Large Heterogeneity Among Minimal Clinically Important Differences for Hip Arthroscopy Outcomes: A Systematic Review of Reporting Trends and Quantification Methods.

PURPOSE: To perform a systematic review of reporting trends and quantification methods for the minimal clinically important difference (MCID) within the hip arthroscopy literature. METHODS: Cochrane, PubMed, and OVID/MEDLINE databases were queried for hip arthroscopy articles that reported the MCID. Studies were classified as (1) calculating new MCID values for their specific study-population or (2) referencing previously established MCID values. Data pertaining to patient demographics, study characteristics, outcome measures, method of MCID quantification, MCID value, anchor questions, measurement error, and study from which referenced MCID values were obtained were extracted. RESULTS: A total of 59 articles with 18,830 patients (19,867 hips) was included. A total of 19 unique outcome measures was reported. A total of 33 (n = 55.9%) studies (follow-up range 6-60 months) used previously established MCID values to assess their study population (MCID values established at a follow-up range 6-31 months). The remaining 26 studies (44.1%) performed new MCID calculations. The MCID values were inconsistent and varied widely (Hip Outcome Score-Activities of Daily Living: 5.0-15.4; Hip Outcome Score-Sports Subscale: 6-25; modified Harris hip score: 2.4-20.9). Among the 33 studies that used previously established MCID values, 10 different studies were cited as the reference. Among the remaining 26 studies that calculated a new MCID value, the most common method was 0.5 standard deviation method (n = 21, 80.8%). Only 3 of 26 (11.5%) studies reported a measurement of error in conjunction with their MCID values. CONCLUSIONS: Inconsistencies in MCID reporting and quantification methods led to a wide range of MCID values for commonly administered outcome measures within the hip arthroscopy literature-even for the same outcome measures. The majority of studies referenced previously established MCID values with variable ranges of follow-up and applied those values to assess their specific study population at varying follow-ups. LEVEL OF EVIDENCE: IV, systematic review.

Inline Reaction Monitoring of Amine-Catalyzed Acetylation of Benzyl Alcohol Using a Microfluidic Stripline Nuclear Magnetic Resonance Setup.

We present an in-depth study of the acetylation of benzyl alcohol in the presence of N, N-diisopropylethylamine (DIPEA) by nuclear magnetic resonance (NMR) monitoring of the reaction from 1.5 s to several minutes. We have adapted the NMR setup to be compatible to microreactor technology, scaling down the typical sample volume of commercial NMR probes (500 µL) to a microfluidic stripline setup with 150 nL detection volume. Inline spectra are obtained to monitor the kinetics and unravel the reaction mechanism of this industrially relevant reaction. The experiments are combined with conventional 2D NMR measurements to identify the reaction products. In addition, we replace DIPEA with triethylamine and pyridine to validate the reaction mechanism for different amine catalysts. In all three acetylation reactions, we find that the acetyl ammonium ion is a key intermediate. The formation of ketene is observed during the first minutes of the reaction when tertiary amines were present. The pyridine-catalyzed reaction proceeds via a different mechanism.

Continuous Flow 1H and 13C NMR Spectroscopy in Microfluidic Stripline NMR Chips.

Microfluidic stripline NMR technology not only allows for NMR experiments to be performed on small sample volumes in the submicroliter range, but also experiments can easily be performed in continuous flow because of the stripline's favorable geometry. In this study we demonstrate the possibility of dual-channel operation of a microfluidic stripline NMR setup showing one- and two-dimensional 1H, 13C and heteronuclear NMR experiments under continuous flow. We performed experiments on ethyl crotonate and menthol, using three different types of NMR chips aiming for straightforward microfluidic connectivity. The detection volumes are approximately 150 and 250 nL, while flow rates ranging from 0.5 µL/min to 15 µL/min have been employed. We show that in continuous flow the pulse delay is determined by the replenishment time of the detector volume, if the sample trajectory in the magnet toward NMR detector is long enough to polarize the spin systems. This can considerably speed up quantitative measurement of samples needing signal averaging. So it can be beneficial to perform continuous flow measurements in this setup for analysis of, e.g., reactive, unstable, or mass-limited compounds.

In-line sample concentration by evaporation through porous hollow fibers and micromachined membranes embedded in microfluidic devices.

Two types of microfluidic systems, a porous hollow fiber and a thin supported membrane with an array of micromachined holes, are investigated for concentrating mass-limited analyte samples. Water evaporation is driven by the partial pressure difference across the hydrophobic membrane, induced by dry sweeping gas on the permeate side. An analytical model permitting clarification of the contribution of design and process parameters on acquisition of concentrated solution and prediction of achievable concentration factors is presented. Concentrating an exemplary solution utilizing the two systems has been studied at different experimental conditions to validate the model. The results show that the hollow fiber gives controllable concentration factors of more than 10. For the micromachined membrane concentrator concentration factors of 6-8 were achieved, at much lower flow rates than predicted by the model. Because of the asymptotic dependence of concentration factor on flow rate, accurate control of the liquid feed is extremely critical in the flow rate range where high concentration factors are obtained, and the smallest variations in liquid flow rate may easily lead to supersaturation and deposition of solutes in the pores. This changes membrane porosity in an unpredictable way and limits the maximum attainable concentration factor.

