Publications

BACKGROUND: Platelet (PLT) transfusion is indicated either prophylactically or therapeutically to reduce the risk of bleeding or to control active bleeding. Significant uncertainty exists regarding the appropriate use of PLT transfusion and the optimal threshold for transfusion in various settings. We formulated 12 key questions to assess the role of PLT transfusion.

STUDY DESIGN AND METHODS: We performed a systematic review (SR) of randomized controlled trials (RCTs) and observational studies. A comprehensive search of PubMed, Web of Science, and Cochrane registry of controlled trials was performed. Methodologic quality of included studies was assessed and a meta-analysis was performed if more than two studies with similar designs were identified for a specific question.

RESULTS: Seventeen RCTs and 55 observational studies were included in the final SR. Results from RCTs showed a beneficial effect of prophylactic compared with therapeutic transfusion for the prevention of significant bleeding in patients with hematologic disorders undergoing chemotherapy or stem cell transplantation. We found no difference in significant bleeding events related to the PLT count threshold for transfusion or the dose of PLTs transfused. Overall methodologic quality of RCTs was moderate. Results from observational studies showed no evidence that PLT transfusion prevented significant bleeding in patients undergoing central venous catheter insertions, lumbar puncture, or other surgical procedures. The methodologic quality of observational studies was very low.

CONCLUSION: We provide a comprehensive assessment of evidence on the use of PLT transfusions in a variety of clinical settings. Our report summarizes current knowledge and identifies gaps to be addressed in future research.

OBJECTIVES: Published negative studies should have the same rigour of methodological quality as studies with positive findings. However, the methodological quality of negative versus positive studies is not known. The objective was to assess the reported methodological quality of positive versus negative studies published in Indian medical journals.

DESIGN: A systematic review (SR) was performed of all comparative studies published in Indian medical journals with a clinical science focus and impact factor >1 between 2011 and 2013. The methodological quality of randomised controlled trials (RCTs) was assessed using the Cochrane risk of bias tool, and the Newcastle-Ottawa scale for observational studies. The results were considered positive if the primary outcome was statistically significant and negative otherwise. When the primary outcome was not specified, we used data on the first outcome reported in the history followed by the results section. Differences in various methodological quality domains between positive versus negative studies were assessed by Fisher's exact test.

RESULTS: Seven journals with 259 comparative studies were included in this SR. 24% (63/259) were RCTs, 24% (63/259) cohort studies, and 49% (128/259) case-control studies. 53% (137/259) of studies explicitly reported the primary outcome. Five studies did not report sufficient data to enable us to determine if results were positive or negative. Statistical significance was determined by p value in 78.3% (199/254), CI in 2.8% (7/254), both p value and CI in 11.8% (30/254), and only descriptive in 6.3% (16/254) of studies. The overall methodological quality was poor and no statistically significant differences between reporting of methodological quality were detected between studies with positive versus negative findings.

CONCLUSIONS: There was no difference in the reported methodological quality of positive versus negative studies. However, the uneven reporting of positive versus negative studies (72% vs 28%) indicates a publication bias in Indian medical journals with an impact factor of >1.

BACKGROUND: Decision-making relies on both analytical and emotional thinking. Cognitive reasoning styles (e.g. maximizing and satisficing tendencies) heavily influence analytical processes, while affective processes are often dependent on regret. The relationship between regret and cognitive reasoning styles has not been well studied in physicians, and is the focus of this paper.

METHODS: A regret questionnaire and 6 scales measuring individual differences in cognitive styles (maximizing-satisficing tendencies; analytical vs. intuitive reasoning; need for cognition; intolerance toward ambiguity; objectivism; and cognitive reflection) were administered through a web-based survey to physicians of the University of South Florida. Bonferroni's adjustment was applied to the overall correlation analysis. The correlation analysis was also performed without Bonferroni's correction, given the strong theoretical rationale indicating the need for a separate hypothesis. We also conducted a multivariate regression analysis to identify the unique influence of predictors on regret.

RESULTS: 165 trainees and 56 attending physicians (age range 25 to 69) participated in the survey. After bivariate analysis we found that maximizing tendency positively correlated with regret with respect to both decision difficulty (r=0.673; p<0.001) and alternate search strategy (r=0.239; p=0.002). When Bonferroni's correction was not applied, we also found a negative relationship between satisficing tendency and regret (r=-0.156; p=0.021). In trainees, but not faculty, regret negatively correlated with rational-analytical thinking (r=-0.422; p<0.001), need for cognition (r=-0.340; p<0.001), and objectivism (r=-0.309; p=0.003) and positively correlated with ambiguity intolerance (r=0.285; p=0.012). However, after conducting a multivariate regression analysis, we found that regret was positively associated with maximizing only with respect to decision difficulty (r=0.791; p<0.001), while it was negatively associated with satisficing (r=-0.257; p=0.020) and objectivism (r=-0.267; p=0.034). We found no statistically significant relationship between regret and overall accuracy on conditional inferential tasks.

