Observational Database Research Articles

Malpractice litigation after spinal surgery: A review of allegations in France in 1990–2020

ObjectiveSpinal surgery is among the specialities with the highest rates of complications and of peri-operative morbidity and mortality. The number of spinal surgeries performed is rising year on year in France. The objectives of this study were to identify the main reasons for malpractice claims after spinal surgery in France, to evaluate the impact of avoidable errors, and to examine differences between civil lawsuits (private institutions) and administrative lawsuits (public institutions). HypothesisThe leading reasons for malpractice litigation are avoidable errors such as inadequate patient information, errors in indications, and inadequate post-operative monitoring. Material and methodsWe reviewed malpractice claims related to spinal surgery and recorded in two French databases (Legifrance and Doctrine) in 1990–2020. We combined the indexing term “surgery” with any of the following terms: “disc”, “spine”, “cervical”, “vertebral”, “lumbar”, “scoliosis”, “disc replacement”, and “fusion”. The search was performed by three orthopaedic surgeons who were blind to patient data and recorded the allegations, verdicts, and pay-outs. ResultsWe included 275 claims. The main plaintiff allegations were inadequate information (34.5%), infection (22%), spinal-cord injury (17%), and errors in indication (13%). Among these reasons, 56.7% may have been partly avoidable. In private institutions, claims for infection and for erroneous indication were significantly more common than in public institutions (p=0.02 for both), whereas the opposite was true for spinal-cord injury (p<0.001). ConclusionAfter spinal surgery in France, the most common malpractice allegation was inadequate information. Over half the claims were related to potentially avoidable factors. Among allegations, errors in indication were significantly more common in civil than in administrative lawsuits. Level of proofIV, retrospective observational database study.

External validation of existing dementia prediction models on observational health data

BackgroundMany dementia prediction models have been developed, but only few have been externally validated, which hinders clinical uptake and may pose a risk if models are applied to actual patients regardless. Externally validating an existing prediction model is a difficult task, where we mostly rely on the completeness of model reporting in a published article.In this study, we aim to externally validate existing dementia prediction models. To that end, we define model reporting criteria, review published studies, and externally validate three well reported models using routinely collected health data from administrative claims and electronic health records.MethodsWe identified dementia prediction models that were developed between 2011 and 2020 and assessed if they could be externally validated given a set of model criteria. In addition, we externally validated three of these models (Walters’ Dementia Risk Score, Mehta’s RxDx-Dementia Risk Index, and Nori’s ADRD dementia prediction model) on a network of six observational health databases from the United States, United Kingdom, Germany and the Netherlands, including the original development databases of the models.ResultsWe reviewed 59 dementia prediction models. All models reported the prediction method, development database, and target and outcome definitions. Less frequently reported by these 59 prediction models were predictor definitions (52 models) including the time window in which a predictor is assessed (21 models), predictor coefficients (20 models), and the time-at-risk (42 models). The validation of the model by Walters (development c-statistic: 0.84) showed moderate transportability (0.67–0.76 c-statistic). The Mehta model (development c-statistic: 0.81) transported well to some of the external databases (0.69–0.79 c-statistic). The Nori model (development AUROC: 0.69) transported well (0.62–0.68 AUROC) but performed modestly overall. Recalibration showed improvements for the Walters and Nori models, while recalibration could not be assessed for the Mehta model due to unreported baseline hazard.ConclusionWe observed that reporting is mostly insufficient to fully externally validate published dementia prediction models, and therefore, it is uncertain how well these models would work in other clinical settings. We emphasize the importance of following established guidelines for reporting clinical prediction models. We recommend that reporting should be more explicit and have external validation in mind if the model is meant to be applied in different settings.

Association of Adherence to Endocrine Therapy Among Patients With Breast Cancer and Potential Drug-Drug Interactions

