Novel closed-loop control system of dual rotary blood pumps in total artificial heart based on the circulatory equilibrium framework: a proof-of-concept in vivo study.

OBJECTIVE: Total artificial heart (TAH) using dual rotary blood pumps (RBPs) is a potential treatment for end-stage heart failure. A well-noted challenge with RBPs is their low sensitivity to preload, which can lead to venous congestion and ventricular suction. To address this issue, we have developed an innovative closed-loop control system of dual RBPs in TAH. This system emulates the Frank-Starling law of the heart in controlling RBPs while monitoring stressed blood volume (V) based on the circulatory equilibrium framework. We validated the system in in-vivo experiments. METHODS: In 9 anesthetized dogs, we prepared a TAH circuit using 2 centrifugal-type RBPs. We first investigated whether the flow and inlet atrial pressure in each RBP adhered to a logarithmic Frank-Starling curve. We then examined whether the RBP flows and atrial pressures were maintained stably during aortic occlusion (AO) and pulmonary cannula stenosis (PS), whether averaged flow of dual RBPs and bilateral atrial pressures were controlled to their predefined target values for a specific V, and whether this system could maintain the atrial pressures within predefined control ranges under significant changes in V. RESULTS: This system effectively emulated the logarithmic Frank-Starling curve. It robustly stabilized the flow and atrial pressures during AO and PS without venous congestion or ventricular suction, accurately achieved target values in averaged flow and atrial pressures, and efficaciously maintained these pressures within the control ranges. CONCLUSION: This system controls dual RBPs in TAH accurately and stably. SIGNIFICANCE: This system may accelerate clinical application of TAH with dual RBPs.

Computer-controlled closed-loop norepinephrine infusion system for automated control of mean arterial pressure in dogs under isoflurane-induced hypotension: a feasibility study.

Introduction: Intra-operative hypotension is a common complication of surgery under general anesthesia in dogs and humans. Computer-controlled closed-loop infusion systems of norepinephrine (NE) have been developed and clinically applied for automated optimization of arterial pressure (AP) and prevention of intra-operative hypotension in humans. This study aimed to develop a simple computer-controlled closed-loop infusion system of NE for the automated control of the mean arterial pressure (MAP) in dogs with isoflurane-induced hypotension and to validate the control of MAP by the developed system. Methods: NE was administered via the cephalic vein, whereas MAP was measured invasively by placing a catheter in the dorsal pedal artery. The proportional-integral-derivative (PID) controller in the negative feedback loop of the developed system titrated the infusion rate of NE to maintain the MAP at the target value of 60 mmHg. The titration was updated every 2 s. The performance of the developed system was evaluated in six laboratory Beagle dogs under general anesthesia with isoflurane. Results: In the six dogs, when the concentration [median (interquartile range)] of inhaled isoflurane was increased from 1.5 (1.5-1.5)% to 4 (4-4)% without activating the system, the MAP was lowered from 95 (91-99) to 41 (37-42) mmHg. In contrast, when the concentration was increased from 1.5 (1.0-1.5)% to 4 (4-4.8)% for a 30-min period and the system was simultaneously activated, the MAP was temporarily lowered from 92 (89-95) to 47 (43-49) mmHg but recovered to 58 (57-58) mmHg owing to the system-controlled infusion of NE. If the acceptable target range for MAP was defined as target MAP ±5 mmHg (55 ≤ MAP ≤65 mmHg), the percentage of time wherein the MAP was maintained within the acceptable range was 96 (89-100)% in the six dogs during the second half of the 30-min period (from 15 to 30 min after system activation). The median performance error, median absolute performance error, wobble, and divergence were - 2.9 (-4.7 to 1.9)%, 2.9 (2.0-4.7)%, 1.3 (0.8-1.8)%, and - 0.24 (-0.34 to -0.11)%·min-1, respectively. No adverse events were observed during the study period, and all dogs were extubated uneventfully. Conclusion: This system was able to titrate the NE infusion rates in an accurate and stable manner to maintain the MAP within the predetermined target range in dogs with isoflurane-induced hypotension. This system can be a potential tool in daily clinical practice for the care of companion dogs.

Short-term dynamic characteristics of diuresis during exogenous pressure perturbations with and without arterial baroreflex control.

