Institutional databases may underestimate the risk factors for 30-day unplanned readmissions compared to national databases

OBJECTIVE

The National Surgical Quality Improvement Program (NSQIP) and National Readmissions Database (NRD) are two widely used databases for research studies. However, they may not provide generalizable information in regard to individual institutions. Therefore, the objective of the present study was to evaluate 30-day readmissions following anterior cervical discectomy and fusion (ACDF) and posterior lumbar fusion (PLF) procedures by using these two national databases and an institutional cohort.

METHODS

The NSQIP and NRD were queried for patients undergoing elective ACDF and PLF, with the addition of an institutional cohort. The outcome of interest was 30-day readmissions following ACDF and PLF, which were unplanned and related to the index procedure. Subsequently, univariable and multivariable analyses were conducted to determine the predictors of 30-day readmissions by using both databases and the institutional cohort.

RESULTS

Among all identified risk factors, only hypertension was found to be a common risk factor between NRD and the institutional cohort following ACDF. NSQIP and the institutional cohort both showed length of hospital stay to be a significant predictor for 30-day related readmission following PLF. There were no overlapping variables among all 3 cohorts for either ACDF or PLF. Additionally, the national databases identified a greater number of risk factors for 30-day related readmissions than did the institutional cohort for both procedures.

CONCLUSIONS

Overall, significant differences were seen among all 3 cohorts with regard to top predictors of 30-day unplanned readmissions following ACDF and PLF. The higher quantity of significant predictors found in the national databases may suggest that looking at single-institution series for such analyses may result in underestimation of important variables affecting patient outcomes, and that big data may be helpful in addressing this concern.

Development of a postoperative ileus risk assessment scale: identification of intraoperative opioid exposure as a significant predictor after spinal surgery

OBJECTIVE

Postoperative ileus (POI) is associated with abdominal pain, nausea, vomiting, and delayed mobilization that in turn lead to diminished patient satisfaction, increased hospital length of stay (LOS), and increased healthcare costs. In this study, the authors developed a risk assessment scale to predict the likelihood of developing POI following spinal surgery.

METHODS

The authors undertook a retrospective review of a prospectively maintained registry of consecutive patients who underwent arthrodesis/fusion surgeries between May 2013 and December 2017. They extracted clinical information, including cumulative intraoperative and postoperative opioid doses using standardized converted morphine milligram equivalent (MME) values. Univariate and multivariate analyses were performed and several categorical and continuous variables were evaluated in a binary logistic regression model built with backward elimination to assess for independent predictors. A points-based prediction model was developed and validated to determine the risk of POI.

RESULTS

A total of 334 patients who underwent spinal fusion surgeries were included. Fifty-six patients (16.8%) developed POI, more frequently in those who underwent long-segment surgeries compared to short-segment surgeries (33.3% vs 10.4%; p < 0.001). POI was associated with an increased LOS when compared with patients who did not develop POI (8.0 ± 4.5 days vs 4.4 ± 2.4 days; p < 0.01). The incidences of liver disease (16% vs 3.7%; p = 0.01) and substance abuse history (12.0% vs 3.2%; p = 0.04) were higher in POI patients than non-POI patients undergoing short-segment surgeries. While the incidences of preoperative opioid intake (p = 0.23) and cumulative 24-hour (87.7 MME vs 73.2 MME; p = 0.08) and 72-hour (225.6 MME vs 221.4 MME; p = 0.87) postoperative opioid administration were not different, remifentanil (3059.3 µg vs 1821.5 µg; p < 0.01) and overall intraoperative opioid (326.7 MME vs 201.7 MME; p < 0.01) dosing were increased in the POI group. The authors derived a multivariate model based on the 5 most significant factors predictive of POI (number of surgical levels, intraoperative MME, liver disease, age, and history of substance abuse) and calculated relative POI risks using a derived 32-point system.

CONCLUSIONS

Intraoperative opioid administration, incorporated in a comprehensive risk assessment scale, represents an early and potentially modifiable predictor of POI. These data indicate that potential preventive strategies, implemented as part of enhanced recovery after surgery protocols, could be instituted in the preoperative phase of care to reduce POI incidence.