Dataset Information

Readmission prediction after colorectal cancer surgery: A derivation and validation study.

ABSTRACT:

Background

Unplanned readmissions after colorectal cancer (CRC) surgery are common, expensive, and result from failure to progress in postoperative recovery. The context of their preventability and extent of predictability remains undefined. This study aimed to define the 30-day unplanned readmission (UR) rate after CRC surgery, identify risk factors, and develop a prediction model with external validation.

Methods

Consecutive patients who underwent CRC surgery between 2012 and 2017 at Christchurch Hospital were retrospectively identified. The primary outcome was UR within 30 days after index discharge. Statistically significant risk factors were identified and incorporated into a predictive model. The model was then externally evaluated on a prospectively recruited dataset from 2018 to 2019.

Results

Of the 701 patients identified, 15.1% were readmitted within 30 days of discharge. Stoma formation (OR 2.45, 95% CI 1.59-3.81), any postoperative complications (PoCs) (OR 2.27, 95% CI 1.48-3.52), high-grade PoCs (OR 2.52, 95% CI 1.18-5.11), and rectal cancer (OR 2.11, 95% CI 1.48-3.52) were statistically significant risk factors for UR. A clinical prediction model comprised of rectal cancer and high-grade PoCs predicted UR with an AUC of 0.64 and 0.62 on internal and external validation, respectively.

Conclusions

URs after CRC surgery are predictable and occur within 2 weeks of discharge. They are driven by PoCs, most of which are of low severity and develop after discharge. Atleast 16% of readmissions are preventable by management in an outpatient setting with appropriate surgical expertise. Targeted outpatient follow-up within two weeks of discharge is therefore the most effective transitional-care strategy for prevention.

SUBMITTER: D'Souza J

PROVIDER: S-EPMC10309978 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Readmission prediction after colorectal cancer surgery: A derivation and validation study.

D'Souza Joel J Eglinton Timothy T Frizelle Frank F

PloS one 20230629 6

<h4>Background</h4>Unplanned readmissions after colorectal cancer (CRC) surgery are common, expensive, and result from failure to progress in postoperative recovery. The context of their preventability and extent of predictability remains undefined. This study aimed to define the 30-day unplanned readmission (UR) rate after CRC surgery, identify risk factors, and develop a prediction model with external validation.<h4>Methods</h4>Consecutive patients who underwent CRC surgery between 2012 and 20 ...[more]

PMID: 37384713

Similar Datasets

Project description:BACKGROUND:Risk-prediction indices are one category of the many tools implemented to guide efforts to decrease readmissions. However, using fied models to predict a complex process can prove challenging. In addition, no risk-prediction index has been developed for patients undergoing colorectal surgery. Therefore, we evaluated the performance of a widely utilized simplified index developed at the hospital level - LACE (length of stay, acute admission, Charlson comorbidity index score, and emergency department visits) and developed and evaluated a novel index in predicting readmissions in this patient population. METHODS:Using a retrospective split-sample cohort, patients discharged after colorectal surgery were identified within the inpatient databases of the Healthcare Cost and Utilization Project for the states of New York, California, and Florida (2006-2014). The primary outcome was death or readmission within 30 days after discharge. Multivariable logistic regression models incorporated patient comorbidities, postoperative complications, and hospitalization details, and were evaluated using the C statistic. RESULTS:A total of 440,742 patients met eligibility criteria. The rate of death or readmission within 30 days after discharge was 14.0% (n = 61,757). When applied to surgical patients, the LACE index demonstrated a poor model fit (C = 0.631). The model fit improved significantly-but remained poor (C = 0.654; P < .001)-with the addition of the following variables, which are known to be associated with readmission after colorectal surgery: age, indication for surgery, and creation of a new ostomy. A novel, simplified model also yielded a poor model fit (C = 0.660). CONCLUSION:Postdischarge death or readmission after colorectal surgery is not accurately modeled using existing, modified, or novel simplified risk prediction models. Payers and providers must ensure that quality improvement efforts applying simplified models to complex processes, such as readmissions following colorectal surgery, may not be appropriate, and that models reflect the relevant patient population.

Dataset Information

Readmission prediction after colorectal cancer surgery: A derivation and validation study.

Background

Methods

Results

Conclusions

Publications

Readmission prediction after colorectal cancer surgery: A derivation and validation study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets