Jonathan E. Helm

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

Readmissions after radical cystectomy are common, burdensome, and poorly understood. For these reasons, the authors conducted a population‐based study that focused on the causes of and time to readmission after radical cystectomy.

Understanding hospital readmission intensity after radical cystectomy.

PURPOSE Hospital readmissions after radical cystectomy vary with respect to intensity in terms of impact on patients and health care systems. Therefore, we conducted a population based study to examine factors associated with increasing readmission intensity after radical cystectomy for bladder cancer. MATERIALS AND METHODS Using SEER (Surveillance, Epidemiology, and End Results)-Medicare data we identified 1,782 patients who underwent radical cystectomy from 2003 to 2009. We defined readmission intensity in terms of length of stay (days) divided into quartiles of less than 3 (lowest), 3 to 4, 5 to 7 and more than 7 (highest). We used logistic regression to examine factors associated with readmission intensity. RESULTS More than half of the patients with the highest intensity readmissions were readmitted within the first week and 77% were readmitted within 2 weeks of discharge. Patients with the highest intensity readmissions were similar in age, gender, race, socioeconomic status, pathological stage, comorbidity, neoadjuvant chemotherapy use and urinary diversion type compared to patients with the lowest intensity readmissions. After multivariable adjustment, complications during the index cystectomy admission (p <0.001), readmission week (p=0.04), and the interaction between index length of stay and discharge to a skilled nursing facility (p=0.04) were associated with the highest readmission intensity. CONCLUSIONS Readmission intensity differs widely after discharge following radical cystectomy. As postoperative efforts to minimize the readmission burden increase, a better understanding of the factors that contribute to the highest intensity readmissions will help direct limited resources (eg telephone calls, office visits) toward high yield areas.

Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support

In managing chronic diseases such as glaucoma, the timing of periodic examinations is crucial, as it may significantly impact patients’ outcomes. We address the question of when to monitor a glaucoma patient by integrating a dynamic, stochastic state space system model of disease evolution with novel optimization approaches to predict the likelihood of progression at any future time. Information about each patient’s disease state is learned sequentially through a series of noisy medical tests. This information is used to determine the best time to next test based on each patient’s individual disease trajectory as well as population information. We develop closed-form solutions and study structural properties of our algorithm. While some have proposed that fixed-interval monitoring can be improved upon, our methodology validates a sophisticated model-based approach to doing so. Based on data from two large-scale, 10+ years clinical trials, we show that our methods significantly outperform fixed-interval sc...

A model to optimize followup care and reduce hospital readmissions after radical cystectomy

PURPOSE Radical cystectomy has one of the highest readmission rates across all surgical procedures at approximately 25%. We developed a mathematical model to optimize outpatient followup regimens for radical cystectomy. MATERIALS AND METHODS We used delay-time analysis, a systems engineering approach, to maximize the probability of detecting patients susceptible to readmission through office visits and telephone calls. Our data source includes patients readmitted after radical cystectomy from the Healthcare Cost and Utilization Project State Inpatient Databases in 2009 and 2010 as well as from our institutional bladder cancer database from 2007 to 2011. We measured the interval from hospital discharge to the point when a patient first exhibits concerning symptoms. Our primary end point is 30-day hospital readmission. Our model optimized the timing and sequence of followup care after radical cystectomy. RESULTS The timing of office visits and telephone calls is more important in detecting a patient at risk for readmission than the sequence of these encounters. Patients are most likely to exhibit concerning symptoms between 4 and 5 days after discharge home. An optimally scheduled office visit can detect up to 16% of potential readmissions, which can be increased to 36% with 1 office visit followed by 4 telephone calls. CONCLUSIONS Our model improves the detection of concerning symptoms after radical cystectomy by optimizing the timing and number of outpatient encounters. By understanding how to design better outpatient followup care for patients treated with radical cystectomy we can help reduce the readmission burden for this population.