Suboptimal adherence to endocrine therapy (ET) among patients with hormone-receptor-positive breast cancer significantly affects survival outcomes and is associated with higher hospitalization rates and health care costs. Weak adherence to long-term treatments has multiple determinants, including disease characteristics, treatment adverse effects, and patients' attributes, such as age and comorbidities. To examine whether potential drug-drug interactions (PDDI) with tamoxifen or aromatase inhibitor were associated with adherence to ET in patients with early and advanced breast cancer. This cohort study used anonymized health record data of women with breast cancer who received ET in a private observational primary care database. Patients eligible for analysis included women aged 18 years or older who had a reported diagnosis of breast cancer and received ET with tamoxifen or aromatase inhibitor between 1994 and 2021. Data were analyzed 2021. Adherence to ET during a given year was defined by a medication possession ratio of 80% or greater over 1-year prescription periods. PDDI were categorized into absent, minor (a combination to take into account), moderate (combination requiring precautions for use), major (combination not recommended), and contraindicated according to guidelines in the Claude Bernard Drug Database. We used regression models to estimate odds ratios (ORs) and 95% CIs for the associations between adherence and age, baseline comorbidities, PDDI, and adherence to ET during the previous year. A total of 10 863 patients who were prescribed ET for breast cancer were eligible for the analysis (age 70 years or older, 3509 patients [32.3%]). In the tamoxifen cohort (3564 patients), PDDI were reported in 497 of 3670 patients (13.5%) at baseline (moderate, 254 patients [51.1%]; major, 227 patients [45.7%]), 2047 of 4831 patients (42.4%) at year 1, 1127 of 2751 patients (41.0%) at year 2, 761 of 1861 patients (40.9%) at year 3, 376 of 1058 patients (35.5%) at year 4, and 201 of 593 patients (33.9%) at year 5. In the aromatase inhibitor cohort (7299 patients), PDDI were reported in 592 of 7437 patients (8.0%) at baseline (moderate in 588 of 592 patients [99.3%]), which reached 2875 of 9031 patients (31.8%) at year 1 and ranged between 31.4% (1802 of 5730 patients in year 2) and 32.8% (791 of 2411 in year 4) throughout the study period. No association between adherence and PDDI was found in the tamoxifen (OR, 0.99; 95% CI, 0.91-1.08) or aromatase inhibitor (OR, 1.05; 95% CI, 0.95-1.15) cohort. In this cohort of patients with hormone-receptor-positive breast cancer, PDDI with tamoxifen and aromatase inhibitors were not associated with adherence to ET.

Phenotype Algorithms to Identify Hidradenitis Suppurativa Using Real-World Data: Development and Validation Study.

Hidradenitis suppurativa (HS) is a potentially debilitating, chronic, recurring inflammatory disease. Observational databases provide opportunities to study the epidemiology of HS. This study's objective was to develop phenotype algorithms for HS suitable for epidemiological studies based on a network of observational databases. A data-driven approach was used to develop 4 HS algorithms. A literature search identified prior HS algorithms. Standardized databases from the Observational Medical Outcomes Partnership (n=9) were used to develop 2 incident and 2 prevalent HS phenotype algorithms. Two open-source diagnostic tools, CohortDiagnostics and PheValuator, were used to evaluate and generate phenotype performance metric estimates, including sensitivity, specificity, positive predictive value (PPV), and negative predictive value. We developed 2 prevalent and 2 incident HS algorithms. Validation showed that PPV estimates were highest (mean 86%) for the prevalent HS algorithm requiring at least two HS diagnosis codes. Sensitivity estimates were highest (mean 58%) for the prevalent HS algorithm requiring at least one HS code. This study illustrates the evaluation process and provides performance metrics for 2 incident and 2 prevalent HS algorithms across 9 observational databases. The use of a rigorous data-driven approach applied to a large number of databases provides confidence that the HS algorithms can correctly identify HS subjects.

Mapping of snow water equivalent by a deep-learning model assimilating snow observations

