Quality Of Care Metrics Research Articles

Narrowing performance gap between rural and urban hospitals for acute myocardial infarction care

BackgroundRural communities experience significant barriers to quality healthcare, including disparities in medical care following acute myocardial infarctions (AMI). This study sought to determine if the population density of the county where Medicare patients were hospitalized following AMI predicted short-term outcomes and to quantify longitudinal changes in hospital performance on quality of care metrics. MethodsHospital-level data was queried from the 2012 and 2018 Centers for Medicare & Medicaid Services archives. Each hospital was classified based on residing county using the National Center for Health Statistics Rural-Urban Continuum Codes (RUCC). Variations and longitudinal changes in risk-adjusted outcomes and quality of care metrics were stratified by RUCC classification and analyzed. ResultsAmong the 4798 hospitals identified, rural hospitals had significantly higher risk-adjusted 30-day mortality (rs = 0.095, p < 0.001) and decreased statin prescribed at discharge (rs = −0.066, p = 0.004). Only aspirin (R2 = 0.003, p = 0.024) and statin (R2 = 0.006, p = 0.001) prescribed at discharge were correlated with improved 30-day mortality. Despite these differences, from 2012 to 2018 the performance gap between rural and urban hospitals narrowed for all but one quality of care metric, with concurrent 1.83% [95% CI 1.76–1.90] and 3.37% [95% CI 3.30–3.44] reductions in mortality and hospital readmissions, respectively. ConclusionsIn the United States, only modest variations currently exist between rural and urban hospitals in the medical care of AMI. Although the performance gap has narrowed, new strategies to improve timely and effective care are necessary to alleviate residual cardiovascular healthcare disparities in rural communities.

Does increasing time to surgery affect survival in stage I renal cell carcinoma? Analysis of the national cancer database.

635 Background: Optimal timing for surgical treatment of localized renal cell carcinoma (RCC) remains undefined. We sought to determine the survival impact of time to definitive surgical treatment for Stage 1 RCC and elucidate factors associated with a delay in surgical care utilizing the National Cancer Database (NCDB). Methods: The NCDB was queried for Stage 1 RCC cases (cT1N0M0) from 2004-2013 treated with partial or radical nephrectomy. Quartiles were formed from the range of time to surgery of the entire cohort in days: early defined as the first two quartiles and delayed as the fourth. Descriptive analyses were conducted between early and delayed groups. Overall survival (OS) between early and delayed groups was calculated with Kaplan-Meier analysis. Multivariable analysis was performed to determine factors associated with delay in surgical care. Results: 38,859 patients were analyzed. Median time to treatment was 40 days (IQR 22-68). Early (≤40 days, n = 23,712) and delayed ( &gt; 68 days, n = 15,147) groups had a median follow-up of 44.8 and 41 months, respectively (p &lt; 0.001). Delayed surgery was more frequent with African-Americans (14.8% vs. 9.1%, p &lt; 0.001), patients with government or no insurance (53.7% vs. 45.1%, p &lt; 0.001), males (60.7% vs. 58.3%, p = 0.001), and Charlson Comorbidity Index (CCI) ≥2 (9.7% vs. 6.7%, p &lt; 0.001). Kaplan-Meier analysis demonstrated survival benefit to the earlier treatment group, with 5 year OS of 85.5% and 80.9% (p &lt; 0.001; Figure). On multivariable analysis, increasing age (OR = 1.001, p = 0.015), African-American race (OR = 1.5, p &lt; 0.001), increasing distance from treatment center (OR = 1.005, p = 0.001), residence in areas with low high school graduation rates (OR = 1.42, p &lt; 0.001), residence in an area of &gt; 1 million population (OR = 1.6, p &lt; 0.001), and CCI ≥2 (OR = 1.4, p &lt; 0.001) were independently associated with increasing time to surgery. Conclusions: Surgery of T1 RCC carried out beyond 9 weeks after diagnosis is associated with reduced overall survival compared to patients treated within 6 weeks. Time to definitive surgical treatment should be a quality of care metric, with special attention given to populations most at risk for delays in care.

Thirty-Day Readmissions in Hematopoietic Stem Cell Transplantation Recipients: Insight from Nationwide Readmission Database

