Decreasing Opioid Addiction and Diversion Using Behavioral Economics Applied Through a Digital Engagement Solution: Protocol for a Randomized Controlled Trial

BACKGROUND

Despite strong and growing interest in ending the ongoing opioid health crisis, there has been limited success in reducing the prevalence of opioid addiction and the number of deaths associated with opioid overdoses. Further, 1 explanation for this is that existing interventions target those who are opiate-dependent but do not prevent opioid-naïve patients from becoming addicted.

OBJECTIVE

Leveraging behavioral economics at the patient level could help patients successfully use, discontinue, and dispose of their opioid medications in an acute pain setting. The primary goal of this project is to evaluate the effect of the 3 versions of the Opioid Management for You (OPY) tool on measures of opioid use relative to the standard of care by leveraging a pragmatic randomized controlled trial (RCT).

METHODS

A team of researchers from the Center for Learning Health System Sciences (CLHSS) at the University of Minnesota partnered with M Health Fairview to design, build, and test the 3 versions of the OPY tool: social influence, precommitment, and testimonial version. The tool is being built using the Epic Care Companion (Epic Inc) platform and interacts with the patient through their existing MyChart (Epic Systems Corporation) personal health record account, and Epic patient portal, accessed through a phone app or the MyChart website. We have demonstrated feasibility with pilot data of the social influence version of the OPY app by targeting our pilot to a specific cohort of patients undergoing upper-extremity procedures. This study will use a group sequential RCT design to test the impact of this important health system initiative. Patients who meet OPY inclusion criteria will be stratified into low, intermediate, and high risk of opiate use based on their type of surgery.

RESULTS

This study is being funded and supported by the CLHSS Rapid Prospective Evaluation and Digital Technology Innovation Programs, and M Health Fairview. Support and coordination provided by CLHSS include the structure of engagement, survey development, data collection, statistical analysis, and dissemination. The project was initially started in August 2022. The pilot was launched in February 2023 and is still running, with the data last counted in August 2023. The actual RCT is planned to start by early 2024.

CONCLUSIONS

Through this RCT, we will test our hypothesis that patient opioid use and diverted prescription opioid availability can both be improved by information delivery applied through a behavioral economics lens via sending nudges directly to the opioid users through their personal health record.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06124079; https://clinicaltrials.gov/study/NCT06124079.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/52882.

A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients

Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases. A manual case review to obtain this estimate indicated our sample incidence of PASC (13%) was representative of the estimated population proportion (95% CI: 19±6.22%). However, the high number of cases classified as indeterminate demonstrates the challenges in classifying PASC even among experienced clinicians. Lastly, this study developed a dashboard to display views of aggregated PASC symptoms and measured its utility using the System Usability Scale. Overall comments related to the dashboard's potential were positive. This pipeline is crucial for monitoring post-COVID-19 patients with potential for use in clinical settings.

Robust Methods for Quantifying the Effect of a Continuous Exposure From Observational Data

A cornerstone of clinical medicine is intervening on a continuous exposure, such as titrating the dosage of a pharmaceutical or controlling a laboratory result. In clinical trials, continuous exposures are dichotomized into narrow ranges, excluding large portions of the realistic treatment scenarios. The existing computational methods for estimating the effect of continuous exposure rely on a set of strict assumptions. We introduce new methods that are more robust towards violations of these assumptions. Our methods are based on the key observation that changes of exposure in the clinical setting are often achieved gradually, so effect estimates must be "locally" robust in narrower exposure ranges. We compared our methods with several existing methods on three simulated studies with increasing complexity. We also applied the methods to data from 14 k sepsis patients at M Health Fairview to estimate the effect of antibiotic administration latency on prolonged hospital stay. The proposed methods achieve good performance in all simulation studies. When the assumptions were violated, the proposed methods had estimation errors of one half to one fifth of the state-of-the-art methods. Applying our methods to the sepsis cohort resulted in effect estimates consistent with clinical knowledge.

Comparison of Outcomes and Process of Care for Patients Treated at Hospitals Dedicated for COVID-19 Care vs Other Hospitals

IMPORTANCE

Early in the SARS-CoV-2 pandemic, the M Health Fairview Hospital System established dedicated hospitals for establishing cohorts and caring for patients with COVID-19, yet the association between treatment at COVID-19-dedicated hospitals and mortality and complications is not known.

OBJECTIVE

To analyze the mortality rate and complications associated with treatment at the COVID-19-dedicated hospitals.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study evaluated data prospectively collected from March 1, 2020, through June 30, 2021, from 11 hospitals in Minnesota, including 2 hospitals created solely to care for patients with COVID-19. Data obtained included demographic characteristics, treatments, and outcomes of interest for all patients with a confirmed COVID-19 infection admitted to this hospital system during the study period.

EXPOSURES

Patients were grouped based on whether they received treatment from 1 of the 2 COVID-19-dedicated hospitals compared with the remainder of the hospitals within the hospital system.

MAIN OUTCOMES AND MEASURES

Multivariate analyses, including risk-adjusted logistic regression and propensity score matching, were performed to evaluate the primary outcome of in-hospital mortality and secondary outcomes, including complications and use of COVID-specific therapeutics.