To estimate snowpack water storage in mountain basins, this study presents a framework that couples a deep-learning long short-term memory (LSTM) model and a zonal bias-correction approach for assimilating ground snow observations. We explored the framework’s ability for snow water equivalent (SWE) mapping and forecasting in a domain, including the Feather, Yuba-Bear, and American River basins in California’s Sierra Nevada. Through a series of different modeling experiments, performances of the LSTM model and bias-correction approach were independently investigated. The LSTM model trained with meteorological forcing (precipitation and temperature), landscape attributes, and antecedent lagged snow conditions, showed good agreement with SWE reanalysis data (Nash-Sutcliffe efficiency, NSE = 0.99). However, for continuous SWE projection without using SWE reanalysis as the antecedent snow condition, the LSTM model underestimated basin-scale and site-scale SWE (NSE = 0.19), reflecting large discrepancies since model bias accumulates over time. Thus, a zonal inverse-distance-weighting (IDW) bias-correction approach was proposed to assimilate ground observations and correct model trajectory. With the bias correction, SWE estimates were significantly improved (NSE = 0.79). The zonal IDW approach accounts for snow differences across different areas by zonal distance, based on historical snow patterns, showing better predictions than the original IDW, which showed larger biases in complex terrain with a rain shadow. We observed that SWE estimates from the framework were not sensitive to the difference in precipitation and temperature forcings between observation-based data and one-day-ahead weather forecasts, suggesting that the framework can use weather-forecast forcing for snowpack forecasting. Comparison between results from monthly and daily bias corrections showed that increasing bias-correction frequency and integrating more snow observations can further improve SWE estimates. Using the continuous SWE products, analyses of 170 precipitation events showed that snowmelt often occurred when daily temperature was above 3.7 °C, augmenting runoff. The proposed framework leverages ground observations and a deep-learning model to provide daily gridded snowpack estimates, which are important for runoff forecasting and water supply.

Incidence, prevalence, and comorbidities of juvenile idiopathic arthritis in Germany: a retrospective observational cohort health claims database study

BackgroundJuvenile idiopathic arthritis (JIA) describes heterogenous categories of chronic inflammatory rheumatic conditions of unknown origin in children and adolescents. Epidemiological data in the literature vary, depending on geographic location, ethnicity and the case definition used. We evaluated epidemiology, especially that of the categories defined by the International League of Associations for Rheumatology (ILAR).MethodsUsing data from two different longitudinal health claims databases (WIG2 and InGef) from January 1st, 2013 to December 31st, 2019, we looked at patients aged 2 to 15 years old with at least one main inpatient or two secondary inpatient/verified outpatient ICD-10 diagnoses in at least two different quarters within one calendar year. We calculated prevalence and incidence (per 100,000 patients) and extrapolated data to the entire German population, looking at differences in gender and age groups. Additionally, we collected data on “other” not necessary comorbidities in our JIA patient population.ResultsOf the 3–4 million patients in the databases (respectively) in 2018, we found a total of 546 (WIG2) and 849 (InGef) patients that met our JIA case definition, with an incidence of 34 (29–41) and 60 (53–67) and prevalence of 133 (122–145) and 168 (157–179). Both incidence and prevalence throughout the age range were mostly higher in females than males, however the difference between females and males increased with increasing age. Of the ILAR categories, oligoarthritis was the most prevalent (70 and 91 per 100,000), with about half of our JIA patients in this category, followed by undifferentiated arthritis (49 and 56 cases per 100,000) and rheumatoid factor negative (RF-) (31 and 39 per 100,000). Incidence in 2018 was the highest in these three categories. Atopic dermatitis, vasomotor and allergic rhinitis, and uveitis were the pre-defined comorbidities seen most often in both databases.ConclusionThis study provides current incidence and prevalence JIA data in Germany, contributing to knowledge on burden of disease and tools for healthcare planning.

Epidemiology of Non-Hodgkin Lymphoma: Global Patterns of Incidence, Mortality, and Trends

