Using machine learning to predict antibody response to SARS-CoV-2 vaccination in solid organ transplant recipients: the multicentre ORCHESTRA cohort.

OBJECTIVES: The study aim was to assess predictors of negative antibody response (AbR) in solid organ transplant (SOT) recipients after the first booster of SARS-CoV-2 vaccination. METHODS: Solid organ transplant recipients receiving SARS-CoV-2 vaccination were prospectively enrolled (March 2021-January 2022) at six hospitals in Italy and Spain. AbR was assessed at first dose (t0), second dose (t1), 3 ± 1 month (t2), and 1 month after third dose (t3). Negative AbR at t3 was defined as an anti-receptor binding domain titre <45 BAU/mL. Machine learning models were developed to predict the individual risk of negative (vs. positive) AbR using age, type of transplant, time between transplant and vaccination, immunosuppressive drugs, type of vaccine, and graft function as covariates, subsequently assessed using a validation cohort. RESULTS: Overall, 1615 SOT recipients (1072 [66.3%] males; mean age±standard deviation [SD], 57.85 ± 13.77) were enrolled, and 1211 received three vaccination doses. Negative AbR rate decreased from 93.66% (886/946) to 21.90% (202/923) from t0 to t3. Univariate analysis showed that older patients (mean age, 60.21 ± 11.51 vs. 58.11 ± 13.08), anti-metabolites (57.9% vs. 35.1%), steroids (52.9% vs. 38.5%), recent transplantation (<3 years) (17.8% vs. 2.3%), and kidney, heart, or lung compared with liver transplantation (25%, 31.8%, 30.4% vs. 5.5%) had a higher likelihood of negative AbR. Machine learning (ML) algorithms showing best prediction performance were logistic regression (precision-recall curve-PRAUC mean 0.37 [95%CI 0.36-0.39]) and k-Nearest Neighbours (PRAUC 0.36 [0.35-0.37]). DISCUSSION: Almost a quarter of SOT recipients showed negative AbR after first booster dosage. Unfortunately, clinical information cannot efficiently predict negative AbR even with ML algorithms.

Host immunological responses facilitate development of SARS-CoV-2 mutations in patients receiving monoclonal antibody treatments.

BackgroundThe role of host immunity in emergence of evasive SARS-CoV-2 Spike mutations under therapeutic monoclonal antibody (mAb) pressure remains to be explored.MethodsIn a prospective, observational, monocentric ORCHESTRA cohort study, conducted between March 2021 and November 2022, mild-to-moderately ill COVID-19 patients (n = 204) receiving bamlanivimab, bamlanivimab/etesevimab, casirivimab/imdevimab, or sotrovimab were longitudinally studied over 28 days for viral loads, de novo Spike mutations, mAb kinetics, seroneutralization against infecting variants of concern, and T cell immunity. Additionally, a machine learning-based circulating immune-related biomarker (CIB) profile predictive of evasive Spike mutations was constructed and confirmed in an independent data set (n = 19) that included patients receiving sotrovimab or tixagevimab/cilgavimab.ResultsPatients treated with various mAbs developed evasive Spike mutations with remarkable speed and high specificity to the targeted mAb-binding sites. Immunocompromised patients receiving mAb therapy not only continued to display significantly higher viral loads, but also showed higher likelihood of developing de novo Spike mutations. Development of escape mutants also strongly correlated with neutralizing capacity of the therapeutic mAbs and T cell immunity, suggesting immune pressure as an important driver of escape mutations. Lastly, we showed that an antiinflammatory and healing-promoting host milieu facilitates Spike mutations, where 4 CIBs identified patients at high risk of developing escape mutations against therapeutic mAbs with high accuracy.ConclusionsOur data demonstrate that host-driven immune and nonimmune responses are essential for development of mutant SARS-CoV-2. These data also support point-of-care decision making in reducing the risk of mAb treatment failure and improving mitigation strategies for possible dissemination of escape SARS-CoV-2 mutants.FundingThe ORCHESTRA project/European Union's Horizon 2020 research and innovation program.

Impact of COVID-19 and Antibiotic Treatments on Gut Microbiome: A Role for Enterococcus spp.

