Psychometric properties of the Swiss version of the Nursing Home Survey on Patient Safety Culture

The Nursing Home Survey on Patient Safety Culture (NHSPSC) was specifically developed for nursing homes to assess a facility’s safety climate and it consists of 12 dimensions. After its pilot testing, however, no fur- ther psychometric analyses were performed on the instrument. For this study of safety climate in Swiss nursing home units, the NHSPSC was linguistically adapted to the Swiss context and to address the unit as well as facility level, with the aim of testing aspects of the validity and reliability of the Swiss version before its use in Swiss nursing home units. Psychometric analyses were performed on data from 367 nurs- ing personnel from nine nursing homes in the German-speaking part of Switzerland (response rate = 66%), and content validity (CVI) examined. The statistical influence of unit membership on respondents’ answers, and on their agreement concerning their units’ safety climate, was tested using intraclass corre- lation coefficients (ICCs) and the rWG(J) interrater agreement index. A multilevel exploratory factor analysis (MEFA) with oblimin rotation was applied to examine the questionnaire’s dimensionality. Cronbach’s alpha and Raykov’s rho were calculated to assess factor reliability. The relationship of safety climate dimensions with clinical outcomes was explored. Expert feedback confirmed the relevance of the instru- ment’s items (CVI = 0.93). Personnel showed strong agreement in their perceptions in three dimensions of the questionnaire. ICCs supported a multilevel analysis. MEFA produced nine factors at the within-level (in comparison to 12 in the original version) and two factors at the between-level with satisfactory fit statis- tics. Raykov’s Rho for the single level factors ranged between 0.67 and 0.86. Some safety climate dimen- sions show moderate, but non-significant correlations with the use of bedrails, physical restraint use, and fall-related injuries. The Swiss version of the NHSPSC needs further refinement and testing before its use can be recommended in Swiss nursing homes: its dimensionality needs further clarification, particularly to distinguish items addressing the unit-level safety climate from those at the facility level.

A tutorial on data-driven methods for statistically assessing ERP topographies

Dynamic changes in ERP topographies can be conveniently analyzed by means of microstates, the so-called "atoms of thoughts", that represent brief periods of quasi-stable synchronized network activation. Comparing temporal microstate features such as on- and offset or duration between groups and conditions therefore allows a precise assessment of the timing of cognitive processes. So far, this has been achieved by assigning the individual time-varying ERP maps to spatially defined microstate templates obtained from clustering the grand mean data into predetermined numbers of topographies (microstate prototypes). Features obtained from these individual assignments were then statistically compared. This has the problem that the individual noise dilutes the match between individual topographies and templates leading to lower statistical power. We therefore propose a randomization-based procedure that works without assigning grand-mean microstate prototypes to individual data. In addition, we propose a new criterion to select the optimal number of microstate prototypes based on cross-validation across subjects. After a formal introduction, the method is applied to a sample data set of an N400 experiment and to simulated data with varying signal-to-noise ratios, and the results are compared to existing methods. In a first comparison with previously employed statistical procedures, the new method showed an increased robustness to noise, and a higher sensitivity for more subtle effects of microstate timing. We conclude that the proposed method is well-suited for the assessment of timing differences in cognitive processes. The increased statistical power allows identifying more subtle effects, which is particularly important in small and scarce patient populations.

Frequency of patient-reported infections among sicker adults in high-income countries: an international perspective

