Association of Neighborhood Disadvantage with Short- and Long-Term Outcomes After Pancreatectomy for Pancreatic Ductal Adenocarcinoma.

BACKGROUND: While lower socioeconomic status has been shown to correlate with worse outcomes in cancer care, data correlating neighborhood-level metrics with outcomes are scarce. We aim to explore the association between neighborhood disadvantage and both short- and long-term postoperative outcomes in patients undergoing pancreatectomy for pancreatic ductal adenocarcinoma (PDAC). PATIENTS AND METHODS: We retrospectively analyzed 243 patients who underwent resection for PDAC at a single institution between 1 January 2010 and 15 September 2021. To measure neighborhood disadvantage, the cohort was divided into tertiles by Area Deprivation Index (ADI). Short-term outcomes of interest were minor complications, major complications, unplanned readmission within 30 days, prolonged hospitalization, and delayed gastric emptying (DGE). The long-term outcome of interest was overall survival. Logistic regression was used to test short-term outcomes; Cox proportional hazards models and Kaplan-Meier method were used for long-term outcomes. RESULTS: The median ADI of the cohort was 49 (IQR 32-64.5). On adjusted analysis, the high-ADI group demonstrated greater odds of suffering a major complication (odds ratio [OR], 2.78; 95% confidence interval [CI], 1.26-6.40; p = 0.01) and of an unplanned readmission (OR, 3.09; 95% CI, 1.16-9.28; p = 0.03) compared with the low-ADI group. There were no significant differences between groups in the odds of minor complications, prolonged hospitalization, or DGE (all p > 0.05). High ADI did not confer an increased hazard of death (p = 0.63). CONCLUSIONS: We found that worse neighborhood disadvantage is associated with a higher risk of major complication and unplanned readmission after pancreatectomy for PDAC.

Prior emergency medical services utilization is a risk factor for in-hospital death among patients with substance misuse: a retrospective cohort study.

BACKGROUND: Substance misuse poses a significant public health challenge, characterized by premature morbidity and mortality, and heightened healthcare utilization. While studies have demonstrated that previous hospitalizations and emergency department visits are associated with increased mortality in patients with substance misuse, it is unknown whether prior utilization of emergency medical service (EMS) is similarly associated with poor outcomes among this population. The objective of this study is to determine the association between EMS utilization in the 30 days before a hospitalization or emergency department visit and in-hospital outcomes among patients with substance misuse. METHODS: We conducted a retrospective analysis of adult emergency department visits and hospitalizations (referred to as a hospital encounter) between 2017 and 2021 within the Substance Misuse Data Commons, which maintains electronic health records from substance misuse patients seen at two University of Wisconsin hospitals, linked with state agency, claims, and socioeconomic datasets. Using regression models, we examined the association between EMS use and the outcomes of in-hospital death, hospital length of stay, intensive care unit (ICU) admission, and critical illness events, defined by invasive mechanical ventilation or vasoactive drug administration. Models were adjusted for age, comorbidities, initial severity of illness, substance misuse type, and socioeconomic status. RESULTS: Among 19,402 encounters, individuals with substance misuse who had at least one EMS incident within 30 days of a hospital encounter experienced a higher likelihood of in-hospital mortality (OR 1.52, 95% CI [1.05 - 2.14]) compared to those without prior EMS use, after adjusting for confounders. Using EMS in the 30 days prior to an encounter was associated with a small increase in hospital length of stay but was not associated with ICU admission or critical illness events. CONCLUSIONS: Individuals with substance misuse who have used EMS in the month preceding a hospital encounter are at an increased risk of in-hospital mortality. Enhanced monitoring of EMS users in this population could improve overall patient outcomes.

Accuracy of models to prognosticate survival after surgery for pancreatic cancer in the era of neoadjuvant therapy.

BACKGROUND: Outcomes for pancreatic adenocarcinoma (PDAC) remain difficult to prognosticate. Multiple models attempt to predict survival following the resection of PDAC, but their utility in the neoadjuvant population is unknown. We aimed to assess their accuracy among patients that received neoadjuvant chemotherapy (NAC). METHODS: We performed a multi-institutional retrospective analysis of patients who received NAC and underwent resection of PDAC. Two prognostic systems were evaluated: the Memorial Sloan Kettering Cancer Center Pancreatic Adenocarcinoma Nomogram (MSKCCPAN) and the American Joint Committee on Cancer (AJCC) staging system. Discrimination between predicted and actual disease-specific survival was assessed using the Uno C-statistic and Kaplan-Meier method. Calibration of the MSKCCPAN was assessed using the Brier score. RESULTS: A total of 448 patients were included. There were 232 (51.8%) females, and the mean age was 64.1 years (±9.5). Most had AJCC Stage I or II disease (77.7%). For the MSKCCPAN, the Uno C-statistic at 12-, 24-, and 36-month time points was 0.62, 0.63, and 0.62, respectively. The AJCC system demonstrated similarly mediocre discrimination. The Brier score for the MSKCCPAN was 0.15 at 12 months, 0.26 at 24 months, and 0.30 at 36 months, demonstrating modest calibration. CONCLUSIONS: Current survival prediction models and staging systems for patients with PDAC undergoing resection after NAC have limited accuracy.

Progress Note Understanding - Assessment and Plan Reasoning: Overview of the 2022 N2C2 Track 3 shared task.

Daily progress notes are a common note type in the electronic health record (EHR) where healthcare providers document the patient's daily progress and treatment plans. The EHR is designed to document all the care provided to patients, but it also enables note bloat with extraneous information that distracts from the diagnoses and treatment plans. Applications of natural language processing (NLP) in the EHR is a growing field with the majority of methods in information extraction. Few tasks use NLP methods for downstream diagnostic decision support. We introduced the 2022 National NLP Clinical Challenge (N2C2) Track 3: Progress Note Understanding - Assessment and Plan Reasoning as one step towards a new suite of tasks. The Assessment and Plan Reasoning task focuses on the most critical components of progress notes, Assessment and Plan subsections where health problems and diagnoses are contained. The goal of the task was to develop and evaluate NLP systems that automatically predict causal relations between the overall status of the patient contained in the Assessment section and its relation to each component of the Plan section which contains the diagnoses and treatment plans. The goal of the task was to identify and prioritize diagnoses as the first steps in diagnostic decision support to find the most relevant information in long documents like daily progress notes. We present the results of the 2022 N2C2 Track 3 and provide a description of the data, evaluation, participation and system performance.

DR.BENCH: Diagnostic Reasoning Benchmark for Clinical Natural Language Processing.

The meaningful use of electronic health records (EHR) continues to progress in the digital era with clinical decision support systems augmented by artificial intelligence. A priority in improving provider experience is to overcome information overload and reduce the cognitive burden so fewer medical errors and cognitive biases are introduced during patient care. One major type of medical error is diagnostic error due to systematic or predictable errors in judgement that rely on heuristics. The potential for clinical natural language processing (cNLP) to model diagnostic reasoning in humans with forward reasoning from data to diagnosis and potentially reduce cognitive burden and medical error has not been investigated. Existing tasks to advance the science in cNLP have largely focused on information extraction and named entity recognition through classification tasks. We introduce a novel suite of tasks coined as Diagnostic Reasoning Benchmarks, Dr.Bench, as a new benchmark for developing and evaluating cNLP models with clinical diagnostic reasoning ability. The suite includes six tasks from ten publicly available datasets addressing clinical text understanding, medical knowledge reasoning, and diagnosis generation. DR.BENCH is the first clinical suite of tasks designed to be a natural language generation framework to evaluate pre-trained language models for diagnostic reasoning. The goal of DR. BENCH is to advance the science in cNLP to support downstream applications in computerized diagnostic decision support and improve the efficiency and accuracy of healthcare providers during patient care. We fine-tune and evaluate the state-of-the-art generative models on DR.BENCH. Experiments show that with domain adaptation pre-training on medical knowledge, the model demonstrated opportunities for improvement when evaluated in DR. BENCH. We share DR. BENCH as a publicly available GitLab repository with a systematic approach to load and evaluate models for the cNLP community. We also discuss the carbon footprint produced during the experiments and encourage future work on DR.BENCH to report the carbon footprint.

