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Research has demonstrated concussion likely causes autonomic dysfunction leading to exercise intolerance. To measure improvement in exercise intolerance due to suspected dysautonomia associated with protracted concussion recovery, using objective measurements on a Buffalo Concussion Treadmill Test (BCTT) following participation in a prescribed exercise program. This is a retrospective cohort study of 101 patient charts post-concussion. Exercise intolerance was assessed using a BCTT to identify suspected dysautonomia and an exercise prescription was provided using guidelines for treating concussion-associated exercise intolerance. Patients without symptom improvement and/or inability to achieve 80–85% of age-expected maximum heart rate (HR) without symptom exacerbation received a repeat BCTT. Twelve patient charts met inclusion criteria and were included in data analysis. There were significant improvements from pre-intervention to post-intervention testing in: maximum BCTT stage mean scores (p =.02); maximum HR mean scores (p =.01); prescription HR (RxHR) mean scores (p =.01); and HR delta (HR $$\delta $$ δ) mean scores (p =.00). Maximum stage, HR threshold, RxHR, and newly identified HR delta (HR $$\delta $$ δ) are potential objective measurements of progress for dysautonomia treatment post-concussion. Future studies are indicated to create a tailored protocol in the management of protracted concussion-associated dysautonomia. [ABSTRACT FROM AUTHOR]

Background: Mainly due to their cultural attitudes toward symptoms and breast cancer, Asian American breast cancer survivors tend to suffer from symptoms and often delay in getting treatments, information, and support. To improve their symptom management, it would be important to determine risk groups among them. Decision tree analyses reportedly help determine risk groups by identifying the characteristics that are directly associated with target health outcomes. Objective: Using a decision tree analysis, this study aimed at identifying the characteristics that were closely linked to the symptom experience of Asian American breast cancer survivors. Methods: This was a part of a parent randomized controlled trial among Asian American breast cancer survivors. Only the data from 135 women at the pre-test were included. Multiple instruments were used to collect the data: the Memorial Symptom Assessment Scale-Short Form, the Cancer Behavior Inventory, the PRQ-2000, the Perceived Isolation Scale, and the Supportive Care Needs Survey-Short Form 34. The data were analyzed using latent profile analysis and decision tree analyses. Results: Two most frequently found profiles included the low symptom experience profile (72.6%) and the high symptom experience profile (27.4%). The high symptom experience profile was predicted by 2 combined characteristics; (a) high psychological needs for help (over 60.00 points), and (b) low psychological needs for help (cut point = 60.00), high perceived barriers (cut point = 1.62), and high social isolation (social support) (cut point = 2.33). Conclusions: These characteristics linked to Asian American breast cancer survivors with high symptom experience need to be considered in future intervention development. [ABSTRACT FROM AUTHOR]

This study identified the process and agency characteristics associated with poor utilization outcomes – higher percentages of patients (i) admitted to an acute care organization and (ii) visited an emergency room (ER) unplanned without hospitalization – for home health agencies (HHAs) in the United States. We conducted a secondary analysis of data about HHAs' various characteristics, process adherence levels, and utilization outcomes collected from disparate public repositories for 2010–2022. We developed descriptive tree-based models using HHAs' hospital admission or ER visit percentages as response variables. Across the board, hospital admission percentages have steadily improved while ER percentages deteriorated for an extended period. Recently, checking for fall risks and depression was associated with improved outcomes for urban agencies. In general, rural HHAs had worse utilization outcomes than urban HHAs. Targeted investments and improvement initiatives can help rural HHAs close the urban-rural gap in the future. [ABSTRACT FROM AUTHOR]

Background and Objective: Computer-based neurocognitive tests are widely used in sport-related concussion management, but the performance of these tests is not well understood in the participant population with attention-deficit/hyperactivity disorder (ADHD) and/or learning disorder (LD). This research estimates the sensitivity and specificity performance of the Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) computer-based neurocognitive test in identifying concussion in this population. Methods: Study participants consisted of collegiate university athletes and military service academy cadets from the National Collegiate Athletic Association-Department of Defense CARE Consortium who completed the ImPACT test between 2014 and 2021. Participants who self-identified as belonging to one of the subgroups of interest (ADHD with or without LD [ADHD:LD+/−], LD with or without ADHD [LD:ADHD+/−], ADHD and/or LD [ADHD a/o LD]) and completed a baseline (1874 ADHD:LD+/−, 779 LD:ADHD+/−, 2338 ADHD a/o LD) or 24–48 h post-concussion (175 ADHD:LD+/−, 77 LD:ADHD+/−, 216 ADHD a/o LD) ImPACT assessment were included. Sensitivity and specificity were calculated using a normative data method and three machine learning classification methods: logistic regression, classification and regression tree, and random forest. Results: Using the four methods, participants with ADHD:LD+/− had sensitivities that ranged from 0.474 to 0.697, and specificities that ranged from 0.538 to 0.686. Participants with LD:ADHD+/− had sensitivities that ranged from 0.455 to 0.688, and specificities that ranged from 0.456 to 0.588. For participants with ADHD a/o LD, sensitivities ranged from 0.542 to 0.755, and specificities ranged from 0.451 to 0.724. Conclusions: For all subgroups and analytical methods, the results illustrate sensitivity and specificity values below typically accepted levels indicative of clinical utility. These findings support that using ImPACT alone may be insufficient to inform concussion diagnoses and encourages the use of a multi-dimensional concussion assessment. [ABSTRACT FROM AUTHOR]

Aim: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients. Materials and Methods: Clinical and radiographic data from four different centres across four continents (two in Europe, one in the United States, and one in China) including 515 patients and 3157 molars were collected and used to train and test different types of machine‐learning algorithms for their prognostic ability of molar loss over 10 years. The following models were trained: logistic regression, support vector machine, K‐nearest neighbours, decision tree, random forest, artificial neural network, gradient boosting, and naive Bayes. In addition, different models were aggregated by means of the ensembled stacking method. The primary outcome of the study was related to the prediction of overall molar loss (MLO) in patients after active periodontal treatment. Results: The general performance in the external validation settings (aggregating three cohorts) revealed that the ensembled model, which combined neural network and logistic regression, showed the best performance among the different models for the prediction of MLO with an area under the curve (AUC) = 0.726. The neural network model showed the best AUC of 0.724 for the prediction of periodontitis‐related molar loss. In addition, the ensembled model showed the best calibration performance. Conclusions: Through a multi‐centre collaboration, both prognostic models for the prediction of molar loss were developed and externally validated. The ensembled model showed the best performance in terms of both discrimination and validation, and it is made freely available to clinicians for widespread use in clinical practice. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: Critics have charged that evidence‐based medicine (EBM) overemphasises algorithmic rules over unstructured clinical experience and intuition, but the role of structured decision support systems in improving health outcomes remains uncertain. We aim to assess if delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to an algorithm based on evidence‐based clinical practice guideline (CPG) improved clinical outcomes compared with administration of anticoagulant treatment given at individual practitioners' discretion. Methods: An observational design consisting of the analysis of all acutely ill, consecutive patients (n = 1783) with confirmed COVID‐19 diagnosis admitted between 10 March 2020 to 11 January 2022 to an US academic center. American Society of Haematology CPG for anticoagulant prophylaxis in hospitalised patients with COVID‐19 was converted into a clinical pathway and translated into fast‐and‐frugal decision (FFT) tree ('algorithm'). We compared delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to the FFT algorithm with administration of anticoagulant treatment given at individual practitioners' discretion. Results: In an adjusted analysis, using combination of Lasso (least absolute shrinkage and selection operator) and propensity score based weighting [augmented inverse‐probability weighting] statistical techniques controlling for cluster data, the algorithm did not reduce death, venous thromboembolism, or major bleeding, but helped avoid longer hospital stay [number of patients needed to be treated (NNT) = 40 (95% CI: 23–143), indicating that for every 40 patients (23–143) managed on FFT algorithm, one avoided staying in hospital longer than 10 days] and averted admission to intensive‐care unit (ICU) [NNT = 19 (95% CI: 13–40)]. All model's selected covariates were well balanced. The results remained robust to sensitivity analyses used to test the stability of the findings. Conclusions: When delivered using a structured FFT algorithm, CPG shortened the hospital stay and help avoided admission to ICU, but it did not affect other relevant outcomes. [ABSTRACT FROM AUTHOR]

