Bartholow TL, Becich MJ, Chandran UR, Parwani AV. Immunohistochemical staining of slit2 in primary and metastatic prostatic adenocarcinoma. Transl Oncol. 2011 Oct;4(5):314-20. Epub 2011 Oct 1. PMC 3162306
BACKGROUND: Conflicting roles for Slit2, a protein involved in mediating the processes of cell migration and chemotactic response, have been previously described in prostate cancer. Here we use immunohistochemistry to evaluate the expression of Slit2 in normal donor prostate (NDP), benign prostatic hyperplasia (BPH), high-grade prostatic intraepithelial neoplasia (HGPIN), normal tissue adjacent to prostatic adenocarcinoma (NAC), primary prostatic adenocarcinoma (PCa), and metastatic prostatic adenocarcinoma (Mets). METHODS: Tissue microarrays were immunostained for Slit2. The staining intensities were quantified using automated image analysis software. The data was statistically analyzed using one-way analysis of variance with subsequent Tukey tests for multiple comparisons or a nonparametric equivalent. Eleven cases of NDP, 35 cases of NAC, 15 cases of BPH, 35 cases of HGPIN, 106 cases of PCa, and 37 cases of Mets were analyzed. RESULTS: Specimens of PCa and HGPIN had the highest absolute staining for Slit2. Significant differences were seen between PCa and NDP (P < .05), PCa and NAC (P < .05), HGPIN and NDP (P < .05), and HGPIN and NAC (P < .05). Whereas the average Mets staining was not significantly different from NDP or NAC, several individual Mets cases featured intense staining. CONCLUSIONS: To our knowledge, this represents the first study comparing the immunohistochemical profiles of Slit2 in PCa and Mets to specimens of HGPIN, BPH, NDP, and NAC. These findings suggest that Slit2 expression can be increased in HGPIN, PCa, and Mets, making it a potentially important biomarker for prostate cancer.
Assessing the Usability of a Telemedicine-based Medication Delivery Unit for Older Adults through Inspection Methods
Ligons FM, Romagnoli KM, Browell S, Hochheiser H, Handler SM. Assessing the Usability of a Telemedicine-based Medication Delivery Unit for Older Adults through Inspection Methods. AMIA 2011. 2011.
Polypharmacy and medication non-adherence are common in older adults, potentially leading to medication-related problems and increased healthcare expenditures. Medication Delivery Units (MDUs) may improve adherence, but their interfaces may present usability challenges for older adults with age-related impairments. We used a combination of three inspection methods - heuristic evaluation, cognitive walkthrough, and simulated elderly interaction, to identify potential concerns with the usability of a commercially available telemedicine MDU. Each method revealed different problems, with repeated discoveries via different methods providing triangulated evidence. Despite the MDU's general usability, issues of varying severity were discovered. Significant usability issues associated with physical interactions with the MDU included loading and unloading the medication blister packs, and opening the delivered medication prior to administration. Less severe issues centered on small text sizes and poor user feedback. Further usability testing, involving older adults with a variety of impairments, is needed to validate findings.
Kohle-Ersher A, Chatterjee P, Osmanbeyoglu HU, Hochheiser H, Bartos CE. Evaluating the Barriers to Point-of-Care Documentation for Nursing Staff. Comput Inform Nurs. 2011 Oct 21. PubMed PMID: 22024972
Jiang X, R.E. Neapolitan, M.M. Barmada, S.Visweswaran. Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinformatics; 2011: 12(89). PMID: 21453508
BACKGROUND: Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL.Although BNs are a promising tool for learning epistatic relationships from data, we cannot confidently use them in this domain until we determine which scoring criteria work best or even well when we try learning the correct model without knowledge of the number of SNPs in that model. RESULTS: We evaluated the performance of 22 BN scoring criteria using 28,000 simulated data sets and a real Alzheimer's GWAS data set. Our results were surprising in that the Bayesian scoring criterion with large values of a hyperparameter called α performed best. This score performed better than other BN scoring criteria and MDR at recall using simulated data sets, at detecting the hardest-to-detect models using simulated data sets, and at substantiating previous results using the real Alzheimer's data set. CONCLUSIONS: We conclude that representing epistatic interactions using BN models and scoring them using a BN scoring criterion holds promise for identifying epistatic genetic variants in data. In particular, the Bayesian scoring criterion with large values of a hyperparameter α appears more promising than a number of alternatives.
