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Dorry L Segev - One of the best experts on this subject based on the ideXlab platform.
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sequelae of early Hospital Readmission after kidney transplantation
American Journal of Transplantation, 2014Co-Authors: Mara A Mcadamsdemarco, Elizabeth A King, Morgan E Grams, Niraj M Desai, Dorry L SegevAbstract:We recently elucidated risk factors for early Hospital Readmission (EHR) following kidney transplantation (KT). We now sought to quantify the independent associations between EHR and post-KT outcomes, including late Hospital Readmission (LHR: 1 year after EHR window), death-censored graft loss and mortality, among Medicare-primary KT recipients (2000-2005). Of 32961 KT recipients, 7.7% had at least one Readmission within 3 days of discharge, 14.8% within 7 days, 22.4% within 14 days and 30.5% within 30 days of discharge after the initial KT Hospitalization. KT recipients who experienced EHR within 30 days of discharge after the initial KT Hospitalization were more likely to have experienced LHR (29.6% vs. 9.0%, p<0.001) and were at 3.02 times higher (95% CI: 2.82-3.23, p<0.001) risk of LHR. Additionally, EHR was associated with death-censored graft loss (deceased donor recipients hazard ratio [HR]: 1.43, 95% CI: 1.36-1.51, p<0.001 and live donor recipients HR: 1.54, 95% CI: 1.40-1.70, p<0.001) and mortality (deceased donor recipients HR: 1.50, 95% CI: 1.43-1.58, p<0.001 and live donor recipients HR: 1.45, 95% CI: 1.32-1.60, p<0.001). Thirty days posttransplant represents a high-risk window for KT recipients and the Readmissions during this window are strong predictors of adverse sequelae, particularly LHRs. Efforts should be made to implement and improve systems to reduce LHR and subsequent graft loss and mortality among recipients with EHR.
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frailty and early Hospital Readmission after kidney transplantation
American Journal of Transplantation, 2013Co-Authors: Mara A Mcadamsdemarco, Morgan E Grams, Dorry L Segev, Andrew Law, Megan L Salter, Eric K H Chow, Jeremy D WalstonAbstract:Early Hospital Readmission (EHR) after kidney transplantation (KT) is associated with increased morbidity and higher costs. Registry-based recipient, transplant and center-level predictors of EHR are limited, and novel predictors are needed. We hypothesized that frailty, a measure of physiologic reserve initially described and validated in geriatrics and recently associated with early KT outcomes, might serve as a novel, independent predictor of EHR in KT recipients of all ages. We measured frailty in 383 KT recipients at Johns Hopkins Hospital. EHR was ascertained from medical records as ≥1 Hospitalization within 30 days of initial post-KT discharge. Frail KT recipients were much more likely to experience EHR (45.8% vs. 28.0%, p = 0.005), regardless of age. After adjusting for previously described registry-based risk factors, frailty independently predicted 61% higher risk of EHR (adjusted RR = 1.61, 95% CI: 1.18-2.19, p = 0.002). In addition, frailty improved EHR risk prediction by improving the area under the receiver operating characteristic curve (p = 0.01) as well as the net reclassification index (p = 0.04). Identifying frail KT recipients for targeted outpatient monitoring and intervention may reduce EHR rates.
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early Hospital Readmission after kidney transplantation patient and center level associations
American Journal of Transplantation, 2012Co-Authors: Mara A Mcadamsdemarco, Morgan E Grams, Dorry L Segev, Erin C Hall, Josef CoreshAbstract:Early Hospital Readmission (EHR) is associated with increased morbidity, costs and transition-of-care errors. We sought to quantify rates of and risk factors for EHR after kidney transplantation (KT). We studied 32 961 Medicare primary KT recipients (2000–2005) linked to Medicare claims through the United States Renal Data System. EHR was defined as at least one Hospitalization within 30 days of initial discharge after KT. The association between EHR and recipient and transplant factors was explored using Poisson regression; hierarchical modeling was used to account for study center-level differences. The overall EHR rate was 31%, and 19 independent patient-level factors associated with EHR were identified: recipient factors included older age, African American race and various comorbidities; transplant factors included ECD, length of stay and lack of induction therapy. The unadjusted rate of EHR by center ranged from 18% to 47%, but conventional center-level factors (percent African American, percent age > 60, percent deceased donor and percent expanded criteria donor) were not associated with EHR. However, intermediate total volume and average length of stay were associated with increased EHR risk. Better identification of patients at risk for early Hospital Readmission following KT may guide discharge planning and early posttransplant outpatient monitoring.
