Recent Methodological Trends in Epidemiology: No Need for Data-Driven Variable Selection?

Variable selection in regression models is a particularly important issue in epidemiology, where one usually encounters observational studies. In contrast to randomized trials or experiments, confounding is often not controlled by the study design, but has to be accounted for by suitable statistical methods. For instance, when risk factors should be identified with unconfounded effect estimates, multivariable regression techniques can help to adjust for confounders. We investigated the current practice of variable selection in 4 major epidemiologic journals in 2019 and found that the majority of articles used subject-matter knowledge to determine a priori the set of included variables. In comparison with previous reviews from 2008 and 2015, fewer articles applied data-driven variable selection. Furthermore, for most articles the main aim of analysis was hypothesis-driven effect estimation in rather low-dimensional data situations (i.e., large sample size compared with the number of variables). Based on our results, we discuss the role of data-driven variable selection in epidemiology.

Online Compassion Focused Therapy for overeating: Feasibility and acceptability pilot study.

OBJECTIVE: This pilot study aims to investigate the feasibility, acceptability, and potential effectiveness of online Compassion Focused Therapy for overeating (CFT-OE). METHOD: Eighteen Portuguese women seeking treatment for overeating were enrolled in this study, and 15 participants completed the CFT-OE. This was a single-arm study. Participants were assessed at pre- and post-intervention and 3-month follow-up. All participants completed measures assessing binge eating, cognitive restraint, uncontrolled eating, emotional eating, general eating psychopathology, general and body shame, self-criticism, self-compassion, and fears of self-compassion. RESULTS: The treatment attrition rate was 16.7%, which is relatively low compared to other similar online interventions. Participants gave positive feedback on the program and indicated they would recommend it to people with similar difficulties. CFT-OE improved self-compassion and reduced eating psychopathology symptoms, general and body shame, self-criticism, and fears of self-compassion. Clinical significance analysis showed that the majority of participants were classified as in recovery in all measures at post-intervention and 3-month follow-up. DISCUSSION: Preliminary results suggest that the online CFT-OE program is an acceptable and feasible intervention. Results also suggest that CFT-OE is beneficial for the treatment of women with difficulties with overeating. A future randomized controlled trial is necessary to establish the effectiveness of the CFT-OE. PUBLIC SIGNIFICANCE: This study indicates that online CFT-OE is a feasible and adequate intervention for women who struggle with overeating. This therapy showed promising results in reducing eating disorder symptoms, shame, and self-criticism and improving self-compassion. As an online intervention, CFT-OE may be more accessible and offer an alternative to in-person therapy.

Ability of a polygenic risk score to refine colorectal cancer risk in Lynch syndrome.

BACKGROUND: Polygenic risk scores (PRSs) have been used to stratify colorectal cancer (CRC) risk in the general population, whereas its role in Lynch syndrome (LS), the most common type of hereditary CRC, is still conflicting. We aimed to assess the ability of PRS to refine CRC risk prediction in European-descendant individuals with LS. METHODS: 1465 individuals with LS (557 MLH1, 517 MSH2/EPCAM, 299 MSH6 and 92 PMS2) and 5656 CRC-free population-based controls from two independent cohorts were included. A 91-SNP PRS was applied. A Cox proportional hazard regression model with 'family' as a random effect and a logistic regression analysis, followed by a meta-analysis combining both cohorts were conducted. RESULTS: Overall, we did not observe a statistically significant association between PRS and CRC risk in the entire cohort. Nevertheless, PRS was significantly associated with a slightly increased risk of CRC or advanced adenoma (AA), in those with CRC diagnosed <50 years and in individuals with multiple CRCs or AAs diagnosed <60 years. CONCLUSION: The PRS may slightly influence CRC risk in individuals with LS in particular in more extreme phenotypes such as early-onset disease. However, the study design and recruitment strategy strongly influence the results of PRS studies. A separate analysis by genes and its combination with other genetic and non-genetic risk factors will help refine its role as a risk modifier in LS.

The benefit of knowledge: postural response modulation by foreknowledge of equilibrium perturbation in an upper limb task.

