Potential impact of blood cholesterol guidelines on statin treatment in the U.S. population using interrupted time series analysis.

BACKGROUND: The 2013 ACC/AHA Guideline was a paradigm shift in lipid management and identified the four statin-benefit groups. Many have studied the guideline's potential impact, but few have investigated its potential long-term impact on MACE. Furthermore, most studies also ignored the confounding effect from the earlier release of generic atorvastatin in Dec 2011. METHODS: To evaluate the potential (long-term) impact of the 2013 ACC/AHA Guideline release in Nov 2013 in the U.S., we investigated the association of the 2013 ACC/AHA Guideline with the trend changes in 5-Year MACE survival and three other statin-related outcomes (statin use, optimal statin use, and statin adherence) while controlling for generic atorvastatin availability using interrupted time series analysis, called the Chow's test. Specifically, we conducted a retrospective study using U.S. nationwide de-identified claims and electronic health records from Optum Labs Database Warehouse (OLDW) to follow the trends of 5-Year MACE survival and statin-related outcomes among four statin-benefit groups that were identified in the 2013 ACC/AHA Guideline. Then, Chow's test was used to discern trend changes between generic atorvastatin availability and guideline potential impact. RESULTS: 197,021 patients were included (ASCVD: 19,060; High-LDL: 33,907; Diabetes: 138,159; High-ASCVD-Risk: 5,895). After the guideline release, the long-term trend (slope) of 5-Year MACE Survival for the Diabetes group improved significantly (P = 0.002). Optimal statin use for the ASCVD group also showed immediate improvement (intercept) and long-term positive changes (slope) after the release (P < 0.001). Statin uses did not have significant trend changes and statin adherence remained unchanged in all statin-benefit groups. Although no other statistically significant trend changes were found, overall positive trend change or no changes were observed after the 2013 ACC/AHA Guideline release. CONCLUSIONS: The 2013 ACA/AHA Guideline release is associated with trend improvements in the long-term MACE Survival for Diabetes group and optimal statin use for ASCVD group. These significant associations might indicate a potential positive long-term impact of the 2013 ACA/AHA Guideline on better health outcomes for primary prevention groups and an immediate potential impact on statin prescribing behaviors in higher-at-risk groups. However, further investigation is required to confirm the causal effect of the 2013 ACA/AHA Guideline.

Unraveling the multiple chronic conditions patterns among people with Alzheimer's disease and related dementia: A machine learning approach to incorporate synergistic interactions.

INTRODUCTION: Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD. METHODS: We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi-label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors. RESULTS: Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictive power, syndemic factors related to dementia, social behaviors, mental and physical health can improve model performance further. DISCUSSION: Our study demonstrated that the MCC patterns among people with ADRD can be learned using a machine-learning approach with syndemic framework adjustments. HIGHLIGHTS: Machine learning models can learn the MCC patterns for people with ADRD. The learned MCC patterns should be adjusted and individualized by syndemic factors. The model can predict which disease is developing based on existing MCC patterns. As a result, this model enables early specific MCC identification and prevention.

Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning.

Almost half of Americans 65 years of age and older take statins, which are highly effective in lowering low-density lipoprotein cholesterol, preventing atherosclerotic cardiovascular disease (ASCVD), and reducing all-cause mortality. Unfortunately, ∼50% of patients prescribed statins do not obtain these critical benefits because they discontinue use within one year of treatment initiation. Therefore, statin discontinuation has been identified as a major public health concern due to the increased morbidity, mortality, and healthcare costs associated with ASCVD. In clinical practice, statin-associated symptoms (SAS) often result in dose reduction or discontinuation of these life-saving medications. Currently, physician decision-making in statin prescribing typically relies on only a few patient data elements. Physicians then employ reactive strategies to manage SAS concerns after they manifest (e.g., offering an alternative statin treatment plan or a statin holiday). A preferred approach would be a proactive strategy to identify the optimal treatment plan (statin agent + dosage) to prevent/minimize SAS and statin discontinuation risks for a particular individual prior to initiating treatment. Given that using a single patient's data to identify the optimal statin regimen is inadequate to ensure that the harms of statin use are minimized, alternative tactics must be used to address this problem. In this proof-of-concept study, we explore the use of a machine-learning personalized statin treatment plan (PSTP) platform to assess the numerous statin treatment plans available and identify the optimal treatment plan to prevent/minimize harms (SAS and statin discontinuation) for an individual. Our study leveraged de-identified administrative insurance claims data from the OptumLabs® Data Warehouse, which includes medical and pharmacy claims, laboratory results, and enrollment records for more than 130 million commercial and Medicare Advantage (MA) enrollees, to successfully develop the PSTP platform. In this study, we found three results: (1) the PSTP platform recommends statin prescription with significantly lower risks of SAS and discontinuation compared with standard-practice, (2) because machine learning can consider many more dimensions of data, the performance of the proactive prescription strategy with machine-learning support is better, especially the artificial neural network approach, and (3) we demonstrate a method of incorporating optimization constraints for individualized patient-centered medicine and shared decision making. However, more research into its clinical use is needed. These promising results show the feasibility of using machine learning and big data approaches to produce personalized healthcare treatment plans and support the precision-health agenda.

