Patient care in the United States has become increasingly more fragmented, and the discharge summary serves as a critical tool for transmitting information on a patient's hospital admission to the primary care clinician. Some guidelines regarding how to write discharge summaries exist, but few are focused on prioritizing content that is most important to optimize a patient's transition of care.We conducted a national survey across various medical primary care specialties, including trainees and advanced practice providers, to understand the priorities of primary care clinicians. We distributed the survey to 2184 clinicians affiliated with 8 large academic institutions. Our response rate was 21%.Hospital course, discharge diagnoses, medication reconciliation, and follow-up sections were ranked as the most important categories with a 95.5% concordance rate among surveyed institutions. The least important sections were contact numbers for inpatient clinicians, ancillary services, weight-bearing status, and wound care. Similar themes were also identified via consensus review of the free-texted comments, adding that discharge summary style was also important. Other identified barriers to high-quality transition of care are both the limited time primary care clinicians can spend reviewing discharge summaries and lack of adequate communication between hospitalists and the outpatient clinician.High-yield content should be presented at the beginning of the discharge summary and conveyed in a brief, succinct manner to ensure maximal utility of the document as a transition of care tool.
Abstract White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables us to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based grey and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in grey and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.
Background With the increase use of pay for performance in healthcare, 30-day readmissions after discharges are critically important. Objective A team-based psychiatric consultation approach was tested in an inpatient hospital setting. This is the first study that examines 30-day readmission rate with this approach. Methods In this quality improvement study, 164 patients received a team-based psychiatric consultation that included daily meetings during the weekdays between psychiatrists and hospitalists and 436 received care of treatment-as-usual or traditional consultation–liaison services. Results Overall 30-day readmission rate was not significantly different between intervention and nonintervention groups. However, in subgroups with high risk of mortality or severe illness, the intervention group had a 0% 30-day readmission rate for both high risk of mortality and severe illness subgroups, while the nonintervention group’s readmission rate was 5% for high risk of mortality group and 3% for severely ill patients. Annual hospital cost saving is estimated between a quarter million and 1.5 million dollars for these subgroups. Conclusion The team-based psychiatric consultation approach demonstrated the potential for substantial cost savings in providing care for patients with high risk of mortality and severe illness. Thus, this intervention may be very useful in caring for patients with complex chronic conditions.
Abstract Background The striking increase in COVID-19 severity in older adults provides a clear example of immunesenescence, the age-related remodelling of the immune system. To better characterise the association between convalescent immunesenescence and acute disease severity, we determined the immune phenotype of COVID-19 survivors and non-infected controls. Results We performed detailed immune phenotyping of peripheral blood mononuclear cells isolated from 103 COVID-19 survivors 3–5 months post recovery who were classified as having had severe ( n = 56; age 53.12 ± 11.30 years), moderate ( n = 32; age 52.28 ± 11.43 years) or mild ( n = 15; age 49.67 ± 7.30 years) disease and compared with age and sex-matched healthy adults ( n = 59; age 50.49 ± 10.68 years). We assessed a broad range of immune cell phenotypes to generate a composite score, IMM-AGE, to determine the degree of immune senescence. We found increased immunesenescence features in severe COVID-19 survivors compared to controls including: a reduced frequency and number of naïve CD4 and CD8 T cells ( p < 0.0001); increased frequency of EMRA CD4 ( p < 0.003) and CD8 T cells ( p < 0.001); a higher frequency ( p < 0.0001) and absolute numbers ( p < 0.001) of CD28 −ve CD57 +ve senescent CD4 and CD8 T cells; higher frequency ( p < 0.003) and absolute numbers ( p < 0.02) of PD-1 expressing exhausted CD8 T cells; a two-fold increase in Th17 polarisation ( p < 0.0001); higher frequency of memory B cells ( p < 0.001) and increased frequency ( p < 0.0001) and numbers ( p < 0.001) of CD57 +ve senescent NK cells. As a result, the IMM-AGE score was significantly higher in severe COVID-19 survivors than in controls ( p < 0.001). Few differences were seen for those with moderate disease and none for mild disease. Regression analysis revealed the only pre-existing variable influencing the IMM-AGE score was South Asian ethnicity ( $$\beta$$ β = 0.174, p = 0.043), with a major influence being disease severity ( $$\beta$$ β = 0.188, p = 0.01). Conclusions Our analyses reveal a state of enhanced immune ageing in survivors of severe COVID-19 and suggest this could be related to SARS-Cov-2 infection. Our data support the rationale for trials of anti-immune ageing interventions for improving clinical outcomes in these patients with severe disease.
