Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Variation in monitoring: Glucose measurement in the ICU as a case study to preempt spurious correlations.

OBJECTIVE: Health inequities can be influenced by demographic factors such as race and ethnicity, proficiency in English, and biological sex. Disparities may manifest as differential likelihood of testing which correlates directly with the likelihood of an intervention to address an abnormal finding. Our retrospective observational study evaluated the presence of variation in glucose measurements in the Intensive Care Unit (ICU). METHODS: Using the MIMIC-IV database (2008-2019), a single-center, academic referral hospital in Boston (USA), we identified adult patients meeting sepsis-3 criteria. Exclusion criteria were diabetic ketoacidosis, ICU length of stay under 1 day, and unknown race or ethnicity. We performed a logistic regression analysis to assess differential likelihoods of glucose measurements on day 1. A negative binomial regression was fitted to assess the frequency of subsequent glucose readings. Analyses were adjusted for relevant clinical confounders, and performed across three disparity proxy axes: race and ethnicity, sex, and English proficiency. RESULTS: We studied 24,927 patients, of which 19.5% represented racial and ethnic minority groups, 42.4% were female, and 9.8% had limited English proficiency. No significant differences were found for glucose measurement on day 1 in the ICU. This pattern was consistent irrespective of the axis of analysis, i.e. race and ethnicity, sex, or English proficiency. Conversely, subsequent measurement frequency revealed potential disparities. Specifically, males (incidence rate ratio (IRR) 1.06, 95% confidence interval (CI) 1.01 - 1.21), patients who identify themselves as Hispanic (IRR 1.11, 95% CI 1.01 - 1.21), or Black (IRR 1.06, 95% CI 1.01 - 1.12), and patients being English proficient (IRR 1.08, 95% CI 1.01 - 1.15) had higher chances of subsequent glucose readings. CONCLUSION: We found disparities in ICU glucose measurements among patients with sepsis, albeit the magnitude was small. Variation in disease monitoring is a source of data bias that may lead to spurious correlations when modeling health data.

Do Thresholds for Invasive Ventilation in Hypoxemic Respiratory Failure Exist? A Cohort Study.

Rationale: Invasive ventilation is a significant event for patients with respiratory failure. Physiologic thresholds standardize the use of invasive ventilation in clinical trials, but it is unknown whether thresholds prompt invasive ventilation in clinical practice. Objectives: To measure, in patients with hypoxemic respiratory failure, the probability of invasive ventilation within 3 hours after meeting physiologic thresholds. Methods: We studied patients admitted to intensive care receiving FiO2 of 0.4 or more via nonrebreather mask, noninvasive positive pressure ventilation, or high-flow nasal cannula, using data from the Medical Information Mart for Intensive Care (MIMIC)-IV database (2008-2019) and the Amsterdam University Medical Centers Database (AmsterdamUMCdb) (2003-2016). We evaluated 17 thresholds, including the ratio of arterial to inspired oxygen, the ratio of saturation to inspired oxygen ratio, composite scores, and criteria from randomized trials. We report the probability of invasive ventilation within 3 hours of meeting each threshold and its association with covariates using odds ratios (ORs) and 95% credible intervals (CrIs). Measurements and Main Results: We studied 4,726 patients (3,365 from MIMIC, 1,361 from AmsterdamUMCdb). Invasive ventilation occurred in 28% (1,320). In MIMIC, the highest probability of invasive ventilation within 3 hours of meeting a threshold was 20%, after meeting prespecified neurologic or respiratory criteria while on vasopressors, and 19%, after a ratio of arterial to inspired oxygen of <80 mm Hg. In AmsterdamUMCdb, the highest probability was 34%, after vasopressor initiation, and 25%, after a ratio of saturation to inspired oxygen of <90. The probability after meeting the threshold from randomized trials was 9% (MIMIC) and 13% (AmsterdamUMCdb). In MIMIC, a race/ethnicity of Black (OR, 0.75; 95% CrI, 0.57-0.96) or Asian (OR, 0.6; 95% CrI, 0.35-0.95) compared with White was associated with decreased probability of invasive ventilation after meeting a threshold. Conclusions: The probability of invasive ventilation within 3 hours of meeting physiologic thresholds was low and associated with patient race/ethnicity.