Knowledge Discovery With Machine Learning for Hospital-Acquired Catheter-Associated Urinary Tract Infections.

Massive generation of health-related data has been key in enabling the big data science initiative to gain new insights in healthcare. Nursing can benefit from this era of big data science, as there is a growing need for new discoveries from large quantities of nursing data to provide evidence-based care. However, there are few nursing studies using big data analytics. The purpose of this article is to explain a knowledge discovery and data mining approach that was employed to discover knowledge about hospital-acquired catheter-associated urinary tract infections from multiple data sources, including electronic health records and nurse staffing data. Three different machine learning techniques are described: decision trees, logistic regression, and support vector machines. The decision tree model created rules to interpret relationships among associated factors of hospital-acquired catheter-associated urinary tract infections. The logistic regression model showed what factors were related to a higher risk of hospital-acquired catheter-associated urinary tract infections. The support vector machines model was included to compare performance with the other two interpretable models. This article introduces the examples of cutting-edge machine learning approaches that will advance secondary use of electronic health records and integration of multiple data sources as well as provide evidence necessary to guide nursing professionals in practice.

Factors Associated With Healthcare-Acquired Catheter-Associated Urinary Tract Infections: Analysis Using Multiple Data Sources and Data Mining Techniques.

PURPOSE: The purpose of this study was to identify factors associated with healthcare-acquired catheter-associated urinary tract infections (HA-CAUTIs) using multiple data sources and data mining techniques. SUBJECTS AND SETTING: Three data sets were integrated for analysis: electronic health record data from a university hospital in the Midwestern United States was combined with staffing and environmental data from the hospital's National Database of Nursing Quality Indicators and a list of patients with HA-CAUTIs. METHODS: Three data mining techniques were used for identification of factors associated with HA-CAUTI: decision trees, logistic regression, and support vector machines. RESULTS: Fewer total nursing hours per patient-day, lower percentage of direct care RNs with specialty nursing certification, higher percentage of direct care RNs with associate's degree in nursing, and higher percentage of direct care RNs with BSN, MSN, or doctoral degree are associated with HA-CAUTI occurrence. The results also support the association of the following factors with HA-CAUTI identified by previous studies: female gender; older age (>50 years); longer length of stay; severe underlying disease; glucose lab results (>200 mg/dL); longer use of the catheter; and RN staffing. CONCLUSIONS: Additional findings from this study demonstrated that the presence of more nurses with specialty nursing certifications can reduce HA-CAUTI occurrence. While there may be valid reasons for leaving in a urinary catheter, findings show that having a catheter in for more than 48 hours contributes to HA-CAUTI occurrence. Finally, the findings suggest that more nursing hours per patient-day are related to better patient outcomes.

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance.

A systems approach to designing effective clinical trials using simulations.