Introduction Hematopoietic Stem Cell Transplant HSCT is an established therapeutic modality for hematological and non hematological diseases. There is a paucity of information regarding the rate, causes and predictors of readmission in HSCT patients. Hospital readmission burden is an important health care quality metric. We aimed to estimate the 30- day readmission burden post HSCT. Methods We used the Nationwide Readmission Database NRD from 2012-2014. HSCT recipients were identified using ICD-9 codes. December was excluded from the index admission as NRD doesn't follow patients over the years, therefore, 30 days readmissions will not be available for these index admission if included. We stratified the patients according to age below and above 18 years and by the type of HSCT to either Bone Marrow or Cord Blood Transplant. We demonstrated the rate and most common causes of readmission through using ICD-9 codes. Multivariate analysis was performed to identify the independent predictors of the 30 day readmissions in HSCT. Results A total of 20,691 HSCT recipients were identified through NRD 2012-2014. Patients above and below 18 years were 92.5% and 7.5% respectively. Bone Marrow Transplant accounted for 97.4% while Cord Blood Transplant was 2.6%. Median age of the adult group was 57 years and of the pediatric group was 8 years, median length of stay was 21 days in adult cohort versus 35 days in pediatric cohort. 58.8% of the whole cohort were male. All cause 30-day readmission rate in HSCT recipients was 21.8% with a median number of 8 days till readmission. In pediatrics cohort, all cause 30-day readmission rate was 39.6% compared to 20.3% in adult cohort. Furthermore, when we stratified according to source of transplant, Cord Blood readmission rate was 42.4 % versus 21.2% in Bone Marrow Transplant. The most common causes of 30-day readmissions were infection, fluid-electrolyte disorder, acute kidney injury and relapse. Among the infection group, the top three causes were bacteremia, clostridium difficile (c.diff), and cytomegalovirus (CMV) infections respectively. On multivariate analysis, Cord Blood Transplant and below 18 years were among the significant predictors of 30-day readmission (HR=2.3, 95% CI (1.9-2.8); p=0.001, and HR=2.4, 95% CI (2.2-2.7); p=0.001, respectively). The estimated total cost of 30-day readmissions in HSCT patients was $6.66 billion, and the mortality rate was 4.4%. Trends of 30-day readmission over 2012-2014 years were rising; 30.2%, 32.2%, and 37.2%, respectively. Conclusions The highest burden of 30-day readmission rates and costs in HSCT recipients in the US were demonstrated among the pediatric and Cord Blood groups. The most common cause of readmission was infection with bacteremia, c. diff and CMV were the highest observed etiologies. Further researches are needed to identify strategies to cut down the rate and cost of readmissions

Neoadjuvant Chemotherapy Use for Nonmetastatic Breast Cancer at Five Public South African Hospitals and Impact on Time to Initial Cancer Therapy.

In the U.S., neoadjuvant chemotherapy (NAC) for nonmetastatic breast cancer (BC) is used with extensive disease and aggressive molecular subtypes. Little is known about the influence of demographic characteristics, clinical factors, and resource constraints on NAC use in Africa. We studied NAC use in a cohort of women with stage I-III BC enrolled in the South African Breast Cancer and HIV Outcomes study at five hospitals. We analyzed associations between NAC receipt and sociodemographic and clinical factors, and we developed Cox regression models for predictors of time to first treatment with NAC versus surgery. Of 810 patients, 505 (62.3%) received NAC. Multivariate analysis found associations between NAC use and black race (odds ratio [OR] 0.49; 95% confidence limit [CI], 0.25-0.96), younger age (OR 0.95; 95% CI, 0.92-0.97 for each year), T-stage (T4 versus T1: OR 136.29; 95% CI, 41.80-444.44), N-stage (N2 versus N0: OR 35.64; 95% CI, 16.56-76.73), and subtype (triple-negative versus luminal A: OR 5.16; 95% CI, 1.88-14.12). Sites differed in NAC use (Site D versus Site A: OR 5.73; 95% CI, 2.72-12.08; Site B versus Site A: OR 0.37; 95% CI, 0.16-0.86) and time to first treatment: Site A, 50 days to NAC versus 30 days to primary surgery (hazard ratio [HR] 1.84; 95% CI, 1.25-2.71); Site D, 101 days to NAC versus 126 days to primary surgery (HR 0.49; 95% CI, 0.27-0.89). NAC use for BC at these South African hospitals was associated with both tumor characteristics and heterogenous resource constraints. Using data from a large breast cancer cohort treated in South Africa's public healthcare system, the authors looked at determinants of neoadjuvant chemotherapy use and time to initiate treatment. It was found that neoadjuvant chemotherapy was associated with increasing tumor burden and aggressive molecular subtypes, demonstrating clinically appropriate care in a lower resource setting. Results of this study also showed that time to treatment differences between chemotherapy and surgery varied by hospital, suggesting that differences in resource limitations were influencing clinical decision making. Practice guidelines and care quality metrics designed for low- and middle-income countries should accommodate heterogeneity of available resources.

Establishing a denominator for palliative care quality metrics for patients with advanced cancer.

