EMERGE: Evaluating the value of Measuring Random Plasma Glucose Values for Managing Hyperglycemia in the Inpatient Setting.

IMPORTANCE: A diagnosis of diabetes is considered when a patient has hyperglycemia with a random plasma glucose ≥200 mg/dL. However, in the inpatient setting, hyperglycemia is frequently non-specific, especially among patients who are acutely unwell. As a result, patients with transient hyperglycemia may be incorrectly labeled as having diabetes, leading to unnecessary treatment, and potential harm. DESIGN, SETTING, AND PARTICIPANTS: We conducted a multicenter cohort study of patients hospitalized at six hospitals in Ontario, Canada, and identified those with a glucose value ≥200 mg/dL (including standing measurements and randomly drawn). We validated a definition for diabetes using manual chart review that included physician notes, pharmacy notes, home medications, and hemoglobin A1C. Among patients with a glucose value ≥200 mg/dL (11.1 mmol/L), we identified patients without diabetes who received a diabetes medication, and the number who experienced hypoglycemia during the same admission. MAIN OUTCOMES AND MEASURES: To determine the diagnostic value of using random blood glucose to diagnose diabetes in the inpatient setting, and its impact on patient outcomes. RESULTS: We identified 328,786 hospitalizations from hospital between 2010 and 2020. A blood glucose value of ≥200 mg/dL (11.1 mmol/L) had a positive predictive value of 68% and a negative predictive value of 90% for a diagnosis of diabetes. Of the 76,967 patients with an elevated glucose value reported, 16,787 (21.8%) did not have diabetes, and of these, 5375 (32%) received a diabetes medication. Hypoglycemia was frequently reported among the 5375 patients that received a diabetes medication, with 1406 (26.2%) experiencing hypoglycemia and 405 (7.5%) experiencing severe hypoglycemia. CONCLUSIONS AND RELEVANCE: Hyperglycemia in hospital is common but does not necessarily indicate a patient has diabetes. Furthermore, it can lead to treatment with diabetes medications with potential harm. Our findings highlight that clinicians should be cautious when responding to elevated random plasma glucose tests in the inpatient setting.

Phenotype clustering of hospitalized high-risk patients with COVID-19 - a machine learning approach within the multicentre, multinational PCHF-COVICAV registry.

IMTRODUCTION: The high-risk population of patients with cardiovascular (CV) disease or risk factors (RF) suffering from COVID-19 is heterogeneous. Several predictors for impaired prognosis have been identified. However, with machine learning (ML) approaches, certain phenotypes may be confined to classify the affected population and to predict outcome. This study aimed to phenotype patients using unsupervised ML technique within the International Postgraduate Course Heart Failure Registry for patients hospitalized with COVID-19 and Cardiovascular disease and/or RF (PCHF-COVICAV). MATERIAL AND METHODS: Patients from the eight centres with follow-up data available from the PCHF-COVICAV registry were included in this ML analysis (K-medoids algorithm). RESULTS: Out of 617 patients included into the prospective part of the registry, 458 [median age: 76 (IQR:65-84) years, 55% male] were analyzed and 46 baseline variables, including demographics, clinical status, comorbidities and biochemical characteristics were incorporated into the ML. Three clusters were extracted by this ML method. Cluster 1 (n = 181) represents mainly women with the least number of overall comorbidities and cardiovascular RF. Cluster 2 (n = 227) is characterized mainly by men with non-CV conditions and less severe symptoms of infection. Cluster 3 (n=50) mainly represents men with the highest prevalence of cardiac comorbidities and RF, more extensive inflammation and organ dysfunction with the highest 6-month all-cause mortality risk. CONCLUSIONS: The ML process has identified three important clinical clusters from hospitalized COVID-19 CV and/or RF patients. The cluster of males with severe CV disease, particularly HF, and multiple RF presenting with increased inflammation had a particularly poor outcome.

Heart failure in COVID-19: the multicentre, multinational PCHF-COVICAV registry.

AIMS: We assessed the outcome of hospitalized coronavirus disease 2019 (COVID-19) patients with heart failure (HF) compared with patients with other cardiovascular disease and/or risk factors (arterial hypertension, diabetes, or dyslipidaemia). We further wanted to determine the incidence of HF events and its consequences in these patient populations. METHODS AND RESULTS: International retrospective Postgraduate Course in Heart Failure registry for patients hospitalized with COVID-19 and CArdioVascular disease and/or risk factors (arterial hypertension, diabetes, or dyslipidaemia) was performed in 28 centres from 15 countries (PCHF-COVICAV). The primary endpoint was in-hospital mortality. Of 1974 patients hospitalized with COVID-19, 1282 had cardiovascular disease and/or risk factors (median age: 72 [interquartile range: 62-81] years, 58% male), with HF being present in 256 [20%] patients. Overall in-hospital mortality was 25% (n = 323/1282 deaths). In-hospital mortality was higher in patients with a history of HF (36%, n = 92) compared with non-HF patients (23%, n = 231, odds ratio [OR] 1.93 [95% confidence interval: 1.44-2.59], P < 0.001). After adjusting, HF remained associated with in-hospital mortality (OR 1.45 [95% confidence interval: 1.01-2.06], P = 0.041). Importantly, 186 of 1282 [15%] patients had an acute HF event during hospitalization (76 [40%] with de novo HF), which was associated with higher in-hospital mortality (89 [48%] vs. 220 [23%]) than in patients without HF event (OR 3.10 [2.24-4.29], P < 0.001). CONCLUSIONS: Hospitalized COVID-19 patients with HF are at increased risk for in-hospital death. In-hospital worsening of HF or acute HF de novo are common and associated with a further increase in in-hospital mortality.