Association between histamine 2 receptor antagonists and sepsis outcomes in ICU patients: a retrospective analysis using the MIMI-IV database.

BACKGROUND: Sepsis is marked by elevated histamine, which is a vasodilator that increases vascular permeability. Although human studies are lacking, murine models of sepsis have indicated potential protective effects of histamine 2 receptor antagonist administration (H2RAs). OBJECTIVE: To assess any association between H2RA use in sepsis-3 patients admitted to the ICU and mortality, mechanical ventilation, length of stay, and markers of renal, liver, and lung dysfunction. DESIGN: Retrospective cohort study. SETTING: Intensive care units of the Beth Israel Deaconess Medical Center (BIDMC) accessed via the MIMIC-IV database spanning an 11-year period from 2008 to 2019. PATIENTS (OR PARTICIPANTS): A total of 30,591 patients met the inclusion criteria for sepsis-3 on admission (mean age 66.49, standard deviation 15.92). MAIN MEASURES: We collected patient age, gender, ethnicity, comorbidities (contained within the Charlson comorbidity index), SOFA score, OASIS score, APS III score, SAPS II score, H2RA use, creatinine, BUN, ALT, AST, and P/F ratios. Primary outcomes were mortality, mechanical ventilation, and ICU length of stay. KEY RESULTS: A total of 30,591 patients met inclusion criteria over the 11-year sample period. The 28-day in hospital mortality rate was significantly lower among patients who received an H2RA (12.6% vs 15.1%, p < 0.001) as compared to those who did not receive an H2RA. Patients receiving an H2RA had significantly lower adjusted odds of mortality (0.802, 95% CI 0.741-0.869, p < 0.001), but significantly higher adjusted odds of invasive mechanical ventilation (4.426, 95% CI 4.132-4.741, p < 0.001) and significantly higher ICU LOS (3.2 days vs. 2.4 days, p < 0.001) as compared to the non-H2RA group. H2RA use was also associated with decreased severity of acute respiratory distress syndrome (ARDS) and lower serum creatinine. CONCLUSION: Among patients hospitalized in the ICU for sepsis, the use of an H2RA was associated with significantly lower odds of mortality, decreased severity of ARDS, and a lower incidence of renal insufficiency.

Machine Learning Prediction of Death in Critically Ill Patients With Coronavirus Disease 2019.

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.

Ensuring trust in COVID-19 data: A retrospective cohort study.

ABSTRACT: There are no standardized methods for collecting and reporting coronavirus disease-2019 (COVID-19) data. We aimed to compare the proportion of patients admitted for COVID-19-related symptoms and those admitted for other reasons who incidentally tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Retrospective cohort studyData were sampled twice weekly between March 26 and June 6, 2020 from a "COVID-19 dashboard," a system-wide administrative database that includes the number of hospitalized patients with a positive SARS-CoV-2 polymerase chain reaction test. Patient charts were subsequently reviewed and the principal reason for hospitalization abstracted.Data collected during a statewide lockdown revealed that 92 hospitalized patients had positive SARS-CoV-2 test results. Among these individuals, 4.3% were hospitalized for reasons other than COVID-19-related symptoms but were incidentally found to be SARS-CoV-2-positive. After the lockdown was suspended, the total inpatient census of SARS-CoV-2-positive patients increased to 128, 20.3% of whom were hospitalized for non-COVID-19-related complaints.In the absence of a statewide lockdown, there was a significant increase in the proportion of patients admitted for non-COVID-19-related complaints who were incidentally found to be SARS-CoV-2-positive. In order to ensure data integrity, coding should distinguish between patients with COVID-19-related symptoms and asymptomatic patients carrying the SARS-CoV-2 virus.

Smoking-associated interstitial lung disease: update and review.

INTRODUCTION: Smoking-associated interstitial lung disease manifests as several heterogeneous disorders involving the airways, pleura, and lung parenchyma with various radiological patterns. The clinical history, radiologic, and pathologic findings are important to distinguish these more uncommon diseases. A multidisciplinary approach is recommended for diagnosis and to manage these conditions appropriately. AREAS COVERED: This review provides an overview of the epidemiology, risk factors, pathogenesis, clinical features, diagnosis, and treatment of acute eosinophilic pneumonia, e-cigarettes, or vaping associated lung injury, respiratory bronchiolitis interstitial lung disease, desquamative interstitial pneumonitis, pulmonary Langerhans cell histiocytosis, idiopathic pulmonary fibrosis, and combined pulmonary fibrosis emphysema. EXPERT OPINION: Cigarette smoking is associated with a variety of pathologic conditions that affect the airways and lungs. E-cigarette use and vaping present new challenges to the clinician. Consensus between the clinical, radiographic, and pathologic findings is important in identifying and differentiating between the various entities to properly diagnose smoking-related interstitial lung diseases discussed in this review.