Clinical Trial Design Innovations for Precision Medicine in Asthma.

Severe asthma is a spectrum disorder with numerous subsets, many of which are defined by clinical history and a general predisposition for T2 inflammation. Most of the approved therapies for severe asthma have required clinical trial designs with population enrichment for exacerbation frequency and/or elevation of blood eosinophils. Moving beyond this framework will require trial designs that increase efficiency for studying nondominant subsets and continue to improve upon biomarker signatures. In addition to reviewing the current literature on biomarker-informed trials for severe asthma, this chapter will also review the advantages of master protocols and adaptive design methods for establishing the efficacy of new interventions in prospectively defined subsets of patients. The incorporation of methods that allow for data collection outside of traditional study visits at academic centers, called remote decentralized trial design, is a growing trend that may increase diversity in study participation and allow for enhanced resiliency during the COVID-19 pandemic. Finally, reaching the goals of precision medicine in asthma will require increased emphasis on effectiveness studies. Recent advances in real-world data utilization from electronic health records are also discussed with a view toward pragmatic trial designs that could also incorporate the evaluation of biomarker signatures.

Internal and External Validation of an Alcohol Biomarker for Screening in Trauma.

OBJECTIVE: We aimed to examine biomarkers for screening unhealthy alcohol use in the trauma setting. SUMMARY AND BACKGROUND DATA: Self-report tools are the practice standard for screening unhealthy alcohol use; however, their collection suffers from recall bias and incomplete collection by staff. METHODS: We performed a multi-center prospective clinical study of 251 adult patients who arrived within 24 hours of injury with external validation in another 60 patients. The Alcohol Use Disorders Identification Test served as the reference standard. The following biomarkers were measured: (1) PEth; (2) ethyl glucuronide; (3) ethyl sulfate; (4) gamma-glutamyl-transpeptidase; (5) carbohydrate deficient transferrin; and (6) blood alcohol concentration (BAC). Candidate single biomarkers and multivariable models were compared by considering discrimination (AUROC). The optimal cutpoint for the final model was identified using a criterion for setting the minimum value for specificity at 80% and maximizing sensitivity. Decision curve analysis was applied to compare to existing screening with BAC. RESULTS: PEth alone had an AUROC of 0.93 [95% confidence interval (CI): 0.92-0.93] in internal validation with an optimal cutpoint of 25 ng/mL. A 4- variable biomarker model and the addition of any single biomarker to PEth did not improve AUROC over PEth alone ( P > 0.05). Decision curve analysis showed better performance of PEth over BAC across most predicted probability thresholds. In external validation, sensitivity and specificity were 76.0% (95% CI: 53.0%-92.0%) and 73.0% (95% CI: 56.0%-86.0%), respectively.Conclusion and Relevance: PEth alone proved to be the single best biomarker for screening of unhealthy alcohol use and performed better than existing screening systems with BAC. PEth may overcome existing screening barriers.

Research Needs for Inpatient Management of Severe Alcohol Withdrawal Syndrome: An Official American Thoracic Society Research Statement.

Background: Severe alcohol withdrawal syndrome (SAWS) is highly morbid, costly, and common among hospitalized patients, yet minimal evidence exists to guide inpatient management. Research needs in this field are broad, spanning the translational science spectrum. Goals: This research statement aims to describe what is known about SAWS, identify knowledge gaps, and offer recommendations for research in each domain of the Institute of Medicine T0-T4 continuum to advance the care of hospitalized patients who experience SAWS. Methods: Clinicians and researchers with unique and complementary expertise in basic, clinical, and implementation research related to unhealthy alcohol consumption and alcohol withdrawal were invited to participate in a workshop at the American Thoracic Society 2019 International Conference. The committee was subdivided into four groups on the basis of interest and expertise: T0-T1 (basic science research with translation to humans), T2 (research translating to patients), T3 (research translating to clinical practice), and T4 (research translating to communities). A medical librarian conducted a pragmatic literature search to facilitate this work, and committee members reviewed and supplemented the resulting evidence, identifying key knowledge gaps. Results: The committee identified several investigative opportunities to advance the care of patients with SAWS in each domain of the translational science spectrum. Major themes included 1) the need to investigate non-γ-aminobutyric acid pathways for alcohol withdrawal syndrome treatment; 2) harnessing retrospective and electronic health record data to identify risk factors and create objective severity scoring systems, particularly for acutely ill patients with SAWS; 3) the need for more robust comparative-effectiveness data to identify optimal SAWS treatment strategies; and 4) recommendations to accelerate implementation of effective treatments into practice. Conclusions: The dearth of evidence supporting management decisions for hospitalized patients with SAWS, many of whom require critical care, represents both a call to action and an opportunity for the American Thoracic Society and larger scientific communities to improve care for a vulnerable patient population. This report highlights basic, clinical, and implementation research that diverse experts agree will have the greatest impact on improving care for hospitalized patients with SAWS.

