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Objective: Self-harm risk prediction models developed using health system data (electronic health records and insurance claims information) often use patient information from up to several years prior to the index visit when the prediction is made. Measurements from some time periods may not be available for all patients. Using the framework of algorithm-agnostic variable importance, we study the predictive potential of variables corresponding to different time horizons prior to the index visit and demonstrate the application of variable importance techniques in the biomedical informatics setting., Materials and Methods: We use variable importance to quantify the potential of recent (up to three months before the index visit) and distant (more than one year before the index visit) patient mental health information for predicting self-harm risk using data from seven health systems. We quantify importance as the decrease in predictiveness when the variable set of interest is excluded from the prediction task. We define predictiveness using discriminative metrics: area under the receiver operating characteristic curve (AUC), sensitivity, and positive predictive value., Results: Mental health predictors corresponding to the three months prior to the index visit show strong signal of importance; in one setting, excluding these variables decreased AUC from 0.85 to 0.77. Predictors corresponding to more distant information were less important., Discussion: Predictors from the months immediately preceding the index visit are highly important. Implementation of self-harm prediction models may be challenging in settings where recent data are not completely available (e.g., due to lags in insurance claims processing) at the time a prediction is made., Conclusion: Clinically derived variables from different time frames exhibit varying levels of importance for predicting self-harm. Variable importance analyses can inform whether and how to implement risk prediction models into clinical practice given real-world data limitations. These analyses be applied more broadly in biomedical informatics research to provide insight into general clinical risk prediction tasks., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Charles J. Wolock reports financial support was provided by National Science Foundation. R. Yates Coley reports financial support was provided by National Institute of Mental Health. Susan M. Shortreed reports a relationship with Bristol Myers Squibb Co that includes: funding grants. Susan M. Shortreed reports a relationship with Pfizer Inc that includes: funding grants. Karen J. Coleman reports a relationship with Janssen Pharmaceuticals Inc that includes: funding grants. Susan M. Shortreed reports a relationship with Syneos Health Inc that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Combination monoclonal broadly neutralizing antibodies (bnAbs) are currently being developed for preventing HIV-1 acquisition. Recent work has focused on predicting in vitro neutralization potency of both individual bnAbs and combination regimens against HIV-1 pseudoviruses using Env sequence features. To predict in vitro combination regimen neutralization potency against a given HIV-1 pseudovirus, previous approaches have applied mathematical models to combine individual-bnAb neutralization and have predicted this combined neutralization value; we call this the combine-then-predict (CP) approach. However, prediction performance for some individual bnAbs has exceeded that for the combination, leading to another possibility: combining the individual-bnAb predicted values and using these to predict combination regimen neutralization; we call this the predict-then-combine (PC) approach. We explore both approaches in both simulated data and data from the Los Alamos National Laboratory's Compile, Neutralize, and Tally NAb Panels repository. The CP approach is superior to the PC approach when the neutralization outcome of interest is binary (e.g., neutralization susceptibility, defined as inhibitory 80% concentration < 1 μg/mL). For continuous outcomes, the CP approach performs nearly as well as the PC approach when the individual-bnAb prediction algorithms have strong performance, and is superior to the PC approach when the individual-bnAb prediction algorithms have poor performance. This knowledge may be used when building prediction models for novel antibody combinations in the absence of in vitro neutralization data for the antibody combination; this, in turn, will aid in the evaluation and down-selection of these antibody combinations into prevention efficacy trials., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Williamson et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Metabolomics has been used extensively to capture the exposome. We investigated whether prospectively measured metabolites provided predictive power beyond well-established risk factors among 758 women with adjudicated cancers [ n = 577 breast (BC) and n = 181 colorectal (CRC)] and n = 758 controls with available specimens (collected mean 7.2 years prior to diagnosis) in the Women's Health Initiative Bone Mineral Density subcohort. Fasting samples were analyzed by LC-MS/MS and lipidomics in serum, plus GC-MS and NMR in 24 h urine. For feature selection, we applied LASSO regression and Super Learner algorithms. Prediction models were subsequently derived using logistic regression and Super Learner procedures, with performance assessed using cross-validation (CV). For BC, metabolites did not increase predictive performance over established risk factors (CV-AUCs~0.57). For CRC, prediction increased with the addition of metabolites (median CV-AUC across platforms increased from ~0.54 to ~0.60). Metabolites related to energy metabolism: adenosine, 2-hydroxyglutarate, N -acetyl-glycine, taurine, threonine, LPC (FA20:3), acetate, and glycerate; protein metabolism: histidine, leucic acid, isoleucine, N -acetyl-glutamate, allantoin, N -acetyl-neuraminate, hydroxyproline, and uracil; and dietary/microbial metabolites: myo-inositol, trimethylamine- N -oxide, and 7-methylguanine, consistently contributed to CRC prediction. Energy metabolism may play a key role in the development of CRC and may be evident prior to disease development.

Objective: To present a general framework providing high-level guidance to developers of computable algorithms for identifying patients with specific clinical conditions (phenotypes) through a variety of approaches, including but not limited to machine learning and natural language processing methods to incorporate rich electronic health record data., Materials and Methods: Drawing on extensive prior phenotyping experiences and insights derived from 3 algorithm development projects conducted specifically for this purpose, our team with expertise in clinical medicine, statistics, informatics, pharmacoepidemiology, and healthcare data science methods conceptualized stages of development and corresponding sets of principles, strategies, and practical guidelines for improving the algorithm development process., Results: We propose 5 stages of algorithm development and corresponding principles, strategies, and guidelines: (1) assessing fitness-for-purpose, (2) creating gold standard data, (3) feature engineering, (4) model development, and (5) model evaluation., Discussion and Conclusion: This framework is intended to provide practical guidance and serve as a basis for future elaboration and extension., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Importance: High-risk medications that contribute to adverse health outcomes are frequently prescribed to older adults. Deprescribing interventions reduce their use, but studies are often not designed to examine effects on patient-relevant health outcomes., Objective: To test the effect of a health system-embedded deprescribing intervention targeting older adults and their primary care clinicians for reducing the use of central nervous system-active drugs and preventing medically treated falls., Design, Setting, and Participants: In this cluster randomized, parallel-group, clinical trial, 18 primary care practices from an integrated health care delivery system in Washington state were recruited from April 1, 2021, to June 16, 2022, to participate, along with their eligible patients. Randomization occurred at the clinic level. Patients were community-dwelling adults aged 60 years or older, prescribed at least 1 medication from any of 5 targeted medication classes (opioids, sedative-hypnotics, skeletal muscle relaxants, tricyclic antidepressants, and first-generation antihistamines) for at least 3 consecutive months., Intervention: Patient education and clinician decision support. Control arm participants received usual care., Main Outcomes and Measures: The primary outcome was medically treated falls. Secondary outcomes included medication discontinuation, sustained medication discontinuation, and dose reduction of any and each target medication. Serious adverse drug withdrawal events involving opioids or sedative-hypnotics were the main safety outcome. Analyses were conducted using intent-to-treat analysis., Results: Among 2367 patient participants (mean [SD] age, 70.6 [7.6] years; 1488 women [63%]), the adjusted cumulative incidence rate of a first medically treated fall at 18 months was 0.33 (95% CI, 0.29-0.37) in the intervention group and 0.30 (95% CI, 0.27-0.34) in the usual care group (estimated adjusted hazard ratio, 1.11 (95% CI, 0.94-1.31) (P = .11). There were significant differences favoring the intervention group in discontinuation, sustained discontinuation, and dose reduction of tricyclic antidepressants at 6 months (discontinuation adjusted rate: intervention group, 0.23 [95% CI, 0.18-0.28] vs usual care group, 0.13 [95% CI, 0.09-0.17]; adjusted relative risk, 1.79 [95% CI, 1.29-2.50]; P = .001) and secondary time points (9, 12, and 15 months)., Conclusions and Relevance: In this randomized clinical trial of a health system-embedded deprescribing intervention targeting community-dwelling older adults prescribed central nervous system-active medications and their primary care clinicians, the intervention was no more effective than usual care in reducing medically treated falls. For health systems that attend to deprescribing as part of routine clinical practice, additional interventions may confer modest benefits on prescribing without a measurable effect on clinical outcomes., Trial Registration: ClinicalTrials.gov Identifier: NCT05689554.