Using a sample of 18,152 pediatric hospice patients, this study assessed the cost-effectiveness of concurrent care over standard hospice care. Analysis of incremental cost-effectiveness ratios with bootstrapping simulations showed concurrent care was more effective but at a higher cost. [ABSTRACT FROM AUTHOR]

Annual turnover of home care workers represents a huge loss of revenue and is a key source of inefficiency in the home health care industry. In this article, we propose a data-driven approach to monitor employee churn and to capture the evolution of employee intent to leave. Unlike most papers in the literature, we use machine learning techniques to analyze over 2 million visits in the US, Canada, and Australia between 2016 and 2019. Results show that the gap between the number of hours worked and in the contract is the most important factor to predict employee intent to leave, which means an employee should be given as many hours as requested in the contract to improve retention. Secondary results show that having diverse shift lengths and continuity in services and patients seem to be associated with less turnover. [ABSTRACT FROM AUTHOR]

The risk for sexual victimization in college is high, and although there can be significant mental and physical consequences for the survivor, resource utilization for this issue is extremely low. The current study used the frameworks of both the ecological model and the theory of planned behavior to examine factors related to resource use in undergraduate survivors of unwanted sexual experiences. The sample consisted of 217 undergraduates who reported at least one unwanted sexual experience while in college (89% female, 76.5% Caucasian, M age 19.6). Data were collected in an online survey from a private liberal arts college, and were analyzed using decision tree modeling. Results suggested that resource use was less related to demographic or event characteristics, but rather related to victim response to the event as well as perceptions about control over potential resource use. In addition to key assault prevention efforts, it is thus important to also support survivor mental health and self-efficacy postassault. [ABSTRACT FROM AUTHOR]

Objectives: To analyze the predictors of health care utilization among respondents to the National Agricultural Worker Survey. Specifically, we hypothesized that English proficiency would predict utilization of health care services within the last 2 years. Methods: Using the 2015–2016 National Agricultural Worker Survey, we performed a secondary data analysis to analyze the predictors of health care utilization within the last 2 years in the United States' agricultural worker population. Data were cleaned and analyzed using decision tree analysis, which produced a classification tree model that was trained on 90% of the data and validated on 10%. Results: Exposure to English was not a predictor of health care utilization in our classification tree. The first major partition that predicted utilization was insurance status. Additional partitions were on age, gender, hypertension diagnosis, and public aid. Conclusions: By partitioning on insurance status and use of public aid, the decision tree provided evidence that systemic factors are key determinants of health care utilization in the agricultural worker community. This highlights the importance of agencies that connect agricultural workers with resources that provide insurance and improve access to health care. This is especially important given that agricultural workers are one of the highest risk groups for occupational injury or death in the United States. [ABSTRACT FROM AUTHOR]

Aims: To assess the speed and accuracy of a checklist user interface for the Diagnostic Criteria for Temporomandibular Disorders (DC/TMD). Methods: A diagnostic tool formatted as a checklist was developed and compared to an existing diagnostic tool, the DC/TMD diagnsostic decision trees. Both types of tools use the DC/TMD and were tested by dental students, interns, and residents in the USA and Japan for diagnosis of hypothetical patients. The comparisons were done in a randomized, crossover, controlled, double-blinded trial. Results: Overall, subjects using the experimental tool answered 25% more correct diagnoses (P < .001) and missed 27% fewer diagnoses (P < .01). They were also able to finalize their diagnoses faster than those using the control tool, in 16% less time (P < .05). The difference in accuracy was more pronounced in complex cases, while the difference in speed was more pronounced in simple cases. Conclusion: This checklist is an alternative user interface for the DC/TMD. [ABSTRACT FROM AUTHOR]

Feedback to evaluees in forensic mental health assessment (FMHA) is an understudied and underdiscussed topic in the professional literature. In other psychological disciplines, feedback is a traditional part of the psychological assessment process, though there are potential reasons that feedback in FMHA might or should be treated differently. Because feedback will often be a matter of evaluator discretion, we determined that reviewing relevant factors for carefully considered practice could be useful to the forensic evaluator community. Thus, based on the results of three focus groups with forensic evaluators, this article offers guidance and a decision tree to help guide decision making about feedback in FMHA. Although feedback will not be appropriate in every situation, thoughtful consideration of the issue—and providing feedback when appropriate—is one way of respecting the rights and dignity of forensic evaluees. Public Significance Statement: This article provides concrete considerations for forensic evaluators when considering whether to provide feedback to evaluees. It offers factors to guide idiographic decision making about feedback while highlighting considerations unique to forensic mental health assessment. [ABSTRACT FROM AUTHOR]

• We modeled population genomic FH screening for cost-effectiveness. • Population FH genomic screening is not cost-effective at current willingness-to-pay. • Population genomic FH screening improves overall population health. • Lower genomic costs and/or multiplex screening are needed to be cost-effective. Population genomic screening for familial hypercholesterolemia (FH) in unselected individuals can prevent premature cardiovascular disease. To estimate the clinical and economic outcomes of population-wide FH genomic screening versus no genomic screening. We developed a decision tree plus 10-state Markov model evaluating the identification of patients with an FH variant, statin treatment status, LDL-C levels, MI, and stroke to compare the costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness of population-wide FH genomic screening. FH variant prevalence (0.4%) was estimated from the Geisinger MyCode Community Health Initiative (MyCode). Genomic test costs were assumed to be $200. Age and sex-based estimates of MI, recurrent MI, stroke, and recurrent stroke were obtained from Framingham risk equations. Additional outcomes independently associated with FH variants were derived from a retrospective analysis of 26,025 participants screened for FH. Sensitivity and threshold analyses were conducted to evaluate model assumptions and uncertainty. FH screening was most effective at younger ages; screening unselected 20-year-olds lead to 111 QALYs gained per 100,000 individuals screened at an incremental cost of $20 M. The incremental cost-effectiveness ratio (ICER) for 20-year-olds was $181,000 per QALY, and there was a 38% probability of cost-effectiveness at a $100,000 per QALY willingness-to-pay threshold. If genomic testing cost falls to $100, the ICER would be $91,000 per QALY. Population FH screening is not cost-effective at current willingness to pay thresholds. However, reducing test costs, testing at younger ages, or including FH within broader multiplex screening panels may improve clinical and economic value. [ABSTRACT FROM AUTHOR]

