Early detection of cognitive disability is challenging. We assessed the domain-specific, concurrent validity of the ages and stages questionnaire (ASQ-3) and the Bayley Scales of Infant and Toddler Development (BSID-III), and their ability to predict cognitive delay at school age.Within a longitudinal birth cohort study, a nested cohort of children was assessed using ASQ-3 and BSID-III at 24 months, and at 5 years using the Kaufmann brief IQ test (KBIT).p value, sensitivity:specificity) of same domain delay for motor  and language  at 2 years, but poor ability to detect cognitive delay compared to BSID-III . 204/278 children were assessed at 5 years. BSID-III language and cognition domains showed better correlation with verbal and nonverbal IQ . Both assessments showed high specificity and low sensitivity for predicting delay at 5 years.278 children were assessed using BSID-III and ASQ-3 at 24-months; mean(SD) BW = 3445(506) grams, M:F ratio=52:48. ASQ-3 had reasonable predictive ability (AUROC, The ASQ-3 cognitive domain showed poor concurrent validity with BSID-III cognitive score. Both ASQ-3 and BSID-III at 2 years poorly predict cognitive delay at 5 years.The ASQ-3 does not adequately detect cognitive delay or predict cognitive delay at 5 years, particularly for children with mild to moderate delay.The ASQ-3 shows reasonable concurrent validity with the motor and language subscales of the BSID-III. Neither early screening nor formal developmental testing demonstrated significant predictive validity to screen for cognitive delay at school age.This article highlights the need to analyse our existing model of using the ASQ-3 to screen for cognitive delay in children aged 2 years. These developmental milestones are, in themselves, approximate metrics for later cognition. The ICD-11 defines neurodevelopmental disorders as “behavioural and cognitive disorders that arise during the developmental period that involve significant difficulties in the acquisition and execution of specific intellectual, motor, or social functions”.5 The prevalence of neurodevelopmental delay has been reported as high as fifteen to eighteen percent, with approximately three percent having severe delay.8 Incidence increases significantly in high risk groups such as small for gestational age9 and preterm infants ranging from 50.2–62.5% in those born <29 weeks’ gestation.11 The American Academy of Paediatrics recommends periodic screening using a validated tool at nine, eighteen and thirty months of age to allow targeted early intervention.12Early intellectual function is a key predictor of adult health and well-being.5 helping to identify children requiring further developmental evaluation.8 The Bayley Scales of Infant and Toddler Development III (BSID-III) is a more comprehensive developmental assessment undertaken by a trained healthcare professional. These tools are further described in the methods section. Together, these tools comprise a valuable resource in clinical practice, however, there is a paucity of research, with often contradictory views on their validity, agreement and predictive value for later cognitive outcome. One systematic review identified three papers which assessed the ASQ-3’s validity in conjunction with the BSID-III (the accepted gold standard at this age).16 The ASQ-3 self-reports a sensitivity of 86% and a specificity of 85%.5 However, independent studies report sensitivity and specificity ranging from 67–100% and 65–93%, respectively21 when compared to the BSID-III (as a gold standard). Velikonja et al. indicated that the lack of research and quality of studies made it difficult to draw any clear conclusions on sensitivities nor specificities of the ASQ-3.16 The ASQ has been demonstrated to have a higher sensitivity in high-risk groups, particularly for those with severe delay. Unfortunately, the rate of detection is markedly lower for mild and moderate delay, a group with a much higher prevalence in the general population.19 As many countries are using the ASQ-3 as the standard developmental screening tool for healthy population screening, it is important to establish its validity and reliability for both detecting developmental and cognitive delay in addition to predicting outcomes at school age.The Ages and Stages Questionnaire 3 (ASQ-3) is a parentally completed developmental screening tool which can be performed in 10–15 minutes22 By including high risk infants in the cohort developing the BSID-III, the scores in each domain is right skewed which lowers cut-off values for delay. This increases the likelihood of children with mild to moderate delay remaining undetected by this tool. The ability of the BSID-III to detect intellectual disability has been questioned.23 This study aimed to assess the domain specific, concurrent validity of the ASQ-3 to predict cognitive delay as detected by the BSID-III at 24 months. We also aimed to assess the predictive ability of both the ASQ-3 and the BSID-III to predict cognitive outcome at school age.The BSID-III is equally not without its own confounding factors. Ten percent of the cohort used for establishing normative ranges for each of its three domains; motor, verbal and non-verbal; was comprised of high-risk infants including children with trisomy 21, cerebral palsy, pervasive developmental disorder, premature birth, specific language impairment, prenatal alcohol exposure, birth asphyxiation, small for gestational age.24 Participants were recruited from the Cork BASELINE Birth Cohort Study born between March 2009 and September 2011. BASELINE (http://www.baselinestudy.net) was established in 2008 as a follow-up to the Screening for Pregnancy Endpoints (SCOPE) pregnancy study. The study participants, aims and methods of the Cork BASELINE birth cohort study have been previously reported.28 Ethical approval for the Cork BASELINE Birth Cohort Study was granted by the Clinical Research Ethics Committee of the Cork Teaching Hospitals (Ref: ECM 5 (9) 01/ 07/2008), and the study is registered (Ref: NCT01498965) with the United States National Institutes of Health Clinical Trials Registry .This study was a secondary data analysis of a nested cohort of the Cork BASELINE Birth Cohort Study.28 (SGA and TGA) were invited for additional assessments using the BSID-III at 24 months. TGA refers to infants with a body fat mass <10th percentile. The outcome of these children and their sex and age matched controls has been previously described.28 Controls were born at term and had birth weights which were appropriate for gestational age (AGA). All BASELINE cohort participants were invited to attend for an IQ assessment at 5 years using the KBIT.Within the birth cohort, a nested cohort of children who were small or thin for gestational age5 It aims to identify children not achieving age-appropriate developmental milestones across these domains and subsequently refer them for further assessment. It’s simplicity and short completion time allow for quick evaluation and identification of children requiring further developmental evaluation.8 The BSID-III is undertaken by a trained healthcare professional over one to two sessions requiring direct observation of skills in the domains of cognition, language, social-emotional, motor and adaptive behaviour. It is used to identify children with developmental delay who may require intervention services. The KBIT is a professionally administered intelligence quotient assessment which measures verbal and non-verbal intelligence from the ages of 4–90 years.Three developmental assessment tools were utilised in this study; the ASQ-3, BSID-III and KBIT. The Ages and Stages Questionnaire 3 (ASQ-3) is a parentally completed screening tool which can be performed in 10–15 minutes assessing the domains of communication, gross motor, fine motor, problem solving, and personal-social at a variety of ages and in multiple languages.The BSID-III assessments were administered by research psychologists trained in BSID-III. ASQ-3 questionnaires were posted to the parents the week prior to assessment and they were asked to complete in advance. Any queries were answered on the day of assessment. KBIT was performed by a research nurse trained in the administration of the test. Parents and caregivers were contacted by telephone.29Statistical analysis: The ASQ-3 was compared with the BSID-III across three domains; non-verbal, verbal and motor. The ASQ-3 and the BSID-III were also compared against the KBIT the using verbal and non-verbal domains. The cut-offs which normally trigger further investigation lie one standard deviation (SD) below the population mean (i.e. a score of less than 85) in the BSID-III and KBIT. These cut-off values were adjusted to one SD below the study cohort’s mean scores in each domain to ensure a geographically relevant cut-off and to account for the Flynn effect.For the purposes of our analyses, ASQ-3 scores were treated as pass or fail on each domain. An overall domain score landing in the black area which means that the child needs further assessment was deemed a referral trigger. These “fails” were compared with BSID-III which fell <1 SD below the cohort mean. A score in the grey zone which indicates that a child should be rescreened within six months was not considered a fail for the purposes of this study. When comparing the ASQ-3 and the BSID-III, the fine and gross motor domains for the ASQ-3 were combined to allow direct comparison to the BSID-III motor composite score. If a child triggered a referral for either fine motor, gross motor or both, then this was considered a referral for the purposes of comparison with the BSID-III motor composite score. The communication domain of the ASQ-3 was compared with the language domain of the BSID-III. Finally, the problem-solving score of the ASQ-3 was compared with the cognitive domain of the BSID-III. The problem-solving score (ASQ-3) and the cognitive domain (BSID-III) represent non-verbal for the purposes of our analyses and were compared with non-verbal scores for the KBIT.The ASQ-3, the BSID-III and the KBIT were analysed for their levels of agreement and suitability as screening and assessment tools. Statistical analyses were completed using IBM SPSS Version 26 software. Descriptive statistics, Pearson correlations and concurrent validity and predictive ability were measured using sensitivity, specificity and area under receiver operating characteristic curve (AUROC). Sensitivity and specificity were used instead of positive and negative predictive value as this nested cohort of children who were small