RESULTS

There were 5504 patients with COVID-19 admitted during the study period (median age, 62.5 [IQR, 45.0-75.6] years; 2854 women [51.9%]). Of these, 2077 patients (37.7%) (median age, 63.4 [IQR, 50.7-76.1] years; 1080 men [52.0%]) were treated at 1 of the 2 COVID-19-dedicated hospitals compared with 3427 (62.3%; median age, 62.0 [40.0-75.1] years; 1857 women (54.2%) treated at other hospitals. The mortality rate was 11.6% (n = 241) at the dedicated hospitals compared with 8.0% (n = 274) at the other hospitals (P < .001). However, risk-adjusted in-hospital mortality was significantly lower for patients in the COVID-19-dedicated hospitals in both the unmatched group (n = 2077; odds ratio [OR], 0.75; 95% CI, 0.59-0.95) and the propensity score-matched group (n = 1317; OR, 0.78; 95% CI, 0.58-0.99). The rate of overall complications in the propensity score-matched group was significantly lower (OR, 0.81; 95% CI, 0.66-0.99) and the use of COVID-19-specific therapeutics including deep vein thrombosis prophylaxis (83.9% vs 56.9%; P < .001), high-dose corticosteroids (56.1% vs 22.2%; P < .001), remdesivir (61.5% vs 44.5%; P < .001), and tocilizumab (7.9% vs 2.0; P < .001) was significantly higher.

CONCLUSIONS AND RELEVANCE

In this cohort study, COVID-19-dedicated hospitals had multiple benefits, including providing high-volume repetitive treatment and isolating patients with the infection. This experience suggests improved in-hospital mortality for patients treated at dedicated hospitals owing to improved processes of care and supports the use of establishing cohorts for future pandemics.

Implementation of an Anticoagulation Practice Guideline for COVID-19 via a Clinical Decision Support System in a Large Academic Health System and Its Evaluation: Observational Study

BACKGROUND

Studies evaluating strategies for the rapid development, implementation, and evaluation of clinical decision support (CDS) systems supporting guidelines for diseases with a poor knowledge base, such as COVID-19, are limited.

OBJECTIVE

We developed an anticoagulation clinical practice guideline (CPG) for COVID-19, which was delivered and scaled via CDS across a 12-hospital Midwest health care system. This study represents a preplanned 6-month postimplementation evaluation guided by the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework.

METHODS

The implementation outcomes evaluated were reach, adoption, implementation, and maintenance. To evaluate effectiveness, the association of CPG adherence on hospital admission with clinical outcomes was assessed via multivariable logistic regression and nearest neighbor propensity score matching. A time-to-event analysis was conducted. Sensitivity analyses were also conducted to evaluate the competing risk of death prior to intensive care unit (ICU) admission. The models were risk adjusted to account for age, gender, race/ethnicity, non-English speaking status, area deprivation index, month of admission, remdesivir treatment, tocilizumab treatment, steroid treatment, BMI, Elixhauser comorbidity index, oxygen saturation/fraction of inspired oxygen ratio, systolic blood pressure, respiratory rate, treating hospital, and source of admission. A preplanned subgroup analysis was also conducted in patients who had laboratory values (D-dimer, C-reactive protein, creatinine, and absolute neutrophil to absolute lymphocyte ratio) present. The primary effectiveness endpoint was the need for ICU admission within 48 hours of hospital admission.

RESULTS

A total of 2503 patients were included in this study. CDS reach approached 95% during implementation. Adherence achieved a peak of 72% during implementation. Variation was noted in adoption across sites and nursing units. Adoption was the highest at hospitals that were specifically transformed to only provide care to patients with COVID-19 (COVID-19 cohorted hospitals; 74%-82%) and the lowest in academic settings (47%-55%). CPG delivery via the CDS system was associated with improved adherence (odds ratio [OR] 1.43, 95% CI 1.2-1.7; P<.001). Adherence with the anticoagulation CPG was associated with a significant reduction in the need for ICU admission within 48 hours (OR 0.39, 95% CI 0.30-0.51; P<.001) on multivariable logistic regression analysis. Similar findings were noted following 1:1 propensity score matching for patients who received adherent versus nonadherent care (21.5% vs 34.3% incidence of ICU admission within 48 hours; log-rank test P<.001).

CONCLUSIONS

Our institutional experience demonstrated that adherence with the institutional CPG delivered via the CDS system resulted in improved clinical outcomes for patients with COVID-19. CDS systems are an effective means to rapidly scale a CPG across a heterogeneous health care system. Further research is needed to investigate factors associated with adherence at low and high adopting sites and nursing units.

Outpatient and inpatient anticoagulation therapy and the risk for hospital admission and death among COVID-19 patients

BACKGROUND

Coronavirus disease 2019 (COVID-19) is associated with a hypercoagulable state. Limited data exist informing the relationship between anticoagulation therapy and risk for COVID-19 related hospitalization and mortality.

METHODS

We evaluated all patients over the age of 18 diagnosed with COVID-19 in a prospective cohort study from March 4th to August 27th, 2020 among 12 hospitals and 60 clinics of M Health Fairview system (USA). We investigated the relationship between (1) 90-day anticoagulation therapy among outpatients before COVID-19 diagnosis and the risk for hospitalization and mortality and (2) Inpatient anticoagulation therapy and mortality risk.

