Development of a postoperative delirium risk scoring tool using data from the Australian and New Zealand Hip Fracture Registry: an analysis of 6672 patients 2017-2018

Background and purposeThis study aimed to determine the incidence, predictors of postoperative delirium and develop a post-surgery delirium risk scoring tool.Patients and MethodsA total of 6672 hip fracture patients with documented assessment for delirium were analyzed from the Australia and New Zealand Hip Fracture Registry between June 2017 and December 2018.Thirty-six variables for the prediction of delirium using univariate and multivariate logistic regression were assessed. The models were assessed for diagnostic accuracy using C-statistic and calibration using Hosmer-Lemeshow goodness-of-fit test. A Delirium Risk Score was developed based on the regression coefficients.ResultsDelirium developed in 2599/6672 (39.0%) hip fracture patients. Seven independent predictors of delirium were identified; age above 80 years (OR=1.6 CI 1.4-1.9; p=0.001), male (OR=1.3 CI 1.1-1.5; p=0.007), absent pre-operative cognitive assessment (OR=1.5 CI 1.3-1.9; p=0.001), impaired pre-operative cognitive state (OR=1.7 CI 1.3 -2.1; p=0.001), surgery delay (OR=1.7 CI 1.2-2.5; p=0.002) and mobilisation day 1 post-surgery (OR=1.9 CI 1.4-2.6; p=0.001). The C-statistics for the training and validation datasets were 0.74 and 0.75, respectively. Calibration was good (χ2=35.72 (9); p<0.001). The Delirium Risk Score for patients ranged from 0 to 42 in the validation data and when used alone as a risk predictor, had similar levels of diagnostic accuracy (C-statistic=0.742) indicating its potential for use as a stand-alone risk scoring tool.ConclusionWe have designed and validated a delirium risk score for predicting delirium following surgery for a hip fracture using seven predicting factors. This could assist clinicians in identifying high risk patients requiring higher levels of observation and post-surgical care.     

Total Hip Arthroplasty in Ankylosing Spondylitis With Extension Contracture of Hips

Background

Despite significant pain relief following total hip arthroplasty (THA) in patients with ankylosing spondylitis, a small subset of patients presenting with extra-articular extension contracture of hips remains unsatisfied.

Effect of hyperdry amniotic membrane in preventing tendon adhesion in a rabbit model

BackgroundNo anti-adhesive materials are currently in clinical use for orthopaedic surgery. We developed a hyperdry amniotic membrane (HD-AM) for easy storage and transplantation as amniotic membrane. The purpose of this study was to examine the application of HD-AM to reduce peritendinous adhesions without impairing tendon healing.MethodsWe randomly divided 3 digits (2nd, 3rd, and 4th digits) from each rabbit into three groups: a tendon repair group; a tendon repair with HD-AM group (HD-AM group); and a control group (cast only). The effects of HD-AM on peritendinous adhesions and tendon healing were examined using microscopic, histological, and mechanical analyses in a rabbit flexor digitorum profundus tendon model.ResultsAdhesions on macroscopic evaluation of the tendon repair site were significantly smaller in the HD-AM group than in the tendon repair group. Little adhesion formation or foreign body reactions were seen by on histologic evaluation in the HD-AM group. Range of motion following tendon repair was significantly better in the HD-AM group than in the tendon repair group. Maximal tensile strength required to pull the tendon from the site of adhesion was significantly smaller in the HD-AM group than in the tendon repair group. As for tendon repair site, no significant difference was seen between the tendon repair and HD-AM groups.ConclusionsHD-AM prevented peritendinous adhesion macroscopically, pathologically, and mechanically without impairing the sutured tendon. HD-AM has already been clinically applied in neurosurgery, ophthalmology, and otolaryngology, and clinical application as an anti-adhesive materials may be achieved in the future.     

Anatomic vs. reverse shoulder arthroplasty for glenohumeral osteoarthritis with intact rotator cuff: a retrospective comparison of patient-reported outcomes using the systems outcomes database with up to 5-year follow-up

