Observational study of changes in utilization and outcomes in non-invasive ventilation in COVID-19 RationaleThe role of non-invasive ventilation (NIV) in severe COVID-19 remains a matter of debate.

ObjectivesTo determine the utilization and outcome of NIV in COVID-19 in an unbiased cohort.

MethodsObservational study of confirmed COVID-19 cases of claims data of the Local Health Care Funds comparing patients with non-invasive and invasive mechanical ventilation (IMV) between spring versus autumn period 2020.

Measurements and Main ResultsNationwide cohort of 7490 cases (median/IQR age 70/60-79 years, 66% male) 3851 (51%) patients primarily received IMV without NIV, 1614 (22%) patients received NIV without subsequent intubation, and 1247 (17%) patients had NIV failure (NIV-F), defined by subsequent endotracheal intubation. The proportion of patients who received invasive MV decreased from 74% to 39% during the second period. Accordingly, the proportion of patients with NIV exclusively increased from 10% to 28%, and those failing NIV increased from 9% to 21%. Median length of hospital stay decreased from 26 to 22 days, and duration of MV decreased from 11.6 to 7.6 days. The NIV failure rate decreased from 49% to 42%. Overall mortality remained unchanged (51% versus 53%). Mortality was 39% with NIV-only, 52% with IMV and 66% with NIV-F with mortality rates steadily increasing from 58% in early NIV-F (day 1) to 75% in late NIV-F (>4 days).

ConclusionUtilization of NIV rapidly increased during the autumn period, which was associated with a reduced duration of MV, but not with overall mortality. High NIV-F rates are associated with increased mortality, particularly in late NIV-F.

FundingInstitutional support and physical resources were provided by the University Witten/Herdecke and Kliniken der Stadt Koln and the Federal Association of the Local Health Care Funds.

At a Glance CommentaryO_ST_ABSScientific Knowledge on the SubjectC_ST_ABSCurrent management of ventilatory support in COVID-19 patients with respiratory failure is heterogeneous. Despite increasing use of non-invasive ventilation (NIV), defining intubation criteria still remains a matter of uncertainty and discussion, especially with regard to the balance between the NIV benefits and the risk of NIV failure. In addition, robust data concerning the influence of the duration and failure of NIV on intubation and mortality rates are still missing, although the time span between initiation of NIV and subsequent intubation in case of respiratory failure progression is suggested to influence patient outcome.

What This Study Adds to the FieldThis is the first large observational study describing differences of ventilatory strategies between the spring and autumn period of the SARS-CoV-2 pandemic in Germany and provides the in-hospital mortality rate of 7,490 patients who received mechanical ventilation. The increased utilization of NIV from 10% (first period) to 29% (second period) was associated with overall reduced durations of mechanical ventilation and length of hospital stay, but overall mortality remained comparably high and reached 51%, 53% respectively. Patients succeeding with NIV had lower mortality rates than those getting intubated without preceding NIV attempts, but those failing NIV had higher mortality rates, respectively, and this became even more predominant in late NIV failure. The present observational study shows the increasing role of NIV in the concert of ICU medicine related to COVID-19, but also clearly addresses its risks in addition to its benefits, both impacting on mortality.
Distinct temporal characteristics of circulating alveolar epithelial and endothelial injury markers in ARDS with COVID-19: a preliminary retrospective report The time course and specific contributions of alveolar epithelial and endothelial injury to the pathogenesis of acute respiratory distress syndrome (ARDS) with coronavirus disease (COVID-19) remain unclear. Here, we evaluated the characteristics of circulating markers of alveolar epithelial and endothelial injury in serum samples from eleven ARDS patients and ten non-ARDS patients, all with COVID-19. Our results indicates that the alveolar epithelial injury at the very early disease stage and the endothelial injury which continues to exacerbate during the later disease stage seem to be the hallmarks of ARDS with COVID-19.
How Informative Were Early SARS-CoV-2 Treatment and Prevention Trials? A longitudinal cohort analysis of trials registered on clinicaltrials.gov BackgroundEarly in the SARS-CoV-2 pandemic, commentators warned that some COVID trials were inadequately conceived, designed and reported. Here, we retrospectively assess the prevalence of informative COVID trials launched in the first 6 months of the pandemic.

MethodsWe created a cohort of SARS-CoV-2 treatment and prevention efficacy trials that were initiated from 2020-01-01 to 2020-06-30 using ClinicalTrials.gov registration records. We evaluated trials on 3 criteria of informativeness: potential redundancy, design quality and feasibility of patient-participant recruitment. The study protocol was prospectively registered with the Open Science Framework (https://osf.io/fp726/).

ResultsWe included 500 trials in our cohort, 58% of which were Phase 2 and 84.8% were directed towards the treatment of SARS-CoV-2. Close to one third of trials met all three criteria and were deemed informative (29.0% (95% Confidence Interval 23.7 - 36.9)). The proportion of potentially redundant trials in our cohort was 4.1%. Over half of the trials in our cohort (56.2%) did not meet our criteria for high quality trial design. The proportion of trials with infeasible patient-participant recruitment was 22.6%.

