Hair Loss Profiles and Ritlecitinib Efficacy in Patients with Alopecia Areata: Post Hoc Analysis of the ALLEGRO Phase 2b/3 Study.

INTRODUCTION: Ritlecitinib demonstrated efficacy in patients with alopecia areata (AA) in the ALLEGRO phase 2b/3 study (NCT03732807). However, hair loss presentation may vary based on location (e.g., scalp, eyebrow/eyelash, body). Here, we sought to identify distinct hair loss profiles at baseline and evaluate whether they affected the efficacy of ritlecitinib. METHODS: Patients with AA aged ≥ 12 years with ≥ 50% scalp hair loss were randomized to daily ritlecitinib 10 mg (assessed for dose ranging only), 30 or 50 mg (± 4-week, 200-mg loading dose), or placebo for 24 weeks. Latent class analysis (LCA) identified hair loss profiles based on four baseline measurements: clinician-reported extent of scalp (Severity of Alopecia Tool score), eyebrow hair loss, eyelash hair loss, and patient-reported body hair loss. Logistic regression evaluated ritlecitinib (50 and 30 mg) efficacy vs placebo using Patient Global Impression of Change (PGI-C) and Patient Satisfaction with Hair Growth (P-Sat; amount, quality, and overall satisfaction) responses at Week 24, adjusting for key covariates, including latent class membership. RESULTS: LCA identified five latent classes: (1) primarily non-alopecia totalis (AT; complete loss of scalp hair); (2) non-AT with moderate non-scalp involvement; (3) extensive scalp, eyebrow, and eyelash involvement; (4) AT with moderate non-scalp involvement; and (5) primarily alopecia universalis (complete scalp, face, and body hair loss). Adjusting for latent class membership, patients receiving ritlecitinib 30 or 50 mg were significantly more likely to achieve PGI-C response (30 mg: odds ratio, 8.62 [95% confidence interval, 4.42-18.08]; 50 mg: 12.29 [6.29-25.85]) and P-Sat quality of hair regrowth (30 mg: 6.71 [3.53-13.51]; 50 mg: 8.17 [4.30-16.46]) vs placebo at Week 24. Results were similar for P-Sat overall satisfaction and amount of hair regrowth. CONCLUSION: Distinct and clinically relevant hair loss profiles were identified in ALLEGRO-2b/3 participants. Ritlecitinib was efficacious compared with placebo, independent of hair loss profile at baseline. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT03732807.

The Application of Adaptive Minimum Match k-Nearest Neighbors to Identify At-Risk Students in Health Professions Education.

INTRODUCTION: When learners fail to reach milestones, educators often wonder if any warning signs could have allowed them to intervene sooner. Machine learning can predict which students are at risk for failing a high-stakes certification examination. If predictions can be made well before the examination, educators can meaningfully intervene before students take the examination to reduce their chances of failing. METHODS: The authors used already-collected, first-year student assessment data from 5 cohorts in a single Master of Physician Assistant Studies program to implement an "adaptive minimum match" version of the k-nearest neighbors algorithm using changing numbers of neighbors to predict each student's future examination scores on the Physician Assistant National Certifying Exam (PANCE). Validation occurred in 2 ways by using leave-one-out cross-validation (LOOCV) and by evaluating predictions in a new cohort. RESULTS: "Adaptive minimum match" version of the k-nearest neighbors algorithm achieved an accuracy of 93% in LOOCV. "Adaptive minimum match" version of the k-nearest neighbors algorithm generates a predicted PANCE score for each student one year before they take the examination. Students are classified into extra support, optional extra support, or no extra support categories. Then, one year remains to provide appropriate support to each category of student. DISCUSSION: Predictive analytics can identify at-risk students who might need additional support or remediation before high-stakes certification examinations. Educators can use the included methods and code to generate predicted test outcomes for students. The authors recommend that educators use predictive modeling responsibly and transparently, as one of many tools used to support students. More research is needed to test alternative machine learning methods across a variety of educational programs.

Modeling of Treatment Outcomes with Tofacitinib Maintenance Therapy in Patients with Ulcerative Colitis: A Post Hoc Analysis of Data from the OCTAVE Clinical Program.