Although body fluid volume control by the kidneys may be classified as a long-term arterial pressure (AP) control system, it does not necessarily follow that the urine flow (UF) response to changes in AP is slow. We quantified the dynamic characteristics of the UF response to short-term AP changes by changing mean AP between 60 mmHg and 100 mmHg every 10 s according to a binary white noise sequence in anesthetized rats (n = 8 animals). In a baro-on trial (the carotid sinus baroreflex was enabled), the UF response represented the combined synergistic effects of pressure diuresis (PD) and neurally mediated antidiuresis (NMA). In a baro-fix trial (the carotid sinus pressure was fixed at 100 mmHg), the UF response mainly reflected the effect of PD. The UF step response was quantified using the sum of two exponential decay functions. The fast and slow components had time constants of 6.5 ± 3.6 s and 102 ± 85 s (means ± SD), respectively, in the baro-on trial. Although the gain of the fast component did not differ between the two trials (0.49 ± 0.21 vs. 0.66 ± 0.22 µL·min-1·kg-1·mmHg-1), the gain of the slow component was greater in the baro-on than in the baro-fix trial (0.51 ± 0.14 vs. 0.09 ± 0.39 µL·min-1·kg-1·mmHg-1, P = 0.023). The magnitude of NMA relative to PD was calculated to be 32.2 ± 29.8%. In conclusion, NMA contributed to the slow component, and its magnitude was approximately one-third of that of the effect of PD.NEW & NOTEWORTHY We quantified short-term dynamic characteristics of the urine flow (UF) response to arterial pressure (AP) changes using white noise analysis. The UF step response approximated the sum of two exponential decay functions with time constants of ∼6.5 s and 102 s. The neurally mediated antidiuretic (NMA) effect contributed to the slow component of the UF step response, with the magnitude of approximately one-third of that of the pressure diuresis (PD) effect.

Inverse ESPVR Estimation with Singularity Avoidance via Constrained EDPVR Parameter Optimization.

Left ventricular end-systolic elastance Ees, as an index of cardiac contractility, can play a key role in continuous patient monitoring during cardiac treatment scenarios such as drug therapies. The clinical feasibility of Ees estimation remains challenging because most techniques have been built on left ventricular pressure and volume, which are difficult to measure or estimate in the regular ICU/CCU setting. The purpose of this paper is to propose and validate a novel approach to estimate Ees, which is independent of left ventricular pressure and volume. Our methods first derive an analytical representation of Ees as the inverse function of the gradient of the Frank-Starling Curve based on cardiac mechanics. Second, elucidating the mechanism of singularities in the inverse function, we derive multiple conditions in both end-systolic pressure-volume relationship (ESPVR) and end-diastolic pressure-volume relationship (EDPVR) parameters to avoid these singularities analytically. Third, we formulate a constrained nonlinear least squares problem to optimize both ESPVR and EDPVR parameters simultaneously to avoid singularities. The effectiveness of the proposed method in avoiding singularities was evaluated in an animal experiment. Compared to the conventional Ees estimation by linear regression, our proposed method reproduced in-vivo hemodynamics more accurately when simulating the estimated Ees variation during drug administration. Our method can be applied using the available data in the regular ICU/CCU setting. The improved clinical feasibility can support not only physicians' decision-making, including adjusting drug dosages in current clinical treatment, but also a closed-loop hemodynamic control system requiring accurate continuous Ees estimation.

Analytical Representation of Four-dimensional Hemodynamics for Drug Therapy Simulation in Acute Heart Failure Treatment.

Acute heart failure imperils multiple organs, including the heart. Elucidating the impact of drug therapies across this multidimensional hemodynamic system remains a challenge. This paper proposes a simulator that analyzes the impact of drug therapies on four dimensions of hemodynamics: left atrial pressure, cardiac output, mean arterial pressure, and myocardial oxygen consumption. To mathematically formulate hemodynamics, the analytical solutions of four-dimensional hemodynamics and the direction of its change are derived as functions of cardiovascular parameters: systemic vascular resistance, cardiac contractility, heart rate, and stressed blood volume. Furthermore, a drug library which represents the multi-dependency effect of drug therapies on cardiovascular parameters was identified in animal experiments. In evaluating the accuracy of our derived hemodynamic direction, the average angular error of predicted versus observed direction was 18.85[deg] after four different drug infusions for acute heart failure in animal experiments. Finally, the impact of drug therapies on four-dimensional hemodynamics was analyzed in three different simulation settings. One result showed that, even when drug therapies were simulated with simple rules according to the Forrester classification, the predicted direction of hemodynamic change matched the expected direction in more than 80% in 963 different AHF patient scenarios. Our developed simulator visualizes the impact of drug therapies on four-dimensional hemodynamics so intuitively that it can support clinicians' decision-making to protect multiple organs.

