Machine learning, infection, microbial toxins profile and health monitoring pre/post general surgeries during COVID-19 pandemic

Although almost 2 years have passed since the beginning of the coronavirus disease 2019 (COVID-19) pandemic in the world, there is still a threat to the health of people at risk and patients. Specialists in various sciences conduct various research in order to eliminate or reduce the problems caused by this disease. Surgery is one of the sciences that plays a critical role in this regard. Both physicians and patients should pay attention to the potent steps of different infections’ key-points during pre/post-general surgeries in the case of preventing or accelerating the healing process of nosocomial acquired COVID-19. The relationship between COVID-19 and general surgical events is one of the factors that could directly or indirectly play a key role in the body's resilience to COVID-19. In this article, we introduce a link between pre/post-general surgery steps, human microbial toxin profiles, and the incidence of acquired COVID-19 in patients. In linking the components of this network, artificial intelligence (AI), machine learning (ML) and data mining (DM) can be important strategies to assist health providers in choosing the best decision based on a patient’s history. 

How severe is antibiotic pharmacokinetic variability in critically ill patients and what can be done about it?

The pharmacokinetics (PK) of antimicrobial agents administered to critically ill patients exhibit marked variability. This variability results from pathophysiological changes that occur in critically ill patients. Changes in volume of distribution, clearance, and tissue penetration all affect the drug concentrations at the site of infection. PK-pharmacodynamic indices (fC(max):MIC; AUC(0-24):MIC; fT(>MIC); fC(min):MIC) for both antimicrobial effect and suppression of emergence of resistance are described for many antimicrobial drugs. Changing the regimen by which antimicrobial drugs are delivered can help overcome the PK variability and optimise target attainment. This will deliver optimised antimicrobial chemotherapy to individual critically ill patients. Delivery of beta-lactams antimicrobial agents by infusions, rather than bolus dosing, is effective at increasing the duration of the dosing interval that the drug concentration is above the MIC. Therapeutic drug monitoring, utilising population PK mathematical models with Bayesian estimation, can also be used to optimise regimens following measurement of plasma drug concentrations. Clinical trials are required to establish if patient outcomes can be improved by implementing these techniques. (C) 2014 Elsevier Inc. All rights reserved

Prediction of nosocomial infections associated with surgical interventions

Nosocomial infections represent an ongoing challenge to healthcare quality and patient safety, negatively impacting clinical outcomes and increasing the burden on healthcare systems. Thus, controlling this type of infection plays a very important role in ensuring a better quality of life for patients. Although the control and prevention measures for these infections are well defined, their signaling and detection is carried out manually and sometimes late, which compromises the health status of patients and everyone around them. In this context, this study emerged with the aim of exploring the potential of data mining techniques to predict the occurrence of nosocomial infections, with a specific focus on infections associated with surgical interventions. Using datasets for the period between 2018 and 2022, sourced from a Portuguese hospital and duly anonymized to protect patient privacy, several classification algorithms and data balancing techniques were analyzed to deal with the uneven nature of the data and the presence of minority classes. Among the algorithms and balancing techniques used, it was found that the Random Forest algorithm combined with the Oversampling technique showed superior performance in identifying cases of nosocomial infections associated with surgical interventions. The results of this study highlight the importance of collaboration between medicine and technology, indicating that the integration of data mining techniques can prove to be valuable tools to improve clinical decision-making and infection management in surgical context.This research was funded by Fundação para a Ciência e Tecnologia, within the Project Scope: UIDB/00319/2020

Using risk adjustment to improve the interpretation of global inpatient pediatric antibiotic prescribing.

Objectives Assessment of regional pediatric last-resort antibiotic utilization patterns is hampered by potential confounding from population differences. We developed a risk-adjustment model from readily available, internationally used survey data and a simple patient classification to aid such comparisons. Design We investigated the association between pediatric conserve antibiotic (pCA) exposure and patient / treatment characteristics derived from global point prevalence surveys of antibiotic prescribing, and developed a risk-adjustment model using multivariable logistic regression. The performance of a simple patient classification of groups with different expected pCA exposure levels was compared to the risk model. Setting 226 centers in 41 countries across 5 continents. Participants Neonatal and pediatric inpatient antibiotic prescriptions for sepsis/bloodstream infection for 1281 patients. Results Overall pCA exposure was high (35%), strongly associated with each variable (patient age, ward, underlying disease, community acquisition or nosocomial infection and empiric or targeted treatment), and all were included in the final risk-adjustment model. The model demonstrated good discrimination (c-statistic = 0.83) and calibration (p = 0.38). The simple classification model demonstrated similar discrimination and calibration to the risk model. The crude regional pCA exposure rates ranged from 10.3% (Africa) to 67.4% (Latin America). Risk adjustment substantially reduced the regional variation, the adjusted rates ranging from 17.1% (Africa) to 42.8% (Latin America). Conclusions Greater comparability of pCA exposure rates can be achieved by using a few easily collected variables to produce risk-adjusted rates