104 Background: Measurement and feedback of palliative care quality metrics for patients with advanced cancer is an important aspect of supporting a shared mental model for the early integration of palliative care within the oncology clinic. Accurately identifying the subset of advanced cancer patients within a population of cancer patients is critical to the development of quality improvement activities; however, there is not a methodology of identifying these patients in real time. Methods: We evaluated two main approaches to identifying patients with advanced cancer among active cancer patients defined as patients who were seen by an oncologist at least twice in the last 6 months and who had received chemotherapy or radiation at least once in the last two years. These approaches included: 1) Pattern matching of words indicating advanced cancer (e.g. metastatic, advanced) in oncology notes, radiology imaging and problem lists and 2) ICD-10 codes. To determine the final set of ICD-10 codes for the second approach, we used a conceptual model of the meaning of advanced cancer (evidence of distant metastasis and/or poor prognostic cancer with &gt; 50% mortality rate at 5 years) and iterative chart review. In order to test our final definitions, we randomly selected 588 charts of patients with active cancer who see one of 64 oncologists in our health system. These charts were abstracted by an oncologist to establish a gold standard for advanced cancer. We evaluated the sensitivity and specificity of our approaches to identify advanced cancer patients compared to this gold standard. Results: We found that the methods used to identify patients using pattern matching had a specificity of 76% and a sensitivity of 80%. Using our final ICD-10 algorithm we achieved a specificity of 93% and a sensitivity of 68%. We improved our sensitivity to 74% while maintaining our specificity at 92% when we excluded oncologists who predominantly see hematological malignancies. Conclusions: We achieved high specificity and reasonable sensitivity for an advanced cancer quality metric denominator using an ICD-10 algorithm within an academic oncology practice. This will help inform quality improvement efforts locally and beyond.

Blood Pressure Control and Other Quality of Care Metrics for Patients with Obesity and Diabetes: A Population-Based Cohort Study.

There are no population-level estimates in the United States for achievement of blood pressure goals in patients with diabetes and hypertension by obesity weight class. We sought to examine the relationship between the extent of obesity and the achievement of guideline-recommended blood pressure goals and other quality of care metrics among patients with diabetes. We conducted an observational population-based cohort study of electronic health data of three large health systems from 2010-2012 in rural, urban and suburban settings of 51,229 adults with diabetes. Outcomes were achievement of diabetes quality of care metrics: blood pressure, A1c, and LDL control, and A1c and LDL testing. Two blood pressure goals were examined given the recommendation for adults with diabetes of 130/80mmHg from JNC7 and the recommendation of 140/90mmHg from JNC8 in 2014. Patients in obesity classes I, II, and III with diagnosed hypertension were less likely to achieve blood pressure control at both the 140/90mmHg and 130/80mmHg control levels. The patients from obesity class III had the lowest likelihood of achieving control at the 130/80mmHg goal, and control was markedly worse for the 130/80mmHg threshold in all weight classes. There were minimal to no differences by weight class in LDL and A1c control and LDL and A1c testing. Although the cardiovascular risk for patients with obesity and diabetes is greater than for non-obese patients with diabetes, we found that patients with obesity are even further behind in achieving blood pressure control.

Predicting ICU readmission among surgical ICU patients: Development and validation of a clinical nomogram

BackgroundUnplanned intensive care unit readmission within 72 hours is an established metric of hospital care quality. However, it is unclear what factors commonly increase the risk of intensive care unit readmission in surgical patients. The objective of this study was to evaluate predictors of readmission among a diverse sample of surgical patients and develop an accurate and clinically applicable nomogram for prospective risk prediction. MethodsWe retrospectively evaluated patient demographic characteristics, comorbidities, and physiologic variables collected within 48 hours before discharge from a surgical intensive care unit at an academic center between April 2010 and July 2015. Multivariable regression models were used to assess the association between risk factors and unplanned readmission back to the intensive care unit within 72 hours. Model selection was performed using lasso methods and validated using an independent data set by receiver operating characteristic area under the curve analysis. The derived nomogram was then prospectively assessed between June and August 2017 to evaluate the correlation between perceived and calculated risk for intensive care unit readmission. ResultsAmong 3,109 patients admitted to the intensive care unit by general surgery (34%), transplant (9%), trauma (43%), and vascular surgery (14%) services, there were 141 (5%) unplanned readmissions within 72 hours. Among 179 candidate predictor variables, a reduced model was derived that included age, blood urea nitrogen, serum chloride, serum glucose, atrial fibrillation, renal insufficiency, and respiratory rate. These variables were used to develop a clinical nomogram, which was validated using 617 independent admissions, and indicated moderate performance (area under the curve: 0.71). When prospectively assessed, intensive care unit providers’ perception of respiratory risk was moderately correlated with calculated risk using the nomogram (ρ: 0.44; P < .001), although perception of electrolyte abnormalities, hyperglycemia, renal insufficiency, and risk for arrhythmias were not correlated with measured values. ConclusionIntensive care unit readmission risk for surgical patients can be predicted using a simple clinical nomogram based on 7 common demographic and physiologic variables. These data underscore the potential of risk calculators to combine multiple risk factors and enable a more accurate risk assessment beyond perception alone.

