The American Association for Thoracic Surgery (AATS) 2022 Expert Consensus Document: The use of mechanical circulatory support in lung transplantation

ObjectiveThe use of mechanical circulatory support (MCS) in lung transplantation has been steadily increasing over the prior decade, with evolving strategies for incorporating support in the preoperative, intraoperative, and postoperative settings. There is significant practice variability in the use of these techniques, however, and relatively limited data to help establish institutional protocols. The objective of the AATS Clinical Practice Standards Committee (CPSC) expert panel was to review the existing literature and establish recommendations about the use of MCS before, during, and after lung transplantation.MethodsThe AATS CPSC assembled an expert panel of 16 lung transplantation physicians who developed a consensus document of recommendations. The panel was broken into subgroups focused on preoperative, intraoperative, and postoperative support, and each subgroup performed a focused literature review. These subgroups formulated recommendation statements for each subtopic, which were evaluated by the entire group. The statements were then developed via discussion among the panel and refined until consensus was achieved on each statement.ResultsThe expert panel achieved consensus on 36 recommendations for how and when to use MCS in lung transplantation. These recommendations included the use of veno-venous extracorporeal membrane oxygenation (ECMO) as a bridging strategy in the preoperative setting, a preference for central veno-arterial ECMO over traditional cardiopulmonary bypass during the transplantation procedure, and the benefit of supporting selected patients with MCS postoperatively.ConclusionsAchieving optimal results in lung transplantation requires the use of a wide range of strategies. MCS provides an important mechanism for helping these critically ill patients through the peritransplantation period. Despite the complex nature of the decision making process in the treatment of these patients, the expert panel was able to achieve consensus on 36 recommendations. These recommendations should provide guidance for professionals involved in the care of end-stage lung disease patients considered for transplantation.     

基层医疗卫生机构居家护理服务的安全支持和医务人员服务意愿调查研究

背景 人口老龄化趋势下，罹患多种慢性病的失能或半失能老年人对居家医疗护理服务需求迫切，但基层医务人员提供居家医疗护理服务存在较大的医疗风险和安全隐患，加强居家医疗护理服务的安全管理，有利于提高基层医务人员提供居家医疗护理服务的意愿。 目的 调查基层医疗卫生机构对居家护理服务的安全支持情况和医务人员的服务意愿，为进一步推动居家医疗护理服务发展提供参考依据。 方法 2021年8—10月，采用便利抽样法在四川省五大经济区抽取49所基层医疗卫生机构的1 131例医务人员。自行设计调查问卷，以"问卷星"电子问卷的形式收集资料。问卷内容包括医务人员的一般情况（8个条目）、提供居家医疗护理服务的经历（2个条目）、医疗机构对医务人员的安全支持（7个条目）、医务人员的居家医疗护理服务意愿（1个条目）。比较不同特征医务人员提供居家医疗护理服务的意愿，采用二元Logistic回归分析医务人员提供居家医疗护理服务意愿的影响因素。 结果 1 131例基层医务人员中，692例（61.18%）报告有过居家医疗护理服务的经历，193例（17.06%）报告在服务过程中发生过不良事件/安全事件，531例（46.95%）报告其所在医疗机构未评估过患者接受居家医疗护理服务的安全风险，199例（17.60%）表示所在机构评估了医务人员提供居家医疗护理服务的安全风险，299例（26.44%）在居家医疗护理服务过程中使用APP定位系统，273例（24.14%）由机构配备报警、延迟预警等设备，807例（71.35%）表示居家医疗护理服务过程中没有获得所在机构内其他医务人员的支持措施，303例（26.79%）表示机构曾组织居家医疗护理服务的专项研讨活动，352例（31.12%）表示接受过有关居家医疗护理服务专项培训，853例（75.42%）表示愿意提供居家医疗护理服务。二元Logistic回归分析结果显示，就职的基层医疗卫生机构类型，目前受聘的专业技术职称，医疗机构是否评估过患者接受居家医疗护理服务的安全风险，在居家医疗护理服务过程中是否配备报警、延迟预警等设备，是否参加过有关居家医疗护理服务的专项培训，对医务人员提供居家医疗护理服务意愿有影响（P<0.05）。 结论 现阶段居家医疗护理服务的安全支持不足，多种因素影响基层医务人员提供居家医疗护理服务的意愿。基层医疗卫生机构应加强对患者接受居家医疗护理服务安全风险的评估，为提供居家医疗护理服务的医务人员配备报警、延迟预警等设备，积极举办居家医疗护理服务相关的专项培训并鼓励医务人员参加，以降低医务人员提供居家医疗护理服务的风险，保障其人身安全。     

