Anthony L. Asher, Mohamad Bydon, and Robert E. Harbaugh
Walter A. Hall, Edward Rustamzadeh, and Anthony L. Asher
The poor prognosis associated with the current management of malignant gliomas has led investigators to develop alternative treatments such as targeted toxin therapy. The optimal method for administering these agents is under development but appears to be convection-enhanced delivery (CED).
The direct intratumoral infusion of targeted toxins was first performed in nude mouse flank tumor models of human malignant glioma. After the demonstration of in vivo efficacy, these potent cytotoxic compounds were tested in Phase I and Phase II clinical trials.
Using a high-flow microinfusion technique, volumes of up to 180 ml were infused by CED through catheters placed directly into brain tumors. Minor systemic toxicity was seen in the form of hepatic enzyme elevation. Neural toxicity manifested as seizure activity and hemiparesis resulted from peritumoral edema that followed the completion of the infusion. Peritumoral toxicity was believed to be more related to the concentration of the infused immunotoxin than to the infusion volume. In approximately half of patients treated with CED a stable disease course, a partial response, or a complete response was demonstrated in some clinical trials.
Targeted toxin therapy has clinical efficacy in patients with malignant gliomas. Convection-enhanced delivery appears to represent an effective method for administering these agents in patients with malignant brain tumors.
Anthony L. Asher, Matthew J. McGirt, and Zoher Ghogawala
Christopher M. Holland, Kevin T. Foley, and Anthony L. Asher
Matthew J. McGirt, Ahilan Sivaganesan, Anthony L. Asher, and Clinton J. Devin
Lumbar spine surgery has been demonstrated to be efficacious for many degenerative spine conditions. However, there is wide variability in outcome after spine surgery at the individual patient level. All stakeholders in spine care will benefit from identification of the unique patient or disease subgroups that are least likely to benefit from surgery, are prone to costly complications, and have increased health care utilization. There remains a large demand for individual patient-level predictive analytics to guide decision support to optimize outcomes at the patient and population levels.
One thousand eight hundred three consecutive patients undergoing spine surgery for various degenerative lumbar diagnoses were prospectively enrolled and followed for 1 year. A comprehensive patient interview and health assessment was performed at baseline and at 3 and 12 months after surgery. All predictive covariates were selected a priori. Eighty percent of the sample was randomly selected for model development, and 20% for model validation. Linear regression was performed with Bayesian model averaging to model 12-month ODI (Oswestry Disability Index). Logistic regression with Bayesian model averaging was used to model likelihood of complications, 30-day readmission, need for inpatient rehabilitation, and return to work. Goodness-of-fit was assessed via R2 for 12-month ODI and via the c-statistic, area under the receiver operating characteristic curve (AUC), for the categorical endpoints. Discrimination (predictive performance) was assessed, using R2 for the ODI model and the c-statistic for the categorical endpoint models. Calibration was assessed using a plot of predicted versus observed values for the ODI model and the Hosmer-Lemeshow test for the categorical endpoint models.
On average, all patient-reported outcomes (PROs) were improved after surgery (ODI baseline vs 12 month: 50.4 vs 29.5%, p < 0.001). Complications occurred in 121 patients (6.6%), 108 (5.9%) were readmitted within 30 days of surgery, 188 (10.3%) required discharge to inpatient rehabilitation, 1630 (88.9%) returned to work, and 449 (24.5%) experienced an unplanned outcome (no improvement in ODI, a complication, or readmission). There were 45 unique baseline variable inputs, derived from 39 clinical variables and 38 questionnaire items (ODI, SF-12, MSPQ, VAS-BP, VAS-LP, VAS-NP), included in each model. For prediction of 12-month ODI, R2 was 0.51 for development and 0.47 for the validation study. For prediction of a complication, readmission, inpatient rehabilitation, and return to work, AUC values ranged 0.72-0.84 for development and 0.79-0.84 for validation study.
A novel prediction model utilizing both clinical data and patient interview inputs explained the majority of variation in outcome observed after lumbar spine surgery and reliably predicted 12-month improvement in physical disability, return to work, major complications, readmission, and need for inpatient rehabilitation for individual patients. Application of these models may allow clinicians to offer spine surgery specifically to those who are most likely to benefit and least likely to incur complications and excess costs.
Anthony L. Asher, Clinton J. Devin, Robert E. Harbaugh, and Mohamad Bydon
Anthony L. Asher, Paul C. McCormick, and Douglas Kondziolka
Scott L. Parker, Anthony L. Asher, Saniya S. Godil, Clinton J. Devin, and Matthew J. McGirt
The health care landscape is rapidly shifting to incentivize quality of care rather than quantity of care. Quality and outcomes registry platforms lie at the center of all emerging evidence-driven reform models and will be used to inform decision makers in health care delivery. Obtaining real-world registry outcomes data from patients 12 months after spine surgery remains a challenge. The authors set out to determine whether 3-month patient-reported outcomes accurately predict 12-month outcomes and, hence, whether 3-month measurement systems suffice to identify effective versus noneffective spine care.
