Clinical-Bladder cancer
Dynamic readmission prediction using routine postoperative laboratory results after radical cystectomy

https://doi.org/10.1016/j.urolonc.2019.11.011Get rights and content

Highlights

Abstract

Objective

To determine if the addition of electronic health record data enables better risk stratification and readmission prediction after radical cystectomy. Despite efforts to reduce their frequency and severity, complications and readmissions following radical cystectomy remain common. Leveraging readily available, dynamic information such as laboratory results may allow for improved prediction and targeted interventions for patients at risk of readmission.

Methods

We used an institutional electronic medical records database to obtain demographic, clinical, and laboratory data for patients undergoing radical cystectomy. We characterized the trajectory of common postoperative laboratory values during the index hospital stay using support vector machine learning techniques. We compared models with and without laboratory results to assess predictive ability for readmission.

Results

Among 996 patients who underwent radical cystectomy, 259 patients (26%) experienced a readmission within 30 days. During the first week after surgery, median daily values for white blood cell count, urea nitrogen, bicarbonate, and creatinine differentiated readmitted and nonreadmitted patients. Inclusion of laboratory results greatly increased the ability of models to predict 30-day readmissions after cystectomy.

Conclusions

Common postoperative laboratory values may have discriminatory power to help identify patients at higher risk of readmission after radical cystectomy. Dynamic sources of physiological data such as laboratory values could enable more accurate identification and targeting of patients at greatest readmission risk after cystectomy. This is a proof of concept study that suggests further exploration of these techniques is warranted.

Introduction

Radical cystectomy has one of the highest rates of complications and readmissions of any surgical procedure, with 25% of patients experiencing unplanned readmission within 30 days [1], [2], [3], [4]. These high readmission rates, coupled with increasing policy focus on reducing readmissions, have motivated investigations into identification and optimization of patients at highest readmission risk. However, the ability to predict readmission using traditional administrative data is limited making it unclear where and when to focus resources, leaving readmission rates largely unchanged [5,6].

There is increasing interest in incorporating dynamic data sources into readmission prediction models to better enable identification of high-risk cohorts [7]. While traditional administrative data are typically limited to static factors (eg, demographics, comorbidities), widespread use of electronic health records has made dynamic sources of data, laboratory results for example, readily available. The degree to which readily available laboratory data used to guide day-to-day clinical decision-making might impact readmission risk prediction after cystectomy is unknown. Indeed, such variables can be successfully incorporated into prediction models to improve performance for other outcomes ranging from transfer to the intensive care unit to mortality [8], [9], [10], [11]. For cystectomy patients with frequent postoperative lab draws, models using dynamic laboratory data could allow for better risk stratification and postoperative planning.

In this context, we used data from our institutional electronic health record to examine whether incorporating dynamic laboratory data into readmission prediction models improved risk stratification after radical cystectomy. Specifically, we assessed daily postoperative values for commonly obtained laboratory tests, and used machine learning techniques to compare values between readmitted and nonreadmitted patients. This study as a proof of concept, exploratory work demonstrates the unique promise of readily available, dynamic data to inform risk stratification of patients most likely to be readmitted after cystectomy.

Section snippets

Data source

We used an institutional database containing records on all inpatient and outpatient visits at our tertiary care facilities. This dataset was queried for all inpatient encounters associated with a diagnosis of bladder cancer (International Classification of Diseases 9th Revision code 188.X) and procedural codes for radical cystectomy (57.71) for the period from 2006 to 2016. This yielded a cohort of 996 patients who underwent radical cystectomy during the study period.

Outcomes and covariates

Our primary outcome for

Results

Among the 996 patients included in this cohort, 259 (26%) were readmitted within 30 days of discharge. Readmitted and nonreadmitted patients were similar in their demographic and clinical characteristics, though readmitted patients had higher BMI values, on average (Table 1, P < 0.01). Most patients were older, married, Caucasian men, and the minority were treated with robotic cystectomy.

As illustrated in Fig. 1, several of the laboratory tests in this study showed differences between

Discussion

Using machine learning techniques, we found differences in common postoperative laboratory values between readmitted and nonreadmitted patients treated with cystectomy. We also calculated threshold values to help differentiate patients at high and low risk of readmission within 30 days of discharge. Moreover, incorporating daily postoperative laboratory value thresholds into our readmission prediction models increased discriminatory power as measured by the c-statistic, when compared to models

Conclusions

We found that readmission risk assessment following radical cystectomy is significantly improved by the addition of dynamic physiologic data collected in modern electronic health record systems. Future work should refine these algorithms and study their implementation into daily practice in order to help guide clinical decision-making. While the problem of readmission after radical cystectomy appears refractory, innovative, dynamic approaches to existing data sources appear poised to enable

Conflicts of Interest

None.

References (22)

  • GJ Escobar et al.

    Nonelective rehospitalizations and postdischarge mortality

    Med Care

    (2015)
  • Cited by (2)

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