In research assessing the effect of an intervention or exposure, a key secondary objective often involves assessing differential effects of this intervention or exposure in subgroups of interest; this is often referred to as assessing effect modification or heterogeneity of treatment effects (HTE). Observed HTE can have important implications for policy, including intervention strategies (e.g., will some patients benefit more from intervention than others?) and prioritizing resources (e.g., to reduce observed health disparities). Analysis of HTE is well understood in studies where the independent unit is an individual. In contrast, in studies where the independent unit is a cluster (e.g., a hospital or school) and a cluster-level outcome is used in the analysis, it is less well understood how to proceed if the HTE analysis of interest involves an individual-level characteristic (e.g., self-reported race) that must be aggregated at the cluster level. Through simulations, we show that only individual-level models have power to detect HTE by individual-level variables; if outcomes must be defined at the cluster level, then there is often low power to detect HTE by the corresponding aggregated variables. We illustrate the challenges inherent to this type of analysis in a study assessing the effect of an intervention on increasing COVID-19 booster vaccination rates at long-term care centers., (© 2023. The Author(s).)

In the ENSEMBLE randomized, placebo-controlled phase 3 trial (NCT04505722), estimated single-dose Ad26.COV2.S vaccine efficacy (VE) was 56% against moderate to severe-critical COVID-19. SARS-CoV-2 Spike sequences were determined from 484 vaccine and 1,067 placebo recipients who acquired COVID-19. In this set of prespecified analyses, we show that in Latin America, VE was significantly lower against Lambda vs. Reference and against Lambda vs. non-Lambda [family-wise error rate (FWER) p < 0.05]. VE differed by residue match vs. mismatch to the vaccine-insert at 16 amino acid positions (4 FWER p < 0.05; 12 q-value ≤ 0.20); significantly decreased with physicochemical-weighted Hamming distance to the vaccine-strain sequence for Spike, receptor-binding domain, N-terminal domain, and S1 (FWER p < 0.001); differed (FWER ≤ 0.05) by distance to the vaccine strain measured by 9 antibody-epitope escape scores and 4 NTD neutralization-impacting features; and decreased (p = 0.011) with neutralization resistance level to vaccinee sera. VE against severe-critical COVID-19 was stable across most sequence features but lower against the most distant viruses., (© 2024. The Author(s).)

Objectives: Automated phenotyping algorithms can reduce development time and operator dependence compared to manually developed algorithms. One such approach, PheNorm, has performed well for identifying chronic health conditions, but its performance for acute conditions is largely unknown. Herein, we implement and evaluate PheNorm applied to symptomatic COVID-19 disease to investigate its potential feasibility for rapid phenotyping of acute health conditions., Materials and Methods: PheNorm is a general-purpose automated approach to creating computable phenotype algorithms based on natural language processing, machine learning, and (low cost) silver-standard training labels. We applied PheNorm to cohorts of potential COVID-19 patients from 2 institutions and used gold-standard manual chart review data to investigate the impact on performance of alternative feature engineering options and implementing externally trained models without local retraining., Results: Models at each institution achieved AUC, sensitivity, and positive predictive value of 0.853, 0.879, 0.851 and 0.804, 0.976, and 0.885, respectively, at quantiles of model-predicted risk that maximize F1. We report performance metrics for all combinations of silver labels, feature engineering options, and models trained internally versus externally., Discussion: Phenotyping algorithms developed using PheNorm performed well at both institutions. Performance varied with different silver-standard labels and feature engineering options. Models developed locally at one site also worked well when implemented externally at the other site., Conclusion: PheNorm models successfully identified an acute health condition, symptomatic COVID-19. The simplicity of the PheNorm approach allows it to be applied at multiple study sites with substantially reduced overhead compared to traditional approaches., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

In many applications, it is of interest to identify a parsimonious set of features, or panel, from multiple candidates that achieves a desired level of performance in predicting a response. This task is often complicated in practice by missing data arising from the sampling design or other random mechanisms. Most recent work on variable selection in missing data contexts relies in some part on a finite-dimensional statistical model, e.g., a generalized or penalized linear model. In cases where this model is misspecified, the selected variables may not all be truly scientifically relevant and can result in panels with suboptimal classification performance. To address this limitation, we propose a nonparametric variable selection algorithm combined with multiple imputation to develop flexible panels in the presence of missing-at-random data. We outline strategies based on the proposed algorithm that achieve control of commonly used error rates. Through simulations, we show that our proposal has good operating characteristics and results in panels with higher classification and variable selection performance compared to several existing penalized regression approaches in cases where a generalized linear model is misspecified. Finally, we use the proposed method to develop biomarker panels for separating pancreatic cysts with differing malignancy potential in a setting where complicated missingness in the biomarkers arose due to limited specimen volumes., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

