Publications

Diagnosis and management of traumatic brain injury (TBI) is crucial to improve patient outcomes. While initial head computed tomography (CT) scan is the optimum tool for quick and accurate detection of intracranial hemorrhage, the guidelines on use of repeat CT differ among institutions. Three systematic reviews have been conducted on a similar topic; none have performed a comprehensive meta-analysis of all studies. Search of Medline, the Cochrane Library database, and Clinicaltrials.gov , and a hand search of conference abstracts and references for all completed studies reporting data on change in management following repeat CT was conducted. Two authors reviewed all studies and extracted data using a standardized form. A proportional meta-analysis was conducted using the random-effects model for outcomes related to any change in management following repeat CT. Any change in management included intracranial intervention, change in intracranial pressure monitoring, and/or administration of drug therapy. Search results yielded 6982 references. In all, 41 studies enrolling 10,501 patients were included. Change in management following repeat CT was reported in 13 prospective and 28 retrospective studies and yielded a pooled proportion of 11.4% (95% confidence interval [CI] 5.9-18.4) and 9.6% (95% CI 6.5-13.2), respectively. In a subgroup analysis of mild TBI patients (Glasgow Coma Scale score 13 to 15), five prospective and nine retrospective studies reported on change in management following repeat CT with the pooled proportion across prospective studies at 2.3% (95% CI 0.3-6.3) and across retrospective studies at 3.9% (95% CI 2.3-5.7), respectively. The evidence suggests that repeat CT in patients with TBI results in a change in management for only a minority of patients. Better designed studies are needed to address the issue of the value of repeat CT in the management of TBI.

INTRODUCTION: Symptomatic hip osteoarthritis (OA) is a disabling condition with up to a 25% cumulative lifetime risk. Total hip arthroplasty (THA) is effective in relieving patients' symptoms and improving function. It is, however, associated with substantial risk of complications, pain and major functional limitation before patients can return to full function. In contrast, hip arthroscopy (HA) is less invasive and can postpone THA. However, there is no evidence regarding the delay in the need for THA that patients would find acceptable to undergoing HA. Knowing patients' values and preferences (VP) on this expected delay is critical when making recommendations regarding the advisability of HA. Furthermore, little is known on the optimal amount of information regarding interventions and outcomes needed to present in order to optimally elicit patients' VP.

METHODS AND ANALYSIS: We will perform a multinational, structured interview-based survey of preference in delay time for THA among patients with non-advanced OA who failed to respond to conservative therapy. We will combine these interviews with a randomised trial addressing the optimal amount of information regarding the interventions and outcomes required to elicit preferences. Eligible patients will be randomly assigned (1 : 1) to either a short or a long format of health scenarios of THA and HA. We will determine each patient's VP using a trade-off and anticipated regret exercises. Our primary outcomes for the combined surveys will be: (1) the minimal delay time in the need for THA surgery that patients would find acceptable to undertaking HA, (2) patients' satisfaction with the amount of information provided in the health scenarios used to elicit their VPs.

ETHICS AND DISSEMINATION: The protocol has been approved by the Hamilton Integrated Research Ethics Board (HIREB13-506). We will disseminate our study findings through peer-reviewed publications and conference presentations, and make them available to guideline makers issuing recommendations addressing HA and THA.

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.

Failure to detect and manage heterogeneity between clinical trials included in meta-analysis may lead to misinterpretation of summary effect estimates. This may ultimately compromise the validity of the results of the meta-analysis. Typically, when heterogeneity between trials is detected, researchers use sensitivity or subgroup analysis to manage it. However, both methods fail to explain why heterogeneity existed in the first place. Here we propose a novel methodology that relies on Rough Set Theory (RST) to detect, explain, and manage the sources of heterogeneity applicable to meta-analysis performed on individual patient data (IPD). The method exploits the RST relations of discernibility and indiscernibility to create homogeneous groups of patients. We applied our methodology on a dataset of 1,111 patients enrolled in 9 randomized controlled trials studying the effect of two transplantation procedures in the management of hematologic malignancies. Our method was able to create three subgroups of patients with remarkably low statistical heterogeneity values (16.8%, 0% and 0% respectively). The proposed methodology has the potential to automatize and standardize the process of detecting and managing heterogeneity in IPD meta-analysis. Future work involves investigating the applications of the proposed methodology in analyzing treatment effects in patients belonging to different risk groups, which will ultimately assist in personalized healthcare decision making.

OBJECTIVE: To use regret decision theory methodology to assess three treatment strategies in pancreatic adenocarcinoma.

