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To evaluate how often newly developed risk prediction models undergo external validation and how well they perform in such validations. We reviewed derivation studies of newly proposed risk models and their subsequent external validations. Study characteristics, outcome(s), and models' discriminatory performance [area under the curve, (AUC)] in derivation and validation studies were extracted. We estimated the probability of having a validation, change in discriminatory performance with more stringent external validation by overlapping or different authors compared to the derivation estimates. We evaluated 127 new prediction models. Of those, for 32 models (25%), at least an external validation study was identified; in 22 models (17%), the validation had been done by entirely different authors. The probability of having an external validation by different authors within 5 years was 16%. AUC estimates significantly decreased during external validation vs. the derivation study [median AUC change: −0.05 (P < 0.001) overall; −0.04 (P = 0.009) for validation by overlapping authors; −0.05 (P < 0.001) for validation by different authors]. On external validation, AUC decreased by at least 0.03 in 19 models and never increased by at least 0.03 (P < 0.001). External independent validation of predictive models in different studies is uncommon. Predictive performance may worsen substantially on external validation.

Phosphorus (P) is an important substance for the growth of phytoplankton and an efficient index to assess the water quality. However, estimation of the TP concentration in waters by remote sensing must be associated with optical substances such as the chlorophyll-a (Chla) and the suspended particulate matter (SPM). Based on the good correlation between the suspended inorganic matter (SPIM) and P in Lake Hongze, we used the direct and indirect derivation methods to develop algorithms for the total phosphorus (TP) estimation with the MODIS/Aqua data. Results demonstrate that the direct derivation algorithm based on 645 nm and 1240 nm of the MODIS/Aqua performs a satisfied accuracy (R2 = 0.75, RMSE = 0.029mg/L, MRE = 39% for the training dataset, R2 = 0.68, RMSE = 0.033mg/L, MRE = 47% for the validate dataset), which is better than that of the indirect derivation algorithm. The 645 nm and 1240 nm of MODIS are the main characteristic band of the SPM, so that algorithm can effectively reflect the P variations in Lake Hongze. Additionally, the ratio of the TP to the SPM is positively correlated with the accuracy of the algorithm as well. The proportion of the SPIM in the SPM has a complex effect on the accuracy of the algorithm. When the SPIM accounts for 78%, the algorithm achieves the highest accuracy. Furthermore, the performance of this direct derivation algorithm was examined in two inland lakes in China (Lake Nanyi and Lake Chaohu), it derived the expected P distribution in Lake Nanyi whereas the algorithm failed in Lake Chaohu. Different water properties influence significantly the accuracy of this direct derivation algorithm, while the TP, Chla, and suspended particular inorganic matter (SPOM) of Lake Chaohu are much higher than those of the other two lakes, thus it is difficult to estimate the TP concentration by a simple band combination in Lake Chaohu. Although the algorithm depends on the dataset used in the development, it usually presents a good estimation for those waters where the SPIM dominated, especially when the SPIM accounts for 60% to 80% of the SPM. This research proposed a direct derivation algorithm for the TP estimation for the turbid lake and will provide a theoretical and practical reference for extending the optical remote sensing application and the TP empirical algorithm of Lake Hongze’s help for the local government management water quality.

