Smartphone Bolus Feature Increases Number of Insulin Boluses in People With Low Bolus Frequency.

BACKGROUND: The t:connect mobile app from Tandem Diabetes Care recently added a feature to allow t:slim X2 insulin pump users to initiate an insulin bolus from their personal smartphone. User experience and user interface considerations prioritized safety and ease of use, and we examined whether the smartphone bolus feature changed bolus behavior in individuals who bolused less than three times/day. METHODS: We performed a retrospective analysis of t:slim X2 insulin pump users in the United States who had remotely updated their insulin pump software to be compatible with the smartphone bolus version of the app and who gave less than three boluses per day prior to the smartphone bolus update. RESULTS: Of the 4470 early adopters who met these criteria, the median number of boluses was 2.2 per day (prior to smartphone bolus update) versus 2.7 per day (after smartphone bolus update), equating to approximately half a bolus more delivered per day (P < .001). Overall, a median of one bolus per day was administered by smartphone app as opposed to being initiated from the screen on the insulin pump. CONCLUSION: This analysis found a significant increase in bolusing behavior among early adopters of the smartphone bolus feature of the t:connect mobile app.

Investigating insomnia in United States deployed military forces: A topic modeling approach.

STUDY OBJECTIVES: This retrospective study analyzed free-text clinical notes from medical encounters for insomnia among a sample of deployed US military personnel. Topic modeling, a natural language processing technique, was used to identify thematic patterns in the clinical notes that were potentially related to insomnia diagnosis. METHODS: Clinical notes of patient clinical encounters coded for insomnia from the US Department of Defense Military Health System Theater Medical Data Store were analyzed. Following preprocessing of the free text in the clinical notes, topic modeling was employed to identify relevant underlying topics or themes in 32,864 unique patients. The machine-learned topics were validated using human-coded potential insomnia etiological issues. RESULTS: A 12-topic model was selected based on quantitative metrics, interpretability, and coherence of terms comprising topics. The topics were assigned the following labels: personal/family history, stimulants, stress, family/relationships, other sleep disorders, depression, schedule/environment, anxiety, other medication, headache/concussion, pain, and medication refill. Validation of these topics (excluding the two medication topics) against their corresponding human-coded potential etiological issues showed strong agreement for the assessed topics. CONCLUSIONS: Analysis of free-text clinical notes using topic modeling resulted in the identification of thematic patterns that largely mirrored known correlates of insomnia. These findings reveal multiple potential etiologies for deployment-related insomnia. The identified topics may augment electronic health record diagnostic codes and provide valuable information for sleep researchers and providers. As both civilian and military healthcare systems implement electronic health records, topic modeling may be a valuable tool for analyzing free-text data to investigate health outcomes.

Cellular Heterogeneity of Activated Primary Human Macrophages and Associated Drug-Gene Networks: From Biology to Precision Therapeutics.

BACKGROUND: Interferon-γ (IFNγ) signaling plays a complex role in atherogenesis. IFNγ stimulation of macrophages permits in vitro exploration of proinflammatory mechanisms and the development of novel immune therapies. We hypothesized that the study of macrophage subpopulations could lead to anti-inflammatory interventions. METHODS: Primary human macrophages activated by IFNγ (M(IFNγ)) underwent analyses by single-cell RNA sequencing, time-course cell-cluster proteomics, metabolite consumption, immunoassays, and functional tests (phagocytic, efferocytotic, and chemotactic). RNA-sequencing data were analyzed in LINCS (Library of Integrated Network-Based Cellular Signatures) to identify compounds targeting M(IFNγ) subpopulations. The effect of compound BI-2536 was tested in human macrophages in vitro and in a murine model of atherosclerosis. RESULTS: Single-cell RNA sequencing identified 2 major clusters in M(IFNγ): inflammatory (M(IFNγ)i) and phagocytic (M(IFNγ)p). M(IFNγ)i had elevated expression of inflammatory chemokines and higher amino acid consumption compared with M(IFNγ)p. M(IFNγ)p were more phagocytotic and chemotactic with higher Krebs cycle activity and less glycolysis than M(IFNγ)i. Human carotid atherosclerotic plaques contained 2 such macrophage clusters. Bioinformatic LINCS analysis using our RNA-sequencing data identified BI-2536 as a potential compound to decrease the M(IFNγ)i subpopulation. BI-2536 in vitro decreased inflammatory chemokine expression and secretion in M(IFNγ) by shrinking the M(IFNγ)i subpopulation while expanding the M(IFNγ)p subpopulation. BI-2536 in vivo shifted the phenotype of macrophages, modulated inflammation, and decreased atherosclerosis and calcification. CONCLUSIONS: We characterized 2 clusters of macrophages in atherosclerosis and combined our cellular data with a cell-signature drug library to identify a novel compound that targets a subset of macrophages in atherosclerosis. Our approach is a precision medicine strategy to identify new drugs that target atherosclerosis and other inflammatory diseases.

