ABSTRACT
Acute coronary syndromes (ACS) may complicate the clinical course of patients with Coronavirus Disease 2019 (COVID-19). It is still unclear whether this condition is a direct consequence of the primary disease. However, several mechanisms including direct cellular damage, endothelial dysfunction, in-situ thrombosis, systemic inflammatory response, and oxygen supply-demand imbalance have been described in patients with COVID-19. The onset of a pro-thrombotic state may also be facilitated by the endothelial dysfunction secondary to the systemic inflammatory response and to the direct viral cell damage. Moreover, dysfunctional endothelial cells may enhance vasospasm and platelet aggregation.The combination of these factors promotes atherosclerotic plaque instability, thrombosis and, consequently, type 1 myocardial infarction.Furthermore, severe hypoxia due to extensive pulmonary involvement, in association with other conditions described in COVID-19 such as sepsis, tachyarrhythmias, anemia, hypotension, and shock, may lead to mismatch between oxygen supply and demand, and cause type 2 myocardial infarction.A deeper understanding of the potential pathophysiological mechanisms underlying ACS in patients with COVID-19 could help the therapeutic management of these very high-risk patients.
ABSTRACT
Introduction: Women are twice as likely to develop insomnia across their lifetime compared with men. This may be explained, in part, by changes in hormones and menstrual cycle phase in reproductive-aged women. Intra- and inter-variability of menstrual cycle timing can make it difficult to accurately measure sleep quality and quantity in sleep research studies. This study aimed to examine the role of menstrual cycle phase in daily self-report and actigraphy-assessed sleep across two consecutive menstrual cycles. Materials and Methods: Fifty-one women (43% Caucasian) between the ages of 18 and 35 (m age = 23.67, SD = 4.68) completed continuous sleep monitoring via actigraphy and daily sleep diaries over two menstrual cycles (m days = 51.29). Cycles were identified via first date of menstrual bleeding and midcycle urinary ovulation testing and were coded into four phases: perimenstrual, mid-follicular, periovulatory, and mid-luteal. The perimenstrual phase was defined as the 3 days prior to and the first 3 days of menstrual bleeding. Within- and between-person relationships between menstrual phase and sleep parameters were estimated using multistep hierarchical linear modeling. Subjective and objective measures yielded the following sleep variables: Total Wake Time (TWTsub and TWTobj), Sleep Efficiency (SEsub and SEobj), and subjective sleepiness. Pandemic-related stress and daily US and region-specific COVID-19 case counts were included as covariates in adjusted models. Results: The sample had a mean a cycle length of 28.61 days (SD = 2.69). Regarding actigraphy data, menstrual phase predicted TWTobj and SEobj. Women spent 4-7 fewer minutes awake during the mid-follicular (m = 61.54, SE = 3.37) and mid-luteal phases (m = 63.11, SE = 3.29), compared to the perimenstrual phase (m = 67.54, SE = 3.37;p <.001). Sleep efficiency was higher in the mid-luteal phase (m = 82.50, SE = 0.79) compared to the perimenstrual phase (m = 80.71, SE = 0.82, p =.006). Subjective ratings indicated that during the perimenstrual phase women spent 8-16 minutes longer awake (m = 52.23, SE = 5.01, p <.001) and experienced reduced sleep efficiency of between 1-3 percentage points (m = 89.70, SE = 0.10, p <.001) compared to all other phases. Women also reported increased morning sleepiness in the perimenstrual (m = 4.71, SE = 0.21) compared to the periovulatory phase (m = 4.34, SE = 0.22, p =.02). Random coefficients models for objective and subjective sleep variables were nonsignificant, indicating that these relationships did not vary significantly between participants. Conclusions: To our knowledge, this is one of the first studies to examine subjective and objective sleep prospectively across two consecutive menstrual cycles. Disturbed sleep was highest in the perimenstrual phase. Future studies should measure menstrual cycle phase when investigating sleep in reproductive age women.
ABSTRACT
Introduction: Women experience increased risk for sleep and affective disorders compared to men, attributed in part to monthly oscillations in sex hormones. Emotional functioning worsens during the perimenstrual phase. There is increasing evidence that sleep continuity also decreases during this phase. Thus, this study examined the interactive effects of sleep and menstrual phase on emotion across two menstrual cycles in healthy women. Methods: Participants (N=51, 43% Caucasian) aged 18-35 (m=23.67) completed actigraphy and daily sleep/emotion diaries over two menstrual cycles (m days=51.29). Cycles were identified via date of menses and urinary ovulation detection, and coded into four phases: perimenstrual, mid-follicular, periovulatory, and mid-luteal. The perimenstrual phase was defined the 3 days prior to and the 3 days following menses onset. Variables included diary and actigraphic total wake time (TWT), daily ratings of positive (happy, calm, enthusiastic) and negative (angry, afraid, sad) affect using a 9-point scale. Relationships between phase, sleep, and emotion were estimated using multistep hierarchical linear modeling. Pandemic-related stress and daily US and region-specific COVID-19 case counts were included as covariates in adjusted models. Results: Mean menstrual cycle length was 28.61±2.69 days. Menstrual phase was first entered into models as predictors for sleep and emotion variables independently. The perimenstrual phase positively predicted anger (p<.001) but no other emotions. Additionally, the perimenstrual phase predicted higher rates of TWT, such that diary-reported TWT was 8-16 minutes longer during the perimenstrual (m=67.54, SE=3.37) compared to other phases (p<.001). Actigraphic TWT was also increased by 4-7 minutes (m=61.54, SE=3.37) in the perimenstrual phase (p<.001). A second model included the interaction term, TWT∗phase to the original model. Positive emotions were .05- .10 points lower (ps=.006-.02) when TWT was greater in the perimenstrual phase. Conclusion: Menstruating women experienced greater rates of anger and sleep disruption during the perimenstrual phase compared to other phases. When poor sleep occurred during the perimenstrual phase, however, women reported reduced positive emotions. Sleep disruptions, particularly occurring during the perimenstrual phase, may be an important intervention target for women at risk for affective disorders. Future studies should be mindful to assess menstrual phases when assessing sleep and circadian rhythm.
ABSTRACT
The number of survivors of cancer is increasing substantially. Current models of care are unsustainable and fail to address the many unmet needs of survivors of cancer. Numerous trials have investigated alternate models of care, including models led by primary-care providers, care shared between oncology specialists and primary-care providers, and care led by oncology nurses. These alternate models appear to be at least as effective as specialist-led care and are applicable to many survivors of cancer. Choosing the most appropriate care model for each patient depends on patient-level factors (such as risk of longer-term effects, late effects, individual desire, and capacity to self-manage), local services, and health-care policy. Wider implementation of alternative models requires appropriate support for non-oncologist care providers and endorsement of these models by cancer teams with their patients. The COVID-19 pandemic has driven some changes in practice that are more patient-centred and should continue. Improved models should shift from a predominant focus on detection of cancer recurrence and seek to improve the quality of life, functional outcomes, experience, and survival of survivors of cancer, reduce the risk of recurrence and new cancers, improve the management of comorbidities, and reduce costs to patients and payers. This Series paper focuses primarily on high-income countries, where most data have been derived. However, future research should consider the applicability of these models in a wider range of health-care settings and for a wider range of cancers.