or thin for gestational age is at higher risk of developmental delay than the general population.Four hundred and ten subjects were invited for both ASQ-3 and the BSID-III at 24 months. Participant flow chart and reasons for exclusion are outlined in Fig. The subjects excluded due to non-completion of both assessments were compared with those who completed both (ASQ-3 and BSID-III). No significant differences were observed between the two groups (completed versus non-complete data groups) in their gestational age in weeks; sex distribution; maternal education; maternal marital status and maternal occupation. Figure Outcome at 24 months: At 24 months, the mean(SD) BSID-III cognitive composite score of the cohort was 98.63 11.30). The cohort specific cut-off of <1 SD was calculated as 87, with 29/278 (10.4%) children having a score below this cut-off. At 24 months, only 14/278(5%) children failed the problem-solving domain of the ASQ-3 including 8 false positives. For explanation of each domain’s geographically relevant cut-off  had a low average verbal KBIT score and 24/204(11.8%) had a low average non-verbal KBIT score. The KBIT IQ composite score n, mean, (SD) = 204, 105.14, (8.88). The correlation between the BSID-III domains and KBIT scores at 5 years is displayed in Table .6% had a19We have shown that the ASQ-3 has poor sensitivity for detecting mild-moderate delay when compared with the BSID-III at 24 months. Both the ASQ-3 and BSID-III demonstrate poor ability to predict low average cognitive outcomes at school age. However, BSID-III has slightly better sensitivities and significant AUROC’s whereas ASQ-3’s AUROCs are not statistically significant. The motor and language domains of the ASQ-3 performed best, whilst the cognitive domain showed the lowest concurrent validity and predictive ability at both time-points. The specificity of the ASQ-3 was high in all domains. This confirms the ability of the ASQ-3 to identify normative development, but also highlights that many children with cognitive difficulties at school age will be missed in countries relying on the ASQ-3 as a screening tool. Similar profiles of low sensitivity and high specificity have been reported for the ASQ-2 when compared with the Bayley Scales.30 The ease of administration, requiring care-giver report only, makes it attractive for widespread screening, particularly in low-resource countries. Adequate reliability and validity of the ASQ have been demonstrated in U.S. populations and other high-resource countries.31 Validity reported by the developers of the ASQ has used comparisons to early intervention/early childhood special education eligibility evaluations using the Battelle Developmental Inventory in high-risk groups referred for further assessment.14 These groups consisted of more than 50% deemed eligible for special education, making it difficult to compare this to the widespread use of the ASQ as a developmental screener in a healthy population. Data on the predictive ability of the ASQ has been reported in a number of countries, using locally adjusted versions of the ASQ in non-English speaking cohorts with calculated ROC ranging from 0.66 to 0.87.32 Data in English-speaking populations are limited with a systematic review having identified 32 publications related to this from a combined ten cohorts; of these ten cohorts only one used the English language ASQ.33The ASQ is one of the most widely used screening assessments of early child development, forming part of the recommended screening schedule in many countries, including the public health assessment protocol in Ireland, where this study was developed.34 and Gollenberg et al.18 . When one looks at all-domain performance, 84.6% of those classified by the BSID-III as having severe delay were detected by the ASQ-3. Only 45.5% were detected in the mild to moderate category. Often, children with severe delay will likely be detected on routine observation by parents and medical professionals without the requirement for validated screening tools. Most children with cognitive delay fall into the mild to moderate34 category which is the cohort least likely to be detected by the ASQ-3 alone. These children are also the group who may benefit most from early detection and implementation of early intervention services.Although the ASQ is designed to give a global impression of the child’s development, the assessment is divided into individual domains. Our focus for this study was the prediction of cognitive delay. Whilst language and motor assessment concurrent validity between the ASQ and the BSID-III was more robust, detection of mild-moderate cognitive delay was very poor. We have also shown that the ASQ-3 is more effective at detecting severe delay than mild or moderate delay, in keeping with previous reports from Sheldrick et al.35 It should be noted that although KBIT is an abbreviated IQ assessment and is not a comprehensive test. It has demonstrated good reliability in assessing cognitive outcomes at school age compared to formal testing using either WISC-IV General Ability Index or the WAIS-III. It has been used in a number of large birth cohorts due to its ease of administration. It does not however give a detailed cognitive profile allowing separation of individual cognitive functions. Nor does it predict specific learning disabilities.38 Whilst the ASQ-3 showed excellent specificity, the sensitivity for the detection of low average non-verbal IQ was very low. This raises questions regarding the utility of the ASQ-3 as a screening tool for cognitive delay. We require screening tools to identify children suitable for further testing. As it is currently scored, the ASQ-3 has such high specificity that further testing is almost not required, whilst its low sensitivity means that the majority of cases of cognitive delay will be missed.This study focused on assessing the ASQ-3’s performance at two years, as recommended in Ireland’s national public health screening schedule, and prediction of cognitive outcomes at school-age. The communication and problem-solving domains of the ASQ-3 were assessed for their ability to predict future performance in the verbal, non-verbal and total intelligence domains of the KBIT respectively. The incidence of delay was similar to that seen in the total BASELINE cohort at 5 years.40 Our study supports the need for better early assessments of cognitive ability and executive function to allow useful intervention and support prior to school entry. Both assessments were performed once at 24 months and it may be that repeated measures to assess the child’s trajectory over time may improve prediction.41The BSID-III performed better than the ASQ-3 for prediction of outcome at 5 years. This is to be expected with a directly administered test. The overall prediction of both verbal and non-verbal IQ was acceptable, with AUROCs between 0.695 and 0.723. However, this was driven by very high specificity, meaning that the majority of children with low average IQ scores at 5 years were not detected by a BSID-III at 24 months. It is estimated that less than half of children with developmental delay are detected prior to school entrance, with the vast majority of those detected receiving no intervention in the very early years.1 We must recognise that current screening, whether by caregiver report or direct developmental assessment does not detect the majority of cases of low average cognitive ability. New methods of early assessment are required if we are to give these children opportunities to reach their full potential. This particularly applies to mild-moderate delay where early intervention may improve that child’s academic attainment and consequently their future quality of life.46It is not surprising that the ASQ assessments based on caregiver report perform better in the quantification of tangible skills such as speech and motor milestones. As with all parentally reported assessments, there is an inherent bias, which may be influenced by the parents own socio-economic background, parenting experience or education level. Care-givers assessment of a child’s early problem-solving skills is inherently more difficult. However, it is these early executive functions which are most highly predictive of the lifetime course, opportunities and health of that child.33 This study utilised BSID-III as the gold standard at two years of age. It should be noted that BSID-IV was released in 2019. It should be noted that the Bayley Scales of Infant and Toddler Development IV did not include the high-risk populations in the normative group as was the case for the BSID-III. Outcomes at five years of age will not be available with the BSID-IV for several years. The BSID-IV has updated its administrative protocol for ease of use and to shorten the assessment, without altering expected scores. Thus we feel that our findings are relevant for current use of either BSID-III or BSID-IV.47 There were some incomplete data in this study as patients were excluded due to non-completion of either the ASQ-3, the BSID-III or both. Subsequently, 74 further patients did not complete the school-age assessment at five years. However, the children with incomplete data did not differ from the group in their demographic or socioeconomic variables. The amount of time taken to complete the BSID-III may be a barrier to its completion with 45/278 requiring more than one session to complete the assessment. Each session may take 60–90 min. However, each subject was afforded the opportunity of multiple sessions to complete the BSID-III assessment. Our study was not focused on examining the acceptability or ease of use of both methods of assessment, but instead a direct comparison of detection of domain-specific delay.We have focused on examining the agreement between neurodevelopmental performance at 24 months and subsequent performance at school age as data on predictive performance is scarce.In summary, the parentally completed ASQ-3 has poor concurrent validity with the cognitive scales of the BSID-III at the same age. It is more effective at identifying children with severe developmental delay but does not achieve sensitivities requisite with an effective screening tool. Both the ASQ-3 and the BSID-III demonstrate poor ability to predict cognitive ability at school-age in both verbal and non-verbal domains. Both tests have high specificity and are adept at predicting a normal performance at 5 years. The majority of children with low average IQ at school age will not be detected using current screening methods.


Background: Defining the characteristics of healthcare worker (HCW) attitudes toward the coronavirus disease 2019 (COVID-19) vaccine can provide insights into vaccine hesitancy. This study’s goal is to determine HCWs’ attitudes regarding the COVID-19 vaccination and reasons for vaccine hesitancy. Methods: This cross-sectional study surveyed HCWs working in institutions in Saginaw, Sanilac, and Wayne counties in Michigan (N = 120) using tipping-scale questions. Analysis of variance and t-test were used to measure HCWs’ attitudes toward the COVID-19 virus and vaccines. Results: Most HCWs received (95.9%) and recommended (98.3%) a COVID-19 vaccine. The top three factors that HCWs cited for recommending a COVID-19 vaccine were: (1) efficacy of the vaccine, (2) current exposure to patients with active COVID-19 infection and risk of virus spread, and (3) safety of vaccine and long-term follow-up. Female HCWs or HCWs aged 25–54 years were more concerned about contracting COVID-19. Physicians or HCWs aged 55–64 were less concerned regarding the effectiveness and side effects of the vaccine. Conclusions: Gender, age, ethnicity, provider type, and medical specialty showed statistically significant differences among COVID-19 attitudes. Focusing educational efforts on HCW demographics who are more likely to have negative attitudes can potentially decrease vaccine hesitancy. Coronavirus disease 2019 (COVID-19) is a disease caused by the SARS-CoV-2 virus that was first identified in Wuhan, China, and spreads from human-to-human through airborne particles and droplets ,4. HowevAs preliminary research on a coronavirus vaccine was being conducted, herd immunity became the standard for gaining control of the COVID-19 pandemic . Herd imA review summarized the findings of 74 studies that investigated COVID-19 vaccination acceptance among HCWs, illustrating that almost two-thirds of HCWs were willing to accept a COVID-19 vaccine. This review highlighted common reasons for hesitancy, such as concerns about vaccine safety and efficacy, speed of development, certain roles of HCWs, and mistrust in the public health response . HoweverEven in the face of growing vaccine hesitancy, HCWs still remain the most important source of information regarding vaccines and their efficacy to the general public ,25. The This cross-sectional, pilot study collected data using questionnaires at healthcare institutions in Michigan. This study was conducted from 12 July 2021 to 30 November 2021  26]. Re. Re26]. Participants were recruited from outpatient clinics, an academic hospital, and a specialty clinic in four counties throughout Michigan: Isabella, Saginaw, Sanilac, and Wayne counties in Michigan . This inA 45-item anonymous online-based questionnaire was distributed to the HCWs . The surA sample size of N = 120 is estimated to have 80% power at a two-tailed 0.05 level of significance to detect a medium effect size . Independent two-sample t-tests and analysis of variance were used to examine the HCWs’ demographics and their attitudes toward the COVID-19 virus and vaccines. We used the Statistical Package for Social Sciences (SPSS) for all analyses reported in this study.The cohort consisted of 120 HCWs residing in 16 counties in Michigan. The demographic profile presented in Literature-validated ,33,34,35When comparing the responses between female and male HCWs , female When comparing the responses between different age groups , those bWhen comparing the responses between race and ethnicity , both raWhen comparing the responses between provider types , nurse pWhen comparing the responses between family medicine specialties and non-family medicine specialties , more faWhen comparing the responses between primary care specialties and non-primary care specialties , more noBased on frequency, the two most important factors for recommending a COVID-19 vaccine to a patient were: efficacy of the vaccine, and current exposure to patients with active COVID-19 infection and risk of virus spread. These two factors accounted for 74% of all responses, as presented in Out of all of the participants, only five HCWs said they were unlikely to receive a COVID-19 vaccine . Those who selected “Definitely will not” selected that “I am NOT planning on getting vaccinated” on the survey. The HCW who selected “Very unlikely” selected “Yes, I have received the first dose of a two dose COVID-19 vaccine ”. All five of these HCWs selected that (1) the COVID-19 vaccines did not have adequate testing and results, (2) the vaccines were developed and tested too quickly, and (3) they were concerned about the side effects and long-term effects of the COVID-19 vaccines. When asked “Given what you currently know, how likely would you recommend a COVID-19 vaccine when it becomes available to the patients you consult or treat?”, three HCWs selected “Somewhat likely”, one HCW selected “Not too likely”, and one HCW selected “Not at all likely”. The demographic characteristics of these five HCWs varied among gender, age, ethnicity, provider type, and specialty. The ranking of the most important factors for recommending a COVID-19 vaccine varied among these five HCWs.Previous studies have utilized certain HCWs’ views as predictors of their intent to get vaccinated and to recommend vaccination to high-risk patients . In addiThe impact of this study is strengthened by the 29-item opinions based on previous literature. This 29-item approach allowed our conclusions to support or contrast findings in other studies. It also allowed us to look at many different attitudes and concerns regarding both the COVID-19 virus and vaccines, which allowed us to find a diversity of associations. In addition, this study specifically focused on HCWs, a demographic that is less studied when it comes to vaccine hesitancy, and HCWs of diverse provider types ,11. Finap = 0.011). Elevated concerns may be associated with increased vaccine uptake. A study on gender gaps in COVID-19 vaccine research by Vassallo et al. mentioned that the majority of the healthcare and hospital population are women, and when vaccines were rolled out on a risk-based prioritization, more women than men received the COVID-19 vaccine [The study cohort presented a clear dichotomy between the male and female genders on their view of the COVID-19 vaccine: female respondents were significantly more concerned about being infected by the virus than male respondents ( vaccine . p = 0.0037). However, our study focuses on HCWs who are 25 years of age and older, and thus the data reveals the vaccine perception and acceptance in adult HCWs, which is the first report to our knowledge in this target population. Our study demonstrated statistically significant differences among those in the 55–64 years age range and the 65+ years age range with respect to vaccine views and hesitancy. The 55–64 years range was the least concerned about self-infection as compared to all other age ranges. Those in the 65+ age range reported they had negative experiences with previous vaccines, shared concerns over the historical mistreatment of those identifying as Black, and were more concerned about the side effects of the COVID-19 vaccines compared to other age groups. The results of this study differ from the results from Shih et al., which found more of a relationship between risk perception and vaccine acceptance for those aged 18–23 years compared to those older than 56 years [The non-White races, which included Black, Asian, native Hawaiian, or other Pacific Islander, and other racial groups were more likely to agree that the vaccine was developed too quickly and that they were instructed to receive the vaccine. In contrast, people identifying as White were more likely to agree that their social circle was not yet receiving the vaccine.Racial and ethnic disparities exist in all areas of medicine, and this is no different in vaccination acceptance and vaccine course completion. Previous research has shown that certain racial and ethnic groups are traditionally less likely to be vaccinated when compared to White populations within the United States. Li et al. showed lower H1N1 vaccination rates among non-Hispanic Black and American Indian/Alaskan native populations enrolled in Medicaid when compared to White populations enrolled in Medicaid across the United States . HoweverThe similarities seen in the data collected between the two racial categories within this study could be due to similarities in the location of the surveyed individuals and the high baseline of agreement among physicians for vaccine acceptance. As will be discussed at length in the following section, the surveyed physicians had high levels of acceptance towards the COVID-19 vaccine and were willing to receive the vaccine upon availability . ConsideWhen analyzing the cohort by HCW provider type, nurse practitioners and other clinicians-in-training were more concerned about long-term side effects when compared to their physician counterparts. These trends indicate a lower level of trust in the vaccine research amongst those in the nursing profession when compared to the other analyzed provider types. Previous studies have suggested that those within the nursing profession tend to show suboptimal uptake of vaccines ,43,44. AThe significant trend displayed in the HCWs analyzed within this study, with physicians tending to have lower levels of vaccine hesitancy than nurse practitioners and other clinicians-in-training, also matches research with vaccine acceptance amongst physicians. Day et al. surveyed physicians and showed that most of the physicians were willing to receive the COVID-19 vaccine immediately upon availability and felt comfortable easing patients’ concerns regarding the vaccine . AdditioThe trends of vaccine acceptance versus hesitancy amongst the provider types could be directly tied to varying levels of research education and the direct impact of research amongst the varying healthcare professions. Therefore, as the length of time in educational years of training increases, it could be assumed that the healthcare provider would have higher levels of acceptance towards medical research, research scientists, and, therefore, vaccinations. Additionally, it could be assumed that not only the length of educational years, but the length of educational years that focus on the natural sciences rather than solely on clinical skills for patient care could lead to higher levels of trust in research and vaccine acceptance. p = 0.028). A study by Ofei-Dodoo et al. found that a great proportion of family physicians intended to be vaccinated with an approved COVID-19 vaccine. The study surveyed 307 practicing family physicians and unveiled a significantly greater rate of family medicine physicians reporting their intention to be COVID-19 vaccinated than the rate of family medicine physicians who were hesitant to receive the COVID-19 vaccines [Even among clinicians with similar levels of baseline training in the sciences, there can be different viewpoints among various subspecialties. A significant number of family medicine physicians reported that they believed the COVID-19 vaccine had adequate testing compared to non-family medicine physicians (vaccines . It coulp < 0.008). A national survey conducted from February to March 2021 surveying pediatric primary care professionals (N = 1047) found that most primary care physicians (83%) supported COVID-19 vaccination mandates for HCWs, especially among individuals who perceived HCWs to be at a greater risk of contracting the virus [When stratifying the data based on primary care specialties, our own study revealed a statistically significant increase in non-primary care physicians who have a prior negative vaccination experience compared to primary care clinicians  protecting patient safety, (2) improving the occupational health and well-being of the HCWs themselves, (3) role modeling and public trust, as HCWs’ attitudes and behaviors towards vaccination can significantly impact public perceptions and acceptance of a COVID-19 vaccine, and (4) improving the outbreak response and preparedness of these frontline workers. Understanding the attitudes and beliefs of HCWs towards vaccinations and applying these results to build interventions to address vaccination hesitancy and other public health concerns is a critical tool in mitigating the spread of such diseases within the community and public.


