Fully automated, real-time, calibration-free, continuous noninvasive estimation of intracranial pressure in children.

OBJECTIVE: In the search for a reliable, cooperation-independent, noninvasive alternative to invasive intracranial pressure (ICP) monitoring in children, various approaches have been proposed, but at the present time none are capable of providing fully automated, real-time, calibration-free, continuous and accurate ICP estimates. The authors investigated the feasibility and validity of simultaneously monitored arterial blood pressure (ABP) and middle cerebral artery (MCA) cerebral blood flow velocity (CBFV) waveforms to derive noninvasive ICP (nICP) estimates. METHODS: Invasive ICP and ABP recordings were collected from 12 pediatric and young adult patients (aged 2-25 years) undergoing such monitoring as part of routine clinical care. Additionally, simultaneous transcranial Doppler (TCD) ultrasonography-based MCA CBFV waveform measurements were performed at the bedside in dedicated data collection sessions. The ABP and MCA CBFV waveforms were analyzed in the context of a mathematical model, linking them to the cerebral vasculature's biophysical properties and ICP. The authors developed and automated a waveform preprocessing, signal-quality evaluation, and waveform-synchronization "pipeline" in order to test and objectively validate the algorithm's performance. To generate one nICP estimate, 60 beats of ABP and MCA CBFV waveform data were analyzed. Moving the 60-beat data window forward by one beat at a time (overlapping data windows) resulted in 39,480 ICP-to-nICP comparisons across a total of 44 data-collection sessions (studies). Moving the 60-beat data window forward by 60 beats at a time (nonoverlapping data windows) resulted in 722 paired ICP-to-nICP comparisons. RESULTS: Greater than 80% of all nICP estimates fell within ± 7 mm Hg of the reference measurement. Overall performance in the nonoverlapping data window approach gave a mean error (bias) of 1.0 mm Hg, standard deviation of the error (precision) of 5.1 mm Hg, and root-mean-square error of 5.2 mm Hg. The associated mean and median absolute errors were 4.2 mm Hg and 3.3 mm Hg, respectively. These results were contingent on ensuring adequate ABP and CBFV signal quality and required accurate hydrostatic pressure correction of the measured ABP waveform in relation to the elevation of the external auditory meatus. Notably, the procedure had no failed attempts at data collection, and all patients had adequate TCD data from at least one hemisphere. Last, an analysis of using study-by-study averaged nICP estimates to detect a measured ICP > 15 mm Hg resulted in an area under the receiver operating characteristic curve of 0.83, with a sensitivity of 71% and specificity of 86% for a detection threshold of nICP = 15 mm Hg. CONCLUSIONS: This nICP estimation algorithm, based on ABP and bedside TCD CBFV waveform measurements, performs in a manner comparable to invasive ICP monitoring. These findings open the possibility for rational, point-of-care treatment decisions in pediatric patients with suspected raised ICP undergoing intensive care.

Cervical mismatch: the normative value of T1 slope minus cervical lordosis and its ability to predict ideal cervical lordosis.

OBJECTIVE: Numerous studies have attempted to delineate the normative value for T1S-CL (T1 slope minus cervical lordosis) as a marker for both cervical deformity and a goal for correction similar to how PI-LL (pelvic incidence-lumbar lordosis) mismatch informs decision making in thoracolumbar adult spinal deformity (ASD). The goal of this study was to define the relationship between T1 slope (T1S) and cervical lordosis (CL). METHODS: This is a retrospective review of a prospective database. Surgical ASD cases were initially analyzed. Analysis across the sagittal parameters was performed. Linear regression analysis based on T1S was used to provide a clinically applicable equation to predict CL. Findings were validated using the postoperative alignment of the ASD patients. Further validation was then performed using a second, normative database. The range of normal alignment associated with horizontal gaze was derived from a multilinear regression on data from asymptomatic patients. RESULTS: A total of 103 patients (mean age 54.7 years) were included. Analysis revealed a strong correlation between T1S and C0-7 lordosis (r = 0.886), C2-7 lordosis (r = 0.815), and C0-2 lordosis (r = 0.732). There was no significant correlation between T1S and T1S-CL. Linear regression analysis revealed that T1S-CL assumed a constant value of 16.5° (R2 = 0.664, standard error 2°). These findings were validated on the postoperative imaging (mean absolute error [MAE] 5.9°). The equation was then applied to the normative database (MAE 6.7° controlling for McGregor slope [MGS] between -5° and 15°). A multilinear regression between C2-7, T1S, and MGS demonstrated a range of T1S-CL between 14.5° and 26.5° was necessary to maintain horizontal gaze. CONCLUSIONS: Normative CL can be predicted via the formula CL = T1S - 16.5° ± 2°. This implies a threshold of deformity and aids in providing a goal for surgical correction. Just as pelvic incidence (PI) can be used to determine the ideal LL, T1S can be used to predict ideal CL. This formula also implies that a kyphotic cervical alignment is to be expected for individuals with a T1S < 16.5°.

A 6-minute sub-maximal run test to predict VO2 max.