CONCLUSION: Regret in physicians is strongly associated with their tendency to maximize; i.e. the tendency to consider more choices among abundant options leads to more regret. However, physicians who exhibit satisficing tendency - the inclination to accept a "good enough" solution - feel less regret. Our observation that objectivism is a negative predictor of regret indicates that the tendency to seek and use empirical data in decision-making leads to less regret. Therefore, promotion of evidence-based reasoning may lead to lower regret.

Dual Processing Theories (DPT) assume that human cognition is governed by two distinct types of processes typically referred to as type 1 (intuitive) and type 2 (deliberative). Based on DPT we have derived a Dual Processing Model (DPM) to describe and explain therapeutic medical decision-making. The DPM model indicates that doctors decide to treat when treatment benefits outweigh its harms, which occurs when the probability of the disease is greater than the so called "threshold probability" at which treatment benefits are equal to treatment harms. Here we extend our work to include a wider class of decision problems that involve diagnostic testing. We illustrate applicability of the proposed model in a typical clinical scenario considering the management of a patient with prostate cancer. To that end, we calculate and compare two types of decision-thresholds: one that adheres to expected utility theory (EUT) and the second according to DPM. Our results showed that the decisions to administer a diagnostic test could be better explained using the DPM threshold. This is because such decisions depend on objective evidence of test/treatment benefits and harms as well as type 1 cognition of benefits and harms, which are not considered under EUT. Given that type 1 processes are unique to each decision-maker, this means that the DPM threshold will vary among different individuals. We also showed that when type 1 processes exclusively dominate decisions, ordering a diagnostic test does not affect a decision; the decision is based on the assessment of benefits and harms of treatment. These findings could explain variations in the treatment and diagnostic patterns documented in today's clinical practice.

BACKGROUND: This paper explores and evaluates the application of classical and dominance-based rough set theory (RST) for the development of data-driven prognostic classification models for hospice referral. In this work, rough set based models are compared with other data-driven methods with respect to two factors related to clinical credibility: accuracy and accessibility. Accessibility refers to the ability of the model to provide traceable, interpretable results and use data that is relevant and simple to collect.

METHODS: We utilize retrospective data from 9,103 terminally ill patients to demonstrate the design and implementation RST- based models to identify potential hospice candidates. The classical rough set approach (CRSA) provides methods for knowledge acquisition, founded on the relational indiscernibility of objects in a decision table, to describe required conditions for membership in a concept class. On the other hand, the dominance-based rough set approach (DRSA) analyzes information based on the monotonic relationships between condition attributes values and their assignment to the decision class. CRSA decision rules for six-month patient survival classification were induced using the MODLEM algorithm. Dominance-based decision rules were extracted using the VC-DomLEM rule induction algorithm.

RESULTS: The RST-based classifiers are compared with other predictive and rule based decision modeling techniques, namely logistic regression, support vector machines, random forests and C4.5. The RST-based classifiers demonstrate average AUC of 69.74 % with MODLEM and 71.73 % with VC-DomLEM, while the compared methods achieve average AUC of 74.21 % for logistic regression, 73.52 % for support vector machines, 74.59 % for random forests, and 70.88 % for C4.5.

CONCLUSIONS: This paper contributes to the growing body of research in RST-based prognostic models. RST and its extensions posses features that enhance the accessibility of clinical decision support models. While the non-rule-based methods-logistic regression, support vector machines and random forests-were found to achieve higher AUC, the performance differential may be outweighed by the benefits of the rule-based methods, particularly in the case of VC-DomLEM. Developing prognostic models for hospice referrals is a challenging problem resulting in substandard performance for all of the evaluated classification methods.

UNLABELLED: Transcatheter arterial chemoembolization (TACE) is the first-line therapy recommended for patients with intermediate hepatocellular carcinoma (HCC). However, in clinical practice, these patients are often referred to surgical teams to be evaluated for hepatectomy. After making a treatment decision (e.g., TACE or surgery), physicians may discover that the alternative treatment would have been preferable, which may bring a sense of regret. Under this premise, it is postulated that the optimal decision will be the one associated with the least amount of regret. Regret-based decision curve analysis (Regret-DCA) was performed on a Cox's regression model developed on 247 patients with cirrhosis resected for intermediate HCC. Physician preferences on surgery versus TACE were elicited in terms of regret; threshold probabilities (Pt) were calculated to identify the probability of survival for which physicians are uncertain of whether or not to perform a surgery. A survey among surgeons and hepatologists regarding three hypothetical clinical cases of intermediate HCC was performed to assess treatment preference domains. The 3- and 5-year overall survival rates after hepatectomy were 48.7% and 33.8%, respectively. Child-Pugh score, tumor number, and esophageal varices were independent predictors of survival (P<0.05). Regret-DCA showed that for physicians with Pt values of 3-year survival between 35% and 70%, the optimal strategy is to rely on the prediction model; for physicians with Pt<35%, surgery should be offered to all patients; and for Pt values>70%, the least regretful strategy is to perform TACE on all patients. The survey showed a significant separation among physicians' preferences, indicating that surgeons and hepatologists can uniformly act according to the regret threshold model.