INTRODUCTION: Tofacitinib is an oral small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). This post hoc analysis assessed whether various statistical techniques could predict outcomes of tofacitinib maintenance therapy in patients with UC. METHODS: Data from patients who participated in a 52-week, phase III maintenance study (OCTAVE Sustain) and an open-label long-term extension study (OCTAVE Open) were included in this analysis. Patients received tofacitinib 5 or 10 mg twice daily (BID) or placebo (OCTAVE Sustain only). Logistic regression analyses were performed to generate models using clinical and laboratory variables to predict loss of responder status at week 8 of OCTAVE Sustain, steroid-free remission (defined as a partial Mayo score of 0-1 in the absence of corticosteroid use) at week 52 of OCTAVE Sustain, and delayed response at week 8 of OCTAVE Open. Furthermore, differences in loss of response/discontinuation patterns between treatment groups in OCTAVE Sustain were established. RESULTS: The generated prediction models demonstrated insufficient accuracy for determining loss of response at week 8, steroid-free remission at week 52 in OCTAVE Sustain, or delayed response in OCTAVE Open. Both tofacitinib doses demonstrated comparable response/remission patterns based on visualizations of disease activity over time. The rectal bleeding subscore was the primary determinant of disease worsening (indicated by an increased total Mayo score), and the endoscopy subscore was the primary determinant of disease improvement (indicated by a decreased total Mayo score). CONCLUSION: Visualizations of disease activity subscores revealed distinct patterns among patients with UC that had disease worsening and disease improvement. The statistical models assessed in this analysis could not accurately predict loss of responder status, steroid-free remission, or delayed response to tofacitinib. Possible reasons include the small sample size or missing data related to yet unknown key variables that were not collected during these trials.

Doctors use tofacitinib (Xeljanz^®) to treat people with moderate to severe ulcerative colitis. Patients who respond to (have improved symptoms following) treatment with tofacitinib 10 mg twice a day for 8 weeks, or up to 16 weeks if they do not respond initially (known as induction treatment), can receive tofacitinib treatment at the lowest effective dose to sustain their response (called maintenance treatment). Predicting how patients respond to tofacitinib maintenance treatment may help clinicians work out the lowest effective dose for each patient. In this study, data from the tofacitinib clinical trials were used to assess the ability to predict maintenance therapy response or failure in patients with ulcerative colitis. Differences between patients who received tofacitinib 5 or 10 mg twice a day and who either stopped responding to treatment or stopped taking treatment were looked at. The study could not accurately predict which patients would experience disease worsening, steroid-free remission (very mild or no symptoms, and not taking steroids), or take longer to respond following tofacitinib maintenance treatment. Patterns of patients who had stopped responding to treatment, or stopped taking treatment, were similar between patients who received tofacitinib 5 or 10 mg twice daily. When reviewed using doctor- and patient-reported scores that measure ulcerative colitis disease activity, different factors were important in patients with disease worsening compared with disease improvement. The results suggest that further research is needed to more accurately predict how patients with ulcerative colitis will respond to tofacitinib maintenance treatment.

The Objective Structured Teaching Encounter (OSTE) in health professions education: A systematic review.

PURPOSE: New emphasis on the assessment of health professions educators' teaching competence has led to greater use of the Objective Structured Teaching Encounter (OSTE). The purpose of this study is to review and further describe the current uses and learning outcomes of the OSTE in health professions education. MATERIALS AND METHODS: PubMed, MEDLINE, and CINAHL (March 2010 to February 2022) were searched for English-language studies describing the use of an OSTE for any educational purpose within health professions education. RESULTS: Of the 29 articles that met inclusion criteria, over half of the studies (17 of 29, 58.6%) were published during or after 2017. Seven studies described OSTE use outside of the traditional medical education context. These new contexts included basic sciences, dental, pharmacy, and Health Professions Education program graduates. Eleven articles described novel OSTE content, which included leadership skills, emotional intelligence, medical ethics, inter-professional conduct, and a procedural OSTE. There is increasing evidence supporting the use of OSTEs for the assessment of clinical educators' teaching skills. CONCLUSIONS: The OSTE is a valuable tool for the improvement and assessment of teaching within a variety of health professions education contexts. Further study is required to determine the impact of OSTEs on teaching behaviors in real-life contexts.

In Situ Simulation as a Tool to Longitudinally Identify and Track Latent Safety Threats in a Structured Quality Improvement Initiative for SARS-CoV-2 Airway Management: A Single-Center Study.

