Extended Length of Stay After Lumbar Spine Surgery: Sick Patients, Postoperative Complications, or Practice Style Differences Among Hospitals and Physicians?

Adogwa, Owoicho; Lilly, Daniel T.; Khalid, Syed I.; Desai, Shyam A.; Vuong, Victoria D.; Davison, Mark A.; Ouyang, Bo; Bagley, Carlos A.; Cheng, Joseph

doi:10.1016/j.wneu.2018.12.016

Cited by 37 publications

(29 citation statements)

References 14 publications

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“…Moreover, Kobayashi et al found that elevated ASA Class and longer operating times were associated with increased LOS after posterior spine fusion, of which the results of this study concur with [ 30 ]. Adogwa et al identified surgeon practice style and preference as risk factors for extended LOS, which aligns with our study’s findings that surgeon Work RVU and operating time were associated with increased LOS [ 31 ]. However, operating time cannot be solely attributed to surgeon style or preference, as there may be patient comorbidities that lead to an increased operative time [ 32 ].…”

Section: Discussionsupporting

confidence: 91%

Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery

Zhang

Veeramani

Quinn

et al. 2021

JCM

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(1) Background: Length of stay (LOS) is a commonly reported metric used to assess surgical success, patient outcomes, and economic impact. The focus of this study is to use a variety of machine learning algorithms to reliably predict whether a patient undergoing posterior spinal fusion surgery treatment for Adult Spine Deformity (ASD) will experience a prolonged LOS. (2) Methods: Patients undergoing treatment for ASD with posterior spinal fusion surgery were selected from the American College of Surgeon’s NSQIP dataset. Prolonged LOS was defined as a LOS greater than or equal to 9 days. Data was analyzed with the Logistic Regression, Decision Tree, Random Forest, XGBoost, and Gradient Boosting functions in Python with the Sci-Kit learn package. Prediction accuracy and area under the curve (AUC) were calculated. (3) Results: 1281 posterior patients were analyzed. The five algorithms had prediction accuracies between 68% and 83% for posterior cases (AUC: 0.566–0.821). Multivariable regression indicated that increased Work Relative Value Units (RVU), elevated American Society of Anesthesiologists (ASA) class, and longer operating times were linked to longer LOS. (4) Conclusions: Machine learning algorithms can predict if patients will experience an increased LOS following ASD surgery. Therefore, medical resources can be more appropriately allocated towards patients who are at risk of prolonged LOS.

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Section: Discussionsupporting

confidence: 91%

Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery

Zhang

Veeramani

Quinn

et al. 2021

JCM

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“…Previous literature iterated that CHF was markedly present among patients who endured extensive lengths of stay (highest quartile of the cohort) after undergoing lumbar spine surgery. 22 A study examining the risk factors for readmission after lumbar surgery identified certain complications predictive of readmission within 90 days. 23 Interestingly, a history of CHF was the only preoperative factor predictive of this outcome.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Postoperative Outcomes of Spine Fusion Patients With History of Cardiac Disease