ConclusionsLess than one third of COVID-19 trials registered on ClinicalTrials.gov during the first six months met all three criteria for informativeness. Shortcomings in trial design, recruitment feasibility and redundancy reflect longstanding weaknesses in the clinical research enterprise that were likely amplified by the exceptional circumstances of a pandemic. The Addiction Risk Factor: A Unitary Genetic Vulnerability Characterizes Substance Use Disorders and Their Associations with Common Correlates Substance use disorders commonly co-occur with one another and with other psychiatric disorders. They share common features including high impulsivity, negative affect, and lower executive function. We tested whether a common genetic factor undergirds liability to problematic alcohol use (PAU), problematic tobacco use (PTU), cannabis use disorder (CUD), and opioid use disorder (OUD) by applying genomic structural equation modelling to genome-wide association study summary statistics for individuals of European ancestry (Total N = 1,019,521; substance specific Ns range: 82,707-435,563), while adjusting for the genetics of substance use (Ns = 184,765-632,802). We also tested whether shared liability across SUDs is associated with behavioral constructs (risk taking, executive function, neuroticism; Ns = 328,339-427,037) and non-substance use psychopathology (psychotic, compulsive, and early neurodevelopmental disorders). Shared genetic liability to PAU, PTU, CUD, and OUD was characterized by a unidimensional addiction risk factor (termed The Addiction-Risk-Factor, independent of substance use. OUD and CUD demonstrated the largest loadings, while problematic tobacco use showed the lowest loading. The Addiction-Risk-Factor was associated with risk taking, neuroticism, executive function, and non-substance psychopathology, but retained specific variance before and after accounting for genetics of substance use. Thus, a common genetic factor partly explains susceptibility for alcohol, tobacco, cannabis, and opioid use disorder. The Addiction-Risk-Factor has a unique genetic architecture that is not shared with normative substance use or non-substance psychopathology, suggesting that addiction is not the linear combination of substance use and psychopathology. Longitudinal determination of mRNA-vaccination induced strongly binding SARS-CoV-2 IgG antibodies in a cohort of Romanian healthcare workers Mass vaccination against the disease caused by the novel coronavirus (COVID-19) was a crucial step in slowing the spread of SARS-CoV-2 in 2021. Even in the face of new variants, it still remains extremely important for reducing hospitalizations and COVID-19 deaths. Only limited data exists about the short- and long-term dynamics of humoral immune response. We present a longitudinal analysis of post-vaccination IgG levels in a cohort of 166 healthcare workers vaccinated with BNT162b2 with weekly follow-up until 35 days past the first dose and monthly follow-up up to 6 months post-vaccination. A subset of the patients continued with follow-up after 6 months and either received a booster dose or got infected during the Delta wave in Romania. Tests were carried out on 1697 samples using a CE-marked IgG ELISA assay developed in-house, containing S1 and N antigens of the wild type virus.

Participants infected with SARS-CoV-2 before vaccination mount a quick immune response, reaching peak IgG levels two weeks after the first dose, while IgG levels of previously uninfected participants mount gradually, increasing abruptly after the second dose. Overall higher IgG levels are maintained for the previously infected group 35-70 days after vaccination. The decrease of IgG levels is gradual, with lower overall values in the infection naive cohort even 7-8 months after vaccination, compared to the previously infected cohort. Administration of a booster dose yielded higher average IgG antibody levels than post second dose in the infection naive group and comparable levels in the previously infected group. Time-Varying Effectiveness of Three Covid-19 Vaccines in Puerto Rico BackgroundAs of October 1, 2021 2,217,547 individuals were fully vaccinated against COVID-19 in Puerto Rico. Since the vaccination process commenced on December 15, 2020 111,052 laboratory-confirmed SARS-CoV-2 infections have been reported. These data permitted us to quantify the benefits of the immunization campaign and to compare effectiveness of the mRNA-1273 (Moderna), BNT162b2 (Pfizer), and Ad26.COV2.S (J&J) vaccines.

MethodsDepartment of Health databases holding vaccination status, SARS-CoV-2 test results, and COVID-19 hospitalizations and deaths were integrated. We fit a statistical model that adjusted for time-varying incidence rates and age to estimate vaccine effectiveness and hospitalization and death relative risks. Code and data are provided here: https://github.com/rafalab/vax-eff-pr.

ResultsAt the peak of their protection, mRNA-1273, BNT162b2, and Ad26.COV2.S had an effectiveness of 90% (88%-91%), 87% (85%-89%), and 58% (51%-65%), respectively. After four months, effectiveness waned to about 70%, 60%, and 30%. We found no evidence that effectiveness was different after the Delta variant became dominant. For those infected, the vaccines provided further protection against hospitalization and deaths across all age groups. All vaccines had a lower effectiveness for those over 85 years, with a larger decrease for the Ad26.COV2.S vaccine. Overall, thousands of hospitalizations and deaths were avoided thanks to the vaccines.

