Reducing the Frequency of Pulse Oximetry Alarms at a Children's Hospital.

BACKGROUND AND OBJECTIVES: Alarm fatigue is exacerbated by frequent, nonactionable physiologic monitor alarms. Overutilization of pulse oximetry (SpO2) compounds this alarm burden. Narrow default alarm limits and overutilization of continuous (CSpO2) rather than intermittent monitoring contribute to nonactionable alarms. There were 1.12 million SpO2 alarms on included units during the baseline period, of which 41.0% were for SpO2 ≥ 88%. We aimed to decrease SpO2 alarms per patient day by 20% within 12 months. METHODS: This quality improvement study included patients admitted January 2019 to June 2022. Intensive care and cardiology units were excluded. Interventions included (1) changing default alarm SpO2 limits on monitors from <90% to <88%, (2) changing SpO2 order default from continuous to intermittent, and (3) adding indication requirements for CSpO2. Outcome measures were total SpO2 alarms and alarms for SpO2 ≥ 88% per patient day. Balancing measures were high acuity transfers and code blues without CSpO2 ordered. Control charts were used for each. RESULTS: Our study included 120 408 patient days with 2.98 million SpO2 alarms. Total SpO2 alarms and alarms for SpO2 ≥ 88% per patient day decreased by 5.48 (30.57 to 25.09; 17.9%) and 4.48 (12.50 to 8.02; 35.8%), respectively. Special cause improvement was associated with changing default monitor alarm parameters. Balancing measures remained stable. CONCLUSIONS: SpO2 monitors alarm frequently at our children's hospital. Widening default alarm limits was associated with decreased SpO2 alarms, particularly nonactionable alarms (≥88%). This high-reliability intervention may be applied, when appropriate, to other monitor alarm parameters to further mitigate alarm burden.

Medicaid Expenditures among Children with Documented Obesity.

Background: Obesity rates continue to rise among children, but knowledge regarding medical expenditures of Medicaid enrollees with documented obesity is lacking. We aim to describe Medicaid expenditure patterns among children with documented obesity and determine the degree to which specific clinical characteristics and conditions contribute to high expenditures. Methods: We performed a retrospective cross-sectional analysis of children aged 2-17 years with a diagnosis code of obesity continuously enrolled in the 2017 Medicaid MarketScan database. Children were grouped based on annual expenditure percentiles: <80th, 80 to <95th, 95 to <99th, and ≥99th. Inpatient, outpatient, and pharmacy expenditures were analyzed. Covariates included demographics, common obesity comorbid conditions (e.g., hypertension), complex chronic conditions (CCCs), and mental health conditions (MHCs). Logistic regression assessed demographic and clinical characteristics associated with high-spending groups (≥95th%). Results: We identified 300,286 children with a diagnosis of obesity. The 1% of children with the highest spending accounted for 25.4% of annual expenditures among children with documented obesity. Annual expenditures in the highest spending groups were driven primarily by inpatient and outpatient mental health services. Characteristics associated with high-spending groups included the following: age 12-17 years, obesity comorbid conditions, and having ≥1 CCC or MHC. These associations increased with increasing number of CCCs or MHCs. Conclusions: Inpatient and outpatient mental health expenditures made up a large proportion of spending among Medicaid-enrolled children with documented obesity. Important drivers of cost in this population were medical complexity and comorbid MHCs. Future research is needed to determine if some of these costs are avoidable in children with obesity.

Connecting omics signatures and revealing biological mechanisms with iLINCS.

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Here we present iLINCS ( http://ilincs.org ), an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates mining and re-analysis of the large collection of omics datasets (>34,000), pre-computed signatures (>200,000), and their connections, as well as the analysis of user-submitted omics signatures of diseases and cellular perturbations. iLINCS analysis workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures. iLINCS user-friendly interfaces enable execution of sophisticated analyses of omics signatures, mechanism of action analysis, and signature-driven drug repositioning. We illustrate the utility of iLINCS with three use cases involving analysis of cancer proteogenomic signatures, COVID 19 transcriptomic signatures and mTOR signaling.

