ALBERT

Description: Introduction: The Patient Reported Outcomes Measurement Information System (PROMIS) includes tools to assess pain interference and pain behavior for pediatric patients. These domains are especially relevant for children with sickle cell disease who have recurring pain. Though these domains have been shown to be reliable and valid in this patient population, their adoption in practice is significantly limited by lack of clinical interpretation of the scores. The objective of this study was to add clinical meaning to the self-reported pain inference and pain behavior scores for children with sickle cell disease. Methods: We recruited a convenience sample of children with sickle cell disease, 8-17 years of age at Children's Hospital of Wisconsin to complete PROMIS surveys. The pediatric PROMIS measures are scored on a T-score metric with a mean of 50 and standard deviation of 10, where 50 represents the mean of the pediatric sample in which the item response theory parameters for the measures were estimated. The PROMIS measures for pain interference and pain behavior use a Likert response scale and have a one week recall period. The Likert scale responses were characterized into three groups to support clinical interpretation of PROMIS scores. Patients who reported "never" or "almost never" for all items on the pain interference domain were considered to be having "no or minor pain". Patients who reported "often" or "almost always" for all items were considered to be having "substantial" pain. All other patients were classified as having "mixed" pain interference. Similarly, in the pain behavior domain, patients were categorized as having 'no or minor' (response to all items: "had no pain", "never", "almost never"), or 'substantial' (response to all items: "often" and "almost always") pain behavior, with remaining considered as having mixed pain behavior. For the domains of pain interference and pain behavior, higher scores mean more impairment. The range of scores for patients with no or minor, mixed and substantial problems on the measures were used to determine the clinical thresholds to identify those with mild, moderate and severe symptoms. The thresholds were validated by comparing the distribution of patients who needed to take pain medications in the past 7 days among the groups of patients with mild, moderate and severe symptoms, using Chi-square tests and adjusting for multiple pairwise comparisons. Results: Our study included 115 eligible children (mean (sd) age = 11.8 (2.8) years), of which 54% were females and 95% were African Americans. For pain interference, there were 36 patients reporting no or minor issues, 16 had substantial issues and the remaining 62 were considered to have mixed pain interference (T-score was missing for one patient). Based on the T-score ranges of these groups, the clinical thresholds of mild and severe pain interference were set to be ≤ 48.3 and ≥ 63.6 respectively. For the domain of pain behavior, 30 children reported having no or minor problems, 6 had substantial problems and the remaining 79 endorsed mixed responses on the items. The range-based thresholds for mild and severe pain behavior were similar at ≤ 41.3 and ≥ 57.3 respectively. Figure 1 shows an example of the distribution of item-level response for one item within the severity groups. There were significant differences in the proportion of patients needing to take pain medications at home in the prior 7 days among those with mild, moderate and severe pain interference (% patients needing pain medications, Mild= 7%; Moderate = 28%; Severe = 44%; p = 0.0095) and pain behavior (% patients needing pain medications, Mild= 6%; Moderate = 32%; Severe = 30%; p = 0.0013). The pairwise comparisons indicate that there were significant differences in proportion of patients needing pain medications between the mild and the moderate/severe groups. However, there were no significant differences in pain medication use between the moderate and severe patients. Conclusions: We show that T-scores ≤ 48.3 and ≤ 41.3 on the respective pain interference and pain behavior domains serve as thresholds for children with no or only mild pain. Whereas children with severe pain have T-scores ≥ 63.6 and ≥ 57.3 on pain interference and pain behavior domains respectively. This facilitates the clinical interpretation of PROMIS pain interference and pain behavior scores for children with sickle cell disease. Disclosures Panepinto: National Institute of Arthritis and Musculoskeletal and Skin Diseases: Research Funding; Health Resources and Services Administration: Research Funding.

Description: Flying ad hoc networks (FANETs) are a collection of unmanned aerial vehicles that communicate without any predefined infrastructure. FANET, being one of the most researched topics nowadays, finds its scope in many complex applications like drones used for military applications, border surveillance systems and other systems like civil applications in traffic monitoring and disaster management. Quality of service (QoS) performance parameters for routing e.g. delay, packet delivery ratio, jitter and throughput in FANETs are quite difficult to improve. Mobility models play an important role in evaluating the performance of the routing protocols. In this paper, the integration of two selected mobility models, i.e. random waypoint and Gauss–Markov model, is implemented. As a result, the random Gauss integrated model is proposed for evaluating the performance of AODV (ad hoc on-demand distance vector), DSR (dynamic source routing) and DSDV (destination-Sequenced distance vector) routing protocols. The simulation is done with an NS2 simulator for various scenarios by varying the number of nodes and taking low- and high-node speeds of 50 and 500, respectively. The experimental results show that the proposed model improves the QoS performance parameters of AODV, DSR and DSDV protocol.

