22 research outputs found

    Machine learning-based clinical decision support for infection risk prediction

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    BackgroundHealthcare-associated infection (HAI) remains a significant risk for hospitalized patients and a challenging burden for the healthcare system. This study presents a clinical decision support tool that can be used in clinical workflows to proactively engage secondary assessments of pre-symptomatic and at-risk infection patients, thereby enabling earlier diagnosis and treatment.MethodsThis study applies machine learning, specifically ensemble-based boosted decision trees, on large retrospective hospital datasets to develop an infection risk score that predicts infection before obvious symptoms present. We extracted a stratified machine learning dataset of 36,782 healthcare-associated infection patients. The model leveraged vital signs, laboratory measurements and demographics to predict HAI before clinical suspicion, defined as the order of a microbiology test or administration of antibiotics.ResultsOur best performing infection risk model achieves a cross-validated AUC of 0.88 at 1 h before clinical suspicion and maintains an AUC >0.85 for 48 h before suspicion by aggregating information across demographics and a set of 163 vital signs and laboratory measurements. A second model trained on a reduced feature space comprising demographics and the 36 most frequently measured vital signs and laboratory measurements can still achieve an AUC of 0.86 at 1 h before clinical suspicion. These results compare favorably against using temperature alone and clinical rules such as the quick sequential organ failure assessment (qSOFA) score. Along with the performance results, we also provide an analysis of model interpretability via feature importance rankings.ConclusionThe predictive model aggregates information from multiple physiological parameters such as vital signs and laboratory measurements to provide a continuous risk score of infection that can be deployed in hospitals to provide advance warning of patient deterioration

    Frequency of Laboratory Test Utilization in the Intensive Care Unit and Its Implications for Large-Scale Data Collection Efforts

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    Objective: Mapping local use names to standardized nomenclatures such as LOINC (Logical Observation Identifiers Names and Codes) is a time-consuming task when done retrospectively or during the configuration of new information systems. The author sought to identify a subset of intensive care unit (ICU) laboratory tests, which, because of their frequency of use, should be the focus of efforts to standardize test names in ICU information systems. Design: The author reviewed the ordering practices in medical, surgical, and pediatric ICUs within a large university teaching hospital to identify the subset of laboratory tests that represented the majority of tests performed in these settings. The author compared the results of his findings with the laboratory tests required to complete several of the most frequently used ICU acuity scoring systems. Results: It was found that between 104 and 202 tests and profiles represented 99% of all testing in the three ICUs. All the laboratory studies needed for six commonly used ICU scoring systems fell into the top 21 laboratory studies and profiles performed in each ICU. Conclusion: The author identified a small subset of the LOINC database that should be the focus of efforts to standardize test names in ICU information systems. Mapping this subset of laboratory tests and profiles to LOINC vocabulary will simplify the process of collecting data for large-scale databases such as ICU scoring systems and the configuration of new ICU information systems

    Tracheal bronchus: a cause of prolonged atelectasis in intubated children

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    Tracheal bronchus is a common anomaly that occurs in approximately 2% of people. Two children with multiple medical problems which led to endotracheal intubation are described. The hospital course for each child was complicated by persistent right upper lobe atelectasis. The presence of a tracheal bronchus was not recognized in either case initially; identification of this anatomic variant allowed appropriate changes in airway management. The potential for tracheal bronchus to cause, or be associated with, localized pulmonary problems is reviewed. The diagnosis of tracheal bronchus should be considered early in the course of intubated patients with right upper lobe complications

    An unusual cause of respiratory distress in a neonate

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    Congenital nasolacrimal duct obstruction with cystic extension into the nasopharynx (dacryocystocele) is a rare cause of respiratory distress in the neonate. We describe the pediatric intensive care unit (PICU) course of a newborn with this disorder who had severe distress and in whom the diagnosis was originally missed
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