Gonadal androgens are associated with decreased type I interferon production by plasmacytoid dendritic cells and increased IgG titres to BNT162b2 following co-vaccination with live attenuated influenza vaccine in adolescents

mRNA vaccine technologies introduced following the SARS-CoV-2 pandemic have highlighted the need to better understand the interaction of adjuvants and the early innate immune response. Type I interferon (IFN-I) is an integral part of this early innate response that primes several components of the adaptive immune response. Women are widely reported to respond better than men to tri- and quadrivalent influenza vaccines. Plasmacytoid dendritic cells (pDCs) are the primary cell type responsible for IFN-I production, and female pDCs produce more IFN-I than male pDCs since the upstream pattern recognition receptor Toll-like receptor 7 (TLR7) is encoded by X chromosome and is biallelically expressed by up to 30% of female immune cells. Additionally, the TLR7 promoter contains several putative androgen response elements, and androgens have been reported to suppress pDC IFN-I in vitro. Unexpectedly, therefore, we recently observed that male adolescents mount stronger antibody responses to the Pfizer BNT162b2 mRNA vaccine than female adolescents after controlling for natural SARS-CoV-2 infection. We here examined pDC behaviour in this same cohort to determine the impact of IFN-I on anti-spike and anti-receptor-binding domain IgG titres to BNT162b2. Through flow cytometry and least absolute shrinkage and selection operator (LASSO) modelling, we determined that serum-free testosterone was associated with reduced pDC IFN-I, but contrary to the well-described immunosuppressive role for androgens, the most bioactive androgen dihydrotestosterone was associated with increased IgG titres to BNT162b2. Also unexpectedly, we observed that co-vaccination with live attenuated influenza vaccine boosted the magnitude of IgG responses to BNT162b2. Together, these data support a model where systemic IFN-I increases vaccine-mediated immune responses, yet for vaccines with intracellular stages, modulation of the local IFN-I response may alter antigen longevity and consequently improve vaccine-driven immunity.

The potential impact of advanced footwear technology on the recent evolution of elite sprint performances

Background

Elite track and field sprint performances have reached a point of stability as we near the limits of human physiology, and further significant improvements may require technological intervention. Following the widely reported performance benefits of new advanced footwear technology (AFT) in road-running events, similar innovations have since been applied to sprint spikes in hope of providing similar performance enhancing benefits. However, it is not yet clear based on current evidence whether there have been subsequent improvements in sprint performance. Therefore, the aims of this study were to establish if there have been recent year-to-year improvements in the times of the annual top 100 and top 20 athletes in the men's and women's sprint events, and to establish if there is an association between the extensive use of AFT and potential recent improvements in sprint performances.

Methods

For the years 2016-19 and 2021-2022, the season best performances of the top 100 athletes in each sprint event were extracted from the World Athletics Top lists. Independent t-tests with Holm corrections were performed using the season's best performance of the top 100 and top 20 athletes in each year to identify significant differences between years for each sprint discipline. Following the classification of shoes worn by the top 20 athletes in each event during their annual best race (AFT or non-AFT), separate linear mixed-model regressions were performed to determine the influence of AFT on performance times.

Results

For the top 100 and top 20 athletes, there were no significant differences year-to-year in any sprint event prior to the release of AFT (2016-2019). There were significant differences between AFT years (2021 or 2022) and pre-AFT years (2016-2019) in eight out of 10 events. These differences ranged from a 0.40% improvement (men's 100 m) to a 1.52% improvement (women's 400 m hurdles). In the second analysis, multiple linear mixed model regressions revealed that the use of AFT was associated with improved performance in six out of ten events, including the men's and women's 100 m, women's 200 m, men's 110 m hurdles, women's 100 m hurdles and women's 400 m hurdles (estimate range: -0.037 - 0.521, p = <0.001 - 0.021). Across both analyses, improvements were more pronounced in women's sprint events than men's sprint events.

Conclusion

Following a period of stability, there were significant improvements in most sprint events which may be partly explained by advances in footwear technology. These improvements appear to be mediated by event, sex and potentially level of athlete.

Accounting for clustering in automated variable selection using hospital data: a comparison of different LASSO approaches

BACKGROUND

Automated feature selection methods such as the Least Absolute Shrinkage and Selection Operator (LASSO) have recently gained importance in the prediction of quality-related outcomes as well as the risk-adjustment of quality indicators in healthcare. The methods that have been used so far, however, do not account for the fact that patient data are typically nested within hospitals.

METHODS

Therefore, we aimed to demonstrate how to account for the multilevel structure of hospital data with LASSO and compare the results of this procedure with a LASSO variant that ignores the multilevel structure of the data. We used three different data sets (from acute myocardial infarcation, COPD, and stroke patients) with two dependent variables (one numeric and one binary), on which different LASSO variants with and without consideration of the nested data structure were applied. Using a 20-fold sub-sampling procedure, we tested the predictive performance of the different LASSO variants and examined differences in variable importance.

RESULTS

For the metric dependent variable Duration Stay, we found that inserting hospitals led to better predictions, whereas for the binary variable Mortality, all methods performed equally well. However, in some instances, the variable importances differed greatly between the methods.

CONCLUSION

We showed that it is possible to take the multilevel structure of data into account in automated predictor selection and that this leads, at least partly, to better predictive performance. From the perspective of variable importance, including the multilevel structure is crucial to select predictors in an unbiased way under consideration of the structural differences between hospitals.

[Time to Antibiotics (TTA) - Reassessment from the German Working Group for Fever and Neutropenia in Children and Adolescents (DGPI/GPOH)]

BACKGROUND

The current German guidance from 2016 recommends a Time to Antibiotics (TTA) of<60 min in children and adolescents with febrile neutropenia (FN).

METHODS

Critical analysis of available studies and recent meta-analyses, and discussion of the practical consequences in the FN working group of the German Societies for Paediatric Oncology and Haematology and Paediatric Infectious Diseases.

RESULTS

The available evidence does not support a clinically significant outcome benefit of a TTA<60 min in all paediatric patients with FN. Studies suggesting such a benefit are biased (mainly triage bias), use different TTA definitions and display further methodical limitations. In any case, a TTA<60 min remains an essential component of the 1st hour-bundle in paediatric cancer patients with septic shock or sepsis with organ dysfunction.

CONCLUSION

Provided that all paediatric FN patients receive a structured medical history and physical examination (including vital signs) by experienced and trained medical personnel in a timely fashion, and provided that a sepsis triage and management bundle is established and implemented, a TTA lower than 3 hours is sufficient and reasonable in stable paediatric cancer patients with FN.

Which trunk muscle parameter is the best predictor for physical function in older adults?

Background

Despite preliminary evidence demonstrating the relevance of trunk muscle strength for physical function in older adults, it is not clear which muscle-related trunk parameter is the best predictor for physical functions. Therefore, this study aimed to compare trunk muscle morphology or strength parameters regarding their predictive ability for physical functions.

Methods

Seventy-four older adults (38 men, 36 women, mean age 76.85 years) were tested for maximum absolute and relative isokinetic trunk flexion and extension strength, trunk lean mass, and trunk muscle quality. Functional assessment included normal and fast walking speed, repeated sit-to-stand transfer, timed up and go, and postural sway during a closed-feet and a semi-tandem stance adjusted for body height. Pearson's correlations were used to compare relationship between trunk strength adjusted and unadjusted for body weight to physical functions. Linear regression analysis including sex and age as co-variables was performed between trunk muscle and functional test parameters.

Results

Relative back extension strength was the most consistent significant predictor for all physical function tests (p = 0.004-0.04) except for postural sway. Relative trunk flexion strength was related to normal walking speed (p = 0.024). Trunk lean mass was related to timed up and go performance (p = 0.024).

Conclusion

Relative back extension strength is associated with better performance in nearly all standard tests for physical function in older adults, while trunk flexion strength and lean mass seem to play a minor role. Our findings emphasize the importance of trunk muscle strength, especially the back extensor muscles, for physical function in older adults.

Intrafraction Dosimetric Evaluation of Biology-Guided Radiotherapy to a Target Under Respiratory Motion

PURPOSE/OBJECTIVE(S)

To evaluate the reproducibility and variability of biology-guided radiotherapy (BgRT) treatments using a large anthropomorphic phantom modeling the motion amplitude of a lung tumor.