Objectives: The COVID‐19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratifying patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID‐19. Methods: All patients greater than 18 years presenting to a single academic ED who were tested for COVID‐19 during this index ED evaluation were included. Outcome was defined as the result of COVID‐19 polymerase chain reaction (PCR) testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease‐specific screening questions, and laboratory data. Three models were developed with a split‐sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient‐boosted decision tree methods. Model discrimination was evaluated comparing area under the receiver operator curve (AUC) and point statistics at a predefined threshold. Results: A total of 1,026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% confidence interval [CI] = 0.84 to 0.94) when including four features: exposure history, temperature, white blood cell count (WBC), and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI = 0.79 to 0.92) and gradient boosting had an AUC of 0.85 (95% CI = 0.79 to 0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2–0.39) compared to 0.2 (0.14–0.28) for random forest and 0.22 (0.15–0.3) for the gradient‐boosted method. Conclusion: The derived predictive models offer good discriminating capacity for COVID‐19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and chest X‐ray result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model‐based approach to COVID‐19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty. [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most common brain disorders that greatly affects patients' quality of life and poses serious risks to their health. While the majority of the patients positively respond to the existing anti-epilepsy drugs, others who developed the refractory type of epilepsy show resistance against drug therapy and need to undergo advance treatments such as surgery. Given that identifying such patients is not a straightforward process and requires long courses of trial and error with anti-epilepsy drugs, this study aims at predicting those at-risk patients using clinical and demographic data obtained from electronic medical records. Specifically, the study employs several predictive analytics machine-learning methods, equipped with a novel approach for data balancing, to identify drug-resistant patients using their comorbidities and demographic information along with the initial epilepsy-related diagnosis made by their physician. The promising results we obtained highlight the potential use of machine-learning techniques in facilitating medical decisions and suggest the possibility of extending the proposed approach for developing a clinical decision support system for medical professionals. [ABSTRACT FROM AUTHOR]

Background: The study of physical activity in cancer survivors has been limited to one cause, one effect relationships. In this exploratory study, we used recursive partitioning to examine multiple correlates that influence physical activity compliance rates in cancer survivors. Methods: African American breast cancer survivors (N = 267, Mean age = 54 years) participated in an online survey that examined correlates of physical activity. Recursive partitioning (RP) was used to examine complex and nonlinear associations between sociodemographic, medical, cancer-related, theoretical, and quality of life indicators. Results: Recursive partitioning revealed five distinct groups. Compliance with physical activity guidelines was highest (82% met guidelines) among survivors who reported higher mean action planning scores (P < 0.001) and lower mean barriers to physical activity (P = 0.035). Compliance with physical activity guidelines was lowest (9% met guidelines) among survivors who reported lower mean action and coping (P = 0.002) planning scores. Similarly, lower mean action planning scores and poor advanced lower functioning (P = 0.034), even in the context of higher coping planning scores, resulted in low physical activity compliance rates (13% met guidelines). Subsequent analyses revealed that body mass index (P = 0.019) and number of comorbidities (P = 0.003) were lowest in those with the highest compliance rates. Conclusion: Our findings support the notion that multiple factors determine physical activity compliance rates in African American breast cancer survivors. Interventions that encourage action and coping planning and reduce barriers in the context of addressing function limitations may increase physical activity compliance rates. [ABSTRACT FROM AUTHOR]

Objective  American College of Cardiology and American Heart Association (ACC/AHA) published new guidelines which lower the cut-off for hypertension. We sought to evaluate the impact of these guidelines to cost and benefit of various low-dose aspirin prophylaxis approaches. Study Design  Decision tree analysis was created using R software to evaluate four approaches to aspirin prophylaxis in the United States: no aspirin, United States Preventive Service Task Force (USPSTF) with Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure (JNC7) hypertension guidelines, USPSTF with ACC/AHA hypertension guidelines, as well as universal aspirin prophylaxis. This model was executed to simulate a hypothetical cohort of 4 million pregnant women in the United States. Results  The new guidelines would expand the aspirin eligibility by 8% (76,953 women) in the USPSTF guidelines. Even with this increased eligibility, the USPSTF guidelines continue to be the approach with the most cost savings ($386.5 million) when compared with universal aspirin and no aspirin prophylaxis. The new hypertension guidelines are projected to increase the cost savings of the USPSTF approach by $9.4 million. Conclusion  Despite the small change in aspirin prophylaxis, using ACC/AHA definition of hypertension still results in an annual cost-saving of $9.4 million in the United States when compared with JNC7. [ABSTRACT FROM AUTHOR]

Background Patients living with human immunodeficiency virus (PLWH) with low CD4 counts are at high risk for immune reconstitution inflammatory syndrome (IRIS) and death at antiretroviral therapy (ART) initiation. Methods We investigated the clinical impact of IRIS in PLWH and CD4 counts <100 cells/μL starting ART in an international, prospective study in the United States, Thailand, and Kenya. An independent review committee adjudicated IRIS events. We assessed associations between baseline biomarkers, IRIS, immune recovery at week 48, and death by week 48 with Cox models. Results We enrolled 506 participants (39.3% were women). Median age was 37 years, and CD4 count was 29 cells/μL. Within 6 months of ART, 97 (19.2%) participants developed IRIS and 31 (6.5%) died. Participants with lower hemoglobin at baseline were at higher IRIS risk (hazard ratio [HR], 1.2; P =.004). IRIS was independently associated with increased risk of death after adjustment for known risk factors (HR, 3.2; P =.031). Being female (P =.004) and having a lower body mass index (BMI; P =.003), higher white blood cell count (P =.005), and higher D-dimer levels (P =.044) were also significantly associated with increased risk of death. Decision-tree analysis identified hemoglobin <8.5 g/dL as predictive of IRIS and C-reactive protein (CRP) >106 μg/mL and BMI <15.6 kg/m2 as predictive of death. Conclusions For PLWH with severe immunosuppression initiating ART, baseline low BMI and hemoglobin and high CRP and D-dimer levels may be clinically useful predictors of IRIS and death risk. [ABSTRACT FROM AUTHOR]

Aim Although obesity is an established risk factor for cancer mortality among women, little is known about how body mass index ( BMI) is interacting with certain lifestyle behaviors and sociodemographic characteristics to increase the risk of cancer mortality. The purpose of this study was to use classification trees to examine possible interactions between BMI, smoking, age, poverty level and marital status in identifying high-risk subgroups for all-cause cancer mortality in women. Material and Methods Data from the US National Health Interview Survey linked with the National Death Index from 1990-2004 were used in this study. The data were restricted to women with complete information on all risk factors considered in the analyses. The data were analyzed using descriptive statistics and classification trees. Results Findings revealed that younger women (41-50 years old) who smoked had higher cancer mortality if they were underweight. Even among 51-60-year-old never smokers, we observed an inverse relationship between BMI and mortality. High BMI, however, was not a protective factor in 51-60-year-old women who were not married and did not smoke. Conclusion Classification tree analysis confirms and extends current knowledge about the role of BMI and smoking on cancer mortality by providing preliminary risk profiles. The findings indicate that it is imperative for cancer-related studies to examine BMI and smoking within age-specific groups and in the context of several sociodemographic factors that are known to independently affect cancer mortality. [ABSTRACT FROM AUTHOR]