Patient diversity and author representation in clinical studies supporting the Surviving Sepsis Campaign guidelines for management of sepsis and septic shock 2021: a systematic review of citations.

BACKGROUND: The generalizability of the Surviving Sepsis Campaign (SSC) guidelines to various patient populations and hospital settings has been debated. A quantitative assessment of the diversity and representation in the clinical evidence supporting the guidelines would help evaluate the generalizability of the recommendations and identify strategic research goals and priorities. In this study, we evaluated the diversity of patients in the original studies, in terms of sex, race/ethnicity, and geographical location. We also assessed diversity in sex and geographical representation among study first and last authors. METHODS: All clinical studies cited in support of the 2021 SSC adult guideline recommendations were identified. Original clinical studies were included, while editorials, reviews, non-clinical studies, and meta-analyses were excluded. For eligible studies, we recorded the proportion of male patients, percentage of each represented racial/ethnic subgroup (when available), and countries in which they were conducted. We also recorded the sex and location of the first and last authors. The World Bank classification was used to categorize countries. RESULTS: The SSC guidelines included six sections, with 85 recommendations based on 351 clinical studies. The proportion of male patients ranged from 47 to 62%. Most studies did not report the racial/ ethnic distribution of the included patients; when they did so, most were White patients (68-77%). Most studies were conducted in high-income countries (77-99%), which included Europe/Central Asia (33-66%) and North America (36-55%). Moreover, most first/last authors were males (55-93%) and from high-income countries (77-99%). CONCLUSIONS: To enhance the generalizability of the SCC guidelines, stakeholders should define strategies to enhance the diversity and representation in clinical studies. Though there was reasonable representation in sex among patients included in clinical studies, the evidence did not reflect diversity in the race/ethnicity and geographical locations. There was also lack of diversity among the first and last authors contributing to the evidence.

Impact of macrolide treatment on long-term mortality in patients admitted to the ICU due to CAP: a targeted maximum likelihood estimation and survival analysis.

INTRODUCTION: Patients with community-acquired pneumonia (CAP) admitted to the intensive care unit (ICU) have high mortality rates during the acute infection and up to ten years thereafter. Recommendations from international CAP guidelines include macrolide-based treatment. However, there is no data on the long-term outcomes of this recommendation. Therefore, we aimed to determine the impact of macrolide-based therapy on long-term mortality in this population. METHODS: Registered patients in the MIMIC-IV database 16 years or older and admitted to the ICU due to CAP were included. Multivariate analysis, targeted maximum likelihood estimation (TMLE) to simulate a randomised controlled trial, and survival analyses were conducted to test the effect of macrolide-based treatment on mortality six-month (6 m) and twelve-month (12 m) after hospital admission. A sensitivity analysis was performed excluding patients with Pseudomonas aeruginosa or MRSA pneumonia to control for Healthcare-Associated Pneumonia (HCAP). RESULTS: 3775 patients were included, and 1154 were treated with a macrolide-based treatment. The non-macrolide-based group had worse long-term clinical outcomes, represented by 6 m [31.5 (363/1154) vs 39.5 (1035/2621), p < 0.001] and 12 m mortality [39.0 (450/1154) vs 45.7 (1198/2621), p < 0.001]. The main risk factors associated with long-term mortality were Charlson comorbidity index, SAPS II, septic shock, and respiratory failure. Macrolide-based treatment reduced the risk of dying at 6 m [HR (95% CI) 0.69 (0.60, 0.78), p < 0.001] and 12 m [0.72 (0.64, 0.81), p < 0.001]. After TMLE, the protective effect continued with an additive effect estimate of - 0.069. CONCLUSION: Macrolide-based treatment reduced the hazard risk of long-term mortality by almost one-third. This effect remains after simulating an RCT with TMLE and the sensitivity analysis for the HCAP classification.

The Digital Divide in Brazil and Barriers to Telehealth and Equal Digital Health Care: Analysis of Internet Access Using Publicly Available Data.