Sharonlise T Normand - One of the best experts on this subject based on the ideXlab platform.
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Hospital Readmission risk isolating Hospital effects from patient effects
The New England Journal of Medicine, 2017Co-Authors: Harlan M Krumholz, Zhenqiu Lin, Elizabeth E Drye, Susannah M Bernheim, Kun Wang, Kumar Dharmarajan, Leora I Horwitz, Joseph S Ross, Sharonlise T NormandAbstract:BackgroundTo isolate Hospital effects on risk-standardized Hospital-Readmission rates, we examined Readmission outcomes among patients who had multiple admissions for a similar diagnosis at more than one Hospital within a given year. MethodsWe divided the Centers for Medicare and Medicaid Services Hospital-wide Readmission measure cohort from July 2014 through June 2015 into two random samples. All the patients in the cohort were Medicare recipients who were at least 65 years of age. We used the first sample to calculate the risk-standardized Readmission rate within 30 days for each Hospital, and we classified Hospitals into performance quartiles, with a lower Readmission rate indicating better performance (performance-classification sample). The study sample (identified from the second sample) included patients who had two admissions for similar diagnoses at different Hospitals that occurred more than 1 month and less than 1 year apart, and we compared the observed Readmission rates among patients who ha...
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abstract 136 the relationship of changing Hospital Readmission rates and mortality rates after Hospitalization for heart failure acute myocardial infarction and pneumonia
Circulation-cardiovascular Quality and Outcomes, 2017Co-Authors: Kumar Dharmarajan, Zhenqiu Lin, Elizabeth E Drye, Susannah M Bernheim, Leora I Horwitz, Joseph S Ross, Yongfei Wang, Nihar R Desai, Lisa G Suter, Sharonlise T NormandAbstract:Background: It is unknown if financial pressures to reduce Hospital Readmission rates following passage of the Affordable Care Act (ACA) have had the unintended effect of increasing mortality rates after Hospitalization. We therefore examined correlations between paired changes in Hospital 30-day Readmission rates and 30-day mortality rates among Medicare fee-for-service beneficiaries Hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia from 2008 to 2014. Methods: We used linear regression to calculate monthly changes in Hospitals’ 30-day risk-adjusted Readmission rates (RARRs) and 30-day risk-adjusted mortality rates (RAMRs) after discharge for HF, AMI, and pneumonia from 2008 to 2014. Adjustment was made for patient age, sex, comorbidities, Hospital length of stay, and season. We then examined the correlation of Hospitals’ paired monthly changes in 30-day RARRs and monthly changes in 30-day RAMRs after discharge. Results: From 2008 to 2014, we identified 2,962,554, 1,229,939, and 2,544,530 Hospitalizations for HF, AMI, and pneumonia at 5,016, 4,772, and 5,057 Hospitals, respectively. Hospital 30-day RARRs declined for all three conditions from 2008 to 2014; the monthly change in RARRs was -0.053 (95% CI -0.055, -0.051) for HF, -0.044 (95% CI -0.047, -0.041) for AMI, and -0.033 (95% CI -0.035, -0.031) for pneumonia. In contrast, the monthly change in Hospital 30-day RAMRs after discharge varied by admitting condition and was 0.008 (95% CI 0.007, 0.010) for HF, -0.003 (95% CI -0.006, -0.001) for AMI, and 0.001 (95% CI -0.001, 0.003) for pneumonia. The correlation between monthly changes in Hospitals’ 30-day RARRs and 30-day RAMRs after discharge was 0.060 for HF (p Conclusion: Changes in Hospital Readmission rates for HF, AMI, and pneumonia were poorly correlated with changes in mortality rates after Hospitalization between 2008 and 2014. These findings suggest that financial incentives to improve Hospitals’ Readmission performance have not increased mortality after Hospitalization.
