Towards a lifelong personalized brain health program: empowering individuals to define, pursue, and monitor meaningful outcomes.

Incorporating person-centered outcomes into clinical trials for neurodegenerative diseases has been challenging due to a deficiency in quantitative measures. Meanwhile, the integration of personally meaningful treatment targets in clinical practice remains qualitative, failing to truly inform evaluations, therapeutic interventions and longitudinal monitoring and support. We discuss the current advances and future directions in capturing individualized brain health outcomes and present an approach to integrate person-centered outcome in a scalable manner. Our approach stems from the evidence-based electronic Person-Specific Outcome Measure (ePSOM) program which prompts an individual to define personally meaningful treatment priorities and report level of confidence in managing items that matter to the individual the most (e.g., "Do I feel confident in my ability to contribute to a conversation?"). Deployed either as a single version (person only) or a dyad version (person and care partner), our proposed tool could be used as an endpoint in clinical trials, offering proof of meaningful intervention benefits and in clinical practice, by establishing an anchor for the therapeutic objectives sought by the individual.

Digital Clock and Recall is superior to the Mini-Mental State Examination for the detection of mild cognitive impairment and mild dementia.

BACKGROUND: Disease-modifying treatments for Alzheimer's disease highlight the need for early detection of cognitive decline. However, at present, most primary care providers do not perform routine cognitive testing, in part due to a lack of access to practical cognitive assessments, as well as time and resources to administer and interpret the tests. Brief and sensitive digital cognitive assessments, such as the Digital Clock and Recall (DCR™), have the potential to address this need. Here, we examine the advantages of DCR over the Mini-Mental State Examination (MMSE) in detecting mild cognitive impairment (MCI) and mild dementia. METHODS: We studied 706 participants from the multisite Bio-Hermes study (age mean ± SD = 71.5 ± 6.7; 58.9% female; years of education mean ± SD = 15.4 ± 2.7; primary language English), classified as cognitively unimpaired (CU; n = 360), mild cognitive impairment (MCI; n = 234), or probable mild Alzheimer's dementia (pAD; n = 111) based on a review of medical history with selected cognitive and imaging tests. We evaluated cognitive classifications (MCI and early dementia) based on the DCR and the MMSE against cohorts based on the results of the Rey Auditory Verbal Learning Test (RAVLT), the Trail Making Test-Part B (TMT-B), and the Functional Activities Questionnaire (FAQ). We also compared the influence of demographic variables such as race (White vs. Non-White), ethnicity (Hispanic vs. Non-Hispanic), and level of education (≥ 15 years vs. < 15 years) on the DCR and MMSE scores. RESULTS: The DCR was superior on average to the MMSE in classifying mild cognitive impairment and early dementia, AUC = 0.70 for the DCR vs. 0.63 for the MMSE. DCR administration was also significantly faster (completed in less than 3 min regardless of cognitive status and age). Among 104 individuals who were labeled as "cognitively unimpaired" by the MMSE (score ≥ 28) but actually had verbal memory impairment as confirmed by the RAVLT, the DCR identified 84 (80.7%) as impaired. Moreover, the DCR score was significantly less biased by ethnicity than the MMSE, with no significant difference in the DCR score between Hispanic and non-Hispanic individuals. CONCLUSIONS: DCR outperforms the MMSE in detecting and classifying cognitive impairment-in a fraction of the time-while being not influenced by a patient's ethnicity. The results support the utility of DCR as a sensitive and efficient cognitive assessment in primary care settings. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT04733989.

Reducing Avoidable Emergency Visits and Hospitalizations With Patient Risk-Based Prescriptive Analytics: A Quality Improvement Project at an Oncology Care Model Practice.

