Meaningful Clinical Interpretation Research Articles

Comparison of Feature Selection Methods—Modelling COPD Outcomes

Selecting features associated with patient-centered outcomes is of major relevance yet the importance given depends on the method. We aimed to compare stepwise selection, least absolute shrinkage and selection operator, random forest, Boruta, extreme gradient boosting and generalized maximum entropy estimation and suggest an aggregated evaluation. We also aimed to describe outcomes in people with chronic obstructive pulmonary disease (COPD). Data from 42 patients were collected at baseline and at 5 months. Acute exacerbations were the aggregated most important feature in predicting the difference in the handgrip muscle strength (dHMS) and the COVID-19 lockdown group had an increased dHMS of 3.08 kg (CI95 ≈ [0.04, 6.11]). Pack-years achieved the highest importance in predicting the difference in the one-minute sit-to-stand test and no clinical change during lockdown was detected. Charlson comorbidity index was the most important feature in predicting the difference in the COPD assessment test (dCAT) and participants with severe values are expected to have a decreased dCAT of 6.51 points (CI95 ≈ [2.52, 10.50]). Feature selection methods yield inconsistent results, particularly extreme gradient boosting and random forest with the remaining. Models with features ordered by median importance had a meaningful clinical interpretation. Lockdown seem to have had a negative impact in the upper-limb muscle strength.

On color adjustment potential and color blending threshold of dental composite resins.

Color Adjustment Potential evaluates the color blending of dental Composite Resins. While Color Adjustment Potential is simple, its clinical relevance is unclear. This research aims to understand it better and to create an index for Composite Resins with meaningful clinical interpretation. Single and double shade composite disks of various diameters and opacities were created to test the indices. Color measurements used a dental colorimeter, avoiding subjective assessments. Color Adjustment Potential analysis of each material revealed insights, leading to the creation of a new Color Blending Threshold, providing a clinically relevant numerical value for Composite Resins. Color Adjustment Potential's numerical significance was clarified and introduced a new index for clinical applications. Color adaptation of each test shade to all Vita shades was also calculated, useful for single-shade restorations in open and closed cavity types. The proposed Color Blending Threshold defines the open/closed cavity dimension that can be adequately restored with a single shade of resin composite. Understanding how dental materials adapt to surrounding tooth colors enhances esthetic restorations, simplifies shade matching, and optimizes resin composite production. The proposed Color Blending Threshold is a parameter that directly relates to the clinical significance of a material's true color blending ability. It defines the cavity dimension that can be adequately restored with a single shade of resin composite while ensuring that the resulting color difference falls below a predetermined threshold, meeting the clinical requirements for an esthetic restoration.

Transcriptome from Paired Samples Improves the Power of Comprehensive COVID-19 Host-Viral Characterization.

Previous transcriptome profiling studies showed significantly upregulated genes and altered biological pathways in acute COVID-19. However, changes in the transcriptional signatures during a defined time frame are not yet examined and described. The aims of this study included viral metagenomics and evaluation of the total expression in time-matched and tissue-matched paired COVID-19 samples with the analysis of the host splicing profile to reveal potential therapeutic targets. Prospective analysis of paired nasopharyngeal swabs (NPS) and blood (BL) samples from 18 COVID-19 patients with acute and resolved infection performed using Kallisto, Suppa2, Centrifuge, EdgeR, PantherDB, and L1000CDS2 tools. In NPS, we discovered 6 genes with changed splicing and 40 differentially expressed genes (DEG) that yielded 88 altered pathways. Blood samples yielded 15 alternatively spliced genes. Although the unpaired DEG analysis failed, pairing identified 78 genes and 242 altered pathways with meaningful clinical interpretation and new candidate drug combinations with up to 65% overlap. Metagenomics analyses showed SARS-CoV-2 dominance during and after the acute infection, with a significant reduction in NPS (0.008 vs. 0.002, p = 0.019). Even though both NPS and BL give meaningful insights into expression changes, this is the first demonstration of how the power of blood analysis is vastly maximized by pairing. The obtained results essentially showed that pairing is a determinant between a failed and a comprehensive study. Finally, the bioinformatics results prove to be a comprehensive tool for full-action insights, drug development, and infectious disease research when designed properly.

Joint inference for competing risks data using multiple endpoints.