BACKGROUND: Pancreatic adenocarcinoma is uniformly fatal without operative intervention. Resection can prolong survival in some patients; however, it is associated with significant morbidity and mortality. Regret theory serves as a novel framework linking both rationality and intuition to determine the optimal course for physicians facing difficult decisions related to treatment.

METHODS: We used the Cox proportional hazards model to predict survival of patients with pancreatic adenocarcinoma and generated a decision model using regret-based decision curve analysis, which integrates both the patient's prognosis and the physician's preferences expressed in terms of regret associated with a certain action. A physician's treatment preferences are indicated by a threshold probability, which is the probability of death/survival at which the physician is uncertain whether or not to perform surgery. The analysis modeled 3 possible choices: perform surgery on all patients; never perform surgery; and act according to the prediction model.

RESULTS: The records of 156 consecutive patients with pancreatic adenocarcinoma were retrospectively evaluated by a single surgeon at a tertiary referral center. Significant independent predictors of overall survival included preoperative stage [P = 0.005; 95% confidence interval (CI), 1.19-2.27], vitality (P < 0.001; 95% CI, 0.96-0.98), daily physical function (P < 0.001; 95% CI, 0.97-0.99), and pathological stage (P < 0.001; 95% CI, 3.06-16.05). Compared with the "always aggressive" or "always passive" surgical treatment strategies, the survival model was associated with the least amount of regret for a wide range of threshold probabilities.

CONCLUSIONS: Regret-based decision curve analysis provides a novel perspective for making treatment-related decisions by incorporating the decision maker's preferences expressed as his or her estimates of benefits and harms associated with the treatment considered.

BACKGROUND: According to the threshold model, when faced with a decision under diagnostic uncertainty, physicians should administer treatment if the probability of disease is above a specified threshold and withhold treatment otherwise. The objectives of the present study are to a) evaluate if physicians act according to a threshold model, b) examine which of the existing threshold models [expected utility theory model (EUT), regret-based threshold model, or dual-processing theory] explains the physicians' decision-making best.

METHODS: A survey employing realistic clinical treatment vignettes for patients with pulmonary embolism and acute myeloid leukemia was administered to forty-one practicing physicians across different medical specialties. Participants were randomly assigned to the order of presentation of the case vignettes and re-randomized to the order of "high" versus "low" threshold case. The main outcome measure was the proportion of physicians who would or would not prescribe treatment in relation to perceived changes in threshold probability.

RESULTS: Fewer physicians choose to treat as the benefit/harms ratio decreased (i.e. the threshold increased) and more physicians administered treatment as the benefit/harms ratio increased (and the threshold decreased). When compared to the actual treatment recommendations, we found that the regret model was marginally superior to the EUT model [Odds ratio (OR) = 1.49; 95% confidence interval (CI) 1.00 to 2.23; p = 0.056]. The dual-processing model was statistically significantly superior to both EUT model [OR = 1.75, 95% CI 1.67 to 4.08; p < 0.001] and regret model [OR = 2.61, 95% CI 1.11 to 2.77; p = 0.018].

CONCLUSIONS: We provide the first empirical evidence that physicians' decision-making can be explained by the threshold model. Of the threshold models tested, the dual-processing theory of decision-making provides the best explanation for the observed empirical results.

BACKGROUND: Patient outcomes critically depend on accuracy of physicians' judgment, yet little is known about individual differences in cognitive styles that underlie physicians' judgments. The objective of this study was to assess physicians' individual differences in cognitive styles relative to age, experience, and degree and type of training.

METHODS: Physicians at different levels of training and career completed a web-based survey of 6 scales measuring individual differences in cognitive styles (maximizing v. satisficing, analytical v. intuitive reasoning, need for cognition, intolerance toward ambiguity, objectivism, and cognitive reflection). We measured psychometric properties (Cronbach's α) of scales; relationship of age, experience, degree, and type of training; responses to scales; and accuracy on conditional inference task.

RESULTS: The study included 165 trainees and 56 attending physicians (median age 31 years; range 25-69 years). All 6 constructs showed acceptable psychometric properties. Surprisingly, we found significant negative correlation between age and satisficing (r = -0.239; P = 0.017). Maximizing (willingness to engage in alternative search strategy) also decreased with age (r = -0.220; P = 0.047). Number of incorrect inferences negatively correlated with satisficing (r = -0.246; P = 0.014). Disposition to suppress intuitive responses was associated with correct responses on 3 of 4 inferential tasks. Trainees showed a tendency to engage in analytical thinking (r = 0.265; P = 0.025), while attendings displayed inclination toward intuitive-experiential thinking (r = 0.427; P = 0.046). However, trainees performed worse on conditional inference task.

