594 research outputs found

    Diagnostic and prognostic prediction models in ventilator-associated pneumonia: Systematic review and meta-analysis of prediction modelling studies

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    Machine learning; Mechanical ventilation; Prognostic modelAprenentatge automàtic; Ventilació mecànica; Model pronòsticAprendizaje automático; Ventilacion mecanica; Modelo pronósticoPurpose Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics. Methods Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed. Results Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability. Conclusions The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.The project is supported by the Academy of Finland (project number 326291) and the University of Oulu

    Machine Learning Methods Applied to Predict Ventilator-Associated Pneumonia with Pseudomonas aeruginosa Infection via Sensor Array of Electronic Nose in Intensive Care Unit

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    One concern to the patients is the off-line detection of pneumonia infection status after using the ventilator in the intensive care unit. Hence, machine learning methods for ventilator-associated pneumonia (VAP) rapid diagnose are proposed. A popular device, Cyranose 320 e-nose, is usually used in research on lung disease, which is a highly integrated system and sensor comprising 32 array using polymer and carbon black materials. In this study, a total of 24 subjects were involved, including 12 subjects who are infected with pneumonia, and the rest are non-infected. Three layers of back propagation artificial neural network and support vector machine (SVM) methods were applied to patients' data to predict whether they are infected with VAP with Pseudomonas aeruginosa infection. Furthermore, in order to improve the accuracy and the generalization of the prediction models, the ensemble neural networks (ENN) method was applied. In this study, ENN and SVM prediction models were trained and tested. In order to evaluate the models' performance, a fivefold cross-validation method was applied. The results showed that both ENN and SVM models have high recognition rates of VAP with Pseudomonas aeruginosa infection, with 0.9479 ± 0.0135 and 0.8686 ± 0.0422 accuracies, 0.9714 ± 0.0131, 0.9250 ± 0.0423 sensitivities, and 0.9288 ± 0.0306, 0.8639 ± 0.0276 positive predictive values, respectively. The ENN model showed better performance compared to SVM in the recognition of VAP with Pseudomonas aeruginosa infection. The areas under the receiver operating characteristic curve of the two models were 0.9842 ± 0.0058 and 0.9410 ± 0.0301, respectively, showing that both models are very stable and accurate classifiers. This study aims to assist the physician in providing a scientific and effective reference for performing early detection in Pseudomonas aeruginosa infection or other diseases.This research was funded by the Taiwan carbon nanometer technology corporation, Taiwan

    Microbial and non-microbial volatile fingerprints : potential clinical applications of electronic nose for early diagnoses and detection of diseases

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    This is the first study to explore the potential applications of using qualitative volatile fingerprints (electronic nose) for early detection and diagnosis of diseases such as dermatophytosis, ventilator associated pneumonia and upper gastrointestinal cancer. The investigations included in vitro analysis of various dermatophyte species and strains, antifungal screening, bacterial cultures and associated clinical specimens and oesophageal cell lines. Mass spectrometric analyses were attempted to identify possible markers. The studies that involved e-nose comparisons indicated that the conducting polymer system was unable to differentiate between any of the treatments over the experimental period (120 hours). Metal oxide-based sensor arrays were better suited and differentiated between four dermatophyte species within 96 hours of growth using principal component analysis and cluster analysis (Euclidean distance and Ward’s linkage) based on their volatile profile patterns. Studies on the sensitivity of detection showed that for Trichophyton mentagrophytes and T. rubrum it was possible to differentiate between log3, log5 and log7 inoculum levels within 96 hours. The probabilistic neural network model had a high prediction accuracy of 88 to 96% depending on the number of sensors used. Temporal volatile production patterns studied at a species level for a Microsporum species, two Trichophyton species and at a strain level for the two Trichophyton species; showed possible discrimination between the species from controls after 120 hours. The predictive neural network model misclassified only one sample. Data analysis also indicated probable differentiation between the strains of T. rubrum while strains of T. mentagrophytes clustered together showing good similarity between them. Antifungal treatments with itraconazole on T. mentagrophytes and T. rubrum showed that the e-nose could differentiate between untreated fungal species from the treated fungal species at both temperatures (25 and 30°C). However, the different antifungal concentrations of 50% fungal inhibition and 2 ppm could not be separated from each other or the controls based on their volatiles. Headspace analysis of bacterial cultures in vitro indicated that the e-nose could differentiate between the microbial species and controls in 83% of samples (n=98) based on a four group model (gram-positive, gram-negative, fungi and no growth). Volatile fingerprint analysis of the bronchoalveolar lavage fluid accurately separated growth and no growth in 81% of samples (n=52); however only 63% classification accuracy was achieved with a four group model. 12/31 samples were classified as infected by the e-nose but had no microbiological growth, further analysis suggested that the traditional clinical pulmonary infection score (CPIS) system correlated with the e-nose prediction of infection in 68% of samples (n=31). No clear distinction was observed between various human cell lines (oesophageal and colorectal) based on volatile fingerprints within one to four hours of incubation, although they were clearly separate from the blank media. However, after 24 hours one of the cell lines could be clearly differentiated from the others and the controls. The different gastrointestinal pathologies (forming the clinical samples) did not show any specific pattern and thus could not be distinguished. Mass spectrometric analysis did not detect distinct markers within the fungal and cell line samples, but potential identifiers in the fungal species such as 3-Octanone, 1-Octen-3-ol and methoxybenzene including high concentration of ammonia, the latter mostly in T. mentagrophytes, followed by T. rubrum and Microsporum canis, were found. These detailed studies suggest that the approach of qualitative volatile fingerprinting shows promise for use in clinical settings, enabling rapid detection/diagnoses of diseases thus eventually reducing the time to treatment significantly.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Advances in Electronic-Nose Technologies Developed for Biomedical Applications

