Dataset Information

Complications after discharge predict readmission after colorectal surgery.

ABSTRACT:

Background

Health care providers, hospitals, and pay-for-performance programs are focused on strategies identifying patients at highest risk for re-admission after colorectal surgery. The study objective was to determine characteristics most associated with re-admission after elective colorectal surgery using a conceptual framework approach.

Methods

This is an observational study of Michigan Surgical Quality Collaborative clinical registry data for 8962 colorectal surgery cases between July-2012 and April-2015. Separate mixed models were fit using known re-admission risk factors aligned in categories that may impact re-admissions by different mechanisms. Overall model discrimination was evaluated using Area Under the Curve estimated on a hold-out data set and examining differences in predicted versus observed re-admission across risk quintiles.

Results

The overall 30-day re-admission rate was 10.5%. From Model 1 to Model 6, discrimination of re-admission was poor until Model 6 (AUC, 0.56, 0.61, 0.65, 0.63, 0.72, 0.81). Differences for observed re-admission rates comparing 'very low' versus 'very high' risk strata from Model 1 to Model 6 were 6%, 11%, 15%, 14%, 20%, and 30% respectively, and all comparisons were significant (p < 0.01). Though there were significant predictors in the first five models, most were no longer significant when additional predictors were included in subsequent models. Complications identified after discharge significantly increased the likelihood of re-admission and were the strongest predictors.

Conclusion

Statistical models that include complications identified after discharge predict re-admission. Strategies to reduce re-admission after colorectal surgery should emphasize prevention of complications and more effective interventions to manage and ameliorate evolving complications identified after discharge.

SUBMITTER: Albright J

PROVIDER: S-EPMC6488217 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Complications after discharge predict readmission after colorectal surgery.

Albright Jeremy J Batool Farwa F Cleary Robert K RK Mullard Andrew J AJ Kreske Edward E Ferraro Jane J Regenbogen Scott E SE

Surgical endoscopy 20180827 4

<h4>Background</h4>Health care providers, hospitals, and pay-for-performance programs are focused on strategies identifying patients at highest risk for re-admission after colorectal surgery. The study objective was to determine characteristics most associated with re-admission after elective colorectal surgery using a conceptual framework approach.<h4>Methods</h4>This is an observational study of Michigan Surgical Quality Collaborative clinical registry data for 8962 colorectal surgery cases be ...[more]

PMID: 30167952

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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.

Project description:Importance:Promoting patient mobility during hospitalization is associated with improved outcomes and reduced risk of hospitalization-associated functional decline. Therefore, accurate measurement of mobility with high-information content data may be key to improved risk prediction models, identification of at-risk patients, and the development of interventions to improve outcomes. Remote monitoring enables measurement of multiple ambulation metrics incorporating both distance and speed. Objective:To evaluate novel ambulation metrics in predicting 30-day readmission rates, discharge location, and length of stay using a real-time location system to continuously monitor the voluntary ambulations of postoperative cardiac surgery patients. Design, Setting, and Participants:This prognostic cohort study of the mobility of 100 patients after cardiac surgery in a progressive care unit at Johns Hopkins Hospital was performed using a real-time location system. Enrollment occurred between August 29, 2016, and April 4, 2018. Data analysis was performed from June 2018 to December 2019. Main Outcomes and Measures:Outcome measures included 30-day readmission, discharge location, and length of stay. Digital records of all voluntary ambulations were created where each ambulation consisted of multiple segments defined by distance and speed. Ambulation profiles consisted of 19 parameters derived from the digital ambulation records. Results:A total of 100 patients (81 men [81%]; mean [SD] age, 63.1 [11.6] years) were evaluated. Distance and speed were recorded for more than 14 000 segments in 840 voluntary ambulations, corresponding to a total of 127.8 km (79.4 miles) using a real-time location system. Patient ambulation profiles were predictive of 30-day readmission (sensitivity, 86.7%; specificity, 88.2%; C statistic, 0.925 [95% CI, 0.836-1.000]), discharge to acute rehabilitation (sensitivity, 84.6%; specificity, 86.4%; C statistic, 0.930 [95% CI, 0.855-1.000]), and length of stay (correlation coefficient, 0.927). Conclusions and Relevance:Remote monitoring provides a high-information content description of mobility, incorporating elements of step count (ambulation distance and related parameters), gait speed (ambulation speed and related parameters), frequency of ambulation, and changes in parameters on successive ambulations. Ambulation profiles incorporating multiple aspects of mobility enables accurate prediction of clinically relevant outcomes.