It has been suggested that people with mental disorders have an elevated risk to acquire severe acute respiratory syndrome coronavirus 2 and to be disproportionally affected by coronavirus disease 19 (COVID-19) once infected. We aimed to analyse the COVID-19 infection rate, course and outcome, including mortality and long COVID, in people with anxiety, depressive, neurodevelopmental, schizophrenia spectrum and substance use disorders relative to control subjects without these disorders.This study constitutes a preregistered systematic review and random-effects frequentist and Bayesian meta-analyses. Major databases were searched up until 27 June 2023.n per effect-size = 114837) regarding associations of interest. Infection risk was not significantly increased for any mental disorder that we investigated relative to samples of people without these disorders. The course of COVID-19, however, is relatively severe, and long COVID and COVID-19-related hospitalization are more likely in all patient samples that we investigated. The odds of dying from COVID-19 were high in people with most types of mental disorders, except for those with anxiety and neurodevelopmental disorders relative to non-patient samples . Bayesian analyses confirmed the findings from the frequentist approach and complemented them with estimates of the strength of evidence.Eighty-one original articles were included reporting 304 cross-sectional and prospective effect size estimates (median Once infected, people with pre-existing mental disorders are at an elevated risk for a severe COVID-19 course and outcome, including long COVID and mortality, relative to people without pre-existing mental disorders, despite an infection risk not significantly increased. Articles were excluded when (1) no relevant outcome data could be extracted, even after we had been in contact (or had made reasonable attempts hereto) with the corresponding author of the article, or when (2) no original data were reported . When articles reported on overlapping data sets, we included the article that was most informative for our purposes .healthy control). Outcome variables included (1) infection risk – including break-through infection –, presented as a percentage of SARS-CoV-2 positive tests/self-reports in the populations under study and (2) COVID-19 course variables, further specified as (a) indicators of the severity of the disease , (b) hospitalization rates, (c) intensive care unit (ICU) admission rates, (d) presence of symptoms of long-COVID/persistent COVID-19 symptoms of any kind  assessed according to diagnostic systems such as Diagnostic and Statistical Manual of Mental Disorders, Fourth or Fifth Edition (DSM-IV or V)  From included papers, we extracted demographic data , clinical data , suspected virus type (estimated from the time frame in which the data were collected), methodological data  and outcome data  on outcome data). Data extraction was performed independently by two members , MM) of the review team.et al., We extracted odds ratios (ORs) and corresponding 95% CIs as measures of effect. Where reported, we extracted data from analyses that controlled for the largest number of potential confounders or that came from propensity-matched samples. When results were reported as hazard ratios (HRs) or risk ratios (RRs) and raw data were not available, we interpreted these as an OR when the incidence of the reported outcome was <20%. HRs and RRs based on data reporting on an incidence of the outcome of ≥20% were transformed . Sensitivity analyses were used to assess the potential effects of controlling for confounders and the time frame of diagnostic assessment  and virus variant . Data on the latter variable were estimated based on the time frame in which the data were gathered and the geographic location where this was done. We followed the advice of the Cochrane handbook to interpret results from sensitivity and moderator only when there were at least 10 studies available per meta-regression analysis , the strength of evidence for the null or the alternative hypothesis. BFs were calculated for an effect-size estimate (logOR) of 0.00 with a standard deviation of 0.50. We used the thresholds suggested by Heck et al.  lists all the articles that were included for full-text assessment as well as reasons for inclusion and exclusion. See The median and mean ages of the included samples ranged between 9 and 81 years. The percentage of females per sample ranged from 16% to 86%. The median sample size per analysis was 114837 . The methodological quality of most input studies was moderate to high (see Table S6 for scores per study).no effect of mental disorder on the risk to acquire SARS-CoV-2 , a small significant increase in the risk of breakthrough infection in patients with several kinds of mental disorders (range adjusted relative RRs: 1.03–1.16). Wang et al. . Results from Bayesian meta-analys in line with the results from those derived through the classical frequentist approach. The evidence for the hypotheses of the existence of an association between having mental disorders and COVID-19-related hospitalization and morbidity was notably strong in most cases. For the outcome of ICU admission, the level of evidence was only anecdotal or moderate see .z = 2.85, P < 0.01). Two, uncontrolled studies investigating the association between psychosis spectrum disorder and COVID-19-related mortality yielded higher effect-size estimates relative to studies that applied statistical control  . Analyses of local and nationwide data yielded consistent results (see Table S9 and dataset S1). Associations were not moderated by geographic location, average age, percentage of females and methodological quality (see Table S10). In a few analyses, we found evidence of publication bias ]. Relative to the other outcomes, these studies on long COVID quite often applied a prospective design (50% vs. 11%). Both Jones et al. and Thompson et al. report on UK nationwide primary care data. To avoid overlapping data, we once ran a meta-analysis with Jones et al. included and Thompson et al. excluded and one with Jones et al. excluded and Thompson et al. included. Al-Aly et al. . Pooled ORs ranged between 1.68 and 1.75, with corresponding P-values < 0.0001. Bayesian analyses showed that the evidence for the alternative hypothesis of a negative effect of pre-existing mental disorders on long COVID symptoms is extremely strong . Due to a lack of data, we were not able to run analyses per type of mental disorder.Eight studies reported on the association between pre-existing mental disorders and long COVID/persistent symptoms. [Al-Aly y et al.  and Wang .no effect of mental disorder on the risk to acquire SARS-CoV-2  may be important mediators of COVID-19 progression and severity  reported data on convenience samples in which people with a disorder were compared to people without the disorder . The use of such control conditions comes with limitations. The concept of psychiatric comorbidity is ignored. Associations that we now assign to for instance a SUD, can easily reflect associations that are due to a SUD comorbid with depression or psychosis. Data exist that indeed suggests that patterns of comorbidity can be the driving force underlying the relatively poor COVID-19 course observed in people with mental disorders  could not be considered. The predictor categories in our study were more fine-grained than those in earlier meta-analyses  ability to cope with events and challenges”  disadvantaged when it comes to coping with the disease. This conclusion underscores an eminent element in current definitions of Molero et al. supplementary materialMolero et al. supplementary material


The American College of Cardiology (ACC) and American Heart Association (AHA) released a new blood cholesterol treatment guideline in November 2013. It is unknown how the new recommendations have affected cholesterol medication use and adherence in a commercial health plan.To evaluate the effect of the 2013 guideline release on antihyperlipidemic treatment patterns and statin adherence in patients with atherosclerotic cardiovascular disease (ASCVD) compared with a historical control group.This study was a historical cohort analysis of adult patients (aged 21-75 years) with clinical ASCVD enrolled in a SelectHealth commercial health plan. Patients were included in the guideline implementation cohort if they had a medical claim with an ICD-9-CM diagnosis of ASCVD in the year before the November 2013 ACC/AHA guideline release. The index date was defined as the first outpatient medical claim with an ICD-9-CM for ASCVD in the first 6 months after the guideline was released. Patients were required to have continuous enrollment for ≥ 1 year before and after the index date. These same criteria were applied to patients exactly 4 years earlier to identify a historical control group. Patients meeting these criteria formed the antihyperlipidemic treatment patterns cohort. Of these, patients who also had ≥1 pharmacy claim for a statin in the 1-year pre- and post-index periods were included in the statin adherence cohort. Antihyperlipidemic treatment patterns were assessed using pharmacy claims for antihyperlipidemic medications in the 1-year pre- and post-index periods. Antihyperlipidemic medication claims were classified as a nonstatin cholesterol medication, low-intensity statin, moderate-intensity statin, or high-intensity statin. To address differences in pre-index antihyperlipidemic medications between the guideline implementation and historical control groups, patients were randomly matched 1:1 based on pre-index classification in a post hoc analysis. Post-index antihyperlipidemic classifications were compared between groups using a Stuart-Maxwell test. The change in mean statin adherence (proportion of days covered [PDC]) was compared within and between groups using paired and independent t-tests, respectively. The proportion of adherent patients (PDC ≥ 0.80) in the pre- and post-index periods was compared between groups using a chi-square test. A multivariable logistic regression was used to compare the likelihood of being adherent in the post-index period while controlling for pre-index adherence and other potential confounders.P < 0.001), and more were male  than the historical control group. In the matched cohort, there was a significant difference in the post-index antihyperlipidemic classification (P < 0.001), which appeared to be a result of the difference in nonstatin cholesterol medications  and high-intensity statins . Of the 1,841 patients in the antihyperlipidemic treatment patterns cohort, 919 patients met inclusion criteria for the statin adherence analysis. Although PDC decreased over time in both groups, significantly more patients in the guideline implementation group were adherent in the post-index period than the historical control group . Additionally, patients in the guideline implementation group were more likely than the historical control to be adherent in the post-index period when adjusting for potential confounders .A total of 7,818 adult members with ASCVD in the index period and 1 year before the index period were identified. Of those, 1,841 patients met the criteria to be included in the analysis, and 1,526 patients were matched on antihyperlipidemic classification and included in the antihyperlipidemic treatment patterns analysis. Baseline characteristics were similar, although the guideline implementation group was younger (58.3 vs. 60.5 years, Since the release of the updated ACC/AHA treatment guideline, more commercial health plan patients with ASCVD used high-intensity statins and fewer used nonstatin cholesterol medications than historical controls. Additionally, since the guideline release, more patients are adherent to statin therapy than historical controls. This study provides managed care organizations with valuable information regarding the effect of the 2013 ACC/AHA guideline. What is already known about this subjectThe updated 2013 American College of Cardiology (ACC) and American Heart Association (AHA) blood cholesterol treatment guideline represents a major shift in treating atherosclerotic cardiovascular disease (ASCVD).The 2013 guideline recommendations were projected to increase the number of persons using statin therapy, but real-world observations of how treatment use has changed has been limited.Adherence to statin medications has been shown to be relatively low, and the effect of removing the treat to target cholesterol goals has not been studied.What this study addsThis study compared, among patients with ASCVD, the effect of the ACC/AHA cholesterol guideline on antihyperlipidemic treatment patterns and adherence to a historical control group.More patients with ASCVD received high-intensity statins and fewer received nonstatin cholesterol medications after the guideline release than historical controls.A greater proportion of patients with ASCVD were adherent to statin therapy after the guideline release than historical controls.2 Rather than recommending specific low-density lipoprotein cholesterol (LDL-C) goals, the guideline endorses treatment with certain “statin intensities” based on patient characteristics and risk factors.1 The new guideline identifies 4 major statin benefit groups, 1 of which is patients with clinical atherosclerotic cardiovascular disease (ASCVD) taking statins for secondary prevention. For patients aged > 75 years with clinical ASCVD, a moderate-intensity statin is recommended; for adults aged ≤ 75 years with clinical ASCVD, a high-intensity statin is recommended.1In November 2013, the American College of Cardiology (ACC) and the