Branko Miladinovic

Do Community Health Worker Interventions Improve Rates of Screening Mammography in the United States? A Systematic Review

Background: Community health workers (CHW) are lay individuals who are trained to serve as liaisons between members of their communities and health care providers and services. Methods: A systematic review was conducted to synthesize evidence from all prospective controlled studies on effectiveness of CHW programs in improving screening mammography rates. Studies reported in English and conducted in the United States were included if they: (i) evaluated a CHW intervention designed to increase screening mammography rates in women 40 years of age or older without a history of breast cancer; (ii) were a randomized controlled trial (RCT), case–controlled study, or quasi-experimental study; and (iii) evaluated a CHW intervention outside of a hospital setting. Results: Participation in a CHW intervention was associated with a statistically significant increase in receipt of screening mammography [risk ratio (RR): 1.06 (favoring intervention); 95% CI: 1.02–1.11, P = 0.003]. The effect remained when pooled data from only RCTs were included in meta-analysis (RR: 1.07; 95% CI: 1.03–1.12, P = 0.0005) but was not present using pooled data from only quasi-experimental studies (RR: 1.03; 95% CI: 0.89–1.18, P = 0.71). In RCTs, participants recruited from medical settings (RR: 1.41; 95% CI: 1.09–1.82, P = 0.008), programs conducted in urban settings (RR: 1.23; 95% CI: 1.09, 1.39, P = 0.001), and programs where CHWs were matched to intervention participants on race or ethnicity (RR: 1.58, 95% CI: 1.29–1.93, P = 0.0001) showed stronger effects on increasing mammography screening rates. Conclusions: CHW interventions are effective for increasing screening mammography in certain settings and populations. Impact: CHW interventions are especially associated with improvements in rate of screening mammography in medical settings, urban settings, and in participants who are racially or ethnically concordant with the CHW. Cancer Epidemiol Biomarkers Prev; 20(8); 1580–98. ©2011 AACR.

Survival of patients with non-small cell lung cancer without treatment: a systematic review and meta-analysis

BackgroundLung cancer is considered a terminal illness with a five-year survival rate of about 16%. Informed decision-making related to the management of a disease requires accurate prognosis of the disease with or without treatment. Despite the significance of disease prognosis in clinical decision-making, systematic assessment of prognosis in patients with lung cancer without treatment has not been performed. We conducted a systematic review and meta-analysis of the natural history of patients with confirmed diagnosis of lung cancer without active treatment, to provide evidence-based recommendations for practitioners on management decisions related to the disease. Specifically, we estimated overall survival when no anticancer therapy is provided.MethodsRelevant studies were identified by search of electronic databases and abstract proceedings, review of bibliographies of included articles, and contacting experts in the field. All prospective or retrospective studies assessing prognosis of lung cancer patients without treatment were eligible for inclusion. Data on mortality was extracted from all included studies. Pooled proportion of mortality was calculated as a back-transform of the weighted mean of the transformed proportions using the random-effects model. To perform meta-analysis of median survival, published methods were used to pool the estimates as mean and standard error under the random-effects model. Methodological quality of the studies was examined.ResultsSeven cohort studies (4,418 patients) and 15 randomized controlled trials (1,031 patients) were included in the meta-analysis. All studies assessed mortality without treatment in patients with non-small cell lung cancer (NSCLC). The pooled proportion of mortality without treatment in cohort studies was 0.97 (95% CI: 0.96 to 0.99) and 0.96 in randomized controlled trials (95% CI: 0.94 to 0.98) over median study periods of eight and three years, respectively. When data from cohort and randomized controlled trials were combined, the pooled proportion of mortality was 0.97 (95% CI: 0.96 to 0.98). Test of interaction showed a statistically non-significant difference between subgroups of cohort and randomized controlled trials. The pooled mean survival for patients without anticancer treatment in cohort studies was 11.94 months (95% CI: 10.07 to 13.8) and 5.03 months (95% CI: 4.17 to 5.89) in RCTs. For the combined data (cohort studies and RCTs), the pooled mean survival was 7.15 months (95% CI: 5.87 to 8.42), with a statistically significant difference between the two designs. Overall, the studies were of moderate methodological quality.ConclusionSystematic evaluation of evidence on prognosis of NSCLC without treatment shows that mortality is very high. Untreated lung cancer patients live on average for 7.15 months. Although limited by study design, these findings provide the basis for future trials to determine optimal expected improvement in mortality with innovative treatments.

Medical research: Trial unpredictability yields predictable therapy gains

In decades of clinical-trial data, new treatments are better than standard ones just over half the time. Thats as it should be, say Benjamin Djulbegovic and colleagues.

Trial unpredictability yields predictable therapy gains

In decades of clinical-trial data, new treatments are better than standard ones just over half the time. Thats as it should be, say Benjamin Djulbegovic and colleagues.

Circulating long noncoding RNA GAS5 levels are correlated to prevalence of type 2 diabetes mellitus

Background Diabetes mellitus (DM), a metabolic disease, is characterized by impaired fasting glucose levels. Type 2 DM is adult onset diabetes. Long non-coding RNAs (lncRNAs) regulate gene expression and multiple studies have linked lncRNAs to human diseases. Methods Serum samples obtained from 96 participating veterans at JAH VA were deposited in the Research Biospecimen Repository. We used a two-stage strategy to identify an lncRNA whose levels correlated with T2DM. Initially we screened five serum samples from diabetic and non-diabetic individuals using lncRNA arrays. Next, GAS5 lncRNA levels were analyzed in 96 serum samples using quantitative PCR. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cutoff GAS5 for diagnosis of DM. Results Our results demonstrate that decreased GAS5 levels in serum were associated with diabetes in a cohort of US military veterans. The ROC analysis revealed an optimal cutoff GAS5 value of less than or equal to 10. qPCR results indicated that individuals with absolute GAS5 < 10 ng/μl have almost twelve times higher odds of having diabetes (Exact Odds Ratio [OR] = 11.79 (95% CI: 3.97, 37.26), p < 0.001). Analysis indicated area under curve (AUC) of ROC of 0.81 with 85.1% sensitivity and 67.3% specificity in distinguishing non-diabetic from diabetic subjects. The positive predictive value is 71.4%. Conclusion lncRNA GAS5 levels are correlated to prevalence of T2DM. General Significance Assessment of GAS5 in serum along with other parameters offers greater accuracy in identifying individuals at-risk for diabetes.

