INSPEX: Optimize Range Sensors for Environment Perception as a Portable System.

Environment perception is crucial for the safe navigation of vehicles and robots to detect obstacles in their surroundings. It is also of paramount interest for navigation of human beings in reduced visibility conditions. Obstacle avoidance systems typically combine multiple sensing technologies (i.e., LiDAR, radar, ultrasound and visual) to detect various types of obstacles under different lighting and weather conditions, with the drawbacks of a given technology being offset by others. These systems require powerful computational capability to fuse the mass of data, which limits their use to high-end vehicles and robots. INSPEX delivers a low-power, small-size and lightweight environment perception system that is compatible with portable and/or wearable applications. This requires miniaturizing and optimizing existing range sensors of different technologies to meet the user's requirements in terms of obstacle detection capabilities. These sensors consist of a LiDAR, a time-of-flight sensor, an ultrasound and an ultra-wideband radar with measurement ranges respectively of 10 m, 4 m, 2 m and 10 m. Integration of a data fusion technique is also required to build a model of the user's surroundings and provide feedback about the localization of harmful obstacles. As primary demonstrator, the INSPEX device will be fixed on a white cane.

Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: the SHARPn project.

The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation's health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation's many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or 'liquidity' of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed clinical models and Consolidated Health Informatics standard terminologies, which were (4) accessed by a phenotyping service using normalized data specifications. The architecture of this prototype SHARPn platform is presented. The EHR data throughput demonstration showed success in normalizing native EHR data, both structured and narrative, from two independent organizations and EHR systems. Based on the demonstration, observed challenges for standardization of EHR data for interoperable secondary use are discussed.

Association of the Intermountain Risk Score with major adverse health events in patients positive for COVID-19: an observational evaluation of a US cohort.

OBJECTIVES: The Intermountain Risk Score (IMRS), composed using published sex-specific weightings of parameters in the complete blood count (CBC) and basic metabolic profile (BMP), is a validated predictor of mortality. We hypothesised that IMRS calculated from prepandemic CBC and BMP predicts COVID-19 outcomes and that IMRS using laboratory results tested at COVID-19 diagnosis is also predictive. DESIGN: Prospective observational cohort study. SETTING: Primary, secondary, urgent and emergent care, and drive-through testing locations across Utah and in sections of adjacent US states. Viral RNA testing for SARS-CoV-2 was conducted from 3 March to 2 November 2020. PARTICIPANTS: Patients aged ≥18 years were evaluated if they had CBC and BMP measured in 2019 and tested positive for COVID-19 in 2020. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was a composite of hospitalisation or mortality, with secondary outcomes being hospitalisation and mortality separately. RESULTS: Among 3883 patients, 8.2% were hospitalised and 1.6% died. Subjects with low, mild, moderate and high-risk IMRS had the composite endpoint in 3.5% (52/1502), 8.6% (108/1256), 15.5% (152/979) and 28.1% (41/146) of patients, respectively. Compared with low-risk, subjects in mild-risk, moderate-risk and high-risk groups had HR=2.33 (95% CI 1.67 to 3.24), HR=4.01 (95% CI 2.93 to 5.50) and HR=8.34 (95% CI 5.54 to 12.57), respectively. Subjects aged <60 years had HR=3.06 (95% CI 2.01 to 4.65) and HR=7.38 (95% CI 3.14 to 17.34) for moderate and high risks versus low risk, respectively; those ≥60 years had HR=1.95 (95% CI 0.99 to 3.86) and HR=3.40 (95% CI 1.63 to 7.07). In multivariable analyses, IMRS was independently predictive and was shown to capture substantial risk variation of comorbidities. CONCLUSIONS: IMRS, a simple risk score using very basic laboratory results, predicted COVID-19 hospitalisation and mortality. This included important abilities to identify risk in younger adults with few diagnosed comorbidities and to predict risk prior to SARS-CoV-2 infection.

The Summit Score Stratifies Mortality and Morbidity in Chronic Obstructive Pulmonary Disease.

