Pancreatitis Secondary to Paxlovid-Induced Tacrolimus Toxicity

Introduction: Pancreatitis is a very common gastrointestinal disease that results in hospital admission. Early detection and treatment leads to better outcomes. This is the first reported case of pancreatitis secondary to elevated tacrolimus in a patient with prior renal transplantation after receiving Paxlovid for a COVID-19 infection. Case Description/Methods: A 57-year-old male with past medical history of 4 renal transplants secondary to posterior urethral valves who presented to the emergency room with acute onset epigastric pain for 24 hours. He was on tacrolimus 5 mg every 48 hours monotherapy for his immunosuppression. 10 days prior to his presentation he had developed chills and anxiety. He tested positive for COVID-19 at that time on a home rapid test. His symptoms had not significantly improved and given his immunosuppressed state he was given Paxlovid (Nirmatrelvir/ritonavir). He took 2 days of Paxlovid, however after his second day of treatment he developed severe epigastric pain requiring him to go to the emergency room. On admission his labs were notable for a lipase of 150 U/L (ULN 63 U/L). He underwent a CT scan was notable for an enlarged pancreatic head and neck with peripancreatic fat stranding (Figure). He also had a right upper quadrant ultrasound without any cholelithiasis and only trace sludge noted. His creatinine was noted to be 1.81 mg/dl which was above his baseline of 1.2 mg/dl. His tacrolimus trough level resulted at a level 45.6 ng/ml and later peaked at 82.2 ng/ml. His liver enzymes were normal. He was treated as acute pancreatitis with hydration and his tacrolimus was held with overall clinical improvement. Discussion(s): Tacrolimus is one of the most common medications used in solid organ transplantation. It is a calcineurin inhibitor that inhibits both T-lymphocyte signal transduction and IL-2 transcription. It is metabolized by the protein CYP3A and levels are monitored closely. Paxlovid is currently prescribed as an antiviral therapy for COVID-19 infection. The ritonavir compound in Paxlovid is potent inhibitor of CYP3A. Currently the guidelines do not recommend Paxlvoid as a therapeutic in patients taking tacrolimus as there is concern about increased drug levels. There have been several case reports of pancreatitis in setting of tacrolimus. This case report helps to demonstrate the need for close monitoring of therapeutics levels, especially in medications with high risk of drug to drug interaction to help prevent serious side effects such as tacrolimus induced pancreatitis.

Personalised recommendations for hospitalised patients with Acute Kidney Injury using a Kidney Action Team (KAT-AKI): protocol and early data of a randomised controlled trial.

INTRODUCTION: Although studies have examined the utility of clinical decision support tools in improving acute kidney injury (AKI) outcomes, no study has evaluated the effect of real-time, personalised AKI recommendations. This study aims to assess the impact of individualised AKI-specific recommendations delivered by trained clinicians and pharmacists immediately after AKI detection in hospitalised patients. METHODS AND ANALYSIS: KAT-AKI is a multicentre randomised investigator-blinded trial being conducted across eight hospitals at two major US hospital systems planning to enrol 4000 patients over 3 years (between 1 November 2021 and 1 November 2024). A real-time electronic AKI alert system informs a dedicated team composed of a physician and pharmacist who independently review the chart in real time, screen for eligibility and provide combined recommendations across the following domains: diagnostics, volume, potassium, acid-base and medications. Recommendations are delivered to the primary team in the alert arm or logged for future analysis in the usual care arm. The planned primary outcome is a composite of AKI progression, dialysis and mortality within 14 days from randomisation. A key secondary outcome is the percentage of recommendations implemented by the primary team within 24 hours from randomisation. The study has enrolled 500 individuals over 8.5 months. Two-thirds were on a medical floor at the time of the alert and 17.8% were in an intensive care unit. Virtually all participants were recommended for at least one diagnostic intervention. More than half (51.6%) had recommendations to discontinue or dose-adjust a medication. The median time from AKI alert to randomisation was 28 (IQR 15.8-51.5) min. ETHICS AND DISSEMINATION: The study was approved by the ethics committee of each study site (Yale University and Johns Hopkins institutional review board (IRB) and a central IRB (BRANY, Biomedical Research Alliance of New York). We are committed to open dissemination of the data through clinicaltrials.gov and sharing of data on an open repository as well as publication in a peer-reviewed journal on completion. TRIAL REGISTRATION NUMBER: NCT04040296.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity.