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID-19 in-patient treatment clinical trial, where the outcomes of COVID-19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinicalimplications.

Clothing condition does not affect meaningful clinical interpretation in markerless motion capture

Markerless motion capture allows whole-body movements to be captured without the need for physical markers to be placed on the body. This enables motion capture analyses to be conducted in more ecologically valid environments. However, the influences of varied clothing on video-based markerless motion capture assessments remain largely unexplored. This study investigated two types of clothing conditions, “Sport” (gym shirt and shorts) and “Street” (unrestricted casual clothing), on gait parameters during overground walking by 29 participants at self-selected speeds using markerless motion capture. Segment lengths, gait spatiotemporal parameters, and lower-limb kinematics were compared between the two clothing conditions. Mean differences in segment length for the forearm, upper arm, thigh, and shank between clothing conditions ranged from 0.2 cm for the forearm to 0.9 cm for the thigh (p < 0.05 for thigh and shank) but below typical marker placement errors (1 – 2 cm). Seven out of 9 gait spatiotemporal parameters demonstrated statistically significant differences between clothing conditions (p < 0.05), however, these differences were approximately ten times smaller than minimal detectable changes in movement-related pathologies including multiple sclerosis and cerebral palsy. Hip, knee, and ankle joint angle root-mean-square deviation values averaged 2.6° and were comparable to previously reported average inter-session variability for this markerless system (2.8°). The results indicate that clothing, a potential limiting factor in markerless motion capture performance, would negligibly alter meaningful clinical interpretations under the conditions investigated.

Clothing Condition Does Not Affect Meaningful Clinical Interpretation in Markerless Motion Capture

Clothing Condition Does Not Affect Meaningful Clinical Interpretation in Markerless Motion Capture

Normative data for peripheral quantitative computed tomography (pQCT) bone parameters in Australian men

BackgroundPeripheral quantitative computed tomography (pQCT) can provide information complementary to dual x-ray absorptiometry (DXA), however, there is sparse normative data to enable meaningful clinical interpretation and comparison. This study aimed to develop age-stratified normative data for pQCT-derived bone parameters in Australian men. MethodsParticipants were men (n = 508, age 33-96 yr) from the Geelong Osteoporosis Study. Bone parameters at 4% (n = 469) and 66% (n = 436) of radial length, and 4% (n = 449) and 66% (n = 438) of tibial length were acquired using pQCT (XCT 2000, Stratec Medizintechnik, Pforzheim, Germany). Best models of age, height and weight for each parameter were developed and where parameters exhibited variation with age, age decade mean (±SD) values were determined. Scatterplots were used to visualise the relationships between each of the parameters and age, height and weight. ResultsThirteen parameters at tibial and radial sites were correlated with age, height and weight, allowing for their inclusion in multiple linear regression models. A positive association with age was found for total area of the tibia or radius (as appropriate) (mm2) at all sites, trabecular bone area (mm2) at 4% sites, and total bone area (both long bones) (mm2) at 66% sites. A negative association with age was found for cortical density (mg/cm3) and cortical thickness (mm) at both radial and tibial 66% sites, but total density (mg/cm3) at the 66% radial site and total cortical density of both long bones (mg/cm3) at the 66% tibial site only. ConclusionThis study presents normative data for pQCT-derived bone parameters and describes age related associations in a number of these variables. Broadly, parameters of bone area were positively associated with age, whereas parameters associated with bone density and structure were negatively associated with age. These data have the potential to be used in clinical settings when assessing age-related decline in bone health. Mini abstractNormative data for pQCT parameters in Australian men are presented, adjusted for age, height and weight.

Minimal clinically important difference, substantial clinical benefit, and patient acceptable symptom state of PROMIS upper extremity after total shoulder arthroplasty.