Identifying High-Risk Subphenotypes and Associated Harms From Delayed Antibiotic Orders and Delivery.

OBJECTIVES: Early antibiotic administration is a central component of sepsis guidelines, and delays may increase mortality. However, prior studies have examined the delay to first antibiotic administration as a single time period even though it contains two distinct processes: antibiotic ordering and antibiotic delivery, which can each be targeted for improvement through different interventions. The objective of this study was to characterize and compare patients who experienced order or delivery delays, investigate the association of each delay type with mortality, and identify novel patient subphenotypes with elevated risk of harm from delays. DESIGN: Retrospective analysis of multicenter inpatient data. SETTING: Two tertiary care medical centers (2008-2018, 2006-2017) and four community-based hospitals (2008-2017). PATIENTS: All patients admitted through the emergency department who met clinical criteria for infection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patient demographics, vitals, laboratory values, medication order and administration times, and in-hospital survival data were obtained from the electronic health record. Order and delivery delays were calculated for each admission. Adjusted logistic regression models were used to examine the relationship between each delay and in-hospital mortality. Causal forests, a machine learning method, was used to identify a high-risk subgroup. A total of 60,817 admissions were included, and delays occurred in 58% of patients. Each additional hour of order delay (odds ratio, 1.04; 95% CI, 1.03-1.05) and delivery delay (odds ratio, 1.05; 95% CI, 1.02-1.08) was associated with increased mortality. A patient subgroup identified by causal forests with higher comorbidity burden, greater organ dysfunction, and abnormal initial lactate measurements had a higher risk of death associated with delays (odds ratio, 1.07; 95% CI, 1.06-1.09 vs odds ratio, 1.02; 95% CI, 1.01-1.03). CONCLUSIONS: Delays in antibiotic ordering and drug delivery are both associated with a similar increase in mortality. A distinct subgroup of high-risk patients exist who could be targeted for more timely therapy.

Determining the Electronic Signature of Infection in Electronic Health Record Data.

OBJECTIVES: Recent sepsis studies have defined patients as "infected" using a combination of culture and antibiotic orders rather than billing data. However, the accuracy of these definitions is unclear. We aimed to compare the accuracy of different established criteria for identifying infected patients using detailed chart review. DESIGN: Retrospective observational study. SETTING: Six hospitals from three health systems in Illinois. PATIENTS: Adult admissions with blood culture or antibiotic orders, or Angus International Classification of Diseases infection codes and death were eligible for study inclusion as potentially infected patients. Nine-hundred to 1,000 of these admissions were randomly selected from each health system for chart review, and a proportional number of patients who did not meet chart review eligibility criteria were also included and deemed not infected. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The accuracy of published billing code criteria by Angus et al and electronic health record criteria by Rhee et al and Seymour et al (Sepsis-3) was determined using the manual chart review results as the gold standard. A total of 5,215 patients were included, with 2,874 encounters analyzed via chart review and a proportional 2,341 added who did not meet chart review eligibility criteria. In the study cohort, 27.5% of admissions had at least one infection. This was most similar to the percentage of admissions with blood culture orders (26.8%), Angus infection criteria (28.7%), and the Sepsis-3 criteria (30.4%). Sepsis-3 criteria was the most sensitive (81%), followed by Angus (77%) and Rhee (52%), while Rhee (97%) and Angus (90%) were more specific than the Sepsis-3 criteria (89%). Results were similar for patients with organ dysfunction during their admission. CONCLUSIONS: Published criteria have a wide range of accuracy for identifying infected patients, with the Sepsis-3 criteria being the most sensitive and Rhee criteria being the most specific. These findings have important implications for studies investigating the burden of sepsis on a local and national level.

