BackgroundPopulation‐based studies have revealed declining acute ischemic stroke (AIS) hospitalization rates in the United States, but no study has assessed recent temporal trends in race/ethnic‐, age‐, and sex‐specific AIS hospitalization rates.Methods and ResultsTemporal trends in hospitalization for AIS from 2000 to 2010 were assessed among adults ≥25 years using the Nationwide Inpatient Sample. Age‐, sex‐, and race/ethnic‐specific and age‐adjusted stroke hospitalization rates were calculated using the weighted number of hospitalizations and US census data. From 2000 to 2010, age‐adjusted stroke hospitalization rates decreased from 250 to 204 per 100 000 (overall rate reduction 18.4%). Age‐specific AIS hospitalization rates decreased for individuals aged 65 to 84 years (846 to 605 per 100 000) and ≥85 years (2077 to 1618 per 100 000), but increased for individuals aged 25 to 44 years (16 to 23 per 100 000) and 45 to 64 years (149 to 156 per 100 000). Blacks had the highest age‐adjusted yearly hospitalization rates, followed by Hispanics and whites (358, 170, and 155 per 100 000 in 2010). Age‐adjusted AIS hospitalization rates increased for blacks but decreased for Hispanics and whites. Age‐adjusted AIS hospitalization rates were lower in women and declined more steeply compared to men (272 to 212 per 100 000 in women versus 298 to 245 per 100 000 in men).ConclusionsAlthough overall stroke hospitalizations declined in the United States, the reduction was more pronounced among older individuals, women, Hispanics, and whites. Renewed efforts at targeting risk factor control among vulnerable individuals may be warranted.

The COVID-19 pandemic has created unprecedented challenges for the U.S. healthcare system due to the mismatch between healthcare system capacity and patient demand. The healthcare industry has been a slow adopter of digital innovation due to the conventional belief that humans need to be at the center of every healthcare delivery task. In the setting of the COVID-19 pandemic, however, artificial intelligence (AI) may be used to carry out specific tasks such as pre-hospital triage and allow clinicians to deliver care at scale. Recognizing that the majority of COVID-19 cases are mild and do not require hospitalization, Partners HealthCare implemented an automated pre-hospital triage solution to direct patients to the appropriate care setting before they showed up at the emergency department, which would otherwise consume resources, expose other patients and staff to potential viral transmission, and further exacerbate supply-and demand mismatching. Although the use of AI has been well-established in other industries to optimize supply and demand matching, the introduction of AI to perform tasks remotely that were traditionally performed in-person by clinical staff represents a significant milestone in healthcare operations strategy.

Meningiomas are central nervous system tumors that originate from the meningeal coverings of the brain and spinal cord. Most meningiomas are pathologically benign or atypical, but 3–5% display malignant features. Despite previous studies on benign and atypical meningiomas, the key molecular pathways involved in malignant transformation remain to be determined, as does the extent of epigenetic alteration in malignant meningiomas. In this study, we explored the landscape of DNA methylation in ten benign, five atypical and four malignant meningiomas. Compared to the benign tumors, the atypical and malignant meningiomas demonstrate increased global DNA hypomethylation. Clustering analysis readily separates malignant from atypical and benign tumors, implicating that DNA methylation patterns may serve as diagnostic biomarkers for malignancy. Genes with hypermethylated CpG islands in malignant meningiomas (such as HOXA6 and HOXA9) tend to coincide with the binding sites of polycomb repressive complexes (PRC) in early developmental stages. Most genes with hypermethylated CpG islands at promoters are suppressed in malignant and benign meningiomas, suggesting the switching of gene silencing machinery from PRC binding to DNA methylation in malignant meningiomas. One exception is the MAL2 gene that is highly expressed in benign group and silenced in malignant group, representing de novo gene silencing induced by DNA methylation. In summary, our results suggest that malignant meningiomas have distinct DNA methylation patterns compared to their benign and atypical counterparts, and that the differentially methylated genes may serve as diagnostic biomarkers or candidate causal genes for malignant transformation.

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