Improving Outpatient Provider Communication for High-Risk Discharges From the Hospitalist Service.

BACKGROUND: Patients are at risk for adverse events during inpatient-to-outpatient transitions of care. Previous improvement work has been targeted at this care transition, but gaps in discharge communication still exist. We aimed to increase documentation of 2-way communication between hospitalists and primary care providers (PCPs) for high-risk discharges from pediatric hospital medicine (PHM) services from 7% to 60% within 30 months. METHODS: A3 improvement methodology was used. A list of high-risk discharge communication criteria was developed through engagement of PCPs and hospitalists. A driver diagram guided interventions. The outcome measure was documentation of successful 2-way communication with the PCP. Any documented 2-way discharge communication attempt was the process measure. Via a survey, hospitalist satisfaction with the discharge communication expectation served as the balancing measure. All patients discharged from PHM services meeting ≥1 high-risk criterion were included. Statistical process control charts were used to assess changes over time. RESULTS: There were 3241 high-risk discharges (442 baseline: November 2017 to January 2018; 2799 intervention and sustain: February 2018 to June 2020). The outcome measure displayed iterative special cause variation from a mean baseline of 7% to peak of 39% but regressed and was sustained at 27%. The process measure displayed iterative special cause variation from a 13% baseline mean to a 64% peak, with regression to 41%. The balancing measure worsened from baseline of 5% dissatisfaction to 13%. Interventions temporally related to special cause improvements were education, division-level performance feedback, standardization of documentation, and offloading the task of communication coordination from hospitalists to support staff. CONCLUSIONS: Improvement methodology resulted in modestly sustained improvements in PCP communication for high-risk discharges from the PHM services.

Pandemic-Related Shifts in New Patients Admitted to Children's Hospitals.

OBJECTIVES: During the coronavirus disease 2019 pandemic, professional organizations recommended preferential transfer of pediatric patients from general hospitals to children's hospitals. Patients previously receiving all care at other facilities would be new to children's hospitals. As a proxy for care consolidation, we sought to describe changes in new patient encounters at children's hospitals and test associations between local severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) incidences and new patient encounters. METHODS: This retrospective cohort study included patients aged 6 months to 18 years admitted to children's hospitals from March 15, 2019, to June 30, 2019 (control) and 2020 (pandemic period). Primary outcome was odds ratio of being a new versus established patient by study period. Generalized linear models estimated odds of being a new patient with adjustment for diagnosis. Analyses were also stratified by local SARS-CoV-2 transmission. RESULTS: There were 205 283 encounters (45.3% new patients). New patients were more common in the pandemic period than in the control (46.4 vs 44.7%, OR 1.07, 95% confidence interval [CI]: 1.05 to 1.09). After adjusting for diagnosis, pandemic new patients were no more common than control new patients (adjusted odds ratio 1.00, 95% CI: 0.98 to 1.02). Compared with hospitals experiencing low local SARS-CoV-2 transmission, admission encounters at both medium and high transmission hospitals were more likely to be new (adjusted odds ratio 1.08, 95% CI: 1.03 to 1.14 and 1.09, 95% CI: 1.03 to 1.15, respectively). CONCLUSIONS: During the early coronavirus disease 2019 pandemic, proportional increases in new patients to children's hospitals appeared to be due to changes in diagnoses but were also associated with local SARS-CoV-2 transmission. Pediatric care consolidation may have occurred; how this may have impacted outcomes for hospitalized children is unclear.