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19.

A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission during an emergency department attendance. We then evaluate our model's performance on attendances occurring pre-pandemic (AUROC of 0.856 with 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC of 0.826 with 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature's SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.

Subtyping CKD Patients by Consensus Clustering: The Chronic Renal Insufficiency Cohort (CRIC) Study.

BACKGROUND: CKD is a heterogeneous condition with multiple underlying causes, risk factors, and outcomes. Subtyping CKD with multidimensional patient data holds the key to precision medicine. Consensus clustering may reveal CKD subgroups with different risk profiles of adverse outcomes. METHODS: We used unsupervised consensus clustering on 72 baseline characteristics among 2696 participants in the prospective Chronic Renal Insufficiency Cohort (CRIC) study to identify novel CKD subgroups that best represent the data pattern. Calculation of the standardized difference of each parameter used the cutoff of ±0.3 to show subgroup features. CKD subgroup associations were examined with the clinical end points of kidney failure, the composite outcome of cardiovascular diseases, and death. RESULTS: The algorithm revealed three unique CKD subgroups that best represented patients' baseline characteristics. Patients with relatively favorable levels of bone density and cardiac and kidney function markers, with lower prevalence of diabetes and obesity, and who used fewer medications formed cluster 1 (n=1203). Patients with higher prevalence of diabetes and obesity and who used more medications formed cluster 2 (n=1098). Patients with less favorable levels of bone mineral density, poor cardiac and kidney function markers, and inflammation delineated cluster 3 (n=395). These three subgroups, when linked with future clinical end points, were associated with different risks of CKD progression, cardiovascular disease, and death. Furthermore, patient heterogeneity among predefined subgroups with similar baseline kidney function emerged. CONCLUSIONS: Consensus clustering synthesized the patterns of baseline clinical and laboratory measures and revealed distinct CKD subgroups, which were associated with markedly different risks of important clinical outcomes. Further examination of patient subgroups and associated biomarkers may provide next steps toward precision medicine.

Prediction of outcomes after acute kidney injury in hospitalised patients: protocol for a systematic review.

INTRODUCTION: Acute kidney injury (AKI) is common and is associated with negative long-term outcomes. Given the heterogeneity of the syndrome, the ability to predict outcomes of AKI may be beneficial towards effectively using resources and personalising AKI care. This systematic review will identify, describe and assess current models in the literature for the prediction of outcomes in hospitalised patients with AKI. METHODS AND ANALYSIS: Relevant literature from a comprehensive search across six databases will be imported into Covidence. Abstract screening and full-text review will be conducted independently by two team members, and any conflicts will be resolved by a third member. Studies to be included are cohort studies and randomised controlled trials with at least 100 subjects, adult hospitalised patients, with AKI. Only those studies evaluating multivariable predictive models reporting a statistical measure of accuracy (area under the receiver operating curve or C-statistic) and predicting resolution of AKI, progression of AKI, subsequent dialysis and mortality will be included. Data extraction will be performed independently by two team members, with a third reviewer available to resolve conflicts. Results will be reported using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Risk of bias will be assessed using Prediction model Risk Of Bias ASsessment Tool. ETHICS AND DISSEMINATION: We are committed to open dissemination of our results through the registration of our systematic review on PROSPERO and future publication. We hope that our review provides a platform for future work in realm of using artificial intelligence to predict outcomes of common diseases. PROSPERO REGISTRATION NUMBER: CRD42019137274.

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity. (preprint)

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole exome sequencing data of about 4,000 SARS-CoV-2-positive individuals were used to define an interpretable machine learning model for predicting COVID-19 severity. Firstly, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthly, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

The Yale Department of Medicine COVID-19 Data Explorer and Repository (DOM-CovX): An Innovative Approach to Promoting Collaborative Scholarship During a Pandemic (preprint)

