Delay discounting is associated with addiction and mental health measures while controlling for health behaviors and health barriers in a large US sample.

Background: Excessive discounting of future rewards [delay discounting (DD)] may be a transdiagnostic process and treatment target underlying behavioral health outcomes, including trauma, depression, anxiety, and problematic substance use. However, multiple health behaviors and barriers are also related to these outcomes, including social media usage, adverse childhood experiences (ACEs), sleep quality, healthcare access, housing status, and exercise. To extend research examining DD as transdiagnostic process, we recruited a large, heterogenous sample to examine the association between DD, problematic substance use, and mental health outcomes while controlling for certain health behaviors and health barriers. Method: In a cross-sectional online survey of 3992 US residents, we administered validated measures of PTSD, depression, anxiety, and problematic alcohol, stimulant, and opioid use. Using linear or ordinal logistic models, scores for each outcome were regressed onto DD while controlling for demographics, health behaviors, and health barriers. Results: Including only DD and demographics, DD was associated with each outcome at low effect sizes (ƒ2 = .013, OR range = 1.08-1.16). Except for opioid ASSIST scores, these relationships held when controlling for social media usage, sleep, housing status, healthcare access, ACEs, physical exercise, and demographic variables (ƒ2 = .002, OR range = 1.03-1.12), increasing confidence that DD concurrently and directly relates to four of these five clinical outcomes. Discussion: These findings support the conceptualization of DD as a transdiagnostic process underlying certain psychopathologies and suggest targeting DD in co-occurring substance use disorder and/or mental health treatments may result in clinically significant outcomes.

Factors Shaping the Implementation of Strategies to Prevent Acute Kidney Injury: A Qualitative Study.

Reducing the prevalence of acute kidney injury (AKI) is an important patient safety objective set forth by the National Quality Forum. Despite international guidelines to prevent AKI, there continues to be an inconsistent uptake of these interventions by cardiac teams across practice settings. The IMPROVE-AKI study was designed to test the effectiveness and implementation of AKI preventive strategies delivered through team-based coaching activities. Qualitative methods were used to identify factors that shaped sites' implementation of AKI prevention strategies. Semi-structured interviews were conducted with staff in a range of roles within the cardiac catheterization laboratories, including nurses, laboratory managers, and interventional cardiologists (N = 50) at multiple time points over the course of the study. Interview transcripts were qualitatively coded, and aggregated code reports were reviewed to construct main themes through memoing. In this paper, we report insights from semi-structured interviews regarding workflow, organizational culture, and leadership factors that impacted implementation of AKI prevention strategies.

Episodic future thinking in type 2 diabetes: Further development and validation of the Health Information Thinking control for clinical trials.

Episodic Future Thinking (EFT) reduces delay discounting and may have the potential as a clinical tool to increase the likelihood of health-promoting behaviors. However, evaluations of EFT in clinical settings require control conditions that match the effort and frequency of cue generation, as well as participants' expectations of improvement. The Health Information Thinking (HIT) control addresses these issues, but how this control affects delay discounting in individuals with diabetes and obesity when utilizing diabetes-management specific health-information vignettes is unknown. Moreover, little research has explored whether EFT reduces delay discounting in individuals with type 2 diabetes. To this end, we examined the impact of EFT, HIT, and a secondary no-cue control condition (NCC; assessments as usual) on delay discounting in 434 adults with self-reported type 2 diabetes and obesity recruited using Amazon Mechanical Turk. After completing an initial screening questionnaire, eligible participants reported demographics, then were randomized to EFT, HIT, or NCC conditions. Following the generation of seven EFT or HIT cues, participants assigned to EFT or HIT conditions completed a delay discounting task while imagining EFT or HIT cues; no-cue participants completed the task without cues. EFT participants demonstrated significantly lower delay discounting levels than HIT or NCC participants; no differences in delay discounting between HIT and NCC participants were observed. These results suggest that engaging in EFT, but not diabetes-specific HIT, results in lower delay discounting in adults with type 2 diabetes and obesity. This provides further evidence for the appropriateness of the HIT control for clinical trials examining the effect of EFT on delay discounting in adults with self-reported type 2 diabetes.

Novel integration of governmental data sources using machine learning to identify super-utilization among U.S. counties.

Background: Super-utilizers consume the greatest share of resource intensive healthcare (RIHC) and reducing their utilization remains a crucial challenge to healthcare systems in the United States (U.S.). The objective of this study was to predict RIHC among U.S. counties, using routinely collected data from the U.S. government, including information on consumer spending, offering an alternative method for identifying super-utilization among population units rather than individuals. Methods: Cross-sectional data from 5 governmental sources in 2017 were used in a machine learning pipeline, where target-prediction features were selected and used in 4 distinct algorithms. Outcome metrics of RIHC utilization came from the American Hospital Association and included yearly: (1) emergency rooms visit, (2) inpatient days, and (3) hospital expenditures. Target-prediction features included: 149 demographic characteristics from the U.S. Census Bureau, 151 adult and child health characteristics from the Centers for Disease Control and Prevention, 151 community characteristics from the American Community Survey, and 571 consumer expenditures from the Bureau of Labor Statistics. SHAP analysis identified important target-prediction features for 3 RIHC outcome metrics. Results: 2475 counties with emergency rooms and 2491 counties with hospitals were included. The median yearly emergency room visits per capita was 0.450 [IQR:0.318, 0.618], the median inpatient days per capita was 0.368 [IQR: 0.176, 0.826], and the median hospital expenditures per capita was $2104 [IQR: $1299.93, 3362.97]. The coefficient of determination (R2), calculated on the test set, ranged between 0.267 and 0.447. Demographic and community characteristics were among the important predictors for all 3 RIHC outcome metrics. Conclusions: Integrating diverse population characteristics from numerous governmental sources, we predicted 3-outcome metrics of RIHC among U.S. counties with good performance, offering a novel and actionable tool for identifying super-utilizer segments in the population. Wider integration of routinely collected data can be used to develop alternative methods for predicting RIHC among population units.

Sex differences in outcomes among adults undergoing abdominal aortic aneurysm repair.

OBJECTIVE: Although the differences in short-term outcomes between male and female patients in abdominal aortic aneurysm (AAA) repair have been well studied, it remains unclear if these sex disparities extend to other long-term adverse outcomes after AAA repair, such as reintervention and late rupture. METHODS: We performed a retrospective cohort study of 13,007 patients who underwent either endovascular (EVAR) or open AAA repair (OAR) between 2003 and 2015 using data from the Vascular Quality Initiative registries. Eligible patients were linked to fee-for-service Medicare claims to identify late outcomes of rupture and aneurysm-specific reintervention. RESULTS: The mean age of our cohort was 76 ± 6.7 years, 22% were female, 94% were White, and 77% underwent EVAR. The 10-year rupture incidence was slightly higher for women at 4.8 per 1000 person-years, vs 3.9 for men, but this difference was not statistically significant after risk adjustment (hazard ratio [HR] = 1.13, 95% confidence interval [CI]: 0.74-1.73). Likewise, we found no sex difference in reintervention rates (5.1 vs 4.8 in women per 1000 person-years) even after risk adjustment (HR = 0.95, 95% CI: 0.83-1.09). Regression models suggest effect modification by repair type for reintervention, where women who underwent index EVAR had a higher risk of reintervention than men (HR = 1.08, 95% CI: 0.93-1.26), whereas women who underwent OAR were at a lower risk of reintervention than men (HR = 0.79, 95% CI: 0.58-1.08); however, neither effect reached statistical significance within each subgroup. In addition, we found that the risk of reintervention for women vs men varied by clinical presentation, where women were less likely to undergo reintervention after an elective or symptomatic AAA repair but were more likely to undergo reintervention after a repair for AAA rupture (HR = 1.70, 95% CI: 1.05-2.75). CONCLUSIONS: Male and female patients who underwent AAA repair had similar rates of reintervention and late aneurysm rupture in the 10 years after their procedure. However, our findings suggest that repair type and clinical presentation may affect the role of sex in clinical outcomes and warrant further exploration in these subgroups.

Association between sex and long-term outcomes of endovascular treatment for peripheral artery disease.

