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Abstract Background Transcatheter aortic valve replacement (TAVR) continues to be increasingly performed. While machine learning has demonstrated utility in predicting adverse outcomes in various cardiovascular procedures, its effectiveness in predicting in-hospital mortality in TAVR remains uncertain. Purpose We aimed to examine the accuracy of machine learning in predicting in-hospital mortality in TAVR. Methods We searched PubMed and Cochrane databases up to February 2024 for observational studies assessing the diagnostic accuracy of machine learning for predicting in-hospital mortality in TAVR. Extracted data included study characteristics, patient demographics, type of predictive model used, and area under the receiver operating characteristic curves (AUC). Predictive accuracy was assessed by pooling AUC of machine-learning models across studies. AUC values ranging from 0.80 to 0.90, 0.70 to 0.80, 0.60 to 0.70, and 0.50 to 0.60 corresponded to excellent, good, fair, and poor discrimination ability, respectively (1). We used a random effects model to estimate the overall effect size with a 95% confidence interval (CI). Heterogeneity was evaluated with the I2 test.We conducted subgroup analyses based on the type of machine-learning model utilized to assess for any potential differences in predictive accuracy. Results A total of three studies were identified, comprising a study population of 70,714 patients. Across studies, eight machine learning models were utilized, consisting of three artificial neural network models, one logistic regression model, one naive Bayes model, two random forest models, and one support vector machine model. Pooled AUC showed excellent accuracy of machine learning in predicting in-hospital mortality in TAVR (0.92; 95% CI 0.90 - 0.95). Subgroup analyses revealed similar yet excellent accuracy across all types of machine learning models utilized: artificial neural network (0.92; 95% CI 0.86-0.99), logistic regression (0.92; 95% CI 0.89-0.95), naive Bayes (0.90; 95% CI 0.88-0.92), random forest (0.94; 95% CI 0.87-1.00), and support vector machine (0.94; 95% CI 0.91-0.96). Conclusion Our study suggests that utilizing machine learning can accurately predict in-hospital mortality in TAVR. Operators of TAVR procedures may benefit from incorporating machine learning prediction models in their clinical practice. Further exploration of machine learning on outcomes in TAVR is needed to verify our findings.

Objective: To evaluate the reproductive experiences of physicians across gender and specialty. Patients and Methods: Between November and December 2021, we surveyed nontrainee physicians of all genders at a single quaternary institution using a modified version of an existing survey instrument. Experiences with family planning, fertility, pregnancy, and parental leave were assessed. Results: There were 422 completed responses. Respondents reported a higher prevalence of infertility as compared to the general U.S. population (26% versus 19%), with no difference in infertility or obstetrical complications by specialty. Most respondents (75%) reported stigma regarding having children in medicine, and 71% reported delaying childbearing. These trends were strongest in the subanalysis of female respondents. Forty-five percent of respondents reported that their work increased the risk for subfertility, infertility, or pregnancy complications. Surgeons were significantly more likely to report physically demanding work conditions (75% versus 30%, p < 0.001), radiation exposure (39% versus 14%, p < 0.001), and bloodborne pathogen exposure (25% versus 12%, p = 0.03) as occupational reproductive hazards. Only 55% of respondents with a pregnancy history reported ever taking parental leave. Among those who took less than the full amount offered, 63% cited concerns about falling behind educationally or professionally as significantly influencing this decision. Conclusions: These results support previous trends showing delayed childbearing and increased infertility among physicians while shedding new light on stigma associated with childbearing and parental leave. A better understanding of the reproductive experiences of physicians is critical to recruiting and retaining a skilled workforce and fostering career and life satisfaction in this profession.

Importance While the typical onset of multiple sclerosis (MS) occurs in early adulthood, 2% to 10% of cases initially present prior to age 18 years, and approximately 5% after age 50 years. Guidance on approaches to differential diagnosis in suspected MS specific to these 2 age groups is needed. Observations There are unique biological factors in children younger than 18 years and in adults older than age 50 years compared to typical adult-onset MS. These biological differences, particularly immunological and hormonal, may influence the clinical presentation of MS, resilience to neuronal injury, and differential diagnosis. While mimics of MS at the typical age at onset have been described, a comprehensive approach focused on the younger and older ends of the age spectrum has not been previously published. Conclusions and Relevance An international committee of MS experts in pediatric and adult MS was formed to provide consensus guidance on diagnostic approaches and key clinical and paraclinical red flags for non-MS diagnosis in children and older adults.

BACKGROUND: Cardiac troponin (cTn) is key in diagnosing myocardial infarction (MI). After MI, the clinically observed half-life of cTn has been reported to be 7 to 20 hours, but this estimate reflects the combined elimination and simultaneous release of cTn from cardiomyocytes. More precise timing of myocardial injuries necessitates separation of these 2 components. We used a novel method for determination of isolated cTn elimination kinetics in humans. METHODS: Patients with MI were included within 24 hours after revascularization and underwent plasmapheresis to obtain plasma with a high cTn concentration. After at least 3 weeks, patients returned for an autologous plasma retransfusion followed by blood sampling for 8 hours. cTn was measured with 5 different high-sensitivity cTn assays. RESULTS: Of 25 included patients, 20 participants (mean age, 64.5 years; SD, 8.2 years; 4 women [20%]) received a retransfusion after a median of 5.8 weeks (interquartile range, 5.0–6.9 weeks) after MI. After retransfusion of a median of 620 mL (range, 180–679 mL) autologous plasma, the concentration of cTn in participants’ blood increased 4 to 445 times above the upper reference level of the 5 high-sensitivity cTn assays. The median elimination half-life ranged from 134.1 minutes (95% CI, 117.8–168.0) for the Elecsys high-sensitivity cTnT assay to 239.7 minutes (95% CI, 153.7–295.1) for the Vitros high-sensitivity cTnI assay. The median clearance of cTnI ranged from 40.3 mL/min (95% CI, 32.0–44.9) to 52.7 mL/min (95% CI, 42.2–57.8). The clearance of cTnT was 77.0 mL/min (95% CI, 45.2–95.0). CONCLUSIONS: This novel method showed that the elimination half-life of cTnI and cTnT was 5 to 16 hours shorter than previously reported. This indicates a considerably longer duration of cardiomyocyte cTn release after MI than previously thought. Improved knowledge of timing of myocardial injury may call for changes in the management of MI and other disorders with myocardial injury.