Jiang, X., D.B. Neill, and G.F. Cooper, On the robustness of Bayesian network based spatial event surveillance. International Journal of Approximate Reasoning, 51 (2010) p 224-239. http://dx.doi.org/10.1016/j.ijar.2009.01.001
Methods for spatial cluster detection attempt to locate spatial subregions of some larger region where the count of some occurrences is higher than expected. Event surveillance consists of monitoring a region in order to detect emerging patterns that are indicative of some event of interest. In spatial event surveillance, we search for emerging patterns in spatial subregions. A well-known method for spatial cluster detection is Kulldorff’s [M. Kulldorff, A spatial scan statistic, Communications in Statistics: Theory and Methods 26 (6) (1997)] spatial scan statistic, which directly analyzes the counts of occurrences in the subregions. Neill et al. [D.B. Neill, A.W. Moore, G.F. Cooper, A Bayesian spatial scan statistic, Advances in Neural Information Processing Systems (NIPS) 18 (2005)] developed a Bayesian spatial scan statistic called BSS, which also directly analyzes the counts. We developed a new Bayesian-network-based spatial scan statistic, called BNetScan, which models the relationships among the events of interest and the observable events using a Bayesian network. BNetScan is an entity-based Bayesian network that models the underlying state and observable variables for each individual in a population. We compared the performance of BNetScan to Kulldorff’s spatial scan statistic and BSS using simulated outbreaks of influenza and cryptosporidiosis injected into real Emergency Department data from Allegheny County, Pennsylvania. It is an open question whether we can obtain acceptable results using a Bayesian network if the probability distributions in the network do not closely reflect reality, and thus, we examined the robustness of BNetScan relative to the probability distributions used to generate the data in the experiments. Our results indicate that BNetScan outperforms the other methods and its performance is robust relative to the probability distribution that is used to generate the data.
Landis Lewis Z, Mello-Thoms C, Gadabu OJ, Gillespie EM, Douglas GP, Crowley RS. The Feasibility of Automating Audit and Feedback for ART Guideline Adherence in Malawi. Accepted to J Am Med Inform Assoc, (JAMIA) April 19, 2011.
ABSTRACT Objective: To determine the feasibility of using electronic medical record (EMR) data to provide audit and feedback of anti-retroviral therapy (ART) clinical guideline adherence to healthcare workers (HCWs) in Malawi. Materials and methods: We evaluated recommendations from Malawi’s ART guidelines using GuideLine Implementability Appraisal criteria. Recommendations that passed selected criteria were converted into ratio-based performance measures. We queried representative EMR data to determine the feasibility of generating feedback for each performance measure, summed clinical encounters representing each performance measure’s denominator, and then measured the distribution of encounter frequency for individual HCWs across nurse and clinical officer groups. Results: We analyzed 423 831 encounters in the EMR data and generated automated feedback for 21 recommendations (12%) from Malawi’s ART guidelines. We identified 11 nurse recommendations and eight clinical officer recommendations. Individual nurses and clinical officers had an average of 45 and 59 encounters per month, per recommendation, respectively. Another 37 recommendations (21%) would support audit and feedback if additional routine EMR data are captured and temporal constraints are modeled. Discussion: It appears feasible to implement automated guideline adherence feedback that could potentially improve HCW performance and supervision. Feedback reports may support workplace learning by increasing HCWs’ opportunities to reflect on their performance. Conclusion: A moderate number of recommendations from Malawi’s ART guidelines can be used to generate automated guideline adherence feedback using existing EMR data. Further study is needed to determine the receptivity of HCWs to peer comparison feedback and barriers to implementation of automated audit and feedback in low-resource settings.