Racial disparity in quality of care and overall survival among black vs. white patients with muscle-invasive bladder cancer treated with radical cystectomy: A national cancer database analysis

ObjectivesTo examine the impact of race on quality of care and overall survival (OS) among patients with muscle invasive bladder cancer (MIBC) treated with radical cystectomy (RC) in the U.S. Materials & MethodsOur cohort consisted of 12,652 patients receiving RC for MIBC within the National Cancer Database from 2004 to 2012. Patients were stratified by race (Black non-Hispanic vs. White non-Hispanic) and imbalances in patient characteristics mitigated using propensity score weighting. Logistic and Cox regressions examined the impact of race on quality of care metrics (receipt of pelvic lymph node dissection (PLND), lymph node count, hospital volume, length of stay, delay of treatment) and on OS. The difference in OS was expressed as Delta, and stratified by facility-type, hospital volume, and region. ResultsBlacks were less likely to receive PLND (odds ratio [OR] 0.70, 95% confidence interval [CI]: 0.55–0.91), or to have a greater number of lymph nodes removed (OR 0.76, 95%CI: 0.64–0.90). They exhibited greater length of stay (OR 1.34, 95%CI: 1.13–1.59), and delay of RC among recipients of neoadjuvant chemotherapy (OR 2.59, 95%CI: 1.77–3.85) (all P ≤ 0.001). Notably, utilization of neoadjuvant chemotherapy in advanced disease stages was more common in blacks (OR 2.82, 95%CI: 1.93–4.13, P < 0.001). Additionally, Black race was associated with inferior OS (Hazard ratio 0.87, 95%CI: 0.79–0.97, P < 0.014). Disparities in OS varied based on facility type and geographical region, but not hospital volume. Specifically, Blacks had worse OS when treated in a community cancer program (Delta 0.42, 95%CI: 0.28–0.57,P < 0.001), or within New England/Middle Atlantic region (Delta 0.16, 95% CI: 0.07–0.24,P < 0.001). ConclusionBlack race is an independent predictor of inferior quality of care and OS in patients undergoing RC for MIBC. Survival disparities vary based on geographical region and facility type. Notably, the OS disparity appears to have narrowed in comparison to previous studies.

Data concordance between ESRD Medical Evidence Report and Medicare claims: is there any improvement?

BackgroundMedicare is one of the world’s largest health insurance programs. It provides health insurance to nearly 44 million beneficiaries whose entitlements are based on age, disability, or end-stage renal disease (ESRD). Data of these ESRD beneficiaries are collected in the US Renal Data System (USRDS), which includes comorbidity information entered at the time of dialysis initiation (medical evidence data), and are used to shape health care policy. One limitation of USRDS data is the lack of validation of these medical evidence comorbidities against other comorbidity data sources, such as medical claims data.MethodsWe examined the potential for discordance between USRDS Medical Evidence and medical claims data for 11 comorbid conditions amongst Medicare beneficiaries in 2011–2013 via sensitivity, specificity, kappa and hierarchical logistic regression.ResultsAmong 61,280 patients, most comorbid conditions recorded on the Medical Evidence forms showed high specificity (>0.9), compared to prior medical claims as reference standard. However, both sensitivity and kappa statistics varied greatly and tended to be low (most <0.5). Only diabetes appeared accurate, whereas tobacco use and drug dependence showed the poorest quality (sensitivity and kappa <0.1). Institutionalization and patient region of residency were associated with data discordance for six and five comorbidities out of 11, respectively, after conservative adjustment of multiple testing. Discordance appeared to be non-informative for congestive heart failure but was most varied for drug dependence.ConclusionsWe conclude that there is no improvement in comorbidity data quality in incident ESRD patients over the last two decades. Since these data are used in case-mix adjustment for outcome and quality of care metrics, the findings in this study should press regulators to implement measures to improve the accuracy of comorbidity data collection.

Characteristics of healthcare organisations struggling to improve quality: results from a systematic review of qualitative studies.

