medRxiv : the preprint server for health sciences最新文献

A fundamental question of any program focused on the testing and timely diagnosis of a communicable disease is its effectiveness in reducing transmission. Here, we introduce testing effectiveness (TE)-the fraction by which testing and post-diagnosis isolation reduce transmission at the population scale-and a model that incorporates test specifications and usage, within-host pathogen dynamics, and human behaviors to estimate TE. Using TE to guide recommendations, we show that today's rapid diagnostics should be used immediately upon symptom onset to control influenza A and respiratory syncytial virus (RSV), but delayed by up to 2d to control omicron-era SARS-CoV-2. Furthermore, while rapid tests are superior to RT-qPCR for control of founder-strain SARS-CoV-2, omicron-era changes in viral kinetics and rapid test sensitivity cause a reversal, with higher TE for RT-qPCR despite longer turnaround times. Finally, we illustrate the model's flexibility by quantifying tradeoffs in the use of post-diagnosis testing to shorten isolation times.

Background: Pathogenic variants in cancer susceptibility genes can now be tested efficiently and economically with the wide availability of multi-gene panel testing. This has resulted in an unprecedented rate of identifying individuals carrying pathogenic variants. These carriers need to be counselled about their future cancer risk conferred by the specific gene mutation. An important cancer susceptibility gene is PALB2. Several studies reported risk estimates for breast cancer (BC) associated with pathogenic variants in PALB2. Because of the variety of modalities (age specific risk, odds ratio, relative risk, and standardized incidence ratio) and effect sizes of these risk estimates, a meta-analysis of all of these estimates of BC risk is necessary to provide accurate counseling of patients with pathogenic variants in PALB2. The challenge, though, in combining these estimates is the heterogeneity of studies in terms of study design and risk measure.

Methods: We utilized a recently proposed novel Bayesian random-effects meta-analysis method that can synthesize and combine information from such heterogeneous studies. We applied this method to combine estimates from twelve different studies on BC risk for carriers of pathogenic PALB2 mutations, out of which two report age-specific penetrance, one reports relative risk, and nine report odds ratios.

Results: The estimated overall (meta-analysis based) risk of BC is 12.80% by age 50 (6.11%- 22.59%) and 48.47% by age 80 (36.05%-61.74%).

Conclusion: Pathogenic mutations in PALB2 makes women more susceptible to BC. Our risk estimates can help clinically manage patients carrying pathogenic variants in PALB2.

Importance: Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up.

Objective: A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler. Here, we deploy DASSi to patients with no or mild/moderate AS at baseline to identify AS development and progression.

Design setting and participants: We defined two cohorts of patients without severe AS undergoing echocardiography in the Yale-New Haven Health System (YNHHS) (2015-2021, 4.1[IQR:2.4-5.4] follow-up years) and Cedars-Sinai Medical Center (CSMC) (2018-2019, 3.4[IQR:2.8-3.9] follow-up years). We further developed a novel computational pipeline for the cross-modality translation of DASSi into cardiac magnetic resonance (CMR) imaging in the UK Biobank (2.5[IQR:1.6-3.9] follow-up years). Analyses were performed between August 2023-February 2024.

Exposure: DASSi (range: 0-1) derived from AI applied to echocardiography and CMR videos.

Main outcomes and measures: Annualized change in peak aortic valve velocity (AV-V_max) and late (>6 months) aortic valve replacement (AVR).

Results: A total of 12,599 participants were included in the echocardiographic study (YNHHS: n=8,798, median age of 71 [IQR (interquartile range):60-80] years, 4250 [48.3%] women, and CSMC: n=3,801, 67 [IQR:54-78] years, 1685 [44.3%] women). Higher baseline DASSi was associated with faster progression in AV-V_max (per 0.1 DASSi increments: YNHHS: +0.033 m/s/year [95%CI:0.028-0.038], n=5,483, and CSMC: +0.082 m/s/year [0.053-0.111], n=1,292), with levels ≥ vs <0.2 linked to a 4-to-5-fold higher AVR risk (715 events in YNHHS; adj.HR 4.97 [95%CI: 2.71-5.82], 56 events in CSMC: 4.04 [0.92-17.7]), independent of age, sex, ethnicity/race, ejection fraction and AV-V_max. This was reproduced across 45,474 participants (median age 65 [IQR:59-71] years, 23,559 [51.8%] women) undergoing CMR in the UK Biobank (adj.HR 11.4 [95%CI:2.56-50.60] for DASSi ≥vs<0.2). Saliency maps and phenome-wide association studies supported links with traditional cardiovascular risk factors and diastolic dysfunction.