Abstract The COVID-19 pandemic continues to impose a significant burden on global health infrastructure. While identification and containment of new cases remains important, laboratories must now pivot and consider assessment of SARS-CoV-2 immunity in the setting of the recent availability of multiple COVID-19 vaccines. Here we have utilized the latest Abbott Alinity semi-quantitative IgM and quantitative IgG spike protein (SP) serology assays (IgM SP and IgG SP ) in combination with Abbott Alinity IgG nucleocapsid (NC) antibody test (IgG NC ) to assess antibody responses in a cohort of 1236 unique participants comprised of naïve, SARS-CoV-2 infected, and vaccinated (including both naïve and recovered) individuals. The IgM SP and IgG SP assays were highly specific (100%) with no cross-reactivity to archived samples recovered prior to the emergence of SARS-CoV-2, including those from individuals with seasonal coronavirus infections. Clinical sensitivity was 96% after 15 days for both IgM SP and IgG SP assays individually. When considered together, the sensitivity was 100%. A combination of NC- and SP-specific serologic assays clearly differentiated naïve, SARS-CoV-2-infected, and vaccine-related immune responses. Vaccination resulted in a significant increase in IgG SP and IgM SP titers, with a major rise in IgG SP following the booster (second) dose in the naïve group. In contrast, SARS-CoV-2 recovered individuals had several fold higher IgG SP responses than naïve following the primary dose, with a comparatively dampened response following the booster. This work illustrates the strong clinical performance of these new serological assays and their utility in evaluating and distinguishing serological responses to infection and vaccination.
This illustration is a humorous take on experts' disagreement about the care of a patient.Breaking the deadlock requires much effort, and a focus on the patient can restore common ground.Patients should always be the center of our care universe.
Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since birth. Thus, it conveys poorly recent or contemporaneous aging trends, which can be better quantified by the (temporal) pace P of brain aging. Many approaches to map P , however, rely on quantifying DNA methylation in whole-blood cells, which the blood–brain barrier separates from neural brain cells. We introduce a three-dimensional convolutional neural network (3D-CNN) to estimate P noninvasively from longitudinal MRI. Our longitudinal model (LM) is trained on MRIs from 2,055 CN adults, validated in 1,304 CN adults, and further applied to an independent cohort of 104 CN adults and 140 patients with Alzheimer’s disease (AD). In its test set, the LM computes P with a mean absolute error (MAE) of 0.16 y (7% mean error). This significantly outperforms the most accurate cross-sectional model, whose MAE of 1.85 y has 83% error. By synergizing the LM with an interpretable CNN saliency approach, we map anatomic variations in regional brain aging rates that differ according to sex, decade of life, and neurocognitive status. LM estimates of P are significantly associated with changes in cognitive functioning across domains. This underscores the LM’s ability to estimate P in a way that captures the relationship between neuroanatomic and neurocognitive aging. This research complements existing strategies for AD risk assessment that estimate individuals’ rates of adverse cognitive change with age.
Longitudinal imaging data are routinely acquired for health studies and patient monitoring. A central goal in longitudinal studies is tracking relevant change over time. Traditional methods remove nuisance variation with custom pipelines to focus on significant changes. In this work, we present a machine learning–based method that automatically ignores irrelevant changes and extracts the time-varying signal of interest. Our method, called Learning-based Inference of Longitudinal imAge Changes (LILAC), performs a pairwise comparison of longitudinal images in order to make a temporal difference prediction. LILAC employs a convolutional Siamese architecture to extract feature pairs, followed by subtraction and a bias-free fully connected layer to learn meaningful temporal image differences. We first showcase LILAC’s ability to capture key longitudinal changes by simply training it to predict the temporal ordering of images. In our experiments, temporal ordering accuracy exceeded 0.98, and predicted time differences were strongly correlated with actual changes in relevant variables (Pearson Correlation Coefficient r = 0.911 with embryo phase change, and r = 0.875 with time interval in wound healing). Next, we trained LILAC to explicitly predict specific targets, such as the change in clinical scores in patients with mild cognitive impairment. LILAC models achieved over a 40% reduction in root mean square error compared to baseline methods. Our empirical results demonstrate that LILAC effectively localizes and quantifies relevant individual-level changes in longitudinal imaging data, offering valuable insights for studying temporal mechanisms or guiding clinical decisions.
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