Temporal Changes in Innate and Adaptive Immunity During Sepsis as Determined by ELISpot

Background

The inability to evaluate host immunity in a rapid quantitative manner in patients with sepsis has severely hampered development of novel immune therapies. The ELISpot assay is a functional bioassay that measures the number of cytokine-secreting cells and the relative amount of cytokine produced at the single-cell level. A key advantage of ELISpot is its excellent dynamic range enabling a more precise quantifiable assessment of host immunity. Herein, we tested the hypothesis on whether the ELISpot assay can detect dynamic changes in both innate and adaptive immunity as they often occur during sepsis. We also tested whether ELISpot could detect the effect of immune drug therapies to modulate innate and adaptive immunity.

Methods

Mice were made septic using sublethal cecal ligation and puncture (CLP). Blood and spleens were harvested serially and ex vivo IFN-γ and TNF-α production were compared by ELISpot and ELISA. The capability of ELISpot to detect changes in innate and adaptive immunity due to in vivo immune therapy with dexamethasone, IL-7, and arginine was also evaluated.

Results

ELISpot confirmed a decreased innate and adaptive immunity responsiveness during sepsis progression. More importantly, ELISpot was also able to detect changes in adaptive and innate immunity in response to immune-modulatory reagents, for example dexamethasone, arginine, and IL-7 in a readily quantifiable manner, as predicted by the reagents known mechanisms of action. ELISpot and ELISA results tended to parallel one another although some differences were noted.

Conclusion

ELISpot offers a unique capability to assess the functional status of both adaptive and innate immunity over time. The results presented herein demonstrate that ELISpot can also be used to detect and follow the in vivo effects of drugs to ameliorate sepsis-induced immune dysfunction. This capability would be a major advance in guiding new immune therapies in sepsis.

Systematic review of approaches to use of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions

BACKGROUND

Despite a large body of literature investigating how the environment influences health outcomes, most published work to date includes only a limited subset of the rich clinical and environmental data that is available and does not address how these data might best be used to predict clinical risk or expected impact of clinical interventions.

OBJECTIVE

Identify existing approaches to inclusion of a broad set of neighborhood-level risk factors with clinical data to predict clinical risk and recommend interventions.

METHODS

A systematic review of scientific literature published and indexed in PubMed, Web of Science, Association of Computing Machinery (ACM) and SCOPUS from 2010 through October 2020 was performed. To be included, articles had to include search terms related to Electronic Health Record (EHR) data Neighborhood-Level Risk Factors (NLRFs), and Machine Learning (ML) Methods. Citations of relevant articles were also reviewed for additional articles for inclusion. Articles were reviewed and coded by two independent reviewers to capture key information including data sources, linkage of EHR to NRLFs, methods, and results. Articles were assessed for quality using a modified Quality Assessment Tool for Systematic Reviews of Observational Studies (QATSO).

RESULTS

A total of 334 articles were identified for abstract review. 36 articles were identified for full review with 19 articles included in the final analysis. All but two of the articles included socio-demographic data derived from the U.S. Census and we found great variability in sources of NLRFs beyond the Census. The majority or the articles (14 of 19) included broader clinical (e.g. medications, labs and co-morbidities) and demographic information about the individual from the EHR in addition to the clinical outcome variable. Half of the articles (10) had a stated goal to predict the outcome(s) of interest. While results of the studies reinforced the correlative association of NLRFs to clinical outcomes, only one article found that adding NLRFs into a model with other data added predictive power with the remainder concluding either that NLRFs were of mixed value depending on the model and outcome or that NLRFs added no predictive power over other data in the model. Only one article scored high on the quality assessment with 13 scoring moderate and 4 scoring low.

