Combinatorial expression motifs in signaling pathways

In animal cells, molecular pathways often comprise families of variant components, such as ligands or receptors. These pathway components are differentially expressed by different cell types, potentially tailoring pathway function to cell context. However, it has remained unclear how pathway expression profiles are distributed across cell types and whether similar profiles can occur in dissimilar cell types. Here, using single-cell gene expression datasets, we identified pathway expression motifs, defined as recurrent expression profiles that are broadly distributed across diverse cell types. Motifs appeared in core pathways, including TGF-β, Notch, Wnt, and the SRSF splice factors, and involved combinatorial co-expression of multiple components. Motif usage was weakly correlated between pathways in adult cell types and during dynamic developmental transitions. Together, these results suggest a mosaic view of cell type organization, in which different cell types operate many of the same pathways in distinct modes.

Informing healthcare operations with integrated pathology, clinical, and epidemiology data: Lessons from a single institution in Kenya during COVID-19 waves

Pathology, clinical care teams, and public health experts often operate in silos. We hypothesized that large data sets from laboratories when integrated with other healthcare data can provide evidence that can be used to optimize planning for healthcare needs, often driven by health-seeking or delivery behavior. From the hospital information system, we extracted raw data from tests performed from 2019 to 2021, prescription drug usage, and admission patterns from pharmacy and nursing departments during the COVID-19 pandemic in Kenya (March 2020 to December 2021). Proportions and rates were calculated. Regression models were created, and a t-test for differences between means was applied for monthly or yearly clustered data compared to pre-COVID-19 data. Tests for malaria parasite, Mycobacterium tuberculosis, rifampicin resistance, blood group, blood count, and histology showed a statistically significant decrease in 2020, followed by a partial recovery in 2021. This pattern was attributed to restrictions implemented to control the spread of COVID-19. On the contrary, D-dimer, fibrinogen, CRP, and HbA1c showed a statistically significant increase (p-value <0.001). This pattern was attributed to increased utilization related to the clinical management of COVID-19. Prescription drug utilization revealed a non-linear relationship to the COVID-19 positivity rate. The results from this study reveal the expected scenario in the event of similar outbreaks. They also reveal the need for increased efforts at diabetes and cancer screening, follow-up of HIV, and tuberculosis patients. To realize a broader healthcare impact, pathology departments in Africa should invest in integrated data analytics, for non-communicable diseases as well.

COVID-19 Surveillance in the Biobank at the Colorado Center for Personalized Medicine: Observational Study

BACKGROUND

Characterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources, such as electronic health records (EHRs) or cross-sectional surveys.

OBJECTIVE

This study aims to describe testing behaviors, symptoms, impact, vaccination status, and case ascertainment during the COVID-19 pandemic using integrated data sources.

METHODS

In summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM; N=180,599) about their experience with COVID-19. The prevalence of testing, symptoms, and impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Survey respondents who reported receiving a positive COVID-19 test result were considered a "confirmed case" of COVID-19. Using EHRs, we compared COVID-19 case ascertainment and characteristics in EHRs versus the survey. Positive cases were identified in EHRs using the International Statistical Classification of Diseases, 10th revision (ICD-10) diagnosis codes, health care encounter types, and encounter primary diagnoses.

RESULTS

Of the 25,063 (13.9%) survey respondents, 10,661 (42.5%) had been tested for COVID-19, and of those, 1366 (12.8%) tested positive. Nearly half of those tested had symptoms or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (n=13,688, 54.6%) and family life (n=12,233, 48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. Of the 10,249 individuals who responded to vaccination questions from version 2 of the survey (summer 2021), 9770 (95.3%) had received the vaccine. After integration with EHR data up to the time of the survey completion, 1006 (4%) of the survey respondents had a discordant COVID-19 case status between EHRs and the survey. Using all longitudinal EHR and survey data, we identified 11,472 (6.4%) COVID-19-positive cases among Biobank participants. In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic.