FINDINGS

Of 6195 patients, 598 were immediately hospitalized and 5597 were treated as outpatients. The overall case-fatality rate was 2•8% (n = 175 deaths). Among the patients who were hospitalized, the inpatient mortality was 13%. Among the 5597 COVID-19 patients initially treated as outpatients, 160 (2.9%) were on anticoagulation and 331 were eventually hospitalized (5.9%). In a multivariable analysis, outpatient anticoagulation use was associated with a 43% reduction in risk for hospital admission, HR (95% CI = 0.57, 0.38-0.86), p = 0.007, but was not associated with mortality, HR (95% CI=0.88, 0.50 - 1.52), p = 0.64. Inpatients who were not on anticoagulation (before or after hospitalization) had an increased risk for mortality, HR (95% CI = 2.26, 1.17-4.37), p = 0.015.

INTERPRETATION

Outpatients with COVID-19 who were on outpatient anticoagulation at the time of diagnosis experienced a 43% reduced risk of hospitalization. Failure to initiate anticoagulation upon hospitalization or maintaining outpatient anticoagulation in hospitalized COVID-19 patients was associated with increased mortality risk.

FUNDING

No funding was obtained for this study.

NLP Methods for Extraction of Symptoms from Unstructured Data for Use in Prognostic COVID-19 Analytic Models

Statistical modeling of outcomes based on a patient's presenting symptoms (symptomatology) can help deliver high quality care and allocate essential resources, which is especially important during the COVID-19 pandemic. Patient symptoms are typically found in unstructured notes, and thus not readily available for clinical decision making. In an attempt to fill this gap, this study compared two methods for symptom extraction from Emergency Department (ED) admission notes. Both methods utilized a lexicon derived by expanding The Center for Disease Control and Prevention's (CDC) Symptoms of Coronavirus list. The first method utilized a word2vec model to expand the lexicon using a dictionary mapping to the Uni ed Medical Language System (UMLS). The second method utilized the expanded lexicon as a rule-based gazetteer and the UMLS. These methods were evaluated against a manually annotated reference (f1-score of 0.87 for UMLS-based ensemble; and 0.85 for rule-based gazetteer with UMLS). Through analyses of associations of extracted symptoms used as features against various outcomes, salient risks among the population of COVID-19 patients, including increased risk of in-hospital mortality (OR 1.85, p-value < 0.001), were identified for patients presenting with dyspnea. Disparities between English and non-English speaking patients were also identified, the most salient being a concerning finding of opposing risk signals between fatigue and in-hospital mortality (non-English: OR 1.95, p-value = 0.02; English: OR 0.63, p-value = 0.01). While use of symptomatology for modeling of outcomes is not unique, unlike previous studies this study showed that models built using symptoms with the outcome of in-hospital mortality were not significantly different from models using data collected during an in-patient encounter (AUC of 0.9 with 95% CI of [0.88, 0.91] using only vital signs; AUC of 0.87 with 95% CI of [0.85, 0.88] using only symptoms). These findings indicate that prognostic models based on symptomatology could aid in extending COVID-19 patient care through telemedicine, replacing the need for in-person options. The methods presented in this study have potential for use in development of symptomatology-based models for other diseases, including for the study of Post-Acute Sequelae of COVID-19 (PASC).

Outpatient metformin use is associated with reduced severity of COVID-19 disease in adults with overweight or obesity

Observational studies suggest outpatient metformin use is associated with reduced mortality from coronavirus disease-2019 (COVID-19). Metformin is known to decrease interleukin-6 and tumor-necrosis factor-α, which appear to contribute to morbidity in COVID-19. We sought to understand whether outpatient metformin use was associated with reduced odds of severe COVID-19 disease in a large US healthcare data set. Retrospective cohort analysis of electronic health record (EHR) data that was pooled across multiple EHR systems from 12 hospitals and 60 primary care clinics in the Midwest between March 4, 2020 and December 4, 2020. Inclusion criteria: data for body mass index (BMI) > 25 kg/m2 and a positive SARS-CoV-2 polymerase chain reaction test; age ≥ 30 and ≤85 years. Exclusion criteria: patient opt-out of research. Metformin is the exposure of interest, and death, admission, and intensive care unit admission are the outcomes of interest. Metformin was associated with a decrease in mortality from COVID-19, OR 0.32 (0.15, 0.66; p = .002), and in the propensity-matched cohorts, OR 0.38 (0.16, 0.91; p = .030). Metformin was associated with a nonsignificant decrease in hospital admission for COVID-19 in the overall cohort, OR 0.78 (0.58-1.04, p = .087). Among the subgroup with a hemoglobin HbA1c available (n = 1193), the adjusted odds of hospitalization (including adjustment for HbA1c) for metformin users was OR 0.75 (0.53-1.06, p = .105). Outpatient metformin use was associated with lower mortality and a trend towards decreased admission for COVID-19. Given metformin's low cost, established safety, and the mounting evidence of reduced severity of COVID-19 disease, metformin should be prospectively assessed for outpatient treatment of COVID-19.

Heterogeneity in COVID-19 Patients at Multiple Levels of Granularity: From Biclusters to Clinical Interventions

Several studies have shown that COVID-19 patients with prior comorbidities have a higher risk for adverse outcomes, resulting in a disproportionate impact on older adults and minorities that fit that profile. However, although there is considerable heterogeneity in the comorbidity profiles of these populations, not much is known about how prior comorbidities co-occur to form COVID-19 patient subgroups, and their implications for targeted care. Here we used bipartite networks to quantitatively and visually analyze heterogeneity in the comorbidity profiles of COVID-19 inpatients, based on electronic health records from 12 hospitals and 60 clinics in the greater Minneapolis region. This approach enabled the analysis and interpretation of heterogeneity at three levels of granularity (cohort, subgroup, and patient), each of which enabled clinicians to rapidly translate the results into the design of clinical interventions. We discuss future extensions of the multigranular heterogeneity framework, and conclude by exploring how the framework could be used to analyze other biomedical phenomena including symptom clusters and molecular phenotypes, with the goal of accelerating translation to targeted clinical care.

