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External Validity of Electronic Sniffers for Automated Recognition of Acute Respiratory Distress Syndrome.

ABSTRACT: INTRODUCTION:Automated electronic sniffers may be useful for early detection of acute respiratory distress syndrome (ARDS) for institution of treatment or clinical trial screening. METHODS:In a prospective cohort of 2929 critically ill patients, we retrospectively applied published sniffer algorithms for automated detection of acute lung injury to assess their utility in diagnosis of ARDS in the first 4 ICU days. Radiographic full-text reports were searched for "edema" OR ("bilateral" AND "infiltrate") and a more detailed algorithm for descriptions consistent with ARDS. Patients were flagged as possible ARDS if a radiograph met search criteria and had a PaO2/FiO2 or SpO2/FiO2 of 300 or 315, respectively. Test characteristics of the electronic sniffers and clinical suspicion of ARDS were compared to a gold standard of 2-physician adjudicated ARDS. RESULTS:Thirty percent of 2841 patients included in the analysis had gold standard diagnosis of ARDS. The simpler algorithm had sensitivity for ARDS of 78.9%, specificity of 52%, positive predictive value (PPV) of 41%, and negative predictive value (NPV) of 85.3% over the 4-day study period. The more detailed algorithm had sensitivity of 88.2%, specificity of 55.4%, PPV of 45.6%, and NPV of 91.7%. Both algorithms were more sensitive but less specific than clinician suspicion, which had sensitivity of 40.7%, specificity of 94.8%, PPV of 78.2%, and NPV of 77.7%. CONCLUSIONS:Published electronic sniffer algorithms for ARDS may be useful automated screening tools for ARDS and improve on clinical recognition, but they are limited to screening rather than diagnosis because their specificity is poor.

SUBMITTER: McKown AC

PROVIDER: S-EPMC5821594 | biostudies-literature | 2019 Nov-Dec

REPOSITORIES: biostudies-literature

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External Validity of Electronic Sniffers for Automated Recognition of Acute Respiratory Distress Syndrome.

McKown Andrew C AC Brown Ryan M RM Ware Lorraine B LB Wanderer Jonathan P JP

Journal of intensive care medicine 20170724 11-12

<h4>Introduction</h4>Automated electronic sniffers may be useful for early detection of acute respiratory distress syndrome (ARDS) for institution of treatment or clinical trial screening.<h4>Methods</h4>In a prospective cohort of 2929 critically ill patients, we retrospectively applied published sniffer algorithms for automated detection of acute lung injury to assess their utility in diagnosis of ARDS in the first 4 ICU days. Radiographic full-text reports were searched for "edema" OR ("bilate ...[more]

PMID: 28737058

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Project description:BackgroundThe acute respiratory distress syndrome (ARDS) results in substantial mortality but remains underdiagnosed in clinical practice. Automated ARDS "sniffer" systems, tools that can automatically analyze electronic medical record data, have been developed to improve recognition of ARDS in clinical practice.ObjectivesTo perform a systematic review examining the evidence underlying automated sniffer systems for ARDS detection.Data sourcesMEDLINE and Scopus databases through November 2018 to identify studies of tools using routinely available clinical data to detect patients with ARDS.Data extractionStudy design, tool description, and diagnostic performance were extracted by two reviewers. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate each study for risk of bias in four domains: patient selection, index test, reference standard, and study flow and timing.SynthesisAmong 480 studies identified, 9 met inclusion criteria, and they evaluated six unique ARDS sniffer tools. Eight studies had derivation and/or temporal validation designs, with one also evaluating the effects of implementing a tool in clinical practice. A single study performed an external validation of previously published ARDS sniffer tools. Studies reported a wide range of sensitivities (43-98%) and positive predictive values (26-90%) for detection of ARDS. Most studies had potential for high risk of bias identified in their study design, including patient selection (five of nine), reference standard (four of nine), and flow and timing (three of nine). In the single external validation without any perceived risks of biases, the performance of ARDS sniffer tools was worse.ConclusionsSniffer systems developed to detect ARDS had moderate to high predictive value in their derivation cohorts, although most studies had the potential for high risks of bias in study design. Methodological issues may explain some of the variability in tool performance. There remains an ongoing need for robust evaluation of ARDS sniffer systems and their impact on clinical practice. Systematic review registered with PROSPERO (CRD42015026584).

