Guidance on Conversations About Race and Racism in Pediatric Clinical Settings.

OBJECTIVE: To develop guidance for pediatric clinicians on how to discuss race and racism in pediatric clinical settings. METHODS: We conducted a modified Delphi study from 2021 to 2022 with a panel of pediatric clinicians, psychologists, parents, and adolescents with expertise in racism and child health through scholarship or lived experience. Panelists responded to an initial survey with open-ended questions about how to talk to youth about race and racism. We coded the responses using qualitative methods and presented them back to the panelists. In iterative surveys, panelists reached a consensus on which themes were most important for the conversation. RESULTS: A total of 29 of 33 panelists completed the surveys and a consensus was reached about the concepts pediatric clinicians should consider before, during, and after conversations about race and racism and impediments clinicians may face while having these discussions. Panelists agreed that it was within the pediatric clinician's role to have these conversations. An overarching theme was the importance of having background knowledge about the systemic nature of racism. Panelists agreed that being active listeners, learning from patients, and addressing intersectionality were important for pediatric clinicians during conversations. Panelists also agreed that short- and long-term benefits may result from these conversations; however, harm could be done if pediatric clinicians do not have adequate training to conduct the conversations. CONCLUSIONS: These principles can help guide conversations about race and racism in the pediatric clinical setting, equipping clinicians with tools to offer care that acknowledges and addresses the racism many of their patients face.

Improving Prediction of Survival for Extremely Premature Infants Born at 23 to 29 Weeks Gestational Age in the Neonatal Intensive Care Unit: Development and Evaluation of Machine Learning Models.

BACKGROUND: Infants born at extremely preterm gestational ages are typically admitted to the neonatal intensive care unit (NICU) after initial resuscitation. The subsequent hospital course can be highly variable, and despite counseling aided by available risk calculators, there are significant challenges with shared decision-making regarding life support and transition to end-of-life care. Improving predictive models can help providers and families navigate these unique challenges. OBJECTIVE: Machine learning methods have previously demonstrated added predictive value for determining intensive care unit outcomes, and their use allows consideration of a greater number of factors that potentially influence newborn outcomes, such as maternal characteristics. Machine learning-based models were analyzed for their ability to predict the survival of extremely preterm neonates at initial admission. METHODS: Maternal and newborn information was extracted from the health records of infants born between 23 and 29 weeks of gestation in the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database. Applicable machine learning models predicting survival during the initial NICU admission were developed and compared. The same type of model was also examined using only features that would be available prepartum for the purpose of survival prediction prior to an anticipated preterm birth. Features most correlated with the predicted outcome were determined when possible for each model. RESULTS: Of included patients, 37 of 459 (8.1%) expired. The resulting random forest model showed higher predictive performance than the frequently used Score for Neonatal Acute Physiology With Perinatal Extension II (SNAPPE-II) NICU model when considering extremely preterm infants of very low birth weight. Several other machine learning models were found to have good performance but did not show a statistically significant difference from previously available models in this study. Feature importance varied by model, and those of greater importance included gestational age; birth weight; initial oxygenation level; elements of the APGAR (appearance, pulse, grimace, activity, and respiration) score; and amount of blood pressure support. Important prepartum features also included maternal age, steroid administration, and the presence of pregnancy complications. CONCLUSIONS: Machine learning methods have the potential to provide robust prediction of survival in the context of extremely preterm births and allow for consideration of additional factors such as maternal clinical and socioeconomic information. Evaluation of larger, more diverse data sets may provide additional clarity on comparative performance.

Improving pain communication between limited English-speaking Hmong patients, medical interpreters, and health care providers in primary care: A pilot study.