For whole-body sway patterns, a compound motor response following an external stimulus may comprise reflexes, postural adjustments (anticipatory or compensatory), and voluntary muscular activity. Responses to equilibrium destabilization may depend on both motor set and a subject`s expectation of the disturbing stimulus. To disentangle these influences on lower limb responses, we studied a model in which subjects (n = 14) were suspended in the air, without foot support, and performed a fast unilateral wrist extension (WE) in response to a passive knee flexion (KF) delivered by a robot. To characterize the responses, electromyographic activity of rectus femoris and reactive leg torque was obtained bilaterally in a series of trials, with or without the requirement of WE (motor set), and/or beforehand information about the upcoming velocity of KF (subject`s expectation). Some fast-velocity trials resulted in StartReact responses, which were used to subclassify leg responses. When subjects were uninformed about the upcoming KF, large rectus femoris responses concurred with a postural reaction in conditions without motor task, and with both postural reaction and postural adjustment when WE was required. WE in response to a low-volume acoustic signal elicited no postural adjustments. When subjects were informed about KF velocity and had to perform WE, large rectus femoris responses corresponded to anticipatory postural adjustment rather than postural reaction. In conclusion, when subjects are suspended in the air and have to respond with WE, the prepared motor set includes anticipatory postural adjustments if KF velocity is known, and additional postural reactions if KF velocity is unknown.

Statistical learning for sparser fine-mapped polygenic models: The prediction of LDL-cholesterol.

Polygenic risk scores quantify the individual genetic predisposition regarding a particular trait. We propose and illustrate the application of existing statistical learning methods to derive sparser models for genome-wide data with a polygenic signal. Our approach is based on three consecutive steps. First, potentially informative loci are identified by a marginal screening approach. Then, fine-mapping is independently applied for blocks of variants in linkage disequilibrium, where informative variants are retrieved by using variable selection methods including boosting with probing and stochastic searches with the Adaptive Subspace method. Finally, joint prediction models with the selected variants are derived using statistical boosting. In contrast to alternative approaches relying on univariate summary statistics from genome-wide association studies, our three-step approach enables to select and fit multivariable regression models on large-scale genotype data. Based on UK Biobank data, we develop prediction models for LDL-cholesterol as a continuous trait. Additionally, we consider a recent scalable algorithm for the Lasso. Results show that statistical learning approaches based on fine-mapping of genetic signals result in a competitive prediction performance compared to classical polygenic risk approaches, while yielding sparser risk models.

GenRisk: a tool for comprehensive genetic risk modeling.

SUMMARY: The genetic architecture of complex traits can be influenced by both many common regulatory variants with small effect sizes and rare deleterious variants in coding regions with larger effect sizes. However, the two kinds of genetic contributions are typically analyzed independently. Here, we present GenRisk, a python package for the computation and the integration of gene scores based on the burden of rare deleterious variants and common-variants-based polygenic risk scores. The derived scores can be analyzed within GenRisk to perform association tests or to derive phenotype prediction models by testing multiple classification and regression approaches. GenRisk is compatible with VCF input file formats. AVAILABILITY AND IMPLEMENTATION: GenRisk is an open source publicly available python package that can be downloaded or installed from Github (https://github.com/AldisiRana/GenRisk). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Boosting distributional copula regression.

Capturing complex dependence structures between outcome variables (e.g., study endpoints) is of high relevance in contemporary biomedical data problems and medical research. Distributional copula regression provides a flexible tool to model the joint distribution of multiple outcome variables by disentangling the marginal response distributions and their dependence structure. In a regression setup, each parameter of the copula model, that is, the marginal distribution parameters and the copula dependence parameters, can be related to covariates via structured additive predictors. We propose a framework to fit distributional copula regression via model-based boosting, which is a modern estimation technique that incorporates useful features like an intrinsic variable selection mechanism, parameter shrinkage and the capability to fit regression models in high-dimensional data setting, that is, situations with more covariates than observations. Thus, model-based boosting does not only complement existing Bayesian and maximum-likelihood based estimation frameworks for this model class but rather enables unique intrinsic mechanisms that can be helpful in many applied problems. The performance of our boosting algorithm for copula regression models with continuous margins is evaluated in simulation studies that cover low- and high-dimensional data settings and situations with and without dependence between the responses. Moreover, distributional copula boosting is used to jointly analyze and predict the length and the weight of newborns conditional on sonographic measurements of the fetus before delivery together with other clinical variables.

