Sensitivity analysis approach for reduced‐order approximations of optimal control problems governed by Burgers equation

The reduced‐order model of the optimal control problem governed by Burgers equation is derived using the proper orthogonal decomposition (POD) method. The reduced‐order solution depending on parameters, which are different from the nominal values, may not be accurate if the POD basis functions depending on the nominal values are used to derive the reduced‐order model. It is known that Burgers equation is sensitive to the perturbations in the diffusion term, so we use the sensitivity information to improve the robustness of the POD solution by generating two new bases: extrapolated and expanded POD basis. We compare these different bases in terms of accuracy, robustness, and computational time. Copyright © 2015 John Wiley & Sons, Ltd.     

Fourier Collocation and Reduced Basis Methods for Fast Modeling of Compressible Flows

A projection-based reduced order model (ROM) based on the Fourier collocation method is proposed for compressible flows. The incorporation of localized artificial viscosity model and filtering is pursued to enhance the robustness and accuracy of the ROM for shock-dominated flows. Furthermore, for Euler systems, ROMs built on the conservative and the skew-symmetric forms of the governing equation are compared. To ensure efficiency, the discrete empirical interpolation method (DEIM) is employed. An alternative reduction approach, exploring the sparsity of viscosity is also investigated for the viscous terms. A number of one- and two-dimensional benchmark cases are considered to test the performance of the proposed models. Results show that stable computations for shock-dominated cases can be achieved with ROMs built on both the conservative and the skew-symmetric forms without additional stabilization components other than the viscosity model and filtering. Under the same parameters, the skew-symmetric form shows better robustness and accuracy than its conservative counterpart, while the conservative form is superior in terms of efficiency.     

Implementation of an artificial neuronal network to predict shunt necessity in carotid surgery

In carotid surgery, it could be useful to know which patient will tolerate carotid cross-clamping in order to minimize the risks of perioperative strokes. In this clinical study, an artificial neuronal network (ANN) was applied and compared with conventional statistical methods to assess the value of various parameters to predict shunt necessity. Eight hundred and fifty patients undergoing carotid endarterectomy for a high-grade internal carotid artery stenosis under local anesthesia were analyzed regarding shunt necessity using a standard feed-forward, backpropagation ANN (NeuroSolutions); NeuroDimensions, Gainesville, FL) with three layers (one input layer, one hidden layer, one output layer). Among the input neurons, preoperative clinical (n = 9) and intraoperative hemodynamic (n = 3) parameters were examined separately. The accuracy of prediction was compared to the results of a regression analysis using the same variables. In 173 patients (20%) a shunt was used because hemispheric deficits or unconsciousness occurred during cross-clamping. With the ANN, not needing a shunt was predicted by preoperative and intraoperative parameters with an accuracy of 96% and 91%, respectively, where the regression analysis showed an accuracy of 98% and 96%, respectively. Those patients who needed a shunt were identified by preoperative parameters in 9% and by intraoperative parameters in 56% when the ANN was used. Regression analysis predicted shunt use correctly in 10% using preoperative parameters and 41% using intraoperative parameters. Intraoperative hemodynamic parameters are more suitable than preoperative parameters to indicate shunt necessity where the application of an ANN provides slightly better results compared to regression analysis. However, the overall accuracy is too low to renounce perioperative neuromonitoring methods like local anesthesia.     

Range of motion and function are similar in patients undergoing TKA with posterior stabilised and high-flexion inserts

