Establishing Shock Index, Pediatric Age-Adjusted (SIPA) Cut Points in Trauma Patients Under 1 Year of Age

PURPOSE

Shock Index (SI) is used to predict injury severity and adverse outcomes in trauma patients, but age-adjusted shock index (SIPA) has superior performance in pediatric patients over the age of 1 year. SIPA scores under the age of 1 have not been well studied. This project aimed to establish and evaluate SIPA cut point data points for patients under 1 year of age.

METHODS

Using age-based vital signs, we developed cut point values for patients under 1 year old using our institutional trauma data. All trauma patients under the age of 12 months were included, and clinical outcomes were recorded. SIPA cut points were defined using age specific vital sign limits (SIPA-VS) and tested against optimal cut points defined by ROC analysis (SIPA-ROC) and a cut point of 1.2 (SIPA-Nordin), which is used for patients ages 1-4 years. Student's t-test, chi-square tests, ANOVA, and test characteristics were used to analyze groups.

RESULTS

A total of 609 pediatric trauma patients under the age of 12 months were identified from 2018 to 2022. SIPA scores were calculated for 483 patients. There were 406 patients with blunt trauma and 17 with penetrating. SIPA-Nordin was elevated in 81.6% (n = 397) of patients, compared to SIPA-VS 21% (n = 101) and SIPA-ROC 31% (n = 150). In comparison to SIPA-Nordin, both SIPA-VS and SIPA-ROC score exhibited superior specificity and negative predictive values (NPV) for multiple outcomes. Elevated SIPA-ROC scores had statistically significant associations with ICU admission, mechanical ventilation, severe anemia, transfusion during hospital admission, and in-hospital mortality.

CONCLUSION

SIPA is a useful tool in identifying patients at risk for several complications of severe traumatic injury. SIPA cut points had high NPV and specificity for many outcomes. This study proposes cut point values that may aid in clinical decision-making for trauma patients under 1 year of age.Level of Evidence: Level IV Retrospective Review.

Contraception use among individuals with substance use disorder increases tenfold with patient-centered, mobile services: a quasi-experimental study

BACKGROUND

Individuals with substance use disorders (SUD) have disproportionately high rates of unintended pregnancy. Reducing harm associated with this risk and its biopsychosocial consequences requires evidence-based, non-coercive interventions that ensure access to contraception for individuals who choose to prevent pregnancy. We examined feasibility and impact of SexHealth Mobile, a mobile unit-based intervention that aimed to increase access to patient-centered contraceptive care for individuals in SUD recovery programs.

METHODS

We conducted a quasi-experimental study (enhanced usual care [EUC] followed by intervention) at three recovery centers with participants (n = 98) at risk for unintended pregnancy. EUC participants were offered printed information on community locations where they could access contraception care. SexHealth Mobile participants were offered same-day, onsite clinical consultation on a medical mobile unit and contraception if desired. The primary outcome was use of contraception (hormonal or intrauterine device) at one-month post-enrollment. Secondary outcomes were at two-weeks and three-months. Confidence in preventing unintended pregnancy, reasons for non-use of contraception at follow-up, and intervention feasibility were also assessed.

RESULTS

Participants (median age = 31, range 19-40) enrolled in the intervention period were almost 10 times more likely to be using contraception at one-month (51.5%) versus the those enrolled in the EUC period (5.4%) (unadjusted relative risk [URR] = 9.3 [95%CI: 2.3-37.1]; adjusted relative risk [ARR] = 9.8 [95%CI: 2.4-39.2]). Intervention participants were also more likely to be using contraception at 2-weeks (38.7% vs. 2.6%; URR = 14.3 [95%CI: 2.0-104.1]) and three-months (40.9% vs. 13.9%; URR = 2.9 [95% CI: 1.1-7.4]). EUC participants reported more barriers (cost, time) and less confidence in preventing unintended pregnancies. Mixed-methods feasibility data indicated high acceptability and feasible integration into recovery settings.

CONCLUSIONS

Mobile contraceptive care based on principles of reproductive justice and harm reduction reduces access barriers, is feasible to implement in SUD recovery settings, and increases contraception use. Expanding interventions like SexHealth Mobile may help reduce harm from unintended pregnancies among individuals in SUD recovery. Trial Registration NCT04227145.

Diagnostic yield of genetic testing in 324 infants with hypotonia

This retrospective cohort study was designed to determine the yield of genetic tests in hypotonic infants and develop a diagnostic algorithm. Out of 496 patients identified by International Classification of Diseases (ICD) 9/10 coding, 324 patients met the inclusion criteria. Diagnostic yields were 32% for karyotype, 19% for microarray, 30% for targeted genetic tests, 38% for gene panels, and 31% for exome sequencing. In addition, we considered the diagnostic contribution of ancillary tests, including neuroimaging, metabolic tests, and so forth. The combination of microarray and exome sequencing gave the highest diagnostic yield. None of the other tests added significant value in arriving at a diagnosis. Based on these results we propose that the vast majority of infants with congenital hypotonia should start with a microarray and proceed with exome sequencing, with the notable exception of infants with clearly syndromic features in whom karyotyping or targeted testing may be more appropriate.