Boosting multivariate structured additive distributional regression models.

We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approach enables the simultaneous modeling of all distribution parameters of an arbitrary parametric distribution of a multivariate response conditional on explanatory variables, while being applicable to potentially high-dimensional data. Moreover, the boosting algorithm incorporates data-driven variable selection, taking various different types of effects into account. As a special merit of our approach, it allows for modeling the association between multiple continuous or discrete outcomes through the relevant covariates. After a detailed simulation study investigating estimation and prediction performance, we demonstrate the full flexibility of our approach in three diverse biomedical applications. The first is based on high-dimensional genomic cohort data from the UK Biobank, considering a bivariate binary response (chronic ischemic heart disease and high cholesterol). Here, we are able to identify genetic variants that are informative for the association between cholesterol and heart disease. The second application considers the demand for health care in Australia with the number of consultations and the number of prescribed medications as a bivariate count response. The third application analyses two dimensions of childhood undernutrition in Nigeria as a bivariate response and we find that the correlation between the two undernutrition scores is considerably different depending on the child's age and the region the child lives in.

Maintenance tocolysis, tocolysis in preterm premature rupture of membranes and in cervical cerclage - a Germany-wide survey on the current practice after dissemination of the German guideline.

OBJECTIVES: To investigate the adherence of German perinatal specialist units and those of basic obstetric care to the national guideline we compared data from a nation-wide survey on the practice of maintenance tocolysis, tocolysis in preterm premature rupture of membranes and in the perioperative setting of cervical cerclage, and bedrest during and after tocolysis with recommendations from the current German Guideline 015/025 "Prevention and Treatment of Preterm Birth". METHODS: A total of 632 obstetric clinics in Germany were approached and received a link to an online questionnaire. Data were descriptively analyzed by performing measures of frequency. To compare two or more groups Fisher's exact test was used. RESULTS: The response rate was 19%; 23 (19.2%) of respondents did not perform maintenance tocolysis, while 97 (80.8%) conducted maintenance tocolysis; 30 (25.0%) of obstetric units performed cervical cerclage without tocolysis and 90 (75.0%) combined cervical cerclage with tocolysis; 11 (9.2%) of respondents did not use tocolytics in patients with preterm premature rupture of membranes, while 109 (90.8%) conducted tocolysis in these patients; 69 (57.5%) of obstetric units did not recommend bed rest during tocolysis, whereas 51 (42.5%) favored bedrest. Perinatal care centers of basic obstetric care recommend bed arrest during tocolysis statistically significant more often to their patients than those of higher perinatal care levels (53.6 vs. 32.8%, p=0.0269). CONCLUSIONS: The results of our survey are in accordance to others from different countries and reveal considerable discrepancies between evidence-based guideline recommendations and daily clinical practice.

Lack of evidence for effects of lockdowns on stillbirth rates during the SARS-CoV-2 pandemic in Bavaria: analysis of the Bavarian perinatal survey from 2010 to 2020.

BACKGROUND: Internationally, potential effects of national SARS-CoV-2-related lockdowns on stillbirth rates have been reported, but data for Germany, including risk factors for fetal pregnancy outcome, are lacking. The aim of this study is to compare the stillbirth rates during the two first lockdown periods in 2020 with previous years from 2010 to 2019 in a large Bavarian cohort. METHODS: This study is a secondary analysis of the Bavarian perinatal data from 2010 to 2020, including 349,245 births. Univariate and multivariable regression analyses were performed to investigate the effect of two Bavarian lockdowns on the stillbirth rate in 2020 compared to the corresponding periods from 2010 to 2019. RESULTS: During the first lockdown, the stillbirth rate was significantly higher compared to the reference period (4.04 vs. 3.03 stillbirths per 1000 births; P = 0.03). After adjustment for seasonal and long-term trends, this effect can no longer be observed (P = 0.2). During the second lockdown, the stillbirth rate did not differ in univariate (3.46 vs. 2.93 stillbirths per 1000 births; P = 0.22) as well as in multivariable analyses (P = 0.68), compared to the years 2010 to 2019. CONCLUSION: After adjustment for known long-term effects, in this study we did not find evidence that the two Bavarian lockdowns had an effect on the rate of stillbirths.

