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

Validation of a tool for estimating clinician recognition of ARDS using data from the international LUNG SAFE study

Under-recognition of acute respiratory distress syndrome (ARDS) by clinicians is an important barrier to adoption of evidence-based practices such as low tidal volume ventilation. The burden created by the COVID-19 pandemic makes it even more critical to develop scalable data-driven tools to improve ARDS recognition. The objective of this study was to validate a tool for accurately estimating clinician ARDS recognition rates using discrete clinical characteristics easily available in electronic health records. We conducted a secondary analysis of 2,705 ARDS and 1,261 non-ARDS hypoxemic patients in the international LUNG SAFE cohort. The primary outcome was validation of a tool that estimates clinician ARDS recognition rates from health record data. Secondary outcomes included the relative impact of clinical characteristics on tidal volume delivery and clinician documentation of ARDS. In both ARDS and non-ARDS patients, greater height was associated with lower standardized tidal volume (mL/kg PBW) (ARDS: adjusted β = -4.1, 95% CI -4.5 --3.6; non-ARDS: β = -7.7, 95% CI -8.8 --6.7, P<0.00009 [where α = 0.01/111 with the Bonferroni correction]). Standardized tidal volume has already been normalized for patient height, and furthermore, height was not associated with clinician documentation of ARDS. Worsening hypoxemia was associated with both increased clinician documentation of ARDS (β = -0.074, 95% CI -0.093 --0.056, P<0.00009) and lower standardized tidal volume (β = 1.3, 95% CI 0.94-1.6, P<0.00009) in ARDS patients. Increasing chest imaging opacities, plateau pressure, and clinician documentation of ARDS also were associated with lower tidal volume in ARDS patients. Our EHR-based data-driven approach using height, gender, ARDS documentation, and lowest standardized tidal volume yielded estimates of clinician ARDS recognition rates of 54% for mild, 63% for moderate, and 73% for severe ARDS. Our tool replicated clinician-reported ARDS recognition in the LUNG SAFE study, enabling the identification of ARDS patients at high risk of being unrecognized. Our approach can be generalized to other conditions for which there is a need to increase adoption of evidence-based care.

Pulse oximetry for the diagnosis and management of acute respiratory distress syndrome

The diagnosis of acute respiratory distress syndrome (ARDS) traditionally requires calculation of the ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO2/FiO2) using arterial blood, which can be costly and is not possible in many resource-limited settings. By contrast, pulse oximetry is continuously available, accurate, inexpensive, and non-invasive. Pulse oximetry-based indices, such as the ratio of pulse-oximetric oxygen saturation to FiO2 (SpO2/FiO2), have been validated in clinical studies for the diagnosis and risk stratification of patients with ARDS. Limitations of the SpO2/FiO2 ratio include reduced accuracy in poor perfusion states or above oxygen saturations of 97%, and the potential for reduced accuracy in patients with darker skin pigmentation. Application of pulse oximetry to the diagnosis and management of ARDS, including formal adoption of the SpO2/FiO2 ratio as an alternative to PaO2/FiO2 to meet the diagnostic criterion for hypoxaemia in ARDS, could facilitate increased and earlier recognition of ARDS worldwide to advance both clinical practice and research.

Recognizing Pediatric ARDS: Provider Use of the PALICC Recommendations in a Tertiary Pediatric ICU

BACKGROUND

For almost 50 years, pediatricians used adult guidelines to diagnose ARDS. In 2015, specific criteria for pediatric ARDS were defined. However, it remains unclear how frequently providers recognize pediatric ARDS and whether recognition affects adherence to consensus recommendations.

METHODS

This was a mixed-method, retrospective study of mechanically ventilated pediatric subjects after the release of the pediatric ARDS recommendation statement. Pediatric ARDS cases were identified according to the new criteria. Provider recognition was defined by documentation in the medical record. Pediatric ARDS subjects with and without provider recognition were compared quantitatively according to clinical characteristics, adherence to lung-protective ventilation (LPV), adjunctive therapies, and outcomes. A qualitative document analysis (QDA) was performed to evaluate knowledge and beliefs surrounding the Pediatric Acute Lung Injury Consensus Conference recommendations.

