin-1 phosphorylation through Western blots. Our findings show that PSP treatment before infection leads to an overall lower infectivity. Additionally, PKR and IRE1α show to be key regulators in cofilin-1 phosphorylation and viral restriction.

The criticality Index-mortality: A dynamic machine learning prediction algorithm for mortality prediction in children cared for in an ICU.

Background: The Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change. Objectives: To assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions. Population: There were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing. Model: A neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression. Results: Discrimination assessed across all time periods found an AUROC of 0.851 (0.841-0.862) and an AUPRC was 0.443 (0.417-0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689-0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058-0.328) and a maximum value of 0.499 (0.229-0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the R2 was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed a priori clinical expectations about the trajectories of death and survivors. Conclusions: The Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.

National Intravenous Acetaminophen Use in Pediatric Inpatients From 2011-2016.

OBJECTIVE: To 1) determine current intravenous (IV) acetaminophen use in pediatric inpatients; and 2) determine the association between opioid medication duration when used with or without IV acetaminophen. METHODS: A retrospective analysis of pediatric inpatients exposed to IV acetaminophen from January 2011 to June 2016, using the national database Health Facts. RESULTS: Eighteen thousand one hundred ninety-seven (2.0%) of 893,293 pediatric inpatients received IV acetaminophen for a median of 14 doses per patient (IQR, 8-56). A greater proportion of IV acetaminophen patients were admitted to the intensive care unit (ICU) (14.8% vs 5.1%, p < 0.0001), received positive pressure ventilation (2.0% vs 1.5%, p < 0.0001), had a higher hospital mortality rate (0.9% vs 0.3%, p < 0.0001), and were operative (35.5% vs 12.8%, p < 0.001) than those not receiving IV acetaminophen. The most common operations associated with IV acetaminophen use were musculoskeletal and digestive system operations. Prescription of IV acetaminophen increased over time, both in prescription rates and number of per patient doses. Of the 18,197 patients prescribed IV acetaminophen, 16,241 (89.2%) also were prescribed opioids during their hospitalization. A multivariate analysis revealed patients prescribed both IV acetaminophen and opioids had a 54.8% increase in opioid duration as compared with patients who received opioids alone. CONCLUSIONS: This is the first study to assess IV acetaminophen prescription practices for pediatric inpatients. Intravenous acetaminophen prescription was greater in the non-operative pediatric inpatient population than operative patients. Intravenous acetaminophen prescription was associated with an increase in opioid duration as compared with patients who received opioids alone, suggesting that it is commonly used to supplement opioids for pain relief.

Dynamic Mortality Risk Predictions for Children in ICUs: Development and Validation of Machine Learning Models.

OBJECTIVES: Assess a machine learning method of serially updated mortality risk. DESIGN: Retrospective analysis of a national database (Health Facts; Cerner Corporation, Kansas City, MO). SETTING: Hospitals caring for children in ICUs. PATIENTS: A total of 27,354 admissions cared for in ICUs from 2009 to 2018. INTERVENTIONS: None. MAIN OUTCOME: Hospital mortality risk estimates determined at 6-hour time periods during care in the ICU. Models were truncated at 180 hours due to decreased sample size secondary to discharges and deaths. MEASUREMENTS AND MAIN RESULTS: The Criticality Index, based on physiology, therapy, and care intensity, was computed for each admission for each time period and calibrated to hospital mortality risk (Criticality Index-Mortality [CI-M]) at each of 29 time periods (initial assessment: 6 hr; last assessment: 180 hr). Performance metrics and clinical validity were determined from the held-out test sample (n = 3,453, 13%). Discrimination assessed with the area under the receiver operating characteristic curve was 0.852 (95% CI, 0.843-0.861) overall and greater than or equal to 0.80 for all individual time periods. Calibration assessed by the Hosmer-Lemeshow goodness-of-fit test showed good fit overall (p = 0.196) and was statistically not significant for 28 of the 29 time periods. Calibration plots for all models revealed the intercept ranged from--0.002 to 0.009, the slope ranged from 0.867 to 1.415, and the R2 ranged from 0.862 to 0.989. Clinical validity assessed using population trajectories and changes in the risk status of admissions (clinical volatility) revealed clinical trajectories consistent with clinical expectations and greater clinical volatility in deaths than survivors (p < 0.001). CONCLUSIONS: Machine learning models incorporating physiology, therapy, and care intensity can track changes in hospital mortality risk during intensive care. The CI-M's framework and modeling method are potentially applicable to monitoring clinical improvement and deterioration in real time.

