Home healthcare patient, caregiver and provider perspectives on use of unscheduled acute care and the usability and acceptability of on-demand telehealth solutions.

BACKGROUND/OBJECTIVES: Home health care (HHC) agencies provide an important role in helping to transition patients from acute care to independent residential living. Telehealth has the potential to transform care delivery in HHC, however the majority of studies in HHC have focused on the use of telemonitoring for patients with specific chronic conditions. The objective of this study was to examine reasons HHC patients use acute care services and assess the acceptability of on-demand telehealth services among HHC patients, caregivers and personnel to help alleviate the need for seeking in-person acute care. Design/Setting/Participants/Measures: This study was a secondary analysis of qualitative data from in-depth interviews of 30 HHC personnel, patients and caregivers from a Medicare-certified HHC agency affiliated with a large healthcare system from January through May 2020. A conventional content analysis approach was used to identify themes. RESULTS: Themes associated with reasons for seeking acute care included: sense of urgency, behavioral and psychosocial factors, and access to care. Participants described their perceptions of the benefits, usability and acceptability and barriers to using telehealth. Patients and HHC personnel agreed that on-demand telehealth should not replace in-person visits but all identified roles that on-demand telehealth services could play in improving communication and access to care. The biggest barriers to use of telehealth identified by HHC personnel were cost, access and ability to use technology by HHC patients. CONCLUSION: This study identified reasons HHC patients seek unscheduled acute care and the usability and acceptability of on-demand telehealth services to increase access to care among HHC patients. These findings underscore the need to improve communication and coordination between patients, HHC personnel, and primary care providers and the role that on-demand telehealth services can have in transforming HHC.

Non-HLA type 1 diabetes genes modulate disease risk together with HLA-DQ and islet autoantibodies.

The possible interrelations between human leukocyte antigen (HLA)-DQ, non-HLA single-nucleotide polymorphisms (SNPs) and islet autoantibodies were investigated at clinical onset in 1-34-year-old type 1 diabetes (T1D) patients (n=305) and controls (n=203). Among the non-HLA SNPs reported by the Type 1 Diabetes Genetics Consortium, 24% were supported in this Swedish replication set including that the increased risk of minor PTPN22 allele and high-risk HLA was modified by GAD65 autoantibodies. The association between T1D and the minor AA+AC genotype in ERBB3 gene was stronger among IA-2 autoantibody-positive patients (comparison P=0.047). The association between T1D and the common insulin (AA) genotype was stronger among insulin autoantibody (IAA)-positive patients (comparison P=0.008). In contrast, the association between T1D and unidentified 26471 gene was stronger among IAA-negative (comparison P=0.049) and IA-2 autoantibody-negative (comparison P=0.052) patients. Finally, the association between IL2RA and T1D was stronger among IAA-positive than among IAA-negative patients (comparison P=0.028). These results suggest that the increased risk of T1D by non-HLA genes is often modified by both islet autoantibodies and HLA-DQ. The interactions between non-HLA genes, islet autoantibodies and HLA-DQ should be taken into account in T1D prediction studies as well as in prevention trials aimed at inducing immunological tolerance to islet autoantigens.

Risk factors for methicillin-resistant Staphylococcus aureus bacteraemia differ depending on the control group chosen.

Methicillin-resistant Staphylococcus aureus (MRSA) bacteraemia cause significant morbidity and mortality in hospitalized patients. Using a nested case-control design, 204 MRSA bacteraemia cases were compared to 301 unmatched methicillin-susceptible Staphylococcus aureus (MSSA) bacteraemia controls and were matched 1:2 with non-infected controls. The independent risk factors for MRSA bacteraemia compared to MSSA bacteraemia were older age (P = 0·048), major organ transplant during current hospital stay (P = 0·016) and quinolone use (P = 0·016). Cases were more likely than non-infected controls to have renal failure (P = 0·003), cirrhosis (P = 0·013), and a central venous catheter (P = 0·003) after controlling for other risk factors. This large case-control study made it possible to assess risk factors for MRSA bacteraemia using two sets of controls and showed that risk factors differed greatly depending on the control group chosen. These results confirm the need for careful selection of appropriate control groups and the need to carefully adjust for underlying severity of illness.

Islet autoantibodies and residual beta cell function in type 1 diabetes children followed for 3-6 years.

AIMS: To test if islet autoantibodies at diagnosis of type 1 diabetes (T1DM) and after 3-6 years with T1D predict residual beta-cell function (RBF) after 3-6 years with T1D. METHODS: T1D children (n=260, median age at diagnosis 9.4, range 0.9-14.7 years) were tested for GAD65, IA-2, ZnT8R, ZnT8W and ZnT8Q autoantibodies (A) at diagnosis, and 3-6 years after diagnosis when also fasting and stimulated RBF were determined. RESULTS: For every 1-year increase in age at diagnosis of TID, the odds of detectable C-peptide increased 1.21 (1.09, 1.34) times for fasting C-peptide and 1.28 (1.15, 1.42) times for stimulated C-peptide. Based on a linear model for subjects with no change in IA-2A levels, the odds of detectable C-peptide were 35% higher than for subjects whose IA-2A levels decreased by half (OR=1.35 (1.09, 1.67), p=0.006); similarly for ZnT8WA (OR=1.39 (1.09, 1.77), p=0.008) and ZnT8QA (OR=1.55 (1.06, 2.26) p=0.024). Such relationship was not detected for GADA or ZnT8RA. All OR adjusted for confounders. CONCLUSIONS: Age at diagnosis with T1D was the major predictor of detectable C-peptide 3-6 years post-diagnosis. Decreases in IA-2A, and possibly ZnT8A, levels between diagnosis and post-diagnosis were associated with a reduction in RBF post-diagnosis.