Factors associated with phosphatidylethanol (PEth) sensitivity for detecting unhealthy alcohol use: An individual patient data meta-analysis.

BACKGROUND: Objective measurement of alcohol consumption is important for clinical care and research. Adjusting for self-reported alcohol use, we conducted an individual participant data (IPD) meta-analysis to examine factors associated with the sensitivity of phosphatidylethanol (PEth), an alcohol metabolite, among persons self-reporting unhealthy alcohol consumption. METHODS: We identified 21 eligible studies and obtained 4073 observations from 3085 participants with Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) positive scores (≥3 for women and ≥4 for men) and PEth measurements. We conducted 1-step IPD meta-analysis using mixed effects models with random intercepts for study site. We examined the associations between demographic (sex, race/ethnicity, and age) and biologic (body mass index-BMI, hemoglobin, HIV status, liver fibrosis, and venous versus finger-prick blood collection) variables with PEth sensitivity (PEth≥8 ng/ml), adjusting for the level of self-reported alcohol use using the AUDIT-C score. RESULTS: One third (31%) of participants were women, 32% were African, 28% African American, 28% White, and 12% other race/ethnicity. PEth sensitivity (i.e., ≥8 ng/ml) was 81.8%. After adjusting for AUDIT-C, we found no associations of sex, age, race/ethnicity, or method of blood collection with PEth sensitivity. In models that additionally included biologic variables, those with higher hemoglobin and indeterminate and advanced liver fibrosis had significantly higher odds of PEth sensitivity; those with higher BMI and those living with HIV had significantly lower odds of PEth sensitivity. African Americans and Africans had higher odds of PEth sensitivity than whites in models that included biologic variables. CONCLUSIONS: Among people reporting unhealthy alcohol use, several biological factors (hemoglobin, BMI, liver fibrosis, and HIV status) were associated with PEth sensitivity. Race/ethnicity was associated with PEth sensitivity in some models but age, sex, and method of blood collection were not. Clinicians should be aware of these factors, and researchers should consider adjusting analyses for these characteristics where possible.

Pre-training phenotyping classifiers.

Recent transformer-based pre-trained language models have become a de facto standard for many text classification tasks. Nevertheless, their utility in the clinical domain, where classification is often performed at encounter or patient level, is still uncertain due to the limitation on the maximum length of input. In this work, we introduce a self-supervised method for pre-training that relies on a masked token objective and is free from the limitation on the maximum input length. We compare the proposed method with supervised pre-training that uses billing codes as a source of supervision. We evaluate the proposed method on one publicly-available and three in-house datasets using the standard evaluation metrics such as the area under the ROC curve and F1 score. We find that, surprisingly, even though self-supervised pre-training performs slightly worse than supervised, it still preserves most of the gains from pre-training.

The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.

OBJECTIVES: Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results at the time of the blood culture order using routine data in the electronic health record. DESIGN: Retrospective analysis of a large, multicenter inpatient data. SETTING: Two academic tertiary medical centers between the years 2007 and 2018. SUBJECTS: All hospitalized patients who received a blood culture during hospitalization. INTERVENTIONS: The dataset was partitioned temporally into development and validation cohorts: the logistic regression and gradient boosting machine models were trained on the earliest 80% of hospital admissions and validated on the most recent 20%. MEASUREMENTS AND MAIN RESULTS: There were 252,569 blood culture days-defined as nonoverlapping 24-hour periods in which one or more blood cultures were ordered. In the validation cohort, there were 50,514 blood culture days, with 3,762 cases of bacteremia (7.5%) and 370 cases of fungemia (0.7%). The gradient boosting machine model for bacteremia had significantly higher area under the receiver operating characteristic curve (0.78 [95% CI 0.77-0.78]) than the logistic regression model (0.73 [0.72-0.74]) (p < 0.001). The model identified a high-risk group with over 30 times the occurrence rate of bacteremia in the low-risk group (27.4% vs 0.9%; p < 0.001). Using the low-risk cut-off, the model identifies bacteremia with 98.7% sensitivity. The gradient boosting machine model for fungemia had high discrimination (area under the receiver operating characteristic curve 0.88 [95% CI 0.86-0.90]). The high-risk fungemia group had 252 fungemic cultures compared with one fungemic culture in the low-risk group (5.0% vs 0.02%; p < 0.001). Further, the high-risk group had a mortality rate 60 times higher than the low-risk group (28.2% vs 0.4%; p < 0.001). CONCLUSIONS: Our novel models identified patients at low and high-risk for bacteremia and fungemia using routinely collected electronic health record data. Further research is needed to evaluate the cost-effectiveness and impact of model implementation in clinical practice.