Project description:ObjectivesAcute respiratory distress syndrome is frequently under recognized and associated with increased mortality. Previously, we developed a model that used machine learning and natural language processing of text from radiology reports to identify acute respiratory distress syndrome. The model showed improved performance in diagnosing acute respiratory distress syndrome when compared to a rule-based method. In this study, our objective was to externally validate the natural language processing model in patients from an independent hospital setting.DesignSecondary analysis of data across five prospective clinical studies.SettingAn urban, tertiary care, academic hospital.PatientsAdult patients admitted to the medical ICU and at-risk for acute respiratory distress syndrome.InterventionsNone.Measurements and main resultsThe natural language processing model was previously derived and internally validated in burn, trauma, and medical patients at Loyola University Medical Center. Two machine learning models were examined with the following text features from qualifying radiology reports: 1) word representations (n-grams) and 2) standardized clinical named entity mentions mapped from the National Library of Medicine Unified Medical Language System. The models were externally validated in a cohort of 235 patients at the University of Chicago Medicine, among which 110 (47%) were diagnosed with acute respiratory distress syndrome by expert annotation. During external validation, the n-gram model demonstrated good discrimination between acute respiratory distress syndrome and nonacute respiratory distress syndrome patients (C-statistic, 0.78; 95% CI, 0.72-0.84). The n-gram model had a higher discrimination for acute respiratory distress syndrome when compared with the standardized named entity model, although not statistically significant (C-statistic 0.78 vs 0.72; p = 0.09). The most important features in the model had good face validity for acute respiratory distress syndrome characteristics but differences in frequencies did occur between hospital settings.ConclusionsOur computable phenotype for acute respiratory distress syndrome had good discrimination in external validation and may be used by other health systems for case-identification. Discrepancies in feature representation are likely due to differences in characteristics of the patient cohorts.

Project description:ImportanceDespite its efficacy, low tidal volume ventilation (LTVV) remains severely underutilized for patients with acute respiratory distress syndrome (ARDS). Physician under-recognition of ARDS is a significant barrier to LTVV use. We propose a computational method that addresses some of the limitations of the current approaches to automated measurement of whether ARDS is recognized by physicians.ObjectiveTo quantify patient and physician factors affecting physicians' tidal volume selection and to build a computational model of physician recognition of ARDS that accounts for these factors.Design, setting, and participantsIn this cross-sectional study, electronic health record data were collected for 361 ARDS patients and 388 non-ARDS hypoxemic (control) patients in nine adult intensive care units at four hospitals between June 24 and December 31, 2013.MethodsStandardized tidal volumes (mL/kg predicted body weight) were chosen as a proxy for physician decision-making behavior. Using data-science approaches, we quantified the effect of eight factors (six severity of illness, two physician behaviors) on selected standardized tidal volumes in ARDS and control patients. Significant factors were incorporated in computational behavioral models of physician recognition of ARDS.ResultsHypoxemia severity and ARDS documentation in physicians' notes were associated with lower standardized tidal volumes in the ARDS cohort. Greater patient height was associated with lower standardized tidal volumes (which is already normalized for height) in both ARDS and control patients. The recognition model yielded a mean (99% confidence interval) physician recognition of ARDS of 22% (9%-42%) for mild, 34% (19%-49%) for moderate, and 67% (41%-100%) for severe ARDS.Conclusions and relevanceIn this study, patient characteristics and physician behaviors were demonstrated to be associated with differences in ventilator management in both ARDS and control patients. Our model of physician ARDS recognition measurement accounts for these clinical variables, providing an electronic approach that moves beyond relying on chart documentation or resource intensive approaches.

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External Validity of Electronic Sniffers for Automated Recognition of Acute Respiratory Distress Syndrome.

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External Validity of Electronic Sniffers for Automated Recognition of Acute Respiratory Distress Syndrome.

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