The aim of our study was to characterize the etiology of prosthetic joint infections (PJIs)—including multidrug-resistant organisms (MDRO)—by category of infection. A multicenter study of 2544 patients with PJIs was performed. We analyzed the causative microorganisms according to the Tsukayama’s scheme (early postoperative, late chronic, and acute hematogenous infections (EPI, LCI, AHI) and “positive intraoperative cultures” (PIC)). Non-hematogenous PJIs were also evaluated according to time since surgery: 12 months. AHIs were mostly caused by Staphylococcus aureus (39.2%) and streptococci (30.2%). EPIs were characterized by a preponderance of virulent microorganisms (S. aureus, Gram-negative bacilli (GNB), enterococci), MDROs (24%) and polymicrobial infections (27.4%). Conversely, coagulase-negative staphylococci (CoNS) and Cutibacterium species were predominant in LCIs (54.5% and 6.1%, respectively) and PICs (57.1% and 15.1%). The percentage of MDROs isolated in EPIs was more than three times the percentage isolated in LCIs (7.8%) and more than twice the proportion found in AHI (10.9%). There was a significant decreasing linear trend over the four time intervals post-surgery for virulent microorganisms, MDROs, and polymicrobial infections, and a rising trend for CoNS, streptococci and Cutibacterium spp. The observed differences have important implications for the empirical antimicrobial treatment of PJIs.

Pneumonia is the leading cause of hospital admission and mortality in coronavirus disease 2019 (COVID-19). We aimed to identify the cytokines responsible for lung damage and mortality. We prospectively recruited 108 COVID-19 patients between March and April 2020 and divided them into four groups according to the severity of respiratory symptoms. Twenty-eight healthy volunteers were used for normalization of the results. Multiple cytokines showed statistically significant differences between mild and critical patients. High HGF levels were associated with the critical group (OR = 3.51; p < 0.001; 95%CI = 1.95–6.33). Moreover, high IL-1α (OR = 1.36; p = 0.01; 95%CI = 1.07–1.73) and low IL-27 (OR = 0.58; p < 0.005; 95%CI = 0.39–0.85) greatly increased the risk of ending up in the severe group. This model was especially sensitive in order to predict critical status (AUC = 0.794; specificity = 69.74%; sensitivity = 81.25%). Furthermore, high levels of HGF and IL-1α showed significant results in the survival analysis (p = 0.033 and p = 0.011, respectively). HGF, IL-1α, and IL 27 at hospital admission were strongly associated with severe/critical COVID-19 patients and therefore are excellent predictors of bad prognosis. HGF and IL-1α were also mortality biomarkers.

Objectives. To assess the influence of corticosteroid pulses on 60‐day mortality in hospitalized patients with severe COVID‐19. Methods. We designed a multicenter retrospective cohort study in three teaching hospitals of Castilla y León, Spain (865,096 people). We selected patients with confirmed COVID‐19 and lung involvement with a pO2/FiO2<300, excluding those exposed to immunosuppressors before or during hospitalization, patients terminally ill at admission, or those who died in the first 24 hours. We performed a propensity score matching (PSM) adjusting covariates that modify the probability of being treated. Then, we used a Cox regression model in the PSM group to consider factors affecting mortality. Results. From 2933 patients, 257 fulfilled the inclusion and exclusion criteria. 124 patients were on corticosteroid pulses (250 mg of methylprednisolone for three days), and 133 were not. 30.3% (37/122) of patients died in the corticosteroid pulse group and 42.9% (57/133) in the nonexposed cohort. These differences (12.6%, 95% CI [8·54‐16.65]) were statically significant (log‐rank 4.72, p = 0, 03). We performed PSM using the exact method. Mortality differences remained in the PSM group (log‐rank 5.31, p = 0.021) and were still significant after a Cox regression model (HR for corticosteroid pulses 0.561; p = 0.039). Conclusions. This study provides evidence about treatment with corticosteroid pulses in severe COVID‐19 that might significantly reduce mortality. Strict inclusion and exclusion criteria with that selection process set a reliable frame to compare mortality in both the exposed and nonexposed groups.

