Classification of patients with osteoarthritis through clusters of comorbidities using 633 330 individuals from Spain.

OBJECTIVES: To explore clustering of comorbidities among patients with a new diagnosis of OA and estimate the 10-year mortality risk for each identified cluster. METHODS: This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand or 'unspecified' site between 2006 and 2020, using SIDIAP (a primary care database representative of Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in ≥1% of the individuals (n = 35) were fitted into two cluster algorithms, k-means and latent class analysis. Models were assessed using a range of internal and external evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards. RESULTS: We identified 633 330 patients with a diagnosis of OA. Our proposed best solution used latent class analysis to identify four clusters: 'low-morbidity' (relatively low number of comorbidities), 'back/neck pain plus mental health', 'metabolic syndrome' and 'multimorbidity' (higher prevalence of all studied comorbidities). Compared with the 'low-morbidity' cluster, the 'multimorbidity' cluster had the highest risk of 10-year mortality (adjusted hazard ratio [HR]: 2.19 [95% CI: 2.15, 2.23]), followed by the 'metabolic syndrome' cluster (adjusted HR: 1.24 [95% CI: 1.22, 1.27]) and the 'back/neck pain plus mental health' cluster (adjusted HR: 1.12 [95% CI: 1.09, 1.15]). CONCLUSION: Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for microscopic polyangiitis.

OBJECTIVE: To develop and validate classification criteria for microscopic polyangiitis (MPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for MPA consisted of 149 cases of MPA and 408 comparators. The validation set consisted of an additional 142 cases of MPA and 414 comparators. From 91 candidate items, regression analysis identified 10 items for MPA, 6 of which were retained. The final criteria and their weights were as follows: perinuclear antineutrophil cytoplasmic antibody (ANCA) or anti-myeloperoxidase-ANCA positivity (+6), pauci-immune glomerulonephritis (+3), lung fibrosis or interstitial lung disease (+3), sino-nasal symptoms or signs (-3), cytoplasmic ANCA or anti-proteinase 3 ANCA positivity (-1) and eosinophil count ≥1×109/L (-4). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having MPA with a cumulative score of ≥5 points. When these criteria were tested in the validation data set, the sensitivity was 91% (95% CI 85% to 95%) and the specificity was 94% (95% CI 92% to 96%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for MPA are now validated for use in clinical research.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for granulomatosis with polyangiitis.

OBJECTIVE: To develop and validate revised classification criteria for granulomatosis with polyangiitis (GPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate criteria items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for GPA consisted of 578 cases of GPA and 652 comparators. The validation set consisted of an additional 146 cases of GPA and 161 comparators. From 91 candidate items, regression analysis identified 26 items for GPA, 10 of which were retained. The final criteria and their weights were as follows: bloody nasal discharge, nasal crusting or sino-nasal congestion (+3); cartilaginous involvement (+2); conductive or sensorineural hearing loss (+1); cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3 ANCA positivity (+5); pulmonary nodules, mass or cavitation on chest imaging (+2); granuloma or giant cells on biopsy (+2); inflammation or consolidation of the nasal/paranasal sinuses on imaging (+1); pauci-immune glomerulonephritis (+1); perinuclear ANCA or antimyeloperoxidase ANCA positivity (-1); and eosinophil count ≥1×109 /L (-4). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having GPA if the cumulative score was ≥5 points. When these criteria were tested in the validation data set, the sensitivity was 93% (95% CI 87% to 96%) and the specificity was 94% (95% CI 89% to 97%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology classification criteria for GPA demonstrate strong performance characteristics and are validated for use in research.

2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Eosinophilic Granulomatosis with Polyangiitis.