BACKGROUND: Pharmacogenetics in warfarin clinical trials have failed to show a significant benefit in comparison with standard clinical therapy. This study demonstrates a computational framework to systematically evaluate preclinical trial design of target population, pharmacogenetic algorithms, and dosing protocols to optimize primary outcomes. METHODS AND RESULTS: We programmatically created an end-to-end framework that systematically evaluates warfarin clinical trial designs. The framework includes options to create a patient population, multiple dosing strategies including genetic-based and nongenetic clinical-based, multiple-dose adjustment protocols, pharmacokinetic/pharmacodynamics modeling and international normalization ratio prediction, and various types of outcome measures. We validated the framework by conducting 1000 simulations of the applying pharmacogenetic algorithms to individualize dosing of warfarin (CoumaGen) clinical trial primary end points. The simulation predicted a mean time in therapeutic range of 70.6% and 72.2% (P=0.47) in the standard and pharmacogenetic arms, respectively. Then, we evaluated another dosing protocol under the same original conditions and found a significant difference in the time in therapeutic range between the pharmacogenetic and standard arm (78.8% versus 73.8%; P=0.0065), respectively. CONCLUSIONS: We demonstrate that this simulation framework is useful in the preclinical assessment phase to study and evaluate design options and provide evidence to optimize the clinical trial for patient efficacy and reduced risk.

Detecting Multimorbidity Patterns with Association Rule Mining in Patients with Alzheimer's Disease and Related Dementias.

Researchers estimate the number of dementia patients to triple by 20501. Dementia seldom occurs in isolation; it's frequently accompanied by other health conditions2. The coexistence of conditions further complicates the management of dementia. In this study, we embarked on an innovative approach, applying association rule mining to analyze National Alzheimer's Coordinating Center (NACC) data. First, we completed a literature review on the utilization of association rules, heatmaps, and network analysis to detect and visualize comorbidities. Then, we conducted a secondary data analysis on the NACC data using association rule mining. This algorithm uncovers associations of comorbidities that are diagnosed together in patients who have Alzheimer's disease and related dementias (ADRD). Also, for these patients, the algorithm provides the probability of a patient developing another comorbidity given the diagnosis of an associated comorbidity. These findings can enhance treatment planning, advance research on high-association diseases, and ultimately enhance healthcare for dementia patients.

Measuring the Impact of Primary Care Team Composition on Patient Activation Utilizing Electronic Health Record Big Data Analytics.

Purpose: Team-based care has been linked to key outcomes associated with the Quadruple Aim and a key driver of high-value patient-centered care. Use of the electronic health record (EHR) and machine learning have significant potential to overcome previous barriers to studying the impact of teams, including delays in accessing data to improve teamwork and optimize patient outcomes. Methods: This study utilized a large EHR dataset (n=316,542) from an urban health system to explore the relationship between team composition and patient activation, a key driver of patient engagement. Teams were operationalized using consensus definitions of teamwork from the literature. Patient activation was measured using the Patient Activation Measure (PAM). Results from multilevel regression analyses were compared to machine learning analyses using multinomial logistic regression to calculate propensity scores for the effect of team composition on PAM scores. Under the machine learning approach, a causal inference model with generalized overlap weighting was used to calculate the average treatment effect of teamwork. Results: Seventeen different team types were observed in the data from the analyzed sample (n=12,448). Team sizes ranged from 2 to 5 members. After controlling for confounding variables in both analyses, more diverse, multidisciplinary teams (team size of 4 or more) were observed to have improved patient activation scores. Conclusions: This is the first study to explore the relationship between team composition and patient activation using the EHR and big data analytics. Implications for further research using EHR data and machine learning to study teams and other patient-centered care are promising and could be used to advance team science.

Development and Validation of the Pharmacological Statin-Associated Muscle Symptoms Risk Stratification Score Using Electronic Health Record Data.

Statin-associated muscle symptoms (SAMS) can lead to statin nonadherence. This paper aims to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using a previously developed PSAMS phenotyping algorithm that distinguishes objective vs. nocebo SAMS using electronic health record (EHR) data. Using our PSAMS phenotyping algorithm, SAMS cases and controls were identified from Minnesota Fairview EHR, with the statin user cohort divided into derivation (January 1, 2010, to December 31, 2018) and validation (January 1, 2019, to December 31, 2020) cohorts. A Least Absolute Shrinkage and Selection Operator regression model was applied to identify significant features for PSAMS. PSAMS-RS scores were calculated and the clinical utility of stratifying PSAMS risk was assessed by comparing hazard ratios (HRs) between fourth vs. first score quartiles. PSAMS cases were identified in 1.9% (310/16,128) of the derivation and 1.5% (64/4,182) of the validation cohorts. Sixteen out of 38 clinical features were determined to be significant predictors for PSAMS risk. Patients within the fourth quartile of the PSAMS scores had an over sevenfold (HR: 7.1, 95% confidence interval (CI): 4.03-12.45, derivation cohort) or sixfold (HR: 6.1, 95% CI: 2.15-17.45, validation cohort) higher hazard of developing PSAMS vs. those in their respective first quartile. The PSAMS-RS score is a simple tool to stratify patients' risk of developing PSAMS after statin initiation which could inform clinician-guided pre-emptive measures to prevent PSAMS-related statin nonadherence.