BackgroundThe Patient-Reported Outcomes Measurement Information System minimal clinically important difference (PROMIS MCID), substantial clinical benefit (SCB), and patient acceptable symptom state (PASS) of patient-reported outcome measures provide clinical significance to patient-reported outcome measures scores. The goal of this study is to measure the MCID, SCB, and PASS of PROMIS Upper Extremity v2.0 (PROMIS UE) in patients undergoing total shoulder arthroplasty (TSA).MethodsAll patients who underwent TSA since October 2017 were identified from our institutional database. Patients who had completed the PROMIS UE outcome measure before surgery were asked to complete a PROMIS UE and anchor survey that contained two transition questions to assess patient satisfaction and change in symptoms since treatment. The anchor-based MCID, SCB, and PASS were calculated as the change in PROMIS UE score that represented the optimal cutoff for a receiver operating characteristic curve. The distribution-based MCID was calculated as a range between the average standard error of measurement multiplied by 2 different constants: 1 and 2.77.ResultsThis study enrolled 165 patients. The anchor-based MCID for PROMIS UE was calculated to be 8.05 with an AUC of 0.814. The anchor-based SCB was calculated to be 10.0 with an AUC of 0.727. The distribution-based MCID was calculated to be between 3.12 and 8.65. The PASS was calculated to be 37.2 with an AUC of 0.90.ConclusionsThe establishment of MCID, SCB, and PASS for PROMIS UE scores after shoulder arthroplasty provides meaningful and objective clinical interpretation of the improvements in outcome scores after TSA.

DATA-GRU: Dual-Attention Time-Aware Gated Recurrent Unit for Irregular Multivariate Time Series

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly and different physiological variables are examined at each visit, producing large amounts of irregular multivariate time series (IMTS) data with missing values and varying intervals. Existing methods process IMTS into regular data so that standard machine learning models can be employed. However, time intervals are usually determined by the status of patients, while missing values are caused by changes in symptoms. Therefore, we propose a novel end-to-end Dual-Attention Time-Aware Gated Recurrent Unit (DATA-GRU) for IMTS to predict the mortality risk of patients. In particular, DATA-GRU is able to: 1) preserve the informative varying intervals by introducing a time-aware structure to directly adjust the influence of the previous status in coordination with the elapsed time, and 2) tackle missing values by proposing a novel dual-attention structure to jointly consider data-quality and medical-knowledge. A novel unreliability-aware attention mechanism is designed to handle the diversity in the reliability of different data, while a new symptom-aware attention mechanism is proposed to extract medical reasons from original clinical records. Extensive experimental results on two real-world datasets demonstrate that DATA-GRU can significantly outperform state-of-the-art methods and provide meaningful clinical interpretation.

Motion Estimation in Echocardiography Using Sparse Representation and Dictionary Learning.

This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic simulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors.This paper introduces a new method for cardiac motion estimation in 2-D ultrasound images. The motion estimation problem is formulated as an energy minimization, whose data fidelity term is built using the assumption that the images are corrupted by multiplicative Rayleigh noise. In addition to a classical spatial smoothness constraint, the proposed method exploits the sparse properties of the cardiac motion to regularize the solution via an appropriate dictionary learning step. The proposed method is evaluated on one data set with available ground-truth, including four sequences of highly realistic simulations. The approach is also validated on both healthy and pathological sequences of in vivo data. We evaluate the method in terms of motion estimation accuracy and strain errors and compare the performance with state-of-the-art algorithms. The results show that the proposed method gives competitive results for the considered data. Furthermore, the in vivo strain analysis demonstrates that meaningful clinical interpretation can be obtained from the estimated motion vectors.

A feature fusion system for basal cell carcinoma detection through data-driven feature learning and patient profile.

Basal cell carcinoma (BCC) is the most common skin cancer, which is highly damaging in its advanced stages. Computer-aided techniques provide a feasible option for early detection of BCC. However, automated BCC detection techniques immensely rely on handcrafting high-level precise features. Such features are not only computationally complex to design but can also represent a very limited aspect of the lesion characteristics. This paper proposes an automated BCC detection technique that directly learns the features from image data, eliminating the need for handcrafted feature design. The proposed method is composed of 2 parts. First, an unsupervised feature learning framework is proposed which attempts to learn hidden characteristics of the data including vascular patterns directly from the images. This is done through the design of a sparse autoencoder (SAE). After the unsupervised learning, we treat each of the learned kernel weights of the SAE as a filter. Convolving each filter with the lesion image yields a feature map. Feature maps are condensed to reduce the dimensionality and are further integrated with patient profile information. The overall features are then fed into a softmax classifier for BCC classification. On a set of 1199 BCC images, the proposed framework achieved an area under the curve of 91.1%, while the visualization of learned features confirmed meaningful clinical interpretation of the features. The proposed framework provides a non-invasive fast BCC detection tool that incorporates both dermoscopic lesional features and clinical patient information, without the need for complex handcrafted feature extraction.

