To date, the psychological impact of COVID-19 epidemic among family members of health care workers (HCWs) in China has been neglected. This cross-sectional study investigates the mental health status and related factors in families of HCWs employed in designated hospitals in Ningbo, China.Family members of HCWs in five designated hospitals in Ningbo, China, were recruited in February, 2020 for this study. Demographic variables, COVID-19-related events in the lives of the participants, knowledge of COVID-19, and the working status of family members  were collected using online self-administered questionnaires. Mental health status was assessed using the Chinese versions of the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9). Multivariable logistic regression analyses were performed to identify the main factors associated with the mental health conditions.In total, 845 participants completed the questionnaires correctly (95.80% response rate). The prevalence of anxiety and depression symptoms were respectively 33.73% (95% CI: 30.53–36.92%) and 29.35% (95% CI: 26.27–32.43%) when a cut-off score of 5 was used for GAD-7 and PHQ-9. Risk factors for anxiety symptoms included more time (hours) spent thinking about the COVID-19, and whether or not family members  had direct contact with confirmed or suspected COVID-19 patients while high participants’ self-reported safety scores for HCW’s protective equipment was a protective factor. More time (hours) spent thinking about COVID-19, longer average working time per week worked by family members , and being parents and other next of kin of HCWs were risk factors for depressive symptoms. Compared to participants who were HCWs, participants who were private sector workers were more likely to develop depressive symptoms, while government or institutional employees were less likely to suffer from depressive symptoms.Psychological responses to COVID-19 have been dramatic among family members of HCWs during the rising phase of the outbreak. Our findings provide strong evidence to examine and attend to the mental health of this population during the COVID-19 epidemic. In December 2019, a coronavirus disease 2019 (COVID-19) outbreak occurred in Wuhan, Hubei Province, China, which has spread rapidly throughout China and even the world .Due to the Chinese Lunar New Year holiday, more than 43,000 people traveled from Wuhan to Ningbo before the start of the travel ban on January 23 and the first confirmed case of COVID-19 in Ningbo was reported on January 21, 2020 ; and setCompelling evidence suggests that infectious disease pandemics, including the severe acute respiratory syndrome (SARS), the Middle East respiratory syndrome (MERS), and the 2009 novel influenza A (H1N1), were associated with mental health problems among the general population , health The severity of the COVID-19 outbreak in China is also causing mental health problems among HCWs such as stress, anxiety, and depressive symptoms . One of However, the psychological response to the COVID-19 epidemic among HCW family members in China has been neglected. In the National Health Commission guidelines for emergency psychological crisis intervention for people affected by COVID-19, HCW family members were only ranked as the third priority group . MoreoveTo address this gap, the aim of the current study was to evaluate the mental health status of family members of HCWs and influencing factors who had direct or indirect contact with confirmed or suspected COVID-19 patients, and to provide data support to develop targeted interventions for this population to help them cope with psychological problems during the COVID-19 epidemic.This was a cross-sectional study conducted in Ningbo in between February 10 and 20, 2020.A convenience sampling method was applied. First, with the help of the Health Commission of Ningbo, five designated hospitals in Ningbo that have treated confirmed or suspected COVID-19 patients were asked to participate in this survey. Second, we sent the online questionnaire to every department director of the five designated hospitals, which they then forwarded to every subordinate. Finally, HCWs forwarded the online questionnaire to their family members.To prevent the spread of COVID-19 through droplets or contact, we used an online-based survey via the WeChat-based survey program “Questionnaire Star” to collect data.The inclusion criteria of this study were (1) being the next of kin of HCWs from hospitals designated for medical treatment of COVID-19 in Ningbo and (2) having access to the Internet. Exclusion criteria were as follows: (1) self-reported history of neurological disorders, mental illness, and other serious systemic disorders; and (2) self-reported substance abuse. All participants were informed of the study procedure and were invited to sign an informed consent form online upon their recruitment. Participants who passed the initial self-screening phase were asked to complete the self-administered questionnaires . Of the http://www.chictr.org (registration number: ChiCTR2000030697).This study was approved by the Ethical Committee of Ningbo First Hospital, Ningbo, China , and registered with the registry website We employed the Chinese version of PHQ-9 to assess the depressive symptoms of HCW families. PHQ-9 is a 9-item self-report measure to assess severity of depression. Participants rated each item in accordance with the frequency of symptoms over the past 2 weeks on a 4-point scale from 0  to 3 (nearly every day). Total scores ranged from 0 to 27, with highest scores indicating greater severity of depressive symptoms . The PHQWe employed the Chinese version of GAD-7 to assess the anxiety symptoms of HCW families. GAD-7 is a self-report questionnaire that screens and measures severity of generalized anxiety disorder. Participants rated seven items according to the frequency of symptoms in the past two weeks on a 4-point scale from 0  to 3 (nearly every day). Total scores ranged from 0 to 21, with higher scores indicating greater severity of anxiety symptoms . The GADThe demographic characteristics included age, gender, educational level, occupation, and nature relationship with HCWs. Occupation included the following five types: (1) HCWs; (2) private sector workers; (3) government or institutional employees; (4) students; and (5) others, which consisted of freelancers, retirees, social workers, and other relevant staffs. The nature relationship with HCWs included the following four types: (1) spouses; (2) children; (3) parents; and (4) other next of kin.The questionnaires for working status of family members , the participants’ knowledge of COVID-19, and COVID-19-related events in the lives of participants were self-developed specifically for this study, as there were no suitable scales available for measuring factors related to HCW families during the COVID-19 outbreak.The working status of family members  included: (1) whether HCWs were in direct contact with confirmed or suspected COVID-19 patients; (2) the average working time (hours) per week for HCWs; (3) participants’ self-reported safety score for protective equipment of HCWs ; and (4) the department of HCWs, which was categorized into five types: (a) frontline departments, including the respiratory department, infectious diseases department, the intensive care unit, the fever clinic, and the isolation ward; (b) the medical technology department ; (c) the nursing department; (d) the logistics department; and (e) other departments.Participants’ knowledge of COVID-19 was assessed based on responses to the following five COVID-19-related single-topic questions: (1) “Which of the following symptoms is not a common symptom of COVID-19?” with possible response options being fever, stuffy and runny nose, fatigue, and dry cough; (2) “how many days do people returning from Hubei Province need to be quarantined under observation?” with possible response options being 10 days, 12 days, 14 days, and 15 days; (3) “which of the following masks can prevent COVID-19?” with possible response options being activated carbon mask, cotton mask, sponge mask, and medical surgical mask; (4) “the known transmission routes of COVID-19 do not include which of the following?” with possible response options being contact transmission, droplet transmission, soil transmission, and aerosol transmission; and (5) “with regard to the disposal of discarded masks, what is incorrect about the following statement?” with possible response options being throw it away at any time when you run out of it; masks worn by people with fever need to be disinfected, sealed, and discarded; wash your hands immediately after handling the mask; and discarded masks should be discarded into hazardous trash cans. Of the above five questions, one point was given for each correct answer and no points were given for incorrect answers. A total score was calculated by summing points for each of the five questions, ranging from 0 to 5, with higher scores indicating a better knowledge of COVID-19.COVID-19-related events in the lives of participants included: (1) whether there had been confirmed COVID-19 cases in families or friends; (2) whether there had been suspected COVID-19 cases in families or friends; and (3) time spent thinking about COVID-19, which was measured by the average number of hours spent thinking about the COVID-19 information every day.The Kolmogorov-Smirnov test was used to test the normal distribution of continuous data. Continuous variables were presented as mean ± standard deviation or medians (interquartile range [IQR]) depending on the distribution of the data. Categorical variables were presented as percentages. Univariate analysis was performed using the Student’s t-test or the Mann-Whitney U-test for continuous variables depending on the distribution of the data, and the chi-square tests for categorical variables.P value of < 0.20 in univariate analysis were considered potential factors for inclusion into the multivariate logistic model [Multivariate logistic regression analyses were used to assess the independent association of symptoms of depression and anxiety with independent variables. Variables with a ic model , all theP < 0.05 was considered statistically significant.Statistical analyses were performed using SPSS v.22.0 . Two-sided Table Respectively 0.36 and 8.28% of participants had confirmed and suspected COVID-19 cases in families or friends. Most of the participants focused on the COVID-19 by spending more than 1 h every day  The median score of participants’ knowledge of COVID-19 was 5.00 (IQR 4.00–5.00). Almost one sixth (15.5%) of the participants’ family members  