Decision rules for personalized statin treatment prescriptions over multi-objectives.

In our previous study, we demonstrated the feasibility of producing a proactive statin prescription strategy - a personalized statin treatment plan (PSTP) - using neural networks with big data. However, its non-transparency limited result interpretations and clinical usability. To improve the transparency of our previous approach with minimal compromise to the maximal statin treatment benefit-to-risk ratio, this study proposed a five-step pipeline approach called the decision rules for statin treatment (DRST). Steps 1-3 of our proposed pipeline improved our previous PSTP model in optimizing individual benefit-to-risk ratio; Step 4 used a decision tree model (DRST) to provide straightforward rules in the initial statin treatment plan; Step 5 aimed to evaluate the efficacy of these decision rules by conducting a clinical trial simulation. We included 107,739 de-identified patient data from Optum Labs Database Warehouse in this study. The final decision rules were compact and efficient, resulting from a decision tree with only a maximum depth of 3 and 11 nodes. The DRST identified three factors that are easily obtainable at the point of care: age, low-density lipoprotein cholesterol (LDL-C) level, and age-adjusted Charlson score. Moreover, it also identified six subpopulations that can benefit most from these decision rules. In our clinical trial simulations, DRST was found to improve statin benefit in LDL-C reduction by 4.15 percentage points (pp) and reduce risks of statin-associated symptoms (SAS) and statin discontinuation by 11.71 and 3.96 pp, respectively, when compared to the standard of care. Moreover, these DRST results were only less than 0.6 pp suboptimal to PSTP, demonstrating that building DRST that provide transparency with minimal compromise to the maximal benefit-to-risk ratio of statin treatments is feasible.

Development and validation of the pharmacological statin-associated muscle symptoms risk stratification (PSAMS-RS) score using real-world electronic health record data.

Introduction: Statin-associated muscle symptoms (SAMS) contribute to the nonadherence to statin therapy. In a previous study, we successfully developed a pharmacological SAMS (PSAMS) phenotyping algorithm that distinguishes objective versus nocebo SAMS using structured and unstructured electronic health records (EHRs) data. Our aim in this paper was to develop a pharmacological SAMS risk stratification (PSAMS-RS) score using these same EHR data. Method: Using our PSAMS phenotyping algorithm, SAMS cases and controls were identified using University of Minnesota (UMN) Fairview EHR data. The statin user cohort was temporally divided into derivation (1/1/2010 to 12/31/2018) and validation (1/1/2019 to 12/31/2020) cohorts. First, from a feature set of 38 variables, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model was fitted to identify important features for PSAMS cases and their coefficients. A PSAMS-RS score was calculated by multiplying these coefficients by 100 and then adding together for individual integer scores. The clinical utility of PSAMS-RS in stratifying PSAMS risk was assessed by comparing the hazard ratio (HR) between 4th vs 1st score quartile. Results: PSAMS cases were identified in 1.9% (310/16128) of the derivation and 1.5% (64/4182) of the validation cohort. After fitting LASSO regression, 16 out of 38 clinical features were determined to be significant predictors for PSAMS risk. These factors are male gender, chronic pulmonary disease, neurological disease, tobacco use, renal disease, alcohol use, ACE inhibitors, polypharmacy, cerebrovascular disease, hypothyroidism, lymphoma, peripheral vascular disease, coronary artery disease and concurrent uses of fibrates, beta blockers or ezetimibe. After adjusting for statin intensity, patients in the PSAMS score 4th quartile had an over seven-fold (derivation) (HR, 7.1; 95% CI, 4.03-12.45) and six-fold (validation) (HR, 6.1; 95% CI, 2.15-17.45) higher hazard of developing PSAMS versus those in 1st score quartile. Conclusion: The PSAMS-RS score can be a simple tool to stratify patients' risk of developing PSAMS after statin initiation which can facilitate clinician-guided preemptive measures that may prevent potential PSAMS-related statin non-adherence.

