Sleep-specific outcomes attributable to digitally delivered cognitive behavioral therapy for insomnia in adults with insomnia and depressive symptoms.

OBJECTIVE: Randomized controlled trials (RCTs) of digitally delivered Cognitive Behavioral Therapy for insomnia (CBT-I) have demonstrated reductions in insomnia severity, depression symptoms, anxiety symptoms, and suicidal ideation. The present study aimed to evaluate the effectiveness of self-guided, digital CBT-I to improve sleep-specific outcomes. METHOD: An RCT of Australian adults with insomnia and depressive symptoms (N = 1149) compared SHUTi, a digital CBT-I intervention, with HealthWatch, an attention-matched control internet program, at baseline, posttest (9 weeks) and at 6-, 12-, and 18-month follow-ups. Online sleep diaries were used to derive measures of sleep-onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), number of awakenings, sleep quality, and total sleep time (TST). RESULTS: Participants in the SHUTi condition had greater improvements at posttest compared with control for: SOL, WASO, SE, number of awakenings, and sleep quality. These improvements were sustained at every follow-up (p < .02 for all outcomes except TST, in which statistically significant increases were observed only at 12- and 18-months). CONCLUSIONS: Digitally delivered CBT-I produced lasting improvements in sleep outcomes among adults with insomnia and depressive symptoms. Findings provide further evidence of long-term improvements associated with a digital therapeutic for insomnia, compared to an attention-control condition.

A Prescription Digital Therapeutic to Support Unsupervised Buprenorphine Initiation for Patients With Opioid Use Disorder: Protocol for a Proof-of-Concept Study.

BACKGROUND: Home-based (unsupervised) buprenorphine initiation is considered safe and effective, yet many patients report barriers to successful treatment initiation. Prescription digital therapeutics (PDTs) are software-based disease treatments regulated by the US Food and Drug Administration (FDA). The reSET-O PDT was authorized by the FDA in 2018 and delivers behavioral treatment for individuals receiving buprenorphine for opioid use disorder (OUD). A prototype PDT (PEAR-002b) designed for use with reSET-O was developed to assist in unsupervised buprenorphine initiation. OBJECTIVE: The primary objective of this pilot study is to evaluate the acceptability of PEAR-002b in individuals with OUD who use it to support buprenorphine initiation, their unsupervised buprenorphine initiation success rate, and their medication adherence. METHODS: Ten adults with OUD will be recruited for acceptability and feasibility testing. Outcomes will be assessed using week-1 visit attendance, participant interviews and satisfaction surveys, and urine drug screening (UDS). Three tools will be used in the study: PEAR-002b, reSET-O, and EmbracePlus. PEAR-002b includes a new set of features designed for use with reSET-O. The mechanism of action for the combined PEAR-002b and reSET-O treatment is a program of medication dosing support during week 1 of the initiation phase, cognitive behavioral therapy, and contingency management. During the medication initiation phase, participants are guided through a process to support proper medication use. PEAR-002b advises them when to take their buprenorphine based on provider inputs (eg, starting dose), self-reported substance use, and self-reported withdrawal symptoms. This study also administers the EmbracePlus device, a medical-grade smartwatch, to pilot methods for collecting physiologic data (eg, heart rate and skin conductance) and evaluate the device's potential for use along with PDTs that are designed to improve OUD treatment initiation. Home buprenorphine initiation success will be summarized as the proportion of participants attending the post-buprenorphine initiation visit (week 1) and the proportion of participants who experience buprenorphine initiation-related adverse events (eg, precipitated withdrawal). Acceptability of PEAR-002b will be evaluated based on individual participants' ratings of ease of use, satisfaction, perceived helpfulness, and likelihood of recommending PEAR-002b. Medication adherence will be evaluated by participant self-report data and confirmed by UDS. UDS data will be summarized as the mean of individual participants' proportion of total urine samples testing positive for buprenorphine or norbuprenorphine over the 4-week study. RESULTS: This project was funded in September 2019. As of September 2022, participant enrollment is ongoing. CONCLUSIONS: This is the first study to our knowledge to develop a PDT that assists with unsupervised buprenorphine initiation with the intent to better support patients and prescribers during this early phase of treatment. This pilot study will assess the acceptability and utility of a digital therapeutic to assist individuals with OUD with unsupervised buprenorphine initiation. TRIAL REGISTRATION: ClinicalTrials.gov NCT05412966; https://clinicaltrials.gov/ct2/show/NCT05412966. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/43122.

Real-world evidence from users of a behavioral digital therapeutic for chronic insomnia.