Research Letter: Prevalence of Spine Injuries Among US Military Personnel With Combat-Related Concussion.

OBJECTIVE: To describe the prevalence of spine injuries among US service members with combat-related concussion. DESIGN AND PARTICIPANTS: A retrospective review of medical records for US service members injured during combat operations in Iraq and Afghanistan between 2002 and 2020. The study sample included 27 897 service members categorized into 3 groups: concussion with loss of consciousness (LOC, n = 4631), concussion non-LOC ( n = 5533), and non-concussion ( n = 17 333). MAIN MEASURES: Spine injuries were identified by International Classification of Diseases, Ninth Revision, Clinical Modification ( ICD-9-CM ) codes and classified by body region and nature of injury using the Barell injury diagnosis matrix. Differences in prevalence of spine injuries by concussion group were evaluated using χ 2 tests. RESULTS: Spine injuries were most prevalent among service members with concussion LOC (31.1%), followed by concussion non-LOC (18.3%), and non-concussion (10.0%, P < .001). Sprains and strains were the most prevalent spine injury category, with injuries to the cervical, thoracic, and lumbar regions significantly more prevalent in the concussion groups ( P values < .001), particularly individuals with LOC compared with non-concussion. CONCLUSION: The US military personnel with combat-related concussion, especially individuals with LOC, may also have spine injuries. Routine assessment for spine injury is recommended during concussion screening because this may impact clinical management and rehabilitation.

Combat injury profiles among U.S. military personnel who survived serious wounds in Iraq and Afghanistan: A latent class analysis.

BACKGROUND: The U.S. military conflicts in Iraq and Afghanistan had the most casualties since Vietnam with more than 53,000 wounded in action. Novel injury mechanisms, such as improvised explosive devices, and higher rates of survivability compared with previous wars led to a new pattern of combat injuries. The purpose of the present study was to use latent class analysis (LCA) to identify combat injury profiles among U.S. military personnel who survived serious wounds. METHODS: A total of 5,227 combat casualty events with an Injury Severity Score (ISS) of 9 or greater that occurred in Iraq and Afghanistan from December 2002 to July 2019 were identified from the Expeditionary Medical Encounter Database for analysis. The Barell Injury Diagnosis Matrix was used to classify injuries into binary variables by site and type of injury. LCA was employed to identify injury profiles that accounted for co-occurring injuries. Injury profiles were described and compared by demographic, operational, and injury-specific variables. RESULTS: Seven injury profiles were identified and defined as: (1) open wounds (18.8%), (2) Type 1 traumatic brain injury (TBI)/facial injuries (14.2%), (3) disseminated injuries (6.8%), (4) Type 2 TBI (15.4%), (5) lower extremity injuries (19.8%), (6) burns (7.4%), and (7) chest and/or abdominal injuries (17.7%). Profiles differed by service branch, combat location, year of injury, injury mechanism, combat posture at the time of injury, and ISS. CONCLUSION: LCA identified seven distinct and interpretable injury profiles among U.S. military personnel who survived serious combat injuries in Iraq or Afghanistan. These findings may be of interest to military medical planners as resource needs are evaluated and projected for future conflicts, and medical professionals involved in the rehabilitation of wounded service members.

Symptom profiles following combat injury and long-term quality of life: a latent class analysis.