MATERIALS/METHODS

RefleXion X1 is equipped with two opposing 90 degrees PET detector arcs to capture the radionuclide emissions and direct the 6MV Linac to treat the lesions in real time. A custom-built phantom filled with a liquid [¹⁸F]Fluorodeoxyglucose (FDG) solution was used. Fillable target and OAR structures were 3D printed and attached to motion stages. The GTV = CTV was matched to the spherical 22 mm diameter target, and the PTV was a 5 mm expansion from the CTV volume. The Biology Tracking Zone (BTZ) was generated after adding 5 mm margin to the motion extent of the CTV. The OAR was a large C-shape annulus (emulating a heart) that was approximately 3 cm from the target. The 3D independent motion trajectory of the target was designed to mimic lung motion: range of +5.8 mm to -4.9 mm in LR, range of +14.4 mm to -11.3 mm in SI, and range of +5.2 mm to -5.1 mm in AP directions. The OAR motion waveform used a 1D sinusoidal pattern with a 5 mm amplitude in SI direction. The target and the OAR were filled with 40 kBq/mL while the background had 5 kBq/mL FDG. A BgRT Modeling (imaging-only) PET acquisition was performed using RefleXion X1 and used to generate a 4-fraction BgRT treatment plan prescribing 10 Gy/fraction to PTV. For each delivery, target, OAR and background were filled with the same FDG concentrations as in the BgRT Modeling PET planning scan. Dosimetry to the target and OAR were both measured using an ion-chamber (Exradin A14SL) and film in the coronal plane through the center of the GTV for all 4 fractions.

RESULTS

The mean activity concentration within the (BTZ) was 7.4 ± 0.8 kBq/mL. The calculated signal-to-noise ratio metric (Normalized Target Signal) within the BTZ was 4.0 ± 0.3. Total treatment times were all less than 35 minutes (34.3 ± 0.2). Prescription dose coverage to the CTV for all 4 fractions was 100%. Ion chamber measurements in the CTV were -1.6 ± 1.3% relative to the planned dose over the active area of the ion-chamber. Minimum and maximum doses to the CTV, measured on film, were -7.7 ± 2.2% and 1.3 ± 1.4%, calculated relative to the planned dose distribution, respectively. The OAR maximum point dose measured on film was -8.7 ± 2.9%, calculated relative to the maximum OAR dose predicted on the bounded dose-volume histogram.

CONCLUSION

Based on this initial study, accurate and reproducible dosimetry can be achieved for targets under respiratory motion using biology-guided radiotherapy over the course of a complete course of treatment. Further studies are needed to evaluate the intrafraction dosimetry of BgRT delivery under various motion models and tumor sizes.

Decay Series Dose Delivery Validation of a Biology-Guided Radiotherapy (BgRT) Methodology

PURPOSE/OBJECTIVE(S)

In this work, we compared the accuracy between the planned dose estimated by a commercial BgRT treatment planning system to the measured delivered dose by the BgRT treatment delivery device. The dose delivered was measured with the Sun Nuclear ArcCHECK device containing a customized phantom filled with fluorodeoxyglucose F18 (FDG) with a fixed target to background activity concentration ratio. The BgRT approach was evaluated for its capacity to accurately deliver a planned dose to a phantom target of an increasingly limited PET signal due to radioactive decay.

MATERIALS/METHODS

The Sun Nuclear ArcCHECK is a helical detector assembly of 1,386 diodes around a 15 cm diameter cavity designed for quality assurance of linear accelerator (LINAC) driven therapies. In this work, the ArcCHECK was loaded with a cylindrical phantom containing a 22 mm diameter homogenous ball and a C-shape insert. The ball target was designated as an organ at risk (OAR) with appropriate dose constraints applied, and the C-shaped insert was designated as the target. The C-shaped target was used to simulate a tumor with a necrotic core. A cylindrical planning/gross tumor volume was placed around the C-shape, providing a test case of delivery to a partially PET-avid target. The target and OAR were filled with 58.46 kBq/mL of FDG and the background with 7.30 kBq/mL, giving an approximate target to background ratio of 8:1. A kVCT localization scan, a short PET pre-treatment scan, and a LINAC treatment sequence (1000 cGy per fraction) were performed each run with four runs performed over a duration of ∼3.5 hours (1.9 half-lives). The ArcCHECK measured the delivered dose during each LINAC treatment sequence and compared it to the plan predicted dose. The relative dose (RD) and absolute dose (AD) gamma values were then calculated for each run using Sun Nuclear's proprietary software with a gamma pass rate criterion of 3mm/3%.

RESULTS

The PET scans for runs 1-4 were completed with background activity concentrations of 5.49 kBq/ml, 3.95 kBq/ml, 2.79 kBq/ml, and 2.02 kBq/ml, respectively. The scans further reported 17.48 kBq/ml, 12.26 kBq/ml, 7.93 kBq/ml, and 6.95 kBq/ml as the mean activity concentrations for the cylindrical gross tumor volume of the planned treatment. After treatment delivery, the resulting RD gamma values were 98%, 94%, 93%, and 95% and AD gamma values were 98%, 93%, 92%, and 94% for runs 1-4.

CONCLUSION

Results from this study demonstrated treatment delivery stability with consistent repeatability in the 8:1 target to background contrast condition even with diminishing PET signal from the phantom target as the activity decayed. This work shows that BgRT is capable of delivering to a cylindrical target volume that is very different from the PET avid C-shaped that was used for the plan and delivery.

Dosimetric Accuracy of Multi-Target Biology-Guided Radiotherapy Treatments in a Single Session

PURPOSE/OBJECTIVE(S)

We present the first dosimetric measurements of single session, multi-target BgRT deliveries using a clinically realistic motion phantom on a research-only version of the RefleXion X1 system.

MATERIALS/METHODS

A custom-made anthropomorphic phantom of a human torso with embedded fillable targets mimicking 18F-FDG-avid lesions was used. From the three embedded spherical targets, Target 1 was 26 mm in diameter coupled with a 3D independent respiratory motion with 22 mm range, whereas Target 2 and 3 were 22 mm in diameter and moved with a 1D 5 mm maximum sinusoidal motion. The 18F-FDG concentration in the background cavity of the phantom was 5 kBq/ml, and the targets were loaded with 10:1, 8:1 and 6:1 contrast relative to the background for Targets 1, 2, 3, respectively. Spherical structures were contoured as GTVs (CTV = GTV) and a 5 mm margin was added to create PTVs. Motion extent of the tumors were captured to create biological tracking zones for each target. Treatment plans were generated using a research version of the Reflexion treatment planning software to deliver 8 Gy/fx to the PTVs. The treatment delivery was repeated 2 times, and each time the phantom was refilled according to the plan. PET image evaluation metrics for each of the three targets were also recorded. Target dosimetry was measured using a combination of radiographic film and ion chamber. The maximum distance between the 97% prescription isodose line from the plan and the film measurements was used to characterize the dosimetric accuracy of the tracked deliveries. CTV and PTV min, max, and mean doses measured on film were also recorded for each target.

RESULTS

Treatment plans were successfully created with 100% prescription dose coverage to each target loaded with different FDG ratios. Total treatment times for the single-plan, three-target deliveries were less than 80 minutes. PET evaluation metrics at imaging-only and pre-scan, and planning and film dosimetry to the GTV and PTV for each of the three targets is shown in table below (mean ± standard deviation of both deliveries). The CTV dose coverage was maintained for all targets. The shrinkage distance of the 97% prescription dose isodose line on the film plane for all three targets was less than 3 mm for both tests, and ranged from -0.4 to -2.34 mm.

CONCLUSION

These results demonstrate that high tracking accuracy and dosimetric accuracy can be achieved in single session, multi-target deliveries over a range of target-to-background 18F-FDG concentrations and target motion patterns.

Characterization of Biology-Guided Radiotherapy Accuracy as a Function of PET Tracer Uptake

PURPOSE/OBJECTIVE(S)

To characterize the tracking capability and dosimetric accuracy of biology-guided radiotherapy (BgRT) under clinically relevant PET tracer uptake scenarios relative to the background.

MATERIALS/METHODS

A custom-made anthropomorphic phantom filled with a liquid 18F-FDG solution including two embedded fillable 22 mm diameter spherical structures mimicking GTV (= CTV) and OAR was coupled to motion stages to create an independent 3D respiratory motion with 22 mm maximum range for target and a 5 mm 1D sinusoidal motion in the OAR. The biology-tracking zone (BTZ) was generated by adding 5 mm margin to the motion extent. The three BgRT scenarios studied were representative of tumors with good (8:1), borderline (4:1) and undesired (2:1) PET biodistributions compared to background. The clinical safety limit of BgRT uses Activity Concentration within the BTZ (AC ≥ 5 kBq/ml) and Normalized Target Signal as a contrast metric (NTS ≧ 2.7 for planning and ≧ 2 for delivery). The BgRT deliveries were repeated 3 times with radiochromic film and integrated ion chamber capturing the target and OAR doses. Tracked dosimetry was assessed using a margin-loss calculation defined as the maximum linear difference in distance between the planned and delivered 97% prescription iso-dose lines.

