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Paid family and medical leave (FML) has significant benefits to organizations, including improvements in employee recruitment and retention, workplace culture, and employee morale and productivity, and is supported by evidence for overall cost savings. Furthermore, paid FML related to childbirth has significant benefits to individuals and families, including but not limited to improved maternal and infant health outcomes and improved breastfeeding initiation and duration. In the case of nonchildbearing parental leave, paid FML is associated with more equitable long-term division of household labor and childcare. Paid FML is increasingly being recognized as an important issue in medicine, as evidenced by the recent passage of policies by national societies and governing bodies, including the American Board of Medical Specialties, American Board of Radiology, Accreditation Council for Graduate Medical Education (ACGME), American College of Radiology, and American Medical Association. Implementation of paid FML requires adherence to federal, state, and local laws as well as institutional requirements. Specific requirements pertain to trainees from national governing bodies, such as the ACGME and medical specialty boards. Flexibility, work coverage, culture, and finances are additional considerations for ensuring an optimal paid FML policy that accounts for concerns of all impacted individuals.

BACKGROUND. CT-based body composition (BC) measurements have historically been too resource intensive to analyze for widespread use and have lacked robust comparison with traditional weight metrics for predicting cardiovascular risk. OBJECTIVE. The aim of this study was to determine whether BC measurements obtained from routine CT scans by use of a fully automated deep learning algorithm could predict subsequent cardiovascular events independently from weight, BMI, and additional cardiovascular risk factors. METHODS. This retrospective study included 9752 outpatients (5519 women and 4233 men; mean age, 53.2 years; 890 patients self-reported their race as Black and 8862 self-reported their race as White) who underwent routine abdominal CT at a single health system from January 2012 through December 2012 and who were given no major cardiovascular or oncologic diagnosis within 3 months of undergoing CT. Using publicly available code, fully automated deep learning BC analysis was performed at the L3 vertebral body level to determine three BC areas (skeletal muscle area [SMA], visceral fat area [VFA], and subcutaneous fat area [SFA]). Age-, sex-, and race-normalized reference curves were used to generate z scores for the three BC areas. Subsequent myocardial infarction (MI) or stroke was determined from the electronic medical record. Multivariable-adjusted Cox proportional hazards models were used to determine hazard ratios (HRs) for MI or stroke within 5 years after CT for the three BC area z scores, with adjustment for normalized weight, normalized BMI, and additional cardiovascular risk factors (smoking status, diabetes diagnosis, and systolic blood pressure). RESULTS. In multivariable models, age-, race-, and sex-normalized VFA was associated with subsequent MI risk (HR of highest quartile compared with lowest quartile, 1.31 [95% CI, 1.03-1.67], p = .04 for overall effect) and stroke risk (HR of highest compared with lowest quartile, 1.46 [95% CI, 1.07-2.00], p = .04 for overall effect). In multivariable models, normalized SMA, SFA, weight, and BMI were not associated with subsequent MI or stroke risk. CONCLUSION. VFA derived from fully automated and normalized analysis of abdominal CT examinations predicts subsequent MI or stroke in Black and White patients, independent of traditional weight metrics, and should be considered an adjunct to BMI in risk models. CLINICAL IMPACT. Fully automated and normalized BC analysis of abdominal CT has promise to augment traditional cardiovascular risk prediction models.