INTRODUCTION The burden of cancer incidence and mortality including non-Hodgkin lymphoma (NHL) is expected to grow worldwide as populations grow, age, and adopt lifestyle behaviors that increase cancer risk, many of which are associated with socioeconomic development. Describing the current and future epidemiologic impact of NHL is crucial to evaluate the therapeutic demand and to inform adequate healthcare resource allocation. However, very few recent studies have described global estimates of non-Hodgkin lymphoma diagnoses, cancer burden, and future projection. In this study, we summarize the current burden, trends, risk factors, prevention, early detection, and survivorship of non-Hodgkin lymphoma that account for approximately 2.9% of the cancer burden among both men and women worldwide. METHODS Estimates of new cancer cases (incidence data) and cancer deaths (mortality data) in the year 2020 were extracted from Global Cancer Observatory (GLOBOCAN) database. GLOBOCAN provides country-specific estimates of cancer incidence, mortality, and prevalence for 185 countries or territories and 36 cancer types by age group and sex. Global geographic variability was assessed using 20 predefined world regions. For US rates, data from National Cancer Institute's (NCI's) Surveillance, Epidemiology, and End Results (SEER) 22 were assessed. All mortality data in the GLOBOCAN database was extracted from the World Health Organization (WHO) mortality database available through 2019. Rates are age-standardized (ASRs) (per 100,000 person-years) using the world Standard Population. We characterize the burden according to the 4-tier Human Development Index (HDI) by the United Nations Development Program. Finally, we provide a prediction of the future burden of NHL in 2040 based on demographic projections, assuming that national rates estimated in 2020 remain constant. RESULTS NHL ranked as the 5th to 9th most common cancer in most countries globally, with an estimated 544,000 new cancer cases and 260,000 cancer deaths in 2020. NHL incidence rates varied by approximately 5-fold in both men and women across WHO areas. The highest age-standardized incidence rates for both sexes (per 100,000) were observed in Australia and New Zealand (12.5) and North America (12), while the lowest rates were in South-Central Asia (2.7), a 4.6-fold difference. Regions with very high HDI had higher rates of NHL (9.3) than regions with medium HDI (3.1) and lower HDI (4.5). However, international variability in NHL mortality rates is smaller: 2.5-fold for both sexes. The lowest mortality rates were in South-Central Asia (1.6) and the highest rates were in Melanesia (4). In the United States, the age-standardized NHL incidence rate (per 100,000) doubled from 1975 (11) to 2007 (21.3) and then stabilized, while the 5-year relative survival rates improved from 47% in 1975 to 73.8% in 2018. These changes have significantly impacted age-adjusted NHL mortality rates (per 100,000), which were 5.6 in 1975, peaked at 8.9 in 1997, and have been decreasing since that time, most recently at 5 in 2019. Similar broad temporal trend patterns were observed in other western countries. In Asia, NHL incidence rates have not plateaued, and have shown rises in Japan during 1993-2010 and in Republic of Korea during 1999-2012. Worldwide, an estimated 779,000 new NHL cases are projected to occur in 2040, a 43% increase from the corresponding 544,000 cases in 2020, assuming national rates remain constant. CONCLUSION There is marked variation in NHL incidence rates among different populations of the world. NHL incidence and mortality rates have steadily increased in the United States and other populations over the past few decades, making NHL an increasingly important contributor to the global cancer burden. This epidemiologic assessment also reveals substantial growth in new NHL diagnoses over the next two decades. Keeping track of new cases, deaths, trends, and survival over time helps us understand where progress is made and where additional research is needed to address challenges. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Global Carbon Budget 2022

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the year 2021, EFOS increased by 5.1 % relative to 2020, with fossil emissions at 10.1 ± 0.5 GtC yr−1 (9.9 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 1.1 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission (including the cement carbonation sink) of 10.9 ± 0.8 GtC yr−1 (40.0 ± 2.9 GtCO2). Also, for 2021, GATM was 5.2 ± 0.2 GtC yr−1 (2.5 ± 0.1 ppm yr−1), SOCEAN was 2.9 ± 0.4 GtC yr−1, and SLAND was 3.5 ± 0.9 GtC yr−1, with a BIM of −0.6 GtC yr−1 (i.e. the total estimated sources were too low or sinks were too high). The global atmospheric CO2 concentration averaged over 2021 reached 414.71 ± 0.1 ppm. Preliminary data for 2022 suggest an increase in EFOS relative to 2021 of +1.0 % (0.1 % to 1.9 %) globally and atmospheric CO2 concentration reaching 417.2 ppm, more than 50 % above pre-industrial levels (around 278 ppm). Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2021, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use change emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extratropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set. The data presented in this work are available at https://doi.org/10.18160/GCP-2022 (Friedlingstein et al., 2022b).

Central Retinal Vein Occlusion 36-Month Outcomes with Anti-VEGF: The Fight Retinal Blindness! Registry