Maximal oxygen uptake ([Formula: see text] max) is a key indicator to assess health as well as sports performance. Currently, maximal exercise testing is the most accurate measure of maximal aerobic power, since submaximal approaches are still imprecise. In this paper, we propose a new method to predict [Formula: see text] max from a submaximal, low intensity, test in sports men and women. 182 males and 108 females from the High Performance Center of Pontevedra (Spain), aged 10-46 years old, with a [Formula: see text] max between 30.1 and 81.2 mL·min-1·kg-1, completed a maximal incremental test to volitional exhaustion. The test began at a speed of 6 km·h-1 and increased by 0.25 km·h-1 every 15 seconds. Using the data gathered during the first 6 minutes of the test, two different regression models were adjusted using functional data analysis and a traditional linear regression model with scalar covariates. The functional regression model obtained the best results, adjusted r2 = 0.845 and RMSE = 2.8 mL·min-1·kg-1, but the linear regression model also obtained a good fit, adjusted r2 = 0.798 and RMSE = 3.5 mL·min-1·kg-1. Both methods are more accurate than classical submaximal tests, although oxygen consumption needs to be measured during the test.

Sacroiliac joint motion in patients with degenerative lumbar spine disorders.

OBJECT Usually additional anchors into the ilium are necessary in long fusion to the sacrum for degenerative lumbar spine disorders (DLSDs), especially for adult spine deformity. Although the use of anchors is becoming quite common, surgeons must always keep in mind that the sacroiliac (SI) joint is mobile and they should be aware of the kinematic properties of the SI joint in patients with DLSDs, including adult spinal deformity. No previous study has clarified in vivo kinematic changes in the SI joint with respect to patient age, sex, or parturition status or the presence of DLSDs. The authors conducted a study to clarify the mobility and kinematic characteristics of the SI joint in patients with DLSDs in comparison with healthy volunteers by using in vivo 3D motion analysis with voxel-based registration, a highly accurate, noninvasive method. METHODS Thirteen healthy volunteers (the control group) and 20 patients with DLSDs (the DLSD group) underwent low-dose 3D CT of the lumbar spine and pelvis in 3 positions (neutral, maximal trunk flexion, and maximal trunk extension). SI joint motion was calculated by computer processing of the CT images (voxel-based registration). 3D motion of the SI joint was expressed as both 6 df by Euler angles and translations on the coordinate system and a helical axis of rotation. The correlation between joint motion and the cross-sectional area of the trunk muscles was also investigated. RESULTS SI joint motion during trunk flexion-extension was minute in healthy volunteers. The mean rotation angles during trunk flexion were 0.07° around the x axis, -0.02° around the y axis, and 0.16° around the z axis. The mean rotation angles during trunk extension were 0.38° around the x axis, -0.08° around the y axis, and 0.08° around the z axis. During trunk flexion-extension, the largest amount of motion occurred around the x axis. In patients with DLSDs, the mean rotation angles during trunk flexion were 0.57° around the x axis, 0.01° around the y axis, and 0.19° around the z axis. The mean rotation angles during trunk extension were 0.68° around the x axis, -0.11° around the y axis, and 0.05° around the z axis. Joint motion in patients with DLSDs was significantly greater, with greater individual difference, than in healthy volunteers. Among patients with DLSDs, women had significantly more motion than men did during trunk extension. SI joint motion was significantly negatively correlated with the cross-sectional area of the trunk muscles during both flexion and extension of the trunk. CONCLUSIONS The authors elucidated the mobility and kinematic characteristics of the SI joint in patients with DLSDs compared with healthy volunteers for the first time. This information is useful for spine surgeons because of the recent increase in spinopelvic fusion for the treatment of DLSDs.

Estimating EQ-5D values from the Neck Disability Index and numeric rating scales for neck and arm pain.

OBJECT: The Neck Disability Index (NDI) and numeric rating scales (0 to 10) for neck pain and arm pain are widely used cervical spine disease-specific measures. Recent studies have shown that there is a strong relationship between the SF-6D and the NDI such that using a simple linear regression allows for the estimation of an SF-6D value from the NDI alone. Due to ease of administration and scoring, the EQ-5D is increasingly being used as a measure of utility in the clinical setting. The purpose of this study is to determine if the EQ-5D values can be estimated from commonly available cervical spine disease-specific health-related quality of life measures, much like the SF-6D. METHODS: The EQ-5D, NDI, neck pain score, and arm pain score were prospectively collected in 3732 patients who presented to the authors' clinic with degenerative cervical spine disorders. Correlation coefficients for paired observations from multiple time points between the NDI, neck pain and arm pain scores, and EQ-5D were determined. Regression models were built to estimate the EQ-5D values from the NDI, neck pain, and arm pain scores. RESULTS: The mean age of the 3732 patients was 53.3 ± 12.2 years, and 43% were male. Correlations between the EQ-5D and the NDI, neck pain score, and arm pain score were statistically significant (p < 0.0001), with correlation coefficients of -0.77, -0.62, and -0.50, respectively. The regression equation 0.98947 + (-0.00705 × NDI) + (-0.00875 × arm pain score) + (-0.00877 × neck pain score) to predict EQ-5D had an R-square of 0.62 and a root mean square error (RMSE) of 0.146. The model using NDI alone had an R-square of 0.59 and a RMSE of 0.150. The model using the individual NDI items had an R-square of 0.46 and an RMSE of 0.172. The correlation coefficient between the observed and estimated EQ-5D scores was 0.79. There was no statistically significant difference between the actual EQ-5D score (0.603 ± 0.235) and the estimated EQ-5D score (0.603 ± 0.185) using the NDI, neck pain score, and arm pain score regression model. However, rounding off the coefficients to fewer than 5 decimal places produced less accurate results. CONCLUSIONS: The regression model estimating the EQ-5D from the NDI, neck pain score, and arm pain score accounted for 60% of the variability of the EQ-5D with a relatively large RMSE. This regression model may not be sufficient to accurately or reliably estimate actual EQ-5D values.