Introduction: Tobacco use and other cardiovascular risk factors often accompany chronic obstructive pulmonary disease (COPD). This study derived and validated the Summit Score to predict mortality in people with COPD and cardiovascular risks. Methods: SUMMIT trial subjects (N=16,485) ages 40-80 years with COPD were randomly assigned 50%/50% to derivation (N=8181) and internal validation (N=8304). Three external COPD validations from Intermountain Healthcare included outpatients with cardiovascular risks (N=9251), outpatients without cardiovascular risks (N=8551), and inpatients (N=26,170). Cox regression evaluated 40 predictors of all-cause mortality. SUMMIT treatments including combined fluticasone furoate (FF) 100µg/vilanterol 25µg (VI) were not included in the score. Results: Mortality predictors were FEV1, heart rate, systolic blood pressure, body mass index, age, smoking pack-years, prior COPD hospitalizations, myocardial infarction, heart failure, diabetes, anti-thrombotics, anti-arrhythmics, and xanthines. Combined in the Summit Score (derivation: c=0.668), quartile 4 vs 1 had HR=4.43 in SUMMIT validation (p<0.001, 95% CI=3.27, 6.01, c=0.662) and HR=8.15 in Intermountain cardiovascular risk COPD outpatients (p<0.001, 95% CI=5.86, 11.34, c=0.736), and strongly predicted mortality in the other Intermountain COPD populations. Among all SUMMIT subjects with scores 14-19, FF 100µg/VI 25µg vs placebo had HR=0.76 (p=0.0158, 95% CI=0.61, 0.95), but FF 100µg/VI 25µg was not different from placebo for scores <14 or >19. Conclusion: In this post hoc analysis of SUMMIT trial data, the Summit Score was derived and validated in multiple Intermountain COPD populations. The score was used to identify a subpopulation in which mortality risk was lower for FF 100µg/VI 25µg treatment. Trial Registration: The SUMMIT trial is registered at ClinicalTrials.gov as number NCT01313676.

Blood Eosinophil Count and Hospital Readmission in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

Purpose: This retrospective, observational cohort study investigated the association of blood eosinophil counts within 1 week of hospitalization for acute exacerbation of COPD (AECOPD) with subsequent risk of all-cause and COPD-related readmission from a large integrated health system. Patients and Methods: Electronic medical records were extracted for index hospitalization for AECOPD at all Intermountain Healthcare hospitals. The primary outcome was the relationship of blood eosinophil count to 30-day all-cause readmission; secondary outcomes were 60-day, 90-day, and 12-month all-cause readmission, COPD-related readmission, and empiric derivation of the eosinophil count with the highest area under the curve (AUC) for predicting 30-day all-cause readmission. Results: Of 2445 included patients, 1935 (79%) had a blood eosinophil count <300 cells/µL and 510 (21%) had a count ≥300 cells/µL. Using a 300-cells/µL threshold, there was no significant difference between high and low eosinophil groups in 30-day (odds ratio [OR]=1.05, 95% confidence interval [CI]=0.75-1.47) or 60-day (OR=1.15, 95% CI=0.88-1.51) all-cause readmissions. However, patients with greater (versus lesser) eosinophil counts had increased 90-day and 12-month all-cause readmissions (OR=1.35, 95% CI=1.06-1.72, and OR=1.32, 95% CI=1.07-1.62). COPD-related readmission rates were significantly greater for patients with greater (versus lesser) eosinophil counts at 30, 60, and 90 days and 12 months (OR range=1.52-1.97). A total of 70 cells/µL had the most discriminatory power to predict 30-day all-cause readmission (highest AUC). Conclusion: Eosinophil counts in patients with COPD were not associated with a difference in 30-day all-cause readmissions. However, greater eosinophil counts were associated with increased risk of all-cause readmission at 90 days and 12 months and COPD-related readmission at 30, 60, and 90 days and 12 months. Patients with eosinophils <70 cells/µL had the lowest risk for 30-day all-cause readmission. Blood eosinophils in patients hospitalized with AECOPD may be a useful biomarker for the risk of hospital readmission.

Comparative evaluation of the clinical laboratory-based Intermountain risk score with the Charlson and Elixhauser comorbidity indices for mortality prediction.

