Shih-Pin Lin

Eyes in the needle: novel epidural needle with embedded high-frequency ultrasound transducer--epidural access in porcine model.

Background:Epidural needle insertion is usually a blind technique where the rate of adverse events depends on the experience of the operator. A novel ultrasound method to guide epidural catheter insertion is described. Methods:An ultrasound transducer (40 MHz, a −6 dB fractional bandwidth of 50%) was placed into the hollow chamber of an 18-gauge Tuohy needle. The single crystal was polished to a thickness of 50 &mgr;m, with a width of 0.5 mm. Tissue planes were identified from the reflected signals in an A-mode display. The device was inserted three times into both the lumbar and thoracic regions of five pigs (average weight, 20 kg) using a paramedian approach at an angle of 35–40°. The epidural space was identified using signals from the ligamentum flavum and dura mater. Epidural catheters were placed with each attempt and placement confirmed by contrast injection. Results:The ligamentum flavum was identified in 83.3% of insertions and the dura mater in all insertions. The dura mater signal was stronger than that of the ligamentum flavum and served as a landmark in all epidural catheter insertions. Contrast studies confirmed correct placement of the catheter in the epidural space of all study animals. Conclusions:This is the first study to introduce a new ultrasound probe embedded in a standard epidural needle. It is anticipated that this technique could reduce failed epidural blocks and complications caused by dural puncture.

Clindamycin-induced Anaphylactic Shock During General Anesthesia

Clindamycin-related anaphylactic reaction is rarely reported. We report a male patient with buccal cancer who was undergoing radical neck dissection when life-threatening anaphylactic shock developed soon after intravenous infusion of clindamycin. Immediate cardiopulmonary resuscitation was performed, and the patient recovered uneventfully. Perioperative anaphylactic shock is a serious problem due to the difficulty of judgment and potentially disastrous outcome. Immediate diagnosis and halting of drug infusion should be the first actions taken.

Evaluating the influence of perceived organizational learning capability on user acceptance of information technology among operating room nurse staff

OBJECTIVES Medical institutions are eager to introduce new information technology to improve patient safety and clinical efficiency. However, the acceptance of new information technology by medical personnel plays a key role in its adoption and application. This study aims to investigate whether perceived organizational learning capability (OLC) is associated with user acceptance of information technology among operating room nurse staff. MATERIALS AND METHODS Nurse anesthetists and operating room nurses were recruited in this questionnaire survey. A pilot study was performed to ensure the reliability and validity of the translated questionnaire, which consisted of 14 items from the four dimensions of OLC, and 16 items from the four constructs of user acceptance of information technology, including performance expectancy, effort expectancy, social influence, and behavioral intention. Confirmatory factor analysis was applied in the main survey to evaluate the construct validity of the questionnaire. Structural equation modeling was used to test the hypothetical relationships between the four dimensions of user acceptance of information technology and the second-ordered OLC. Goodness of fit of the hypothetic model was also assessed. RESULTS Performance expectancy, effort expectancy, and social influence positively influenced behavioral intention of users of the clinical information system (all p < 0.001) and accounted for 75% of its variation. The second-ordered OLC was positively associated with performance expectancy, effort expectancy, and social influence (all p < 0.001). However, the hypothetic relationship between perceived OLC and behavioral intention was not significant (p = 0.87). The fit statistical analysis indicated reasonable model fit to data (root mean square error of approximation = 0.07 and comparative fit index = 0.91). CONCLUSION Perceived OLC indirectly affects user behavioral intention through the mediation of performance expectancy, effort expectancy, and social influence in the operating room setting.

Priming with Rocuronium to Accelerate the Onset Time of Cisatracurium During Intubation

