Urinary Tract Infections in Hospitalized COVID-19 Patients, What's Up, Doc?

Use of machine learning to identify protective factors for death from COVID-19 in the ICU: a retrospective study

Background

Patients in serious condition due to COVID-19 often require special care in intensive care units (ICUs). This disease has affected over 758 million people and resulted in 6.8 million deaths worldwide. Additionally, the progression of the disease may vary from individual to individual, that is, it is essential to identify the clinical parameters that indicate a good prognosis for the patient. Machine learning (ML) algorithms have been used for analyzing complex medical data and identifying prognostic indicators. However, there is still an urgent need for a model to elucidate the predictors related to patient outcomes. Therefore, this research aimed to verify, through ML, the variables involved in the discharge of patients admitted to the ICU due to COVID-19.

Methods

In this study, 126 variables were collected with information on demography, hospital length stay and outcome, chronic diseases and tumors, comorbidities and risk factors, complications and adverse events, health care, and vital indicators of patients admitted to an ICU in southern Brazil. These variables were filtered and then selected by a ML algorithm known as decision trees to identify the optimal set of variables for predicting patient discharge using logistic regression. Finally, a confusion matrix was performed to evaluate the model's performance for the selected variables.

Results

Of the 532 patients evaluated, 180 were discharged: female (16.92%), with a central venous catheter (23.68%), with a bladder catheter (26.13%), and with an average of 8.46- and 23.65-days using bladder catheter and submitted to mechanical ventilation, respectively. In addition, the chances of discharge increase by 14% for each additional day in the hospital, by 136% for female patients, 716% when there is no bladder catheter, and 737% when no central venous catheter is used. However, the chances of discharge decrease by 3% for each additional year of age and by 9% for each other day of mechanical ventilation. The performance of the training data presented a balanced accuracy of 0.81, sensitivity of 0.74, specificity of 0.88, and the kappa value was 0.64. The test performance had a balanced accuracy of 0.85, sensitivity 0.75, specificity 0.95, and kappa value of 0.73. The McNemar test found that there were no significant differences in the error rates in the training and test data, suggesting good classification. This work showed that female, the absence of a central venous catheter and bladder catheter, shorter mechanical ventilation, and bladder catheter duration were associated with a greater chance of hospital discharge. These results may help develop measures that lead to a good prognosis for the patient.

A comprehensive status update on modification of foley catheter to combat catheter-associated urinary tract infections and microbial biofilms

Present-day healthcare employs several types of invasive devices, including urinary catheters, to improve medical wellness, the clinical outcome of disease, and the quality of patient life. Among urinary catheters, the Foley catheter is most commonly used in patients for bladder drainage and collection of urine. Although such devices are very useful for patients who cannot empty their bladder for various reasons, they also expose patients to catheter-associated urinary tract infections (CAUTIs). Catheter provides an ideal surface for bacterial colonization and biofilm formation, resulting in persistent bacterial infection and severe complications. Hence, rigorous efforts have been made to develop catheters that harbour antimicrobial and anti-fouling properties to resist colonization by bacterial pathogens. In this regard, catheter modification by surface functionalization, impregnation, blending, or coating with antibiotics, bioactive compounds, and nanoformulations have proved to be effective in controlling biofilm formation. This review attempts to illustrate the complications associated with indwelling Foley catheters, primarily focussing on challenges in fighting CAUTI, catheter colonization, and biofilm formation. In this review, we also collate scientific literature on catheter modification using antibiotics, plant bioactive components, bacteriophages, nanoparticles, and studies demonstrating their efficacy through in vitro and in vivo testing.

Machine learning-driven development of a disease risk score for COVID-19 hospitalization and mortality: a Swedish and Norwegian register-based study

Aims

To develop a disease risk score for COVID-19-related hospitalization and mortality in Sweden and externally validate it in Norway.