BackgroundThe COVID-19 pandemic has led to an explosion of research publications spanning epidemiology, basic and clinical science. While a digital revolution has allowed for open access to large datasets enabling real-time tracking of the epidemic, detailed, locally-specific clinical data has been less readily accessible to a broad range of academic faculty and their trainees. This perpetuates the separation of the primary missions of clinically-focused and primary research faculty resulting in lost opportunities for improved understanding of the local epidemic; expansion of the scope of scholarship; limitation of the diversity of the research pool; lack of creation of initiatives for growth and dissemination of research skills needed for the training of the next generation of clinicians and faculty. ObjectivesCreate a common, easily accessible and up-to-date database that would promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise. By providing a robust dataset, a broad range of researchers (faculty, trainees) and clinicians are encouraged to explore and collaborate on novel clinically relevant research questions. MethodsWe constructed a research platform called the Yale Department of Medicine COVID-19 Explorer and Repository (DOM-CovX), to house cleaned, highly granular, de-identified, continually-updated data from over 7,000 patients hospitalized with COVID-19 (1/2020-present) across the Yale New Haven Health System. This included a front-end user interface for simple data visualization of aggregate data and more detailed clinical datasets for researchers after a review board process. The goal is to promote access to local COVID-19 clinical data, thereby increasing efficiency, streamlining and democratizing the research enterprise. Expected OutcomesO_LIAccelerate generation of new knowledge and increase scholarly productivity with particular local relevance C_LIO_LIImprove the institutional academic climate by: O_LIBroadening research scope C_LIO_LIExpanding research capability to more diverse group of stakeholders including clinical and research-based faculty and trainees C_LIO_LIEnhancing interdepartmental collaborations C_LI C_LI ConclusionsThe DOM-CovX Data Explorer and Repository have great potential to increase academic productivity. By providing an accessible tool for simple data analysis and access to a consistently updated, standardized and large-scale dataset, it overcomes barriers for a wide variety of researchers. Beyond academic productivity, this innovative approach represents an opportunity to improve the institutional climate by fostering collaboration, diversity of scholarly pursuits and expanding medical education. It provides a novel approach that can be expanded to other diseases beyond COVID 19.

The Impact of Baseline A1C and Admission Glucose on Mortality among Patients with Diabetes Hospitalized with COVID-19

We sought to determine the associations between hemoglobin A1c (A1c) and admission glucose with in-hospital mortality among patients with diabetes mellitus (DM) hospitalized with COVID-19. Adults hospitalized between 3/5/20 and 12/1/20 in a Connecticut health care system were included if they had prior DM diagnosis, an in-hospital A1c, and a positive RT-PCR nasopharyngeal swab for SARS-CoV-2. A1c was stratified into <7%, 7-<9%, and ≥9%. Both bivariate and multi-variable adjusted logistic regression analyses were performed to determine the association of A1c categories and admission glucose >200 mg/dL with mortality (in-hospital death or transition to hospice) and with intensive care unit (ICU) use. Models were adjusted for demographics and 8 relevant comorbidities. Among 733 patients (median age 67 years [interquartile range, 56-77], 48.3% female, 43.11% White, 35.47% Black, 24.97% Hispanic, 1.64% Asian), 31.7% had A1c <7%, 40.5% 7-<9%, 27.8% ≥9%, and 38.1% admission glucose >200 mg/dL. During hospitalization, 111 (15.1%) patients died or transitioned to hospice and 230 (31.4%) required ICU care. In 2 multi-variable adjusted analyses, neither A1c category nor high admission glucose were significantly associated with mortality (A1c 7-<9%: OR 0.89, 95% CI 0.53-1.49;A1c >9% OR 1.3, CI 0.72-2.35 compared with A1c <7%;glucose >200 OR 1.34, CI 0.72-2.35) or ICU care (A1c 7-<9% OR 1.30, 95% CI 0.88-1.93;A1c ≥9% OR 1.35, CI 0.86-2.1 compared with A1c <7%;glucose >200 OR 1.26, CI 0.9-1.78). Age (per year OR 1.06, CI 1.04-1.08), male sex (OR 1.78, CI 1.14-2.81), obesity (OR 1.85, CI 1.16-2.96) and CKD (OR 1.90, CI 1.19-3.03) were significantly associated with mortality. Only female sex (OR 0.67, CI 0.48-0.93) was significantly associated with ICU care. In our retrospective study of hospitalized patients with DM, neither A1c nor admission glucose were prognostic of COVID-19 mortality or ICU care. In those with DM, male sex, obesity and CKD predicted worse outcomes.