BACKGROUND: There have been many research trials of various digital therapeutics, but few real world evaluations of their efficacy. This type of data, however, can provide a more rounded understanding of their impact, utility, reach, and adoption. Findings presented here focus on outcome and patient engagement data of SHUTi (Sleep Health Using the Internet), a digital therapeutic delivering Cognitive Behavioral Therapy for insomnia (CBT-I), in a large real-world dataset of adults with insomnia. METHODS: 7216 adults who purchased access to SHUTi between December 2015 and February 2019 are included in the analysis. The Insomnia Severity Index (ISI) was administered at the beginning of each of six treatment Cores of the intervention. Users entered sleep diaries between Cores to track changes in sleep over time and obtain tailored sleep recommendations. Number of Cores completed and sleep diaries entered indicate program usage. RESULTS: Users showed a reduction in mean ISI scores and a corresponding increase in effect size at the start of each subsequent Core (compared to Core 1) (range: d = 0.3-1.9). Effect sizes at the last Core relative to the first were moderate-to-large for diary-derived sleep onset latency and wake after sleep onset. A reduction in number of medicated nights was also found, with those with severe insomnia showing the largest reduction from last-to-first week of treatment (d = 0.3). At the last Core, 61% met criteria for meaningful treatment response (reduction of >7 points on ISI) and 40% met criteria for remission (ISI<8). Engagement was comparable to SHUTi research trials. CONCLUSION: Consistent with controlled trials, real-world data suggest that digital therapeutics can result in relatively high levels of engagement and clinically meaningful sleep improvements.

A Smartphone-Based Intervention as an Adjunct to Standard-of-Care Treatment for Schizophrenia: Randomized Controlled Trial.

BACKGROUND: Antipsychotic medications have limited benefits in schizophrenia, and cognitive behavioral therapy may be beneficial as an adjunct. There may be potential for implementing mobile cognitive behavioral therapy-based treatment for schizophrenia in addition to standard antipsychotic medications. OBJECTIVE: This study aims to determine whether PEAR-004, a smartphone-based investigational digital therapeutic, improves the symptoms of an acute psychotic exacerbation of schizophrenia when it is added to standard treatments. METHODS: This was a 12-week, multicenter, randomized, sham-controlled, rater-blinded, parallel group proof­of­concept study of 112 participants with moderate acute psychotic exacerbation in schizophrenia. This study was conducted in 6 clinical trial research sites in the United States from December 2018 to September 2019. The primary outcome, change in Positive and Negative Syndrome Scale (PANSS) from baseline to week 12 or the last available visit, was analyzed using the mixed-effects regression model for repeated measures, applied to an intent-to-treat sample. RESULTS: The total PANSS scores slightly decreased from baseline over the study period in both groups; the treatment difference at day 85 between PEAR-004 and sham was 2.7 points, in favor of the sham (2-sided P=.09). The secondary scales found no benefit, except for transient improvement in depressive symptoms with PEAR-004. Application engagement was good, and patient and clinical investigator satisfaction was high. No safety concerns were observed. There was some evidence of study site heterogeneity for the onboarding processes and directions on PEAR-004 product use at baseline and throughout the study. However, these differences did not affect the efficacy results. CONCLUSIONS: In the largest-to-date randomized, sham-controlled study of a digital therapeutic in schizophrenia, PEAR-004 did not demonstrate an effect on the primary outcome-total PANSS scores-when compared with a nonspecific digital sham control. The secondary and exploratory results also did not demonstrate any notable benefits, except for possible temporary improvement in depressive symptoms. This study provided many useful scientific and operational insights that can be used in the further clinical development of PEAR-004 and other investigational digital therapeutics. TRIAL REGISTRATION: ClinicalTrials.gov NCT03751280; https://clinicaltrials.gov/ct2/show/NCT03751280.

Patient Engagement With a Game-Based Digital Therapeutic for the Treatment of Opioid Use Disorder: Protocol for a Randomized Controlled Open-Label, Decentralized Trial.

