Pragmatic characteristics of patient-reported outcome measures are important for use in clinical practice

OBJECTIVES: Measures for assessing patient-reported outcomes (PROs) that may have initially been developed for research are increasingly being recommended for use in clinical practice as well. Although psychometric rigor is essential, this article focuses on pragmatic characteristics of PROs that may enhance uptake into clinical practice. STUDY DESIGN AND SETTING: Three sources were drawn on in identifying pragmatic criteria for PROs: (1) selected literature review including recommendations by other expert groups; (2) key features of several model public domain PROs; and (3) the authors' experience in developing practical PROs. RESULTS: Eight characteristics of a practical PRO include: (1) actionability (i.e., scores guide diagnostic or therapeutic actions/decision making); (2) appropriateness for the relevant clinical setting; (3) universality (i.e., for screening, severity assessment, and monitoring across multiple conditions); (4) self-administration; (5) item features (number of items and bundling issues); (6) response options (option number and dimensions, uniform vs. varying options, time frame, intervals between options); (7) scoring (simplicity and interpretability); and (8) accessibility (nonproprietary, downloadable, available in different languages and for vulnerable groups, and incorporated into electronic health records). CONCLUSION: Balancing psychometric and pragmatic factors in the development of PROs is important for accelerating the incorporation of PROs into clinical practice

The Minimal Clinically Important Difference for the Mayo-Portland Adaptability Inventory

OBJECTIVES: To determine the Minimal Clinically Important Difference (MCID) and Robust Clinically Important Difference (RCID) of the Mayo-Portland Adaptability Inventory-4 (MPAI-4) as measures of response to intervention. METHODS: Retrospective analysis of existing data. Both distribution- and anchor-based methods were used to triangulate on the MCID and to identify a moderate, that is, more robust, level of change (RCID) for the MPAI-4. These were further evaluated with respect to clinical provider ratings. PARTICIPANTS: Data for individuals with acquired brain injury in rehabilitation programs throughout the United States in the OutcomeInfo Database (n = 3087) with 2 MPAI-4 ratings. MAIN MEASURES: MPAI-4, Supervision Rating Scale, Clinician Rating of Global Clinical Improvement. RESULTS: Initial analyses suggested 5 T-score points (5T) as the MCID and 9T as the RCID. Eighty-one percent to 87% of clinical raters considered a 5T change and 99% considered a 9T change to indicate meaningful improvement. CONCLUSIONS: 5T represents the MCID for the MPAI-4 and 9T, the RCID. Both values are notably less than the Reliable Change Index (RCI). While the RCI indicates change with a high level of statistical confidence, it may be insensitive to change that is considered meaningful by providers and participants as indicated by the MCID

Risk Stratification Strategies for Colorectal Cancer Screening: From Logistic Regression to Artificial Intelligence

Risk stratification is a system or process by which clinically-meaningful separation of risk is achieved in a group of otherwise similar persons. While parametric logistic regression dominates risk prediction, use of nonparametric methods such as classification and regression trees, artificial neural networks, and other machine-learning methods are increasing. Collectively, these learning methods are referred to as “artificial intelligence” (AI). The persuasive nature of AI requires knowledge of study validity, an understanding of model metrics, and determination of whether and to what extent the model can and should be applied to the patient or population under consideration. Further investigation is needed, especially in model validation and impact assessment

The resilience in illness model, part 1: exploratory evaluation in adolescents and young adults with cancer

BACKGROUND: Resilience is a positive health outcome identified by the Committee on Future Direction for Behavioral and Social Sciences as a research priority for the National Institutes of Health. The Resilience in Illness Model (RIM) was developed from a series of qualitative and quantitative studies, to increase understanding of how positive health protective factors (ie, social integration, family environment, courageous coping, and derived meaning) may influence resilience outcomes. The RIM also includes 2 risk factors: illness-related distress and defensive coping. OBJECTIVE: The purpose of this 2-part article was to report on evaluation of the RIM for adolescents/young adults (AYAs) with cancer. Here, in part 1, our purpose was to describe the exploratory RIM evaluation, and in part 2 we describe the confirmatory RIM evaluation. METHODS: An exploratory evaluation of RIM was done using exploratory latent variable structural equation modeling with a combined sample from 2 studies of preadolescents and AYAs with cancer aged 10 to 26 years (n = 202). RESULTS: Results, including goodness-of-fit indices, support the RIM as a theory with a high level of explained variance for outcomes of resilience (67%) and self-transcendence (63%). Variance explained for proximal outcomes ranged from 18% to 76%. CONCLUSIONS: Findings indicate that, following confirmatory testing, the RIM may be a useful guide to developing targeted interventions that are grounded in the experiences of the AYAs. IMPLICATIONS FOR PRACTICE: Understanding of the AYA cancer experience to improve holistic care is increased

