Sharpening the focus on causes and timing of readmission after radical cystectomy for bladder cancer

Hu, Michael S.; Jacobs, Bruce L.; Montgomery, Jeffrey S.; He, Chang; Ye, Jun; Zhang, Yun; Brathwaite, Julien; Morgan, Todd M.; Hafez, Khaled S.; Weizer, Alon Z.; Gilbert, Scott M.; Lee, Cheryl T.; Lavieri, Mariel S.; Helm, Jonathan E.; Hollenbeck, Brent K.; Skolarus, Ted A.

doi:10.1002/cncr.28586

Cited by 74 publications

(65 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ninety-day mortality was seen at a significantly higher rate in the LACE high-risk group (9.1 vs. 3.5%, p < 0.001), and the LACE score predicted an adjusted odds of 2.09 for morality with a reasonable predictive value (AUC of 0.7181). The overall 90-day re-admission rate was 18.4%, and consistent with reported rates in the literature (16-25%) [2,3,4,5,6]. Higher readmission rates were seen in high-risk patients as identified by LACE score (22.8 vs. 17.7%, p = 0.002), and while uni-variate analysis demonstrated a strong relationship with high-risk designation and readmission, adjusted multi-variate analysis demonstrated only a borderline increase in risk with poor predictive value for 90-day readmission (AUC = 0.5812).…”

Section: Discussionsupporting

confidence: 79%

“…In addition to having the longest postoperative length of stay of all urological procedures, radical cystectomies also have the highest 90-day readmission rate (16-25%) [2,3,4,5,6] and 90-day mortality rate (3.7- 10.6%) [2,7,8]. Over 65% of these readmissions occur within 10-12 days of discharge, suggesting that detailed discharge planning and close outpatient follow-up are warranted [3,4]. …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The LACE Score as a Tool to Identify Radical Cystectomy Patients at Increased Risk of 90-Day Readmission and Mortality

Saluk¹,

Blackwell²,

Gange³

et al. 2018

Current Urology

View full text Add to dashboard Cite

Introduction: Radical cystectomy for bladder cancer is associated with high rates of readmission. We investigated the LACE score, a validated prediction tool for readmission and mortality, in the radical cystectomy population. Materials & Methods: Patients who underwent radical cystectomy for bladder cancer were identified by ICD-9 codes from the Healthcare Cost and Utilization Project State Inpatient Database for California years 2007-2010. The LACE score was calculated as previously described, with components of L: length of stay, A: acuity of admission, C: comorbidity, and E: number of emergency department visits within 6 months preceding surgery. Results: Of 3,470 radical cystectomy patients, 638 (18.4%) experienced 90-day readmission, and 160 (4.6%) 90-day mortality. At a previously validated “high-risk” LACE score ≥ 10, patients experienced an increased risk of 90-day readmission (22.8 vs. 17.7%, p = 0.002) and mortality (9.1 vs. 3.5%, p < 0.001). On adjusted multivariable analysis, “high risk” patients by LACE score had increased 90-day odds of readmission (adjusted OR = 1.24, 95% CI: 0.99-1.54, p = 0.050) and mortality (adjusted OR = 2.09, 95% CI: 1.47-2.99, p < 0.001). Conclusion: The LACE score reasonably identifies patients at risk for 90-day mortality following radical cystectomy, but only poorly predicts readmission. Providers may use the LACE score to target high-risk patients for closer follow-up or intervention.

show abstract

Section: Discussionsupporting

confidence: 79%

Section: Introductionmentioning

confidence: 99%

The LACE Score as a Tool to Identify Radical Cystectomy Patients at Increased Risk of 90-Day Readmission and Mortality