OBJECTIVE: To develop and validate revised classification criteria for eosinophilic granulomatosis with polyangiitis (EGPA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in five phases: (1) identification of candidate criteria items using consensus methodology, (2) prospective collection of candidate items present at the time of diagnosis, (3) data-driven reduction of the number of candidate items, (4) expert panel review of cases to define the reference diagnosis and (5) derivation of a points-based risk score for disease classification in a development set using least absolute shrinkage and selection operator logistic regression, with subsequent validation of performance characteristics in an independent set of cases and comparators. RESULTS: The development set for EGPA consisted of 107 cases of EGPA and 450 comparators. The validation set consisted of an additional 119 cases of EGPA and 437 comparators. From 91 candidate items, regression analysis identified 11 items for EPGA, 7 of which were retained. The final criteria and their weights were as follows: maximum eosinophil count ≥1×109/L (+5), obstructive airway disease (+3), nasal polyps (+3), cytoplasmic antineutrophil cytoplasmic antibody (ANCA) or anti-proteinase 3-ANCA positivity (-3), extravascular eosinophilic predominant inflammation (+2), mononeuritis multiplex/motor neuropathy not due to radiculopathy (+1) and haematuria (-1). After excluding mimics of vasculitis, a patient with a diagnosis of small- or medium-vessel vasculitis could be classified as having EGPA if the cumulative score was ≥6 points. When these criteria were tested in the validation data set, the sensitivity was 85% (95% CI 77% to 91%) and the specificity was 99% (95% CI 98% to 100%). CONCLUSION: The 2022 American College of Rheumatology/European Alliance of Associations for Rheumatology Classification Criteria for Eosinophilic Granulomatosis with Polyangiitis demonstrate strong performance characteristics and are validated for use in research.

2022 American College of Rheumatology/EULAR classification criteria for Takayasu arteritis.

OBJECTIVE: To develop and validate new classification criteria for Takayasu arteritis (TAK). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in six phases: (1) identification of candidate criteria items, (2) collection of candidate items present at diagnosis, (3) expert panel review of cases, (4) data-driven reduction of candidate items, (5) derivation of a points-based classification score in a development data set and (6) validation in an independent data set. RESULTS: The development data set consisted of 316 cases of TAK and 323 comparators. The validation data set consisted of an additional 146 cases of TAK and 127 comparators. Age ≤60 years at diagnosis and imaging evidence of large-vessel vasculitis were absolute requirements to classify a patient as having TAK. The final criteria items and weights were as follows: female sex (+1), angina (+2), limb claudication (+2), arterial bruit (+2), reduced upper extremity pulse (+2), reduced pulse or tenderness of a carotid artery (+2), blood pressure difference between arms of ≥20 mm Hg (+1), number of affected arterial territories (+1 to +3), paired artery involvement (+1) and abdominal aorta plus renal or mesenteric involvement (+3). A patient could be classified as having TAK with a cumulative score of ≥5 points. When these criteria were tested in the validation data set, the model area under the curve was 0.97 (95% CI 0.94 to 0.99) with a sensitivity of 93.8% (95% CI 88.6% to 97.1%) and specificity of 99.2% (95% CI 96.7% to 100.0%). CONCLUSION: The 2022 American College of Rheumatology/EULAR classification criteria for TAK are now validated for use in research.

2022 American College of Rheumatology/EULAR classification criteria for giant cell arteritis.

OBJECTIVE: To develop and validate updated classification criteria for giant cell arteritis (GCA). METHODS: Patients with vasculitis or comparator diseases were recruited into an international cohort. The study proceeded in six phases: (1) identification of candidate items, (2) prospective collection of candidate items present at the time of diagnosis, (3) expert panel review of cases, (4) data-driven reduction of candidate items, (5) derivation of a points-based risk classification score in a development data set and (6) validation in an independent data set. RESULTS: The development data set consisted of 518 cases of GCA and 536 comparators. The validation data set consisted of 238 cases of GCA and 213 comparators. Age ≥50 years at diagnosis was an absolute requirement for classification. The final criteria items and weights were as follows: positive temporal artery biopsy or temporal artery halo sign on ultrasound (+5); erythrocyte sedimentation rate ≥50 mm/hour or C reactive protein ≥10 mg/L (+3); sudden visual loss (+3); morning stiffness in shoulders or neck, jaw or tongue claudication, new temporal headache, scalp tenderness, temporal artery abnormality on vascular examination, bilateral axillary involvement on imaging and fluorodeoxyglucose-positron emission tomography activity throughout the aorta (+2 each). A patient could be classified as having GCA with a cumulative score of ≥6 points. When these criteria were tested in the validation data set, the model area under the curve was 0.91 (95% CI 0.88 to 0.94) with a sensitivity of 87.0% (95% CI 82.0% to 91.0%) and specificity of 94.8% (95% CI 91.0% to 97.4%). CONCLUSION: The 2022 American College of Rheumatology/EULAR GCA classification criteria are now validated for use in clinical research.