In the Antibody Mediated Prevention (AMP) trials (HVTN 704/HPTN 085 and HVTN 703/HPTN 081), prevention efficacy (PE) of the monoclonal broadly neutralizing antibody (bnAb) VRC01 (vs. placebo) against HIV-1 acquisition diagnosis varied according to the HIV-1 Envelope (Env) neutralization sensitivity to VRC01, as measured by 80% inhibitory concentration (IC80). Here, we performed a genotypic sieve analysis, a complementary approach to gaining insight into correlates of protection that assesses how PE varies with HIV-1 sequence features. We analyzed HIV-1 Env amino acid (AA) sequences from the earliest available HIV-1 RNA-positive plasma samples from AMP participants diagnosed with HIV-1 and identified Env sequence features that associated with PE. The strongest Env AA sequence correlate in both trials was VRC01 epitope distance that quantifies the divergence of the VRC01 epitope in an acquired HIV-1 isolate from the VRC01 epitope of reference HIV-1 strains that were most sensitive to VRC01-mediated neutralization. In HVTN 704/HPTN 085, the Env sequence-based predicted probability that VRC01 IC80 against the acquired isolate exceeded 1 µg/mL also significantly associated with PE. In HVTN 703/HPTN 081, a physicochemical-weighted Hamming distance across 50 VRC01 binding-associated Env AA positions of the acquired isolate from the most VRC01-sensitive HIV-1 strain significantly associated with PE. These results suggest that incorporating mutation scoring by BLOSUM62 and weighting by the strength of interactions at AA positions in the epitope:VRC01 interface can optimize performance of an Env sequence-based biomarker of VRC01 prevention efficacy. Future work could determine whether these results extend to other bnAbs and bnAb combinations., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background: COVID-19 vaccination rates among long-term care center (LTCC) workers are among the lowest of all frontline health care workers. Current efforts to increase COVID-19 vaccine uptake generally focus on strategies that have proven effective for increasing influenza vaccine uptake among health care workers including educational and communication strategies. Experimental evidence is lacking on the comparative advantage of educational strategies to improve vaccine acceptance and uptake, especially in the context of COVID-19. Despite the lack of evidence, education and communication strategies are recommended to improve COVID-19 vaccination rates and decrease vaccine hesitancy (VH), especially strategies using tailored messaging for disproportionately affected populations., Methods: We describe a cluster-randomized comparative effectiveness trial with 40 LTCCs and approximately 4000 LTCC workers in 2 geographically, culturally, and ethnically distinct states. We compare the effectiveness of two strategies for increasing COVID-19 booster vaccination rates and willingness to promote COVID-19 booster vaccination: co-design processes for tailoring educational messages vs. an enhanced usual care comparator. Our study focuses on the language and/or cultural groups that are most disproportionately affected by VH and low COVID-19 vaccine uptake in these LTCCs., Conclusion: Finding effective methods to increase COVID-19 vaccine uptake and decrease VH among LTCC staff is critical. Beyond COVID-19, better approaches are needed to improve vaccine uptake and decrease VH for a variety of existing vaccines as well as vaccines created to address novel viruses as they emerge., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Increasing emphasis on the use of real-world evidence (RWE) to support clinical policy and regulatory decision-making has led to a proliferation of guidance, advice, and frameworks from regulatory agencies, academia, professional societies, and industry. A broad spectrum of studies use real-world data (RWD) to produce RWE, ranging from randomized trials with outcomes assessed using RWD to fully observational studies. Yet, many proposals for generating RWE lack sufficient detail, and many analyses of RWD suffer from implausible assumptions, other methodological flaws, or inappropriate interpretations. The Causal Roadmap is an explicit, itemized, iterative process that guides investigators to prespecify study design and analysis plans; it addresses a wide range of guidance within a single framework. By supporting the transparent evaluation of causal assumptions and facilitating objective comparisons of design and analysis choices based on prespecified criteria, the Roadmap can help investigators to evaluate the quality of evidence that a given study is likely to produce, specify a study to generate high-quality RWE, and communicate effectively with regulatory agencies and other stakeholders. This paper aims to disseminate and extend the Causal Roadmap framework for use by clinical and translational researchers; three companion papers demonstrate applications of the Causal Roadmap for specific use cases., Competing Interests: The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement by, FDA/HHS, the US Government, or the authors’ affiliations. LED reports tuition and stipend support from a philanthropic gift from the Novo Nordisk corporation to the University of California, Berkeley, to support the Joint Initiative for Causal Inference. IJD is the principal investigator of a research agreement between Harvard University and Sanofi and reports consulting fees from Moderna. EK is employed by Microsoft. DA is employed by Pfizer Inc. and holds stocks in Pfizer Inc. CYV and RAH are employed by Syneos Health. CYV reports that her husband is employed by Galapagos. HR and KK are employed by Novo Nordisk A/S and own stocks in Novo Nordisk A/S. RP has received the following (directed to his institution): speaker fees from Merck and Novo Nordisk; consulting fees from Bayer AG, Corcept Therapeutics Incorporated, Dexcom, Endogenex, Inc., Gasherbrum Bio, Inc., Hanmi Pharmaceutical Co., Hengrui (USA) Ltd., Lilly, Merck, Novo Nordisk, Pfizer, Rivus Pharmaceuticals Inc., Sanofi, Scohia Pharma Inc., and Sun Pharmaceutical Industries; and grants from Hanmi Pharmaceuticals Co., Metavention, Novo Nordisk, and Poxel SA. DM is employed by Moderna. CEB is co-founder of Adaptic Health Inc., Managing Director of Pivotal Strategic Consulting, LLC, and receives consulting fees from Graticule Inc. and Sophic Alliance Inc. MA is employed by Novartis Pharma AG. JBB reports contracted fees and travel support for contracted activities for consulting work paid to the University of North Carolina by Novo Nordisk; grant support by Dexcom, NovaTarg, Novo Nordisk, Sanofi, Tolerion and vTv Therapeutics; personal compensation for consultation from Alkahest, Altimmune, Anji, AstraZeneca, Bayer, Biomea Fusion Inc., Boehringer-Ingelheim, CeQur, Cirius Therapeutics Inc., Corcept Therapeutics, Eli Lilly, Fortress Biotech, GentiBio, Glycadia, Glyscend, Janssen, MannKind, Mellitus Health, Moderna, Pendulum Therapeutics, Praetego, Sanofi, Stability Health, Terns Inc., Valo and Zealand Pharma; stock/options in Glyscend, Mellitus Health, Pendulum Therapeutics, PhaseBio, Praetego, and Stability Health; and board membership of the Association of Clinical and Translational Science. MvdL and SG report that they are co-founders of the statistical software start-up company TLrevolution, Inc. MvdL and MP report personal compensation for consultation from Novo Nordisk., (© The Author(s) 2023.)

Background: Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals., Methods: The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence., Results: In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative - a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping., Conclusion: These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis., Competing Interests: LED reports tuition and stipend support from a philanthropic gift from the Novo Nordisk corporation to the University of California, Berkeley to support the Joint Initiative for Causal Inference. SG reports that she is a co-founder of the statistical software start-up company TL Revolution, Inc. AW is employed by Parexel., (© The Author(s) 2023.)

Combination monoclonal broadly neutralizing antibody (bnAb) regimens are in clinical development for HIV prevention, necessitating additional knowledge of bnAb neutralization potency/breadth against circulating viruses. Williamson et al. (2021) described a software tool, Super LeArner Prediction of NAb Panels (SLAPNAP), with application to any HIV bnAb regimen with sufficient neutralization data against a set of viruses in the Los Alamos National Laboratory's Compile, Neutralize, and Tally Nab Panels repository. SLAPNAP produces a proteomic antibody resistance (PAR) score for Env sequences based on predicted neutralization resistance and estimates variable importance of Env amino acid features. We apply SLAPNAP to compare HIV bnAb regimens undergoing clinical testing, finding improved power for downstream sieve analyses and increased precision for comparing neutralization potency/breadth of bnAb regimens due to the inclusion of PAR scores of Env sequences with much larger sample sizes available than for neutralization outcomes. SLAPNAP substantially improves bnAb regimen characterization, ranking, and down-selection., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Background: Central nervous system (CNS) active medications have been consistently linked to falls in older people. However, few randomized trials have evaluated whether CNS-active medication reduction reduces falls and fall-related injuries. The objective of the Reducing CNS-active Medications to Prevent Falls and Injuries in Older Adults (STOP-FALLS) trial is to test the effectiveness of a health-system-embedded deprescribing intervention focused on CNS-active medications on the incidence of medically treated falls among community-dwelling older adults., Methods: We will conduct a pragmatic, cluster-randomized, parallel-group, controlled clinical trial within Kaiser Permanente Washington to test the effectiveness of a 12-month deprescribing intervention consisting of (1) an educational brochure and self-care handouts mailed to older adults prescribed one or more CNS-active medications (aged 60 + : opioids, benzodiazepines and Z-drugs; aged 65 + : skeletal muscle relaxants, tricyclic antidepressants, and antihistamines) and (2) decision support for their primary health care providers. Outcomes are examined over 18-26 months post-intervention. The primary outcome is first incident (post-baseline) medically treated fall as determined from health plan data. Our sample size calculations ensure at least 80% power to detect a 20% reduction in the rate of medically treated falls for participants receiving care within the intervention (n = 9) versus usual care clinics (n = 9) assuming 18 months of follow-up. Secondary outcomes include medication discontinuation or dose reduction of any target medications. Safety outcomes include serious adverse drug withdrawal events, unintentional overdose, and death. We will also examine medication signetur fields for attempts to decrease medications. We will report factors affecting implementation of the intervention., Discussion: The STOP-FALLS trial will provide new information about whether a health-system-embedded deprescribing intervention that targets older participants and their primary care providers reduces medically treated falls and CNS-active medication use. Insights into factors affecting implementation will inform future research and healthcare organizations that may be interested in replicating the intervention., Trial Registration: ClinicalTrial.gov NCT05689554. Registered on 18 January 2023, retrospectively registered., (© 2023. The Author(s).)

