There is limited information available describing the clinical and epidemiological features of Spanish patients requiring hospitalization for coronavirus disease 2019 (COVID-19). In this ...observational study, we aimed to describe the clinical characteristics and epidemiological features of severe (non-ICU) and critically patients (ICU) with COVID-19 at triage, prior to hospitalization. Forty-eight patients (27 non-ICU and 21 ICU) with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were analyzed (mean age, 66 years, range, 33–88 years; 67% males). There were no differences in age or sex among groups. Initial symptoms included fever (100%), coughing (85%), dyspnea (76%), diarrhea (42%) and asthenia (21%). ICU patients had a higher prevalence of dyspnea compared to non-ICU patients (95% vs. 61%, p = 0.022). ICU-patients had lymphopenia as well as hypoalbuminemia. Lactate dehydrogenase (LDH), C-reactive protein (CRP), and procalcitonin were significantly higher in ICU patients compared to non-ICU (p < 0.001). Lower albumin levels were associated with poor prognosis measured as longer hospital length (r = −0.472, p < 0.001) and mortality (r = −0.424, p = 0.003). As of 28 April 2020, 10 patients (8 ICU and 2 non-ICU) have died (21% mortality), and while 100% of the non-ICU patients have been discharged, 33% of the ICU patients still remained hospitalized (5 in ICU and 2 had been transferred to ward). Critically ill patients with COVID-19 present lymphopenia, hypoalbuminemia and high levels of inflammation.
The use of Artificial Intelligence (AI) and Machine Learning (ML) techniques has improved a sepsis (SE) and septic shock (SS) early detection compared with traditional rules according to recent ...retrospective, prospective and meta-analysis (1). Develop predictive models using algorithms based on AI-ML techniques and compare with fixed rules for SE/SS detection, assessing whether these new models improve predictive capability.
We carried out an observational, retrospective non interventional study developed in our University General Hospital. The period assessed was from January 2014 to October 2018. The diagnosis and validation of each SE or SS case were made prospectively by the clinical experts of the Multidisciplinary Sepsis Unit (MSU). We used a Sepsis 2 definition. We developed AI-ML techniques from historical data from the Electronic Health Record (EHR). The structured variables were obtained from different data sources and from non-structured text from the Triage and Emergency Department (ED). The Mann-Whitney-Wilcoxon test was used to identify statistically significant clinical and analytical variables, as well as wrapper techniques, with a significance level of 0.01 and to obtain relevant unstructured data using a Natural Language Processing (NLP) techniques.
A total of 815,170 records of the EHR have been assessed. We included 218.562 adult patients from all hospital departments. We divided into 2 groups: 1) with SE/SS were 9301 (4.6%); and 2) 209,261 (95.4%) who did NOT have sepsis (NSE). A total of 3927 variables have been extracted from the different data sources. By relevance and after being validated by the UMS team, 244 (6.2%) both structured and unstructured variables were associated with the detection of SE/SS. Within the structured variables, we identified many that are not blackened by the classic scorings of SE/SS, such as hemoglobin or eosinopenia. We developed about 30 different predictive models for SE/SS detection, using fixed rules individually, using only AI-ML based algorithms or the combination of fixed rules with AI-ML techniques. The best model using only fixed rules was the one using the Sepsis.2 criterion, while the best model using AI-ML techniques was called BISEPRO and was a combination of SEPSIS.2 with AI-ML techniques.
In this retrospective study including adult patients in all areas of a hospital the use of AI-ML based techniques was significantly superior for the detection of SE/SS.
1. Lucas M. Fleuren; Patrick Thoral, Duncan Shillan, et all. Machine learning in intensive care medicine: ready for take-off. Intensive Care Medicine. Jul 2020.46:1486–1488
No score is available to assess severity and stratify mortality risk in ventilator-associated pneumonia (VAP). Our objective was to develop a severity assessment tool for VAP patients.