ConclusionsThe mRNA-1273 and BNT162b2 vaccines were highly effective across all age groups. They were still effective after four months although the protection waned. The Ad26.COV2.S vaccine was effective but to a lesser degree, especially for older age groups. Substance Use Onset in High-Risk 9-13 Year-Olds in the ABCD Study AimA key aim of the Adolescent Brain Cognitive DevelopmentSM (ABCD) Study is to document substance use onset, patterns, and sequelae across adolescent development. However, substance use misreporting obscures accurate drug use characterization. Hair toxicology tests provide objective historical substance use data, but are rarely investigated in youth. Here, we compare objective hair toxicology results with self-reported substance use in youth.

MethodsA literature-based substance use risk algorithm identified 696 ABCD Study(R) participants for hair sample collections between baseline and 2-year follow-up (spanning ages 9-13) for laboratory analysis. Chi-square and t-tests assessed differences between participants demographics, positive and negative hair tests, risk algorithm scores, and self-reported substance use.

ResultsHair testing confirmed that 17% of at-risk 9-13 year-olds had evidence of past 3-month use of one (n=99), two (n=17), three (n=3), or four (n=2) drug classes. After considering prescribed medication use, 11% had a positive test incongruent with self- or parent-report. No participant with a positive result self-reported substance use consistent with their toxicology results. Participants with positive tests under-reported use (p<.001), reported less sipping of alcohol (p<.001), and scored higher on the risk algorithm (p<.001) than those with negative hair toxicology.

ConclusionsAn alarming 11% of tested samples in at-risk 9-13 year-olds were positive for at least one unreported substance, suggesting underreporting in this population when participating in a national healthy development study. The degree of underreporting cannot yet be calculated, as at-risk samples were prioritized for assays. Expanded toxicology testing is key to characterize substance use in youth. Deceased donor kidney degradomics indicates cytoskeletal proteolytic alterations impacting post-transplant function. BackgroundIn brain death, cerebral injury contributes to systemic biological dysregulation, causing significant cellular stress in donor kidneys that adversely impacts the quality of grafts. Here, we hypothesized that DBD kidneys may undergo proteolytic processes that renders grafts susceptible to post-transplant dysfunction.

Material & MethodsUsing mass spectrometry and immunoblotting analyses, we profiled degradation patterns of cytoskeletal proteins in deceased (n=55) and living (n=10) donor kidneys.

ResultsWe found that in DBD kidneys, key podocyte cytoskeletal proteins had been proteolytically cleaved. Generated degradation profiles were not associated to donor related demographic and clinical factors but were associated to suboptimal post-transplant function. Strikingly, -actinin -4 and Talin-1 degradation profiles were not observed in circulatory-death or living-donor kidneys. As Talin-1 is a specific proteolytic target of Calpain-1, we investigated a potential trigger of Calpain activation and Talin-1 degradation using ex-vivo precision-cut human kidney slices and in-vitro immortalised human podocytes. Notably, we found that Transforming-Growth Factor-{beta} (TGF-{beta}) activated Calpain-1 and proteolytically cleaved Talin-1 to generate distinct peptide fragments. These peptide fragments were of similar size and matched the degradation patterns observed in DBD kidneys. Talin-1 degradation was prevented in vitro by Calpain-1 inhibition.

ConclusionsHere, we provide initial evidence that DBD kidneys are susceptible to cytoskeletal protein degradation that impacts posttransplant kidney function. Subsequent studies should aim to further investigate the link between brain death and activation of proteolytic pathways exploring new therapeutic opportunities. Reports of myocarditis and pericarditis following mRNA COVID-19 vaccines: A systematic review of spontaneously reported data from the UK, Europe, and the US and of the literature ObjectivesTo bring together spontaneously reported data from around the world to estimate the reporting rate in different countries and better understand the risk factors for myocarditis and pericarditis following exposure to COVID-19 mRNA vaccines.

DesignSystematic review of the literature and systematic review of spontaneously reported data from the United Kingdom (UK), United States (US), and European Union/European Economic Area (EU/EEA).

Data sourcesSpontaneously reported data for COVID-19 Vaccine Pfizer/BioNTech (Comirnaty) and COVID-19 Vaccine Moderna (Spikevax) were obtained using outputs from the UKs Yellow Card scheme (covering the period 09/12/2020 - 20/10/2021), the Vaccine Adverse Event Reporting System (VAERS; US; covering the period 23/08/2021 - 21/10/2021), and EudraVigilance (EU/EEA; covering the period 21/12/2020 - 21/10/2021). Systematic searches of PubMed and Embase literature databases were performed with a datalock point of 11 October 2021.

Eligibility criteriaAll publicly available spontaneous reporting data from the three regions of interest were included. Data on events labelled "Myocarditis" and "Pericarditis" were included. No restrictions on age or gender were applied. For the systematic literature review, all pharmacoepidemiological studies of observational design investigating myocarditis and pericarditis following mRNA COVID-19 vaccines identified by our search were included. No restrictions on language or date were applied. Pre-print manuscripts were included if no peer-reviewed version was available. Studies of non-observational design, case reports, and case series were excluded. Study quality was assessed using relevant Critical Appraisal Skills Programme (CASP) tools. All studies were considered of sufficient quality for inclusion.