RESULTS

The imaging-only PET images used to create BgRT plans had an AC of 7.0, 5.3, and 1.6 kBq/ml with an NTS of 6.8, 5.3, and 1.8 for 8:1, 4:1, and 2:1 concentrations, respectively. Qualitatively, the target was not visible on the planning PET images 2:1 loading scenario. At delivery, the mean pre-scan activity concentrations were 6.8, 4.7, and 3.7 kBq/ml with corresponding mean NTS of 3.7, 2.6, 1.5 for 8:1, 4:1 and 2:1 deliveries. The pre-scan values of AC or NTS did not satisfy the clinical system safety limits for 4:1 and 2:1 ratio experiments, but the engineering software allowed for the delivery to capture the resulting doses. The deliveries showed a prescription dose coverage to the CTV of 100% for the 8:1 and 4:1 cases, but 88% for the 2:1 case. When compared to the planned dose values, the delivered minimum doses were -7.6%, -8.6% and -10.9%, whereas the maximum dose differences in CTV were 1.2%, 0% and -4.8% of the planned dose distributions of the 8:1, 4:1 and 2:1 cases, respectively. Calculated margin losses were -2.3, -3.8, and -5.5 mm, for the 8:1, 4:1, and 2:1 cases, respectively. The maximum OAR doses were less than the maximum doses predicted on the bounded DVH curves for all scenarios.

CONCLUSION

With sufficient tracer uptake in the target, BgRT can deliver tracked dosimetry for targets with a large respiratory motion profile. Both the good BgRT candidate and borderline cases produced clinically acceptable delivered doses, even though the borderline case was flagged by the clinical system safety checks. As expected, the delivered BgRT dose distributions were suboptimal with reduced tumor over background PET contrast.

Infection pre-Ad26.COV2.S-vaccination primes greater class switching and reduced CXCR5 expression by SARS-CoV-2-specific memory B cells

Neutralizing antibodies strongly correlate with protection for COVID-19 vaccines, but the corresponding memory B cells that form to protect against future infection are relatively understudied. Here we examine the effect of prior SARS-CoV-2 infection on the magnitude and phenotype of the memory B cell response to single dose Johnson and Johnson (Ad26.COV2.S) vaccination in South African health care workers. Participants were either naïve to SARS-CoV-2 or had been infected before vaccination. SARS-CoV-2-specific memory B-cells expand in response to Ad26.COV2.S and are maintained for the study duration (84 days) in all individuals. However, prior infection is associated with a greater frequency of these cells, a significant reduction in expression of the germinal center chemokine receptor CXCR5, and increased class switching. These B cell features correlated with neutralization and antibody-dependent cytotoxicity (ADCC) activity, and with the frequency of SARS-CoV-2 specific circulating T follicular helper cells (cTfh). Vaccination-induced effective neutralization of the D614G variant in both infected and naïve participants but boosted neutralizing antibodies against the Beta and Omicron variants only in participants with prior infection. In addition, the SARS-CoV-2 specific CD8+ T cell response correlated with increased memory B cell expression of the lung-homing receptor CXCR3, which was sustained in the previously infected group. Finally, although vaccination achieved equivalent B cell activation regardless of infection history, it was negatively impacted by age. These data show that phenotyping the response to vaccination can provide insight into the impact of prior infection on memory B cell homing, CSM, cTfh, and neutralization activity. These data can provide early signals to inform studies of vaccine boosting, durability, and co-morbidities.

Slow progression of pediatric HIV associates with early CD8+ T cell PD-1 expression and a stem-like phenotype

HIV nonprogression despite persistent viremia is rare among adults who are naive to antiretroviral therapy (ART) but relatively common among ART-naive children. Previous studies indicate that ART-naive pediatric slow progressors (PSPs) adopt immune evasion strategies similar to those described in natural hosts of SIV. However, the mechanisms underlying this immunophenotype are not well understood. In a cohort of early-treated infants who underwent analytical treatment interruption (ATI) after 12 months of ART, expression of PD-1 on CD8+ T cells immediately before ATI was the main predictor of slow progression during ATI. PD-1+CD8+ T cell frequency was also negatively correlated with CCR5 and HLA-DR expression on CD4+ T cells and predicted stronger HIV-specific T lymphocyte responses. In the CD8+ T cell compartment of PSPs, we identified an enrichment of stem-like TCF-1+PD-1+ memory cells, whereas pediatric progressors and viremic adults had a terminally exhausted PD-1+CD39+ population. TCF-1+PD-1+ expression on CD8+ T cells was associated with higher proliferative activity and stronger Gag-specific effector functionality. These data prompted the hypothesis that the proliferative burst potential of stem-like HIV-specific cytotoxic cells could be exploited in therapeutic strategies to boost the antiviral response and facilitate remission in infants who received early ART with a preserved and nonexhausted T cell compartment.

Regularization approaches in clinical biostatistics: A review of methods and their applications

A range of regularization approaches have been proposed in the data sciences to overcome overfitting, to exploit sparsity or to improve prediction. Using a broad definition of regularization, namely controlling model complexity by adding information in order to solve ill-posed problems or to prevent overfitting, we review a range of approaches within this framework including penalization, early stopping, ensembling and model averaging. Aspects of their practical implementation are discussed including available R-packages and examples are provided. To assess the extent to which these approaches are used in medicine, we conducted a review of three general medical journals. It revealed that regularization approaches are rarely applied in practical clinical applications, with the exception of random effects models. Hence, we suggest a more frequent use of regularization approaches in medical research. In situations where also other approaches work well, the only downside of the regularization approaches is increased complexity in the conduct of the analyses which can pose challenges in terms of computational resources and expertise on the side of the data analyst. In our view, both can and should be overcome by investments in appropriate computing facilities and educational resources.

Predictive modeling of lower extremity injury risk in male elite youth soccer players using least absolute shrinkage and selection operator regression

PURPOSE

To (1) identify neuromuscular and biomechanical injury risk factors in elite youth soccer players and (2) assess the predictive ability of a machine learning approach.

MATERIAL AND METHODS

Fifty-six elite male youth soccer players (age: 17.2 ± 1.1 years; height: 179 ± 8 cm; mass: 70.4 ± 9.2 kg) performed a 3D motion analysis, postural control testing, and strength testing. Non-contact lower extremities injuries were documented throughout 10 months. A least absolute shrinkage and selection operator (LASSO) regression model was used to identify the most important injury predictors. Predictive performance of the LASSO model was determined in a leave-one-out (LOO) prediction competition.

RESULTS

Twenty-three non-contact injuries were registered. The LASSO model identified concentric knee extensor peak torque, hip transversal plane moment in the single-leg drop landing task and center of pressure sway in the single-leg stance test as the three most important predictors for injury in that order. The LASSO model was able to predict injury outcomes with a likelihood of 58% and an area under the ROC curve of 0.63 (sensitivity = 35%; specificity = 79%).

CONCLUSION

The three most important variables for predicting the injury outcome suggest the importance of neuromuscular and biomechanical performance measures in elite youth soccer. These preliminary results may have practical implications for future directions in injury risk screening and planning, as well as for the development of customized training programs to counteract intrinsic injury risk factors. However, the poor predictive performance of the final model confirms the challenge of predicting sports injuries, and the model must therefore be evaluated in larger samples.

Editorial special issue: Statistics in sports

Triggered by advances in data gathering technologies, the use of statistical analyzes, predictions and modeling techniques in sports has gained a rapidly growing interest over the last decades. Today, professional sports teams have access to precise player positioning data and sports scientists design experiments involving non-standard data structures like movement-trajectories. This special issue on statistics in sports is dedicated to further foster the development of statistics and its applications in sports. The contributed articles address a wide range of statistical problems such as statistical methods for prediction of game outcomes, for prevention of sports injuries, for analyzing sports science data from movement laboratories, for measurement and evaluation of player performance, etc. Finally, also SARS-CoV-2 pandemic-related impacts on the sport's framework are investigated.