CONCLUSIONS

In spite of growing interest in combining NLRFs with EHR data for clinical prediction, we found limited evidence that NLRFs improve predictive power in clinical risk models. We found these data and methods are being used in four ways. First, early approaches to include broad NLRFs to predict clinical risk primarily focused on dimension reduction for feature selection or as a data preparation step to input into regression analysis. Second, more recent work incorporates NLRFs into more advanced predictive models, such as Neural Networks, Random Forest, and Penalized Lasso to predict clinical outcomes or predict value of interventions. Third, studies that test how inclusion of NLRFs predict clinical risk have shown mixed results regarding the value of these data over EHR or claims data alone and this review surfaced evidence of potential quality challenges and biases inherent to this approach. Finally, NLRFs were used with unsupervised learning to identify underlying patterns in patient populations to recommend targeted interventions. Further access to computable, high quality data is needed along with careful study design, including sub-group analysis, to better determine how these data and methods can be used to support decision making in a clinical setting.

Development of A Blockchain Framework for Virtual Clinical Trials

Clinical trials are essential for discovering new treatments, but there are multiple challenges to patient recruitment, patient engagement, and cost containment. Virtual clinical trials (VCT) are an innovative approach that provides potential solutions by conducting home-based, rather than site-based, clinical trials. Virtual clinical trials are still the exception rather than general practice due to technical barriers. "Blockchain," a distributed ledger technology, is a perfect match for virtual clinical trials. Its peer-to-peer design, security settings, and data transparency meet the needs of many healthcare applications. The programmable "Smart Contract" feature makes blockchain more suitable and feasible for VCT by solving computational issues. Our previous work has shown the power of applying blockchain to clinical trial recruitment. This work develops a comprehensive blockchain framework, with simulations and case studies, including patient recruitment, patient engagement, and persistent monitoring modules.

Applying Blockchain Technology for Health Information Exchange and Persistent Monitoring for Clinical Trials

"Blockchain" is a distributed ledger technology originally applied in the financial sector. This technology ensures the integrity of transactions without third-party validation. Its functions of decentralized transaction validation, data provenance, data sharing, and data integration are a good fit for the needs of health information exchange and clinical trials. We investigated the current workflow of Health Information Exchange and clinical trials; conducted design thinking processes with clinicians, trial managers, informaticians, and blockchain professionals; and implemented a private blockchain model to tackle known issues. We used coded Smart Contract regulations to simulate several scenarios in healthcare processes. This proof-of-concept work provides a feasible simulation for potential solutions to monitor clinical trials across different census regions persistently. Various levels of data access privileges have been designed to utilize a suite of customized Smart Contract settings. These settings emulate the workflow protocols for the monitoring entities, trial sponsors, clinical sponsors and participating subjects. Keywords: Blockchain, Smart Contract, Health Information Exchange, Clinical Trial, Persistent Monitoring.

The Effect of Neighborhood Disadvantage on Diabetes Prevalence

Patient socioeconomic data is not usually included in medical records nor easily accessible to clinicians, yet socioeconomic disadvantage can be an important guide to disease management. This study evaluated the neighborhood-level Area Deprivation Index (ADI), a measure of neighborhood socioeconomic disadvantage, as a factor in diabetes mellitus prevalence. Electronic health records at an academic hospital system identified 4,770 Medicare beneficiaries. Logistic regression of diabetes diagnosis (ICD9=250.x) against ADI quintile, age, gender, and race/ethnicity found all these patient characteristics to be significantly associated. Diabetes prevalence was lowest in the least disadvantaged quintile of neighborhoods after adjusting for age, gender, and race/ethnicity. The positive non-linear association of diabetes prevalence with ADI demonstrates the power of this index to practically quantify socioeconomic disadvantage. The ADI may be suitable for clinical decision support, and for informing the policy changes which are needed to reduce socioeconomic disparities in diabetes prevalence and other health outcomes.

Combining Contrast Mining with Logistic Regression To Predict Healthcare Utilization in a Managed Care Population

BACKGROUND

Because 5% of patients incur 50% of healthcare expenses, population health managers need to be able to focus preventive and longitudinal care on those patients who are at highest risk of increased utilization. Predictive analytics can be used to identify these patients and to better manage their care. Data mining permits the development of models that surpass the size restrictions of traditional statistical methods and take advantage of the rich data available in the electronic health record (EHR), without limiting predictions to specific chronic conditions.

OBJECTIVE

The objective was to demonstrate the usefulness of unrestricted EHR data for predictive analytics in managed healthcare.