BACKGROUND: Prescription digital therapeutics are software-based disease treatments that are regulated by the US Food and Drug Administration; the reSET-O prescription digital therapeutic was authorized in 2018 and delivers behavioral treatment for individuals receiving buprenorphine for opioid use disorder. Although reSET-O improves outcomes for individuals with opioid use disorder, most of the therapeutic content is delivered as narrative text. PEAR-008 is an investigational device based on reSET-O that uses an interactive, game-based platform to deliver similar therapeutic content designed to enhance patient engagement, which may further improve treatment outcomes. OBJECTIVE: We aim to investigate how participants interact with the prescription digital therapeutic's new content delivery format. Secondary objectives include evaluating treatment success, symptoms of co-occurring mental health disorders, recovery capital, and skill development. METHODS: Due to the COVID-19 pandemic, this study was redesigned using a decentralized model because it was not possible to conduct medication initiation and study visits in person, as initially intended. A decentralized, randomized controlled trial design will be utilized to compare patient engagement with PEAR-008 and that with reSET-O using both subjective and objective assessments. The study population will consist of approximately 130 individuals with opioid use disorder (based on Diagnostic and Statistical Manual of Mental Disorders 5 criteria) who have recently started buprenorphine treatment for opioid use disorder. Participants will be virtually recruited and randomly assigned to receive either PEAR-008 or reSET-O. All study sessions will be virtual, and the duration of the study is 12 weeks. The primary outcome measure of engagement is operationalized as the number of active sessions per week with either PEAR-008 or reSET-O. (An active session is any session that contains some active participation in the app, such as navigating to a different screen, engaging with a learning module, or responding to a notification.) Subjective dimensions of engagement will be assessed with participant surveys. The hypothesis is that PEAR-008 will have significantly greater participant engagement than reSET-O. RESULTS: As of February 2021, participant enrollment is ongoing. CONCLUSIONS: This randomized controlled trial will investigate if changing the delivery format and enhancing the content of a prescription digital therapeutic for opioid use disorder will affect how participants use and interact with the prescription digital therapeutic. The study design may serve as a useful model for conducting decentralized studies in this patient population. TRIAL REGISTRATION: ClinicalTrials.gov NCT04542642; https://clinicaltrials.gov/ct2/show/NCT04542642. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/32759.

Engagement patterns with a digital therapeutic for substance use disorders: Correlations with abstinence outcomes.

INTRODUCTION: Patient engagement may play a key role in the success or failure of treatments for substance use disorder (SUD). This exploratory analysis of data from a large, multisite effectiveness trial (NCT01104805) sought to determine how patient engagement with a digital therapeutic for SUD delivered at clinics was associated with abstinence outcomes. METHODS: The study evaluated engagement for 206 participants enrolled in a treatment program for SUDs related to cocaine, alcohol, cannabis, or other stimulants who were randomized to receive treatment as usual (TAU) or reduced TAU plus the digital Therapeutic Education System (TES) for 12 weeks. Participants were eligible for contingency management incentives for module completion (modules cover Community Reinforcement Approach topic areas) and negative urine drug screens. Analyses examined the association of module completion with end-of-treatment abstinence. RESULTS: Participants completed a mean of 38.8 (range 0-72) TES modules over 12 weeks of treatment. Study completers (n = 157) completed a mean of 45.5 (range 9-72) TES modules, whereas study noncompleters (n = 49) completed a mean of 17.4 (range 0-45) TES modules. The study observed a strong positive correlation between TES engagement (i.e., total number of modules completed) and the probability of abstinence during weeks 9-12 of treatment among 157 study completers (OR = 1.11; 95% CI 1.08-1.14). Each module completed increased the odds of abstinence during weeks 9-12 by approximately 11% for study completers and 9% for the full sample. The study observed a similar, but weaker, association between engagement and abstinence among 49 patients who did not complete the study (OR = 1.02; 95% CI 0.98-1.07). CONCLUSIONS: Greater engagement with a digital therapeutic for patients with SUD (i.e., number of modules completed over time) was strongly associated with the probability of abstinence in the last four weeks of treatment among those who completed the recommended 12-week treatment. TRIALS REGISTRATION: ClinicalTrials.gov Identifier: NCT01104805.

Real-world use and clinical outcomes after 24 weeks of treatment with a prescription digital therapeutic for opioid use disorder.