Coronary artery inflammation inhibits adipogenesis in adjacent perivascular fat. A novel imaging biomarker—the perivascular fat attenuation index (FAI)—captures coronary inflammation by mapping spatial changes of perivascular fat attenuation on coronary computed tomography angiography (CTA). However, the ability of the perivascular FAI to predict clinical outcomes is unknown. In the Cardiovascular RISk Prediction using Computed Tomography (CRISP-CT) study, we did a post-hoc analysis of outcome data gathered prospectively from two independent cohorts of consecutive patients undergoing coronary CTA in Erlangen, Germany (derivation cohort) and Cleveland, OH, USA (validation cohort). Perivascular fat attenuation mapping was done around the three major coronary arteries—the proximal right coronary artery, the left anterior descending artery, and the left circumflex artery. We assessed the prognostic value of perivascular fat attenuation mapping for all-cause and cardiac mortality in Cox regression models, adjusted for age, sex, cardiovascular risk factors, tube voltage, modified Duke coronary artery disease index, and number of coronary CTA-derived high-risk plaque features. Between 2005 and 2009, 1872 participants in the derivation cohort underwent coronary CTA (median age 62 years [range 17–89]). Between 2008 and 2016, 2040 patients in the validation cohort had coronary CTA (median age 53 years [range 19–87]). Median follow-up was 72 months (range 51–109) in the derivation cohort and 54 months (range 4–105) in the validation cohort. In both cohorts, high perivascular FAI values around the proximal right coronary artery and left anterior descending artery (but not around the left circumflex artery) were predictive of all-cause and cardiac mortality and correlated strongly with each other. Therefore, the perivascular FAI measured around the right coronary artery was used as a representative biomarker of global coronary inflammation (for prediction of cardiac mortality, hazard ratio [HR] 2·15, 95% CI 1·33–3·48; p=0·0017 in the derivation cohort, and 2·06, 1·50–2·83; p<0·0001 in the validation cohort). The optimum cutoff for the perivascular FAI, above which there is a steep increase in cardiac mortality, was ascertained as −70·1 Hounsfield units (HU) or higher in the derivation cohort (HR 9·04, 95% CI 3·35–24·40; p<0·0001 for cardiac mortality; 2·55, 1·65–3·92; p<0·0001 for all-cause mortality). This cutoff was confirmed in the validation cohort (HR 5·62, 95% CI 2·90–10·88; p<0·0001 for cardiac mortality; 3·69, 2·26–6·02; p<0·0001 for all-cause mortality). Perivascular FAI improved risk discrimination in both cohorts, leading to significant reclassification for all-cause and cardiac mortality. The perivascular FAI enhances cardiac risk prediction and restratification over and above current state-of-the-art assessment in coronary CTA by providing a quantitative measure of coronary inflammation. High perivascular FAI values (cutoff ≥–70·1 HU) are an indicator of increased cardiac mortality and, therefore, could guide early targeted primary prevention and intensive secondary prevention in patients. British Heart Foundation, and the National Institute of Health Research Oxford Biomedical Research Centre.

Cardiac surgery is associated with a high risk of postoperative acute kidney injury (AKI) and subsequent loss of kidney function. We explored the clinical utility of urinary dickkopf-3 (DKK3), a renal tubular stress marker, for preoperative identification of patients at risk for AKI and subsequent kidney function loss. This observational cohort study included patients who had cardiac surgery in a derivation cohort and those who had cardiac surgery in a validation cohort (RenalRIP trial). The study comprised consecutive patients who had elective cardiac surgery at the Saarland University Medical Centre (Homburg, Germany; derivation cohort) and those undergoing elective cardiac surgery (selected on the basis of a Cleveland Clinical Foundation score of 6 or higher) who were enrolled in the prospective RenalRIP multicentre trial (validation cohort) and who were randomly assigned to remote ischaemic preconditioning or a sham procedure. The association between the ratio of preoperative urinary concentrations of DKK3 to creatinine (DKK3:creatinine) and postoperative AKI, defined according to the Kidney Disease Improving Global Outcomes criteria, and subsequent kidney function loss, as determined by estimated glomerular filtration rate, was assessed. In the 733 patient in the derivation cohort, urinary concentrations of DKK3 to creatinine that were higher than 471 pg/mg were associated with significantly increased risk for AKI (odds ratio [OR] 1·65, 95% CI 1·10–2·47, p=0·015), independent of baseline kidney function. Compared with clinical and other laboratory measurements, urinary concentrations of DKK3:creatinine significantly improved AKI prediction (net reclassification improvement 0·32, 95% CI 0·23–0·42, p<0·0001). High urinary DKK3:creatinine concentrations were independently associated with significantly lower kidney function at hospital discharge and after a median follow-up of 820 days (IQR 733–910). In the RenalRIP trial, preoperative urinary DKK3:creatinine concentrations higher than 471 pg/mg were associated with a significantly higher risk for AKI (OR 1·94, 95% CI 1·08–3·47, p=0·026), persistent renal dysfunction (OR 6·67, 1·67–26·61, p=0·0072), and dialysis dependency (OR 13·57, 1·50–122·77, p=0·020) after 90 days compared with DKK3:creatinine concentrations of 471 pg/mg or less. Urinary DKK3:creatinine concentrations higher than 471 pg/mg were associated with significantly higher risk for AKI (OR 2·79, 95% CI 1·45–5·37) and persistent renal dysfunction (OR 3·82, 1·32–11·05) only in patients having a sham procedure, but not remote ischaemic preconditioning (AKI OR 1·35, 0·76–2·39 and persistent renal dysfunction OR 1·05, 0·12–9·45). Preoperative urinary DKK3 is an independent predictor for postoperative AKI and for subsequent loss of kidney function. Urinary DKK3 might aid in the identification of patients in whom preventive treatment strategies are effective. No study funding.