BACKGROUND: In situ simulation has emerged as a powerful tool for identifying latent safety threats (LSTs). After the first wave of the SARS-CoV-2 pandemic, an urban community emergency department (ED) identified opportunities for improvement surrounding acute airway management and particularly focused on infection control precautions, equipment availability, and interprofessional communication during acute resuscitation. Using the Model for Improvement, a hybrid in situ/quality improvement initiative was implemented using Plan-Do-Study-Act (PDSA) cycles to enhance systems for intubating patients with SARS-CoV-2. METHODS: Three PDSA cycles consisting of 10 simulations each were conducted from June 2020 through February 2021. Latent safety threats (LST) were identified through an in situ simulation scenario involving a patient with SARS-CoV-2 in acute respiratory failure. LSTs were collected through structured debriefs focused on (1) infection control, (2) equipment availability, and (3) communication. The SAFER-Matrix was used to score LSTs according to frequency and likelihood of harm by members of the ED QI team (SAFER score). The research team worked with the same QI leaders to implement action plans based on scored threats using cause-and-effect and driver diagrams. The Donabedian model was used to conceptually evaluate the quality of interventions upon conclusion of the third PDSA cycle. RESULTS: The median SAFER score decreased from 10.94 in PDSA cycle 1 to 6.77 in PDSA cycle 2 to 4.71 in PDSA cycle 3. Across all identified LSTs, the SAFER score decreased by 3.114 for every additional PDSA cycle ( P = 0.0167). When evaluating for threats identified as being primarily structure based, there was a decrease in SAFER score of 1.28 per every additional PDSA cycle ( P = 0.001). There was a decrease in total count of LST of 0.20 per additional simulation run ( P = 0.02) after controlling for shift type, census, perceived workload, team size, and prior attendance in simulations across all PDSA cycles. CONCLUSIONS: This study presents a blueprint for the utilization of in situ simulation through multiple waves of the SARS-CoV-2 pandemic to identify LSTs and use the SAFER score as a surrogate marker to monitor the impact of interventions for a safer environment for both medical staff and patients.

How Medical Students Apply Their Biomedical Science Knowledge to Patient Care in the Family Medicine Clerkship.

Medical students enter clerkships with the requisite biomedical science knowledge to engage in supervised patient care. While poised to apply this knowledge, students face the cognitive challenge of transfer: applying knowledge learned in one context (i.e., preclinical classroom) to solve problems in a different context (i.e., patients in the clinic). To help students navigate this challenge, a structured reflection exercise was developed using Kolb's experiential learning cycle as an organizing framework. Students selected a patient encounter (concrete experience), wrote and addressed biomedical science learning objectives related to the care of the patient (reflective observation), reflected on how addressing the learning objectives influenced patient care (abstract conceptualization), and described their attending engaging in a similar process (active experimentation). A directed content analysis of students' written reflections revealed that most students wrote clinical science learning objectives in addition to biomedical science learning objectives. When viewed through the lenses of knowledge encapsulation theory and illness script theory, some students recognized knowledge encapsulation as a process beginning to occur in their own approach and their attendings' approach to clinical reasoning. Students readily applied their biomedical science knowledge to explain the pathophysiologic basis of disease (fault illness script domain) and signs and symptoms (consequence illness script domain), with fewer addressing predisposing conditions (enabling conditions illness script domain). Instances in which students observed their attending applying biomedical science knowledge were rare. Implications for using structured reflective writing as a tool to facilitate student application of their biomedical science knowledge in clerkships are discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-022-01697-5.

A Two-Step, Trajectory-Focused, Analytics Approach to Attempt Prediction of Analgesic Response in Patients with Moderate-to-Severe Osteoarthritis.

INTRODUCTION: We sought to predict analgesic response to daily oral nonsteroidal anti-inflammatory drugs (NSAIDs) or subcutaneous tanezumab 2.5 mg (every 8 weeks) at week 16 in patients with moderate-to-severe osteoarthritis, based on initial treatment response over 8 weeks. METHODS: Data were derived from three randomized controlled trials of osteoarthritis. A two-step, trajectory-focused, analytics approach was used to predict patients as responders or non-responders at week 16. Step 1 identified patients using a data-element combination method (based on pain score at baseline, pain score at week 8, pain score monotonicity at week 8, pain score path length at week 8, and body site [knee or hip]). Patients who could not be identified in step 1 were predicted in step 2 using a k-nearest neighbor method based on pain score and pain response level at week 8. RESULTS: Our approach predicted response with high accuracy in NSAID-treated (83.2-90.2%, n = 931) and tanezumab-treated (84.6-91.0%, n = 1430) patients regardless of the efficacy measure used to assess pain, or the threshold used to define response (20%, 30%, or 50% improvement from baseline). Accuracy remained high using 50% or 20% response thresholds, with 50% and 20% yielding generally slightly better negative and positive predictive value, respectively, relative to 30%. Accuracy was slightly better in patients aged ≥ 65 years relative to younger patients across most efficacy measure/response threshold combinations. CONCLUSIONS: Analyzing initial 8-week analgesic responses using a two-step, trajectory-based approach can predict future response in patients with moderate-to-severe osteoarthritis treated with NSAIDs or 2.5 mg tanezumab. These findings demonstrate that prediction of treatment response based on a single dose of a novel therapeutic is possible and that predicting future outcomes based on initial response offers a way to potentially advance the approach to clinical management of patients with osteoarthritis. GOV IDENTIFIERS: NCT02528188, NCT02709486, NCT02697773.

