Injury patterns and healthcare utilisation by runners of the New York City Marathon.

Objectives: The purpose of this study was to describe injury patterns and healthcare utilisation of marathon runners. Methods: This was a previously reported 16-week prospective observational study of runners training for the New York City Marathon. Runners completed a baseline survey including demographics, running experience and marathon goal. Injury surveys were collected every 4 weeks during training, as well as 1 week before and 1 week after the race. Injury details collected included anatomic location, diagnosis, onset, and treatment received. Results: A total of 1049 runners were enrolled. Injuries were reported by 398 (38.4%) during training and 128 (14.1%) during the marathon. The overall prevalence of injury was 447/1049 (42.6%). Foot, knee and hip injuries were most common during training, whereas knee, thigh and foot injuries were most common during the race. The most frequent tissue type affected was the category of muscle, tendon/fascia and bursa. The prevalence of overuse injuries increased, while acute injuries remained constant throughout training. Hamstring injuries had the highest prevalence of diagnosis with 38/564 injuries (6.7%). Of the 447 runners who reported an injury, 224 (50.1%) received medical care. Physical therapy was the most common medical care received with 115/1037 (11.1%) runners during training and 44/907 (4.9%) postrace. Conclusion: Runners training and participating in a marathon commonly experience injuries, especially of the foot and knee, which often are overuse soft tissue injuries. Half of the injured runners sought out medical care for their injury. Understanding the patterns of injuries affecting marathon runners could help guide future injury prevention efforts.

Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes.

BACKGROUND: Unplanned hospital readmissions after total joint arthroplasty (TJA) represent potentially serious adverse events and remain a critical measure of hospital quality. Predicting the risk of readmission after TJA may provide patients and clinicians with valuable information for preoperative decision-making. QUESTIONS/PURPOSES: (1) Can nonlinear machine-learning models integrating preoperatively available patient, surgeon, hospital, and county-level information predict 30-day unplanned hospital readmissions in a large cohort of nationwide Medicare beneficiaries undergoing TJA? (2) Which predictors are the most important in predicting 30-day unplanned hospital readmissions? (3) What specific information regarding population-level associations can we obtain from interpreting partial dependency plots (plots describing, given our modeling choice, the potentially nonlinear shape of associations between predictors and readmissions) of the most important predictors of 30-day readmission? METHODS: National Medicare claims data (chosen because this database represents a large proportion of patients undergoing TJA annually) were analyzed for patients undergoing inpatient TJA between October 2016 and September 2018. A total of 679,041 TJAs (239,391 THAs [61.3% women, 91.9% White, 52.6% between 70 and 79 years old] and 439,650 TKAs [63.3% women, 90% White, 55.2% between 70 and 79 years old]) were included. Model features included demographics, county-level social determinants of health, prior-year (365-day) hospital and surgeon TJA procedure volumes, and clinical classification software-refined diagnosis and procedure categories summarizing each patient's Medicare claims 365 days before TJA. Machine-learning models, namely generalized additive models with pairwise interactions (prediction models consisting of both univariate predictions and pairwise interaction terms that allow for nonlinear effects), were trained and evaluated for predictive performance using area under the receiver operating characteristic (AUROC; 1.0 = perfect discrimination, 0.5 = no better than random chance) and precision-recall curves (AUPRC; equivalent to the average positive predictive value, which does not give credit for guessing "no readmission" when this is true most of the time, interpretable relative to the base rate of readmissions) on two holdout samples. All admissions (except the last 2 months' worth) were collected and split randomly 80%/20%. The training cohort was formed with the random 80% sample, which was downsampled (so it included all readmissions and a random, equal number of nonreadmissions). The random 20% sample served as the first test cohort ("random holdout"). The last 2 months of admissions (originally held aside) served as the second test cohort ("2-month holdout"). Finally, feature importances (the degree to which each variable contributed to the predictions) and partial dependency plots were investigated to answer the second and third research questions. RESULTS: For the random holdout sample, model performance values in terms of AUROC and AUPRC were 0.65 and 0.087, respectively, for THA and 0.66 and 0.077, respectively, for TKA. For the 2-month holdout sample, these numbers were 0.66 and 0.087 and 0.65 and 0.075. Thus, our nonlinear models incorporating a wide variety of preoperative features from Medicare claims data could not well-predict the individual likelihood of readmissions (that is, the models performed poorly and are not appropriate for clinical use). The most predictive features (in terms of mean absolute scores) and their partial dependency graphs still confer information about population-level associations with increased risk of readmission, namely with older patient age, low prior 365-day surgeon and hospital TJA procedure volumes, being a man, patient history of cardiac diagnoses and lack of oncologic diagnoses, and higher county-level rates of hospitalizations for ambulatory-care sensitive conditions. Further inspection of partial dependency plots revealed nonlinear population-level associations specifically for surgeon and hospital procedure volumes. The readmission risk for THA and TKA decreased as surgeons performed more procedures in the prior 365 days, up to approximately 75 TJAs (odds ratio [OR] = 1.2 for TKA and 1.3 for THA), but no further risk reduction was observed for higher annual surgeon procedure volumes. For THA, the readmission risk decreased as hospitals performed more procedures, up to approximately 600 TJAs (OR = 1.2), but no further risk reduction was observed for higher annual hospital procedure volumes. CONCLUSION: A large dataset of Medicare claims and machine learning were inadequate to provide a clinically useful individual prediction model for 30-day unplanned readmissions after TKA or THA, suggesting that other factors that are not routinely collected in claims databases are needed for predicting readmissions. Nonlinear population-level associations between low surgeon and hospital procedure volumes and increased readmission risk were identified, including specific volume thresholds above which the readmission risk no longer decreases, which may still be indirectly clinically useful in guiding policy as well as patient decision-making when selecting a hospital or surgeon for treatment. LEVEL OF EVIDENCE: Level III, therapeutic study.