This pilot study assessed the feasibility of implementing a pain assessment information visualization (InfoViz) tool to address cultural and language barriers among limited English proficiency (LEP) Hmong patients in primary care. We used a static group comparison design to collect data from 20 patient, interpreter, and provider triads under usual care (i.e., interpreter using verbal pain descriptions), followed by another 20 triads under the intervention (i.e., interpreter using verbal pain descriptions and the InfoViz tool). Feasibility outcomes included recruitment and retention rates, InfoViz tool completion, acceptability, and fidelity. We also assessed mutual understanding (MU) and pain electronic health record (EHR) documentation. Descriptive data were calculated and thematic analysis was conducted. Thirty-six LEP Hmong patients (n = 29 female, mean age = 59.03), 27 providers (n = 15 female), and four interpreters participated in this study. The patient recruitment rate was 18% while the retention rate was 81%. Interpreter recruitment rate was 80%, and 75% for retention rate. The intervention fidelity mean score was 83%. In the intervention condition, patient-provider MU of pain severity improved by 30%, coupled with a 28% increase in pain severity EHR documentation compared to usual care. While communication of pain quality did not improve, there was a higher mean number of pain descriptors (3.31 in the intervention vs. 1.79 in usual care) in EHR documentation. All participants had a positive experience with the tool, reporting it as valuable with 100% completeness of all tools. Findings revealed the tool was acceptable and feasible to use among LEP patients-interpreters-providers, providing support for an efficacy study.

Using a Machine Learning Algorithm to Predict Online Patient Portal Utilization: A Patient Engagement Study.

Objective: There is a low rate of online patient portal utilization in the U.S. This study aimed to utilize a machine learning approach to predict access to online medical records through a patient portal. Methods: This is a cross-sectional predictive machine learning algorithm-based study of Health Information National Trends datasets (Cycles 1 and 2; 2017-2018 samples). Survey respondents were U.S. adults (≥18 years old). The primary outcome was a binary variable indicating that the patient had or had not accessed online medical records in the previous 12 months. We analyzed a subset of independent variables using k-means clustering with replicate samples. A cross-validated random forest-based algorithm was utilized to select features for a Cycle 1 split training sample. A logistic regression and an evolved decision tree were trained on the rest of the Cycle 1 training sample. The Cycle 1 test sample and Cycle 2 data were used to benchmark algorithm performance. Results: Lack of access to online systems was less of a barrier to online medical records in 2018 (14%) compared to 2017 (26%). Patients accessed medical records to refill medicines and message primary care providers more frequently in 2018 (45%) than in 2017 (25%). Discussion: Privacy concerns, portal knowledge, and conversations between primary care providers and patients predict portal access. Conclusion: Methods described here may be employed to personalize methods of patient engagement during new patient registration.

Longitudinal K-means approaches to clustering and analyzing EHR opioid use trajectories for clinical subtypes.

Identification of patient subtypes from retrospective Electronic Health Record (EHR) data is fraught with inherent modeling issues, such as missing data and variable length time intervals, and the results obtained are highly dependent on data pre-processing strategies. As we move towards personalized medicine, assessing accurate patient subtypes will be a key factor in creating patient specific treatment plans. Partitioning longitudinal trajectories from irregularly spaced and variable length time intervals is a well-established, but open problem. In this work, we present and compare k-means approaches for subtyping opioid use trajectories from EHR data. We then interpret the resulting subtypes using decision trees, examining how each subtype is influenced by opioid medication features and patient diagnoses, procedures, and demographics. Finally, we discuss how the subtypes can be incorporated in static machine learning models as features in predicting opioid overdose and adverse events. The proposed methods are general, and can be extended to other EHR prescription dosage trajectories.

A Comparative Retrospective Study of Immunotherapy RANO Versus Standard RANO Criteria in Glioblastoma Patients Receiving Immune Checkpoint Inhibitor Therapy.