BackgroundIdentifying characteristics associated with struggling healthcare organisations may help inform improvement. Thus, we systematically reviewed the literature to: (1) Identify organisational factors associated with struggling healthcare organisations and (2) Summarise these factors into actionable domains.MethodsSystematic review of qualitative studies that evaluated organisational characteristics of healthcare organisations that were struggling as defined by below-average patient outcomes (eg, mortality) or quality of care metrics (eg, Patient Safety Indicators). Searches were conducted in MEDLINE (via Ovid), EMBASE, Cochrane Library, CINAHL, and Web of Science from database inception through February 8 2018. Qualitative data were analysed using framework-based synthesis and summarised into key domains. Study quality was evaluated using the Critical Appraisal Skills Program tool.ResultsThirty studies (33 articles) from multiple countries and settings (eg, acute care, outpatient) with a diverse range of interviewees (eg, nurses, leadership, staff) were included in the final analysis. Five domains characterised struggling healthcare organisations: poor organisational culture (limited ownership, not collaborative, hierarchical, with disconnected leadership), inadequate infrastructure (limited quality improvement, staffing, information technology or resources), lack of a cohesive mission (mission conflicts with other missions, is externally motivated, poorly defined or promotes mediocrity), system shocks (ie, events such as leadership turnover, new electronic health record system or organisational scandals that detract from daily operations), and dysfunctional external relations with other hospitals, stakeholders, or governing bodies.ConclusionsStruggling healthcare organisations share characteristics that may affect their ability to provide optimal care. Understanding and identifying these characteristics may provide a first step to helping low performers address organisational challenges to improvement.Systematic review registrationPROSPERO: CRD42017067367.

Thirty-day readmission rate as a surrogate marker for quality of care in neurosurgical patients: a single-center Canadian experience.

Readmission to the hospital within 30 days of discharge is used as a surrogate marker for quality and value of care in the United States (US) healthcare system. Concern exists regarding the value of 30-day readmission as a quality of care metric in neurosurgical patients. Few studies have assessed 30-day readmission rates in neurosurgical patients outside the US. The authors performed a retrospective review of all adult neurosurgical patients admitted to a single Canadian neurosurgical academic center and who were discharged to home to assess for the all-cause 30-day readmission rate, unplanned 30-day readmission rate, and avoidable 30-day readmission rate. A retrospective review was performed assessing 30-day readmission rates after discharge to home in all neurosurgical patients admitted to a single academic neurosurgical center from January 1, 2011, to December 31, 2011. The primary outcomes included rates of all-cause, unplanned, and avoidable readmissions within 30 days of discharge. Secondary outcomes included factors associated with unplanned and avoidable 30-day readmissions. A total of 184 of 950 patients (19.4%) were readmitted to the hospital within 30 days of discharge. One-hundred three patients (10.8%) were readmitted for an unplanned reason and 81 (8.5%) were readmitted for a planned or rescheduled operation. Only 19 readmissions (10%) were for a potentially avoidable reason. Univariate analysis identified factors associated with readmission for a complication or persistent/worsening symptom, including age (p = 0.009), length of stay (p = 0.007), general neurosurgery diagnosis (p < 0.001), cranial pathology (p < 0.001), intensive care unit (ICU) admission (p < 0.001), number of initial admission operations (p = 0.01), and shunt procedures (p < 0.001). Multivariate analysis identified predictive factors of readmission, including diagnosis (p = 0.002, OR 2.4, 95% CI 1.4-5.3), cranial pathology (p = 0.002, OR 2.7, 95% CI 1.4-5.3), ICU admission (p = 0.004, OR 2.4, 95% CI 1.3-4.2), and number of first admission operations (p = 0.01, OR 0.51, 95% CI 0.3-0.87). Univariate analysis performed to identify factors associated with potentially avoidable readmissions included length of stay (p = 0.03), diagnosis (p < 0.001), cranial pathology (p = 0.02), and shunt procedures (p < 0.001). Multivariate analysis identified only shunt procedures as a predictive factor for avoidable readmission (p = 0.02, OR 5.6, 95% CI 1.4-22.8). Almost one-fifth of neurosurgical patients were readmitted within 30 days of discharge. However, only about half of these patients were admitted for an unplanned reason, and only 10% of all readmissions were potentially avoidable. This study demonstrates unique challenges encountered in a publicly funded healthcare setting and supports the growing literature suggesting 30-day readmission rates may serve as an inappropriate quality of care metric in neurosurgical patients. Potentially avoidable readmissions can be predicted, and further research assessing predictors of avoidable readmissions is warranted.

IMPACT OF A QUALITY IMPROVEMENT INITIATIVE ON UNPLANNED EXTUBATION (UE) RATE IN THE NEONATAL INTENSIVE CARE UNIT (NICU)