BACKGROUND: Endovascular peripheral vascular intervention (PVI) has become the primary revascularization technique used for peripheral artery disease (PAD). Yet, there is limited understanding of long-term outcomes of PVI among women versus men. In this study, our objective was to investigate sex differences in the long-term outcomes of patients undergoing PVI. METHODS: We performed a cohort study of patients undergoing PVI for PAD from January 1, 2010 to September 30, 2015 using data in the Vascular Quality Initiative (VQI) registry. Patients were linked to fee-for-service Medicare claims to identify late outcomes including major amputation, reintervention, major adverse limb event (major amputation or reintervention [MALE]), and mortality. Sex differences in outcomes were evaluated using cumulative incidence curves, Gray's test, and mixed effects Cox proportional hazards regression accounting for patient and lesion characteristics using inverse probability weighted estimates. RESULTS: In this cohort of 15,437 patients, 44% (n = 6731) were women. Women were less likely to present with claudication than men (45% vs. 49%, p < 0.001, absolute standardized difference, d = 0.08) or be able to ambulate independently (ambulatory: 70% vs. 76%, p < 0.001, d = 0.14). There were no major sex differences in lesion characteristics, except for an increased frequency of tibial artery treatment in men (23% vs. 18% in women, p < 0.001, d = 0.12). Among patients with claudication, women had a higher risk-adjusted rate of major amputation (hazard ratio [HR] = 1.72, 95% confidence interval [CI]: 1.18-2.49), but a lower risk of mortality (HR = 0.86, 95% CI: 0.75-0.99). There were no sex differences in reintervention or MALE for patients with claudication. However, among patients with chronic limb-threatening ischemia, women had a lower risk-adjusted hazard of major amputation (HR = 0.79, 95% CI: 0.67-0.93), MALE (HR = 0.86, 95% CI: 0.78-0.96), and mortality (HR = 0.86, 95% CI: 0.79-0.94). CONCLUSION: There is significant heterogeneity in PVI outcomes among men and women, especially after stratifying by symptom severity. A lower overall mortality in women with claudication was accompanied by a higher risk of major amputation. Men with chronic limb-threatening ischemia had a higher risk of major amputation, MALE, and mortality. Developing sex-specific approaches to PVI that prioritizes limb outcomes in women can improve the quality of vascular care for men and women.

Team-Based Coaching Intervention to Improve Contrast-Associated Acute Kidney Injury: A Cluster-Randomized Trial.

BACKGROUND: Up to 14% of patients in the United States undergoing cardiac catheterization each year experience AKI. Consistent use of risk minimization preventive strategies may improve outcomes. We hypothesized that team-based coaching in a Virtual Learning Collaborative (Collaborative) would reduce postprocedural AKI compared with Technical Assistance (Assistance), both with and without Automated Surveillance Reporting (Surveillance). METHODS: The IMPROVE AKI trial was a 2×2 factorial cluster-randomized trial across 20 Veterans Affairs medical centers (VAMCs). Participating VAMCs received Assistance, Assistance with Surveillance, Collaborative, or Collaborative with Surveillance for 18 months to implement AKI prevention strategies. The Assistance and Collaborative approaches promoted hydration and limited NPO and contrast dye dosing. We fit logistic regression models for AKI with site-level random effects accounting for the clustering of patients within medical centers with a prespecified interest in exploring differences across the four intervention arms. RESULTS: Among VAMCs' 4517 patients, 510 experienced AKI (235 AKI events among 1314 patients with preexisting CKD). AKI events in each intervention cluster were 110 (13%) in Assistance, 122 (11%) in Assistance with Surveillance, 190 (13%) in Collaborative, and 88 (8%) in Collaborative with Surveillance. Compared with sites receiving Assistance alone, case-mix-adjusted differences in AKI event proportions were -3% (95% confidence interval [CI], -4 to -3) for Assistance with Surveillance, -3% (95% CI, -3 to -2) for Collaborative, and -5% (95% CI, -6 to -5) for Collaborative with Surveillance. The Collaborative with Surveillance intervention cluster had a substantial 46% reduction in AKI compared with Assistance alone (adjusted odds ratio=0.54; 0.40-0.74). CONCLUSIONS: This implementation trial estimates that the combination of Collaborative with Surveillance reduced the odds of AKI by 46% at VAMCs and is suggestive of a reduction among patients with CKD. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: IMPROVE AKI Cluster-Randomized Trial (IMPROVE-AKI), NCT03556293.