Next-generation sequencing and PCR technologies in monitoring the hospital microbiome and its drug resistance

The hospital environment significantly contributes to the onset of healthcare-associated infections (HAIs), which represent one of the most frequent complications occurring in healthcare facilities worldwide. Moreover, the increased antimicrobial resistance (AMR) characterizing HAI-associated microbes is one of the human health’s main concerns, requiring the characterization of the contaminating microbial population in the hospital environment. The monitoring of surface microbiota in hospitals is generally addressed by microbial cultural isolation. However, this has some important limitations mainly relating to the inability to define the whole drug-resistance profile of the contaminating microbiota and to the long time period required to obtain the results. Hence, there is an urgent need to implement environmental surveillance systems using more effective methods. Molecular approaches, including next-generation sequencing and PCR assays, may be useful and effective tools to monitor microbial contamination, especially the growing AMR of HAI-associated pathogens. Herein, we summarize the results of our recent studies using culture-based and molecular analyses in 12 hospitals for adults and children over a 5-year period, highlighting the advantages and disadvantages of the techniques used

Improved diagnosis and management of sepsis and bloodstream infection

Sepsis is a severe organ dysfunction triggered by infections, and a leading cause of hospitalization and death. Concurrent bloodstream infection (BSI) is common and around one third of sepsis patients have positive blood cultures. Prompt diagnosis and treatment is crucial, but there is a trade-off between the negative effects of over diagnosis and failure to recognize sepsis in time. The emerging crisis of antimicrobial resistance has made bacterial infections more difficult to treat, especially gram-negative pathogens such as Pseudomonas aeruginosa. The overall aim with this thesis was to improve diagnosis, assess the influence of time to antimicrobial treatment and explore prognostic bacterial virulence markers in sepsis and BSI. The papers are based on observational data from 7 cohorts of more than 100 000 hospital episodes. In addition, whole genome sequencing has been performed on approximately 800 invasive P. aeruginosa isolates collected from centers in Europe and Australia. Paper I showed that automated surveillance of sepsis incidence using the Sepsis-3 criteria is feasible in the non-ICU setting, with examples of how implementing this model generates continuous epidemiological data down to the ward level. This information can be used for directing resources and evaluating quality-of-care interventions. In Paper II, evidence is provided for using peripheral oxygen saturation (SpO2) to diagnose respiratory dysfunction in sepsis, proposing the novel thresholds 94% and 90% to get 1 and 2 SOFA points, respectively. This has important implications for improving sepsis diagnosis, especially when conventional arterial blood gas measurements are unavailable. Paper III verified that sepsis surveillance data can be utilized to develop machine learning screening tools to improve early identification of sepsis. A Bayesian network algorithm trained on routine electronic health record data predicted sepsis onset within 48 hours with better discrimination and earlier than conventional NEWS2 outside the ICU. The results suggested that screening may primarily be suited for the early admission period, which have broader implications also for other sepsis screening tools. Paper IV demonstrated that delays in antimicrobial treatment with in vitro pathogen coverage in BSI were associated with increased mortality after 12 hours from blood culture collection, but not at 1, 3, and 6 hours. This indicates a time window where clinicians should focus on the diagnostic workup, and proposes a target for rapid diagnostics of blood cultures. Finally, Paper V showed that the virulence genotype had some influence on mortality and septic shock in P. aeruginosa BSI, however, it was not a major prognostic determinant. Together these studies contribute to better understanding of the sepsis and BSI populations, and provide several suggestions to improve diagnosis and timing of treatment, with implications for clinical practice. Future works should focus on the implementation of sepsis surveillance, clinical trials of time to antimicrobial treatment and evaluating the prognostic importance of bacterial genotype data in larger populations from diverse infection sources and pathogens

Early-onset ventilator-associated pneumonia incidence in intensive care units: a surveillance-based study