METHODS

In a population of 9,568 Medicare and Medicaid beneficiaries, patients in the highest 5% of charges were compared to equal numbers of patients with the lowest charges. Contrast mining was used to discover the combinations of clinical attributes frequently associated with high utilization and infrequently associated with low utilization. The attributes found in these combinations were then tested by multiple logistic regression, and the discrimination of the model was evaluated by the c-statistic.

RESULTS

Of 19,014 potential EHR patient attributes, 67 were found in combinations frequently associated with high utilization, but not with low utilization (support>20%). Eleven of these attributes were significantly associated with high utilization (p<0.05). A prediction model composed of these eleven attributes had a discrimination of 84%.

CONCLUSIONS

EHR mining reduced an unusably high number of patient attributes to a manageable set of potential healthcare utilization predictors, without conjecturing on which attributes would be useful. Treating these results as hypotheses to be tested by conventional methods yielded a highly accurate predictive model. This novel, two-step methodology can assist population health managers to focus preventive and longitudinal care on those patients who are at highest risk for increased utilization.

Identifying Patients at Risk of High Healthcare Utilization

Objective. To develop a systematic and reproducible way to identify patients at increased risk for higher healthcare costs. Methods. Medical records were analyzed for 9,581 adults who were primary care patients in the University of Missouri Health System and who were enrolled in Medicare or Medicaid. Patients were categorized into one of four risk tiers as of October 1, 2013, and the four tiers were compared on demographic characteristics, number of healthcare episodes, and healthcare charges in the year before and the year after cohort formation. Results. The mean number of healthcare episodes and the sum of healthcare charges in the year following cohort formation were higher for patients in the higher-risk tiers. Conclusions. Retrospective information that is easily extracted from medical records can be used to create risk tiers that provide highly useful information about the prospective risk of healthcare utilization and costs.

Contrast Mining for Pattern Discovery and Descriptive Analytics to Tailor Sub-Groups of Patients Using Big Data Solutions

The shift to electronic health records has created a plethora of information ready to be examined and acted upon by those in the medical and computational fields. While this allows for novel research on a scale unthinkable in the past, all discoveries still rely on some initial insight leading to a hypothesis. As the size and variety of data grows so do the number of potential findings, making it necessary to optimize hypothesis generation to increase the rate and importance of discoveries produced from the data. By using distributed Association Rule Mining and Contrast Mining in a big data ecosystem, it is possible to discover discrepancies within large, complex populations which are inaccessible using traditional methods. These discrepancies, when used as hypotheses, can help improve patient care through decision support, population health analytics, and other areas of healthcare.

The Paradox of Higher Charges for Lower-Risk Inpatient Admissions: When Healthier Patients Cost More

Risk stratification is essential to achieving the Triple Aim of better health, better care, and lower costs. Although risk tiers based on chronic disease diagnoses and recent healthcare utilization were predictive of healthcare utilization and charges in a managed population, their correlation with specific high-cost outcomes was unknown. More detailed analyses were performed to confirm that admissions for higher-risk patients were more expensive. However, these analyses found that charges for admissions of high-risk patients were actually not more expensive but 33% less expensive. The billing categories of implants, surgery, and supplies accounted for 93% of this difference. These findings may reflect that high-risk patients are less often appropriate candidates for elective surgery. An understanding of this difference, especially if validated by claims data and replicated in other populations, may lead to important insights into using risk stratification for predicting health services utilization in managed care populations.

Longitudinal Changes in Risk Stratification for a Managed Population

The LIGHT² project managed the care of approximately 10,000 Medicare (primarily elderly) and Medicaid (low income) patients between 2013 and 2015. Risk tiers based on chronic disease diagnoses and recent healthcare utilization were strongly predictive of future healthcare utilization, and the authors expected that the members of an aging and well-insured population would gradually rise in risk of healthcare utilization over the course of three years. Various analytic techniques were used to characterize the members of higher risk tiers. However, retrospective cohort analysis and simple data visualization discovered the tendency of patients in lower initial risk tiers to remain healthy, and the tendency of patients in higher initial risk tiers to improve. In a time frame of three years, this return to stability was a more important influence on healthcare utilization than risk or aging.