Data extraction and synthesisOne researcher extracted data from spontaneous reporting systems and published pharmacoepidemiological studies. Spontaneously reported events of myocarditis and pericarditis were presented for each individual data source, and stratified by vaccine, age, sex, and dose where this information was available. Vaccination data were obtained to the same date, and reporting rates were calculated for the events of myocarditis and pericarditis for each population of interest. For published pharmacoepidemiological studies, the study design, participant characteristics, and study results were tabulated.

ResultsOverall, there were 435 reports of myocarditis and 327 reports of pericarditis reported to the UKs Yellow Card scheme up to 20 October 2021. To 21 October 2021, in the US, 1936 reports of myocarditis and 1271 reports of pericarditis had been submitted to VAERS; there had been 2924 reports of myocarditis and 1855 reports of pericarditis submitted to EudraVigilance to the same date. Most reports (73.3%) had been submitted following Comirnaty (Pfizer/BioNTech). Males represented 74.9% of reports of myocarditis and 56.9% of pericarditis overall (for US and EU/EEA populations only), and the majority of reports concerned vaccinees ages under 40 years. Reports were more frequent following the second dose of mRNA COVID-19 vaccine. Reporting rates of myocarditis and pericarditis were consistent between the data sources. Seven pharmacoepidemiological studies were included; results of these studies were largely consistent with results of our spontaneous report analyses.

ConclusionsThis study provides evidence that younger vaccinees more frequently report myocarditis and pericarditis following mRNA COVID-19 vaccines compared with older vaccinees. These events had mild clinical course followed by full recovery in most cases. Results from published literature supported the results of our analyses.

Strengths and Limitations of the StudyO_LIThis is the first study to bring together spontaneously reported data from the United Kingdom, United States, and Europe on myocarditis and pericarditis following mRNA COVID-19 vaccines.
C_LIO_LIResults from this study provide evidence on the frequency of reported events of myocarditis and pericarditis following mRNA vaccines in different age groups, and by sex and vaccine dose. Analyses of spontaneous reports were consolidated with results of published literature, identified by systematic review.
C_LIO_LIResults may have been influenced by biases including different vaccination policies in each region examined, and publicity on events of myocarditis and pericarditis following mRNA vaccines.
C_LIO_LIThe study relied on outputs from spontaneous reporting systems in which the level of detail differed between the systems examined; furthermore, it is not possible to estimate incidence rates using spontaneous reports due to the lack of data on the exposed population, and there is no unvaccinated comparison group.
C_LIO_LIThere is an urgent need for further pharmacoepidemiological studies to be conducted to provide more accurate estimates of the frequency, clinical course, long term outcome, effects of treatment and impact on quality of life, to address many of the limitations of spontaneous reporting.
C_LI Investigating trends in those who experience menstrual bleeding changes after SARS-CoV-2 vaccination Early in 2021, many people began sharing that they experienced unexpected menstrual bleeding after SARS-CoV-2 inoculation. We investigated this emerging phenomenon of changed menstrual bleeding patterns among a convenience sample of currently and formerly menstruating people using a web-based survey. In this sample, 42% of people with regular menstrual cycles bled more heavily than usual while 44% reported no change after being vaccinated. Among respondents who typically do not menstruate, 71% of people on long-acting reversible contraceptives, 39% of people on gender-affirming hormones, and 66% of post-menopausal people reported breakthrough bleeding. We found increased/breakthrough bleeding was significantly associated with age, systemic vaccine side effects (fever, fatigue), history of pregnancy or birth, and ethnicity. Generally, changes to menstrual bleeding are not uncommon nor dangerous, yet attention to these experiences is necessary to build trust in medicine.

TeaserIncreased bleeding can occur post SARS-CoV-2 vaccines; this study investigates patterns in who experiences these changes. Melodic Intonation Therapy for aphasia: A multi-level meta-analysis of randomised-controlled-trial and individual-participant data AO_SCPLOWBSTRACTC_SCPLOWMelodic Intonation Therapy (MIT) is a prominent rehabilitation programme for individuals with post-stroke aphasia. The present meta-analysis investigated the efficacy of MIT while considering outcome measure quality, experimental design, influence of spontaneous recovery, MIT protocol, and level of generalisation.

An extensive literature search identified 606 studies in major databases and trials registers; of those, 22 studies, overall 129 participants, met all eligibility criteria. Multi-level mixed- and random-effects models served to separately meta-analyse RCT and non-RCT data.

RCT evidence on validated measures revealed a small-to-moderate standardised effect in non-communicative language expression for MIT, with substantial uncertainty. Unvalidated measures attenuated MITs effect size compared to validated tests. MITs effect size was 5.7 times larger for non-RCT data compared to RCT data. Effect size in non-RCT data decreased with number of months post-stroke, suggesting confound through spontaneous recovery. Variation from the original MIT protocol did not systematically alter benefit from treatment. Progress on validated tests arose mainly from gains in repetition tasks rather than other domains of verbal expression such as everyday communication ability.