BACKGROUND: The COVID-19 pandemic has increased the use of digital solutions in medical care, especially for patients in remote areas and those requiring regular medical care. However, internet access is essential for the implementation of digital health care. The digital divide is the unequal distribution of access to digital technology, and the first level digital divide encompasses structural barriers. Brazil, a country with economic inequality and uneven population distribution, faces challenges in achieving internet access for all. OBJECTIVE: This study aims to provide a comprehensive overview of the first-level digital divide in Brazil, estimate the relationship between variables, and identify the challenges and opportunities for digital health care implementation. METHODS: Data were retrieved from the Brazilian Institute of Geography and Statistics National Continuous House survey database, including demographic, health, and internet-related variables. Statistical analysis included 2-tailed t tests, chi-square, and multivariate logistic regression to assess associations between variables. RESULTS: Our analysis included 279,382 interviews throughout Brazil. The sample included more houses from the northeast (n=99,553) and fewer houses from the central west (n=30,804). A total of 223,386 (80.13%) of the interviewed population used the internet, with urban areas having higher internet access (187,671/212,109, 88.48%) than rural areas (35,715/67,077, 53.24%). Among the internet users, those interviewed who lived in urban houses, were women, were younger, and had higher income had a statistically higher prevalence (P<.001). Cell phones were the most common device used to access the internet (141,874/143,836, 98.63%). Reasons for not using the internet included lack of interest, knowledge, availability, and cost, with regional variations. The prevalence of internet access also varied among races, with 84,747 of 98,968 (85.63%) White respondents having access, compared to 22,234 of 28,272 (78.64%) Black respondents, 113,518 of 148,191 (76.6%) multiracial respondents, and 2887 of 3755 (76.88%) other respondents. In the southeast, central west, and south regions, the numbers of people with internet access were 49,790 of 56,298 (88.44%), 27,209 of 30,782 (88.39%), and 27,035 of 31,226 (86.58%), respectively, and in the north and northeast, 45,038 of 61,404 (73.35%) and 74,314 of 99,476 (74.7%). The income of internet users was twice the income of internet nonusers. Among those with diabetes-related limitations in daily activities, 945 of 2377 (39.75%) did not have internet access, and among those with daily activity restrictions, 1381 of 3644 (37.89%) did not have access. In a multivariate logistic regression analysis, women (odds ratio [OR] 1.147, 95% CI 0.118-0.156; P<.001), urban households (OR 6.743, 95% CI 1.888-1.929; P<.001), and those earning more than the minimum wage (OR 2.087, 95% CI 0.716-0.756; P<.01) had a positive association with internet access. CONCLUSIONS: Brazil's diverse regions have different demographic distributions, house characteristics, and internet access levels, requiring targeted measures to address the first-level digital divide in rural areas and reduce inequalities in digital health solutions. Older people, poor, and rural populations face the greatest challenges in the first level digital divide in Brazil, highlighting the need to tackle the digital divide in order to promote equitable access to digital health care.

Digital Education for the Deployment of Artificial Intelligence in Health Care.

Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health care professionals must acquire basic knowledge of machine learning and neural networks, critical evaluation of data sets, integration within clinical workflows, bias control, and human-machine interaction in clinical settings. Additionally, they should understand the legal and ethical aspects of digital health care and the impact of AI adoption. Misconceptions and fears about AI systems could jeopardize its real-life implementation. However, there are multiple barriers to promoting electronic health literacy, including time constraints, overburdened curricula, and the shortage of capacitated professionals. To overcome these challenges, partnerships among developers, professional societies, and academia are essential. Integrating specialists from different backgrounds, including data specialists, lawyers, and social scientists, can significantly contribute to combating digital illiteracy and promoting safe AI implementation in health care.

Racial Disparities in Invasive ICU Treatments Among Septic Patients: High Resolution Electronic Health Records Analysis from MIMIC-IV.