American Heart Association (AHA) released the 2013 ACC/AHA Guideline on the Treatment of Blood Cholesterol to Reduce Atherosclerotic Cardiovascular Risk in Adults, which updated lipid treatment and replaced the long-standing method of treating to blood cholesterol targets established by the Adult Treatment Panel III (ATP III) guideline.3 the number of patients eligible for statin therapy as secondary prevention may also increase. However, because of the similar eligibility criteria outlined in the ATP III guideline, this increase will not likely be as drastic.3 While observational studies are beginning to examine changes to antihyperlipidemic treatment patterns after the release of the 2013 ACC/AHA guideline, they have thus far been limited to a single health system with a relatively small sample size.4 The impact of the 2013 ACC/AHA guideline on broader populations remains unknown.Because of the 2013 guideline recommendations, a projected 12.8 million additional U.S. adults may be eligible for statin therapy. While the majority of this increase will likely occur among adults using statins for the primary prevention of cardiovascular events,1 Adherence to statin therapies is critical to reducing the risk of cardiovascular events, and statin nonadherence is a major challenge to optimal management.7 A meta-analysis of 20 studies revealed that adherence to statins is poor; only 57% of patients using statins for primary prevention and 76% using them for secondary prevention are considered adherent.8 Furthermore, adherence to statins decreases over time, with as few as 26% of patients considered adherent after 5 years.9 Nonadherent patients receive little or no benefit in reduction of cardiovascular events or mortality.13 By eliminating the emphasis of LDL-C treatment targets during follow-up, the treatment process may be simplified, but removing LDL-C targets may also affect positive reinforcement of patient adherence behaviors. Overall, the effect that the 2013 ACC/AHA guideline may have had on statin adherence remains unknown.The 2013 ACC/AHA guideline emphasizes adherence to lifestyle modifications and statins as essential components of primary and secondary prevention of ASCVD.The purpose of this study was to examine the effect of the 2013 ACC/AHA guideline release on antihyperlipidemic treatment patterns and statin adherence in adult members of a regional managed care organization using a historical control group.This study was a historical cohort analysis of adult patients with clinical ASCVD enrolled in a SelectHealth commercial health plan. SelectHealth is a nonprofit regional managed care organization that covers approximately 800,000 lives from the Intermountain region, with commercial, Medicare, and Medicaid plans available. Additionally, SelectHealth is part of an integrated health system with Intermountain Healthcare, which is a nonprofit health system with 22 hospitals and over 185 clinics across Utah and Idaho. Data for the study were collected from SelectHealth pharmacy and medical claims. The Intermountain Healthcare Institutional Review Board reviewed and approved this study.1 Patients were also required to have a medical claim with an International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis of ASCVD . Patients meeting all of these criteria comprised the statin adherence cohort.1 If patients did not have any prescription claims for an antihyperlipidemic medication, they were classified as having none. Patients were then assigned a classification in the pre- and post-index years according to the highest intensity statin received during the period. If patients received a nonstatin cholesterol medication and a statin, they were classified according to the statin intensity received.Prescription claims for antihyperlipidemic medications were identified using Generic Product Identifier codes in the 1-year pre- and post-index periods. During the pre- and post-index periods, each claim for an antihyperlipidemic medication was classified as a nonstatin cholesterol medication, low-intensity statin, moderate-intensity statin, or high-intensity statin according to the 2013 ACC/AHA guideline definition.Statin adherence in the pre- and post-index periods was calculated using the proportion of days covered (PDC). PDC was calculated by dividing the number of days supplied by 365 days, taking into account early refills by shifting the start date of the prescription filled early to the end of the days supply of the previous prescription fill and only considering the 365 days during the 1-year pre- and post-index periods. Statin adherence was estimated at the class level, which allowed patients to switch between statins. Fill dates were adjusted if switches were filled early. Additionally, prescription claims from before the start of the pre-index period with a days supply that carried over into the pre-index period were included. If this occurred, only the days actually within the 365-day pre-index period were included in calculating PDC . Pharmacy claims from the pre-index period with a days supply that carried over into the post-index period were handled similarly. Patients were considered adherent if they had a PDC ≥ 0.80 and nonadherent if the PDC was < 0.80.Baseline characteristics were identified in the 1-year pre-index period and included demographics  and clinical characteristics  as wellAntihyperlipidemic treatment patterns were described using frequencies and percentages and were compared using chi-square tests. However, given the significant differences in antihyperlipidemic classification identified between the groups in the pre-index period, a post hoc analysis was used to better understand and describe changes in treatment patterns. In the post hoc analysis, the post-index antihyperlipidemic classifications were compared between groups after patients in the guideline implementation group were randomly matched 1:1 to patients in the historical control group on pre-index antihyperlipidemic classification. Patients who did not match on pre-index antihyperlipidemic class were excluded from the post hoc analysis. In the post hoc analysis, baseline characteristics were compared between the groups using paired t-tests and McNemar’s test or Stuart-Maxwell test, as appropriate, and antihyperlipidemic treatments in the post-index period were compared using the Stuart-Maxwell test.P value of > 0.2 as a threshold.14 Covariates considered clinically meaningful were included regardless of the P value obtained in the stepwise selection process. The final covariates included in the model were age, sex, statin adherence in the pre-index period, mean statin copay in pre-index period, hypertension, anxiety, and depression. All statistical analyses were performed using R version 3.1 .For the statin adherence cohort, the change in mean statin adherence from the pre- to the post-index periods was compared within the guideline implementation and historical control groups using paired t-tests and between groups using an independent t-test. The proportion of patients considered adherent in the pre- and post-index periods was compared between the guideline implementation and historical control groups using a chi-square test. A multivariable logistic regression was then used to compare the likelihood of being adherent in the post-index period between the guideline implementation and historical control cohorts, while controlling for baseline adherence classification and other potential confounders . A backward stepwise approach was used in covariate selection for the multivariable logistic regression using a A total of 7,818 adult patients were identified with an outpatient medical claim for ASCVD in the 6-month index period period, as well as a claim with a diagnosis of ASCVD in the 1 year before . Of thosP < 0.001), had a higher proportion with a diagnosis for obesity , and had a lower proportion with a diagnosis for bipolar disorder . Among those patients with ≥1 antihyperlipidemic fill, patients in the guideline implementation group had a lower mean antihyperlipidemic copay  and fewer mean antihyperlipidemic fills . The characteristics for the matched groups in the post hoc analysis were similar to those before matching on pre-index antihyperlipidemic class, although the difference in bipolar diagnosis was no longer significant . Additionally, there was a significant difference in the statin intensity classification in the pre-index period between the groups .A total of 588 patients in the guideline implementation and 361 in the historical control groups met all the inclusion criteria to be included in the analysis . The guiP value for change) in the post-index period was 0.79  for the overall group, 0.81  for the guideline implementation group, and 0.75  for the historical control group. The proportion of patients with a PDC ≥ 0.80 in the pre-index period was not significantly different between the groups . When controlling for potential confounders, patients in the guideline implementation group were significantly more likely than the historical control group  to be adherent in the post-index period (P < 0.001).In the overall statin adherence cohort, the mean [SD] PDC in the pre-index period was 0.82 [0.22]; mean PDC was 0.83 [0.20] in the guideline implementation and 0.80 [0.23] in the historical control groups. Adherence decreased from the pre- to the post-index periods for each group. The mean . While the magnitude of effect seen was greater than in the current study, there were several key differences. First, the previous study only included patients seen in primary care clinics from a single university health system and had a relatively small sample size. These parameters may limit the generalizability of the results. Additionally, the previous study used medical records and examined statins prescribed, not necessarily filled. Further, it only examined statin prescription orders for patients during 1 period of time, either before or after the release of the 2013 ACC/AHA guideline. Patients with orders in both periods were randomly assigned to one of the periods. Thus, it did not necessarily consider changes to therapy for patients. The current study overcomes some of these limitations by using claims data from a regional managed care organization, including a historical cohort for comparison, and looking specifically at changes to antihyperlipidemic therapy.The antihyperlipidemic treatment pattern analysis results are similar in direction to a previously published study that examined prescribing patterns in patients with ASCVD seen in primary care clinics in a university health system.Several limitations of this study are worthy of discussion. First, the index date was assumed to be a point at which the provider and patient had the opportunity to discuss adjusting cholesterol medications and address statin adherence. This was a surrogate marker for the provider’s implementation of the new cholesterol guidelines. This surrogate does not identify actual uptake of the guideline in clinical practice, and future research should consider examining this. Historically, guideline adoption has been slow, but information dissemination has changed significantly, and it is unknown how quickly guidelines are adopted. Also, since only claims data were used for this analysis, patients who were not candidates for statin therapy, for reasons such as previous intolerance or ineffectiveness, could not be identified. Future studies may consider using claims and medical record data to overcome this.Second, the current study only considered patients with ASCVD and did not examine other groups to which statins offer benefits identified in the guideline. Similarly, SelectHealth launched Medicare and managed Medicaid plans in 2013, so these populations were not included in the analysis because of the lack of available data during the study period. Along these lines, Medicare star ratings include a statin adherence measure, and it is unknown how this might have impacted the focus on statin adherence in the overall health plan. While this analysis did not include Medicare patients, the addition of a Medicare plan between the historical group and guideline cohorts may have provided an incidental focus on statin adherence. Future studies should include the effect of the ACC/AHA guideline on Medicare and Medicaid populations.Finally, patients were only required to have a single ASCVD diagnosis in the pre-index period. Some patients in the antihyperlipidemic treatment cohort may have thus been relatively newly diagnosed and not yet started treatment. Also, by requiring at least 1 claim for a statin in the pre- and post-index periods for the statin adherence cohort, included patients had received statin therapy for some time. This may limit the generalizability of the results of the adherence analysis.Since the release of the updated ACC/AHA treatment guideline, more patients with ASCVD are using high-intensity statins, and fewer are using nonstatin cholesterol medications than historical controls. The significant difference between the types of antihyperlipidemic medication use could be a result of prescribing based on the guideline and/or practicing of evidence-based medicine. Additionally, since the guideline release, more patients were adherent to statin therapy than historical controls. The difference in statin adherence could be a result of provider emphasis on adherence, as recommended by the 2013 ACC/AHA guideline, but previous statin adherence was still the largest predictor of adherence. However, the effect of a possible emphasis on improving statin adherence was not studied and should be examined in future studies.