Evaluation of an Intrahospital Telemedicine Program for Patients Admitted With COVID-19: Mixed Methods Study

BACKGROUND

The increasing incidence of COVID-19 infection has challenged health care systems to increase capacity while conserving personal protective equipment (PPE) supplies and minimizing nosocomial spread. Telemedicine shows promise to address these challenges but lacks comprehensive evaluation in the inpatient environment.

OBJECTIVE

The aim of this study is to evaluate an intrahospital telemedicine program (virtual care), along with its impact on exposure risk and communication.

METHODS

We conducted a natural experiment of virtual care on patients admitted for COVID-19. The primary exposure variable was documented use of virtual care. Patient characteristics, PPE use rates, and their association with virtual care use were assessed. In parallel, we conducted surveys with patients and clinicians to capture satisfaction with virtual care along the domains of communication, medical treatment, and exposure risk.

RESULTS

Of 137 total patients in our primary analysis, 43 patients used virtual care. In total, there were 82 inpatient days of use and 401 inpatient days without use. Hospital utilization and illness severity were similar in patients who opted in versus opted out. Virtual care was associated with a significant reduction in PPE use and physical exam rate. Surveys of 41 patients and clinicians showed high rates of recommendation for further use, and subjective improvements in communication. However, providers and patients expressed limitations in usability, medical assessment, and empathetic communication.

CONCLUSIONS

In this pilot natural experiment, only a subset of patients used inpatient virtual care. When used, virtual care was associated with reductions in PPE use, reductions in exposure risk, and patient and provider satisfaction.

Metformin and Covid-19: Focused Review of Mechanisms and Current Literature Suggesting Benefit

Metformin is the first-line medication for type 2 diabetes, but it also has a long history of improved outcomes in infectious diseases, such as influenza, hepatitis C, and in-vitro assays of zika. In the current Covid-19 pandemic, which has rapidly spread throughout the world, 4 observational studies have been published showing reduced mortality among individuals with home metformin use. There are several potential overlapping mechanisms by which metformin may reduce mortality from Covid-19. Metformin's past anti-infectious benefits have been both against the infectious agent directly, as well as by improving the underlying health of the human host. It is unknown if the lower mortality suggested by observational studies in patients infected with Covid-19 who are on home metformin is due to direct activity against the virus itself, improved host substrate, or both.

35. Missed and Unrecorded Drug Use Among Infective Endocarditis Cases Is Associated with Underestimated Burden of Disease and Fragmented Care: Evidence from Six States

Background

Studies using national administrative data suggest that hospitalizations for drug use-associated infective endocarditis (DUA-IE) have increased over the last ten years. However, drug use as a contributing factor to IE hospitalizations is often missed or not included in coding documentation, resulting in undercount of DUA-IE. We assessed whether missed drug use during IE hospitalizations was associated with higher levels of fragmented care and underestimation of DUA-IE burden.

Methods

We analyzed data from State Inpatient Databases and State Emergency Department Databases from six states (FL, GA, IA, NY, UT, VT) from 2011–2015. Patients older than 16 with ICD-9/10 codes for admissions with IE were included. IE was categorized as DUA using ICD-9/10 codes for drugs/conditions associated with injection drug use. We labeled IE cases as a “missed” DUA-IE case if they had no diagnosis of drug use during their index hospitalization but received a drug use diagnosis during an ED visit or inpatient stay in the calendar year of their index IE hospitalization. We compared “missed” DUA-IE cases to DUA-IE cases where drug use was identified in the index hospitalization and non-DUE-IE cases with respect to demographics, length of stay (LOS) and total charges. To assess care fragmentation, we stratified IE groups by whether the patient was admitted to 1 or >1 hospital within 90-days of the index IE admission.

Results

There were 52147 non-DUA-IE cases, 6872 DUA-IE cases, and 2676 “missed” DUA-IE cases identified by linking drug use across multiple encounters. Missed cases represented a 39% increase in total DUA-IE cases. Compared to DUA-IE cases identified at index hospitalizations, missed cases were more likely to be older, Black, insured by Medicare, and from rural areas. They also had higher 30-day readmission rate (23.2% vs 14.5%, p< 0.001) and higher charges (p< 0.001), with similar LOS. Fragmented care was most common among patients with missed DUA-IE (33.3%), followed by DUA-IE cases identified during index hospitalization (20.5%) and non-DUA-IE cases (13.7%).

Table 1

Table 2

Conclusion

Missed and/or unrecorded drug use and fragmented care are common features of DUA-IE. This results in underestimation of both DUA-IE prevalence and hospital utilization due to DUA-IE.

Disclosures

All Authors: No reported disclosures

Characterizing COVID-19 Clinical Phenotypes and Associated Comorbidities and Complication Profiles

BACKGROUND

There is limited understanding of heterogeneity in outcomes across hospitalized patients with coronavirus disease 2019 (COVID-19). Identification of distinct clinical phenotypes may facilitate tailored therapy and improve outcomes.

OBJECTIVE

Identify specific clinical phenotypes across COVID-19 patients and compare admission characteristics and outcomes.