Abstract BACKGROUND Unplanned extubations (UEs) refer to any removal of an endotracheal tube not directly ordered or intended by the medical team. It is the fourth most common adverse event in the neonatal intensive care unit (NICU) and can lead to significant morbidity in this vulnerable population. A large proportion of UEs in the NICU are deemed preventable and UE rates are increasingly being recognized as an important quality of care metric. OBJECTIVES To examine the effectiveness of an education-based quality improvement initiative to decrease UE rate in a level II/III NICU with a high rate of UEs DESIGN/METHODS Pre-intervention UE rate was determined by retrospective chart review for all intubated neonates admitted to a 25 bed level II/III NICU from January 2013 until December 2013. UEs were recorded along with demographic information including reason for extubation. UE rate was calculated by number of UEs/100 ventilator days and the data was analyzed to determine any significantly associated risk factors. The UE quality improvement initiative included: multi-disciplinary NICU staff education sessions, placement of educational posters in the NICU and identification of high risk neonates as defined by the pre-intervention study. High risk neonates were given additional signage to alert care providers. Standardized documentation was implemented to track and record UEs prospectively. Six months post-implementation, all UE events were reviewed from December 2015 until May 2016 and the post-intervention UE rate was calculated. RESULTS The UE rate was 3.28 UEs/100 ventilator days in the pre-intervention cohort with neonatal movement and adhesive failure accounting for over 50% of the documented UEs. Regression analysis revealed total ventilation time to be the only significant (p&lt;0.05) risk factor for an UE. In the post-intervention cohort the UE rate was 1.45 UEs/100 ventilator days, a 56% decrease from the pre-intervention rate. Rates of re-intubation following an UE were 78.3% and 50% for the pre- and post-implementation cohorts respectively. CONCLUSION Reduction in UE rate from 3.28 to 1.45 was achieved with an education-based multi-disciplinary quality improvement initiative. Rates of re-intubation following an UE were similar between cohorts. Further study is necessary to evaluate whether the effectiveness of this intervention will persist over time and whether results are generalizable to other NICUs.

Mandated Quality of Care Metrics for Medicare Patients: Examining New or Worsened Pressure Ulcers and Rehabilitation Outcomes in United States Inpatient Rehabilitation Facilities

ObjectiveTo examine the association between the Medicare pressure ulcer quality indicator (the development of new or worsened pressure ulcers) and rehabilitation outcomes among Medicare patients seen in an inpatient rehabilitation facility (IRF). DesignRetrospective descriptive study. SettingIRFs subscribed to the Uniform Data System for Medical Rehabilitation. ParticipantsNearly 500,000 IRF Medicare patients who were discharged between January 2013 and September 2014 were examined. InterventionsNot applicable. Main Outcome MeasuresFunctional independence, functional change (gain), and discharge destination. ResultsThe pressure ulcer quality indicator was associated with poorer rehabilitation outcomes; patients were less likely to achieve functional independence (odds ratio [OR], .47; 95% confidence interval [CI], .44–.51), were less likely to be discharged to a community setting (OR, .88; 95% CI, .82–.95), and made less functional gain during their IRF stay (a difference of 6 FIM points). ConclusionsThese results support that the pressure ulcer quality indicator is associated with lower quality of rehabilitation outcomes; however, given that those patients with a new or worsened pressure injury still made functional gains and most were discharged to the community, the risk of pressure injury development should not preclude the admission of these cases to an IRF.

Semi-Automated Visualization and ANalysis of Trends: A "SAVANT" for Facilitating Antimicrobial Stewardship Using Antistaphylococcal Resistance and Consumption as a Prototype.

Governments and health care regulators now require hospitals and nursing homes to establish programs to monitor and report antimicrobial consumption and resistance. However, additional resources were not provided. We sought to develop an approach for monitoring antimicrobial resistance and consumption that health care systems can implement with minimal added costs or modifications to existing diagnostic and informatics infrastructure. Using (1) the electronic laboratory information system of a nationwide managed care network, (2) the 3 most widely used commercial microbiology diagnostic platforms, and (3) Staphylococcus aureus, one of the most common causes of infections worldwide, as a prototype, we validated the approach dubbed "SAVANT" for Semi-Automated Visualization and ANalysis of Trends. SAVANT leverages 3 analytical methods (time series analysis, the autoregressive integrated moving average, and generalized linear regression) on either commercial or open source software to report trends in antistaphylococcal use and resistance. All laboratory results from January 2010 through December 2015 from an annual average of 9.2 million health care beneficiaries were queried. Inpatient and outpatient prescription rates were calculated for 8 key antistaphylococcal compounds. Trends and relationships of antistaphylococcal consumption and resistance among 81 840 unique S. aureus isolates from >6.5 million cultures were revealed. Using existing or freely available resources, SAVANT was successfully implemented across a complex and geographically dispersed 280-hospital network, bridging a critical gap between medical informatics, large-scale data analytics, and mandatory reporting of health care quality metrics.

3:09 PM Abstract No. 52 Risk factors for adverse events following inferior vena cava filter placement: analysis of 69,588 cases

Improved inferior vena cava filters (IVCF) have led to liberalization of the indications for insertion, however, sparse data exists identifying risk factors for adverse events (AEs) following IVCF placement. In this study, we identify potential factors and analyzed their association with three quality of care metrics: 30-day readmission, 1-year mortality, and long hospital stay. The New York Statewide Planning and Research Cooperative System, which covers all emergency room visits in New York State, was queried from 2007 to 2014 for emergent IVCF insertion (ICD-9 38.7). The American Hospital Association Dataviewer was used to supplement each procedure with information on the associated facility. High-volume physicians and high-volume hospitals were identified such that each accounted for 25% of all cases. The cutoff for long stay (>22 days) was set such that 25% of cases could be considered long stays. Multivariate analysis was performed to determine adjusted odds ratios, taking into account age, ethnicity, race, insurance, comorbidities, hospital teaching affiliation, hospital setting, and procedure year. A total of 69,588 cases were identified. Of these, 22,401 (32.2%) were identified as following a pulmonary embolism (PE), 33,866 (48.7%) were identified as being related to a deep vein thrombosis (DVT) only, and 13,321 (19.1%) did not have DVT or PE listed as a diagnosis, but were presumably at high risk for a venous thromboembolic event. Female gender and IVCF placement performed more recently were found to be protective for all three quality of care metrics, while procedures performed by high-volume physicians were protective against long hospital stay and 30-day readmissions. Finally, African-American race was found to be associated with poorer outcomes in readmissions and long stay. In the setting of IVCF placement, female gender and more recently placed IVCF were associated with improvement in all three of the quality of care metrics. Procedures performed by high volume physicians were associated with decreased rates of long hospital stay and 30-day readmissions while African-American race was associated with increased rates of those same metrics.

Association of shared decision-making on patient-reported health outcomes and healthcare utilization

BackgroundShared decision-making (SDM) is a process that respects the rights of patients to be fully involved in decisions about their care. By evaluating all available healthcare options and weighing patients' personal values and preferences against available unbiased evidence, patients and healthcare professionals can make health-related decisions together, as partners. We sought to evaluate the impact of perceived SDM on patient-reported outcomes, healthcare quality, and healthcare utilization. MethodsPatients were identified from the 2010–2014 Medical Expenditure Panel Survey (MEPS) cohort. The Consumer Assessment of Healthcare Providers and Systems (CAHPS) survey was levied to create a weighted composite score of satisfaction with SDM on a 12-point scale, and then categorized as optimal, average or poor SDM based on weighted scores. Weighting and variance techniques were applied to assure results were representative of the U.S. civilian population. Chi-square analysis was used to estimate differences across SDM groupings and multivariate logistic regression was performed to generate odds ratios (OR) and confidence intervals (CI). ResultsThe study cohort included 63,931 responses to the survey tool. Results of SDM satisfaction across the three categories were skewed, with 46.6% (n = 29,807) of the respondents reporting optimal SDM, 42.1% (n = 26,887) reporting average scores and only 11.3% (n = 7237) reporting poor perceived SDM. Non-white race, lower educational level, low socioeconomic status, non-married status, and uninsured or underinsured status were all associated with higher incidence of poor perceived SDM (p < .05). Poor SDM was associated with increased odds of poor physical health scores (OR: 1.17; 95% CI 1.01–1.36) and poor mental health scores (OR: 1.53; 95% CI 1.25–1.86). Poor SDM was associated with lower use of statins (OR: 0.77; 95% CI 0.68–0.87) and aspirin (OR: 0.86; 95% CI 0.77–0.95), both of which are established quality of care metrics. Poor SDM was also associated with increased emergency department (ED) utilization, with an increased likelihood of 2 or more ED visits associated with poor SDM (OR: 1.25; 95% CI 1.06–1.49). ConclusionsPoor SDM was associated with worse patient-reported health outcomes, worse established quality indicators, and higher healthcare utilization. While increasing physician education may help optimize SDM, differences in patient-perceived SDM were also strongly driven by inherent patient characteristics.

Abstract WP306: Race-Ethnic Disparities in 30-Day Readmission After Ischemic Stroke Among Medicare Beneficiaries in the Florida Puerto Rico Collaboration to Reduce Stroke Disparities (FL-PR CReSD) Study