Characterizing an effective hospital admissions scheduling and control management system: a genetic algorithm approach

Proper management of hospital inpatient admissions involves a large number of decisions that have complex and uncertain consequences for hospital resource utilization and patient flow. Further, inpatient admissions has a significant impact on the hospitals profitability, access, and quality of care. Making effective decisions to drive high quality, efficient hospital behavior is difficult, if not impossible, without the aid of sophisticated decision support. Hancock and Walter (1983) developed such a management system with documented implementation success, but for each hospital the system parameters are “optimized” manually. We present a framework for valuing instances of this management system via simulation and optimizing the system parameters using a genetic algorithm based search. This approach reduces the manual overhead in designing a hospital management system and enables the creation of Pareto efficiency curves to better inform management of the trade-offs between critical hospital metrics when designing a new control system.

No Differences in Population-based Readmissions After Open and Robotic-assisted Radical Cystectomy: Implications for Post-discharge Care

OBJECTIVE To inform whether readmission reduction strategies should consider surgical approach, we examined readmission differences between open and robotic-assisted radical cystectomy (RARC) using population-based data. METHODS We identified patients who underwent cystectomy between January 2010 and September 2013 based on International Classification of Diseases-9th edition codes and administrative claims from a large, national US health insurer (Clinformatics Data Mart Database, OptumInsight, Eden Prairie, MN). We assessed post-discharge health system utilization and tested for differences in readmissions after the 2 surgical approaches. RESULTS We identified 935 patients treated with cystectomy: open = 785 (84%) and RARC = 150 (16%). Patients undergoing RARC were slightly older, male, had more ileal conduit urinary reconstruction, and less need for intensive care. Index length of stay was shorter for RARC than for open surgery (7 days vs 8 days, P < .001). However, we found no differences in 30-day readmission rates (24% open vs 29% RARC, P = .26) or other readmission parameters, including readmission length of stay (5 days open vs 4 days RARC, P = .32), emergency department use (22% open vs 24% RARC, P = .86), reasons for readmission, or timing of first outpatient visits (11.5 days open vs 9 days RARC, P = .41). For both approaches, the majority of patients were readmitted within 2 weeks. CONCLUSION The surgical approach to cystectomy does not appear to impact readmissions. Strategies to reduce the readmission burden after cystectomy do not need to consider surgical approach but should focus on timing of medical contacts.

Using filtered forecasting techniques to determine personalized monitoring schedules for patients with open-angle glaucoma

PURPOSE To determine whether dynamic and personalized schedules of visual field (VF) testing and intraocular pressure (IOP) measurements result in an improvement in disease progression detection compared with fixed interval schedules for performing these tests when evaluating patients with open-angle glaucoma (OAG). DESIGN Secondary analyses using longitudinal data from 2 randomized controlled trials. PARTICIPANTS A total of 571 participants from the Advanced Glaucoma Intervention Study (AGIS) and the Collaborative Initial Glaucoma Treatment Study (CIGTS). METHODS Perimetric and tonometric data were obtained for AGIS and CIGTS trial participants and used to parameterize and validate a Kalman filter model. The Kalman filter updates knowledge about each participants disease dynamics as additional VF tests and IOP measurements are obtained. After incorporating the most recent VF and IOP measurements, the model forecasts each participants disease dynamics into the future and characterizes the forecasting error. To determine personalized schedules for future VF tests and IOP measurements, we developed an algorithm by combining the Kalman filter for state estimation with the predictive power of logistic regression to identify OAG progression. The algorithm was compared with 1-, 1.5-, and 2-year fixed interval schedules of obtaining VF and IOP measurements. MAIN OUTCOME MEASURES Length of diagnostic delay in detecting OAG progression, efficiency of detecting progression, and number of VF and IOP measurements needed to assess for progression. RESULTS Participants were followed in the AGIS and CIGTS trials for a mean (standard deviation) of 6.5 (2.8) years. Our forecasting model achieved a 29% increased efficiency in identifying OAG progression (P<0.0001) and detected OAG progression 57% sooner (reduced diagnostic delay) (P = 0.02) than following a fixed yearly monitoring schedule, without increasing the number of VF tests and IOP measurements required. The model performed well for patients with mild and advanced disease. The model performed significantly more testing of patients who exhibited OAG progression than nonprogressing patients (1.3 vs. 1.0 tests per year; P<0.0001). CONCLUSIONS Use of dynamic and personalized testing schedules can enhance the efficiency of OAG progression detection and reduce diagnostic delay compared with yearly fixed monitoring intervals. If further validation studies confirm these findings, such algorithms may be able to greatly enhance OAG management.