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abstract 13 risks of death and Hospital Readmission by time following Hospitalization for heart failure and acute myocardial infarction
Circulation-cardiovascular Quality and Outcomes, 2013Co-Authors: Kumar Dharmarajan, Sharonlise T Normand, Elizabeth E Drye, Susannah M Bernheim, Leora I Horwitz, Joseph S Ross, Lisa G Suter, Angela F Hsieh, Vivek T Kulkarni, Harlan M KrumholzAbstract:Background: After Hospitalization for heart failure (HF) and acute myocardial infarction (AMI), patients experience increased risk of death and Hospital Readmission. Defining the trajectory and timing of this period of risk may help guide interventions to improve post-discharge outcomes. Methods: We used 2008-10 Medicare data to identify patients ≥65 years discharged alive after HF or AMI Hospitalization. Using hazard rates, we characterized the risks of death and first Readmission on each day after discharge to describe (1) the maximum daily risks of death and Readmission after discharge; (2) risks of death and Readmission 1 year after discharge; (3) the time in days after discharge for the risks of death and Readmission to reach their maximum daily rates and 50% of their maximum daily rates to characterize the rapidity of decline in risk. We created separate survival models for death and first Readmission. Data were censored after 1 year follow up. The Readmission model also censored for death prior to Readmission. Results: Of 878,963 HF Hospitalizations, 367,542 (41.8%) died and 618,283 (70.3%) were readmitted in 1 year. Of 350,509 AMI Hospitalizations, 90,623 (25.9%) died and 177,031(50.5%) were readmitted in 1 year. The Figure shows hazard rates by time after discharge. For HF, daily risk of death was 0.0056 maximally and 0.0011 at 1 year (19% of maximum). Daily risk of Readmission was 0.013 maximally and 0.002 at 1 year (16% of maximum). Daily risk of death was highest 1 day after discharge and 50% less 11 days after discharge. Daily risk of Readmission was highest 4 days after discharge and 50% less 49 days after discharge. For AMI, daily risk of death was 0.010 maximally and 0.0004 at 1 year (4% of maximum). Daily risk of Readmission was 0.015 maximally and 0.0011 at 1 year (7% of maximum). Daily risk of death was highest 1 day after discharge and 50% less 6 days after discharge. Daily risk of Readmission was highest 2 days after discharge and 50% less 13 days after discharge. Conclusions: After Hospitalization for HF and AMI, risk of death is highest on day 1 after discharge and then declines rapidly. In contrast, risk of Readmission peaks later and declines more slowly. This extended period of risk for Readmission may justify continued vigilance beyond the 30-day period used by Medicare to evaluate Hospital Readmission performance. ![Graphic][1] [1]: /embed/inline-graphic-1.gif
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is same Hospital Readmission rate a good surrogate for all Hospital Readmission rate
Medical Care, 2010Co-Authors: Khurram Nasir, Zhenqiu Lin, Hector Bueno, Sharonlise T Normand, Elizabeth E Drye, Patricia S Keenan, Harlan M KrumholzAbstract:Background: The Centers for Medicare & Medicaid Services (CMS) Readmission measure is based on all-cause Readmissions to any Hospital within 30 days of discharge. Whether a measure based on same-Hospital Readmission, an outcome that is easier for Hospitals and some systems to track, could serve as a proxy for the all-Hospital measure is not known. Objectives: Evaluate whether same-Hospital Readmission rate is a good surrogate for all-Hospital Readmission rate. Research Design: The study population was derived from the Medicare inpatient, outpatient, and carrier (physician) Standard Analytic Files. Thirty-day risk-standardized Readmission rates (RSRRs) for heart failure (HF) for both all-Hospital Readmission and same-Hospital Readmission were assessed by using hierarchical logistic regression models. Subjects: The sample consisted of 501,234 Hospitalizations in 4674 Hospitals with at least 1 Hospitalization. Measures: Thirty-day Readmission was defined as occurrence of at least 1 Hospitalization in any US acute care Hospital for any cause within 30 days of discharge after an index Hospitalization. Same-Hospital Readmission was considered if the patient was admitted to the Hospital that produced the original discharge within 30 days. Results: Overall, 80.9% of all HF Readmissions occurred in the same-Hospital, whereas 19.1% of Readmissions occurred in a different Hospital. The mean difference between all- versus same-Hospital RSRR was 4.7 ± 1.0%, ranging from 0.9% to 10.5% across these Hospitals with 25th, 50th, and 75th percentiles of 4.1%, 4.7%, and 5.2%, respectively, and was variable across the range of average RSRR. Conclusion: Same-Hospital Readmission rate is an unreliable and biased indicator of all-Hospital Readmission rate with limited value as a benchmark for quality of care processes.