Mycobacterial infections in adults with haematological malignancies and haematopoietic stem cell transplants: guidelines from the 8th European Conference on Infections in Leukaemia

Mycobacterial infections, both tuberculosis and nontuberculous, are more common in patients with haematological malignancies and haematopoietic stem cell transplant recipients than in the general population-although these infections remain rare. Mycobacterial infections pose both diagnostic and therapeutic challenges. The management of mycobacterial infections is particularly complicated for patients in haematology because of the many drug-drug interactions between antimycobacterial drugs and haematological and immunosuppressive treatments. The management of mycobacterial infections must also consider the effect of delaying haematological management. We surveyed the management practices for latent tuberculosis infection (LTBI) in haematology centres in Europe. We then conducted a meticulous review of the literature on the epidemiology, diagnosis, and management of LTBI, tuberculosis, and nontuberculous mycobacterial infections among patients in haematology, and we formulated clinical guidelines according to standardised European Conference on Infections in Leukaemia (ECIL) methods. In this Review, we summarise the available literature and the recommendations of ECIL 8 for managing mycobacterial infections in patients with haematological malignancies.

Correlation of Walking Activity and Cardiac Hospitalizations in Coronary Patients for 1 Year Post Cardiac Rehabilitation: The More Steps, the Better!

Problem:

Reducing risk by improving fitness is one of the main objectives of cardiac rehabilitation (CR). To estimate how the number of steps/day post-CR affects coronary patients’ prognosis, we analyzed its correlation with the occurrence of death, hospitalizations, and heart complaints, and if and how other variables (ejection fraction (EF), gender, age) relate to those.

Methods:

One hundred eleven patients (male = 91, female = 20; average age ± standard deviation (SD): 61 ± 11 years) who had been in CR due to recent coronary revascularization or chronic coronary syndrome could be enrolled. Patients were advised to document their steps (daily), blood pressure (daily), weight (weekly) and occurrences of a cardiac event in a diary for 1 year post-CR. A Cox proportional hazard model was used to examine the influence of steps/day, EF, gender, and age until the occurrence of an event. Kaplan-Meier curves were generated to compare patients’ profiles.

Results:

Average steps/day of patients post-CR were 7333 (SD 4426). Increased walking activity reduced risk for cardiac hospitalization (constant steps/day: 5000 vs 7500, hazard rate (HR) reduction of 0.43; 10 000 vs 12 500, HR reduction of 0.20) and risk was higher in patients with an EF < 55% versus EF ⩾ 55% (HR increase of 2.88). Median follow-up time post-CR was 218 days. No patient died, 25 were hospitalized.

Discussion:

Monitoring the number of steps of coronary patients post CR could be valuable for estimating patients’ prognosis.

Interactions of scores derived from two groups of variables: Alternating lasso regularization avoids overfitting and finds interpretable scores

Regression models with interaction terms are common models for moderating relationships. When effects of several predictors from one group-for example, genetic variables-are potentially moderated by several predictors from another-for example, environmental variables-many interaction terms result. This complicates model interpretation, especially when coefficient signs point in different directions. By first forming a score for each group of predictors, the interaction model's dimension is severely reduced. The hierarchical score model is an elegant one-step approach: Score weights and regression model coefficients are estimated simultaneously by an alternating optimization (AO) algorithm. Especially in high dimensional settings, scores remain an effective technique to reduce interaction model dimension, and we propose regularization to ensure sparsity and interpretability of the score weights. A nontrivial extension of the original AO algorithm is presented, which adds a lasso penalty, resulting in the alternating lasso optimization algorithm (ALOA). The hierarchical score model with ALOA is an interpretable statistical learning technique for moderation in potentially high dimensional applications, and encompasses generalized linear models for the main interaction model. In addition to the lasso regularization, a screening procedure called regularization and residualization (RR) is proposed to avoid spurious interactions. ALOA tuning parameter choice and the RR screening procedure are investigated by simulations, and two illustrative applications to depression risk are provided. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Machine learning and conventional statistics: making sense of the differences

The application of machine learning (ML) to the field of orthopaedic surgery is rapidly increasing, but many surgeons remain unfamiliar with the nuances of this novel technique. With this editorial, we address a fundamental topic-the differences between ML techniques and traditional statistics. By doing so, we aim to further familiarize the reader with the new opportunities available thanks to the ML approach.

Predicting Hospital Readmissions from Health Insurance Claims Data: A Modeling Study Targeting Potentially Inappropriate Prescribing

BACKGROUND

 Numerous prediction models for readmissions are developed from hospital data whose predictor variables are based on specific data fields that are often not transferable to other settings. In contrast, routine data from statutory health insurances (in Germany) are highly standardized, ubiquitously available, and would thus allow for automatic identification of readmission risks.

OBJECTIVES

 To develop and internally validate prediction models for readmissions based on potentially inappropriate prescribing (PIP) in six diseases from routine data.

METHODS

 In a large database of German statutory health insurance claims, we detected disease-specific readmissions after index admissions for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type-2 diabetes mellitus (DM), and osteoporosis (OS). PIP at the index admission was determined by the STOPP/START criteria (Screening Tool of Older Persons' Prescriptions/Screening Tool to Alert doctors to the Right Treatment) which were candidate variables in regularized prediction models for specific readmission within 90 days. The risks from disease-specific models were combined ("stacked") to predict all-cause readmission within 90 days. Validation performance was measured by the c-statistics.

RESULTS

 While the prevalence of START criteria was higher than for STOPP criteria, more single STOPP criteria were selected into models for specific readmissions. Performance in validation samples was the highest for DM (c-statistics: 0.68 [95% confidence interval (CI): 0.66-0.70]), followed by COPD (c-statistics: 0.65 [95% CI: 0.64-0.67]), S/AF (c-statistics: 0.65 [95% CI: 0.63-0.66]), HF (c-statistics: 0.61 [95% CI: 0.60-0.62]), AMI (c-statistics: 0.58 [95% CI: 0.56-0.60]), and OS (c-statistics: 0.51 [95% CI: 0.47-0.56]). Integrating risks from disease-specific models to a combined model for all-cause readmission yielded a c-statistics of 0.63 [95% CI: 0.63-0.64].

CONCLUSION

 PIP successfully predicted readmissions for most diseases, opening the possibility for interventions to improve these modifiable risk factors. Machine-learning methods appear promising for future modeling of PIP predictors in complex older patients with many underlying diseases.

Artificial intelligence and machine learning: an introduction for orthopaedic surgeons

The application of artificial intelligence (AI) and machine learning to the field of orthopaedic surgery is rapidly increasing. While this represents an important step in the advancement of our specialty, the concept of AI is rich with statistical jargon and techniques unfamiliar to many clinicians. This knowledge gap may limit the impact and potential of these novel techniques. We aim to narrow this gap in a way that is accessible for all orthopaedic surgeons. With this manuscript, we introduce the concept of AI and machine learning and give examples of how it can impact clinical practice and patient care.Level of evidence VI.

Maximum isometric torque at individually-adjusted joint angles exceeds eccentric and concentric torque in lower extremity joint actions

Background

Previous research indicates the high relevance of optimal joint angles for individual isometric strength assessment. The objective was to compare lower limb peak isometric muscle strength abilities at the strongest joint angles with those of dynamic contractions in healthy young adults.

Methods

Eighteen young male adults performed maximum concentric, isometric, and eccentric contractions of the ankle, knee, and hip flexors and extensors, and hip adductors and abductors in a randomized sequence on an isokinetic dynamometer (ISOMED 2000). Angular velocity was set at 60°/s. The peak of concentric contraction torque curves was used to define optimal joint angles best suited to generate maximum torque during isometric contractions. Maximum voluntary contraction torque of all contraction conditions was adjusted for limb weight and analyzed via a generalized linear mixed gamma regression model (GLMM).

Results

The gamma GLMM revealed strongly significant effects for all three categorical covariates (contraction types, muscle group, and test order) (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p < 2 \times 10^{ - 16}$$\end{document}p<2×10-16). Eccentric contraction increases the muscle torque (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\beta }_{k} = 0.147$$\end{document}β^k=0.147) compared to concentric contraction, and isometric contraction even more (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\beta }_{k} = 0.258$$\end{document}β^k=0.258). A moderate individual-specific variation was found (random effects standard deviation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma_{b} = 0.093$$\end{document}σb=0.093).

Conclusion

The results support the importance of optimal joint angles for isometric maximum strength assessment. When such conditions are given, isometric contractions can produce higher muscle torques than eccentric contractions in the lower body.