Background: Low-resolution administrative databases can give biased results, whereas high-resolution, time-stamped variables from clinical databases like MIMIC-IV might provide nuanced insights. We evaluated racial-ethnic disparities in life-sustaining ICU-treatments (Invasive Mechanical Ventilation (IMV), Renal Replacement Therapy (RRT), and Vasopressors (VP)) among patients with sepsis. Methods: In this observational retrospective cohort study, patients fulfilling sepsis-3 criteria were categorized by treatment assignment within the first 4 days. The outcomes were treatment allocations. The likelihood of receiving treatment was calculated by race-ethnicity (Racial-ethnic group (REG) or White group (WG)) using 5-fold sub-sampling nested logistic regression and XGBoost. Results: In 23,914 admissions, 82% were White, 42% were women. REG were less likely to receive IMV across all eligibility days (day 1 odds ratio (OR) 0.87, 95% confidence interval (CI) 0.83-0.94, day 4 OR 0.80, 95% CI 0.72 - 0.87). There were no differences in RRT (day 1 OR 1.00, 95% CI 0.96-1.09, day 4 OR 1.00, 95% CI 0.94-1.06). REG were also less likely to be treated with VP at days 1 to 3 (day 1 OR 0.87, 95% CI 0.76-0.94), but not at day 4 (OR 0.95, 95% CI 0.87-1.01). These findings remained robust when relaxing eligibility criteria for treatment allocation. Conclusion: Our findings reveal significant disparities in the use of invasive life-saving ICU treatments among septic patients from racial and ethnic minority backgrounds, particularly with respect to IMV and VP use. These disparities underscore not only the need to address inequality in critical care settings, but also highlight the importance of high-resolution data.

Rural-Urban Differences in Influenza Vaccination Among Adults in the United States, 2018-2019.

Objectives. To provide adjusted rates of self-reported receipt of the influenza vaccine in the 2018-2019 flu season among adults in large metropolitan, medium and small metropolitan, and nonmetropolitan areas of the United States by age group, gender, and race. Methods. We queried the 2019 National Health Interview Survey for respondents aged 18 years and older. To provide national estimates of influenza vaccination coverage, we performed sample-weighted multivariable logistic regressions and predicted marginal modeling while adjusting for age, gender, race/ethnicity, and urban-rural household designation. Results. After weighting, 48.1%, 46.2%, and 43.6% of adults from large metropolitan, small and medium metropolitan, and nonmetropolitan areas, respectively, received the influenza vaccine. Additionally, there was a trend toward declining influenza vaccination status from large metropolitan to rural areas in all age groups, both genders, and multiple racial/ethnic groups. Conclusions. Self-reported influenza vaccination rates were lower in rural than in urban areas among adults of all age groups and both genders. Using community leaders for health promotion, augmentation of the community health care workforce, and provision of incentives for providers to integrate influenza vaccination in regular visits may expand influenza vaccine coverage. (Am J Public Health. 2022;112(2):304-307. https://doi.org/10.2105/AJPH.2021.306575).

A novel Vascular Leak Index identifies sepsis patients with a higher risk for in-hospital death and fluid accumulation.

PURPOSE: Sepsis is a leading cause of morbidity and mortality worldwide and is characterized by vascular leak. Treatment for sepsis, specifically intravenous fluids, may worsen deterioration in the context of vascular leak. We therefore sought to quantify vascular leak in sepsis patients to guide fluid resuscitation. METHODS: We performed a retrospective cohort study of sepsis patients in four ICU databases in North America, Europe, and Asia. We developed an intuitive vascular leak index (VLI) and explored the relationship between VLI and in-hospital death and fluid balance using generalized additive models (GAM). RESULTS: Using a GAM, we found that increased VLI is associated with an increased risk of in-hospital death. Patients with a VLI in the highest quartile (Q4), across the four datasets, had a 1.61-2.31 times increased odds of dying in the hospital compared to patients with a VLI in the lowest quartile (Q1). VLI Q2 and Q3 were also associated with increased odds of dying. The relationship between VLI, treated as a continuous variable, and in-hospital death and fluid balance was statistically significant in the three datasets with large sample sizes. Specifically, we observed that as VLI increased, there was increase in the risk for in-hospital death and 36-84 h fluid balance. CONCLUSIONS: Our VLI identifies groups of patients who may be at higher risk for in-hospital death or for fluid accumulation. This relationship persisted in models developed to control for severity of illness and chronic comorbidities.

Prediction of blood lactate values in critically ill patients: a retrospective multi-center cohort study.

Elevations in initially obtained serum lactate levels are strong predictors of mortality in critically ill patients. Identifying patients whose serum lactate levels are more likely to increase can alert physicians to intensify care and guide them in the frequency of tending the blood test. We investigate whether machine learning models can predict subsequent serum lactate changes. We investigated serum lactate change prediction using the MIMIC-III and eICU-CRD datasets in internal as well as external validation of the eICU cohort on the MIMIC-III cohort. Three subgroups were defined based on the initial lactate levels: (i) normal group (< 2 mmol/L), (ii) mild group (2-4 mmol/L), and (iii) severe group (> 4 mmol/L). Outcomes were defined based on increase or decrease of serum lactate levels between the groups. We also performed sensitivity analysis by defining the outcome as lactate change of > 10% and furthermore investigated the influence of the time interval between subsequent lactate measurements on predictive performance. The LSTM models were able to predict deterioration of serum lactate values of MIMIC-III patients with an AUC of 0.77 (95% CI 0.762-0.771) for the normal group, 0.77 (95% CI 0.768-0.772) for the mild group, and 0.85 (95% CI 0.840-0.851) for the severe group, with only a slightly lower performance in the external validation. The LSTM demonstrated good discrimination of patients who had deterioration in serum lactate levels. Clinical studies are needed to evaluate whether utilization of a clinical decision support tool based on these results could positively impact decision-making and patient outcomes.

Predicting hypoglycemia in critically Ill patients using machine learning and electronic health records.

Hypoglycemia is a common occurrence in critically ill patients and is associated with significant mortality and morbidity. We developed a machine learning model to predict hypoglycemia by using a multicenter intensive care unit (ICU) electronic health record dataset. Machine learning algorithms were trained and tested on patient data from the publicly available eICU Collaborative Research Database. Forty-four features including patient demographics, laboratory test results, medications, and vitals sign recordings were considered. The outcome of interest was the occurrence of a hypoglycemic event (blood glucose < 72 mg/dL) during a patient's ICU stay. Machine learning models used data prior to the second hour of the ICU stay to predict hypoglycemic outcome. Data from 61,575 patients who underwent 82,479 admissions at 199 hospitals were considered in the study. The best-performing predictive model was the eXtreme gradient boosting model (XGBoost), which achieved an area under the received operating curve (AUROC) of 0.85, a sensitivity of 0.76, and a specificity of 0.76. The machine learning model developed has strong discrimination and calibration for the prediction of hypoglycemia in ICU patients. Prospective trials of these models are required to evaluate their clinical utility in averting hypoglycemia within critically ill patient populations.

Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.

The unprecedented global crisis brought about by the COVID-19 pandemic has sparked numerous efforts to create predictive models for the detection and prognostication of SARS-CoV-2 infections with the goal of helping health systems allocate resources. Machine learning models, in particular, hold promise for their ability to leverage patient clinical information and medical images for prediction. However, most of the published COVID-19 prediction models thus far have little clinical utility due to methodological flaws and lack of appropriate validation. In this paper, we describe our methodology to develop and validate multi-modal models for COVID-19 mortality prediction using multi-center patient data. The models for COVID-19 mortality prediction were developed using retrospective data from Madrid, Spain (N = 2547) and were externally validated in patient cohorts from a community hospital in New Jersey, USA (N = 242) and an academic center in Seoul, Republic of Korea (N = 336). The models we developed performed differently across various clinical settings, underscoring the need for a guided strategy when employing machine learning for clinical decision-making. We demonstrated that using features from both the structured electronic health records and chest X-ray imaging data resulted in better 30-day mortality prediction performance across all three datasets (areas under the receiver operating characteristic curves: 0.85 (95% confidence interval: 0.83-0.87), 0.76 (0.70-0.82), and 0.95 (0.92-0.98)). We discuss the rationale for the decisions made at every step in developing the models and have made our code available to the research community. We employed the best machine learning practices for clinical model development. Our goal is to create a toolkit that would assist investigators and organizations in building multi-modal models for prediction, classification, and/or optimization.

Generalisability through local validation: overcoming barriers due to data disparity in healthcare.

Cho et al. report deep learning model accuracy for tilted myopic disc detection in a South Korean population. Here we explore the importance of generalisability of machine learning (ML) in healthcare, and we emphasise that recurrent underrepresentation of data-poor regions may inadvertently perpetuate global health inequity.Creating meaningful ML systems is contingent on understanding how, when, and why different ML models work in different settings. While we echo the need for the diversification of ML datasets, such a worthy effort would take time and does not obviate uses of presently available datasets if conclusions are validated and re-calibrated for different groups prior to implementation.The importance of external ML model validation on diverse populations should be highlighted where possible - especially for models built with single-centre data.

Use of Do-Not-Resuscitate Orders for Critically Ill Patients with ESKD.

BACKGROUND: Despite having high comorbidity rates and shortened life expectancy, patients with ESKD may harbor unrealistically optimistic expectations about their prognoses. Whether this affects resuscitation orders is unknown. METHODS: To determine whether do-not-resuscitate (DNR) orders differ among patients with ESKD compared with other critically ill patients, including those with diseases of other major organs, we investigated DNR orders on admission to intensive care units (ICUs) among 106,873 patients in the United States. RESULTS: Major organ disease uniformly associated with increased risk of hospital mortality, particularly for cirrhosis (adjusted odds ratio [aOR], 2.67; 95% confidence interval [95% CI], 2.30 to 3.08), and ESKD (aOR, 1.47; 95% CI, 1.31 to 1.65). Compared with critically ill patients without major organ disease, patients with stroke, cancer, heart failure, dementia, chronic obstructive pulmonary disease, and cirrhosis were statistically more likely to have a DNR order on ICU admission; those with ESKD were not. Findings were similar when comparing patients with a single organ disease with those without organ disease. The disconnect between prognosis and DNR use was most notable among Black patients, for whom ESKD (compared with no major organ disease) was associated with a 62% (aOR, 1.62; 95% CI, 1.27 to 2.04) higher odds of hospital mortality, but no appreciable difference in DNR utilization (aOR, 1.06; 95% CI, 0.66 to 1.62). CONCLUSIONS: Unlike patients with diseases of other major organs, critically ill patients with ESKD were not more likely to have a DNR order than patients without ESKD. Whether this reflects a greater lack of advance care planning in the nephrology community, as well as a missed opportunity to minimize potentially needless patient suffering, requires further study.

Distance-learning collaborations for rapid knowledge sharing to the occupied Palestinian territory during the COVID-19 response: experience from the OxPal partnership.

Distance-learning increasingly forms part of many academic and professional courses. OxPal, a collaboration between University of Oxford medical students and doctors and their counterparts in the occupied Palestinian territory (oPt), sought to evaluate the feasibility and efficacy of interactive online distance-learning through a pre-established international partnership as a method for rapid knowledge sharing during the novel coronavirus disease 2019 (COVID-19) response. Two interactive online lectures for medical students and clinicians in the oPt on the COVID-19 pandemic were conducted. The first lecture was an 'Introduction to COVID-19' and the second focused on 'Data-sharing during the pandemic'. 212 and 174 attended each lecture, respectively. Feedback was via an online questionnaire. >95% of respondents indicated the lectures covered a gap in their medical education. 87% and 77% of respondents rated lecture quality 'Good' or 'Excellent' for the two lectures respectively. Qualitative feedback elicited requests for more clinically focused lectures, which have since been provided. Online lectures are feasible and effective for rapid education of medical students and clinicians in the oPt in a public health emergency. We hope this encourages other institutions to provide similar support in the oPt and other 'tutor-deplete' regions facing specific geopolitical challenges to local medical education.

Treatment in Disproportionately Minority Hospitals Is Associated With Increased Risk of Mortality in Sepsis: A National Analysis.

