No abstract

Blood pressure management is a central concern in critical care patients. For a variety of reasons, titration of vasopressor infusions may be an ideal use-case for computer assistance. Using our previous experience gained in the bench-to-bedside development of a computer-assisted fluid management system, we have developed a novel controller for this purpose. The aim of this preliminary study was to assess the feasibility of using this controller in simulated patients to maintain a target blood pressure in both stable and variable blood-pressure scenarios. We tested the controller in two sets of simulation scenarios: one with stable underlying blood pressure and a second with variable underlying blood pressure. In addition, in the variable phase of the study, we tested infusion-line delays of 8-60 s. The primary outcome for both testing conditions (stable and variable) was % case time in target range. We determined a priori that acceptable performance on the first phase of the protocol would require greater than 95% case-time in-target given the simple nature of the protocol, and for the second phase of the study 80% or greater given the erratic nature of the blood pressure changes taking place. 250 distinct cases for each simulation condition, both managed and unmanaged, were run over 4 days. In the stable hemodynamic conditions, the unmanaged group had an MAP of 57.5 ± 4.6 mmHg and spent only 5.6% of case time in-target. The managed group had an MAP of 70.3 ± 2.6 and spent a total of 99.5% of case time in-target (p < 0.00001 for both comparisons between groups). In the variable hemodynamic conditions, the unmanaged group had an MAP of 53.1 ± 5.0 mmHg and spent 0% of case time in-target. The managed group had an MAP of 70.5 ± 3.2 mmHg (p < 0.00001 compared to unmanaged group) and spent 88.6% of case time in-target (p < 0.00001 compared to unmanaged group), with 6.4% of case time over and 5.1% of case time under target. Increasing infusion lag increased coefficient of variation by about 10% per 15 s of lag (p = 0.001). This study demonstrated that this novel controller for vasopressor administration is able to main a target mean arterial pressure in a simulated physiologic model in the face of random disturbances and infusion-line lag.

This review over a 5-year longitudinal period demonstrates statistically significant improvement from baseline.

IntroductionOnabotulinumtoxinA (OBTA) is approved for treating chronic headaches and migraines in adults, but there is limited scientific literature on the outcomes in pediatric patients. The aim of this study was to determine if subjects treated with OBTA reported a statistically significant improvement in the primary features (frequency, intensity, duration and disability scoring) associated with migraines compared with placebo at follow-up visits.MethodsAfter obtaining approval by the appropriate local (HS# 2016–3108) and federal institutions, the principal investigator enrolled candidates aged 8 to 17 years old diagnosed with chronic migraines (at least 6 months), and 15 or more headache days in a 4-week baseline period. This randomized control trial consisted of two phases: double-blind and open-label for the first two and last two sets of treatments, respectively. Subjects were randomly assigned to receive a treatment protocol—155 units at 31 injection sites—in 3-month intervals and follow-up visits every 6 weeks. Non-parametric testing (Wilcoxon signed-rank test) was performed using widely available open-source statistical software (‘R’).ResultsFrom February 2017 to November 2018, 17 subjects presented for a screening visit; 15 met eligibility criteria. Subjects that received OBTA reported a statistically significant decrease from the following baseline values compared with placebo 6-week post-treatment compared with placebo: frequency (20 (7 to 17) vs 28 (23 to 28); p=0.038), intensity (5 (3 to 7) vs 7 (5 to 9); p=0.047), and PedMIDAS (Pediatric Migraine Disability Score) (3 (2 to 4) vs 4 (4 to 4); p=0.047). There was no statistically significant difference in the duration (10 (2 to 24) vs 24 (4 to 24); p=0.148) of migraines between the two groups.DiscussionOnabotulinumtoxinA showed a statistically significant decrease in frequency and intensity of migraines compared with placebo. No adverse effects or serious adverse events related to the use of OBTA were reported. In the future, we aim to evaluate the specific nature of migraines, for example, quality/location of pain presented during an initial consult to predict the likelihood of OBTA being a truly effective modality of pain management for pediatric migraineurs.Trial registration numberNCT03055767.

Background Accurate, objective pain assessment is required in the health care domain and clinical settings for appropriate pain management. Automated, objective pain detection from physiological data in patients provides valuable information to hospital staff and caregivers to better manage pain, particularly for patients who are unable to self-report. Galvanic skin response (GSR) is one of the physiologic signals that refers to the changes in sweat gland activity, which can identify features of emotional states and anxiety induced by varying pain levels. This study used different statistical features extracted from GSR data collected from postoperative patients to detect their pain intensity. To the best of our knowledge, this is the first work building pain models using postoperative adult patients instead of healthy subjects. Objective The goal of this study was to present an automatic pain assessment tool using GSR signals to predict different pain intensities in noncommunicative, postoperative patients. Methods The study was designed to collect biomedical data from postoperative patients reporting moderate to high pain levels. We recruited 25 participants aged 23-89 years. First, a transcutaneous electrical nerve stimulation (TENS) unit was employed to obtain patients' baseline data. In the second part, the Empatica E4 wristband was worn by patients while they were performing low-intensity activities. Patient self-report based on the numeric rating scale (NRS) was used to record pain intensities that were correlated with objectively measured data. The labels were down-sampled from 11 pain levels to 5 different pain intensities, including the baseline. We used 2 different machine learning algorithms to construct the models. The mean decrease impurity method was used to find the top important features for pain prediction and improve the accuracy. We compared our results with a previously published research study to estimate the true performance of our models. Results Four different binary classification models were constructed using each machine learning algorithm to classify the baseline and other pain intensities (Baseline [BL] vs Pain Level [PL] 1, BL vs PL2, BL vs PL3, and BL vs PL4). Our models achieved higher accuracy for the first 3 pain models than the BioVid paper approach despite the challenges in analyzing real patient data. For BL vs PL1, BL vs PL2, and BL vs PL4, the highest prediction accuracies were achieved when using a random forest classifier (86.0, 70.0, and 61.5, respectively). For BL vs PL3, we achieved an accuracy of 72.1 using a k-nearest-neighbor classifier. Conclusions We are the first to propose and validate a pain assessment tool to predict different pain levels in real postoperative adult patients using GSR signals. We also exploited feature selection algorithms to find the top important features related to different pain intensities. In...