The frequency of patient-reported health care-associated infections across several high-income countries was analyzed in representative population samples based on data from "The Commonwealth Fund's 2011 International Survey of Sicker Adults in Eleven countries." Across countries, 8.9% of patients who were hospitalized and/or had surgery reported an infection, but this rate varied considerably from 5.3% in the United States to 11.9% in New Zealand. Patients who reported infection were more likely to rate the quality of medical care received as fair or poor (odds ratio [OR], 2.4; 95% confidence interval [CI]: 1.9-3.1, P < .001). Female sex (OR, 1.2; 95% CI: 1.0-1.5, P = .027), reporting 2 or more chronic conditions (OR, 1.5; 95% CI: 1.1-2.0, P = .004), poor health (OR, 1.6; 95% CI: 1.2-2.1, P < .001), and surgery (OR, 1.8; 95% CI: 1.4-2.3, P < .001) were significant predictors for health care-associated infection across countries. Being above 64 years of age (OR, 0.78; 95% CI: 0.64-0.95, P = .013) and day-surgery (OR, 0.62; 95% CI: 0.48-0.79, P < .001) decreased the likelihood for reporting infection.

A very rare cancer in Down syndrome: medulloblastoma. Epidemiological data from 13 countries

Persons with Down syndrome (DS) uniquely have an increased frequency of leukemias but a decreased total frequency of solid tumors. The distribution and frequency of specific types of brain tumors have never been studied in DS. We evaluated the frequency of primary neural cell embryonal tumors and gliomas in a large international data set. The observed number of children with DS having a medulloblastoma, central nervous system primitive neuroectodermal tumor (CNS-PNET) or glial tumor was compared to the expected number. Data were collected from cancer registries or brain tumor registries in 13 countries of Europe, America, Asia and Oceania. The number of DS children with each category of tumor was treated as a Poisson variable with mean equal to 0.000884 times the total number of registrations in that category. Among 8,043 neural cell embryonal tumors (6,882 medulloblastomas and 1,161 CNS-PNETs), only one patient with medulloblastoma had DS, while 7.11 children in total and 6.08 with medulloblastoma were expected to have DS. (p 0.016 and 0.0066 respectively). Among 13,797 children with glioma, 10 had DS, whereas 12.2 were expected. Children with DS appear to be specifically protected against primary neural cell embryonal tumors of the CNS, whereas gliomas occur at the same frequency as in the general population. A similar protection against neuroblastoma, the principal extracranial neural cell embryonal tumor, has been observed in children with DS. Additional genetic material on the supernumerary chromosome 21 may protect against embryonal neural cell tumor development.

Combining follow-up and change data is valid in meta-analyses of continuous outcomes: a meta-epidemiological study

OBJECTIVE To investigate whether it is valid to combine follow-up and change data when conducting meta-analyses of continuous outcomes. STUDY DESIGN AND SETTING Meta-epidemiological study of randomized controlled trials in patients with osteoarthritis of the knee/hip, which assessed patient-reported pain. We calculated standardized mean differences (SMDs) based on follow-up and change data, and pooled within-trial differences in SMDs. We also derived pooled SMDs indicating the largest treatment effect within a trial (optimistic selection of SMDs) and derived pooled SMDs from the estimate indicating the smallest treatment effect within a trial (pessimistic selection of SMDs). RESULTS A total of 21 meta-analyses with 189 trials with 292 randomized comparisons in 41,256 patients were included. On average, SMDs were 0.04 standard deviation units more beneficial when follow-up values were used (difference in SMDs: -0.04; 95% confidence interval: -0.13, 0.06; P=0.44). In 13 meta-analyses (62%), there was a relevant difference in clinical and/or significance level between optimistic and pessimistic pooled SMDs. CONCLUSION On average, there is no relevant difference between follow-up and change data SMDs, and combining these estimates in meta-analysis is generally valid. Decision on which type of data to use when both follow-up and change data are available should be prespecified in the meta-analysis protocol.

An analysis of variance type method to describe and compare steady states in clinical data

Identifying and comparing different steady states is an important task for clinical decision making. Data from unequal sources, comprising diverse patient status information, have to be interpreted. In order to compare results an expressive representation is the key. In this contribution we suggest a criterion to calculate a context-sensitive value based on variance analysis and discuss its advantages and limitations referring to a clinical data example obtained during anesthesia. Different drug plasma target levels of the anesthetic propofol were preset to reach and maintain clinically desirable steady state conditions with target controlled infusion (TCI). At the same time systolic blood pressure was monitored, depth of anesthesia was recorded using the bispectral index (BIS) and propofol plasma concentrations were determined in venous blood samples. The presented analysis of variance (ANOVA) is used to quantify how accurately steady states can be monitored and compared using the three methods of measurement.