BACKGROUND: The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile. How IMRS, Charlson, and Elixhauser relate to each other is unknown. METHODS: All inpatient admissions except obstetric patients at Intermountain Healthcare's 21 adult care hospitals from 2010-2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient's index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality). RESULTS: Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p<0.001) in univariate analyses. Dual score analysis of Charlson (HR = 1.79, CI = 1.66, 1.92) with IMRS (HR = 13.10, CI = 11.53, 14.87) and of Elixhauser (HR = 3.00, CI = 2.80, 3.21) with IMRS (HR = 11.42, CI = 10.09, 12.92) found significance for both scores in each model. Results were similar for 30-day, 1-year, and 5-year mortality. CONCLUSIONS: IMRS provided the strongest ability to predict mortality, adding to and attenuating the predictive ability of the Charlson and Elixhauser indices whose mortality associations remained statistically significant. IMRS uses common, standardized, objective laboratory data and should be further evaluated for integration into mortality risk evaluations.

Laboratory-based Intermountain Validated Exacerbation (LIVE) Score stability in patients with chronic obstructive pulmonary disease.

BACKGROUND: The Laboratory-based Intermountain Validated Exacerbation (LIVE) Score is associated with mortality and chronic obstructive pulmonary disease (COPD) exacerbation risk across multiple health systems. However, whether the LIVE Score and its associated risk is a stable patient characteristic is unknown. METHODS: We validated the LIVE Score in a fourth health system. Then we determined the LIVE Score stability in a retrospective cohort of 98 766 patients with COPD in four health systems where it was previously validated. We assessed whether LIVE Scores changed or remained the same over time. Stability was defined as a majority of surviving patients having the same LIVE Score 4 years later. RESULTS: The LIVE Score separated patients into three LIVE Score risk groups of low, medium, and high mortality and LIVE Score stability. Mortality ranged from 6.2% for low-risk LIVE to 45.8% for high-risk LIVE (p<0.001). We found that low-risk LIVE groups were stable and high-risk LIVE groups were unstable. Low-risk LIVE group patients remained low risk, but few high-risk LIVE group patients remained high risk (79.0% high vs 48.1% medium vs 8.8% low, p<0.001 for all pairwise comparisons). CONCLUSION: The LIVE Score identifies three major clinically actionable cohorts: a stable low-risk LIVE group, an unstable high-risk LIVE group with high mortality rates, and a medium-risk LIVE group. These observations further our understanding of how existing data used to calculate the LIVE Score may target interventions across risk cohorts of patients with COPD in a health system.

Epidemiology and Clinical Features of Invasive Fungal Infection in a US Health Care Network.

BACKGROUND: A better understanding of the epidemiology and clinical features of invasive fungal infection (IFI) is integral to improving outcomes. We describe a novel case-finding methodology, reporting incidence, clinical features, and outcomes of IFI in a large US health care network. METHODS: All available records in the Intermountain Healthcare Enterprise Data Warehouse from 2006 to 2015 were queried for clinical data associated with IFI. The resulting data were overlaid in 124 different combinations to identify high-probability IFI cases. The cohort was manually reviewed, and exclusions were applied. European Organization for Research and Treatment of Cancer/Invasive Fungal Infections Cooperative Group and the National Institute of Allergy and Infectious Diseases Mycoses Study Group Consensus Group definitions were adapted to categorize IFI in a broad patient population. Linear regression was used to model variation in incidence over time. RESULTS: A total of 3374 IFI episodes occurred in 3154 patients. The mean incidence was 27.2 cases/100 000 patients per year, and there was a mean annual increase of 0.24 cases/100 000 patients (P = .21). Candidiasis was the most common (55%). Dimorphic fungi, primarily Coccidioides spp., comprised 25.1% of cases, followed by Aspergillus spp. (8.9%). The median age was 55 years, and pediatric cases accounted for 13%; 26.1% of patients were on immunosuppression, 14.9% had autoimmunity or immunodeficiency, 13.3% had active malignancy, and 5.9% were transplant recipients. Lymphopenia preceded IFI in 22.1% of patients. Hospital admission occurred in 76.2%. The median length of stay was 16 days. All-cause mortality was 17.0% at 42 days and 28.8% at 1 year. Forty-two-day mortality was highest in Aspergillus spp. (27.5%), 20.5% for Candida, and lowest for dimorphic fungi (7.5%). CONCLUSIONS: In this population, IFI was not uncommon, affected a broad spectrum of patients, and was associated with high crude mortality.