The current results confirm the promising role of MIT in improving trained/untrained performance with unvalidated measures, alongside validated repetition tasks; whilst highlighting possible limitations in promoting everyday communication ability. Strain-resolved analysis in a randomized trial of antibiotic pretreatment and maintenance dose delivery mode with fecal microbiota transplant for ulcerative colitis Fecal microbiota transplant is a promising therapy for ulcerative colitis. Parameters maximizing effectiveness and tolerability are not yet clear, and it is not known how import the transmission of donor microbes to patients is. Here (clinicaltrails.gov: NCT03006809) we have tested the effects of antibiotic pretreatment and compared two modes of maintenance dose delivery, capsules versus enema, in a randomized, pilot, open-label, 2x2 factorial design with 22 patients analyzed with mild to moderate UC. Clinically, the treatment was well-tolerated with favorable safety profile. Of patients who received antibiotic pretreatment, 6 of 11 experienced remission after six weeks of treatment, versus 2 of 11 non-pretreated patients (odds ratio: 1.69, 95% confidence interval: -0.25 to 3.62). No significant differences were found between maintenance dosing via capsules versus enema. In exploratory analyses, microbiome turnover at both the species and strain levels was extensive and significantly more pronounced in the pretreated patients. Associations were also revealed between taxonomic turnover and changes in the composition of primary and secondary bile acids. Together these findings suggest that antibiotic pretreatment contributes to microbiome engraftment and possibly clinical effectiveness, and validate longitudinal strain tracking as a powerful way to monitor the dynamics and impact of microbiota transfer. The Ophthalmic Surgical Backlog caused by the COVID-19 Pandemic: A population-based and microsimulation modelling study BackgroundJurisdictions worldwide ramped down ophthalmic surgeries to mitigate the effects of coronavirus disease 2019 (COVID-19), creating a global surgical backlog. We sought to predict the long-term impact of COVID-19 on ophthalmology surgical care delivery.

MethodsThis is a population-based and a microsimulation modelling study. Provincial administrative data from January 2019 to May 2021 was used to estimate the backlog size and wait-times following the COVID-19 pandemic. For the post-pandemic recovery phase, we estimated the resources required to clear the backlog of patients accumulated on the waitlist during the pandemic.

ResultsA total of 56,923 patients were on the waitlist in the province of Ontario awaiting non-emergency ophthalmic surgery as of March 15, 2020. The number of non-emergency surgeries performed in the province decreased by 45-98% from March to May 2020, and 48-80% from April to May 2021 compared to the same months in 2019. By 2 years and 3 years, the overall estimated number of patients awaiting surgery grew by 129% and 150%, respectively. The estimated mean wait-time for patients for all subspecialty surgeries increased to 282 (SD 91) in March 2023 compared to 94 days (SD 97) in 2019. The provincial monthly additional resources required to clear the backlog by March 2023 was estimated to be a 34% escalation from the pre-pandemic volumes (4,626 additional surgeries).

InterpretationThe magnitude of the ophthalmic surgical backlog from COVID-19 has important implications for the recovery phase. The estimates from this microsimulation modelling can be adapted to other jurisdictions to assist with recovery planning for vision saving surgeries. Diffusion Kurtosis Imaging of neonatal Spinal Cord in clinical routine Diffusion Kurtosis Imaging (DKI) has undisputed advantages over more classical diffusion Magnetic Resonance Imaging (dMRI), as witnessed by a fast-increasing number of clinical applications and software packages widely adopted in brain imaging. However, in the neonatal setting, DKI is still largely underutilized in Spinal Cord (SC) imaging because of its inherently demanding technological requirements.

Due to its extreme sensitivity to non-gaussian diffusion, DKI proves particularly suitable for detecting complex, subtle, fast microstructural changes occurring in this area at this early and critical stage of development, and not identifiable with only DTI. Given the multiplicity of congenital anomalies of the spinal canal, their crucial effect on later developmental outcome, and the close interconnection between SC region and the above brain, managing to apply such a method to neonatal cohort becomes of utmost importance.

This study will (i) review the current methodological challenges associated with application of advanced dMRI methods like DKI in early infancy, (ii) illustrate the first semi-automated pipeline built on Spinal Cord Toolbox (SCT) for handling with DKI data of neonatal SC, from acquisition setting to estimation of diffusion measures, through accurate adjustment of processing algorithms customized for adult SC, and (iii) present preliminary results of its application in a pilot clinical case study.