A machine learning approach for modelling the occurrence of Galba truncatula as the major intermediate host for Fasciola hepatica in Switzerland

Fasciolosis caused by the trematode Fasciola hepatica is an important parasitosis in both livestock and humans across the globe. Chronic infections in cattle are associated with considerable economic losses. As a prerequisite for an effective control and prevention of fasciolosis in cattle fine-scale predictive models on farm-level are needed. Since disease transmission will only occur where the mollusc intermediate host is present, the objective of our research was to develop a regression model that allows to predict the local presence or absence of Galba truncatula as principal intermediate host for Fasciola hepatica in Switzerland. By implementing generalized linear mixed models (GLMMs) a total amount of 70 variables were analysed for their potential influence on the likelihood π_i of finding Galba truncatula at a certain site. Important site-specific features could be considered by selecting suitable modelling procedures. The statistical software R was used to conduct regression analysis, performing the grplasso and the glmmLasso method. The selection of parameters was based on 10-fold cross validation and the Bayesian Information Criterion (BIC). This yielded a total number of 19 potential predictor variables for the grplasso and 13 variables for the glmmLasso model, which also included random effects. Nine variables appeared to be relevant predictors for the occurrence of Galba truncatula in both models. These included reed/humid area, spring water, water bodies within a 100 m radius, and trees/bushes as powerful positive predictors. High soil depth, temperatures frequently exceeding 30 °C in the year preceding the search for snails and temperatures below 0 °C especially in the second year before were identified to exert an adverse effect on the occurrence of Galba truncatula. Temperatures measured near ground level proved to be more powerful predictors than macroclimatic parameters. Precipitation values seemed to be of minor impact in the given setting. Both regression models may be convenient for a fine-scale prediction of the occurrence of Galba truncatula, and thus provide useful approaches for the development of future spatial transmission models, mapping the risk of fasciolosis in Switzerland on farm-level.

Sex differences in injury rates in team-sport athletes: A systematic review and meta-regression analysis

BACKGROUND

Team-sport players have a particularly high injury risk. Although female sex is considered a risk factor, it is still unknown whether female and male team-sport players, in fact, differ in their injury rates. We aimed to compare injury rates between female and male players by systematically reviewing and meta-analyzing injury surveillance studies of both sexes in order to evaluate sex-specific differences in team-sport injuries.

METHODS

Studies that prospectively collected injury data for high-level female and male players (age ≥16 years) in basketball, field hockey, football (soccer), handball, rugby (union and sevens), and volleyball were included. Two reviewers (AZ and ALR) independently assessed study quality and extracted data for overall, match, training, and severe injuries (>28 days' time loss) as well as data regarding injury locations and types. Incidence rate ratios (IRRs) were pooled in a meta-analysis, and meta-regression analysis was performed when 10 or more studies were available.

RESULTS

Of 20 studies, 9 studies reported injury data from football, 3 studies from rugby, 3 studies from handball, 1 study from basketball, 1 study from field hockey, 2 studies from volleyball, and 1 study from basketball and field hockey. For overall injuries, the pooled IRR = 0.86 (95% confidence interval (95%CI): 0.76-0.98) indicated significantly more injuries in male than in female players. For injury location, the pooled IRR showed higher injury rates in male athletes than in female athletes for upper extremity, hip/groin, thigh, and foot injuries. Female players had a significantly higher rate of anterior cruciate ligament injuries (IRR = 2.15, 95%CI: 1.27-3.62) than male players. No significant sex-specific differences in IRR were found for match, training, severe injuries, concussions, or ankle sprains.

CONCLUSION

Our meta-analysis provides evidence for sex-specific differences in the injury rates in team sports. Further epidemiological studies including both sexes in sports other than football are needed in order to strengthen the evidence.

Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants

BACKGROUND

Decision making for the "best" treatment is particularly challenging in situations in which individual patient response to drugs can largely differ from average treatment effects. By estimating individual treatment effects (ITEs), we aimed to demonstrate how strokes, major bleeding events, and a composite of both could be reduced by model-assisted recommendations for a particular direct oral anticoagulant (DOAC).

METHODS

In German claims data for the calendar years 2014-2018, we selected 29 901 new users of the DOACs rivaroxaban and apixaban. Random forests considered binary events within 1 y to estimate ITEs under each DOAC according to the X-learner algorithm with 29 potential effect modifiers; treatment recommendations were based on these estimated ITEs. Model performance was evaluated by the c-for-benefit statistics, absolute risk reduction (ARR), and absolute risk difference (ARD) by trial emulation.

RESULTS

A significant proportion of patients would be recommended a different treatment option than they actually received. The stroke model significantly discriminated patients for higher benefit and thus indicated improved decisions by reduced outcomes (c-for-benefit: 0.56; 95% confidence interval [0.52; 0.60]). In the group with apixaban recommendation, the model also improved the composite endpoint (ARR: 1.69 % [0.39; 2.97]). In trial emulations, model-assisted recommendations significantly reduced the composite event rate (ARD: -0.78 % [-1.40; -0.03]).

CONCLUSIONS

If prescribers are undecided about the potential benefits of different treatment options, ITEs can support decision making, especially if evidence is inconclusive, risk-benefit profiles of therapeutic alternatives differ significantly, and the patients' complexity deviates from "typical" study populations. In the exemplary case for DOACs and potentially in other situations, the significant impact could also become practically relevant if recommendations were available in an automated way as part of decision making.HighlightsIt was possible to calculate individual treatment effects (ITEs) from routine claims data for rivaroxaban and apixaban, and the characteristics between the groups with recommendation for one or the other option differed significantly.ITEs resulted in recommendations that were significantly superior to usual (observed) treatment allocations in terms of absolute risk reduction, both separately for stroke and in the composite endpoint of stroke and major bleeding.When similar patients from routine data were selected (precision cohorts) for patients with a strong recommendation for one option or the other, those similar patients under the respective recommendation showed a significantly better prognosis compared with the alternative option.Many steps may still be needed on the way to clinical practice, but the principle of decision support developed from routine data may point the way toward future decision-making processes.

1162. Antifungal Use in Immunocompromised Children in Europe: a 12-week Multicenter Modified Point prevalence Study (CALYPSO)

Background

While antifungal consumption in immunocompromised patients appears high, data on children are limited. We analyzed antifungal use in hospitalized immunocompromised children across Europe in order to better organize a European pediatric antifungal stewardship programs (pAFS).

Methods

A multicenter 12-wk modified point-prevalence study was conducted. All patients hospitalized in hematology-oncology (HO) or bone marrow/solid organ transplant (BMT/SOT) units across Europe and receiving systemic antifungals were included. Data on ward demographics and policies were collected once at the beginning; weekly ward and patient data were prospectively collected during the 12-wk study period and entered in REDCap. Systemic antifungals administered were recorded (doses, duration, reason for administration or discontinuation).

Results

Twenty-one HO and 10 BMT/SOT units from 18 hospitals in 11 European countries participated in the study and 572 antifungal prescriptions were recorded. The most common underlying conditions were: 69% malignancy (81% hematologic, 19% solid tumors), 20% BMT, 6% hematologic diseases except malignancy and 4% primary immunodeficiency. Indication of antifungals was prophylaxis for 439 (77%) and treatment for 133 (23%) prescriptions (62 empirical, 43 pre-emptive, 28 targeted). Most common reasons for empirical, pre-emptive and targeted treatment were antibiotic-resistant febrile neutropenia (52%), abnormalities on chest-CT with/without positive galactomannan (72%) and candidiasis (82%), respectively. For targeted treatment, the most frequent pathogens were C. albicans (50%), C. parapsilosis (11%) and A. fumigatus (11%). Overall, fluconazole and liposomal amphotericin B were the most frequently prescribed agents both for prophylaxis (31% and 21%) and treatment (32% and 23%). However, in BMT patients the most frequently prescribed antifungal agents were fluconazole or micafungin for prophylaxis and caspofungin and voriconazole for treatment (Table).

Antifungal agents used per underlying condition

Conclusion

Most systemic antifungal prescribing across European HO and BMT/SOT units is for prophylaxis, and fluconazole is the main antifungal prescribed. Results from this multicenter study can be a first step to guide a Europe-wide pAFS.