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    The research and development of new electronic-nose applications in the biomedical field has accelerated at a phenomenal rate over the past 25 years. Many innovative e-nose technologies have provided solutions and applications to a wide variety of complex biomedical and healthcare problems. The purposes of this review are to present a comprehensive analysis of past and recent biomedical research findings and developments of electronic-nose sensor technologies, and to identify current and future potential e-nose applications that will continue to advance the effectiveness and efficiency of biomedical treatments and healthcare services for many years. An abundance of electronic-nose applications has been developed for a variety of healthcare sectors including diagnostics, immunology, pathology, patient recovery, pharmacology, physical therapy, physiology, preventative medicine, remote healthcare, and wound and graft healing. Specific biomedical e-nose applications range from uses in biochemical testing, blood-compatibility evaluations, disease diagnoses, and drug delivery to monitoring of metabolic levels, organ dysfunctions, and patient conditions through telemedicine. This paper summarizes the major electronic-nose technologies developed for healthcare and biomedical applications since the late 1980s when electronic aroma detection technologies were first recognized to be potentially useful in providing effective solutions to problems in the healthcare industry

    Ventilator associated pneumonia : analyses of volatile fingerprints for identification of causative microorganisms, assessment of anti-fungals and use of in vitro models for early clinical sample prediction

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    This study has involved the analysis of volatile fingerprints using a hybrid electronic nose (e-nose) to discriminate between and diagnose the microorganisms which cause ventilator–associated pneumonia (VAP), one of the most important infections in the hospital environment. This infection occurs in hospitalised patients with 48-72 hrs of mechanical ventilation. VAP diagnostics still remains a problem due to the lack of a precise diagnostic tool. The current tests are mostly based on quantitative cultures of samples from the lower lung airways with clinical findings, which do not often result in accurate diagnoses of the disease. Cont/d.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Ventilator associated pneumonia : analyses of volatile fingerprints for identification of causative microorganisms, assessment of anti-fungals and use of in vitro models for early clinical sample prediction

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    This study has involved the analysis of volatile fingerprints using a hybrid electronic nose (e-nose) to discriminate between and diagnose the microorganisms which cause ventilator–associated pneumonia (VAP), one of the most important infections in the hospital environment. This infection occurs in hospitalised patients with 48-72 hrs of mechanical ventilation. VAP diagnostics still remains a problem due to the lack of a precise diagnostic tool. The current tests are mostly based on quantitative cultures of samples from the lower lung airways with clinical findings, which do not often result in accurate diagnoses of the disease. Cont/d.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Nursing-Relevant Patient Outcomes and Clinical Processes in Data Science Literature: 2019 Year in Review

