Research Spotlight

Posted May 15th 2018

Trajectories of quality of life following breast cancer diagnosis.

Elizabeth Z. Naftalis M.D.

Elizabeth Z. Naftalis M.D.

Goyal, N. G., B. J. Levine, K. J. Van Zee, E. Naftalis and N. E. Avis (2018). “Trajectories of quality of life following breast cancer diagnosis.” Breast Cancer Res Treat 169(1): 163-173.

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PURPOSE: Although quality of life (QoL) improves over time for most breast cancer survivors (BCS), BCS may show different patterns of QoL. This study sought to identify distinct QoL trajectories among BCS and to examine characteristics associated with trajectory group membership. METHODS: BCS (N = 653) completed baseline assessments within 8 months of diagnosis. QoL was assessed by the Functional Assessment of Cancer Therapy-Breast (FACT-B) at baseline and 6, 12, and 18 months later. Finite mixture modeling was used to determine QoL trajectories of the trial outcome index (TOI; a composite of physical well-being, functional well-being, and breast cancer-specific subscales) and emotional and social/family well-being subscales. Chi-square tests and F tests were used to examine group differences in demographic, cancer-related, and psychosocial variables. RESULTS: Unique trajectories were identified for all three subscales. Within each subscale, the majority of BCS had consistently medium or high QoL. The TOI analysis revealed only stable or improving groups, but the emotional and social/family subscales had groups that were stable, improved, or declined. Across all subscales, women in “consistently high” groups had the most favorable psychosocial characteristics. For the TOI and emotional subscales, psychosocial variables also differed significantly between women who started similarly but had differing trajectories. CONCLUSIONS: The majority of BCS report good QoL as they transition from treatment to survivorship. However, some women have persistently low QoL in each domain and some experience declines in emotional and/or social/family well-being. Psychosocial variables are consistently associated with improving and/or declining trajectories of physical/functional and emotional well-being.


Posted May 15th 2018

Employment status at the time of heart transplant listing.

Shelley A. Hall M.D.

Shelley A. Hall M.D.

Felius, J. and S. A. Hall (2018). “Employment status at the time of heart transplant listing.” J Heart Lung Transplant 37(5): 575-576.

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For those living with a chronic illness, employment status can be seen as a proxy for both functional status and quality of life. In solid-organ transplantation, it has been established that work status immediately before transplantation is associated with clinical outcomes and with the probability of returning to work post-transplantation. Although previous research has established beneficial outcomes linked to work status after heart transplantation, it was heretofore unknown whether work status at the time of transplant listing (as well as in the pre-operative period) is associated with outcomes after transplantation. Although it may be challenging to tease apart the effects of employment during the pre-operative period on clinical outcomes after heart transplantation from potential confounders, such as age, socioeconomic status, and comorbidities, it may prove very useful indeed if employment status per se could be identified as a modifiable risk factor . . . Ravi et al present an analysis of 23,228 adult heart transplant recipients in the United States over the period from 2001 to 2014 through the United Network for Organ Sharing (UNOS) database in their assessment of the relationships between work status before/after transplantation and short, intermediate, and long-term survival . . . The authors concluded that recipients who were working at the time of transplant listing showed better long-term (5- and 10-year) survival, but the differences in 30-day and 1-year survival did not reach statistical significance. Recipients who were working at the time of transplantation showed better post-transplant survival at each time-point. (Excerpts from text; no abstract available.)


Posted May 15th 2018

Can use of an administrative database improve accuracy of hospital-reported readmission rates?

James R. Edgerton M.D.

James R. Edgerton M.D.

Edgerton, J. R., M. A. Herbert, B. L. Hamman and W. S. Ring (2018). “Can use of an administrative database improve accuracy of hospital-reported readmission rates?” J Thorac Cardiovasc Surg 155(5): 2043-2047.

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OBJECTIVES: Readmission rates after cardiac surgery are being used as a quality indicator; they are also being collected by Medicare and are tied to reimbursement. Accurate knowledge of readmission rates may be difficult to achieve because patients may be readmitted to different hospitals. In our area, 81 hospitals share administrative claims data; 28 of these hospitals (from 5 different hospital systems) do cardiac surgery and share Society of Thoracic Surgeons (STS) clinical data. We used these 2 sources to compare the readmissions data for accuracy. METHODS: A total of 45,539 STS records from January 2008 to December 2016 were matched with the hospital billing data records. Using the index visit as the start date, the billing records were queried for any subsequent in-patient visits for that patient. The billing records included date of readmission and hospital of readmission data and were compared with the data captured in the STS record. RESULTS: We found 1153 (2.5%) patients who had STS records that were marked “No” or “missing,” but there were billing records that showed a readmission. The reported STS readmission rate of 4796 (10.5%) underreported the readmission rate by 2.5 actual percentage points. The true rate should have been 13.0%. Actual readmission rate was 23.8% higher than reported by the clinical database. Approximately 36% of readmissions were to a hospital that was a part of a different hospital system. CONCLUSIONS: It is important to know accurate readmission rates for quality improvement processes and institutional financial planning. Matching patient records to an administrative database showed that the clinical database may fail to capture many readmissions. Combining data with an administrative database can enhance accuracy of reporting.