CONCLUSIONS

We found that the COVID-19 pandemic has had far-reaching and varying effects among our Biobank participants. Integrated data assets, such as the Biobank at the CCPM, are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic.

Predicting the Aortic Aneurysm Postoperative Risks Based on Russian Integrated Data

This article describes the results of feature extraction from unstructured medical records and prediction of postoperative complications for patients with thoracic aortic aneurysm operations using machine learning algorithms. The datasets from two different medical centers were integrated. Seventy-two features were extracted from Russian unstructured medical records. We formulated 8 target features: Mortality, Temporary neurological deficit (TND), Permanent neurological deficit (PND), Prolonged (> 7 days) lung ventilation (LV), Renal replacement therapy (RRT), Bleeding, Myocardial infarction (MI), Multiple organ failure (MOF). XGBoost showed the best performance for most target variables (F-measure 0.74-0.95) which is comparable to recent results in cardiovascular postoperative risks prediction.

A randomized trial of permanent supportive housing for chronically homeless persons with high use of publicly funded services

Objective

To examine whether randomization to permanent supportive housing (PSH) versus usual care reduces the use of acute health care and other services among chronically homeless high users of county‐funded services.

Data Sources

Between 2015 and 2019, we assessed service use from Santa Clara County, CA, administrative claims data for all county‐funded health care, jail and shelter, and mortality.

Study Design

We conducted a randomized controlled trial among chronically homeless high users of multiple systems. We compared postrandomization outcomes from county‐funded systems using multivariate regression analysis.

Data Collection

We extracted encounter data from an integrated database capturing health care at county‐funded facilities, shelter and jails, county housing placement, and death certificates.

Principal Findings

We enrolled 423 participants (199 intervention; 224 control). Eighty‐six percent of those randomized to PSH received housing compared with 36 percent in usual care. On average, the 169 individuals housed by the PSH intervention have remained housed for 28.8 months (92.9 percent of the study follow‐up period). Intervention group members had lower rates of psychiatric ED visits IRR 0.62; 95% CI [0.43, 0.91] and shelter days IRR 0.30; 95% CI [0.17, 0.53], and higher rates of ambulatory mental health services use IRR 1.84; 95% CI [1.43, 2.37] compared to controls. We found no differences in total ED or inpatient use, or jail. Seventy (37 treatment; 33 control) participants died.

Conclusions

The intervention placed and retained frequent user, chronically homeless individuals in housing. It decreased psychiatric ED visits and shelter use, and increased outpatient mental health care, but not medical ED visits or hospitalizations. Limitations included more than one‐third of usual care participants received another form of subsidized housing, potentially biasing results to the null, and loss of power due to high death rates. PSH can house high‐risk individuals and reduce emergent psychiatric services and shelter use. Reductions in hospitalizations may be more difficult to realize.

Conversion between the Modified Mini-Mental State Examination (3MSE) and the Mini-Mental State Examination (MMSE)

BACKGROUND

The Modified Mini-Mental State Examination (3MSE) and the Mini-Mental State Examination (MMSE) are two commonly used instruments for assessing cognitive function. Although conversion between 3MSE and MMSE is useful in applications such as integrative data analysis, there are limited published reports on the topic. Our objective is to provide a dual tool: (1) an item-level conversion tool to score responses for deriving both 3MSE and MMSE measures, and (2) cross-walk tables to facilitate quick conversion between 3MSE and MMSE.

METHODS

An SAS program tool allows scoring of 3MSE item-level responses into MMSE score. Using integrated data sets (n = 8346), actual 3MSE and MMSE scores obtained from the same individuals were linked to form cross-walk tables.

RESULTS

An SAS conversion program was made available. Cross-walk tables were derived. Validation sample shows bias is -0.11 (standard deviation = 1.02) in 3MSE→MMSE; the converse had substantially large bias.

DISCUSSION

The 3MSE→MMSE conversion table can be used in clinical practice and legacy system data.