RESULTS

Of 1,983 subject encounters, pediatric ARDS was identified in 321 (16%). Provider recognition was present in 97 (30%) cases and occurred more often in subjects who were older, had worse oxygenation deficits, or were bone marrow transplant recipients. Recognition rates increased each studied year. LPV practices did not differ based on provider recognition; however, subjects who received it were more likely to experience permissive hypoxemia and adherence to extrapulmonary recommendations. Ultimately, there was no differences in outcomes between the provider recognition and non-provider recognition groups. Three themes emerged from the QDA: (1) pediatric ARDS presents within a complex, multidimensional context, with potentially competing organ system failures; (2) similar to historical conceptualizations, pediatric ARDS was often considered a visual diagnosis, with measures of oxygenation unreferenced; and (3) emphasis was placed on non-evidence-based interventions, such as pulmonary clearance techniques, rather than on consensus recommendations.

CONCLUSIONS

Among mechanically ventilated children, pediatric ARDS was common but recognized in a minority of cases. Potential opportunities, such as an opt-out approach to LPV, may exist for improved dissemination and implementation of recommended best practices.

The first step is recognizing there is a problem: a methodology for adjusting for variability in disease severity when estimating clinician performance

Background

Adoption of innovations in the field of medicine is frequently hindered by a failure to recognize the condition targeted by the innovation. This is particularly true in cases where recognition requires integration of patient information from different sources, or where disease presentation can be heterogeneous and the recognition step may be easier for some patients than for others.

Methods

We propose a general data-driven metric for clinician recognition that accounts for the variability in patient disease severity and for institutional standards. As a case study, we evaluate the ventilatory management of 362 patients with acute respiratory distress syndrome (ARDS) at a large academic hospital, because clinician recognition of ARDS has been identified as a major barrier to adoption to evidence-based ventilatory management. We calculate our metric for the 48 critical care physicians caring for these patients and examine the relationships between differences in ARDS recognition performance from overall institutional levels and provider characteristics such as demographics, social network position, and self-reported barriers and opinions.

Results

Our metric was found to be robust to patient characteristics previously demonstrated to affect ARDS recognition, such as disease severity and patient height. Training background was the only factor in this study that showed an association with physician recognition. Pulmonary and critical care medicine (PCCM) training was associated with higher recognition (β = 0.63, 95% confidence interval 0.46–0.80, p < 7 × 10^− 5). Non-PCCM physicians recognized ARDS cases less frequently and expressed greater satisfaction with the ability to get the information needed for making an ARDS diagnosis (p < 5 × 10^− 4), suggesting that lower performing clinicians may be less aware of institutional barriers.

Conclusions

We present a data-driven metric of clinician disease recognition that accounts for variability in patient disease severity and for institutional standards. Using this metric, we identify two unique physician populations with different intervention needs. One population consistently recognizes ARDS and reports barriers vs one does not and reports fewer barriers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01543-7.

A Quantitative and Radiomics approach to monitoring ARDS in COVID-19 patients based on chest CT: a retrospective cohort study

Rationale: Acute respiratory distress syndrome (ARDS) is one of the major reasons for ventilation and intubation management of COVID-19 patients but there is no noninvasive imaging monitoring protocol for ARDS. In this study, we aimed to develop a noninvasive ARDS monitoring protocol based on traditional quantitative and radiomics approaches from chest CT. Methods: Patients diagnosed with COVID-19 from Jan 20, 2020 to Mar 31, 2020 were enrolled in this study. Quantitative and radiomics data were extracted from automatically segmented regions of interest (ROIs) of infection regions in the lungs. ARDS existence was measured by Pa02/Fi02 <300 in artery blood samples. Three different models were constructed by using the traditional quantitative imaging metrics, radiomics features and their combinations, respectively. Receiver operating characteristic (ROC) curve analysis was used to assess the effectiveness of the models. Decision curve analysis (DCA) was used to test the clinical value of the proposed model. Results: The proposed models were constructed using 352 CT images from 86 patients. The median age was 49, and the male proportion was 61.9%. The training dataset and the validation dataset were generated by randomly sampling the patients with a 2:1 ratio. Chi-squared test showed that there was no significant difference in baseline of the enrolled patients between the training and validation datasets. The areas under the ROC curve (AUCs) of the traditional quantitative model, radiomics model and combined model in the validation dataset was 0.91, 0.91 and 0.94, respectively. Accordingly, the sensitivities were 0.55, 0.82 and 0.58, while the specificities were 0.97, 0.86 and 0.98. The DCA curve showed that when threshold probability for a doctor or patients is within a range of 0 to 0.83, the combined model adds more net benefit than "treat all" or "treat none" strategies, while the traditional quantitative model and radiomics model could add benefit in all threshold probability. Conclusions: It is feasible to monitor ARDS from CT images using radiomics or traditional quantitative analysis in COVID-19. The radiomics model seems to be the most practical one for possible clinical use. Multi-center validation with a larger number of samples is recommended in the future.