Abstract P258: Racial Disparity in 90-Day Post-Stroke Readmission - An MUSC Perspective

Circulation ◽  
2017 ◽  
Vol 135 (suppl_1) ◽  
Author(s):  
Pratik Y Chhatbar ◽  
Jihad S Obeid ◽  
Yujing Zhao ◽  
Daniel T Lackland ◽  
Robert J Adams
Keyword(s):  
South Carolina ◽  
Quality Of Care ◽  
African Americans ◽  
Emergency Room ◽  
International Classification Of Diseases ◽  
University Of South Carolina ◽  
Readmission Rates ◽  
Hospital Readmission Rates ◽  
Classification Of Diseases

Background: Readmissions after acute hospitalizations are a cause of both risk and expense, and many of them are potentially preventable. Importantly, risk-standardized hospital readmission rates are sometimes used as a yardstick of the quality of care offered. However, racial variability in readmissions might involve factors beyond quality of care and has not been studied extensively. Objective: To identify differences in readmissions between African Americans and other races and determine preventable readmissions from a pragmatic viewpoint. Methods: We obtained deidentified data from Medical University of South Carolina (MUSC) Electronic Data Warehouse (EDW) on adult admissions with index diagnosis considered as an ischemic stroke (or closely related) using International Classification of Diseases, Ninth Revision (ICD-9) codes 433.x, 434.x, 436.x, 437.x between January 2011 and June 2014. Of these, we determined readmission and reason for readmission over 90-day period. Readmission can be hospital or emergency room readmission. We obtained race as the only linked demographic. Results: Of the 1953 patients admitted with index diagnoses of stroke, 765 (39%), 1148 (59%) and 50 (1%) were African Americans, Caucasians and others, respectively. At 90-days, 256 patients were readmitted as in-patient, of which 128 (50%), 126 (49%) and 2 (1%) were African Americans, Caucasians and others, respectively. On the other hand, 241 patients visited Emergency Room, of which 175 (73%), 65 (26%) and 1 (1%) were African Americans, Caucasians and others, respectively. On adjusting readmissions to index admissions, 17%, 11% and 4% of African Americans, Caucasians and others, respectively, were readmitted in hospital, while 23%, 6% and 2% of African Americans, Caucasians and others, respectively, visited Emergency Room over 90-days period. Conclusions: 90-days readmission rates involve African Americans in a disproportionate manner. This demands further investigation on the etiology of readmission and the care offered.

Abstract P352: Racial Disparity in Post-stroke Readmission - a South Carolina Perspective

Circulation ◽  
2018 ◽  
Vol 137 (suppl_1) ◽  
Author(s):  
Pratik Y Chhatbar ◽  
Jihad S Obeid ◽  
Daniel T Lackland ◽  
Suzanne P Burns ◽  
Joy N Buie ◽  
...  
Keyword(s):  
South Carolina ◽  
Quality Of Care ◽  
African Americans ◽  
Racial Disparity ◽  
Clinical Care ◽  
University Of South Carolina ◽  
Readmission Rates ◽  
Post Stroke ◽  
Medical University

Background: Readmissions after acute hospitalizations are a cause of both risk and expense, and many of them are potentially preventable. Importantly, risk-standardized hospital readmission rates are sometimes used as a yardstick of the quality of care offered. However, racial variability in readmissions might involve factors beyond quality of care and has not been studied extensively. During our pilot investigation using 90-day post-stroke readmissions data at Medical University of South Carolina (MUSC), we found significant disparities between African Americans and Caucasians. Objective: To identify differences in readmissions between African Americans and other races and determine preventable readmissions from a pragmatic viewpoint. Methods: We obtained deidentified data from Health Sciences South Carolina (HSSC) Clinical Data Warehouse (CDW). The data was comprised of three institutions: Medical University of South Carolina (MUSC), Palmetto Health and Greenville Hospital System University Medical Center. The data consisted of on adult admissions with index diagnosis considered as an ischemic stroke (or closely related) using International Classification of Diseases, Ninth and Tenth Revision (ICD-9, ICD-10) codes between January 2011 and April 2017. Of these, we will determine readmission and reason for readmission over 90-day period. Readmission can be hospital or emergency room readmission. Results: Our database contains 32,548 patients who have been provided clinical care for stroke. Out of these patients 8,308 (25.5%), 23,085 (70.9%) and 1,155 (3.5%) are African Americans, Caucasians and others, respectively. We will present weekly readmission trends over 90 days and evaluate if there are disparities across races. We will apply chi-square test and Student’s t-test to determine statistical significance. For weekly readmission trends over 90 days, we will apply Kolmogorov-Smirnov test to identify difference in readmission patterns across races. We will also identify confounders like socioeconomic status and age and their influence in the racial disparity. Conclusions: From a single center retrospective data, we found that 90-days readmission rates involve African Americans in a disproportionate manner. This multicenter data analysis will further shed light on the etiology of readmission, confounders and the care offered.

