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  • Journal of Neurosurgery Publishing Group (JNSPG)  (2)
  • Mummaneni, Praveen V.  (2)
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  • Journal of Neurosurgery Publishing Group (JNSPG)  (2)
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  • 1
    In: Journal of Neurosurgery: Spine, Journal of Neurosurgery Publishing Group (JNSPG), Vol. 38, No. 2 ( 2023-02-01), p. 182-191
    Abstract: Prior studies have revealed that a body mass index (BMI) ≥ 30 is associated with worse outcomes following surgical intervention in grade 1 lumbar spondylolisthesis. Using a machine learning approach, this study aimed to leverage the prospective Quality Outcomes Database (QOD) to identify a BMI threshold for patients undergoing surgical intervention for grade 1 lumbar spondylolisthesis and thus reliably identify optimal surgical candidates among obese patients. METHODS Patients with grade 1 lumbar spondylolisthesis and preoperative BMI ≥ 30 from the prospectively collected QOD lumbar spondylolisthesis module were included in this study. A 12-month composite outcome was generated by performing principal components analysis and k-means clustering on four validated measures of surgical outcomes in patients with spondylolisthesis. Random forests were generated to determine the most important preoperative patient characteristics in predicting the composite outcome. Recursive partitioning was used to extract a BMI threshold associated with optimal outcomes. RESULTS The average BMI was 35.7, with 282 (46.4%) of the 608 patients from the QOD data set having a BMI ≥ 30. Principal components analysis revealed that the first principal component accounted for 99.2% of the variance in the four outcome measures. Two clusters were identified corresponding to patients with suboptimal outcomes (severe back pain, increased disability, impaired quality of life, and low satisfaction) and to those with optimal outcomes. Recursive partitioning established a BMI threshold of 37.5 after pruning via cross-validation. CONCLUSIONS In this multicenter study, the authors found that a BMI ≤ 37.5 was associated with improved patient outcomes following surgical intervention. These findings may help augment predictive analytics to deliver precision medicine and improve prehabilitation strategies.
    Type of Medium: Online Resource
    ISSN: 1547-5654
    RVK:
    Language: Unknown
    Publisher: Journal of Neurosurgery Publishing Group (JNSPG)
    Publication Date: 2023
    Location Call Number Limitation Availability
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  • 2
    In: Neurosurgical Focus, Journal of Neurosurgery Publishing Group (JNSPG), Vol. 51, No. 6 ( 2021-12), p. E2-
    Abstract: There is a learning curve for surgeons performing “awake” spinal surgery. No comprehensive guidelines have been proposed for the selection of ideal candidates for awake spinal fusion or decompression. The authors sought to formulate an algorithm to aid in patient selection for surgeons who are in the startup phase of awake spinal surgery. METHODS The authors developed an algorithm for selecting patients appropriate for awake spinal fusion or decompression using spinal anesthesia supplemented with mild sedation and local analgesia. The anesthetic protocol that was used has previously been reported in the literature. This algorithm was formulated based on a multidisciplinary team meeting and used in the first 15 patients who underwent awake lumbar surgery at a single institution. RESULTS A total of 15 patients who underwent decompression or lumbar fusion using the awake protocol were reviewed. The mean patient age was 61 ± 12 years, with a median BMI of 25.3 (IQR 2.7) and a mean Charlson Comorbidity Index of 2.1 ± 1.7; 7 patients (47%) were female. Key patient inclusion criteria were no history of anxiety, 1 to 2 levels of lumbar pathology, moderate stenosis and/or grade I spondylolisthesis, and no prior lumbar surgery at the level where the needle is introduced for anesthesia. Key exclusion criteria included severe and critical central canal stenosis or patients who did not meet the inclusion criteria. Using the novel algorithm, 14 patients (93%) successfully underwent awake spinal surgery without conversion to general anesthesia. One patient (7%) was converted to general anesthesia due to insufficient analgesia from spinal anesthesia. Overall, 93% (n = 14) of the patients were assessed as American Society of Anesthesiologists class II, with 1 patient (7%) as class III. The mean operative time was 115 minutes (± 60 minutes) with a mean estimated blood loss of 46 ± 39 mL. The median hospital length of stay was 1.3 days (IQR 0.1 days). No patients developed postoperative complications and only 1 patient (7%) required reoperation. The mean Oswestry Disability Index score decreased following operative intervention by 5.1 ± 10.8. CONCLUSIONS The authors propose an easy-to-use patient selection algorithm with the aim of assisting surgeons with patient selection for awake spinal surgery while considering BMI, patient anxiety, levels of surgery, and the extent of stenosis. The algorithm is specifically intended to assist surgeons who are in the learning curve of their first awake spinal surgery cases.
    Type of Medium: Online Resource
    ISSN: 1092-0684
    Language: Unknown
    Publisher: Journal of Neurosurgery Publishing Group (JNSPG)
    Publication Date: 2021
    detail.hit.zdb_id: 2026589-X
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