Cardiac surgeons' concerns, perceptions, and responses during the COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has had an unprecedented impact on health care and cardiac surgery. We report cardiac surgeons' concerns, perceptions, and responses during the COVID-19 pandemic. METHODS: A detailed survey was sent to recruit participating adult cardiac surgery centers in North America. Data regarding cardiac surgeons' perceptions and changes in practice were analyzed. RESULTS: Our study comprises 67 institutions with diverse geographic distribution across North America. Nurses were most likely to be redeployed (88%), followed by advanced care practitioners (69%), trainees (28%), and surgeons (25%). Examining surgeon concerns in regard to COVID-19, they were most worried with exposing their family to COVID-19 (81%), followed by contracting COVID-19 (68%), running out of personal protective equipment (PPE) (28%), and hospital resources (28%). In terms of PPE conservation strategies among users of N95 respirators, nearly half were recycling via decontamination with ultraviolet light (49%), followed by sterilization with heat (13%) and at home or with other modalities (13%). Reuse of N95 respirators for 1 day (22%), 1 week (21%) or 1 month (6%) was reported. There were differences in adoption of methods to conserve N95 respirators based on institutional pandemic phase and COVID-19 burden, with higher COVID-19 burden institutions more likely to resort to PPE conservation strategies. CONCLUSIONS: The present study demonstrates the impact of COVID-19 on North American cardiac surgeons. Our study should stimulate further discussions to identify optimal solutions to improve workforce preparedness for subsequent surges, as well as facilitate the navigation of future healthcare crises.

Emergency department admissions during COVID-19: explainable machine learning to characterise data drift and detect emergent health risks (preprint)

Supervised machine learning algorithms deployed in acute healthcare settings use data describing historical episodes to predict clinical outcomes. Clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (a phenomenon known as data drift), and so can the relationship between episode characteristics and associated clinical outcomes (so-called, concept drift). We demonstrate how explainable machine learning can be used to monitor data drift in a predictive model deployed within a hospital emergency department. We use the COVID-19 pandemic as an exemplar cause of data drift, which has brought a severe change in operational circumstances. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission to hospital during an emergency department attendance. We evaluate our model's performance on attendances occurring pre-pandemic (AUROC 0.856 95\%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC 0.826 95\%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature's SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.

International transfers of personal data for health research following Schrems II: a problem in need of a solution.

On 16 July 2020, the Court of Justice of the European Union issued their decision in the Schrems II case concerning Facebook's transfers of personal data from the EU to the US. The decision may have significant effects on the legitimate transfer of personal data for health research purposes from the EU. This article aims: (i) to outline the consequences of the Schrems II decision for the sharing of personal data for health research between the EU and third countries, particularly in the context of the COVID-19 pandemic; and, (ii) to consider certain options available to address the consequences of the decision and to facilitate international data exchange for health research moving forward.

A Descriptive-Multivariate Analysis of Community Knowledge, Confidence, and Trust in COVID-19 Clinical Trials among Healthcare Workers in Uganda.

Background-misinformation and mistrust often undermines community vaccine uptake, yet information in rural communities, especially of developing countries, is scarce. This study aimed to identify major challenges associated with coronavirus disease 2019 (COVID-19) vaccine clinical trials among healthcare workers and staff in Uganda. Methods-a rapid exploratory survey was conducted over 5 weeks among 260 respondents (66% male) from healthcare centers across the country using an online questionnaire. Twenty-seven questions assessed knowledge, confidence, and trust scores on COVID-19 vaccine clinical trials from participants in 46 districts in Uganda. Results-we found low levels of knowledge (i.e., confusing COVID-19 with Ebola) with males being more informed than females (OR = 1.5, 95% CI: 0.7-3.0), and mistrust associated with policy decisions to promote herbal treatments in Uganda and the rushed international clinical trials, highlighting challenges for the upcoming Oxford-AstraZeneca vaccinations. Knowledge, confidence and trust scores were higher among the least educated (certificate vs. bachelor degree holders). We also found a high level of skepticism and possible community resistance to DNA recombinant vaccines, such as the Oxford-AstraZeneca vaccine. Preference for herbal treatments (38/260; 14.6%, 95% CI: 10.7-19.3) currently being promoted by the Ugandan government raises major policy concerns. High fear and mistrust for COVID-19 vaccine clinical trials was more common among wealthier participants and more affluent regions of the country. Conclusion-our study found that knowledge, confidence, and trust in COVID-19 vaccines was low among healthcare workers in Uganda, especially those with higher wealth and educational status. There is a need to increase transparency and inclusive participation to address these issues before new trials of COVID-19 vaccines are initiated.