Usability Analysis Of A Customized Documentation System For Nurse Population-Health Managers

The University of Missouri's population health management pilot employs 22 Nurse Care Managers (NCM) to manage medical casework for approximately 10,000 patients. We studied the NCMs' documentation system to identify interruptions to their clinical workflow, and identify missing functionality. We used an interview script and Morae software to observe and record five NCMs at work, measuring time on task, click counts, and task completion. We also documented quantitative and qualitative responses to a directed interview. All tasks were completed satisfactorily, with completion times of 443±275 seconds and click counts of 58±23. Surveys on a scale of 1 (worst) to 5 (best) scored "ease of use" at 3.4±1.1, "integration of functions" at 3.0±1.2, and "ease of learning" at 2.6±1.5. Overall, the system is functional and reliable but could be improved to support workflow.

Optimizing the txt2MEDLINE search portal for low-resource clinical decision support

txt2MEDLINE provides access to high-quality medical evidence via text-messaging in settings with inadequate Internet access. We optimized the txt2MEDLINE search technique by parsing queries for MeSH (Medical Subject Heading) terms and searching MEDLINE for articles containing these terms in their titles or abstracts. We compared our results to the existing txt2MEDLINE tool by compiling benchmark queries from low-income and low-middle-income countries, and asking doctors and nurses with practice experience in low-resource areas to evaluate them. The median scores on a 5-point Likert scale were 2.9 for the existing txt2MEDLINE vs. 3.8 for the modified version (p=0.015). This reached our predefined criterion for clinical significance, a difference of 0.5 standard deviations. Improving this technology could improve clinical information resources in the world's most medically underserved communities.

Do language fluency and other socioeconomic factors influence the use of PubMed and MedlinePlus?

BACKGROUND

Increased usage of MedlinePlus by Spanish-speakers was observed after introduction of MedlinePlus in Spanish. This probably reflects increased usage of MEDLINE and PubMed by those with greater fluency in the language in which it is presented; but this has never been demonstrated in English speakers. Evidence that lack of English fluency deters international healthcare personnel from using PubMed could support the use of multi-language search tools like Babel-MeSH.

OBJECTIVES

This study aims to measure the effects of language fluency and other socioeconomic factors on PubMed MEDLINE and MedlinePlus access by international users.

METHODS

We retrospectively reviewed server pageviews of PubMed and MedlinePlus from various periods of time, and analyzed them against country statistics on language fluency, GDP, literacy rate, Internet usage, medical schools, and physicians per capita, to determine whether they were associated.

RESULTS

We found fluency in English to be positively associated with pageviews of PubMed and MedlinePlus in countries with high literacy rates. Spanish was generally found to be positively associated with pageviews of MedlinePlus en Español. The other parameters also showed varying degrees of association with pageviews.

CONCLUSIONS

After adjusting for the other factors investigated in this study, language fluency was a consistently significant predictor of the use of PubMed, MedlinePlus English and MedlinePlus en Español. This study may support the need for multi-language search tools and may increase access of health information resources from non-English speaking countries.

Usability of selected databases for low-resource clinical decision support

BACKGROUND

Smartphones are increasingly important for clinical decision support, but smartphone and Internet use are limited by cost or coverage in many settings. txt2MEDLINE provides access to published medical evidence by text messaging. Previous studies have evaluated this approach, but we found no comparisons with other tools in this format.

OBJECTIVES

To compare txt2MEDLINE with other databases for answering clinical queries by text messaging in low-resource settings.

METHODS

Using varied formats, we searched txt2MEDLINE and five other search portals (askMEDLINE, Cochrane, DynaMed, PubMed PICO, and UpToDate) to develop optimal strategies for each. We then searched each database again with five benchmark queries, using the customized search-optimization formats. We truncated the results to less than 480 characters each to simulate delivering them to a maximum of three text messages. Clinicians with practice experience in low-resource areas scored the results on a 5-point Likert scale.

RESULTS

Median scores and standard deviations from 17 reviewers were: txt2M2MEDLINE, 3.2±0.82 (control); askMEDLINE, 3.2±0.90 (p = 0.918); Cochrane, 3.8±0.58 (p = 0.073); DynaMed, 3.6±0.65 (p = 0.105); PubMed PICO, 3.6±0.82 (p = 0.005); and UpToDate, 4.0±0.52 (p = 0.002). Our sample size was sufficiently powered to find differences of 1.0 point.