CONCLUSION: Regret theory provides a new perspective for treatment-related decisions applicable to the setting of intermediate HCC.

OBJECTIVE: To understand how often 'breakthroughs,' that is, treatments that significantly improve health outcomes, can be developed.

DESIGN: We applied weighted adaptive kernel density estimation to construct the probability density function for observed treatment effects from five publicly funded cohorts and one privately funded group.

DATA SOURCES: 820 trials involving 1064 comparisons and enrolling 331,004 patients were conducted by five publicly funded cooperative groups. 40 cancer trials involving 50 comparisons and enrolling a total of 19,889 patients were conducted by GlaxoSmithKline.

RESULTS: We calculated that the probability of detecting treatment with large effects is 10% (5-25%), and that the probability of detecting treatment with very large treatment effects is 2% (0.3-10%). Researchers themselves judged that they discovered a new, breakthrough intervention in 16% of trials.

CONCLUSIONS: We propose these figures as the benchmarks against which future development of 'breakthrough' treatments should be measured.

CK1δ (Casein kinase I isoform delta) is a member of CK1 kinase family protein that mediates neurite outgrowth and the function as brain-specific microtubule-associated protein. ATP binding kinase domain of CK1δ is essential for regulating several key cell cycle signal transduction pathways. Mutation in CK1δ protein is reported to cause cancers and affects normal brain development. S97C mutation in kinase domain of CK1δ protein has been involved to induce breast cancer and ductal carcinoma. We performed molecular docking studies to examine the effect of this mutation on its ATP-binding affinity. Further, we conducted molecular dynamics simulations to understand the structural consequences of S97C mutation over the kinase domain of CK1δ protein. Docking results indicated the loss of ATP-binding affinity of mutant structure, which were further rationalized by molecular dynamics simulations, where a notable loss in 3-D conformation of CK1δ kinase domain was observed in mutant as compared to native. Our results explained the underlying molecular mechanism behind the observed cancer associated phenotype caused by S97C mutation in CK1δ protein.

Computational prediction of cancer associated SNPs from the large pool of SNP dataset is now being used as a tool for detecting the probable oncogenes, which are further examined in the wet lab experiments. The lack in prediction accuracy has been a major hurdle in relying on the computational results obtained by implementing multiple tools, platforms and algorithms for cancer associated SNP prediction. Our result obtained from the initial computational compilations suggests the strong chance of Aurora-A G325W mutation (rs11539196) to cause hepatocellular carcinoma. The implementation of molecular dynamics simulation (MDS) approaches has significantly aided in raising the prediction accuracy of these results, but measuring the difference in the convergence time of mutant protein structures has been a challenging task while setting the simulation timescale. The convergence time of most of the protein structures may vary from 10 ns to 100 ns or more, depending upon its size. Thus, in this work we have implemented 200 ns of MDS to aid the final results obtained from computational SNP prediction technique. The MDS results have significantly explained the atomic alteration related with the mutant protein and are useful in elaborating the change in structural conformations coupled with the computationally predicted cancer associated mutation. With further advancements in the computational techniques, it will become much easier to predict such mutations with higher accuracy level.

BACKGROUND: Randomized controlled trials (RCTs) are considered the gold standard for assessing the efficacy of new treatments compared to standard treatments. However, the reasoning behind treatment selection in RCTs is often unclear. Here, we focus on a cohort of RCTs in multiple myeloma (MM) to understand the patterns of competing treatment selections.

METHODS: We used social network analysis (SNA) to study relationships between treatment regimens in MM RCTs and to examine the topology of RCT treatment networks. All trials considering induction or autologous stem cell transplant among patients with MM were eligible for our analysis. Medline and abstracts from the annual proceedings of the American Society of Hematology and American Society for Clinical Oncology, as well as all references from relevant publications were searched. We extracted data on treatment regimens, year of publication, funding type, and number of patients enrolled. The SNA metrics used are related to node and network level centrality and to node positioning characterization.

RESULTS: 135 RCTs enrolling a total of 36,869 patients were included. The density of the RCT network was low indicating little cohesion among treatments. Network Betweenness was also low signifying that the network does not facilitate exchange of information. The maximum geodesic distance was equal to 4, indicating that all connected treatments could reach each other in four "steps" within the same pathway of development. The distance between many important treatment regimens was greater than 1, indicating that no RCTs have compared these regimens.

CONCLUSION: Our findings show that research programs in myeloma, which is a relatively small field, are surprisingly decentralized with a lack of connectivity among various research pathways. As a result there is much crucial research left unexplored. Using SNA to visually and analytically examine treatment networks prior to designing a clinical trial can lead to better designed studies.