One- and 2-year incidence of osteoporotic fracture: a multi-cohort observational study using routinely collected real-world data.

We estimated and characterized the imminent fracture risk (1-2 years) of high-risk fracture patients through a multinational (UK, Spain, Denmark) cohort study. Older individuals with newly diagnosed osteoporosis and individuals who had a fracture while on treatment with a bisphosphonate were at a high risk of imminent fracture. PURPOSE: To characterize and estimate 1- to 2-year fracture risk in high-risk fracture patients. METHODS: Multi-cohort study in (database/study period) UK (CPRD/1995-2017), Spain (SIDIAP/2006-2016) and Denmark (DHR/1995-2016) including individuals ≥ 50 years old in NDO (newly diagnosed osteoporosis), OFx (incident osteoporotic fracture), BP (incident oral bisphosphonates use) or FWOT (fracture while on treatment with bisphosphonates). Outcomes (ICD-10/READ): hip, clinical spine, non-hip, non-spine and hip/humerus/distal forearm fracture. FOLLOW-UP: from cohort entry until death, migration/transfer or end of the study. STATISTICS: baseline characteristics and incidence rate (IR per 1000 persons). RESULTS (1-YEAR IR): NDO included 69,899 (UK), 37,901 (Spain) and 158,191 (Denmark) individuals. Spanish-IR was lowest for hip (4.7), clinical spine (2.5) and major osteoporotic fracture (MOF) (17.3) and highest in Denmark (74.2, 26.0 and 120.1, respectively). OFx included 83,514 (UK), 51,044 (Spain) and 509,551 (Denmark) individuals. IR in Denmark was highest for hip (24.1) and MOF (47.2), in Spain was highest for the clinical spine (9.4) and lowest for hip (9.5) and in the UK was lowest for the clinical spine (2.8) and MOF (20.7). BP included 148,507 (UK), 52,037 (Spain) and 204,010 (Denmark) individuals. Spanish-IR was lowest for hip (5.0) and MOF (21.1) and highest in Denmark (20.3 and 48.6, respectively). FWOT included 28,930 (UK), 1,865 (Spain) and 31,882 (Denmark) individuals. Clinical spine-IR was highest for Spain (12.0). Hip-IR was lowest for Spain (7.6) and highest for Denmark (33.6). Comparing young subjects, those who have FWOT started with an increased fracture rate. CONCLUSION: OFx and FWOT individuals experience higher re-fracture incidence rates than those with osteoporosis with or without treatment.

Symptom scores in the diagnosis of pediatric cow's milk protein allergy: A systematic review.

BACKGROUND: Cow's milk protein allergy (CMPA) is an immune-mediated allergic response to proteins in milk that is common in infants. Broad CMPA symptoms make diagnosis a challenge, particularly in primary care. Symptom scores may improve a clinician's awareness of symptoms, indicating a need for further testing. This systematic review examined the development and evaluation of such symptom scores for use in infants. METHODS: CENTRAL, MEDLINE, EMBASE and CINAHL databases were searched from inception to 3 December 2019 (Updated 14 November 2020) for diagnostic accuracy studies, randomised controlled trials, observational studies, economic evaluations, qualitative studies and studies reporting development of the tools. Data were not suitable for meta-analysis due to clinical and methodological heterogeneity, so were narratively synthesised. RESULTS: We found two symptom scores evaluated in one and fourteen studies, respectively. Estimated sensitivity and specificity ranged from 37% to 98% and 38% to 93%. The evaluations of each tool were at high risk of bias or failed to address issues such as clinical and cost-effectiveness. CONCLUSIONS: Estimates of accuracy of symptom scores for CMPA offered so far should be interpreted cautiously. Rigorous, conflict-free research based on well-defined roles for the tools is urgently required.

Association of Tramadol vs Codeine Prescription Dispensation With Mortality and Other Adverse Clinical Outcomes.