CONCLUSIONS

Comparing several possible sources for texting-based clinical-decision-support information, our results did not demonstrate one-point differences in usefulness on a scale of 1 to 5. PubMed PICO and UpToDate were significantly better than txt2MEDLINE, but with relatively small improvements in Likert score (0.4 and 0.8, respectively). In a texting-only setting, txt2MEDLINE is comparable to simulated alternatives based on established reference sources.

A systematic review of healthcare applications for smartphones

BACKGROUND

Advanced mobile communications and portable computation are now combined in handheld devices called "smartphones", which are also capable of running third-party software. The number of smartphone users is growing rapidly, including among healthcare professionals. The purpose of this study was to classify smartphone-based healthcare technologies as discussed in academic literature according to their functionalities, and summarize articles in each category.

METHODS

In April 2011, MEDLINE was searched to identify articles that discussed the design, development, evaluation, or use of smartphone-based software for healthcare professionals, medical or nursing students, or patients. A total of 55 articles discussing 83 applications were selected for this study from 2,894 articles initially obtained from the MEDLINE searches.

RESULTS

A total of 83 applications were documented: 57 applications for healthcare professionals focusing on disease diagnosis (21), drug reference (6), medical calculators (8), literature search (6), clinical communication (3), Hospital Information System (HIS) client applications (4), medical training (2) and general healthcare applications (7); 11 applications for medical or nursing students focusing on medical education; and 15 applications for patients focusing on disease management with chronic illness (6), ENT-related (4), fall-related (3), and two other conditions (2). The disease diagnosis, drug reference, and medical calculator applications were reported as most useful by healthcare professionals and medical or nursing students.

CONCLUSIONS

Many medical applications for smartphones have been developed and widely used by health professionals and patients. The use of smartphones is getting more attention in healthcare day by day. Medical applications make smartphones useful tools in the practice of evidence-based medicine at the point of care, in addition to their use in mobile clinical communication. Also, smartphones can play a very important role in patient education, disease self-management, and remote monitoring of patients.

Unsupported labeling of race as a risk factor for certain diseases in a widely used medical textbook

PURPOSE

Confounding of racial, socioeconomic, and behavioral characteristics may lead to the misidentification of race as a risk factor for certain diseases. The authors evaluated the validity of attributing race as a risk factor in a widely used pathology textbook.

METHOD

In 2009-2010, the authors searched Robbins and Cotran Pathologic Basis of Disease, Professional Edition (8th ed) for assertions that African Americans have a different disease profile than do individuals of other races. They evaluated the references cited in the textbook, assessing the quality of the sources and whether those authors controlled for confounding variables, such as socioeconomic status and behavioral characteristics. For statements that remained unconfirmed, the authors searched the literature for evidence supporting or contradicting the claims made in the textbook. The authors classified each statement from the textbook as supported, unconfirmed, or contradicted.

RESULTS

Of the 31 relevant statements, 11 were supported by the literature review, 17 remained unconfirmed, and three were contradicted.

CONCLUSIONS

About two-thirds of the assertions that different risk factors exist for African Americans found in a widely used pathology textbook could not be supported by the published literature. Failure to separate race from other socioeconomic and behavioral characteristics as risk factors for certain diseases may contribute to the misdiagnosis of patients and interfere with efforts to identify and remove causes of health disparities.

Liver transplantation

Liver transplantation celebrated its twenty-fifth anniversary in 1988. However, only in the last decade has it been accepted as a viable treatment alternative for patients with end-stage liver disease. There currently are over 70 centers in the United States with liver transplant programs. Over 1500 transplants were performed in 1988, and there were 644 people awaiting suitable liver donors, with an average of 150 to 200 patients added to the list each month. This growth has resulted from many factors, including improved operative techniques and anesthetic management, immunosuppressive therapies, and improvements in donor recovery, which has served to increase the acceptable organs available for transplantation. The future of liver transplantation, however, may be somewhat uncertain. Will the cost of patient care continue to be supported by the economy? The goal of liver transplantation is simple: to provide the patient with a better quality and longer life. The question remains, is this a goal that society will deem financially feasible?

