Parameters of Spinal Cord Stimulation in Complex Regional Pain Syndrome: Systematic Review and Meta-analysis of Randomized Controlled Trials.

BACKGROUND: Complex Regional Pain Syndrome (CRPS) is a chronic debilitating neuropathic pain condition characterized by autonomic and inflammatory features that typically occurs after a traumatic event. Spinal cord stimulation (SCS) has been shown to be effective in the treatment of chronic CRPS refractory to conventional treatment modalities. The collective evidence of novel parameters of SCS for treating CRPS has not been characterized extensively. OBJECTIVE: To provide evidence for the use of SCS to treat CRPS and characterize the additional benefits of various SCS waveforms. STUDY DESIGN: Systematic Review and Meta-analysis. METHODS: PubMed, Embase and CINHLA were screened for all randomized controlled trials (RCT) comparing SCS parameters for the treatment of CRPS. RESULTS: Four RCTs were identified that included SCS as a treatment arm for CRPS. Of these, one study compared low frequency tonic SCS (LF-SCS) versus conventional physical therapy, 2 studies compared placebo/sham SCS with LF-SCS and a multitude of waveforms, and one study compared LF-SCS with high-frequency SCS (HF-SCS). Two of the studies were rated as having a low risk of bias, one study was rated as having some concerns for bias, while the final study was rated as having a high risk of bias. A meta-analysis of 4 studies comparing conventional therapy/placebo SCS stimulation against LF-SCS revealed increased benefit of LF-SCS in pain reduction up to a month (mean difference [MD] = -1.17 points; 95% CI = -1.61 to -0.73; P < 0.001, I2 = 42%). Another meta-analysis of 2 studies showed that LF-SCS results in higher global perceived effect scores relative to conventional therapy/placebo SCS stimulation (MD = 1.58; 95% CI = 1.00 to 2.15; P < 0.001, I2 = 0%). LIMITATIONS: A pooled analysis using different designs for RCTs was conducted. Some studies folded in multiple neuropathic pain pathologies in addition to CRPS. One study was at a high risk for bias in at least one domain. CONCLUSION: LF-SCS is superior to conventional therapy/placebo SCS stimulation. However, more evidence is required to demonstrate that novel SCS parameters are superior to LF-SCS in improving pain scores and functional outcomes.

Design of condition-based maintenance framework for process operations management in pharmaceutical continuous manufacturing.

Continuous manufacturing, an emerging technology in the pharmaceutical industry, has the potential to increase the efficiency, and agility of pharmaceutical manufacturing processes. To realize these potential benefits of continuous operations, effectively managing materials, equipment, analyzers, and data is vital. Developments for continuous pharmaceutical manufacturing have led to novel technologies and methods for processing material, designing and configuring individual equipment and process analyzers, as well as implementing strategies for active process control. However, limited work has been reported on managing abnormal conditions during operations to prevent unplanned deviations and downtime and sustain system capabilities. Moreover, although the sourcing, analysis, and management of real-time data have received growing attention, limited discussion exists on the continued verification of the infrastructure for ensuring reliable operations. Hence, this work introduces condition-based maintenance (CBM) as a general strategy for continually verifying and sustaining advanced pharmaceutical manufacturing systems, with a focus on the continuous manufacture of oral solid drug products (OSD-CM). Frameworks, such as CBM, benefit unified efforts towards continued verification and operational excellence by leveraging process knowledge and the availability of real-time data. A vital implementation consideration for manufacturing operations management applications, such as CBM, is a systems architecture and an enabling infrastructure. This work outlines the systems architecture design for CBM in OSD-CM and highlights sample fault scenarios involving equipment and process analyzers. For illustrative purposes, this work also describes the infrastructure implemented on an OSD-CM testbed, which uses commercially available automation systems and leverages enterprise architecture standards. With the increasing digitalization of manufacturing operations in the pharmaceutical industry, proactively using process data towards modernizing maintenance practices is relevant to a single unit operation as well as to a series of physically integrated unit operations.

Data reconciliation in the Quality-by-Design (QbD) implementation of pharmaceutical continuous tablet manufacturing.

Data provided by in situ sensors is always affected by some level of impreciseness as well as uncertainty in the measurements due to process operation disturbance or material property variance. In-process data precision and reliability should be considered when implementing active product quality control and real-time process decision making in pharmaceutical continuous manufacturing. Data reconciliation is an important strategy to address such imperfections effectively, and to exploit the data redundancy and data correlation based on process understanding. In this study, a correlation between tablet weight and main compression force in a rotary tablet press was characterized by the classical Kawakita equation. A load cell, situated at the exit of the tablet press chute, was also designed to measure the tablet production rate as well as the tablet weight. A novel data reconciliation strategy was proposed to reconcile the tablet weight measurement subject to the correlation between tablet weight and main compression force, in such, the imperfect tablet weight measurement can be reconciled with the much more precise main compression force measurement. Special features of the Welsch robust estimator to reject the measurement gross errors and the Kawakita model parameter estimation to monitor the material property variance were also discussed. The proposed data reconciliation strategy was first evaluated with process control open-loop and closed-loop experimental data and then integrated into the process control system in a continuous tablet manufacturing line. Specifically, the real-time reconciled tablet weight measurements were independently verified with an at-line Sotax Auto Test 4 tablet weight measurements every five minutes. Promising and reliable performance of the reconciled tablet weight measurement was demonstrated in achieving process automation and quality control of tablet weight in pilot production runs.

Steady-State Data Reconciliation Framework for a Direct Continuous Tableting Line.

Purpose: Reliable process monitoring in real-time remains a challenge for the pharmaceutical industry. Dealing with random and gross errors in the process measurements in a systematic way is a potential solution. In this paper, we present a process model-based framework, which for given sensor network and measurement uncertainties will predict the most likely state of the process. Thus, real-time process decisions, whether for process control or exceptional events management, can be based on the most reliable estimate of the process state. Methods: Reliable process monitoring is achieved by using data reconciliation (DR) and gross error detection (GED) to mitigate the effects of random measurement errors and non-random sensor malfunctions. Steady-state data reconciliation (SSDR) is the simplest forms of DR but offers the benefits of short computational times. We also compare and contrast the model-based DR approach (SSDR-M) to the purely data-driven approach (SSDR-D) based on the use of principal component constructions. Results: We report the results of studies on a pilot plant-scale continuous direct compression-based tableting line at steady-state in two subsystems. If the process is linear or mildly nonlinear, SSDR-M and SSDR-D give comparable results for the variables estimation and GED. SSDR-M also complies with mass balances and estimate unmeasured variables. Conclusions: SSDR successfully estimates the true state of the process in presence of gross errors, as long as steady state is maintained and the redundancy requirement is met. Gross errors are also detected while using SSDR-M or SSDR-D. Process monitoring is more reliable while using the SSDR framework.