Background: The priming technique, in which a small dose of nondepolarizing muscle relaxant is administered 3–6 minutes before giving the intubation dose, can speed up the onset of muscle relaxation in patients with paralysis during intubation. We investigated the priming technique and compared 2 different priming agents (rocuronium and cisatracurium) at a priming time of 3 minutes and its effect on decreasing the onset time of cisatracurium. Methods: A total of 60 patients with ASA physical status I–II scheduled for elective surgery were enrolled. After induction with propofol and fentanyl, the patients were randomized into 1 of 3 groups. Group 1 received rocuronium 0.06 mg/kg as a priming dose. Group 2 received cisatracurium 0.01 mg/kg as a priming dose. Group 3 received normal saline and constituted the control group. After a 3‐minute priming time, intubation doses of cisatracurium were given (Groups 1 and 2, 0.14 mg/kg; Group 3, 0.15 mg/kg). First twitch height percentage (T1/T0%; % of control) and train‐of‐four percentage (T4/T1%) were recorded every 10 seconds from baseline until T1/T0% reached 0. Results: Rocuronium (Group 1) and cisatracurium (Group 2) significantly accelerated the onset of cisatracurium (Group 1, 117.0 ± 29.0 seconds; Group 2, 151.0 ± 37.5 seconds; Group 3, 221.5 ± 36.6 seconds; all p < 0.001). Conclusion: Priming with rocuronium or cisatracurium for 3 minutes significantly accelerated the onset of cisatracurium. Priming with rocuronium for 3 minutes improved the onset time of cisatracurium even more than priming with cisatracurium itself.

Portable optical epidural needle-a CMOS-based system solution and its circuit design.

Epidural anesthesia is a common anesthesia method yet up to 10% of procedures fail to provide adequate analgesia. This is usually due to misinterpreting the tactile information derived from the advancing needle through the complex tissue planes. Incorrect placement also can cause dural puncture and neural injury. We developed an optic system capable of reliably identifying tissue planes surrounding the epidural space. However the new technology was too large and cumbersome for practical clinical use. We present a miniaturized version of our optic system using chip technology (first generation CMOS-based system) for logic functions. The new system was connected to an alarm that was triggered once the optic properties of the epidural were identified. The aims of this study were to test our miniaturized system in a porcine model and describe the technology to build this new clinical tool. Our system was tested in a porcine model and identified the epidural space in the lumbar, low and high thoracic regions of the spine. The new technology identified the epidural space in all but 1 of 46 attempts. Experimental results from our fabricated integrated circuit and animal study show the new tool has future clinical potential.

Serial Analgesic Consumptions and Predictors of Intravenous Patient-controlled Analgesia with Cluster Analysis

Objectives:To elucidate the dynamics of analgesic consumption regarding intravenous patient controlled analgesia (IVPCA) during postoperative period is rather complex partly due to between-patient variation and partly due to within-patient variation. A statistical method was proposed to classify serial analgesic consumption into different classifications that were further taken as the multiple outcomes on which to explore the associated predictors. Methods:We retrospectively included 3284 patients administrated by IVPCA for 3 days after surgery. A repeated measurement design corresponding to serial analgesic consumption variables defined as six-hour total analgesic consumptions was adopted. After determining the numbers of clusters, serial analgesic consumptions were classified into several homogeneous subgroups. Factors associated with new classifications were identified and quantified with a multinominal logistic regression model. Results:Three distinct analgesic classifications were aggregated, including “high”, ”middle” and “low” level of analgesic consumption of IVPCA. The mean analgesic consumptions on 12 successive analgesic consumptions at 6-hour interval of each classification consistently revealed a decreasing trend. As the trends were almost parallel with time, this suggests the time-invariant proportionality of analgesic consumption between the levels of analgesic consumption of IVPCA. Patient’s characteristics, like age, gender, weight, height, and cancer status, were significant factors associated with analgesic classifications. Surgical sites had great impacts on analgesic classifications. Discussion:The serial analgesic consumptions were simplified into 3 analgesic consumptions classifications. The identified predictors are useful to recognize patient’s analgesic classifications before using IVPCA. This study explored a new approach to analysing dynamic changes of postoperative analgesic consumptions.

Predicting postoperative vomiting among orthopedic patients receiving patient-controlled epidural analgesia using SVM and LR

Patient-controlled epidural analgesia (PCEA) has been applied to reduce postoperative pain in orthopedic surgical patients. Unfortunately, PCEA is occasionally accompanied by nausea and vomiting. The logistic regression (LR) model is widely used to predict vomiting, and recently support vector machines (SVM), a supervised machine learning method, has been used for classification and prediction. Unlike our previous work which compared Artificial Neural Networks (ANNs) with LR, this study uses a SVM-based predictive model to identify patients with high risk of vomiting during PCEA and comparing results with those derived from the LR-based model. From January to March 2007, data from 195 patients undergoing PCEA following orthopedic surgery were applied to develop two predictive models. 75% of the data were randomly selected for training, while the remainder was used for testing to validate predictive performance. The area under curve (AUC) was measured using the Receiver Operating Characteristic curve (ROC). The area under ROC curves of LR and SVM models were 0.734 and 0.929, respectively. A computer-based predictive model can be used to identify those who are at high risk for vomiting after PCEA, allowing for patient-specific therapeutic intervention or the use of alternative analgesic methods.