Disclosures

Emmanuel Roilides, MD, PhD, ECMM (Research Grant or Support, Other Financial or Material Support, ECMM grant for this study)

An HLA-I signature favouring KIR-educated Natural Killer cells mediates immune control of HIV in children and contrasts with the HLA-B-restricted CD8+ T-cell-mediated immune control in adults

Natural Killer (NK) cells contribute to HIV control in adults, but HLA-B-mediated T-cell activity has a more substantial impact on disease outcome. However, the HLA-B molecules influencing immune control in adults have less impact on paediatric infection. To investigate the contribution NK cells make to immune control, we studied >300 children living with HIV followed over two decades in South Africa. In children, HLA-B alleles associated with adult protection or disease-susceptibility did not have significant effects, whereas Bw4 (p = 0.003) and low HLA-A expression (p = 0.002) alleles were strongly associated with immunological and viral control. In a comparator adult cohort, Bw4 and HLA-A expression contributions to HIV disease outcome were dwarfed by those of protective and disease-susceptible HLA-B molecules. We next investigated the immunophenotype and effector functions of NK cells in a subset of these children using flow cytometry. Slow progression and better plasma viraemic control were also associated with high frequencies of less terminally differentiated NKG2A+NKp46+CD56^dim NK cells strongly responsive to cytokine stimulation and linked with the immunogenetic signature identified. Future studies are indicated to determine whether this signature associated with immune control in early life directly facilitates functional cure in children.

In adults, immune control of HIV is strongly influenced by antiviral CD8+ T-cell responses restricted by ‘protective’ HLA class I molecules, such as HLA-B*57, and by ‘disease-susceptible’ HLA class I molecules such as HLA-B*58:02. By contrast, Natural Killer (NK) cells responses make a smaller, albeit significant, contribution. In this study, we evaluate in children living with HIV the contribution of NK cell responses to immune control of HIV, in an age group where HIV-specific CD8+ T-cell responses have less impact on disease outcome. A cohort of >300 therapy-naïve children living with HIV shows that a genetic signature favouring a KIR-education on NK cells is associated with slow progression and better viraemic control. Consistent with this, we observed control of HIV viraemia and lower total HIV DNA levels among children was associated with a less differentiated NKG2A+NKp46+ CD56^dim NK cell population that functionally was highly responsive to cytokine stimulation. Thus, the study identifies a signature that can impact future therapeutic strategies to achieve remission in children.

Joint Modelling Approaches to Survival Analysis via Likelihood-Based Boosting Techniques

Joint models are a powerful class of statistical models which apply to any data where event times are recorded alongside a longitudinal outcome by connecting longitudinal and time-to-event data within a joint likelihood allowing for quantification of the association between the two outcomes without possible bias. In order to make joint models feasible for regularization and variable selection, a statistical boosting algorithm has been proposed, which fits joint models using component-wise gradient boosting techniques. However, these methods have well-known limitations, i.e., they provide no balanced updating procedure for random effects in longitudinal analysis and tend to return biased effect estimation for time-dependent covariates in survival analysis. In this manuscript, we adapt likelihood-based boosting techniques to the framework of joint models and propose a novel algorithm in order to improve inference where gradient boosting has said limitations. The algorithm represents a novel boosting approach allowing for time-dependent covariates in survival analysis and in addition offers variable selection for joint models, which is evaluated via simulations and real world application modelling CD4 cell counts of patients infected with human immunodeficiency virus (HIV). Overall, the method stands out with respect to variable selection properties and represents an accessible way to boosting for time-dependent covariates in survival analysis, which lays a foundation for all kinds of possible extensions.

The Interval Between Matches Significantly Influences Injury Risk in Field Hockey

Fixture congestion increases injury risk in football, but how it impacts other sports is unclear. The aim of this study was to identify associations between match density and injury incidence in field hockey players. Injury data from a prospective cohort study of professional and youth players was analysed in two ways. Inter-match intervals were clustered into<2424-hours, 3–7-days, and 13 + days, and injury rate ratios (IRR) were calculated to identify differences between clusters in match injuries. Separately, a Lasso-penalised Poisson regression model was used to determine the association between match load across the previous 24-hours, 3-days, 7-days and 14-days, and match and training injuries. Injury rates in matches within 24-hours of the previous match were mostly significantly higher when compared to matches after 3–7-days (IRRs: 3.78; 6.77, P = 0.003; 0.005). While a higher match exposure in the preceding 24-hour and 3-day periods was associated with higher combined match and training injury rates (β̂ = 0.0001; 0.0018), a higher match exposure in the previous 7-and 14-day periods was associated with a reduced injury rate (β̂ = –0.0001; –0.0005). Due to the increased injury risk in matches 3-days and especially 24-hours following the previous fixture, match distribution should be cautiously planned.

Addressing cluster-constant covariates in mixed effects models via likelihood-based boosting techniques

Boosting techniques from the field of statistical learning have grown to be a popular tool for estimating and selecting predictor effects in various regression models and can roughly be separated in two general approaches, namely gradient boosting and likelihood-based boosting. An extensive framework has been proposed in order to fit generalized mixed models based on boosting, however for the case of cluster-constant covariates likelihood-based boosting approaches tend to mischoose variables in the selection step leading to wrong estimates. We propose an improved boosting algorithm for linear mixed models, where the random effects are properly weighted, disentangled from the fixed effects updating scheme and corrected for correlations with cluster-constant covariates in order to improve quality of estimates and in addition reduce the computational effort. The method outperforms current state-of-the-art approaches from boosting and maximum likelihood inference which is shown via simulations and various data examples.

Early initiation of antiretroviral therapy following in utero HIV infection is associated with low viral reservoirs but other factors determine subsequent plasma viral rebound

Background

Early HIV diagnosis allows combination antiretroviral therapy (cART) initiation in the first days of life following in utero (IU) infection. The impact of early cART initiation on infant viral reservoir size in the setting of high-frequency cART nonadherence is unknown.

Methods

Peripheral blood total HIV DNA from 164 early treated (day 0–21 of life) IU HIV-infected South African infants was measured using droplet digital PCR at birth and following suppressive cART. We evaluated the impact of cART initiation timing on HIV reservoir size and decay, and on the risk of subsequent plasma viremia in cART-suppressed infants.

Results

Baseline HIV DNA (median 2.8 log₁₀ copies/million peripheral blood mononuclear cells, range 0.7–4.8) did not correlate with age at cART initiation (0–21 days) but instead with maternal antenatal cART use. In 98 infants with plasma viral suppression on cART, HIV DNA half-life was 28 days. However, the probability of maintenance of plasma aviremia was low (0.46 at 12 months) and not influenced by HIV DNA load. Unexpectedly, longer time to viral suppression was associated with protection against subsequent viral rebound.

Conclusions

With effective prophylaxis against mother-to-child transmission, cART initiation timing in the first 3 weeks of life is not critical to reservoir size.

Disease-dependent variations in the timing and causes of readmissions in Germany: A claims data analysis for six different conditions

BACKGROUND

Hospital readmissions place a major burden on patients and health care systems worldwide, but little is known about patterns and timing of readmissions in Germany.

METHODS

We used German health insurance claims (AOK, 2011-2016) of patients ≥ 65 years hospitalized for acute myocardial infarction (AMI), heart failure (HF), a composite of stroke, transient ischemic attack, or atrial fibrillation (S/AF), chronic obstructive pulmonary disease (COPD), type 2 diabetes mellitus, or osteoporosis to identify hospital readmissions within 30 or 90 days. Readmissions were classified into all-cause, specific, and non-specific and their characteristics were analyzed.

RESULTS

Within 30 and 90 days, about 14-22% and 27-41% index admissions were readmitted for any reason, respectively. HF and S/AF contributed most index cases, and HF and COPD accounted for most all-cause readmissions. Distributions and ratios of specific to non-specific readmissions were disease-specific with highest specific readmissions rates among COPD and AMI.

CONCLUSION

German claims are well-suited to investigate readmission causes if longer periods than 30 days are evaluated. Conditions closely related with the primary disease are the most frequent readmission causes, but multiple comorbidities among readmitted cases suggest that a multidisciplinary care approach should be implemented vigorously addressing comorbidities already during the index hospitalization.

Game Exposure, Player Characteristics, and Neuromuscular Performance Influence Injury Risk in Professional and Youth Field Hockey Players

BACKGROUND

Identifying factors that contribute to the occurrence of injury is an important step in designing training programs to minimize the risk of injury. However, despite high injury rates, variables contributing to injury in field hockey players remain relatively unexplored.

PURPOSE

To identify factors that may predict injury in professional and youth field hockey players.

STUDY DESIGN

Cohort study; Level of evidence, 2.

