946 research outputs found

    Deep Learning to Quantify Pulmonary Edema in Chest Radiographs

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    Purpose: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. Materials and Methods: In this retrospective study, 369,071 chest radiographs and associated radiology reports from 64,581 (mean age, 51.71; 54.51% women) patients from the MIMIC-CXR chest radiograph dataset were included. This dataset was split into patients with and without congestive heart failure (CHF). Pulmonary edema severity labels from the associated radiology reports were extracted from patients with CHF as four different ordinal levels: 0, no edema; 1, vascular congestion; 2, interstitial edema; and 3, alveolar edema. Deep learning models were developed using two approaches: a semi-supervised model using a variational autoencoder and a pre-trained supervised learning model using a dense neural network. Receiver operating characteristic curve analysis was performed on both models. Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0.99 for the semi-supervised model and 0.87 for the pre-trained models. Performance of the algorithm was inversely related to the difficulty in categorizing milder states of pulmonary edema (shown as AUCs for semi-supervised model and pre-trained model, respectively): 2 versus 0, 0.88 and 0.81; 1 versus 0, 0.79 and 0.66; 3 versus 1, 0.93 and 0.82; 2 versus 1, 0.69 and 0.73; and, 3 versus 2, 0.88 and 0.63. Conclusion: Deep learning models were trained on a large chest radiograph dataset and could grade the severity of pulmonary edema on chest radiographs with high performance.Comment: The two first authors contributed equall

    Systematic review of studies investigating ventilator associated pneumonia diagnostics in intensive care

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    Abstract Background Ventilator-associated pneumonia (VAP) is an important diagnosis in critical care. VAP research is complicated by the lack of agreed diagnostic criteria and reference standard test criteria. Our aim was to review which reference standard tests are used to evaluate novel index tests for suspected VAP. Methods We conducted a comprehensive search using electronic databases and hand reference checks. The Cochrane Library, MEDLINE, CINHAL, EMBASE, and web of science were searched from 2008 until November 2018. All terms related to VAP diagnostics in the intensive treatment unit were used to conduct the search. We adopted a checklist from the critical appraisal skills programme checklist for diagnostic studies to assess the quality of the included studies. Results We identified 2441 records, of which 178 were selected for full-text review. Following methodological examination and quality assessment, 44 studies were included in narrative data synthesis. Thirty-two (72.7%) studies utilised a sole microbiological reference standard; the remaining 12 studies utilised a composite reference standard, nine of which included a mandatory microbiological criterion. Histopathological criteria were optional in four studies but mandatory in none. Conclusions Nearly all reference standards for VAP used in diagnostic test research required some microbiological confirmation of infection, with BAL culture being the most common reference standard used

    Inflammation Profiling of Critically Ill Coronavirus Disease 2019 Patients.

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    Objectives: Coronavirus disease 2019 is caused by severe acute respiratory syndrome-coronavirus-2 infection to which there is no community immunity. Patients admitted to ICUs have high mortality, with only supportive therapies available. Our aim was to profile plasma inflammatory analytes to help understand the host response to coronavirus disease 2019. Design: Daily blood inflammation profiling with immunoassays. Setting: Tertiary care ICU and academic laboratory. Subjects: All patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome-coronavirus-2, using standardized hospital screening methodologies, had daily blood samples collected until either testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative), or until ICU day 7 if the patient was positive (coronavirus disease 2019 positive). Interventions: None. Measurements and Main Results: Age- and sex-matched healthy controls and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well-balanced with the exception that coronavirus disease 2019 positive patients were more likely than coronavirus disease 2019 negative patients to suffer bilateral pneumonia. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. We measured 57 inflammatory analytes and then analyzed with both conventional statistics and machine learning. Twenty inflammatory analytes were different between coronavirus disease 2019 positive patients and healthy controls ( Conclusions: While many inflammatory analytes were elevated in coronavirus disease 2019 positive ICU patients, relative to healthy controls, the top six analytes distinguishing coronavirus disease 2019 positive ICU patients from coronavirus disease 2019 negative ICU patients were tumor necrosis factor, granzyme B, heat shock protein 70, interleukin-18, interferon-gamma-inducible protein 10, and elastase 2