ABSTRACT: BACKGROUND: The incidence of ventilator-associated pneumonia (VAP) within the first 48 hours of intensive care unit (ICU) stay has been poorly investigated. The objective was to estimate early-onset VAP occurrence in ICUs within 48 hours after admission. METHODS: We analyzed data from prospective surveillance between 01/01/2001 and 31/12/2009 in 11 ICUs of Lyon hospitals (France). The inclusion criteria were: first ICU admission, not hospitalized before admission, invasive mechanical ventilation during first ICU day, free of antibiotics at admission, and ICU stay >=48 hours. VAP was defined according to a national protocol. Its incidence was the number of events per 1,000 invasive mechanical ventilation-days. The Poisson regression model was fitted from day 2 (D2) to D8 to incident VAP to estimate the expected VAP incidence from D0 to D1 of ICU stay. RESULTS: Totally, 367 (10.8%) of 3,387 patients in 45,760 patient-days developed VAP within the first 9 days. The predicted cumulative VAP incidence at D0 and D1 was 5.3 (2.6-9.8) and 8.3 (6.1-11.1), respectively. The predicted cumulative VAP incidence was 23.0 (20.8-25.3) at D8. The proportion of missed VAP within 48 hours from admission was 11% (9%-17%). CONCLUSIONS: Our study indicates underestimation of early-onset VAP incidence in ICUs, if only VAP occurring [greater than or equal to]48 hours is considered to be hospital-acquired. Clinicians should be encouraged to develop a strategy for early detection after ICU admission

In vivo monitoring of therapeutic efficacy and virulence profile by bioluminescent Klebsiella pneumoniae.

Klebsiella pneumoniae causes an acute respiratory infection in human with severe outcomes and high mortality rates even with antibiotic treatment. Even with its critical clinical importance, few virulence systems have been identified for K. pneumoniae limiting the development of new therapeutic strategies. Accordingly, we performed Next Generation sequencing for the strain ATCC 43816, a virulent strain in mouse respiratory disease models, and compared its genomic data with two previously sequenced strains NTUH-K2044 and MGH 78578 for the purpose of identifying genes required for colonizing host lungs. Furthermore, the virulence potential of the three K. pneumoniae strains were tested in a mouse model of pulmonary disease uniquely generated by our group to insure the specific delivery of an inoculum into host lungs allowing for studying diseases associated specifically with the lower respiratory tract. To monitor disease progression noninvasively, a bioluminescent K. pneumoniae strain was engineered which allowed for monitoring meropenem therapeutic efficacy against the bacteria in real time. A transposon mutant library was generated in the bioluminescent strain and introduced into mice lungs in order identify critical fitness factors required by K. pneumoniae to survive the selective pressure of host lung. The attenuation of known and potential virulence factors, including capsular polysaccharide (CPS) and type 6 secretion systems (T6SSs), were tested in our lung-specific murine model of respiratory disease. Similar to previous findings, manC capsule mutant was attenuated in our lung-specific disease model whereas for the vgrG T6SSs mutants, only cluster one illustrated some potential attenuation in the host, and future studies will be conducted to confirm these outcomes. K. pneumoniae is thought to be an extracellular pathogen but we have provided the first evidence suggesting that this dogma might not be entirely true by demonstrating the capability of the bacteria to proliferate within cultured macrophages in addition to the ability of a subpopulation of K. pneumoniae to become intracellular within mice lungs. Further studies will need to be conducted to identify the role(s) of the intracellular lifestyle for K. pneumoniae during the pulmonary disease

Predictive Analysis of Healthcare-Associated Blood Stream Infections in the Neonatal Intensive Care Unit Using Artificial Intelligence: A Single Center Study

Background: Neonatal infections represent one of the six main types of healthcare-associated infections and have resulted in increasing mortality rates in recent years due to preterm births or problems arising from childbirth. Although advances in obstetrics and technologies have minimized the number of deaths related to birth, different challenges have emerged in identifying the main factors affecting mortality and morbidity. Dataset characterization: We investigated healthcare-associated infections in a cohort of 1203 patients at the level III Neonatal Intensive Care Unit (ICU) of the “Federico II” University Hospital in Naples from 2016 to 2020 (60 months). Methods: The present paper used statistical analyses and logistic regression to identify an association between healthcare-associated blood stream infection (HABSIs) and the available risk factors in neonates and prevent their spread. We designed a supervised approach to predict whether a patient suffered from HABSI using seven different artificial intelligence models. Results: We analyzed a cohort of 1203 patients and found that birthweight and central line catheterization days were the most important predictors of suffering from HABSI. Conclusions: Our statistical analyses showed that birthweight and central line catheterization days were significant predictors of suffering from HABSI. Patients suffering from HABSI had lower gestational age and birthweight, which led to longer hospitalization and umbilical and central line catheterization days than non-HABSI neonates. The predictive analysis achieved the highest Area Under Curve (AUC), accuracy and F1-macro score in the prediction of HABSIs using Logistic Regression (LR) and Multi-layer Perceptron (MLP) models, which better resolved the imbalanced dataset (65 infected and 1038 healthy)