METHOD

Professional and youth hockey players completed preseason neuromuscular performance testing and were monitored for injuries, training, and game exposure throughout the subsequent 12-month indoor and outdoor hockey season. Least absolute shrinkage and selection operator analysis was conducted to identify injury risk factors. Receiver operating characteristic curves were then calculated to determine the individual predictive accuracy of the identified variables.

RESULTS

A total of 83 players (mean ± SD age, 20.7 ± 4.9 years; 34.9% female) with complete performance, injury, and exposure data were included for data analysis. Almost half of players (44.6%) sustained a time-loss injury during the season, and 73% of these injuries occurred in the lower limb. Playing more games and having an older age, asymmetrical and poor dynamic postural control, and better explosive performance were identified as jointly influencing the risk of injury. When considered individually, number of games played throughout the season was the most accurate predictor of injury risk (area under the curve [AUC] = 0.74; P < .001), while asymmetrical and poor dynamic postural control (AUCs = 0.61-0.65; P = .01-.04) and better explosive athletic performance (AUCs = 0.65-0.67; P < .01) were identified as moderate individual predictors of sustaining a general or lower limb injury.

CONCLUSION

A number of modifiable factors were individually and jointly associated with an increased injury risk in field hockey players, providing initial evidence for the design of targeted and sport-specific training programs to mitigate the risk of injury.

Using the Causal Inference Framework to Support Individualized Drug Treatment Decisions Based on Observational Healthcare Data

When healthcare professionals have the choice between several drug treatments for their patients, they often experience considerable decision uncertainty because many decisions simply have no single “best” choice. The challenges are manifold and include that guideline recommendations focus on randomized controlled trials whose populations do not necessarily correspond to specific patients in everyday treatment. Further reasons may be insufficient evidence on outcomes, lack of direct comparison of distinct options, and the need to individually balance benefits and risks. All these situations will occur in routine care, its outcomes will be mirrored in routine data, and could thus be used to guide decisions. We propose a concept to facilitate decision-making by exploiting this wealth of information. Our working example for illustration assumes that the response to a particular (drug) treatment can substantially differ between individual patients depending on their characteristics (heterogeneous treatment effects, HTE), and that decisions will be more precise if they are based on real-world evidence of HTE considering this information. However, such methods must account for confounding by indication and effect measure modification, eg, by adequately using machine learning methods or parametric regressions to estimate individual responses to pharmacological treatments. The better a model assesses the underlying HTE, the more accurate are predicted probabilities of treatment response. After probabilities for treatment-related benefit and harm have been calculated, decision rules can be applied and patient preferences can be considered to provide individual recommendations. Emulated trials in observational data are a straightforward technique to predict the effects of such decision rules when applied in routine care. Prediction-based decision rules from routine data have the potential to efficiently supplement clinical guidelines and support healthcare professionals in creating personalized treatment plans using decision support tools.

Author Correction: Sex-specific innate immune selection of HIV-1 in utero is associated with increased female susceptibility to infection

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

HIGH-FREQUENCY failure of combination antiretroviral therapy in paediatric HIV infection is associated with unmet maternal needs causing maternal NON-ADHERENCE

BACKGROUND

Early combination antiretroviral therapy (cART) reduces the size of the viral reservoir in paediatric and adult HIV infection. Very early-treated children may have higher cure/remission potential.

METHODS

In an observational study of 151 in utero (IU)-infected infants in KwaZulu-Natal, South Africa, whose treatment adhered strictly to national guidelines, 76 infants diagnosed via point-of-care (PoC) testing initiated cART at a median of 26 h (IQR 18-38) and 75 infants diagnosed via standard-of-care (SoC) laboratory-based testing initiated cART at 10 days (IQR 8-13). We analysed mortality, time to suppression of viraemia, and maintenance of aviraemia over the first 2 years of life.

FINDINGS

Baseline plasma viral loads were low (median 8000 copies per mL), with 12% of infants having undetectable viraemia pre-cART initiation. However, barely one-third (37%) of children achieved suppression of viraemia by 6 months that was maintained to >12 months. 24% had died or were lost to follow up by 6 months. Infant mortality was 9.3%. The high-frequency virological failure in IU-infected infants was associated not with transmitted or acquired drug-resistant mutations but with cART non-adherence (plasma cART undetectable/subtherapeutic, p<0.0001) and with concurrent maternal cART failure (OR 15.0, 95%CI 5.6-39.6; p<0.0001). High-frequency virological failure was observed in PoC- and SoC-tested groups of children.

INTERPRETATION

The success of early infant testing and cART initiation strategies is severely limited by subsequent cART non-adherence in HIV-infected children. Although there are practical challenges to administering paediatric cART formulations, these are overcome by mothers who themselves are cART-adherent. These findings point to the ongoing obligation to address the unmet needs of the mothers. Eliminating the particular barriers preventing adequate treatment for these vulnerable women and infants need to be prioritised in order to achieve durable suppression of viraemia on cART, let alone HIV cure/remission, in HIV-infected children.

FUNDING

Wellcome Trust, National Institutes of Health.

Plasma IL-5 but Not CXCL13 Correlates With Neutralization Breadth in HIV-Infected Children

Children may be the optimal target for HIV vaccine development as they generate substantially more frequent and more potent broadly HIV neutralizing antibodies (bnAbs) than adults. Development of a biomarker that correlates with neutralization breadth in this group could function as a powerful tool to facilitate the development of an HIV vaccine. Previously, we observed that this preferential ability in HIV-infected children over adults to generate bnAbs is associated with an enrichment of circulating follicular helper T-cells (TFH) with an effector phenotype, and the presence of IL-21 secreting HIV-specific TFH within lymphoid tissue germinal centers (GC). In adults, bnAbs development has been linked with high plasma levels of CXCL13, a chemoattractant for CXCR5-expressing TFH cells to the lymph node GC. We sought to test this relationship in HIV-infected children, but found no association between neutralization breadth and plasma levels of CXCL13, or with the Th2 cytokines IL-4 and IL-13, or the TFH associated factor Activin A. However, we did find an unexpected association between plasma IL-5 levels and bnAb development in these children. Importantly, although CXCL13 correlated with total circulating TFH cells, it was not associated with effector TFH. Additionally, raised CXCL13 expression was associated with a lower CD4 percentage, higher viral load and a loss of immune function, implying it is associated with progressive disease rather than HIV-specific GC activity in these subjects. Taken together, our data suggests that IL-5 should be evaluated further as a candidate plasma biomarker for HIV neutralization breadth and for monitoring vaccine responses in the pediatric age group.

Increased Regulatory T-Cell Activity and Enhanced T-Cell Homeostatic Signaling in Slow Progressing HIV-infected Children

Pediatric slow progressors (PSP) are rare ART-naïve, HIV-infected children who maintain high CD4 T-cell counts and low immune activation despite persistently high viral loads. Using a well-defined cohort of PSP, we investigated the role of regulatory T-cells (TREG) and of IL-7 homeostatic signaling in maintaining normal-for-age CD4 counts in these individuals. Compared to children with progressive disease, PSP had greater absolute numbers of TREG, skewed toward functionally suppressive phenotypes. As with immune activation, overall T-cell proliferation was lower in PSP, but was uniquely higher in central memory TREG (CM TREG), indicating active engagement of this subset. Furthermore, PSP secreted higher levels of the immunosuppressive cytokine IL-10 than children who progressed. The frequency of suppressive TREG, CM TREG proliferation, and IL-10 production were all lower in PSP who go on to progress at a later time-point, supporting the importance of an active TREG response in preventing disease progression. In addition, we find that IL-7 homeostatic signaling is enhanced in PSP, both through preserved surface IL-7receptor (CD127) expression on central memory T-cells and increased plasma levels of soluble IL-7receptor, which enhances the bioactivity of IL-7. Combined analysis, using a LASSO modeling approach, indicates that both TREG activity and homeostatic T-cell signaling make independent contributions to the preservation of CD4 T-cells in HIV-infected children. Together, these data demonstrate that maintenance of normal-for-age CD4 counts in PSP is an active process, which requires both suppression of immune activation through functional TREG, and enhanced T-cell homeostatic signaling.

Prediction of Drug-Related Risks Using Clinical Context Information in Longitudinal Claims Data

OBJECTIVES

To develop and internally validate prediction models for medication-related risks arising from overuse, misuse, and underuse that utilize clinical context information and are suitable for routine risk assessment in claims data (i.e., medication-based models predicting the risk for hospital admission apparent in routine claims data or MEDI-RADAR).