Validation of a Scale to Measure Patient-Perceived Barriers to Medication Use

AIMS: Medication adherence may explain why patients show very different clinical outcomes. Previous assessments of adherence have used refill rates and pill counts. Few studies have investigated patient-identified barriers to using medications as prescribed. This is particularly true for persons with diabetes, most of whom are using poly-pharmacy regimens. We created a questionnaire to measure patient perceptions of barriers to medication adherence (PBMA) targeting a predominately low income, inner-city minority population. METHODS: Twenty items (Likert scale) leading with "I sometimes don't take my diabetes medicines because..." were developed from 5 focus groups (N=48). A questionnaire including these items was mailed to 1000 patients who have diabetes. RESULTS: Using data from 267 respondents (Mean age=58, 74%female, 43% non-Hispanic Caucasian, 77% income<$15,000), exploratory factor analyses with varimax rotation identified 5 factors, that may contribute to poor medication adherence: personal access to medications (F1); communication with providers (F2); understanding or appropriately following the prescribed regimen (F3); side effects (F4) and system factors that inhibited access to medication (F5). Cronbach alphas ranged from .73 to .83 for the five factors and was .92 for total scale score. No relationships were found between total scores and gender, race, or income. Greater perception of barriers was significantly (p<0.01) associated with being younger (r= -0.21), being bothered more by physical (r= -0.40) and emotional side effects (r= 0.43), and less satisfaction with control of blood sugar by diabetes medications (r= 0.45). CONCLUSIONS: This instrument is reliable, factorially valid, and consistent with clinical observation regarding factors known to be associated with patient medication adherence. Although study participants were patients with diabetes, the PBMA may be applicable to other therapeutic areas

Metabolic syndrome and total cancer mortality in the Third National Health and Nutrition Examination Survey

PURPOSE: Although metabolic syndrome incidence has substantially increased during the last few decades, it largely remains unclear whether this metabolic disorder is associated with total cancer mortality. The present study was carried out to investigate this important question. METHODS: A total of 687 cancer deaths were identified from 14,916 participants in the third National Health and Nutrition Examination Survey by linking them to the National Death Index database through December 31, 2006. Cox proportional hazards regression was performed to calculate hazard ratios (HR) and 95% confidence intervals (CI) for total cancer mortality in relation to metabolic syndrome and its individual components. RESULTS: After adjustment for confounders, a diagnosis of metabolic syndrome was associated with 33% elevated total cancer mortality. Compared with individuals without metabolic syndrome, those with 3, 4 and 5 abnormal components had HRs (95% CIs) of 1.28 (1.03-1.59), 1.24 (0.96-1.60), and 1.87 (1.34-2.63), respectively (p-trend = 0.0003). Systolic blood pressure and serum glucose were associated with an increased risk of death from total cancer [HR (95% CI) for highest vs. lowest quartiles: 1.67 (1.19-2.33), p-trend = 0.002 and 1.34 (1.04-1.74), p-trend = 0.003, respectively]. Overall null results were obtained for lung cancer mortality. The effects of metabolic syndrome and its components on non-lung cancer mortality were generally similar to, but somewhat larger than, those for total cancer mortality. CONCLUSION: Our study is among the first to reveal that metabolic syndrome is associated with increased total cancer mortality

Group variable selection via convex log-exp-sum penalty with application to a breast cancer survivor study

In many scientific and engineering applications, covariates are naturally grouped. When the group structures are available among covariates, people are usually interested in identifying both important groups and important variables within the selected groups. Among existing successful group variable selection methods, some methods fail to conduct the within group selection. Some methods are able to conduct both group and within group selection, but the corresponding objective functions are non-convex. Such a non-convexity may require extra numerical effort. In this article, we propose a novel Log-Exp-Sum(LES) penalty for group variable selection. The LES penalty is strictly convex. It can identify important groups as well as select important variables within the group. We develop an efficient group-level coordinate descent algorithm to fit the model. We also derive non-asymptotic error bounds and asymptotic group selection consistency for our method in the high-dimensional setting where the number of covariates can be much larger than the sample size. Numerical results demonstrate the good performance of our method in both variable selection and prediction. We applied the proposed method to an American Cancer Society breast cancer survivor dataset. The findings are clinically meaningful and may help design intervention programs to improve the qualify of life for breast cancer survivors

Performance Characteristics of Fecal Immunochemical Tests for Colorectal Cancer and Advanced Adenomatous Polyps: A Systematic Review and Meta-analysis