To analyze the 3-year outcomes in a broad population of patients starting VEGF inhibitors for central retinal vein occlusion (CRVO) in routine clinical practice. Observational database study. Overall, 527 treatment-naïve CRVO eyes that commenced VEGF inhibitors between December 1, 2010 and 2018 were tracked in the Fight Retinal Blindness! registry. Longitudinal models were used to plot changes in visual acuity (VA) and central subfield thickness (CST). Mean change in VA from baseline to 36 months, injections, visits, completion, switching, and suspensions of therapy > 180 days at the final review. Overall (527 eyes) mean VA change (95% confidence interval [CI]) was+ 10 (7, 12) letters, 37% had final VA ≥ 70 and 30% ≤ 35 letters, mean CST changed-306 μm. Completers (257/527, 49%) had mean 36-month changes in VA and CST of+ 12 letters and-324 μm with a median of 18 injections at 26 visits. The adjusted mean VA change was similar to each VEGF inhibitor (mean,+ 11.4 letters) despite a greater reduction in CST with aflibercept (-310 μm) versus ranibizumab (-258 μm) versus bevacizumab (-216 μm; P < 0.001). Eyes with baseline VA that was trial-eligible (19-73 letters; 356/527, 68%) gained 7 letters, very poor (< 19 letters; 129/527, 24%) gained 22 letters, or very good (> 73 letters; 42/527, 8%) lost 7 letters. Switching (160/527, 30%) was most often to aflibercept (79 eyes). By using suspensions and discontinuation reasons, we identified similar proportions had ceased therapy (154/527, 29%) and were still receiving it at 36 months (165/527, 31%). Only 62/527 eyes (12%) had resolution of macular edema without treatment for > 6 months. Patients with CRVO that commenced VEGF inhibitors in routine care for whom follow-up was available had VA improvements of around 12 letters at 3 years, but with > 50% lost to follow-up, the VA outcome for the entire group was likely worse. The choice of VEGF inhibitor influenced CST but not VA outcomes. We estimated that around half of the eyes were still receiving injections after 36 months. Proprietary or commercial disclosure may be found after the references.

Using cloud radar to investigate the effect of rainfall on migratory insect flight

Abstract The fate of migrating insects that encounter rainfall in flight is a critical consideration when modelling insect movement, but few field observations of this common phenomenon have ever been collected due to the logistical challenges of witnessing these encounters. Operational cloud radars have been deployed around the world by meteorological agencies to study precipitation physics, and as a byproduct, provide a rich database of insect observations that is freely available to researchers. Although considered unwanted ‘clutter’ by the meteorologists who collect the data, the analysis method presented here enables ecologists to delineate co‐occurring signals from insects and raindrops. We present a method that uses image processing techniques on cloud radar velocity spectra to examine the fate of migrating insects when they encounter precipitation. By analysing velocity spectra, we can distinguish flying insects from falling rain and compare the relative density of insects in flight before, during and after the rainfall. We demonstrate the method on a case of insect migration in Oklahoma, USA. Using this method, we show the first reconstructed images of migrating insect layers in flight during rainfall. Our analysis shows that mild to moderate rainfall diminishes the number of insects aloft but does not cause full termination of migratory flight, as has previously been suggested. We hope this technique will spur further investigations of how changing weather conditions impact insect migration, and enable some of the first of such studies in regions of the world that are underrepresented in the literature.

Physician consultation rates and characteristics among workers with chronic pain or headache who participated in a behavioural change program: a retrospective database analysis using real-world healthcare data

ObjectiveTo assess the potential benefit of a behavioural change programme in working individuals with chronic pain or headache, in the form of increased physician consultation.DesignRetrospective observational database study.SettingMembers of employment-based...

The 2021 Loyalty Islands Earthquake (Mw 7.7): Tsunami Waveform Inversion and Implications for Tsunami Forecasting for New Zealand

AbstractA tsunamigenic earthquake with thrust faulting mechanism occurred southeast of the Loyalty Islands, New Caledonia, in the Southern Vanuatu subduction zone on the 10th of February 2021. The tsunami was observed at coastal gauges in the surrounding islands and in New Zealand. The tsunami was also recorded at a new DART network designed to enhance the tsunami forecasting capability of the Southwestern Pacific. We used the tsunami waveforms in an inversion to estimate the fault slip distribution. The estimated major slip region is located near the trench with maximum slip of 4 m. This source model with an assumed rupture velocity of 1.0 km/s can reproduce the observed seismic waves. We evaluated two tsunami forecasting approaches for coastal regions in New Zealand: selecting a pre‐computed scenario, and interpolating between two pre‐computed scenarios. For the evaluation, we made a reference map of tsunami threat levels in New Zealand using the estimated source model. The results show that the threat level maps from the pre‐computed Mw 7.7 scenario located closest to the epicenter, and from an interpolation of two scenarios, match the reference threat levels in most coastal regions. Further improvements to enhance the system toward more robust warnings include expansion of scenario database and incorporation of tsunami observation around tsunami source regions. We also report on utilization of the coastal gauge and DART station data for updating forecasts in real‐time during the event and discuss the differences between the rapid‐response forecast and post‐event retrospective forecasts.