Background: Minimal clinically important difference (MCID) scores for outcome measures are frequently used evidence-based guides to gage meaningful changes. There are numerous outcome instruments used for analyzing pain, disability, and dysfunction of the low back; perhaps the most common of these is the Oswestry disability index (ODI). A single agreed-upon MCID score for the ODI has yet to be established. What is also unknown is whether selected baseline variables will be universal predictors regardless of the MCID used for a particular outcome measure. Objective: To explore the relationship between predictive models and the MCID cutpoint on the ODI. Setting: Data were collected from 16 outpatient physical therapy clinics in 10 states. Design: Secondary database analysis using backward stepwise deletion logistic regression of data from a randomized controlled trial (RCT) to create prognostic clinical prediction rules (CPR). Participants and Interventions: One hundred and forty-nine patients with low back pain (LBP) were enrolled in the RCT. All were treated with manual therapy, with a majority also receiving spine-strengthening exercises. Results: The resultant predictive models were dependent upon the MCID used and baseline sample characteristics. All CPR were statistically significant (P < 0.01). All six MCID cutpoints used resulted in completely different significant predictor variables with no predictor significant across all models. Limitations: The primary limitations include sub-optimal sample size and study design. Conclusions: There is extreme variability among predictive models created using different MClDs on the ODI within the same patient population. Our findings highlight the instability of predictive modeling, as these models are significantly affected by population baseline characteristics along with the MCID used. Clinicians must be aware of the fragility of CPR prior to applying each in clinical practice. [ABSTRACT FROM AUTHOR]

Supplemental digital content is available in the text. OBJECTIVE: The aim of this study was to describe the relationships between intent to leave, reasons nurses intend to leave, and the nursing work environment in military hospitals. BACKGROUND: Intention to leave is a precursor of nurse turnover. The reasons nurses intend to leave may be influenced by leader interventions and potentially preventable. METHODS: This descriptive, correlational secondary analysis included 724 nurse survey responses from 23 US Army hospitals. Bivariate correlations and predictive modeling techniques were used. RESULTS: Forty-nine percent of nurses indicated they intended to leave, 44% for potentially preventable reasons. Dissatisfaction with management and the nursing work environment were the top potentially preventable reasons to leave. Nurses who intended to leave for potentially preventable reasons scored aspects of the nursing work environment significantly lower than those intending to leave for nonpreventable reasons. CONCLUSIONS: Identifying potentially preventable reasons in conjunction with intent to leave can provide leaders opportunities to intervene and influence turnover intention. [ABSTRACT FROM AUTHOR]

Every year, abnormal uterine bleeding (AUB) exacts a heavy toll on women's health and leads to high costs for the US health care system. The literature shows that endometrial ablation results in fewer complications, shorter recovery and lower costs than more commonly performed hysterectomy procedures. The objective of this study was to model clinical-economic outcomes, budget impact, and cost-effectiveness of global endometrial ablation (GEA) versus outpatient hysterectomy (OPH) and inpatient hysterectomy (IPH) procedures. A decision tree, state-transition (semi-Markov) economic model was developed to simulate 3 hypothetical cohorts of women who received surgical treatment for AUB (GEA, OPH, and IPH) over 1, 2, and 3 years to evaluate clinical and economic outcomes for GEA vs. OPH and GEA vs. IPH. Two versions of the model were created to reflect both commercial health care payer and US Medicaid perspectives, and analyses were conducted for both payer types. Total health care costs in the first year after GEA were substantially lower compared with those for IPH and OPH. Budget impact analysis results showed that increasing GEA utilization yields total annual cost savings of about $906,000 for a million-member commercial health plan and about $152,000 in cost savings for a typical-sized state Medicaid plan with 1.4 million members. Cost-effectiveness analysis results for both perspectives showed GEA as economically dominant (conferring greater benefit at lower cost) over both OPH and IPH in the 1-year commercial scenario. This study demonstrates that, for some patients, GEA may prove to be a safe, uterus-sparing, cost-effective alternative to OPH and IPH for the surgical treatment of AUB. [ABSTRACT FROM AUTHOR]

Background: Tier 1 exposure tools recommended for use under REACH are designed to easily identify situations that may pose a risk to health through conservative exposure predictions. However, no comprehensive evaluation of the performance of the lower tier tools has previously been carried out. The ETEAM project aimed to evaluate several lower tier exposure tools (ECETOC TRA, MEASE, and EMKG-EXPO-TOOL) as well as one higher tier tool (STOFFENMANAGER®). This paper describes the results of the external validation of tool estimates using measurement data. Methods: Measurement data were collected from a range of providers, both in Europe and United States, together with contextual information. Individual measurement and aggregated measurement data were obtained. The contextual information was coded into the tools to obtain exposure estimates. Results were expressed as percentage of measurements exceeding the tool estimates and presented by exposure category (non-volatile liquid, volatile liquid, metal abrasion, metal processing, and powder handling). We also explored tool performance for different process activities as well as different scenario conditions and exposure levels. Results: In total, results from nearly 4000 measurements were obtained, with the majority for the use of volatile liquids and powder handling. The comparisons of measurement results with tool estimates suggest that the tools are generally conservative. However, the tools were more conservative when estimating exposure from powder handling compared to volatile liquids and other exposure categories. In addition, results suggested that tool performance varies between process activities and scenario conditions. For example, tools were less conservative when estimating exposure during activities involving tabletting, compression, extrusion, pelletisation, granulation (common process activity PROC14) and transfer of substance or mixture (charging and discharging) at nondedicated facilities (PROC8a; powder handling only). With the exception of STOFFENMANAGER® (for estimating exposure during powder handling), the tools were less conservative for scenarios with lower estimated exposure levels. Conclusions: This is the most comprehensive evaluation of the performance of REACH exposure tools carried out to date. The results show that, although generally conservative, the tools may not always achieve the performance specified in the REACH guidance, i.e. using the 75th or 90th percentile of the exposure distribution for the risk characterisation. Ongoing development, adjustment, and recalibration of the tools with new measurement data are essential to ensure adequate characterisation and control of worker exposure to hazardous substances. [ABSTRACT FROM AUTHOR]

Objective. We evaluated the extent to which children and adolescents were not vaccinated against measles ('unvaccinated'), clustering within U.S. counties, and factors associated with unvaccination, including parents' vaccine-related beliefs and missed opportunities. Methods. We analyzed data from the 2010-2013 National Immunization Survey (NIS) and NIS-Teen Survey of households with 19- to 35-month-old children and 13- to 17-year-old adolescents, respectively. We used provider-reported vaccination histories to assess measles vaccination status. Results. In 2013, 7.5% of children and 4.5% of adolescents were unvaccinated against measles. Four-fifths (80.0%) of unvaccinated children lived in counties containing 41.9% of the nation's children, and 80.0% of unvaccinated adolescents lived in counties containing 30.4% of the nation's adolescents. Multivariable statistical analyses found that 74.6% of children who were unvaccinated against measles missed being vaccinated for reasons other than parents' negative vaccine-related beliefs, and 89.6% could be deemed as having at least one missed opportunity for being vaccinated against measles because they were administered at least one dose of other recommended vaccines after 12 months of age. Among adolescents, multivariable analyses found that only demographic factors, not vaccine-related parental beliefs, were independently associated with being unvaccinated. Conclusions. Reasons other than negative vaccine-related beliefs, including missed opportunities, accounted for the vast majority of unvaccinated children and adolescents. [ABSTRACT FROM AUTHOR]

The article presents research which analyzed the cost-effectiveness of early and delayed repair for anterior cruciate ligament injuries. Topics covered include the importance of surgical reconstruction to treat tear in anterior cruciate ligament (ACL) as well as use of Markov decision model to assess the repair. Also mentioned is the application of the Multicenter Orthopaedic Outcomes Network (MOON) database in the study.

