Creating a Three-Dimensional Reconstruction of the Glenohumeral Joint From Magnetic Resonance Imaging to Assist in Surgical Decision-Making.

Understanding the anatomical structure of a patient's shoulder joint is essential in surgical decision-making, especially regarding glenohumeral bone loss. The use of various imaging techniques, such as magnetic resonance imaging (MRI) and computed tomography (CT), bring certain advantages and disadvantages in assessing joint structure. Before a surgical procedure, bone loss can be observed and measured using these imaging techniques in both 2-dimensional and 3-dimensional (3D) views. The ability to visualize the shoulder joint in a 3D manner, as commonly done with CT scans, is helpful in assessing bone loss; however, CT involves exposure to radiation, additional time, and greater costs. The process of obtaining a 3D view of the shoulder joint from an MRI, although less common, can be completed effectively to assess bone loss while also solving some issues surrounding CT scans. By loading MRI datasets into an image-reformation program, such as 3D Slicer, the anatomical structures can be segmented to create realistic 3D models of the shoulder joint. Surgical direction can be determined after bone loss measurements and structural assessment of these models, without the need for CT scans. This technique can also be applied to other skeletal joints, in addition to the shoulder.

A High-Sensitivity International Knee Documentation Committee Survey Index From the PROMIS System: The Next-Generation Patient-Reported Outcome for a Knee Injury Population.

BACKGROUND: Patient-reported outcomes (PROs) measure progression and quality of care. While legacy PROs such as the International Knee Documentation Committee (IKDC) survey are well-validated, a lengthy PRO creates a time burden on patients, decreasing adherence. In recent years, PROs such as the Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function and Pain Interference surveys were developed as computer adaptive tests, reducing time to completion. Previous studies have examined correlation between legacy PROs and PROMIS; however, no studies have developed effective prediction models utilizing PROMIS to create an IKDC index. While the IKDC is the standard knee PRO, computer adaptive PROs offer numerous practical advantages. PURPOSE: To develop a nonlinear predictive model utilizing PROMIS Physical Function and Pain Interference to estimate IKDC survey scores and examine algorithm sensitivity and validity. STUDY DESIGN: Cohort study (diagnosis); Level of evidence, 3. METHODS: The MOTION (Military Orthopaedics Tracking Injuries and Outcomes Network) database is a prospectively collected repository of PROs and intraoperative variables. Patients undergoing knee surgery completed the IKDC and PROMIS surveys at varying time points. Nonlinear multivariable predictive models using Gaussian and beta distributions were created to establish an IKDC index score, which was then validated using leave-one-out techniques and minimal clinically important difference analysis. RESULTS: A total of 1011 patients completed the IKDC and PROMIS Physical Function and Pain Interference, providing 1618 complete observations. The algorithms for the Gaussian and beta distribution were validated to predict the IKDC (Pearson = 0.84-0.86; R2 = 0.71-0.74; root mean square error = 9.3-10.0). CONCLUSION: The publicly available predictive models can approximate the IKDC score. The results can be used to compare PROMIS Physical Function and Pain Interference against historical IKDC scores by creating an IKDC index score. Serial use of the IKDC index allows for a lower minimal clinically important difference than the conventional IKDC. PROMIS can be substituted to reduce patient burden, increase completion rates, and produce orthopaedic-specific survey analogs.

Generating the American Shoulder and Elbow Surgeons Score Using Multivariable Predictive Models and Computer Adaptive Testing to Reduce Survey Burden.

BACKGROUND: The preferred patient-reported outcome measure for the assessment of shoulder conditions continues to evolve. Previous studies correlating the Patient-Reported Outcomes Measurement Information System (PROMIS) computer adaptive tests (CATs) to the American Shoulder and Elbow Surgeons (ASES) score have focused on a singular domain (pain or physical function) but have not evaluated the combined domains of pain and physical function that compose the ASES score. Additionally, previous studies have not provided a multivariable prediction tool to convert PROMIS scores to more familiar legacy scores. PURPOSE: To establish a valid predictive model of ASES scores using a nonlinear combination of PROMIS domains for physical function and pain. STUDY DESIGN: Cohort study (Diagnosis); Level of evidence, 3. METHODS: The Military Orthopaedics Tracking Injuries and Outcomes Network (MOTION) database is a prospectively collected repository of patient-reported outcomes and intraoperative variables. Patients in MOTION research who underwent shoulder surgery and completed the ASES, PROMIS Physical Function, and PROMIS Pain Interference at varying time points were included in the present analysis. Nonlinear multivariable predictive models were created to establish an ASES index score and then validated using "leave 1 out" techniques and minimal clinically important difference /substantial clinical benefit (MCID/SCB) analysis. RESULTS: A total of 909 patients completed the ASES, PROMIS Physical Function, and PROMIS Pain Interference at presurgery, 6 weeks, 6 months, and 1 year after surgery, providing 1502 complete observations. The PROMIS CAT predictive model was strongly validated to predict the ASES (Pearson coefficient = 0.76-0.78; R2 = 0.57-0.62; root mean square error = 13.3-14.1). The MCID/SCB for the ASES was 21.7, and the best ASES index MCID/SCB was 19.4, suggesting that the derived ASES index is effective and can reliably re-create ASES scores. CONCLUSION: The PROMIS CAT predictive models are able to approximate the ASES score within 13 to 14 points, which is 7 points more accurate than the ASES MCID/SCB derived from the sample. Our ASES index algorithm, which is freely available online (https://osf.io/ctmnd/), has a lower MCID/SCB than the ASES itself. This algorithm can be used to decrease patient survey burden by 11 questions and provide a reliable ASES analog to clinicians.