Posted May 15th 2018

Peak Timing for Complications Following Adult Spinal Deformity Surgery.

Michael O'Brien M.D.

Michael O’Brien M.D.

Daniels, A. H., S. Bess, B. Line, A. E. M. Eltorai, D. B. C. Reid, V. Lafage, B. A. Akbarnia, C. P. Ames, O. Boachie-Adjei, D. C. Burton, V. Deviren, H. J. Kim, R. A. Hart, K. M. Kebaish, E. O. Klineberg, M. Gupta, G. M. Mundis, Jr., R. A. Hostin, Jr., M. O’Brien, F. J. Schwab, C. I. Shaffrey and J. S. Smith (2018). “Peak Timing for Complications Following Adult Spinal Deformity Surgery.” World Neurosurg. Apr 21. [Epub ahead of print].

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BACKGROUND: Overall complication rates for adult spinal deformity (ASD) surgery have been reported, however little data exists on the peak timing associated with specific complications. This study quantifies the peak timing for multiple complication types in an ASD cohort at minimum 2-year follow up. METHODS: Multi-center, prospective analysis of all complications following ASD surgery in a consecutively enrolled cohort was performed. Inclusion criteria were ASD, age >/= 18 years, spinal fusion >/= 4 levels, and minimum 2-year follow-up. Complications included major and minor as well as specific complication types. Peak timing of specific complications were identified and described. Regression analysis was performed to assess correlation between patient/surgical factors and complication timing. RESULTS: 280 patients met inclusion criteria. Mean follow-up time was 2.9 years (range 2-5 years). 209 (74.6%) patients had at least one complication accounting for 529 total complications (258 minor, 271 major). Both major and minor complications peaked at <3 months. Infection and neurologic complications peaked at <3 months. PJK had bimodal peaks at <3 and >24 months. Implant failure peaked at 12-24 and >24 months. There was a significant positive correlation between preoperative SVA and total complications at 6-12 months, major complications at 24-months, and reoperation. BMI was associated with total complications and implant failure at 12-24 and >24 months. CONCLUSIONS: The peak timing of specific complications following ASD surgery are identifiable. Understanding when these complications are likely to occur may improve patient counseling, early diagnosis, and prophylactic interventions, and may help inform future reimbursement models.


Posted May 15th 2018

Renal Effects and Associated Outcomes During Angiotensin-Neprilysin Inhibition in Heart Failure.

Milton Packer M.D.

Milton Packer M.D.

Damman, K., M. Gori, B. Claggett, P. S. Jhund, M. Senni, M. P. Lefkowitz, M. F. Prescott, V. C. Shi, J. L. Rouleau, K. Swedberg, M. R. Zile, M. Packer, A. S. Desai, S. D. Solomon and J. J. V. McMurray (2018). “Renal Effects and Associated Outcomes During Angiotensin-Neprilysin Inhibition in Heart Failure.” JACC Heart Fail. Apr 11. [Epub ahead of print].

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OBJECTIVES: The purpose of this study was to evaluate the renal effects of sacubitril/valsartan in patients with heart failure and reduced ejection fraction. BACKGROUND: Renal function is frequently impaired in patients with heart failure with reduced ejection fraction and may deteriorate further after blockade of the renin-angiotensin system. METHODS: In the PARADIGM-HF (Prospective Comparison of ARNI with ACE inhibition to Determine Impact on Global Mortality and Morbidity in Heart Failure) trial, 8,399 patients with heart failure with reduced ejection fraction were randomized to treatment with sacubitril/valsartan or enalapril. The estimated glomerular filtration rate (eGFR) was available for all patients, and the urinary albumin/creatinine ratio (UACR) was available in 1872 patients, at screening, randomization, and at fixed time intervals during follow-up. We evaluated the effect of study treatment on change in eGFR and UACR, and on renal and cardiovascular outcomes, according to eGFR and UACR. RESULTS: At screening, the eGFR was 70 +/- 20 ml/min/1.73 m(2) and 2,745 patients (33%) had chronic kidney disease; the median UACR was 1.0 mg/mmol (interquartile range: 0.4 to 3.2 mg/mmol) and 24% had an increased UACR. The decrease in eGFR during follow-up was less with sacubitril/valsartan compared with enalapril (-1.61 ml/min/1.73 m(2)/year; [95% confidence interval: -1.77 to -1.44 ml/min/1.73 m(2)/year] vs. -2.04 ml/min/1.73 m(2)/year [95% CI: -2.21 to -1.88 ml/min/1.73 m(2)/year ]; p < 0.001) despite a greater increase in UACR with sacubitril/valsartan than with enalapril (1.20 mg/mmol [95% CI: 1.04 to 1.36 mg/mmol] vs. 0.90 mg/mmol [95% CI: 0.77 to 1.03 mg/mmol]; p < 0.001). The effect of sacubitril/valsartan on cardiovascular death or heart failure hospitalization was not modified by eGFR, UACR (p interaction = 0.70 and 0.34, respectively), or by change in UACR (p interaction = 0.38). CONCLUSIONS: Compared with enalapril, sacubitril/valsartan led to a slower rate of decrease in the eGFR and improved cardiovascular outcomes, even in patients with chronic kidney disease, despite causing a modest increase in UACR.