Shorter androgen receptor polyQ alleles protect against life-threatening COVID-19 disease in European males.

BACKGROUND: While SARS-CoV-2 similarly infects men and women, COVID-19 outcome is less favorable in men. Variability in COVID-19 severity may be explained by differences in the host genome. METHODS: We compared poly-amino acids variability from WES data in severely affected COVID-19 patients versus SARS-CoV-2 PCR-positive oligo-asymptomatic subjects. FINDINGS: Shorter polyQ alleles (≤22) in the androgen receptor (AR) conferred protection against severe outcome in COVID-19 in the first tested cohort (both males and females) of 638 Italian subjects. The association between long polyQ alleles (≥23) and severe clinical outcome (p = 0.024) was also validated in an independent cohort of Spanish men <60 years of age (p = 0.014). Testosterone was higher in subjects with AR long-polyQ, possibly indicating receptor resistance (p = 0.042 Mann-Whitney U test). Inappropriately low serum testosterone level among carriers of the long-polyQ alleles (p = 0.0004 Mann-Whitney U test) predicted the need for intensive care in COVID-19 infected men. In agreement with the known anti-inflammatory action of testosterone, patients with long-polyQ and age ≥60 years had increased levels of CRP (p = 0.018, not accounting for multiple testing). INTERPRETATION: We identify the first genetic polymorphism that appears to predispose some men to develop more severe disease. Failure of the endocrine feedback to overcome AR signaling defects by increasing testosterone levels during the infection leads to the polyQ tract becoming dominant to serum testosterone levels for the clinical outcome. These results may contribute to designing reliable clinical and public health measures and provide a rationale to test testosterone as adjuvant therapy in men with COVID-19 expressing long AR polyQ repeats. FUNDING: MIUR project "Dipartimenti di Eccellenza 2018-2020" to Department of Medical Biotechnologies University of Siena, Italy (Italian D.L. n.18 March 17, 2020) and "Bando Ricerca COVID-19 Toscana" project to Azienda Ospedaliero-Universitaria Senese. Private donors for COVID-19 research and charity funds from Intesa San Paolo.

Post-Mendelian genetic model in COVID-19 (preprint)

Host genetics is an emerging theme in COVID-19 and few common polymorphisms and some rare variants have been identified, either by GWAS or candidate gene approach, respectively. However, an organic model is still missing. Here, we propose a new model that takes into account common and rare germline variants applied in a cohort of 1,300 Italian SARS-CoV-2 positive individuals. Ordered logistic regression of clinical WHO grading on sex and age was used to obtain a binary phenotypic classification. Genetic variability from WES was synthesized in several boolean representations differentiated according to allele frequencies and genotype effect. LASSO logistic regression was used for extracting relevant genes. We defined about 100 common driver polymorphisms corresponding to classical "threshold model". Extracted genes were demonstrated to be gender specific. Stochastic rare more penetrant events on about additional 100 extracted genes, when occurred in a medium or severe background (common within the family), simulate Mendelian inheritance in 14% of subjects (having only 1 mutation) or oligogenic inheritance (in 10% having 2 mutations, in 11% having 3 mutations, etc). The combined effect of common and rare results can be described as an integrated polygenic score computed as: (nseverity - nmildness) + F (mseverity - mmildness) where n is the number of common driver genes, m is the number of driver rare variants and F is a factor for appropriately weighing the more powerful rare variants. We called the model "post-Mendelian". The model well describes the cohort, and patients are clustered in severe or mild by the integrated polygenic scores, the F factor being calibrated around 2, with a prediction capacity of 65% in males and 70% in females. In conclusion, this is the first comprehensive model interpreting host genetics in a holistic post-Mendelian manner. Further validations are needed in order to consolidate and refine the model which however holds true in thousands of SARS-CoV-2 Italian subjects.

Employing a systematic approach to biobanking and analyzing clinical and genetic data for advancing COVID-19 research.