Computational pathology for musculoskeletal conditions using machine learning: advances, trends, and challenges.

Histopathology is widely used to analyze clinical biopsy specimens and tissues from pre-clinical models of a variety of musculoskeletal conditions. Histological assessment relies on scoring systems that require expertise, time, and resources, which can lead to an analysis bottleneck. Recent advancements in digital imaging and image processing provide an opportunity to automate histological analyses by implementing advanced statistical models such as machine learning and deep learning, which would greatly benefit the musculoskeletal field. This review provides a high-level overview of machine learning applications, a general pipeline of tissue collection to model selection, and highlights the development of image analysis methods, including some machine learning applications, to solve musculoskeletal problems. We discuss the optimization steps for tissue processing, sectioning, staining, and imaging that are critical for the successful generalizability of an automated image analysis model. We also commenting on the considerations that should be taken into account during model selection and the considerable advances in the field of computer vision outside of histopathology, which can be leveraged for image analysis. Finally, we provide a historic perspective of the previously used histopathological image analysis applications for musculoskeletal diseases, and we contrast it with the advantages of implementing state-of-the-art computational pathology approaches. While some deep learning approaches have been used, there is a significant opportunity to expand the use of such approaches to solve musculoskeletal problems.

Patient and Surgeon Risk-Taking Regarding Total Joint Arthroplasty.

BACKGROUND: Decisions regarding care for osteoarthritis involve physicians helping patients understand likely benefits and harms of treatment. Little work has directly compared patient and surgeon risk-taking attitudes, which may help inform strategies for shared decision-making and improve patient satisfaction. METHODS: We surveyed patients contemplating total joint arthroplasty visiting a high-volume specialty hospital regarding general questions about risk-taking, as well as willingness to undergo surgery under hypothetical likelihoods of moderate improvement and complications. We compared responses from surgeons answering similar questions about willingness to recommend surgery. RESULTS: Altogether 82% (162/197) of patients responded, as did 65% (30/46) of joint replacement surgeons. Mean age among patients was 66.4 years; 58% were female. Surgeons averaged 399 surgeries in 2019. Responses were similar between groups for general, health, career, financial, and sports/leisure risk-taking (P > .20); surgeons were marginally more risk-taking in driving (P = .05). For willingness to have or recommend surgery, as the chance of benefit decreased, or the chance of harm increased, the percentage willing to have or recommend surgery decreased. Between a 70% and 95% chance of moderate improvement (for a 2% complication risk), as well as between a 90% and 95% chance of moderate improvement (for 4% and 6% complication risks), the percentage willing to have or recommend surgery was indistinguishable between patients and surgeons. However, for lower likelihoods of improvement, a higher percentage of patients were willing to undergo surgery than surgeons recommended. Patients were also more often indifferent between complication risks. CONCLUSION: Although patients and surgeons were often willing to have or recommend joint replacement surgery at similar rates, they diverged for lower-benefit higher-harm scenarios.

History of COVID-19 Was Not Associated With Length of Stay or In-Hospital Complications After Elective Lower Extremity Joint Replacement.