Mara A Mcadamsdemarco - One of the best experts on this subject based on the ideXlab platform.
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early Hospital Readmission after simultaneous pancreas kidney transplantation patient and center level factors
American Journal of Transplantation, 2016Co-Authors: Elizabeth A King, Lauren M Kucirka, Mara A Mcadamsdemarco, Allan B Massie, Al F Ammary, Rizwan Ahmed, Morgan E GramsAbstract:Early Hospital Readmission is associated with increased morbidity, mortality, and cost. Following simultaneous pancreas-kidney transplantation, rates of Readmission and risk factors for Readmission are unknown. We used United States Renal Data System data to study 3643 adult primary first-time simultaneous pancreas-kidney recipients from December 1, 1999 to October 31, 2011. Early Hospital Readmission was any Hospitalization within 30 days of discharge. Modified Poisson regression was used to determine the association between Readmission and patient-level factors. Empirical Bayes statistics were used to determine the variation attributable to center-level factors. The incidence of Readmission was 55.5%. Each decade increase in age was associated with an 11% lower risk of Readmission to age 40, beyond which there was no association. Donor African-American race was associated with a 13% higher risk of Readmission. Each day increase in length of stay was associated with a 2% higher risk of Readmission until 14 days, beyond which each day increase was associated with a 1% reduction in the risk of Readmission. Center-level factors were not associated with Readmission. The high incidence of early Hospital Readmission following simultaneous pancreas-kidney transplant may reflect clinical complexity rather than poor quality of care.
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sequelae of early Hospital Readmission after kidney transplantation
American Journal of Transplantation, 2014Co-Authors: Mara A Mcadamsdemarco, Elizabeth A King, Morgan E Grams, Niraj M Desai, Dorry L SegevAbstract:We recently elucidated risk factors for early Hospital Readmission (EHR) following kidney transplantation (KT). We now sought to quantify the independent associations between EHR and post-KT outcomes, including late Hospital Readmission (LHR: 1 year after EHR window), death-censored graft loss and mortality, among Medicare-primary KT recipients (2000-2005). Of 32961 KT recipients, 7.7% had at least one Readmission within 3 days of discharge, 14.8% within 7 days, 22.4% within 14 days and 30.5% within 30 days of discharge after the initial KT Hospitalization. KT recipients who experienced EHR within 30 days of discharge after the initial KT Hospitalization were more likely to have experienced LHR (29.6% vs. 9.0%, p<0.001) and were at 3.02 times higher (95% CI: 2.82-3.23, p<0.001) risk of LHR. Additionally, EHR was associated with death-censored graft loss (deceased donor recipients hazard ratio [HR]: 1.43, 95% CI: 1.36-1.51, p<0.001 and live donor recipients HR: 1.54, 95% CI: 1.40-1.70, p<0.001) and mortality (deceased donor recipients HR: 1.50, 95% CI: 1.43-1.58, p<0.001 and live donor recipients HR: 1.45, 95% CI: 1.32-1.60, p<0.001). Thirty days posttransplant represents a high-risk window for KT recipients and the Readmissions during this window are strong predictors of adverse sequelae, particularly LHRs. Efforts should be made to implement and improve systems to reduce LHR and subsequent graft loss and mortality among recipients with EHR.
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frailty and early Hospital Readmission after kidney transplantation
American Journal of Transplantation, 2013Co-Authors: Mara A Mcadamsdemarco, Morgan E Grams, Dorry L Segev, Andrew Law, Megan L Salter, Eric K H Chow, Jeremy D WalstonAbstract:Early Hospital Readmission (EHR) after kidney transplantation (KT) is associated with increased morbidity and higher costs. Registry-based recipient, transplant and center-level predictors of EHR are limited, and novel predictors are needed. We hypothesized that frailty, a measure of physiologic reserve initially described and validated in geriatrics and recently associated with early KT outcomes, might serve as a novel, independent predictor of EHR in KT recipients of all ages. We measured frailty in 383 KT recipients at Johns Hopkins Hospital. EHR was ascertained from medical records as ≥1 Hospitalization within 30 days of initial post-KT discharge. Frail KT recipients were much more likely to experience EHR (45.8% vs. 28.0%, p = 0.005), regardless of age. After adjusting for previously described registry-based risk factors, frailty independently predicted 61% higher risk of EHR (adjusted RR = 1.61, 95% CI: 1.18-2.19, p = 0.002). In addition, frailty improved EHR risk prediction by improving the area under the receiver operating characteristic curve (p = 0.01) as well as the net reclassification index (p = 0.04). Identifying frail KT recipients for targeted outpatient monitoring and intervention may reduce EHR rates.