PURPOSE: To evaluate real-world prescription digital therapeutic (PDT) use and associated clinical outcomes among patients with opioid use disorder (OUD). PATIENTS AND METHODS: A real-world observational evaluation of patients who filled either a 12- or 24-week (refill) prescription for the reSET-O® PDT. The PDT content consists of 67 interactive lessons unlocked in sequence during use as well as the chance to earn rewards for progress and/or negative urine screens. Engagement/retention data (ongoing engagement in weeks 9-12, or 21-24) were collected via the PDT and analyzed with descriptive statistics. Substance use was evaluated as a composite of patient self-reports and urine drug screens (UDS). Missing UDS data were assumed to be positive. A regression analyses of hospital encounters for 12- vs. 24-week prescriptions controlling for covariates was conducted. RESULTS: In a cohort of 3,817 individuals with OUD who completed a 12-week PDT prescription, a cohort of 643 was prescribed a second 12-week 'refill' prescription, for a total treatment time of 24 weeks. Mean age of the 24-week cohort was 39 years, 56.7% female. At 24 weeks of total treatment: abstinence in the last 4 weeks of treatment was 86% in an analysis in which patients with no data are assumed to be positive for illicit opioids. Over 91% of patients were retained in treatment. An analysis of matched insurance claims showed that those treated for 24 weeks had a 27% decrease in unique hospital encounters compared to those who got the first 12-week prescription only. CONCLUSIONS: These data present real-world evidence that a second prescription (24 weeks) of a PDT for OUD is associated with improved outcomes, high levels of retention, and fewer hospital encounters compared to a single prescription for a PDT.PLAIN LANGUAGE SUMMARYPrescription digital therapeutics (PDTs) are software-based treatments that are FDA-authorized to improve clinical outcomes for serious diseases and conditions. The reSET-O PDT consists of 67 interactive lessons unlocked in sequence during use as well as the chance to earn rewards for progress and/or negative urine screens. Multiple studies show that a single 12-week PDT prescription for opioid use disorder (OUD) helps patients engage in treatment, reduces substance use, and helps patients remain in treatment, but to date there has been no evaluation of how patients who receive a 'refill' second prescription engage with the therapeutic and whether the positive effects on substance use and retention are durable across a second 12 weeks (total of 24 weeks) of treatment.This real-world analysis evaluated 643 patients from 12 U.S. states who were prescribed a second PDT prescription. 93% of this cohort completed 8 or more core lesson modules in the second prescription period, 85% completed at least half of core modules, and 64% completed all 32 core modules. Patients used the PDT outside of clinic hours about 40% of the time. 94.4% of patients had 80% or greater negative reports of opioid use across the second 12 weeks of treatment. A 27% decrease in unique hospital encounters was observed in patients who completed a second prescription vs. patients who completed only one prescription.These data show that a second prescription of a PDT for OUD is associated with postive patient outcomes. Patients showed durable and high levels of engagement with the PDT, reduced substance use, and improved treatment retention through 24 weeks of treatment.

Protocol for Digital Real-world Evidence trial for Adults with insomnia treated via Mobile (DREAM): an open-label trial of a prescription digital therapeutic for treating patients with chronic insomnia.

Background: Cognitive behavioral therapy for insomnia (CBT-I) is underused in healthcare settings and is challenging for people with insomnia to access because of uneven geographical distribution of behavioral sleep medicine providers. Prescription digital therapeutics can overcome these barriers. This study evaluates the effectiveness of a specific digital CBT-I therapeutic. Materials & methods:Digital Real-world Evidence trial for Adults with insomnia treated via Mobile (DREAM) is a 9-week, open-label, decentralized clinical trial to collect real-world evidence for a digital therapeutic (Somryst™) delivering CBT-I to patients with chronic insomnia. The primary objective is to examine the effectiveness of Somryst to reduce self-reported insomnia symptoms and severity in a real-world population (n = 350). Conclusion: This pragmatic study seeks to assess the potential benefits of treating insomnia with an asynchronous, mobile, tailored prescription digital therapeutic. Clinical trial registration: NCT04325464 (ClinicalTrials.gov).

Lay abstract Chronic insomnia is linked to a range of health problems, including heart disease, chronic pain, high blood pressure and depression. A behavioral treatment called cognitive behavioral therapy for insomnia (CBT-I) is considered the first choice for helping patients overcome insomnia and reduce their risks of insomnia-related problems. Although the benefits of CBT-I have been established, it can be difficult for patients to access trained CBT-I therapists. One possible solution is to use digital forms of CBT-I, which patients can access on mobile devices. Somryst™ is a prescription digital therapeutic, which means it is authorized by the US FDA and has been proven effective in carefully-controlled clinical trials. Less is known, however, about how well the prescription digital therapeutic works in real-world settings. The Digital Real-world Evidence trial for Adults with insomnia treated via Mobile study (DREAM) will explore this question by evaluating a range of symptoms and outcomes in at least 350 patients with chronic insomnia who will use Somryst and be followed for 1 year.

Evaluation of the cost-utility of a prescription digital therapeutic for the treatment of opioid use disorder.