OBJECTIVES: Treatment in a disproportionately minority-serving hospital has been associated with worse outcomes in a variety of illnesses. We examined the association of treatment in disproportionately minority hospitals on outcomes in patients with sepsis across the United States. DESIGN: Retrospective cohort analysis. Disproportionately minority hospitals were defined as hospitals having twice the relative minority patient population than the surrounding geographical mean. Minority hospitals for Black and Hispanic patient populations were identified based on U.S. Census demographic information. A multivariate model employing a validated algorithm for mortality in sepsis using administrative data was used. SETTING: The National Inpatient Sample from 2008 to 2014. PATIENTS: Patients over 18 years of age with sepsis. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 4,221,221 patients with sepsis were identified. Of these, 612,217 patients (14.5%) were treated at hospitals disproportionately serving the black community (Black hospitals), whereas 181,141 (4.3%) were treated at hospitals disproportionately serving the Hispanic community (Hispanic hospitals). After multivariate analysis, treatment in a Black hospital was associated with a 4% higher risk of mortality compared to treatment in a nonminority hospital (odds ratio, 1.04; 95% CI, 1.03-1.05; p < 0.01). Treatment in a Hispanic hospital was associated with a 9% higher risk of mortality (odds ratio, 1.09; 95% CI, 1.07-1.11; p < 0.01). Median hospital length of stay was almost 1 day longer at each of the disproportionately minority hospitals (nonminority hospitals: 5.9 d; interquartile range, 3.1-11.0 d vs Hispanic: 6.9 d; interquartile range, 3.6-12.9 d and Black: 6.7 d, interquartile range, 3.4-13.2 d; both p < 0.01). CONCLUSIONS: Patients with sepsis regardless of race who were treated in disproportionately high minority hospitals suffered significantly higher rates of in-hospital mortality.

The Impact of Chronic Ozone and Particulate Air Pollution on Mortality in Patients With Sepsis Across the United States.

OBJECTIVE: The impact of chronic exposure to air pollution on mortality in patients with sepsis is unknown. We attempted to quantify the relationship between air pollution, notably excess ozone, and particulate matter (PM), with in-hospital mortality in patients with sepsis nationwide. METHODS: The 2011 Nationwide Inpatient Sample (NIS) was linked with ambient air pollution data from the Environmental Protection Agency for both 8-hour ozone exposure and annual mean 2.5-micron PM (PM2.5) pollution levels. A validated severity of illness model for sepsis using administrative data was used to control for sepsis severity. RESULTS: The records of 8 023 590 hospital admissions from the 2011 NIS sample were analyzed. Of these, there were 444 928 patients who met the Angus definition of sepsis, treated in hospitals for which air pollution data were available. The cohort had an overall mortality of 11.2%. After adjustment for severity of sepsis, increasing exposure to ozone pollution was associated with increased risk of mortality (odds ratio [OR]: 1.04 for each 0.01 ppm increase, 95% confidence interval [CI]: 1.03-1.05; P < .01). Particulate matter was not associated with mortality (OR: 0.99 for each 5 µg/m3 increase, 95% CI: 0.97-1.01; P = .28). When stratified by sepsis source, ozone pollution had a higher impact on patients with pneumonia (OR: 1.06, 95% CI: 1.04-1.08; P < .01) compared to those patients without pneumonia (OR: 1.02, 95% CI: 1.01-1.03; P < .01). CONCLUSION: Exposure to increased levels of ozone but not particulate air pollution was associated with higher risk of mortality in patients with sepsis. This association was strongest in patients with pneumonia but persisted in all sources of sepsis. Further work is needed to understand the relationship between ambient ozone air pollution and sepsis outcomes.

Vasopressin Administration Is Associated With Rising Serum Lactate Levels in Patients With Sepsis.

BACKGROUND: Vasopressin is used in conjunction with norepinephrine during treatment of patients with septic shock. Serum lactate is often used in monitoring of patients with sepsis; however, its importance as a therapeutic target is unclear. The objective of this study is to examine the relationship of vasopressin use on serum lactate levels in patients with sepsis. METHODS: This study uses electronic heath records available via the Medical Information Mart for Intensive Care III. Patients were required to have a serum lactate monitoring during the intensive care unit (ICU) stay. The treatment was the administration of vasopressin between hours 3 and 18 of the ICU stay. Analysis was performed using a matched design. RESULTS: Patients receiving vasopressin were more likely to have their serum lactate levels rise when compared to matched patients who did not receive vasopressin (odds ratio: 6.6; 95% confidence interval: 3.0-14.6, P < .001). Patients who received vasopressin had a median increase in serum lactate of 0.3 mmol/L, while patients who did not receive vasopressin had a median decrease in serum lactate of 0.7 mmol/L (P < .001). There was no statistically significant difference between the control and treated groups' lactate trajectories prior to possible administration of vasopressin (P = .15). The results did not change significantly when norepinephrine initiation was used as the index time. CONCLUSIONS: In patients with sepsis, the administration of vasopressin was associated with a statistically significant difference in lactate change over the course of 24 hours when compared to matched patients who did not receive vasopressin.