Recovering from substance use disorders during the early months of the COVID-19 pandemic: A mixed-methods longitudinal study of women in Kansas City

AIMS

During the early months of the U.S. COVID-19 outbreak, women suffered disproportionate burdens of pandemic-related psychological and economic distress. We aimed to describe the experiences of women in substance use disorder (SUD) recovery programs by (1) exploring the pandemic's impact on their lives, sobriety, and recovery capital and (2) tracking COVID-19 perceptions and preventative behaviors.

METHODS

We conducted monthly semistructured interviews with women in residential and outpatient SUD recovery programs in Kansas City in April, May, and June 2020. Participants described the pandemic's impact on their life and sobriety and completed survey items on factors related to COVID-19 preventative behaviors. We interpreted qualitative themes longitudinally alongside quantitative data.

RESULTS

In 64 interviews, participants (n = 24) described reduced access to recovery capital, or resources that support sobriety, such as social relationships, housing, employment, and health care. Most experienced negative impacts on their lives and feelings of stability in March and April but maintained sobriety. Four women described relapse, all attributed to pandemic stressors. Participants described relief related to societal re-opening in May and June, and increased engagement with their communities, despite rising infection rates.

CONCLUSIONS

For women recovering from SUDs during COVID-19, securing recovery capital often meant assuming greater COVID-19 risk. As substance use appeared to have increased during the pandemic and COVID-19 transmission continues, public health planning must prioritize adequate and safe access to recovery capital and timely distribution of vaccines to people struggling with SUDs.

Effects of an MRI Try Without program on patient access

BACKGROUND

Pediatric patients are often sedated for magnetic resonance imaging (MRI) scans to ensure images are of diagnostic quality. However, access time for MRIs requiring sedation is often long due to high patient volumes and limited sedation resources.

OBJECTIVE

This study examined the effectiveness of an MRI Try Without sedation program to decrease the wait time for obtaining an MRI while simultaneously ensuring diagnostic-quality images.

MATERIALS AND METHODS

A retrospective chart review was performed on subjects who utilized the MRI Try Without program from April 2014 through June 2015 at a dedicated pediatric institution. Child life specialist preparations and access time (i.e. time from exam ordered to exam completed) were recorded in each patient's electronic medical record. MRI images were evaluated for image quality by a pediatric neuroradiologist.

RESULTS

A total of 134 patients participated in the MRI Try Without program (mean age: 6.9±1.7 years), all of whom received interventions from a child life specialist. The average number of days between when the order was placed and when the MRI was completed using the MRI Try Without program was 15.4±18.5 days, while the third-available appointment for sedation/anesthesia was 46.2 days (standard deviation [SD]±15.7 days). Nearly all patients received a "good" or "very good" image quality determination (87.3%) and only 5 (3.8%) patients were recommended for repeat examination for diagnostic-quality images.

CONCLUSION

Utilization of an MRI Try Without sedation program, with child life specialist interventions, decreased the wait time for obtaining an MRI while still providing diagnostic-quality images.

Subject level clustering using a negative binomial model for small transcriptomic studies

Background

Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput ‘omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies.

Results

We have developed a Negative Binomial Model-Based (NBMB) method to cluster samples using a stochastic version of the expectation-maximization algorithm. A simulation study involving various scenarios was completed to compare the performance of NBMB to Gaussian model-based or Gaussian mixture modeling (GMM). NBMB was also applied for the clustering of two RNA-seq studies; type 2 diabetes study (N = 96) and TCGA study of ovarian cancer (N = 295). Simulation results showed that NBMB outperforms GMM applied with different transformations in majority of scenarios with limited sample size. Additionally, we found that NBMB outperformed GMM for small clusters distance regardless of sample size. Increasing total number of genes with fixed proportion of differentially expressed genes does not change the outperformance of NBMB, but improves the overall performance of GMM. Analysis of type 2 diabetes and ovarian cancer tumor data with NBMB found good agreement with the reported disease subtypes and the gene expression patterns. This method is available in an R package on CRAN named NB.MClust.

Conclusion

Use of Negative Binomial model based clustering is advisable when clustering over dispersed RNA-seq count data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2556-9) contains supplementary material, which is available to authorized users.