External Validation of an Acute Respiratory Distress Syndrome Prediction Model Using Radiology Reports.

OBJECTIVES: Acute 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. DESIGN: Secondary analysis of data across five prospective clinical studies. SETTING: An urban, tertiary care, academic hospital. PATIENTS: Adult patients admitted to the medical ICU and at-risk for acute respiratory distress syndrome. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The 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. CONCLUSIONS: Our 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.

Trends, Cost, and Mortality From Sepsis After Trauma in the United States: An Evaluation of the National Inpatient Sample of Hospitalizations, 2012-2016.

OBJECTIVES: Identification and outcomes in patients with sepsis have improved over the years, but little data are available in patients with trauma who develop sepsis. We aimed to examine the cost and epidemiology of sepsis in patients hospitalized after trauma. DESIGN: Retrospective cohort study. PATIENTS: National Inpatient Sample. INTERVENTIONS: Sepsis was identified between 2012 and 2016 using implicit and explicit International Classification of Diseases, Ninth and Tenth Revision codes. Analyses were stratified by injury severity score greater than or equal to 15. Annual trends were modeled using generalized linear models. Survey-adjusted logistic regression was used to compare the odds for in-hospital mortality, and the average marginal effects were calculated to compare the cost of hospitalization with and without sepsis. MEASUREMENTS AND MAIN RESULTS: There were 320,450 (SE = 3,642) traumatic injury discharges from U.S. hospitals with sepsis between 2012 and 2016, representing 6.0% (95% CI, 5.9-6.0%) of the total trauma population (n = 5,329,714; SE = 47,447). In-hospital mortality associated with sepsis after trauma did not change over the study period (p > 0.40). In adjusted analysis, severe (injury severity score ≥ 15) and nonsevere injured septic patients had an odds ratio of 1.39 (95% CI, 1.31-1.47) and 4.32 (95% CI, 4.06-4.59) for in-hospital mortality, respectively. The adjusted marginal cost for sepsis compared with nonsepsis was $16,646 (95% CI, $16,294-$16,997), and it was greater than the marginal cost for severe injury compared with nonsevere injury $8,851 (95% CI, $8,366-$8,796). CONCLUSIONS: While national trends for sepsis mortality have improved over the years, our analysis of National Inpatient Sample did not support this trend in the trauma population. The odds risk for death after sepsis and the cost of care remained high regardless of severity of injury. More rigor is needed in tracking sepsis after trauma and evaluating the effectiveness of hospital mandates and policies to improve sepsis care in patients after trauma.

Identifying Novel Sepsis Subphenotypes Using Temperature Trajectories.