Importance: Although tramadol is increasingly used to manage chronic noncancer pain, few safety studies have compared it with other opioids. Objective: To assess the associations of tramadol, compared with codeine, with mortality and other adverse clinical outcomes as used in outpatient settings. Design, Setting, and Participants: Retrospective, population-based, propensity score-matched cohort study using a primary care database with routinely collected medical records and pharmacy dispensations covering more than 80% of the population of Catalonia, Spain (≈6 million people). Patients 18 years or older with 1 or more year of available data and dispensation of tramadol or codeine (2007-2017) were included and followed up to December 31, 2017. Exposures: New prescription dispensation of tramadol or codeine (no dispensation in the previous year). Main Outcomes and Measures: Outcomes studied were all-cause mortality, cardiovascular events, fractures, constipation, delirium, falls, opioid abuse/dependence, and sleep disorders within 1 year after the first dispensation. Absolute rate differences (ARDs) and hazard ratios (HRs) with 95% confidence intervals were calculated using cause-specific Cox models. Results: Of the 1 093 064 patients with a tramadol or codeine dispensation during the study period (326 921 for tramadol, 762 492 for codeine, 3651 for both drugs concomitantly), a total of 368 960 patients (184 480 propensity score-matched pairs) were included after study exclusions and propensity score matching (mean age, 53.1 [SD, 16.1] years; 57.3% women). Compared with codeine, tramadol dispensation was significantly associated with a higher risk of all-cause mortality (incidence, 13.00 vs 5.61 per 1000 person-years; HR, 2.31 [95% CI, 2.08-2.56]; ARD, 7.37 [95% CI, 6.09-8.78] per 1000 person-years), cardiovascular events (incidence, 10.03 vs 8.67 per 1000 person-years; HR, 1.15 [95% CI, 1.05-1.27]; ARD, 1.36 [95% CI, 0.45-2.36] per 1000 person-years), and fractures (incidence, 12.26 vs 8.13 per 1000 person-years; HR, 1.50 [95% CI, 1.37-1.65]; ARD, 4.10 [95% CI, 3.02-5.29] per 1000 person-years). No significant difference was observed for the risk of falls, delirium, constipation, opioid abuse/dependence, or sleep disorders. Conclusions and Relevance: In this population-based cohort study, a new prescription dispensation of tramadol, compared with codeine, was significantly associated with a higher risk of subsequent all-cause mortality, cardiovascular events, and fractures, but there was no significant difference in the risk of constipation, delirium, falls, opioid abuse/dependence, or sleep disorders. The findings should be interpreted cautiously, given the potential for residual confounding.

Combating Glioblastoma by Codelivering the Small-Molecule Inhibitor of STAT3 and STAT3siRNA with α5ß1 Integrin Receptor-Selective Liposomes.

Glioblastoma multiforme (GBM) is one of the most aggressive tumors with a median survival of only 15 months. Effective therapeutics need to overcome the formidable challenge of crossing the blood-brain barrier (BBB). Receptors and transporters overexpressed on BCECs are being used for designing liposomes, polymers, polymeric micelles, peptides, and dendrimer-based drug carriers for combating brain tumors. Herein, using the orthotopic mouse glioblastoma model, we show that codelivering a small-molecule inhibitor of the JAK/STAT pathway (WP1066) and STAT3siRNA with nanometric (100-150 nm) α5ß1 integrin receptor-selective liposomes of RGDK-lipopeptide holds therapeutic promise in combating glioblastoma. Rh-PE (red)-labeled liposomes of RGDK-lipopeptide were found to be internalized in GL261 cells via integrin α5ß1 receptors. Intravenously administered near-infrared (NIR)-dye-labeled α5ß1 integrin receptor-selective liposomes of RGDK-lipopeptide were found to be accumulated preferentially in the mouse brain tumor tissue. Importantly, we show that iv injection of WP1066 (a commercially sold small-molecule inhibitor of the JAK/STAT pathway) and STAT3siRNA cosolubilized within the liposomes of RGDK-lipopeptide leads to significant inhibition (>350% compared to the untreated mice group) of orthotopically growing mouse glioblastoma. The present strategy may find future use in combating GBM.

Identifying individuals with chronic pain after knee replacement: a population-cohort, cluster-analysis of Oxford knee scores in 128,145 patients from the English National Health Service.

