BOLD: Blood-gas and Oximetry Linked Dataset - Open Source Research.

Pulse oximeters measure peripheral arterial oxygen saturation (SpO 2 ) noninvasively, while the gold standard (SaO 2 ) involves arterial blood gas measurement. There are known racial and ethnic disparities in their performance. BOLD is a new comprehensive dataset that aims to underscore the importance of addressing biases in pulse oximetry accuracy, which disproportionately affect darker-skinned patients. The dataset was created by harmonizing three Electronic Health Record databases (MIMIC-III, MIMIC-IV, eICU-CRD) comprising Intensive Care Unit stays of US patients. Paired SpO 2 and SaO 2 measurements were time-aligned and combined with various other sociodemographic and parameters to provide a detailed representation of each patient. BOLD includes 49,099 paired measurements, within a 5-minute window and with oxygen saturation levels between 70-100%. Minority racial and ethnic groups account for ∼25% of the data - a proportion seldom achieved in previous studies. The codebase is publicly available. Given the prevalent use of pulse oximeters in the hospital and at home, we hope that BOLD will be leveraged to develop debiasing algorithms that can result in more equitable healthcare solutions.

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88-0.90), sensitivity of 0.77 (95% CI = 0.75-0.78), specificity 0.85 (95% CI = 085-0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81-0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18-40) (AUROC = 0.93 [0.92-0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81-0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.

Experience With Cardiology-Oriented Outcomes in Critically Ill Patients With Coronavirus Disease 2019.

OBJECTIVES: Coronavirus disease 2019 is associated with high mortality rates and multiple organ damage. There is increasing evidence that these patients are at risk for various cardiovascular insults; however, there are currently no guidelines for the diagnosis and management of such cardiovascular complications in patients with coronavirus disease 2019. We share data and recommendations from a multidisciplinary team to highlight our institution's clinical experiences and guidelines for managing cardiovascular complications of coronavirus disease 2019. DESIGN SETTING AND PATIENTS: This was a retrospective cohort study of patients admitted to one of six ICUs dedicated to the care of patients with coronavirus disease 2019 located in three hospitals within one academic medical center in Atlanta, Georgia. MEASUREMENTS/INTERVENTIONS: Chart review was conducted for sociodemographic, laboratory, and clinical data. Rates of specific cardiovascular complications were assessed, and data were analyzed using a chi-square or Wilcoxon rank-sum test for categorical and continuous variables. Additionally, certain cases are presented to demonstrate the sub committee's recommendations. MAIN RESULTS: Two-hundred eighty-eight patients were admitted to the ICU with coronavirus disease 2019. Of these, 86 died (29.9%), 242 (84.03%) had troponin elevation, 70 (24.31%) had dysrhythmias, four (1.39%) had ST-elevation myocardial infarction, eight (2.78%) developed cor pulmonale, and 190 (65.97%) with shock. There was increased mortality risk in patients with greater degrees of troponin elevation (p < 0.001) and with the development of arrhythmias (p < 0.001), cor pulmonale (p < 0.001), and shock (p < 0.001). CONCLUSIONS: While there are guidelines for the diagnosis and management of pulmonary complications of coronavirus disease 2019, there needs to be more information regarding the management of cardiovascular complications as well. These recommendations garnered from the coronavirus disease 2019 cardiology subcommittee from our institution will add to the existing knowledge of these potential cardiovascular insults as well as highlight suggestions for the diagnosis and management of the range of cardiovascular complications of coronavirus disease 2019. Additionally, with the spread of coronavirus disease 2019, our case-based recommendations provide a bedside resource for providers newly caring for patients with coronavirus disease 2019.

Physical characteristics of catheters used for subarachnoid drainage.

BACKGROUND: Cerebrospinal fluid diversion through lumbar drainage catheters is a routine procedure that is used in a variety of clinical applications. Fracture of catheters during insertion or removal may result in retained foreign bodies that may lead to patient morbidity with potential legal ramifications. Fracture resistance is an important component of drain selection. OBJECTIVE: To analyze catheter integrity to determine which commercially available catheters are most robust and resistant to fracture. METHODS: Physical properties of the commercially available drainage catheters that can be advanced through a Touhy needle were assessed using laboratory equipment and reported. Five types of catheter were analyzed for break load, tensile strength, extension at break, and shear strength. RESULTS AND CONCLUSIONS: Of the five types of catheter, the Arrow catheter showed the greatest resistance to fracture. Of the drainage catheters, the Codman catheter showed the greatest resistance to fracture.