Rationale: Sepsis is a heterogeneous syndrome, and identifying clinically relevant subphenotypes is essential.Objectives: To identify novel subphenotypes in hospitalized patients with infection using longitudinal temperature trajectories.Methods: In the model development cohort, inpatient admissions meeting criteria for infection in the emergency department and receiving antibiotics within 24 hours of presentation were included. Temperature measurements within the first 72 hours were compared between survivors and nonsurvivors. Group-based trajectory modeling was performed to identify temperature trajectory groups, and patient characteristics and outcomes were compared between the groups. The model was then externally validated at a second hospital using the same inclusion criteria.Measurements and Main Results: A total of 12,413 admissions were included in the development cohort, and 19,053 were included in the validation cohort. In the development cohort, four temperature trajectory groups were identified: "hyperthermic, slow resolvers" (n = 1,855; 14.9% of the cohort); "hyperthermic, fast resolvers" (n = 2,877; 23.2%); "normothermic" (n = 4,067; 32.8%); and "hypothermic" (n = 3,614; 29.1%). The hypothermic subjects were the oldest and had the most comorbidities, the lowest levels of inflammatory markers, and the highest in-hospital mortality rate (9.5%). The hyperthermic, slow resolvers were the youngest and had the fewest comorbidities, the highest levels of inflammatory markers, and a mortality rate of 5.1%. The hyperthermic, fast resolvers had the lowest mortality rate (2.9%). Similar trajectory groups, patient characteristics, and outcomes were found in the validation cohort.Conclusions: We identified and validated four novel subphenotypes of patients with infection, with significant variability in inflammatory markers and outcomes.

Evaluation and Management of Obesity Hypoventilation Syndrome. An Official American Thoracic Society Clinical Practice Guideline.

Background: The purpose of this guideline is to optimize evaluation and management of patients with obesity hypoventilation syndrome (OHS).Methods: A multidisciplinary panel identified and prioritized five clinical questions. The panel performed systematic reviews of available studies (up to July 2018) and followed the Grading of Recommendations, Assessment, Development, and Evaluation evidence-to-decision framework to develop recommendations. All panel members discussed and approved the recommendations.Recommendations: After considering the overall very low quality of the evidence, the panel made five conditional recommendations. We suggest that: 1) clinicians use a serum bicarbonate level <27 mmol/L to exclude the diagnosis of OHS in obese patients with sleep-disordered breathing when suspicion for OHS is not very high (<20%) but to measure arterial blood gases in patients strongly suspected of having OHS, 2) stable ambulatory patients with OHS receive positive airway pressure (PAP), 3) continuous positive airway pressure (CPAP) rather than noninvasive ventilation be offered as the first-line treatment to stable ambulatory patients with OHS and coexistent severe obstructive sleep apnea, 4) patients hospitalized with respiratory failure and suspected of having OHS be discharged with noninvasive ventilation until they undergo outpatient diagnostic procedures and PAP titration in the sleep laboratory (ideally within 2-3 mo), and 5) patients with OHS use weight-loss interventions that produce sustained weight loss of 25% to 30% of body weight to achieve resolution of OHS (which is more likely to be obtained with bariatric surgery).Conclusions: Clinicians may use these recommendations, on the basis of the best available evidence, to guide management and improve outcomes among patients with OHS.

Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients.

BACKGROUND: Automated de-identification methods for removing protected health information (PHI) from the source notes of the electronic health record (EHR) rely on building systems to recognize mentions of PHI in text, but they remain inadequate at ensuring perfect PHI removal. As an alternative to relying on de-identification systems, we propose the following solutions: (1) Mapping the corpus of documents to standardized medical vocabulary (concept unique identifier [CUI] codes mapped from the Unified Medical Language System) thus eliminating PHI as inputs to a machine learning model; and (2) training character-based machine learning models that obviate the need for a dictionary containing input words/n-grams. We aim to test the performance of models with and without PHI in a use-case for an opioid misuse classifier. METHODS: An observational cohort sampled from adult hospital inpatient encounters at a health system between 2007 and 2017. A case-control stratified sampling (n = 1000) was performed to build an annotated dataset for a reference standard of cases and non-cases of opioid misuse. Models for training and testing included CUI codes, character-based, and n-gram features. Models applied were machine learning with neural network and logistic regression as well as expert consensus with a rule-based model for opioid misuse. The area under the receiver operating characteristic curves (AUROC) were compared between models for discrimination. The Hosmer-Lemeshow test and visual plots measured model fit and calibration. RESULTS: Machine learning models with CUI codes performed similarly to n-gram models with PHI. The top performing models with AUROCs > 0.90 included CUI codes as inputs to a convolutional neural network, max pooling network, and logistic regression model. The top calibrated models with the best model fit were the CUI-based convolutional neural network and max pooling network. The top weighted CUI codes in logistic regression has the related terms 'Heroin' and 'Victim of abuse'. CONCLUSIONS: We demonstrate good test characteristics for an opioid misuse computable phenotype that is void of any PHI and performs similarly to models that use PHI. Herein we share a PHI-free, trained opioid misuse classifier for other researchers and health systems to use and benchmark to overcome privacy and security concerns.