PURPOSE: Cancer-related emergency department (ED) visits and hospitalizations that would have been appropriately managed in the outpatient setting are avoidable and detrimental to patients and health systems. This quality improvement (QI) project aimed to leverage patient risk-based prescriptive analytics at a community oncology practice to reduce avoidable acute care use (ACU). METHODS: Using the Plan-Do-Study-Act (PDSA) methodology, we implemented the Jvion Care Optimization and Recommendation Enhancement augmented intelligence (AI) tool at an Oncology Care Model (OCM) practice, the Center for Cancer and Blood Disorders practice. We applied continuous machine learning to predict risk of preventable harm (avoidable ACU) and generated patient-specific recommendations that nurses implemented to avert it. RESULTS: Patient-centric interventions included medication/dosage changes, laboratory tests/imaging, physical/occupational/psychologic therapy referral, palliative care/hospice referral, and surveillance/observation. Nurses contacted patients every 1-2 weeks after initial outreach to assess and maintain adherence to recommended interventions. Per 100 unique OCM patients, monthly ED visits dropped from 13.7 to 11.5 (18%), a sustained month-over-month improvement. Quarterly admissions dropped from 19.5 to 17.1 (13%), a sustained quarter-over-quarter improvement. Overall, the practice realized potential annual savings of $2.8 million US dollars (USD) on avoidable ACU. CONCLUSION: The AI tool has enabled nurse case managers to identify and resolve critical clinical issues and reduce avoidable ACU. Effects on outcomes can be inferred from the reduction; targeting short-term interventions toward patients most at-risk translates to better long-term care and outcomes. QI projects involving predictive modeling of patient risk, prescriptive analytics, and nurse outreach may reduce ACU.

Estimating dementia risk in an African American population using the DCTclock.

The prevalence of Alzheimer's disease (AD) and related dementias (ADRD) is increasing. African Americans are twice as likely to develop dementia than other ethnic populations. Traditional cognitive screening solutions lack the sensitivity to independently identify individuals at risk for cognitive decline. The DCTclock is a 3-min AI-enabled adaptation of the well-established clock drawing test. The DCTclock can estimate dementia risk for both general cognitive impairment and the presence of AD pathology. Here we performed a retrospective analysis to assess the performance of the DCTclock to estimate future conversion to ADRD in African American participants from the Rush Alzheimer's Disease Research Center Minority Aging Research Study (MARS) and African American Clinical Core (AACORE). We assessed baseline DCTclock scores in 646 participants (baseline median age = 78.0 ± 6.4, median years of education = 14.0 ± 3.2, 78% female) and found significantly lower baseline DCTclock scores in those who received a dementia diagnosis within 3 years. We also found that 16.4% of participants with a baseline DCTclock score less than 60 were significantly more likely to develop dementia in 5 years vs. those with the highest DCTclock scores (75-100). This research demonstrates the DCTclock's ability to estimate the 5-year risk of developing dementia in an African American population. Early detection of elevated dementia risk using the DCTclock could provide patients, caregivers, and clinicians opportunities to plan and intervene early to improve cognitive health trajectories. Early detection of dementia risk can also enhance participant selection in clinical trials while reducing screening costs.

Impact of Augmented Intelligence on Utilization of Palliative Care Services in a Real-World Oncology Setting.

PURPOSE: For patients with advanced cancer, timely referral to palliative care (PC) services can ensure that end-of-life care aligns with their preferences and goals. Overestimation of life expectancy may result in underutilization of PC services, counterproductive treatment measures, and reduced quality of life for patients. We assessed the impact of a commercially available augmented intelligence (AI) tool to predict 30-day mortality risk on PC service utilization in a real-world setting. METHODS: Patients within a large hematology-oncology practice were scored weekly between June 2018 and October 2019 with an AI tool to generate insights into short-term mortality risk. Patients identified by the tool as being at high or medium risk were assessed for a supportive care visit and further referred as appropriate. Average monthly rates of PC and hospice referrals were calculated 5 months predeployment and 17 months postdeployment of the tool in the practice. RESULTS: The mean rate of PC consults increased from 17.3 to 29.1 per 1,000 patients per month (PPM) pre- and postdeployment, whereas the mean rate of hospice referrals increased from 0.2 to 1.6 per 1,000 PPM. Eliminating the first 6 months following deployment to account for user learning curve, the mean rate of PC consults nearly doubled over baseline to 33.0 and hospice referrals increased 12-fold to 2.4 PPM. CONCLUSION: Deployment of an AI tool at a hematology-oncology practice was found to be feasible for identifying patients at high or medium risk for short-term mortality. Insights generated by the tool drove clinical practice changes, resulting in significant increases in PC and hospice referrals.

Augmented intelligence to predict 30-day mortality in patients with cancer.

Aim: An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. Patients & methods: An algorithm to predict 30-day mortality risk was developed using socioeconomic and clinical data from patients in a large community hematology/oncology practice. Patients were scored weekly; algorithm performance was assessed using dates of death in patients' electronic health records. Results: For patients scored as highest risk for 30-day mortality, the event rate was 4.9% (vs 0.7% in patients scored as low risk; a 7.4-times greater risk). Conclusion: The development and validation of a decision tool to accurately identify patients with cancer who are at risk for short-term mortality is feasible.

Using applied machine learning to predict healthcare utilization based on socioeconomic determinants of care.