Project description:BACKGROUND:Restrictive transfusion strategies supported by large randomized trials are resulting in decreased blood utilization in cardiac surgery. What remains to be determined, however, is the impact of lower discharge hemoglobin (Hb) levels on readmission rates. We assessed patients with higher versus lower Hb levels on discharge to compare 30-day readmission rates after coronary artery bypass grafting (CABG). METHODS:We retrospectively evaluated 1552 patients undergoing isolated CABG at our institution from January 2013 to May 2016. We evaluated 2 Hb cohorts: "high" (above) and "low" (below) the mean discharge Hb level of 9.4 g/dL, comparing patient characteristics, blood utilization, and clinical outcomes including 30-day readmission rates. We further evaluated the effects of the lowest (<8 g/dL) discharge Hb levels on 30-day readmission rates by dividing the patients into 4 anemia cohorts based on discharge Hb levels: "no anemia" (>12 g/dL), "mild anemia" (10-11.9 g/dL), "moderate anemia" (8-9.9 g/dL), and "severe anemia" (<8 g/dL). Risk adjustment accounted for age, sex, Charlson comorbidity index, preoperative comorbidities, revision sternotomy, and patient blood management program implementation. RESULTS:The "high" and "low" groups had similar patient characteristics except for Hb levels (mean discharge Hb was 10.4 ± 0.9 vs 8.5 ± 0.6 g/dL, respectively). Notably, no evidence for a difference in 30-day readmission rates was noted between the "high" (76/746; 10.2%) and "low" (97/806; 12.0%) (P = .25) Hb cohorts. The 4 anemia cohorts had differences in age, revision sternotomy incidence, Hb levels, certain patient comorbidities, and time to readmission. On multivariable analysis, the risk-adjusted odds of readmission in the "low" Hb cohort (odds ratio, 1.16; 95% confidence interval, 0.84-1.61; P = .36) was not significant compared to the "high" Hb cohort. Compared to patients with discharge Hb ?8 g/dL, patients with Hb <8 g/dL had a higher incidence of readmission (22/129; 17.1% vs 151/1423; 10.6%; P = .036). On multivariable analysis, Hb <8 g/dL on discharge was predictive of readmission (odds ratio, 1.77; 95% confidence interval, 1.05-2.88; P = .03). The most common reason for readmission was volume overload, followed by infection and arrhythmias. CONCLUSIONS:A discharge Hb level below the institution mean for CABG patients does not provide evidence for an association with an increased 30-day readmission rate. In the small number of patients discharged with Hb <8 g/dL, there is a suggestion of increased risk for readmission and larger more controlled studies are needed to verify or refute this finding.

Project description:The Centers for Medicare and Medicaid Services have implemented penalties for hospitals with above-average readmission rates under the Hospital Readmissions Reductions Program. These changes will likely be extended to affect postoperative readmissions in the future.To identify variables that place patients at risk for readmission, develop a predictive nomogram, and validate this nomogram.Retrospective review and prospective validation of a predictive nomogram. A predictive nomogram was developed with the linear predictor method using the American College of Surgeons National Surgical Quality Improvement Program database paired with institutional billing data for patients who underwent nonemergent inpatient general surgery procedures. The nomogram was developed from August 1, 2006, through December 31, 2011, in 2799 patients and prospectively validated from November 1, 2013, through December 19, 2013, in 255 patients at a single academic institution. Area under the curve and positive and negative predictive values were calculated.The outcome of interest was readmission within 30 days of discharge following an index hospitalization for a surgical procedure.Bleeding disorder (odds ratio, 2.549; 95% CI, 1.464-4.440), long operative time (odds ratio, 1.601; 95% CI, 1.186-2.160), in-hospital complications (odds ratio, 16.273; 95% CI, 12.028-22.016), dependent functional status, and the need for a higher level of care at discharge (odds ratio, 1.937; 95% CI, 1.176-3.190) were independently associated with readmission. The nomogram accurately predicted readmission (C statistic?=?0.756) in a prospective evaluation. The negative predictive value was 97.9% in the prospective validation, while the positive predictive value was 11.1%.Development of an online calculator using this predictive model will allow us to identify patients who are at high risk for readmission at the time of discharge. Patients with increased risk may benefit from more intensive postoperative follow-up in the outpatient setting.

Dataset Information

Complications after discharge predict readmission after colorectal surgery.

Background

Methods

Results

Conclusion

Publications

Complications after discharge predict readmission after colorectal surgery.

Similar Datasets

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