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Sepsis is the syndrome of life-threatening organ dysfunction resulting from dysregulated host response to infection. Aspirin, an anti-inflammatory agent, may play a role in attenuating the inflammatory response during infection. We evaluated the association between aspirin use and long-term rates of sepsis as well as sepsis outcomes.We analyzed data from 30,239 adults ≥45 years old in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. The primary exposure was aspirin use, identified via patient interview. The primary outcome was sepsis hospitalization, defined as admission for infection with two or more systemic inflammatory response syndrome criteria. We fit Cox proportional hazards models assessing the association between aspirin use and rates of sepsis, adjusted for participant demographics, health behaviors, chronic medical conditions, medication adherence, and biomarkers. We used a propensity-matched analysis and a series of sensitivity analyses to assess the robustness of our results. We also examined risk of organ dysfunction and hospital mortality during hospitalization for sepsis.Among 29,690 REGARDS participants with follow-up data available, 43% reported aspirin use (n = 12,869). Aspirin users had higher sepsis rates (hazard ratio 1.35; 95% CI: 1.22-1.49) but this association was attenuated following adjustment for potential confounders (adjusted HR 0.99; 95% CI: 0.88-1.12). We obtained similar results in propensity-matched and sensitivity analyses. Among sepsis hospitalizations, aspirin use was not associated with organ dysfunction or hospital death.In the REGARDS cohort, baseline aspirin use was not associated with long-term rates of sepsis.

ObjectivesLumbar puncture (LP) is an important technique for assessing and treating neurological symptoms. The objective of this study was to describe the characteristics of diagnostic lumbar punctures performed on hospitalized patients in the United States.MethodsWe analyzed data from the 2010 National Inpatient Sample (NIS) and the National Emergency Department Survey (NEDS). We included patients treated in the Emergency Department (ED) as well as those admitted to an inpatient bed through the ED. We identified patients undergoing LPs from ICD-9 procedural code 03.31 and CPT code 62270. We generated nationally weighted estimates of the total number of LPs. We also assessed patient and hospital characteristics of cases undergoing LP.ResultsOf an estimated 135 million hospitalizations (ED + admission, or ED only), there were an estimated 362,718 LPs (331,248-394,188), including 273,612 (251,850-295,375) among adults and 89,106 (71,870-106,342) among children (ConclusionsLumbar Puncture remains an important procedure for diagnostic and therapeutic uses in United States Hospitals.

Variation exists in cardiac arrest (CA) survival among institutions. We sought to determine institutional-level characteristics of academic medical centers (AMCs) associated with CA survival.We examined discharge data from AMCs participating with Vizient clinical database-resource manager. We identified cases using ICD-9 diagnosis code 427.5 (CA) or procedure code 99.60 (CPR). We estimated hospital-specific risk-standardized survival rates (RSSRs) using mixed effects logistic regression, adjusting for individual mortality risk. Institutional and community characteristics of AMCs with higher than average survival were compared with those with lower survival.We analyzed data on 3,686,296 discharges in 2012, of which 33,700 (0.91%) included a CA diagnosis. Overall survival was 42.3% (95% CI 41.8-42.9) with median institutional RSSR of 42.6% (IQR 35.7-51.0; Min-Max 19.4-101.6). We identified 28 AMCs with above average survival (median RSSR 61.8%) and 20 AMCs with below average survival (median RSSR 26.8%). Compared to AMCs with below average survival, those with high CA survival had higher CA volume (median 262 vs.119 discharges, p = 0.002), total beds (722 vs. 452, p = 0.02), and annual surgical volume (24,939 vs. 13,109, p

Background. While often first treated in the emergency department (ED), identification of sepsis is difficult. Electronic medical record (EMR) clinical decision tools offer a novel strategy for identifying patients with sepsis. The objective of this study was to test the accuracy of an EMR-based, automated sepsis identification system.Methods. We tested an EMR-based sepsis identification tool at a major academic, urban ED with 64,000 annual visits. The EMR system collected vital sign and laboratory test information on all ED patients, triggering a “sepsis alert” for those with ≥2 SIRS (systemic inflammatory response syndrome) criteria (fever, tachycardia, tachypnea, leukocytosis) plus ≥1 major organ dysfunction (SBP ≤ 90 mm Hg, lactic acid ≥2.0 mg/dL). We confirmed the presence of sepsis through manual review of physician, nursing, and laboratory records. We also reviewed a random selection of ED cases that did not trigger a sepsis alert. We evaluated the diagnostic accuracy of the sepsis identification tool.Results. From January 1 through March 31, 2012, there were 795 automated sepsis alerts. We randomly selected 300 cases without a sepsis alert from the same period. The true prevalence of sepsis was 355/795 (44.7%) among alerts and 0/300 (0%) among non-alerts. The positive predictive value of the sepsis alert was 44.7% (95% CI [41.2–48.2%]). Pneumonia and respiratory infections (38%) and urinary tract infection (32.7%) were the most common infections among the 355 patients with true sepsis (true positives). Among false-positive sepsis alerts, the most common medical conditions were gastrointestinal (26.1%), traumatic (25.7%), and cardiovascular (20.0%) conditions. Rates of hospital admission were: true-positive sepsis alert 91.0%, false-positive alert 83.0%, no sepsis alert 5.7%.Conclusions. This ED EMR-based automated sepsis identification system was able to detect cases with sepsis. Automated EMR-based detection may provide a viable strategy for identifying sepsis in the ED.