OBJECTIVES: Real-time assessment of treatment response in glioblastoma (GBM) patients on immune checkpoint blockade (ICB) remains challenging because inflammatory effects of therapy may mimic progressive disease, and the temporal evolution of these inflammatory findings is poorly understood. We compare GBM patient response during ICB as assessed with the Immunotherapy Response Assessment in Neuro-Oncology (iRANO) and the standard Response Assessment in Neuro-Oncology (RANO) radiological criteria. METHODS: 49 GBM patients (seven newly diagnosed and 42 recurrent) treated with ICBs at a single institution were identified. Tumor burden was quantified on serial MR scans according to RANO criteria during ICB. Radiographic response assessment by iRANO and RANO were compared. RESULTS: 82% (40/49) of patients received anti-PD-1, 16% (8/49) received anti-PD-L1, and 2% (1/49) received anti-PD-1 and anti-CTLA4 treatment. Change in tumor burden and best overall response ranged from -100 to +557% (median: +48%). 12% (6/49) of patients were classified as concordant non-progressors by both RANO and iRANO (best response: one CR, one PR, and four SD). Another12% (6/49) had discordant assessments: 15% (6/41) of RANO grade progressive disease (PD) patients had iRANO grade of progressive disease unconfirmed (PDU). The final classification of these discordant patients was pseudoprogression (PsP) in three of six, PD in two of six, and PDU in one of six who went off study before the iRANO assessment of PDU. iRANO delayed diagnosis of PD by 42 and 93 days in the two PD patients. 76% (37/49) patients were classified as concordant PD by both RANO and iRANO. 12% (6/49) of all patients were classified as PsP, starting at a median of 12 weeks (range, 4-30 weeks) after ICB initiation. CONCLUSIONS: Standard RANO and iRANO have high concordance for assessing PD in patients within 6 months of ICB initiation. iRANO was beneficial in 6% (3/49) cases later proven to be PsP, but delayed confirmation of PD by <3 months in 4% (2/49). PsP occurred in 12% of patients, starting at up to 7 months after initiation of ICB. Further study to define the utility of modified RANO compared with iRANO in ICB GBM patients is needed.

Clinical Validation of Automatable Gaussian Normalized CBV in Brain Tumor Analysis: Superior Reproducibility and Slightly Better Association with Survival than Current Standard Manual Normal Appearing White Matter Normalization.

PURPOSE: To validate Gaussian normalized cerebral blood volume (GN-nCBV) by association with overall survival (OS) in newly diagnosed glioblastoma patients and compare this association with current standard white matter normalized cerebral blood volume (WN-nCBV). METHODS: We retrieved spin-echo echo-planar dynamic susceptibility contrast MRI acquired after maximal resection and prior to radiation therapy between 2006 and 2011 in 51 adult patients (28 male, 23 female; age 23-87 years) with newly diagnosed glioblastoma. Software code was developed in house to perform Gaussian normalization of CBV to the standard deviation of the whole brain CBV. Three expert readers manually selected regions of interest in tumor and normal-appearing white matter on CBV maps. Receiver operating characteristics (ROC) curves associating nCBV with 15-month OS were calculated for both GN-nCBV and WN-nCBV. Reproducibility and interoperator variability were compared using within-subject coefficient of variation (wCV) and intraclass correlation coefficients (ICCs). RESULTS: GN-nCBV ICC (≥0.82) and wCV (≤21%) were superior to WN-nCBV ICC (0.54-0.55) and wCV (≥46%). The area under the ROC curve analysis demonstrated both GN-nCBV and WN-nCBV to be good predictors of OS, but GN-nCBV was consistently superior, although the difference was not statistically significant. CONCLUSION: GN-nCBV has a slightly better association with clinical gold standard OS than conventional WM-nCBV in our glioblastoma patient cohort. This equivalent or superior validity, combined with the advantages of higher reproducibility, lower interoperator variability, and easier automation, makes GN-nCBV superior to WM-nCBV for clinical and research use in glioma patients. We recommend widespread adoption and incorporation of GN-nCBV into commercial dynamic susceptibility contrast processing software.

Normalization of ADC does not improve correlation with overall survival in patients with high-grade glioma (HGG).