Background: The opioid epidemic continues to generate a significant mental and physical health burden on patients, and claims the life of almost 150 Americans daily. Making matters worse, an increase in relapses and/or opioid-related deaths has been reported in more than 40 U.S. states since the start of the COVID-19 pandemic. Opioid use disorder (OUD) is one of the single most expensive disorders in the United States, generating average medical costs of $60B from just 2 million Americans diagnosed with the disorder. In commercial use since 2019, reSET-O is a non-drug, prescription digital therapeutic (PDT) that delivers evidence-based neurobehavioral treatment for OUD and helps overcome the barriers associated with access to care, stigma, and social distancing. Although shown to be cost effective and efficacious in clinical trials and real-world evidence studies, respectively, information on its value for money from a health utilities and cost per quality-adjusted life-year is needed to inform policy discussions.Objectives: To evaluate the impact of reSET-O on health utilities and assess its overall cost per quality-adjusted life year (QALY) gained vs. treatment-as-usual (TAU).Methods: Decision analytic model comparing reSET-O plus TAU to TAU alone (i.e. buprenorphine, face-to-face counseling, and contingency management) over 12 weeks. Clinical effectiveness data (abstinence and health utility) were obtained from a clinical trial, and resource utilization and cost data were adapted from a recent claims data analysis to reflect less frequent face-to-face counseling with the therapeutic.Results: The addition of reSET-O to TAU decreases total health care costs by -$131 and resulted in post-treatment utility values within population norms, with a corresponding gain of 0.003 QALYs. reSET-O when used adjunctively to TAU was economically dominant (less costly, more effective) vs. TAU alone.Conclusion: reSET-O is an economically-dominant adjunctive treatment for OUD and is associated with an overall reduction in total incremental cost vs TAU.

Real-world evidence for a prescription digital therapeutic to treat opioid use disorder.

OBJECTIVES: To evaluate patient engagement and usage of a prescription digital therapeutic (PDT) and associated outcomes of opioid use and treatment retention in a large real-world dataset of patients with opioid use disorder (OUD) treated with buprenorphine medication for opioid use disorder (MOUD). PDTs are software-based disease treatments evaluated for safety and effectiveness in randomized clinical trials (RCTs), and authorized by the U.S. Food and Drug Administration (FDA) to treat disease with approved directions for use (label). METHODS: A real-world observational evaluation of an all-comer population of patients who redeemed a 12-week prescription for the reSET-O PDT. Engagement and therapeutic use data were collected and analysed on a population level. Substance use was evaluated as a composite of self-reports recorded with reSET-O and urine drug screens (UDS). RESULTS: Data from 3144 individuals with OUD were evaluated. 45.5% were between ages 30 and 39 years. 80% completed at least 8 of the 67 possible therapeutic modules, 66% completed half of all modules, and 49% completed all modules. Abstinence during the last 4 weeks of treatment was calculated with two imputation methodologies: 66% abstinent using "missing data excluded (patients with no data as positive)", and 91% abstinent with "missing data removed (patients with no data excluded)". 91% of patients met the responder definition of ≥80% of self-report or UDS negative. 74.2% of patients were retained through the last 4 weeks of treatment. Subgroup analysis of patients using reSET-O appropriately (4 or more modules per week for the first 4 weeks) showed 88.1% abstinence using "missing data excluded (patients with no data as positive)", and retention at weeks 9-12 of 85.8%. CONCLUSIONS: Results demonstrate that reSET-O is readily and broadly used by patients with OUD and that high real-world engagement with the therapeutic is positively associated with abstinence and retention in treatment. ReSET-O is a potentially valuable adjunct to buprenorphine MOUD therapy for patients with OUD.

The SLE-key test serological signature: new insights into the course of lupus.

Objective: We previously described the multiplex autoantibody SLE-key Rule-Out test, which detects a signature of autoantibody reactivity that distinguishes healthy subjects from SLE patients with 94% sensitivity, 75% specificity and 93% negative predictive value; thus, an individual manifesting a positive Rule-Out test score is unlikely to have SLE (e.g. lupus is excluded). The objective of this current study was to evaluate the stability of the lupus-associated signature over time. Methods: We used banked serum samples from healthy subjects (n = 51) and lupus patients (n = 50 individual samples and n = 181 paired samples, for a total of n = 412 serum samples). The samples were drawn at different times after diagnosis to analyse the impact on the SLE-key Rule-Out test of time elapsed since diagnosis and any changes in disease activity (as reflected by the SLEDAI score). Results: The SLE signature remains stable for the first 10 years after diagnosis; in this time frame, <10% of patients manifested a positive Rule-Out score and the SLE-key Rule-Out score was independent of the underlying disease activity as reflected by the SLEDAI score. After ⩾10 years, ∼30% of lupus subjects scored as SLE Ruled-Out; the proportion of patients manifesting this status was even greater in the subset of individuals with a SLEDAI score of 0. Conclusion: These findings raise the possibility that a significant number of SLE patients manifest a change in their serological signature over time, and that such a signature change may signify an evolution in the immunological features of their disease relevant to patient management.

