569 Advanced heart failure patients: insight clinical characteristics and prognosis

Aims The prevalence of patients with advanced heart failure (HF) ranges from 1% to 10% of HF population. A recent position statement of Heart Failure Association (HFA) of European Society of Cardiology (ESC), defined advanced HF according four criteria including LV dysfunction, symptoms severity, and HF hospital admission.1 In this study we would like to evaluate clinical and prognostic characteristics of HF patients in advanced stage. Methods and results This is an observational retrospective study enrolling patients with diagnosis of acute heart failure (AHF) de novo or not, admitted to our department from January 2015 to January 2019 within 12 h from emergency department admission. Patients underwent to clinical examination, laboratory analysis (NTproBNP, renal function haemoglobin) and echocardiography. Advanced heart failure patients were defined on the basis of ESC recent criteria.1 Patients were followed for 1 year after hospital discharge for the composite outcome of HF re-hospitalization and cardiovascular death through 1 year. A total of 601 AHF patients were included in this analysis. Median age was 78 (70–83) years, median of left ventricular ejection fraction (LVEF) was 45 (33–55)% and the median of serum levels of NTproBNP was 7851 (3222–17 543) pg/ml. In our sample we found 122 patients who met the criteria of advanced HF. Comparing patients with advanced HF and without we found that advanced HF patients were more frequent affected by not ischaemic cardiomyopathy with respect to patients not advanced (85% vs. 49%; P < 0.001). Moreover, patients not advanced were more frequent de novo HF with respect to advanced ones (54% vs. 34%; P < 0.001). Advanced heart failure patients showed higher values of NTproBNP [12 375 (5346–26 375) vs. 6652 (2552–13 782) pg/ml; P < 0.001] and creatinine [1.20 (0.88–1.99) vs. 1.07 (0.82–1.50) mg/dL; P = 0.002] and lower values of TAPSE [18 (15–20) vs. 16 (13–19) mm; P < 0.001], eGFR [48 (30–66) vs. 52 (38–38) ml/min/m2; P = 0.05], and serum sodium [140 (138–143) vs. 139 (136–141) mEq/l; P = 0.001] with respect to not advanced HF patients. Univariate analysis showed that advanced HF was related to poor prognosis in terms of one year cardiovascular death or HF re-hospitalization [HR: 1.83 (1.32–2.54); P < 0.001] and in terms of in-hospital mortality [HR: 2.53 (1.01–6.53); P = 0.05]. Conclusions Advanced HF patients showed a worse neuro-hormonal and renal pattern compared to not advanced ones. Similarly, these patients experienced a worse right ventricular function and were more prone no adverse events development at 1 year. Further study are mandatory to better manage these patients improving their outcome.

Describing agreement in the Main Condition coding field using Canadian ICD-11 inpatient data

IntroductionCountries use varying coding standards, which impact international coded data comparability. The `main condition' (MC) field is coded within the Discharge Abstract Database as "reason for admission" or "largest resource use". ObjectiveWe offer a preliminary analysis on the frequency of and contributing factors to MC definition agreements within an inpatient Canadian dataset. MethodsSix professional coders performed a chart review between August 2016 and June 2017 on 3,000 randomly selected inpatient charts from three acute care hospitals in Calgary, Alberta. Coders classified the MC as "reason for admission", "largest resource use" or "both". Patients were admitted between 1st January and 30th June 2015 and met the inclusion criteria if they were >18 years, had an Alberta personal health care number, and had an inpatient visit for any service outside of obstetrics. Agreement between the two MC definitions was stratified by length of stay (LOS), emergency department admission, hospital of origin, discharge location, age, sex, procedures, and comorbidities. Chi-square analysis and frequency of inconsistencies were reported. ResultsOnly 34 (1.51%) of the 2,250 patient charts had disagreeing MC definitions. Age, emergency visit on admit, LOS, hospital, and discharge location were associated with MC agreement. Chronic conditions were seen more often in MC definition agreements, and acute conditions seen within those disagreeing. ConclusionThere was a small proportion of cases in which the condition bringing the patient to hospital was not also the condition occupying the largest resources. Within disagreements, further research using a larger sample size is needed to explore the presence of MC in a secondary/tertiary condition, the association between patient complexity and disagreeing MC definitions, and the nature of the conditions seen in the inconsistent MC definitions.

Acute and sustained increase in endothelial biomarkers in COVID-19

Endothelial injury is related to poor outcomes in respiratory infections yet little is known in relation to COVID-19. Performing a longitudinal analysis (on emergency department admission and post-hospitalisation follow-up), we evaluated endothelial damage via surrogate systemic endothelial biomarkers, that is, proadrenomedullin (proADM) and proendothelin, in patients with COVID-19. Higher proADM and/or proendothelin levels at baseline were associated with the most severe episodes and intensive care unit admission when compared with ward-admitted individuals and outpatients. Elevated levels of proADM or proendothelin at day 1 were associated with in-hospital mortality. High levels maintained after discharge were associated with reduced diffusing capacity.