Stability of Frequency of Severe Chronic Obstructive Pulmonary Disease Exacerbations and Health Care Utilization in Clinical Populations.

Rationale: Although chronic obstructive pulmonary disease (COPD) exacerbation frequency is stable in research cohorts, whether severe COPD exacerbation frequency can be used to identify patients at high risk for future severe COPD exacerbations and/or mortality is unknown. Methods: Severe COPD exacerbation frequency stability was determined in 3 distinct clinical cohorts. A total of 17,450 patients with COPD in Intermountain Healthcare were categorized based on the number of severe COPD exacerbations per year. We determined whether exacerbation frequency was stable and whether it predicted mortality. These findings were validated in 83,134 patients from the U.S. Veterans Affairs (VA) nationwide health care system and 3326 patients from the University of Chicago Medicine health system. Results: In the Intermountain Healthcare cohort, the majority (84%, 14,706 patients) had no exacerbations in 2009 and were likely to remain non-exacerbators with a significantly lower 6-year mortality compared with frequent exacerbators (2 or more exacerbations per year) (25% versus 57%, p<0.001). Similar findings were noted in the VA health system and the University of Chicago Medicine health system. Non-exacerbators were likely to remain non-exacerbators with the lowest overall mortality. In all cohorts, frequent exacerbator was not a stable phenotype until patients had at least 2 consecutive years of frequent exacerbations. COPD exacerbation frequency predicted any cause mortality. Conclusions: In clinical datasets across different organizations, severe COPD exacerbation frequency was stable after at least 2 consecutive years of frequent exacerbations. Thus, severe COPD exacerbation frequency identifies patients across a health care system at high risk for future COPD-related health care utilization and overall mortality.

The Laboratory-Based Intermountain Validated Exacerbation (LIVE) Score Identifies Chronic Obstructive Pulmonary Disease Patients at High Mortality Risk.

Background: Identifying COPD patients at high risk for mortality or healthcare utilization remains a challenge. A robust system for identifying high-risk COPD patients using Electronic Health Record (EHR) data would empower targeting interventions aimed at ensuring guideline compliance and multimorbidity management. The purpose of this study was to empirically derive, validate, and characterize subgroups of COPD patients based on routinely collected clinical data widely available within the EHR. Methods: Cluster analysis was used in 5,006 patients with COPD at Intermountain to identify clusters based on a large collection of clinical variables. Recursive Partitioning (RP) was then used to determine a preferred tree that assigned patients to clusters based on a parsimonious variable subset. The mortality, COPD exacerbations, and comorbidity profile of the identified groups were examined. The findings were validated in an independent Intermountain cohort and in external cohorts from the United States Veterans Affairs (VA) and University of Chicago Medicine systems. Measurements and Main Results: The RP algorithm identified five LIVE Scores based on laboratory values: albumin, creatinine, chloride, potassium, and hemoglobin. The groups were characterized by increasing risk of mortality. The lowest risk, LIVE Score 5 had 8% 4-year mortality vs. 56% in the highest risk LIVE Score 1 (p < 0.001). These findings were validated in the VA cohort (n = 83,134), an expanded Intermountain cohort (n = 48,871) and in the University of Chicago system (n = 3,236). Higher mortality groups also had higher COPD exacerbation rates and comorbidity rates. Conclusions: In large clinical datasets across different organizations, the LIVE Score utilizes existing laboratory data for COPD patients, and may be used to stratify risk for mortality and COPD exacerbations.

Characterizing the Structure of a Patient's Care Team through Electronic Encounter Data Analysis.

As the field of medicine grows more complicated and doctors become more specialized in a particular field, the number of healthcare providers involved in healing an individual patient increases. This is particularly true of patients with multiple chronic conditions. Establishing effective communications among the care providers becomes critical to facilitate care coordination and more efficient resource use, which will ultimately result in health outcome improvement. The first step for care coordination is to understand who have been involved in a patient's care and their relationships with the patient. The widespread adoption of Electronic Health Records provides us an opportunity to explore solutions to well-coordinated care. This paper presents the concept of a patient's care team and demonstrates the feasibility of identifying relevant healthcare providers for an individual patient by leveraging electronic patient encounter data. Combined with network analysis techniques, we further visualize the care team structure with quantified strength of patient-provider relationships. Our work is foundational to the larger goal of patient-centered, coordinated care in the digital age of healthcare.