Safety Events in Children's Hospitals During the COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has impacted hospitals, potentially affecting quality and safety. Our objective was to compare pediatric hospitalization safety events during the pandemic versus previous years. METHODS: In this retrospective cohort study of hospitalizations in the Pediatric Health Information System, we compared Pediatric Quality Indicator (PDI) rates from March 15 to May 31, 2017-2019 (pre-COVID-19), with those from March 15 to May 31, 2020 (during COVID-19). Generalized linear mixed-effects models with adjustment for patient characteristics (eg, diagnosis, clinical severity) were used. RESULTS: There were 399 113 discharges pre-COVID-19 and 88 140 during COVID-19. Unadjusted PDI rates were higher during versus pre-COVID-19 for overall PDIs (6.39 vs 5.05; P < .001). In adjusted analyses, odds of postoperative sepsis were higher during COVID-19 versus pre-COVID-19 (adjusted odds ratio 1.28 [95% confidence interval 1.04-1.56]). The remainder of the PDIs did not have increased adjusted odds during compared with pre-COVID-19. CONCLUSIONS: Postoperative sepsis rates increased among children hospitalized during COVID-19. Efforts are needed to improve safety of postoperative care for hospitalized children.

Variations in Sedated Echocardiography and Association With Repeat Echocardiography in Nonrefractory Kawasaki Disease.

OBJECTIVES: Echocardiography performed under sedation allows for better coronary artery visualization in children with Kawasaki disease. We sought to describe hospital-level variability in the percentage of sedated echocardiography (SE) among children with nonrefractory Kawasaki disease (NRKD) and then test its association with repeat echocardiography, length of stay (LOS), and costs. METHODS: We identified children in the Pediatric Health Information System <36 months of age hospitalized with NRKD from March 2010 to February 2017. Hospital-level percentage of SE was the primary outcome. Secondary outcomes were repeat echocardiography during the same hospitalization, LOS, and costs. We used χ2 and Wilcoxon rank tests to compare differences in repeat echocardiography between SE and nonsedated echocardiography. Pearson correlation was used to test associations between SE and repeat echocardiography, LOS, and costs. RESULTS: There were 2887 NRKD hospitalizations from 40 children's hospitals. Initial SE varied from 0.0% to 87.0% (median 5.9%; interquartile range 1.4%-21.1%). Of initial echocardiographies, 22.4% were sedated and 10.1% of all hospitalizations had a repeat echocardiography. Use of SE at the hospital level was associated with a lower likelihood for repeat echocardiography (r = -0.32; 95% confidence interval -0.58 to -0.01; P = .042). Absolute risk reduction was 3.5% and the number needed to sedate was 29. Initial SE was not associated with LOS or hospital-level costs but was associated with increased patient-level costs. CONCLUSIONS: Significant variation exists in the use of SE for children <36 months of age with NRKD at children's hospitals. Our results suggest that determination of SE should not be driven by concern for repeat echocardiography or LOS considerations.

A quality improvement project to increase palliative care team involvement in pediatric oncology patients.

BACKGROUND: Pediatric palliative care (PPC) for oncology patients improves quality of life and the likelihood of goal-concordant care. However, barriers to involvement exist. OBJECTIVES: We aimed to increase days between PPC consult and death for patients with refractory cancer from a baseline median of 13.5 days to ≥30 days between March 2019 and March 2020. METHODS: Outcome measure was days from PPC consult to death; process measure was days from diagnosis to PPC consult. The project team surveyed oncologists to identify barriers. Plan-do-study-act cycles included establishing target diagnoses, offering education, standardizing documentation, and sending reminders. RESULTS: The 24-month baseline period included 30 patients who died and 25 newly diagnosed patients. The yearlong intervention period included six patients who died and 16 newly diagnosed patients. Interventions improved outcome and process measures. Targeted patients receiving PPC ≥30 days prior to death increased from 43% to 100%; median days from consult to death increased from 13.5 to 159.5. Targeted patients receiving PPC within 30 days of diagnosis increased from 28% to 63%; median days from diagnosis to consult decreased from 221.5 to 14. Of those without PPC consult ≤ 30 days after diagnosis, 17% had template documentation of the rationale. CONCLUSION: Interventions utilized met the global aim, outcome, and process measures. Use of QI methodology empowered providers to involve PPC. Poor template use was a barrier to identifying further drivers. Future directions for this project relate to expanding the target list, creating long-term sustainability, formalizing standards, and surveying patients and families.