Background: Studies report inconsistent performance of fecal immunochemical tests (FITs) for colorectal cancer (CRC) and advanced adenomas. Purpose: To summarize performance characteristics of FITs for CRC and advanced adenomas in average-risk persons undergoing screening colonoscopy (reference standard) and to identify factors affecting these characteristics. Data Sources: Ovid MEDLINE, PubMed, Embase, and the Cochrane Library from inception through October 2018; reference lists of studies and reviews. Study Selection: Two reviewers independently screened records to identify published English-language prospective or retrospective observational studies that evaluated FIT sensitivity and specificity for colonoscopic findings in asymptomatic, average-risk adults. Data Extraction: Two authors independently extracted data and evaluated study quality. Data Synthesis: Thirty-one studies (120 255 participants; 18 FITs) were included; all were judged to have low to moderate risk of bias. Performance characteristics depended on the threshold for a positive result. A threshold of 10 µg/g resulted in sensitivity of 0.91 (95% CI, 0.84 to 0.95) and a negative likelihood ratio of 0.10 (CI, 0.06 to 0.19) for CRC, whereas a threshold of greater than 20 µg/g resulted in specificity of 0.95 (CI, 0.94 to 0.96) and a positive likelihood ratio of 15.49 (CI, 9.82 to 22.39). For advanced adenomas, sensitivity was 0.40 (CI, 0.33 to 0.47) and the negative likelihood ratio was 0.67 (CI, 0.57 to 0.78) at 10 µg/g, and specificity was 0.95 (CI, 0.94 to 0.96) and the positive likelihood ratio was 5.86 (CI, 3.77 to 8.97) at greater than 20 µg/g. Studies had low to high heterogeneity, depending on the threshold. Although several FITs had adequate performance, sensitivity and specificity for CRC for 1 qualitative FIT were 0.90 and 0.91, respectively, at its single threshold of 10 µg/g; positive and negative likelihood ratios were 10.13 and 0.11, respectively. Comparison of 3 FITs at 3 thresholds was inconclusive: CIs overlapped, and the comparisons were across rather than within studies. Limitations: Only English-language studies were included. Incomplete reporting limited quality assessment of some evidence. Performance characteristics are for 1-time rather than serial testing. Conclusion: Single-application FITs have moderate to high sensitivity and specificity for CRC, depending on the positivity threshold. Sensitivity of 1-time testing for advanced adenomas is low, regardless of the threshold

The Healthy Aging Brain Care (HABC) Monitor: validation of the Patient Self-Report Version of the clinical tool designed to measure and monitor cognitive, functional, and psychological health

BACKGROUND: Primary care providers need an inexpensive, simple, user-friendly, easily standardized, sensitive to change, and widely available multidomain instrument to measure the cognitive, functional, and psychological symptoms of patients suffering from multiple chronic conditions. We previously validated the Caregiver Report Version of the Healthy Aging Brain Care Monitor (HABC Monitor) for measuring and monitoring the severity of symptoms through caregiver reports. The purpose of this study was to assess the reliability and validity of the Patient Self-Report Version of the HABC Monitor (Self-Report HABC Monitor). DESIGN: Cross-sectional study. SETTING: Primary care clinics affiliated with a safety net urban health care system in Indianapolis, Indiana, USA. SUBJECTS: A total of 291 subjects aged ≥65 years with a mean age of 72.7 (standard deviation 6.2) years, 76% female, and 56% African Americans. ANALYSIS: Psychometric validity and reliability of the Self-Report HABC Monitor. RESULTS: Among 291 patients analyzed, the Self-Report HABC Monitor demonstrated excellent fit for the confirmatory factor analysis model (root mean square error of approximation =0.030, comparative fit index =0.974, weighted root mean square residual =0.837) and good internal consistency (0.78-0.92). Adequate convergent-divergent validity (differences between the Telephone Interview for Cognitive Status test-based cognitive function impairment versus nonimpairment groups) was demonstrated only when patients were removed from analysis if they had both cognitive function test impairment and suspiciously perfect self-report HABC Monitor cognitive floor scores of 0. CONCLUSION: The Self-Report HABC Monitor demonstrates good reliability and validity as a clinically practical multidimensional tool for measuring symptoms. The tool can be used along with its caregiver version to provide useful feedback (via monitoring of symptoms) for modifying care plans. Determining the validity of HABC Monitor scores from patients who self-report a perfect cognitive score of 0 requires cognitive function test results (eg, Telephone Interview for Cognitive Status or Mini Mental State Examination) or Caregiver Report HABC Monitor scores or further clinical examination to rule out the possibility that the patient is denying or unaware of their cognitive symptoms