Ahmad

Fernandez

Bell

et al. 2022

J Am Acad Orthop Surg

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Introduction: There is paucity on the effect of different cardiac diagnoses on outcomes in elective spine fusion patients. Methods: Patients undergoing elective spine fusion surgery were isolated in the PearlDiver database. Patients were stratified by having a previous history of coronary artery disease (CAD), congestive heart failure (CHF), valve disorder (valve), dysrhythmia, and no heart disease (control). Means comparison tests (chi-squared and independent samples t-tests, as appropriate) compared differences in demographics, diagnoses, comorbidities, procedural characteristics, length of stay, complication outcomes, and total hospital charges among the cohort. Results: In total, 537,252 elective spine fusion patients were stratified into five groups: CAD, CHF, valve, dysrhythmia, and control. Among the cohort, patients with CHF had significantly higher rates of morbid obesity, peripheral vascular disease, and chronic kidney disease (P , 0.001 for all). Patients with CAD had significantly higher rates of chronic obstructive pulmonary disease, diabetes, hypertension, and hyperlipidemia (all P , 0.001). Comparing postoperative outcomes for CAD and control subjects, CAD was associated with higher odds of myocardial infarction (odds ratio [OR]: 1.64 [1.27 to 2.11]) (P , 0.05). Assessing postoperative outcomes for CHF versus control subjects, patients with CHF had higher rates of pneumonia, cerebrovascular accident (CVA), myocardial infarction, sepsis, and death (P , 0.001). Compared with control subjects, CHF was a significant predictor of death in spine fusion patients (OR: 2.0 [1.1 to 3.5], P = 0.022). Patients with valve disorder compared with control displayed significantly higher rates of 30-day readmission (P , 0.05) and 1.38• greater odds of CVA by 90 days postoperatively (OR: 1.4 [1.1 to 1.7], P = 0.007). Patients with dysrhythmia were associated with significantly higher odds of CVA (OR: 1.8 [1.4 to 2.3], P , 0.001) by 30 days postoperatively. Conclusion: Heart disease presents an additional challenge to spine fusion patients who are undergoing a challenging and risky procedure.

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“…Adogwa et al performed a retrospective database study using the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database from January 2008 through December 2014 and analyzed 23,102 patients undergoing lumbar decompression and fusion procedures; the authors found that patients with extended LHS had a higher incidence of post-operative MI: 0.16% for those with normal LHS and 1.29% for those with extended LHS [ 11 ]. Similarly, another investigation by Adogwa et al of adult patients aged 65 and older in the ACS NSQIP database who underwent certain SF procedures ( n = 4730) from 2008 through 2014 also found that patients with extended LHS had a higher incidence of post-operative MI: 0.06% for those with normal LHS and 1.18% for those with extended LHS [ 12 ].…”

Section: Discussionmentioning

confidence: 99%

The incidence of acute myocardial infarction after elective spinal fusions or joint replacement surgery in the United States: a large-scale retrospective observational cohort study in 322,585 patients

Arena

Liu

et al. 2021

Patient Saf Surg

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Background Acute myocardial infarction (AMI) is an uncommon but fatal complication among patients undergoing elective spinal fusion surgery (SF), total hip arthroplasty (THA), and total knee arthroplasty (TKA). Our objective was to estimate the incidence of AMI among adults undergoing elective SF, THA, and TKA in different post-operative risk windows and characterize high-risk sub-populations in the United States. Methods A retrospective cohort study was conducted using data from a longitudinal electronic healthcare record (EHR) database from January 1, 2007 to June 30, 2018. ICD codes were used to identify SF, THA, TKA, AMI, and selected clinical characteristics. Incidence proportions (IPs) and 95% confidence intervals were estimated in the following risk windows: index hospitalization, ≤ 30, ≤ 90, ≤ 180, and ≤ 365 days post-operation. Results A total of 67,533 SF patients, 87,572 THA patients, and 167,480 TKA patients were eligible for the study. The IP of AMI after SF, THA, and TKA ranged from 0.36, 0.28, and 0.25% during index hospitalization to 1.05, 0.93, and 0.85% ≤ 365 days post-operation, respectively. The IP of AMI was higher among patients who were older, male, with longer hospital stays, had a history of AMI, and had a history of diabetes. Conclusion The IP of post-operative AMI was generally highest among the SF cohort compared to the THA and TKA cohorts. Additionally, potential high-risk populations were identified. Future studies in this area are warranted to confirm these findings via improved confounder control and to identify effect measure modifiers.

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Extended Length of Stay After Lumbar Spine Surgery: Sick Patients, Postoperative Complications, or Practice Style Differences Among Hospitals and Physicians?

Cited by 37 publications

References 14 publications

Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery

Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery

Assessment of Postoperative Outcomes of Spine Fusion Patients With History of Cardiac Disease

The incidence of acute myocardial infarction after elective spinal fusions or joint replacement surgery in the United States: a large-scale retrospective observational cohort study in 322,585 patients

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