Acute effects of empagliflozin on open-loop baroreflex function and urinary glucose excretion in rats with chronic myocardial infarction.

Sodium-glucose cotransporter 2 (SGLT2) inhibitors have exerted cardioprotective effects in clinical trials, but underlying mechanisms are not fully understood. As mitigating sympathetic overactivity is of major clinical concern in the mechanisms of heart failure treatments, we examined the effects of modulation of glucose handling on baroreflex-mediated sympathetic nerve activity and arterial pressure regulations in rats with chronic myocardial infarction (n = 9). Repeated 11-min step input sequences were used for an open-loop analysis of the carotid sinus baroreflex. An SGLT2 inhibitor, empagliflozin, was intravenously administered (10 mg/kg) after the second sequence. Neither the baroreflex neural nor peripheral arc significantly changed during the last observation period (seventh and eighth sequences) compared with the baseline period although urinary glucose excretion increased from near 0 (0.0089 ± 0.0011 mg min-1 kg-1) to 1.91 ± 0.25 mg min-1 kg-1. Hence, empagliflozin does not acutely modulate the baroreflex regulations of sympathetic nerve activity and arterial pressure in this rat model of chronic myocardial infarction.

Impact of vericiguat on baroreflex-mediated sympathetic circulatory regulation: An open-loop analysis.

AIMS: To quantify in vivo the effects of the soluble guanylate cyclase (sGC) stimulator, vericiguat, on autonomic cardiovascular regulation in comparison with the nitric oxide (NO) donor, sodium nitroprusside. METHODS: In anesthetized Wistar-Kyoto rats, baroreflex-mediated changes in sympathetic nerve activity (SNA), arterial pressure (AP), central venous pressure (CVP), and aortic flow (AoF) were examined before and during the intravenous continuous administration (10 µg·kg-1·min-1) of vericiguat or sodium nitroprusside (n = 8 each). Systemic vascular resistance (SVR) was calculated as SVR = (AP-CVP) / AoF. RESULTS: Neither vericiguat nor sodium nitroprusside affected fitted parameters of the baroreflex-mediated SNA response. Both vericiguat and sodium nitroprusside decreased the AP mainly through their peripheral effects. Vericiguat halved the slope of the SNA-SVR relationship from 0.012 ± 0.002 to 0.006 ± 0.002 mmHg·min·mL-1·%-1 (P = 0.008), whereas sodium nitroprusside caused a near parallel downward shift in the SNA-SVR relationship with a reduction of the SVR intercept from 1.235 ± 0.187 to 0.851 ± 0.123 mmHg·min/mL (P = 0.008). CONCLUSION: Neither vericiguat nor sodium nitroprusside significantly affected the baroreflex-mediated SNA response. The vasodilative effect of vericiguat became greater toward high levels of SNA and AP, possibly reflecting the increased sGC sensitivity to endogenous NO. By contrast, the effect of sodium nitroprusside was more uniform over the range of SNA. These results help better understand cardiovascular effects of vericiguat.

Acute effects of empagliflozin on open-loop baroreflex function and urine glucose excretion in Goto-Kakizaki diabetic rats.

Although suppression of sympathetic activity is suggested as one of the underlying mechanisms for the cardioprotective effects afforded by sodium-glucose cotransporter 2 (SGLT2) inhibitors, whether the modulation of glucose handling acutely affects sympathetic regulation of arterial pressure remains to be elucidated. In Goto-Kakizaki diabetic rats, we estimated the open-loop static characteristics of the carotid sinus baroreflex together with urine glucose excretion using repeated 11-min step input sequences. After the completion of the 2nd sequence, an SGLT2 inhibitor empagliflozin (10 mg kg-1) or vehicle solution was administered intravenously (n = 7 rats each). Empagliflozin did not significantly affect the baroreflex neural or peripheral arc, despite significantly increasing urine glucose excretion (from 0.365 ± 0.216 to 8.514 ± 0.864 mg·min-1·kg-1, P < 0.001) in the 7th and 8th sequences. The possible sympathoinhibitory effect of empagliflozin may be an indirect effect associated with chronic improvements in renal energy status and general disease conditions.