Derivation of a Three Biomarker Panel to Improve Diagnosis in Patients with Mild Traumatic Brain Injury.

BACKGROUND: Nearly 5 million emergency department (ED) visits for head injury occur each year in the United States, of which <10% of patients show abnormal computed tomography (CT) findings. CT negative patients frequently suffer protracted somatic, behavioral, and neurocognitive dysfunction. Our goal was to evaluate biomarkers to identify mild TBI (mTBI) in patients with suspected head injury. METHODS: An observational ED study of head-injured and control patients was conducted at Johns Hopkins University (HeadSMART). Head CT was obtained (ACEP criteria) in patients with Glasgow Coma Scale scores of 13-15 and aged 18-80. Three candidate biomarker proteins, neurogranin (NRGN), neuron-specific enolase (NSE), and metallothionein 3 (MT3), were evaluated by immunoassay (samples <24 h from injury). American Congress of Rehabilitation Medicine (ACRM) criteria were used for diagnosis of mTBI patients for model building. Univariate analysis, logistic regression, and random forest (RF) algorithms were used for data analysis in R. Overall, 662 patients were studied. Statistical models were built using 328 healthy controls and 179 mTBI patients. RESULTS: Median time from injury was 5.9 h (IQR, 4.0; range 0.8-24 h). mTBI patients had elevated NSE, but decreased MT3 versus controls (p < 0.01 for each). NRGN was also elevated but within 2-6 h after injury. In the derivation set, the best model to distinguish mTBI from healthy controls used three markers, age, and sex as covariates (C-statistic = 0.91, sensitivity 98%, specificity 72%). Panel test accuracy was validated with the 155 remaining ACRM+ mTBI patients. Applying the RF model to the ACRM+ mTBI validation set resulted in 78% correctly classified as mTBI (119/153). CT positive and CT negative validation subsets were 91% and 75% correctly classified. In samples taken <2 h from injury, 100% (10/10) samples classified correctly, indicating that hyperacute testing is possible with these biomarker assays. The model accuracy varied from 72-100% overall, and had greater accuracy with increasing severity, as shown by comparing CT+ with CT- (91% versus 75%), and Injury Severity Score ≥16 versus <16 (88% versus 72%, respectively). Objective blood tests, detecting NRGN, NSE, and MT3, can be used to identify mTBI, irrespective of neuroimaging findings.

An ImmunoSignature test distinguishes Trypanosoma cruzi, hepatitis B, hepatitis C and West Nile virus seropositivity among asymptomatic blood donors.

BACKGROUND: The complexity of the eukaryotic parasite Trypanosoma (T.) cruzi manifests in its highly dynamic genome, multi-host life cycle, progressive morphologies and immune-evasion mechanisms. Accurate determination of infection or Chagas' disease activity and prognosis continues to challenge researchers. We hypothesized that a diagnostic platform with higher ligand complexity than previously employed may hold value. METHODOLOGY: We applied the ImmunoSignature Technology (IST) for the detection of T. cruzi-specific antibodies among healthy blood donors. IST is based on capturing the information in an individual's antibody repertoire by exposing their peripheral blood to a library of >100,000 position-addressable, chemically-diverse peptides. PRINCIPAL FINDINGS: Initially, samples from two Chagas cohorts declared positive or negative by bank testing were studied. With the first cohort, library-peptides displaying differential binding signals between T. cruzi sero-states were used to train an algorithm. A classifier was fixed and tested against the training-independent second cohort to determine assay performance. Next, samples from a mixed cohort of donors declared positive for Chagas, hepatitis B, hepatitis C or West Nile virus were assayed on the same library. Signals were used to train a single algorithm that distinguished all four disease states. As a binary test, the accuracy of predicting T. cruzi seropositivity by IST was similar, perhaps modestly reduced, relative to conventional ELISAs. However, the results indicate that information beyond determination of seropositivity may have been captured. These include the identification of cohort subclasses, the simultaneous detection and discerning of other diseases, and the discovery of putative new antigens. CONCLUSIONS & SIGNIFICANCE: The central outcome of this study established IST as a reliable approach for specific determination of T. cruzi seropositivity versus disease-free individuals or those with other diseases. Its potential contribution for monitoring and controlling Chagas lies in IST's delivery of higher resolution immune-state readouts than obtained with currently-used technologies. Despite the complexity of the ligand presentation and large quantitative readouts, performing an IST test is simple, scalable and reproducible.