Lipoprotein (a): Examination of Cardiovascular Risk in a Pediatric Referral Population

Atherosclerotic cardiovascular disease (CVD), a leading cause of death globally, has origins in childhood. Major risk factors include family history of premature CVD, dyslipidemia, diabetes mellitus, and hypertension. Lipoprotein (a) [Lp(a)], an inherited lipoprotein, is associated with premature CVD, but its impact on cardiovascular health during childhood is less understood. The objective of the study was to examine the relationship between Lp(a), family history of premature CVD, dyslipidemia, and vascular function and structure in a high-risk pediatric population. This is a single-center, cross-sectional study of 257 children referred to a preventive cardiology clinic. The independent variable, Lp(a), separated children into high-Lp(a) [Lp(a) ≥ 30 mg/dL] and normal-Lp(a) groups [Lp(a) < 30 mg/dL]. Dependent variables included family history of premature CVD; dyslipidemia, defined as low-density lipoprotein cholesterol > 130 mg/dL, high-density lipoprotein cholesterol (HDL-C) < 45 mg/dL, triglycerides (TG) > 100 mg/dL; and vascular changes suggesting early atherosclerosis, as measured by carotid-femoral pulse wave velocity (PWV) and carotid artery intima-media thickness (CIMT). Of the 257 children, 110 (42.8%) had high Lp(a) and 147 (57.2%) had normal Lp(a). There was a higher prevalence of African-American children in the high-Lp(a) group (19.3%) compared to the normal-Lp(a) group (2.1%) (p < 0.001). High Lp(a) was associated with positive family history of premature CVD (p = 0.03), higher-than-optimal HDL-C (p = 0.02), and lower TG (p < 0.001). There was no difference in PWV or CIMT between groups. High Lp(a) in children is associated with family history of premature CVD and is prevalent in African-American children. In children with high Lp(a), promotion of intensive lifestyle modifications is prudent to decrease premature CVD-related morbidity.

Adherence With Multiple National Healthy Lifestyle Recommendations in a Large Pediatric Center Electronic Health Record and Reduced Risk of Obesity

OBJECTIVE

To evaluate the utility of a routine assessment of lifestyle behaviors incorporated into the electronic health record (EHR) to quantify lifestyle practices and obesity risk at a pediatric primary care center.

PATIENTS AND METHODS

Participants included 24,255 patients aged 2 to 18 years whose parent/caregiver completed a self-report lifestyle assessment during a well-child examination (January 1, 2013, through June 30, 2016). Cross-sectional analyses of age, race/ethnicity, body mass index, and lifestyle assessment responses were performed. Outcome measures included prevalence of patients meeting consensus recommendations for physical activity; screen time; and dairy, water, and fruit/vegetable consumption and the odds of obesity based on reported lifestyle behaviors.

RESULTS

Prevalence of meeting recommendations for lifestyle behaviors was highest for physical activity (84%), followed by screen time (61%) and consumption of water (51%), dairy (27%), and fruits/vegetables (10%). Insufficient physical activity was the strongest predictor of obesity (odds ratio [OR], 1.65; 95% CI, 1.51-1.79), followed by excess screen time (OR, 1.36; 95% CI, 1.27-1.45). Disparities existed across ages, races/ethnicities, and sexes for multiple lifestyle habits. Youth who met 0 or 1 lifestyle recommendation were 1.45 to 1.71 times more likely to have obesity than those meeting all 5 recommendations.

CONCLUSION

Healthy behaviors vary in prevalence, as does their association with obesity. This variation is partially explained by age, sex, and race/ethnicity. Meeting national recommendations for specific behaviors is negatively associated with obesity in a dose-dependent manner. These findings support the assessment of lifestyle behaviors in primary care as one component of multilevel initiatives to prevent childhood obesity.

Assessment of data transformations for model-based clustering of RNA-Seq data

Quality control, global biases, normalization, and analysis methods for RNA-Seq data are quite different than those for microarray-based studies. The assumption of normality is reasonable for microarray based gene expression data; however, RNA-Seq data tend to follow an over-dispersed Poisson or negative binomial distribution. Little research has been done to assess how data transformations impact Gaussian model-based clustering with respect to clustering performance and accuracy in estimating the correct number of clusters in RNA-Seq data. In this article, we investigate Gaussian model-based clustering performance and accuracy in estimating the correct number of clusters by applying four data transformations (i.e., naïve, logarithmic, Blom, and variance stabilizing transformation) to simulated RNA-Seq data. To do so, an extensive simulation study was carried out in which the scenarios varied in terms of: how genes were selected to be included in the clustering analyses, size of the clusters, and number of clusters. Following the application of the different transformations to the simulated data, Gaussian model-based clustering was carried out. To assess clustering performance for each of the data transformations, the adjusted rand index, clustering error rate, and concordance index were utilized. As expected, our results showed that clustering performance was gained in scenarios where data transformations were applied to make the data appear "more" Gaussian in distribution.