Background. Static cold storage is a mainstay of the heart transplantation (HTx) process. However, the temperature distribution within the organ at each stage of HTx is unknown. In this study, we aimed to quantify how long it took for the heart to warm up and cool down and the nature of temperature distribution with the organ at each stage of HTx. Methods. We used high-fidelity computational time-varying biothermal modeling on an anatomical human heart model to model the HTx process in 5 interdependent stages, including cardioplegia, back-table preparation, static cold storage ice box storage and transport, back-table preparation at the recipient institution and warm-up within the recipient body before cross-clamp release. Results. Results indicate that the heart experiences roller-coaster-like temperature changes in stage, including rapid cool down from body temperature to <10 °C within 15 min in stage 1 with a maximum cooling rate of 5 °C/min. This was followed by cooling and extended duration of temperatures <2 °C in the ice box and rapid warming up to body temperature within 10 min at rates of 2 °C/min and 4 °C/min for the left and right sides, respectively, during implantation. Temperature distribution throughout the heart was heterogeneous, with right-sided temperature change occurring nearly 2× faster than on the left side. Conclusions. We present, for the first time, detailed temperature distributions and evolution at each stage of HTx. Quantification of the rapid and heterogeneous temperature changes is crucial to optimize HTx and improve organ viability.

ImportanceAdjuvant ovarian function suppression (OFS) with oral endocrine therapy improves outcomes for premenopausal patients with hormone receptor–positive (HR+) breast cancer but adds adverse effects. A genomic biomarker for selecting patients most likely to benefit from OFS-based treatment is lacking.ObjectiveTo assess the predictive and prognostic performance of the Breast Cancer Index (BCI) for OFS benefit in premenopausal women with HR+ breast cancer.Design, Setting, and ParticipantsThis prospective-retrospective translational study used all available tumor tissue samples from female patients from the Suppression of Ovarian Function Trial (SOFT). These individuals were randomized to receive 5 years of adjuvant tamoxifen alone, tamoxifen plus OFS, or exemestane plus OFS. BCI testing was performed blinded to clinical data and outcome. The a priori hypothesis was that BCI HOXB13/IL17BR ratio (BCI[H/I])–high tumors would benefit more from OFS and high BCI portended poorer prognosis in this population. Settings spanned multiple centers internationally. Participants included premenopausal female patients with HR+ early breast cancer with specimens in the International Breast Cancer Study Group tumor repository available for RNA extraction. Data were collected from December 2003 to April 2021 and were analyzed from May 2022 to October 2022.Main Outcomes and MeasuresPrimary end points were breast cancer–free interval (BCFI) for the predictive analysis and distant recurrence-free interval (DRFI) for the prognostic analyses.ResultsTumor specimens were available for 1718 of the 3047 female patients in the SOFT intention-to-treat population. The 1687 patients (98.2%) who had specimens that yielded sufficient RNA for BCI testing represented the parent trial population. The median (IQR) follow-up time was 12 (10.5-13.4) years, and 512 patients (30.3%) were younger than 40 years. Tumors were BCI(H/I)-low for 972 patients (57.6%) and BCI(H/I)-high for 715 patients (42.4%). Patients with tumors classified as BCI(H/I)-low exhibited a 12-year absolute benefit in BCFI of 11.6% from exemestane plus OFS (hazard ratio [HR], 0.48 [95% CI, 0.33-0.71]) and an absolute benefit of 7.3% from tamoxifen plus OFS (HR, 0.69 [95% CI, 0.48-0.97]) relative to tamoxifen alone. In contrast, patients with BCI(H/I)-high tumors did not benefit from either exemestane plus OFS (absolute benefit, −0.4%; HR, 1.03 [95% CI, 0.70-1.53]; P for interaction = .006) or tamoxifen plus OFS (absolute benefit, −1.2%; HR, 1.05 [95% CI, 0.72-1.54]; P for interaction = .11) compared with tamoxifen alone. BCI continuous index was significantly prognostic in the N0 subgroup for DRFI (n = 1110; P = .004), with 12-year DRFI of 95.9%, 90.8%, and 86.3% in BCI low-risk, intermediate-risk, and high-risk N0 cancers, respectively.Conclusions and RelevanceIn this prospective-retrospective translational study of patients enrolled in SOFT, BCI was confirmed as prognostic in premenopausal women with HR+ breast cancer. The benefit from OFS-containing adjuvant endocrine therapy was greater for patients with BCI(H/I)-low tumors than BCI(H/I)-high tumors. BCI(H/I)-low status may identify premenopausal patients who are likely to benefit from this more intensive endocrine therapy.