A prospective, ...observational, cohort study was performed including 441 patients with VAP in three multidisciplinary ICUs. Multivariate logistic regression was performed to identify variables independently associated with ICU mortality. Results were converted into a four-variable score based on the PIRO (predisposition, insult, response, organ dysfunction) concept for ICU mortality risk stratification in VAP patients.
Comorbidities (COPD, immunocompromise, heart failure, cirrhosis, or chronic renal failure); bacteremia; systolic BP < 90 mm Hg; and ARDS. A simple, four-variable VAP PIRO score was obtained at VAP onset. Mortality varied significantly according to VAP PIRO score (p < 0.001). On the basis of observed mortality for each VAP PIRO score, patients were stratified into three levels of risk: (1) mild, 0 to 1 points; (2) high, 2 points; (3) very high, 3 to 4 points. VAP PIRO score was associated with higher risk of death in Cox regression analysis in the high-risk group (hazard ratio, 2.14; 95% confidence interval CI, 1.19 to 3.86) and the very-high-risk group (hazard ratio, 4.63; 95% confidence interval, 2.68 to 7.99). Moreover, medical resource use after VAP diagnosis was higher in high-risk and very-high-risk levels compared to patients at mild risk, evaluated using length of ICU stay (mean ± SD, 22.0 ± 10.6 d vs 18.7 ± 12.8 d, p < 0.05) and duration of mechanical ventilation (18.3 ± 10.1 d vs 15.1 ± 11.5 d, p < 0.05).
VAP PIRO score is a simple, practical clinical tool for predicting ICU mortality and health-care resources use that is likely to assist clinicians in determining VAP severity.
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•New non-invasive method for COVID-19 diagnosis with fast turnaround time (<10 min).•Nanoparticle biosensors detect SARS-CoV-2 antigens trapped in surgical face masks.•Excellent ...sensitivity and specificity, even with asymptomatic patients.•Signals are read with a smartphone, ideal for decentralized mass screenings.•Potential for detecting other antigens as well as biomarkers of inflammation.
Detecting SARS-CoV-2 antigens in respiratory tract samples has become a widespread method for screening new SARS-CoV-2 infections. This requires a nasopharyngeal swab performed by a trained healthcare worker, which puts strain on saturated healthcare services. In this manuscript we describe a new approach for non-invasive COVID-19 diagnosis. It consists of using mobile biosensors for detecting viral antigens trapped in surgical face masks worn by patients. The biosensors are made of filter paper containing a nanoparticle reservoir. The nanoparticles transfer from the biosensor to the mask on contact, where they generate colorimetric signals that are quantified with a smartphone app. Sample collection requires wearing a surgical mask for 30 min, and the total assay time is shorter than 10 min. When tested in a cohort of 27 patients with mild or no symptoms, an area under the receiving operating curve (AUROC) of 0.99 was obtained (96.2 % sensitivity and 100 % specificity). Serial measurements revealed a high sensitivity and specificity when masks were worn up to 6 days after diagnosis. Surgical face masks are inexpensive and widely available, which makes this approach easy to implement anywhere. The excellent sensitivity, even when tested with asymptomatic patient samples, along with the mobile detection scheme and non-invasive sampling procedure, makes this biosensor design ideal for mass screening.
Stratifying patients according to disease severity has been a major hurdle during the COVID-19 pandemic. This usually requires evaluating the levels of several biomarkers, which may be cumbersome ...when rapid decisions are required. In this manuscript we show that a single nanoparticle aggregation test can be used to distinguish patients that require intensive care from those that have already been discharged from the intensive care unit (ICU). It consists of diluting a platelet-free plasma sample and then adding gold nanoparticles. The nanoparticles aggregate to a larger extent when the samples are obtained from a patient in the ICU. This changes the color of the colloidal suspension, which can be evaluated by measuring the pixel intensity of a photograph. Although the exact factor or combination of factors behind the different aggregation behavior is unknown, control experiments demonstrate that the presence of proteins in the samples is crucial for the test to work. Principal component analysis demonstrates that the test result is highly correlated to biomarkers of prognosis and inflammation that are commonly used to evaluate the severity of COVID-19 patients. The results shown here pave the way to develop nanoparticle aggregation assays that classify COVID-19 patients according to disease severity, which could be useful to de-escalate care safely and make a better use of hospital resources.