All patients undergoing lumbar spine surgery for degenerative disease at a single medical institution over a 2-year period were enrolled in a prospective longitudinal registry. Patient-reported outcome instruments (numeric rating scale [NRS], Oswestry Disability Index [ODI], 12-Item Short Form Health Survey [SF-12], EQ-5D, and the Zung Self-Rating Depression Scale) were recorded prospectively at baseline and at 3 months and 12 months after surgery. Linear regression was performed to determine the independent association of 3- and 12-month outcome. Receiver operating characteristic (ROC) curve analysis was performed to determine whether improvement in general health state (EQ-5D) and disability (ODI) at 3 months accurately predicted improvement and achievement of minimum clinical important difference (MCID) at 12 months.
A total of 593 patients undergoing elective lumbar surgery were included in the study. There was a significant correlation between 3-month and 12-month EQ-5D (r = 0.71; p < 0.0001) and ODI (r = 0.70; p < 0.0001); however, the authors observed a sizable discrepancy in achievement of a clinically significant improvement (MCID) threshold at 3 versus 12 months on an individual patient level. For postoperative disability (ODI), 11.5% of patients who achieved an MCID threshold at 3 months dropped below this threshold at 12 months; 10.5% of patients who did not meet the MCID threshold at 3 months continued to improve and ultimately surpassed the MCID threshold at 12 months. For ODI, achieving MCID at 3 months accurately predicted 12-month MCID with only 62.6% specificity and 86.8% sensitivity. For postoperative health utility (EQ-5D), 8.5% of patients lost an MCID threshold improvement from 3 months to 12 months, while 4.0% gained the MCID threshold between 3 and 12 months postoperatively. For EQ-5D (quality-adjusted life years), achieving MCID at 3 months accurately predicted 12-month MCID with only 87.7% specificity and 87.2% sensitivity.
In a prospective registry, patient-reported measures of treatment effectiveness obtained at 3 months correlated with 12-month measures overall in aggregate, but did not reliably predict 12-month outcome at the patient level. Many patients who do not benefit from surgery by 3 months do so by 12 months, and, conversely, many patients reporting meaningful improvement by 3 months report loss of benefit at 12 months. Prospective longitudinal spine outcomes registries need to span at least 12 months to identify effective versus noneffective patient care.
Scott L. Parker, Ahilan Sivaganesan, Silky Chotai, Matthew J. McGirt, Anthony L. Asher, and Clinton J. Devin
Hospital readmissions lead to a significant increase in the total cost of care in patients undergoing elective spine surgery. Understanding factors associated with an increased risk of postoperative readmission could facilitate a reduction in such occurrences. The aims of this study were to develop and validate a predictive model for 90-day hospital readmission following elective spine surgery.
All patients undergoing elective spine surgery for degenerative disease were enrolled in a prospective longitudinal registry. All 90-day readmissions were prospectively recorded. For predictive modeling, all covariates were selected by choosing those variables that were significantly associated with readmission and by incorporating other relevant variables based on clinical intuition and the Akaike information criterion. Eighty percent of the sample was randomly selected for model development and 20% for model validation. Multiple logistic regression analysis was performed with Bayesian model averaging (BMA) to model the odds of 90-day readmission. Goodness of fit was assessed via the C-statistic, that is, the area under the receiver operating characteristic curve (AUC), using the training data set. Discrimination (predictive performance) was assessed using the C-statistic, as applied to the 20% validation data set.
A total of 2803 consecutive patients were enrolled in the registry, and their data were analyzed for this study. Of this cohort, 227 (8.1%) patients were readmitted to the hospital (for any cause) within 90 days postoperatively. Variables significantly associated with an increased risk of readmission were as follows (OR [95% CI]): lumbar surgery 1.8 [1.1–2.8], government-issued insurance 2.0 [1.4–3.0], hypertension 2.1 [1.4–3.3], prior myocardial infarction 2.2 [1.2–3.8], diabetes 2.5 [1.7–3.7], and coagulation disorder 3.1 [1.6–5.8]. These variables, in addition to others determined a priori to be clinically relevant, comprised 32 inputs in the predictive model constructed using BMA. The AUC value for the training data set was 0.77 for model development and 0.76 for model validation.
Identification of high-risk patients is feasible with the novel predictive model presented herein. Appropriate allocation of resources to reduce the postoperative incidence of readmission may reduce the readmission rate and the associated health care costs.