乳腺癌随访和药物副作用管理策略的研究进展

乳腺癌是全球女性发病率最高的恶性肿瘤,随着诊断技术及治疗手段的不断提高,乳腺癌患者接受系统治疗后生存期明显延长。乳腺癌患者需要长时间的随访以便及早监测到局部复发和远处转移。此外,激素敏感性乳腺癌患者还需要给予5~10年的内分泌治疗,针对其副作用的全程管理也需要随访,进而提高患者的生活质量,减轻患者疾病负担。本文就近年来乳腺癌随访相关方面进行简要综述。     

医疗领域人工智能应用的研究进展＊

人工智能引发了医疗领域的数字革命，在推动行业发展方面具有极大潜力。本研究围绕临床诊疗、医学研究和公共卫生三个基本场景，聚焦人工智能与传统中医药的交叉与融合，并着重介绍人工智能在疾病诊断、决策支持、医学研究以及重大公共卫生事件中的应用。虽然人工智能在诸多方面显示出独特优势，但仍存在透明度不高、缺乏安全性评估和相关法律法规监管等问题需要谨慎解决，以促进人工智能技术在医疗领域的推广。     

Do social support and community engagement act as mechanisms in the association between neighbourhood income inequality and the mental health of mothers in Calgary,Canada? A mediation analysis

PurposeAccording to the social determinants of health framework, income inequality is a potential risk factor for adverse mental health. However, few studies have explored the mechanisms suspected to mediate this relationship. The current study addresses this gap through a mediation analysis to determine if social support and community engagement act as mediators linking neighbourhood income inequality to maternal anxiety and depressive symptoms within a cohort of new mothers living in the City of Calgary, Canada.MethodsData collected at three years postpartum from mothers belonging to the All Our Families (AOF) cohort were used in the current study. Maternal data were collected between 2012 and 2015 and linked to neighbourhood socioeconomic data from the 2006 Canadian Census. Income inequality was measured using Gini coefficients derived from 2006 after-tax census data. Generalized structural equation models were used to quantify the associations between income inequality and mental health symptoms, and to assess the potential direct and indirect mediating effects of maternal social support and community engagement.ResultsIncome inequality was not significantly associated with higher depressive symptoms (β = 0.32, 95%CI = −0.067, 0.70), anxiety symptoms (β = 0.11, 95%CI = −0.39, 0.60), or lower social support. Income inequality was not associated with community engagement. For the depression models, higher social support was significantly associated with lower depressive symptoms (β = −0.13, 95%CI = −0.15, −0.097), while community engagement was not significantly associated with depressive symptoms (β = 0.059, 95%CI = −0.15, 0.27). Similarly, for the anxiety models, lower anxiety symptoms were significantly associated with higher levels of social support (β = −0.17, 95%CI = −0.20, −0.13) but not with higher levels of community engagement (β = 0.14, 95%CI = −0.14, 0.41).ConclusionThe current study did not find clear evidence for social support or community engagement mediating the relationship between neighbourhood income inequality and maternal mental health. Future investigations should employ a broader longitudinal approach to capture changes in income inequality, potential mediators, and mental health symptomatology over time.     

COVID-19 Gender Disparities and Mitigation Recommendations: A Narrative Review

The coronavirus disease 2019 (COVID-19) pandemic has rapidly created widespread impacts on global health and the economy. Data suggest that women are less susceptible to severe illness. However, sex-disaggregated data are incomplete, leaving room for misinterpretation, and focusing only on biologic sex underestimates the gendered impact of the pandemic on women. This narrative review summarizes what is known about gender disparities during the COVID-19 pandemic and the economic, domestic, and health burdens along with overlapping vulnerabilities related to the pandemic. In addition, this review outlines recommended strategies that advocacy groups, community leaders, and policymakers should implement to mitigate the widening gender disparities related to COVID-19.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.