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•A single AuNPs test for assessing the severity of COVID-19 disease is developed.•The method is based on diluting a platelet-free plasma sample, adding AuNPs and observing color changes.•AuNPs aggregate according to changes in plasma composition triggered by elevated inflammatory status.sepsis
Since March 2008, several linezolid and teicoplanin-resistant Staphylococcus hominis (S. hominis) isolates have been recovered from patients admitted to the two major hospitals on the island of ...Majorca, Spain. For this reason, a study was conducted to determine the molecular epidemiology of these isolates and the mechanism of linezolid resistance.
The molecular epidemiology study was performed by pulsed-field gel electrophoresis (PFGE) analysis, after digestion with ApaI. Linezolid resistance mechanisms were evaluated by PCR amplification of a fragment of the domain V of the 23S rRNA gene (followed by sequencing) and cfr gene.
From March 2008 to February 2009, 15 linezolid and teicoplanin-resistant S. hominis isolates were recovered from 14 patients. All of them, except one, were hospitalised in the intensive care units of either of the two institutions. Isolates were obtained mainly from blood cultures (9). The majority of infected patients (12 of 15 infectious episodes, 80.0%) had received courses of linezolid prior to detection of the resistant isolate. PFGE analysis revealed the presence of a unique clone among linezolid resistant S. hominis isolates. The G2576T mutation was detected in all the linezolid resistant strains. None of the resistant isolates showed a positive PCR for the cfr gene. All of the isolates were also resistant to penicillin, oxacillin, trimethoprim-sulfamethoxazole, ciprofloxacin, levofloxacin, and tobramicin; whereas all of them were susceptible to erythromycin, tetracycline, gentamicin, and daptomycin. The MIC of vancomycin was 4μg/ml for all the strains.
The detection of linezolid resistant Staphylococci highlights the need to rationalise the use of linezolid, and maintain an active surveillance of its resistance to preserve the clinical usefulness of this antimicrobial.
Around one-third of patients diagnosed with COVID-19 develop a severe illness that requires admission to the Intensive Care Unit (ICU). In clinical practice, clinicians have learned that patients ...admitted to the ICU due to severe COVID-19 frequently develop ventilator-associated lower respiratory tract infections (VA-LRTI). This study aims to describe the clinical characteristics, the factors associated with VA-LRTI, and its impact on clinical outcomes in patients with severe COVID-19. This was a multicentre, observational cohort study conducted in ten countries in Latin America and Europe. We included patients with confirmed rtPCR for SARS-CoV-2 requiring ICU admission and endotracheal intubation. Only patients with a microbiological and clinical diagnosis of VA-LRTI were included. Multivariate Logistic regression analyses and Random Forest were conducted to determine the risk factors for VA-LRTI and its clinical impact in patients with severe COVID-19. In our study cohort of 3287 patients, VA-LRTI was diagnosed in 28.8% 948/3287. The cumulative incidence of ventilator-associated pneumonia (VAP) was 18.6% 610/3287, followed by ventilator-associated tracheobronchitis (VAT) 10.3% 338/3287. A total of 1252 bacteria species were isolated. The most frequently isolated pathogens were Pseudomonas aeruginosa (21.2% 266/1252), followed by Klebsiella pneumoniae (19.1% 239/1252) and Staphylococcus aureus (15.5% 194/1,252). The factors independently associated with the development of VA-LRTI were prolonged stay under invasive mechanical ventilation, AKI during ICU stay, and the number of comorbidities. Regarding the clinical impact of VA-LRTI, patients with VAP had an increased risk of hospital mortality (OR 95% CI of 1.81 1.40-2.34), while VAT was not associated with increased hospital mortality (OR 95% CI of 1.34 0.98-1.83). VA-LRTI, often with difficult-to-treat bacteria, is frequent in patients admitted to the ICU due to severe COVID-19 and is associated with worse clinical outcomes, including higher mortality. Identifying risk factors for VA-LRTI might allow the early patient diagnosis to improve clinical outcomes.Trial registration: This is a prospective observational study; therefore, no health care interventions were applied to participants, and trial registration is not applicable.
