Inflation Forecasting Post‐COVID‐19: Evidence From Germany

The first-line treatment of obstructive sleep apnea syndrome in children is adenotonsillectomy, but this may result in perioperative respiratory adverse events (PRAEs). The primary aim of this study is to examine whether the McGill oximetry score (MOS) and other polysomnography parameters can predict major PRAEs following adenotonsillectomy. We secondarily evaluated the MOS interrater reliability and correlation with other polysomnography parameters. This retrospective study included all children aged 0-18 years who underwent preoperative polysomnography between June 2010 and January 2016 prior to adenotonsillectomy at a tertiary pediatric institution. Oximetries from polysomnograms were assigned an MOS. Univariable and multivariable models for prediction of major PRAEs were constructed. MOS was correlated with polysomnography parameters and interrater reliability was evaluated. This study included 106 children; 15 had a major PRAE. A multivariable prediction model that combined MOS and age showed evidence for the ability to predict major PRAEs with an area under the receiver operating characteristic curve of 0.68 (95% confidence interval: 0.52, 0.84), whereby increased MOS and younger age were associated with PRAEs, but apnea-hypopnea index was not. MOS had excellent interrater reliability (κ = 0.95) and was highly correlated with oxygen saturation nadir and cumulative time percentage with oxygen saturation less than 90%. A prediction model including MOS and age may predict PRAEs following adenotonsillectomy. This suggests that nocturnal oximetry provides the most essential information of polysomnography measures to direct postoperative monitoring following adenotonsillectomy. Xiao L, Barrowman N, Momoli F, etal. Polysomnography parameters as predictors of respiratory adverse events following adenotonsillectomy in children. J Clin Sleep Med. 2021;17(11):2215-2223.

This study aimed to evaluate the impact of race on in-hospital outcomes of Takotsubo cardiomyopathy using the National Inpatient Sample. We conducted a retrospective study using data from the National Inpatient Sample database 2006 to 2018. We focused on Takotsubo cardiomyopathy hospitalizations, excluding those with acute coronary syndrome as the primary diagnosis. Two study groups consisted of White patients or Black patients. Univariate and multivariable logistic models evaluated race's effect on death, cardiac arrest, cardiogenic shock, length of stay, while adjusting for potential confounders. The Bayesian model averaging technique was used to further elucidate the factors influencing death within each racial group. Significant differences were observed between the 2 racial groups. Black patients presented at a younger age, had a higher proportion of men, a higher burden of comorbidities, and a lower median household income compared with their White counterparts. In the univariate model, the Black cohort showed an increased risk of cardiac arrest (odds ratio, 1.45 [95% CI, 1.15-1.82]). However, the difference did not reach statistical significance in the multivariable model. Black patients also had a significantly longer hospital stay in both the univariate model (risk ratio, 1.26 [95% CI, 1.22-1.31]) and the multivariable model (risk ratio, 1.06 [95% CI, 1.04-1.07]). No significant difference in all-cause death was observed between the racial groups. The outcome differences between 2 racial groups in our study are likely influenced by racial disparities in demographics, comorbidities, and socioeconomic factors. Individualized care based on racial group needs is crucial in clinical practice.

The energy demand is increasing due to population growth and economic development. To satisfy this energy demand, the use of renewable energy is essential to face global warming and the depletion of fossil fuels. Photovoltaic energy is one of the renewable energy sources, widely used by several countries over the world. The integration of PV energy into the grid brings significant benefits to the economy and environment, however, high penetration of this energy also brings some challenges to the stability of the electrical grid, due to the intermittency of solar energy. To overcome this issue, the use of a forecasting system is one of the solutions to guarantee an effective integration of PV plants in the electrical grid. In this paper, a PV power ultra short term forecasting has been done by using univariate and multivariate LSTM models. Different combinations of input variables of the models and different timesteps forecasting were tested and compared. The main aim of this work is to study the influence of the different combinations of variables on the accuracy of the LSTM models for one-step forecasting and multistep forecasting and comparing the univariate and multivariate LSTM models with MLP and CNN models . The results show that for one step forecasting, the use of a univariate model based on historical data of PV output power is sufficient to get accurate forecasting with 28.98W in MAE compared to multivariate models that can reach 35.39W. Meanwhile, for multistep forecasting, it is mandatory to use a multivariate model that has historical data of meteorological variables and PV output power in the input of LSTM model. Moreover, The LSTM model shows great accuracy compared to MLP and CNN especially in multistep PV power forecasting.