OBJECTIVES: To determine if it is possible to risk-stratify avoidable utilization without clinical data and with limited patient-level data. STUDY DESIGN: The aim of this study was to demonstrate the influences of socioeconomic determinants of health (SDH) with regard to avoidable patient-level healthcare utilization. The study investigated the ability of machine learning models to predict risk using only publicly available and purchasable SDH data. A total of 138,115 patients were analyzed from a deidentified database representing 3 health systems in the United States. METHODS: A hold-out methodology was used to ensure that the model's performance could be tested on a completely independent set of subjects. A proprietary decision tree methodology was used to make the predictions. Only the socioeconomic features-age group, gender, and race-were used in the prediction of a patient's risk of admission. RESULTS: The decision tree-based machine learning approach analyzed in this study was able to predict inpatient and emergency department utilization with a high degree of discrimination using only purchasable and publicly available data on SDH. CONCLUSIONS: This study indicates that it is possible to risk-stratify patients' risk of utilization without interacting with the patient or collecting information beyond the patient's age, gender, race, and address. The implications of this application are wide and have the potential to positively affect health systems by facilitating targeted patient outreach with specific, individualized interventions to tackle detrimental SDH at not only the individual level but also the neighborhood level.

Improving Outcomes in Patients With Sepsis.

Sepsis mortality may be improved by early recognition and appropriate treatment based on evidence-based guidelines. An intervention was developed that focused on earlier identification of sepsis, early antimicrobial administration, and an educational program that was disseminated throughout all hospital units and services. There were 1331 patients with sepsis during the intervention period and 1401 patients with sepsis during the control period. After controlling for expected mortality, patients in the intervention period had 30% lower odds of dying (odds ratio = 0.70, 95% confidence interval [CI] = 0.57 to 0.84). They also had 1.07 fewer days on average in the intensive care unit (95% CI = -1.98 to -0.16), 2.15 fewer hospital days (95% CI = -3.45 to -0.86), and incurred on average $1949 less in hospital costs, although the effect on costs was not statistically significant. Continued incremental improvement and sustainment is anticipated through organizational oversight, continued education, and initiation of an automated electronic sepsis alert function.

Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS): architecture.

We describe the architecture of the Patient Centered Outcomes Research Institute (PCORI) funded Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS, http://www.SCILHS.org) clinical data research network, which leverages the $48 billion dollar federal investment in health information technology (IT) to enable a queryable semantic data model across 10 health systems covering more than 8 million patients, plugging universally into the point of care, generating evidence and discovery, and thereby enabling clinician and patient participation in research during the patient encounter. Central to the success of SCILHS is development of innovative 'apps' to improve PCOR research methods and capacitate point of care functions such as consent, enrollment, randomization, and outreach for patient-reported outcomes. SCILHS adapts and extends an existing national research network formed on an advanced IT infrastructure built with open source, free, modular components.

The impact of obesity on sepsis mortality: a retrospective review.

BACKGROUND: Recent sepsis guidelines have focused on the early identification and risk stratification of patients on presentation. Obesity is associated with alterations in multiple inflammatory regulators similar to changes seen in sepsis, suggesting a potential interaction between the presence of obesity and the severity of illness in sepsis. METHODS: We performed a retrospective chart review of patients admitted with a primary billing diagnosis of sepsis at a single United States university hospital from 2007 to 2010. Seven hundred and ninety-two charts were identified meeting inclusion criteria. Obesity was defined as a body mass index (BMI) ≥ 30 kg/m2. The data recorded included age, race, sex, vital signs, laboratory values, length of stay, comorbidities, weight, height, and survival to discharge. A modified APACHE II score was calculated to estimate disease severity. The primary outcome variable was inpatient mortality. RESULTS: Survivors had higher average BMI than nonsurvivors (27.6 vs. 26.3 kg/m2, p = 0.03) in unadjusted analysis. Severity of illness and comorbid conditions including cancer were similar across BMI categories. Increased incidence of diabetes mellitus type 2 was associated with increasing BMI (p < 0.01) and was associated with decreased mortality, with an odds ratio of 0.53 compared with nondiabetic patients. After adjusting for age, gender, race, severity of illness, length of stay, and comorbid conditions, the trend of decreased mortality for increased BMI was no longer statistically significant, however diabetes continued to be strongly protective (odds ratio 0.52, p = 0.03). CONCLUSIONS: This retrospective analysis suggests obesity may be protective against mortality in septic inpatients. The protective effect of obesity may be dependent on diabetes, possibly through an unidentified hormonal intermediary. Further prospective studies are necessary to elaborate the specific mechanism of this protective effect.