Results of this application agree with findings achieved in a corresponding adult survey, thus confirming validity of adopted pipeline and diagnostic value of DKI in pediatrics. iBKH: The integrative Biomedical Knowledge Hub The rapidly increasing biomedical knowledge, derived from biological experiments or gained from clinical practice, has become the important treasure in the biomedical research. The emerging knowledge graphs (KGs) provide an efficient and effective way to organize and retrieval the huge and increasing volume of biomedical knowledge. A biomedical KG (BKG) typically stores and represents knowledge by constructing a semantic network describing entities and the relationships between them. Previous efforts have been conducted to construct and curate BKGs by comprehensively integrating various biomedical data resources. Though the resulting BKGs have made a significant progress in this filed in advancing biological and medical research, there remain a big gap to a perfect one that is comprehensive and fine-grained enough. To this end, in the present study, we collected and integrated data from diverse well-curated biomedical knowledge bases and BKGs to curate a more comprehensive one, named the integrative Biomedical Knowledge Hub (iBKH). To enhance the usage in accelerating biomedical research, we deployed iBKH using the famous graph database, Neo4j. This is a continuing effort and we are adding in more and more contents in iBKH to support the various complex needs in biomedical data analysis. Please contact us if you have better ideas and suggestions. The Acute Treatment of Migraine with Low-Dose Naltrexone/Acetaminophen Combinations and Each Component: Findings of a Small, Randomized, Double-Blind, and Placebo-Controlled Clinical Trial We tested two low-dose naltrexone and acetaminophen combinations and each component in the acute treatment of migraine. The patients use a single-dose of the study medication for a moderate or severe pain intensity migraine attack. Patients were adults with migraine with or without aura experiencing 2 to 20 (average 6.4) monthly migraine days. The co-primary endpoints were pain-freedom and absence of prospectively-identified most bothersome migraine-associated symptom 2 hours after dosing. We randomized 92 patients; 72 completed the study (mean age, 43 years; 75% women). Pain-freedom at 2 hours was 10.2% higher than placebo with naltrexone 2.25 mg/acetaminophen 325 mg, 10.9% with naltrexone 3.25 mg/acetaminophen 325 mg, 17.3% with naltrexone 2.25 mg, and 31.3% with acetaminophen 325 mg. The treatment groups migraine burden at baseline was unbalanced due to randomized patients uneven study completion. The acetaminophen group had the lowest migraine burden, giving its results lower credibility.

Saliently, Low-dose naltrexone alone (n=19) had a 17.3% higher response rate for headache pain-freedom at 2 hours than placebo (n=17). The naltrexone and the placebo groups were the largest and had a balanced disease burden, implying higher credibility to the naltrexone group results. We found low-dose naltrexone and acetaminophen combination, low-dose naltrexone, and acetaminophen had higher response rates than placebo in treating headache pain. The most commonly reported adverse events were sedation, nausea, and dizziness. We postulate that naltrexones toll-like receptor (TLR4) antagonism properties prevent pro-inflammatory cytokines production in the trigeminal ganglion averting "overactive nerves" (laymans term) and migraine. Although this trial used low-dose naltrexone (defined as 1 - 5 mg/day), future phase 3 studies will test a range of naltrexone and acetaminophen combination doses. Simple risk scores to predict hospitalization or death in outpatients with COVID-19 including the Omicron variant ImportanceOutpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, in particular whether to hospitalize a patient and if managed as an outpatient, how closely to follow them.

ObjectiveTo develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging.

DesignDerivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules.

SettingPrimary and Express care clinics in a Pennsylvania health system.

ParticipantsPatients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19.

Main outcomes and measuresClassification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC).

ResultsOverall, 7.4% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21) and 5.5% in the late validation cohort (3806 patients presenting 3/1/21 or later) were ultimately hospitalized. We developed and temporally validated three risk scores that all included age, dyspnea, and the presence of comorbidities, adding respiratory rate for the second score and oxygen saturation for the third. All had very good overall accuracy (AUC 0.77 to 0.78) and classified over half of patients in the validation cohort as very low risk with a 1.7% or lower likelihood of hospitalization. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization (2.8%).

Conclusions and relevanceSimple risk scores applicable to outpatient and telehealth settings can identify patients with very low (1.6% to 1.7%), low (5.2% to 5.9%), moderate (14.7% to 15.6%), and high risk (32.0% to 34.2%) of hospitalization. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/. Correction of measurement error in a commercial multiple-breath washout device RationaleNitrogen multiple-breath washout (N2MBW) is an established technique to assess functional residual capacity (FRC) and ventilation inhomogeneity in the lung. Accurate measurement of gas concentrations is essential for the appropriate calculation of clinical outcomes.

ObjectivesWe investigated the accuracy of oxygen and carbon dioxide measurements used for the indirect calculation of nitrogen concentration in a commercial MBW device (Exhalyzer D, Eco Medics AG, Duernten, Switzerland) and its impact on FRC and lung clearance index (LCI).

MethodsHigh precision calibration gas mixtures and mass spectrometry were used to evaluate sensor output. We assessed the impact of corrected signal processing on FRC and LCI in a dataset of healthy children and children with cystic fibrosis using custom analysis software.

ResultsWe found inadequate correction for the cross sensitivity of the oxygen and carbon dioxide sensors in the Exhalyzer D device. This results in an overestimation of expired nitrogen concentration, and consequently FRC and LCI outcomes. Breath-by-breath correction of this error reduced mean (SD) FRC by 8.9 (2.2)% and LCI by 11.9 (4.0)%. It also resulted in almost complete disappearance of the tissue nitrogen signal at the end of measurements.

ConclusionsInadequate correction for cross sensitivity between the oxygen and carbon dioxide gas sensors of the Exhalyzer D device leads to an overestimation of FRC and LCI. Correction of this error is possible and could be applied by re-analysing the measurements breath-by-breath in an updated software version.