Injury Characteristics and von Willebrand Factor for the Prediction of Acute Respiratory Distress Syndrome in Patients With Burn Injury: Development and Internal Validation.

OBJECTIVE: To derive and validate a prediction model for the development of ARDS in burn-injured patients. SUMMARY BACKGROUND DATA: Burn injury carries the highest incidence of acute respiratory distress syndrome (ARDS) among all predisposing conditions, but few studies exist on risk factors in these patients. Studies employing biomarkers and clinical risk factors for predicting ARDS mortality have recently been examined but none exist for onset of ARDS nor in patients with burn injury. METHODS: This was a prospective multicenter study of 113 patients with isolated burn injury or inhalation injury. Clinical variables and plasma biomarkers representative of endothelial injury, epithelial injury, or inflammation were collected within 24 hours of admission. The most parsimonious model was chosen by considering discrimination, calibration, and model fit. RESULTS: Among the biomarkers measured in patients with burn injuries, a one-standard deviation increase in log-transformed levels of the A2 domain of von Willebrand factor in the first 24 hours was most strongly associated with the development of ARDS (OR 7.72; 95% CI: 1.64-36.28, P = 0.03). Of candidate models, a 3-variable model with %TBSA, inhalation injury, and von Willebrand factor-A2 had comparable discrimination to more complex models (area under the curve: 0.90; 95% CI 0.85-0.96). The 3-variable model had good model fit by Hosmer-Lemeshow test (P = 0.74) and maintained similar discrimination after accounting for performance optimism (Bootstrapped area under the curve: 0.90; 95% CI: 0.84-0.95). CONCLUSIONS: The 3-variable model with %TBSA, inhalation injury, and von Willebrand factor could be used to better identify at-risk patients for both the study and prevention of ARDS in patients with burn injury.

Patient Outcomes and Cost-Effectiveness of a Sepsis Care Quality Improvement Program in a Health System.

OBJECTIVES: Assess patient outcomes in patients with suspected infection and the cost-effectiveness of implementing a quality improvement program. DESIGN, SETTING, AND PARTICIPANTS: We conducted an observational single-center study of 13,877 adults with suspected infection between March 1, 2014, and July 31, 2017. The 18-month period before and after the effective date for mandated reporting of the sepsis bundle was examined. The Sequential Organ Failure Assessment score and culture and antibiotic orders were used to identify patients meeting Sepsis-3 criteria from the electronic health record. INTERVENTIONS: The following interventions were performed as follows: 1) multidisciplinary sepsis committee with sepsis coordinator and data abstractor; 2) education campaign; 3) electronic health record tools; and 4) a Modified Early Warning System. MAIN OUTCOMES AND MEASURES: Primary health outcomes were in-hospital death and length of stay. The incremental cost-effectiveness ratio was calculated and the empirical 95% CI for the incremental cost-effectiveness ratio was estimated from 5,000 bootstrap samples. RESULTS: In multivariable analysis, the odds ratio for in-hospital death in the post- versus pre-implementation periods was 0.70 (95% CI, 0.57-0.86) in those with suspected infection, and the hazard ratio for time to discharge was 1.25 (95% CI, 1.20-1.29). Similarly, a decrease in the odds for in-hospital death and an increase in the speed to discharge was observed for the subset that met Sepsis-3 criteria. The program was cost saving in patients with suspected infection (-$272,645.7; 95% CI, -$757,970.3 to -$79,667.7). Cost savings were also observed in the Sepsis-3 group. CONCLUSIONS AND RELEVANCE: Our health system's program designed to adhere to the sepsis bundle metrics led to decreased mortality and length of stay in a cost-effective manner in a much larger catchment than just the cohort meeting the Centers for Medicare and Medicaid Services measures. Our single-center model of interventions may serve as a practice-based benchmark for hospitalized patients with suspected infection.