Results of a Prospective Study Evaluating a Noninvasive Method of Hemoglobin Adjustment for Determining the Diffusing Capacity of the Lung.

Measurement of the diffusing capacity of the lung for carbon monoxide (DlCO) is significantly influenced by the pulmonary capillary blood volume. Consequently, measurements require adjustment for blood hemoglobin concentration (DlCOadj) to allow meaningful clinical interpretation. Noninvasive point-of-care devices that measure hemoglobin transcutaneously provide immediate values for hemoglobin that may be useful in pulmonary function laboratories for determining DlCOadj. To test the hypothesis that DlCOadj determinations obtained with a commercially available device for noninvasive, point-of-care measurement of blood hemoglobin concentrations are not significantly different from determinations obtained using hemoglobin concentrations measured conventionally in venous blood samples. In a prospective open trial, hemoglobin measurements were obtained with the Pronto-7 spot check pulse CO-oximeter (Massimo, Irvine, CA) and by venipuncture for 205 patients referred for DlCO testing at Cincinnati Children's Hospital. Hemoglobin and DlCOadj measurements were compared between the two methods, using Student paired t tests and Bland-Altman plots. To assess variability, the differences in DlCOadj between the two methods were also compared by a modification of the current standard for acceptable within-session variability for DlCO. Clinical interpretation for individual DlCO tests based on DlCOadj values obtained from the two methods were compared statistically using Kendall's coefficient of concordance to determine whether the Pronto-7 altered the classification of the severity of DlCO defects. Measurements of hemoglobin concentration by the Pronto-7 analyzer were significantly lower than those obtained from venipuncture blood samples (13.1 ± 1.8 vs. 13.4 ± 2.0; P = 0.01). However, there were no differences for DlCOadj between both methods (23.6 ± 7.7 vs. 23.7 ± 7.5 ml/min/mm Hg; P = 0.42). There was strong correlation between the Pronto-7 and venipuncture DlCOadj values (r = 0.99, P < 0.0001). Variability between the two methods was low for DlCOadj, with a bias of -0.07. More than 96% of tests met acceptable within-session variability. There was no significant difference in the clinical interpretation of the DlCO test based on DlCOadj values recovered from both methods (Kendall's coefficient, 0.96). Noninvasive measurement of hemoglobin for determination of DlCOadj was accurate and provided acceptable within-session variability. The results obtained noninvasively did not alter clinical interpretation of test results compared with venipuncture. These findings support noninvasive point-of-care devices as an alternative to venipuncture for determining hemoglobin to measure DlCOadj in most patients.

Mendelian Randomization.

Confounding and reverse causality have prevented us from drawing meaningful clinical interpretation even in well-powered observational studies. Confounding may be attributed to our inability to randomize the exposure variable in observational studies. Mendelian randomization (MR) is one approach to overcome confounding. It utilizes one or more genetic polymorphisms as a proxy for the exposure variable of interest. Polymorphisms are randomly distributed in a population, they are static throughout an individual's lifetime, and may thus help in inferring directionality in exposure-outcome associations. Genome-wide association studies (GWAS) or meta-analyses of GWAS are characterized by large sample sizes and the availability of many single nucleotide polymorphisms (SNPs), making GWAS-based MR an attractive approach. GWAS-based MR comes with specific challenges, including multiple causality. Despite shortcomings, it still remains one of the most powerful techniques for inferring causality.With MR still an evolving concept with complex statistical challenges, the literature is relatively scarce in terms of providing working examples incorporating real datasets. In this chapter, we provide a step-by-step guide for causal inference based on the principles of MR with a real dataset using both individual and summary data from unrelated individuals. We suggest best possible practices and give recommendations based on the current literature.