The relative area score for sublingual varices reliability measurement: a diagnostic study.

BACKGROUND: Sublingual varices (SV) and their predictive potential for other clinical parameters is a much studied topic in oral medicine. SVs have been well studied as predictive markers for many common diseases such as arterial hypertension, cardiovascular disease, smoking, type 2 diabetes mellitus and age. Despite many prevalence studies, it is still unclear how the reliability of SV inspection affects its predictive power. The aim of this study was to quantify the inspection reliability of SV. METHODS: In a diagnostic study, the clinical inspection of 78 patients by 23 clinicians was examined for the diagnosis of SV. Digital images of the underside of the tongue were taken from each patient. The physicians were then asked to rate them for the presence of sublingual varices (0/1) in an online inspection experiment. Statistical analysis for inter-item and inter-rater reliability was performed in a τ-equivalent measurement model with Cronbach's [Formula: see text] and Fleiss κ. RESULTS: The interrater reliability for sublingual varices was relatively low with κ = 0.397. The internal consistency of image findings for SV was relatively high with α≈ 0.937. This shows that although SV inspection is possible in principle, it has a low reliability R. This means that the inspection finding (0/1) of individual images often cannot be reproduced stably. Therefore, SV inspection is a difficult task of clinical investigation. The reliability R of SV inspection also limits the maximum linear correlation [Formula: see text] of SV with an arbitrary other parameter Y. The reliability of SV inspection R = 0.847 limits the maximum correlation to [Formula: see text] (SV, Y) = 0,920-a 100% correlation was a priori not achievable in our sample. To overcome the problem of low reliability in SV inspection, we propose the RA (relative area) score as a continuous classification system for SV, which normalises the area of visible sublingual veins to the square of the length of the tongue, providing a dimensionless measure of SV. CONCLUSIONS: The reliability of the SV inspection is relatively low. This limits the maximum possible correlation of SV with other (clinical) parameters. SV inspection reliability is an important indicator for the quality of SV as a predictive marker. This should be taken into account when interpreting previous studies on SV and has implications for future studies. The RA score could help to objectify the SV examination and thus increase its reliability.

Cerebroplacental versus Umbilicocerebral Ratio-Analyzing the Predictive Value Regarding Adverse Perinatal Outcomes in Low- and High-Risk Fetuses at Term.

Background and Objectives: The aim of this study was to investigate the prediction of adverse perinatal outcomes using the cerebroplacental (CPR) and umbilicocerebral (UCR) ratios in different cohorts of singleton pregnancies. Materials and Methods: In this retrospective cohort study, we established our own Multiple of Median (MoM) for CPR and UCR. The predictive value for both ratios was studied in the following outcome parameters: emergency cesarean delivery, operative intervention (OI), OI due to fetal distress, 5-min Apgar < 7, admission to neonatal intensive care unit, and composite adverse perinatal outcome. The performance of the ratios was assessed in the following cohorts: total cohort (delivery ≥ 37 + 0 weeks gestation, all birth weight centiles), low-risk cohort (delivery ≥ 37 + 0 weeks gestation, birth weight ≥ 10. centile), prolonged pregnancy cohort (delivery ≥ 41 + 0 weeks gestation, birth weight ≥ 10. centile) and small-for-gestational-age fetuses (delivery ≥ 37 + 0 weeks gestation, birth weight < 10. centile). The underlying reference values for MoM were estimated using quantile regression depending on gestational age. Prediction performance was evaluated using logistic regression models assessing the corresponding Brier score, combining discriminatory power and calibration. Results: Overall, 3326 cases were included. Across all cohorts, in the case of a significant association between a studied outcome parameter and CPR, there was an association with UCR, respectively. The Brier score showed only minimal differences for both ratios. Conclusions: Our study provides further evidence regarding predictive values of CPR and UCR. The results of our study suggest that reversal of CPR to UCR does not improve the prediction of adverse perinatal outcomes.