Printed Silk Microelectrode Arrays for Electrophysiological Recording and Controlled Drug Delivery.

The use of soft and flexible bioelectronic interfaces can enhance the quality for recording cells' electrical activity by ensuring a continuous and intimate contact with the smooth, curving surfaces found in the physiological environment. This work develops soft microelectrode arrays (MEAs) made of silk fibroin (SF) films for recording interfaces that can also serve as a drug delivery system. Inkjet printing is used as a tool to deposit the substrate, conductive electrode, and insulator, as well as a drug-delivery nanocomposite film. This approach is highly versatile, as shown in the fabrication of carbon microelectrodes, sandwiched between a silk substrate and a silk insulator. The technique permits the development of thin-film devices that can be employed for in vitro extracellular recordings of HL-1 cell action potentials. The tuning of SF by applying an electrical stimulus to produce a permeable layer that can be used in on-demand drug delivery systems is also demonstrated. The multifunctional MEA developed here can pave the way for in vitro drug screening by applying time-resolved and localized chemical stimuli.

Filtration-processed biomass nanofiber electrodes for flexible bioelectronics.

An increasing demand for bioelectronics that interface with living systems has driven the development of materials to resolve mismatches between electronic devices and biological tissues. So far, a variety of different polymers have been used as substrates for bioelectronics. Especially, biopolymers have been investigated as next-generation materials for bioelectronics because they possess interesting characteristics such as high biocompatibility, biodegradability, and sustainability. However, their range of applications has been restricted due to the limited compatibility of classical fabrication methods with such biopolymers. Here, we introduce a fabrication process for thin and large-area films of chitosan nanofibers (CSNFs) integrated with conductive materials. To this end, we pattern carbon nanotubes (CNTs), silver nanowires, and poly (3,4-ethylenedioxythiophene):poly (styrenesulfonate) (PEDOT:PSS) by a facile filtration process that uses polyimide masks fabricated via laser ablation. This method yields feedlines of conductive material on nanofiber paper and demonstrates compatibility with conjugated and high-aspect-ratio materials. Furthermore, we fabricate a CNT neural interface electrode by taking advantage of this fabrication process and demonstrate peripheral nerve stimulation to the rapid extensor nerve of a live locust. The presented method might pave the way for future bioelectronic devices based on biopolymer nanofibers.

System Design for Optimizing Drug Infusions Using Cardiovascular Space Mapping for Acute Heart Failure.

Acute heart failure is caused by various factors and requires multiple drug therapies to remedy underlying causes. Due to the complexity of pharmacologic effects of cardiovascular agents, few studies have theoretically addressed the multidrug optimization problem. This paper proposes a drug infusion system for acute heart failure that controls cardiovascular performance metrics (cardiac output, left atrial pressure, and mean arterial pressure) within desired ranges as dictated by the cardiovascular parameters (systemic vascular resistance, cardiac contractility, heart rate, and stressed blood volume). The key to our system design is modeling and controlling cardiovascular parameters to yield the desired cardiovascular metrics. A 'tailored drug infusion' technique controls parameters by solving the optimization problem in order to conquer the complexity of multi-dependencies and the different dosage limits among multiple drugs. A 'cardiovascular space mapping' technique identifies the desired parameters from the desired metrics by deriving the analytical solutions of the metrics as functions of the parameters. To facilitate clinical discussions, parameters were set to realistic values in 5,600 simulated patients. Our results showed not only that the optimized drug combinations and dosages controlled the cardiovascular metrics to within the desired ranges, but also that they mostly corresponded to the recommended clinical use guidelines. An additional value of our system is that it proactively predicts the limitations of the tailored drug therapy, which supports the clinical decision of pivoting to alternative treatment strategies such as mechanical circulatory support.

Integrating Machine Learning With Microsimulation to Classify Hypothetical, Novel Patients for Predicting Pregabalin Treatment Response Based on Observational and Randomized Data in Patients With Painful Diabetic Peripheral Neuropathy.