BACKGROUND: Approximately one in five patients undergoing knee replacement surgery experience chronic pain after their operation, which can negatively impact on their quality of life. In order to develop and evaluate interventions to improve the management of chronic post-surgical pain, we aimed to derive a cut-off point in the Oxford Knee Score pain subscale to identify patients with chronic pain following knee replacement, and to characterise these patients using self-reported outcomes. METHODS: Data from the English Patient-Reported Outcome Measures (PROMs) programme were used. This comprised patient-reported data from 128,145 patients who underwent primary knee replacement surgery in England between 2012 and 2015. Cluster analysis was applied to derive a cut-off point on the pain subscale of the Oxford Knee Score. RESULTS: A high-pain group was identified, described by a maximum of 14 points in the Oxford Knee Score pain subscale six months after surgery. The high-pain group, comprising 15% of the sample, was characterised by severe and frequent problems in all pain dimensions, particularly in pain severity, night pain and limping, as well as in all dimensions of health-related quality of life. CONCLUSIONS: Patients with Oxford Knee Score pain subscale scores of 14 or less at six months after knee replacement can be considered to be in chronic pain that is likely to negatively affect their quality of life. This derived cut-off can be used for patient selection in research settings to design and assess interventions that support patients in their management of chronic post-surgical pain.

Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017.

BACKGROUND: Malaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. METHODS: In this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000-17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program-Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000-16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. FINDINGS: Malaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10-4 for Pakistan, 4·86 × 10-6 for India, and 1·32 × 10-5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. INTERPRETATION: Malaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. FUNDING: NIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.

Device-Measured Physical Activity in 3506 Individuals with Knee or Hip Arthroplasty.

PURPOSE: Hip and knee arthroplasty aims to reduce joint pain and increase functional mobility in patients with osteoarthritis; however, the degree to which arthroplasty is associated with higher physical activity is unclear. The current study sought to assess the association of hip and knee arthroplasty with objectively measured physical activity. METHODS: This cross-sectional study analyzed wrist-worn accelerometer data collected in 2013-2016 from UK Biobank participants (aged 43-78 yr). Multivariable linear regression was performed to assess step count, cadence, overall acceleration, and activity behaviors between nonarthritic controls, end-stage arthritic, and postoperative cohorts, controlling for demographic and behavioral confounders. From a cohort of 94,707 participants with valid accelerometer wear time and complete self-reported data, electronic health records were used to identify 3506 participants having undergone primary or revision hip or knee arthroplasty and 68,389 nonarthritic controls. RESULTS: End-stage hip or knee arthritis was associated with taking 1129 fewer steps per day (95% confidence interval (CI), 811-1447; P < 0.001) and having 5.8 fewer minutes per day (95% CI, 3.0-8.7; P < 0.001) of moderate-to-vigorous activity compared with nonarthritic controls. Unilateral primary hip and knee arthroplasties were associated with 877 (95% CI, 284-1471; P = 0.004) and 893 (95% CI, 232-1554; P = 0.008) more steps than end-stage osteoarthritic participants, respectively. Postoperative unilateral hip arthroplasty participants demonstrated levels of moderate-to-vigorous physical activity and daily step count equivalent to nonarthritic controls. No difference in physical activity was observed between any cohorts in terms of overall acceleration, or time spent in daily light activity, sedentary behavior, or sleep. CONCLUSIONS: Hip and knee arthroplasties are associated with higher levels of physical activity compared with participants with end-stage arthritis. Unilateral hip arthroplasty patients, in particular, demonstrate equivalence to nonarthritic peers at more than 1 yr after surgery.

Self-Supervised Machine Learning to Characterise Step Counts from Wrist-Worn Accelerometers in the UK Biobank.