    Severe community-acquired pneumonia

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    Identifying Patients with Pneumonia from Free-Text Intensive Care Unit Reports

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    Abstract Clinical research studying critical illness phenotypes relies on the identification of clinical syndromes defined by consensus definitions. Pneumonia is a prime example. Historically, identifying pneumonia has required manual chart review, which is a time and resource intensive process. The overall research goal of our work is to develop automated approaches that accurately identify critical illness phenotypes. In this paper, we describe our approach to the identification of pneumonia from electronic medical records, present our preliminary results, and describe future steps

    Pseudomonas aeruginosa Nosocomial Pneumonia: Impact of Pneumonia Classification

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    OBJECTIVE To describe and compare the mortality associated with nosocomial pneumonia due to Pseudomonas aeruginosa (Pa-NP) according to pneumonia classification (community-onset pneumonia [COP], hospital-acquired pneumonia [(HAP], and ventilator-associated pneumonia [VAP]). DESIGN We conducted a retrospective cohort study of adults with Pa-NP. We compared mortality for Pa-NP among patients with COP, HAP, and VAP and used logistic regression to identify risk factors for hospital mortality and inappropriate initial antibiotic therapy (IIAT). SETTING Twelve acute care hospitals in 5 countries (United States, 3; France, 2; Germany, 2; Italy, 2; and Spain, 3). PATIENTS/PARTICIPANTS A total of 742 patients with Pa-NP. RESULTS Hospital mortality was greater for those with VAP (41.9%) and HAP (40.1%) compared with COP (24.5%) (P<.001). In multivariate analyses, independent predictors of hospital mortality differed by pneumonia classification (COP: need for mechanical ventilation and intensive care; HAP: multidrug-resistant isolate; VAP: IIAT, increasing age, increasing Charlson comorbidity score, bacteremia, and use of vasopressors). Presence of multidrug resistance was identified as an independent predictor of IIAT for patients with COP and HAP, whereas recent antibiotic administration was protective in patients with VAP. CONCLUSIONS Among patients with Pa-NP, pneumonia classification identified patients with different risks for hospital mortality. Specific risk factors for hospital mortality also differed by pneumonia classification and multidrug resistance appeared to be an important risk factor for IIAT. These findings suggest that pneumonia classification for P. aeruginosa identifies patients with different mortality risks and specific risk factors for outcome and IIAT

    Screening for Sepsis: A Key Strategy for Early Identification and Management of Septic Patients

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    Background: Sepsis, defined as a systemic inflammatory response to infection, is a life-threatening medical condition that rapidly progresses from severe sepsis (characterized by signs of organ dysfunction) to septic shock with fluid-refractory hypotension. Adherence to the Surviving Sepsis Campaign guidelines for the management of severe sepsis and septic shock have been associated with improved delivery of care and reduced mortality. Delays in recognition of sepsis has been identified as a barrier to achieving early goal-directed therapy targets. Purpose: To determine if a sepsis screening protocol could facilitate earlier identification of patients with sepsis Methods: A retrospective medical record review was conducted for adult patients with a primary or secondary diagnosis of sepsis using ICD-9 codes 038.9 (unspecified septicemia), 995.91 (sepsis), 995.92 (severe sepsis), and 785.52 (septic shock). A sepsis screening strategy was applied retrospectively to simulate implementation of a screening protocol. Application of the screening strategy was performed to quantify the interval between when clinicians first recognized sepsis and when patients first exhibited signs of systemic inflammatory response syndrome (SIRS). Results: The median interval of time between when a clinician recognized sepsis and when a patient first exhibited signs of sepsis was 222 minutes. A difference in time occurred in 22% of the cases. Duration of the interval was positively correlated with hospital length of stay (rs = .65, n = 17, p = .005). Conclusion: The interval between when patients with sepsis were first identified by a clinician (without screening) and when those patients could have been recognized utilizing a screening protocol was quantified. Results suggest that more than one in five patients would have been identified earlier using a screening protocol. A pilot study to further investigate the potential impact of sepsis screening on time to identification is warranted
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