worked in the front-line departments, while 33.96% worked in the nursing department and 37.28% worked in other departments; nearly half (48.05%) had direct contact with confirmed or suspected COVID-19 patients. The median working time per week worked by HCWs was 40.00 h (IQR 35.00–45.00), and the median points of participants’ self-reported safety score for protective equipment of HCWs was 4.00 (IQR 3.00–5.00).The prevalence of the symptoms of anxiety and depression and results of thee univariate analysis of potential factors related to anxiety and depression symptoms among HCW families are shown in Table The overall prevalence of anxiety and depression symptoms were 33.73% (95% CI: 30.53–36.92%) and 29.35% (95% CI: 26.27–32.43%), respectively.Univariate analysis showed that gender, age, nature of relationship with HCWs, whether there had been suspected COVID-19 cases in families or friends, time spent thinking about COVID-19 per day (hours), whether HCWs had directly contacted with confirmed or suspected COVID-19 patients, average working time per week worked by HCWs (hours), and participants’ self-reported safety score for protective equipment of HCWs were potential factors related to anxiety symptoms, whereas educational level, occupation, whether there had been confirmed COVID-19 cases in families or friends, knowledge of COVID-19, the department where the HCW worked were not.Univariate analysis also indicated that gender, age, occupation, nature of relationship with HCWs, time spent thinking about COVID-19 per day (hours), average working time per week worked by HCWs (hours) and participants’ self-reported safety score for protective equipment of HCWs were potential factors related to depressive symptoms, whereas educational level, whether there had been confirmed COVID-19 cases in families or friends, whether there had been suspected COVID-19 cases in families or friends, knowledge of COVID-19, the department where the HCW worked, whether or not HCWs were directly in contact with confirmed or suspected COVID-19 patients were not.The association between influence factors with anxiety and depression symptoms among HCW families during the COVID-19 epidemic are given in Tables 2 = 5.906, degree of freedom = 8, P = 0.658).In multiple logistic regression analysis, participants who spent more time (hours) thinking about the COVID-19  and whose family members  had direct contact with confirmed or suspected COVID-19 patients  were significantly more likely to develop anxiety symptoms, while higher participants’ self-reported safety score for protective equipment of HCWs  was a significantly protective factor for participants to suffer anxiety symptoms. In addition, female participants were marginally significantly more likely to have anxiety symptoms  than male participants. The final model showed good discrimination  and good calibration .Multiple logistic regression analysis also demonstrated that more time (hours) to focus on the COVID-19  and longer average working time per week worked by HCWs  were significantly associated with a higher risk of depressive symptoms among participants. Compared to participants who were HCWs, private sector workers were significantly more likely to develop depressive symptoms , while government or institutional employees  were significantly less likely to have depressive symptoms. Compared to participants who are spouses of HCWs, parents , and other next of kin  were significantly likely to develop depressive symptoms. The final model showed good discrimination  and good calibration  and 29.35% (95% CI: 26.27–32.43%) of family members of HCWs reported symptoms of anxiety and depression, respectively, which was much higher than the levels reported among the general population of China . It is wStudies have demonstrated that hospital-related transmission was suspected to be the possible mechanism of infection for affected HCWs . Thus, iOur findings demonstrated that longer average working time per week worked by HCWs was significantly associated with a higher risk of depressive symptoms among participants. Longer working time means that HCWs have to spend more time in contact with confirmed or suspected COVID-19 patients, which may increase their chances of being infected with COVID-19 , and cauThe only factor significantly associated with symptoms of both anxiety and depression was the time spent thinking about COVID-19, which was consistent with findings in the general population during the COVID-19 outbreak . During Our study findings indicate that insufficient and inadequate attention is being paid to family members of HCWs during the COVID-19 epidemic in China. Given the increasingly serious outbreak outside of China, the main implication of this study is that our findings could assist in developing mechanisms to help family members of HCWs in similar situations in other countries. In order to alleviate symptoms of anxiety and depression among this vulnerable population, several appropriate interventions are recommended as follows: first, health policy makers and stakeholders should collaborate to provide high-quality, timely crisis psychological services to HCW families. Online psychological self-help intervention systems, including online cognitive behavioral therapy for depression and anxiety , would be appropriate for HCW families . Second,This study has some important strengths. First, to the best of our knowledge, this is the first study to explore the mental health problems and related factors among family members of HCWs during the COVID-19 outbreak and one of the first to investigate this issue during an infectious epidemic. Second, mental health status was assessed using previously developed measurement tools with good reliability and validity. Third, the use of multivariate analysis may cause type I error inflation.Nevertheless, several limitations need to be considered. First, because we performed a cross-sectional study, our results do not show a causal relationship. Second, due to the sudden outbreak of the epidemic, baseline data on mental health conditions in the target population during normal conditions without an outbreak were not available so that excess morbidity in terms of anxiety and depression symptoms due to the outbreak cannot be correctly determined. Thus, we compared the prevalence of depression and anxiety symptoms among target populations with that observed in the general population in China during the normal conditions, as we discussed above. Third, to prevent potential COVID-19 infection from spreading, a web-based survey was conducted; study sampling was therefore voluntary, resulting in possible selection bias. Fourth, our sample is not highly representative, as the respondents were all from Ningbo City.The present study provided evidence of a major mental health burden of family members of HCWs in designated hospitals during the COVID-19 epidemic in Ningbo, China, particularly in participants who spent more time thinking about COVID-19, family members  who had direct contact with confirmed or suspected COVID-19 patients, family members  with longer average working time per week, and those who were in non-spousal relationships with HCWs. On the contrary, participants’ high self-reported safety score for protective equipment of HCWs was a protective factor against psychological problems. Compared to participants who were HCWs, those who were enterprise workers were more likely to develop mental health problems, while those who were government or institutional employees were less likely to report psychological issues. In summary, we suggest that more attention should be paid to the mental health of this vulnerable population during an infectious disease outbreak. In addition, our findings are important in enabling the government to allocate health resources and offer appropriate treatment for family members of HCWs who suffer mental health problems during the COVID-19 epidemic or any other infectious disease outbreak in the future.


Difficulties have risen while managing Acute Respiratory Distress Syndrome (ARDS) caused by COVID-19, although it meets the Berlin definition. Severe hypoxemia with near-normal compliance was noted along with coagulopathy. Understanding the precise pathophysiology of this atypical ARDS will assist researchers and physicians in improving their therapeutic approach. Previous work is limited to postmortem studies, while our report addresses patients under protective lung mechanical ventilation. An open-lung minithoracotomy was performed in 3 patients who developed ARDS related to COVID-19 and were admitted to the intensive care unit to carry out a pathological and microbiological analysis on lung tissue biopsy. Diffused alveolar damage with hyaline membranes was found, as well as plurifocal fibrin microthrombi and vascular congestion in all patients' specimens. Microbiological cultures were negative, whereas qualitative Reversed Transcriptase Polymerase Chain Reaction (RT-PCR) detected SARS-CoV-2 in the pulmonary parenchyma and pleural fluid in two patients. COVID-19 causes progressive ARDS with onset of severe hypoxemia, underlying a dual mechanism: shunt effect through diffused alveolar damage and dead space effect through thrombotic injuries in microvascular beds. It seems reasonable to manage this ventilation-perfusion ratio mismatch using a high dose of anticoagulant combined with glucocorticoids. In the early months of 2020, the world witnessed an outbreak of the severe respiratory syndrome coronavirus (SARS-Cov-2), which caused a tremendous flood of coronavirus-related pneumonia. In most cases, coronavirus disease 2019 (COVID-19) is rapidly resolved, whereas 26% require intensive care unit admission [Clinical manifestations of COVID-19 vary from mild pneumonia to progressive Acute Respiratory Distress Syndrome (ARDS). Its singular features are severe hypoxemia often associated with near-normal respiratory system compliance at the beginning . On thesARDS does not take into account pathologic findings which leaves a considerable gap in categorizing COVID-19 ARDS (C-ARDS) and its peripheral vascular changes. On the other hand, previous work studied the coinfection rate between SARS-CoV-2 and other respiratory pathogens and focused only on nasopharyngeal swabs [In 1967, Ashbaugh et al. assumed that diffuse alveolar damage (DAD) on lung histology is the pathological hallmark of ARDS . HoweverUnderstanding the precise pathophysiology of C-ARDS will assist researchers and physicians in improving their therapeutic approach. Here, we conducted a descriptive study performing an open-lung biopsy (OLB); taking into account the