METHODS

Based on nationwide claims from health-insured persons in Germany between 2010 and 2012, we drew a random sample of people aged ≥65 years (N = 22,500 randomly allocated to training set, N = 7500 to validation set). Individual duration of drug supply was estimated from prescription patterns to yield time-varying drug exposure windows. Together with concurrent medical conditions (ICD-10 diagnoses), exposure to the STOPP/START (screening tool of older persons' potentially inappropriate prescriptions/screening tool to alert doctors to the right treatment) criteria was derived. These were tested as time-dependent covariates together with time-constant covariates (patient demographics, baseline comorbidities) in regularized Cox regression models.

RESULTS

STOPP/START variables were iteratively refined and selected by regularization to include 2 up to 11 START variables and 8 up to 31 STOPP variables in parsimonious and liberal selections in the prediction modeling. The models discriminated well between patients with and without all-cause hospitalizations, potentially drug-induced hospitalizations, and mortality (parsimonious model c-indices with 95% confidence intervals: 0.63 [0.62-0.64], 0.67 [0.65-0.68], and 0.78 [0.76-0.80]).

CONCLUSIONS

The STOPP/START criteria proved to efficiently predict medication-related risk in models possessing good performance. Timely detection of such risks by routine monitoring in claims data can support tailored interventions targeting these modifiable risk factors. Their impact on older peoples' medication safety and effectiveness can now be explored in future implementation studies.

Walking activity during ambulant cardiac rehabilitation is related to maximum working capacity, age, and smoking behavior

BACKGROUND

A total of 6,500 to 8,000 steps per day are recommended for cardiovascular secondary prevention. The aim of this research was to examine how many steps per day patients achieve during ambulant cardiac rehabilitation (CR), and if there is a correlation between the number of steps and physical and cardiological parameters.

METHODS

In all, 192 stable CR patients were included and advised for sealed pedometry. The assessed parameters included maximum working capacity and heart rate, body mass index (BMI), New York Heart Association (NYHA) class, ejection fraction (EF), coronary artery disease status, beta-blocker medication, age, sex, smoking behavior, and laboratory parameters. A regularized regression approach called least absolute shrinkage and selection operator (LASSO) was used to detect a small set of explanatory variables associated with the response for steps per day. Based on these selected covariates, a sparse additive regression model was fitted.

RESULTS

The model noted that steps per day had a strong positive correlation with maximum working capacity (P=0.001), a significant negative correlation with higher age (P=0.01) and smoking (smoker: P<0.05; ex-smoker: P=0.01), a positive correlation with high-density lipoprotein (HDL), and a negative correlation with beta-blockers. Correlation between BMI and walking activity was nonlinear (BMI 18.5-24: 7,427±2,730 steps per day; BMI 25-29: 6,448±2,393 steps/day; BMI 30-34: 6,751±2,393 steps per day; BMI 35-39: 5,163±2,574; BMI >40: 6,077±1,567).

CONCLUSION

Walking activity during CR is reduced in patients who are unfit, older, smoke, or used to smoke. In addition to training recommendations, estimated steps per day during CR could be seen as a baseline orientation that helps patients to stay generally active or even to increase activity after CR.

Predicting matches in international football tournaments with random forests

Many approaches that analyse and predict results of international matches in football are based on statistical models incorporating several potentially influential covariates with respect to a national team's success, such as the bookmakers’ ratings or the FIFA ranking. Based on all matches from the four previous FIFA World Cups 2002–2014, we compare the most common regression models that are based on the teams’ covariate information with regard to their predictive performances with an alternative modelling class, the so-called random forests. Random forests can be seen as a mixture between machine learning and statistical modelling and are known for their high predictive power. Here, we consider two different types of random forests depending on the choice of response. One type of random forests predicts the precise numbers of goals, while the other type considers the three match outcomes—win, draw and loss—using special algorithms for ordinal responses. To account for the specific data structure of football matches, in particular at FIFA World Cups, the random forest methods are slightly altered compared to their standard versions and adapted to the specific needs of the application to FIFA World Cup data.

[Recommendations for Diagnostics and Therapy of Children with Cancer Presenting with Fever and Neutropenia - Comparison of Two Current Guidelines]

Immunocompromised children and adolescents receiving treatment for cancer have a considerably increased risk for infection. Neutropenia is the most important single risk factor for infectious complications, and fever in neutropenia is considered as an emergency. Whereas guidelines for the management of fever in neutropenic adults have been established for decades, specific pediatric guidelines have not been developed until recently. As children differ in many aspects from adults such as in the underlying malignancy or in the availability and dosing of antimicrobial compounds, guidelines for pediatric patients are important. This article reviews similarities and differences between the recently published German interdisciplinary guideline of the German Societies of Pediatric Infectious Diseases and Pediatric Oncology and Hematology and a guideline developed by a panel of international experts for the management of fever in neutropenia in children and adolescents.

A generalized additive model approach to time-to-event analysis

Abstract:

This tutorial article demonstrates how time-to-event data can be modelled in a very flexible way by taking advantage of advanced inference methods that have recently been developed for generalized additive mixed models. In particular, we describe the necessary pre-processing steps for transforming such data into a suitable format and show how a variety of effects, including a smooth nonlinear baseline hazard, and potentially nonlinear and nonlinearly time-varying effects, can be estimated and interpreted. We also present useful graphical tools for model evaluation and interpretation of the estimated effects. Throughout, we demonstrate this approach using various application examples. The article is accompanied by a new R -package called pammtools implementing all of the tools described here.

Malnutrition in HIV-Infected Children Is an Indicator of Severe Disease with an Impaired Response to Antiretroviral Therapy

This observational study aimed to describe immunopathogenesis and treatment outcomes in children with and without severe acute malnutrition (SAM) and HIV-infection. We studied markers of microbial translocation (16sDNA), intestinal damage (iFABP), monocyte activation (sCD14), T-cell activation (CD38, HLA-DR) and immune exhaustion (PD1) in 32 HIV-infected children with and 41 HIV-infected children without SAM prior to initiation of antiretroviral therapy (ART) and cross-sectionally compared these children to 15 HIV-uninfected children with and 19 HIV-uninfected children without SAM. We then prospectively measured these markers and correlated them to treatment outcomes in the HIV-infected children at 48 weeks following initiation of ART. Plasma levels of 16sDNA, iFABP and sCD14 were measured by quantitative real time PCR, ELISA and Luminex, respectively. T cell phenotype markers were measured by flow cytometry. Multiple regression analysis was performed using generalized linear models (GLMs) and the least absolute shrinkage and selection operator (LASSO) approach for variable selection. Microbial translocation, T cell activation and exhaustion were increased in HIV-uninfected children with SAM compared to HIV-uninfected children without SAM. In HIV-infected children microbial translocation, immune activation, and exhaustion was strongly increased but did not differ by SAM-status. SAM was associated with increased mortality rates early after ART initiation. Malnutrition, age, microbial translocation, monocyte, and CD8 T cell activation were independently associated with decreased rates of CD4% immune recovery after 48 weeks of ART. SAM is associated with increased microbial translocation, immune activation, and immune exhaustion in HIV-uninfected children and with worse prognosis and impaired immune recovery in HIV-infected children on ART.

Nonprogressing HIV-infected children share fundamental immunological features of nonpathogenic SIV infection

Disease-free infection in HIV-infected adults is associated with human leukocyte antigen-mediated suppression of viremia, whereas in the sooty mangabey and other healthy natural hosts of simian immunodeficiency virus (SIV), viral replication continues unabated. To better understand factors preventing HIV disease, we investigated pediatric infection, where AIDS typically develops more rapidly than in adults. Among 170 nonprogressing antiretroviral therapy-naïve children aged >5 years maintaining normal-for-age CD4 T cell counts, immune activation levels were low despite high viremia (median, 26,000 copies/ml). Potent, broadly neutralizing antibody responses in most of the subjects and strong virus-specific T cell activity were present but did not drive pediatric nonprogression. However, reduced CCR5 expression and low HIV infection in long-lived central memory CD4 T cells were observed in pediatric nonprogressors. These children therefore express two cardinal immunological features of nonpathogenic SIV infection in sooty mangabeys-low immune activation despite high viremia and low CCR5 expression on long-lived central memory CD4 T cells-suggesting closer similarities with nonpathogenetic mechanisms evolved over thousands of years in natural SIV hosts than those operating in HIV-infected adults.