Predicting postoperative vomiting for orthopedic patients receiving patient-controlled epidural analgesia with the application of an artificial neural network.

Patient-controlled epidural analgesia (PCEA) was used in many patients receiving orthopedic surgery to reduce postoperative pain but is accompanied with certain incidence of vomiting. Predictions of the vomiting event, however, were addressed by only a few authors using logistic regression (LR) models. Artificial neural networks (ANN) are pattern-recognition tools that can be used to detect complex patterns within data sets. The purpose of this study was to develop the ANN based predictive model to identify patients with high risk of vomiting during PCEA used. From January to March 2007, the PCEA records of 195 patients receiving PCEA after orthopedic surgery were used to develop the two predicting models. The ANN model had a largest area under curve (AUC) in receiver operating characteristic (ROC) curve. The areas under ROC curves of ANN and LR models were 0.900 and 0.761, respectively. The computer-based predictive model should be useful in increasing vigilance in those patients most at risk for vomiting while PCEA is used, allowing for patient-specific therapeutic intervention, or even in suggesting the use of alternative methods of analgesia.

Risk factors of vomiting among females on patient-controlled epidural analgesia.

Background: Postoperative pain and postoperative vomiting (POV) are both sources of distress in the postoperative period. Patient‐controlled epidural analgesia (PCEA) is used in patients undergoing lower extremity surgery to improve postoperative quality but is accompanied by a certain incidence of vomiting. We wanted to determine the risk factors of POV in patients using PCEA with the aim of improving the quality of the postoperative period. Methods: We conducted a retrospective study to analyze the risk factors among patients using PCEA after lower‐limb surgery under regional anesthesia. A total of 195 patients (91 males, 104 females) were enrolled. They were categorized into 2 groups: vomiting and non‐vomiting. We found that female gender predominated in the vomiting group. Hence, we analyzed the female subgroup in order to find the risk factors of vomiting in the female PCEA population. Results: Female gender was the most significant factor related to vomiting (crude OR, 11.55; 95% CI, 4.88–27.33). From analysis of the female subgroup, puncture site (OR, 4.07; 95% CI, 1.41–11.79), catheter length in the epidural space (OR, 0.28; 95% CI, 0.16–0.50) and patients height (OR, 1.07; 95% CI, 1.00–1.14) were also factors for vomiting, i.e. higher epidural catheter puncture site, shorter length in the epidural space, and greater height caused a higher incidence of POV. Conclusion: The most important risk factor for POV in patients using PCEA was female gender. Among the female subgroup, the risk factors for POV included higher epidural catheter puncture site, shorter length in the epidural space and greater body height.

Patient response prediction with logistic regression in gastrointestinal endoscopy under midazolam-alfentanil sedation performed as well as response surface model

Background: Researchers have used logistic regression (LR) and non‐linear response surface models (RSMs) to predict patient responses to sedation. The reduced Greco and hierarchy RSMs have proven to be more appropriate than other RSMs in gastrointestinal endoscopies using midazolam and alfentanil. In this study, we evaluate the performance of a simpler model, LR, and compared it with that of RSM. Methods: Thirty‐three patients who received esophagogastroduodenoscopy (EGD) and colonoscopy sedation with midazolam and alfentanil were enrolled in the study. LR was performed for the EGD group and validated using the colonoscopy group. The two RSMs were performed using the same process, and performances and receiver operating characteristic (ROC) curves of the models were evaluated. Results: The native EGD LR model had an ROC curve area of 0.94. For external validation, the ROC curves were 0.92, 0.94, and 0.94 for the reduced Greco, hierarchy, and LR models, respectively. Pairwise comparison between models was not significant. Conclusion: The LR model performed as well as RSM in generalizing the predicted sedative effect of midazolam and alfentanil during gastrointestinal endoscopies. LR may be used for generalization across patients experiencing procedures with similar stimulus intensities.