Ajman Specialty General Hospital

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer type globally and contributes significantly to burden of disease in South Asia. In Pakistan, HNSCC is among the most commonly diagnosed cancer in males and females. The increasing regional burden of HNSCC along with a unique set of risk factors merited a deeper investigation of the disease at the genomic level. Whole exome sequencing of HNSCC samples and matched normal genomic DNA analysis (n=7) was performed. Significant somatic single nucleotide variants (SNVs) were identified and pathway analysis performed to determine frequently affected signaling pathways. We identified significant, novel recurrent mutations in ASNS (asparagine synthetase) that may affect substrate binding, and variants in driver genes including TP53, PIK3CA, FGFR2, ARID2, MLL3, MYC and ALK. Using the IntOGen platform, we identified MAP kinase, cell cycle, actin cytoskeleton regulation, PI3K-Akt signaling and other pathways in cancer as affected in the samples. This data is the first of its kind from the Pakistani population. The results of this study can guide a better mechanistic understanding of HNSCC in the population, ultimately contributing new, rational therapeutic targets for the treatment of the disease.

Objective: U.S. hospitals face substantial financial challenges as policymakers try to reduce healthcare spending. As a result, it is likely that hospital closures will accelerate in the coming years. Hospital closures threaten access to care, and may negatively impact patient outcomes by lengthening travel times, particularly for illnesses for which treatment depends heavily on timely receipt of treatment. Therefore, our objective was to assess the impact of hospital closures on patient outcomes for acute myocardial infarction (AMI) and stroke over the past decade. Methods: We used data from Medicare files as well as the American Hospital Association from 2002 through 2010 to identify hospital closures. For each hospital that closed, we identified Medicare fee-for-service patients that sought care at that hospital for AMI or stroke in the year prior to closure. We then determined the nearest alternative hospital using geocoding for each patient in the sample, and determined the additional travel time associated with this alternative site of care. We compared the characteristics of the closing versus alternative hospitals. We then created multivariate hierarchical logistic regression models in which we reassigned each of the patients at a hospital that closed to the nearest alternative hospital, and compared mortality rates between the closed and alternative facilities. Results: Between 2002 and 2010, we identified 121 hospital closures. There were 1,109 patients that received AMI care and 1,104 patients that received stroke care at a hospital that closed in the subsequent year. The median age was 79 for both conditions; of the AMI patients, 45% were male, and of the stroke patients, 39% were male. Travel time increased by 2.8 minutes for AMI patients and by 1.5 minutes for stroke patients. For AMI patients, the alternative hospital to which they would have traveled was more often large (0% versus 28%, p<0.001), non-profit (63% versus 79%, p<0.001), and teaching (1.1% versus 8.2%, p<0.001) compared with hospitals that closed. Patterns were similar for patients with stroke. For AMI, there was no difference in 30-day risk-adjusted mortality rates for the closed versus alternative hospitals (19.03% versus 19.17%, difference 0.14%, p=0.90). Similarly, for stroke, there was no difference in 30-day risk-adjusted mortality rates (19.57% versus 19.96%, difference 0.39%, p=0.75). Conclusions: We found no evidence that hospital closure was associated with worse clinical outcomes for AMI or stroke for patients. Though there were increased travel times for both conditions, this was likely offset by an increase in hospital quality in the alternative hospitals. These findings should provide some reassurance to those concerned that hospital closures will lead to significantly worse patient outcomes, even for conditions for which timely receipt of treatment is critical.

Objective: The 30-day readmission rate in the Medicare population is near 18%, with an associated cost of $16 billion dollars annually. Policy makers have become focused on trying to identify successful strategies to reduce both the clinical and economic burden of rehospitalizations, and to this end, the Affordable Care Act sets up penalties for hospitals with high readmission rates. Despite the national attention to readmissions, there are many hospitals that have failed to improve their readmission rates. Understanding who these persistently poor-performing hospitals are is key to helping them improve. Methods: We used national Medicare data from 2007 through 2009 to calculate mean readmission rates across six common conditions (heart failure, acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, stroke and gastrointestinal bleeding) for all acute-care hospitals in the U.S. We identified poor baseline performers as those hospitals with performance in the worst quartile of readmission rates in 2007. We then categorized these hospitals into two groups: those who improved by 2009 and those who did not. We compared the characteristics of hospitals and markets in each of these groups. Results: Our sample was comprised of 869 poor-performing acute-care hospitals. Baseline median composite readmission rates were 27.8% (IQR 25.8%-32.5%). Of these, 214 (24.6%) hospitals failed to improve their readmission rates by 2009; the median 2009 readmission rate for persistently poor performers was 32.0% (IQR 27.1%-38.0%) while the rate fo hospitals that improved was 20.9% (IQR 18.5%-23.6%). Persistently poor performers were more likely to be small hospitals (71% versus 32%, p<0.001), publicly owned (32% versus 21%, p=0.003), rural (43% versus 18%, p=0.02), non-teaching hospitals (86% versus 74%, p<0.001). They were less likely to have a medical intensive care unit (20% versus 52%, p=0.001). Persistently poor performers were located in areas with fewer specialist physicians (7 per 100,000 population versus 9 per 100,000 population, p<0.001) and lower median income ($33,299 versus $34,523, p<0.001). In multivariate logistic regression analyses, the strongest predictors of being a persistently poor performer were being a small hospital (odds ratio 13.3, p<0.001) and lacking a medical intensive care unit (odds ratio 2.9, p<0.001). Conclusions and Implications: Between 2007 and 2009, small, public, non-teaching hospitals were far less likely to improve their readmission rates than others; in general, persistently poor performers were hospitals with lower resource levels and more socioeconomically disadvantaged populations. As hospitals face looming penalties for high readmission rates, our findings raise concern about the ability of the small, worst-performing hospitals and those with poor resource bases to improve their outcomes, and thus raise concerns about the potential of readmissions penalties to widen disparities in care. Policymakers may need to consider coupling readmission penalties with programs and resources to help these vulnerable hospitals improve in order to avoid unintended consequences of this policy initiative.