benefit-to-risk ratio, in patients with C-ARDS. This study is aimed at determining C-ARDS pathological characteristics and coinfection with other pathogens in lung tissue.th of April to 25th June 2020—who later developed ARDS that met the Berlin definition [We selected patients with laboratory-confirmed SARS-CoV-2 infection—admitted to the COVID-19 ICU of the University Hospital from the 25finition  and wereAn open-lung minithoracotomy with rib spreading was performed using the wedge resection technique from the anterolateral segment with a stapler . The antBiopsy lung tissue was analyzed with hematoxylin-eosin and periodic acid-Schiff for detecting bacterial and fungal infection and also Masson trichrome staining to identify pulmonary interstitial fibrosis. The slides have been digitized using a Leica SCN400 Slide Scanner, and then images of tissue sections were captured. Pleural effusion fluid and biopsy lung tissue were tested for SARS-CoV-2 by RT-PCR and a panel of non-SARS-CoV-2 respiratory viral pathogens , along with standard bacterial and fungal respiratory cultures.An open-lung biopsy in 3 patients with C-ARDS was carried out. All of them were male and tested positive for SARS-CoV-2 by nasopharyngeal swab at the time of hospital admission. The median age was 65 years . The median duration from symptoms to admission is 10 days , and the median duration from admission to death was 9.6 days . Initial symptoms in 3 patients were mainly fever, dry cough, and shortness of breath, whereas Case 3 reported anosmia.Hypertension was found in all patients as preexisting comorbidity, Case 1 had hyperthyroidism and benign hypertrophy of the prostate, and Case 3 reported being a chronic smoker for 20 years with dyslipidemia .All patients were managed with the same national Moroccan protocol, which is hydroxychloroquine combined with azithromycin, zinc, vitamin C, and therapeutic dose of Low Molecular Weight Heparin (LMWH), in addition to acetylsalicylic acid. Two patients had A+ blood type and Case 1 had O+. The median duration of noninvasive ventilation management was 6.3 days , and the median duration of mechanical ventilation was 3.3 days . Open-lung biopsy was performed on the first day of endotracheal intubation in all patients.μg/mL, 2.27μg/mL, and 0.31 μg/mL, respectively, whereas at their last day were elevated to 21.27μg/mL, 22.95 μg/mL, and 1.1 μg/mL  culture returned negative. Qualitative RT-PCR detected SARS-CoV-2 in the pulmonary parenchyma and pleural fluid of Cases 2 and 3.Herein, an open-lung minithoracotomy was performed in 3 patients, who developed C-ARDS, in the first 24 hours of endotracheal intubation. Then, pathological, viral, and bacterial elements of lung tissues were examined. In all samples, the early phase of proliferative DAD was found associated with unexpected thrombosis of alveolar capillaries.A/Q) mismatch caused by COVID-19. The first evident explanation is that thickening of alveoli walls and hyaline membranes is responsible for hypoxemia, but not solely [A/Q. Finding DAD in our patients' lung corroborates with previous postmortem pathological data of C-ARDS [It seems plausible to hypothesize that, as a signature of ARDS, there were two intriguing mechanisms of hypoxemia and ventilation-to-perfusion ratio  virus was responsible for a global pandemic alert . CapelozIt is noteworthy that fibrin thrombi were found in all patients as well as high levels of serum D-dimer. An increasing number of researchers highlighted coagulopathy abnormalities and thrombosis with severe COVID-19 patients . MoreoveMost host responses in severe cases of COVID-19 reflect hyperinflammation and cytokine storm , which lA/Q mismatch might be the last straw to further amplify inflammation and increase blood viscosity [The presence of SARS-CoV-2 viral elements within the endothelial cells and the accumulation of inflammatory cells  may geneiscosity , throughiscosity .In this study, bacterial and fungal cultures were negative, in contrast with the recent finding of bacterial abscesses in 4 patients among 38 who succumbed to COVID-19, with a single fungal abscess in one . SARS-CoThis research was carried out primarily to formulate adequate therapeutic strategies based on the uncovered pathogenesis of C-ARDS. Increasingly, it was pointed out that LMWH administration is tied to a better outcome in patients with severe COVID-19 that are showing an elevated level of D-dimers . In addiNCT04401293, NCT04409834, and NCT04362085), as no data is available at this time for this indication. Even though minimal alveolar hemorrhage was found in the biopsy, managing the plurifocal fibrin microthrombi outweighs the risk of bleeding, which seems to be consistent with the low incidence of hemorrhagic complications in patients with severe COVID-19 coagulopathy [There are several possible explanations for the inefficacy of LMWH administered in our patients. On the one hand, perhaps an asymptomatic pulmonary embolism was already present before the hospitalization of our hypercoagulable patients , consideFrom another angle, these findings would seem to support the role of corticosteroid therapy. It is well known that glucocorticoids reduce endothelial leakage through decreasing capillary permeability and lower leukocyte migration to the inflammation site, while effectively stopping the inflammatory cascade. It is interesting to note that concerning C-ARDS, it was reported that methylprednisolone appears to have reduced the risk of death ; moreoveThe present study has not investigated the samples using immunohistochemical staining for additional insights, due to lack of materials. Despite the sample size, we believe our preliminary work could be a starting point to further provide concrete and substantial answers regarding the mechanism and pathogenesis of severe hypoxemia in C-ARDS and its management.A/Q mismatch in severe C-ARDS hypoxemia. Additionally, this paper points out the crucial benefit of anticoagulant therapy and corticosteroids in terms of dead space and shunt effect, respectively. Further investigation should be conducted in order to determine pertinently C-ARDS hypoxemia mechanisms and the proper ventilation and therapeutic management.The evidence from this study allows us to not only hypothesize that SARS-CoV-2 directly attacks lung alveoli leading to diffuse alveolar damage and more particularly plurifocal fibrin microthrombi in the peripheral vasculature beds, but also elucidate the mechanism behind V


The COVID-19 outbreak is a great threat to public health worldwide. Lung injury is the main outcome of COVID-19 infection; however, damage can occur in other organs including the liver. Currently, limited data are available that link underlying liver injury with the severe SARS-CoV-2 infection. This review summarizes the available data on liver test abnormalities in COVID-19 patients; critically evaluates the possible causes of liver injury and provides recommendations for clinicians. In laboratory tests, serum levels of liver test markers notably transaminase, gamma-glutamyl transferase and total bilirubin were significantly higher in severe patients with COVID-19 infection. The use of certain drugs especially lopinavir and ritonavir showed an association with the progression of liver damage in severe cases. Available data suggest that liver injury in COVID-19 patients may result from direct effect by the virus, immune-mediated inflammation or drug-induced toxicity. Some studies demonstrated that COVID-19 patients with pre-existing liver disease are at higher risk for hospitalizations and mortality. Therefore, the impact of pre-existing liver disease on treatment and clinical outcomes of COVID-19 should be determined. Large-scale clinical studies are needed to identify the causes of liver injury in patients with COVID-19 infection. The COVID-19 pandemic poses a great challenge to the international healthcare system. Older adults and those at any age with hypertension, coronary heart disease and diabetes are at higher risk of SARS-CoV-2 infection and severe disease course  in ICU admitted severe patients than non-severe patients , aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT) levels accompanied by moderately elevated prothrombin time (PT), and total bilirubin (TB) levels , 17–27. patients . In thatpatients . Moreovepatients . Similarpatients . Furtherpatients . Anotherpatients . Therefop < 0.001). Moreover, using antibiotics in the treatment also showed an association with the increased prevalence of liver test abnormalities in the regression model; however in other models this association was not significant  in the USA (p < 0.001) in patients with pre-existing liver disease than patients without liver disease and the relative risk was markedly higher in patients with cirrhosis (p < 0.001)  . Moreove< 0.001) . The aut< 0.001) . In that< 0.001) .Although, there is no direct evidence on liver failure in COVID-19 patients without pre-existing liver disease; several measures should be considered for all patients with COVID-19 during hospitalization. For example, individuals who are at high risk of hepatitis A infection should maintain personal hygiene and avoid large gatherings during this pandemic. Presently there is no evidence that people with hepatitis B and hepatitis C are at higher risk of infections. However, people living with hepatitis B and C should follow a healthy lifestyle and should also continue their regular treatments if they are affected by COVID-19 infection. People with pre-existing liver disease or after liver transplantation should follow the same preventive measures being followed by people with other medical conditions to avoid getting sick with or spreading COVID-19 infection. Moreover, attention should be paid to older people with severe medical conditions including liver test abnormalities as they are at higher risk of becoming severely ill from this infection. In addition, (i) the liver test markers should be measured and monitored regularly for all COVID-19 patients, (ii) Serologic testing for hepatitis B and C should take into consideration for COVID-19 patients during the measure of liver test biochemistries, (iii) Up to now, data are missing on the safety of drugs used for the treatment of COVID-19 patients with liver dysfunction, therefore, the detrimental effects of drugs on the liver injury during the treatment should be monitored and assessed frequently.Abnormal liver function test markers are more common in patients with severe COVID-infection. Available study findings support the hypothesis that liver injury might be associated with severe outcomes in COVID-19 patients. However, there is still lacks important data on larger studies to determine causality between COVID-19 and liver damage. The mechanism of liver injury in COVID-19 infection is not well-understood yet and seems to be multifactorial. The patients with advanced hepatic disease and those after liver transplantation are at high risk of progression to severe disease course. Therefore, it is recommended to identify and care for patients with chronic liver disease with priority within the limited healthcare resources. COVID-19 patients with liver injury are advised to be treated with drugs that are able to inhibit inflammatory responses and protect liver functions. In addition, the detrimental effects of certain drugs on the liver injury during hospitalization should be monitored and evaluated regularly. Attention should also pay toward modulating innate immune dysfunction in severe cases. Moreover, the effects of pre-existing liver disease on treatment and clinical outcomes of COVID-19 should be determined. Future research should focus on the potential causes of liver damage in severe patients with COVID-19 infection.The author confirms being the sole contributor of this work and has approved it for publication.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