Background: 30-day readmission after ischemic stroke is an important health quality of care metric, but little is known about potential race-ethnic disparities for this outcome. We examined 30-day rates and causes for readmission among Medicare beneficiaries in the NINDS-funded Florida-Puerto Rico Collaboration to Reduce Stroke Disparities (CReSD) Study. Methods: We examined 30-day all-cause readmission for Medicare fee-for-service beneficiaries treated for ischemic stroke (ICD-9 433,434, 436) in FL CReSD hospitals from 2010-2013. We fit a Cox proportional hazards model that censored for death and adjusted for age, sex, and comorbidities to assess race-ethnic differences in readmission, and identified reasons for readmissions. Results: Among 16,952 unique ischemic stroke patients (54% women, 75% white, 8% black, 15% Hispanic), all-cause 30-day readmission was 15%. Readmission rates were 17.2% for Blacks, 16.7% for Hispanics, 14.4% for Whites, and 14.7% for Others (p=0.003). There was a median of 11 days between discharge and first readmission. In risk-adjusted analyses, there was no significant difference in 30-day readmission for Blacks (hazard ratio 1.15, 95% confidence interval 0.99-1.33), Hispanics (1.00, 0.90-1.13), and those of other race (0.91, 0.71-1.16) compared to Whites. Nearly 1 in 4 readmissions were attributable to an acute cerebrovascular event: 16.6% ischemic stroke or transient ischemic attack, 1.5% hemorrhagic stroke, and 5.2% cerebral artery intervention (carotid endarterectomy or stenting, endovascular). Interventions were more common among Whites (6.0%) and those of other race (7.5%) than among Blacks (1.8%) and Hispanics (3.8%; p=0.029). Readmission due to pneumonia or urinary tract infection was 8.2%. Conclusions: Readmissions varied by race/ethnicity, but generally occurred within two weeks of hospital discharge. Our results underscore the importance of post-discharge transition of care and the need for better secondary stroke prevention strategies early after ischemic stroke. Implementation of effective transition of care after acute stroke hospitalization may reduce potentially preventable readmissions, particularly among minority populations.

Abstract WP273: Infections Increase The Risk Of 30-day Readmissions Among Stroke Survivors: An Analysis Of The National Readmission Database

Background and Purpose: Hospitals are increasingly utilizing 30-day readmission as a quality of care metric. We hypothesize that common infections common during the stroke stay are associated with 30-day readmission (30dRA). Methods: We conducted a weighted analysis of the federally managed 2013 National Readmission Database to assess the relationship between infection during a stroke hospitalization and 30dRA among ischemic stroke survivors. Ischemic stroke, common infections (defined as sepsis, pneumonia (PNA), and urinary tract infection (UTI)), and comorbidities were identified using ICD-9 diagnosis codes, and IV-tPA or intra-arterial therapy were identified using ICD-9 procedure codes. Survey design logistic regression models were fit to estimate crude and adjusted odds ratios (OR) and 95% confidence intervals (CI) for the association between infections and 30d-RA. Results: Of the 402042 ischemic stroke patients, 11.2% (n=45247) were readmitted within 30 days, and ~16% (n=7240) had an infection during their index hospitalization. Patients who had a common stroke infection during their stroke admission had a 36% higher odds of being readmitted than patients without any type of infection (OR 1.36, 95%CI 1.30-1.42; Figure 1). The increased odds of readmission remained after adjusting for confounders (OR 1.16, 95%CI 1.11-1.22; Figure 1). Stratifying by age, patients under 65 who had an infection during their stroke admission had an increased adjusted odds of 30d-RA (OR 1.38, 95%CI 1.28-1.49), than those over 65 (OR 1.11, 95%CI 1.06-1.17). The increased odds of readmission remained for those who had sepsis, PNA and a UTI (Figure 1), even after adjusting for covariates of interest. Conclusions: Using a nationally representative cohort we show that patients who have a common infection during their stroke stay are at increased odds of being readmitted. This presents a unique patients population who may benefit from an earlier post-stroke follow-up.

Association of hospice utilization and publicly reported outcomes following hospitalization for pneumonia or heart failure: a retrospective cohort study

BackgroundThe Center for Medicare and Medicaid Services (CMS) and the Hospital Quality Alliance began collecting and reporting United States hospital performance in the treatment of pneumonia and heart failure in 2008. Whether the utilization of hospice might affect CMS-reported mortality and readmission rates is not known.MethodsHospice utilization (mean days on hospice per decedent) for 2012 from the Dartmouth Atlas (a project of the Dartmouth Institute that reports a variety of public health and policy-related statistics) was merged with hospital-level 30-day mortality and readmission rates for pneumonia and heart failure from CMS. The association between hospice use and outcomes was analyzed with multivariate quantile regression controlling for quality of care metrics, acute care bed availability, regional variability and other measures.Results2196 hospitals reported data to both CMS and the Dartmouth Atlas in 2012. Higher rates of hospice utilization were associated with lower rates of 30-day mortality and readmission for pneumonia but not for heart failure. Higher quality of care was associated with lower rates of mortality for both pneumonia and heart failure. Greater acute care bed availability was associated with increased readmission rates for both conditions (p < 0.05 for all).ConclusionsHigher rates of hospice utilization were associated with lower rates of 30-day mortality and readmission for pneumonia as reported by CMS. While causality is not established, it is possible that hospice referrals might directly affect CMS outcome metrics. Further clarification of the relationship between hospice referral patterns and publicly reported CMS outcomes appears warranted.

Impact of regionalisation of a national rheumatic heart disease registry: the Ugandan experience

ObjectivesRheumatic heart disease (RHD) remains a major driver of cardiovascular morbidity and mortality in low-resource settings. Registry-based care for RHD has been advocated as a powerful tool to improve clinical...