The effect of a provider-enhanced clinical decision support tool for guiding venous thromboembolism pharmacoprophylaxis in low-risk patients.

BACKGROUND: Computer-based tools to assess venous thromboembolism (VTE) risk have been shown to increase VTE pharmacoprophylaxis rates and decrease VTE incidence in high-risk hospitalized patients. However, VTE risk may be underestimated using computer-based tools alone. We tested the effect of a provider-enhanced clinical decision support (CDS) tool on VTE pharmacoprophylaxis and VTE incidence in patients who would have been deemed "low risk" using a computer-based risk-assessment tool alone. METHODS: The study sample was adult patients hospitalized during a 13-month period who were determined to be at low risk for VTE with a computer-based risk-assessment tool. The provider-enhanced CDS tool was implemented 4 months into the study period and required providers to stratify patients as being at high, moderate, or low risk for VTE. We compared rates of VTE pharmacoprophylaxis and VTE incidence before and after implementation of the provider-enhanced CDS tool. RESULTS: There were 1322 patients in the 4-month pre-implementation period and 3347 patients in the 9-month post-implementation period who were determined to be at low risk for VTE based on a computer-based risk-assessment tool. Using the provider-enhanced CDS tool, providers stratified 31% of these computer-assigned low-risk patients as being at moderate risk for VTE and 7% as being at high risk for VTE. The rate of VTE pharmacoprophylaxis increased from 27% to 34% (P < 0.01). The venous thromboprophylaxis rate decreased from 0.98% to 0.42% after implementation of the provider-enhanced CDS tool (P < 0.02). CONCLUSION: Patients identified as being at low risk for VTE solely by computer-based algorithms may include patients whom providers determine to be at risk for VTE. Provider-enhanced CDS tools may outperform computer-based VTE risk-stratification algorithms.

Validity of the brief inpatient screen for intimate partner violence among adult women.

BACKGROUND: Identifying intimate partner violence in healthcare settings is becoming the standard of care. The Brief Inpatient Screen was designed to assess recent emotional, physical, and sexual abuse in a general inpatient medical-surgical setting and compared to the Composite Abuse Scale. METHODS: Researchers matched "cases" (inpatients screening Brief Inpatient Screen-positive) to up to four "controls" (inpatients screening Brief Inpatient Screen-negative). Forty-six female hospital inpatients ages 18-64 years completed a self-administered survey. The sensitivity and specificity of the Brief Inpatient Screen and its subscales were compared to the Composite Abuse Scale. Researchers examined the performance of the Brief Inpatient Screen when used as a verbal screen versus an anonymous written screen. RESULTS: Twelve of 46 participants (26%) had a positive screen. Compared to the Composite Abuse Scale, the overall sensitivity and specificity of the verbal Brief Inpatient Screen were 52.6% (95% CI 28.9-75.6) and 92.6% (95% CI 75.7-99.1), respectively. The written Brief Inpatient Screen showed improved sensitivity overall (68.4%, 95% CI 43.5-87.4) for the most severe intimate partner violence. Subscale analysis revealed greater sensitivity for emotional and severe combined intimate partner violence. CONCLUSIONS: The verbal Brief Inpatient Screen, when compared to the Composite Abuse Scale, was limited in its ability to identify intimate partner violence. An anonymous written format improved sensitivity. Future research should optimize intimate partner violence screening among inpatients.

Effect of standardized electronic discharge instructions on post-discharge hospital utilization.

BACKGROUND: Several physician organizations and the Centers for Medicare and Medicaid Services (CMS) support compliance measures for written discharge instructions. CMS has identified clear discharge instructions with specific attention to medication management as a necessary intervention. OBJECTIVE: We tested the hypothesis that implementing a standardized electronic discharge instructions document with embedded computerized medication reconciliation would decrease post-discharge hospital utilization. DESIGN: Retrospective pre- and post-implementation comparison cohort study. PATIENTS: Subjects were hospitalized patients age 18 and older discharged between November 1, 2005 and October 31, 2006 (n = 16,572) and between March 1, 2007 and February 28, 2008 (n = 17,516). INTERVENTION: Implementation of a standardized, templated electronic discharge instructions document with embedded computerized medication reconciliation on December 18, 2006. MAIN MEASURES: The primary outcome was a composite variable of readmission or Emergency Department (ED) visit within 30 days of discharge. Secondary outcomes were the individual variables of readmissions and ED visits within 30 days. KEY RESULTS: The implementation of standardized electronic discharge instructions with embedded computerized medication reconciliation was not associated with a change in the primary composite outcome (adjusted OR 1.04, 95% CI 0.98-1.10) or the secondary outcome of 30-day ED visits (adjusted OR 0.98, 95% CI 0.98-1.10). There was an unexpected small but statistically significant increase in 30-day readmissions (adjusted OR 1.08, 95% CI 1.01-1.16). CONCLUSIONS: Implementation of standardized electronic discharge instructions was not associated with reduction in post-discharge hospital utilization. More studies are needed to determine the reasons for post-discharge hospital utilization and to examine outcomes associated with proposed process-related recommendations.