The best assay or marker to define mRNA-1273 vaccine-induced antibodies as a correlate of protection (CoP) is unclear. In the COVE trial, participants received two doses of the mRNA-1273 COVID-19 vaccine or placebo. We previously assessed IgG binding antibodies to the spike protein (spike IgG) or receptor binding domain (RBD IgG) and pseudovirus neutralizing antibody 50 or 80% inhibitory dilution titer measured on day 29 or day 57, as correlates of risk (CoRs) and CoPs against symptomatic COVID-19 over 4 months after dose. Here, we assessed a new marker, live virus 50% microneutralization titer (LV-MN 50 ), and compared and combined markers in multivariable analyses. LV-MN 50 was an inverse CoR, with a hazard ratio of 0.39 (95% confidence interval, 0.19 to 0.83) at day 29 and 0.51 (95% confidence interval, 0.25 to 1.04) at day 57 per 10-fold increase. In multivariable analyses, pseudovirus neutralization titers and anti-spike binding antibodies performed best as CoRs; combining antibody markers did not improve correlates. Pseudovirus neutralization titer was the strongest independent correlate in a multivariable model. Overall, these results supported pseudovirus neutralizing and binding antibody assays as CoRs and CoPs, with the live virus assay as a weaker correlate in this sample set. Day 29 markers performed as well as day 57 markers as CoPs, which could accelerate immunogenicity and immunobridging studies.

Background: There has been increasing interest in physiologic pacing techniques that directly activate the specialized conduction system. We aimed to assess outcomes of conduction system pacing (CSP) in patients with prosthetic heart valves., Methods: This systematic review was performed according to PRISMA guidelines. Freeman-Tukey double arcsine transformation with the random-effect model was used to summarize the data. Outcomes studied were 1) implant success (defined as ability to recruit the His-Purkinje system or the distal Purkinje system); (2) lead parameters at implant and follow-up; and (3) procedure-related complications., Results: This systematic review of 7 studies included 267 unique patients in whom CSP was attempted with either HBP or LBBAP for pacing indications after a prosthetic valve. HBP was attempted in 38% (n = 108), while LBBAP in 62% (n = 175) patients. The overall success rate of CSP was 87%, while in patients post-TAVR, the overall success rate was 83.2%. In the subgroup analysis, LBBAP had a significant higher overall success rate compared to HBP (94.3% vs. 76.5%, p interaction  = 0.02) and post-TAVR patients (94.3 vs. 66.9%, p interaction  < 0.01), respectively. The LBBAP thresholds were significantly lower compared to HBP both at implant (0.67 ± 0.4 @ 0.44 ms vs. 1.35 ± 1 @ 0.85 ms, p interaction  < 0.01) and at a mean follow-up of 12.4 ± 8 months (0.73 ± 0.1 @ 0.44 ms vs. 1.39 ± 1 @ 0.85 ms, p interaction  < 0.01), respectively., Conclusion: CSP is safe and feasible in patients with a prosthetic valve, with a significantly higher success rate and superior lead parameters with LBBAP than HBP, especially in patients post-TAVR., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

In many applications, it is of interest to assess the relative contribution of features (or subsets of features) toward the goal of predicting a response - in other words, to gauge the variable importance of features. Most recent work on variable importance assessment has focused on describing the importance of features within the confines of a given prediction algorithm. However, such assessment does not necessarily characterize the prediction potential of features, and may provide a misleading reflection of the intrinsic value of these features. To address this limitation, we propose a general framework for nonparametric inference on interpretable algorithm-agnostic variable importance. We define variable importance as a population-level contrast between the oracle predictiveness of all available features versus all features except those under consideration. We propose a nonparametric efficient estimation procedure that allows the construction of valid confidence intervals, even when machine learning techniques are used. We also outline a valid strategy for testing the null importance hypothesis. Through simulations, we show that our proposal has good operating characteristics, and we illustrate its use with data from a study of an antibody against HIV-1 infection.

Background: The identification of baseline host determinants that associate with robust HIV-1 vaccine-induced immune responses could aid HIV-1 vaccine development. We aimed to assess both the collective and relative performance of baseline characteristics in classifying individual participants in nine different Phase 1-2 HIV-1 vaccine clinical trials (26 vaccine regimens, conducted in Africa and in the Americas) as High HIV-1 vaccine responders., Methods: This was a meta-analysis of individual participant data, with studies chosen based on participant-level (vs. study-level summary) data availability within the HIV-1 Vaccine Trials Network. We assessed the performance of 25 baseline characteristics (demographics, safety haematological measurements, vital signs, assay background measurements) and estimated the relative importance of each characteristic in classifying 831 participants as High (defined as within the top 25th percentile among positive responders or above the assay upper limit of quantification) versus Non-High responders. Immune response outcomes included HIV-1-specific serum IgG binding antibodies and Env-specific CD4+ T-cell responses assessed two weeks post-last dose, all measured at central HVTN laboratories. Three variable importance approaches based on SuperLearner ensemble machine learning were considered., Findings: Overall, 30.1%, 50.5%, 36.2%, and 13.9% of participants were categorized as High responders for gp120 IgG, gp140 IgG, gp41 IgG, and Env-specific CD4+ T-cell vaccine-induced responses, respectively. When including all baseline characteristics, moderate performance was achieved for the classification of High responder status for the binding antibody responses, with cross-validated areas under the ROC curve (CV-AUC) of 0.72 (95% CI: 0.68, 0.76) for gp120 IgG, 0.73 (0.69, 0.76) for gp140 IgG, and 0.67 (95% CI: 0.63, 0.72) for gp41 IgG. In contrast, the collection of all baseline characteristics yielded little improvement over chance for predicting High Env-specific CD4+ T-cell responses [CV-AUC: 0.53 (0.48, 0.58)]. While estimated variable importance patterns differed across the three approaches, female sex assigned at birth, lower height, and higher total white blood cell count emerged as significant predictors of High responder status across multiple immune response outcomes using Approach 1. Of these three baseline variables, total white blood cell count ranked highly across all three approaches for predicting vaccine-induced gp41 and gp140 High responder status., Interpretation: The identified features should be studied further in pursuit of intervention strategies to improve vaccine responses and may be adjusted for in analyses of immune response data to enhance statistical power., Funding: National Institute of Allergy and Infectious Diseases (UM1AI068635 to YH, UM1AI068614 to GDT, UM1AI068618 to MJM, and UM1 AI069511 to MCK), the Duke CFAR P30 AI064518 to GDT, and National Institute of Dental and Craniofacial Research (R01DE027245 to JJK). This work was also supported by the Bill and Melinda Gates Foundation. The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding sources., Competing Interests: Declaration of interests Y.H. declares contract payments to her institution from the World Health Organization to conduct statistical analysis work (outside the scope of the current work; related to COVID-19 vaccines) and plan to submit the results for publication, within the past 36 months, as well as service to WHV as an SMB (payment directly to her) within the past 36 months. K.E.S. and J.H. declare coverage of travel expenses for attendance at the HVTN Full Group Meeting in 2022 (reimbursement by the HVTN); K.E.S. additionally declares payment of a registration fee for virtual seminar attendance directly to Keystone Symposium by the HVTN. S.D.R. declares contracts awarded to his institution from Battelle and from Janssen, and grants awarded to his institution from the Gates Medical Research Institute and from the Paul G. Allen Family Foundation, within the past 36 months. P.A.G. declares consulting fees received from Johnson and Johnson within the past 36 months, as well as a patent for a COVID-19 monoclonal antibody that is not yet clinically available. K.W.C. declares funding from the Bill and Melinda Gates Foundation in the form of payments to her institution within the last 36 months. B.D.W. declares grant payments made to his institution within the past 36 months from the following entities: Centers for Disease Control and Prevention, Patient-Centered Outcomes Research Institute, National Institute of Mental Health, National Institute of Allergy and Infectious Diseases, National Cancer Institute, and the National Institute of General Medical Studies; an honorarium for delivering an invited webinar to the American Statistical Association: Statistical Learning and Data Science Section; travel support through a grant from the National Institute of Mental Health; and retirement accounts invested in mutual funds operated by Vanguard and by TIAA (he does not choose individual stocks). L.-G.B. declares payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Gilead Sciences, Janssen, and Merck PTY LTD within the past 36 months, as well as participation on a Data Safety Monitoring Board or Advisory Board for the PrEPVAC study within the past 36 months. G.D.T. declares consulting fees received from Axon and from Janssen (not related to this work), serving as an Advisory Board member for the NIH VRC, UNC CFAR, and Johns Hopkins (not related to this work), and serving as a scientific review member for Gilead (not related to this work), all within the past 36 months, as well as travel as part of research funding (more than 3 years prior, with funding paid to her institution). J.T. declares consulting fees from Vaccitech within the past 36 months., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