PURPOSE: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices against camera-annotated ground truth. This study aims to describe the development and validation of step count derived from a wrist-worn accelerometer and assess its association with cardiovascular and all-cause mortality in a large prospective cohort. METHODS: We developed and externally validated a self-supervised machine learning step detection model, trained on an open-source and step-annotated free-living dataset. 39 individuals will free-living ground-truth annotated step counts were used for model development. An open-source dataset with 30 individuals was used for external validation. Epidemiological analysis was performed using 75,263 UK Biobank participants without prevalent cardiovascular disease (CVD) or cancer. Cox regression was used to test the association of daily step count with fatal CVD and all-cause mortality after adjustment for potential confounders. RESULTS: The algorithm substantially outperformed reference models (free-living mean absolute percent error of 12.5%, versus 65-231%). Our data indicate an inverse dose-response association, where taking 6,430-8,277 daily steps was associated with 37% [25-48%] and 28% [20-35%] lower risk of fatal CVD and all-cause mortality up to seven years later, compared to those taking fewer steps each day. CONCLUSIONS: We have developed an open and transparent method that markedly improves the measurement of steps in large-scale wrist-worn accelerometer datasets. The application of this method demonstrated expected associations with CVD and all-cause mortality, indicating excellent face validity. This reinforces public health messaging for increasing physical activity and can help lay the groundwork for the inclusion of target step counts in future public health guidelines.

Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity.

Intersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond "White", "Black", "Asian", "Mixed" and "Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.

Incident Use of Hydroxychloroquine for the Treatment of Rheumatoid Arthritis and Systemic Lupus Erythematosus During the COVID-19 Pandemic.

OBJECTIVE: We studied whether the use of hydroxychloroquine (HCQ) for COVID-19 resulted in supply shortages for patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). METHODS: We used US claims data (IQVIA PHARMETRICS® Plus for Academics [PHARMETRICS]) and hospital electronic records from Spain (Institut Municipal d'Assistència Sanitària Information System [IMASIS]) to estimate monthly rates of HCQ use between January 2019 and March 2022, in the general population and in patients with RA and SLE. Methotrexate (MTX) use was estimated as a control. RESULTS: More than 13.5 million individuals (13,311,811 PHARMETRICS, 207,646 IMASIS) were included in the general population cohort. RA and SLE cohorts enrolled 135,259 and 39,295 patients, respectively, in PHARMETRICS. Incidence of MTX and HCQ were stable before March 2020. On March 2020, the incidence of HCQ increased by 9- and 67-fold in PHARMETRICS and IMASIS, respectively, and decreased in May 2020. Usage rates of HCQ went back to prepandemic trends in Spain but remained high in the United States, mimicking waves of COVID-19. No significant changes in HCQ use were noted among patients with RA and SLE. MTX use rates decreased during HCQ approval period for COVID-19 treatment. CONCLUSION: Use of HCQ increased dramatically in the general population in both Spain and the United States during March and April 2020. Whereas Spain returned to prepandemic rates after the first wave, use of HCQ remained high and followed waves of COVID-19 in the United States. However, we found no evidence of general shortages in the use of HCQ for both RA and SLE in the United States.

Research Protocol for an Observational Health Data Analysis on the Adverse Events of Systemic Treatment in Patients with Metastatic Hormone-sensitive Prostate Cancer: Big Data Analytics Using the PIONEER Platform.

Combination therapies in metastatic hormone-sensitive prostate cancer (mHSPC), which include the addition of an androgen receptor signaling inhibitor and/or docetaxel to androgen deprivation therapy, have been a game changer in the management of this disease stage. However, these therapies come with their fair share of toxicities and side effects. The goal of this observational study is to report drug-related adverse events (AEs), which are correlated with systemic combination therapies for mHSPC. Determining the optimal treatment option requires large cohorts to estimate the tolerability and AEs of these combination therapies in "real-life" patients with mHSPC, as provided in this study. We use a network of databases that includes population-based registries, electronic health records, and insurance claims, containing the overall target population and subgroups of patients defined by unique certain characteristics, demographics, and comorbidities, to compute the incidence of common AEs associated with systemic therapies in the setting of mHSPC. These data sources are standardised using the Observational Medical Outcomes Partnership Common Data Model. We perform the descriptive statistics as well as calculate the AE incidence rate separately for each treatment group, stratified by age groups and index year. The time until the first event is estimated using the Kaplan-Meier method within each age group. In the case of episodic events, the anticipated mean cumulative counts of events are calculated. Our study will allow clinicians to tailor optimal therapies for mHSPC patients, and they will serve as a basis for comparative method studies.