It is debatable whether high-flexion (HF) total knee arthroplasty (TKA) designs will improve postoperative flexion and function or will diminish the need for manipulation under anaesthesia (MUA). We retrospectively analysed range of motion (ROM), flexion, Knee Society Score (KSS), and rate of MUA in a consecutive group of patients who underwent TKA with a conventional posterior stabilised (PS) insert or an HF insert using identical surgical technique, implant design, and postoperative care. Fifty TKAs with a standard PS insert were matched for patient’s age, gender, preoperative ROM, and KSS with 50 TKA performed with an HF insert. The patient’s ROM and KSS were evaluated at six weeks, four months, and one year postoperatively. The outcome variables (flexion, ROM, KSS, and manipulation rate) in both groups were compared using the generalised estimating equations method. A second analysis of patients with preoperative flexion ≥120° was performed. The ROM, flexion, and patient-reported KSS was similar in both groups at each time period. The rate of MUA was also similar. Patients with a preoperative ROM of at least 120° showed similar results. Our study found that one year after surgery, patients who underwent TKA with a PS or an HF insert achieved similar flexion, ROM, and function.     

术后谵妄评估和预测的研究进展

术后谵妄（POD）是手术患者常见的并发症,严重影响患者远期认知功能、生理功能和社会功能。既往对POD的评估多以量表为基础,评估结果存在主观性,如何科学客观地进行POD的评估值得深入探究。POD可在早期预测并干预,从而进行有效预防,基于临床预测模型进行POD的风险预测已成为研究热点。本文从神经心理学量表、血清标志物、脑脊液标志物和脑电标志物等多个维度对POD的评估方法进行归纳,并针对POD临床预测模型的预测对象与预测指标进行总结,以期为POD评估和预测提供科学、可行、有效的参考依据。     

Muscle strength and knee range of motion after femoral lengthening

Background and purpose — Femoral lengthening may result in decrease in knee range of motion (ROM) and quadriceps and hamstring muscle weakness. We evaluated preoperative and postoperative knee ROM, hamstring muscle strength, and quadriceps muscle strength in a diverse group of patients undergoing femoral lengthening. We hypothesized that lengthening would not result in a significant change in knee ROM or muscle strength.

Patients and methods — This prospective study of 48 patients (mean age 27 (9–60) years) compared ROM and muscle strength before and after femoral lengthening. Patient age, amount of lengthening, percent lengthening, level of osteotomy, fixation time, and method of lengthening were also evaluated regarding knee ROM and strength. The average length of follow-up was 2.9 (2.0–4.7) years.

Results — Mean amount of lengthening was 5.2 (2.4–11.0) cm. The difference between preoperative and final knee flexion ROM was 2° for the overall group. Congenital shortening cases lost an average of 5% or 6° of terminal knee flexion, developmental cases lost an average of 3% or 4°, and posttraumatic cases regained all motion. The difference in quadriceps strength at 45° preoperatively and after lengthening was not statistically or clinically significant (2.7?Nm; p = 0.06). Age, amount of lengthening, percent lengthening, osteotomy level, fixation time, and lengthening method had no statistically significant influence on knee ROM or quadriceps strength at final follow-up.

Interpretation — Most variables had no effect on ROM or strength, and higher age did not appear to be a limiting factor for femoral lengthening. Patients with congenital causes were most affected in terms of knee flexion.     

Comparison of different methods for hemodialysis evaluation by means of ROC curves: from artificial intelligence to current methods