The efficacy of multifaceted versus single anesthesia work area infection control measures and the importance of surgical site infection follow-up duration.

BACKGROUND: Earlier a randomized trial showed efficacy of a multifaceted intervention approach for reducing surgical site infection: hand hygiene, vascular care, environmental cleaning, patient decolonization (nasal povidone iodine, chlorhexidine wipes), with feedback on pathogen transmission. The follow-up prospective observational study showed effectiveness when applied to all operating rooms of an inpatient surgical suite. In practice, many organizations will at baseline not be using conditions equivalent to the control groups but instead functionally have had ongoing a single intervention for infection control (e.g., encouraging better hand hygiene). Organizations also differ in how well and long they survey every surgical patient for postoperative surgical site infection. Thus, estimation of the expected net cost savings from implementing multifaceted intervention depends on the relative efficacy of multifaceted approach versus single intervention approaches and on the incidence of surgical site infection, the latter depending itself on the monitoring period for infection development. METHODS: The retrospective cohort analysis included 4865 patients from two single intervention and two multifaceted studies, each of the four studies with matched control groups. We used Poisson regression with robust variance to estimate the relative risk reduction in surgical site infections for the multifaceted approach versus single interventions and, with 30-day follow-up versus ≥60-day follow-up for infection. RESULTS: The multifaceted approach was associated with an estimated 68% reduction in postoperative surgical site infections relative to single interventions (risk ratio 0.32, 97.5% confidence interval 0.15-0.70, P = 0.001). There were approximately 2.61-fold more surgical site infections detected with follow-up for at least 60 days of medical records relative to 30 days of records reviewed (97.5% CI 1.62 to 4.21, P < 0.001). CONCLUSIONS: An evidence-based, multifaceted approach to anesthesia work area infection control can generate substantial reductions in surgical site infections. A follow-up period of at least 60-days is indicated for infection detection.

The importance of targeting intraoperative transmission of bacteria with antibiotic resistance and strain characteristics.

BACKGROUND: Evidence-based intraoperative infection control measures can reduce Staphylococcus aureus transmission and infections. We aimed to determine whether transmitted S. aureus isolates were associated with increased risk of multidrug resistance and associated traits. METHODS: S. aureus isolates obtained from intraoperative environmental, patient skin, and provider hand reservoirs among 274 operating room case pairs (1st and 2nd case of the day) across 3 major academic medical centers from March 2009 to February 2010 underwent systematic-phenotypic-genomic analysis to identify clonal transmission events. The association of clonal S. aureus transmission with multidrug resistance and resistance traits was investigated. Transmission dynamics were characterized. RESULTS: Transmitted isolates (N=58) were associated with increased risk of multi-drug antibiotic resistance [33% (19/58) transmitted vs. 10% (12/115) other isolates, risk ratio 3.14, 99% CI 1.34-7.38, P=0.0006]. Transmission was associated with a significant increase in resistance traits including mecA [40% transmitted isolates vs. 17% other isolates, risk ratio 2.28, P=0.0026] and ant (6)-Ia [26% transmitted isolates vs. 9% other isolates, risk ratio 2.97, P=0.0050]. Provider hands were a frequent reservoir of origin, between-case a common mode of transmission, and patient skin and provider hands frequent transmission locations for multidrug resistant pathogens. CONCLUSIONS: Intraoperative S. aureus transmission was associated with multidrug resistance and resistance traits. Proven infection control measures should be leveraged to target intraoperative transmission of multidrug resistant pathogens.

Integrating Clinical and Air Quality Data to Improve Prediction of COPD Exacerbations.