BackgroundThe growing enthusiasm for the use of reverse shoulder arthroplasty (RSA) in the treatment of primary glenohumeral osteoarthritis (GHOA) with an intact rotator cuff is based on data derived from single-center studies with limited generalizability and follow-up. This study compared patient-reported outcomes (PROs) between RSA and total shoulder arthroplasty (TSA) for the treatment of primary GHOA with up to 5-year follow-up and examined temporal trends in the treatment of GHOA between 2012 and 2021.MethodsA retrospective review was performed on patients with primary GHOA undergoing primary arthroplasty surgery from the Surgical Outcomes System global registry between 2012 and 2021. PROs including the American Shoulder and Elbow Surgeons (ASES) score, Single Assessment Numeric Evaluation (SANE) score, and visual analog scale (VAS) for pain were compared between RSA and TSA at 1, 2, and 5 years postoperatively.ResultsA total of 4451 patients were included, with 2693 (60.5%) undergoing TSA and 1758 (39.5%) undergoing RSA. Both RSA and TSA provided clinically excellent outcomes at 1 year postoperatively (ASES: 80.8 ± 17.9 vs. 85.9 ± 15.2, respectively; SANE: 74.8 ± 24.7 vs. 79.5 ± 22.9; VAS pain: 1.3 ± 2.0 vs. 1.1 ± 1.7; all P < .05) that were maintained at 2 years (ASES: 81.3 ± 19.3 vs. 87.3 ± 14.9; SANE: 74.8 ± 26.2 vs. 79.7 ± 24.7; VAS pain: 1.3 ± 2.1 vs. 1.0 ± 1.6; all P < .05) and 5 years (ASES: 81.7 ± 16.5 vs. 86.9 ± 15.3; SANE: 71.6 ± 28.5 vs. 78.2 ± 25.9; VAS pain: 1.0 ± 1.7 vs. 1.0 ± 1.7; all P < .05), with statistical significance favoring TSA. After controlling for age and sex, there was an adjusted difference of 4.5 units in the ASES score favoring TSA (P = .005) at 5 years postoperatively but no differences in adjusted SANE (P = .745) and VAS pain (P = .332) scores. The use of RSA for GHOA grew considerably over time, from representing only 17% of all replacements performed for GHOA in 2012 to nearly half (47%) in 2021 (P < .001).ConclusionRSA as a treatment for GHOA with an intact rotator cuff seems to yield PROs that are largely clinically equivalent to TSA extending to 5 years postoperatively. The observed statistical significance favoring TSA appears to be of marginal clinical benefit based on established minimal clinically important differences and may be a result of the large sample size. Further research using more granular clinical data and examining differences in range of motion and complications is warranted as it may change the value analysis.     

Comparing Artificial Intelligence and Traditional Methods to Identify Factors Associated With Pediatric Asthma Readmission

ObjectiveTo identify and contrast risk factors for six-month pediatric asthma readmissions using traditional models (Cox proportional-hazards and logistic regression) and artificial neural-network modeling.MethodsThis retrospective cohort study of the 2013 Nationwide Readmissions Database included children 5 to 18 years old with a primary diagnosis of asthma. The primary outcome was time to asthma readmission in the Cox model, and readmission within 180 days in logistic regression. A basic neural network construction with 2 hidden layers and multiple replications considered all dataset variables and potential variable interactions to predict 180-day readmissions. Logistic regression and neural-network models were compared on area-under-the receiver-operating curve.ResultsOf 18,489 pediatric asthma hospitalizations, 1858 were readmitted within 180 days. In Cox and logistic models, longer index length of stay, public insurance, and nonwinter index admission seasons were associated with readmission risk, whereas micropolitan county was protective. In neural-network modeling, 9 factors were significantly associated with readmissions. Four overlapped with the Cox model (nonwinter-month admission, long length of stay, public insurance, and micropolitan hospitals), whereas 5 were unique (age, hospital bed number, teaching-hospital status, weekend index admission, and complex chronic conditions). The area under the curve was 0.592 for logistic regression and 0.637 for the neural network.ConclusionsDifferent methods can produce different readmission models. Relying on traditional modeling alone overlooks key readmission risk factors and complex factor interactions identified by neural networks.     

Patient-Centered Framework for Rehabilitation Research in Outpatient Settings

Conducting high-quality clinical research is dependent on merging scientific rigor with the clinical environment. This is often a complex endeavor that may include numerous barriers and competing interests. Overcoming these challenges and successfully integrating clinical research programs into clinical practice settings serving rehabilitation outpatients is beneficial from both a logistical perspective (eg, supports efficient and successful research procedures) and the establishment of a truly patient-centered research approach. Leveraging our experience with navigating this research-clinical care relationship, this article (1) proposes the Patient-Centered Framework for Rehabilitation Research, a model for integrating patient-centered research in an outpatient clinical setting that incorporates a collaborative, team-based model encompassing patient-centered values, as well as strategies for recruitment and retention, with a focus on populations living with disabilities or chronic diseases; (2) describes application of this framework in a comprehensive specialty multiple sclerosis center with both general strategies and specific examples to guide adaptation and implementation in other settings; and (3) discusses the effect of the framework as a model in 1 center, as well as the need for additional investigation and adaptation for other populations. The 5 interconnected principles incorporated in the Framework and which prioritize patient-centeredness include identifying shared values, partnering with the clinical setting, engaging with the population, building relationships with individuals, and designing accessible procedures. The Patient-Centered Framework for Rehabilitation Research is a model presented as an adaptable roadmap to guide researchers in hopes of not only improving individual patients’ experiences but also the quality and relevance of rehabilitation research as a whole. Future investigation is needed to test the Framework in other settings.     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.