The influence of modifiable risk factors on short-term postoperative outcomes following cervical spine surgery: A retrospective propensity score matched analysis

Abstract

Background: Modifiable risk factors (MRFs) represent patient variables associated with increased complication rates that may be prevented. There exists a paucity of studies that comprehensively analyze MRF subgroups and their independent association with postoperative complications in patients undergoing cervical spine surgery. Therefore, the purpose of this study is to compare outcomes between patients receiving cervical spine surgery with reported MRFs. Methods: Retrospective analysis of the Nationwide Readmissions Database (NRD) from the years 2016 and 2017, a publicly available and purchasable data source, to include adult patients undergoing cervical fusion. MRF cohorts were separated into three categories: substance abuse (alcohol, tobacco/nicotine, opioid abuse); vascular disease (hypertension, dyslipidemia); and dietary factors (malnutrition, obesity). Three-way nearest-neighbor propensity score matching for demographics, hospital, and surgical characteristics was implemented. Findings: We identified 9601 with dietary MRFs (D-MRF), 9654 with substance abuse MRFs (SA-MRF), and 9503 with vascular MRFs (V-MRF). Those with D-MRFs had significantly higher rates of medical complications (9.3%), surgical complications (8.1%), and higher adjusted hospital costs compared to patients with SA-MRFs and V-MRFs. Patients with D-MRFs (16.3%) and V-MRFs (14.0%) were independently non-routinely discharged at a significantly higher rate compared to patients with SA-MRFs (12.6%) (p<0.0001 and p = 0.0037). However, those with substance abuse had the highest readmission rate and were more commonly readmitted for delayed procedure-related infections. Interpretation: A large proportion of patients who receive cervical spine surgery have potential MRFs that uniquely influence their postoperative outcomes. A thorough understanding of patient-specific MRF subgroups allows for improved preoperative risk stratification, tailored patient counseling, and postoperative management planning.

Developing Mixed-effects Models to Optimize Prediction of Postoperative Outcomes in a Modern Sample of Over 450,000 Patients Undergoing Elective Cervical Spine Fusion Surgery

“…While comorbidity indices such as CCI and ECI create one score accounting for all comorbidities present within a patient, previous studies have evaluated the influence of specific risk factor categories on patient outcomes following spine surgery. Within the domain of cervical spine surgery specifically, a prior study by Shahrestani et al 17 demonstrated that substance abuse (SA), vascular (V), and dietary (D) MRFs may uniquely influence postoperative outcomes following cervical spine fusion procedures. Using a combined comorbidity score such as ECI, we still capture SA, V, and D diagnoses as individual points added to an overall score, but lose some granularity in terms of comorbidity etiology.…”

“…With the analytical advantages of big data and machine learning, we are now entering an era where training and clinical experience can be enhanced with the aid of high data density diagnostics, prognostication, and outcomes prediction. Utilizing these tools with large multicenter collaborative efforts such as nationally representative outcomes databases, studies have found that morbidity and mortality in cervical fusion surgery are associated with specific characteristics: age, [13][14][15] modifiable risk factors (MRFs) such as obesity and malnutrition, [16][17][18][19] and the influence of certain nonmodifiable factors, for example, frailty. 20,21 For these reasons, comorbidity indices such as the Elixhauser Comorbidity Index (ECI) and Charlson Comorbidity Index (CCI) have been developed to augment clinical prognostication efforts.…”

Comparison of Clinical Data Between Patients With Complications and Without Complications After Spinal Tuberculosis Surgery: A Propensity Score Matching Analysis

“…PSM analysis strategies, such as tumor chemotherapy ( 47 ), cervical spine surgery ( 48 ), robot-assisted surgery ( 49 ), and other areas, are widely used in medical research. The PSM analysis can reduce the difference between the two groups of comparable data and decrease the effect of interfering factors on the results by adjusting the balance of baseline data ( 22 ).…”

Put Down the Tin

“…3,4 Some modifiable risk factors for complications after lumbar fusion include substance abuse, such as alcohol, opioids, and tobacco use, hypertension, dyslipidemia, malnutrition, and obesity. 5 Tobacco use is a prevalent modifiable risk factor that has been demonstrated to be associated with wound complications, deep infections, and pseudarthrosis, as well as an increase in the risk of inhospital mortality in patients undergoing orthopedic procedures. 6,7 Tobacco use includes both smoking and smokeless tobacco, such as e-cigarettes or chewing tobacco.…”