Administrative claims datasets have great potential for health services researchers who wish to evaluate patient care on a large scale across providers, but categorizing patients’ primary health conditions from these data can be challenging. The goal of this work is to describe and evaluate a methodology to assign workers compensation claimants to meaningful groups within back and shoulder injuries using claims data. Claims data from a large multi-state workers compensation insurance dataset were used to assign eligible claimants to condition and subcondition groups using available ICD9 codes. Assignments were evaluated against body part indicators, severity indicators, resource utilization, and specific clinical interventions. Of the 575,967 claimants who met inclusion criteria, 54,066 claimants were designated as shoulder injuries and 118,772 were designated as back injuries. Within back and shoulder injuries, claimants were assigned to more specific groups known as subconditions. For both back and shoulder injuries, there were statistically significant differences between subconditions in several categories of resource utilization ( p < 0.01 for all). For each of nine specific clinical interventions, the hypothesized corresponding subcondition had statistically significantly higher utilization than other subconditions ( p < 0.01). This methodology could be an important tool to health services researchers who wish to target interventions or examine trends in cost and service utilization among meaningful groups of claimants. [ABSTRACT FROM AUTHOR]

Background: Pressure ulcers are associated with a nearly three-fold increase in in-hospital mortality. It is essential to investigate how other factors besides the Braden scale could enhance the prediction of pressure ulcers. Data mining modeling techniques can be beneficial to conduct this type of analysis. Data mining techniques have been applied extensively in health care, but are not widely used in nursing research. Purpose: To remedy this methodological gap, this paper will review, explain, and compare several data mining models to examine patient level factors associated with pressure ulcers based on a four year study from military hospitals in the United States. Methods: The variables included in the analysis are easily accessible demographic information and medical measurements. Logistic regression, decision trees, random forests, and multivariate adaptive regression splines were compared based on their performance and interpretability. Results: The random forests model had the highest accuracy (C-statistic) with the following variables, in order of importance, ranked highest in predicting pressure ulcers: days in the hospital, serum albumin, age, blood urea nitrogen, and total Braden score. Conclusion: Data mining, particularly, random forests are useful in predictive modeling. It is important for hospitals and health care systems to use their own data over time for pressure ulcer risk prediction, to develop risk models based upon more than the total Braden score, and specific to their patient population. [ABSTRACT FROM AUTHOR]

Background: Early medical attention after concussion may minimize symptom duration and burden; however, many concussions are undiagnosed or have a delay in diagnosis after injury. Many concussion symptoms (eg, headache, dizziness) are not visible, meaning that early identification is often contingent on individuals reporting their injury to medical staff. A fundamental understanding of the types and levels of factors that explain when concussions are reported can help identify promising directions for intervention. Purpose: To identify individual and institutional factors that predict immediate (vs delayed) injury reporting. Study Design: Case-control study; Level of evidence, 3. Methods: This study was a secondary analysis of data from the Concussion Assessment, Research and Education (CARE) Consortium study. The sample included 3213 collegiate athletes and military service academy cadets who were diagnosed with a concussion during the study period. Participants were from 27 civilian institutions and 3 military institutions in the United States. Machine learning techniques were used to build models predicting who would report an injury immediately after a concussive event (measured by an athletic trainer denoting the injury as being reported "immediately" or "at a delay"), including both individual athlete/cadet and institutional characteristics. Results: In the sample as a whole, combining individual factors enabled prediction of reporting immediacy, with mean accuracies between 55.8% and 62.6%, depending on classifier type and sample subset; adding institutional factors improved reporting prediction accuracies by 1 to 6 percentage points. At the individual level, injury-related altered mental status and loss of consciousness were most predictive of immediate reporting, which may be the result of observable signs leading to the injury report being externally mediated. At the institutional level, important attributes included athletic department annual revenue and ratio of athletes to athletic trainers. Conclusion: Further study is needed on the pathways through which institutional decisions about resource allocation, including decisions about sports medicine staffing, may contribute to reporting immediacy. More broadly, the relatively low accuracy of the machine learning models tested suggests the importance of continued expansion in how reporting is understood and facilitated. [ABSTRACT FROM AUTHOR]

People with aphasia may produce speech errors or pauses during speaking turns. A communication partner may choose to guess the person's intended meaning, or may allow the person time to repair their turns (i.e., edited turns). The aim of this study was to understand the topic-related effects that occur when speakers without aphasia allow their partners with aphasia to complete their own repairs. We analyzed videos of 10 people with aphasia engaged in social conversations with communication partners. We identified and extracted "PWA-initiated segments" (PWA-IS). These segments begin with turns in which PWAs carry out repairs and introduce new topics (both actions occur in a single turn). PWA-ISes continue until the last turn about the topic is completed. We calculated descriptive statistics detailing the proportion of turns/words in each conversation represented by PWAIS's, as well as the range and mean of these proportions across conversations. This approach allowed us to understand how many of the turns in each conversation were dependent on the new topics introduced by people with aphasia when they were afforded time for self-expression during their edited utterances. On average, 35% of turns and 38% of words occurred during PWA-ISes. In general, substantial proportions of conversations we analyzed were related to topics introduced by people with aphasia during PWA-ISes. Edited turns initiate segments composed of topically-related turns, and these segments occupied large portions of the conversations we analyzed. Giving people with aphasia time to edit their own turns may thus play an important role in steering conversation toward the topics and interactional agendas that people with aphasia wish to pursue. [ABSTRACT FROM AUTHOR]

Suicide is the second leading cause of death among individuals aged 5 to 24 in the United States (US). However, the precursors to suicide often do not surface, making suicide prevention challenging. This study aims to develop a machine learning model for predicting suicide ideation (SI), suicide planning (SP), and suicide attempts (SA) among adolescents in the US during the coronavirus pandemic. We used the 2021 Adolescent Behaviors and Experiences Survey Data. Class imbalance was addressed using the proposed data augmentation method tailored for binary variables, Modified Synthetic Minority Over-Sampling Technique. Five different ML models were trained and compared. SHapley Additive exPlanations analysis was conducted for explainability. The Logistic Regression model, identified as the most effective, showed superior performance across all targets, achieving high scores in recall: 0.82, accuracy: 0.80, and area under the Receiver Operating Characteristic curve: 0.88. Variables such as sad feelings, hopelessness, sexual behavior, and being overweight were noted as the most important predictors. Our model holds promise in helping health policymakers design effective public health interventions. By identifying vulnerable sub-groups within regions, our model can guide the implementation of tailored interventions that facilitate early identification and referral to medical treatment. [ABSTRACT FROM AUTHOR]

Purpose: The Fragile X Messenger Ribonucleoprotein-1 (FMR1) premutation (FXpm) is a genetic variant that is common in the general population and is associated with health symptoms and disease in adulthood. However, poor understanding of the clinical phenotype during childhood has hindered the development of clinical practice guidelines for screening and intervention. Given that social communication difficulties have been widely documented in adults with the FXpm and are linked with reduced psychosocial functioning, the present study aimed to characterize the communication profile of the FXpm during early childhood. Method: Eighteen children with the FXpm who were identified through cascade testing (89%) or screening at birth (11%) were compared to 21 matched typically developing children, aged 2–4 years. Participants completed standardized assessments of language (Mullen Scales of Early Learning) and adaptive communication (Vineland Adaptive Behavior Scales-II). Social communication was rated from seminaturalistic interaction samples using the Brief Observation of Social Communication Change. Results: Children with the FXpm showed delayed social communication development, with the magnitude of group differences highlighting social communication as a feature that distinguishes children with the FXpm from their peers (p= .046, ηp ² = .12). The groups did not differ on the standardized language and adaptive communication measures (ps > .297, ηp ² s < .03). Conclusions: Early screening and treatment of social communication delays may be key to optimizing outcomes for children with the FXpm. Further research is needed to replicate findings in a larger sample, delineate the trajectory and consequences of social communication difficulties across the life span in the FXpm, and determine the potential epidemiological significance of FMR1 as a mediator of developmental communication differences within the general population. [ABSTRACT FROM AUTHOR]