Introduction: Large rectal lesions can conceal submucosal invasion and cancer nodules. Despite the increasing diffusion of high-definition endoscopes and the importance of an accurate morphological evaluation, a complete assessment in this setting can be challenging. Endoscopic ultrasound (EUS) plays an established role in the locoregional staging of rectal cancer, although this technique has a tendency toward the over-estimation of the loco-regional (T) staging. However, there are still few data on contrast-enhanced endoscopic ultrasound (CH-EUS), especially if this ancillary technique may increase the accuracy for predicting invasive nodules among large rectal lesions. Material and Methods: Consecutive large (≥20 mm) superficial rectal lesions with high-definition endoscopy, characterized by focal areas suggestive for invasive cancer/2B type according to JNET classification, were considered for additional standardized evaluation via CH-EUS with Sonovue ©. Results: From 2020 to 2023, we evaluated 12 consecutive superficial rectal lesions with sizes ranging from 20 to 180 mm. This evaluation provided additional elements to support the therapeutic decision made. Lesions were treated with surgical (3/12) or endoscopic treatment (9/12) according to their morphology and CH-EUS evaluation. Conclusion: Contrast-enhanced endoscopic ultrasound can provide an additional evaluation for large and difficult-to-classify rectal lesions. In our experience, CH-EUS staging corresponded to the final pathological stages in 9/12 (75%) lesions, improving the distinction between T1 and T2 lesions. Larger prospective studies and randomized trials should be conducted to support and standardize this approach. Colorectal cancer causes 700,000 deaths every year, being the fourth cause of cancer death worldwide; therefore, endoscopy plays a key role in the early diagnosis and removal of these lesions . In the According to the decision algorithm proposed by the current European Guidelines ,9,10,11,Endoscopic ultrasound (EUS) plays a role in the locoregional staging of rectal cancer both for the evaluation of the degree of infiltration (T) and mostly for lymph node assessment staging (N) . The accHowever, the advent of ultrasound image enhancement systems, such as ultrasound contrast agents, brought a valuable aid for improving the definition of the degree of parietal infiltration. Contrast-enhanced harmonic ultrasound (CH-EUS) uses a micro-bubble-based solution which, once injected into the bloodstream, breaks or refracts the ultrasound signal coming from the probe, generating an amplified acoustic signal. Today, the most widely used ultrasound contrast agent, both in trans-abdominal ultrasound and in EUS, is SonoVue © , which is composed of sulfur particles coated with a phospholipid membrane.While the role of CH-EUS has been widely studied at the level of pancreatic lesions and submucosal lesions, both in terms of detection and differential diagnosis , its rol-Paris and Laterally Spreading Tumor (LST) classification corresponding to 0-Is, 0-IIa, 0-IIb or mixed lesions (0-IIc + IIa or 0-IIa + IIc); LST nongranular ; or LST granular nodular mixed type.-The concomitant presence of a demarcated focal area depressed or with an irregular surface pattern or bulky component corresponding to JNET 2B/3 .Consecutive patients with rectal lesions ≥20 mm and with the following characteristics were included:All suspected lesions were evaluated during the same session with a linear endoscopic ultrasound . PatientThe evaluation was performed by two experienced endoscopists in EUS. The endoscopic evaluation was performed under conscious sedation using the same high-resolution processor. Following the B-mode examination of the rectal lesions, CH-EUS was performed using Sonovue © 2.4 mL. The contrast medium was activated with 45 s of agitation and then injected into a cubital vein. After the injection, 10 mL of saline was administered. The ultrasound machine was set with an MI of 0.21. After the contrast administration, arterial, venous and late uptake were assessed at 30 s, 60 s and 90 s, respectively.-Standard EUS for the definition of the degree of parietal infiltration without contrast agent and the search for pathological lymph nodes providing staging.-The injection of the intravenous contrast agent (Sonovue ©) and the assessment of the entirety of the various layers of the rectum wall arterial and portal venous phases . FinallySpecifically, EUS examination was standardized as follows:Descriptive statistics for nonparametric distribution were used. Means, medians and standard deviations were used to report the results, as appropriate.From January 2020 to January 2023, a total of 12 patients were included . Indications for CH-EUS comprised rectal lesions with a median size of 35 mm (20–180). The baseline characteristics are reported in We present the first case series of rectal lesions where the use of contrast agent during endoscopic ultrasound is proposed as an additional tool to support therapeutic decision making. Large rectal lesions can be difficult to examine properly even in expert hands . LesionsEvaluation with EUS and microvascular pattern distribution with contrast enhancement can be performed in the same endoscopy session, without any additional bowel preparation. It can be targeted on individual areas that are more suspicious and worthy of study without having to perform a global image, as is the case with MRI. Rectal cancer upon EUS evaluation appears as a hypoechoic mass where the regular stratification wall is altered. T1 lesions of the rectum are limited to the mucosa and submucosa (up to the third layer), with no signs of extension to the fourth layer. T2 lesions infiltrate the muscle layer (IV stratum) but do not pass through the serosa (V stratum). A first attempt of the classification of submucosal infiltration was proposed by Hurlstone et al. in 2007 using a high-frequency miniprobe and compared to histopathology : SM1: hyBeyond adenomas with dysplasia, the addition of CH-EUS proved beneficial overall in distinguishing T1 and T2 lesions. The advancement of certain endoscopic techniques could allow the use of CH-EUS even more focused on a technical study of the lesion to choose which endoscopic technique is most suitable. The first Endoscopic Intermuscular Dissection (EID) feasibility and safety study on cases of rectal lesions with suspected deep submucosal invasion was recently published . This adWe are aware that this study has many limitations. Firstly, it is a retrospective case series, and individual cases were submitted to CH-EUS not systematically but only in the case of doubt due to the size and heterogeneity of the lesion. Furthermore, all lesions were studied with a linear echoendoscope, as this is proven to be non-inferior to a radial one . ClearlyIn our experience, performing CH-EUS in the evaluation of voluminous and complex rectal lesions in experienced hands could improve accuracy in rectal cancer staging, giving a guide for the therapeutic decision, without any additional risk for the patients. CH-EUS staging can provide useful information regarding either the integrity of the muscular layer and the presence of vascularization, which are both factors known to be predictive for non-curative endoscopic resection. The limited experience in this first case series does not allow for a definition of accuracy, but it shows encouraging results, as it allowed a clinically successful therapeutic decision in the majority of cases.The dissemination of this method for these lesions could lead to a future definition of the agreement between experts and thus a shared classification to define the degree of deep infiltration. Pre-interventional CH-EUS could promote the adoption of intermuscular dissection techniques or more advanced resections beyond the submucosal plane, avoiding surgical overtreatment. Large, prospective studies are needed to define the accuracy of this method in this setting, also with respect to the evolution of endoscopic therapy techniques.