Characterizing 16-Week Responder Profiles Using Group-Based Trajectory Modeling in Over 4300 Clinical Trial Participants Receiving Pharmaceutical Treatment for Moderate to Severe Osteoarthritis.

INTRODUCTION: We sought to identify and characterize distinct responder profiles among osteoarthritis (OA) subjects treated with tanezumab, nonsteroidal anti-inflammatory drugs (NSAIDs), or placebo. METHODS: Subject-level data were derived from three randomized, double-blind, placebo- or NSAID-controlled trials of tanezumab in subjects with moderate-to-severe OA. Subjects received subcutaneous tanezumab (2.5 mg, n = 1527; 5 mg, n = 1279) every 8 weeks, oral NSAIDs (n = 994) daily, or placebo (n = 513). Group-based trajectory modeling (GBTM, an application of finite mixture statistical modeling that uses response trajectory to identify and summarize complex patterns in longitudinal data) was used to identify subgroups of subjects following similar patterns of response in each treatment arm, based on daily pain intensity scores from baseline through Week 16. We then examined whether subject-related variables were associated with any of the subgroups using multinomial logistic regression. RESULTS: A three-subgroup/four-inflection point trajectory model was selected based on clinical and statistical considerations. The subgroups were high responders (substantial pain improvement and a large majority of members achieved ≥ 30% improvement before Week 16), medium responders (gradual pain improvement and a majority of members achieved ≥ 30% improvement by Week 16), and non-responders (little to no pain improvement over 16 weeks). Across all treatments, fluctuation in pain intensity in the week prior to treatment was consistently associated with treatment response. Other variables were positively (age, body mass index, days of rescue medication use) or negatively (severity of disease based on Kellgren-Lawrence grading) associated with response but effects were small and/or varied across treatments. CONCLUSIONS: Across all treatments, GBTM identified three subgroups of subjects that were characterized by extent of treatment response (high, medium, and non-responders). Similar analyses (e.g., grouping of subjects based on response trajectory and identification of subgroup-related variables) in other studies of OA could inform clinical trial design and/or treatment approaches. (NCT02697773; NCT02709486; NCT02528188).

Static and group-based trajectory analyses of factors associated with non-adherence in patients with multiple sclerosis newly-initiating once- or twice-daily oral disease-modifying therapy.

Background: Increased understanding of adherence may facilitate optimal targeting of interventions. Objective: To utilize group-based trajectory modeling (GBTM) to understand longitudinal patterns of adherence and factors associated with non-adherence in patients with multiple sclerosis (MS) newly-initiating once-/twice-daily oral disease-modifying therapy (DMT) (fingolimod, dimethyl fumarate, or teriflunomide). Methods: Commercial plan data were analyzed using proportion of days covered (PDC) to evaluate factors associated with non-adherence. GBTM clustered patient subgroups with similar longitudinal patterns of adherence measured by monthly PDC (≥80%) and multinomial logistic regression identified factors associated with adherence trajectory subgroups. Results: Among 7689 patients, 39.5% were non-adherent to once-/twice-daily oral DMTs. Characteristics associated with non-adherence (PDC<80%) included younger age, female, depression or migraine, switching during follow-up, more frequent dosing, relapse, and absence of magnetic resonance imaging. GBTM elucidated three adherence subgroups: Immediately Non-Adherent (14.9%); Gradually Non-Adherent (19.5%), and Adherent (65.6%). Additional factors associated with adherence (i.e. region, chronic lung disease) were identified and factors differed among trajectory subgroups. Conclusion: These analyses confirmed that a significant proportion of patients with MS are non-adherent to once-/twice-daily oral DMTs. Unique patterns of non-adherence and factors associated with patterns of adherence emerged. The approach demonstrated how quantitative trajectories can help clinicians develop tailored interventions.