Machine Learning for Risk Factor Identification and Cardiovascular Mortality Prediction Among Patients with Osteoporosis.

Risk prediction tools are increasingly popular aids in clinical decision-making. However, the underlying models are often trained on data from general patient cohorts and may not be representative of and suitable for use with targeted patient groups in actual clinical practice, such as in the case of osteoporosis patients who may be at elevated risk of mortality. We developed and internally validated a cardiovascular mortality risk prediction model tailored to individuals with osteoporosis using a range of machine learning models. We compared the performance of machine learning models with existing expert-based models with respect to data-driven risk factor identification, discrimination, and calibration. The proposed models were found to outperform existing cardiovascular mortality risk prediction tools for the osteoporosis population. External validation of the model is recommended.Clinical Relevance- This study presents the performance of machine learning models for cardiovascular death prediction among osteoporotic patients as well as the risk factors identified by the models to be important predictors.

Random effects modelling versus logistic regression for the inclusion of cluster-level covariates in propensity score estimation: A Monte Carlo simulation and registry cohort analysis.

Purpose: Surgeon and hospital-related features, such as volume, can be associated with treatment choices and outcomes. Accounting for these covariates with propensity score (PS) analysis can be challenging due to the clustered nature of the data. We studied six different PS estimation strategies for clustered data using random effects modelling (REM) compared with logistic regression. Methods: Monte Carlo simulations were used to generate variable cluster-level confounding intensity [odds ratio (OR) = 1.01-2.5] and cluster size (20-1,000 patients per cluster). The following PS estimation strategies were compared: i) logistic regression omitting cluster-level confounders; ii) logistic regression including cluster-level confounders; iii) the same as ii) but including cross-level interactions; iv), v), and vi), similar to i), ii), and iii), respectively, but using REM instead of logistic regression. The same strategies were tested in a trial emulation of partial versus total knee replacement (TKR) surgery, where observational versus trial-based estimates were compared as a proxy for bias. Performance metrics included bias and mean square error (MSE). Results: In most simulated scenarios, logistic regression, including cluster-level confounders, led to the lowest bias and MSE, for example, with 50 clusters × 200 individuals and confounding intensity OR = 1.5, a relative bias of 10%, and MSE of 0.003 for (i) compared to 32% and 0.010 for (iv). The results from the trial emulation also gave similar trends. Conclusion: Logistic regression, including patient and surgeon-/hospital-level confounders, appears to be the preferred strategy for PS estimation.

Changes in air pollution exposure after residential relocation and body mass index in children and adolescents: A natural experiment study.

Air pollution exposure may affect child weight gain, but observational studies provide inconsistent evidence. Residential relocation can be leveraged as a natural experiment by studying changes in health outcomes after a sudden change in exposure within an individual. We aimed to evaluate whether changes in air pollution exposure due to residential relocation are associated with changes in body mass index (BMI) in children and adolescents in a natural experiment study. This population-based study included children and adolescents, between 2 and 17 years, who moved during 2011-2018 and were registered in the primary healthcare in Catalonia, Spain (N = 46,644). Outdoor air pollutants (nitrogen dioxides (NO2), particulate matter <10 µm (PM10) and <2.5 µm (PM2.5)) were estimated at residential census tract level before and after relocation; tertile cut-offs were used to define changes in exposure. Routinely measured weight and height were used to calculate age-sex-specific BMI z-scores. A minimum of 180 days after moving was considered to observe zBMI changes according to changes in exposure using linear fixed effects regression. The majority of participants (60-67% depending on the pollutant) moved to areas with similar levels of air pollution, 15-49% to less polluted, and 14-31% to more polluted areas. Moving to areas with more air pollution was associated with zBMI increases for all air pollutants (ß NO2 = 0.10(95%CI 0.09; 0.12), ß PM2.5 0.06(0.04; 0.07), ß PM10 0.08(0.06; 0.10)). Moving to similar air pollution areas was associated with decreases in zBMI for all pollutants. No associations were found for those moving to less polluted areas. Associations with moving to more polluted areas were stronger in preschool- and primary school-ages. Associations did not differ by area deprivation strata. This large, natural experiment study suggests that increases in outdoor air pollution may be associated with child weight gain, supporting ongoing efforts to lower air pollution levels.