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early Hospital Readmission after kidney transplantation patient and center level associations
American Journal of Transplantation, 2012Co-Authors: Mara A Mcadamsdemarco, Morgan E Grams, Dorry L Segev, Erin C Hall, Josef CoreshAbstract:Early Hospital Readmission (EHR) is associated with increased morbidity, costs and transition-of-care errors. We sought to quantify rates of and risk factors for EHR after kidney transplantation (KT). We studied 32 961 Medicare primary KT recipients (2000–2005) linked to Medicare claims through the United States Renal Data System. EHR was defined as at least one Hospitalization within 30 days of initial discharge after KT. The association between EHR and recipient and transplant factors was explored using Poisson regression; hierarchical modeling was used to account for study center-level differences. The overall EHR rate was 31%, and 19 independent patient-level factors associated with EHR were identified: recipient factors included older age, African American race and various comorbidities; transplant factors included ECD, length of stay and lack of induction therapy. The unadjusted rate of EHR by center ranged from 18% to 47%, but conventional center-level factors (percent African American, percent age > 60, percent deceased donor and percent expanded criteria donor) were not associated with EHR. However, intermediate total volume and average length of stay were associated with increased EHR risk. Better identification of patients at risk for early Hospital Readmission following KT may guide discharge planning and early posttransplant outpatient monitoring.
Michael J Mcwilliams - One of the best experts on this subject based on the ideXlab platform.
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assessment of the effect of adjustment for patient characteristics on Hospital Readmission rates implications for pay for performance
JAMA Internal Medicine, 2018Co-Authors: Eric T Roberts, Michael L Barnett, Michael J Mcwilliams, Alan M Zaslavsky, Bruce E Landon, Lin DingAbstract:Importance In several pay-for-performance programs, Medicare ties payments to Readmission rates but accounts only for a limited set of patient characteristics—and no measures of social risk—when assessing performance of health care providers (clinicians, practices, Hospitals, or other organizations). Debate continues over whether accounting for social risk would mitigate inappropriate penalties or would establish lower standards of care for disadvantaged patients if they are served by lower-quality providers. Objectives To assess changes in Hospital performance on Readmission rates after adjusting for additional clinical and social patient characteristics by using methods that distinguish the association between patient characteristics and Readmission from between-Hospital differences in quality. Design, Setting, and Participants Using Medicare claims for admissions in 2013 through 2014 and linked US Census data, we assessed several clinical and social characteristics of patients that are not currently used for risk adjustment in the Hospital Readmission Reduction Program. We compared Hospital Readmission rates with and without adjustment for these additional characteristics, using only the average within-Hospital associations between patient characteristics and Readmission as the basis for adjustment, thereby appropriately excluding Hospitals’ distinct contributions to Readmission from the adjustment. Main Outcomes and Measures All-cause Readmission within 30 days of discharge. Results The study sample consisted of 1 169 014 index admissions among 1 003 664 unique Medicare beneficiaries (41.5% men; mean [SD] age, 79.9 [8.3] years) in 2215 Hospitals. Compared with adjustment for patient characteristics currently implemented by Medicare, adjustment for the additional characteristics reduced overall variation in Hospital Readmission rates by 9.6%, changed rates upward or downward by 0.37 to 0.72 percentage points for the 10% of Hospitals most affected by the additional adjustments (±30.3% to ±58.9% of the Hospital-level standard deviation), and would be expected to reduce penalties (in relative terms) by 52%, 46%, and 41% for Hospitals with the largest 1%, 5%, and 10% of penalty reductions, respectively. The additional adjustments reduced the mean difference in Readmission rates between Hospitals in the top and bottom quintiles of high-risk patients by 0.53 percentage points (95% CI, 0.50-0.55;P Conclusions and Relevance Hospitals serving higher-risk patients may be penalized substantially because of the patients they serve rather than their quality of care. Adjusting solely for within-Hospital associations may allow adjustment for additional patient characteristics to mitigate unintended consequences of pay for performance without holding Hospitals to different standards because of the patients they serve.
