Accuracy of continuous glucose monitoring in the hospital setting: an observational study.

AIMS/HYPOTHESIS: Continuous glucose monitoring (CGM) improves glycaemic outcomes in the outpatient setting; however, there are limited data regarding CGM accuracy in hospital. METHODS: We conducted a prospective, observational study comparing CGM data from blinded Dexcom G6 Pro sensors with reference point of care and laboratory glucose measurements during participants' hospitalisations. Key accuracy metrics included the proportion of CGM values within ±20% of reference glucose values >5.6 mmol/l or within ±1.1 mmol/l of reference glucose values ≤5.6 mmol/l (%20/20), the mean and median absolute relative difference between CGM and reference value (MARD and median ARD, respectively) and Clarke error grid analysis (CEGA). A retrospective calibration scheme was used to determine whether calibration improved sensor accuracy. Multivariable regression models and subgroup analyses were used to determine the impact of clinical characteristics on accuracy assessments. RESULTS: A total of 326 adults hospitalised on 19 medical or surgical non-intensive care hospital floors were enrolled, providing 6648 matched glucose pairs. The %20/20 was 59.5%, the MARD was 19.2% and the median ARD was 16.8%. CEGA showed that 98.2% of values were in zone A (clinically accurate) and zone B (benign). Subgroups with lower accuracy metrics included those with severe anaemia, renal dysfunction and oedema. Application of a once-daily morning calibration schedule improved accuracy (MARD 11.4%). CONCLUSIONS/INTERPRETATION: The CGM accuracy when used in hospital may be lower than that reported in the outpatient setting, but this may be improved with appropriate patient selection and daily calibration. Further research is needed to understand the role of CGM in inpatient settings.

Obesity, Motility, Diet, and Intestinal Microbiota-Connecting the Dots.

PURPOSE OF REVIEW: The goal of the present review is to explore the relationship between dietary changes and alterations in gut microbiota that contribute to disorders of gut motility and obesity. RECENT FINDINGS: We review the microbiota changes that are seen in obesity, diarrhea, and constipation and look at potential mechanisms of how dysbiosis can predispose to these. We find that microbial metabolites, particularly short chain fatty acids, can lead to signaling changes in the host enterocytes. Microbial alteration leading to both motility disorders and obesity may be mediated by the release of hormones including glucagon-like peptides 1 and 2 (GLP-1, GLP-2) and polypeptide YY (PYY). These pathways provide avenues for microbiota-targeted interventions that can treat both disorders of motility and obesity. In summary, multiple mechanisms contribute to the interplay between the microbial dysbiosis, obesity, and dysmotility.

Leveraging Large Language Models to Analyze Continuous Glucose Monitoring Data: A Case Study.

Continuous glucose monitors (CGM) provide patients and clinicians with valuable insights about glycemic control that aid in diabetes management. The advent of large language models (LLMs), such as GPT-4, has enabled real-time text generation and summarization of medical data. Further, recent advancements have enabled the integration of data analysis features in chatbots, such that raw data can be uploaded and analyzed when prompted. Studying both the accuracy and suitability of LLM-derived data analysis performed on medical time series data, such as CGM data, is an important area of research. The objective of this study was to assess the strengths and limitations of using an LLM to analyze raw CGM data and produce summaries of 14 days of data for patients with type 1 diabetes. This study used simulated CGM data from 10 different cases. We first evaluated the ability of GPT-4 to compute quantitative metrics specific to diabetes found in an Ambulatory Glucose Profile (AGP). Then, using two independent clinician graders, we evaluated the accuracy, completeness, safety, and suitability of qualitative descriptions produced by GPT-4 across five different CGM analysis tasks. We demonstrated that GPT-4 performs well across measures of accuracy, completeness, and safety when producing summaries of CGM data across all tasks. These results highlight the capabilities of using an LLM to produce accurate and safe narrative summaries of medical time series data. We highlight several limitations of the work, including concerns related to how GPT-4 may misprioritize highlighting instances of hypoglycemia and hyperglycemia. Our work serves as a preliminary study on how generative language models can be integrated into diabetes care through CGM analysis, and more broadly, the potential to leverage LLMs for streamlined medical time series analysis.