The absolute abundance of bacterial taxa in human host-associated environments plays a critical role in reproductive and gastrointestinal health. However, obtaining the absolute abundance of many bacterial species is typically prohibitively expensive. In contrast, relative abundance data for many species are comparatively cheap and easy to collect (e.g., with universal primers for the 16S rRNA gene). In this paper, we propose a method to jointly model relative abundance data for many taxa and absolute abundance data for a subset of taxa. Our method provides point and interval estimates for the absolute abundance of all taxa. Crucially, our proposal accounts for differences in the efficiency of taxon detection in the relative and absolute abundance data. We show that modeling taxon-specific efficiencies substantially reduces the estimation error for absolute abundance, and controls the coverage of interval estimators. We demonstrate the performance of our proposed method via a simulation study, a study of the effect of HIV acquisition on microbial abundances, and a sensitivity study where we jackknife the taxa with observed absolute abundances., (© 2021 The Authors. Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.)

In trials of oral HIV pre-exposure prophylaxis (PrEP), multiple approaches have been used to measure adherence, including self-report, pill counts, electronic dose monitoring devices, and biological measures such as drug levels in plasma, peripheral blood mononuclear cells, hair, and/or dried blood spots. No one of these measures is ideal and each has strengths and weaknesses. However, accurate estimates of adherence to oral PrEP are important as drug efficacy is closely tied to adherence, and secondary analyses of trial data within identified adherent/non-adherent subgroups may yield important insights into real-world drug effectiveness. We develop a statistical approach to combining multiple measures of adherence and show in simulated data that the proposed method provides a more accurate measure of true adherence than self-report. We then apply the method to estimate adherence in the ADAPT study (HPTN 067) in South African women., (© 2022 John Wiley & Sons Ltd.)

Background: In the CYD14 (NCT01373281) and CYD15 (NCT01374516) dengue vaccine efficacy trials, month 13 neutralizing antibody (nAb) titers correlated inversely with risk of symptomatic, virologically confirmed dengue (VCD) between month 13 (1 month after final dose) and month 25. We assessed nAb titer as a correlate of instantaneous risk of hospitalized VCD (HVCD), for which participants were continually surveilled for 72 months., Methods: Using longitudinal nAb titers from the per-protocol immunogenicity subsets, we estimated hazard ratios (HRs) of HVCD by current nAb titer value for 3 correlate/endpoint pairs: average titer across all 4 serotypes/HVCD of any serotype (HVCD-Any), serotype-specific titer/homologous HVCD, and serotype-specific titer/heterologous HVCD., Results: Baseline-seropositive placebo recipients with higher average titer had lower instantaneous risk of HVCD-Any in 2- to 16-year-olds and in 9- to 16-year-olds (HR, 0.26 or 0.15 per 10-fold increase in average titer by 2 methods [95% confidence interval {CI}, .14-.45 and .07-.34, respectively]) pooled across both trials. Results were similar for homologous HVCD. There was evidence suggesting increased HVCD-Any risk in participants with low average titer (1:10 to 1:100) compared to seronegative participants (HR, 1.85 [95% CI, .93-3.68])., Conclusions: Natural infection-induced nAbs were inversely associated with hospitalized dengue, upon exceeding a relatively low threshold., (© The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Background: While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases-a common situation in biomedical studies, which often have rare outcomes and covariates whose measurement is resource-intensive., Methods: Using an immunologic marker dataset from a phase III HIV vaccine efficacy trial, we seek to optimize random forest prediction performance using combinations of variable screening, class balancing, weighting, and hyperparameter tuning., Results: Our experiments show that while class balancing helps improve random forest prediction performance when variable screening is not applied, class balancing has a negative impact on performance in the presence of variable screening. The impact of the weighting similarly depends on whether variable screening is applied. Hyperparameter tuning is ineffective in situations with small sample sizes. We further show that random forests under-perform generalized linear models for some subsets of markers, and prediction performance on this dataset can be improved by stacking random forests and generalized linear models trained on different subsets of predictors, and that the extent of improvement depends critically on the dissimilarities between candidate learner predictions., Conclusion: In small datasets from two-phase sampling design, variable screening and inverse sampling probability weighting are important for achieving good prediction performance of random forests. In addition, stacking random forests and simple linear models can offer improvements over random forests., (© 2021. The Author(s).)

Motivation: A single monoclonal broadly neutralizing antibody (bnAb) regimen was recently evaluated in two randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g. cocktails, multi-specific antibodies), which demonstrate higher potency and breadth in vitro compared to single bnAbs. Given the large number of potential regimens, methods for down-selecting these regimens into efficacy trials are of great interest., Results: We developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization sensitivity of HIV Envelope (Env) pseudoviruses to a given single or combination bnAb regimen, based on Env amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. By predicting bnAb coverage of circulating sequences, SLAPNAP can improve ranking of bnAb regimens by their potential prevention efficacy. In addition, SLAPNAP can improve sieve analysis by defining sequence features that impact bnAb prevention efficacy., Availabilityand Implementation: SLAPNAP is a freely available docker image that can be downloaded from DockerHub (https://hub.docker.com/r/slapnap/slapnap). Source code and documentation are available at GitHub (https://github.com/benkeser/slapnap and https://benkeser.github.io/slapnap/)., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: A weight-based heparin dosing policy adjusted for preprocedural oral anticoagulation was implemented to reduce the likelihood of subtherapeutic dosing during left atrial catheter ablation procedures. We hypothesized that initiation of the protocol would result in a greater prevalence of therapeutic activated clotting time (ACT) values and decreased time to therapeutic ACT during left atrial ablation procedures., Methods: A departmental protocol was initiated for which subjects received intravenous unfractionated heparin (UFH) to achieve and maintain a goal of ACT >300 s. Initial bolus dose was adjusted for pre-procedure oral anticoagulation and weight as follows: 50 units/kg for those receiving warfarin, 75 units/kg for those not anticoagulated, and 120 units/kg for those on direct oral anticoagulants (DOACs). A UFH infusion was initiated at 10% of the bolus per hour. One hundred consecutive left atrial ablation procedures treated with Protocol Guided heparin dosing were compared with a retrospective consecutive cohort of Usual Care heparin dosing., Results: When the Usual Care and Protocol Guided cohorts were compared, significant findings were limited to those on pre-procedure DOAC. The initial UFH bolus increased from 99.3 ± 24.8 to 118.2 ± 22.8 units/kg (p < .001), the proportion of therapeutic ACT on the first draw after heparin administration increased from 57.7% to 76.6% (p = .010), and the time to therapeutic ACT after UFH administration decreased from 37.8 ± 19.8 to 30.2 ± 16.4 min (p = .032)., Conclusion: A weight-based protocol for periprocedural UFH administration resulted in a higher proportion of therapeutic ACT values and decreased the time to therapeutic ACT for those on pre-procedure DOAC., (© 2021 Wiley Periodicals LLC.)

