Dyspnea and Wheezing after Adenosine Injection in a Patient with Eosinophilic Bronchitis

A 58-year-old nonsmoker female was referred for evaluation of chronic cough of 13 months duration. After an initial work-up, the patient was diagnosed to have chronic cough due to eosinophilic bronchitis. The diagnostic work-up for eosinophilic bronchitis and bronchial biopsy is discussed. Eosinophilic bronchitis is differentiated from asthma. In addition, the patient developed dyspnea, flushing, and wheezing after the administration of adenosine during a cardiac stress test in spite of a negative methacholine challenge. This indirect stimulus of airway hyperresponsiveness suggests the possible involvement of mast cells in eosinophilic bronchitis

Acute Kidney Injury in ADPKD Patients with Pneumonia

Background. In animal models, polycystic kidneys are susceptible to acute kidney injury (AKI). We examined the occurrence of AKI in a cohort of autosomal dominant polycystic kidney disease (ADPKD) and non-ADPKD patients with acute pneumonia. Design. All ADPKD patients admitted to Mayo Clinic Rochester for pneumonia from January 1990 to April 2010 were examined. Sixty-three patients had lobar infiltration and consolidation on chest X-ray. After excluding patients on dialysis, with organ transplantation, and on chronic immunosuppression, 24 remaining ADPKD patients were enrolled. Twenty-three of the 24 were matched with 92 (1 : 4 ratio) non-ADPKD pneumonia patients based on their baseline eGFR. AKI was defined as serum creatinine elevation ≥0.3 mg/dL. Results. Sixteen of the 23 ADPKD patients (69.6%) and 36 of the 92 (39.1%) non-ADPKD patients developed AKI, P = 0.008. In both groups, those who developed AKI had a lower baseline eGFR (41.1 ± 5.00 versus 58.7 ± 11.8 in ADPKD and 40.2 ± 3.65 versus 51.8 ± 2.24 mL/min/1.73 m2 in the non-ADPKD group), more intensive care unit admissions, and longer hospital stays. AKI was associated with a reduced survival in both groups. Conclusions. Patients with ADPKD admitted for acute pneumonia had more frequent episodes of AKI than non-ADPKD patients with comparable kidney function

Individuals With Scleroderma May Have Increased Risk of Sleep-Disordered Breathing.

STUDY OBJECTIVES:Scleroderma is associated with abnormal skin thickening, interstitial lung disease, pulmonary hypertension, and abnormalities of the upper airway. These changes can cause cardiopulmonary complications, potentially including sleep-disordered breathing. The objective of this study is to examine the risk of sleep-disordered breathing in patients with scleroderma. METHODS:We retrospectively identified patients with documented scleroderma. We abstracted data from their electronic health records, including findings from antibody tests, serial pulmonary function tests, transthoracic echocardiography, high-resolution computed tomography, and overnight forehead oximetry. RESULTS:We identified 171 patients with scleroderma. Mean age at the time of initial consult was 56.5 years (range, 18-96 years), and 150 (86.7%) were women. Scleroderma was categorized as limited disease for 108 (62.4%), diffuse disease for 59 (34.1%), and mixed connective tissue disease for 6 (3.5%). Fifty-four patients (31.2%) had abnormal overnight forehead oximetry results, defined as an oxygen desaturation index greater than 5 or a baseline mean arterial oxygen saturation level less than 90%. CONCLUSIONS:Cardiopulmonary complications are common in patients with scleroderma, one of which may be sleep-disordered breathing. In our cohort, approximately one-third of individuals with scleroderma had evidence of sleep-disordered breathing. Moreover, the rate of sleep-disordered breathing in our population of scleroderma patients was twice the rate of pulmonary hypertension and was approximately the same as the rate of interstitial lung disease. Future prospective studies are needed to further assess the role of sleep-disordered breathing in scleroderma clinical outcomes

Towards the prevention of acute lung injury: a population based cohort study protocol

<p>Abstract</p> <p>Background</p> <p>Acute lung injury (ALI) is an example of a critical care syndrome with limited treatment options once the condition is fully established. Despite improved understanding of pathophysiology of ALI, the clinical impact has been limited to improvements in supportive treatment. On the other hand, little has been done on the prevention of ALI. Olmsted County, MN, geographically isolated from other urban areas offers the opportunity to study clinical pathogenesis of ALI in a search for potential prevention targets.</p> <p>Methods/Design</p> <p>In this population-based observational cohort study, the investigators identify patients at high risk of ALI using the prediction model applied within the first six hours of hospital admission. Using a validated system-wide electronic surveillance, Olmsted County patients at risk are followed until ALI, death or hospital discharge. Detailed in-hospital (second hit) exposures and meaningful short and long term outcomes (quality-adjusted survival) are compared between ALI cases and high risk controls matched by age, gender and probability of developing ALI. Time sensitive biospecimens are collected for collaborative research studies. Nested case control comparison of 500 patients who developed ALI with 500 matched controls will provide an adequate power to determine significant differences in common hospital exposures and outcomes between the two groups.</p> <p>Discussion</p> <p>This population-based observational cohort study will identify patients at high risk early in the course of disease, the burden of ALI in the community, and the potential targets for future prevention trials.</p

Alcohol Consumption and Development of Acute Respiratory Distress Syndrome: A Population-Based Study

This retrospective population-based study evaluated the effects of alcohol consumption on the development of acute respiratory distress syndrome (ARDS). Alcohol consumption was quantified based on patient and/or family provided information at the time of hospital admission. ARDS was defined according to American-European consensus conference (AECC). From 1,422 critically ill Olmsted county residents, 1,357 had information about alcohol use in their medical records, 77 (6%) of whom developed ARDS. A history of significant alcohol consumption (more than two drinks per day) was reported in 97 (7%) of patients. When adjusted for underlying ARDS risk factors (aspiration, chemotherapy, high-risk surgery, pancreatitis, sepsis, shock), smoking, cirrhosis and gender, history of significant alcohol consumption was associated with increased risk of ARDS development (odds ratio 2.9, 95% CI 1.3–6.2). This population-based study confirmed that excessive alcohol consumption is associated with higher risk of ARDS

Validation of Automated Data Abstraction for SCCM Discovery VIRUS COVID-19 Registry: Practical EHR Export Pathways (VIRUS-PEEP)

BACKGROUND: The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities. OBJECTIVE: This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients. MATERIALS AND METHODS: This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen\u27s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson\u27s correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00). MEASUREMENTS AND MAIN RESULTS: The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%. CONCLUSION AND RELEVANCE: Our study confirms the feasibility and validity of an automated process to gather data from the EHR

Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

BackgroundThe gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.ObjectiveThis study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.Materials and methodsThis observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen’s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson’s correlation coefficient and Bland–Altman plots. The strength of agreement was defined as almost perfect (0.81–1.00), substantial (0.61–0.80), and moderate (0.41–0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00–0.30), low (0.30–0.50), moderate (0.50–0.70), high (0.70–0.90), and extremely high (0.90–1.00).Measurements and main resultsThe cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.Conclusion and relevanceOur study confirms the feasibility and validity of an automated process to gather data from the EHR