Using artificial intelligence to improve pain assessment and pain management: a scoping review.

CONTEXT: Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research. OBJECTIVES: This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients. METHODS: The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality. RESULTS: This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively. CONCLUSIONS: Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.

Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students.

Chronic absenteeism is an administrative term defining extreme failure for students to be present at school, which can have devastating long-term impacts on students. Although numerous prior studies have investigated associated variables and interventions, there are few studies that utilize both theory-driven and data-informed approaches to investigate absenteeism. The current study applied data-driven machine learning techniques, grounded in "The Kids and Teens at School" (KiTeS) theoretical framework, to student-level data (N = 121,005) to identify risk and protective variables that are highly associated with school absences. A total of 18 risk and protective variables were identified; all 18 variables were characteristics of the microsystem or mesosystem, emphasizing school absences' proximity to variables within inner ecological systems rather than the exosystem or macrosystem. Implications for future studies and health infrastructure are discussed.

Development and application of pharmacological statin-associated muscle symptoms phenotyping algorithms using structured and unstructured electronic health records data.

Importance: Statins are widely prescribed cholesterol-lowering medications in the United States, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation. Objectives: In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview. Materials and Methods: We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the published SAMS-Clinical Index tool from clinical notes in 200 patients. We developed machine learning algorithms and rule-based algorithms that incorporated various criteria, including ICD codes, statin allergy, creatine kinase elevation, and keyword mentions in clinical notes. We applied the best-performing algorithm to the statin cohort to identify SAMS. Results: We identified 16 889 patients who started statins in the Fairview EHR system from 2010 to 2020. The combined rule-based (CRB) algorithm, which utilized both clinical notes and structured data criteria, achieved similar performance compared to machine learning algorithms with a precision of 0.85, recall of 0.71, and F1 score of 0.77 against the gold standard set. Applying the CRB algorithm to the statin cohort, we identified the pharmacological SAMS prevalence to be 1.9% and selective risk factors which included female gender, coronary artery disease, hypothyroidism, and use of immunosuppressants or fibrates. Discussion and Conclusion: Our study developed and validated a simple pharmacological SAMS phenotyping algorithm that can be used to create SAMS case/control cohort to enable further analysis which can lead to the development of a SAMS risk prediction model.

Development and Application of Pharmacological Statin-Associated Muscle Symptoms Phenotyping Algorithms Using Structured and Unstructured Electronic Health Records Data.

Background: Statins are widely prescribed cholesterol-lowering medications in the US, but their clinical benefits can be diminished by statin-associated muscle symptoms (SAMS), leading to discontinuation. In this study, we aimed to develop and validate a pharmacological SAMS clinical phenotyping algorithm using electronic health records (EHRs) data from Minnesota Fairview. Methods: We retrieved structured and unstructured EHR data of statin users and manually ascertained a gold standard set of SAMS cases and controls using the SAMS-CI tool from clinical notes in 200 patients. We developed machine learning algorithms and rule-based algorithms that incorporated various criteria, including ICD codes, statin allergy, creatine kinase elevation, and keyword mentions in clinical notes. We applied the best performing algorithm to the statin cohort to identify SAMS. Results: We identified 16,889 patients who started statins in the Fairview EHR system from 2010-2020. The combined rule-based (CRB) algorithm, which utilized both clinical notes and structured data criteria, achieved similar performance compared to machine learning algorithms with a precision of 0.85, recall of 0.71, and F1 score of 0.77 against the gold standard set. Applying the CRB algorithm to the statin cohort, we identified the pharmacological SAMS prevalence to be 1.9% and selective risk factors which included female gender, coronary artery disease, hypothyroidism, use of immunosuppressants or fibrates. Conclusion: Our study developed and validated a simple pharmacological SAMS phenotyping algorithm that can be used to create SAMS case/control cohort for further analysis such as developing SAMS risk prediction model.

Individualized patient-centered lifestyle recommendations: an expert system for communicating patient specific cardiovascular risk information and prioritizing lifestyle options.