A Microdebriefing Crisis Resource Management Program for Simulated Pediatric Resuscitation in a Community Hospital: A Feasibility Study.

INTRODUCTION: Crisis Resource Management (CRM) is a team training tool used in healthcare to enhance team performance and improve patient safety. Our program intends to determine the feasibility of high-fidelity simulation for teaching CRM to an interprofessional team in a community hospital and whether a microdebriefing intervention can improve performance during simulated pediatric resuscitation. METHODS: We conducted a single-center prospective interventional study with 24 teams drawn from 4 departments. The program was divided into an initial assessment simulation case (pre), a 40-minute microdebriefing intervention, and a final assessment simulation case (post). Post and pre results were analyzed for each team using t tests and Wilcoxon signed-rank tests. Primary outcome measures included (a) completion of program, (b) percent enrollment, (c) participant reaction, and (d) support of continued programs on completion. Secondary outcomes included (a) change in teamwork performance, measured by the Clinical Teamwork Scale; (b) change in time to initiation of chest compressions and defibrillation; and (c) pediatric advanced life support adherence, measured by the Clinical Performance Tool. RESULTS: We successfully completed a large-scale training program with high enrollment. Twenty-four teams with 162 participants improved in Clinical Teamwork Scale scores (42.8%-57.5%, P < 0.001), Clinical Performance Tool scores (61.7%-72.1%, P < 0.001), and time to cardiopulmonary resuscitation initiation (70.6-34.3 seconds, P < 0.001). CONCLUSIONS: Our center ran a well-attended, well-received interprofessional program in a community hospital site demonstrating that teaching CRM skills can improve simulated team performance in a diverse experienced cohort.

Prediction of early clinical response in patients receiving tofacitinib in the OCTAVE Induction 1 and 2 studies.

INTRODUCTION: Tofacitinib is an oral, small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). Outcome prediction based on early treatment response, along with clinical and laboratory variables, would be very useful for clinical practice. The aim of this study was to determine early variables predictive of responder status in patients with UC treated with tofacitinib. METHODS: Data were collected from patients treated with tofacitinib 10 mg twice daily in the OCTAVE Induction 1 and 2 studies (NCT01465763 and NCT01458951). Logistic regression and random forest analyses were performed to determine the power of clinical and/or laboratory variables to predict 2- and 3-point partial Mayo score responder status of patients at Weeks 4 or 8 after baseline. RESULTS: From a complete list of variables measured in OCTAVE Induction 1 and 2, analyses identified partial Mayo score, partial Mayo subscore (stool frequency, rectal bleeding, and Physician Global Assessment), cholesterol level, and C-reactive protein level as sufficient variables to predict responder status. Using these variables at baseline and Week 2 predicted responder status at Week 4 with 84-87% accuracy and Week 8 with 74-79% accuracy. Variables at baseline, Weeks 2 and 4 could predict responder status at Week 8 with 85-87% accuracy. CONCLUSION: Using a limited set of time-dependent variables, statistical and machine learning models enabled early and clinically meaningful predictions of tofacitinib treatment outcomes in patients with moderately to severely active UC.

A scoping review of adaptive expertise in education.

Adaptive expertise encompasses efficiency and innovation; however little is known about the state of research of adaptive expertise in medical education. Our scoping review summarizes existing evidence in the conceptual frameworks, development, and measurement for adaptive expertise. We searched Pubmed, MEDLINE, ERIC, CINAHL and PsycINFO for original research articles published from 1986 onwards in English. Given the heterogeneity of the studies, no quantitative syntheses were conducted and the articles were summarized qualitatively. Of the 48 articles that met inclusion criteria, 19 examined conceptual frameworks, 24 explored interventions supporting development and 5 examined measurement. Conceptual frameworks are consistent within and beyond health professions education. Factors influencing development include: predisposing factors such as knowledge (ability to integrate knowledge and innovate), beliefs and attitudes (high motivation and humility), enabling factors such as skills (people skills, implementing reflection and scholarly activities), resources such as curricular enablers (providing variability of cases, allowing flexibility to generate solutions, critical appraisal of textbooks) and reinforcing factors such as mentor-guided feedback and constant curricular review. Two validated measurement tools exist for adaptive expertise. Substantial research opportunities exist in studying interventions involving the development of adaptive expertise. Notable gaps exist in the development and validation of measurement tools.

Real-world adherence to, and persistence with, once- and twice-daily oral disease-modifying drugs in patients with multiple sclerosis: a systematic review and meta-analysis.