Conclusions and relevance: In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker is independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

In early 2020, the Coronavirus Disease 19 (COVID-19) rapidly spread across the United States (US), exhibiting significant geographic variability. While several studies have examined the predictive relationships of differing factors on COVID-19 deaths, few have looked at spatiotemporal variation at refined geographic scales. The objective of this analysis is to examine this spatiotemporal variation in COVID-19 deaths with respect to association with socioeconomic, health, demographic, and political factors. We use multivariate regression applied to Health and Human Services (HHS) regions as well as nationwide county-level geographically weighted random forest (GWRF) models. Analyses were performed on data from three separate time frames which correspond to the spread of distinct viral variants in the US: pandemic onset until May 2021, May 2021 through November 2021, and December 2021 until April 2022. Multivariate regression results for all regions across three time windows suggest that existing measures of social vulnerability for disaster preparedness (SVI) are predictive of a higher degree of mortality from COVID-19. In comparison, GWRF models provide a more robust evaluation of feature importance and prediction, exposing the value of local features for prediction, such as obesity, which is obscured by coarse-grained analysis. Overall, GWRF results indicate that this more nuanced modeling strategy is useful for determining the spatial variation in the importance of sociodemographic risk factors for predicting COVID-19 mortality.

For in vitro neutrophil functional assays, neutrophils are typically isolated from whole blood, having the target cells exposed to an artificial microenvironment with altered kinetics. Isolated neutrophils exhibit limited lifespans of only a few hours ex vivo, significantly shorter than the 3-5 day lifespan of neutrophils in vivo. In addition, due to neutrophil inherently high sensitivity, neutrophils removed from whole blood exhibit stochastic non-specific activation that contributes to assay variability. Here we present a method - named micro-Blood - that enables functional neutrophil assays using a microliter of unprocessed whole blood. micro-Blood allows multiple phenotypic readouts of neutrophil function (including cell/nucleus morphology, motility, recruitment, and pathogen control). In micro-Blood, neutrophils show sustained migration and limited non-specific activation kinetics (<0.1% non-specific activation) over 3-6 days. In contrast, neutrophils isolated using traditional methods show increased and divergent activation kinetics (10-70% non-specific activation) in only 3 h. Finally, micro-Blood allows the capture and quantitative comparison of distinct neutrophil functional heterogeneity between healthy donors and cancer patients in response to microbial stimuli with the preserved physiological lifespan over 6 days.

Objective: Large Language Models such as GPT-4 previously have been applied to differential diagnostic challenges based on published case reports. Published case reports have a sophisticated narrative style that is not readily available from typical electronic health records (EHR). Furthermore, even if such a narrative were available in EHRs, privacy requirements would preclude sending it outside the hospital firewall. We therefore tested a method for parsing clinical texts to extract ontology terms and programmatically generating prompts that by design are free of protected health information.

Materials and methods: We investigated different methods to prepare prompts from 75 recently published case reports. We transformed the original narratives by extracting structured terms representing phenotypic abnormalities, comorbidities, treatments, and laboratory tests and creating prompts programmatically.

Results: Performance of all of these approaches was modest, with the correct diagnosis ranked first in only 5.3-17.6% of cases. The performance of the prompts created from structured data was substantially worse than that of the original narrative texts, even if additional information was added following manual review of term extraction. Moreover, different versions of GPT-4 demonstrated substantially different performance on this task.

Discussion: The sensitivity of the performance to the form of the prompt and the instability of results over two GPT-4 versions represent important current limitations to the use of GPT-4 to support diagnosis in real-life clinical settings.

Conclusion: Research is needed to identify the best methods for creating prompts from typically available clinical data to support differential diagnostics.

