Study Design Retrospective case series. Objective To determine risk factors associated with prolonged opioid use after lumbar fusion and to elucidate the effect of opioid use on patient-reported outcome measures (PROMs) after surgery. Methods Patients who underwent 1–3 level lumbar decompression and fusion with at least one-year follow-up were identified. Opioid data were collected through the Pennsylvania Prescription Drug Monitoring Program. Preoperative “chronic use” was defined as consumption of >90 days in the one-year before surgery. Postoperative “prolonged use” was defined as a filled prescription 90-days after surgery. PROMs included the following: Short Form-12 Health Survey PCS-12 and MCS-12, ODI, and VAS-Back and Leg scores. Logistic regression was performed to determine independent predictors for prolonged opioid use. Results The final analysis included 260 patients. BMI >35 (OR: .44 [.20, .90], P = .03) and current smoking status (OR: 2.73 [1.14, 6.96], P = .03) significantly predicted postoperative opioid usage. Chronic opioid use before surgery was associated with greater improvements in MCS-12 (β= 5.26 [1.01, 9.56], P = .02). Patients with prolonged opioid use self-reported worse VAS-Back (3.4 vs 2.1, P = .003) and VAS-Leg (2.6 vs 1.2, P = .03) scores after surgery. Prolonged opioid use was associated with decreased improvement in VAS-Leg over time (β = .14 [.15, 1.85], P = .02). Conclusions Current smoking status and lower BMI were significantly predictive of prolonged opioid use. Excess opioid use before and after surgery significantly affected PROMs after lumbar fusion.
This paper presents a novel application of commodity Graphics Processing Units (GPU), the OpenMP CPU parallelization library, and the Qt4.8 framework for a real-time characterization and calibration utility created to meet the needs of the Imaging Spectroscopy community. Until now FPA calibration was performed offline in MATLAB or IDL due to the high throughput required to process incoming data - a staggering 1.1 Gbit/sec on a CPU. Techniques better suited to this sort of high-throughput processing are described, as well as pitfalls. The real-time analysis of data produced by these algorithms is used to identify sources of electronic noise and interference in the imaging spectrometer and to characterize the FPA. Additionally, the methods for optimizing the calculation routines used in the software, such as Fourier Transforms (FFT) and Dark Subtraction Filters (DSF), are described along with their applications. This software has been used to provide critical support for the testing of several imaging spectrometers, such as AVIRIS Next Generation 2 , NEON-3 3 , PRISM 4 and CWIS 5 .
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