BACKGROUND: Nonadherence to disease-modifying drugs (DMDs) for multiple sclerosis (MS) is associated with poorer clinical outcomes, including higher rates of relapse and disease progression, and higher medical resource use. A systematic review and quantification of adherence and persistence with oral DMDs would help clarify the extent of nonadherence and nonpersistence in patients with MS to help prescribers make informed treatment plans and optimize patient care. The objectives were to: 1) conduct a systematic literature review to assess the availability and variability of oral DMD adherence and/or persistence rates across 'real-world' data sources; and 2) conduct meta-analyses of the rates of adherence and persistence for once- and twice-daily oral DMDs in patients with MS using real-world data. METHODS: A systematic review of studies published between January 2010 and April 2018 in the PubMed database was performed. Only studies assessing once- and twice-daily oral DMDs were available for inclusion in the analysis. Study quality was evaluated using a modified version of the Newcastle-Ottawa Scale, a tool for assessing quality of observational studies. The random effects model evaluated pooled summary estimates of nonadherence. RESULTS: From 510 abstracts, 31 studies comprising 16,398 patients with MS treated with daily oral DMDs were included. Overall 1-year mean medication possession ratio (MPR; n = 4 studies) was 83.3% (95% confidence interval [CI] 74.5-92.1%) and proportion of days covered (PDC; n = 4 studies) was 76.5% (95% CI 72.0-81.1%). Pooled 1-year MPR ≥80% adherence (n = 6) was 78.5% (95% CI 63.5-88.5%) and PDC ≥80% (n = 5 studies) was 71.8% (95% CI 59.1-81.9%). Pooled 1-year discontinuation (n = 20) was 25.4% (95% CI 21.6-29.7%). CONCLUSIONS: Approximately one in five patients with MS do not adhere to, and one in four discontinue, daily oral DMDs before 1 year. Opportunities to improve adherence and ultimately patient outcomes, such as patient education, medication support/reminders, simplified dosing regimens, and reducing administration or monitoring requirements, remain. Implementation of efforts to improve adherence are essential to improving care of patients with MS.

Examining the Baby Café Model and Mothers' Breastfeeding Duration, Meeting of Goals, and Exclusivity.

Objective: Accessible community lactation support impacts a woman's breastfeeding success by offering timely intervention and solutions, thereby allowing mothers to achieve breastfeeding goals and improve overall breastfeeding rates. Although the impact of breastfeeding support has been well established, there is a lack of consistency in the development and evaluation of support models. This report examines two differing populations of Baby Café attendees. The study evaluated the mother's achievement of personal and nationally recommended breastfeeding goals, the frequency of attending a Baby Café, and their ratings of the program as helpful in solving breastfeeding problems. Methods: A total of 559 mothers attending two Baby Cafés, one in Massachusetts and the other in southern Texas, were surveyed when their babies were 6 months old and again over age 12 months. Actual breastfeeding duration was compared with the mothers' initially stated goals and American Academy of Pediatrics (AAP) recommended goals, and then evaluated against the number of Café attendances. The mother's rating of the Café for helpfulness was measured using a 1-5 effectiveness scale. Results: Results show that mothers attending either of the surveyed Baby Cafés that served distinctly different populations reported higher breastfeeding exclusivity rates and higher rates of 12-month breastfeeding duration than national rates reported by the Centers for Disease Control and Prevention (CDC). More than 70% of all mothers surveyed rated the Café as most effective. Conclusions: The Baby Café model was shown to be effective at helping mothers reach breastfeeding goals regardless of the Café's different geographical settings and the socioeconomic characteristics of the populations served.

Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.