Soluble ST2 and Risk of Arrhythmias, Heart Failure, or Death in Patients with Mildly Symptomatic Heart Failure: Results from MADIT-CRT.

Soluble ST2 is an established biomarker of heart failure (HF) progression. Data about its prognostic implications in patients with mildly symptomatic HF eligible to receive cardiac resynchronization therapy defibrillators (CRT-D) are limited. In a cohort of 684 patients enrolled in Multicenter Automated Defibrillator Implantation Trial (MADIT)-CRT, levels of soluble ST2 (sST2) were serially assessed at baseline and 1 year (n = 410). In multivariable-adjusted models, elevated baseline sST2 was associated with an increased risk of death, death or HF, and death or ventricular arrhythmia (VA) even when adjusting for baseline brain natriuretic protein (BNP) levels. In addition, patients with lower baseline sST2 levels had greater risk reduction with CRT-D (p = 0.006). Serial assessment revealed increased risk of VA and death or VA (HR per 10 % increase in sST2 1.11 (1.04-1.20), p = 0.004). Among patients with mildly symptomatic HF and eligibility for CRT-D, baseline and serial assessments sST2 may provide important information for risk stratification.

SLE-key(®) rule-out serologic test for excluding the diagnosis of systemic lupus erythematosus: Developing the ImmunArray iCHIP(®).

We describe here the development, verification and validation of the SLE-key(®) rule-out test for a definitive rule-out of a diagnosis of systemic lupus erythematosus (SLE). The test uses the proprietary iCHIP(®) micro-array technology platform (Fattal et al., 2010) to identify discriminating patterns of circulating autoantibodies among SLE patients compared with self-declared healthy individuals. Given the challenges associated with the diagnosis of SLE and the healthcare costs of delayed diagnosis and misdiagnosis, a definitive rule-out test can provide significant clinical benefits to patients and potentially major cost savings to healthcare systems.

Validation of a multi-marker model for the prediction of incident type 2 diabetes mellitus: combined results of the Inter99 and Botnia studies.

PURPOSE: To assess performance of a biomarker-based score that predicts the five-year risk of diabetes (Diabetes Risk Score, DRS) in an independent cohort that included 15-year follow-up. METHOD: DRS was developed on the Inter99 cohort, and validated on the Botnia cohort. Performance was benchmarked against other risk-assessment tools comparing calibration, time to event analysis, and net reclassification. RESULTS: The area under the receiver-operating characteristic curve (AUC) was 0.84 for the Inter99 cohort and 0.78 for the Botnia cohort. In the Botnia cohort, DRS provided better discrimination than fasting plasma glucose (FPG), homeostasis model assessment of insulin resistance, oral glucose tolerance test or risk scores derived from Framingham or San Antonio Study cohorts. Overall reclassification with DRS was significantly better than using FPG and glucose tolerance status (p < 0.0001). In time to event analysis, rates of conversion to diabetes in low, moderate, and high DRS groups were significantly different (p < 0.001). CONCLUSION: This study validates DRS performance in an independent population, and provides a more accurate assessment of T2DM risk than other methods.

Biomarkers in Type 2 diabetes: improving risk stratification with the PreDx ® Diabetes Risk Score.

Type 2 diabetes is a chronic, debilitating and often deadly disease that has reached epidemic proportions. The onset of diabetes can be delayed or prevented in high-risk individuals by diet and lifestyle changes and medications, and hence a key element for addressing the diabetes epidemic is to identify those most at risk of developing diabetes so that preventative measures can be effectively focused. The PreDx(®) Diabetes Risk Score is a multimarker tool for assessing a patient's risk of developing diabetes within the next 5 years. Requiring a simple blood draw using standard sample collection and handling procedures, the PreDx Diabetes Risk Score is easily implemented in clinical practice and provides an assessment of diabetes risk that is superior to other measures, including fasting plasma glucose, glycated hemoglobin, measures of insulin resistance and other clinical measures. In this article, we provide an overview of the PreDx Diabetes Risk Score.

Biomarkers in fasting serum to estimate glucose tolerance, insulin sensitivity, and insulin secretion.