Association of Intravenous Acetaminophen Administration With the Duration of Intravenous Opioid Use Among Hospitalized Pediatric Patients.

Importance: Adoption of multimodal pain regimens that incorporate nonopioid analgesic medications to reduce inpatient opioid administration can prevent serious opioid-related adverse effects in children, including tolerance, withdrawal, delirium, and respiratory depression. Intravenous (IV) acetaminophen is in widespread pediatric use; however, its effectiveness as an opioid-sparing agent has not been evaluated in general pediatric inpatients. Objective: To determine if IV acetaminophen administered prior to IV opioids is associated with a reduction in the total duration of IV opioids administered compared with IV opioids administered without IV acetaminophen in general pediatric inpatients. Design, Setting, and Participants: This comparative effectiveness research study included data on pediatric inpatients from 274 US hospitals between January 2011 and June 2016 collected from a national database. Outcomes were compared with a propensity score-matched analysis of pediatric inpatients administered IV opioids without IV acetaminophen (control) and those administered IV acetaminophen prior to IV opioids (intervention). Data were analyzed from January 2020 through October 2021. Exposures: Patients in the intervention group received IV acetaminophen prior to IV opioids. Patients in the control group received IV opioids without IV acetaminophen. Main Outcomes and Measures: Total duration of all IV opioids administered during a patient's hospitalization. Results: Of 893 293 pediatric inpatients, a total of 104 579 were included in analysis (median [IQR] age, 1.3 [0-14.7] years; 59 806 [57.2%] female; 21 485 [21.5%] African American, 56 309 [53.8%] White), of whom 18 197 (2.0%) received IV acetaminophen, and 287 504 (34.0%) received IV opioids. After applying exclusion criteria, among patients who received IV acetaminophen, 1739 (10.8%) received IV acetaminophen prior to IV opioids within a median (IQR) treatment time of 1.5 (0.02-7.3) hours. After propensity score matching produced comparable groups in the control and intervention groups (with 839 patients in each group), the multivariable model estimated a 15.5% shorter duration of IV opioid use in the intervention group, with an absolute IV opioid reduction of 7.5 hours (95% CI, 0.7-15.8 hours). Conclusions and Relevance: In this comparative effectiveness study, IV acetaminophen administered prior to IV opioids was associated with a reduction in IV opioid duration by 15.5%. Multimodal pain regimens that use IV acetaminophen prior to IV opioids could reduce IV opioid duration.

Predicting Future Care Requirements Using Machine Learning for Pediatric Intensive and Routine Care Inpatients.

Develop and compare separate prediction models for ICU and non-ICU care for hospitalized children in four future time periods (6-12, 12-18, 18-24, and 24-30 hr) and assess these models in an independent cohort and simulated children's hospital. DESIGN: Predictive modeling used cohorts from the Health Facts database (Cerner Corporation, Kansas City, MO). SETTING: Children hospitalized in ICUs. PATIENTS: Children with greater than or equal to one ICU admission (n = 20,014) and randomly selected routine care children without ICU admission (n = 20,130) from 2009 to 2016 were used for model development and validation. An independent 2017-2018 cohort consisted of 80,089 children. INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Initially, we undersampled non-ICU patients for development and comparison of the models. We randomly assigned 64% of patients for training, 8% for validation, and 28% for testing in both clinical groups. Two additional validation cohorts were tested: a simulated children's hospitals and the 2017-2018 cohort. The main outcome was ICU care or non-ICU care in four future time periods based on physiology, therapy, and care intensity. Four independent, sequential, and fully connected neural networks were calibrated to risk of ICU care at each time period. Performance for all models in the test sample were comparable including sensitivity greater than or equal to 0.727, specificity greater than or equal to 0.885, accuracy greater than 0.850, area under the receiver operating characteristic curves greater than or equal to 0.917, and all had excellent calibration (all R2 s > 0.98). Model performance in the 2017-2018 cohort was sensitivity greater than or equal to 0.545, specificity greater than or equal to 0.972, accuracy greater than or equal to 0.921, area under the receiver operating characteristic curves greater than or equal to 0.946, and R2 s greater than or equal to 0.979. Performance metrics were comparable for the simulated children's hospital and for hospitals stratified by teaching status, bed numbers, and geographic location. CONCLUSIONS: Machine learning models using physiology, therapy, and care intensity predicting future care needs had promising performance metrics. Notably, performance metrics were similar as the prediction time periods increased from 6-12 hours to 24-30 hours.