PURPOSE: Variability in patient treatment responses can be a barrier to effective care. Utilization of available patient databases may improve the prediction of treatment responses. We evaluated machine learning methods to predict novel, individual patient responses to pregabalin for painful diabetic peripheral neuropathy, utilizing an agent-based modeling and simulation platform that integrates real-world observational study (OS) data and randomized clinical trial (RCT) data. PATIENTS AND METHODS: The best supervised machine learning methods were selected (through literature review) and combined in a novel way for aligning patients with relevant subgroups that best enable prediction of pregabalin responses. Data were derived from a German OS of pregabalin (N=2642) and nine international RCTs (N=1320). Coarsened exact matching of OS and RCT patients was used and a hierarchical cluster analysis was implemented. We tested which machine learning methods would best align candidate patients with specific clusters that predict their pain scores over time. Cluster alignments would trigger assignments of cluster-specific time-series regressions with lagged variables as inputs in order to simulate "virtual" patients and generate 1000 trajectory variations for given novel patients. RESULTS: Instance-based machine learning methods (k-nearest neighbor, supervised fuzzy c-means) were selected for quantitative analyses. Each method alone correctly classified 56.7% and 39.1% of patients, respectively. An "ensemble method" (combining both methods) correctly classified 98.4% and 95.9% of patients in the training and testing datasets, respectively. CONCLUSION: An ensemble combination of two instance-based machine learning techniques best accommodated different data types (dichotomous, categorical, continuous) and performed better than either technique alone in assigning novel patients to subgroups for predicting treatment outcomes using microsimulation. Assignment of novel patients to a cluster of similar patients has the potential to improve prediction of patient outcomes for chronic conditions in which initial treatment response can be incorporated using microsimulation. CLINICAL TRIAL REGISTRIES: www.clinicaltrials.gov: NCT00156078, NCT00159679, NCT00143156, NCT00553475.

Assessing the Value of Time Series Real-World and Clinical Trial Data vs. Baseline-Only Data in Predicting Responses to Pregabalin Therapy for Patients with Painful Diabetic Peripheral Neuropathy.

BACKGROUND AND OBJECTIVE: Treatment challenges necessitate new approaches to customize care to individual patient needs. Integrating data from randomized controlled trials and observational studies may reduce potential covariate biases, yielding information to improve treatment outcomes. The objective of this study was to predict pregabalin responses, in individuals with painful diabetic peripheral neuropathy, by examining time series data (lagged inputs) collected after treatment initiation vs. baseline using microsimulation. METHODS: The platform simulated pregabalin-treated patients to estimate hypothetical future pain responses over 6 weeks based on six distinct time series regressions with lagged variables as inputs (hereafter termed "time series regressions"). Data were from three randomized controlled trials (N = 398) and an observational study (N = 3159). Regressions were derived after performing a hierarchical cluster analysis with a matched patient dataset from coarsened exact matching. Regressions were validated using unmatched (observational study vs. randomized controlled trial) patients. Predictive implications (of 6-week outcomes) were compared using only baseline vs. 1- to 2-week prior data. RESULTS: Time series regressions for pain performed well (adjusted R2 0.85-0.91; root mean square error 0.53-0.57); those with only baseline data performed less well (adjusted R2 0.13-0.44; root mean square error 1.11-1.40). Simulated patient distributions yielded positive predictive values for > 50% pain score improvements from baseline for the six clusters (287-777 patients each; range 0.87-0.98). CONCLUSIONS: Effective prediction of pregabalin response for painful diabetic peripheral neuropathy was accomplished through combining cluster analyses, coarsened exact matching, and time series regressions, reflecting distinct patterns of baseline and "on-treatment" variables. These results advance the understanding of microsimulation to predict patient treatment responses through integration and inter-relationships of multiple, complex, and time-dependent characteristics.

WHY COMBINE DIFFERENT DATA SOURCES?: Analyzing the tremendous amount of patient data can provide meaningful insights to improve healthcare quality. Using statistical methods to combine data from clinical trials with real-world studies can improve overall data quality (e.g., reducing biases related to real-world patient variability). WHY CONSIDER A TIME SERIES ANALYSIS?: The best predictor of future outcomes is past outcomes. A "time series" collects data at regular intervals over time. Statistical analyses of time series data allow us to discern time-dependent patterns to predict future clinical outcomes. Modeling and simulation make it possible to combine enormous amounts of data from clinical trial databases to predict a patient's clinical response based on data from similar patients. This approach improves selecting the right drug/dose for the right patient at the right time (i.e., personalized medicine). Using modeling and simulation, we predicted which patients would show a positive response to pregabalin (a neuropathic pain drug) for painful diabetic peripheral neuropathy. WHAT ARE THE MAJOR FINDINGS AND IMPLICATIONS?: For pregabalin-treated patients, a time series analysis had substantially more predictive value vs. analysis only of baseline data (i.e., data collected at treatment initiation). The ability to best predict which patients will respond to therapy has the overall implication of better informing drug treatment decisions. For example, an appropriate modeling and simulation platform complete with relevant historical clinical data could be integrated into a stand-alone device used to monitor and also predict a patient's response to therapy based on daily outcome measures (e.g., smartphone apps, wearable technologies).

Correction: Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

[This corrects the article DOI: 10.1371/journal.pone.0207120.].