PURPOSE: The purpose of this study was to identify symptom profiles among U.S. military personnel within 1 year after combat injury and assess the relationship between the symptom profiles and long-term quality of life (QoL). METHODS: The study sample consisted of 885 military personnel from the Expeditionary Medical Encounter Database who completed (1) a Post-Deployment Health Assessment (PDHA) within 1 year following combat injury in Iraq or Afghanistan, and (2) a survey for the Wounded Warrior Recovery Project (WWRP), a longitudinal study tracking patient-reported outcomes (e.g., QoL) in injured military personnel. Fifteen self-reported symptoms from the PDHA were assessed using latent class analysis to develop symptom profiles. Multivariable linear regression assessed the predictive effect of symptom profiles on QoL using the physical (PCS) and mental (MCS) component summary scores from the 36-Item Short Form Survey included in the WWRP. Time between PDHA and WWRP survey ranged from 4.3 to 10.5 years (M = 6.6, SD = 1.3). RESULTS: Five distinct symptom profiles were identified: low morbidity (50.4%), multimorbidity (15.6%), musculoskeletal (14.0%), psycho-cognitive (11.1%), and auditory (8.9%). Relative to low morbidity, the multimorbidity (ß = - 5.45, p < 0.001) and musculoskeletal (ß = - 4.23, p < 0.001) profiles were associated with lower PCS, while the multimorbidity (ß = - 4.25, p = 0.002) and psycho-cognitive (ß = - 3.02, p = 0.042) profiles were associated with lower MCS. CONCLUSION: Multimorbidity, musculoskeletal, and psycho-cognitive symptom profiles were the strongest predictors of lower QoL. These profiles can be employed during screening to identify at-risk service members and assist with long-term clinical planning, while factoring in patient-specific impairments and preferences.

Multi-Injury Casualty Stream Simulation in a Shipboard Combat Environment.

Accurate forecasts of casualty streams are essential for estimating personnel and materiel requirements for future naval combat engagements. The scarcity of recent naval combat data makes accurate forecasting difficult. Furthermore, current forecasts are based on single injuries only, even though empirical evidence indicates most battle casualties suffer multiple injuries. These anticipated single-injury casualty streams underestimate the needed medical resources. This article describes a method of simulating realistic multi-injury casualty streams in a maritime environment by combining available shipboard data with ground combat blast data. The simulations, based on the Military Combat Injury Scale, are expected to provide a better tool for medical logistics planning.

Combat injury coding: a review and reconfiguration.

BACKGROUND: The current civilian Abbreviated Injury Scale (AIS), designed for automobile crash injuries, yields important information about civilian injuries. It has been recognized for some time, however, that both the AIS and AIS-based scores such as the Injury Severity Score (ISS) are inadequate for describing penetrating injuries, especially those sustained in combat. Existing injury coding systems do not adequately describe (they actually exclude) combat injuries such as the devastating multi-mechanistic injuries resulting from attacks with improvised explosive devices (IEDs). METHODS: After quantifying the inapplicability of current coding systems, the Military Combat Injury Scale (MCIS), which includes injury descriptors that accurately characterize combat anatomic injury, and the Military Functional Incapacity Scale (MFIS), which indicates immediate tactical functional impairment, were developed by a large tri-service military and civilian group of combat trauma subject-matter experts. Assignment of MCIS severity levels was based on urgency, level of care needed, and risk of death from each individual injury. The MFIS was developed based on the casualty's ability to shoot, move, and communicate, and comprises four levels ranging from "Able to continue mission" to "Lost to military." Separate functional impairments were identified for injuries aboard ship. Preliminary evaluation of MCIS discrimination, calibration, and casualty disposition was performed on 992 combat-injured patients using two modeling processes. RESULTS: Based on combat casualty data, the MCIS is a new, simpler, comprehensive severity scale with 269 codes (vs. 1999 in AIS) that specifically characterize and distinguish the many unique injuries encountered in combat. The MCIS integrates with the MFIS, which associates immediate combat functional impairment with minor and moderate-severity injuries. Predictive validation on combat datasets shows improved performance over AIS-based tools in addition to improved face, construct, and content validity and coding inter-rater reliability. Thus, the MCIS has greater relevance, accuracy, and precision for many military-specific applications. CONCLUSION: Over a period of several years, the Military Combat Injury Scale and Military Functional Incapacity Scale were developed, tested and validated by teams of civilian and tri-service military expertise. MCIS shows significant promise in documenting the nature, severity and complexity of modern combat injury.