The aim of the present meta-analysis is to compare the efficacy and safety of low-dose and standard-dose recombinant tissue plasminogen activators (r-tPA) in patients with acute ischemic stroke. The present meta-analysis was conducted according to the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines. We conducted a systematic search in PubMed, Embase, and the Cochrane Library to identify studies published between January 1, 2010, and January 31, 2023, using the following terms: "stroke," "alteplase," "doses," "efficacy," "tissue plasminogen activator," "r-tPA," and "safety." Primary efficacy outcomes included favorable outcomes , while secondary efficacy outcome was all-cause mortality at 90 days. Safety outcomes included asymptomatic intracerebral hemorrhage (ICH) and symptomatic ICH assessed using the National Institute of Neurological Disorders and Stroke (NINDS) study and the Safe Implementation of Thrombolysis in Stroke-Monitoring (SITS-MOST) study. We also compared parenchymal hematomas as safety outcome between the two groups defined by the authors themselves in their research. A total of 16 studies were included in the present meta-analysis. The meta-analysis did not report any significant difference between low-dose and standard-dose r-tPA in terms of mortality, symptomatic intracranial hemorrhage (SICH), asymptomatic ICH, and parenchymal hematomas. However, the favorable outcome was significantly greater in patients receiving a standard dose of r-tPA.  Cerebrovascular stroke is the second and seventh leading cause of death and disability worldwide . Stroke Registry studies conducted in various parts of Asia have produced inconsistent results regarding the use of alteplase -7, but tA 2017 meta-analysis involving 12 studies found no significant difference between low dose and standard dose of r-tPA in terms of mortality and favorable outcomes  and retrAn updated meta-analysis of the existing evidence in this area could provide a more definitive answer to this question by pooling the results of multiple studies and conducting a systematic analysis of the data. This would allow researchers to evaluate the overall effectiveness and safety of low-dose thrombolytic therapy compared to standard-dose therapy. The aim of this meta-analysis is to compare the efficacy and safety of low-dose and standard-dose r-tPA in patients with acute ischemic stroke.MethodologyThe present meta-analysis was conducted according to the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines.Literature Search Strategy and Study SelectionWe conducted a systematic search in PubMed, Embase, and the Cochrane Library to identify studies published between January 1, 2010, and January 31, 2023, using the following terms: "stroke," "alteplase," "doses," "efficacy," "tissue plasminogen activator," "r-tPA," and "safety." To identify relevant articles for this study, a comprehensive search was conducted using Medical Subject Headings (MeSH) terms and boolean algebra operators to combine and refine search keywords in a systematic and structured manner. We manually searched the reference lists of all included studies for additional relevant records. All identified records were imported into an EndNote Library X9, and duplicates were removed. Initial screening was conducted based on the title and abstract of each study, and full texts of all eligible articles were retrieved. We carried out a detailed assessment of eligibility criteria using predefined inclusion and exclusion criteria. Two authors independently conducted the study search and selection process, and any disagreement was resolved through discussion or consensus with a third investigator. Eligibility CriteriaWe included all prospective or retrospective studies that met the following criteria: (a) patients with ischemic stroke receiving alteplase, (b) comparison of two doses of alteplase, and (c) reporting of outcomes assessed in the present analysis. We included only those studies in which patients were presented within 4.5 hours of the onset of stroke. Lastly, we included those studies that were published in the English language only. We defined the standard dose of alteplase as >0.85 mg per kilogram of body weight, while any dose other than the standard dose was regarded as a low dose. We excluded reviews, cross-sectional studies, case reports, and case series.OutcomesPrimary efficacy outcomes included favorable outcomes , while secondary efficacy outcome was all-cause mortality at 90 days. Safety outcomes included asymptomatic intracerebral hemorrhage (ICH) and symptomatic ICH assessed using the National Institute of Neurological Disorders and Stroke (NINDS) study and the Safe Implementation of Thrombolysis in Stroke-Monitoring (SITS-MOST) study at 90 days. We also compared parenchymal hematomas as safety outcome between the two groups defined by the authors themselves in their research.Data ExtractionData extraction was performed using a data collection sheet developed in Microsoft Excel. The data extracted from the included studies included author name, year of publication, study design, dose, sample size, participants' characteristics, and outcome measures. One investigator extracted the data, and the other author cross-checked it and entered it into RevMan software for data analysis.Data AnalysisData analysis was done using RevMan Version 5.4.1 . A risk ratio (RR) with 95% confidence intervals (CIs) was calculated for outcome variables. P-values less than 0.05 were considered significant. A random effect model was used to pool studies with significant heterogeneity as determined by the I-square value of more than 50%. We performed subgroup analysis based on the continent where the study was conducted and also on study design (retrospective and prospective studies). Results One thousand four hundred eighty-two studies were initially retrieved from Embase and PubMed. The majority of records were excluded upon titles or abstracts screening. Full texts of all eligible studies were obtained and assessed for detailed assessment of inclusion and exclusion criteria. After a full-text review of 32 papers, 16 were included in the present meta-analysis. Figure Efficacy OutcomesFavorable outcome: thirteen studies reported favorable outcomes, and 10 of these studies showed fewer favorable outcomes in patients receiving the low dose of alteplase compared to the standard dose. Three studies reached statistical significance ,22. PoolMortality: in the present meta-analysis, a total of 15 studies were included that compared the risk of mortality between low-dose and standard-dose alteplase. The incidence of mortality was lower in the low-dose group (9.72%) compared to the high-dose group , as shown in Figure Safety OutcomesSymptomatic intracranial hemorrhage (SICH): SICH was identified in seven studies according to the NINDS definition and seven studies according to the SITS-MOST definition. Therefore, pooled analyses for SICH were conducted separately based on NINDS and SITS-MOST definitions. There was no significant difference in SICH using the NINDS definition between low dose and standard dose , as shown in Figure Non-symptomatic ICH and parenchymal hematomas: the present meta-analysis revealed no significant difference in non-symptomatic ICH and parenchymal hematomas between standard- and low-dose IV-tPA patients, as shown in Figures Subgroup AnalysisWhen combining only studies conducted in Asia, the pooled estimate for all-cause mortality showed no heterogeneity (I-square: 0%), and no statistical difference was found between the two groups, as shown in Table Based on the study design, the pooled analysis of prospective studies showed a significant reduction of SICH (using definitions of SITS-MOST and NINDS) in the low-dose tPA group compared to the standard group. However, the pooled analysis of retrospective studies did not find any significant difference between the two groups in terms of SICH, as shown in Table DiscussionThe present meta-analysis assessed the differences in safety and efficacy between low-dose and standard-dose intravenous r-tPA therapy in patients with ischemic stroke. The meta-analysis found that patients receiving standard-dose r-tPA had significantly greater favorable outcomes compared to those receiving low-dose r-tPA. A past meta-analysis of 11 studies conducted by Tan et al.  found noThe findings of the present meta-analysis suggest that there is a connection between using a standard dose of r-tPA and an increased likelihood of a favorable outcome. However, it is important to consider that most of the studies used in this analysis were not randomized, which means that there may have been selection bias - patients who received a lower dose may have had a lower chance of a positive outcome. This is because patients who are at a higher risk of bleeding on admission are often given a lower dose of rtPA, and these patients tend to have a larger area of brain damage or other factors that can reduce the likelihood of a favorable outcome at three months. Therefore, the potential benefits of a standard dose of rtPA on positive outcomes at three months should be confirmed by further randomized controlled trials in the future.The study conducted by Liu et al.  found noAlthough developing countries account for 85% of stroke cases globally, the administration of IV-tPA to patients in these countries is quite low . One of The current meta-analysis showed that the standard dose was still the best option for patients with acute ischemic stroke. Regarding individual patients, who might get benefit from a lower dosage? A study involving 3479 patients with acute ischemic stroke found that for those with a moderate stroke (NIHSS 5-14), lower amounts of tPA were linked to a decrease in severe bleeding in the brain and had similar effectiveness to higher doses . Wang etIt is important to note that while the present meta-analysis aimed to provide a more comprehensive estimate of SICH, there may still be some heterogeneity across the included studies that could affect the overall results. Further studies with larger sample sizes and standardized definitions of SICH are needed to provide more conclusive evidence. Additionally, the potential clinical significance of the observed differences in SICH incidence and other hemorrhagic complications between low and standard-dose alteplase warrants careful consideration when making treatment decisions for individual patients. Future research may include a diverse population to assess the impact of tPA on these individuals.Study LimitationsThe present meta-analysis has certain limitations. Firstly, the low dose of alteplase in various included studies ranged from 0.5 to 0.95 mg per kg of body weight. Secondly, the majority of included cohort studies were performed in the Asian population. Therefore, the findings need to be interpreted with caution in the overall world population because of genetic and racial differences. However, we have conducted a subgroup analysis based on continents. Thirdly, only one randomized control trial has been included in the present meta-analysis. Non-randomized designs of studies would cause a selection bias, which may challenge the reliability of outcomes. We did not perform subgroup analysis for randomized trials because of a lack of a number of studies. However, we performed a subgroup analysis based on the prospective and retrospective nature of the studies. Lastly, we did not carry analysis based on the severity of the stroke due to a lack of individual patient data. The present meta-analysis found that compared to a low dose, the standard dose of tPA was effective in enhancing favorable outcomes in patients with acute ischemic stroke without significantly increasing the risk of symptomatic ICH, asymptomatic ICH, and parenchymal hematomas. However, only one randomized trial was included in the present meta-analysis. Given the possibility that efficacy is optimized using the standard of 0.9 mg/ kg tPA, a randomized dose-comparison study is an important research priority to establish the optimal tPA dose in stroke patients. For this reason, RCTs are important to conduct in patients from various backgrounds and stroke severity to determine the optimum dose of tPA. Healthcare professionals need to remain cautious in clinical practice when considering low-dose tPA treatment of acute ischemic patients.