Mixed reports leave uncertainty about whether normalization of apparent diffusion coefficient (ADC) to a within-subject white matter reference is necessary for assessment of tumor cellularity. We tested whether normalization improves the previously reported correlation of resection margin ADC with 15-month overall survival (OS) in HGG patients. Spin-echo echo-planar DWI was retrieved from 3 T MRI acquired between maximal resection and radiation in 37 adults with new-onset HGG (25 glioblastoma; 12 anaplastic astrocytoma). ADC maps were produced with the FSL DTIFIT tool (Oxford Centre for Functional MRI). 3 neuroradiologists manually selected regions of interest (ROI) in normal appearing white matter (NAWM) and in non-enhancing tumor (NT) < 2 cm from the margin of residual enhancing tumor or resection cavity. Normalized ADC (nADC) was computed as the ratio of absolute NT ADC to NAWM ADC. Reproducibility of nADC and absolute ADC among the readers' ROI was assessed using intra-class correlation coefficient (ICC) and within-subject coefficient of variation (wCV). Correlations of ADC and nADC with OS were compared using receiver operating characteristics (ROC) analysis. A p value 0.05 was considered statistically significant. Both mean ADC and nADC differed significantly between patients subgrouped by 15-month OS (p = 0.0014 and 0.0073 respectively). wCV and ICC among the readers were similar for absolute and normalized ADC. In ROC analysis of correlation with OS, nADC did not perform significantly better than absolute ADC. Normalization does not significantly improve the correlation of absolute ADC with OS in HGG, suggesting that normalization is not necessary for clinical or research ADC analysis in HGG patients.

Lung cancer mutation testing: a clinical retesting study of agreement between a real-time PCR and a mass spectrometry test.

To investigate the clinical validity and utility of tests for detecting Epidermal Growth Factor Receptor (EGFR) gene mutations in non-squamous non-small cell lung cancer patients, tumour DNA extracts from 532 patients previously tested by the cobas EGFR Mutation Test (RT-PCR test) were retested by the Sequenom/Agena Biosciences MassArray OncoFocus mass spectrometry test (MS test). Valid results from both tests were available from 470 patients (88%) for agreement analysis. Survival data were obtained for 513 patients (96%) and 77 patients (14%) were treated with EGFR tyrosine kinase inhibitors (TKIs). Agreement analysis revealed moderately high positive (79.8%), negative (96.9%) and overall percentage agreement (93.2%) for the detection of EGFR mutations. However, EGFR mutations were detected by one test and not by the other test in 32 patients (7%). Retesting of discordant samples revealed false-positive and false-negative results generated by both tests. Despite this, treatment and survival outcomes correlated with the results of the RT-PCR and MS tests. In conclusion, this study provides evidence of the clinical validity and utility of the RT-PCR and MS tests for detection of EGFR mutations that predict prognosis and benefit from EGFR-TKI treatment. However, their false-positive and false-negative test results may have important clinical consequences.

EGFR Mutation Testing of non-squamous NSCLC: Impact and Uptake during Implementation of Testing Guidelines in a Population-Based Registry Cohort from Northern New Zealand.