DESIGN, SETTINGS, AND PARTICIPANTS

Retrospective analysis of 1,022 COVID-19 patient admissions from 14 Midwest U.S. hospitals between March 7, 2020 and August 25, 2020.

METHODS

Ensemble clustering was performed on a set of 33 vitals and labs variables collected within 72 hours of admission. K-means based consensus clustering was used to identify three clinical phenotypes. Principal component analysis was performed on the average covariance matrix of all imputed datasets to visualize clustering and variable relationships. Multinomial regression models were fit to further compare patient comorbidities across phenotype classification. Multivariable models were fit to estimate the association between phenotype and in-hospital complications and clinical outcomes. Main outcomes and measures: Phenotype classification (I, II, III), patient characteristics associated with phenotype assignment, in-hospital complications, and clinical outcomes including ICU admission, need for mechanical ventilation, hospital length of stay, and mortality.

RESULTS

The database included 1,022 patients requiring hospital admission with COVID-19 (median age, 62.1 [IQR: 45.9-75.8] years; 481 [48.6%] male, 412 [40.3%] required ICU admission, 437 [46.7%] were white). Three clinical phenotypes were identified (I, II, III); 236 [23.1%] patients had phenotype I, 613 [60%] patients had phenotype II, and 173 [16.9%] patients had phenotype III. When grouping comorbidities by organ system, patients with respiratory comorbidities were most commonly characterized by phenotype III (p=0.002), while patients with hematologic (p<0.001), renal (p<0.001), and cardiac (p<0.001) comorbidities were most commonly characterized by phenotype I. The adjusted odds of respiratory (p<0.001), renal (p<0.001), and metabolic (p<0.001) complications were highest for patients with phenotype I, followed by phenotype II. Patients with phenotype I had a far greater odds of hepatic (p<0.001) and hematological (p=0.02) complications than the other two phenotypes. Phenotypes I and II were associated with 7.30-fold (HR: 7.30, 95% CI: (3.11-17.17), p<0.001) and 2.57-fold (HR: 2.57, 95% CI: (1.10-6.00), p=0.03) increases in the hazard of death, respectively, when compared to phenotype III.

CONCLUSION

In this retrospective analysis of patients with COVID-19, three clinical phenotypes were identified. Future research is urgently needed to determine the utility of these phenotypes in clinical practice and trial design.

Non-alcoholic fatty liver disease (NAFLD) and risk of hospitalization for Covid-19

Background

Covid-19 disease causes significant morbidity and mortality through increase inflammation and thrombosis. Non-alcoholic fatty liver disease and non-alcoholic steatohepatitis are states of chronic inflammation and indicate advanced metabolic disease. We sought to understand the risk of hospitalization for Covid-19 associated with NAFLD/NASH.

Methods

Retrospective analysis of electronic medical record data of 6,700 adults with a positive SARS-CoV-2 PCR from March 1, 2020 to Aug 25, 2020. Logistic regression and competing risk were used to assess odds of being hospitalized. Additional adjustment was added to assess risk of hospitalization among patients with a prescription for metformin use within the 3 months prior to the SARS-CoV-2 PCR result, history of home glucagon-like-peptide 1 receptor agonist (GLP-1 RA) use, and history of metabolic and bariatric surgery (MBS). Interactions were assessed by gender and race.

Results

A history of NAFLD/NASH was associated with increased odds of admission for Covid-19: logistic regression OR 2.04 (1.55, 2.96, p<0.01), competing risks OR 1.43 (1.09-1.88, p<0.01); and each additional year of having NAFLD/NASH was associated with a significant increased risk of being hospitalized for Covid-19, OR 1.86 (1.43-2.42, p<0.01). After controlling for NAFLD/NASH, persons with obesity had decreased odds of hospitalization for Covid-19, OR 0.41 (0.34-0.49, p<0.01). NAFLD/NASH increased risk of hospitalization in men and women, and in all racial/ethnic subgroups. Mediation treatments for metabolic syndrome were associated with non-significant reduced risk of admission: OR 0.42 (0.18-1.01, p=0.05) for home metformin use and OR 0.40 (0.14-1.17, p=0.10) for home GLP-1RA use. MBS was associated with a significant decreased risk of admission: OR 0.22 (0.05-0.98, p<0.05).

Conclusions

NAFLD/NASH is a significant risk factor for hospitalization for Covid-19, and appears to account for risk attributed to obesity. Treatments for metabolic disease mitigated risks from NAFLD/NASH. More research is needed to confirm risk associated with visceral adiposity, and patients should be screened for and informed of treatments for metabolic syndrome.

Diagnostic Discordance, Health Information Exchange, and Inter-Hospital Transfer Outcomes: a Population Study

BACKGROUND

Studying diagnostic error at the population level requires an understanding of how diagnoses change over time.

OBJECTIVE

To use inter-hospital transfers to examine the frequency and impact of changes in diagnosis on patient risk, and whether health information exchange can improve patient safety by enhancing diagnostic accuracy.

DESIGN

Diagnosis coding before and after hospital transfer was merged with responses from the American Hospital Association Annual Survey for a cohort of patients transferred between hospitals to identify predictors of mortality.

PARTICIPANTS

Patients (180,337) 18 years or older transferred between 473 acute care hospitals from NY, FL, IA, UT, and VT from 2011 to 2013.