BACKGROUND: The National Kidney Foundation Guidelines (DOQI) and the European Renal Association (ERA) have set standards for adequacy of hemodialysis treatment. They recommended minimum single pool doses of 1.2 (Kt/Vsp DOQI), and 1.4 (Kt/Vsp ERA) and a "standard" urea removal ratio (URR) of 65%. Here, we compare an Artificial Intelligence Method (AIM) based on an Artificial Neural Network (ANN) and the usual methods for hemodialysis treatment follow-up such as Smye, Daugirdas, standard urea reduction ratio (URR using post-dialysis urea concentration) and modified URR [Cheng et al. 2001] against equilibrated Kt/V and URR calculated using a 60 min post-dialysis urea concentration. METHODS: We used ROC analysis to evaluate and compare these methodologies. We also propose a method to find a minimum target dose that maximizes the sensitivity, specificity and positive predictive values of the diagnostic tool. RESULTS: From a URR point of view, the ANN, stdURR and mURR perform almost equally well with an area under the curve (AUC) of 0.90, 0.93 and 0.92, respectively, but the ANN achieved the lowest false positive rate (FPR = 7.94%) and error rate (ER = 12.7%). When Kt/V is used as a dose index, the logarithmic single-and double-pool equations perform almost equally (AUC 0.957 and 0.962), and the ANN method achieves an AUC of 0.934. The lowest FPR was for ANN and Kt/Vsp (4.76%), which also achieved the lowest ER of 6.39%. CONCLUSIONS: For both cases (URR and Kt/V), the minimum doses required to achieve the lowest FPR and ER for the standard methods (stdURR and Kt/Vsp) were higher than those reported by the DOQI guidelines, being 70% for stdURR and 1.35 for Kt/Vsp, whereas for those methods using the double-pool Kt/V or equilibrated URR, the dose targets were close to those recommended by DOQI and ERA. Our proposed method for target dose selection is easy to understand, and it takes into account both accuracy and confidence of the adequacy tool. We found the ANN method to be superior to the Smye method for estimation of equilibrated urea, and the results presented here suggest that ANN methods could be useful tools in the analysis of nephrology data.     

Prediction of equilibrated postdialysis BUN by an artificial neural network in high-efficiency hemodialysis

In urea kinetic modeling, postdialysis blood urea nitrogen (BUN) is usually underestimated with an overestimation of the Kt/V especially in high-efficiency hemodialysis (HD). Thus, an artificial neural network (ANN) was used to predict the equilibrated BUN (Ceq) and equilibrated Kt/V (eKt/V60) by using both predialysis, postdialysis, and low-flow postdialysis BUN. The results were compared to a Smye formula to predict Ceq and a Daugirdas' formula (eKt/V30) to predict eKt/V60. Seventy-four patients on high-efficiency or high-flux HD were recruited. Their mean urea rebound was 28.6+/-2%. Patients were divided into a "training" set (n = 40) and a validation set (n = 34) for the ANN. Their status was exchanged later, and the two results were pooled. In the prediction of Ceq, both Smye formula and low-flow ANN were equally highly accurate. In patients with a high urea rebound (>30%), although Smye formula lost its accuracy, low-flow ANN remained accurate. In the prediction of eKt/V60, both Daugirdas' formula and low-flow ANN were equally accurate, although the Smye formula was not so accurate. In patients with a high urea rebound, although both Smye and Daugirdas' formulas lost their accuracy, low-flow ANN remained accurate. We concluded that low-flow ANN can accurately predict both Ceq and eKt/V60 regardless of the degree of urea rebound.     

Does hand-behind-back range of motion accurately reflect shoulder internal rotation?

Shoulder internal rotation (IR) is commonly assessed by an indirect method where the hand is placed behind the back and the distance reached by the tip of the extended thumb is recorded. The aim of this study was to assess the validity of measuring active IR range of motion (ROM) by use of the indirect hand-behind-back (HBB) ROM method in subjects with shoulder pain of mechanical origin. We recruited 137 subjects with unilateral shoulder pain. HBB ROM was determined by measuring the distance between the T1 spinous process and the radial styloid process. Active shoulder IR was measured in the supine position in 45 degrees or 90 degrees abduction. Correlation coefficients adjusted for measurement error were calculated. HBB ROM demonstrated only a low to moderate correlation with active shoulder IR. Active HBB ROM is not an accurate method of measuring active shoulder IR in patients with shoulder pain.     

Is Therapy Necessary After Distal Biceps Tendon Repair?