OBJECTIVES:To validate and compare performance of an International Classification of Diseases, tenth revision (ICD-10) version of a combined comorbidity index merging conditions of Charlson and Elixhauser measures against individual measures in the prediction of 30-day mortality. To select a weight derivation method providing optimal performance across ICD-9 and ICD-10 coding systems. RESEARCH DESIGN:Using 2 adult population-based cohorts of patients with hospital admissions in ICD-9 (2005, n=337,367) and ICD-10 (2011, n=348,820), we validated a combined comorbidity index by predicting 30-day mortality with logistic regression. To appreciate performance of the Combined index and both individual measures, factors impacting indices performance such as population characteristics and weight derivation methods were accounted for. We applied 3 scoring methods (Van Walraven, Schneeweiss, and Charlson) and determined which provides best predictive values. RESULTS:Combined index [c-statistics0.853 (95% confidence intervalCI, 0.848–0.856)] performed better than original Charlson [0.841 (95% CI, 0.835–0.844)] or Elixhauser [0.841 (95% CI, 0.837–0.844)] measures on ICD-10 cohort. All weight derivation methods provided close high discrimination results for the Combined index (Van Walraven0.852, Schneeweiss0.851, Charlson0.849). Results were consistent across both coding systems. CONCLUSIONS:The Combined index remains valid with both ICD-9 and ICD-10 coding systems and the 3 weight derivation methods evaluated provided consistent high performance across those coding systems.

Based on Lambert, Leuz, and Verrecchia's (2007) derivation of the cost of equity capital in terms of expected cash flows, we generate a testable hypothesis that relates tax avoidance to a firm's cost of equity capital. Using three broad measures of tax avoidance—book-tax differences, permanent book-tax differences, and long-run cash effective tax rates—to test our hypothesis, we find that the cost of equity is lower for tax-avoiding firms. This effect is stronger for firms with better outside monitoring, firms that likely realize higher marginal benefits from tax savings, and firms with higher information quality. Overall, our results suggest that equity investors generally require a lower expected rate of return due to the positive cash flow effects of corporate tax avoidance.

The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. In this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Three distinct phenotypes were derived in the derivation cohort (n=2667)—phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])—and reproduced in the internal validation cohort (n=1368)—phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2·5% (95% CI 1·4–4·3) for patients with phenotype A, 30·5% (28·5–32·6) for patients with phenotype B, and 60·7% (53·7–67·2) for patients with phenotype C (log-rank test p<0·0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5·3% [95% CI 3·4–8·1] for phenotype A, 31·3% [28·5–34·2] for phenotype B, and 59·5% [48·8–69·3] for phenotype C; external validation cohort: 3·7% [2·0–6·4] for phenotype A, 23·7% [21·8–25·7] for phenotype B, and 51·4% [41·9–60·7] for phenotype C). Patients admitted to hospital with COVID-19 can be classified into three phenotypes that correlate with mortality. We developed and validated a simplified tool for the probabilistic assignment of patients into phenotypes. These results might help to better classify patients for clinical management, but the pathophysiological mechanisms of the phenotypes must be investigated. Instituto de Salud Carlos III, Spanish Ministry of Science and Innovation, and Fundación SEIMC/GeSIDA.

The benefit of oral anticoagulation in atrial fibrillation is based on a balance between reduction in ischaemic stroke and increase in major bleeding. We aimed to develop and validate a new biomarker-based risk score to improve the prognostication of major bleeding in patients with atrial fibrillation. We developed and internally validated a new biomarker-based risk score for major bleeding in 14 537 patients with atrial fibrillation randomised to apixaban versus warfarin in the ARISTOTLE trial and externally validated it in 8468 patients with atrial fibrillation randomised to dabigatran versus warfarin in the RE-LY trial. Plasma samples for determination of candidate biomarker concentrations were obtained at randomisation. Major bleeding events were centrally adjudicated. The predictive values of biomarkers and clinical variables were assessed with Cox regression models. The most important variables were included in the score with weights proportional to the model coefficients. The ARISTOTLE and RE-LY trials are registered with ClinicalTrials.gov, numbers NCT00412984 and NCT00262600, respectively. The most important predictors for major bleeding were the concentrations of the biomarkers growth differentiation factor-15 (GDF-15), high-sensitivity cardiac troponin T (cTnT-hs) and haemoglobin, age, and previous bleeding. The ABC-bleeding score (age, biomarkers [GDF-15, cTnT-hs, and haemoglobin], and clinical history [previous bleeding]) score yielded a higher c-index than the conventional HAS-BLED and the newer ORBIT scores for major bleeding in both the derivation cohort (0·68 [95% CI 0·66–0·70] vs 0·61 [0·59–0·63] vs 0·65 [0·62–0·67], respectively; ABC-bleeding vs HAS-BLED p<0·0001 and ABC-bleeding vs ORBIT p=0·0008). ABC-bleeding score also yielded a higher c-index score in the the external validation cohort (0·71 [95% CI 0·68–0·73] vs 0·62 [0·59–0·64] for HAS-BLED vs 0·68 [0·65–0·70] for ORBIT; ABC-bleeding vs HAS-BLED p<0·0001 and ABC-bleeding vs ORBIT p=0·0016). A modified ABC-bleeding score using alternative biomarkers (haematocrit, cTnI-hs, cystatin C, or creatinine clearance) also outperformed the HAS-BLED and ORBIT scores. The ABC-bleeding score, using age, history of bleeding, and three biomarkers (haemoglobin, cTn-hs, and GDF-15 or cystatin C/CKD-EPI) was internally and externally validated and calibrated in large cohorts of patients with atrial fibrillation receiving anticoagulation therapy. The ABC-bleeding score performed better than HAS-BLED and ORBIT scores and should be useful as decision support on anticoagulation treatment in patients with atrial fibrillation. BMS, Pfizer, Boehringer Ingelheim, Roche Diagnostics.