Unhealthy alcohol use is an independent risk factor for increased COVID-19 disease severity: observational cross-sectional study (Preprint)

BACKGROUND Unhealthy alcohol use (UAU) is known to disrupt pulmonary immune mechanisms and increase the risk of acute respiratory distress syndrome in patients with pneumonia; however, little is known about the effects of UAU on outcomes in patients with COVID-19 pneumonia. To our knowledge, this is the first observational cross-sectional study that aims to understand the effect of UAU on the severity of COVID-19 disease. OBJECTIVE We aim to determine if UAU is associated with more severe clinical presentation and worse health outcomes related to COVID-19 and if socioeconomic status, smoking, age, body mass index (BMI), race/ethnicity, and pattern of alcohol use modify the risk. METHODS In this observational cross-sectional study, we ran a digital machine learning classifier on the electronic health record of patients who tested positive via nasopharyngeal swab for SARS-CoV-2 or had two COVID-19 international classification of disease codes to identify patients with UAU. We then performed a multivariable regression to examine the relationship between UAU and COVID-19 severity as measured by hospital care level, i.e. emergency department admission, emergency department admission with ventilator, or death. We used a predefined cutoff of 0.15 (optimal sensitivity and specificity) on the digital classifier to compare disease severity in patients with versus without UAU. Models were adjusted for age, sex, race/ethnicity, BMI, smoking status, and insurance status. RESULTS Each incremental increase in the predicted probability from the digital alcohol classifier was associated with a greater odds risk for more severe COVID-19 disease(OR 1.15, 95% CI: 1.10 - 1.20). Using the predefined cut-off of 0.15 to group patients into binary unhealthy alcohol group or not, we found that the unhealthy alcohol group had a greater odds risk to develop more severe disease (OR = 1.89, 95% CI: 1.17 - 3.06), suggesting that alcohol positive classification was associated with a 89% increase in the odds of being in a higher severity category. CONCLUSIONS In patients infected with SARS-CoV-2, UAU is an independent risk factor associated with greater disease severity and/or death.

Discrepancy between emergency department admission diagnosis and hospital discharge diagnosis and its impact on length of stay, up-triage to the intensive care unit, and mortality

Objectives Published discrepancy rates between emergency department (ED) and hospital discharge (HD) diagnoses vary widely (from 6.5 to 75.6%). The goal of this study was to determine the extent of diagnostic discrepancy and its impact on length of hospital stay (LOS), up-triage to the intensive care unit (ICU) and in-hospital mortality. Methods A retrospective chart review of adult patients admitted from the ED to a hospitalist service at a tertiary hospital was performed. The ED and HD diagnoses were compared and classified as concordant, discordant, or symptom diagnoses according to predefined criteria. Logistic regression analysis was conducted to examine the associations of diagnostic discordance with in-hospital mortality and up-triage to the ICU. A linear regression model was used for the length of stay. Results Of the 636 patients whose records were reviewed, 418 (217 [51.9%] women, with a mean age of 64.1 years) were included. Overall, 318 patients (76%) had concordant diagnoses, while 91 (21.77%) had discordant diagnoses. Only 9 patients (2.15%) had symptom diagnoses. A discordant diagnosis was associated with increased mortality (OR: 3.64; 95% CI: 1.026–12.91; p=0.045) and up-triage to the ICU (OR: 5.51; 95% CI: 2.43–12.5; p<0.001). The median LOS was significantly greater for patients with discordant diagnoses (7 days) than for those with concordant diagnoses (4.7 days) (p=0.004). Symptom diagnosis did not affect the mortality or ICU up-triage. Conclusions One in five hospitalized patients had discordant HD and admission diagnoses. This diagnostic discrepancy was associated with significant impacts on patient morbidity and mortality.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann–Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

Characterizing safety and efficacy in ovarian cancer patients with thromboembolism treated with rivaroxaban.

e18720 Background: There is a paucity of real-world data regarding rates of recurrent venous thromboembolism (VTE), major bleeding and all-cause mortality among ovarian cancer patients with thrombosis treated with direct oral anticoagulants (DOACs) currently available. We sought to evaluate the effectiveness and safety of rivaroxaban versus low molecular-weight heparin (LMWH) for cancer-associated thrombosis (CAT) treatment in patients with ovarian cancer. Methods: We utilized US Surveillance, Epidemiology and End Results-Medicare–linked data from 2013-2016 to identify adults with active ovarian cancer, undergoing hospitalization/emergency department admission for CAT and prescribed rivaroxaban or LMWH for outpatient anticoagulation. Rivaroxaban and LMWH cohorts were balanced using propensity score overlap weighting . Outcomes included the composite of recurrent thrombosis or major bleeding, each outcome separately and mortality at six-months using an intention-to-treat approach. On-treatment analysis after 12-months was also performed. Hazard ratios (HRs) with 95% confidence intervals using overlap weighted cox regression were reported. Results: We included 33 rivaroxaban and 92 LMWH-managed CAT patients. In each cohort, mean age was 73, 79.5% of patients were white, 46.9% had a history of ≥ stage 3 chronic kidney disease (CKD), two-thirds had metastatic disease at the time of VTE, and 32.6% had a prior VTE. Patients were diagnosed with ovarian cancer an average of 1.40 years prior to the index VTE event and 45.8% had a pulmonary embolism (with or without DVT) as the index event. Our analysis did not detect a significant difference in outcomes with rivaroxaban versus LMWH at six-months (Table). On-treatment analysis at 12-months showed similar results. Conclusions: Rivaroxaban may be a reasonable alternative to LMWH for CAT patients with ovarian cancer. Large observational studies are needed to confirm these results.[Table: see text]