BACKGROUND: Biomarkers for estimating reduced glucose tolerance, insulin sensitivity, or impaired insulin secretion would be clinically useful, since these physiologic measures are important in the pathogenesis of type 2 diabetes mellitus. METHODS: We conducted a cross-sectional study in which 94 individuals, of whom 84 had 1 or more risk factors and 10 had no known risk factors for diabetes, underwent oral glucose tolerance testing. We measured 34 protein biomarkers associated with diabetes risk in 250-µL fasting serum samples. We applied multiple regression selection techniques to identify the most informative biomarkers and develop multivariate models to estimate glucose tolerance, insulin sensitivity, and insulin secretion. The ability of the glucose tolerance model to discriminate between diabetic individuals and those with impaired or normal glucose tolerance was evaluated by area under the ROC curve (AUC) analysis. RESULTS: Of the at-risk participants, 25 (30%) were found to have impaired glucose tolerance, and 11 (13%) diabetes. Using molecular counting technology, we assessed multiple biomarkers with high accuracy in small volume samples. Multivariate biomarker models derived from fasting samples correlated strongly with 2-h postload glucose tolerance (R(2) = 0.45, P < 0.0001), composite insulin sensitivity index (R(2) = 0.91, P < 0.0001), and insulin secretion (R(2) = 0.45, P < 0.0001). Additionally, the glucose tolerance model provided strong discrimination between diabetes vs impaired or normal glucose tolerance (AUC 0.89) and between diabetes and impaired glucose tolerance vs normal tolerance (AUC 0.78). CONCLUSIONS: Biomarkers in fasting blood samples may be useful in estimating glucose tolerance, insulin sensitivity, and insulin secretion.

Development of a type 2 diabetes risk model from a panel of serum biomarkers from the Inter99 cohort.

OBJECTIVE: The purpose of this study was to develop a model for assessing the 5-year risk of developing type 2 diabetes from a panel of 64 circulating candidate biomarkers. RESEARCH DESIGN AND METHODS: Subjects were selected from the Inter99 cohort, a longitudinal population-based study of approximately 6,600 Danes in a nested case-control design with the primary outcome of 5-year conversion to type 2 diabetes. Nondiabetic subjects, aged >or=39 years, with BMI >or=25 kg/m(2) at baseline were selected. Baseline fasting serum samples from 160 individuals who developed type 2 diabetes and from 472 who did not were tested. An ultrasensitive immunoassay was used to measure of 58 candidate biomarkers in multiple diabetes-associated pathways, along with six routine clinical variables. Statistical learning methods and permutation testing were used to select the most informative biomarkers. Risk model performance was estimated using a validated bootstrap bias-correction procedure. RESULTS: A model using six biomarkers (adiponectin, C-reactive protein, ferritin, interleukin-2 receptor A, glucose, and insulin) was developed for assessing an individual's 5-year risk of developing type 2 diabetes. This model has a bootstrap-estimated area under the curve of 0.76, which is greater than that for A1C, fasting plasma glucose, fasting serum insulin, BMI, sex-adjusted waist circumference, a model using fasting glucose and insulin, and a noninvasive clinical model. CONCLUSIONS: A model incorporating six circulating biomarkers provides an objective and quantitative estimate of the 5-year risk of developing type 2 diabetes, performs better than single risk indicators and a noninvasive clinical model, and provides better stratification than fasting plasma glucose alone.

Validation of a multimarker model for assessing risk of type 2 diabetes from a five-year prospective study of 6784 Danish people (Inter99).

BACKGROUND: Improved identification of subjects at high risk for development of type 2 diabetes would allow preventive interventions to be targeted toward individuals most likely to benefit. In previous research, predictive biomarkers were identified and used to develop multivariate models to assess an individual's risk of developing diabetes. Here we describe the training and validation of the PreDx Diabetes Risk Score (DRS) model in a clinical laboratory setting using baseline serum samples from subjects in the Inter99 cohort, a population-based primary prevention study of cardiovascular disease. METHODS: Among 6784 subjects free of diabetes at baseline, 215 subjects progressed to diabetes (converters) during five years of follow-up. A nested case-control study was performed using serum samples from 202 converters and 597 randomly selected nonconverters. Samples were randomly assigned to equally sized training and validation sets. Seven biomarkers were measured using assays developed for use in a clinical reference laboratory. RESULTS: The PreDx DRS model performed better on the training set (area under the curve [AUC] = 0.837) than fasting plasma glucose alone (AUC = 0.779). When applied to the sequestered validation set, the PreDx DRS showed the same performance (AUC = 0.838), thus validating the model. This model had a better AUC than any other single measure from a fasting sample. Moreover, the model provided further risk stratification among high-risk subpopulations with impaired fasting glucose or metabolic syndrome. CONCLUSIONS: The PreDx DRS provides the absolute risk of diabetes conversion in five years for subjects identified to be "at risk" using the clinical factors.