MAIN MEASURES

We identified discordant Elixhauser comorbidities before and after transfer to determine the frequency and developed a weighted score of diagnostic discordance to predict mortality. This was included in a multivariate model with inpatient mortality as the dependent variable. We investigated whether health information exchange (HIE) functionality adoption as reported by hospitals improved diagnostic discordance and inpatient mortality.

KEY RESULTS

Discordance in diagnoses occurred in 85.5% of all patients. Seventy-three percent of patients gained a new diagnosis following transfer while 47% of patients lost a diagnosis. Diagnostic discordance was associated with increased adjusted inpatient mortality (OR 1.11 95% CI 1.10-1.11, p < 0.001) and allowed for improved mortality prediction. Bilateral hospital HIE participation was associated with reduced diagnostic discordance index (3.69 vs. 1.87%, p < 0.001) and decreased inpatient mortality (OR 0.88, 95% CI 0.89-0.99, p < 0.001).

CONCLUSIONS

Diagnostic discordance commonly occurred during inter-hospital transfers and was associated with increased inpatient mortality. Health information exchange adoption was associated with decreased discordance and improved patient outcomes.

Interhospital transfer handoff practices among US tertiary care centers: A descriptive survey

BACKGROUND

Interhospital transfer is an understudied area within transitions of care. The process by which hospitals accept and transfer patients is not well described. National trends and best practices are unclear.

OBJECTIVE

To describe the demographics of large transfer centers, to identify common handoff practices, and to describe challenges and notable innovations involving the interhospital transfer handoff process.

DESIGN AND PARTICIPANTS

A convenience sample of 32 tertiary care centers in the United States was studied. Respondents were typically transfer center directors surveyed by phone.

MAIN MEASURES

Data regarding transfer center demographics, handoff communication practices, electronic infrastructure, and data sharing were obtained.

RESULTS

The median number of patients transferred each month per receiving institution was 700 (range, 250-2500); on average, 28% of these patients were transferred to an intensive care unit. Transfer protocols and practices varied by institution. Transfer center coordinators typically had a medical background (78%), and critical care-trained registered nurse was the most prevalent (38%). Common practices included: mandatory recorded 3-way physician-to-physician conversation (84%) and mandatory clinical status updates prior to patient arrival (81%). However, the timeline of clinical status updates was variable. Less frequent transfer practices included: electronic medical record (EMR) cross-talk availability and utilization (23%), real-time transfer center documentation accessibility in the EMR (32%), and referring center clinical documentation available prior to transport (29%). A number of innovative strategies to address challenges involving interhospital handoffs are reported.

CONCLUSIONS

Interhospital transfer practices vary widely amongst tertiary care centers. Practices that lead to improved patient handoffs and reduced medical errors need additional prospective evaluation. Journal of Hospital Medicine 2016;11:413-417. © 2016 Society of Hospital Medicine.

CHAOTIC BEHAVIOR OF A NEURAL NETWORK WITH DYNAMICAL THRESHOLDS

Models of neural networks which include dynamical thresholds can display motion in pattern space, the space of all memories. We investigate this motion in a particular model which is based on a feedback network of excitatory and inhibitory neurons. We find that small variations in the parameters of the model can lead to big qualitative changes of its behavior. We display results of closed loops and chaotic motion which turn from one to the other through intermittency. We show that the basin of attraction of a closed orbit has a fractal shape, and find that the dimension of the chaotic motion is slightly bigger than 2. The general character of the dynamics of this model is convergence to centers of attraction on short time scales and divergence on long ones.

EXCITATORY–INHIBITORY NETWORKS WITH DYNAMICAL THRESHOLDS

We investigate feedback networks containing excitatory and inhibitory neurons. The couplings between the neurons follow a Hebbian rule in which the memory patterns are encoded as cell assemblies of the excitatory neurons. Using disjoint patterns, we study the attractors of this model and point out the importance of mixed states. The latter become dominant at temperatures above 0.25. We use both numerical simulations and an analytic approach for our investigation. The latter is based on differential equations for the activity of the different memory patterns in the network configuration. Allowing the excitatory thresholds to develop dynamic features which correspond to fatigue of individual neurons, we obtain motion in pattern space, the space of all memories. The attractors turn into transients leading to chaotic motion for appropriate values of the dynamical parameters. The motion can be guided by overlaps between patterns, resembling a process of free associative thinking in the absence of any input.

Nuclease activity of Saccharomyces cerevisiae Mre11 functions in targeted nucleotide alteration

Oligonucleotides can be used to direct site-specific changes in genomic DNA through a process in which mismatched base pairs in the oligonucleotide and the target DNA are created. The mechanism by which these complexes are developed and resolved is being studied by using Saccharomyces cerevisiae as a model system. Genetic analyses have revealed that in all likelihood the reaction occurs in two phases: DNA pairing and DNA repair. While the former phase involves strand assimilation, the latter phase likely involves an endonucleolytic processing step that leads to joint resolution. In this study, we established the importance of a functioning MRE11 gene in the overall reaction, as yeast strains deficient in MRE11 exhibited severely reduced activity. The activity could be rescued by complementation with wild-type MRE11 genes but not with MRE11 alleles lacking the nuclease function. Taken together, the data suggest that Mre11 provides nuclease activity for targeted nucleotide exchange, a process that could be used to reengineer yeast genes.