Healthy Lifestyle and Mood: A Biomedical Informatics Citizen Science Project in a High School Classroom.

Mental health is an increasing concern in adolescents. Mental health disorders can affect academic performance, affect the cultivation of healthy relationships, and even lead to suicide. Healthy lifestyle can improve mental health, though there are gaps in the research, partly resulted from the lack of detailed longitudinal datasets on lifestyle and mental health. To inform and engage students in the research on adolescent lifestyle and mood, the George Washington University and the T.C. Williams High School in Alexandria, Virginia teamed up in a citizen science project. Students generated questions, collected data on themselves, analyzed the data, and produced research reports relating to their mental health and lifestyle. Student feedbacks suggest that the students find the project to be generally interesting and some students (46%) reported that the participation in the project may influence their college and career plans. The anonymized dataset resulted from the project provides another contribution to science.

Severity Trajectories of Pediatric Inpatients Using the Criticality Index.

OBJECTIVES: To assess severity of illness trajectories described by the Criticality Index for survivors and deaths in five patient groups defined by the sequence of patient care in ICU and routine patient care locations. DESIGN: The Criticality Index developed using a calibrated, deep neural network, measures severity of illness using physiology, therapies, and therapeutic intensity. Criticality Index values in sequential 6-hour time periods described severity trajectories. SETTING: Hospitals with pediatric inpatient and ICU care. PATIENTS: Pediatric patients never cared for in an ICU (n = 20,091), patients only cared for in the ICU (n = 2,096) and patients cared for in both ICU and non-ICU care locations (n = 17,023) from 2009 to 2016 Health Facts database (Cerner Corporation, Kansas City, MO). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Criticality Index values were consistent with clinical experience. The median (25-75th percentile) ICU Criticality Index values (0.878 [0.696-0.966]) were more than 80-fold higher than the non-ICU values (0.010 [0.002-0.099]). Non-ICU Criticality Index values for patients transferred to the ICU were 40-fold higher than those never transferred to the ICU (0.164 vs 0.004). The median for ICU deaths was higher than ICU survivors (0.983 vs 0.875) (p < 0.001). The severity trajectories for the five groups met expectations based on clinical experience. Survivors had increasing Criticality Index values in non-ICU locations prior to ICU admission, decreasing Criticality Index values in the ICU, and decreasing Criticality Index values until hospital discharge. Deaths had higher Criticality Index values than survivors, steeper increases prior to the ICU, and worsening values in the ICU. Deaths had a variable course, especially those who died in non-ICU care locations, consistent with deaths associated with both active therapies and withdrawals/limitations of care. CONCLUSIONS: Severity trajectories measured by the Criticality Index showed strong validity, reflecting the expected clinical course for five diverse patient groups.

Criticality: A New Concept of Severity of Illness for Hospitalized Children.

OBJECTIVES: To validate the conceptual framework of "criticality," a new pediatric inpatient severity measure based on physiology, therapy, and therapeutic intensity calibrated to care intensity, operationalized as ICU care. DESIGN: Deep neural network analysis of a pediatric cohort from the Health Facts (Cerner Corporation, Kansas City, MO) national database. SETTING: Hospitals with pediatric routine inpatient and ICU care. PATIENTS: Children cared for in the ICU (n = 20,014) and in routine care units without an ICU admission (n = 20,130) from 2009 to 2016. All patients had laboratory, vital sign, and medication data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A calibrated, deep neural network used physiology (laboratory tests and vital signs), therapy (medications), and therapeutic intensity (number of physiology tests and medications) to model care intensity, operationalized as ICU (versus routine) care every 6 hours of a patient's hospital course. The probability of ICU care is termed the Criticality Index. First, the model demonstrated excellent separation of criticality distributions from a severity hierarchy of five patient groups: routine care, routine care for those who also received ICU care, transition from routine to ICU care, ICU care, and high-intensity ICU care. Second, model performance assessed with statistical metrics was excellent with an area under the curve for the receiver operating characteristic of 0.95 for 327,189 6-hour time periods, excellent calibration, sensitivity of 0.817, specificity of 0.892, accuracy of 0.866, and precision of 0.799. Third, the performance in individual patients with greater than one care designation indicated as 88.03% (95% CI, 87.72-88.34) of the Criticality Indices in the more intensive locations was higher than the less intense locations. CONCLUSIONS: The Criticality Index is a quantification of severity of illness for hospitalized children using physiology, therapy, and care intensity. This new conceptual model is applicable to clinical investigations and predicting future care needs.

The Association of Laboratory Test Abnormalities With Mortality Risk in Pediatric Intensive Care.

OBJECTIVES: To determine the bivariable associations between abnormalities of 28 common laboratory tests and hospital mortality and determine how mortality risks changes when the ranges are evaluated in the context of commonly used laboratory test panels. DESIGN: A 2009-2016 cohort from the Health Facts (Cerner Corporation, Kansas City, MO) database. SETTING: Hospitals caring for children in ICUs. PATIENTS: Children cared for in ICUs with laboratory data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: There were 2,987,515 laboratory measurements in 71,563 children. The distribution of laboratory test values in 10 groups defined by population percentiles demonstrated the midrange of tests was within the normal range except for those measured predominantly when significant abnormalities are suspected. Logistic regression analysis at the patient level combined the population-based groups into ranges with nonoverlapping mortality odds ratios. The most deviant test ranges associated with increased mortality risk (mortality odds ratios > 5.0) included variables associated with acidosis, coagulation abnormalities and blood loss, immune function, liver function, nutritional status, and the basic metabolic profile. The test ranges most associated with survival included normal values for chloride, pH, and bicarbonate/total Co2. When the significant test ranges from bivariable analyses were combined in commonly used test panels, they generally remained significant but were reduced as risk was distributed among the tests. CONCLUSIONS: The relative importance of laboratory test ranges vary widely, with some ranges strongly associated with mortality and others strongly associated with survival. When evaluated in the context of test panels rather than isolated tests, the mortality odds ratios for the test ranges decreased but generally remained significant as risk was distributed among the components of the test panels. These data are useful to develop critical values for children in ICUs, to identify risk factors previously underappreciated, for education and training, and for future risk score development.

Medications for Children Receiving Intensive Care: A National Sample.

OBJECTIVE: To examine medication administration records through electronic health record data to provide a broad description of the pharmaceutical exposure of critically ill children. DESIGN: Retrospective cohort study using the Cerner Health Facts database. SETTING: United States. PATIENTS: A total of 43,374 children 7 days old to less than 22 years old receiving intensive care with available pharmacy data. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 907,440 courses of 1,080 unique medications were prescribed with a median of nine medications (range, 1-99; 25-75th percentile, 5-16) per patient. The most common medications were acetaminophen, ondansetron, and morphine. Only 45 medications (4.2%) were prescribed to more than 5% of patients, and these accounted for 442,067 (48.7%) of the total courses of medications. Each additional medication was associated with increased univariate risk of mortality (odds ratio, 1.05; 95% CI, 1.05-1.06; p < 0.001). CONCLUSIONS: Children receiving intensive care receive a median of nine medications per patient and one quarter are prescribed at least than 16 medications. Only 45 medications were prescribed to more than 5% of patients, but these accounted for almost half of all medication courses.

Sedation, Analgesia, and Neuromuscular Blockade: An Assessment of Practices From 2009 to 2016 in a National Sample of 66,443 Pediatric Patients Cared for in the ICU.

OBJECTIVES: To describe the pharmaceutical management of sedation, analgesia, and neuromuscular blockade medications administered to children in ICUs. DESIGN: A retrospective analysis using data extracted from the national database Health Facts. SETTING: One hundred sixty-one ICUs in the United States with pediatric admissions. PATIENTS: Children in ICUs receiving medications from 2009 to 2016. EXPOSURE/INTERVENTION: Frequency and duration of administration of sedation, analgesia, and neuromuscular blockade medications. MEASUREMENTS AND MAIN RESULTS: Of 66,443 patients with a median age of 1.3 years (interquartile range, 0-14.5), 63.3% (n = 42,070) received nonopioid analgesic, opioid analgesic, sedative, and/or neuromuscular blockade medications consisting of 83 different agents. Opioid and nonopioid analgesics were dispensed to 58.4% (n = 38,776), of which nonopioid analgesics were prescribed to 67.4% (n = 26,149). Median duration of opioid analgesic administration was 32 hours (interquartile range, 7-92). Sedatives were dispensed to 39.8% (n = 26,441) for a median duration of 23 hours (interquartile range, 3-84), of which benzodiazepines were most common (73.4%; n = 19,426). Neuromuscular-blocking agents were dispensed to 17.3% (n = 11,517) for a median duration of 2 hours (interquartile range, 1-15). Younger age was associated with longer durations in all medication classes. A greater proportion of operative patients received these medication classes for a longer duration than nonoperative patients. A greater proportion of patients with musculoskeletal and hematologic/oncologic diseases received these medication classes. CONCLUSIONS: Analgesic, sedative, and neuromuscular-blocking medications were prescribed to 63.3% of children in ICUs. The durations of opioid analgesic and sedative medication administration found in this study can be associated with known complications, including tolerance and withdrawal. Several medications dispensed to pediatric patients in this analysis are in conflict with Food and Drug Administration warnings, suggesting that there is potential risk in current sedation and analgesia practice that could be reduced with practice changes to improve efficacy and minimize risks.