OBJECTIVE: Several studies showed the substantial use of antibiotics and increased risk of antimicrobial resistant infections in patients with COVID-19. The impact of COVID-19-related treatments and antibiotics on gut dysbiosis has not been clarified. DESIGN: The prospective cohort study included hospitalized COVID-19 patients (April-December 2020). The gut microbiome composition was analysed by 16S sequencing. The gut diversity and changes in opportunistic bacteria (OBs) or symbionts were analysed according to clinical parameters, laboratory markers of disease progression, type of non-antibiotic COVID-19 treatments (NACT) and type, WHO AWaRe group, and duration of antibiotic therapy (AT). RESULTS: A total of 82 patients (mean age 66 ± 13 years, 70% males) were enrolled. The relative abundance of Enterococcus was significantly correlated with duration of hospitalization, intensive care unit stay, O2 needs, and D-dimer, ferritin, and IL-6 blood levels. The presence of Enterococcus showed the highest number of correlations with NACT, AT, and AT + NACT (e.g., hydroxychloroquine ± lopinavir/ritonavir) and increased relative abundance with AWaRe Watch/Reserve antibiotics, AT duration, and combinations. Abundance of Dorea, Agathobacter, Roseburia, and Barnesiella was negatively correlated with AT and corticosteroids use. Patients with increased IL-6, D-dimer, and ferritin levels receiving AT were more likely to show dysbiosis with increased abundance of Enterococcus and Bilophila bacteria and decreased abundance of Roseburia compared with those not receiving AT. CONCLUSION: Microbiome diversity is affected by COVID-19 severity. In this context, antibiotic treatment may shift the gut microbiome composition towards OBs, particularly Enterococcus. The impact of treatment-driven dysbiosis on OBs infections and long-term consequences needs further study to define the role of gut homeostasis in COVID-19 recovery and inform targeted interventions.

Evaluation of the Kinetics of Antibody Response to COVID-19 Vaccine in Solid Organ Transplant Recipients: The Prospective Multicenter ORCHESTRA Cohort.

Previous studies assessing the antibody response (AbR) to mRNA COVID-19 vaccines in solid organ transplant (SOT) recipients are limited by short follow-up, hampering the analysis of AbR kinetics. We present the ORCHESTRA SOT recipients cohort assessed for AbR at first dose (t0), second dose (t1), and within 3 ± 1 month (t2) after the first dose. We analyzed 1062 SOT patients (kidney, 63.7%; liver, 17.4%; heart, 16.7%; and lung, 2.5%) and 5045 health care workers (HCWs). The AbR rates in the SOTs and HCWs were 52.3% and 99.4%. The antibody levels were significantly higher in the HCWs than in the SOTs (p < 0.001). The kinetics showed an increase (p < 0.001) in antibody levels up to 76 days and a non-significant decrease after 118 days in the SOT recipients versus a decrease up to 76 days (p = 0.02) and a less pronounced decrease between 76 and 118 days (p = 0.04) in the HCWs. Upon multivariable analysis, liver transplant, ≥3 years from SOT, mRNA-1273, azathioprine, and longer time from t0 were associated with a positive AbR at t2. Older age, other comorbidities, mycophenolate, steroids, and impaired graft function were associated with lower AbR probability. Our results may be useful to optimize strategies of immune monitoring after COVID-19 vaccination and indications regarding timing for booster dosages calibrated on SOT patients' characteristics.

Determinants of persistence of symptoms and impact on physical and mental wellbeing in Long COVID: A prospective cohort study.

BACKGROUND: Residual symptoms can be detected for several months after COVID-19. To better understand the predictors and impact of symptom persistence we analyzed a prospective cohort of COVID-19 patients. METHODS: Patients were followed for 9 months after COVID-19 onset. Duration and predictors of persistence of symptoms, physical health and psychological distress were assessed. RESULTS: 465 patients (54% males, 51% hospitalized) were included; 37% presented with at least 4 symptoms and 42% complained of symptom lasting more than 28 days. At month 9, 20% of patients were still symptomatic, showing mainly fatigue (11%) and breathlessness (8%). Hospitalization and ICU stay vs. non-hospitalized status increased the median duration of fatigue of 8 weeks. Age > 50 years (OR 2.50), ICU stay (OR 2.35), and presentation with 4 or more symptoms (OR 2.04) were independent predictors of persistence of symptoms at month 9. A total of 18% of patients did not return to optimal pre-COVID physical health, while 19% showed psychological distress at month 9. Hospital admission (OR 2.28) and persistence of symptoms at day 28 (OR 2.21) and month 9 (OR 5.16) were independent predictors of suboptimal physical health, while female gender (OR 5.27) and persistence of symptoms at day 28 (OR 2.42) and month 9 (OR 2.48) were risk factors for psychological distress. CONCLUSIONS: Patients with advanced age, ICU stay and multiple symptoms at onset were more likely to suffer from long-term symptoms, which had a negative impact on both physical and mental wellbeing. This study contributes to identify the target populations and Long COVID consequences for planning long-term recovery interventions.

Assessment of COVID-19 progression on day 5 from symptoms onset.