scholarly journals Same-Hospital Readmission Rates as a Measure of Pediatric Quality of Care

2015 ◽  
Vol 169 (10) ◽  
pp. 905 ◽  
Author(s):  
Alisa Khan ◽  
Mari M. Nakamura ◽  
Alan M. Zaslavsky ◽  
Jisun Jang ◽  
Jay G. Berry ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Hospital Readmission ◽  
Readmission Rates ◽  
Hospital Readmission Rates

scholarly journals Quality of surgical care and readmission in elderly glioblastoma patients

2014 ◽  
Vol 1 (2) ◽  
pp. 33-39 ◽  
Author(s):  
Miriam Nuño ◽  
Diana Ly ◽  
Debraj Mukherjee ◽  
Alicia Ortega ◽  
Keith L. Black ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Surgical Care ◽  
Patient Characteristics ◽  
Readmission Rates ◽  
Vein Thrombosis ◽  
Complex Interactions ◽  
Hospital Readmission Rates ◽  
Medicare Database ◽  
Deep Vein

Abstract Background Thirty-day readmissions post medical or surgical discharge have been analyzed extensively. Studies have shown that complex interactions of multiple factors are responsible for these hospitalizations. Methods A retrospective analysis was conducted using the Surveillance, Epidemiology and End Results (SEER) Medicare database of newly diagnosed elderly glioblastoma multiforme (GBM) patients who underwent surgical resection between 1991 and 2007. Hospitals were classified into high- or low-readmission rate cohorts using a risk-adjusted methodology. Bivariate comparisons of outcomes were conducted. Multivariate analysis evaluated differences in quality of care according to hospital readmission rates. Results A total of 1,273 patients underwent surgery in 338 hospitals; 523 patients were treated in 228 high-readmission hospitals and 750 in 110 low-readmission hospitals. Patient characteristics for high-versus low-readmission hospitals were compared. In a confounder-adjusted model, patients treated in high- versus low-readmission hospitals had similar outcomes. The hazard of mortality for patients treated at high- compared to low-readmission hospitals was 1.06 (95% CI, 0.095%–1.19%). While overall complications were comparable between high- and low-readmission hospitals (16.3% vs 14.3%; P = .33), more postoperative pulmonary embolism/deep vein thrombosis complications were documented in patients treated at high-readmission hospitals (7.5% vs 4.1%; P = .01). Adverse events and levels of resection achieved during surgery were comparable at high- and low-readmission hospitals. Conclusions For patients undergoing GBM resection, quality of care provided by hospitals with the highest adjusted readmission rates was similar to the care delivered by hospitals with the lowest rates. These findings provide evidence against the preconceived notion that 30-day readmissions can be used as a metric for quality of surgical and postsurgical care.

scholarly journals Garbage codes as causes of death and quality of mortality statistics in Belo Horizonte, Brazil

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
L Ishitani ◽  
R Teixeira ◽  
D Abreu ◽  
L Paixão ◽  
E França
Keyword(s):  
Causes Of Death ◽  
Health Planning ◽  
Age Groups ◽  
International Classification Of Diseases ◽  
Mortality Statistics ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Hospital Deaths ◽  
Belo Horizonte

Abstract Background Quality of cause-of-death information is fundamental for health planning. Traditionally, this quality has been assessed by the analysis of ill-defined causes from chapter XVIII of the International Classification of Diseases - 10th revision (ICD-10). However, studies have considered other useless diagnoses for public health purposes, defined, in conjunction with ill-defined causes, as garbage codes (GC). In Brazil, despite the high completeness of the Mortality Information System, approximately 30% of deaths are attributable to GCs. This study aims to analyze the frequency of GCs in Belo Horizonte municipality, the capital of Minas Gerais state, Brazil. Methods Data of deaths from 2011 to 2013 in Belo Horizonte were analyzed. GCs were classified according to the GBD 2015 study list. These codes were classified in: a) GCs from chapter XVIII of ICD-10 (GC-R), and b) GC from other chapters of ICD-10 (GC-nonR). Proportions of GC were calculated by sex, age, and place of occurrence. Results In Belo Horizonte, from the total of 44,123 deaths, 5.5% were classified as GC-R. The majority of GCs were GC-nonR (25% of total deaths). We observed a higher proportion of GC in children (1 to 4 years) and in people aged over 60 years. GC proportion was also higher in females, except in the age-groups under 1 year and 30-59 years. Home deaths (n = 7,760) had higher proportions of GCs compared with hospital deaths (n = 30,182), 36.9% and 28.7%, respectively. The leading GCs were the GC-R other ill-defined and unspecified causes of death (ICD-10 code R99)), and the GCs-nonR unspecified pneumonia (J18.9), unspecified stroke (I64), and unspecified septicemia (A41.9). Conclusions Analysis of GCs is essential to evaluate the quality of mortality information. Key messages Analysis of ill-defined causes (GC-R) is not sufficient to evaluate the quality of information on causes of death. Causes of death analysis should consider the total GC, in order to advance the discussion and promote adequate intervention on the quality of mortality statistics.