The EndoButton technique of distal biceps tendon repair provides strong biomechanical fixation. This strength of fixation may allow earlier postoperative range of motion (ROM). A retrospective review of 15 male patients undergoing single incision EndoButton repairs was used. Six subjects participated in conventional supervised postoperative rehabilitation while nine subjects were allowed unrestricted ROM after 2 weeks. Final ROM, time to full ROM, and Disabilities of Arm Shoulder and Hand (DASH) scores were compared. There was a significant difference for time to full ROM (p < 0.05). The mean time to full ROM was 8.67 weeks for the supervised therapy group and 4.38 weeks for the unrestricted group. There were no reruptures in either group. There were no significant differences in final ROM or DASH scores. These data suggest that unrestricted ROM results in a quicker return to full ROM without an increased risk of rerupture.     

Influence of early mobilization program on pain,self-reported and performance based functional measures following total knee replacement

BackgroundTotal knee replacement (TKR) is an optimal treatment for persons with severe knee joint pain and disability, who were unsuccessful with conservative management. Early mobilization can be defined as moving out of bed and/or walking quickly after the surgery for reducing the risks allied with bed rest. There is a paucity of studies on effects of early mobilization on a performance-based measure of timed up and go test (TUG), six-minute walk test (SMWT) and a self-reported disease-specific measure of a knee injury and Osteoarthritis outcome score (KOOS) following TKR.MethodsA prospective pre-post-trial was conducted at Manipal Hospital, Bangalore, India. Participants underwent early (POD ‘0’) mobilization on the same postoperative day within 7 h post-TKR surgery. Outcome measures were recorded by an independent blinded observer. The statistical significance level was set at 'p' value < 0.05. The difference between pre-operative and post-operative outcome measure at 1 month and 3 months post-intervention were analyzed using repeated measures of ANOVA.ResultsThe study included a total of 78 participants (59 Females; 19 Males) and the mean age of the included participants was 64.1 ± 7 years. Amongst, 78 participants, 53 underwent unilateral TKR, 25 underwent bilateral TKR. There were three dropouts in the study due to post-operative complications. Significant improvements from pre-operative to one month were observed following POD ‘0’ mobilization on NPRS (7.35 ± 1.2 to 4.3 ± 1.7), SMWT (169 ± 70 to 236.7 ± 80.7). KOOS subscales of pain, symptom, and quality of life showed significant changes at one month and 3 months. TUG, Knee strength, Knee ROM and KOOS ADL subscale shown improvements only at 3 months post-intervention.ConclusionOur study findings suggest that POD ‘0’ (early) mobilization can result in reduced pain and an increase in walking speed at 1 month. Significant changes were observed in pain, Knee strength, Knee ROM, TUG, SMWT and KOOS subscales at 3 months following total knee replacement.     

Accurate determination of a joint rotation center based on the minimal amplitude point method.

In many computer-assisted surgery procedures in the field of joint replacement a joint rotation center must be located. To this end, joints such as the hip or the shoulder are approximated as spherical joints, and the most commonly used numerical method is the sphere-fitting algorithm. However, this method has a numerical instability where there is a limited range of motion caused by, for example, joint impingement. The aim of the present study was to develop an alternative kinematic method called the minimal amplitude point method. This method estimates the localization of the rotation center and can easily be integrated into program codes of computer-assisted surgery modules. It mainly consists of identifying the point of a mobile segment that moves least in the reference coordinate system of an immobile segment using an optimization procedure (genetic algorithm). The point determined can then be assumed to be the rotation center of the joint. To compare results of the two methods, an experimental set-up of two rigid solids linked by a spherical joint with known geometry was used to compute the rotation center. In contrast to the sphere-fitting method, the minimal amplitude point method permits the evaluation of the rotation center with an error of less than 4.1 mm, having a range of motion (ROM) of 5 degrees. An equivalent accuracy for the sphere-fitting method requires an ROM of 45 degrees.     