BACKGROUND: Since 2013, clinical practice guidelines recommend EGFR mutation testing of non-squamous NSCLC to select advanced-stage patients for first-line treatment using EGFR-TKIs. OBJECTIVE: We aimed to determine population-based trends in the real-world uptake and impact in routine practice of these recently updated testing guidelines. PATIENTS AND METHODS: A population-based observational study was conducted of notifications to the New Zealand Cancer Registry of patients eligible for EGFR testing diagnosed in northern New Zealand between January 2010 and April 2014. The main study variable was EGFR mutation testing. Main outcome measures (overall survival and dispensing of EGFR-TKIs) were extracted from prospectively archived electronic databases until October 2015. RESULTS: The population-based cohort of 1857 patients had an average age of 70 years. Most had adenocarcinoma and metastatic disease at diagnosis. EGFR testing was undertaken in 500 patients (27%) with mutations detected in 109 patients (22%). EGFR testing increased during the period of study from <5% to 67% of patients (P < 0.0001). Full uptake of testing by all eligible patients was limited by a lack of availability of specimens for testing and variable testing referral practices. The proportion of patients treated with EGFR-TKIs decreased during the same time period, both among untested patients (from 12.2% to 2.8% (P < 0.0001)) and in the population as a whole (from 13.7% to 10.6% (P < 0.05)). EGFR testing was associated with prolonged overall survival (Adjusted HR = 0.76 (95% CI, 0.65-0.89) Log-rank P < 0.0001) due at least in part to the much longer overall survival achieved by mutation-positive patients, of whom 79% received EGFR-TKIs. Compared to untested EGFR-TKI-treated patients, mutation-positive EGFR-TKI-treated patients received EGFR-TKIs for longer, and survived longer both from the start of EGFR-TKI treatment and date of their diagnosis. CONCLUSIONS: In this real world setting, high uptake of EGFR testing was achieved and associated with major changes in EGFR-TKI prescribing and improved health outcomes. Modifiable factors determined testing uptake. Study registration ACTRN12615000998549.

Structural remodeling of vagal afferent innervation of aortic arch and nucleus ambiguus (NA) projections to cardiac ganglia in a transgenic mouse model of type 1 diabetes (OVE26).

Diabetes-induced structural changes of vagal aortic afferent and cardiac efferent axons are not well understood. FVB control and OVE26 diabetic mice at different ages received injections of the tracer tetramethylrhodamine dextran (TMR-D) into the nodose ganglion to label vagal aortic afferents (at 3 and 6 months), or DiI injections into the nucleus ambiguus to label vagal cardiac efferents (at 3, 6, and 9 months). The aortic arch and atria were examined by using confocal microscopy. In the aortic arch, TMR-D labeled large and small vagal afferent axons (axons(L) and axons(S)) that formed different types of terminals: axons(L) produced large flower-sprays (flower-sprays(L)) and end-nets (end-nets(L)), whereas axons(S) produced small flower-sprays (flower-sprays(S)) and end-nets (end-nets(S)). In the atria, DiI-labeled vagal efferent axons formed basket endings around ganglion principle neurons (PNs). The vagal afferents, PNs and vagal cardiac efferents in diabetic mice were compared with age-matched control mice. We found (P < 0.05) that: 1) the size of axons(L), flower-sprays(L), flower-sprays(S) and end-nets(S) were reduced at 6 and 9 months; 2) the size of cardiac ganglia and the somatic area of the PNs were decreased, and the PN density in cardiac ganglia was increased at all ages and the PN nuclei/soma area ratio was increased at 9 months; and 3) the percentage of DiI-labeled axons-innervated PNs was decreased at all ages. Furthermore, the number of synaptic-like terminal varicosities around PNs was decreased. Compared with 3 months, more advanced diabetes at 9 months further reduced the number of varicosities/PN. In addition to these changes, swollen axons and terminals, as well as leaky-like DiI-labeled terminals, were observed in long-term diabetic mice (6 and 9 months of age). Taken together, our data show that chronic diabetes induces a significant structural atrophy of vagal aortic afferent and cardiac efferent axons and terminals. Although different morphologies of vagal afferent terminals in the aortic arch may serve as substrates for the future investigation of aortic depressor afferent physiology, structural remodeling of vagal afferents and efferents provides a foundation for further analysis of diabetes-induced impairment of cardiac autonomic regulation.

A study of field independence versus field dependence of school teachers and university students in mathematics.

The Group Embedded Figures Test was administered to 72 secondary school teachers and 54 university students in mathematics to measure their cognitive styles of field independence-dependence. A mean difference was found between the teachers and students as teachers scored more field-independent than the students. There was also a group-by-sex interaction, which indicated that the female teachers scored more field-independent than the male teachers, whereas the male students scored as more field-independent than the female students. Implications of the findings are reflected in the discussion.