Method

We employed linked data from the national health registries of Sweden and Norway to conduct our study. We focused on individuals in Sweden with confirmed SARS-CoV-2 infection through RT-PCR testing up to August 2022 as our study cohort. Within this group, we identified hospitalized cases as those who were admitted to the hospital within 14 days of testing positive for SARS-CoV-2 and matched them with five controls from the same cohort who were not hospitalized due to SARS-CoV-2. Additionally, we identified individuals who died within 30 days after being hospitalized for COVID-19. To develop our disease risk scores, we considered various factors, including demographics, infectious, somatic, and mental health conditions, recorded diagnoses, and pharmacological treatments. We also conducted age-specific analyses and assessed model performance through 5-fold cross-validation. Finally, we performed external validation using data from the Norwegian population with COVID-19 up to December 2021.

Results

During the study period, a total of 124,560 individuals in Sweden were hospitalized, and 15,877 individuals died within 30 days following COVID-19 hospitalization. Disease risk scores for both hospitalization and mortality demonstrated predictive capabilities with ROC-AUC values of 0.70 and 0.72, respectively, across the entire study period. Notably, these scores exhibited a positive correlation with the likelihood of hospitalization or death. In the external validation using data from the Norwegian COVID-19 population (consisting of 53,744 individuals), the disease risk score predicted hospitalization with an AUC of 0.47 and death with an AUC of 0.74.

Conclusion

The disease risk score showed moderately good performance to predict COVID-19-related mortality but performed poorly in predicting hospitalization when externally validated.

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic’s effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

Pattern of antibiotic use and bacterial co-infection in hospitalized Covid-19 patients

Background

There is evidence that bacterial co-infection in respiratory viruses leads to morbidity and mortality. Patients with decreased immunity are prone to bacterial co-infection. A lack of judicious use of antibiotics leads to the spread of multi-drug resistant bacteria (MDR) that have a long-term negative impact. In this study, we attempted to observe the pattern of antibacterial use and its impact on secondary bacterial infection.

Methods

An observational study was conducted at Alexandria Main University Hospital (AMUH) (Alexandria University) from June 2021- February 2022. Study participants were admitted to the Intensive Care Unit (ICU) with confirmed Covid-19 (by Polymerase Chain Reaction (PCR) and Computed tomography (CT) scan). The following data was collected (Demographic, clinical, and laboratory data).In this study, the Pattern of antibiotic use as well as the occurrence of secondary bacterial infections were reported.

Results

Among 121 patients included in the present study, all received antibiotics empirically. Upon admission (19.8%) showed urinary tract infection, (11.5%) had bloodstream infection, and (57.7%) had respiratory tract infection. After 10 days secondary bacterial infection occurred in 38 patients (61.2%) with (24.1%) Urinary tract infection (UTI), (12.9%) Bloodstream infection (BSI), and (72.2%) respiratory tract infection. The respiratory sample size was (45) patients due to Infection Control (IC) restrictions on the aerosol-producing procedure.

Conclusion

Upon admission, all patients received broad-spectrum antibiotics while the incidence of bacterial co-infection was low.

Bacterial Co-Infection in Patients with COVID-19 Hospitalized (ICU and Not ICU): Review and Meta-Analysis

The prevalence of patients hospitalized in ICUs with COVID-19 and co-infected by pathogenic bacteria is relevant in this study, considering the integrality of treatment. This systematic review assesses the prevalence of co-infection in patients admitted to ICUs with SARS-CoV-2 infection, using the PRISMA guidelines. We examined the results of the PubMed, Embase, and SciELO databases, searching for published English literature from December 2019 to December 2021. A total of 542 rec ords were identified, but only 38 were eligible and, and of these only 10 were included. The tabulated studies represented a sample group of 1394 co-infected patients. In total, 35%/138 of the patients were co-infected with Enterobacter spp., 27% (17/63) were co-infected with methicillin-sensitive Staphylococ cus aureus, 21% (84/404) were co-infected with Klebsiella spp., 16% (47/678) of patients were co-infected with coagulase-negative Staphylococcus, 13% (10/80) co-infected with Escherichia coli (ESBL), and 3% (30/1030) of patients were co-infected with Pseudomonas aeruginosa. The most common co-infections were related to blood flow; although in the urinary and respiratory tracts of patients Streptococcus pneumoniae was found in 57% (12/21) of patients, coagulase negative Staphylococcus in 44% (7/16) of patients, and Escherichia coli was found in 37% (11/29) of patients. The present research demonstrated that co-infections caused by bacteria in patients with COVID-19 are a concern.