Elevated beta-hydroxybutyric acid with no ketoacidosis in type 2 diabetic patients using sodium-glucose cotransporter-2 inhibitors

SGLT2 inhibitor (SGLT2i) class of medications are known to cause to euglycemic diabetic ketoacidosis (euDKA) as reported in the article by Lin et al. in your esteemed publication about this entity being reported for the first time in Taiwanese population.We wish to share the findings from our center to further expand the spectrum of findings associated with SGLT2i therapy

Review of clinical profile, risk factors, and outcome in patients with Tuberculosis and COVID -19

Coronavirus disease (COVID 19) has involved millions of people all over the world. Tuberculosis (TB) continues to affect millions of people every year with high mortality. There is limited literature on the occurrence of COVID 19 in patients with TB. We reviewed the available data on various clinical details, management, and outcome among patients with COVID-19 and TB. 8 studies reported a total of 80 patients with this coinfection. These patients were reported from ten different countries, with Italy reporting the largest number of cases. Migrant, males constituted a major proportion of cases. Most reported patients were symptomatic. Fever, dry cough, and dyspnea were the most commonly reported symptoms. Bilateral ground glass opacities were more common in COVID 19 infection and cavitary lesions were more common in patients with TB. Most reported TB patients had been found to have mycobacterium tuberculosis from sputum culture in the background of pulmonary TB. Most patients of TB were treated with multidrug regimen antitubercular therapy. In all 8 studies, COVID 19 was treated as per the local protocol. Mortality was reported in more than 10% of patients. Mortality was higher in elderly patients ( \u3e 70 years) and amongst patient with multiple medical comorbidities

Mechanisms of neurological injury in COVID-19

We read with much interest the article “Neurological Impact of Coronavirus Disease (COVID 19): Practical Considerations for the Neuroscience Community” by Werner et al. published in your esteemed Journal. Authors have described the various neurological details of COVID-19 in detail.1 We believe this topic is important and is continuously evolving. We have the following comments as an addition to the article

Cerebrovascular events in COVID-19 patients

Neurological manifestations in patients with COVID-19 are more frequently being reported. Cerebrovascular events have been reported in around 3% of patients. In this review we summarize the published literature on cerebrovascular events in patients with COVID-19 as available on the PubMed database. So far, 3 studies have reported cerebrovascular events. Cerebrovascular events were identified on screening patients with decreased consciousness or in the presence of focal neurological deficits. These events were common in elderly, critically ill patients and in patients with prior cardio-cerebrovascular comorbidities. The diagnosis of cerebrovascular events was confirmed with computed tomography of the brain in most studies reporting neurological events. Multiple pathological mechanisms have been postulated regarding the process of neurological and vascular injury among which cytokine storm is shown to correlate with mortality. Patients with severe illness are found to have a higher cardio- cerebrovascular comorbidity. With an increasing number of cases and future prospective studies, the exact mechanism by which these cerebrovascular events occur and attribute to the poor outcome will be better understood

Optimal Blood Glucose Monitoring Interval for Insulin Infusion in Critically Ill Non-Cardiothoracic Patients: A Pilot Study

OBJECTIVE: The American Diabetes Association and the Society of Critical Care Medicine recommend monitoring blood glucose (BG) every 1-2 hours in patients receiving insulin infusion to guide titration of insulin infusion to maintain serum glucose in the target range; however, this is based on weak evidence. We evaluated the compliance of hourly BG monitoring and relation of less frequent BG monitoring to glycemic status. MATERIALS AND METHODS: Retrospective chart review performed on 56 consecutive adult patients who received intravenous insulin infusion for persistent hyperglycemia in the ICU at Saint Vincent Hospital, a tertiary care community hospital an urban setting in Northeast region of USA. The frequency of fingerstick blood glucose (FSBG) readings was reviewed for compliance with hourly FSBG monitoring per protocol and the impact of FSBG testing at different time intervals on the glycemic status. Depending on time interval of FSBG monitoring, the data was divided into three groups: Group A ( \u3c 90 min), Group B (91-179 min) and Group C ( \u3e /=180 min). RESULTS: The mean age was 69 years (48% were males), 77% patients had preexisting type 2 diabetes mellitus (T2DM). The mean MPM II score was 41. Of the 1411 readings for BG monitoring on insulin infusion, 467 (33%) were in group A, 806 (57%) in group B and 138 (10%) in group C; hourly BG monitoring compliance was 12.6%. The overall glycemic status was similar among all groups. There were 14 (0.99%) hypoglycemic episodes observed. The rate of hypoglycemic episodes was similar in all three groups (p=0.55). CONCLUSION: In patients requiring insulin infusion for sustained hyperglycemia in ICU, the risk of hypoglycemic episodes was not significantly different with less frequent BG monitoring. The compliance to hourly blood glucose monitoring and ICU was variable, and hypoglycemic episodes were similar across the groups despite the variation in monitoring