Background: Primary care serves as an entry point for many patients to access health care services, especially those who reside in medically underserved areas. Because an initiative exists for family nurse practitioners (FNPs) to fill primary care gaps in medically underserved areas, they must be included in educational strategies to provide quality behavioral health care within their scope of practice. Method: An academic-practice partnership was used to provide FNP residents the opportunity to learn to work within their scope of practice in a behavioral health integration model. Results: The residents noted satisfaction with the learning environment, supervisory relationship, and role of the faculty. Conclusion: Innovative continuing education activities are needed to prepare novice FNPs to safely practice and enter into a health care workforce pipeline focused on reducing disparities in medically underserved areas. [J Contin Educ Nurs. 2024;55(4):203–208.] [ABSTRACT FROM AUTHOR]

Major Depressive Disorder (MDD) and Generalized Anxiety Disorder (GAD) pose significant burdens on individuals and society, necessitating accurate prediction methods. Machine learning (ML) algorithms utilizing electronic health records and survey data offer promising tools for forecasting these conditions. However, potential bias and inaccuracies inherent in subjective survey responses can undermine the precision of such predictions. This research investigates the reliability of five prominent ML algorithms—a Convolutional Neural Network (CNN), Random Forest, XGBoost, Logistic Regression, and Naive Bayes—in predicting MDD and GAD. A dataset rich in biomedical, demographic, and self-reported survey information is used to assess the algorithms' performance under different levels of subjective response inaccuracies. These inaccuracies simulate scenarios with potential memory recall bias and subjective interpretations. While all algorithms demonstrate commendable accuracy with high-quality survey data, their performance diverges significantly when encountering erroneous or biased responses. Notably, the CNN exhibits superior resilience in this context, maintaining performance and even achieving enhanced accuracy, Cohen's kappa score, and positive precision for both MDD and GAD. This highlights the CNN's superior ability to handle data unreliability, making it a potentially advantageous choice for predicting mental health conditions based on self-reported data. These findings underscore the critical importance of algorithmic resilience in mental health prediction, particularly when relying on subjective data. They emphasize the need for careful algorithm selection in such contexts, with the CNN emerging as a promising candidate due to its robustness and improved performance under data uncertainties. [ABSTRACT FROM AUTHOR]

This study's aim was to examine the impact of pandemic emergency Home- and Community-Based Services (HCBS) payments on the continuity and security of people with intellectual and developmental disabilities (IDD). Using a multilevel logistic regression, we analyzed secondary Personal Outcome Measures interviews from 738 people with IDD (March 2020 through April 2022), and state pandemic emergency HCBS payment data from 16 states. The odds of people with IDD experiencing continuity and security during the pandemic increased by 3% for every 1% states increased their payment rates, and by 398% when states offered retainer payments. Increased reimbursement rates and retainer payments can help providers maintain operations and promote the continuity and security of people with IDD. [ABSTRACT FROM AUTHOR]

Purpose. The results of a pharmacoeconomic study of the cost-effectiveness of posaconazole versus fluconazole in preventing invasive fungal infections (IFIs) in patients with graft-versus-host disease (GVHD) are reported. Methods. The results of a randomized clinical trial suggested that posaconazole may be as effective as fluconazole for the prevention of IFIs in recipients of allogeneic cell transplants who develop severe GVHD and that posaconazole may be superior to fluconazole in reducing IFI-related deaths. Using published data from that trial and data from secondary sources, an economic model was developed to estimate the costs, IFIs avoided, and life-years saved with posaconazole versus fluconazole therapy. The results of the modeled 112-day treatment simulation were extrapolated to a lifetime horizon. Results. In the modeled simulation, posaconazole therapy was associated with a lower probability of IFI development (0.05 versus 0.09), increased discounted life-years (7.87 life-years versus 7.66 life-years), and higher discounted costs per patient ($8,860 versus $5,710 in 2006 U.S. dollars) relative to fluconazole therapy. The estimated incremental cost-effectiveness of posaconazole versus fluconazole for IFI prophylaxis was $85,300 per IFI avoided and $15,300 per life-year saved. A sensitivity analysis indicated a 90% probability that the use of posaconazole for this purpose would be cost-effective at a threshold of $50,000 per life-year saved. Conclusion. Posaconazole is in the range of currently accepted criteria for cost-effectiveness relative to fluconazole for the prevention of IFIs among patients with GVHD. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the outcomes of COVID‐19‐related hospitalizations among patients with autoimmune rheumatic diseases (ARDs) in the United States in 2020. The primary outcome was in‐hospital mortality, and secondary outcomes included intubation rate, length of hospital stay (LOS), and total hospital charges (THCs). Methods: Data for the study were obtained from the National Inpatient Sample database and included patients who were hospitalized with a principal diagnosis of COVID‐19. Univariable and multivariable logistic regression analyses were conducted to calculate odds ratios for the outcomes, adjusting for age, sex, and comorbidities. Results: Out of the 1,050,720 COVID‐19 admissions, 30,775 had an ARD diagnosis. The unadjusted analysis showed higher mortality (12.21%) and intubation (9.2%) rates in the ARD group compared with the non‐ARD group (mortality rate: 11.14%, P = 0.013; intubation rate: 8.5%, P = 0.048). However, this difference was not significant after adjusting for confounding factors. The mean LOS and THCs did not differ significantly between the two groups. Among all ARD subgroups, the vasculitis group had significantly higher intubation rate, LOS, and THC. Conclusion: The study suggests that ARD is not associated with an increased risk of mortality or worse outcomes among patients hospitalized with COVID‐19 after adjusting for confounding factors. However, the vasculitis group had poorer outcomes during COVID‐19 hospitalizations. Further studies are needed to evaluate the effect of ARD activity and immunosuppressants on outcomes. Additionally, more research is required to investigate the relationship between COVID‐19 and vasculitis. [ABSTRACT FROM AUTHOR]

Backgro und: Accurate prediction of risk for chronic diseases like type 2 diabetes (T2D) is challenging due to the complex underlying etiology. Integration of more complex data types from sensors and leveraging technologies for collection of -omics datasets may provide greater insights into the specific risk profile for complex diseases. Methods: We performed a literature review to identify feature selection methods and machine learning models for prediction of weight loss in a previously completed clinical trial (NCT02278939) of a behavioral intervention for weight loss in Filipinos at risk for T2D. Features included demographic and clinical characteristics, dietary factors, physical activity, and transcriptomics. Results: We identified four feature selection methods: Correlation-based Feature Subset Selection (CfsSubsetEval) with BestFirst, Kolmogorov–Smirnov (KS) test with correlation featureselection (CFS), DESeq2, and max-relevance-min-relevance (MRMR) with linear forward search and mutual information (MI) and four machine learning algorithms: support vector machine, decision tree, random forest, and extra trees that are applicable to prediction of weight loss using the specified feature types. Conclusion: More accurate prediction of risk for T2D and other complex conditions may be possible by leveraging complex data types from sensors and -omics datasets. Emerging methods for feature selection and machine learning algorithms make this type of modeling feasible. [ABSTRACT FROM AUTHOR]

