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 ß = -4.1, 95% CI -4.5 --3.6; non-ARDS: ß = -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 (ß = -0.074, 95% CI -0.093 --0.056, P<0.00009) and lower standardized tidal volume (ß = 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.

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 (ß = 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.

Firearm laws and illegal firearm flow between US states.

BACKGROUND: Considerable variation in firearm legislation exists. Prior studies show an association between stronger state laws and fewer firearm deaths. We hypothesized that firearms would flow from states with weaker laws to states with stronger laws based on proximity and population. METHODS: Crime gun trace data from 2015 to 2017 was accessed from the Bureau of Alcohol, Tobacco, Firearms and Explosives and compared with the count and composition of firearm legislation in 2015 among the contiguous 48 states. Additional independent variables included population, median household income, distance, and presence or absence of a shared border. We used Exponential Random Graph Models to identify predictors of traced firearm transfers between origin and destination states. RESULTS: After controlling for network structure, firearm laws in origin states were associated with fewer traced firearm transfers (incidence rate ratio [IRR], 0.88; 95% confidence interval [CI], 0.83-0.93; p < 0.001). Conversely, more firearm laws in destination states were associated with more traced firearm transfers (IRR, 1.10; 95% CI, 1.06-1.15; p < 0.001). Larger population at the origin was associated with increased transfers (IRR, 1.38; 95%CI, 1.27-1.50; p < 0.001), as was larger population at the destination state (IRR, 1.45; 95% CI, 1.35-1.56; p < 0.001). Greater distance was associated with fewer transfers (for each 1,000 km; IRR, 0.35; 95% CI, 0.27-0.46; p < 0.001), and transfers were greater between adjacent states (IRR, 2.49; 95% CI, 1.90-3.27; p < 0.001). CONCLUSION: State firearm legislation has a significant impact on gun trafficking even after controlling for network structure. States with stricter firearm legislation are negatively impacted by states with weaker regulations, as crime guns flow from out-of-state. LEVEL OF EVIDENCE: Epidemiologic, level III.

Quantifying the future lethality of terror organizations.

As terror groups proliferate and grow in sophistication, a major international concern is the development of scientific methods that explain and predict insurgent violence. Approaches to estimating a group's future lethality often require data on the group's capabilities and resources, but by the nature of the phenomenon, these data are intentionally concealed by the organizations themselves via encryption, the dark web, back-channel financing, and misinformation. Here, we present a statistical model for estimating a terror group's future lethality using latent-variable modeling techniques to infer a group's intrinsic capabilities and resources for inflicting harm. The analysis introduces 2 explanatory variables that are strong predictors of lethality and raise the overall explained variance when added to existing models. The explanatory variables generate a unique early-warning signal of an individual group's future lethality based on just a few of its first attacks. Relying on the first 10 to 20 attacks or the first 10 to 20% of a group's lifetime behavior, our model explains about 60% of the variance in a group's future lethality as would be explained by a group's complete lifetime data. The model's robustness is evaluated with out-of-sample testing and simulations. The findings' theoretical and pragmatic implications for the science of human conflict are discussed.

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

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.

Leveraging genome-wide datasets to quantify the functional role of the anti-Shine-Dalgarno sequence in regulating translation efficiency.

Studies dating back to the 1970s established that sequence complementarity between the anti-Shine-Dalgarno (aSD) sequence on prokaryotic ribosomes and the 5' untranslated region of mRNAs helps to facilitate translation initiation. The optimal location of aSD sequence binding relative to the start codon, the full extents of the aSD sequence and the functional form of the relationship between aSD sequence complementarity and translation efficiency have not been fully resolved. Here, we investigate these relationships by leveraging the sequence diversity of endogenous genes and recently available genome-wide estimates of translation efficiency. We show that-after accounting for predicted mRNA structure-aSD sequence complementarity increases the translation of endogenous mRNAs by roughly 50%. Further, we observe that this relationship is nonlinear, with translation efficiency maximized for mRNAs with intermediate levels of aSD sequence complementarity. The mechanistic insights that we observe are highly robust: we find nearly identical results in multiple datasets spanning three distantly related bacteria. Further, we verify our main conclusions by re-analysing a controlled experimental dataset.

Analysis of Musculoskeletal Injuries Sustained in Falls From the United States-Mexico Border Fence.

Injuries sustained by unauthorized individuals who jump or fall from the United States-Mexico border fence are frequently treated by trauma centers in border states. The authors investigated patterns of musculoskeletal injury occurring in these individuals to improve emergency department assessment and to identify strategies to prevent future injuries. A retrospective chart review was performed for patients presenting to an urban, level I trauma center with musculoskeletal injuries sustained in a jump or fall from the United States-Mexico border fence between February 2004 and February 2010. Frequency of fracture by site, frequency of open fracture, and associated patterns of injury were recorded. The population was stratified by age and sex to identify disparity in injury pattern. Average length of stay and number of surgical interventions were also recorded. During the study period, 174 individuals who had jumped or fallen from the United States-Mexico border fence were identified. The population contained 93 (53%) women and 81 (47%) men with an average age of 31.5 years (range, 11-56 years). On average (±standard error), men sustained slightly more fractures than women (1.77±0.12 vs 1.43±0.07; P=.015). There were no significant differences in the number of fractures sustained between age groups. Average length of stay for patients admitted to the hospital was 3.5 days. Patients underwent an average of 0.75 surgical interventions during admission. Falls from the United States-Mexico border fence are a significant cause of morbidity among unauthorized immigrants. [Orthopedics. 2017; 40(3):e432-e435.].

Design and implementation of a privacy preserving electronic health record linkage tool in Chicago.

OBJECTIVE: To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research. METHODS: The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches. RESULTS: The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%. CONCLUSIONS: Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.

Adoption of a High-Impact Innovation in a Homogeneous Population.

Adoption of innovations, whether new ideas, technologies, or products, is crucially important to knowledge societies. The landmark studies of adoption dealt with innovations having great societal impact (such as antibiotics or hybrid crops) but where determining the utility of the innovation was straightforward (such as fewer side effects or greater yield). Recent large-scale studies of adoption were conducted within heterogeneous populations and focused on products with little societal impact. Here, we focus on a case with great practical significance: adoption by small groups of highly trained individuals of innovations with large societal impact but for which it is impractical to determine the true utility of the innovation. Specifically, we study experimentally the adoption by critical care physicians of a diagnostic assay that complements current protocols for the diagnosis of life-threatening bacterial infections and for which a physician cannot estimate the true accuracy of the assay based on personal experience. We show through computational modeling of the experiment that infection-spreading models-which have been formalized as generalized contagion processes-are not consistent with the experimental data, while a model inspired by opinion models is able to reproduce the empirical data. Our modeling approach enables us to investigate the efficacy of different intervention schemes on the rate and robustness of innovation adoption in the real world. While our study is focused on critical care physicians, our findings have implications for other settings in education, research, and business, where small groups of highly qualified peers make decisions about the adoption of innovations whose utility is difficult if not impossible to gauge.