Several studies have found associations between air pollution and respiratory disease outcomes. However, there is minimal prognostic research exploring whether integrating air quality into clinical prediction models can improve accuracy and utility. In this study, we built models using both logistic regression and random forests to determine the benefits of including air quality data with meteorological and clinical data in prediction of COPD exacerbations requiring medical care. Logistic models were not improved by inclusion of air quality. However, the net benefit curves of random forest models showed greater clinical utility with the addition of air quality data. These models demonstrate a practical and relatively low-cost way to include environmental information into clinical prediction tools to improve the clinical utility of COPD prediction. Findings could be used to provide population level health warnings as well as individual-patient risk assessments.

Quantifying differences in packaged food and drink purchases among households with diet-related cardiometabolic multi-morbidity: a cross-sectional analysis.

BACKGROUND: Diet is important for chronic disease management, with limited research understanding dietary choices among those with multi-morbidity, the state of having 2 or more chronic conditions. The objective of this study was to identify associations between packaged food and drink purchases and diet-related cardiometabolic multi-morbidity (DRCMM). METHODS: Cross-sectional associations between packaged food and drink purchases and household DRCMM were investigated using a national sample of U.S. households participating in a research marketing study. DRCMM households were defined as household head(s) self-reporting 2 or more diet-related chronic conditions. Separate multivariable logistic regression models were used to model the associations between household DRCMM status and total servings of, and total calories and nutrients from, packaged food and drinks purchased per month, as well as the nutrient density (protein, carbohydrates, and fat per serving) of packaged food and drinks purchased per month, adjusted for household size. RESULTS: Among eligible households, 3795 (16.8%) had DRCMM. On average, households with DRCMM versus without purchased 14.8 more servings per capita, per month, from packaged foods and drinks (p < 0.001). DRCMM households were 1.01 times more likely to purchase fat and carbohydrates in lieu of protein across all packaged food and drinks (p = 0.002, p = 0.000, respectively). DRCMM households averaged fewer grams per serving of protein, carbohydrates, and fat per month across all food and drink purchases (all p < 0.001). When carbonated soft drinks and juices were excluded, the same associations for grams of protein and carbohydrates per serving per month were seen (both p < 0.001) but the association for grams of fat per serving per month attenuated. CONCLUSIONS: DRCMM households purchased greater quantities of packaged food and drinks per capita than non-DRCMM households, which contributed to more fat, carbohydrates, and sodium in the home. However, food and drinks in DRCMM homes on average were lower in nutrient-density. Future studies are needed to understand the motivations for packaged food and drink choices among households with DRCMM to inform interventions targeting the home food environment.

Putting prospection into practice: Methodological considerations in the use of episodic future thinking to reduce delay discounting and maladaptive health behaviors.

In recent years, episodic future thinking (EFT) has emerged as a promising behavioral intervention to reduce delay discounting or maladaptive health behaviors; however, considerable methodological heterogeneity in methods for eliciting engagement in EFT has been observed in prior research. In this narrative review, we briefly describe methods for generating EFT cues, the content of EFT cues, common control conditions for experiments utilizing EFT, and considerations for cue delivery and implementation. Where possible, we make suggestions for current best practices in each category while identifying gaps in knowledge and potential areas of future research. Finally, we conclude by using the NIH Stage model to better frame the current state of the literature on EFT and propose gaps to be addressed if EFT is to be both an efficacious and effective behavioral intervention.

Maintaining a National Acute Kidney Injury Risk Prediction Model to Support Local Quality Benchmarking.