Within the GEN-COVID Multicenter Study, biospecimens from more than 1000 SARS-CoV-2 positive individuals have thus far been collected in the GEN-COVID Biobank (GCB). Sample types include whole blood, plasma, serum, leukocytes, and DNA. The GCB links samples to detailed clinical data available in the GEN-COVID Patient Registry (GCPR). It includes hospitalized patients (74.25%), broken down into intubated, treated by CPAP-biPAP, treated with O2 supplementation, and without respiratory support (9.5%, 18.4%, 31.55% and 14.8, respectively); and non-hospitalized subjects (25.75%), either pauci- or asymptomatic. More than 150 clinical patient-level data fields have been collected and binarized for further statistics according to the organs/systems primarily affected by COVID-19: heart, liver, pancreas, kidney, chemosensors, innate or adaptive immunity, and clotting system. Hierarchical clustering analysis identified five main clinical categories: (1) severe multisystemic failure with either thromboembolic or pancreatic variant; (2) cytokine storm type, either severe with liver involvement or moderate; (3) moderate heart type, either with or without liver damage; (4) moderate multisystemic involvement, either with or without liver damage; (5) mild, either with or without hyposmia. GCB and GCPR are further linked to the GCGDR, which includes data from whole-exome sequencing and high-density SNP genotyping. The data are available for sharing through the Network for Italian Genomes, found within the COVID-19 dedicated section. The study objective is to systematize this comprehensive data collection and begin identifying multi-organ involvement in COVID-19, defining genetic parameters for infection susceptibility within the population, and mapping genetically COVID-19 severity and clinical complexity among patients.

Stress Management: COVID-19 - Psychological and Spiritual Context.

Any crisis (personal or collective) brings along an in-built stress. It cripples people from living a normal life; sometimes it leads people to the extreme condition of permanent damage. The COVID-19 situation has brought in unexpected misery - victimizing millions of people. It has been an equalizer as both the rich and the poor have been affected; both the affluent and the developing countries have become a prey to the pandemic. For nearly one year the world is wondering which way to turn for comfort or solution, as the fear of the second and third waves looms over. Science has not given the timely preventive medication; world leaders are not able to lead the people with effective insight; financial system is on the brink of collapse; and even the faith of people seems to become a question mark. But any problem should have a solution, with often multiple aspects of solutions. Apart from medicine, one could become strengthened to face the situation. Starting with the analysis of the cause and the effect of Coronavirus Pandemic, this talk explores the intellectual and emotional aspects of the situation with the focus on the psychological and spiritual solutions to face the crisis; how to get reconciled with the reality; and how to thrive in his situation. The ultimate goal is to come out of the crisis with a spirit of creativity. If the pandemic is a period of cocoon, the butterfly-joy would be the hope to come out soon. And we need to stay energized to realize the fullness of life with peace of the mind, health of the body, and joy of the heart. Practical suggestions are put forward to face, encounter, and overcome the situation.

Validation of home oxygen saturations as a marker of clinical deterioration in patients with suspected COVID-19 (preprint)

Background The early identification of deterioration in suspected COVID-19 patients managed at home enables a more timely clinical intervention, which is likely to translate into improved outcomes. We undertook an analysis of COVID-19 patients conveyed by ambulance to hospital to investigate how oxygen saturation and measurements of other vital signs correlate to patient outcomes, to ascertain if clinical deterioration can be predicted with simple community physiological monitoring. Methods A retrospective analysis of routinely collected clinical data relating to patients conveyed to hospital by ambulance was undertaken. We used descriptive statistics and predictive analytics to investigate how vital signs, measured at home by ambulance staff from the South Central Ambulance Service, correlate to patient outcomes. Information on patient comorbidities was obtained by linking the recorded vital sign measurements to the patient's electronic health record at the Hampshire Hospitals NHS Foundation Trust. ROC analysis was performed using cross-validation to evaluate, in a retrospective fashion, the efficacy of different variables in predicting patient outcomes. Results We identified 1,080 adults with a COVID-19 diagnosis who were conveyed by ambulance to either Basingstoke & North Hampshire Hospital or the Royal Hampshire County Hospital (Winchester) between March 1st and July 31st and whose diagnosis was clinically confirmed at hospital discharge. Vital signs measured by ambulance staff at first point of contact in the community correlated with patient short-term mortality or ICU admission. Oxygen saturations were the most predictive of mortality or ICU admission (AUROC 0.772 (95 % CI: 0.712-0.833)), followed by the NEWS2 score (AUROC 0.715 (95 % CI: 0.670-0.760), patient age (AUROC 0.690 (95 % CI: 0.642-0.737)), and respiration rate (AUROC 0.662 (95 % CI: 0.599-0.729)). Combining age with the NEWS2 score (AUROC 0.771 (95 % CI: 0.718-0.824)) or the measured oxygen saturation (AUROC 0.820 (95 % CI: 0.785-0.854)) increased the predictive ability but did not reach significance. Conclusions Initial oxygen saturation measurements (on air) for confirmed COVID-19 patients conveyed by ambulance correlated with short-term (30-day) patient mortality or ICU admission, AUROC: 0.772 (95% CI: 0.712-0.833). We found that even small deflections in oxygen saturations of 1-2% below 96% confer an increased mortality risk in those with confirmed COVID at their initial community assessments.