Evaluation of the United States Centers for Disease Control and Prevention (CDC)-defined HCV-related risk factors are not consistently performed as part of routine care, rendering risk-based testing susceptible to clinician bias and missed diagnoses. This work uses natural language processing (NLP) and machine learning to identify patients who are at high risk for HCV infection. Models were developed and validated to predict patients with newly identified HCV infection (detectable RNA or reported HCV diagnosis). We evaluated models with three types of variables: structured (structured-based model), semi-structured and free-text notes (text-based model), and all variables (full-set model). We applied each model to three stratifications of data: patients with no history of HCV prior to 2020, patients with a history of HCV prior to 2020, and all patients. We used XGBoost and ten-fold C-statistic cross-validation to evaluate the generalizability of the models. There were 3564 unique patients, 487 with HCV infection. The average C-statistics on the structured-based, text-based, and full-set models for all the patients were 0.777 (95% CI: 0.744–0.810), 0.677 (95% CI: 0.631–0.723), and 0.774 (95% CI: 0.735–0.813), respectively. The full-set model performed slightly better than the structured-based model and similar to text-based models for patients with no history of HCV prior to 2020; average C-statistics of 0.780, 0.774, and 0.759, respectively. NLP was able to identify six more risk factors inconsistently coded in structured elements: incarceration, needlestick, substance use or abuse, sexually transmitted infections, piercings, and tattoos. The availability of model options (structured-based or text-based models) with a similar performance can provide deployment flexibility in situations where data is limited. [ABSTRACT FROM AUTHOR]

The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke Protected Analytics Computing Environment (PACE) to facilitate data-sharing and big data analytics across the US and Singapore. Data from all colorectal surgery patients between 1 January 2012 and 31 December 2017 in Singapore and, 1 January 2015 to 31 December 2019 in the US were used, and 7585 cases and 3597 single and multiple procedure cases from Singapore and US were included. The ML models were based on categorical gradient boosting (CatBoost) models trained on common data fields shared by both institutions. The procedure codes were based on the Table of Surgical Procedure (TOSP) (Singapore) and the Current Procedural Terminology (CPT) codes (US). The two types of codes were mapped by surgical experts. The CPT codes were then transformed into the relative value unit (RVU). The ML models outperformed the baseline MA models. The MA, scheduled durations and procedure codes were found to have higher loadings as compared to surgeon factors. We further demonstrated the use of the Duke PACE in facilitating data-sharing and big data analytics. [ABSTRACT FROM AUTHOR]

Loan processors and underwriters at mortgage firms seek to gather substantial supporting documentation to properly understand and model loan risks. In doing so, loan originations become prone to closing delays, risking client dissatisfaction and consequent revenue losses. We collaborate with a large national mortgage firm to examine the extent to which these delays are predictable, using internal discussion threads to prioritize interventions for loans most at risk. Substantial work experience is required to predict delays, and we find that even highly trained employees have difficulty predicting delays by reviewing discussion threads. We develop an array of methods to predict loan delays. We apply four modern out‐of‐the‐box sentiment analysis techniques, two dictionary‐based and two rule‐based, to predict delays. We contrast these approaches with domain‐specific approaches, including firm‐provided keyword searches and "smoke terms" derived using machine learning. Performance varies widely across sentiment approaches; while some sentiment approaches prioritize the top‐ranking records well, performance quickly declines thereafter. The firm‐provided keyword searches perform at the rate of random chance. We observe that the domain‐specific smoke term approaches consistently outperform other approaches and offer better prediction than loan and borrower characteristics. We conclude that text mining solutions would greatly assist mortgage firms in delay prevention. [ABSTRACT FROM AUTHOR]

Hydroxychloroquine (HCQ) was proposed as an early therapy for coronavirus disease 2019 (COVID-19) after in vitro studies indicated possible benefit. Previous in vivo observational studies have presented conflicting results, though recent randomized clinical trials have reported no benefit from HCQ among patients hospitalized with COVID-19. We examined the effects of HCQ alone and in combination with azithromycin in a hospitalized population of US veterans with COVID-19, using a propensity score–adjusted survival analysis with imputation of missing data. According to electronic health record data from the US Department of Veterans Affairs health care system, 64,055 US Veterans were tested for the virus that causes COVID-19 between March 1, 2020 and April 30, 2020. Of the 7,193 veterans who tested positive, 2,809 were hospitalized, and 657 individuals were prescribed HCQ within the first 48-hours of hospitalization for the treatment of COVID-19. There was no apparent benefit associated with HCQ receipt, alone or in combination with azithromycin, and there was an increased risk of intubation when HCQ was used in combination with azithromycin (hazard ratio = 1.55; 95% confidence interval: 1.07, 2.24). In conclusion, we assessed the effectiveness of HCQ with or without azithromycin in treatment of patients hospitalized with COVID-19, using a national sample of the US veteran population. Using rigorous study design and analytic methods to reduce confounding and bias, we found no evidence of a survival benefit from the administration of HCQ. [ABSTRACT FROM AUTHOR]

OBJECTIVES: Early recognition of dementia would allow patients and their families to receive care earlier in the disease process, potentially improving care management and patient outcomes, yet nearly half of patients with dementia are undiagnosed. Our aim was to develop and validate an electronic health record (EHR)‐based tool to help detect patients with unrecognized dementia (EHR Risk of Alzheimer's and Dementia Assessment Rule [eRADAR]). DESIGN: Retrospective cohort study. SETTING: Kaiser Permanente Washington (KPWA), an integrated healthcare delivery system. PARTICIPANTS: A total of 16 665 visits among 4330 participants in the Adult Changes in Thought (ACT) study, who undergo a comprehensive process to detect and diagnose dementia every 2 years and have linked KPWA EHR data, divided into development (70%) and validation (30%) samples. MEASUREMENTS: EHR predictors included demographics, medical diagnoses, vital signs, healthcare utilization, and medications within the previous 2 years. Unrecognized dementia was defined as detection in ACT before documentation in the KPWA EHR (ie, lack of dementia or memory loss diagnosis codes or dementia medication fills). RESULTS: Overall, 1015 ACT visits resulted in a diagnosis of incident dementia, of which 498 (49%) were unrecognized in the KPWA EHR. The final 31‐predictor model included markers of dementia‐related symptoms (eg, psychosis diagnoses, antidepressant fills), healthcare utilization pattern (eg, emergency department visits), and dementia risk factors (eg, cerebrovascular disease, diabetes). Discrimination was good in the development (C statistic =.78; 95% confidence interval [CI] =.76‐.81) and validation (C statistic =.81; 95% CI =.78‐.84) samples, and calibration was good based on plots of predicted vs observed risk. If patients with scores in the top 5% were flagged for additional evaluation, we estimate that 1 in 6 would have dementia. CONCLUSION: The eRADAR tool uses existing EHR data to detect patients with good accuracy who may have unrecognized dementia. J Am Geriatr Soc 68:103–111, 2019 [ABSTRACT FROM AUTHOR]