Ethanol-induced conditioned taste avoidance: reward or aversion?

BACKGROUND: Rats avoid intake of a palatable taste cue when paired with all drugs of abuse tested. Evidence suggests that, at least for morphine and cocaine, rats avoid the taste cue because they are anticipating the rewarding properties of the drug. Thus, the suppressive effects of a rewarding sucrose solution and cocaine, but not those of the putatively aversive agent, lithium chloride (LiCl), are exaggerated in drug-sensitive Lewis rats. Likewise, the suppressive effects of sucrose and morphine, but not those of LiCl, are eliminated by bilateral lesions of the gustatory thalamus. Unlike morphine and cocaine, it is less clear whether rewarding or aversive drug properties are responsible for ethanol-induced suppression of intake of a taste cue. The present set of studies tests whether, like cocaine, ethanol-induced suppression of intake of a taste cue also is greater in the drug-sensitive Lewis rats and whether the suppressive effects of the drug are prevented by bilateral lesions of the taste thalamus. METHODS: In Experiment 1, fluid-deprived Lewis and Fischer rats were given 5-minute access to 0.15% saccharin and then injected with saline or a range of doses of ethanol (0.5, 0.75, 1.0, or 1.5 g/kg). There was a total of 6 such pairings. In Experiments 2 and 3, Sprague-Dawley rats received bilateral electrophysiologically guided lesions of the gustatory thalamus. After recovery, suppression of intake of the saccharin cue was evaluated following repeated daily pairings with either a high (1.5 g/kg) or a low (0.75 g/kg) dose of ethanol. RESULTS: Ethanol-induced suppression of intake of the saccharin conditioned stimulus (CS) did not differ between the drug-sensitive Lewis rats relative to the less-sensitive Fischer rats. Lesions of the taste thalamus, however, prevented the suppressive effect of the 0.75 g/kg dose of the drug, but had no impact on the suppressive effect of the 1.5 g/kg dose of ethanol. CONCLUSION: The results suggest that the suppressive effects of ethanol on CS intake are mediated by both rewarding and aversive consequences, varying as a function of dose.

Cardiovascular outcome in hospitalized patients with minimal troponin I elevation and normal creatine phosphokinase.

BACKGROUND: Among patients with acute coronary syndrome, elevated cardiac troponin and creatine phosphokinase MB fraction levels have both prognostic and diagnostic values. However, in hospitalized patients, cardiac biomarkers are measured in a variety of clinical situations including but not limited to acute coronary syndrome. Moreover, these patients may have elevated troponin levels with no increase in creatine phosphokinase MB fraction levels. OBJECTIVE: To evaluate the cardiovascular outcome of acutely ill, hospitalized patients with minimal troponin I increase with normal creatine phosphokinase MB fraction. METHODS: We identified 64 patients retrospectively from our database with minimal troponin I increase and normal creatine phosphokinase MB fraction hospitalized between November 1998 and April 2000. Discharged patients were questioned about re-hospitalization for myocardial infarction, unstable coronary syndrome, congestive heart failure and percutaneous coronary intervention by means of a structured questionnaire. For those patients who died during hospitalization, data were collected from hospital records. For patients who died at home or at a different institution, a surviving relative completed the questionnaire. Primary outcomes were death, myocardial infarction and the need for revascularization or re-hospitalization. RESULTS: Composite endpoint of death, myocardial infarction, percutaneous coronary intervention or coronary artery bypass grafting and re-hospitalization for cardiac cause occurred in 35.95% of patients within 1 year. CONCLUSIONS: There is a significant composite event rate of death, myocardial infarction or re-hospitalization for cardiac causes in acutely ill, hospitalized patients with normal creatine phosphokinase MB fraction and minimally elevated troponin I, regardless of the cause for hospitalization.