In a regression setting, it is often of interest to quantify the importance of various features in predicting the response. Commonly, the variable importance measure used is determined by the regression technique employed. For this reason, practitioners often only resort to one of a few regression techniques for which a variable importance measure is naturally defined. Unfortunately, these regression techniques are often suboptimal for predicting the response. Additionally, because the variable importance measures native to different regression techniques generally have a different interpretation, comparisons across techniques can be difficult. In this work, we study a variable importance measure that can be used with any regression technique, and whose interpretation is agnostic to the technique used. This measure is a property of the true data-generating mechanism. Specifically, we discuss a generalization of the analysis of variance variable importance measure and discuss how it facilitates the use of machine learning techniques to flexibly estimate the variable importance of a single feature or group of features. The importance of each feature or group of features in the data can then be described individually, using this measure. We describe how to construct an efficient estimator of this measure as well as a valid confidence interval. Through simulations, we show that our proposal has good practical operating characteristics, and we illustrate its use with data from a study of risk factors for cardiovascular disease in South Africa., (© 2020 The International Biometric Society.)

BACKGROUNDViral load (VL) surrogate endpoints transformed development of HIV and hepatitis C therapeutics. Surrogate endpoints for CMV-related morbidity and mortality could advance development of antiviral treatments. Although observational data support using CMV VL as a trial endpoint, randomized controlled trials (RCTs) demonstrating direct associations between virological markers and clinical endpoints are lacking.METHODSWe performed CMV DNA PCR on frozen serum samples from the only placebo-controlled RCT of ganciclovir for early treatment of CMV after hematopoietic cell transplantation (HCT). We used established criteria to assess VL kinetics as surrogates for CMV disease or death by weeks 8, 24, and 48 after randomization and quantified antiviral effects captured by each marker. We used ensemble-based machine learning to assess the predictive ability of VL kinetics and performed this analysis on a ganciclovir prophylaxis RCT for validation.RESULTSVL suppression with ganciclovir reduced cumulative incidence of CMV disease and death for 20 years after HCT. Mean VL, peak VL, and change in VL during the first 5 weeks of treatment fulfilled the Prentice definition for surrogacy, capturing more than 95% of ganciclovir's effect, and yielded highly sensitive and specific predictions by week 48. In the prophylaxis trial, the viral shedding rate satisfied the Prentice definition for CMV disease by week 24.CONCLUSIONSOur results support using CMV VL kinetics as surrogates for CMV disease, provide a framework for developing CMV preventative and therapeutic agents, and support reductions in VL as the mechanism through which antivirals reduce CMV disease.FUNDINGMerck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc.

Objectives: This study aimed to characterize corrected QT (QTc) prolongation in a cohort of hospitalized patients with coronavirus disease-2019 (COVID-19) who were treated with hydroxychloroquine and azithromycin (HCQ/AZM)., Background: HCQ/AZM is being widely used to treat COVID-19 despite the known risk of QT interval prolongation and the unknown risk of arrhythmogenesis in this population., Methods: A retrospective cohort of COVID-19 hospitalized patients treated with HCQ/AZM was reviewed. The QTc interval was calculated before drug administration and for the first 5 days following initiation. The primary endpoint was the magnitude of QTc prolongation, and factors associated with QTc prolongation. Secondary endpoints were incidences of sustained ventricular tachycardia or ventricular fibrillation and all-cause mortality., Results: Among 415 patients who received concomitant HCQ/AZM, the mean QTc increased from 443 ± 25 ms to a maximum of 473 ± 40 ms (87 [21%] patients had a QTc ≥500 ms). Factors associated with QTc prolongation ≥500 ms were age (p < 0.001), body mass index <30 kg/m 2 (p = 0.005), heart failure (p < 0.001), elevated creatinine (p = 0.005), and peak troponin (p < 0.001). The change in QTc was not associated with death over the short period of the study in a population in which mortality was already high (hazard ratio: 0.998; p = 0.607). No primary high-grade ventricular arrhythmias were observed., Conclusions: An increase in QTc was seen in hospitalized patients with COVID-19 treated with HCQ/AZM. Several clinical factors were associated with greater QTc prolongation. Changes in QTc were not associated with increased risk of death., Competing Interests: Author Disclosures Dr. Haines has received honoraria from Biosense Webster, Farapulse and Sagentia; and has been a consultant for Affera, Boston Scientific, Integer, Medtronic, Philips Healthcare, and Zoll. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose., (Copyright © 2021 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Coronavirus disease 2019 is a global pandemic affecting >3 million people in >170 countries, resulting in >200 000 deaths; 35% to 40% of patients and deaths are in the United States. The coronavirus disease 2019 crisis is placing an enormous burden on health care in the United States, including residency and fellowship training programs. The balance between mitigation, training and education, and patient care is the ultimate determinant of the role of cardiology fellows in training during the coronavirus disease 2019 crisis. On March 24, 2020, the Accreditation Council for Graduate Medical Education issued a formal response to the pandemic crisis and described a framework for operation of graduate medical education programs. Guidance for deployment of cardiology fellows in training during the coronavirus disease 2019 crisis is based on the principles of a medical mission, and adherence to preparation, protection, and support of our fellows in training. The purpose of this review is to describe our departmental strategic deployment of cardiology fellows in training using the Accreditation Council for Graduate Medical Education framework for pandemic preparedness.

The true population-level importance of a variable in a prediction task provides useful knowledge about the underlying data-generating mechanism and can help in deciding which measurements to collect in subsequent experiments. Valid statistical inference on this importance is a key component in understanding the population of interest. We present a computationally efficient procedure for estimating and obtaining valid statistical inference on the S hapley P opulation V ariable I mportance M easure (SPVIM). Although the computational complexity of the true SPVIM scales exponentially with the number of variables, we propose an estimator based on randomly sampling only Θ( n ) feature subsets given n observations. We prove that our estimator converges at an asymptotically optimal rate. Moreover, by deriving the asymptotic distribution of our estimator, we construct valid confidence intervals and hypothesis tests. Our procedure has good finite-sample performance in simulations, and for an in-hospital mortality prediction task produces similar variable importance estimates when different machine learning algorithms are applied.

HVTN 505 is a preventative vaccine efficacy trial testing DNA followed by recombinant adenovirus serotype 5 (rAd5) in circumcised, Ad5-seronegative men and transgendered persons who have sex with men in the United States. Identified immune correlates of lower HIV-1 risk and a virus sieve analysis revealed that, despite lacking overall efficacy, vaccine-elicited responses exerted pressure on infecting HIV-1 viruses. To interrogate the mechanism of the antibody correlate of HIV-1 risk, we examined antigen-specific antibody recruitment of Fcγ receptors (FcγRs), antibody-dependent cellular phagocytosis (ADCP), and the role of anti-envelope (anti-Env) IgG3. In a prespecified immune correlates analysis, antibody-dependent monocyte phagocytosis and antibody binding to FcγRIIa correlated with decreased HIV-1 risk. Follow-up analyses revealed that anti-Env IgG3 breadth correlated with reduced HIV-1 risk, anti-Env IgA negatively modified infection risk by Fc effector functions, and that vaccine recipients with a specific FcγRIIa single-nucleotide polymorphism locus had a stronger correlation with decreased HIV-1 risk when ADCP, Env-FcγRIIa, and IgG3 binding were high. Additionally, FcγRIIa engagement correlated with decreased viral load setpoint in vaccine recipients who acquired HIV-1. These data support a role for vaccine-elicited anti-HIV-1 Env IgG3, antibody engagement of FcRs, and phagocytosis as potential mechanisms for HIV-1 prevention.