Artificial Neural Network for Prediction of Lymph Node Metastases in Gastric Cancer: A Phase II Diagnostic Study

Background Extension of lymphadenectomy in gastric cancer is controversial, and preoperative diagnosis of lymph node metastases (LNM) is difficult. Therefore, knowledge-based systems such as the Maruyama computer program (MCP) are being developed. MCP shows good prognostic value for the compartments; however, for different lymph node groups (LNG) there are a large number of false positives. The aim of this study was to evaluate artificial neural networks (ANN) for predicting LNM in patients with gastric cancer and to compare the predictive power with that of MCP.Methods A total of 135 consecutive patients who underwent D2 gastrectomy were included. We applied a single-layer perceptron to the data of 4302 patients from the National Cancer Center, Tokyo, and compared the results with those from the MCP.Results Prediction of N⁺ or N0 with ANN-1 (Borrmann classification, T category, and tumor size and location) had an accuracy of 79%. The predictive value for LNM in each of the LNG varied: ANN-1, 64% to 86%; MCP, 42% to 70%. We constructed another ANN by using the additional parameter of metastases in LNG 3 as an example of sentinel node. The accuracy of this ANN was 93%.Conclusions Using an ANN, LNM in each LNG can be accurately predicted. Additional knowledge about one lymph node improves the results.     

Initial biopsy outcome prediction--head-to-head comparison of a logistic regression-based nomogram versus artificial neural network

OBJECTIVES: Nomograms and artificial neural networks (ANNs) represent alternative methodologic approaches to predict the probability of prostate cancer on initial biopsy. We hypothesized that, in a head-to-head comparison, one of the approaches might demonstrate better accuracy and performance characteristics than the other. METHODS: A previously published nomogram, which relies on age, digital rectal examination, serum prostate-specific antigen (PSA), and percent-free PSA, and an ANN, which relies on the same predictors plus prostate volume, were applied to a cohort of 3980 men, who were subjected to multicore systematic prostate biopsy. The accuracy and the performance characteristics were compared between these two approaches. RESULTS: The accuracy of the nomogram was 71% versus 67% for the ANN (p=0.0001). Graphical exploration of the performance characteristics demonstrated virtually perfect predictions for the nomogram. Conversely, the ANN underestimated the observed rate of prostate cancer. CONCLUSIONS: A 4% increase in predictive accuracy implies that the use of the nomogram instead of the ANN will result in 40 additional patients who will be correctly classified between benign and cancer.     

An Efficient Finite Element Method with Exponential Mesh Refinement for the Solution of the Allen-Cahn Equation in Non-Convex Polygons

In this paper we consider the numerical solution of the Allen-Cahn type diffuse interface model in a polygonal domain. The intersection of the interface with the re-entrant corners of the polygon causes strong corner singularities in the solution. To overcome the effect of these singularities on the accuracy of the approximate solution, for the spatial discretization we develop an efficient finite element method with exponential mesh refinement in the vicinity of the singular corners, that is based on ($k$−1)-th order Lagrange elements, $k$≥2 an integer. The problem is fully discretized by employing a first-order, semi-implicit time stepping scheme with the Invariant Energy Quadratization approach in time, which is an unconditionally energy stable method. It is shown that for the error between the exact and the approximate solution, an accuracy of $\mathcal{O}$($h^k$+$τ$) is attained in the $L^2$-norm for the number of $\mathcal{O}$($h^{−2}$ln$h^{−1}$) spatial elements, where $h$ and $τ$ are the mesh and time steps, respectively. The numerical results obtained support the analysis made.     

Intracranial Pressure Processing with Artificial Neural Networks: Prediction of ICP Trends