Desempeño académico, y su relación con calidad de vida y hábitos saludables / Academic Performance and its Association with Quality of Life and Healthy Habits

Introducción: El estudiante de Medicina enfrenta, en muchas ocasiones, entornos de vida y condiciones de aprendizaje poco favorables, así como aspectos psicológicos que pueden impactar de modo negativo en su calidad de vida y rendimiento académico. Objetivo: Evaluar el desempeño académico de los aspirantes a estudiar Medicina en relación con la autopercepción de la calidad de vida y los hábitos saludables. Métodos: Se realizó un estudio transversal, observacional y analítico. Se aplicó una encuesta sociodemográfica y la encuesta WHOQOL-Bref a una muestra de estudiantes del curso propedéutico de Medicina. Resultados: Se encontraron diferencias entre los alumnos que aprobaron y no aprobaron el curso propedéutico en autopercepción de Calidad de Vida (p = 0,01), y los dominios de Salud Física (p = 0,04), Salud Psicológica (p = 0,02) y Ambiente (p = 0,003). También entre los alumnos que desayunaban y los que omitían el desayuno en Autopercepción de Calidad de Vida (p = 0,012), Autopercepción de Salud Física (p = 0,009), y los dominios Salud Psicológica (p = 0,009) y Ambiente (p = 0,024). Conclusiones: Los altos puntajes en la prueba WHOQOL-Bref se correlacionan con el elevado desempeño académico; además, el hábito de omitir el desayuno conlleva a un bajo rendimiento escolar y una menor calidad de vida en los discentes(AU)

Introduction: Medical students face, many times, unfavorable living environments and learning conditions, as well as psychological aspects that can impact negatively on their quality of life and academic outcomes. Objective: To assess the academic performance of applicants to study Medicine in association with self-perception of quality of life and healthy habits. Methods: A cross-sectional, observational and analytical study was carried out. A sociodemographic survey and the WHOQOL-Bref survey were conducted on a sample of medical students of the propaedeutic course. Results: Differences were found between students who passed and those who did not pass the propaedeutic course regarding self-perception of quality of life (p=0.01), and the domains of physical health (p=0.04), psychological health (p=0.02) and environment (p=0.003). Also, we found differences between students who had breakfast and those who omitted breakfast regarding self-perception of quality of life (p=0.012), physical health (p=0.009), and domains of psychological health (p=0.009) and environment (p=0.024). Conclusions: The high scores in the WHOQOL-Bref test are in correlation with high academic performance; also, the habit of omitting breakfast leads to poor school performance and lower quality of life in students(AU)

Sequence optimization and glycosylation of vasoinhibin: Pitfalls of recombinant production.

Vasoinhibin belongs to a family of proteins with antiangiogenic properties derived by proteolytic cleavage from the hormone prolactin (PRL). Vasoinhibin isoforms range from the first 79 to the first 159 residues of PRL. In an attempt to increase the yield of recombinant vasoinhibin and avoid incorrect intra- and inter-disulfide bond formation, the cDNA sequence comprising the first 123 amino acids of human PRL, in which cysteine 58 was or not mutated to serine, was codon-optimized. The optimized constructs achieved a 6-fold increase in mRNA expression but showed no change in protein production and reduced protein secretion when expressed in human embryo kidney (HEK293T/17) cells. Limited vasoinhibin levels associated with the activation of the unfolded protein response (UPR) and endoplasmic reticulum-associated degradation (ERAD) as revealed by the upregulation of UPR (Bip, Xbp-1, and Chop) and ERAD (Hrd1, Os9, and Sel1l) target genes. Mutation to serine introduced a new N-glycosylation site and associated with increased glycosylation and release of glycosylated vasoinhibin isoforms having reduced antiangiogenic properties. We conclude that overexpression and excessive glycosylation lead to protein degradation and reduced bioactivity, respectively, negatively affecting the production of recombinant vasoinhibin in mammalian cells.