PURPOSE: Variability in patient treatment responses can be a barrier to effective care. Utilization of available patient databases may improve the prediction of treatment responses. We evaluated machine learning methods to predict novel, individual patient responses to pregabalin for painful diabetic peripheral neuropathy, utilizing an agent-based modeling and simulation platform that integrates real-world observational study (OS) data and randomized clinical trial (RCT) data. PATIENTS AND METHODS: The best supervised machine learning methods were selected (through literature review) and combined in a novel way for aligning patients with relevant subgroups that best enable prediction of pregabalin responses. Data were derived from a German OS of pregabalin (N=2642) and nine international RCTs (N=1320). Coarsened exact matching of OS and RCT patients was used and a hierarchical cluster analysis was implemented. We tested which machine learning methods would best align candidate patients with specific clusters that predict their pain scores over time. Cluster alignments would trigger assignments of cluster-specific time-series regressions with lagged variables as inputs in order to simulate "virtual" patients and generate 1000 trajectory variations for given novel patients. RESULTS: Instance-based machine learning methods (k-nearest neighbor, supervised fuzzy c-means) were selected for quantitative analyses. Each method alone correctly classified 56.7% and 39.1% of patients, respectively. An "ensemble method" (combining both methods) correctly classified 98.4% and 95.9% of patients in the training and testing datasets, respectively. CONCLUSION: An ensemble combination of two instance-based machine learning techniques best accommodated different data types (dichotomous, categorical, continuous) and performed better than either technique alone in assigning novel patients to subgroups for predicting treatment outcomes using microsimulation. Assignment of novel patients to a cluster of similar patients has the potential to improve prediction of patient outcomes for chronic conditions in which initial treatment response can be incorporated using microsimulation. CLINICAL TRIAL REGISTRIES: www.clinicaltrials.gov: NCT00156078, NCT00159679, NCT00143156, NCT00553475.

Assessing the Value of Time Series Real-World and Clinical Trial Data vs. Baseline-Only Data in Predicting Responses to Pregabalin Therapy for Patients with Painful Diabetic Peripheral Neuropathy.

BACKGROUND AND OBJECTIVE: Treatment challenges necessitate new approaches to customize care to individual patient needs. Integrating data from randomized controlled trials and observational studies may reduce potential covariate biases, yielding information to improve treatment outcomes. The objective of this study was to predict pregabalin responses, in individuals with painful diabetic peripheral neuropathy, by examining time series data (lagged inputs) collected after treatment initiation vs. baseline using microsimulation. METHODS: The platform simulated pregabalin-treated patients to estimate hypothetical future pain responses over 6 weeks based on six distinct time series regressions with lagged variables as inputs (hereafter termed "time series regressions"). Data were from three randomized controlled trials (N = 398) and an observational study (N = 3159). Regressions were derived after performing a hierarchical cluster analysis with a matched patient dataset from coarsened exact matching. Regressions were validated using unmatched (observational study vs. randomized controlled trial) patients. Predictive implications (of 6-week outcomes) were compared using only baseline vs. 1- to 2-week prior data. RESULTS: Time series regressions for pain performed well (adjusted R2 0.85-0.91; root mean square error 0.53-0.57); those with only baseline data performed less well (adjusted R2 0.13-0.44; root mean square error 1.11-1.40). Simulated patient distributions yielded positive predictive values for > 50% pain score improvements from baseline for the six clusters (287-777 patients each; range 0.87-0.98). CONCLUSIONS: Effective prediction of pregabalin response for painful diabetic peripheral neuropathy was accomplished through combining cluster analyses, coarsened exact matching, and time series regressions, reflecting distinct patterns of baseline and "on-treatment" variables. These results advance the understanding of microsimulation to predict patient treatment responses through integration and inter-relationships of multiple, complex, and time-dependent characteristics.

WHY COMBINE DIFFERENT DATA SOURCES?: Analyzing the tremendous amount of patient data can provide meaningful insights to improve healthcare quality. Using statistical methods to combine data from clinical trials with real-world studies can improve overall data quality (e.g., reducing biases related to real-world patient variability). WHY CONSIDER A TIME SERIES ANALYSIS?: The best predictor of future outcomes is past outcomes. A "time series" collects data at regular intervals over time. Statistical analyses of time series data allow us to discern time-dependent patterns to predict future clinical outcomes. Modeling and simulation make it possible to combine enormous amounts of data from clinical trial databases to predict a patient's clinical response based on data from similar patients. This approach improves selecting the right drug/dose for the right patient at the right time (i.e., personalized medicine). Using modeling and simulation, we predicted which patients would show a positive response to pregabalin (a neuropathic pain drug) for painful diabetic peripheral neuropathy. WHAT ARE THE MAJOR FINDINGS AND IMPLICATIONS?: For pregabalin-treated patients, a time series analysis had substantially more predictive value vs. analysis only of baseline data (i.e., data collected at treatment initiation). The ability to best predict which patients will respond to therapy has the overall implication of better informing drug treatment decisions. For example, an appropriate modeling and simulation platform complete with relevant historical clinical data could be integrated into a stand-alone device used to monitor and also predict a patient's response to therapy based on daily outcome measures (e.g., smartphone apps, wearable technologies).