Grants, Gifts, Equipment, DrugsEco Medics AG (Duernten, Switzerland) provided a research version of their commercial software Spiroware 3.2.1 including insight on signal processing algorithms and helped with the acquisition of mass spectrometry measurements. This project was funded by the Swiss National Science Foundation, Grant Nr. 182719 (P. Latzin) and 168173 (K. Ramsey) Complementary frailty and mortality prediction models on older patients as a tool for assessing palliative care needs BackgroundPalliative care (PC) has demonstrated benefits for life-limiting illnesses. Cancer patients have mainly accessed these services, but there is growing consensus about the importance of promoting access for patients with non-malignant disease. Bad survival prognosis and patients frailty are usual dimensions to decide PC inclusion.

ObjectivesThe main aim of this work is to design and evaluate three quantitative models based on machine learning approaches to predict frailty and mortality on older patients in the context of supporting PC decision making: one-year mortality, survival regression and one-year frailty classification.

MethodsThe dataset used in this study is composed of 39,310 hospital admissions for 19,753 older patients (age >= 65) from January 1st, 2011 to December 30th, 2018. All prediction models were based on Gradient Boosting Machines. From the initial pool of variables at hospital admission, 20 were selected by a recursive feature elimination algorithm based on the random forests GINI importance criterion. Besides, we run an independent grid search to find the best hyperparameters in each model. The evaluation was performed by 10-fold cross-validation and area under the receiver operating characteristic curve and mean absolute error were reported. The Cox proportional-hazards model was used to compare our proposed approach with classical survival methods.

ResultsThe one-year mortality model achieved an AUC ROC of 0.87 {+/-} 0.01; the mortality regression model achieved an MAE of 329.97 {+/-} 5.24 days. The one-year frailty classification reported an AUC ROC of 0.9 {+/-} 0.01. The Spearmans correlation between the admission frailty index and the survival time was -0.1, while the point-biserial correlation between one-year frailty index and survival time was -0.16.

ConclusionsOne-year mortality model performance is at a state-of-the-art level. Frailty Index used in this study behaves coherently with other works in the literature. One-year frailty classifier demonstrated that frailty status within the year could be predicted accurately. To our knowledge, this is the first study predicting one-year frailty status based on a frailty index. We found mortality and frailty as two weakly correlated and complementary PC needs assessment criteria. Predictive models are available online at http://demoiapc.upv.es. COVID Outcome Prediction in the Emergency Department (COPE): Development and validation of a model for predicting death and need for intensive care in COVID-19 patients Background and aimCOVID-19 is putting extraordinary pressure on emergency departments (EDs). To support decision making in the ED, we aimed to develop a simple and valid model for predicting mortality and need for intensive care unit (ICU) admission in suspected COVID-19 patients.

MethodsFor model development, we retrospectively collected data of patients who were admitted to 4 large Dutch hospitals with suspected COVID-19 between March and August 2020 (first wave of the pandemic). Based on prior literature we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. Logistic regression analyses with post-hoc uniform shrinkage was used to obtain predicted probabilities of in-hospital death and of the need for ICU admission, both within 28 days after hospital admission. We assessed model performance (Area Under the ROC curve (AUC); calibration plots) with temporal validation in patients who presented between September and December 2020 (second wave). We used multiple imputation to account for missing values.

ResultsThe development data included 5,831 patients, of whom 629 (10.8%) died and 5,070 (86.9%) were discharged within 28 days after admission. ICU admission was fully recorded for 2,633 first wave patients in 2 hospitals, with 214 (8%) ICU admissions within 28 days. A simple model - COVID Outcome Prediction in the Emergency Department (COPE) - with age, respiratory rate, C-reactive protein, lactic dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well-calibrated and showed good discrimination in 3,252 second wave patients (AUC in 4 hospitals: 0.82 [0.78; 0.86]; 0.82 [0.74; 0.90]; 0.79 [0.70; 0.88]; 0.83 [0.79; 0.86]). COPE was also able to identify patients at high risk of needing IC in 706 second wave patients with complete information on ICU admission (AUC: 0.84 [0.78; 0.90]; 0.81 [0.66; 0.95]). The models are implemented in web-based and mobile applications.

ConclusionCOPE is a simple tool that is well able to predict mortality and need for ICU admission for patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

CONTRIBUTION TO THE LITERATUREO_ST_ABSWhat is already known on this topicC_ST_ABSO_LIPrediction models have the potential to support decision making about hospital admission of patients presenting to the emergency department with suspected COVID-19
C_LIO_LIMost currently available models that were independently assessed contain a high risk of bias
C_LIO_LIPromising models were developed in different patient selections and included predictors that are not quickly and objective obtainable in emergency departments
C_LI

What this study addsO_LIA simple and objective tool ("COPE") is well able to predict mortality and need for ICU admission for patients who present to the ED with suspected COVID-19
C_LIO_LICOPE may support ED physicians to identify high-risk patients - i.e. those at high risk of deterioration and/or death - requiring treatment in the ICU, intermediate-risk patients requiring admission to the clinical ward, and low-risk patients who can potentially be sent home
C_LI Machine Learning Approaches for Binary Classification to Discover Liver Diseases using Clinical Data For a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, Multiple Imputation by Chained Equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR, all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed a better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification. What delirium detection tools are used in routine clinical practice in the United Kingdom? Survey results from 91% of acute healthcare organisations. PurposeOur aim was to collect information on delirium assessment processes and pathways in non-intensive care settings in the United Kingdom (UK).