Retrospective clinical trial experimentally validates glioblastoma genome-wide pattern of DNA copy-number alterations predictor of survival.

Modeling of genomic profiles from the Cancer Genome Atlas (TCGA) by using recently developed mathematical frameworks has associated a genome-wide pattern of DNA copy-number alterations with a shorter, roughly one-year, median survival time in glioblastoma (GBM) patients. Here, to experimentally test this relationship, we whole-genome sequenced DNA from tumor samples of patients. We show that the patients represent the U.S. adult GBM population in terms of most normal and disease phenotypes. Intratumor heterogeneity affects ≈ 11 % and profiling technology and reference human genome specifics affect <1% of the classifications of the tumors by the pattern, where experimental batch effects normally reduce the reproducibility, i.e., precision, of classifications based upon between one to a few hundred genomic loci by >30%. With a 2.25-year Kaplan-Meier median survival difference, a 3.5 univariate Cox hazard ratio, and a 0.78 concordance index, i.e., accuracy, the pattern predicts survival better than and independent of age at diagnosis, which has been the best indicator since 1950. The prognostic classification by the pattern may, therefore, help to manage GBM pseudoprogression. The diagnostic classification may help drugs progress to regulatory approval. The therapeutic predictions, of previously unrecognized targets that are correlated with survival, may lead to new drugs. Other methods missed this relationship in the roughly 3B-nucleotide genomes of the small, order of magnitude of 100, patient cohorts, e.g., from TCGA. Previous attempts to associate GBM genotypes with patient phenotypes were unsuccessful. This is a proof of principle that the frameworks are uniquely suitable for discovering clinically actionable genotype-phenotype relationships.

Latanoprost uptake and release from commercial contact lenses.

This study investigated the potential of delivering an anti-glaucoma drug using commercial silicone hydrogel (SiHy) contact lenses. The moderately hydrophobic drug latanoprost was rapidly loaded in 4 min by swelling contact lenses in a solution of the drug in n-propanol. A fraction of the drug was radiolabeled, thus allowing measurement of the uptake and subsequent release of drug into artificial tear fluid. Three questions were addressed: (1) how much drug can be loaded into each type of lens, (2) how fast is drug release, and (3) how are these values related to the contact lens chemistry. The results showed that much more latanoprost could be loaded into SiHy lenses than a conventional contact lens of poly(hydroxyethyl methacrylate). The drug uptake correlated with the amount of swelling in n-propanol, with Galyfilcon lenses having the greatest swelling and highest drug uptake. The drug release from the SiHy lenses occurred over days, whereas the conventional lens released nearly all drug in a burst over a few hours. To examine correlations between lens chemistry, drug chemistry and uptake, and solvent chemistry, the Hansen solubility parameters were calculated using estimates of contact lens chemistry. These results showed that drug uptake in SiHy lenses correlated with favorable solubility parameter interactions between the n-propanol and the lens material, but did not correlate with interactions between the drug and the lens materials.

GSVD- and tensor GSVD-uncovered patterns of DNA copy-number alterations predict adenocarcinomas survival in general and in response to platinum.

More than a quarter of lung, uterine, and ovarian adenocarcinoma (LUAD, USEC, and OV) tumors are resistant to platinum drugs. Only recently and only in OV, patterns of copy-number alterations that predict survival in response to platinum were discovered, and only by using the tensor GSVD to compare Agilent microarray platform-matched profiles of patient-matched normal and primary tumor DNA. Here, we use the GSVD to compare whole-genome sequencing (WGS) and Affymetrix microarray profiles of patient-matched normal and primary LUAD, USEC, and OV tumor DNA. First, the GSVD uncovers patterns similar to one Agilent OV pattern, where a loss of most of the chromosome arm 6p combined with a gain of 12p encode for transformation. Like the Agilent OV pattern, the WGS LUAD and Affymetrix LUAD, USEC, and OV patterns are correlated with shorter survival, in general and in response to platinum. Like the tensor GSVD, the GSVD separates these tumor-exclusive genotypes from experimental inconsistencies. Second, by identifying the shorter survival phenotypes among the WGS- and Affymetrix-profiled tumors, the Agilent pattern proves to be a technology-independent predictor of survival, independent also of the best other indicator at diagnosis, i.e., stage. Third, like no other indicator, the pattern predicts the overall survival of OV patients experiencing progression-free survival, in general and in response to platinum. We conclude that comparative spectral decompositions, such as the GSVD and tensor GSVD, underlie a mathematically universal description of the relationships between a primary tumor's genotype and a patient's overall survival phenotype, which other methods miss.