It is well known that intracranial pressure (ICP) is influenced by an array of predictable and unpredictable factors, which gives rise to a signal heavily loaded with stochastic, i.e. random components. Hence, statistical modelling of this signal has proved to be of limited utility, in spite of the very sophisticated mathematical methods applied. In recent years, neural network algorithms (ANN), which are an alternative to statistical methods, have proved their effectiveness in the prediction of trends, as applied in a variety of medical and non-medical tasks. We therefore attempted to test the efficiency of neural models in the on-line prediction of ICP values, compare their effectiveness to statistically oriented algorithms and combine ANN methods with some newer signal processing algorithms, like wavelet decomposition. Prediction horizons of up to 5 minutes have been tested with various architectures of the neural predictor. For a 3 minute prediction horizon, a satisfactory accuracy of forecasting has been achieved with "plain" ANN, as expressed by the "average relative variance coefficient". This was measured by the ratio of the prediction error obtained, in relation to the error which would occur if a current value were taken as the forecasted one. The prediction quality with statistical autoregressive models has proved unsatisfactory, whilst the result obtained using the ANN model with the wavelet transform incorporated, performed significantly better than the ANN models alone. The prediction quality obtained with the ANN methodology seems to be satisfactory over a short time horizon, though no conclusion can be derived at this stage of the study, as to the clinical utility of this method. In particular, even with this methodology, it is not possible to forecast any sudden dehiscencies of the ICP signal with any practical reliability. From the point of view of modelling theory, such sharp deviations of the signal may be regarded as a "catastrophe". This implies the necessity for a different approach to the ICP signal analysis with the artificial intelligence methodology; one, that is more oriented towards the global properties of the signal.     

Salbutamol premedication in children with a recent respiratory tract infection

Background :  Premedication with β-2 agonists (e.g. salbutamol) is effective in preventing increases in total respiratory resistance and in decreasing the incidence of perioperative bronchospasm in asthmatic children. Because children with recent respiratory tract infection (RTI) exhibit bronchial hyperreactivity similar to that observed in asthmatic children, the use of salbutamol in children with RTI has become popular among pediatric anesthetists for the prevention of perioperative respiratory adverse events (PRAE). In a prospective observational study, we therefore assessed the usefulness of salbutamol premedication on the occurrence of PRAE.
Methods :  Results from 600 children (0–16 years) undergoing general anesthesia were analyzed: 200 children with a recent RTI who received preoperative salbutamol 10–30 min prior to surgery, 200 children with a recent RTI without salbutamol premedication, and 200 children without a RTI during the last 4 weeks. All PRAE (laryngospasm, bronchospasm, oxygen desaturation [<95%], severe coughing) were recorded.
Results :  Children with a recent RTI who received salbutamol demonstrated a significantly reduced incidence of perioperative bronchospasm (5.5% vs 11%, P  = 0.0270) and severe coughing (5.5% vs 11.5%, P  = 0.0314) compared with children who had an RTI but did not receive salbutamol. However, healthy children presented with the lowest rate (bronchospasm 1.5%, severe coughing 4.5%) of respiratory complications compared with children with a recent RTI independent whether or not they received salbutamol preoperatively.
Conclusions :  The results from this audit suggest that children with a history of a recent RTI have significantly less PRAE following a premedication with salbutamol compared with no premedication. Therefore, premedication with salbutamol might be considered in children with recent RTI.     

Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study

OBJECTIVE: To assess the ability of artificial neural networks (ANNs) to predict optimum renal stone fragmentation in patients being managed by extracorporeal shock wave lithotripsy (ESWL). PATIENTS AND METHODS: The study included 82 patients with renal stones who were being treated by ESWL. Data (input and output values) from 60 patients in whom there was optimum fragmentation of stones by ESWL were used for training the ANN. These data mainly included the 24-h urinary variables, the radiological features of the stone disease and the ESWL settings used. The predictability of the trained ANN was tested on 22 subsequent patients, by supplying the input variables of the 22 patients into the trained ANN and recording the output values (predicted values). After subjecting these patients to ESWL, the actual results (observed values) were recorded. The predicted and the observed values were then compared. RESULTS: In the 22 patients in whom predictability was tested, the trained ANN predicted optimum fragmentation at < or = 13 000 shocks/stone (as per study protocol) in 17 and optimum fragmentation at> 13 000 shocks/stone in the other five. In the 17 patients (test set) where the trained ANN had predicted optimum fragmentation at < or = 13 000 shocks/stone, the optimum fragmentation was at that value, although the predicted and observed values were not identical. The overall correlation between the predicted and the observed values was 75.5% (correlation coefficient 0.7547) in these 17 patients. Of the other five patients, none had optimum fragmentation at < 13 000 shocks/stone, as predicted by the trained ANN, giving complete accuracy for this factor. CONCLUSION: This was a pilot study, i.e. an initial attempt to use an ANN in this regard, and although there were few patients, such that it is not possible to make final recommendations, the overall predictability was approximately 75%. An encouraging outcome of the study was that the trained ANN identified patients unlikely to benefit from ESWL. Using a larger dataset and identifying more significant variables, while eliminating inputs with a negative effect, the efficiency and utility of this ANN can probably be enhanced and in future it might be possible to predict stone fragmentation with reasonable accuracy.     