Gait analysis of chicks following treatment with tri-ortho-cresyl phosphate in ovo

Chick embryos were treated during late embryonic development with tri-ortho-cresyl phosphate (TOCP), an organophosphate compound that causes delayed neurotoxicity in humans and some other species. Embryos were treated on incubation d 14 with either 62 or 250 microliters TOCP/kg egg. The higher dose reduced the number hatched, and signs of cholinergic toxicity were apparent in the newly hatched chick. All chicks that survived this dose were unable to stand. Recovery from the cholinergic effects occurred within a few days after hatching, but the chicks remained severely ataxic through 3 wk of observation. The mortality of embryos treated with 62 microliters TOCP/kg egg was not higher than that of controls, and young chicks showed no overt signs of cholinergic toxicity or ataxia. Motor impairment was detected by measuring gait parameters. These chicks had a short stride and walked with a more open angle of foot placement. These are adjustments in gait that provide a more steady base of support. The change in gait developed over a 3-wk period after hatching. The hindlimb motor impairment detected at both doses is consistent with neuropathy such as is seen in the adult chicken. The value of gait analysis is the ability to quantify effects that are not apparent by simple observation.

Morphologic alterations in leg muscles of chicks treated with triorthocresyl phosphate in ovo

Chick embryos were injected on incubation Day 14 with 62 microliter of triorthocresyl phosphate (TOCP)/kg egg. Muscles of the leg were examined from 5 to 25 days after hatching. The sartorius from the thigh and the external gastrocnemius and peroneus longus from the tibial leg region were compared for muscle fiber size and end-plate length over this period. Treated chicks showed no acute toxic effects or overt ataxia and were equal in body weight to controls. At 5, 15, and 25 days after hatching, morphologic alterations consistent with denervation were detected. Muscle fibers were smaller than controls on Day 5 and were hypertrophic on Days 15 and 25. On Day 5 growth of fibers was retarded, an effect consistent with denervation, and the subsequent hypertrophy is predicted as compensation for denervated fibers. Small end plates were seen on Day 15, characteristic of end plates that were delayed in development by denervation. Each of these differences was greater in the tibial muscles than in the more proximally located sartorius. This is consistent with a distal neuropathy, such as that caused by TOCP in adult hens. Some recovery was apparent at the low dose 25 days after hatching. It is suggested that this resulted from reinnervation by repaired axons. This study of the myoneural apparatus and muscle fiber response to TOCP adds evidence to the possibility that the developing chick embryo may develop delayed neuropathy from organophosphorus compounds which produce this effect in adult hens.

Peripheral nerve damage in chicks following treatment with organophosphorus compounds in ovo

Chick embryos were treated with tri-o-cresyl phosphate (TOCP) or leptophos, organophosphorus compounds that cause delayed neurotoxicity. Embryos received either TOCP (62 or 250 microliter/kg egg) or leptophos (125 to 750 mg/kg egg) Day 14 of incubation and were examined after hatching for nerve damage. The high doses caused high embryo mortality. Chicks which survived the high doses were grossly ataxic from hatching until the study was ended at 3 weeks posthatching. On Posthatching Day 2, many degenerating nerve fibers were observed in the profundus/superficialis peroneus nerve in chicks surviving the high doses. TOCP-treated chicks were followed in detail for neuromuscular changes. Twenty days after hatching there were fewer large nerve fibers in the distal ischiadic nerve compared with controls and the largest nerve fibers were absent in the peroneus profundus nerve. Consistent with the evidence of denervation there was increased terminal branching of motor axons in femoral (sartorius) and tibial (external gastrocnemius and peroneus longus) leg muscles. The leg nerves of chicks treated with the low dose of TOCP did not show either an excessive number of degenerating nerve fibers or a detectable loss of large nerve fibers. However, terminal branching of motor axons was increased in the external gastrocnemius and peroneus longus muscles of 5- and 15-day-old chicks, followed by recovery by Day 25. The evidence is interpreted as a distal axonopathy in chicks treated with TOCP during late embryonic development.