Disaggregating asthma: Big investigation versus big data

We are facing a major challenge in bridging the gap between identifying subtypes of asthma to understand causal mechanisms and translating this knowledge into personalized prevention and management strategies. In recent years, “big data” has been sold as a panacea for generating hypotheses and driving new frontiers of health care; the idea that the data must and will speak for themselves is fast becoming a new dogma. One of the dangers of ready accessibility of health care data and computational tools for data analysis is that the process of data mining can become uncoupled from the scientific process of clinical interpretation, understanding the provenance of the data, and external validation. Although advances in computational methods can be valuable for using unexpected structure in data to generate hypotheses, there remains a need for testing hypotheses and interpreting results with scientific rigor. We argue for combining data- and hypothesis-driven methods in a careful synergy, and the importance of carefully characterized birth and patient cohorts with genetic, phenotypic, biological, and molecular data in this process cannot be overemphasized. The main challenge on the road ahead is to harness bigger health care data in ways that produce meaningful clinical interpretation and to translate this into better diagnoses and properly personalized prevention and treatment plans. There is a pressing need for cross-disciplinary research with an integrative approach to data science, whereby basic scientists, clinicians, data analysts, and epidemiologists work together to understand the heterogeneity of asthma.

Variable importance and prediction methods for longitudinal problems with missing variables.

We present prediction and variable importance (VIM) methods for longitudinal data sets containing continuous and binary exposures subject to missingness. We demonstrate the use of these methods for prognosis of medical outcomes of severe trauma patients, a field in which current medical practice involves rules of thumb and scoring methods that only use a few variables and ignore the dynamic and high-dimensional nature of trauma recovery. Well-principled prediction and VIM methods can provide a tool to make care decisions informed by the high-dimensional patient’s physiological and clinical history. Our VIM parameters are analogous to slope coefficients in adjusted regressions, but are not dependent on a specific statistical model, nor require a certain functional form of the prediction regression to be estimated. In addition, they can be causally interpreted under causal and statistical assumptions as the expected outcome under time-specific clinical interventions, related to changes in the mean of the outcome if each individual experiences a specified change in the variable (keeping other variables in the model fixed). Better yet, the targeted MLE used is doubly robust and locally efficient. Because the proposed VIM does not constrain the prediction model fit, we use a very flexible ensemble learner (the SuperLearner), which returns a linear combination of a list of user-given algorithms. Not only is such a prediction algorithm intuitive appealing, it has theoretical justification as being asymptotically equivalent to the oracle selector. The results of the analysis show effects whose size and significance would have been not been found using a parametric approach (such as stepwise regression or LASSO). In addition, the procedure is even more compelling as the predictor on which it is based showed significant improvements in cross-validated fit, for instance area under the curve (AUC) for a receiver-operator curve (ROC). Thus, given that 1) our VIM applies to any model fitting procedure, 2) under assumptions has meaningful clinical (causal) interpretations and 3) has asymptotic (influence-curve) based robust inference, it provides a compelling alternative to existing methods for estimating variable importance in high-dimensional clinical (or other) data.

Covariate-adjusted measures of discrimination for survival data.

MotivationDiscrimination statistics describe the ability of a survival model to assign higher risks to individuals who experience earlier events: examples are Harrell’s C-index and Royston and Sauerbrei’s D, which we call the D-index. Prognostic covariates whose distributions are controlled by the study design (e.g. age and sex) influence discrimination and can make it difficult to compare model discrimination between studies. Although covariate adjustment is a standard procedure for quantifying disease-risk factor associations, there are no covariate adjustment methods for discrimination statistics in censored survival data.ObjectiveTo develop extensions of the C-index and D-index that describe the prognostic ability of a model adjusted for one or more covariate(s).MethodWe define a covariate-adjusted C-index and D-index for censored survival data, propose several estimators, and investigate their performance in simulation studies and in data from a large individual participant data meta-analysis, the Emerging Risk Factors Collaboration.ResultsThe proposed methods perform well in simulations. In the Emerging Risk Factors Collaboration data, the age-adjusted C-index and D-index were substantially smaller than unadjusted values. The study-specific standard deviation of baseline age was strongly associated with the unadjusted C-index and D-index but not significantly associated with the age-adjusted indices.ConclusionsThe proposed estimators improve meta-analysis comparisons, are easy to implement and give a more meaningful clinical interpretation.

Estimating the optimal threshold for a diagnostic biomarker in case of complex biomarker distributions.