Safety and efficacy of drug-eluting stents in women: a patient-level pooled analysis of randomised trials

BACKGROUND The safety and efficacy of drug-eluting stents (DES) in the treatment of coronary artery disease have been assessed in several randomised trials. However, none of these trials were powered to assess the safety and efficacy of DES in women because only a small proportion of recruited participants were women. We therefore investigated the safety and efficacy of DES in female patients during long-term follow-up. METHODS We pooled patient-level data for female participants from 26 randomised trials of DES and analysed outcomes according to stent type (bare-metal stents, early-generation DES, and newer-generation DES). The primary safety endpoint was a composite of death or myocardial infarction. The secondary safety endpoint was definite or probable stent thrombosis. The primary efficacy endpoint was target-lesion revascularisation. Analysis was by intention to treat. FINDINGS Of 43,904 patients recruited in 26 trials of DES, 11,557 (26·3%) were women (mean age 67·1 years [SD 10·6]). 1108 (9·6%) women received bare-metal stents, 4171 (36·1%) early-generation DES, and 6278 (54·3%) newer-generation DES. At 3 years, estimated cumulative incidence of the composite of death or myocardial infarction occurred in 132 (12·8%) women in the bare-metal stent group, 421 (10·9%) in the early-generation DES group, and 496 (9·2%) in the newer-generation DES group (p=0·001). Definite or probable stent thrombosis occurred in 13 (1·3%), 79 (2·1%), and 66 (1·1%) women in the bare-metal stent, early-generation DES, and newer-generation DES groups, respectively (p=0·01). The use of DES was associated with a significant reduction in the 3 year rates of target-lesion revascularisation (197 [18·6%] women in the bare-metal stent group, 294 [7·8%] in the early-generation DES group, and 330 [6·3%] in the newer-generation DES group, p<0·0001). Results did not change after adjustment for baseline characteristics in the multivariable analysis. INTERPRETATION The use of DES in women is more effective and safe than is use of bare-metal stents during long-term follow-up. Newer-generation DES are associated with an improved safety profile compared with early-generation DES, and should therefore be thought of as the standard of care for percutaneous coronary revascularisation in women. FUNDING Women in Innovation Initiative of the Society of Cardiovascular Angiography and Interventions.

Patient-specific finite-element simulation of the human cornea: A clinical validation study on cataract surgery

The planning of refractive surgical interventions is a challenging task. Numerical modeling has been proposed as a solution to support surgical intervention and predict the visual acuity, but validation on patient specific intervention is missing. The purpose of this study was to validate the numerical predictions of the post-operative corneal topography induced by the incisions required for cataract surgery. The corneal topography of 13 patients was assessed preoperatively and postoperatively (1-day and 30-day follow-up) with a Pentacam tomography device. The preoperatively acquired geometric corneal topography – anterior, posterior and pachymetry data – was used to build patient-specific finite element models. For each patient, the effects of the cataract incisions were simulated numerically and the resulting corneal surfaces were compared to the clinical postoperative measurements at one day and at 30-days follow up. Results showed that the model was able to reproduce experimental measurements with an error on the surgically induced sphere of 0.38D one day postoperatively and 0.19D 30 days postoperatively. The standard deviation of the surgically induced cylinder was 0.54D at the first postoperative day and 0.38D 30 days postoperatively. The prediction errors in surface elevation and curvature were below the topography measurement device accuracy of ±5μm and ±0.25D after the 30-day follow-up. The results showed that finite element simulations of corneal biomechanics are able to predict post cataract surgery within topography measurement device accuracy. We can conclude that the numerical simulation can become a valuable tool to plan corneal incisions in cataract surgery and other ophthalmosurgical procedures in order to optimize patients' refractive outcome and visual function.