Correction: Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

[This corrects the article DOI: 10.1371/journal.pone.0207120.].

Using time series analysis approaches for improved prediction of pain outcomes in subgroups of patients with painful diabetic peripheral neuropathy.

Prior work applied hierarchical clustering, coarsened exact matching (CEM), time series regressions with lagged variables as inputs, and microsimulation to data from three randomized clinical trials (RCTs) and a large German observational study (OS) to predict pregabalin pain reduction outcomes for patients with painful diabetic peripheral neuropathy. Here, data were added from six RCTs to reduce covariate bias of the same OS and improve accuracy and/or increase the variety of patients for pain response prediction. Using hierarchical cluster analysis and CEM, a matched dataset was created from the OS (N = 2642) and nine total RCTs (N = 1320). Using a maximum likelihood method, we estimated weekly pain scores for pregabalin-treated patients for each cluster (matched dataset); the models were validated with RCT data that did not match with OS data. We predicted novel 'virtual' patient pain scores over time using simulations including instance-based machine learning techniques to assign novel patients to a cluster, then applying cluster-specific regressions to predict pain response trajectories. Six clusters were identified according to baseline variables (gender, age, insulin use, body mass index, depression history, pregabalin monotherapy, prior gabapentin, pain score, and pain-related sleep interference score). CEM yielded 1766 patients (matched dataset) having lower covariate imbalances. Regression models for pain performed well (adjusted R-squared 0.90-0.93; root mean square errors 0.41-0.48). Simulations showed positive predictive values for achieving >50% and >30% change-from-baseline pain score improvements (range 68.6-83.8% and 86.5-93.9%, respectively). Using more RCTs (nine vs. the earlier three) enabled matching of 46.7% more patients in the OS dataset, with substantially reduced global imbalance vs. not matching. This larger RCT pool covered 66.8% of possible patient characteristic combinations (vs. 25.0% with three original RCTs) and made prediction possible for a broader spectrum of patients. Trial Registration: www.clinicaltrials.gov (as applicable): NCT00156078, NCT00159679, NCT00143156, NCT00553475.

Predicting Responses to Pregabalin for Painful Diabetic Peripheral Neuropathy Based on Trajectory-Focused Patient Profiles Derived from the First 4 Weeks of Treatment.

INTRODUCTION: Prediction of final clinical outcomes based on early weeks of treatment can enable more effective patient care for chronic pain. Our goal was to predict, with at least 90% accuracy, 12- to 13-week outcomes for pregabalin-treated painful diabetic peripheral neuropathy (pDPN) patients based on 4 weeks of pain and pain-related sleep interference data. METHODS: We utilized active treatment data from six placebo-controlled randomized controlled trials (n = 939) designed to evaluate efficacy of pregabalin for reducing pain in patients with pDPN. We implemented a three-step, trajectory-focused analytics approach based upon patient responses collected during the first 4 weeks using monotonicity, path length, frequency domain (FD), and k-nearest neighbor (kNN) methods. The first two steps were based on combinations of baseline pain, pain at 4 weeks, weekly monotonicity and path length during the first 4 weeks, and assignment of patients to one of four responder groups (based on presence/absence of 50% or 30% reduction from baseline pain at 4 and at 12/13 weeks). The third step included agreement between prediction of logistic regression of daily FD amplitudes and assignment made from kNN analyses. RESULTS: Step 1 correctly assigned 520/939 patients from the six studies to a responder group using a 3-metric combination approach based on unique assignment to a 50% responder group. Step 2 (applied to the remaining 419 patients) predicted an additional 121 patients, using a blend of 50% and 30% responder thresholds. Step 3 (using a combination of FD and kNN analyses) predicted 204 of the remaining 298 patients using the 50% responder threshold. Our approach correctly predicted 90.0% of all patients. CONCLUSION: By correctly predicting 12- to 13-week responder outcomes with 90% accuracy based on responses from the first month of treatment, we demonstrated the value of trajectory measures in predicting pDPN patient response to pregabalin. TRIAL REGISTRATION: www.clinicaltrials.gov identifiers, NCT00156078/NCT00159679/NCT00143156/NCT00553475. FUNDING: Pfizer. Plain language summary available for this article.