Treatment Success in Cancer: Industry Compared to Publicly Sponsored Randomized Controlled Trials

Objective To assess if commercially sponsored trials are associated with higher success rates than publicly-sponsored trials. Study Design and Settings We undertook a systematic review of all consecutive, published and unpublished phase III cancer randomized controlled trials (RCTs) conducted by GlaxoSmithKline (GSK) and the NCIC Clinical Trials Group (CTG). We included all phase III cancer RCTs assessing treatment superiority from 1980 to 2010. Three metrics were assessed to determine treatment successes: (1) the proportion of statistically significant trials favouring the experimental treatment, (2) the proportion of the trials in which new treatments were considered superior according to the investigators, and (3) quantitative synthesis of data for primary outcomes as defined in each trial. Results GSK conducted 40 cancer RCTs accruing 19,889 patients and CTG conducted 77 trials enrolling 33,260 patients. 42% (99%CI 24 to 60) of the results were statistically significant favouring experimental treatments in GSK compared to 25% (99%CI 13 to 37) in the CTG cohort (RR = 1.68; p = 0.04). Investigators concluded that new treatments were superior to standard treatments in 80% of GSK compared to 44% of CTG trials (RR = 1.81; p<0.001). Meta-analysis of the primary outcome indicated larger effects in GSK trials (odds ratio = 0.61 [99%CI 0.47–0.78] compared to 0.86 [0.74–1.00]; p = 0.003). However, testing for the effect of treatment over time indicated that treatment success has become comparable in the last decade. Conclusions While overall industry sponsorship is associated with higher success rates than publicly-sponsored trials, the difference seems to have disappeared over time.

Comparative effectiveness of traditional chemoembolization with or without sorafenib for hepatocellular carcinoma.

AIM To compare the overall survival (OS) and progression-free survival (PFS) with associated adverse events (AE) in patients with unresectable hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE) + sorafenib vs TACE alone. METHODS In this retrospective cohort study we collected data on all consecutive patients with a diagnosis of unresectable HCC between 2007 and 2011 who had been treated with TACE + sorafenib or TACE alone. We hypothesized that the combination therapy is superior to TACE alone in improving the survival in these patients. Data extracted included patients demographics, etiology of liver disease, histology of HCC, stage of liver disease with respect to model of end stage liver disease score and Child-Turcotte-Pugh (CTP) classification and Barcelona Clinic Liver Cancer (BCLC) staging for HCC. Computed tomography scan findings, alpha fetoprotein levels, number of treatments and related AE were also recorded and analyzed. RESULTS Of the 43 patients who met inclusion criteria, 13 were treated with TACE + sorafenib and 30 with TACE alone. There was no significant difference in median survival: 20.6 mo (95%CI: 13.4-38.4) for the TACE + sorafenib and 18.3 mo (95%CI: 11.8-32.9) for the TACE alone (P = 0.72). There were also no statistically significant differences between groups in OS (HR = 0.82, 95%CI: 0.38-1.77; P = 0.61), PFS (HR = 0.93, 95%CI: 0.45-1.89; P = 0.83), and treatment-related toxicities (P = 0.554). CTP classification and BCLC staging for HCC were statistically significant (P = 0.001, P = 0.04 respectively) in predicting the survival in patients with HCC. The common AE observed were abdominal pain, nausea, vomiting and mild elevation of liver enzymes. CONCLUSION Combination therapy with TACE + sorafenib is safe and equally effective as TACE alone in patients with unresectable HCC. CTP classification and BCLC staging were the significant predictors of survival. Future trials with large number of patients are needed to further validate this observation.

How do physicians decide to treat: an empirical evaluation of the threshold model

BackgroundAccording 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.MethodsA 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.ResultsFewer 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].ConclusionsWe 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.

Outcomes of a Neonatal Golden Hour Implementation Project

The objective of this study was to implement and evaluate a quality improvement project (the golden hour pathway [GHP]) aimed at improving the quality and efficiency of care delivered to extremely low birth weight (ELBW) infants <28 weeks gestation and/or <1000 g birth weight during the first hour of life. Process improvement and patient data collected during the quality improvement cycles were compared with retrospective data for ELBW infants admitted to the study neonatal intensive care unit during the 2 years prior to GHP implementation. GHP implementation resulted in improvements compared with past internal performance in time to surfactant administration, time to administration of dextrose and amino acids, body temperature at admission, odds of developing chronic lung disease, and odds of developing retinopathy of prematurity. A standardized interdisciplinary approach to the care of ELBW infants in the first hour of life can lead to more efficient care delivery and contribute to improved outcomes.

Defining optimum treatment of patients with pancreatic adenocarcinoma using regret-based decision curve analysis.

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 patients prognosis and the physicians preferences expressed in terms of regret associated with a certain action. A physicians 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 makers preferences expressed as his or her estimates of benefits and harms associated with the treatment considered.