Background: The high effectiveness and safety of the two-drug (2DRs) strategy using dolutegravir (DTG) plus lamivudine (3TC) have led to international guidelines recommending their use for treatment-naive HIV patients. In virologically suppressed patients, de-escalating from 3DRs to DTG plus either rilpivirine (RPV) or 3TC has shown high rates of virological suppression. Objectives: This study aimed to compare the real-life data of two multicenter Spanish cohorts of PLWHIV treated with DTG plus 3TC (SPADE-3) or RPV (DORIPEX) as a switch strategy, not only in terms of virological suppression, safety, and durability but also in terms of immune restoration. The primary endpoint was the percentage of patients with virological suppression on DTG plus 3TC and DTG plus RPV at weeks 24 and 48. The secondary outcomes included the proportion of patients who experienced the protocol-defined loss of virological control by week 48; changes in immune status in terms of CD4+ and CD8+ T lymphocyte counts and the CD4+/CD8+ ratio; the rate, incidence, and reasons for discontinuation of treatment over the 48-week study period; and safety profiles at weeks 24 and 48. Methods: We conducted a retrospective, observational, multicenter study of 638 and 943 virologically suppressed HIV-1-infected patients in two cohorts who switched to 2DRs with DTG plus RPV or DTG plus 3TC. Results: The most frequent reasons for starting DTG-based 2DRs were treatment simplification/pill burden or drug decrease. The virological suppression rates were 96.9%, 97.4%, and 99.1% at weeks 24, 48, and 96, respectively. The proportion of patients with virological failure over the 48-week study period was 0.01%. Adverse drug reactions were uncommon. Patients treated with DTG+3TC increased CD4, CD8, and CD4/CD8 parameters at 24 and 48 weeks. Conclusions: We conclude that DTG-based 2DRs (combined with 3TC or RPV) in clinical practice were effective and safe as a switching strategy, with a low VF and high viral suppression rates. Both regimens were well tolerated, and ADR rates were low, including neurotoxicity and induced treatment discontinuations.

Antigen tests or polymerase chain reaction (PCR) amplification are currently COVID-19 diagnostic tools. However, developing complementary diagnosis tools is mandatory. Thus, we performed a plasma cytokine array in COVID-19 patients to identify novel diagnostic biomarkers. A discovery–validation study in two independent prospective cohorts was performed. The discovery cohort included 136 COVID-19 and non-COVID-19 patients recruited consecutively from 24 March to 11 April 2020. Forty-five cytokines’ quantification by the MAGPIX system (Luminex Corp., Austin, TX, USA) was performed in plasma samples. The validation cohort included 117 patients recruited consecutively from 15 to 25 April 2020 for validating results by ELISA. COVID-19 patients showed different levels of multiple cytokines compared to non-COVID-19 patients. A single chemokine, IP-10, accurately identified COVID-19 patients who required hospital admission (AUC: 0.962; 95%CI (0.933–0.992); p < 0.001)). The results were validated in an independent cohort by multivariable analysis (OR: 25.573; 95%CI (8.127–80.469); p < 0.001) and AUROC (AUC: 0.900; 95%CI (0.846–0.954); p < 0.001). Moreover, showing IP-10 plasma levels over 173.35 pg/mL identified COVID-19 with higher sensitivity (86.20%) than the first SARS-CoV-2 PCR. Our discover–validation study identified IP-10 as a robust biomarker in clinical practice for COVID-19 diagnosis at hospital. Therefore, IP-10 could be used as a complementary tool in clinical practice, especially in emergency departments.

Background/Objectives: Early identification of complications in chronic and infectious diseases can reduce clinical deterioration, lead to early therapeutic interventions and lower morbidity and mortality rates. Here, we aimed to assess the feasibility of a novel clinical decision support system (CDSS) based on the automatic generation of alerts through remote patient monitoring and to identify the patient profile associated with the likelihood of severe medical alerts. Methods: A prospective, multicenter, open-label, randomized controlled trial was conducted. Patients with COVID-19 in home isolation were randomly assigned in a 1:1 ratio to receive either conventional primary care telephone follow-up plus access to a mobile app for self-reporting of symptoms (control group) or conventional primary care telephone follow-up plus access to the mobile app for self-reporting of symptoms and wearable devices for real-time telemonitoring of vital signs (case group). Results: A total of 342 patients were randomized, of whom 247 were included in the per-protocol analysis (103 cases and 144 controls). The case group received a more exhaustive follow-up, with a higher number of alerts (61,827 vs. 1825; p < 0.05) but without overloading healthcare professionals thanks to automatic alert management through artificial intelligence. Baseline factors independently associated with the likelihood of a severe alert were having asthma (OR: 1.74, 95% CI: 1.22–2.48, p = 0.002) and taking corticosteroids (OR: 2.28, 95% CI: 1.24–4.2, p = 0.008). Conclusions: The CDSS could be successfully implemented and enabled real-time telemonitoring of patients’ clinical status, providing valuable information to physicians and public health agencies.