BACKGROUND: The utility of quality dashboards to inform decision-making and improve clinical outcomes is tightly linked to the accuracy of the information they provide and, in turn, accuracy of underlying prediction models. Despite recognition of the need to update prediction models to maintain accuracy over time, there is limited guidance on updating strategies. We compare predefined and surveillance-based updating strategies applied to a model supporting quality evaluations among US veterans. METHODS: We evaluated the performance of a US Department of Veterans Affairs-specific model for postcardiac catheterization acute kidney injury using routinely collected observational data over the 6 years following model development (n=90 295 procedures in 2013-2019). Predicted probabilities were generated from the original model, an annually retrained model, and a surveillance-based approach that monitored performance to inform the timing and method of updates. We evaluated how updating the national model impacted regional quality profiles. We compared observed-to-expected outcome ratios, where values above and below 1 indicated more and fewer adverse outcomes than expected, respectively. RESULTS: The original model overpredicted risk at the national level (observed-to-expected outcome ratio, 0.75 [0.74-0.77]). Annual retraining updated the model 5×; surveillance-based updating retrained once and recalibrated twice. While both strategies improved performance, the surveillance-based approach provided superior calibration (observed-to-expected outcome ratio, 1.01 [0.99-1.03] versus 0.94 [0.92-0.96]). Overprediction by the original model led to optimistic quality assessments, incorrectly indicating most of the US Department of Veterans Affairs' 18 regions observed fewer acute kidney injury events than predicted. Both updating strategies revealed 16 regions performed as expected and 2 regions increasingly underperformed, having more acute kidney injury events than predicted. CONCLUSIONS: Miscalibrated clinical prediction models provide inaccurate pictures of performance across clinical units, and degrading calibration further complicates our understanding of quality. Updating strategies tailored to health system needs and capacity should be incorporated into model implementation plans to promote the utility and longevity of quality reporting tools.

Can diverse population characteristics be leveraged in a machine learning pipeline to predict resource intensive healthcare utilization among hospital service areas?

BACKGROUND: Super-utilizers represent approximately 5% of the population in the United States (U.S.) and yet they are responsible for over 50% of healthcare expenditures. Using characteristics of hospital service areas (HSAs) to predict utilization of resource intensive healthcare (RIHC) may offer a novel and actionable tool for identifying super-utilizer segments in the population. Consumer expenditures may offer additional value in predicting RIHC beyond typical population characteristics alone. METHODS: Cross-sectional data from 2017 was extracted from 5 unique sources. The outcome was RIHC and included emergency room (ER) visits, inpatient days, and hospital expenditures, all expressed as log per capita. Candidate predictors from 4 broad groups were used, including demographics, adults and child health characteristics, community characteristics, and consumer expenditures. Candidate predictors were expressed as per capita or per capita percent and were aggregated from zip-codes to HSAs using weighed means. Machine learning approaches (Random Forrest, LASSO) selected important features from nearly 1,000 available candidate predictors and used them to generate 4 distinct models, including non-regularized and LASSO regression, random forest, and gradient boosting. Candidate predictors from the best performing models, for each outcome, were used as independent variables in multiple linear regression models. Relative contribution of variables from each candidate predictor group to regression model fit were calculated. RESULTS: The median ER visits per capita was 0.482 [IQR:0.351-0.646], the median inpatient days per capita was 0.395 [IQR:0.214-0.806], and the median hospital expenditures per capita was $2,302 [1$,544.70-$3,469.80]. Using 1,106 variables, the test-set coefficient of determination (R2) from the best performing models ranged between 0.184-0.782. The adjusted R2 values from multiple linear regression models ranged from 0.311-0.8293. Relative contribution of consumer expenditures to model fit ranged from 23.4-33.6%. DISCUSSION: Machine learning models predicted RIHC among HSAs using diverse population data, including novel consumer expenditures and provides an innovative tool to predict population-based healthcare utilization and expenditures. Geographic variation in utilization and spending were identified.

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction of 30-day readmission following an acute myocardial infarction. Methods and Results Patients were enrolled into derivation and validation cohorts. The derivation cohort included inpatient discharges from Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary diagnosis of acute myocardial infarction, who were discharged alive, and not transferred from another facility. The validation cohort included patients from Dartmouth-Hitchcock Health Center between April 2, 2011, and December 31, 2016, meeting the same eligibility criteria described above. Data from both sites were linked to Centers for Medicare & Medicaid Services administrative data to supplement 30-day hospital readmissions. Clinical notes from each cohort were extracted, and an NLP model was deployed, counting mentions of 7 social risk factors. Five machine learning models were run using clinical and NLP-derived variables. Model discrimination and calibration were assessed, and receiver operating characteristic comparison analyses were performed. The 30-day rehospitalization rates among the derivation (n=6165) and validation (n=4024) cohorts were 15.1% (n=934) and 10.2% (n=412), respectively. The derivation models demonstrated no statistical improvement in model performance with the addition of the selected NLP-derived social risk factors. Conclusions Social risk factors extracted using NLP did not significantly improve 30-day readmission prediction among hospitalized patients with acute myocardial infarction. Alternative methods are needed to capture social risk factors.