The association between the PTPN22 1858C>T variant and type 1 diabetes depends on HLA risk and GAD65 autoantibodies.

The single nucleotide polymorphism 1858C>T in the PTPN22 gene is associated with type 1 diabetes (T1D) in several populations. Earlier reports have suggested that the association may be modified by human leukocyte antigen (HLA), as well as by islet autoantibodies. In a large case-control study of Swedish incident T1D patients and controls, 0-34 years of age, we tested whether the odds ratio (OR) measure of association was dependent on HLA or autoantibodies against the islet autoantigens glutamic acid decarboxylase 65 kDa autoantibodies (GADA), insulin, islet antigen-2, or islet cell. The association between the carrier status of 1858C>T allele in PTPN22 (PTPN22(CT+TT)) and T1D was modified by HLA. In addition, in GADA-positive T1D, the OR was 2.83 (2.00, 3.99), whereas in GADA-negative T1D, the OR was 1.41 (0.98, 2.04) (P for comparison=0.007). The OR of association between PTPN22(CT+TT) and GADA-positive T1D declined with increasing HLA-risk category from 6.12 to 1.54 (P=0.003); no such change was detected in GADA-negative T1D (P=0.722) (P for comparison=0.001). However, the absolute difference in risk between PTPN22(CC) and PTPN22(CT+TT) subjects with high-risk HLA was five times higher than that for subjects with low-risk HLA. We hypothesize that the altered T-cell function because of the PTPN22(1858C>T) polymorphism is exclusively associated with GADA-positive T1D at diagnosis.

Characterization and functional role of voltage gated cation conductances in the glucagon-like peptide-1 secreting GLUTag cell line.

Glucagon-like peptide-1 (GLP-1) is released from intestinal L-cells in response to nutrient ingestion. It is currently under therapeutic evaluation because it enhances insulin secretion in type 2 diabetes. Previous studies using the GLP-1 secreting cell line GLUTag have shown that the cells are electrically active, and that the action potential frequency is regulated by nutrients. In this study we characterize voltage gated currents underlying this electrical activity and correlate the electrophysiological findings with gene expression determined by microarrays. Whole cell voltage clamp experiments designed to separate different ionic components revealed rapidly inactivating sodium currents sensitive to tetrodotoxin, calcium currents sensitive to nifedipine and omega-conotoxin GVIA, and sustained as well as rapidly inactivating potassium currents, which were sensitive to TEA and 4-AP, respectively. In perforated patch experiments we also observed hyperpolarization-activated currents which were inhibited by ZD7288. The amplitude of the sodium current was approximately 10 times that of the other depolarizing currents and tetrodotoxin abolished action potential firing. In secretion experiments, however, nifedipine, but not tetrodotoxin, omega-conotoxin GVIA or ZD7288, inhibited glucose-induced GLP-1 release. Consistent with this finding, the intracellular Ca2+ response to glucose was impaired by nifedipine but not by tetrodotoxin. Thus, in GLUTag cells, GLP-1 release is not dependent on the firing of Na+-carrying action potentials but requires membrane depolarization and Ca2+ entry through L-type Ca2+ channels. Understanding the characteristics of the currents and the molecular identification of the underlying channels in GLP-1 secreting cells might facilitate the development of agents to enhance GLP-1 secretion in vivo.

Binary tree-structured vector quantization approach to clustering and visualizing microarray data.

MOTIVATION: With the increasing number of gene expression databases, the need for more powerful analysis and visualization tools is growing. Many techniques have successfully been applied to unravel latent similarities among genes and/or experiments. Most of the current systems for microarray data analysis use statistical methods, hierarchical clustering, self-organizing maps, support vector machines, or k-means clustering to organize genes or experiments into 'meaningful' groups. Without prior explicit bias almost all of these clustering methods applied to gene expression data not only produce different results, but may also produce clusters with little or no biological relevance. Of these methods, agglomerative hierarchical clustering has been the most widely applied, although many limitations have been identified. RESULTS: Starting with a systematic comparison of the underlying theories behind clustering approaches, we have devised a technique that combines tree-structured vector quantization and partitive k-means clustering (BTSVQ). This hybrid technique has revealed clinically relevant clusters in three large publicly available data sets. In contrast to existing systems, our approach is less sensitive to data preprocessing and data normalization. In addition, the clustering results produced by the technique have strong similarities to those of self-organizing maps (SOMs). We discuss the advantages and the mathematical reasoning behind our approach.