Multidecadal changes in biology influence the variability of the North Atlantic carbon sink

The North Atlantic Ocean is the most intense marine sink for anthropogenic carbon dioxide (CO2) in the world’s oceans, showing high variability and substantial changes over recent decades. However, the contribution of biology to the variability and trend of this sink is poorly understood. Here we use in situ plankton measurements, alongside observation-based sea surface CO2 data from 1982 to 2020, to investigate the biological influence on the CO2 sink. Our results demonstrate that long term variability in the CO2 sink in the North Atlantic is associated with changes in phytoplankton abundance and community structure. These data show that within the subpolar regions of the North Atlantic, phytoplankton biomass is increasing, while a decrease is observed in the subtropics, which supports model predictions of climate-driven changes in productivity. These biomass trends are synchronous with increasing temperature, changes in mixing and an increasing uptake of atmospheric CO2 in the subpolar North Atlantic. Our results highlight that phytoplankton play a significant role in the variability as well as the trends of the CO2 uptake from the atmosphere over recent decades.

Commentary on the First-Line Support for Assistance in Breathing in Children Trials on Noninvasive Respiratory Support: Taking a Closer Look.

Noninvasive respiratory support modalities such as high-flow nasal cannula (HFNC) therapy and continuous positive airway pressure (CPAP) are used frequently in pediatric critical care to support acutely ill children with respiratory failure (step-up management) and children following extubation (step-down management). Although there are several observational studies and database analyses comparing the efficacy of HFNC and CPAP, and a few small randomized clinical trials (RCTs), until recently, there were no large RCTs comparing the two modalities in a mixed group of critically ill children. In the first half of 2022, results from the First-Line Support for Assistance in Breathing in Children (FIRST-ABC) trials were published; these comprised a master protocol of two trials: one in acutely ill children (step-up RCT) and one in extubated children (step-down RCT). Each of these pragmatic trials randomized 600 children to either HFNC or CPAP when the treating clinician decided that noninvasive respiratory support beyond standard oxygen therapy was required. The primary outcome was time to liberation from all forms of respiratory support (invasive and noninvasive), excluding supplemental oxygen. The FIRST-ABC trials represent a significant advance in the field of noninvasive respiratory support, which has traditionally been evidence-poor and associated with considerable variability in clinical practice. In this article, we provide an overview of how the FIRST-ABC trials were conceived and conducted, our view on the results, and how the trial findings have changed our clinical practice.

Intraoperative subconjunctival steroid reduces the incidence of pseudophakic macular oedema: a cohort study of 20,066 consecutive phacoemulsification surgeries.

Pseudophakic macular oedema remains the most common sight-threatening complication following cataract surgery. This study aims to assess the effect of intraoperative subconjunctival steroids on the rate of pseudophakic cystoid macular oedema. A retrospective, observational database study of 20 066 consecutive phacoemulsification surgeries. The incidence of pseudophakic cystoid macular oedema was compared in eyes that did and did not receive intraoperative subconjunctival steroid injection during routine cataract surgery. Intraoperative subconjunctival injection of dexamethasone or betamethasone sodium phosphate significantly reduced the odds of developing pseudophakic cystoid macular oedema across the cohort (odds ratio: 0.67; 95% confidence interval: 0.46-0.98, p = 0.039). The effect of subconjunctival steroids on pseudophakic macular oedema remained independently associated on multivariate logistic regression analysis (p = 0.028). This study demonstrates that administration of intraoperative subconjunctival steroid injection is associated with a reduced incidence of pseudophakic cystoid macular oedema in routine, uncomplicated cataract surgery.