Dose Titration of Pregabalin in Patients with Painful Diabetic Peripheral Neuropathy: Simulation Based on Observational Study Patients Enriched with Data from Randomized Studies.

INTRODUCTION: Achieving a therapeutic response to pregabalin in patients with painful diabetic peripheral neuropathy (pDPN) requires adequate upward dose titration. Our goal was to identify relationships between titration and response to pregabalin in patients with pDPN. METHODS: Data were integrated from nine randomized, placebo-controlled clinical trials as well as one 6-week open-label observational study conducted by 5808 physicians (2642 patients with pDPN) in standard outpatient settings in Germany. These studies evaluated pregabalin for treatment of pDPN. Using these data, we examined "what if" scenarios using a microsimulation platform that integrates data from randomized and observational sources as well as autoregressive-moving-average with exogenous inputs models that predict pain outcomes, taking into account weekly changes in pain, sleep interference, dose, and other patient characteristics that were unchanging. RESULTS: Final pain levels were significantly different depending on dose changes (P < 0.0001), with greater proportions improving with upward titration regardless of baseline pain severity. Altogether, 78.5% of patients with pDPN had 0-1 dose change, and 15.2% had ≥ 2 dose changes. Simulation demonstrated that the 4.8% of inadequately titrated patients who did not improve/very much improve their pain levels would have benefited from ≥ 2 dose changes. Patient satisfaction with tolerability (range 90.3-96.2%) was similar, regardless of baseline pain severity, number of titrations, or extent of improvement, suggesting that tolerability did not influence treatment response patterns. CONCLUSION: Upward dose titration reduced pain in patients with pDPN who actually received it. Simulation also predicted pain reduction in an inadequately titrated nonresponder subgroup of patients had they actually received adequate titration. The decision not to uptitrate must have been driven by factors other than tolerability. FUNDING: Pfizer, Inc.

Integrating data from randomized controlled trials and observational studies to predict the response to pregabalin in patients with painful diabetic peripheral neuropathy.

BACKGROUND: More patient-specific medical care is expected as more is learned about variations in patient responses to medical treatments. Analytical tools enable insights by linking treatment responses from different types of studies, such as randomized controlled trials (RCTs) and observational studies. Given the importance of evidence from both types of studies, our goal was to integrate these types of data into a single predictive platform to help predict response to pregabalin in individual patients with painful diabetic peripheral neuropathy (pDPN). METHODS: We utilized three pivotal RCTs of pregabalin (398 North American patients) and the largest observational study of pregabalin (3159 German patients). We implemented a hierarchical cluster analysis to identify patient clusters in the Observational Study to which RCT patients could be matched using the coarsened exact matching (CEM) technique, thereby creating a matched dataset. We then developed autoregressive moving average models (ARMAXs) to estimate weekly pain scores for pregabalin-treated patients in each cluster in the matched dataset using the maximum likelihood method. Finally, we validated ARMAX models using Observational Study patients who had not matched with RCT patients, using t tests between observed and predicted pain scores. RESULTS: Cluster analysis yielded six clusters (287-777 patients each) with the following clustering variables: gender, age, pDPN duration, body mass index, depression history, pregabalin monotherapy, prior gabapentin use, baseline pain score, and baseline sleep interference. CEM yielded 1528 unique patients in the matched dataset. The reduction in global imbalance scores for the clusters after adding the RCT patients (ranging from 6 to 63% depending on the cluster) demonstrated that the process reduced the bias of covariates in five of the six clusters. ARMAX models of pain score performed well (R 2 : 0.85-0.91; root mean square errors: 0.53-0.57). t tests did not show differences between observed and predicted pain scores in the 1955 patients who had not matched with RCT patients. CONCLUSION: The combination of cluster analyses, CEM, and ARMAX modeling enabled strong predictive capabilities with respect to pain scores. Integrating RCT and Observational Study data using CEM enabled effective use of Observational Study data to predict patient responses.