Addressing needs of diverse stakeholders: Twelve tips for leaders of health professions education programs.

Designing and evaluating health professions educational programs require a range of skills in a rapidly changing educational and healthcare environment. Not all program directors possess all the required leadership skills. In this twelve tips article, we describe a systematic approach to effectively address the complexity facing program leadership, implement robust programs and meaningfully evaluate their impact. They also offer a roadmap for managing diverse stakeholders with often competing demands. The tips are categorized under three domains: Planning, Initial Implementation, and Monitoring. Specific recommendations are provided on addressing context, organizational culture, and key relationships along with practical techniques adapted from continuous quality improvement programs. An outcomes-based approach ensures that program leaders balance competing demands. The tips provide a structure for educational leaders worldwide to reflect on what is feasible in their own context, understand and address complexities in program design and evaluation, regardless of the resources at their disposal.

Association Between Breastfeeding and Childhood Cardiovascular Disease Risk Factors.

Introduction The immediate benefits of breastfeeding are well-established but the long-term health benefits are less well-known. West Virginia (WV) has a higher prevalence of cardiovascular disease (CVD) and lower breastfeeding rates compared to national averages. There is a paucity of research examining the relationship between breastfeeding and subsequent childhood CVD risk factors, an issue of particular relevance in WV. Methods This study used longitudinally linked data from three cross-sectional datasets in WV (N = 11,980). The information on breastfeeding was obtained retrospectively via parental recall when the child was in the fifth grade. The outcome variables included blood pressure measures [systolic blood pressure (SBP), diastolic blood pressure (DBP)] and lipid profile [total cholesterol (TC), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), non-HDL, and triglycerides (TG)]. Multiple regression analyses were performed, adjusting for childhood body mass index (BMI) and additional covariates. Results Only 43% of mothers self-reported ever breastfeeding. The unadjusted analysis showed that children who were ever vs. never breastfed had significantly lower SBP (b = - 1.39 mmHg; 95% CI - 1.97, - 0.81), DBP (b = - 0.79 mmHg; 95% CI - 1.26, - 0.33), log-TG (b = - 0.08; 95% CI - 0.1, - 0.05), and higher HDL (b = 0.95 mg/dL; 95% CI 0.33, 1.56). After adjustment for the child's BMI, socio-demographic and lifestyle factors, log-TG remained significantly associated with breastfeeding (b = - 0.04; 95% CI - 0.06, - 0.01; p = 0.01). Conclusion The observed protective effect of any breastfeeding on childhood TG level was small but significant. This finding provides some support for a protective effect of breastfeeding on later CVD risk.

Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

Prior work applied hierarchical clustering, coarsened exact matching (CEM), time series regressions with lagged variables as inputs, and microsimulation to data from three randomized clinical trials (RCTs) and a large German observational study (OS) to predict pregabalin pain reduction outcomes for patients with painful diabetic peripheral neuropathy. Here, data were added from six RCTs to reduce covariate bias of the same OS and improve accuracy and/or increase the variety of patients for pain response prediction. Using hierarchical cluster analysis and CEM, a matched dataset was created from the OS (N = 2642) and nine total RCTs (N = 1320). Using a maximum likelihood method, we estimated weekly pain scores for pregabalin-treated patients for each cluster (matched dataset); the models were validated with RCT data that did not match with OS data. We predicted novel 'virtual' patient pain scores over time using simulations including instance-based machine learning techniques to assign novel patients to a cluster, then applying cluster-specific regressions to predict pain response trajectories. Six clusters were identified according to baseline variables (gender, age, insulin use, body mass index, depression history, pregabalin monotherapy, prior gabapentin, pain score, and pain-related sleep interference score). CEM yielded 1766 patients (matched dataset) having lower covariate imbalances. Regression models for pain performed well (adjusted R-squared 0.90-0.93; root mean square errors 0.41-0.48). Simulations showed positive predictive values for achieving >50% and >30% change-from-baseline pain score improvements (range 68.6-83.8% and 86.5-93.9%, respectively). Using more RCTs (nine vs. the earlier three) enabled matching of 46.7% more patients in the OS dataset, with substantially reduced global imbalance vs. not matching. This larger RCT pool covered 66.8% of possible patient characteristic combinations (vs. 25.0% with three original RCTs) and made prediction possible for a broader spectrum of patients. Trial Registration: www.clinicaltrials.gov (as applicable): NCT00156078, NCT00159679, NCT00143156, NCT00553475.