Description: Objective: Published literature shows low and variable compliance with the recommended guidelines for care of children with sickle cell disease (SCD). Our objective was to use an electronic health record (EHR)-based registry to track and improve compliance with the recommendations for the pediatric population of SCD at our institution, using annual transcranial doppler screening (TCD) as an example. Methods: We developed an in-EPIC real time registry for children with SCD in the year 2016 to enhance point of care and improve quality. Since 2017, we have been conducting plan-do-study-act (PDSA) cycles using the registry to improve TCD compliance. We extract data quarterly to examine TCD rates and share with the clinical team the list of children who have not received a TCD screening in the past 18 months. This allows the clinical team to provide case-management to reduce non-compliance. We recently added Child Life support to increase TCD compliance among children

Description: Sickle cell disease (SCD) is an inherited hemoglobinopathy that can affect nearly every organ system. Individuals living with SCD are at high risk of developing serious infections which can further trigger disease related complications and attribute additional morbidity and mortality. In light of the evolving pandemic caused by SARS-CoV-2, the causative agent of COVID-19 disease, and the potential for future infectious disease epidemics, it is important to understand the impact that COVID-19 has on hospitalization rates and mortality in this medically vulnerable population. The objective of this study was to describe hospitalization and case fatality rates secondary to COVID-19 among individuals living with SCD in different age groups and compare these to the general population. The Medical College of Wisconsin established the international SECURE-SCD Registry to collect data on pediatric and adult COVID-19 infections in individuals living with SCD. Providers are instructed to report confirmed COVID-19 cases to the registry after sufficient time has passed to observe the disease course through resolution of acute illness and/or death. For each case, providers complete a short form that includes the following data: patient demographics, COVID-19 related hospitalization, COVID-19 severity/management strategies, if the patient died due to COVID, and other information about SCD complications. Data are de-identified and without protected health information to facilitate rapid and increased reporting. We calculated the hospitalization rate and case fatality rate for individuals with SCD by specific age group and contrasted it with the rates publicly available for the general Black population. We utilized data from California Department of Public Health for case fatality rate comparison in Blacks and data from COVID-NET for hospitalization rate comparison. We used indirect age adjustment to calculate standardized mortality ratios using COVID-19 data from California state as the reference population. As of July 17th 2020, 218 cases of COVID-19 in Blacks with SCD in the US were reported to the registry. There was a slight predominance of females (52.8%) and 32.1% of reported cases were patients 18 years and under. There were 15 deaths reported with overall mortality rate of 6.9%. Figure 1 shows the distribution of cases and deaths by age group and gender. Mortality rate in SCD patients was highest in the 50-64 years age group (23.1%) in contrast to mortality rate peaks seen in the general population in patients older than 80 years (Table 1). Young adult SCD patients aged 18-34 years had a case fatality rate of 3.3% and those aged 34-50 years had a rate of 14.9%. California Department of Public Health report case fatality rates for Blacks are less than 1% in both of these comparative age groups. Age-standardized mortality ratio shows that individuals with SCD are 7.7 times more likely to die due to COVID-19 infection compared to the general population. The overall hospitalization rate in individuals with SCD was 72.5% and 18.8% of reported hospitalized cases were children. Among hospitalized adults with SCD, stratification by age showed that 85% were aged 18-49, whereas only 25.7% of people 18-49 years in the general Black population were hospitalized (Table 2). Our findings show that individuals with SCD who have COVID-19 infection have higher rates of death due to COVID-19 than the general Black population. Also, a large proportion of COVID hospitalization for the SCD population occurs among the younger age group. Further analysis is planned to examine effects of underlying comorbidities and prior SCD-associated complications on the severity of COVID-19 in individuals with SCD. Disclosures Mucalo: NIH/NHLBI: Research Funding; NIH/NINDS: Research Funding. Brandow:Greater Milwaukee Foundation: Research Funding; NIH / NHLBI: Research Funding. Panepinto:HRSA: Research Funding; NINDS: Research Funding; NINDS: Research Funding; NHLBI: Research Funding.

Description: Introduction: By August 1, 2020 in the United States, more than 3 million cases of Coronavirus disease 2019 (COVID-19) had been reported with more than 150,000 deaths due to this disease. Growing evidence suggests that individuals with the pre-existing conditions of hypertension, diabetes, cardiovascular disease and obesity are at a higher risk of more serious COVID-19 illness. However, the impact of COVID-19 on individuals with sickle cell disease and sickle cell trait as compared to those without sickle cell disease or trait is not known. The objective of this study was to determine the rate of hospitalization, disease symptoms and deaths due to COVID-19, in patients with sickle cell disease and sickle cell trait compared to Blacks without sickle cell disease or trait. Methods: We leveraged existing electronic health record (EHR) data from multiple sites that contribute data to a research network, TriNetX. TriNetX query platform was used to identify patients with COVID-19 infection based on ICD diagnoses codes or a positive COVID-19 result from a nucleic acid amplification with probe-based detection test, present any time after January 20, 2020 (this is when the first COVID-19 case was detected in the United States) within the patients' EHR data. We report rates of specific COVID-19 related outcomes among individuals with sickle cell disease and trait, calculated as % of patients in cohort with the particular outcome. Our outcomes of interest included COVD-19 related symptoms, hospitalization, and death, which occurred within 2 weeks of COVID diagnosis. We used propensity score matching (greedy nearest-neighbor matching algorithm with a caliper of 0.1 pooled standard deviations) to create balanced cohorts for comparing outcomes between individuals with sickle cell disease or trait and Blacks without sickle cell disease or trait. Risk ratios and risk differences are reported along with 95% confidence intervals. Given multiple outcomes of interest, we considered a more stringent two-sided alpha of less than