Maintenance of semantic information in capacity-limited item short-term memory

We report a semantic effect in immediate free recall, which is localized at recency and is preserved under articulatory suppression but is highly reduced when recall is delayed after an intervening distractor task. These results are explained by a neurocomputational model based on a limited-capacity short-term memory (STM) store, consisting of activated long-term memory representations. The model makes additional predictions about serial position functions in semantically cued recall, indicating capacity limitations caused by a displacement type mechanism, which are confirmed in a second experiment. This suggests that in addition to the phonological component in verbal STM, there is an activation/item-limited component with semantically sensitive representations.

The time course of perceptual choice: the leaky, competing accumulator model

The time course of perceptual choice is discussed in a model of gradual, leaky, stochastic, and competitive information accumulation in nonlinear decision units. Special cases of the model match a classical diffusion process, but leakage and competition work together to address several challenges to existing diffusion, random walk, and accumulator models. The model accounts for data from choice tasks using both time-controlled (e.g., response signal) and standard reaction time paradigms and its adequacy compares favorably with other approaches. A new paradigm that controls the time of arrival of information supporting different choice alternatives provides further support. The model captures choice behavior regardless of the number of alternatives, accounting for the log-linear relation between reaction time and number of alternatives (Hick's law) and explains a complex pattern of visual and contextual priming in visual word identification.

Stochastic resonance in the speed of memory retrieval

The stochastic resonance (SR) phenomenon in human cognition (memory retrieval speed for arithmetical multiplication rules) is addressed in a behavioral and neurocomputational study. The results of an experiment in which performance was monitored for various magnitudes of acoustic noise are presented. The average response time was found to be minimal for some optimal noise level. Moreover, it was shown that the optimal noise level and the magnitude of the SR effect depend on the difficulty of the task. A computational framework based on leaky accumulators that integrate noisy information and provide the output upon reaching a threshold criterion is used to illustrate the observed phenomena.

The "benefits" of distractibility: mechanisms underlying increased Stroop effects in schizophrenia

Recent studies of selective attention in schizophrenia patients suggest a particular pattern of single-trial Stroop performance: increased facilitation but not interference in reaction times (RTs), combined with increased error interference. Our Stroop task analysis suggests that this pattern can be explained by a selective attention deficit if one accounts for (1) performance in the congruent condition; (2) the nature of the neutral stimulus; (3) the relationship between accuracy and RT; and (4) response set effects. To test these hypotheses, we examined Stroop performance in 40 DSM-IV schizophrenia patients and 20 healthy control subjects, using a range of neutral stimuli (color patches, noncolor words, color words not in the response set). The findings confirmed several of our predictions and the results were consistent with the hypothesis that abnormal Stroop performance in schizophrenia reflects a failure to adequately attend to the task-appropriate stimulus dimension (color). This inattention affects both the congruent and incongruent conditions and multiple points in the information processing pathway.

Mechanisms for spatial integration in visual detection: a model based on lateral interactions

Recent studies of visual detection show a configuration dependent weak improvement of thresholds with the number of targets, which corresponds to a fourth-root power law. We find this result to be inconsistent with probability summation models, and account for it by a model of 'physiological' integration that is based on excitatory lateral interactions in the visual cortex. The model explains several phenomena which are confirmed by the experimental data, such as the absence of spatial and temporal uncertainty effects, temporal summation curves, and facilitation by a pedestal in 2AFC tasks. The summation exponents are dependent on the strength of the lateral interactions, and on the distance and orientation relationship between the elements.

The role of locus coeruleus in the regulation of cognitive performance

Noradrenergic locus coeruleus (LC) neurons were recorded in monkeys performing a visual discrimination task, and a computational model was developed addressing the role of the LC brain system in cognitive performance. Changes in spontaneous and stimulus-induced patterns of LC activity correlated closely with fluctuations in behavioral performance. The model explains these fluctuations in terms of changes in electrotonic coupling among LC neurons and predicts improved performance during epochs of high coupling and synchronized LC firing. Cross correlations of simultaneously recorded LC neurons confirmed this prediction, indicating that electrotonic coupling in LC may play an important role in attentional modulation and the regulation of goal-directed versus exploratory behaviors.

Visual synchrony affects binding and segmentation in perception

The visual system analyses information by decomposing complex objects into simple components (visual features) that are widely distributed across the cortex. When several objects are present simultaneously in the visual field, a mechanism is required to group (bind) together visual features that belong to each object and to separate (segment) them from features of other objects. An attractive scheme for binding visual features into a coherent percept consists of synchronizing the activity of their neural representations. If synchrony is important in binding, one would expect that binding and segmentation are facilitated by visual displays that are temporally manipulated to induce stimulus-dependent synchrony. Here we show that visual grouping is indeed facilitated when elements of one percept are presented at the same time as each other and are temporally separated (on a scale below the integration time of the visual system) from elements of another percept or from background elements. Our results indicate that binding is due to a global mechanism of grouping caused by synchronous neural activation, and not to a local mechanism of motion computation.

Lateral interactions in primary visual cortex: a model bridging physiology and psychophysics

Recent physiological studies show that the spatial context of visual stimuli enhances the response of cells in primary visual cortex to weak stimuli and suppresses the response to strong stimuli. A model of orientation-tuned neurons was constructed to explore the role of lateral cortical connections in this dual effect. The differential effect of excitatory and inhibitory current and noise conveyed by the lateral connections explains the physiological results as well as the psychophysics of pop-out and contour completion. Exploiting the model's property of stochastic resonance, the visual context changes the model's intrinsic input variability to enhance the detection of weak signals.

