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.

Background Identifying the most appropriate antiretroviral regimen for pregnant women with Human Immunodeficiency Virus (HIV-1) infection can be challenging, mainly due to pregnancy-related physiological alterations which can significantly reduce maternal drug plasma concentration. We would like to report our experience as it consists of an unusual case of low plasmatic concentration of antiretroviral drugs despite regimen intensification in a HIV-positive pregnant woman. It also underlines the need for accurate monitoring and treatment adjustment in pregnant women with Human Immunodeficiency Virus (HIV). Case presentation A 26-year-old Brazilian woman with HIV-1 infection attending our out-patient clinic presented with low plasmatic concentration of antiretroviral drugs and persistent detectable viral load despite regimen intensification during pregnancy. Trough plasma concentrations of dolutegravir and darunavir were measured by validated liquid chromatography–mass spectrometry. At 23 weeks of gestation it showed a lower value than expected in non-pregnant adults, compared to a normal level of plasma concentration measured at 10 weeks after delivery. Our patient and the baby had no regimen-related adverse effects. Conclusions Physiological changes during pregnancy can affect pharmacokinetics and reduce a mother’s bioavailability of antiretroviral drugs, potentially altering their pharmacological activity. A personalized treatment and a careful follow-up are hence mandatory for this key population.

IntroductionResidency in LTCFs increases the likelihood of colonization with multidrug resistant Gram-negative bacteria (MDR-GNB). We assessed the prevalence and risk factors for enteric colonization by III-generation cephalosporins-resistant and carbapenem-resistant (CR) GNB in a large group of LTCFs in a high endemic setting. We also assessed the prevalence and risk factors for C. difficile colonization.MethodsA point prevalence survey with rectal screening (RS) was conducted in 27 LTCFs in north Italy. Epidemiological and clinical variables on the survey day, history of hospitalization and surgery within one year, and antibiotics within three months, were collected. The presence of III-generation cephalosporin resistant and CR GNB was assessed using a selective culture on chromogenic medium and PCR for carbapenemase detection. The presence of C. difficile was assessed using ELISA for GDH and RT-PCR to identify toxigenic strains. Multi-variable analyses were performed using two-level logistic regression models.ResultsIn the study period 1947 RSs were performed. The prevalence of colonization by at least one GNB resistant to III-generation cephalosporin was 51% (E. coli 65%, K. pneumoniae 14% of isolates). The prevalence of colonization by CR GNB was 6%. 6% of all isolates (1150 strains) resulted in a carbapenem-resistant K. pneumoniae, and 3% in a carbapenem-resistant E. coli. KPC was the most frequent carbapenemase (73%) identified by PCR, followed by VIM (23%). The prevalence of colonization by C. difficile was 11.7%. The presence of a medical device (OR 2.67) and previous antibiotic use (OR 1.48) were significantly associated with III-generation cephalosporin resistant GNB colonization. The presence of a medical device (OR 2.67) and previous hospitalization (OR 1.80) were significantly associated with CR GNB. The presence of a medical device (OR 2.30) was significantly associated with C. difficile colonization. Main previously used antibiotic classes were fluoroquinolones (32% of previously treated subjects), III-generation cephalosporins (21%), and penicillins (19%).ConclusionAntimicrobial stewardship in LTCFs is a critical issue, being previous antibiotic treatment a risk factor for colonization by MDR-GNB. The prevalence of colonization by III-generation cephalosporin and CR GNB among LTCF residents also underlines the importance to adhere to hand hygiene indications, infection prevention and control measures, and environmental hygiene protocols, more achievable than rigorous contact precautions in this type of social setting.

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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 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. 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. A model assessed at day-5 of symptoms onset including male sex, age >65 years, dyspnea, 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) 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 assessment of 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.