A central assumption in the design and conduct of non-inferiority trials is that the active-control therapy will have the same degree of effectiveness in the planned non-inferiority trial as in the prior placebo-controlled trials used to define the non-inferiority margin. This is referred to as the 'constancy' assumption. If the constancy assumption fails, decisions based on the chosen non-inferiority margin may be incorrect, and the study runs the risk of approving an inferior product or failing to approve a beneficial product. The constancy assumption cannot be validated in a trial without a placebo arm, and it is unlikely ever to be met completely. When there are strong, observable predictors of constancy, such as dosing and adherence to the active-control product, we can specify conditions where the constancy assumption will likely fail. We propose a method for using measurable predictors of active-control effectiveness to specify non-inferiority margins targeted to the planned study population characteristics. We describe a pre-specified method, using baseline characteristics or post-baseline predictors in the active-control arm, to adapt the non-inferiority margin at the end of the study if constancy is violated. Adaptive margins can help adjust for constancy violations that will inevitably occur in real clinical trials, while maintaining pre-specified levels of Type I error and power.

The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons., Competing Interests: The authors have declared that no competing interests exist.

Introduction Readmission following revision orthopedic surgery imposes tremendous costs due to the increased length of stay, procedure complexity, and revision surgery. Following revision total hip arthroplasty, as many as one in five patients are readmitted postoperatively. Readmissions cost the federal government $17.4 billion annually. The purpose of this study was to identify risk factors for unplanned readmission following revision total hip arthroplasty. Methods This was a retrospective case series review of randomized revision total hip arthroplasties (THA) patients between 2008 and 2018. Exclusions were as follows: outside hospital revisions, staged revisions, revisions for infection, and bilateral revisions. Data were collected by manual chart review. Readmissions were tracked from discharge until the final follow-up. Results A total of 61 patients and 85 revision THAs were analyzed. Nineteen patients (31.1%) were readmitted; 31.6% of the readmitted patients had a coronary artery disease compared to 6.5% of non-readmitted patients. Readmission was also associated with obesity, former smokers, and hypertension. Also, the mean duration of follow-up was 26.5 months for readmitted patients as compared to 8.96 for non-readmitted patients. Conclusion Obesity, former tobacco use, younger age, coronary artery disease (CAD), and hypertension were associated with readmission. The medical optimization of patients with these risk factors prior to surgery could significantly lower costs relative to revision THA., Competing Interests: The authors have declared that no competing interests exist.

An individual malignant tumor is composed of a heterogeneous collection of single cells with distinct molecular and phenotypic features, a phenomenon termed intratumoral heterogeneity. Intratumoral heterogeneity poses challenges for cancer treatment, motivating the need for combination therapies. Single-cell technologies are now available to guide effective drug combinations by accounting for intratumoral heterogeneity through the analysis of the signaling perturbations of an individual tumor sample screened by a drug panel. In particular, Mass Cytometry Time-of-Flight (CyTOF) is a high-throughput single-cell technology that enables the simultaneous measurements of multiple ([Formula: see text]40) intracellular and surface markers at the level of single cells for hundreds of thousands of cells in a sample. We developed a computational framework, entitled Drug Nested Effects Models (DRUG-NEM), to analyze CyTOF single-drug perturbation data for the purpose of individualizing drug combinations. DRUG-NEM optimizes drug combinations by choosing the minimum number of drugs that produce the maximal desired intracellular effects based on nested effects modeling. We demonstrate the performance of DRUG-NEM using single-cell drug perturbation data from tumor cell lines and primary leukemia samples., Competing Interests: Conflict of interest statement: A patent application underlying the DRUG-NEM framework has been filed on behalf of Stanford University., (Copyright © 2018 the Author(s). Published by PNAS.)

Objectives: This global, multicenter, prospective study, initiated to meet U.S. Food and Drug Administration condition-of-approval requirements, evaluated the safety and efficacy of the Medtronic magnetic resonance imaging (MRI)-conditional pacing system when used in an MRI environment in routine clinical practice. The primary endpoint was MRI-related complications. The secondary endpoint was the cumulative change in pacing capture threshold (PCT) for patients undergoing multiple MRI scans., Background: Large-scale, real-world evaluation of MRI in patients implanted with an MRI-conditional pacing system remains limited, with few published data for patients who undergo multiple MRI scans., Methods: Patients were enrolled and followed up prospectively from the time of implantation. Evaluation of the pacemaker function was performed before and after MRI. The MRI-related complication-free rate was evaluated. Changes in electrical performance after each scan and cumulative changes over multiple scans were analyzed., Results: In 81 centers, 2,629 patients were implanted with a complete SureScan pacing system (41.8% women, age 70.2 ± 12.5 years). A total of 526 patients (28.5%) received 872 clinically indicated MRI scans, including 58 thoracic scans. No MRI-related complications occurred during or after MRI, meeting the primary objective. Six (1%) MRI-related observations (atrial fibrillation, PCT increase, and chest symptoms) were reported. A total of 171 patients (32.5%) underwent 2 or more scans with no cumulative increase in PCT., Conclusions: This report constitutes the largest longitudinal MRI experience in patients implanted with an MRI-conditional pacing system. Results support the safety profile of the SureScan system and demonstrate for the first time that patients may safely undergo multiple MRI scans. (SureScan Post-Approval Study; NCT01299675)., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To determine the effects of intraset rest frequency and training load on muscle time under tension, external work, and external mechanical power output during back-squat protocols with similar changes in velocity., Methods: Twelve strength-trained men (26.0 ± 4.2 y, 83.1 ± 8.8 kg, 1.75 ± 0.06 m, 1.88:0.19 one-repetition-maximum [1RM] body mass) performed 3 sets of 12 back squats using 3 different set structures: traditional sets with 60% 1RM (TS), cluster sets of 4 with 75% 1RM (CS4), and cluster sets of 2 with 80% 1RM (CS2). Repeated-measures ANOVAs were used to determine differences in peak force (PF), mean force (MF), peak velocity (PV), mean velocity (MV), peak power (PP), mean power (MP), total work (TW), total time under tension (TUT), percentage mean velocity loss (%MVL), and percentage peak velocity loss (%PVL) between protocols., Results: Compared with TS and CS4, CS2 resulted in greater MF, TW, and TUT in addition to less MV, PV, and MP. Similarly, CS4 resulted in greater MF, TW, and TUT in addition to less MV, PV, and MP than TS did. There were no differences between protocols for %MVL, %PVL, PF, or PP., Conclusions: These data show that the intraset rest provided in CS4 and CS2 allowed for greater external loads than with TS, increasing TW and TUT while resulting in similar PP and %VL. Therefore, cluster-set structures may function as an alternative method to traditional strength- or hypertrophy-oriented training by increasing training load without increasing %VL or decreasing PP.

