Digitizing U.S. Air Force Medical Standards for the Creation and Validation of a Readiness Decision Support System.

INTRODUCTION: Deployment-limiting medical conditions are the primary reason why service members are not medically ready. Service-specific standards guide clinicians in what conditions are restrictive for duty, fitness, and/or deployment requirements. The Air Force (AF) codifies most standards in the Medical Standards Directory (MSD). Providers manually search this document, among others, to determine if any standards are violated, a tedious and error-prone process. Digitized, standards-based decision-support tools for providers would ease this workflow. This study digitized and mapped all AF occupations to MSD occupational classes and all MSD standards to diagnosis codes and created and validated a readiness decision support system (RDSS) around this mapping. MATERIALS AND METHODS: A medical coder mapped all standards within the May 2018 v2 MSD to 2018 International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. For the publication of new MSDs, we devised an automated update process using Amazon Web Service's Comprehend Medical and the Unified Medical Language System's Metathesaurus. We mapped Air Force Specialty Codes to occupational classes using the MSD and AF classification directories. We uploaded this mapping to a cloud-based MySQL (v5.7.23) database and built a web application to interface with it using R (v3.5+). For validation, we compared the RDSS to the record review of two subject-matter experts (SMEs) for 200 outpatient encounters in calendar year 2018. We performed four separate analyses: (1) SME vs. RDSS for any restriction; (2) SME interrater reliability for any restriction; (3) SME vs. RDSS for specific restriction(s); and (4) SME interrater reliability for categorical restriction(s). This study was approved as "Not Human Subjects Research" by the Air Force Research Laboratory (FWR20190100N) and Boston Children's Hospital (IRB-P00031397) review boards. RESULTS: Of the 709 current medical standards in the September 2019 MSD, 631 (89.0%) were mapped to ICD-10-CM codes. These 631 standards mapped to 42,810 unique ICD codes (59.5% of all active 2019 codes) and covered 72.3% (7,823/10,821) of the diagnoses listed on AF profiles and 92.8% of profile days (90.7/97.8 million) between February 1, 2007 and January 31, 2017. The RDSS identified diagnoses warranting any restrictions with 90.8% and 90.0% sensitivity compared to SME A and B. For specific restrictions, the sensitivity was 85.0% and 44.8%. The specificity was poor for any restrictions (20.5%-43.4%) and near perfect for specific restrictions (99.5+%). The interrater reliability between SMEs for all comparisons ranged from minimal to moderate (κ = 0.33-0.61). CONCLUSION: This study demonstrated key pilot steps to digitizing and mapping AF readiness standards to existing terminologies. The RDSS showed one potential application. The sensitivity between the SMEs and RDSS demonstrated its viability as a screening tool with further refinement and study. However, its performance was not evenly distributed by special duty status or for the indication of specific restrictions. With machine consumable medical standards integrated within existing digital infrastructure and clinical workflows, RDSSs would remove a significant administrative burden from providers and likely improve the accuracy of readiness metrics.

Variations in the incidence and burden of illnesses and injuries among non-retiree service members in the earliest, middle, and last 6 months of their careers, active component, U.S. Armed Forces, 2000-2015.

This report uses routinely collected data in the Defense Medical Surveillance System (DMSS) to characterize the incidence and burden of medical conditions throughout the careers of service members separating from the active component of the U.S. Armed Forces between 1 October 2014 and 30 September 2015. Three surveillance periods between 30 September 2000 and 30 September 2015 were defined by each individual's time in service: early (first 6 months), middle (middle 6 months), and late (last 6 months). Overall, separating service members were most often aged 25-34 years (59.4%), male (84.0%), non-Hispanic white (64.0%), junior enlisted (52.4%), in the Marine Corps (33.1%), serving in a repair/engineering occupation (33.0%), and had never deployed (52.5%). The top five burden of disease categories across surveillance periods by sex were remarkably similar, including mental health disorders, which exhibited similar upward trends across the three surveillance periods (males: 1.3%, 17.0%, and 35.6%; females: 1.8%, 15.1%, and 32.4%, respectively). The most common diagnoses exhibiting upward, downward, or bimodal trends by incidence rate differences were mental health disorders, respiratory infections/diseases, and musculoskeletal diseases, respectively.

Association of Sickle Cell Trait and Hemoglobin S Percentage with Physical Fitness.

PURPOSE: This study aimed to determine the association between sickle cell trait (SCT) as a binary variable and hemoglobin S percentage as a stratified categorical variable with aerobic and anaerobic fitness. METHODS: This retrospective cohort study included all recruits who entered US Air Force Basic Training between January 2009 and December 2014. Fitness parameters among recruits with and without SCT were compared using a standardized fitness assessment of a 1.5-mile timed run, 1 min of push-ups, and 1 min of sit-ups. Performance was further compared by stratifying those with SCT by their hemoglobin S percentage (20%-29.99%, 30%-39.99%, and ≥40%). RESULTS: Of all recruits (N = 210,461) who entered training during the surveillance period, 2161 (1.0%) had SCT. After adjusting for age, sex, race, body mass index, and ambient temperature while conducting the fitness assessment, recruits with SCT were slower on their initial run than their peers without SCT by a mean (standard error) of 9.4 s (2.6 s) (P < 0.001) and completed 0.5 (0.3) fewer push-ups (P < 0.05); sit-up completion was statistically equivalent between the two groups. When retested 6 wk later, recruits with SCT improved their run time by a margin of 4.3 s (2.1 s) over their counterparts without SCT (P < 0.05). Baseline physical fitness was largely consistent across strata of hemoglobin S percentages; increased percentages were modestly correlated with faster run times (R = 0.374) and fewer push-ups (R = 0.339). CONCLUSIONS: As compared with their peers, recruits with SCT had slightly inferior aerobic fitness and similar anaerobic fitness at the outset of basic training, and gaps further narrowed over 6 wk of training. Stratifying recruits by their hemoglobin S percentage did not dramatically change the strength or direction of association.

A de novo MYOC mutation detected in juvenile open angle glaucoma associated with reduced myocilin protein in aqueous humor.

MYOC mutations were originally identified in patients with juvenile open angle glaucoma (JOAG). Cell culture and mouse studies suggest that MYOC mutations cause glaucoma through a dominant-negative effect on myocilin protein secretion. We tested this hypothesis with patient samples in this study. Glaucoma and control patients underwent complete ocular examination. DNA samples from glaucoma patients, unaffected relatives and controls were used for DNA sequencing of MYOC. Aqueous humor (AH) samples from glaucoma and control patients were obtained at the time of surgery. Myocilin protein in AH was detected by quantitative Western blot analysis. A de novo Val251Ala mutation of MYOC was found to segregate with disease in a family with autosomal dominant JOAG. Myocilin protein was detected in all control AH samples but was nearly undetectable in AH samples from a patient heterozygous for the Val251Ala mutation. Our results using human patient samples are consistent with a dominant-negative effect of pathogenic MYOC mutations on myocilin secretion.