Prognostic accuracy of an artificial neural network in patients undergoing radical cystectomy for bladder cancer: a comparison with logistic regression analysis

OBJECTIVE: To compare the prognostic performance of an artificial neural network (ANN) with that of standard logistic regression (LR), in patients undergoing radical cystectomy for bladder cancer. PATIENTS AND METHODS: From February 1982 to February 1994, 369 evaluable patients with non-metastatic bladder cancer had pelvic lymph node dissection and radical cystectomy for either stage Ta-T1 (any grade) tumour not responding to intravesical therapy, with or with no carcinoma in situ, or stage T2-T4 tumour. LR analysis based on 12 variables was used to identify predictors of overall 5-year survival, and the ANN model was developed to predict the same outcome. The LR analysis, based on statistically significant predictors, and the ANN model were the compared for their accuracy in predicting survival. RESULTS: The median age of the patients was 63 years, and overall 201 of them died. The tumour stage and nodal involvement (both P<0.001) were the only statistically independent predictors of overall 5-year survival on LR analysis. Based on these variables, LR had a sensitivity and specificity for predicting survival of 68.4% and 82.8%, respectively; corresponding values for the ANN were 62.7% and 86.1%. For LR and ANN, the positive predictive values were 78.6% and 76.2%, and the negative predictive values were 73.9% and 76.5%, respectively. The index of diagnostic accuracy was 75.9% for LR and 76.4% for ANN. CONCLUSIONS: The ANN accurately predicted the survival of patients undergoing radical cystectomy for bladder cancer and had a prognostic performance comparable with that of LR. As ANNs are based on easy-to-use software that can identify nonlinear interactions between variables, they might become the preferred tool for predicting outcome.     

Comparison of adductor canal block and IPACK block (interspace between the popliteal artery and the capsule of the posterior knee) with adductor canal block alone after total knee arthroplasty: a prospective control trial on pain and knee function in immediate postoperative period

Background

Adductor canal block (ACB) is a peripheral nerve blockade technique that provides good pain control in patients undergoing total knee arthroplasty which however does not relieve posterior knee pain. The recent technique of an ultrasound-guided local anesthetic infiltration of the interspace between popliteal artery and the capsule of posterior knee (IPACK) has shown promising results in providing significant posterior knee analgesia without affecting the motor nerves.

Materials and methods

A prospective study was conducted from September 2016 to March 2017 in a total of 120 patients undergoing unilateral total knee arthroplasty. The initial 60 consecutive patients received ACB?+?IPACK (Group 1, n?=?60), and the subsequent 60 patients received ACB alone (Group 2, n?=?60). All patients were evaluated with VAS score for pain recorded at 8 h, postoperative day (POD) 1 and POD 2 after the surgery. The secondary outcome measures assessed were the range of movement (ROM) and ambulation distance.

Results

VAS score showed significantly (p?<?0.005) better values in ACB?+?IPACK group compared to the ACB group. The mean ROM of knee and ambulation distance also showed significantly better values in ACB?+?IPACK group compared to the ACB group.

Conclusion

ACB?+?IPACK is a promising technique that offers improved pain management in the immediate postoperative period without affecting the motor function around the knee joint resulting in better ROM and ambulation compared to ACB alone.