Future thinking to decrease real-world drinking in alcohol use disorder: Repairing reinforcer pathology in a randomized proof-of-concept trial.

Reinforcer Pathology theory proposes that expanding the temporal window of reinforcement (i.e., reducing delay discounting) using episodic future thinking (EFT) would decrease alcohol consumption. However, evidence of effectiveness in real-world settings is lacking. Using a randomized proof-of-concept field trial, the current study examined the effect of expanding the temporal window of reinforcement, using remotely delivered EFT, on decreasing real-world alcohol consumption among individuals with alcohol use disorder (AUD). Fifty-two individuals (9 females) aged 18-65 years who met the DSM-5 criteria for moderate or severe AUD and aimed to drink in moderation or abstain from drinking completed the study and were included in analysis. EFT significantly (p = .031) reduced alcohol consumption (mean change of consumption pre-post intervention = -2.18 drinks/day) compared to control episodic recent thinking (ERT; mean change of -0.52 drinks/day). Changes in discounting rates pre-post intervention significantly predicted changes in alcohol consumption (coef. = .424, 95% CI [.043-.813], p = .030) even after controlling for age, gender, race, income, education, marital status, and family history of addiction. Overall satisfaction across groups was rated as 3.92 on a 1 to 5-point scale, suggesting that the current remote approach is feasible and acceptable. The current findings were congruent with the theory, Reinforcer Pathology, that EFT expands the temporal window and decreases alcohol consumption, and the remote approach was considered feasible and acceptable. We believe the present study contributes new knowledge with tangible benefits for scientifically understanding and better defining novel interventions that may be clinically deployed to improve treatment outcomes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.

A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL) long short-term memory (LSTM) models predicting unplanned, all-cause, 30-day readmission were developed and compared to several traditional models. Models used EHR data defined by a Common Data Model. The LSTM model Area Under the Receiver Operating Characteristic Curve (AUROC) was significantly greater than that of the next best traditional model [LSTM 0.79 vs Random Forest (RF) 0.72, p<0.0001]. Experiments showed that performance of the LSTM models increased as prior encounter number increased up to 30 encounters. An LSTM model with 16 selected laboratory tests yielded equivalent performance to a model with all 981 laboratory tests. This new DL model may provide the basis for a more useful readmission risk prediction tool for diabetes patients.

E-liquid purchase as a function of workplace restriction in the experimental tobacco marketplace.

E-cigarette use is prohibited in most smoke-free environments. The effect of this policy on tobacco consumption could be examined using the Experimental Tobacco Marketplace (ETM). The ETM allows observation of policy on smokers' purchasing behavior under conditions that simulate "real-world" circumstances. A within-subject design was used to evaluate the effect of workplace policy (Vaping Allowed vs. Not Allowed) and nicotine concentration (24 mg/mL vs. 0 mg/mL) on tobacco product consumption. Participants (n = 31) completed one sampling and two ETM/workplace sessions per week for 2 weeks. During the sampling session, participants were given an e-cigarette with a 2-day supply of a commercially available e-liquid of their preferred flavor. Before purchasing, participants were informed whether e-cigarette use was permitted. During the four ETM sessions, participants purchased for the following 24 hr, including the 4-hr work shift that started immediately after buying products in the ETM. The workplace session consisted of data entry tasks in a mock office environment. Participants could use any purchased tobacco products during two 15-min breaks. Condition order was counterbalanced. The results show that permitting E-cigarette use in the workplace increased e-liquid purchase on average, but nicotine concentration had no effect on e-liquid demand. Cigarette demand was unaltered across conditions. The present study suggests that allowing e-cigarette use in the workplace would increase demand for e-liquid regardless of nicotine strength. However, it would not change conventional cigarette demand. (PsycInfo Database Record (c) 2022 APA, all rights reserved).