BACKGROUND: The impact of previous SARS-CoV-2 infection on the morbidity of elective total joint arthroplasty (TJA) is not fully understood. This study reports on the association between previous COVID-19 disease, hospital length of stay (LOS), and in-hospital complications after elective primary TJA. METHODS: Demographics, comorbidities, LOS, and in-hospital complications of consecutive 340 patients with a history of COVID-19 were compared with those of 5014 patients without a history of COVID-19 undergoing TJA. History of COVID-19 was defined as a positive IgG antibody test for SARS-CoV-2 before surgery. All patients were given both antibody and polymerase chain reaction tests before surgery. RESULTS: Patients with a history of COVID-19 were more likely to be obese (43.8% vs 32.4%, P < .001), Black (15.6% vs 6.8%, P < .001), or Hispanic (8.5% vs 5.4%, P = .028) than patients without a history of COVID-19. COVID-19 treatment was reported by 6.8% of patients with a history of COVID-19. Patients with a history of COVID-19 did not have a significantly longer median LOS after controlling for other factors (for hip replacements, median 2.9 h longer, 95% confidence interval = -2.0 to 7.8, P = .240; for knee replacements, median 4.1 h longer, 95% confidence interval = -2.4 to 10.5, P = .214), but a higher percentage were discharged to a post-acute care facility (4.7% vs 1.9%, P = .001). There was no significant difference in in-hospital complication rates between the 2 groups (0/340 = 0.0% vs 22/5014 = 0.44%, P = .221). CONCLUSIONS: We do not find differences in LOS or in-hospital complications between the 2 groups. However, more work is needed to confirm these findings, particularly for patients with a history of more severe COVID-19. LEVEL OF EVIDENCE: II.

Presenteeism and absenteeism before and after single-level lumbar spine surgery.

BACKGROUND CONTEXT: Health can impact work performance through absenteeism, time spent away from work, and presenteeism, inhibited at-work performance. Low back pain is common and costly, both in terms of direct medical expenditures and indirect reduced work performance. PURPOSE: Surgery for lumbar spinal pathology is an important part of treatment for patients who do not respond to nonsurgical management. While the indirect costs of return to work and absenteeism among employed patients undergoing lumbar spine surgery have been studied, little work has been done to quantify presenteeism before and after lumbar spine surgery. STUDY DESIGN/SETTING: Prospective cohort study at a single high-volume urban musculoskeletal specialty hospital. PATIENT SAMPLE: Patients undergoing single-level lumbar spinal fusion and/or decompression surgery. OUTCOME MEASURES: Presenteeism and absenteeism were measured using the World Health Organization's Health and Work Performance Questionnaire before surgery, as well as 6 weeks, 6 months, and 12 months after surgery. METHODS: Average presenteeism and absenteeism were evaluated at pre-surgical baseline and each follow-up timepoint. Monthly average time lost to presenteeism and absenteeism were calculated before surgery and 12 months after surgery. Study data were collected and managed using REDCap electronic data capture tools with support from Clinical and Translational Science Center grant, UL1TR002384. One author discloses royalties, private investments, consulting fees, speaking/teaching arrangements, travel, board of directorship, and scientific advisory board membership totaling >$300,000. RESULTS: We enrolled 134 employed surgical patients, among whom 115 (86%) responded at 6 weeks, 105 (78%) responded at 6 months, and 115 (86%) responded at 12 months. Preoperatively, mean age was 56.4 years (median 57.5), and 41.0% were women; 68 (50.7%) had only decompressions, while 66 (49.3%) had fusions. Among respondents at each time point, 98%, 92%, and 92% were still employed, among whom 76%, 96%, and 96% had resumed working, respectively (median 29 days). Average at-work performance among working patients (who responded at each pair of timepoints) moved from 75.4 to 78.7 between baseline and 6 weeks, 71.8 to 85.9 between baseline and 6 months, and 73.0 to 88.1 between baseline and 12 months. Gains were concentrated among the 52.0% of patients whose at-work performance was declining (and low) leading up to surgery. Average absenteeism was relatively unmoved between baseline and each follow-up. Before surgery, the monthly average time lost to presenteeism and absenteeism was 19.8% and 18.9%, respectively; 12 months after surgery, these numbers were 9.7% and 16.0%; changes represent a mitigated loss of 13.0 percentage points of average monthly value. CONCLUSIONS: Presenteeism and absenteeism contributed roughly evenly to preoperative average monthly lost time. Although average changes in absenteeism and 6-week at-work performance were small, average changes in at-work performance at 6 and 12 months were significant. Cost-benefit analyses of lumbar spine surgery should therefore consider improved presenteeism, which appears to offset some of the direct and indirect costs of surgical treatment.

