Jeff Ehresman, Zach Pennington, James Feghali, Andrew Schilling, Andrew Hersh, Bethany Hung, Daniel Lubelski, and Daniel M. Sciubba
More than 8000 patients are treated annually for vertebral column tumors, of whom roughly two-thirds will be discharged to an inpatient facility (nonroutine discharge). Nonroutine discharge is associated with increased care costs as well as delays in discharge and poorer patient outcomes. In this study, the authors sought to develop a prediction model of nonroutine discharge in the population of vertebral column tumor patients.
Patients treated for primary or metastatic vertebral column tumors at a single comprehensive cancer center were identified for inclusion. Data were gathered regarding surgical procedure, patient demographics, insurance status, and medical comorbidities. Frailty was assessed using the modified 5-item Frailty Index (mFI-5) and medical complexity was assessed using the modified Charlson Comorbidity Index (mCCI). Multivariable logistic regression was used to identify independent predictors of nonroutine discharge, and multivariable linear regression was used to identify predictors of prolonged length of stay (LOS). The discharge model was internally validated using 1000 bootstrapped samples.
The authors identified 350 patients (mean age 57.0 ± 13.6 years, 53.1% male, and 67.1% treated for metastatic vs primary disease). Significant predictors of prolonged LOS included higher mCCI score (β = 0.74; p = 0.026), higher serum absolute neutrophil count (β = 0.35; p = 0.001), lower hematocrit (β = −0.34; p = 0.001), use of a staged operation (β = 4.99; p < 0.001), occurrence of postoperative pulmonary embolism (β = 3.93; p = 0.004), and surgical site infection (β = 9.93; p < 0.001). Significant predictors of nonroutine discharge included emergency admission (OR 3.09; p = 0.001), higher mFI-5 score (OR 1.90; p = 0.001), lower serum albumin level (OR 0.43 per g/dL; p < 0.001), and operations with multiple stages (OR 4.10; p < 0.001). The resulting statistical model was deployed as a web-based calculator (https://jhuspine4.shinyapps.io/Nonroutine_Discharge_Tumor/).
The authors found that nonroutine discharge of patients with surgically treated vertebral column tumors was predicted by emergency admission, increased frailty, lower serum albumin level, and staged surgical procedures. The resulting web-based calculator tool may be useful clinically to aid in discharge planning for spinal oncology patients by preoperatively identifying patients likely to require placement in an inpatient facility postoperatively.
Jeff Ehresman, Zach Pennington, Aditya V. Karhade, Sakibul Huq, Ravi Medikonda, Andrew Schilling, James Feghali, Andrew Hersh, A. Karim Ahmed, Ethan Cottrill, Daniel Lubelski, Erick M. Westbroek, Joseph H. Schwab, and Daniel M. Sciubba
Incidental durotomy is a common complication of elective lumbar spine surgery seen in up to 11% of cases. Prior studies have suggested patient age and body habitus along with a history of prior surgery as being associated with an increased risk of dural tear. To date, no calculator has been developed for quantifying risk. Here, the authors’ aim was to identify independent predictors of incidental durotomy, present a novel predictive calculator, and externally validate a novel method to identify incidental durotomies using natural language processing (NLP).
The authors retrospectively reviewed all patients who underwent elective lumbar spine procedures at a tertiary academic hospital for degenerative pathologies between July 2016 and November 2018. Data were collected regarding surgical details, patient demographic information, and patient medical comorbidities. The primary outcome was incidental durotomy, which was identified both through manual extraction and the NLP algorithm. Multivariable logistic regression was used to identify independent predictors of incidental durotomy. Bootstrapping was then employed to estimate optimism in the model, which was corrected for; this model was converted to a calculator and deployed online.
Of the 1279 elective lumbar surgery patients included in this study, incidental durotomy occurred in 108 (8.4%). Risk factors for incidental durotomy on multivariable logistic regression were increased surgical duration, older age, revision versus index surgery, and case starts after 4 pm. This model had an area under curve (AUC) of 0.73 in predicting incidental durotomies. The previously established NLP method was used to identify cases of incidental durotomy, of which it demonstrated excellent discrimination (AUC 0.97).
Using multivariable analysis, the authors found that increased surgical duration, older patient age, cases started after 4 pm, and a history of prior spine surgery are all independent positive predictors of incidental durotomy in patients undergoing elective lumbar surgery. Additionally, the authors put forth the first version of a clinical calculator for durotomy risk that could be used prospectively by spine surgeons when counseling patients about their surgical risk. Lastly, the authors presented an external validation of an NLP algorithm used to identify incidental durotomies through the review of free-text operative notes. The authors believe that these tools can aid clinicians and researchers in their efforts to prevent this costly complication in spine surgery.
Sonia Ajmera, Ryan P. Lee, Andrew Schultz, David S. Hersh, Jacob Lepard, Raymond Xu, Hassan Saad, Olutomi Akinduro, Melissa Justo, Brittany D. Fraser, Mustafa Motiwala, Pooja Dave, Brian Jimenez, David A. Wallace, Olufemi Osikoya, Sebastian Norrdahl, Jennings H. Dooley, Nickalus R. Khan, Brandy N. Vaughn, Cormac O. Maher, and Paul Klimo Jr.
The objective of this study was to analyze the publication output of postgraduate pediatric neurosurgery fellows for a 10-year period as well as identify 25 individual highly productive pediatric neurosurgeons. The correlation between academic productivity and the site of fellowship training was studied.
Programs certified by the Accreditation Council for Pediatric Neurosurgery Fellowships that had 5 or more graduating fellows from 2006 to 2015 were included for analysis. Fellows were queried using Scopus for publications during those 10 years with citation data through 2017. Pearson correlation coefficients were calculated, comparing program rankings of faculty against fellows using the revised Hirsch index (r-index; primary) and Hirsch index (h-index; secondary). A list of 25 highly accomplished individual academicians and their fellowship training locations was compiled.
Sixteen programs qualified with 152 fellows from 2006 to 2015; 136 of these surgeons published a total of 2009 articles with 23,735 citations. Most publications were pediatric-specific (66.7%) clinical articles (93.1%), with middle authorship (55%). Co-investigators were more likely from residency than fellowship. There was a clustering of the top 7 programs each having total publications of around 120 or greater, publications per fellow greater than 12, more than 1200 citations, and adjusted ir10 (revised 10-year institutional h-index) and ih10 (10-year institutional h-index) values of approximately 2 or higher. Correlating faculty and fellowship program rankings yielded correlation coefficients ranging from 0.53 to 0.80. Fifteen individuals (60%) in the top 25 (by r5 index) list completed their fellowship at 1 of these 7 institutions.
Approximately 90% of fellowship-trained pediatric neurosurgeons have 1 or more publications, but the spectrum of output is broad. There is a strong correlation between where surgeons complete their fellowships and postgraduate publications.