We propose a proof-of-concept machine-learning expert system that learned knowledge of lifestyle and the associated 10-year cardiovascular disease (CVD) risks from individual-level data (i.e., Atherosclerosis Risk in Communities Study, ARIC). The expert system prioritizes lifestyle options and identifies the one that maximally reduce an individual's 10-year CVD risk by (1) using the knowledge learned from the ARIC data and (2) communicating for patient-specific cardiovascular risk information and personal limitations and preferences (as defined by variables used in this study). As a result, the optimal lifestyle is not only prioritized based on an individual's characteristics but is also relevant to personal circumstances. We also explored probable uses and tested the system in several examples using real-world scenarios and patient preferences. For example, the system identifies the most effective lifestyle activities as the starting point for an individual's behavior change, shows different levels of BMI changes and the associated CVD risk reductions to encourage weight loss, identifies whether weight loss or smoking cessation is the most urgent change for a diabetes patient, etc. Answers to the questions noted above vary based on an individual's characteristics. Our validation results from clinical trial simulations, which compared original with the optimal lifestyle using an independent dataset, show that the optimal individualized patient-centered lifestyle consistently reduced 10-year CVD risks.

Clinical Data Cohort Quality Improvement: The Case of the Medication Data in The University of Minnesota's Clinical Data Repository.

Clinical and translational research centers (CTRCs) have emerged as key centers for electronic medical record related research through integrated data repositories (IDRs) and the 'secondary use' of clinical data. Researchers accessing and pre-processing ever increasing amounts of electronic medical records for data mining tasks have a growing need for best practice approaches for clinical data quality assessment and improvement. This project focused on a large data extract for 7 statin medication prescriptions for patients with cardiovascular disease. After the initial data extraction, we proceeded to analyze the data for completeness, correctness, currency, and percentage populated using established data quality frameworks. Assessment of the said data was performed through medication possession ratios, medication discontinuation reasons, and drug dosages. When we compared distributions of data elements such as drug dosage before and after changes were introduced by our pre-processing protocols, only a minimal noticeable difference was found as the clinical data cohort quality assessment and pre-processing were completed without substantially altering the original data structure. Our study demonstrated practical steps for clinical data cohort quality improvement using medication data and illustrates a best practice approach in clinical data cohort quality improvement for any data mining tasks.

Comparison of the Warfarin Dosing and Outcomes in Hmong Versus East Asians Patients: Real-World Data From an Integrated Healthcare System.

Background Previous research predicted that Hmong, an understudied East Asian subpopulation, might require significantly lower warfarin doses than East Asian patients partially due to their unique genetic and clinical factors. However, such findings have not been corroborated using real-world data. Methods This was a retrospective cohort study of Hmong and East Asian patients receiving warfarin. Warfarin stable doses (WSD) and time to the composite outcome, including international normalized ratio (INR) greater than four incidences or major bleeding within six months of warfarin initiation, were compared. Results This cohort study included 55 Hmong and 100 East Asian patients. Compared to East Asian patients, Hmong had a lower mean WSD (14.5 vs. 20.4 mg/week, p<0.05). In addition, Hmong had a 3.1-fold (95% CI: 1.1-9.3, p<0.05) higher hazard of the composite outcome. Conclusion Using real-world data, significant differences in warfarin dosing and hazard for the composite outcome of INR>4 and major bleeding were observed between Hmong and East Asian patients. These observations further underscore the importance of recognizing subpopulation-based differences in warfarin dosing and outcomes.

Development, implementation and participant evaluation of combining text messaging and peer group support in a weight management programme for African-American women.

BACKGROUND: Development of highly accessible interventions that are effective in reducing body weight, preventing weight gain, and maintaining weight loss is urgently needed to solve the current obesity epidemic, especially among African-American women. AIMS: The purpose of this paper is to describe the development, implementation, and participant evaluation processes of a combined text messaging and peer support group programme to enhance weight management skills among African-American women. METHODS: The programme's conceptual framework and operational model were developed to enhance the research design and protocol to support the study rationale and to lay a solid theoretical base for programme implementation. The programme curriculum and schedule were established and embedded into the programme protocol. RESULTS: The 16-week text messaging and peer support group intervention was implemented from September 2014 to March 2015. In total, 2089 messages were sent using an online text messaging application. Eight support group sessions were held in the participant's community centre or community church bi-weekly for approximately one hour. CONCLUSIONS: This paper provides a blueprint of the methodological aspects and insights from participants' evaluation of a combined weight management intervention that can be used or adapted by public health nurses and other community health professionals in their work to develop weight management skills among African-American women.