Osteoblast-like cell response to bioactive composites-surface-topography and composition effects.

Two bioactive composites, containing 40 vol % filler in high-density polyethylene (HDPE), were investigated to examine the effects of different filler compositions and different surface patterning. The first composite, known as HAPEX, consists of hydroxyapatite within HDPE, and the second composite, known as AWPEX, consists of glass-ceramic apatite-wollastonite in HDPE. Surface topography effects at 5-50 and 100-150 microm were explored, with cell morphology analyzed with the use of scanning electron microscopy and confocal laser scanning microscopy (CLSM). Biochemical assays of adenosine triphosphate and alkaline phosphatase were used to analyze osteoblast-like cell proliferation and differentiation. For both composites, cell alignment was seen along grooves, pillars, and wells, with preferential cell attachment to ceramic particles within the polymer matrices. HAPEX showed significantly increased cell proliferation over AWPEX (P < 0.005). However, greater cell differentiation occurred for AWPEX over HAPEX (P < 0.005). Polishing significantly increased osteoblast-like cell response over as-cut samples, but surface-topography changes above 50 microm had no consistent effect. Smaller-scale features also showed no significant trend in terms of cell proliferation, but did show significant differences in cell differentiation (P < 0.05). CLSM imaging of actin and vinculin localization within cells showed the greatest change in comparison to polished surface controls for cells cultured on samples with surface features below 50 microm. The fact that similar observations were made for both HAPEX and AWPEX indicated that, for these experiments, the effects of surface topography more strongly influenced cell response than chemical composition.

Bias in recording of body mass index data in the electronic health record.

The relationship between patient disease status and the presence or absence of body mass index (BMI) data in the electronic health record (EHR) has not been characterized. We conducted a descriptive study of the completeness of BMI data for three patient cohorts. Cross-sectional descriptions of BMI presence rates per patient were compared between a cohort having at least one ICD-9-CM code for diabetes mellitus (DM) versus a cohort with no diagnosis constraints. Conversely, frequencies of encounter diagnoses were compared among subgroups having BMI recorded or not in both cohorts described and a third cohort having DM codes from a separate organization's EHR. The data demonstrate a correlation with presence of BMI and higher disease status. This effect may bias the cohort average BMIs, which appear higher than expected. When EHR BMI data are repurposed for research, biases in the selective recording of BMI may affect the results.

Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.

RESEARCH OBJECTIVE: To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND METHODS: Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. RESULTS: Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria. CONCLUSIONS: End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

Preferential expression of a mutant allele of the amplified MDR1 (ABCB1) gene in drug-resistant variants of a human sarcoma.

Activation of the MDR1 (ABCB1) gene is a common event conferring multidrug resistance (MDR) in human cancers. We investigated MDR1 activation in MDR variants of a human sarcoma line, some of which express a mutant MDR1, which facilitated the study of allelic gene expression. Structural alterations of MDR1, gene copy numbers, and allelic expression were analyzed by cytogenetic karyotyping, oligonucleotide hybridization, Southern blotting, polymerase chain reaction, and DNA heteroduplex assays. Both chromosome 7 alterations and several cytogenetic changes involving the 7q21 locus are associated with the development of MDR in these sarcoma cells. Multistep-selected cells and their revertants contain three- to six-fold MDR1 gene amplification compared with that of the drug-sensitive parental cell line MES-SA and single-step doxorubicin-selected mutants. MDR1 gene amplification precedes the emergence of a mutant allele in cells that were coselected with doxorubicin and a cyclosporin inhibitor of P-glycoprotein (P-gp). Allele-specific oligonucleotide hybridization showed that the endogenous mutant allele was present as a single copy, with multiple copies of the normal allele. Reselection of revertant cells with doxorubicin in either the presence or the absence of the P-gp inhibitor resulted in exclusive reexpression of the mutant MDR1 allele, regardless of the presence of multiple wild-type MDR1 alleles. These data provide new insights into how multiple alleles are regulated in the amplicon of drug-resistant cancer cells and indicate that increased expression of an amplified gene can result from selective transcription of a single mutant allele of the gene.