Shorter androgen receptor polyQ alleles protect against life-threatening COVID-19 disease in males. (preprint)

BackgroundCOVID-19 presentation ranges from asymptomatic to fatal. The variability in severity may be due in part to impaired Interferon type I response due to specific mutations in the host genome or to autoantibodies, explaining about 15% of the cases when combined. Exploring the host genome is thus warranted to further elucidate disease variability. MethodsWe developed a synthetic approach to genetic data representation using machine learning methods to investigate complementary genetic variability in COVID-19 infected patients that may explain disease severity, due to poly-amino acids repeat polymorphisms. Using host whole-exome sequencing data, we compared extreme phenotypic presentations (338 severe versus 300 asymptomatic cases) of the entire (men and women) Italian GEN-COVID cohort of 1178 subjects infected with SARS-CoV-2. We then applied the LASSO Logistic Regression model on Boolean gene-based representation of the poly-amino acids variability. FindingsShorter polyQ alleles ([≤]22) in the androgen receptor (AR) conferred protection against a more severe outcome in COVID-19 infection. In the subgroup of males with age <60 years, testosterone was higher in subjects with AR long-polyQ ([≥]23), possibly indicating receptor resistance (p=0.004 Mann-Whitney U test). Inappropriately low testosterone levels for the long-polyQ alleles predicted the need for intensive care in COVID-19 infected men. In agreement with the known anti-inflammatory action of testosterone, patients with long-polyQ ([≥]23) and age>60 years had increased levels of C Reactive Protein (p=0.018). InterpretationOur results may contribute to design reliable clinical and public health measures and provide a rationale to test testosterone treatment as adjuvant therapy in symptomatic COVID-19 men expressing AR polyQ longer than 23 repeats. FundingMIUR project "Dipartimenti di Eccellenza 2018-2020" to Department of Medical Biotechnologies University of Siena, Italy (Italian D.L. n.18 March 17, 2020). Private donors for COVID research and charity funds from Intesa San Paolo. BoxesO_ST_ABSEvidence before this studyC_ST_ABSWe searched on Medline, EMBASE, and Pubmed for articles published from January 2020 to August 2020 using various combinations of the search terms "sex-difference", "gender" AND SARS-Cov-2, or COVID. Epidemiological studies indicate that men and women are similarly infected by COVID-19, but the outcome is less favorable in men, independently of age. Several studies also showed that patients with hypogonadism tend to be more severely affected. A prompt intervention directed toward the most fragile subjects with SARS-Cov2 infection is currently the only strategy to reduce mortality. glucocorticoid treatment has been found cost-effective in improving the outcome of severe cases. Clinical algorithms have been proposed, but little is known on the ability of genetic profiling to predict outcome and disclose novel therapeutic strategies. Added-value of this studyIn a cohort of 1178 men and women with COVID-19, we used a supervised machine learning approach on a synthetic representation of the uncovered variability of the human genome due to poly-amino acid repeats. Comparing the genotype of patients with extreme manifestations (severe vs. asymptomatic), we found that the poly-glutamine repeat of the androgen receptor (AR) gene is relevant for COVID-19 disease and defective AR signaling identifies an association between male sex, testosterone exposure, and COVID-19 outcome. Failure of the endocrine feedback to overcome AR signaling defect by increasing testosterone levels during the infection leads to the fact that polyQ becomes dominant to T levels for the clinical outcome. Implications of all the available evidenceWe identify the first genetic polymorphism predisposing some men to develop a more severe disease irrespectively of age. Based on this, we suggest that sizing the AR poly-glutamine repeat has important implications in the diagnostic pipeline of patients affected by life-threatening COVID-19 infection. Most importantly, our studies open to the potential of using testosterone as adjuvant therapy for severe COVID-19 patients having defective androgen signaling, defined by this study as [≥]23 PolyQ repeats and inappropriate levels of circulating androgens.