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Predictors for Patient Discharge Destination After Elective Anterior Cervical Discectomy and Fusion
John Di Capua1, Sulaiman Somani1, Jun S Kim1
1Department of Orthopedics Surgery, Icahn School of Medicine at Mount Sinai, New York, NY.
Study Design:
Retrospective study of prospectively collected data.
Objective:
To identify risk factors for nonhome patient discharge after elective anterior cervical discectomy and fusion (ACDF).
Summary Of Background Data:
ACDF is one of the most performed spinal procedures and this is expected to increase in the coming years. To effectively deal with an increasing patient volume, identifying variables associated with patient discharge destination can expedite placement applications and subsequently reduce hospital length of stay.
Methods:
The 2011 to 2014 ACS-NSQIP database was queried using Current Procedural Terminology (CPT) codes 22551 or 22554. Patients were divided into two cohorts based on discharge destination. Bivariate and multivariate logistic regression analyses were employed to identify predictors for patient discharge destination and extended hospital length of stay.
Results:
A total of 14,602 patients met the inclusion criteria for the study of which 498 (3.4%) had nonhome discharge. Multivariate logistic regression found that Hispanic versus Black race/ethnicity (odds ratio, OR =0.21, 0.05-0.91, P =0.037), American Indian or Alaska Native, Asian, Native Hawaiian or Pacific Islander versus Black race/ethnicity (OR = 0.52, 0.34-0.80, p-value = 0.003), White versus Black race/ethnicity (OR = 0.55, 0.42-0.71), elderly age ≥65 years (OR = 3.32, 2.72-4.06), obesity (OR = 0.77, 0.63-0.93, P = 0.008), diabetes (OR = 1.32, 1.06-1.65, P = 0.013), independent versus partially/totally dependent functional status (OR = 0.11, 0.08-0.15), operation time ≥4 hours (OR = 2.46, 1.87-3.25), cardiac comorbidity (OR = 1.38, 1.10-1.72, P = 0.005), and ASA Class ≥3 (OR = 2.57, 2.05-3.20) were predictive factors in patient discharge to a facility other than home. In addition, multivariate logistic regression analysis also found nonhome discharge to be the most predictive variable in prolonged hospital length of stay.
Conclusion:
Several predictive factors were identified in patient discharge to a facility other than home, many being preoperative variables. Identification of these factors can expedite patient discharge applications and potentially can reduce hospital stay, thereby reducing the risk of hospital acquired conditions and minimizing health care costs.
Level Of Evidence:
3.