scholarly journals Association Between Diagnosis Code Expansion and Changes in 30‐Day Risk‐Adjusted Outcomes for Cardiovascular Diseases

2021 ◽  
Author(s):  
Lauren Gilstrap ◽  
Rishi K. Wadhera ◽  
Andrea M. Austin ◽  
Stephen Kearing ◽  
Karen E. Joynt Maddox ◽  
...  
Keyword(s):  
Logistic Regression ◽  
International Classification Of Diseases ◽  
Fee For Service ◽  
Readmission Rates ◽  
Difference In Differences ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Changes In Risk ◽  
The Impact

BACKGROUND In January 2011, Centers for Medicare and Medicaid Services expanded the number of inpatient diagnosis codes from 9 to 25, which may influence comorbidity counts and risk‐adjusted outcome rates for studies spanning January 2011. This study examines the association between (1) limiting versus not limiting diagnosis codes after 2011, (2) using inpatient‐only versus inpatient and outpatient data, and (3) using logistic regression versus the Centers for Medicare and Medicaid Services risk‐standardized methodology and changes in risk‐adjusted outcomes. METHODS AND RESULTS Using 100% Medicare inpatient and outpatient files between January 2009 and December 2013, we created 2 cohorts of fee‐for‐service beneficiaries aged ≥65 years. The acute myocardial infarction cohort and the heart failure cohort had 578 728 and 1 595 069 hospitalizations, respectively. We calculate comorbidities using (1) inpatient‐only limited diagnoses, (2) inpatient‐only unlimited diagnoses, (3) inpatient and outpatient limited diagnoses, and (4) inpatient and outpatient unlimited diagnoses. Across both cohorts, International Classification of Diseases, Ninth Revision ( ICD‐9 ) diagnoses and hierarchical condition categories increased after 2011. When outpatient data were included, there were no significant differences in risk‐adjusted readmission rates using logistic regression or the Centers for Medicare and Medicaid Services risk standardization. A difference‐in‐differences analysis of risk‐adjusted readmission trends before versus after 2011 found that no significant differences between limited and unlimited models for either cohort. CONCLUSIONS For studies that span 2011, researchers should consider limiting the number of inpatient diagnosis codes to 9 and/or including outpatient data to minimize the impact of the code expansion on comorbidity counts. However, the 2011 code expansion does not appear to significantly affect risk‐adjusted readmission rate estimates using either logistic or risk‐standardization models or when using or excluding outpatient data.

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

2019 ◽  
Vol 130 (5) ◽  
pp. 1692-1698 ◽  
Author(s):  
Mitchell P. Wilson ◽  
Andrew S. Jack ◽  
Andrew Nataraj ◽  
Michael Chow
Keyword(s):  
Multivariate Analysis ◽  
Quality Of Care ◽  
Readmission Rate ◽  
Univariate Analysis ◽  
Surrogate Marker ◽  
Readmission Rates ◽  
Factors Associated ◽  
Neurosurgical Patients ◽  
Avoidable Readmissions

OBJECTIVEReadmission 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.METHODSA 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.RESULTSA 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).CONCLUSIONSAlmost 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.

Readmission Rates Are a Poor Proxy for Transplant Center Quality of Care among US Kidney Transplant Recipients

2012 ◽  
Vol 94 (10S) ◽  
pp. 185 ◽  
Author(s):  
T. R. Srinivas ◽  
R. Woodward ◽  
A. Tang ◽  
D. Goldfarb ◽  
S. Flechner ◽  
...  
Keyword(s):  
Quality Of Care ◽  
Kidney Transplant ◽  
Transplant Recipients ◽  
Kidney Transplant Recipients ◽  
Readmission Rates ◽  
Transplant Center
Sign in / Sign up

Export Citation Format

Share Document