Predicting mechanism of action of cellular perturbations with pathway activity signatures.

MOTIVATION: Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures (TSeses) a difficult problem. RESULTS: We developed a new computational method for implicating pathway targets of bioactive chemicals and other cellular perturbations by integrated analysis of pathway network topology, the Library of Integrated Network-based Cellular Signature TSes of genetic perturbations of pathway genes and the TS of the perturbation. Our methodology accurately predicts signaling pathways targeted by the perturbation when current pathway analysis approaches utilizing only the TS of the perturbation fail. AVAILABILITY AND IMPLEMENTATION: Open source R package paslincs is available at https://github.com/uc-bd2k/paslincs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Mass spectrometry proteomics reveals a function for mammalian CALCOCO1 in MTOR-regulated selective autophagy.

Macroautophagy/autophagy is suppressed by MTOR (mechanistic target of rapamycin kinase) and is an anticancer target under active investigation. Yet, MTOR-regulated autophagy remains incompletely mapped. We used proteomic profiling to identify proteins in the MTOR-autophagy axis. Wild-type (WT) mouse cell lines and cell lines lacking individual autophagy genes (Atg5 or Ulk1/Ulk2) were treated with an MTOR inhibitor to induce autophagy and cultured in media with either glucose or galactose. Mass spectrometry proteome profiling revealed an elevation of known autophagy proteins and candidates for new autophagy components, including CALCOCO1 (calcium binding and coiled-coil domain protein 1). We show that CALCOCO1 physically interacts with MAP1LC3C, a key protein in the machinery of autophagy. Genetic deletion of CALCOCO1 disrupted autophagy of the endoplasmic reticulum (reticulophagy). Together, these results reveal a role for CALCOCO1 in MTOR-regulated selective autophagy. More generally, the resource generated by this work provides a foundation for establishing links between the MTOR-autophagy axis and proteins not previously linked to this pathway. Abbreviations: ATG: autophagy-related; CALCOCO1: calcium binding and coiled-coil domain protein 1; CALCOCO2/NDP52: calcium binding and coiled-coil domain protein 2; CLIR: MAP1LC3C-interacting region; CQ: chloroquine; KO: knockout; LIR: MAP1LC3-interacting region; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; MEF: mouse embryonic fibroblast; MLN: MLN0128 ATP-competitive MTOR kinase inhibitor; MTOR: mechanistic target of rapamycin kinase; reticulophagy: selective autophagy of the endoplasmic reticulum; TAX1BP1/CALCOCO3: TAX1 binding protein 1; ULK: unc 51-like autophagy activating kinase; WT: wild-type.

Cheminformatics Tools for Analyzing and Designing Optimized Small-Molecule Collections and Libraries.

Libraries of well-annotated small molecules have many uses in chemical genetics, drug discovery, and therapeutic repurposing. Multiple libraries are available, but few data-driven approaches exist to compare them and design new libraries. We describe an approach to scoring and creating libraries based on binding selectivity, target coverage, and induced cellular phenotypes as well as chemical structure, stage of clinical development, and user preference. The approach, available via the online tool http://www.smallmoleculesuite.org, assembles sets of compounds with the lowest possible off-target overlap. Analysis of six kinase inhibitor libraries using our approach reveals dramatic differences among them and led us to design a new LSP-OptimalKinase library that outperforms existing collections in target coverage and compact size. We also describe a mechanism of action library that optimally covers 1,852 targets in the liganded genome. Our tools facilitate creation, analysis, and updates of both private and public compound collections.

Implementation of a General Pediatric Clinical Rounding Checklist.