No score is available to assess severity and stratify mortality risk in ventilator-associated pneumonia (VAP). Our objective was to develop a severity assessment tool for VAP patients.
A prospective, ...observational, cohort study was performed including 441 patients with VAP in three multidisciplinary ICUs. Multivariate logistic regression was performed to identify variables independently associated with ICU mortality. Results were converted into a four-variable score based on the PIRO (predisposition, insult, response, organ dysfunction) concept for ICU mortality risk stratification in VAP patients.
Comorbidities (COPD, immunocompromise, heart failure, cirrhosis, or chronic renal failure); bacteremia; systolic BP < 90 mm Hg; and ARDS. A simple, four-variable VAP PIRO score was obtained at VAP onset. Mortality varied significantly according to VAP PIRO score (p < 0.001). On the basis of observed mortality for each VAP PIRO score, patients were stratified into three levels of risk: (1) mild, 0 to 1 points; (2) high, 2 points; (3) very high, 3 to 4 points. VAP PIRO score was associated with higher risk of death in Cox regression analysis in the high-risk group (hazard ratio, 2.14; 95% confidence interval CI, 1.19 to 3.86) and the very-high-risk group (hazard ratio, 4.63; 95% confidence interval, 2.68 to 7.99). Moreover, medical resource use after VAP diagnosis was higher in high-risk and very-high-risk levels compared to patients at mild risk, evaluated using length of ICU stay (mean +/- SD, 22.0 +/- 10.6 d vs 18.7 +/- 12.8 d, p < 0.05) and duration of mechanical ventilation (18.3 +/- 10.1 d vs 15.1 +/- 11.5 d, p < 0.05).
VAP PIRO score is a simple, practical clinical tool for predicting ICU mortality and health-care resources use that is likely to assist clinicians in determining VAP severity.
Introduction
Early use of corticosteroids in patients affected by pandemic (H1N1)v influenza A infection, although relatively common, remains controversial.
Methods
Prospective, observational, ...multicenter study from 23 June 2009 through 11 February 2010, reported in the European Society of Intensive Care Medicine (ESICM) H1N1 registry.
Results
Two hundred twenty patients admitted to an intensive care unit (ICU) with completed outcome data were analyzed. Invasive mechanical ventilation was used in 155 (70.5%). Sixty-seven (30.5%) of the patients died in ICU and 75 (34.1%) whilst in hospital. One hundred twenty-six (57.3%) patients received corticosteroid therapy on admission to ICU. Patients who received corticosteroids were significantly older and were more likely to have coexisting asthma, chronic obstructive pulmonary disease (COPD), and chronic steroid use. These patients receiving corticosteroids had increased likelihood of developing hospital-acquired pneumonia (HAP) 26.2% versus 13.8%,
p
< 0.05; odds ratio (OR) 2.2, confidence interval (CI) 1.1–4.5. Patients who received corticosteroids had significantly higher ICU mortality than patients who did not (46.0% versus 18.1%,
p
< 0.01; OR 3.8, CI 2.1–7.2). Cox regression analysis adjusted for severity and potential confounding factors identified that early use of corticosteroids was not significantly associated with mortality hazard ratio (HR) 1.3, 95% CI 0.7–2.4,
p
= 0.4 but was still associated with an increased rate of HAP (OR 2.2, 95% CI 1.0–4.8,
p
< 0.05). When only patients developing acute respiratory distress syndrome (ARDS) were analyzed, similar results were observed.
Conclusions
Early use of corticosteroids in patients affected by pandemic (H1N1)v influenza A infection did not result in better outcomes and was associated with increased risk of superinfections.
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