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    Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this paper, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (e.g., natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope the studies described in this paper help readers: (a) understand the breadth and depth of data science’s ability to improve clinical processes and patient outcomes that are relevant to nurses and (b) identify gaps in the literature that are in need of exploration

    Breath biomarkers of inflammation, infection and metabolic derangement in the intensive care unit

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    The analysis of volatile organic compounds (VOCs) in breath may be a useful non-invasive tool in the Intensive Care Unit (ICU) to monitor metabolic and oxidative stress or diagnose pulmonary infection. Acetone is produced during starvation and metabolic stress, hydrogen sulphide (H2S) may be a marker of inflammation and infection and hydrogen cyanide (HCN) may also act as a marker of infection, particularly caused by Pseudomonas aeruginosa. Firstly, the effects on measured VOC concentrations of the breath collection equipment and storage were assessed. Sample humidity declined faster than any analyte. Sample losses of 21%, 25% and 24% for acetone, H2S and HCN, respectively, were seen as a result of being passed through the sampling apparatus. Over 90% of initial breath VOC concentrations were detectable after 90 min storage in Tedlar bags at 40°C. Secondly, a breath collection method for off-line analysis was validated in 20 mechanically ventilated patients in the ICU. The effect on VOC concentrations of breath sampling from two locations after two breathing manoeuvres was explored, revealing significantly higher analyte concentrations in samples from the airways than from a T-piece in the breathing circuit, and after tidal breathing compared to a recruitment-style breath. Practical difficulties were encountered using direct airway sampling and delivering recruitment style breaths; end-tidal breath sampling from the T-piece was simplest to perform and results equally reproducible. Breath samples from 26 healthy anaesthetised controls were used to validate a breath collection method in the operating theatre. The effects of altering anaesthesia machine settings on inspiratory and exhaled acetone concentrations were explored. A difference in median inspiratory, but not exhaled, acetone concentrations was observed between the anaesthesia machines (ADU Carestation 276 ppb, Aysis Carestation 131 ppb, p=0.0005). Closing the adjustable pressure limiting (APL) valve resulted in a reduction in exhaled acetone concentration, as did breath sampling distal to the circuit filter, due to dilution by dead space air. Median (range) breath concentrations for samples collected on the patient side of the circuit filter with the APL valve open (n=22): acetone 738 ppb (257–6594 ppb), H2S 1.00 ppb (0.71-2.49 ppb), HCN 0.82 ppb (0.60-1.51 ppb). Breath acetone concentration was related to plasma acetone (rs=0.80, p<0.0001) and beta-hydroxybutyrate concentrations (rs=0.55, p=0.0075). Finally, breath and blood samples were collected daily from 32 mechanically ventilated patients in the ICU with stress hyperglycaemia (n=11) and/or new pulmonary infiltrates on chest radiograph (n=28). Samples were collected over a median 3 days (1-8 days). Median (range) breath VOC concentrations of all samples collected: acetone 853 ppb (162–11,375 ppb), H2S 0.96 ppb (0.22-5.12 ppb), HCN 0.76 ppb (0.31-11.5 ppb). Median initial breath acetone concentration was higher than in anaesthetised controls (1451 ppb versus 812 ppb; p=0.038). There was a trend towards a reduction in breath acetone concentration over time. Relationships were seen between breath acetone and arterial acetone (rs=0.64, p<0.0001) and beta-hydroxybutyrate (rs=0.52, p<0.0001) concentrations. Several patients remained ketotic despite insulin therapy and normal, or near normal, arterial glucose concentrations. Inspired and exhaled H2S and HCN concentrations were not significantly different. Breath H2S and HCN concentrations could not be used to differentiate between patients with pneumonia and those with pulmonary infiltrates due to other conditions. In conclusion, losses due to the sampling apparatus were determined and linear over the range of concentrations tested. End-tidal breath sampling via the T-piece was the simplest technique, with reproducibility comparable to other methods. It was possible to replicate the breath sampling method in the operating theatre; pre-filter samples with inspiratory gas flow rate 6 L/min and APL valve open provided repeatable results avoiding rebreathing. There was no role for the use of breath H2S or HCN in the diagnosis or monitoring of pneumonia in critical illness. There was no relationship between breath acetone concentration and illness severity, however the utility of breath acetone in the modulation of insulin and feeding in critical illness merits further study