Factors associated with injuries in first-time marathon runners from the New York City marathon.

OBJECTIVES: To determine how baseline characteristics of first-time marathon runners and training patterns are associated with risk of injuries during training and the race. METHODS: First-time adult marathon runners who were registered for the 2017 New York City Marathon were monitored starting 12 weeks prior to the race. Baseline data collection included demographics and running experience. Running frequency, distance, and injury occurrence were self-reported using online surveys every 2 weeks. RESULTS: A total of 720 runners participated of which 675 completed the study. There were 64/675 (9.5%) who had major injuries during training or the race that preventing starting or finishing the race. An additional 332 (49.2%) had minor injuries interfering with training and/or affecting race performance. Injury incidence was not significantly different based on age or sex. Runners who completed a half marathon prior to the study were less likely to report getting injured [multivariable odds ratio (OR) 0.40, (0.22, 0.76), p= 0.005]. Runners who averaged <4 training runs per week during the study were less likely to report getting injured compared to those who averaged ≥4 per week [relative risk 1.36, (1.13-1.63), p= 0.001]. Longest training run distance during the study was inversely associated with race-day injury incidence [OR 0.87 (0.81, 0.94), p< 0.001]. CONCLUSION: Injuries are common among first-time marathon runners. We found that risk of injury during training was associated with lack of half marathon experience and averaging ≥4 training runs per week. Longer training runs were associated with a lower incidence of race-day injuries. These results can inform the development of targeted injury-prevention interventions.

Running races during the COVID-19 pandemic: a 2020 survey of the running community.

OBJECTIVES: To survey runners and triathletes about their willingness to resume in-person racing during the COVID-19 pandemic, health concerns related to mass races and changes in running patterns since the start of the pandemic. DESIGN: An electronic survey was distributed from 15 July to 1 September 2020 to runners and triathletes by New York Road Runners, ASICS North America, and race medical directors, and through social media. PARTICIPANTS: Runners and triathletes 18 years of age or older who participated in at least one race in 2019. RESULTS: A total of 2278 surveys were received. Not all participants answered every question; the denominator represents the number of responses to each question. Most participants were from the USA (1620/1940, 83.5%), of which over half were from New York (812/1475, 55.1%). Regarding when respondents would feel comfortable returning to in-person racing, the most frequent response was 'Whenever local laws allow, but only if there are sufficient precautions' (954/2173, 43.9%), followed by 'Not until there is a vaccine' (540/2173, 24.9%). The most common concerns about in-person races were crowded starting corrals (1802/2084, 86.5%), the number of COVID-19 cases in the race location (1585/2084, 76.1%) and the number of participants (1517/2084, 72.8%). Comparing running patterns before the pandemic to Summer 2020, the mean weekly mileage decreased from 25.5 (SD 15.4) miles to 22.7 (16.2) miles (p<0.001). CONCLUSION: Most runners are willing to return to racing when local laws allow, though as of Summer 2020, many desired certain precautions to feel comfortable.

Causal Language in Observational Orthopaedic Research.

ABSTRACT: With the increasing availability of large clinical registries and administrative data sets, observational (i.e., nonexperimental) orthopaedic research is being performed with increased frequency. While this research substantially advances our field, there are fundamental limitations to what can be determined through a single observational study. Avoiding overstatements and misstatements is important for the sake of accuracy, particularly for ensuring that clinical care is not inadvertently swayed by how an observational study is written up and described. We have noticed that causal language is frequently misused in observational orthopaedic research-that is, language that says or implies that 1 variable definitively causes another, despite the fact that causation can generally only be determined with randomization. In this data-backed commentary, we examine the prevalence of causal language in a random sample of 400 observational orthopaedic studies; we found that causal language was misused in 60% of them. We discuss the implications of these results and how to report observational findings more accurately: the word "association" (and its derivatives) can almost always replace or reframe a causal phrase.

Genomic analysis of diet composition finds novel loci and associations with health and lifestyle.

