Improving Care for People Aging with HIV: A Collaborative Quality Improvement Approach.

Nearly 60% of people with HIV in New York State are over 50 years of age. After town halls and a statewide survey of long-term survivors, older people living with HIV, and their providers, the Quality of Care Program of the AIDS Institute in the New York State Department of Health developed a statewide quality improvement project that aimed to improve screening for functional impairments among people aging with HIV. Thirteen sites reported outcomes of a pilot project using a modification of the World Health Organization's Integrated Care of Older People (ICOPE) intrinsic capacity screen in small scale, short cycle tests of change. A total of 1,629 people were found to be eligible for screening, and of these, 638 people were screened. Both clinical and non-clinical sites were able to identify significant areas of need. Positive screens ranged from a low of 17% for the identification of hearing issues to 49% for vision concerns. Only 11% of people with memory or nutritional concerns were referred for services; hearing loss was the domain with the largest number of referrals, at 27%. Although in many cases, when referrals were not made, patients/clients were already under care for the identified functional deficit, in other cases no services were available for referral or patients/clients declined to use the offered service. Sites also responded to the findings of the screen by initiating process changes, and many reported continuing to screen for functional impairments after the close of the pilot. The modified ICOPE screen is still in use in sites throughout the state. This pilot demonstrated that a collaboration between people with lived HIV experience, the New York State Department of Health, clinicians, and service providers could result in improved quality of care for people aging with HIV.

Risk Prediction Tool for Assessing the Probability of Death or Myocardial Infarction in Patients With Stable Coronary Artery Disease.

Several risk scores in acute coronary syndromes are available, but few models exist for stable coronary artery disease to guide decision-making and prognosis. A multivariate model was developed using 23 baseline candidate variables from the Clinical Outcomes Utilizing Revascularization and Aggressive Drug Therapy EvaluationTrial (n = 2,287 patients). Discrimination of the model was evaluated by the concordance c-index. The procedure was validated using 100 random half samples. We identified 9 independent predictors of death or myocardial infarction (MI) during a 5-year follow-up. The following predictors and points contributing to the risk score were: heart failure (3), number of diseased coronary arteries (1 for each vessel), diabetes (1), age (1 for each 15 years ≥ age 45), previous revascularization (1), current smoking (1), female (1), previous MI (1), and high-density lipoprotein cholesterol (1: 31 to 40 mg/dL; 2: <30 mg/dL). The risk tool had a potential range from 0 to 15, corresponding to 5-year event rates of 5.8% to 56%. C-indices ranged from 0.67 for the full data set to 0.62 for the validating subsamples. Respective observed versus predicted 5-year event rates for 3 predefined risk strata revealed: 30% had a low-risk score of 0 to 3 (9.3% vs 9.3%, or 1.9%/year); 59% had an intermediate-risk score of 4-6 (18.0% vs 18.1%, or 3.6%/year); and 11% had a high-risk score of 7-11 (36% vs 36.5%, or 7.2%/year). This stable coronary artery disease risk score permitted a prognostic assessment of 5-year probability of death or MI with an approximate 4-fold range in event rates from the lowest (9.3%) to the highest (36%) terciles, thus enabling better clinical practice decisions that allow physicians to tailor the intensity of treatment to the level of risk.

Genomic analysis of insertion behavior and target specificity of mini-Tn7 and Tn3 transposons in Saccharomyces cerevisiae.

Transposons are widely employed as tools for gene disruption. Ideally, they should display unbiased insertion behavior, and incorporate readily into any genomic DNA to which they are exposed. However, many transposons preferentially insert at specific nucleotide sequences. It is unclear to what extent such bias affects their usefulness as mutagenesis tools. Here, we examine insertion site specificity and global insertion behavior of two mini-transposons previously used for large-scale gene disruption in Saccharomyces cerevisiae: Tn3 and Tn7. Using an expanded set of insertion data, we confirm that Tn3 displays marked preference for the AT-rich 5 bp consensus site TA[A/T]TA, whereas Tn7 displays negligible target site preference. On a genome level, both transposons display marked non-uniform insertion behavior: certain sites are targeted far more often than expected, and both distributions depart drastically from Poisson. Thus, to compare their insertion behavior on a genome level, we developed a windowed Kolmogorov-Smirnov (K-S) test to analyze transposon insertion distributions in sequence windows of various sizes. We find that when scored in large windows (>300 bp), both Tn3 and Tn7 distributions appear uniform, whereas in smaller windows, Tn7 appears uniform while Tn3 does not. Thus, both transposons are effective tools for gene disruption, but Tn7 does so with less duplication and a more uniform distribution, better approximating the behavior of the ideal transposon.