BACKGROUND: A major limitation of current predictive prognostic models in patients with COVID-19 is the heterogeneity of population in terms of disease stage and duration. This study aims at identifying a panel of clinical and laboratory parameters that at day-5 of symptoms onset could predict disease progression in hospitalized patients with COVID-19. METHODS: Prospective cohort study on hospitalized adult patients with COVID-19. Patient-level epidemiological, clinical, and laboratory data were collected at fixed time-points: day 5, 10, and 15 from symptoms onset. COVID-19 progression was defined as in-hospital death and/or transfer to ICU and/or respiratory failure (PaO2/FiO2 ratio < 200) within day-11 of symptoms onset. Multivariate regression was performed to identify predictors of COVID-19 progression. A model assessed at day-5 of symptoms onset including male sex, age > 65 years, dyspnoea, cardiovascular disease, and at least three abnormal laboratory parameters among CRP (> 80 U/L), ALT (> 40 U/L), NLR (> 4.5), LDH (> 250 U/L), and CK (> 80 U/L) was proposed. Discrimination power was assessed by computing area under the receiver operating characteristic (AUC) values. RESULTS: A total of 235 patients with COVID-19 were prospectively included in a 3-month period. The majority of patients were male (148, 63%) and the mean age was 71 (SD 15.9). One hundred and ninety patients (81%) suffered from at least one underlying illness, most frequently cardiovascular disease (47%), neurological/psychiatric disorders (35%), and diabetes (21%). Among them 88 (37%) experienced COVID-19 progression. The proposed model showed an AUC of 0.73 (95% CI 0.66-0.81) for predicting disease progression by day-11. CONCLUSION: An easy-to-use panel of laboratory/clinical parameters computed at day-5 of symptoms onset predicts, with fair discrimination ability, COVID-19 progression. Assessment of these features at day-5 of symptoms onset could facilitate clinicians' decision making. The model can also play a role as a tool to increase homogeneity of population in clinical trials on COVID-19 treatment in hospitalized patients.

Long-Term Patient-Centred Follow-up in a Prospective Cohort of Patients with COVID-19.

INTRODUCTION: To better define COVID-19 long-term impact we prospectively analysed patient-centred outcomes, including general health and symptom duration. METHODS: Barthel index (BI), St. George's Respiratory Questionnaire adapted to patients with COVID-19 (aSGRQ) and WHO Clinical Progression Scale (CPS) were measured at enrolment and at 6 weeks from the onset of symptoms. Persistence of most frequently reported symptoms was assessed at 6 weeks and, among symptomatic patients, at 12 weeks from the onset of symptoms. Predictors of impaired general health over time were identified using an ordinal multilevel multivariate model. RESULTS: A total of 448 patients (55% men, median age 56 years) were enrolled. WHO-CPS showed mild, moderate and severe disease in 48%, 42% and 10% of patients at admission and mild disease in all patients at follow-up, respectively. BI and aSGRQ were normal in 96% and 93% patients before COVID-19 but only in 47% and 16% at COVID-19 diagnosis and in 87% and 65% at 6-week follow-up. Male gender was identified by all three assessments as a predictor of impaired general health (BI, OR 2.14, p < 0.0001; aSGRQ, OR 0.53, p = 0.003; WHO-CPS, OR 1.56, p = 0.01). Other predictors included age, ICU admission and comorbidities (e.g. cardiovascular disease and cancer) for BI, hospital admission for aSGRQ, age and presence of comorbidities for WHO-CPS. At 6- and 12-week follow-up, 39% and 20% of patients, respectively, were still reporting symptoms. Fatigue and breathlessness were the most frequently reported symptoms. CONCLUSIONS: Long-term follow-up facilitates the monitoring of health impairment and symptom persistence and can contribute to plan tailored interventions.

Pitfalls of Computed Tomography in the Coronavirus 2019 (COVID-19) Era: A New Perspective on Ground-Glass Opacities.

Aim To study ground-glass opacities (GGO) not only from the coronavirus 2019 (COVID-19) pneumonia" perspective but also as a radiological presentation of other pathologies with comparable features. Methods We enrolled 33 patients admitted to Policlinico Universitario G. B. Rossi who underwent non-contrast-enhanced (NCE) or contrast-enhanced (CE) chest computed tomography (CT) between March 12 and April 12. All patients with CT-detected ground-glass opacity (GGO) were included. All patients resulted as COVID-19 negative at the reverse transcription-polymerase chain reaction (RT-PCR) assay. We studied the different pathologies underlying GGO features: neoplastic diseases and non-neoplastic diseases (viral pneumonias, interstitial pneumonias, and cardiopulmonary diseases) in order to avoid pitfalls and to reach the correct diagnosis. Results All CT scans detected GGOs. Symptomatic patients were 25/33 (75.7%). At the clinical presentation, they reported fever and dry cough; in six out of 25 cases, dyspnea was also reported (24%). Thirty-three (33; 100%) showed GGO at CT: 15/33 (45.45%) presented pure GGO, and 18/33 (54.54%) showed GGO with consolidation. The RT-PCR assay was negative in 100%. We investigated other potential underlying diseases to explain imaging features: neoplastic causes (8/33, 24.24%) and non-neoplastic causes, in particular, infectious pneumonias (16/33, 48,48 %, viral and fungal), interstitial pneumonias (4/33, 12,12%), and cardio-pulmonary disease (5/33, 15,15%). Conclusions GGO remains a diagnostic challenge. Although CT represents a fundamental diagnostic tool because of its sensitivity, it still needs to be integrated with clinical data to achieve the best clinical management. In the presence of typical imaging features (e.g. GGO and consolidation), the radiologist should focus on the pandemic and manage a suspect patient as COVID-19 positive until proven to be negative.