Objective. We sought to determine the characteristics of children presenting to United States (US) Emergency Departments (ED) with severe sepsis.Study design. Cross-sectional analysis using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). Using triage vital signs and ED diagnoses (defined by the International Classification of Diseases, Ninth Revision codes), we identified children

RationalePredicting long-term outcomes in sepsis survivors remains a difficult task. Persistent inflammation post-sepsis is associated with increased risk for rehospitalization and death. As surrogate markers of inflammation, complete blood count parameters measured at hospital discharge may have prognostic value for sepsis survivors.ObjectiveTo determine the incremental value of complete blood count parameters over clinical characteristics for predicting 90-day outcomes in sepsis survivors.MethodsElectronic health record data was used to identify sepsis hospitalizations at United States Veterans Affairs hospitals with live discharge and relevant laboratory data (2013 to 2018). We measured the association of eight complete blood count parameters with 90-day outcomes (mortality, rehospitalization, cause-specific rehospitalizations) using multivariable logistic regression models.Measurements and main resultsWe identified 155,988 eligible hospitalizations for sepsis. Anemia (93.6%, N=142,162) and lymphopenia (28.1%, N=29,365) were the most common blood count abnormalities at discharge. In multivariable models, all parameters were associated with the primary outcome of 90-day mortality or rehospitalization and improved model discrimination above clinical characteristics alone (likelihood ratio test, p

OBJECTIVES:. Older age is a key risk factor for adverse outcomes in critically ill patients with COVID-19. However, few studies have investigated whether preexisting comorbidities and acute physiologic ICU factors modify the association between age and death. DESIGN:. Multicenter cohort study. SETTING:. ICUs at 68 hospitals across the United States. PATIENTS:. A total of 5,037 critically ill adults with COVID-19 admitted to ICUs between March 1, 2020, and July 1, 2020. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. The primary exposure was age, modeled as a continuous variable. The primary outcome was 28-day inhospital mortality. Multivariable logistic regression tested the association between age and death. Effect modification by the number of risk factors was assessed through a multiplicative interaction term in the logistic regression model. Among the 5,037 patients included (mean age, 60.9 yr [± 14.7], 3,179 [63.1%] male), 1,786 (35.4%) died within 28 days. Age had a nonlinear association with 28-day mortality (p for nonlinearity

Abstract Background Claims-based algorithms are commonly used to identify sepsis in health services research because the laboratory features required to define clinical criteria may not be available in administrative data. Methods We evaluated claims-based sepsis algorithms among adults in the US aged ≥65 years with Medicare health insurance enrolled in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study. Suspected infections from baseline (2003–2007) through December 31, 2012 were analyzed. Two claims-based algorithms were evaluated: (1) infection plus organ dysfunction diagnoses or sepsis diagnoses (Medicare-Implicit/Explicit) and (2) Centers for Medicare and Medicaid Services Severe Sepsis/Septic Shock Measure diagnoses (Medicare-CMS). Three classifications based on clinical criteria were used as standards for comparison: (1) the sepsis-related organ failure assessment (SOFA) score (REGARDS-SOFA), (2) “quick” SOFA (REGARDS-qSOFA), and (3) Centers for Disease Control and Prevention electronic health record criteria (REGARDS-EHR). Results There were 2217 suspected infections among 9522 participants included in the current study. The total number of suspected infections classified as sepsis was 468 for Medicare-Implicit/Explicit, 249 for Medicare-CMS, 541 for REGARDS-SOFA, 185 for REGARDS-qSOFA, and 331 for REGARDS-EHR. The overall agreement between Medicare-Implicit/Explicit and REGARDS-SOFA, REGARDS-qSOFA, and REGARDS-EHR was 77, 79, and 81%, respectively, sensitivity was 46, 53, and 57%, and specificity was 87, 82, and 85%. Comparing Medicare-CMS and REGARDS-SOFA, REGARDS-qSOFA, and REGARDS-EHR, agreement was 77, 87, and 85%, respectively, sensitivity was 27, 41, and 36%, and specificity was 94, 92, and 93%. Events meeting the REGARDS-SOFA classification had a lower 90-day mortality rate (140.7 per 100 person-years) compared with the Medicare-CMS (296.1 per 100 person-years), REGARDS-qSOFA (238.6 per 100 person-years), Medicare-Implicit/Explicit (219.4 per 100 person-years), and REGARDS-EHR classifications (201.8 per 100 person-years). Conclusion Claims-based sepsis algorithms have high agreement and specificity but low sensitivity when compared with clinical criteria. Both claims-based algorithms identified a patient population with similar 90-day mortality rates as compared with classifications based on qSOFA and EHR criteria but higher mortality relative to SOFA criteria.

