Background Patients often ask their doctors when they can safely return to driving after orthopaedic injuries and procedures, but the data regarding this topic are diverse and sometimes conflicting. Some studies provide observer-reported outcome measures, such as brake response time or simulators, to estimate when patients can safely resume driving after surgery, and patient survey data describing when patients report a return to driving, but they do not all agree. We performed a systematic review and quality appraisal for available data regarding when patients are safe to resume driving after common orthopaedic surgeries and injuries affecting the ability to drive.
Systematic reviews and meta-analyses in orthopaedics sports medicine literature relied on evidence levels 4 and 5 in 53% of studies over the 5-year study period. Overall, PRISMA and AMSTAR scores are high and may be better than those in other disciplines. Readers need to be conscious of potential shortcomings when reading systematic reviews and using them in practice.
Study Design Level III retrospective database study. Objectives The purpose of this study is to determine if machine learning algorithms are effective in predicting unplanned intubation following anterior cervical discectomy and fusion (ACDF). Methods The National Surgical Quality Initiative Program (NSQIP) was queried to select patients who had undergone ACDF. Machine learning analysis was conducted in Python and multivariate regression analysis was conducted in R. C-Statistics area under the curve (AUC) and prediction accuracy were used to measure the classifier’s effectiveness in distinguishing cases. Results In total, 54 502 patients met the study criteria. Of these patients, .51% underwent an unplanned re-intubation. Machine learning algorithms accurately classified between 72%-100% of the test cases with AUC values of between .52-.77. Multivariable regression indicated that the number of levels fused, male sex, COPD, American Society of Anesthesiologists (ASA) > 2, increased operating time, Age > 65, pre-operative weight loss, dialysis, and disseminated cancer were associated with increased risk of unplanned intubation. Conclusions The models presented here achieved high accuracy in predicting risk factors for re-intubation following ACDF surgery. Machine learning analysis may be useful in identifying patients who are at a higher risk of unplanned post-operative re-intubation and their treatment plans can be modified to prophylactically prevent respiratory compromise and consequently unplanned re-intubation.
» Adult spinal deformity (ASD) is a challenging problem for spine surgeons given the high risk of complications, both medical and surgical.» Surgeons should have a high index of suspicion for medical complications, including cardiac, pulmonary, thromboembolic, genitourinary and gastrointestinal, renal, cognitive and psychiatric, and skin conditions, in the perioperative period and have a low threshold for involving specialists.» Surgical complications, including neurologic injuries, vascular injuries, proximal junctional kyphosis, durotomy, and pseudarthrosis and rod fracture, can be devastating for the patient and costly to the health-care system.» Mortality rates have been reported to be between 1.0% and 3.5% following ASD surgery.» With the increasing rate of ASD surgery, surgeons should properly counsel patients about these risks and have a high index of suspicion for complications in the perioperative period.
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