Our study examines the trend of U.S. immigrants' engagement in various e-health activities and how immigrants' use of e-health technologies is associated with their cultural characteristics over years. Aggregating three national representative samples of U.S. immigrants from the Health Information National Trends Survey (HINTS) conducted between 2008 and 2013, our results from trend analyses and logistic regressions revealed that the U.S. immigrants exhibited an increasing trend on two types of e-health engagement: tracking personal health information online and communicating with a doctor online. But we did not find any significant change in the other two e-health activities throughout these years: the percentage of immigrants who sought a health provider online remained moderate (about 37%), whereas the percentage of immigrants who joined an online support group kept low (about 5%). Furthermore, immigrants of different race/ethnicity or length of residence in the United States showed different trends of e-health engagement. Asian immigrants and immigrants with 13–34 years of residence were more proactive to engage in various e-health activities. Our study informs the practice aiming to enhance U.S. immigrants' Internet use for better health-related outcomes. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to report on the findings and lessons that were learned from the many cases of internet fraud complaints that are gathered by the Federal Trade Commission in the USA. The implications that are contained in the behavior of the complaints and trends identified are critical for addressing all types of online criminal activities in the increasing world of cybercrimes.Design/methodology/approach Data for this paper are extracted from the Consumer Sentinel Network Data Handbook covering a period of 13 years. Using the raw data, the occurrences were plotted and trends (number of cycles, time between cycles, and leading state and lagging states) were identified. Descriptive statistics covering the 13 years were examined and discussed. Using 20002 as the base year, the rate of growth of each of the states were ranked annually and were tested for stability and predictability using non-parametric approaches.Findings The key findings are indigenous to the occurrences of online fraud complaints in the USA. However, as the leading nation with the best database on fraud complaints, the findings are mission critical to fraud prevention across the globe. Specifically, this study found that since 2002, there have been four distinct cycles. Each cycle is clearly noticeable because there is a rapid growth in the number of crime complaints in the beginning half of the period followed by a slowing down period. However, the speed of change from one cycle to the next is steeper and the time gap is faster. While it is true that from the perspective of all the 50 states, the ranks appear to change every year and thus are not stable. However, the majority of the changes are relatively small so their relative positions by rank are still predictable.Originality/value The paper extends existing understanding of online fraud occurrences in the USA. The findings are timely and based on longitudinal data that span over a decade. The four new cycles identified are critical to the body of knowledge. The observed change in time from one cycle to another and its changes from a mathematic to a geometric one are also new. The findings are invaluable to persons working in law enforcement related occupations (auditors, lawyers, forensic experts, among others) and who must deal with the increasing problem of online fraud and cybercrimes. [ABSTRACT FROM AUTHOR]

We determined factors associated with diet quality and assessed the relationship between diet quality, birth weight, and gestational age in a prospective national multicenter cohort study. We evaluated diet quality with the Healthy Eating Index (HEI, scale 0-100) in the third trimester of pregnancy with three 24-hr multiple-pass dietary recalls in 266 HIV+ women enrolled in the Pediatric HIV/AIDS Cohort Study. Covariates included demographics, food security, pre-pregnancy body mass index, HIV disease severity, substance use, and antiretroviral exposures. A two-stage multivariate process using classification and regression trees (CART) followed by multiple regression described HEI tendencies, controlled possible confounding effects, and examined the association of HEI with birth weight and gestational age. To assess the stability of the CART solution, both the HEI 2005 and 2010 were evaluated. The mean HEI scores were 56.1 and 47.5 for the 2005 and 2010 HEI, respectively. The first-stage CART analysis examined the relationship between HEI and covariates. Non-US born versus US-born mothers had higher HEI scores (15-point difference, R2 = 0.28). There was a secondary partition due to alcohol/cigarette/illicit drug usage (3.5-point difference, R2 = 0.03) among US-born women. For the second-stage CART adjusted multiple regression, birth weight z-score was positively related to HEI 2005 and 2010 (partial r's > 0.13, P's ≤ 0.0398), but not gestational age ( r = 0.00). We conclude that diet quality among HIV+ women is associated with higher birth weight. Despite the influence of a large cultural effect and poor prenatal behaviors, interventions to improve diet in HIV+ women may help to increase birth weight. [ABSTRACT FROM AUTHOR]

Objective The American College of Rheumatology guidelines for the treatment of lupus nephritis recommend change in induction therapy when response to therapy has not occurred within 6 months. Response is not defined, and renal fibrosis can occur while waiting for this end point. Therefore, a decision support tool to better define response is needed to guide clinicians when starting patients on therapy. This study was undertaken to identify biomarker models with sufficient predictive power to develop such a tool. Methods Urine samples from 140 patients with biopsy-proven lupus nephritis who had not yet started induction therapy were analyzed for a panel of urinary biomarkers. Univariate receiver operating characteristic (ROC) curves were generated for each individual biomarker and compared to the ROC area under the curve values from machine learning models developed using random forest algorithms. Biomarker models of outcome developed with novel markers in addition to clinical markers were compared to those developed with traditional clinical markers alone. Results Models developed with the combined traditional and novel biomarker panels demonstrated clinically meaningful predictive power. Markers most predictive of response were chemokines, cytokines, and markers of cellular damage. Conclusion This is the first study to demonstrate the power of low-abundance biomarker panels and machine learning algorithms for predicting lupus nephritis outcomes. This is a critical first step in research to develop clinically meaningful decision support tools. [ABSTRACT FROM AUTHOR]

This study examined (a) the validity of two accelerometers (ActiGraph GT3X [ActiGraph LLC, Pensacola, FL, USA] and activPAL [PAL Technologies Ltd., Glasgow, Scotland]) for the assessment of sedentary behavior; and (b) the variations in assessment accuracy by setting minimum sedentary bout durations against a proxy for direct observation using an automated wearable camera in free-living environments. Time spent in sedentary behavior estimated from the GT3X, with varying identification methods (i.e., a hybrid machine learning algorithm,Sojourn, and activity count thresholds), and the activPAL were compared to the criterion measure with and without applying minimum sedentary bout durations. The activPAL assessed sedentary behavior most accurately followed by GT3X with the Sojourn method. The performance of the GT3X is improved when identifying sedentary bouts ≥ 15 min with the Sojourn method or a threshold of < 150 cpm. The activPAL should be considered for studies focusing on sedentary behavior. The ability of the GT3X to assess sedentary behavior is improved when focusing on sedentary bouts ≥ 15 min using the Sojourn method or the threshold of < 150 cpm. [ABSTRACT FROM PUBLISHER]

Background The goal of the current study was to use tree-based methods ( Zhang and Singer, 2010, Recursive Partitioning and Applications, 2nd ed. Springer, New York) to identify predictors of abstinence from heavy drinking in COMBINE ( Anton et al. JAMA 2006; 295:2003), the largest study of pharmacotherapy for alcoholism in the United States to date, and to validate these results in PREDICT (Mann et al. Addict Biol 2012; 18:937), a parallel study conducted in Germany. Methods We compared a classification tree constructed according to purely statistical criteria to a tree constructed according to a combination of statistical criteria and clinical considerations for prediction of no heavy drinking during treatment in COMBINE. We considered over 100 baseline predictors. The tree approach was compared to logistic regression. The trees and a deterministic forest identified the most important predictors of no heavy drinking for direct testing in PREDICT. Results The tree built using both clinical and statistical considerations consisted of 4 splits based on consecutive days of abstinence ( CDA) prior to randomization, age, family history of alcoholism, and confidence to resist drinking in response to withdrawal and urges. The tree based on statistical considerations with 4 splits also split on CDA and age but also on gamma-glutamyl transferase level and drinking goal. Deterministic forest identified CDA, age, and drinking goal as the most important predictors. Backward elimination logistic regression among the top 18 predictors identified in the deterministic forest analyses identified only age and CDA as significant main effects. Longer CDA and goal of complete abstinence were associated with better outcomes in both data sets. Conclusions The most reliable predictors of abstinence from heavy drinking were CDA and drinking goal. Trees provide binary decision rules and straightforward graphical representations for identification of subgroups based on response and may be easier to implement in clinical settings. [ABSTRACT FROM AUTHOR]