However, the conventional reconstruction technique is confined to low microbubble concentrations to avoid localization and tracking errors. Several research groups have introduced sparsity- and deep learning-based approaches to overcome this constraint to extract useful vascular structural information from overlapping microbubble signals, but these solutions have not been demonstrated to produce blood flow velocity maps of the microcirculation. Here, we introduce Deep-SMV, a localization free super-resolution microbubble velocimetry technique, based on a long short-term memory neural network, that provides high imaging speed and robustness to high microbubble concentrations, and directly outputs blood velocity measurements at a super-resolution. Deep-SMV is trained efficiently using microbubble flow simulation on real in vivo vascular data and demonstrates real-time velocity map reconstruction suitable for functional vascular imaging and pulsatility mapping at super-resolution. The technique is successfully applied to a wide variety of imaging scenarios, include flow channel phantoms, chicken embryo chorioallantoic membranes, and mouse brain imaging. An implementation of Deep-SMV is openly available at  I.ULTRASOUND localization microscopy (ULM) is a super-resolution imaging technique that relies on the localization and tracking of intravascular microbubble (MB) contrast agents to reconstruct microvasculature. It has been demonstrated by multiple research groups that MBs can act as acoustic point sources that can be localized at a sub-diffraction precision, achieving micrometer-scale vascular fidelity at clinical frequencies without sacrificing imaging depth , 2], , , 5]. Fu. Fu5]. FSeveral ULM approaches have been specifically introduced to handle overlapping MB signals from high-concentration contrast injections. Sparse recovery strategies , 9], su, su9], sHere we introduce DL-based super-resolution microvessel velocimetry (Deep-SMV), a localization free approach that provides high imaging speed and robustness to high MB concentrations, and directly outputs blood velocity measurements at a super-resolution without explicit data association for MB tracking. Deep-SMV leverages the rich spatiotemporal information embedded in ultrasound MB imaging data with a convolutional neural network including long short-term memory (LSTM) blocks to provide both structural super-resolution imaging and time-resolved velocimetry of tissue vasculature, permitting super-resolution pulsatility mapping. LSTM is a type of recurrent neural network (RNN) architecture , 23] wh wh23] whII.A.Our NN model adopts the convolutional LSTM-UNet structure, where the bottleneck layers of the classical UNet were replaced by LSTM layers , Fig. 2.The convolutional-LSTM block takes a new input and the previous hidden state output as it inputs, while maintaining an internal cell status variable. The inputs will first go through a 3*3 convolution layer. The convolution layer output will be split along the channel dimension into four parts, where three will go through a sigmoid activation function and operate as the forget gate, the input gate, and the output gate, respectively. A hyperbolic tangent (tanh) function will be applied to the remaining part. The forget gate determines which part of the previous cell status will be discarded. The input gate determines whether parts of the new input contribute to updating the cell status. A tanh activation function will be applied to the new cell status. Finally, the output gate determines how the cell status propagates to the output. The convolutional LSTM block will reduce the input sequence along the temporal dimension.The decoder block is constructed similarly to the encoder block, with the max pooling layer replaced by spatial upsampling layer. Each decoder block will expand the spatial dimension of its input by 2 and reduce the channel dimension by 2. The output block is a 3*3 convolution layer with output channel size of 2. The final output of the network is a predicted blood flow velocity map with the same spatial dimension as the input data.We used mean squared error (MSE) as the loss function for training the NN, defined as:B.The training dataset of our NN model was simulated using CAM optical images, as well as conventional mouse brain ULM results to serve as a vessel structure template.1)Optical imaging was performed using a Nikon SMZ800N stereomicroscope  with a Plan Apo 1x objective at 1x zoom, captured with a DS-Fi3 microscope camera, and saved as.nd2 format. These were later exported as RGB .tiff files for external analysis . The opt2)threshold_adaptive function available in the scikit-image python package  us us31] us7)6-fold dilution) MB solution into DI water and gently stirring to create movement. MB image patches were extracted using normalized cross correlation with an estimated Gaussian PSF template to create a bank of PSF samples. Simulation MB images were produced by convolution between the MB locations and amplitudes generated in In addition to numerical simulation using ultrasound simulation software, we also explored the idea of incorporating experimental PSF in the simulation pipeline. Ultrasound images of isolated, slow flowing MBs were acquired by injecting heavily diluted (6 × 108)A total of 1300 training samples of 16 frames in the temporal dimension, and 256 * 256 pixels in the spatial dimension were fed to the model in mini-batches of 4 samples per batch. The model was trained with an Adam optimizer to minimize the MSE loss between the model output and the ground truth using back-propagation with a learning rate of 0.001. The ground truth contains two channels of the magnitudes in pixels/ms and angles of the flow velocity map. To avoid complications of training with negative values in the ground truth, the angle map which originally contains values in the range , , , 36], w, w36], wpolation . MBs werpolation , or a Kapolation , where vIII.A.An example of in vivo informed data simulation for Deep-SMV training is demonstrated in B.We then tested the performance of Deep-SMV on an in vitro flow channel phantom  where thC.®GeForce®RTX 2080 Ti GPUs, a near 100X acceleration compared to conventional ULM. The magnitude of flow estimated via Deep-SMV is comparable to the point estimates derived from ULM; however, it is notable that the proportion of vascular lumen space that was reconstructed is much higher than conventional processing. The convention used in all results presented is that the orange color indicates flow toward the transducer, and blue indicates flow away from the transducer.The use of Deep-SMV for the generation of super-resolved velocity maps takes advantage of the fast forward-processing speed of NNs and GPU-based parallelized computing to achieve real-time super-resolution imaging capability. The example 300 x 300-pixel image patch with 64 temporal frames in D.-I, Deep-SMV reconstruction achieved a 2.8x resolution gain as measured by FWHM in comparison to power Doppler (64.8 -III (34.5 After demonstrating good performance in the flow phantom experiment  and for  dataset  shows co dataset  to plot  dataset . The resArt and Vein) that were not apparent in conventional ULM processing. The frequency of the change in blood velocity was around 2-3 Hz, which matches with the Doppler fluctuation, and is close to the reported values for the cardiac cycle of chicken embryos at this stage of development , , , 19]; h; h19]; hWe also demonstrated that the Deep-SMV approach substantially improves super-resolution reconstruction speed, particularly for regions with high vessel densities, allowing for real-time visualization of the microvascular flow dynamics. This is a particularly enticing accomplishment for super-resolution ultrasound imaging, potentially enabling the translation of the technology into functional ultrasound applications where high temporal resolution is critical for monitoring vascular response(s). Although other DL-based approaches have demonstrated rapid localization times, these solutions still rely on conventional ULM pairing and tracking algorithms to estimate velocity. The processing time burden of these algorithms scale with the MB count, making these solutions ineligible for dynamic real-time velocimetry applications. The Deep-SMV network processing time does not depend on the MB concentration, performing consistently across multiple imaging scenarios. However, applying Deep-SMV to MB data with too-low concentration will lead to sparse and noisy reconstruction, due to insufficient information available for reconstruction. Slight underestimation may also be present due to temporal averaging of a sparser estimation. Moreover, since Deep-SMV is trained to produce velocity estimation in pixel per frame, it can be easily adapted for different imaging framerates by adjusting the conversion from pixel-space unit to physical units. The training set would also need to be adjusted to represent the appropriate physical velocity range for accurate results.Although the Deep-SMV approach shows substantial promise over conventional ULM and competing DL solutions, the results presented in this manuscript must be understood within the context of the limitations of the study. The most critical is that there is no established gold-standard technique for measuring microvascular flow velocity to validate the output of the Deep-SMV network. As a compromise, we have used the velocity results of the much more computationally expensive ULM reconstructions as a reference standard; however, this approach comes with some caveats. As discussed, ULM processing is susceptible to spurious MB localizations which may bias the velocity point estimates. We have attempted to account for this by averaging the ULM velocity references across a long acquisition time, which should reduce the impact of this source of error. The network was trained using Field II simulations of MB flow, where the velocity distribution was taken from experimental ULM reconstructions of vasculature. This implicitly tunes some aspects of the Deep-SMV network to the parameters used in the initial ULM reconstruction and is also dependent on the assumptions applied during vascular modeling . This is demonstrated directly by the gain in Deep-SMV performance for organ-specific simulation data in comparison to more generic vascular flow training . HoweverAt present, the flow dynamics modeling we assumed for simulation is relatively simplistic. Employing more advanced features, such as dynamic velocity profile in the vessel cross-section, diameter-dependent viscosity, and pulsatility within a simulated data segment, can potentially improve the robustness of Deep-SMV for complex blood flow patterns. Physiologically relevant constraints in assigning flow velocity and connectivity can also be implemented.In the current training setup, we used MSE loss, which is a loss function based on the Euclidean distance between the target and the estimation. Although MSE has been successfully applied to various image processing tasks, it is not necessarily the most efficient in capturing errors in our desired output data. We believe that a loss function specifically tailored for velocimetry can potentially improve the performance of Deep-SMV.Another loss term that we considered was the structural similarity (SSIM) loss, a loss designed to provide a similarity measurement between two images that reflects how humans perceive them. Since structural similarity is normally applied to images scaled to a uniform range (0-1 or 0-255), using it on its own is not well-suited for our task, where the velocity range can be arbitrary. Instead, we experimented with a combined loss function with MSE as the dominant loss term and SSIM as an additional constraint on vascular structure. We discovered that the addition of SSIM term did not improve the quality of reconstructed vessel structure. This might be due to the fact that perception of structure in natural images is different from that in microvessel images. Small distortions that normally do not heavily affect the image quality of a natural image, and thus penalized less when using SSIM loss, can have greater impact in preserving vascular structure.Deep-SMV provides efficient, effective, and fast super-resolution microvascular structural and velocity reconstruction without requiring MB localization, pairing, and tracking. By eliminating this inefficient ULM processing pipeline, Deep-SMV can capitalize on the rich spatiotemporal information present in the input IQ data, enabling real-time super-resolution imaging that is robust to a wide range of imaging conditions. This technique demonstrated a high temporal resolution, permitting the application of super-resolution velocimetry to functional imaging scenarios and enabling super-resolved pulsatility mapping. By relaxing the strict imaging paradigm that ULM typically requires, Deep-SMV can facilitate the translation of super-resolution ultrasound to a wider range of experimental and clinical applications.supp1-3251197