Dynamic Monitoring and Control of Irreversible Chronic Diseases with Application to Glaucoma

To effectively manage chronic disease patients, clinicians must know (1) how to monitor each patient (i.e., when to schedule the next visit and which tests to take), and (2) how to control the disease (i.e., what levels of controllable risk factors will sufficiently slow progression). Our research addresses these questions simultaneously and provides the optimal solution to a novel linear quadratic Gaussian state space model. For the new objective of minimizing the relative change in state over time (i.e., disease progression), which is necessary for management of irreversible chronic diseases, we show that the classical two-way separation of estimation and control holds, thereby making a previously intractable problem solvable by decomposition into two separate, tractable problems while maintaining optimality. The resulting optimization is applied to the management of glaucoma. Based on data from two large randomized clinical trials, we validate our model and demonstrate how our decision support tool can provide actionable insights to the clinician caring for a patient with glaucoma. This methodology can be applied to a broad range of irreversible chronic diseases to optimally devise patient-specific monitoring and treatment plans.

The Fate of Radical Cystectomy Patients after Hospital Discharge: Understanding the Black Box of the Pre-readmission Interval

BACKGROUND Radical cystectomy has one of the highest 30-d hospital readmission rates but circumstances leading to readmission remain poorly understood. OBJECTIVE To examine the postdischarge period and better understand hospital readmission after radical cystectomy. DESIGN, SETTING, AND PARTICIPANTS We conducted a retrospective cohort study of patients treated with radical cystectomy for bladder cancer from 2005 to 2012 using our institutional database. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS We assessed patient communication with any healthcare system after hospital discharge based on timing, methods, and concern types. Logistic regression and Cox proportional-hazards analyses were used to compare postdischarge concerns among readmitted and nonreadmitted patients. We internally validated the logistic model using a bootstrap resampling technique. RESULTS AND LIMITATIONS One-hundred patients (23%) were readmitted within 30 d of index discharge. Readmitted patients were more likely to use the emergency department with initial concerns compared with nonreadmitted patients (27% vs 1.0%, p<0.001). Patients who took longer to first communicate their concerns and who were able to tolerate their symptoms longer had lower odds of readmission. Patients who reported infection (adjusted hazard ratio: 2.8, 95% confidence interval: 1.4-5.8) and failure to thrive concerns (adjusted hazard ratio: 4.4, 95% confidence interval: 2.0-9.3) were more likely to be readmitted compared with those who communicated noninfectious wounds and/or urinary concerns. CONCLUSIONS Radical cystectomy patients who contact the health system soon after discharge or communicated infectious or failure to thrive symptoms (fever, poor oral intake, or vomiting) are more likely to experience readmission as opposed to those that endorse pain, constipation, or ostomy issues. Better understanding of this pre-readmission interval can optimize postdischarge practices. PATIENT SUMMARY We looked at bladder cancer patients who had surgery and the reasons why they were readmitted to hospital. We found patients who had a fever or difficulty with eating and maintaining their weight had the highest chance of being readmitted.

Predictors and Cost of Readmission in Total Knee Arthroplasty

BACKGROUND The Comprehensive Care for Joint Replacement bundle was created to decrease total knee arthroplasty (TKA) cost. To help accomplish this, there is a focus on reducing TKA readmissions. However, there is a lack of national representative sample of all-payer hospital admissions to direct strategy, identify risk factors for readmission, and understand actual readmission cost. METHODS We used the Nationwide Readmission Database to examine national readmission rates, predictors of readmission, and associated readmission costs for elective TKA procedures. We fit a multivariable logistic regression model to examine factors associated with readmission. Then, we determined mean readmission costs and calculated the readmission cost when distributed across the entire TKA population. RESULTS We identified 224,465 patients having TKA across all states participating in the Nationwide Readmission Database. The mean unadjusted 30-day TKA readmission rate was 4%. The greatest predictors of readmission were congestive heart failure (odds ratio [OR] 2.51, 95% confidence interval [CI] 2.62-2.80), renal disease (OR 2.19, 95% CI 2.03-2.37), and length of stay greater than 4 days (OR 2.4, 95% CI 2.25-2.61). The overall median cost for each readmission was