We conducted genome-wide association studies (GWAS) of relative intake from the macronutrients fat, protein, carbohydrates, and sugar in over 235,000 individuals of European ancestries. We identified 21 unique, approximately independent lead SNPs. Fourteen lead SNPs are uniquely associated with one macronutrient at genome-wide significance (P < 5 × 10-8), while five of the 21 lead SNPs reach suggestive significance (P < 1 × 10-5) for at least one other macronutrient. While the phenotypes are genetically correlated, each phenotype carries a partially unique genetic architecture. Relative protein intake exhibits the strongest relationships with poor health, including positive genetic associations with obesity, type 2 diabetes, and heart disease (rg ≈ 0.15-0.5). In contrast, relative carbohydrate and sugar intake have negative genetic correlations with waist circumference, waist-hip ratio, and neighborhood deprivation (|rg| ≈ 0.1-0.3) and positive genetic correlations with physical activity (rg ≈ 0.1 and 0.2). Relative fat intake has no consistent pattern of genetic correlations with poor health but has a negative genetic correlation with educational attainment (rg ≈-0.1). Although our analyses do not allow us to draw causal conclusions, we find no evidence of negative health consequences associated with relative carbohydrate, sugar, or fat intake. However, our results are consistent with the hypothesis that relative protein intake plays a role in the etiology of metabolic dysfunction.

Impact of COVID-19 on vulnerable patients with rheumatic disease: results of a worldwide survey.

OBJECTIVE: There is emerging evidence that COVID-19 disproportionately affects people from racial/ethnic minority and low socioeconomic status (SES) groups. Many physicians across the globe are changing practice patterns in response to the COVID-19 pandemic. We sought to examine the practice changes among rheumatologists and what they perceive the impact to be on their most vulnerable patients. METHODS: We administered an online survey to a convenience sample of rheumatologists worldwide during the initial height of the pandemic (between 8 April and 4 May 2020) via social media and group emails. We surveyed rheumatologists about their opinions regarding patients from low SES and racial/ethnic minority groups in the context of the COVID-19 pandemic. Mainly, what their specific concerns were, including the challenges of medication access; and about specific social factors (health literacy, poverty, food insecurity, access to telehealth video) that may be complicating the management of rheumatologic conditions during this time. RESULTS: 548 rheumatologists responded from 64 countries and shared concerns of food insecurity, low health literacy, poverty and factors that preclude social distancing such as working and dense housing conditions among their patients. Although 82% of rheumatologists had switched to telehealth video, 17% of respondents estimated that about a quarter of their patients did not have access to telehealth video, especially those from below the poverty line. The majority of respondents believed these vulnerable patients, from racial/ethnic minorities and from low SES groups, would do worse, in terms of morbidity and mortality, during the pandemic. CONCLUSION: In this sample of rheumatologists from 64 countries, there is a clear shift in practice to telehealth video consultations and widespread concern for socially and economically vulnerable patients with rheumatic disease.

When Stars Do Not Align: Overall Hospital Quality Star Ratings and the Volume-Outcome Association.

BACKGROUND: Volume-outcome relationships are well established for coronary artery bypass grafting and total joint arthroplasty surgery. Although the U.S. Centers for Medicare & Medicaid Services (CMS) Overall Hospital Quality Star Ratings program includes outcome quality measures for these procedures, these outcome quality measures are not counted toward the star ratings for low-volume hospitals. We sought to assess whether excluding low-volume hospitals from surgical quality measures with known volume-outcome relationships affects the star ratings. METHODS: We identified quality measures used in CMS's star ratings that are related to surgical procedures with a known volume-outcome relationship and tested for the presence of the volume-outcome association for each of these measures. We then imputed missing values for low-volume hospitals for each measure and otherwise identically repeated the CMS calculations in order to assess the percentages of hospitals with the same, better, or worse ratings. RESULTS: Among the measures used to calculate star ratings, we identified 4 quality measures (2 related to coronary artery bypass grafting and 2 related to total joint arthroplasty) with known volume-outcome relationships that were excluded from the calculations of the star ratings for low-volume hospitals. We confirmed a volume-outcome association in the CMS data for all 4 measures. When total joint arthroplasty complications were imputed for low-volume hospitals and then included in the calculation of the star ratings, over one-third of hospitals received a different rating; both low-volume and other hospitals were more often hurt than helped. Imputing the other 3 quality measures among low-volume hospitals left the ratings unchanged. CONCLUSIONS: The CMS star ratings do not fully represent the risks of undergoing procedures at low-volume hospitals, potentially misrepresent quality across facilities, and hence are of uncertain utility to consumers.

Author Correction: Multi-trait analysis of genome-wide association summary statistics using MTAG.

In the version of the paper initially published, no competing interests were declared. The 'Competing interests' statement should have stated that B.M.N. is on the Scientific Advisory Board of Deep Genomics. The error has been corrected in the HTML and PDF versions of the article.