BackgroundEstimating the optimal threshold (and especially the confidence interval) of a quantitative biomarker to be used as a diagnostic test is essential for medical decision-making. This is often done with simple methods that are not always reliable. More advanced methods work well but only for biomarkers with very simple distributions. In fact, biomarker distributions are often complex because of a natural heterogeneity in marker expression and other heterogeneities due to various disease stages, laboratory equipments, etc. Methods are required to estimate a biomarker optimal threshold in case of heterogeneity and complex distributions.MethodsA previously described Bayesian method developed for normally distributed biomarkers is applied to two flexible distributions; namely, a Student-t and a mixture of Dirichlet processes. Here, numerical studies assess the adequacy of the previous method with both distributions. Two applications are presented: the diagnosis of treatment failure after prostate cancer treated by ultrasound and the early diagnosis of cancers of the upper aerodigestive tract.ResultsBayesian inference provided reliable credible intervals in terms of bias and coverage probability. The two distributions analysed gave meaningful clinical interpretations in both applications.ConclusionsReliable methods can be used to estimate a biomarker optimal threshold, even in case of complex distributions.

Cobas Ampliprep/Cobas TaqMan HIV-1 v2.0 Assay: Consequences at the Cohort Level

BackgroundHigh-sensitive real-time PCR assays are routinely used to monitor HIV-1 infected subjects. Inter-assay discrepancies have been described at the low viral load (VL) end, where clinical decisions regarding possible virological rebound are based.MethodsA retrospective study was performed to analyze frequencies of viral blips after transition to the COBAS Ampliprep/COBAS TaqMan v2.0 HIV-1 assay (Taqman v2.0) in patients with prior undetectable VLs as measured with the Roche Cobas Ampliprep Amplicor HIV-1 Monitor Test, v1.5 (Amplicor) and was evaluated in comparison to a group of patients monitored with the Abbott Real-time HIV-1 assay (Abbott RT) during the same period of time.Results85 of 373 patients with VLs below the limit of quantification with Amplicor had VLs >50 copies/mL after transition to the TaqMan v2.0 assay. Among these 74.1% had VLs ranging from 50–499 copies/mL, 22.9% had VLs >500 copies/mL. From 22 patients with initial Taqman v2.0 based VLs exceeding 500 copies/mL, 6 patients had VLs <20 copies/mL after novel VL measurement on a next visit. In our control group with VL quantification using the Abbott RT assay, only 1 patient became detectable and showed a VL of <40 copies/mL after new measurement.ConclusionsTransition to the Taqman v2.0 assay was accompanied by an increase of quantifiable HIV-1 VLs in patients with long term viral suppression under antiretroviral therapy that might be attributed to technical shortcomings of the Taqman v2.0 assay. A high test variability at the low VL end but also beyond was observed, making meaningful clinical interpretation of viral blips derived from different assays difficult.

Outcome measurement in clinical pediatric neurorehabilitation: assessment of motor and process skills evaluates the effect of therapy in the area of activity of daily living

Aims: The assessment of motor and process skills (AMPS) is an internationally well-established, in German speaking countries rarely known as outcome measurement, which evaluates the performance of activities of daily living (ADLs) in regard to effort, efficiency, safety, and need for assistance (ICF- category activity/participation). It measures motor and process skills. The AMPS was validated worldwide with 150,000 persons for all diagnostic categories and persons aged 2 years and older. After the occupational therapist observed and evaluated 1 person's ADL performance, the raw scores are imputed and analyzed with a Rasch-software that generates graphic and/or written reports. These reports set the person's ADL performance in relation to normative mean values and to standardized scores which enable meaningful clinical interpretations. We scrutinized the ability of this instrument to capture progress in clinical pediatric neurorehabilitation in the area of ADL.

Clinical Value of the 11- to 13+6-Week Sonogram for Detection of Congenital Malformations: A Review

We sought to determine the clinical value of the 11- to 13(+6)-week ultrasound evaluation for diagnosis of congenital abnormalities. A literature search was conducted for publications concerning the use of the 11- to 13(+6)-week ultrasound evaluation for prenatal diagnosis of congenital abnormalities. Series with more than 1000 cases published in the English language were selected. The reported data were integrated and a pooled analysis was performed to provide meaningful clinical interpretations. Our analysis of data from 36,237 pregnancies generated by eight centers suggests that the overall detection rate of major congenital anomalies during the 11- to 13(+6)-week ultrasound evaluation is 29% (95% confidence interval 25 to 33). Prenatal diagnosis at these stages of development could improve if evaluations are implemented in accordance to very well delineated protocols.