    Breath biomarkers of inflammation, infection and metabolic derangement in the intensive care unit

    Get PDF
    The analysis of volatile organic compounds (VOCs) in breath may be a useful non-invasive tool in the Intensive Care Unit (ICU) to monitor metabolic and oxidative stress or diagnose pulmonary infection. Acetone is produced during starvation and metabolic stress, hydrogen sulphide (H2S) may be a marker of inflammation and infection and hydrogen cyanide (HCN) may also act as a marker of infection, particularly caused by Pseudomonas aeruginosa. Firstly, the effects on measured VOC concentrations of the breath collection equipment and storage were assessed. Sample humidity declined faster than any analyte. Sample losses of 21%, 25% and 24% for acetone, H2S and HCN, respectively, were seen as a result of being passed through the sampling apparatus. Over 90% of initial breath VOC concentrations were detectable after 90 min storage in Tedlar bags at 40°C. Secondly, a breath collection method for off-line analysis was validated in 20 mechanically ventilated patients in the ICU. The effect on VOC concentrations of breath sampling from two locations after two breathing manoeuvres was explored, revealing significantly higher analyte concentrations in samples from the airways than from a T-piece in the breathing circuit, and after tidal breathing compared to a recruitment-style breath. Practical difficulties were encountered using direct airway sampling and delivering recruitment style breaths; end-tidal breath sampling from the T-piece was simplest to perform and results equally reproducible. Breath samples from 26 healthy anaesthetised controls were used to validate a breath collection method in the operating theatre. The effects of altering anaesthesia machine settings on inspiratory and exhaled acetone concentrations were explored. A difference in median inspiratory, but not exhaled, acetone concentrations was observed between the anaesthesia machines (ADU Carestation 276 ppb, Aysis Carestation 131 ppb, p=0.0005). Closing the adjustable pressure limiting (APL) valve resulted in a reduction in exhaled acetone concentration, as did breath sampling distal to the circuit filter, due to dilution by dead space air. Median (range) breath concentrations for samples collected on the patient side of the circuit filter with the APL valve open (n=22): acetone 738 ppb (257–6594 ppb), H2S 1.00 ppb (0.71-2.49 ppb), HCN 0.82 ppb (0.60-1.51 ppb). Breath acetone concentration was related to plasma acetone (rs=0.80, p<0.0001) and beta-hydroxybutyrate concentrations (rs=0.55, p=0.0075). Finally, breath and blood samples were collected daily from 32 mechanically ventilated patients in the ICU with stress hyperglycaemia (n=11) and/or new pulmonary infiltrates on chest radiograph (n=28). Samples were collected over a median 3 days (1-8 days). Median (range) breath VOC concentrations of all samples collected: acetone 853 ppb (162–11,375 ppb), H2S 0.96 ppb (0.22-5.12 ppb), HCN 0.76 ppb (0.31-11.5 ppb). Median initial breath acetone concentration was higher than in anaesthetised controls (1451 ppb versus 812 ppb; p=0.038). There was a trend towards a reduction in breath acetone concentration over time. Relationships were seen between breath acetone and arterial acetone (rs=0.64, p<0.0001) and beta-hydroxybutyrate (rs=0.52, p<0.0001) concentrations. Several patients remained ketotic despite insulin therapy and normal, or near normal, arterial glucose concentrations. Inspired and exhaled H2S and HCN concentrations were not significantly different. Breath H2S and HCN concentrations could not be used to differentiate between patients with pneumonia and those with pulmonary infiltrates due to other conditions. In conclusion, losses due to the sampling apparatus were determined and linear over the range of concentrations tested. End-tidal breath sampling via the T-piece was the simplest technique, with reproducibility comparable to other methods. It was possible to replicate the breath sampling method in the operating theatre; pre-filter samples with inspiratory gas flow rate 6 L/min and APL valve open provided repeatable results avoiding rebreathing. There was no role for the use of breath H2S or HCN in the diagnosis or monitoring of pneumonia in critical illness. There was no relationship between breath acetone concentration and illness severity, however the utility of breath acetone in the modulation of insulin and feeding in critical illness merits further study
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