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patient characteristics and differences in Hospital Readmission rates
JAMA Internal Medicine, 2015Co-Authors: Michael L Barnett, John Hsu, Michael J McwilliamsAbstract:Importance Medicare penalizes Hospitals with higher than expected Readmission rates by up to 3% of annual inpatient payments. Expected rates are adjusted only for patients’ age, sex, discharge diagnosis, and recent diagnoses. Objective To assess the extent to which a comprehensive set of patient characteristics accounts for differences in Hospital Readmission rates. Design, Setting, and Participants Using survey data from the nationally representative Health and Retirement Study (HRS) and linked Medicare claims for HRS participants enrolled in Medicare who were Hospitalized from 2009 to 2012 (n = 8067 admissions), we assessed 29 patient characteristics from survey data and claims as potential predictors of 30-day Readmission when added to standard Medicare adjustments of Hospital Readmission rates. We then compared the distribution of these characteristics between participants admitted to Hospitals with higher vs lower Hospital-wide Readmission rates reported by Medicare. Finally, we estimated differences in the probability of Readmission between these groups of participants before vs after adjusting for the additional patient characteristics. Main Outcomes and Measures All-cause Readmission within 30 days of discharge. Results Of the additional 29 patient characteristics assessed, 22 significantly predicted Readmission beyond standard adjustments, and 17 of these were distributed differently between Hospitals in the highest vs lowest quintiles of publicly reported Hospital-wide Readmission rates (P ≤ .04 for all comparisons). Almost all of these differences (16 of 17) indicated that participants admitted to Hospitals in the highest quintile of Readmission rates were more likely to have characteristics that were associated with a higher probability of Readmission. The difference in the probability of Readmission between participants admitted to Hospitals in the highest vs lowest quintile of Hospital-wide Readmission rates was reduced by 48% from 4.41 percentage points with standard adjustments used by Medicare to 2.29 percentage points after adjustment for all patient characteristics assessed (reduction in difference: −2.12; 95% CI, −3.33 to −0.67;P = .003). Conclusions and Relevance Patient characteristics not included in Medicare’s current risk-adjustment methods explained much of the difference in Readmission risk between patients admitted to Hospitals with higher vs lower Readmission rates. Hospitals with high Readmission rates may be penalized to a large extent based on the patients they serve.
Morgan E Grams - One of the best experts on this subject based on the ideXlab platform.
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early Hospital Readmission after simultaneous pancreas kidney transplantation patient and center level factors
American Journal of Transplantation, 2016Co-Authors: Elizabeth A King, Lauren M Kucirka, Mara A Mcadamsdemarco, Allan B Massie, Al F Ammary, Rizwan Ahmed, Morgan E GramsAbstract:Early Hospital Readmission is associated with increased morbidity, mortality, and cost. Following simultaneous pancreas-kidney transplantation, rates of Readmission and risk factors for Readmission are unknown. We used United States Renal Data System data to study 3643 adult primary first-time simultaneous pancreas-kidney recipients from December 1, 1999 to October 31, 2011. Early Hospital Readmission was any Hospitalization within 30 days of discharge. Modified Poisson regression was used to determine the association between Readmission and patient-level factors. Empirical Bayes statistics were used to determine the variation attributable to center-level factors. The incidence of Readmission was 55.5%. Each decade increase in age was associated with an 11% lower risk of Readmission to age 40, beyond which there was no association. Donor African-American race was associated with a 13% higher risk of Readmission. Each day increase in length of stay was associated with a 2% higher risk of Readmission until 14 days, beyond which each day increase was associated with a 1% reduction in the risk of Readmission. Center-level factors were not associated with Readmission. The high incidence of early Hospital Readmission following simultaneous pancreas-kidney transplant may reflect clinical complexity rather than poor quality of care.