This cross-sectional study examines the association between rates of mortality and readmission used by Medicare to determine financial penalties and to rank hospital quality with the case mix of patients with do-not-resuscitate status at US hospitals. Are risk-standardized mortality and readmission rates used in financial penalties and quality star ratings associated with the case mix of patients with do-not-resuscitate status in US hospitals?In this cross-sectional study of mortality cohorts comprising 4 884 237 inpatient encounters and readmission cohorts including 4 450 378 inpatient encounters, hospitals with a greater relative volume of patients with present-on-admission do-not-resuscitate status had statistically significantly higher 30-day risk-standardized mortality rates and statistically significantly greater odds of avoiding a Hospital Readmission Reduction Program financial penalty.Results of this study suggest that risk-standardized hospital outcomes should control for do-not-resuscitate status to avoid potential biases in pay-for-performance programs and hospital quality rankings. The Centers for Medicare and Medicaid Services’s (CMS’s) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings.To identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty.This cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis.International Classification of Diseases, Ninth Revision diagnosis code of V49.86  or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 . Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions.Present-on-admission (POA) DNR status was defined as an Hospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts  and HRRP financial penalty status (yes or no).P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs . A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty .Included in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts  and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts . Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9  vs 12.5 ; This cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance. The 30-day RSMRs and RSRRs are adjusted for approximately 20 to 40 condition categories, which include common comorbidities such as cancer, diabetes, renal failure, and psychiatric disorders. However, a patient’s do-not-resuscitate (DNR) status is not included in risk adjustment despite the greater severity of illness and mortality risk among patients with DNR status present on admission (POA).10The Centers for Medicare and Medicaid Services (CMS) has invested more than $130 million to develop, refine, and disseminate risk-standardized quality outcome models.13 To our knowledge, however, the association between hospital-level DNR prevalence and 30-day RSMRs and RSRRs has not been examined using national data from all Medicare-eligible US hospitals.Evidence is limited on the association between the prevalence of POA DNR status and the risk-standardized outcome rates at hospitals. Although hospital-level prevalence of POA DNR status varies, the DNR status of patients is documented reliably enough for inclusion in risk-standardization models and analyses, suggesting that adjustment for patient-level DNR status improves the predictive accuracy of 30-day RSMRs for AMI, HF, and pneumonia.In this cross-sectional study, we assessed the variation in hospital-level POA DNR status across the condition-specific cohorts used in value-based purchasing reimbursement, HRRP penalty, and the CMS Overall Hospital Quality Star Ratings. Our aim was to identify the association between the prevalence of POA DNR status and condition-specific 30-day RSMRs and RSRRs. We also evaluated the implication of POA DNR status prevalence for the HRRP financial penalties.STROBE) reporting guideline.This cross-sectional study was deemed exempt by the Mayo Clinic Institutional Review Board because the data used have been previously collected and deidentified. Informed consent was not required for this secondary research as contact with participants was not possible. We followed the Strengthening the Reporting of Observational Studies in Epidemiology  codes  and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes  to the index admissions, adhering to the inclusion criteria specified in the CMS inpatient quality-reporting, condition-specific mortality documentation method,14 and we excluded incoming acute care transfers and patients who were discharged against medical advice. In the AMI, pneumonia, and HF mortality cohorts, we also excluded patients who were discharged alive within 1 day of admission who were not transferred to an acute care facility. We did not exclude patients who arrived from hospice or who were discharged to hospice.We obtained data from the 2015 to 2018 CMS Limited Data Set Inpatient Standard Analytical File, which includes 100% of Medicare fee-for-service inpatient encounters across 4484 US hospitals. The condition-specific cohorts for mortality were AMI, HF, stroke, pneumonia, and COPD. To form these condition-specific 30-day mortality cohorts, we applied 15 and excluded all patients who died before discharge, were discharged against medical advice, or were discharged or transferred to an acute care facility. We did not exclude patients who arrived from hospice or were discharged to hospice. An important distinction is that CMS attributes 30-day readmissions to the discharging hospital, whereas 30-day mortalities are attributed to the admitting hospital. Thus, the transfer exclusion criteria for both RSMRs and RSRRs matched the exclusion criteria of the CMS method.The condition-specific cohorts for readmission were AMI, HF, pneumonia, and COPD. To form these condition-specific 30-day readmission cohorts, we followed the CMS inpatient quality-reporting 30-day readmission measures methodThe 30-day mortality study population consisted of all consecutive Medicare inpatient encounters from July 1, 2015, through June 30, 2018, with a principal diagnosis of AMI, HF, stroke, pneumonia, or COPD. The 30-day readmission study population included all consecutive Medicare inpatient encounters from July 1, 2015, through June 30, 2018, with a principal diagnosis of AMI, HF, pneumonia, or COPD. This period matched the data period used for the 30-day RSMRs and RSRRs publicly reported on the CMS Hospital Compare website as of August 2019.16 which provided annual HRRP penalty status by hospital. Any hospital that was not penalized at any time during the 3 fiscal years  was considered to have the binary outcome of avoided HRRP penalization.To ascertain the HRRP penalty status (yes or no) during fiscal years 2016 to 2018, we downloaded the Inpatient Pay-for-Performance Final Impact Summary File from the Advisory Board,ICD-9 diagnosis code of V49.86  or an ICD-10 diagnosis code of Z66  if the DNR order was POA per the corresponding POA indicator on the inpatient claim. Patients without a POA DNR status but who acquired DNR status after admission were included in the analysis. The outcomes were the hospital-level condition-specific 30-day RSMRs for AMI, HF, COPD, pneumonia, and stroke and the 30-day RSRRs for AMI, HF, COPD, and pneumonia as reported on the Hospital Compare website in August 2019. Briefly, the CMS calculates hospital-specific RSMRs and RSRRs by taking each hospital’s predicted vs expected (P/E) 30-day mortality or readmission rates from hierarchical mixed-effects risk-standardization models and multiplying these P/E ratios by the national mean 30-day mortality or readmission rate. We matched at the hospital level the values on the Hospital Compare website to the Inpatient Standard Analytical File data using the 6-digit CMS provider numbers. Hospitals without reported RSMR or RSRR data were excluded.We defined DNR status as an 17 version 3.7 and beta version, we assigned 29 binary comorbidity categories, known as Elixhauser comorbidities,18 to all patients on the basis of corresponding ICD-9 or ICD-10 diagnosis codes and Medicare severity diagnosis related group. We then used the Healthcare Cost and Utilization Project Comorbidity Index program to calculate the Elixhauser comorbidity index score from the weighted sum of these 29 comorbidity categories.20 The Elixhauser comorbidity index score is an integer ranging from –32 (lowest) to 99 (highest) comorbidity burden.Using the Healthcare Cost and Utilization Project Elixhauser Comorbidity Software,21 and with a condition indicator as a third independent variable and an interaction term between the condition and the Elixhauser comorbidity index score. We visually assessed the association between estimated probability of DNR prevalence (y-axis) and comorbidity burden (x-axis) From the logistic regression model, we calculated hospital-level observed vs expected DNR ratios and visually assessed the variation in both unadjusted condition-specific hospital-level DNR prevalence and the observed vs expected value using density plots. This visualization allowed us to consider the extent of interhospital differences in DNR documentation.Within each condition-specific cohort, we used the hospital-level RSMR or RSRR reported by the CMS as dependent variables in distinct inverse-variance–weighted linear regression models with the following independent variables: (1) quintiles of hospital-level DNR prevalence, with prevalence defined as the percentage of patients with POA DNR status; (2) mean hospital-level age