Early detection of sepsis in patients admitted to the emergency department (ED) is an important clinical objective as early identification and treatment can help reduce morbidity and mortality rate of 20% or higher. Hematologic changes during sepsis-associated organ dysfunction are well established and a new biomarker called Monocyte Distribution Width (MDW) has been recently approved by the US Food and Drug Administration for sepsis. However, MDW, which quantifies monocyte activation in sepsis patients, is not a routinely reported parameter and it requires specialized proprietary laboratory equipment. Further, the relative importance of MDW as compared to other routinely available hematologic parameters and vital signs has not been studied, which makes it difficult for resource constrained hospital systems to make informed decisions in this regard. To address this issue, we analyzed data from a cohort of ED patients (n=10,229) admitted to a large regional safety-net hospital in Cleveland, Ohio with suspected infection who later developed poor outcomes associated with sepsis. We developed a new analytical framework consisting of seven data models and an ensemble of high accuracy machine learning (ML) algorithms (accuracy values ranging from 0.83 to 0.90) for the prediction of outcomes more common in sepsis than uncomplicated infection (3-day intensive care unit stay or death). To characterize the contributions of individual hematologic parameters, we applied the Local Interpretable Model-Agnostic Explanation (LIME) and Shapley Additive Value (SHAP) interpretability methods to the high accuracy ML algorithms. The ML interpretability results were consistent in their findings that the value of MDW is grossly attenuated in the presence of other routinely reported hematologic parameters and vital signs data. Further, this study for the first time shows that complete blood count with differential (CBC-DIFF) together with vital signs data can be used as a substitute for MDW in high accuracy ML algorithms to screen for poor outcomes associated with sepsis.

Background: Obesity is associated with obstructive sleep apnea (OSA) and cardiovascular risk. Positive airway pressure (PAP) is the first line treatment for OSA, but evidence on its beneficial effect on major adverse cardiovascular events (MACE) prevention is limited. Using claims data, the effects of PAP on mortality and incidence of MACE among Medicare beneficiaries with OSA were examined.

Methods: A cohort of Medicare beneficiaries with ≥2 distinct OSA claims was defined from multi-state, state-wide, multi-year (2011-2020) Medicare fee-for-service claims data. Evidence of PAP initiation and utilization was based on PAP claims after OSA diagnosis. MACE was defined as a composite of myocardial infarction, heart failure, stroke, or coronary revascularization. Doubly robust Cox proportional hazards models with inverse probability of treatment weights estimated treatment effects controlling for sociodemographic and clinical factors.

Results: Among 888,835 beneficiaries with OSA (median age 73 years; 43.9% women; median follow-up 1,141 days), those with evidence of PAP initiation (32.6%) had significantly lower all-cause mortality (HR [95%CI]: 0.53 [0.52-0.54]) and MACE incidence risk (0.90 [0.89-0.91]). Higher quartiles of annual PAP claims were progressively associated with lower mortality (Q2: 0.84 [0.81-0.87], Q3: 0.76 [0.74-0.79], Q4: 0.74 [0.72-0.77]) and MACE incidence risk (Q2: 0.92 [0.89-0.95], Q3: 0.89 [0.86-0.91], Q4: 0.87 [0.85-0.90]).

Conclusion: PAP utilization was associated with lower all-cause mortality and MACE incidence among Medicare beneficiaries with OSA. Results might inform trials assessing the importance of OSA therapy towards minimizing cardiovascular risk and mortality in older adults.

Neuroinflammation through enhanced innate immunity is thought play a role in the pathogenesis of Parkinson's disease (PD). Methods for monitoring neuroinflammation in living patients with PD are currently limited to positron emission tomography (PET) ligands that lack specificity in labeling immune cells in the nervous system. The colony stimulating factor 1 receptor (CSF1R) plays a crucial role in microglial function, an important cellular contributor to the nervous system's innate immune response. Using immunologic methods, we show that CSF1R in human brain is colocalized with the microglial marker, ionized calcium binding adaptor molecule 1 (Iba1). In PD, CSF1R immunoreactivity is significantly increased in PD across multiple brain regions, with the largest differences in the midbrain versus controls. Autoradiography revealed significantly increased [³H]JHU11761 binding in the inferior parietal cortex of PD patients. PET imaging demonstrated that higher [¹¹C]CPPC binding in the striatum was associated with greater motor disability in PD. Furthermore, increased [¹¹C]CPPC binding in various regions correlated with more severe motor disability and poorer verbal fluency. This study finds that CSF1R expression is elevated in PD and that [¹¹C]CPPC-PET imaging of CSF1R is indicative of motor and cognitive impairments in the early stages of the disease. Moreover, the study underscores the significance of CSF1R as a promising biomarker for neuroinflammation in Parkinson's disease, suggesting its potential use for non-invasive assessment of disease progression and severity, leading to earlier diagnosis and targeted interventions.