Influence of residual normal metaphases in acute myeloid leukemia

Kamis, Kevin F. MPH; Barbera, Lauren MD(c); Abdo, Mona PhD(c); Rowan, Sarah E. MD; Hussain, Cory MD; Gardner, Edward M. MD; Johnson, Steven C. MD; MaWhinney, Samantha ScD; Davis, Amelia J. MD; Carlson, Jesse MPH(c); Kozacka, Katie A. MD; Erlandson, Kristine M. MD, MS Author Information

Prognostic Value of Late Heart Rate Recovery After Treadmill Exercise

ObjectivesSerum ferritin and body mass index (BMI) have been reportedly associated with coronary artery disease (CAD) risk. The aim of the present study was to explore the interaction between serum...

The mixed cumulative probit (MCP), a new, flexible algorithm that accommodates a variety of mean and shape parameters in univariate models and conditional dependence/independence in multivariate models, was used to develop subadult age estimation models. Sixty-two variables were collected on computed tomography (CT) images of 1317 individuals (537 females and 780 males) aged between birth and 21 years from the United States sample in the Subadult Virtual Anthropology Database (SVAD). Long bone measurements (n = 18), stages of epiphyseal fusion and ossification (n = 28), and stages of dental development of permanent teeth (n = 16) were used in univariate, multivariate, and mixed models and compared using test mean log posterior (TMNLP), root mean squared error (RMSE), and percent accuracy on an independent test sample. Out of the six possible parameter combinations, all combinations were accounted for at least once in the data and conditionally dependent models outperformed the conditionally independent models. Overall, multivariate models exhibited smaller TMNLP and RMSE, and an overall greater stability in the age estimations compared to univariate models across all ages and independent of indicator type. Pre-optimized subadult age estimation models are freely available for immediate application through MCP-S-Age, a graphical user interface.

This study intends to determine if the sex of an individual can be identified by foot lengths, shoe lengths, and/or footprints. For this purpose, foot length, foot breadth, and foot heel breadth of 506 subjects, comprising 253 females and 253 males ranging from 17.56 to 82.92 years of age, were taken. In addition, the footprints (length, breadth, and heel breadth) and footwear (length and breadth) of the same subjects were measured. Finally, the shoe size of the subjects was recorded. Univariate and multivariate discriminant function models were developed for sex allocations. Statistical analyses indicated that univariate models correctly assign approximately 67-94% of individuals to their correct sex groups. Among univariate models the most reliable measurement was shoe length. The results of multivariate models were better than those of univariate ones, with an approximately 82-96% correct assignment. The best multivariate model was comprised of four variables: foot length, shoe length, shoe breadth and shoe size. It could be suggested that these discriminant functions can provide useful clues to establish personal identity whenever complete or partial feet, footprints, or footwear are recovered.

Mediators of suicidality 12 years after bariatric surgery relative to a nonsurgery comparison group.