Saluk¹,

Blackwell²,

Gange³

et al. 2018

Current Urology

View full text Add to dashboard Cite

show abstract

“…We confirmed the morbidity revealing a 22.8% readmission, 55.2% complication, and 2.3% in-hospital mortality rate, which is similar to other published series. [7][8][9]13,14 We also revealed that treatment at higher-volume centres or academic centres was not protective or predictive of readmission. Academic centres were less likely to discharge patients to PACFs, which may be protective.…”

mentioning

confidence: 73%

Few modifiable factors predict readmission following radical cystectomy

Minnillo

Maurice

Schiltz

et al. 2015

CUAJ

View full text Add to dashboard Cite

Introduction: We sought to determine the patient and providerrelated factors associated with readmission after radical cystectomy (RC) for bladder cancer. In this era of healthcare reform, hospital performance measures, such as readmission, are beginning to affect provider reimbursement. Given its high readmission rate, RC could be a target for quality improvement. Methods: We reviewed bladder cancer patients who underwent RC in California's State Inpatient Database (2005Database ( -2009) of the Healthcare Cost and Utilization Project. We examined patient-(e.g., race, discharge disposition) and provider-related factors (e.g., volume) and evaluated their association with 30-day readmission. Multivariable logistic regression was used to examine associations of interest. Results: Overall, 22.8% (n = 833) of the 3649 patients who underwent RC were readmitted within 30 days. Regarding disposition, 34.8%, 50.8%, and 12.2% were discharged home, home with home healthcare, and to a post-acute care facility (PACF), respectively. Within 30 days, 20.3%, 20.9%, and 42.3% were discharged home, home with home healthcare, and to a PACF were readmitted, respectively. African Americans (odds ratio [OR] 1.64, 95% confidence interval [CI] 1.07-2.50), having ≥2 comorbidities (OR 1.42, 95% CI 1.06-1.91), receiving a neobladder (OR 1.45, 95% CI 1.09-1.93), and discharged to a PACF (OR 3.79, 95% CI 2.88-4.98) were independent factors associated with readmission. Hospital stays ≥15 days were associated with less readmission (OR 0.43, p = 0.0002). Procedure volume was not associated with complication, in-hospital mortality, or readmission. Conclusions: About one-fifth of patients undergoing RC are readmitted. Patients who are discharged to a PACF, African American, and who have more extensive comorbidities tend to experience more readmissions. Increased efforts with care coordination among these patients may help reduce readmissions.

show abstract

“…This allows the model to capture not only the time to readmission, but also different time to readmission dynamics for different causes of readmission. This could potentially help clinicians better target diagnostic questioning and tests to look for specific readmission causing conditions at different times after discharge; an idea which is supported by the clinical literature (see Hu et al (2014)). Assuming that the vector of frailties ν is drawn from a multivariate distribution with density g(ν 1 , ..., ν M ) and t i for i = 1, .…”

Section: Accepted Articlementioning

confidence: 95%

Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization

Helm

Alaeddini

Stauffer

et al. 2016

Production and Operations Management

Self Cite

View full text Add to dashboard Cite

Hospital readmissions present an increasingly important challenge for healthcare organizations. Readmissions are expensive and often unnecessary, putting patients at risk and costing $15 billion annually in the US alone. Currently, 17% of Medicare patients are readmitted to a hospital within 30 days of initial discharge with readmissions typically being more expensive than the original visit to the hospital. Recent legislation penalizes organizations with a high readmission rate. The medical literature conjectures that many readmissions can be avoided or mitigated by post-discharge monitoring. To develop a good monitoring plan it is critical to anticipate the timing of a potential readmission and to effectively monitor the patient for readmission causing conditions based on that knowledge. This research develops new methods to empirically generate an individualized estimate of the time to readmission density function and then uses this density to optimize a post-discharge monitoring schedule and staffing plan to support monitoring needs. Our approach integrates classical prediction models with machine learning and transfer learning to develop an empirical density that is personalized to each patient. We then transform an intractable monitoring plan optimization with stochastic discharges and health state evolution based on delay-time models into a weakly-coupled network flow model with tractable subproblems after applying a new pruning method that leverages the problem structure. Using this multi-methodologic approach on two large inpatient datasets, we show that optimal readmission prediction and monitoring plans can identify and mitigate 40%-70% of readmissions before they generate an emergency readmission.

show abstract

Sharpening the focus on causes and timing of readmission after radical cystectomy for bladder cancer

Cited by 74 publications

References 27 publications

The LACE Score as a Tool to Identify Radical Cystectomy Patients at Increased Risk of 90-Day Readmission and Mortality

The LACE Score as a Tool to Identify Radical Cystectomy Patients at Increased Risk of 90-Day Readmission and Mortality

Few modifiable factors predict readmission following radical cystectomy

Reducing Hospital Readmissions by Integrating Empirical Prediction with Resource Optimization

Contact Info

Product

Resources

About