Phenothiazine treatment and respiratory distress syndrome in a child

An 11-year-old boy who was treated with a relatively high dose of methotrimeprazine meleate (Levemepromazine) a phenothiazine antipsychotic drug, was admitted to the pediatric intensive care unit suffering from respiratory distress syndrome. He required intensive treatment and support for 13 days. The persistent effects of methotrimeprazine meleate on various organs are typical of the prolonged biological action of the phenothiazine metabolites. The association of phenothiazine overdose and respiratory distress syndrome merits consideration.

Neural Network Modeling of Memory Deterioration in Alzheimer's Disease

The clinical course of Alzheimer's disease (AD) is generally characterized by progressive gradual deterioration, although large clinical variability exists. Motivated by the recent quantitative reports of synaptic changes in AD, we use a neural network model to investigate how the interplay between synaptic deletion and compensation determines the pattern of memory deterioration, a clinical hallmark of AD. Within the model we show that the deterioration of memory retrieval due to synaptic deletion can be much delayed by multiplying all the remaining synaptic weights by a common factor, which keeps the average input to each neuron at the same level. This parallels the experimental observation that the total synaptic area per unit volume (TSA) is initially preserved when synaptic deletion occurs. By using different dependencies of the compensatory factor on the amount of synaptic deletion one can define various compensation strategies, which can account for the observed variation in the severity and progression rate of AD.

A neural model of the dynamic activation of memory

We study an Attractor Neural Network that stores natural concepts, organized in semantic classes. The concepts are represented by distributed patterns over a space of attributes, and are related by both semantic and episodic associations. While semantic relations are expressed through an hierarchical coding over the attribute space, episodic links are realized via specific synaptic projections. Due to dynamic thresholds expressing neuronal fatigue, the network's behavior is characterized by convergence toward the concept patterns on a short time scale, and by transitions between the various patterns on a longer time scale. In its baseline, undamaged state, the network manifests semantic, episodic, and random transitions, and demonstrates the phenomenon of priming. Modeling possible pathological changes, we have found that increasing the 'noise' level or the rate of neuronal fatigue decreases the frequency of semantic transitions. When neurons characterized by large synaptic connectivity are deleted, semantic transitions decay before the episodic ones, in accordance with the findings in patients with Alzheimer's disease.

Segmentation, Binding, and Illusory Conjunctions

We investigate binding within the framework of a model of excitatory and inhibitory cell assemblies that form an oscillating neural network. Our model is composed of two such networks that are connected through their inhibitory neurons. The excitatory cell assemblies represent memory patterns. The latter have different meanings in the two networks, representing two different attributes of an object, such as shape and color. The networks segment an input that contains mixtures of such pairs into staggered oscillations of the relevant activities. Moreover, the phases of the oscillating activities representing the two attributes in each pair lock with each other to demonstrate binding. The system works very well for two inputs, but displays faulty correlations when the number of objects is larger than two. In other words, the network conjoins attributes of different objects, thus showing the phenomenon of “illusory conjunctions,” as in human vision.

Parallel Activation of Memories in an Oscillatory Neural Network

We describe a feedback neural network whose elements possess dynamic thresholds. This network has an oscillatory mode that we investigate by measuring the activities of memory patterns as functions of time. We observe spontaneous and induced transitions between the different oscillating memories. Moreover, the network exhibits pattern segmentation, by oscillating between different memories that are included as a mixture in a constant input. The efficiency of pattern segmentation decreases strongly as the number of the input memories is increased. Using oscillatory inputs we observe resonance behavior.

Schematic and situational determinants of depressed and nondepressed students' interpretation of feedback

We examined whether depressed persons' social skill deficits contribute to their negative cognitions and whether this contribution is independent of their negative schemata. Depressed (n = 60) and nondepressed (n = 60) subjects engaged in group discussions. We assessed subjects' social competence schemata with a questionnaire and subjects' actual level of social competence in the discussion through objective ratings made by codiscussants and outside observers. We found that independently of their negative schemata, depressed subjects' social skill deficits explained a significant portion of the variance in their more negative interpretation of feedback (relative to nondepressed subjects'). This suggests that real deficits in depressed persons' performance compound the effects of their negative schemata and further contribute to their negative cognitions. We also further explored findings by Dykman et al. (1989) and Lewinsohn et al. (1980).

Effect of gentamicin on the auditory brainstem evoked response in term infants: a preliminary report

Seven essentially healthy term infants who received gentamicin starting on the 1st day of life for prolonged rupture of membranes and maternal fever were compared with nine healthy term infants to determine whether this drug induces alterations in the auditory pathway. The auditory pathway was studied on the 3rd day of life by analyzing brainstem auditory evoked potentials elicited by a click stimulus presented at the infant's ears. Latencies of components III and V, interval I-III, and interval I-V were significantly prolonged in the gentamicin group, indicating impairment of the central component of the auditory pathway. Peak and trough serum gentamicin levels all fell within the recommended therapeutic range. The study indicates that short course gentamicin therapy in healthy newborn infants can lead to abnormality of auditory function.

Comparison of Tc-99m methylene diphosphonate with Tc-99m pyrophosphate in the detection of acute myocardial infarction: concise communication

Myocardial scans were obtained in ten patients with proven myocardial infarction. Scintigraphy on consecutive days was performed with technetium-99m methylene diphosphonate and technetium-99m pyrophosphate. Images obtained with the two tracers were generally equal in quality. Those with PPi exhibited denser uptake, while background activity was lower with MDP.