308 Background: The role of chemotherapy for patients with early stage, node-negative gastric adenocarcinoma (T2N0M0) remains unclear. Possible options are surgery alone, surgery followed by adjuvant chemotherapy, or perioperative chemotherapy. The objective of our study was to investigate the role and sequence of chemotherapy in T2N0M0 gastric adenocarcinoma. Methods: The National Cancer Data Base (2006-2017) was used to identify patients with T2N0M0 gastric adenocarcinoma. Patients were categorized into 3 groups, surgery alone (S), surgery followed by adjuvant chemotherapy (AC), and peri-operative chemotherapy (POC). Univariate and multivariable Cox proportional-hazards models were performed to evaluate treatment differences in overall survival (OS). Patients were further sub-categorized into two subgroups based on the number of regional nodes examined, less than 15 (RNE<15) and greater or equal to 15 (RNE>15). Univariate and multivariable Cox proportional-hazards models were performed on each subgroup to assess the treatment differences within two groups. Results: The retrospective study analyzed 3,142 patients between 2006 to 2017. Surgery alone represented 2,090 patients (66.5%), AC group had 360 patients (11.5%), and the POC group 692 patients (22.0%). The median age of the population was 70 yrs. Mean inpatient stay was 12.5 days in S, 9.2 days in AC and 10.1 days in the POC group (p<0.001). In the multivariable model, surgery followed by adjuvant chemotherapy (HR 0.75, p=0.003), RNE>15 (HR 0.73, p<0.001), patients treated at academic facilities (HR 0.84, p=0.005), female sex (HR 0.84, p=0.004), and Asian race (HR 0.69, p=0.002) had better outcomes. There were 1,526 (49.2%) patients with RNE<15 and 1,577 (50.8%) patients with RNE>15. Subgroup analysis showed that peri-operative chemotherapy for RNE<15 was beneficial (HR 0.74, p= 0.029). Patients with RNE>15 had an improved survival with surgery followed by adjuvant chemotherapy (HR 0.72, p= 0.017) in the multivariable model. Conclusions: Surgery followed by adjuvant chemotherapy was found to be beneficial in patients having a T2N0M0 Gastric Cancer. However, the number of lymph nodes examined during surgery influences this association. Perioperative chemotherapy maybe beneficial for patients who were under staged due to inadequate exploration of lymph nodes during surgery.

This paper describes a quantitative approach to understanding the signal, contrast and weighting of magnetic resonance (MR) images. It uses the concept of pulse sequences as tissue property (TP) filters and models the signal, contrast and weighting of sequences using either a single TP-filter (univariate model) or several TP-filters (the multivariate model). For the spin echo (SE) sequence using the Bloch equations, voxel signal intensity is plotted against the logarithm of the value of the TPs contributing to the sequence signal to produce three TP-filters, an exponential ρm-filter, a low pass T1-filter and a high pass T2-filter. Using the univariate model which considers signal changes in only one of ρm, T1, or T2 at a time, the first partial derivative of signal with respect to the natural logarithm of ρm, T1 or T2 is the sequence weighting for each filter (for small changes in each TP). Absolute contrast is then the sequence weighting multiplied by the fractional change in TP for each filter. For large changes in TPs, the same approach is followed, but using the mean slope of the filter as the sequence weighting. These approaches can also be used for fractional contrast. The univariate TP-filter model provides a mathematical framework for converting conventional qualitative univariate weighting as used in everyday clinical practice into quantitative univariate weighting. Using the multivariate model which considers several TP-filters together, the relative contributions of each TP to overall sequence and image weighting are expressed as sequence and imaging weighting ratios respectively. This is not possible with conventional qualitative weighting which is univariate. The same approaches are used for inversion recovery (IR), pulsed gradient SE, spoiled gradient echo (SGE), balanced steady state free precession, ultrashort echo time and other pulse sequences. Other TPs such as susceptibility, chemical shift and flow can be included with phase along the Y axis of the TP-filter. Contrast agent effects are also included. In the text TP-filters are distinguished from k-space filters, signal filters (S-filters) which are used in imaging processing as well as to describe windowing the signal width and level of images, and spatial filters. The TP-filters approach resolves many of the ambiguities and inconsistencies associated with conventional qualitative weighting and provides a variety of new insights into the signal, contrast and weighting of MR images which are not apparent using qualitative weighting. The TP-filter approach relates the preparation component of pulse sequences to voxel signal, and contrast between two voxels. This is complementary to k-space which relates the acquisition component of pulse sequences to the spatial properties of MR images and their global contrast.