modeled with a 5-knot restricted cubic spline function; and (3) mean hospital-level Elixhauser comorbidity index score modeled with a 5-knot restricted cubic spline function.2The inverse-variance weights were estimated from the CIs of the RSMR and RSRR in the Hospital Compare data, where SE = [(Upper CI – Lower CI) / (2 × 1.96)] and weight = 1 / SE.P value for DNR quintiles. We also compared the condition-specific mean RSMR and RSRR of the fifth (highest) quintiles with those of the first (lowest) quintiles using a Bonferroni-corrected P value. We conducted a sensitivity analysis using a more inclusive definition of DNR prevalence, in which patients were considered to have DNR status regardless of whether the DNR order was POA.The adjusted least-squared mean RSMR and RSRR (and 95% CIs) for each quintile of hospital-level DNR prevalence were reported along with the overall Wald To assess the association between hospital-level POA DNR status and HRRP penalty, we pooled patient-level data from the 4 readmission cohorts included in the HRRP penalty calculation and assessed hospital-level POA DNR status. Next, we used logistic regression, adjusting for age and Elixhauser comorbidity index score (using restricted cubic spline functions), to identify the association between POA DNR prevalence (independent variable) and avoidance of HRRP penalty (dependent variable).P < .05 indicated statistical significance. Data were analyzed from July 2019 to March 2020.All analyses were performed with SAS, version 9.4 (SAS Institute Inc). Two-sided This study analyzed 4 884 237 inpatient encounters across the 5 condition-specific 30-day mortality cohorts  and 4 450 378 inpatient encounters across the 4 condition-specific 30-day readmission cohorts . Within the mortality cohorts, DNR status was POA in 790 988 encounters (16.2%) and present at any time in 946 391 encounters (19.4%). Within the readmission cohorts, DNR status was POA in 674 030 encounters (15.1%) and present at any time in 776 071 encounters (17.4%). Condition-specific patient-level characteristics are presented in Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD.Across all condition-specific mortality cohorts, the Elixhauser comorbidity index score showed a statistically significant and generally nonlinear association with DNR status , and an association between RSMR and DNR prevalence quintile was found across all conditions  (P < .001), and an association between RSRR and DNR prevalence quintile was found across all conditions  ; pneumonia: 15.8  vs 15.0 ; COPD: 8.5  vs 8.1 [8.0-8.2]; all  < .001) . In all  < .001) . The sen < .001) .P < .001)  hospitals avoided an HRRP penalty from fiscal years 2016 to 2018. After adjustment for age and Elixhauser comorbidity index score, POA DNR prevalence was associated with HRRP penalty avoidance  .To our knowledge, this study is the first to use national Medicare claims data to assess the association between hospital-level prevalence of patients with POA DNR status and 30-day RSMRs and RSRRs across 5 conditions. A higher prevalence of patients with POA DNR status was associated with a higher RSMR. Conversely, a higher prevalence of patients with POA DNR status was associated with a lower RSRR. The 30-day RSMR and RSRR models do not adjust for POA DNR status, which may be a factor in unearned HRRP financial penalties and inaccurate CMS Overall Hospital Quality Star Ratings.23 Specifically, Walkey and colleagues23 found that hospitals with higher rates of DNR prevalence were more likely to be outliers for pneumonia mortality, and these authors hypothesized that the lack of adjustment for DNR status was associated with biased penalization of hospitals that in reality were providing high-quality, patient-centered care.23 Results of the present analysis not only support this hypothesis and provide expanded evidence of this pattern across multiple conditions and quality outcomes linked with CMS reimbursements but also offer the first direct evidence of the implications of this association for HRRP penalties. In addition, these results are consistent with findings from 2 studies of approximately 300 hospitals in the California State Inpatient Database (CA-SID). The CA-SID study reported an increase in the C statistic from 0.75 to 0.78 when comparing a CMS-based risk-standardized AMI mortality model to an improved risk model that adjusted for DNR status11 and a decrease in readmission rates for patients with pneumonia who had a DNR status.12 In the AMI analysis, Bruckel and colleagues11 found that 6 of 25 high-mortality outlier hospitals were reclassified as nonoutliers under the improved DNR risk-standardized model. Likewise, both the hospital-level prevalence and variation of POA DNR status reported in the CA-SID study  were similar to those in the CMS claims data . This similarity in DNR rates between CMS claims data and the CA-SID study, which showed an 84% agreement between the claims-based DNR status variable and medical record abstraction,24 suggests that POA DNR status is captured in the CMS claims data at acceptable rates to permit its inclusion in risk-standardized models.The association between hospital DNR case mixes and quality outcomes has been examined previously, notably by Walkey and colleagues.12 Under the current paradigm, hospitals that provide treatment to higher proportions of patients with complex conditions, terminal illness, and/or DNR status may be reported to have higher RSMRs and lower RSRRs than if an appropriate DNR adjustment was made.Patients with DNR status do not receive cardiopulmonary resuscitation during an acute event and are expected to have greater risks of 30-day mortality. Conversely, patients with DNR status are more likely to be discharged to hospice or end-of-life care, making 30-day readmission less likely.25 including whether DNR status acquired during hospitalization represented a preventable complication or natural history of disease. However, the same rationale exists for many of the clinical risk factors in the CMS models, and the variation may reflect true prevalence or accuracy of documentation. One example from the CMS models is protein-calorie malnutrition, a comorbidity for which reported hospital prevalence may reflect documentation rather than case mix.26 Furthermore, no evidence is available to suggest that hospitals could take advantage of the system by overdocumenting DNR status. Abusing the system for DNR status is less possible than for other comorbidities in the CMS models because, unlike clinical risk factors, the documentation of DNR status requires the additional burden of obtaining a patient’s legal consent and signature and/or even the presence of a witness in some jurisdictions. Conversely, no signatures or witnesses are needed for a physician to document risk factors, such as protein-calorie malnutrition.The omission of DNR status as a risk-adjustment variable was attributed to concerns about its availability and documentation in claims data,This analysis suggested that there are implications for HRRP penalization if POA DNR status was not controlled for during risk standardization. Specifically, for 2 hospitals of equivalent age and comorbidity case mixes among patients in the readmission cohorts, a hospital with a 10% greater absolute prevalence of POA DNR status (eg: 25% vs 15%) has nearly 2-fold better odds of avoiding HRRP penalization, implying that hospitals with low DNR prevalence could receive either a lower penalty or no penalty if POA DNR status were accounted for in risk standardization. Conversely, hospitals with high DNR prevalence could receive a larger HRRP penalty if POA DNR status were included in risk standardization.We did not assess the association of lower readmissions and higher mortality with value-based purchasing payments in hospitals with high DNR prevalence. Thus, although hospitals with high DNR prevalence may have an advantage under HRRP owing to the lack of risk adjustment for DNR status, it is possible that the reverse is true under value-based purchasing when the opposing effects of 30-day mortality are considered.This study has some strengths. It used a large national sample of CMS claims data from more than 4000 US hospitals. It also closely adhered to the CMS condition-specific cohort inclusion and exclusion criteria.11 which was validated with medical record reviews, we did not validate DNR documentation in this analysis. Furthermore, the inclusion of hospice patients may have introduced bias because these patients were excluded from the CMS metrics. Nevertheless, less than 2% of all encounters were hospice exclusions under the CMS method, so it is unlikely that this small percentage of encounters changed the results.14 We did not assess the association between in-hospital DNR status acquisition and RSMR or RSRR, which could be an important area for future research given the increasing consideration of patient-centered palliative care in the hospital setting. Fourth, socioeconomic and demographic factors were likely associated with patients’ DNR status. We did not adjust for these factors in these analyses, but they may help explain the associations between hospital DNR case mixes and RSMR or RSRR.This study has some limitations. First, it had an ecological design, which makes it possible that patients with POA DNR status were not direct factors in the increased mortality rates at hospitals with high DNR prevalence. However, given that patients with a DNR status are known to have greater mortality risks, there is strong reason to suspect that these patients may be direct contributors to higher mortality rates. Second, some of the variation in DNR status may be associated with interinstitutional coding differences, physician preferences and comfort with discussing DNR status, and patient preferences and values. Given these differences, misclassification of DNR status may have occurred that could have biased the results to the null, particularly among several hospitals with 0% reported DNR prevalence. Third, although the hospital-level DNR prevalence rates in this study were similar to those in the CA-SID study,Under the current CMS risk-standardization models for 30-day mortality and readmission, mortality rates appeared to be worse among hospitals with greater proportions of patients with POA DNR status, whereas readmission rates appeared to be better among hospitals with greater proportions of patients with POA DNR status. Findings from this study suggest that controlling for DNR status would improve estimates for 30-day RSMRs and RSRRs and would have direct implications for HRRP financial penalty and hospital performance rankings.