Publisher Correction: Multi-trait analysis of genome-wide association summary statistics using MTAG.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Can Machine Learning Algorithms Predict Which Patients Will Achieve Minimally Clinically Important Differences From Total Joint Arthroplasty?

BACKGROUND: Identifying patients at risk of not achieving meaningful gains in long-term postsurgical patient-reported outcome measures (PROMs) is important for improving patient monitoring and facilitating presurgical decision support. Machine learning may help automatically select and weigh many predictors to create models that maximize predictive power. However, these techniques are underused among studies of total joint arthroplasty (TJA) patients, particularly those exploring changes in postsurgical PROMs. QUESTION/PURPOSES: (1) To evaluate whether machine learning algorithms, applied to hospital registry data, could predict patients who would not achieve a minimally clinically important difference (MCID) in four PROMs 2 years after TJA; (2) to explore how predictive ability changes as more information is included in modeling; and (3) to identify which variables drive the predictive power of these models. METHODS: Data from a single, high-volume institution's TJA registry were used for this study. We identified 7239 hip and 6480 knee TJAs between 2007 and 2012, which, for at least one PROM, patients had completed both baseline and 2-year followup surveys (among 19,187 TJAs in our registry and 43,313 total TJAs). In all, 12,203 registry TJAs had valid SF-36 physical component scores (PCS) and mental component scores (MCS) at baseline and 2 years; 7085 and 6205 had valid Hip and Knee Disability and Osteoarthritis Outcome Scores for joint replacement (HOOS JR and KOOS JR scores), respectively. Supervised machine learning refers to a class of algorithms that links a mapping of inputs to an output based on many input-output examples. We trained three of the most popular such algorithms (logistic least absolute shrinkage and selection operator (LASSO), random forest, and linear support vector machine) to predict 2-year postsurgical MCIDs. We incrementally considered predictors available at four time points: (1) before the decision to have surgery, (2) before surgery, (3) before discharge, and (4) immediately after discharge. We evaluated the performance of each model using area under the receiver operating characteristic (AUROC) statistics on a validation sample composed of a random 20% subsample of TJAs excluded from modeling. We also considered abbreviated models that only used baseline PROMs and procedure as predictors (to isolate their predictive power). We further directly evaluated which variables were ranked by each model as most predictive of 2-year MCIDs. RESULTS: The three machine learning algorithms performed in the poor-to-good range for predicting 2-year MCIDs, with AUROCs ranging from 0.60 to 0.89. They performed virtually identically for a given PROM and time point. AUROCs for the logistic LASSO models for predicting SF-36 PCS 2-year MCIDs at the four time points were: 0.69, 0.78, 0.78, and 0.78, respectively; for SF-36 MCS 2-year MCIDs, AUROCs were: 0.63, 0.89, 0.89, and 0.88; for HOOS JR 2-year MCIDs: 0.67, 0.78, 0.77, and 0.77; for KOOS JR 2-year MCIDs: 0.61, 0.75, 0.75, and 0.75. Before-surgery models performed in the fair-to-good range and consistently ranked the associated baseline PROM as among the most important predictors. Abbreviated LASSO models performed worse than the full before-surgery models, though they retained much of the predictive power of the full before-surgery models. CONCLUSIONS: Machine learning has the potential to improve clinical decision-making and patient care by helping to prioritize resources for postsurgical monitoring and informing presurgical discussions of likely outcomes of TJA. Applied to presurgical registry data, such models can predict, with fair-to-good ability, 2-year postsurgical MCIDs. Although we report all parameters of our best-performing models, they cannot simply be applied off-the-shelf without proper testing. Our analyses indicate that machine learning holds much promise for predicting orthopaedic outcomes. LEVEL OF EVIDENCE: Level III, diagnostic study.

Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.

Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals.

Here we conducted a large-scale genetic association analysis of educational attainment in a sample of approximately 1.1 million individuals and identify 1,271 independent genome-wide-significant SNPs. For the SNPs taken together, we found evidence of heterogeneous effects across environments. The SNPs implicate genes involved in brain-development processes and neuron-to-neuron communication. In a separate analysis of the X chromosome, we identify 10 independent genome-wide-significant SNPs and estimate a SNP heritability of around 0.3% in both men and women, consistent with partial dosage compensation. A joint (multi-phenotype) analysis of educational attainment and three related cognitive phenotypes generates polygenic scores that explain 11-13% of the variance in educational attainment and 7-10% of the variance in cognitive performance. This prediction accuracy substantially increases the utility of polygenic scores as tools in research.