CONCLUSION: Physicians capable of suppressing an immediate intuitive response to questions and those scoring higher on rational thinking made fewer inferential mistakes. We found a negative correlation between age and maximizing: Physicians who were more advanced in their careers were less willing to spend time and effort in an exhaustive search for solutions. However, they appeared to have maintained their "mindware" for effective problem solving.

OBJECTIVES: The current approach for evaluating the risk of random error in meta-analyses (MAs) using trial sequential analysis (TSA) can accommodate binary and continuous data but not time-to-event data. We conducted a TSA for time-to-event outcomes and applied the method to determine the risk of random error in MAs for treatments of multiple myeloma.

STUDY DESIGN AND SETTING: Literature search identified 11 systematic reviews consisting of 23 MAs. Of the 23 MAs, 13 had overall survival and 10 had progression-free survival as outcome; 48% (11 of 23) reported statistically significant treatment effects. We calculated the optimal a priori diversity-adjusted information size (APDIS) based on the relative risk reduction of 15% and 25%. We also calculated the optimal low-bias information size (LBIS) and low-bias diversity-adjusted information size (LBDIS).

RESULTS: Overall, under APDIS15%, 48% (11 of 23) of MAs were false negative (FN) and 17% (4 of 23) of MAs were false positive. Under APDIS25%, 34% (8 of 23) of MAs were false negative and 4% (1 of 23) of MAs were false positive. LBIS identified 30% (7 of 23) as false negative MAs and 4% (1 of 23) as false positive MAs, whereas LBDIS identified 52% (12 of 23) as false negative MAs and 4% (1 of 23) as false positive MAs.

CONCLUSION: The new method demonstrates the possibility of incorporating time-to-event outcomes into TSA and reveals that some MAs have potentially inconclusive results.

Centrosomes are the key-regulating element of cell cycle progression. Aberrations in their functional mechanism lead to several cancer-related disorders. Aurora A protein is a centrosome-associated protein that regulates the centriole duplication and its abberations are associated with multiple cases of aneuploidy and cancer-related disorders. S155R mutation in Aurora A is reported to induce cancer like phenotype and disrupt its binding with TPX2 protein. In this study, we have demonstrated the structural consequences of Aurora A S155R mutation and the atomic changes that influenced the loss of TPX2-binding affinity. Docking and molecular dynamics simulation results suggested significant loss in atomic contacts between mutant Aurora A and TPX2 protein. Further, we observed a notable changes in conformation of mutant Aurora A-TPX2 docked complex as compared to the native. Loss of binding affinity rendered the TPX2 domain free which then induced unfolding in its coiled region and enabled the overall expansion of mutant complex as compared to the native. The significant outcomes obtained from this study will facilitate in future cancer researches and in developing the potent drug therapies.

OBJECTIVE: To assess the adherence to antiretroviral therapy (ART) in the human immunodeficiency virus (HIV)-infected population in India.

DESIGN: Systematic review and meta-analysis.

MATERIALS AND METHODS: The Medline and Cochrane library database were searched. Any prospective or retrospective study enrolling a minimum of 10 subjects with a primary objective of assessing ART adherence in the HIV population in India was included. Data were extracted on adherence definition, adherence estimates, study design, study population characteristics, recall period and assessment method. For metaanalysis, the pooled proportion was calculated as a back-transform of the weighted mean of the transformed proportions (calculated according to the Freeman-Tukey variant of the arcsine square root) using the random effects model.

RESULTS: There were seven cross-sectional studies and one retrospective study enrolling 1666 participants. Publication bias was significant (P = 0.003). Pooled results showed an ART adherence rate of 70% (95% confidence interval: 59-81%, I(2) = 96.3%). Sensitivity analyses based on study design, adherence assessment method and study region did not influence adherence estimates. Fifty percent (4/8) of the studies reported cost of medication as the most common obstacle for ART adherence. Twenty-five percent (2/8) reported lack of access to medication as the reason for non-adherence and 12% (1/8) cited adverse events as the most prevalent reason for non-adherence. The overall methodological quality of the included studies was poor.

CONCLUSION: Pooled results show that overall ART adherence in India is below the required levels to have an optimal treatment effect. The quality of studies is poor and cannot be used to guide policies to improve ART adherence.