The purpse of this study was to evaluate the ability of various radiologic response criteria to predict patient outcomes after trans-arterial chemo-embolization with drug-eluting beads (DEB-TACE) in patients with advanced-stage (BCLC C) hepatocellular carcinoma (HCC). Hospital records from 2005 to 2011 were retrospectively reviewed. Non-infiltrative lesions were measured at baseline and on follow-up scans after DEB-TACE according to various common radiologic response criteria, including guidelines of the World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), the European Association for the Study of the Liver (EASL), and modified RECIST (mRECIST). Statistical analysis was performed to see which, if any, of the response criteria could be used as a predictor of overall survival (OS) or time-to-progression (TTP). 75 patients met inclusion criteria. Median OS and TTP were 22.6months (95% CI 11.6-24.8) and 9.8months (95% CI 7.1-21.6), respectively. Univariate and multivariate Cox analyses revealed that none of the evaluated criteria had the ability to be used as a predictor for OS or TTP. Analysis of the C index in both univariate and multivariate models showed that the evaluated criteria were not accurate predictors of either OS (C-statistic range: 0.51-0.58 in the univariate model; range: 0.54-0.58 in the multivariate model) or TTP (C-statistic range: 0.55-0.59 in the univariate model; range: 0.57-0.61 in the multivariate model). Current response criteria are not accurate predictors of OS or TTP in patients with advanced-stage HCC after DEB-TACE.

Using a set of signal features (e.g. magnetic-field, light, sound), we can create a model to estimate the location of a user in an indoor environment. In our previous work, we proposed a model with 46 features from the temporal and spectral evolution of the magnetic-field, which enables us to estimate the indoor location using the readings of a magnetometer built in a smartphone. Considering our previous results, some questions arise. Firstly, (1) Is it possible to estimate the location of a user in indoor environments with a single feature of the magnetic-field signal (univariate model)?; (2) Which model, univariate or multivariate, has higher performance in terms of accuracy? and (3) Which type of features (frequency/temporal) outweigh the indoor location? To answer these questions we propose an evaluation which consists of four experiments: i) building a model per feature, ii) constructing a model that contains only spectral features; iii) building a model composed by the temporal features, and iv) create a model that combines all features. Our results indicate that a multivariate model has higher accuracy in comparison to the univariate models, however, in the multivariate model a clever feature selection is needed to reduce the number of features required to explain the variance of our multivariate model.

Multivariate analysis helps to understand the relationships between dependent variables; this methodology has great potential in several areas of knowledge. The aim of this study was to adjust and compare the univariate and multivariate Gompertz and Logistic nonlinear models to describe the productive traits of sunn hemp (Crotalaria juncea L.). Two uniformity trials were performed, and the following productive traits were analyzed in 376 sunn hemp plants along 94 days of observations (four plants per day): the fresh mass of leaves (FML), the fresh mass of stem (FMS), and the fresh mass of the aerial parts (FMAP). The Gompertz and Logistic univariate models were adjusted for each productive trait. To adjust the multivariate models, the errors covariance matrix was calculated. The matrix (Cholesky factor) was obtained for each trait, and the multivariate Gompertz (GG) and Logistic (LL) nonlinear models were generated, together with the combination of both models (GL and LG). To define the best model, the residual standard deviation (RSD), the determination coefficient (R2), the Akaike information criterion (AIC), the mean absolute deviation (MAD), and the measures of intrinsic nonlinearity (INL) and parametric nonlinearity (PNL) were calculated. The nonlinear multivariate model LL was adequate and achieved satisfactory results to describe the productive traits of sunn hemp.