Early machine learning prediction of hospitalized patients at low risk of respiratory deterioration or mortality in community-acquired pneumonia: Derivation and validation of a multivariable model

Current prognostic tools for pneumonia predominantly focus on mortality, often neglecting other crucial outcomes such as the need for advanced respiratory support. The objective of this study was to develop and validate a tool that predicts the early risk of non-occurrence of respiratory deterioration or mortality. We conducted a single-center, retrospective cohort study involving hospitalized adult patients with community-acquired pneumonia (CAP) and acute hypoxic respiratory failure from January 2009 to December 2019 (n = 4379). We employed the gradient boosting machine (GBM) learning to create a model that estimates the likelihood of patients requiring advanced respiratory support (high flow nasal cannula [HFNC], non-invasive mechanical ventilation [NIMV], and invasive mechanical ventilation [IMV]) or facing mortality during hospitalization. This model utilized readily available data including demographic, physiologic, and laboratory data, sourced from electronic health records and obtained within the first six hours of admission. Out of the cohort, 890 patients (25.2%) either required advanced respiratory support or died during their hospital stay. Our predictive model displayed superior discrimination and higher sensitivity (cross-validation C-statistic = 0.71; specificity = 0.56; sensitivity = 0.72) compared to the pneumonia severity index (PSI) (C-statistic = 0.65; specificity = 0.91; sensitivity = 0.24; P value < 0.001), while maintaining a negative predictive value (NPV) of approximately 0.85. These data demonstrate that our machine learning model predicted the non-occurrence of respiratory deterioration or mortality among hospitalized CAP patients more accurately than the PSI. The enhanced sensitivity of this model holds potential for reliably excluding low-risk patients from pneumonia clinical trials

Gaining consensus on expert rule statements for acute respiratory failure digital twin patient model in intensive care unit using a Delphi method

Digital twin technology is a virtual depiction of a physical product and has been utilized in many fields. Digital twin patient model in healthcare is a virtual patient that provides opportunities to test the outcomes of various interventions virtually without subjecting an actual patient to possible harm. This can serve as a decision aid in the complex environment of the intensive care unit (ICU). Our objective is to develop consensus among a multidisciplinary expert panel on statements regarding respiratory pathophysiology contributing to respiratory failure in the medical ICU. We convened a panel of 34 international critical care experts. Our group modeled elements of respiratory failure pathophysiology using directed acyclic graphs (DAGs) and derived expert statements describing associated ICU clinical practices. The experts participated in three rounds of modified Delphi to gauge agreement on 78 final questions (13 statements with 6 substatements for each) using a Likert scale. A modified Delphi process achieved agreement for 62 of the final expert rule statements. Statements with the highest degree of agreement included the physiology, and management of airway obstruction decreasing alveolar ventilation and ventilation-perfusion matching. The lowest agreement statements involved the relationship between shock and hypoxemic respiratory failure due to heightened oxygen consumption and dead space. Our study proves the utility of a modified Delphi method to generate consensus to create expert rule statements for further development of a digital twin-patient model with acute respiratory failure. A substantial majority of expert rule statements used in the digital twin design align with expert knowledge of respiratory failure in critically ill patients

Urgent Need for Field Surveys of Coronaviruses in Southeast Asia to Understand the SARS-CoV-2 Phylogeny and Risk Assessment for Future Outbreaks

Phylogenetic analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is focused on a single isolate of bat coronaviruses (bat CoVs) which does not adequately represent genetically related coronaviruses (CoVs) [...