Effects of treatment with SGLT-2 inhibitors on arginine-related cardiovascular and renal biomarkers.

BACKGROUND: In patients with type 2 diabetes (T2D) sodium-glucose cotransporter 2 (SGLT-2) inhibitors improve glycaemic control as well as cardiovascular and renal outcomes. Their effects on L-arginine (Arg) related risk markers asymmetric and symmetric dimethylarginine (ADMA and SDMA) and the protective biomarker L-homoarginine (hArg) linking T2D to cardiovascular and renal disease have not yet been reported. METHODS: Plasma and 24-h urine samples taken before and after 6 weeks of treatment were available from two prospective, randomized, double-blind, placebo-controlled, cross-over trials with empagliflozin (71 patients analyzed, NCT02471963) and dapagliflozin (59 patients analyzed, NCT02383238). In these samples, concentrations of hArg, Arg, ADMA, SDMA, and creatinine were determined by liquid-chromatography coupled to tandem mass-spectrometry. Additionally, intraindividual changes of the biomarkers in plasma were correlated with intraindividual changes of clinical parameters. RESULTS: Treatment with empagliflozin and dapagliflozin was associated with a reduction of plasma hArg by 17.5% and 13.7% (both p < 0.001), respectively, and increase in plasma SDMA concentration of 6.7% and 3.6%, respectively (p < 0.001 and p < 0.05), while plasma Arg and ADMA concentrations were not significantly altered. 24-h urinary excretion of ADMA was reduced by 15.2% after treatment with empagliflozin (p < 0.001) but not after dapagliflozin treatment, while excretion of the other markers was not significantly altered. Renal clearance of SDMA was reduced by 9.1% and 3.9% for both drugs (both p < 0.05). A reduction in ADMA clearance was observable after empagliflozin treatment only (- 15.5%, p < 0.001), but not after dapagliflozin. Renal clearance of hArg and Arg was not significantly altered. Treatment effects on L-arginine related biomarkers were not constantly correlated with effects on glycated hemoglobin, fasting plasma glucose, body mass index, and systolic blood pressure. CONCLUSIONS: Treatment with SGLT-2 inhibitors has divergent effects on Arg-related biomarkers and could affect risk estimates associated with these markers. The observed effects are unlikely to explain the known cardiovascular and renal benefits of treatment with empagliflozin or dapagliflozin but still may indicate new therapeutic approaches in patients treated with SGLT-2 inhibitors. Trial registration http://www.clinicaltrials.gov : NCT02471963 (registered 15th June 2015, retrospectively registered) and NCT02383238.

Clinical value of pre-operative scoring systems to predict leiomyosarcoma: results of a validation study in 177 patients from the NOGGO-REGSA Registry.

OBJECTIVES: Benign leiomyomas are the most common uterine tumors. In contrast, uterine leiomyosarcomas are malignancies with a poor prognosis due to difficulties in early diagnosis and inappropriate surgical treatment. Most often they are diagnosed incidentally after surgery performed for treatment of leiomyoma. As the appropriate surgical treatment is crucial for survival of the patient, there is a high demand to predict leiomyosarcoma pre-operatively. Available scoring systems to discriminate leiomyoma from leiomyosarcoma are based on retrospective studies with limited numbers of patients and are not implemented in routine clinical practice. METHODS: The aim of our study was to evaluate a recently published score-the pre-operative leiomyosarcoma (pLMS) score-to determine whether it would have been predictive of leiomyosarcoma in 177 patients from the NOGGO-REGSA study, a German register of histologically proven gynecological sarcoma detected during routine clinical investigation. RESULTS: The threshold of the pLMS score for 'leiomyosarcoma not probable' (< -3) failed for 7.5% of the patients and the threshold 'indicator for leiomyosarcoma' (>+1) was true for 39.1% of the patients. 53.4% of the patients were attributed to the group 'additional investigations are recommended' (-3 to +1). The most relevant parameters in our analysis were suspicious sonography and rapid growth, but neither have been quantitatively defined. CONCLUSION: In our validation cohort, the pLMS score seems not to be a reliable tool to predict leiomyosarcoma and therefore we do not recommend its clinical implementation to identify leiomyosarcoma.