Simple and Effective Way for Data Preprocessing Selection Based on Design of Experiments.

The selection of optimal preprocessing is among the main bottlenecks in chemometric data analysis. Preprocessing currently is a burden, since a multitude of different preprocessing methods is available for, e.g., baseline correction, smoothing, and alignment, but it is not clear beforehand which method(s) should be used for which data set. The process of preprocessing selection is often limited to trial-and-error and is therefore considered somewhat subjective. In this paper, we present a novel, simple, and effective approach for preprocessing selection. The defining feature of this approach is a design of experiments. On the basis of the design, model performance of a few well-chosen preprocessing methods, and combinations thereof (called strategies) is evaluated. Interpretation of the main effects and interactions subsequently enables the selection of an optimal preprocessing strategy. The presented approach is applied to eight different spectroscopic data sets, covering both calibration and classification challenges. We show that the approach is able to select a preprocessing strategy which improves model performance by at least 50% compared to the raw data; in most cases, it leads to a strategy very close to the true optimum. Our approach makes preprocessing selection fast, insightful, and objective.

Room-temperature intermediate layer bonding for microfluidic devices.

In this work a novel room-temperature bonding technique based on chemically activated Fluorinated Ethylene Propylene (FEP) sheet as an intermediate between chemically activated substrates is presented. Surfaces of silicon and glass substrates are chemically modified with APTES bearing amine terminal groups, while FEP sheet surfaces are treated to form carboxyl groups and subsequently activated by means of EDC-NHS chemistry. The activation procedures of silicon, glass and FEP sheet are characterized by contact angle measurements and XPS. Robust bonds are created at room-temperature by simply pressing two amine-terminated substrates together with activated FEP sheet in between. Average tensile strengths of 5.9 MPa and 5.2 MPa are achieved for silicon-silicon and glass-glass bonds, respectively, and the average fluidic pressure that can be operated is 10.2 bar. Moreover, it is demonstrated that FEP-bonded microfluidic chips can handle mild organic solvents at elevated pressures without leakage problems. This versatile room-temperature intermediate layer bonding technique has a high potential for bonding, packaging, and assembly of various (bio-) chemical microfluidic systems and MEMS devices.

A microfluidic high-resolution NMR flow probe.

A microfluidic high-resolution NMR flow probe based on a novel stripline detector chip is demonstrated. This tool is invaluable for the in situ monitoring of reactions performed in microreactors. As an example, the acetylation of benzyl alcohol with acetyl chloride was monitored. Because of the uncompromised (sub-Hz) resolution, this probe holds great promise for metabolomics studies, as shown by an analysis of 600 nL of human cerebrospinal fluid.

Spatially resolved spectroscopy using tapered stripline NMR.

Magnetic field B0 gradients are essential in modern Nuclear Magnetic Resonance spectroscopy and imaging. Although RF/B1 gradients can be used to fulfill a similar role, this is not used in common practice because of practical limitations in the design of B1 gradient coils. Here we present a new method to create B1 gradients using stripline RF coils. The conductor-width of a stripline NMR chip and the strength of its radiofrequency field are correlated, so a stripline chip can be tapered to produce any arbitrary shaped B1 field gradient. Here we show the characterization of this tapered stripline configuration and demonstrate three applications: magnetic resonance imaging on samples with nL-µL volumes, reaction monitoring of fast chemical reactions (10(-2)-10(1)s) and the compensation of B0 field gradients to obtain high-resolution spectra in inhomogeneous magnetic fields.

Boosting model performance and interpretation by entangling preprocessing selection and variable selection.

The aim of data preprocessing is to remove data artifacts-such as a baseline, scatter effects or noise-and to enhance the contextually relevant information. Many preprocessing methods exist to deliver one or more of these benefits, but which method or combination of methods should be used for the specific data being analyzed is difficult to select. Recently, we have shown that a preprocessing selection approach based on Design of Experiments (DoE) enables correct selection of highly appropriate preprocessing strategies within reasonable time frames. In that approach, the focus was solely on improving the predictive performance of the chemometric model. This is, however, only one of the two relevant criteria in modeling: interpretation of the model results can be just as important. Variable selection is often used to achieve such interpretation. Data artifacts, however, may hamper proper variable selection by masking the true relevant variables. The choice of preprocessing therefore has a huge impact on the outcome of variable selection methods and may thus hamper an objective interpretation of the final model. To enhance such objective interpretation, we here integrate variable selection into the preprocessing selection approach that is based on DoE. We show that the entanglement of preprocessing selection and variable selection not only improves the interpretation, but also the predictive performance of the model. This is achieved by analyzing several experimental data sets of which the true relevant variables are available as prior knowledge. We show that a selection of variables is provided that complies more with the true informative variables compared to individual optimization of both model aspects. Importantly, the approach presented in this work is generic. Different types of models (e.g. PCR, PLS, …) can be incorporated into it, as well as different variable selection methods and different preprocessing methods, according to the taste and experience of the user. In this work, the approach is illustrated by using PLS as model and PPRV-FCAM (Predictive Property Ranked Variable using Final Complexity Adapted Models) for variable selection.