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sequelae of early Hospital Readmission after kidney transplantation
American Journal of Transplantation, 2014Co-Authors: Mara A Mcadamsdemarco, Elizabeth A King, Morgan E Grams, Niraj M Desai, Dorry L SegevAbstract:We recently elucidated risk factors for early Hospital Readmission (EHR) following kidney transplantation (KT). We now sought to quantify the independent associations between EHR and post-KT outcomes, including late Hospital Readmission (LHR: 1 year after EHR window), death-censored graft loss and mortality, among Medicare-primary KT recipients (2000-2005). Of 32961 KT recipients, 7.7% had at least one Readmission within 3 days of discharge, 14.8% within 7 days, 22.4% within 14 days and 30.5% within 30 days of discharge after the initial KT Hospitalization. KT recipients who experienced EHR within 30 days of discharge after the initial KT Hospitalization were more likely to have experienced LHR (29.6% vs. 9.0%, p<0.001) and were at 3.02 times higher (95% CI: 2.82-3.23, p<0.001) risk of LHR. Additionally, EHR was associated with death-censored graft loss (deceased donor recipients hazard ratio [HR]: 1.43, 95% CI: 1.36-1.51, p<0.001 and live donor recipients HR: 1.54, 95% CI: 1.40-1.70, p<0.001) and mortality (deceased donor recipients HR: 1.50, 95% CI: 1.43-1.58, p<0.001 and live donor recipients HR: 1.45, 95% CI: 1.32-1.60, p<0.001). Thirty days posttransplant represents a high-risk window for KT recipients and the Readmissions during this window are strong predictors of adverse sequelae, particularly LHRs. Efforts should be made to implement and improve systems to reduce LHR and subsequent graft loss and mortality among recipients with EHR.
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frailty and early Hospital Readmission after kidney transplantation
American Journal of Transplantation, 2013Co-Authors: Mara A Mcadamsdemarco, Morgan E Grams, Dorry L Segev, Andrew Law, Megan L Salter, Eric K H Chow, Jeremy D WalstonAbstract:Early Hospital Readmission (EHR) after kidney transplantation (KT) is associated with increased morbidity and higher costs. Registry-based recipient, transplant and center-level predictors of EHR are limited, and novel predictors are needed. We hypothesized that frailty, a measure of physiologic reserve initially described and validated in geriatrics and recently associated with early KT outcomes, might serve as a novel, independent predictor of EHR in KT recipients of all ages. We measured frailty in 383 KT recipients at Johns Hopkins Hospital. EHR was ascertained from medical records as ≥1 Hospitalization within 30 days of initial post-KT discharge. Frail KT recipients were much more likely to experience EHR (45.8% vs. 28.0%, p = 0.005), regardless of age. After adjusting for previously described registry-based risk factors, frailty independently predicted 61% higher risk of EHR (adjusted RR = 1.61, 95% CI: 1.18-2.19, p = 0.002). In addition, frailty improved EHR risk prediction by improving the area under the receiver operating characteristic curve (p = 0.01) as well as the net reclassification index (p = 0.04). Identifying frail KT recipients for targeted outpatient monitoring and intervention may reduce EHR rates.
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early Hospital Readmission after kidney transplantation patient and center level associations
American Journal of Transplantation, 2012Co-Authors: Mara A Mcadamsdemarco, Morgan E Grams, Dorry L Segev, Erin C Hall, Josef CoreshAbstract:Early Hospital Readmission (EHR) is associated with increased morbidity, costs and transition-of-care errors. We sought to quantify rates of and risk factors for EHR after kidney transplantation (KT). We studied 32 961 Medicare primary KT recipients (2000–2005) linked to Medicare claims through the United States Renal Data System. EHR was defined as at least one Hospitalization within 30 days of initial discharge after KT. The association between EHR and recipient and transplant factors was explored using Poisson regression; hierarchical modeling was used to account for study center-level differences. The overall EHR rate was 31%, and 19 independent patient-level factors associated with EHR were identified: recipient factors included older age, African American race and various comorbidities; transplant factors included ECD, length of stay and lack of induction therapy. The unadjusted rate of EHR by center ranged from 18% to 47%, but conventional center-level factors (percent African American, percent age > 60, percent deceased donor and percent expanded criteria donor) were not associated with EHR. However, intermediate total volume and average length of stay were associated with increased EHR risk. Better identification of patients at risk for early Hospital Readmission following KT may guide discharge planning and early posttransplant outpatient monitoring.