Background: Cognitive impairment (CI) is commonly observed after intracerebral hemorrhage (ICH). While a growing number of studies have explored this association, several evidence gaps persist. This review seeks to investigate the relationship between CI and ICH.Methods: A two-stage systematic review of research articles, clinical trials, and case series was performed. Initial search used the keywords [“Intracerebral hemorrhage” OR “ICH”] AND [“Cognitive Impairment” OR “Dementia OR “Cognitive Decline”] within the PubMed (last accessed November 3rd, 2020) and ScienceDirect (last accessed October 27th, 2020) databases, without publication date limits. Articles that addressed CI and spontaneous ICH were accepted if CI was assessed after ICH. Articles were rejected if they did not independently address an adult human population or spontaneous ICH, didn't link CI to ICH, were an unrelated document type, or were not written in English. A secondary snowball literature search was performed using reviews identified by the initial search. The Agency for Healthcare research and Quality's assessment tool was used to evaluate bias within studies. Rates of CI and contributory factors were investigated.Results: Search yielded 32 articles that collectively included 22,631 patients. Present evidence indicates a high rate of post-ICH CI (65–84%) in the acute phase (<4 weeks) which is relatively lower at 3 (17.3–40.2%) and 6 months (19–63.3%). Longer term follow-up (≥1 year) demonstrates a gradual increase in CI. Advanced age, female sex, and prior stroke were associated with higher rates of CI. Associations between post-ICH CI and cerebral microbleeds, superficial siderosis, and ICH volume also exist. Pre-ICH cognitive assessment was missing in 28% of included studies. The Mini Mental State Evaluation (44%) and Montreal Cognitive Assessment (16%) were the most common cognitive assessments, albeit with variable thresholds and definitions. Studies rarely (<10%) addressed racial and ethnic disparities.Discussion: Current findings suggest a dynamic course of post-ICH cognitive impairment that may depend on genetic, sociodemographic and clinical factors. Methodological heterogeneity prevented meta-analysis, limiting results. There is a need for the methodologies and time points of post-ICH cognitive assessments to be harmonized across diverse clinical and demographic populations.

Background The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic. Objective We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization. Methods Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository—the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context. Results The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources. Conclusions A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.

BackgroundCovid-19 has caused significant global mortality. Multiple vaccines have demonstrated efficacy in clinical trials though real-world effectiveness of vaccines against Covid-19 mortality in clinically and demographically diverse populations has not yet been reported.MethodsWe used a retrospective cohort assembled from a cross-institution comprehensive data repository. Established patients of the health care system were categorized as not immunized, partially immunized, or fully immunized against SARS-CoV-2 with an mRNA vaccine through April 4, 2021. Outcomes were Covid-19 related hospitalization and death.ResultsOf the 91,134 established patients, 70.2% were not immunized, 4.5% were partially immunized and 25.4% were fully immunized. Among the fully immunized 0.7% had a Covid-19 hospitalization, whereas 3.4% among the partially immunized and 2.7% non-immunized individuals were hospitalized with Covid-19. Of the 225 deaths among Covid-19 hospitalizations, 219 (97.3%) were in the not immunized, 5 (2.2%) in the partially immunized, and 1 (0.0041%) in the fully immunized group. mRNA vaccines were 96% (95%CI: 95 — 99) effective at preventing Covid-19 related hospitalization and 98.7% (95%CI: 91.0 — 99.8) effective at preventing Covid-19 related death when participants were fully vaccinated. Partial vaccination was 77% (95%CI: 71 — 82) effective at preventing hospitalization and 64.2% (95%CI: 13.0 — 85.2) effective at preventing death. Vaccine effectiveness at preventing hospitalization was conserved across subgroups of age, race, ethnicity, Area Deprivation Index, and Charlson Comorbidity Index.ConclusionsIn a large, diverse cohort in the United States, full immunization with mRNA vaccines was highly effective in the real-world scenario at preventing Covid-19 related hospitalization and death.

Recent reports linked acute COVID-19 infection in hospitalized patients to cardiac abnormalities. Studies have not evaluated presence of abnormal cardiac structure and function before scanning in setting of COVD-19 infection. We sought to examine cardiac abnormalities in consecutive group of patients with acute COVID-19 infection according to the presence or absence of cardiac disease based on review of health records and cardiovascular imaging studies. We looked at independent contribution of imaging findings to clinical outcomes. After excluding patients with previous left ventricular (LV) systolic dysfunction (global and/or segmental), 724 patients were included. Machine learning identified predictors of in-hospital mortality and in-hospital mortality + ECMO. In patients without previous cardiovascular disease, LV EF < 50% occurred in 3.4%, abnormal LV global longitudinal strain (< 16%) in 24%, and diastolic dysfunction in 20%. Right ventricular systolic dysfunction (RV free wall strain < 20%) was noted in 18%. Moderate and large pericardial effusion were uncommon with an incidence of 0.4% for each category. Forty patients received ECMO support, and 79 died (10.9%). A stepwise increase in AUC was observed with addition of vital signs and laboratory measurements to baseline clinical characteristics, and a further significant increase (AUC 0.91) was observed when echocardiographic measurements were added. The performance of an optimized prediction model was similar to the model including baseline characteristics + vital signs and laboratory results + echocardiographic measurements.