Barth syndrome is an X-linked rare disorder that typically affects only males. This study investigates 1) agreement between child self-reports and parent proxy-reports of HRQoL in boys with Barth syndrome and 2) relationship between parental HRQoL and parent proxy-reports of HRQoL for the child.Twenty-eight boys with Barth syndrome and their parents participated in this study. The PedsQL™ 4.0 and the PedsQL™ Family Impact Module were used to measure HRQoL of the boys, and the parents’ HRQoL, respectively. The Intraclass Correlation Coefficient was used to test agreement between the child self-reports and parent proxy-reports of HRQoL. The Spearman correlation coefficient was used to test the relationship between parental HRQoL and parent proxy-reports of HRQoL for the child.p < .05).The agreement between the child self-reports and the parent proxy-reports showed moderate-to-good agreement. Higher parental HRQoL was significantly related to higher ratings of the parents on their children’s HRQoL (This study broadens understanding of HRQoL of boys with Barth syndrome using both child self-reports and parent proxy-reports. The findings indicate that the parent proxy-report of HRQoL should be used in conjunction with the child self-report when making client-centered health decisions. First described in 1983, BTHS has been diagnosed in fewer than 500 living males worldwide; however, evidence suggests that BTHS may be significantly underdiagnosed due to the wide variety of clinical features associated with the disorder2. Therefore, the exact prevalence of BTHS remains unknown, though the disorder is estimated to occur in roughly 1 out of every 300,000 – 400,000 U.S. births3. Once regarded as primarily a cardiac condition, BTHS is now recognized as a multi-system disorder caused by a complex inborn metabolic error4.Barth syndrome (BTHS) is a rare, life-threatening X-linked genetic disorder that typically affects only males6. These symptoms are typically detected in infancy or early childhood, though diagnosis of BTHS is often difficult or delayed7. Though survival rates have improved significantly in recent years, individuals with BTHS remain predisposed to premature death due to cardiac issues and risk of infection related to neutropenia8. BTHS is frequently associated with physical effects, such as fatigue, pain, and feeding problems, as well as mild cognitive effects on visuo-spatial skills and motor learning12. These cognitive and physical effects may contribute to increased functional difficulties in academics, prevocational/vocational activities, and independent living skills13. In addition, boys with BTHS have been reported to have difficulty getting along with peers, often experiencing bullying because of their small stature and limits on physical exertion14. These peer interactions contribute to increased stress and may lead to lowered self-esteem14.While the exact nature and degree of symptoms vary considerably in different individuals, cardinal characteristics of BTHS include cardiomyopathy, neutropenia, muscle hypoplasia and weakness, growth delays and short stature, exercise intolerance, 3-methylglutaconic aciduria, and cardiolipin deficiency 11. Health-related quality of life (HRQoL) is a multidimensional construct that considers the impact of disease or health conditions on overall health and daily living16. Physical, emotional, and social functioning are considered when assessing HRQoL16. When a child can self-report his or her health status, child self-report is typically used17. However, if the child is very young or cannot complete the self-report due to illness or cognitive impairment, parents may be asked to report on their child’s HRQoL via parent proxy-report19. While some earlier studies misguidedly attempted to determine whose report was more “accurate”, current research typically accepts that both the parent and child reports provide invaluable information and that comprehensive evaluation should include consideration of both the child and parent perspectives17.Given the impact of BTHS on an individual’s functioning and well-being, it is understandable that initial research indicates that boys with BTHS have significantly reduced quality of life as related to their health22. However, in general, parents of children with chronic conditions typically report lower HRQoL for the children than the children themselves26. This contrasts with parents of healthy children, who tend to rate their children’s HRQoL higher than the children themselves17. By identifying which aspects of HRQoL show the greatest discrepancies in the level of agreement between child self-reports and parent proxy-reports of HRQoL, clinicians can better understand the differences in perspectives of HRQoL between a child and his or her parents27. This understanding may then provide useful information for guiding clinical decision making, such as when planning rehabilitation therapy goals.Previous studies of children with chronic conditions report varying degrees of agreement between child self-reports and parent proxy-reports of HRQoL28. Indeed, parents with higher perceived levels of parenting burden20 and distress29 generally provide lower ratings for their child’s HRQoL. Thus, the parent’s own HRQoL, including psychosocial factors, should also be considered when using parent- proxy reports or when investigating discrepancies between parent-proxy and child self-reports. This is particularly important for parents of children with BTHS, who may experience increased stress from dealing with the medical management of the condition, as well as emotional impact due to concerns about the child’s future and mortality13. Furthermore, assessing a parent’s HRQoL can help identify ways to better support the parent in caring for their child.Research suggests that the parent’s own psychosocial health may be particularly impactful on their perception of their child’s quality of lifeThere have been very few studies focusing on the HRQoL of children with BTHS, though the psychosocial functioning of children with BTHS and their parents has been studied by13 and Jacob and colleagues11. However, no prior studies have specifically focused on the agreement between child self-reports and parent proxy- reports of HRQoL in boys with BTHS. This is particularly important because research suggests that the nature and extent of discrepancies in parent-proxy reports and child self-reports of HRQoL vary depending on the condition17. Consequently, these factors must be investigated specifically with BTHS in order to best serve the needs of children with BTHS and their families. Therefore, the purpose of this study is to: 1) examine the agreement between child self-reports and parent proxy-reports of HRQoL for boys with BTHS and 2) investigate the relationship between parental HRQoL and parent proxy-reports of HRQoL for the child. We anticipate that parents’ own HRQoL will have positive relationships to the boys’ HRQoL as rated by the parents.Storch and colleaguesA two-group cross-sectional design was used to investigate 1) agreement between child self-reports and parent proxy-reports of HRQoL in boys with Barth syndrome (BTHS); and 2) relationship between parental HRQoL and parent proxy-reports of HRQoL for the child. Participants were boys with BTHS, ages 4 – 17  and one parent of each child (N = 28). Five children fell in the age group of 4 to 7, 12 children in the age 8-to-12 group, and 11 children in the age 13-to-17 group. Participants were recruited at the 2010 and/or 2012 Barth Syndrome International Conference. This conference is a scientific, medical, and family conference held in the United States every two years. The conference was attended by families from the US, Canada, United Kingdom, and France. For participants who attended both the 2010 and 2012 Conferences, only data from the 2012 Conference were used.This study was approved by the University of Florida’s Institutional Review Board. Before data collection, written informed consent was obtained from the parent, and assent obtained from the boy. Boys with BTHS completed an HRQoL self-report, and one of the parents completed the same HRQoL instrument using the parent proxy-report version.30. Subscale scores are calculated by scoring each item on a 5-point scale , then averaged within each subscale. The total score is the mean of all items across the four subscales with higher scores indicating better HRQoL. The reliability and validity of the PedsQL™ were established using 963 children with or without chronic illness and 1,629 parents31. Clinical cut points are established for the total score between 71 and 76 for children with moderate chronic conditions, and 70 or below for children with major chronic conditions32. The PedsQL™ subscale scores  and total score were used as variables in the analyses.The Pediatric Quality of Life Inventory Version 4.0 (PedsQL™) was used to measure the HRQoL of boys with BTHS. Both the child report and the parent report versions were used in this study. The PedsQL™ consists of four subscales: Physical Functioning (8 items), Emotional Functioning (5 items), Social Functioning (5 items), and School Functioning (5 items)33. Parental QoL was used as a variable and calculated as the mean of items from the subscales: 1) Physical Functioning (6 items), 2) Emotional Functioning (5 items), 3) Social Functioning (4 items), and 4) Cognitive Functioning (5 items). In this instrument, each item is scored on a 5-point scale  with higher scores representing better parental QoL. Evidence of reliability and validity of the PedsQL™ FI instrument was established with 458 parents of children or adolescents with chronic pain34. Statistical analysisThe PedsQL™ Family Impact Module (PedsQL™ FI) was used to assess parental quality of life (QoL). The PedsQL™ FI is a 36-item questionnaire measuring the domains of Parental QoL and Family Functioning35. Magnitude of mean differences in child self-reports and parent proxy-reports were analyzed using paired t-tests. Consistency in rank order relationships between the child and parent reports were tested using the Pearson Correlation Coefficient (Pearson r). Spearman Correlation Coefficient (rs) was used to test the relationship between parental HRQoL and parent-rated HRQoL of the child. SPSS version 24 was used for all data analyses .Distributions of the data were tested using the Shapiro-Wilk Test. Levels of agreement between child and parent reports were tested using a Two-Way Mixed-Effect Model  Intraclass Correlation Coefficient (ICC). ICC value of ≤ 0.4 indicates poor-to-fair agreement, 0.41 to 0.60 means moderate agreement, 0.61 to 0.80 is good agreement, and 0.81 to 1.00 represents excellent agreementp > .05). Findings from the ICC indicate that the agreement between child self-reports and parent proxy-reports was moderate-to-good. The results of the agreement in the ICC were good between the child self-reports and parent proxy-reports of HRQoL for Physical Functioning, Emotional Functioning, School Functioning, and Total