Amniotic-Umbilical-to-Cerebral Ratio - A Novel Ratio Combining Doppler Parameters and Amniotic Fluid Volume to Predict Adverse Perinatal Outcome in SGA Fetuses At Term.

PURPOSE: Introduction of a novel ratio - the amniotic-umbilical-to-cerebral ratio (AUCR) - to predict adverse perinatal outcome in SGA fetuses at term and comparison of its predictive accuracy with established parameters. MATERIALS AND METHODS: This retrospective cohort study included 165 singleton pregnancies with SGA fetuses (birth weight < 10th percentile) at term. Cases with planned vaginal delivery and documented pulsatility indices (PI) of the umbilical artery (UA), middle cerebral artery (MCA), and single deepest pocket (SDP) were included. CPR was calculated as the ratio between MCA PI and UA PI, UCR as the ratio between UA PI and MCA PI. AUCR was defined as follows: SDP/(UA PI/MCA PI). Adverse perinatal outcomes were defined as operative intervention (OI), OI due to fetal distress, admission to the neonatal intensive care unit (NICU), and composite adverse perinatal outcome (CAPO). Associations between Doppler parameters and these outcomes were estimated using regression analyses. RESULTS: OI was statistically significantly associated with UCR, SDP, and AUCR, whereas no association was observed for UA PI, MCA PI, and CPR. Fetuses requiring OI due to fetal distress revealed a significantly higher UA PI and UCR as well as a lower MCA PI, CPR, and AUCR. With regard to NICU admission and CAPO, a significantly higher UA PI and lower CPR were found. Furthermore, a significant association was shown for SDP, UCR, and AUCR. AUCR achieved the best area under the curve for all outcome parameters. CONCLUSION: AUCR leads to an improvement in the prediction of unfavorable outcome in SGA fetuses at term. Furthermore, results of our study show that UCR might be superior to CPR.

Influence of Sonographic Fetal Weight Estimation Inaccuracies in Macrosomia on Perinatal Outcome.

OBJECTIVE: To evaluate the influence of inaccurate sonographic fetal weight estimation in macrosomia on the mode of delivery and neonatal outcome (NO). METHODS: In 14 633 pregnancies between 2002 and 2016, this retrospective study evaluated the association between sonographic fetal weight estimation, true birth weight (BW), mode of delivery (primary cesarean section [pCS], secondary cesarean section, vaginal delivery, and operative vaginal delivery rates) and NO parameters (5-min Apgar < 7, pH < 7.1, neonatal intensive care unit [NICU] admission, shoulder dystocia). Singleton pregnancies > 37 + 0 weeks with ultrasound-estimated fetal weight (EFW) within 7 days before delivery were included. The study population was divided into four groups: Group 1 (false-negative): EFW < 4000 g/BW ≥ 4000 g; Group 2 (true-positive): EFW ≥ 4000 g/BW ≥ 4000 g; Group 3 (false-positive): EFW ≥ 4000 g/BW < 4000 g; and Group 4 (true-negative): EFW < 4000 g/BW < 4000 g. RESULTS: As expected, the highest secondary cesarean section (sCS) rate was found in Group 2 (true-positive) (30.62 %), compared with only 17.68 % in Group 4 (true-negative). The sCS rate in the false-positive Group 3 was significantly higher (28.48 %) in comparison with the false-negative Group 1 (21.22 %; OR 1.48; 95 % CI, 1.16 to 1.89; P = 0.002). In comparison with the true-negative Group 4, univariate analyses showed significantly higher rates for sCS in all other groups: odds ratio (OR) 2.06 for Group 2 (95 % CI, 1.74 to 2.42; P < 0.001), 1.85 for Group 3 (95 % CI, 1.54 to 2.22, P < 0.001), and 1.25 for Group 1 (95 % CI, 1.05 to 1.49; P < 0.01). No significant differences were found for NO between Groups 1 and 3 for the parameters 5-min Apgar < 7 (P = 0.75), pH < 7.1 (P = 0.28), or NICU admission (P = 0.54). However, there was a significantly higher chance for shoulder dystocia in Group 1 compared with Group 3 (OR 4.58; 95 % CI, 1.34 to 24.30; P = 0.008). CONCLUSION: Sonographic EFW inaccuracies in fetal macrosomia appear to have a greater impact on the mode of delivery than birth weight itself. Underestimation of fetal weight may be associated with a higher probability of shoulder dystocia.