Contrast-associated acute kidney injury can occur after percutaneous coronary intervention (PCI). Prediction of the contrast-associated acute kidney injury risk is important for a tailored prevention and mitigation strategy. We sought to develop a simple risk score to estimate contrast-associated acute kidney injury risk based on a large contemporary PCI cohort. Consecutive patients undergoing PCI at a large tertiary care centre between Jan 1, 2012, and Dec 31, 2020, with available creatinine measurements both before and within 48 h after the procedure, were included; only patients on chronic dialysis were excluded. Patients treated between 2012 and 2017 comprised the derivation cohort and those treated between 2018 and 2020 formed the validation cohort. The primary endpoint was contrast-associated acute kidney injury, defined according to the Acute Kidney Injury Network. Independent predictors of contrast-associated acute kidney injury were derived from multivariate logistic regression analysis. Model 1 included only pre-procedural variables, whereas Model 2 also included procedural variables. A weighted integer score based on the effect estimate of each independent variable was used to calculate the final risk score for each patient. The impact of contrast-associated acute kidney injury on 1-year deaths was also evaluated. 32 378 PCI procedures were performed and screened for inclusion in the present analysis. After the exclusion of patients without paired creatinine measurements, patients on chronic dialysis, and multiple procedures, 14 616 patients were included in the derivation cohort (mean age 66·2 years, 29·2% female) and 5606 were included in the validation cohort (mean age 67·0 years, 26·4% female). Contrast-associated acute kidney injury occurred in 860 (4·3%) patients. Independent predictors of contrast-associated acute kidney injury included in Model 1 were: clinical presentation, estimated glomerular filtration rate, left ventricular ejection fraction, diabetes, haemoglobin, basal glucose, congestive heart failure, and age. Additional independent predictors in Model 2 were: contrast volume, peri-procedural bleeding, no flow or slow flow post procedure, and complex PCI anatomy. The occurrence of contrast-associated acute kidney injury in the derivation cohort increased gradually from the lowest to the highest of the four risk score groups in both models (2·3% to 34·9% in Model 1, and 2·0% to 38·8% in Model 2). Inclusion of procedural variables in the model only slightly improved the discrimination of the risk score (C-statistic in the derivation cohort: 0·72 for Model 1 and 0·74 for model 2; in the validation cohort: 0·84 for Model 1 and 0·86 for Model 2). The risk of 1-year deaths significantly increased in patients with contrast-associated acute kidney injury (10·2% vs 2·5%; adjusted hazard ratio 1·76, 95% CI 1·31–2·36; p=0·0002), which was mainly due to excess 30-day deaths. A contemporary simple risk score based on readily available variables from patients undergoing PCI can accurately discriminate the risk of contrast-associated acute kidney injury, the occurrence of which is strongly associated with subsequent death. None.

The study focuses on the morphological problems that Saudi students face when translating. The researcher employed a descriptive-analytical strategy to uncover morphological problems. The information on the research problem was obtained from a group of thirty undergraduate students from Al Baha University's College of Arts in Beljurshi, who were mostly boys enrolled in the students' second year of 2019, majoring in English and using written Arabic text. Following the data analysis, morphological flaws were revealed. According to the data, the inflectional morpheme obtained a score of 144 errors with an error rate of 88 percent, while the derivation morpheme received a score of 19 errors with a 19 rate. The study determines that the existence of errors in the inflectional morpheme is greater than errors in the derivation morpheme; the mistakes were brought about by an inability to grasp the contrasts between the languages of English and Arabic, as well as a scarcity of fluency in the language. The researcher suggests that the instructor provide more explanations on how to use inflection and derivation morphemes, as well as add comparative analysis periods to the translation field, to help students learn and practice morphology.