Score. Moderate agreement was found in Social Functioning . Magnitude of PedsQL™ mean scores were significantly different between child self-reports and parent proxy-reports in Physical Functioning (t(27) = 3.267, p = .003) , Emotional Functioning (t(27) = 2.319, p = .028), Social Functioning (t(27) = 3.377, p = .002), and School Functioning (t(27) = 2.559, p = .016), as well as Total Score (t(27) = 4.386, p < .001). Parent proxy scores were lower than child reported scores across all subscales and Total Score   .r (26) = .581, p = .001). Thus, the higher the scores for parental HRQoL, the higher the scores for parent proxy-reports of HRQoL for the child.A positive relationship was found between parental HRQoL and parent proxy-reports of HRQoL for the child  point to the importance of incorporating measurement of both child self-reports and parent proxy-reports of HRQoL into research and clinical practice.Children with disabilities often have constrained social participation43. Findings may reflect parental concern regarding perceived impacts of physical difficulties on their child’s daily life and overall HRQoL. However, findings might also suggest the boys’ reduced awareness of the impacts related to their impaired health status35, or the presence of beneficial coping and/or compensatory strategies. Boys with BTHS grow up with physical symptoms that manifest at young ages4, thus creating opportunity for adjustment to physical constraints early in development. Nonetheless, the mean scores as reported by both the boys and their parents fell below clinically meaningful cutoff scores compared to moderate and major chronic health conditions (76 and 70 respectively)32. Therefore, our findings contribute evidence of diminished HRQoL for children growing up with BTHS.In this study, parents perceived the boys’ HRQoL to be lower than that reported by the boys themselves. This is consistent with previous research in children with chronic diseases, which found that parents tend to underestimate their child’s HRQoL28, parental stress44, and parental health45. Our finding contributes to the existing evidence that points to the importance of assessing the parent’s own HRQoL when the parent rates the child’s HRQoL.In addition, we found a significant positive relationship between parental HRQoL and parent proxy-reports of HRQoL for the child, indicating higher parental HRQoL was significantly related to higher ratings of the parents on their children’s HRQoL. The finding is consistent with other studies that reported associations between parent proxy-report of HRQoL for the child and parental HRQoL46. Assessing the child’s HRQoL provides valuable understanding for client-centered care. Quality of life is inherently subjective, and the child’s perceptions provide salient information. Therefore, children who can report on their perceived quality of life should report themselves. However, parent proxy-reports of HRQoL can provide information different from that reported by the child. Therefore, parent proxy-reports should be used in conjunction with the child self-report when making client-centered health decisions. This is because parent proxy-reports remain an important source of complementary information about the child’s HRQoL.Client-centered care concentrates on the child’s needs and functioning rather than focusing solely on the disease; such care contributes to high-quality, desirable, and effective healthcare services4, our sample size of 28 children represents almost 6% of the known population of individuals with BTHS. Sample size hindered our ability to assess potential impacts of parenting role  on proxy-reporting. Future research should include investigation of factors such as the child’s age, disease severity, family functioning, and family cohesion in order to elucidate child and parental factors affecting level of agreement between child self-reports and parent proxy-reports.This study broadens understanding of HRQoL of boys with BTHS using both child self-reports and parent proxy-reports. HRQoL measurement using both child self-reports and parent proxy-reports can contribute to demonstrating the value of interventions in research and clinical practice. Our sample is not large enough to generalize results. However, it is notable that according to the BTHS registry


A second scan was performed within one week in a subset (n = 5) to determine reproducibility of peak exercise measures. Reliability and agreement of the free-breathing LAX approach with the conventional SAX approach were assessed by intraclass correlation coefficient (ICC) and Bland-Altman plots, respectively. Normal control GLS reserve was also acquired in a separate set of 12 young, healthy control women  for comparison. Comparisons of LV volumes and function among all techniques at rest had good-to-excellent reliability (ICC = 0.80–0.96), and excellent reliability between peak exercise free-breathing LAX and SAX evaluations (ICC = 0.92–0.96). Higher resting heart rates with free-breathing acquisitions compared to breath-hold  reduced reliability for cardiac output (ICC = 0.67–0.79). Reproducibility of the free-breathing LAX approach was good-to-excellent at rest and peak exercise (ICC = 0.74–0.99). GLS exercise reserve was impaired in older women at cardiovascular risk compared to young healthy women . Real-time, free-breathing CMR with LAX evaluation provides a reliable and reproducible method to assess rest and peak exercise cardiac function, including GLS.The conventional approach to cardiac magnetic resonance (CMR) involving breath holds, electrocardiography-gating, and acquisition of a short-axis (SAX) image stack, introduces technical and logistical challenges for assessing exercise left ventricular (LV) function. Real-time, free-breathing CMR acquisition of long-axis (LAX) images overcomes these issues and also enables assessment of global longitudinal strain (GLS). We evaluated the reliability of a free-breathing LAX approach compared to the standard SAX approach and the reproducibility of free-breathing LAX. LV SAX (contiguous stack) and LAX (two-chamber and four-chamber) 3T CMR cine images were acquired four times within one scan in 32 women with cardiovascular risk factors (56±10 years, 28±4 kg/m The study of the mechanisms underlying the spectrum of physical exercise capacity from high performance to disease provides critical insight to our understanding of health, aging, and life span. Reduced exercise capacity is a hallmark of most chronic diseases including cancer, heart disease, lung disease, and diabetes –4 due inCardiac magnetic resonance (CMR) imaging is considered the gold standard for quantification of resting cardiac volumes and function based on its high accuracy and reproducibility. Standard resting CMR image acquisition requires breath holds and ECG-gating, both of which are challenging with exercise due to technical and logistical challenges from increased respiratory rates and cardiac translation. To overcome these exercise-specific issues, La Gerche et al.  evaluate2peak.The primary aim of this study was to describe and evaluate an acquisition and analysis approach using real-time, free-breathing LV biplane LAX images (two-chamber and four-chamber) to measure volumes and function, including GLS at rest and peak in-magnet exercise. First, the reliability and agreement of the real-time, free-breathing LAX (referred to as free-breathing hereafter) approach was compared with the conventional gated-segmented, end-expiration breath-hold (referred to as breath-hold hereafter) acquisition at rest. Second, reliability of the free-breathing LAX approach was compared to the free-breathing SAX approach at rest and at peak in-magnet exercise. Third, reproducibility of the free-breathing LAX approach was evaluated at rest and peak exercise. Lastly, upright cycle ergometer exercise tests were performed to enable the quantification of the extent of exercise stress achieved in-magnet and the cardiac function contributions to VO2), and no chronic medical conditions (or contraindications to CMR) were recruited to provide normal control data for exercise GLS. All participants were recruited by invitation, word of mouth, and posters in Edmonton, Canada. Ethical approval was obtained from the University of Alberta Research Ethics Board and all participants provided written informed consent.Much of the previous exercise CMR work has been performed in young, healthy/athletic and male only or mixed sex participants , 18, 19.All scans were performed on a 3T Siemens PRISMA system  using a 36-element chest/back array for signal reception. Breath-hold acquisitions were performed with ECG-gating at end-expiration and consisted of: 1) segmented contiguous SAX stack of 10–12 slices covering the entire LV; 2) single slices of two-chamber and four-chamber LAX views. Typical imaging parameters were: FOV = 400x300mm, matrix = 256x144, flip angle = 33°, GRAPPA = 3, TE = 1.09ms, TR = 2.38ms, 30 cardiac phases, 8 mm slice thickness, 2 mm gap, 75% phase resolution . Breath-hold, gated imaging was not performed with exercise due to the inability to acquire a usable ECG signal with exercise inside the bore [The full CMR protocol is shown in Localizer images were acquired, followed by gated-segmented, breath-hold acquisitions of SAX and LAX images.Free-breathing acquisitions of SAX and LAX images, with matched slice prescriptions to the breath-hold acquisitions above. Participants were instructed to breath gently.The participant then left the MRI scanner for ~10 minutes, and then was repositioned with an MRI-compatible stepping device attached to the table by medical suction . To prevent sliding on the table, the participant wore a snug-fitting vest that was attached to the stepper and their feet were strapped to each step.New localizer images were acquired, followed by a second set of resting free-breathing acquisition of SAX and LAX images to assess reproducibility with 2) above.The exercise protocol was then initiated without removing the participant from the bore. The protocol started with an initial workload of 20 watts and increased by five watts every 20 seconds. An auditory stimulus (beeping) was provided through the patient headphones to help maintain stepping frequency at 40 steps/minute. During the exercise test, a study investigator remained inside the scanner room to provide verbal encouragement and record the participant’s rating of perceived exertion  [http://segment.heiberg.se) [Breath-hold SAX acquisitions were processed using the freely available software Segment version 2.0 (berg.se) . The SegFor both free-breathing acquisitions (SAX and LAX), image processing with in-house software included: 1) selection of SAX and LAX slices for analysis; 2) identification of a complete cardiac cycle for each slice at similar positions in the respiratory cycle (end-expiration); 3) selection of end-diastolic and end-systolic images; 4) manual tracing of LV endocardial and epicardial contours on end-diastolic and end-systolic images . For freFor free-breathing