Patient Expectations in Internet-Based Self-Help for Social Anxiety

A number of controlled trials have demonstrated the efficacy of Internet-based cognitive-behaviour therapy for treating social anxiety disorder (SAD). However, little is known about what makes those interventions work. The current trial focuses on patient expectations as one common mechanism of change. The study examines whether patients' expectancy predicts outcome, adherence, and dropout in an unguided Internet-based self-help programme for SAD. Data of 109 participants in a 10-week self-help programme for SAD were analysed. Social anxiety measures were administered prior to the intervention, at week 2, and after the intervention. Expectancy was assessed at week 2. Patient expectations were a significant predictor of change in social anxiety (β = - .35 to - .40, all p < .003). Patient expectations also predicted treatment adherence (β = .27, p = .02). Patients with higher expectations showed more adherence and better outcome. Dropout was not predicted by expectations. The effect of positive expectations on outcome was mediated by early symptom change (from week 0 to week 2). Results suggest that positive outcome expectations have a beneficial effect on outcome in Internet-based self-help for SAD. Furthermore, patient expectations as early process predictors could be used to inform therapeutic decisions such as stepping up patients to guided or face-to-face treatment options

Exploring the validity and reliability of a questionnaire for evaluating virtual patient design with a special emphasis on fostering clinical reasoning

Background: The design of Virtual Patients (VPs) is essential. So far there are no validated evaluation instruments for VP design published. Summary of work: We examined three sources of validity evidence of an instrument to be filled out by students aimed at measuring the quality of VPs with a special emphasis on fostering clinical reasoning: (1) Content was examined based on theory of clinical reasoning and an international VP expert team. (2) Response process was explored in think aloud pilot studies with students and content analysis of free text questions accompanying each item of the instrument. (3) Internal structure was assessed by confirmatory factor analysis (CFA) using 2547 student evaluations and reliability was examined utilizing generalizability analysis. Summary of results: Content analysis was supported by theory underlying Gruppen and Frohna’s clinical reasoning model on which the instrument is based and an international VP expert team. The pilot study and analysis of free text comments supported the validity of the instrument. The CFA indicated that a three factor model comprising 6 items showed a good fit with the data. Alpha coefficients per factor were 0,74 - 0,82. The findings of the generalizability studies indicated that 40-200 student responses are needed in order to obtain reliable data on one VP. Conclusions: The described instrument has the potential to provide faculty with reliable and valid information about VP design. Take-home messages: We present a short instrument which can be of help in evaluating the design of VPs.

Rigorously assessing whether the data backs the back school.

A rigorous between-subjects methodology employing independent random samples and having broad clinical applicability was designed and implemented to evaluate the effectiveness of back safety and patient transfer training interventions for both hospital nurses and nursing assistants. Effects upon self-efficacy, cognitive, and affective measures are assessed for each of three back safety procedures. The design solves the problem of obtaining randomly assigned independent controls where all experimental subjects must participate in the training interventions.