Postoperative delirium is an independent factor influencing the length of stay of elderly patients in the intensive care unit and in hospital.

PURPOSE: Postoperative delirium (POD) is an often unrecognized adverse event in older people after surgery. The aim of this subgroup analysis of the PRe-Operative Prediction of postoperative DElirium by appropriate SCreening (PROPDESC) trial in patients aged 70 years and older was to identify preoperative risk factors and the impact of POD on length of stay (LOS) in intensive care unit (ICU) and hospital. METHODS: Of the total 1097 patients recruited at a German university hospital (from September 2018 to October 2019) in the PROPDESC prospective observational study, 588 patients aged 70 years and older (mean age 77.2 ± 4.7 years) were included for subgroup analysis. The primary endpoint POD was considered positive if one of the following tests were positive on any of the five postoperative visit days: Confusion Assessment Method for ICU (CAM-ICU), Confusion Assessment Method (CAM), 4'A's (4AT) and Delirium Observation Scale (DOS). Trained doctoral students carried out these visitations and additionally the nursing staff were interviewed for completion of the DOS. To evaluate the independent effect of POD on LOS in ICU and in hospital, a multi-variable linear regression analysis was performed. RESULTS: The POD incidence was 25.9%. The results of our model showed POD as an independent predictor for a prolonged LOS in ICU (36%; 95% CI 4-78%; < 0.001) and in hospital (22%; 95% CI 4-43%; < 0.001). CONCLUSION: POD has an independent impact on LOS in ICU and in hospital. Based on the effect of POD for the elderly, a standardized risk screening is required. TRAIL REGISTRATION: German Registry for Clinical Studies: DRKS00015715.

Randomized boosting with multivariable base-learners for high-dimensional variable selection and prediction.

BACKGROUND: Statistical boosting is a computational approach to select and estimate interpretable prediction models for high-dimensional biomedical data, leading to implicit regularization and variable selection when combined with early stopping. Traditionally, the set of base-learners is fixed for all iterations and consists of simple regression learners including only one predictor variable at a time. Furthermore, the number of iterations is typically tuned by optimizing the predictive performance, leading to models which often include unnecessarily large numbers of noise variables. RESULTS: We propose three consecutive extensions of classical component-wise gradient boosting. In the first extension, called Subspace Boosting (SubBoost), base-learners can consist of several variables, allowing for multivariable updates in a single iteration. To compensate for the larger flexibility, the ultimate selection of base-learners is based on information criteria leading to an automatic stopping of the algorithm. As the second extension, Random Subspace Boosting (RSubBoost) additionally includes a random preselection of base-learners in each iteration, enabling the scalability to high-dimensional data. In a third extension, called Adaptive Subspace Boosting (AdaSubBoost), an adaptive random preselection of base-learners is considered, focusing on base-learners which have proven to be predictive in previous iterations. Simulation results show that the multivariable updates in the three subspace algorithms are particularly beneficial in cases of high correlations among signal covariates. In several biomedical applications the proposed algorithms tend to yield sparser models than classical statistical boosting, while showing a very competitive predictive performance also compared to penalized regression approaches like the (relaxed) lasso and the elastic net. CONCLUSIONS: The proposed randomized boosting approaches with multivariable base-learners are promising extensions of statistical boosting, particularly suited for highly-correlated and sparse high-dimensional settings. The incorporated selection of base-learners via information criteria induces automatic stopping of the algorithms, promoting sparser and more interpretable prediction models.