MethodsWe sent a Freedom of Information request to 169 UK National Health Service (NHS) hospitals, trusts and health boards (units) in July 2020 to obtain data on usage of delirium assessment tools in clinical practice and delirium pathways or guidelines.

ResultsWe received responses from 154/169 units (91% response rate). Of these, 146/154 (95%) units reported use of formal delirium assessment processes and 131/154 (85%) units had guidelines or pathways in place. The 4  As Test (4AT) was the most widely used tool, with 117/146 (80%) units reporting use. The Confusion Assessment Method was used in 65/146 (45%) units, and the Single Question to identify Delirium (SQiD) in 52/146 (36%) units.

ConclusionsOur findings show that the 4AT is the most commonly used tool in the UK, with 80% of units reporting use. This study adds to our knowledge of real-world uptake of delirium detection methods at scale. Future studies should evaluate real-world implementation of delirium assessment tools further via (i) tool completion rates and (ii) rates of positive scores against the expected prevalence delirium in the clinical population concerned.

KEY SUMMARY POINTSO_ST_ABSAimC_ST_ABSTo seek information on delirium assessment processes and pathways in non-intensive clinical care settings in the United Kingdom (UK), and to assess usage of specific delirium assessment tools: the 4  As Test (4AT), Confusion Assessment Method and Single Question to identify Delirium (SQiD).

FindingsIn total, 95% of National Health Service (NHS) units (hospitals, trusts and health boards) reported use of formal delirium assessment processes and 85% of units had guidelines or pathways in place. The 4AT was the most widely used tool, with 80% of units reporting use; the Confusion Assessment Method was reportedly used in 45% of units and the SQiD in 36% of units.

MessageThis study shows real-world, large-scale uptake of delirium detection methods and delirium guidelines in UK hospitals, which contributes to ongoing efforts to improve delirium care. Effectiveness of mRNA and ChAdOx1 COVID-19 vaccines against symptomatic SARS-CoV-2 infection and severe outcomes with variants of concern in Ontario SARS-CoV-2 variants of concern (VOC) are more transmissible and have the potential for increased disease severity and decreased vaccine effectiveness. We estimated the effectiveness of BNT162b2 (Pfizer-BioNTech Comirnaty), mRNA-1273 (Moderna Spikevax), and ChAdOx1 (AstraZeneca Vaxzevria) vaccines against symptomatic SARS-CoV-2 infection and COVID-19 hospitalization or death caused by the Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), and Delta (B.1.617.2) VOCs in Ontario, Canada using a test-negative design study. Effectiveness against symptomatic infection [&ge;]7 days after two doses was 89-92% against Alpha, 87% against Beta, 88% against Gamma, 82-89% against Beta/Gamma, and 87-95% against Delta across vaccine products. The corresponding estimates [&ge;]14 days after one dose were lower. Effectiveness estimates against hospitalization or death were similar to, or higher than, against symptomatic infection. Effectiveness against symptomatic infection is generally lower for older adults ([&ge;]60 years) compared to younger adults (<60 years) for most of the VOC-vaccine combinations. Preexisting diabetes and COVID-associated hyperglycaemia in patients with COVID-19 pneumonia AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSAimC_ST_ABSThe aim of the current study was to compare clinical characteristics, laboratory findings and major outcomes of patients hospitalized for COVID-19 pneumonia with COVID-associated hyperglycaemia or preexisting diabetes.

MethodsA cohort of 176 adult patients with a diagnosis of pre-existing diabetes (n=112) or COVID-associated hyperglycaemia (n=55) was studied. Clinical outcomes and laboratory findings were analysed according to the presence of the two conditions. The time to viral clearance was assessed during the follow-up after hospital discharge.

ResultPatients with COVID-associated hyperglycaemia had lower BMI, significantly less comorbidities and higher levels of inflammatory markers and indicators of multi-organ injury than those with preexisting diabetes. No differences between preexisting diabetes and COVID-associated hyperglycaemia were evident for symptoms at admission, humoral response against SARS-CoV-2 or autoantibodies to glutamic acid decarboxylase or interferon alpha-4. COVID-associated hyperglycaemia was independently associated with the risk of adverse clinical outcome defined as ICU admission or death (HR 2.11, 95% CI 1.34-3.31; p=0.001), even after adjustment for age, sex and other selected variables associated with COVID-19 severity. Furthermore, we documented a negative association (HR 0.661, 95% CI 0.43-1.02; p=0.063) between COVID-associated hyperglycaemia and the time to swab negativization.

ConclusionsThe recognition of hyperglycaemia as a specific clinical entity associated with COVID-19 pneumonia is relevant for early and appropriate patient management and close monitoring for the progression of disease severity.