THE MULTIPLE LOCATION TIME WEIGHTED INDEX: USING PATIENT ACTIVITY SPACES TO CALCULATE PRIMARY CARE SERVICE AREAS

Geographic health planning analyses, such as service area calculations, are hampered by a lack of patient-specific geographic data. Using the limited patient address information in patient management systems, planners analyze patient origin based on home address. But activity space research done sparingly in public health and extensively in non-health related arenas uses multiple addresses per person when analyzing accessibility. Also, health care access research has shown that there are many non-geographic factors that influence choice of provider. Most planning methods, however, overlook non-geographic factors influencing choice of provider, and the limited data mean the analyses can only be related to home address. This research attempted to determine to what extent geography plays a part in patient choice of provider and to determine if activity space data can be used to calculate service areas for primary care providers. During Spring 2008, a convenience sample of 384 patients of a locally-funded Community Health Center in Houston, Texas, completed a survey that asked about what factors are important when he or she selects a health care provider. A subset of this group (336) also completed an activity space log that captured location and time data on the places where the patient regularly goes. Survey results indicate that for this patient population, geography plays a role in their choice of health care provider, but it is not the most important reason for choosing a provider. Other factors for choosing a health care provider such as the provider offering “free or low cost visits”, meeting “all of the patient’s health care needs”, and seeing “the patient quickly” were all ranked higher than geographic reasons. Analysis of the patient activity locations shows that activity spaces can be used to create service areas for a single primary care provider. Weighted activity-space-based service areas have the potential to include more patients in the service area since more than one location per patient is used. Further analysis of the logs shows that a reduced set of locations by time and type could be used for this methodology, facilitating ongoing data collection for activity-space-based planning efforts.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

A study of patient satisfaction with medical care services in Yanbu al-Sinaiya, Saudi Arabia

This study attempts to provide reliable scientific data that will enable the health services department of the Royal Commission of Yanbu Al Sinaiyah, Saudi Arabia to improve the quality of health care services provided in their facilities. Patient satisfaction and dissatisfaction were investigated along seven dimensions: General satisfaction scale, Communication, Technical quality, Art of care, Continuity of care, Time spent with the doctor, and Access/Convenience/ and availability. Patient satisfaction parameters were compared for Saudi vs. non-Saudi, males vs. females, and for patients seen in the hospital vs. those seen in Al-nawa and Radwa primary care centers. The information was obtained by using a self-administered questionnaire. The results indicate that patients seen in Al-nawa primary care center were more satisfied with care than patients seen in the hospital who in turn were more satisfied than those seen in Radwa primary care center. The non-Saudi patients were more satisfied than the Saudi patients across all three facilities and satisfaction scales. The female patients were more satisfied than the male patients across all three facilities and satisfaction scales. ^

Statistical characterization and development of summary measures of non-normal distributions of nuclear morphometry data from prostate cancer cells for use as prognostic indicators

Nuclear morphometry (NM) uses image analysis to measure features of the cell nucleus which are classified as: bulk properties, shape or form, and DNA distribution. Studies have used these measurements as diagnostic and prognostic indicators of disease with inconclusive results. The distributional properties of these variables have not been systematically investigated although much of the medical data exhibit nonnormal distributions. Measurements are done on several hundred cells per patient so summary measurements reflecting the underlying distribution are needed.^ Distributional characteristics of 34 NM variables from prostate cancer cells were investigated using graphical and analytical techniques. Cells per sample ranged from 52 to 458. A small sample of patients with benign prostatic hyperplasia (BPH), representing non-cancer cells, was used for general comparison with the cancer cells.^ Data transformations such as log, square root and 1/x did not yield normality as measured by the Shapiro-Wilks test for normality. A modulus transformation, used for distributions having abnormal kurtosis values, also did not produce normality.^ Kernel density histograms of the 34 variables exhibited non-normality and 18 variables also exhibited bimodality. A bimodality coefficient was calculated and 3 variables: DNA concentration, shape and elongation, showed the strongest evidence of bimodality and were studied further.^ Two analytical approaches were used to obtain a summary measure for each variable for each patient: cluster analysis to determine significant clusters and a mixture model analysis using a two component model having a Gaussian distribution with equal variances. The mixture component parameters were used to bootstrap the log likelihood ratio to determine the significant number of components, 1 or 2. These summary measures were used as predictors of disease severity in several proportional odds logistic regression models. The disease severity scale had 5 levels and was constructed of 3 components: extracapsulary penetration (ECP), lymph node involvement (LN+) and seminal vesicle involvement (SV+) which represent surrogate measures of prognosis. The summary measures were not strong predictors of disease severity. There was some indication from the mixture model results that there were changes in mean levels and proportions of the components in the lower severity levels. ^