Estimating effective infection fatality rates during the course of the COVID-19 pandemic in Germany.

BACKGROUND: The infection fatality rate (IFR) of the Coronavirus Disease 2019 (COVID-19) is one of the most discussed figures in the context of this pandemic. In contrast to the case fatality rate (CFR), the IFR depends on the total number of infected individuals - not just on the number of confirmed cases. In order to estimate the IFR, several seroprevalence studies have been or are currently conducted. METHODS: Using German COVID-19 surveillance data and age-group specific IFR estimates from multiple international studies, this work investigates time-dependent variations in effective IFR over the course of the pandemic. Three different methods for estimating (effective) IFRs are presented: (a) population-averaged IFRs based on the assumption that the infection risk is independent of age and time, (b) effective IFRs based on the assumption that the age distribution of confirmed cases approximately reflects the age distribution of infected individuals, and (c) effective IFRs accounting for age- and time-dependent dark figures of infections. RESULTS: Effective IFRs in Germany are estimated to vary over time, as the age distributions of confirmed cases and estimated infections are changing during the course of the pandemic. In particular during the first and second waves of infections in spring and autumn/winter 2020, there has been a pronounced shift in the age distribution of confirmed cases towards older age groups, resulting in larger effective IFR estimates. The temporary increase in effective IFR during the first wave is estimated to be smaller but still remains when adjusting for age- and time-dependent dark figures. A comparison of effective IFRs with observed CFRs indicates that a substantial fraction of the time-dependent variability in observed mortality can be explained by changes in the age distribution of infections. Furthermore, a vanishing gap between effective IFRs and observed CFRs is apparent after the first infection wave, while an increasing gap can be observed during the second wave. CONCLUSIONS: The development of estimated effective IFR and observed CFR reflects the changing age distribution of infections over the course of the COVID-19 pandemic in Germany. Further research is warranted to obtain timely age-stratified IFR estimates, particularly in light of new variants of the virus.

Postoperative delirium after cardiac surgery of elderly patients as an independent risk factor for prolonged length of stay in intensive care unit and in hospital.

BACKGROUND: Postoperative delirium (POD) is a relevant and underdiagnosed complication after cardiac surgery that is associated with increased intensive care unit (ICU) and hospital length of stay (LOS). The aim of this subgroup study was to compare the frequency of tested POD versus the coded International Statistical Classification of Diseases and Related Health Problems (ICD) diagnosis of POD and to evaluate the influence of POD on LOS in ICU and hospital. METHODS: 254 elective cardiac surgery patients (mean age, 70.5 ± 6.4 years) at the University Hospital Bonn between September 2018 and October 2019 were evaluated. The endpoint tested POD was considered positive, if one of the tests Confusion Assessment Method for ICU (CAM-ICU) or Confusion Assessment Method (CAM), 4 'A's Test (4AT) or Delirium Observation Scale (DOS) was positive on one day. RESULTS: POD occurred in 127 patients (50.0%). LOS in ICU and hospital were significantly different based on presence (ICU 165.0 ± 362.7 h; Hospital 26.5 ± 26.1 days) or absence (ICU 64.5 ± 79.4 h; Hospital 14.6 ± 6.7 days) of POD (p < 0.001). The multiple linear regression showed POD as an independent predictor for a prolonged LOS in ICU (48%; 95%CI 31-67%) and in hospital (64%; 95%CI 27-110%) (p < 0.001). The frequency of POD in the study participants that was coded with the ICD F05.0 and F05.8 by hospital staff was considerably lower than tests revealed by the study personnel. CONCLUSION: Approximately 50% of elderly patients who underwent cardiac surgery developed POD, which is associated with an increased ICU and hospital LOS. Furthermore, POD is highly underdiagnosed in clinical routine.