Background Residual symptoms can be detected for several months after COVID-19. To better understand the predictors and impact of symptom persistence we analysed 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% hospitalised) 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%). Hospitalisation and ICU stay vs. non-hospitalised 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.
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.
BackgroundMycoplasma genitalium (MG) is one of the most warning emerging sexually transmitted pathogens also due to its ability in developing resistance to antibiotics. MG causes different conditions ranging from asymptomatic infections to acute mucous inflammation. Resistance-guided therapy has demonstrated the best cure rates and macrolide resistance testing is recommended in many international guidelines. However, diagnostic and resistance testing can only be based on molecular methods, and the gap between genotypic resistance and microbiological clearance has not been fully evaluated yet. This study aims at finding mutations associated with MG antibiotic resistance and investigating the relationship with microbiological clearance amongst MSM.MethodsFrom 2017 to 2021, genital (urine) and extragenital (pharyngeal and anorectal swabs) biological specimens were provided by men-who-have-sex-with-men (MSM) attending the STI clinic of the Infectious Disease Unit at the Verona University Hospital, Verona, Italy. A total of 1040 MSM were evaluated and 107 samples from 96 subjects resulted positive for MG. Among the MG-positive samples, all those available for further analysis (n=47) were considered for detection of mutations known to be associated with macrolide and quinolone resistance. 23S rRNA, gyrA and parC genes were analyzed by Sanger sequencing and Allplex™ MG and AziR Assay (Seegene).ResultsA total of 96/1040 (9.2%) subjects tested positive for MG in at least one anatomical site. MG was detected in 107 specimens: 33 urine samples, 72 rectal swabs and 2 pharyngeal swabs. Among them, 47 samples from 42 MSM were available for investigating the presence of mutations associated with macrolide and quinolone resistance: 30/47 (63.8%) showed mutations in 23S rRNA while 10/47 (21.3%) in parC or gyrA genes. All patients with positive Test of Cure (ToC) after first-line treatment with azithromycin (n=15) were infected with 23S rRNA-mutated MG strains. All patients undergoing second-line moxifloxacin treatment (n=13) resulted negative at ToC, even those carrying MG strains with mutations in parC gene (n=6).ConclusionOur observations confirm that mutations in 23S rRNA gene are associated with azithromycin treatment failure and that mutations in parC gene alone are not always associated with phenotypic resistance to moxifloxacin. This reinforces the importance of macrolide resistance testing to guide the treatment and reduce antibiotic pressure on MG strains.
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