LAX acquisitions, EDV and ESV were calculated using the biplane method of disks , 23 with0) to end-systole (L) relative to end-diastolic length, reported as a percentage, (L-L0)/L0*100 (Lagrangian strain) [For all analyses, stroke volume (SV) was measured as the difference between EDV and ESV, and CO measured as the product of SV and heart rate while ejection fraction (EF) was calculated as SV/EDV*100. Additionally, for the breath-hold and free-breathing LAX acquisitions, GLS was calculated as the average of the fractional change in length of the mid-wall contour from end-diastole (L strain)  for the  strain) . For botReproducibility of the free-breathing LAX evaluation approach at rest was assessed from the two sets of acquisitions separated by subject repositioning after leaving the MRI table and a delay of ~10 minutes in the 32 women recruited for reliability and reproducibility. For the evaluation of reproducibility of peak and reserve values, assessments were performed on two different days, separated by 2–7 days, to reduce the impact of fatigue. As the original 32 participants were already performing both the upright cycle ergometer and CMR test within a week, we asked a convenience sample of 5 of the 12 women comprising the young control group to perform the resting assessment and CMR exercise a second time 2–7 days later.2peak) was measured as the highest 20-second VO2 captured by continuous gas analysis . Continuous measurement of heart rate was captured by ECG. The CPET was performed either on a separate day from the MRI scan or on the same day with at least a one-hour break after the MRI exercise test completion according to participant availability and living distance from the testing facility.The 32 participants involved in the reliability assessment also performed a gold standard upright cycle ergometer  cardiopulmonary exercise test (CPET). The cycle ergometer CPET followed the same protocol as with the stepping device . The upright peak power and heart rate were compared to those achieved with the supine, in-magnet, stepper exercise to evaluate the extent of exercise stress. Additionally, oxygen uptake was measured to illustrate the relationship between exercise capacity and CMR-derived CO. Peak oxygen uptake  for two-way mixed methods with absolute agreement for the following comparisons between methods: 1) conventional resting breath-hold vs free-breathing LAX; 2) resting free-breathing SAX vs free-breathing LAX; 3) resting free-breathing SAX vs breath-hold SAX; and 4) peak exercise free-breathing LAX vs free-breathing SAX. Agreement was assessed by Bland-Altman plots and the bias and 95% limit of agreement for the free-breathing LAX method with the gold standard  at rest. The ICC was also calculated to assess reproducibility for our free-breathing LAX method at rest, peak exercise and reserve. Peak power output and heart rate achieved in the CMR exercise test were expressed as a percentage of those achieved in the CPET on the cycle ergometer to assess exercise stress. Linear regression was performed to determine the extent of the variance in VO2peak explained by peak CO . Both VO2peak and CO were normalized to body weight (kg) to standardize units. GLS exercise reserve was compared between the young normal control data and the 32 participants in the reliability assessment by an independent t-test. SPSS version 26.0  was used for all analyses.Age, anthropometrics, and VO2 (20–35 kg/m2), and VO2peak of 26.3±8.2 mL/kg/min (15.4–45.1 mL/kg/min). In terms of cardiovascular risk factors and other medical history, 16 (50%) had a history of cancer and anthracycline treatment, 15 (47%) were former smokers (no current), 14 (44%) were obese, 13 (41%) were overweight, 11 (34%) had arthritis, 6 (19%) were sedentary, 5 (16%) had thyroid disorders, 3 (9%) had osteoporosis, 2 (6%) had hypertension and 2 (6%) had hypercholesterolemia. The twelve women who provided the normal control data for GLS exercise reserve were 25±4 years old with an average body mass index of 22±2 kg/m2 and none of the above-mentioned chronic conditions nor any cardiovascular risk factors.All 32 participants completed the full CMR examination for the reliability assessment and the CPET. These women had an average ± standard deviation (range) age of 56±10 years (32–72 years), body mass index of 28±4 kg/mImage quality was sufficient for analysis for all subjects at rest and peak exercise. At rest, in comparison of the free-breathing LAX approach to the gold standard breath-hold SAX approach, ICC values ranged from 0.76–0.92 for all parameters  and BlanBoth resting heart rate and thus CO were higher with free-breathing compared to breath-hold acquisitions . Bland-AWe also compared the free-breathing SAX acquisitions to the conventional breath-hold SAX acquisitions which had good to excellent reliability .At rest and peak exercise, ICC values for comparing the LAX and SAX free-breathing acquisitions exceeded 0.90 for all parameters Tables .ICC values for reproducibility for LV volumes and function at rest and peak exercise ranged from 0.74 to 0.99 . ICC val2peak, a respiratory exchange ratio exceeding 1.1 [2peak .In this study, we described and evaluated an approach for the quantification of both resting and peak exercise LV volumes and function, including GLS, using real-time, free-breathing biplanar LAX acquisitions. We demonstrated that at rest, this approach had very good to excellent reliability and agreement with the gold standard, gated-segmented, end-expiration breath-hold, SAX evaluations for all parameters. At peak exercise, the free-breathing LAX approach was shown to have excellent reliability compared to a free-breathing SAX approach that has been previously validated against the direct Fick method for quantification of CO . ReproduThe LAX approach to analyzing LV volumes and function offers the advantage of analysis of only two slices, without the requirement of a large number of respiratory-phase matched contiguous slices to generate a volume, as needed for SAX analysis . Gated-sA unique aspect of the LAX approach in the current study was the acquisition of six parallel LAX slices to enable retrospective selection of optimally placed mid-ventricular LAX slices (one two-chamber and one four-chamber view). This technique helped to account for cardiac translation during exercise. The total acquisition time in this study for SAX and LAX slices were similar (~25 seconds each), but the LAX acquisition could be reduced to ~13 seconds with the acquisition of just three (instead of six) contiguous slices each view, which in our experience would be sufficient to account for translation. This is significantly shorter than the conventional technique , which would take 5–7 minutes depending on heart rate and recovery time between breath-holds. However, a limitation of this approach is that without the 3-chamber LAX view, the regional wall motion in the anteroseptum and inferior segments cannot be assessed.A primary motivation to using a LAX approach is the concurrent ability to measure GLS. Resting GLS has been demonstrated to be a strong independent predictor of cardiac events, cardiac mortality and all-cause mortality –12, 29; Peak exercise or reserve of LVEF and GLS could augment sensitivity of the detection of dysfunction over imaging performed at rest. For example, impaired LVEF reserve after just two anthracycline treatments, was shown to predict later development of cardiac dysfunction  and GLS While CMR LV volumes and function were similar for all compared approaches, resting heart rate measured during free-breathing acquisitions was 5 bpm greater than reported with gated, breath-hold acquisitions. This finding is consistent with a previous report of a 4 bpm drop in heart rate during breath holding . CO measOne aim of this study was to evaluate the extent of exercise stress achieved with the supine MRI stepper device compared to a CPET performed on an upright cycle ergometer. The supine body position required for CMR exercise is expected to lead to alternations in cardiovascular parameters compared to an elevated or upright position, including a lower heart rate, increased LV filling or EDV, and increased SV . In thisThe higher relative intensity achieved in our study has implications. First, it illustrates the feasibility of maximal exercise evaluations with CMR imaging in women with a wide range of ages, body sizes, and fitness levels. Second, cardiac function parameters measured at a higher intensity are likely more representative of cardiac function at peak exercise in an upright position. Lastly, the peak power output achieved with the supine stepper device could be taken to be representative of fitness levels, thereby potentially removing the requirement for a second exercise test in some studies.2peak, and increases with exercise intensity determined by both heart rate and SV. In this study, the increase in SV was due primarily to an exercise-related decrease in ESV. EDV changed minimally with exercise due to the supine posture, which is similar to previous reports [2peak was explained by our LAX CMR technique for assessing supine peak CO. The Fick equation states that the other determinants of VO2peak include hemoglobin concentration and arteriovenous oxygen difference [2peak, it is also evident that substantial variation in peak CO exists among participants with very similar VO2peak values. This finding applies across the spectrum of fitness levels measured in this study. This data could be used to determine the extent of central versus peripheral limitations to fitness within individual participants and personalize approaches to improve those limitations [Peak exercise CO is one of the primary determinants of VO reports . Over hafference . While Fitations .We did not compare our free-breathing LAX approach to direct evaluations of cardiac function . However, a free-breathing SAX approach has been previously validated in this way, and our free-breathing LAX data showed excellent agreement with our free-breathing SAX data. Respiration is known to influence cardiac volumes but we did not systematically collect information on the timing of respiration as has been done by others by measuring abdominal pressure with a plethysmograph . HoweverA real-time, free-breathing CMR approach to measure rest and peak exercise cardiac function using two-chamber and four-chamber view LAX images demonstrated good to excellent absolute agreement, with no major systematic differences from the gold standard approach of gated, breath-hold SAX evaluation of LV volumes and function at rest as well as free-breathing SAX evaluation at rest and peak exercise. Test-retest reliability of this approach was very good to excellent at rest and peak exercise. Overall, this real-time, free-breathing LAX CMR technique provides an accurate and reliable method to assess rest and peak cardiac function and could be used to expose clinical abnormalities that may be unidentifiable at rest and assess the contributions of cardiac function to exercise capacity.