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A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.

ABSTRACT: Importance: Despite 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.

Objective: To 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 participants: In 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.

Methods: Standardized 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.

Results: Hypoxemia 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 relevance: In 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.

SUBMITTER: Bechel MA

PROVIDER: S-EPMC6754155 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.

Bechel Meagan A MA Pah Adam R AR Shi Hanyu H Mehrotra Sanjay S Persell Stephen D SD Weiner Shayna S Wunderink Richard G RG Nunes Amaral Luís A LA Weiss Curtis H CH

PloS one 20190920 9

<h4>Importance</h4>Despite 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.<h4>Objective</h4>To quantify patient and physician factors affecting physicians' tidal ...[more]

PMID: 31539417

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Project description:ObjectivesThe acute respiratory distress syndrome is common in critically ill patients. Recognition is crucial because acute respiratory distress syndrome is associated with a high mortality rate, and low tidal volume ventilation improves mortality. However, acute respiratory distress syndrome often goes unrecognized. Risk factors for under-recognition and trends over time have not been fully described.DesignRetrospective chart review of patients with acute respiratory distress syndrome from a prospective cohort study of critically ill patients. For each patient's ICU stay, we searched the chart for terms that indicated that acute respiratory distress syndrome was diagnosed, in the differential diagnosis, or treated with low tidal volume ventilation.SettingICUs at a tertiary hospital at the University of California, San Francisco between 2008 and 2016.PatientsCritically ill patients with acute respiratory distress syndrome.InterventionsNone.Measurements and main resultsAcute respiratory distress syndrome was recognized in 70% of patients, and recognition increased from 60% in 2008-2009 to 92% in 2016 (p = 0.004). Use of tidal volumes less than 6.5 mL/kg also increased (p < 0.001) from 20% to 92%. Increased acute respiratory distress syndrome severity (p = 0.01) and vasopressor use (p = 0.04) were associated with greater recognition. Clinician diagnosis of acute respiratory distress syndrome and inclusion of acute respiratory distress syndrome in the differential diagnosis were associated with tidal volumes less than 6.5 mL/kg (51% use of tidal volume ≤ 6.5 mL/kg if acute respiratory distress syndrome recognized vs 15% if not recognized; p = 0.002). Diagnosing acute respiratory distress syndrome was associated with lower tidal volume in multivariate analysis.ConclusionsAlthough acute respiratory distress syndrome recognition and low tidal volume ventilation use have increased over time, they remain less than universal. Clinician recognition of acute respiratory distress syndrome is associated with both systemic and respiratory severity of illness and is also associated with use of low tidal volume ventilation.

Project description:Acute respiratory distress syndrome is underrecognized in the ICU, but it remains uncertain if acute respiratory distress syndrome recognition affects evidence-based acute respiratory distress syndrome care in the modern era. We sought to determine the rate of clinician-recognized acute respiratory distress syndrome in an academic medical ICU and understand how clinician-recognized-acute respiratory distress syndrome affects clinical care and patient-centered outcomes.DesignObservational cohort study.SettingSingle medical ICU at an academic tertiary-care hospital.PatientsNine hundred seventy-seven critically ill adults (381 with expert-adjudicated acute respiratory distress syndrome) enrolled from 2006 to 2015.InterventionsClinician-recognized-acute respiratory distress syndrome was identified using an electronic keyword search of clinical notes in the electronic health record. We assessed the classification performance of clinician-recognized acute respiratory distress syndrome for identifying expert-adjudicated acute respiratory distress syndrome. We also compared differences in ventilator settings, diuretic prescriptions, and cumulative fluid balance between clinician-recognized acute respiratory distress syndrome and unrecognized acute respiratory distress syndrome.Measurements and main resultsOverall, clinician-recognized-acute respiratory distress syndrome had a sensitivity of 47.5%, specificity 91.1%, positive predictive value 77.4%, and negative predictive value 73.1% for expert-adjudicated acute respiratory distress syndrome. Among the 381 expert-adjudicated acute respiratory distress syndrome cases, we did not observe any differences in ventilator tidal volumes between clinician-recognized-acute respiratory distress syndrome and unrecognized acute respiratory distress syndrome, but clinician-recognized-acute respiratory distress syndrome patients had a more negative cumulative fluid balance (mean difference, -781 mL; 95% CI, [-1,846 to +283]) and were more likely to receive diuretics (49.3% vs 35.7%, p = 0.02). There were no differences in mortality, ICU length of stay, or ventilator-free days.ConclusionsAcute respiratory distress syndrome recognition was low in this single-center study. Although acute respiratory distress syndrome recognition was not associated with lower ventilator volumes, it was associated with differences in behaviors related to fluid management. These findings have implications for the design of future studies promoting evidence-based acute respiratory distress syndrome interventions in the ICU.

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:IntroductionThe clinical use of serial quantitative computed tomography (CT) to characterize lung disease and guide the optimization of mechanical ventilation in patients with acute respiratory distress syndrome (ARDS) is limited by the risk of cumulative radiation exposure and by the difficulties and risks related to transferring patients to the CT room. We evaluated the effects of tube current-time product (mAs) variations on quantitative results in healthy lungs and in experimental ARDS in order to support the use of low-dose CT for quantitative analysis.MethodsIn 14 sheep chest CT was performed at baseline and after the induction of ARDS via intravenous oleic acid injection. For each CT session, two consecutive scans were obtained applying two different mAs: 60 mAs was paired with 140, 15 or 7.5 mAs. All other CT parameters were kept unaltered (tube voltage 120 kVp, collimation 32 × 0.5 mm, pitch 0.85, matrix 512 × 512, pixel size 0.625 × 0.625 mm). Quantitative results obtained at different mAs were compared via Bland-Altman analysis.ResultsGood agreement was observed between 60 mAs and 140 mAs and between 60 mAs and 15 mAs (all biases less than 1%). A further reduction of mAs to 7.5 mAs caused an increase in the bias of poorly aerated and nonaerated tissue (-2.9% and 2.4%, respectively) and determined a significant widening of the limits of agreement for the same compartments (-10.5% to 4.8% for poorly aerated tissue and -5.9% to 10.8% for nonaerated tissue). Estimated mean effective dose at 140, 60, 15 and 7.5 mAs corresponded to 17.8, 7.4, 2.0 and 0.9 mSv, respectively. Image noise of scans performed at 140, 60, 15 and 7.5 mAs corresponded to 10, 16, 38 and 74 Hounsfield units, respectively.ConclusionsA reduction of effective dose up to 70% has been achieved with minimal effects on lung quantitative results. Low-dose computed tomography provides accurate quantitative results and could be used to characterize lung compartment distribution and possibly monitor time-course of ARDS with a lower risk of exposure to ionizing radiation. A further radiation dose reduction is associated with lower accuracy in quantitative results.

Dataset Information

A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.

Publications

A quantitative approach for the analysis of clinician recognition of acute respiratory distress syndrome using electronic health record data.

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