BACKGROUND: Checklists have been found to improve patient outcomes in a variety of health care settings, but use is rare during general pediatric rounds. We aimed to decrease length of stay (LOS) by 10% within 12 months through the standardized delivery of 8 aspects of hospital care for the general pediatric unit. METHODS: For the general pediatrics unit at our freestanding children's hospital, a clinical rounding checklist was developed through a consensus of teaching faculty. Iterative plan-do-study-act cycles were conducted in a targeted medical unit over a 12-month period. We assessed change using control charts. RESULTS: Checklist use increased from 61% to 96% early in the implementation phase. LOS (72 hours) was relatively unchanged. Cardiorespiratory monitor (CRM) duration was reduced by 28% (17.3 hours) within 4 months and was sustained for more than 12 months. A similar CRM reduction of 29% (22.5 hours) was seen in our medically complex patient subgroup. No clinically significant change was appreciated in central line, peripheral intravenous (IV) catheter, IV fluid, or IV medication duration. Thirty-day readmissions, code blue events, and rapid response team activations were not impacted. CONCLUSIONS: The use of a clinical rounding checklist on the general pediatric floor improved care in only 1 of 8 targeted domains. LOS did not change significantly over the project's duration. CRM duration was significantly reduced in both routine patients and medically complex children, and these improvements were sustained without impacting patient safety. More study is needed to determine which checklist components, if any, may prove beneficial to patient outcomes.

The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program that catalogs how human cells globally respond to chemical, genetic, and disease perturbations. Resources generated by LINCS include experimental and computational methods, visualization tools, molecular and imaging data, and signatures. By assembling an integrated picture of the range of responses of human cells exposed to many perturbations, the LINCS program aims to better understand human disease and to advance the development of new therapies. Perturbations under study include drugs, genetic perturbations, tissue micro-environments, antibodies, and disease-causing mutations. Responses to perturbations are measured by transcript profiling, mass spectrometry, cell imaging, and biochemical methods, among other assays. The LINCS program focuses on cellular physiology shared among tissues and cell types relevant to an array of diseases, including cancer, heart disease, and neurodegenerative disorders. This Perspective describes LINCS technologies, datasets, tools, and approaches to data accessibility and reusability.

GRcalculator: an online tool for calculating and mining dose-response data.

BACKGROUND: Quantifying the response of cell lines to drugs or other perturbagens is the cornerstone of pre-clinical drug development and pharmacogenomics as well as a means to study factors that contribute to sensitivity and resistance. In dividing cells, traditional metrics derived from dose-response curves such as IC 50 , AUC, and E max , are confounded by the number of cell divisions taking place during the assay, which varies widely for biological and experimental reasons. Hafner et al. (Nat Meth 13:521-627, 2016) recently proposed an alternative way to quantify drug response, normalized growth rate (GR) inhibition, that is robust to such confounders. Adoption of the GR method is expected to improve the reproducibility of dose-response assays and the reliability of pharmacogenomic associations (Hafner et al. 500-502, 2017). RESULTS: We describe here an interactive website ( www.grcalculator.org ) for calculation, analysis, and visualization of dose-response data using the GR approach and for comparison of GR and traditional metrics. Data can be user-supplied or derived from published datasets. The web tools are implemented in the form of three integrated Shiny applications (grcalculator, grbrowser, and grtutorial) deployed through a Shiny server. Intuitive graphical user interfaces (GUIs) allow for interactive analysis and visualization of data. The Shiny applications make use of two R packages (shinyLi and GRmetrics) specifically developed for this purpose. The GRmetrics R package is also available via Bioconductor and can be used for offline data analysis and visualization. Source code for the Shiny applications and associated packages (shinyLi and GRmetrics) can be accessed at www.github.com/uc-bd2k/grcalculator and www.github.com/datarail/gr_metrics . CONCLUSIONS: GRcalculator is a powerful, user-friendly, and free tool to facilitate analysis of dose-response data. It generates publication-ready figures and provides a unified platform for investigators to analyze dose-response data across diverse cell types and perturbagens (including drugs, biological ligands, RNAi, etc.). GRcalculator also provides access to data collected by the NIH LINCS Program ( http://www.lincsproject.org /) and other public domain datasets. The GRmetrics Bioconductor package provides computationally trained users with a platform for offline analysis of dose-response data and facilitates inclusion of GR metrics calculations within existing R analysis pipelines. These tools are therefore well suited to users in academia as well as industry.