Summary: Many US states published crisis standards of care (CSC) guidelines for allocating scarce critical care resources during the COVID-19 pandemic. However, the performance of these guidelines in maximizing their population benefit has not been well tested. In 2,272 adults with COVID-19 requiring mechanical ventilation drawn from the Study of the Treatment and Outcomes in Critically Ill Patients with COVID-19 (STOP-COVID) multicenter cohort, we test the following three approaches to CSC algorithms: Sequential Organ Failure Assessment (SOFA) scores grouped into ranges, SOFA score ranges plus comorbidities, and a hypothetical approach using raw SOFA scores not grouped into ranges. We find that area under receiver operating characteristic (AUROC) curves for all three algorithms demonstrate only modest discrimination for 28-day mortality. Adding comorbidity scoring modestly improves algorithm performance over SOFA scores alone. The algorithm incorporating comorbidities has modestly worse predictive performance for Black compared to white patients. CSC algorithms should be empirically examined to refine approaches to the allocation of scarce resources during pandemics and to avoid potential exacerbation of racial inequities.

OBJECTIVES:. Critically ill patients with coronavirus disease 2019 have variable mortality. Risk scores could improve care and be used for prognostic enrichment in trials. We aimed to compare machine learning algorithms and develop a simple tool for predicting 28-day mortality in ICU patients with coronavirus disease 2019. DESIGN:. This was an observational study of adult patients with coronavirus disease 2019. The primary outcome was 28-day inhospital mortality. Machine learning models and a simple tool were derived using variables from the first 48 hours of ICU admission and validated externally in independent sites and temporally with more recent admissions. Models were compared with a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 using the area under the receiver operating characteristic curve and calibration. SETTING:. Sixty-eight U.S. ICUs. PATIENTS:. Adults with coronavirus disease 2019 admitted to 68 ICUs in the United States between March 4, 2020, and June 29, 2020. INTERVENTIONS:. None. MEASUREMENTS AND MAIN RESULTS:. The study included 5,075 patients, 1,846 (36.4%) of whom died by day 28. eXtreme Gradient Boosting had the highest area under the receiver operating characteristic curve in external validation (0.81) and was well-calibrated, while k-nearest neighbors were the lowest performing machine learning algorithm (area under the receiver operating characteristic curve 0.69). Findings were similar with temporal validation. The simple tool, which was created using the most important features from the eXtreme Gradient Boosting model, had a significantly higher area under the receiver operating characteristic curve in external validation (0.78) than the Sequential Organ Failure Assessment score (0.69), National Early Warning Score (0.60), and CURB-65 (0.65; p < 0.05 for all comparisons). Age, number of ICU beds, creatinine, lactate, arterial pH, and Pao2/Fio2 ratio were the most important predictors in the eXtreme Gradient Boosting model. CONCLUSIONS:. eXtreme Gradient Boosting had the highest discrimination overall, and our simple tool had higher discrimination than a modified Sequential Organ Failure Assessment score, National Early Warning Score, and CURB-65 on external validation. These models could be used to improve triage decisions and clinical trial enrichment.

Abstract Background Prior studies associate steroid use with infection risk but were limited to select populations and short follow-up periods. The association of steroid use with long-term risk of community-acquired infections is unknown. We sought to determine the association of steroid risk with long-term risks of community- acquired infections and sepsis. Methods We used data on 30,239 adults aged ≥ 45 years old from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. The primary exposure was oral or injectable steroid use, determined from medication inventory obtained at baseline in-home visit. The primary outcome was time to first infection event during 2003–2012, determined through adjudicated review of hospital records. We determined associations between baseline steroid use and first infection hospitalization events using Cox proportional hazards models, adjusting for demographics, health behaviors, chronic medical conditions, and medication adherence. Among the first infection hospitalization events, we also determined the association between baseline steroid use and sepsis. Results Steroid use was reported in 2.24% (n = 677) of the study population. There were 2593 incident infection events during the 10-year follow-up period. Infection incidence rates were higher for steroid than non-steroid users (37.99 vs. 13.79 per 1000 person-years). Steroid use was independently associated with increased risk of infection (adjusted HR 2.10, 95% CI: 1.73–2.56). Among first-infection events, steroid use was associated with increased odds of sepsis (adjusted OR 2.11, 95% CI: 1.33–3.36). The associations persisted in propensity matched analyses as well as models stratified by propensity score and medication adherence. Conclusions In this population-based cohort study, baseline steroid use was associated with increased long-term risks of community-acquired infections and sepsis.