Clustering of Directions Improves Goodness of Fit in Kinematic Data Collected in the Transverse Plane During Robot-Assisted Rehabilitation of Stroke Patients.

The kinematic character of hand trajectory in reaching tasks varies by movement direction. Often, direction is not included as a factor in the analysis of data collected during multi-directional reach tasks; consequently, this directionally insensitive model (DI) may be prone to type-II error due to unexplained variance. On the other hand, directionally specific models (DS) that account separately for each movement direction, may reduce statistical power by increasing the amount of data groupings. We propose a clustered-by-similarity (CS) in which movement directions with similar kinematic features are grouped together, maximizing model fit by decreasing unexplained variance while also decreasing uninformative sub-groupings. We tested model quality in measuring change over time in 10 kinematic features extracted from 72 chronic stroke patients participating in the VA-ROBOTICS trial, performing a targeted reaching task over 16 movement directions (8 targets, back- and forth from center) in the horizontal plane. Across 49 participants surviving a quality control sieve, 4.3 ± 1.1 (min: 3; max: 7) clusters were found among the 16 movement directions; clusters varied between participants. Among 49 participants, and averaged across 10 features, the better-fitting model for predicting change in features was found to be CS assessed by the Akaike Information criterion (61.6 ± 7.3%), versus DS (31.0 ± 7.8%) and DI (7.1 ± 7.1%). Confirmatory analysis via Extra Sum of Squares F-test showed the DS and CS models out-performed the DI model in head-to-head (pairwise) comparison in >85% of all specimens. Thus, we find overwhelming evidence that it is necessary to adjust for direction in the models of multi-directional movements, and that clustering kinematic data by feature similarly may yield the optimal configuration for this co-variate.

P values: from suggestion to superstition.

A threshold probability value of 'p≤0.05' is commonly used in clinical investigations to indicate statistical significance. To allow clinicians to better understand evidence generated by research studies, this review defines the p value, summarizes the historical origins of the p value approach to hypothesis testing, describes various applications of p≤0.05 in the context of clinical research and discusses the emergence of p≤5×10(-8) and other values as thresholds for genomic statistical analyses. Corresponding issues include a conceptual approach of evaluating whether data do not conform to a null hypothesis (ie, no exposure-outcome association). Importantly, and in the historical context of when p≤0.05 was first proposed, the 1-in-20 chance of a false-positive inference (ie, falsely concluding the existence of an exposure-outcome association) was offered only as a suggestion. In current usage, however, p≤0.05 is often misunderstood as a rigid threshold, sometimes with a misguided 'win' (p≤0.05) or 'lose' (p>0.05) approach. Also, in contemporary genomic studies, a threshold of p≤10(-8) has been endorsed as a boundary for statistical significance when analyzing numerous genetic comparisons for each participant. A value of p≤0.05, or other thresholds, should not be employed reflexively to determine whether a clinical research investigation is trustworthy from a scientific perspective. Rather, and in parallel with conceptual issues of validity and generalizability, quantitative results should be interpreted using a combined assessment of strength of association, p values, CIs, and sample size.

Piecewise analysis of patient survival after onset of AKI.

BACKGROUND AND OBJECTIVES: AKI affects approximately 2%-7% of hospitalized patients and >35% of critically ill patients. Survival after AKI may be described as having an acute phase (including an initial hyperacute component) followed by a convalescent phase, which may itself have early and late components. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Data from the Veterans Affairs/National Institutes of Health Acute Renal Failure Trial Network (ATN) study was used to model mortality risk among patients with dialysis-requiring AKI. This study assumed that the mortality hazard can be described by a piecewise log-linear function with change points. Using an average likelihood method, the authors tested for the number of change points in a piecewise log-linear hazard model. The maximum likelihood approach to locate the change point(s) was then adopted, and associated parameters and standard errors were estimated. RESULTS: There were 1124 ATN participants with follow-up to 1 year. The mortality hazard of AKI decreased over time with inflections in the rate of decrease at days 4, 42, and 148, with the sharpest change at day 42. The daily rate of decline in the log of the hazard for death was 0.220 over the first 4 days, 0.046 between day 4 and day 42, 0.017 between day 42 and day 148, and 0.003 between day 148 and day 365. CONCLUSIONS: There appear to be two major phases of mortality risk after AKI: an early phase extending over the first 6 weeks and a late phase from 6 weeks to 1 year. Within the first 42 days, this can be further divided into hyperacute (days 1-4) and acute (days 4-42) phases. After 42 days, there appear to be early (days 42-148) and late (after day 148) convalescent phases. These findings may help to inform the design of AKI clinical trials and assist critical care physicians in prognostic stratification.