Co-infection with coronavirus disease 2019, previously undiagnosed human immunodeficiency virus, Pneumocystis jirovecii pneumonia and cytomegalovirus pneumonitis, with possible immune reconstitution inflammatory syndrome.

BACKGROUND: Case reports, case series and cohort studies have been published describing the clinical course and outcomes of people living with human immunodeficiency virus (PLWH) who contract coronavirus disease 2019 (COVID-19) pneumonia. However, the majority of the published work focuses on patients with well-controlled human immunodeficiency virus (HIV) on antiretroviral therapy (ART). CASE PRESENTATION: We present a case of a new diagnosis of HIV with Acquired Immune Deficiency Syndrome (AIDS) made simultaneously to diagnosis of COVID-19, with co-infection with pneumocystis jirovecii pneumonia (PJP) and possible cytomegalovirus (CMV) pneumonitis. The patient decompensated following initiation of ART, suggestive of possible immune reconstitution inflammatory syndrome (IRIS). CONCLUSIONS: This case illustrates the importance of maintaining a high suspicion for HIV/AIDS in patients with risk factors. Additionally, this case raises the possibility that IRIS may develop in the setting of ART initiation in patients with COVID-19.

Legionella pulmonary abscess and pleural space infection in an immunocompetent patient.

A 34-year-old woman is admitted to the hospital with dyspnoea, dry cough and left-sided flank pain. Her Legionella urinary test was positive and CT imaging demonstrated multifocal pneumonia with pulmonary abscesses. Although she had initial clinical improvement on appropriate antibiotic therapy, her hospital course was complicated by worsening flank pain, hypoxemia and leucocytosis, prompting clinical re-evaluation and assessment for development of complications involving the pleural space. CT imaging revealed interval development of a loculated complicated parapneumonic effusion. Successful treatment required chest tube drainage assisted by fibrinolytic therapy. This case highlights the importance of considering Legionella in patients with pulmonary abscess, demonstrates an approach to a patient with a non-resolving pneumonia and illustrates the management of parapneumonic effusions.

Emerging trends and the clinical impact of food insecurity in patients with diabetes.

Food insecurity is a major public health concern in the United States affecting 15 million households according to data in 2017 from the US Department of Agriculture. Food insecurity, or the inability to consistently obtain nutritious food, disproportionately affects socioeconomically disadvantaged households, as well as those with chronic diseases including diabetes mellitus (DM). This review article explores the literature over the past 10 years pertaining to the complex relationship between food insecurity, social determinants of health, and chronic disease with an emphasis on diabetes and glycemic control. Those with diabetes and food insecurity together have been shown to have worse glycemic control compared to those who are food secure, but it remains unclear exactly how food insecurity affects glycemic control. Prior interventional studies have targeted aspects of food insecurity in patients with diabetes but have reported variable outcomes with respect to improvement in glycemic control despite effectively reducing rates of food insecurity. Additionally, few data exist regarding long-term outcomes and diabetes-related complications in this population. It is likely that many factors at both the community and individual levels impact glycemic control outcomes in the setting of food insecurity. Further studies are needed to better understand these factors and to create multifaceted targets for future interventional studies aimed at improving glycemic control in this population.

Diffuse Idiopathic Pulmonary Neuroendocrine Cell Hyperplasia (DIPNECH) with liver metastases.

Diffuse Idiopathic Pulmonary Neuroendocrine Cell Hyperplasia (DIPNECH) is a rare pulmonary disorder characterised by classic radiological findings and symptoms of obstructive lung disease. DIPNECH is considered a precursor to carcinoid tumours in the lungs. In this case, we describe a patient with years of unexplained dry cough presenting with 2 weeks of progressive nausea and vomiting, and found to have massive hepatomegaly on examination. By CT-PE, she was diagnosed with DIPNECH, and abdominal MRI revealed metastatic carcinoid tumours. Despite its non-specific presentation, DIPNECH has characteristic radiological findings of mosaic attenuation with numerous pulmonary nodules. DIPNECH requires early identification and close surveillance to prevent progression to carcinoid tumours. Thus, it is critical for frontline providers to consider this diagnosis as part of their differential when other common causes of obstructive lung disease have been ruled out.