Putting external validation performance of major bleeding risk models into context

When developing predictive models, model simplicity and performance often need to be balanced. We propose a novel methodology to put the performance of bleeding risk prediction models ORBIT, ATRIA, HAS-BLED, CHADS2, and CHA2DS2-VASc into perspective. Instead of comparing the existing models’ performance against the 0.5–1 AUROC scale, we suggest estimating a prediction task specific AUROC scale, lower bound AUROC (lbAUROC) and upper bound AUROC (ubAUROC), to help assess the balance between model simplicity and performance and determine whether more complex models could significantly improve the ability to predict the outcome. We validate the existing bleeding risk prediction models by applying them to a cohort of new users of warfarin and a cohort of new users of direct oral anticoagulants (DOACs) separately, across a set of four observational databases. Then, we develop the lbAUROC-ubAUROC scale by using the validation data to train regularized logistic regression models. The internal validation AUROC of the model that includes only age and gender variables was used to estimate the lbAUROC. The internal validation AUROC of the model that includes thousands of candidate variables was used to estimate the ubAUROC. The age and gender only models achieved AUROCs between 0.50 and 0.56 (lower bound) and the large-scale models achieved AUROCs between 0.67 and 0.72 and between 0.70 and 0.77 (upper bound) within the target cohorts of warfarin new users and DOACs new users, respectively. The AUROC of existing bleeding risk prediction models fall between the upper-bound and lower-bound of predictive models. Our study showed that this context of the predictability of the outcome is essential when evaluating risk prediction models to be administered in actual practice.

Linkage Between Databases in Joint Arthroplasty and Orthopaedics: The Way Forward?

Each large observational database contains specific data elements. The number of data elements are chosen carefully to cover the essential needs of the database as well as to avoid excessive burden of collection. Frequently, an important study question cannot be answered because one database does not contain some essential data elements. This deficiency may be present because the proposed study is cross-disciplinary, because the study requires more granular information on a specific topic than is practical to collect in a broad-based registry, or because the relevant questions, and hence essential data elements, have changed over time. An obvious way to overcome some such challenges, when one database contains some of the information and another contains the further needed data, is to link different databases. While the prospect of linking databases is appealing, the practicalities of doing so often are daunting. Challenges may be practical (information-technology barriers to crosstalk between the registries), legal, and financial. In the first section of this paper, Dr. Nathanael Heckmann discusses linking large orthopaedic databases, focusing on linking databases with detailed, short-term data to those with longer-term longitudinal data. In the second part of this paper, Nathan Glusenkamp discusses efforts to link the American Joint Replacement Registry (AJRR) to other data sources, an ambition not yet fully realized but one that will bear fruit in the near future.

Optimizing Use of Large Databases in Joint Arthroplasty and Orthopaedics.

The potential users of "big data" need to consider many factors when choosing whether to use a large observational database for their research question and, if so, which database is the best fit for the scientific question. The first section of this paper, written by Dr. James A. Browne, provides a framework (who, what, where, when, and why?) to assess the critical elements that are included in a large database, which allows the user to determine if interrogation of the data is likely to answer the research question. The next section of this paper, written by Dr. Bryan Springer, focuses on the importance of having an a priori research question before deciding the best data source to answer the question; it also elaborates on the differences between administrative databases and clinical databases. The final section of the paper, written by Dr. Kurt P. Spindler, reviews the concepts of hypothesis-generating and hypothesis-testing studies and discusses in detail the differences, strengths, limitations, and appropriate uses of observational data versus randomized controlled trials.

A probabilistic framework to model bond strength between concrete and GFRP rebar using Bayesian linear regression

This study was aimed at developing a probabilistic model using Bayesian linear regression approach to examine bond strength behavior between concrete and GFRP rebars. This probabilistic model presents a quantitative description of the epistemic uncertainties of model parameters and model error, which decrease as new information is received. By using the problem mechanics, variables were selected and explanatory functions were developed. Then, based on an observational database including 405 beam test specimens, 47 candidate models were selected based on different combinations of explanatory variables, and the fittest model was selected based on prediction quality, error non-normality, scattering non-homogeneity, residual correlation, and nonlinearity. Afterward, Bayesian regression was used to obtain the probability distribution of the model parameters, and model error was obtained using Bayesian regression. The paper continues by an in-depth explanation of the observation selection process, model development, model diagnostic, step-by-step model reduction process with the removal of ineffective parameters. The analysis results specified the bar position and transverse reinforcement ratio parameters as ineffective parameters. In addition, a comparison between deterministic and probabilistic models showed that contrary to the deterministic model, the probabilistic one could capture uncertainty in the model.

Voluntary Firearm Divestment and Suicide Risk: Real-World Importance in the Absence of Causal Identification.

Voluntary Firearm Divestment and Suicide Risk: Real-World Importance in the Absence of Causal Identification.