Purpose: To compare the effects of a traditional set structure and 2 cluster set structures on force, velocity, and power during back squats in strength-trained men., Methods: Twelve men (25.8 ± 5.1 y, 1.74 ± 0.07 m, 79.3 ± 8.2 kg) performed 3 sets of 12 repetitions at 60% of 1-repetition maximum using 3 different set structures: traditional sets (TS), cluster sets of 4 (CS4), and cluster sets of 2 (CS2)., Results: When averaged across all repetitions, peak velocity (PV), mean velocity (MV), peak power (PP), and mean power (MP) were greater in CS2 and CS4 than in TS (P < .01), with CS2 also resulting in greater values than CS4 (P < .02). When examining individual sets within each set structure, PV, MV, PP, and MP decreased during the course of TS (effect sizes 0.28-0.99), whereas no decreases were noted during CS2 (effect sizes 0.00-0.13) or CS4 (effect sizes 0.00-0.29)., Conclusions: These results demonstrate that CS structures maintain velocity and power, whereas TS structures do not. Furthermore, increasing the frequency of intraset rest intervals in CS structures maximizes this effect and should be used if maximal velocity is to be maintained during training.

Introduction: Successful global treatment as prevention (TasP) requires identifying HIV-positive individuals at high risk for transmitting HIV, and having impact via potential infections averted. This study estimated the frequency and predictors of numbers of HIV transmissions and bacterial sexually transmitted infection (STI) acquisition among sexually active HIV-positive individuals in care from three representative global settings., Methods: HIV-positive individuals ( n= 749), including heterosexual men, heterosexual women and men who have sex with men (MSM) in HIV care, were recruited from Chiang Mai (Thailand), Rio De Janeiro (Brazil) and Lusaka (Zambia). Participants were assessed on HIV and STI sexual transmission risk variables, psychosocial characteristics and bacterial STIs at enrolment and quarterly for 12 months (covering 15 months). Estimated numbers of HIV transmissions per person were calculated using reported numbers of partners and sex acts together with estimates of HIV transmissibility, accounting for ART treatment and condom use., Results: An estimated 3.81 (standard error, (SE)=0.63) HIV transmissions occurred for every 100 participants over the 15 months, which decreased over time. The highest rate was 19.50 (SE=1.68) for every 100 MSM in Brazil. In a multivariable model, country×risk group interactions emerged: in Brazil, MSM had 2.85 (95% CI=1.45, 4.25, p <0.0001) more estimated transmissions than heterosexual men and 3.37 (95% CI=2.01, 4.74, p <0.0001) more than heterosexual women over the 15 months. For MSM and heterosexual women, the combined 12-month STI incidence rate for the sample was 22.4% (95% CI=18.1%, 27.3%; incidence deemed negligible in heterosexual men). In the multivariable model, MSM had 12.3 times greater odds (95% CI=4.44, 33.98) of acquiring an STI than women, but this was not significant in Brazil. Higher alcohol use on the Alcohol Use Disorders Identification Test (OR=1.04, 95% CI=1.01, 1.08) was also significantly associated with increased STI incidence. In bivariate models for both HIV transmissions and STI incidence, higher depressive symptoms were significant predictors., Conclusions: These data help to estimate the potential number of HIV infections transmitted and bacterial STIs acquired over time in patients established in care, a group typically considered at lower transmission risk, and found substantial numbers of estimated HIV transmissions. These findings provide an approach for evaluating the impact (in phase 2 studies) and potentially cost-effectiveness of global TasP efforts., Competing Interests: The authors have no competing interests to declare.

Aim: To investigate whether there was any association between growing pains (GP) and periodic limb movements of sleep (PLMS) in children referred for polysomnography (PSG), in light of the possible shared genetic determinants between GP and restless legs syndrome., Methods: Records of all 903 children who underwent PSG at a paediatric hospital between January 2009 and May 2010 were retrospectively reviewed. Children aged 3-16 years, without neuromuscular or neurological/developmental disorders who were seen by a single sleep physician were included in the analysis. Data extracted included: current GP, PLMS index, mixed obstructive apnoea/hypopnoea index and patient demographics. GP was diagnosed in the pre-PSG consultation., Results: GP was recorded in 43 of the eligible 230 children referred for PSG of whom 25.6% had a PLMS index ≥ 5/h, significantly higher than in the children without GP (10.2%) (odds ratio 3.04, χ(2) = 6.0, P = 0.014). The adjusted odds ratio for the association between GP and PLMS remained significant at 2.53 (95% CI 1.1-6.1, P < 0.05). A PLMS index ≥ 5/h was recorded in 30 of the 230 participants. GP and PLMS were also significantly multivariately associated with restlessness as the indication for PSG., Conclusions: Children with GP were three times more likely to have a PLMS index ≥ 5/h than children without GP on PSG. These results suggest that GP may be associated with PLMS, giving greater support for the hypothesis that GP might lie on the phenotypic spectrum of restless legs syndrome., (© 2014 The Authors. Journal of Paediatrics and Child Health © 2014 Paediatrics and Child Health Division (Royal Australasian College of Physicians).)

Objectives: Our purpose was to demonstrate that strategically chosen implantable cardioverter-defibrillator (ICD) ventricular tachycardia (VT) or ventricular fibrillation (VF) detection and therapy parameters can reduce the combined incidence of device-delivered shocks, arrhythmic syncope, and untreated sustained symptomatic VT/VF (morbidity index)., Background: Strategically chosen ICD VT/VF detection and therapy parameters have been shown in previous studies to reduce the number of shocked episodes. In the PREPARE (Primary Prevention Parameters Evaluation) study, these prior strategies were combined with additional strategies specific to primary prevention patients., Methods: The PREPARE study was a prospective, cohort-controlled study that analyzed 700 patients (biventricular [Bi-V] ICD and non-Bi-V ICD) with primary prevention indications for an ICD from 38 centers followed for 1 year. VT/VF was detected for rates > or =182 beats/min that were maintained for at least 30 of 40 beats. Antitachycardia pacing was programmed as the first therapy for regular rhythms with rates of 182 to 250 beats/min, and supraventricular tachycardia discriminators were used for rhythms < or =200 beats/min. The control cohort consisted of 689 primary prevention patients from the EMPIRIC (Comparison of Empiric to Physician-Tailored Programming of Implantable Cardioverter Defibrillators Trial) (non-Bi-V ICD, physician arm only) and MIRACLE ICD (Multicenter InSync Implantable Cardioversion Defibrillation Randomized Clinical Evaluation) (Bi-V ICD) trials for whom VT/VF detection and therapy programming were not controlled., Results: The PREPARE programming significantly reduced the morbidity index incidence density (0.26 events/patient-year for PREPARE study patients vs. 0.69 control cohort, p = 0.003). The PREPARE study patients were less likely to receive a shock in the first year compared with control patients (9% vs. 17%, p < 0.01). The incidence of untreated VT and arrhythmic syncope was similar between the PREPARE study patients and the control cohort., Conclusions: Strategically chosen VT/VF detection and therapy parameters can safely reduce shocks and other morbidities associated with ICD therapy in patients receiving an ICD for primary prevention indications. (PREPARE-Primary Prevention Parameters Evaluation; NCT00279279).

Study Objectives: To examine the prevalence of raised periodic limb movements of sleep (PLMS) index in children referred for polysomnography (PSG) and whether parental report of symptoms correlates with objective measurement during PSG., Methods: Records of children undergoing PSG from January 2006 to July 2006 were retrospectively reviewed. At their initial sleep clinic visit, parents had been asked whether their child was restless or moved their legs excessively during sleep. Their response to these questions was compared to the child's PLMS index (number of periodic limb movements per hour) during a full PSG. PLMS were scored according to internationally accepted criteria., Results: Data were examined for 101 children (60 male) with mean age 6.5 years (range 1.2 to 17.6 years). Excessive leg movements were reported by parents in 50% and restlessness in 73%. A raised PLMS index (defined as > or = 5 per hour) was noted in 10 cases (prevalence 10%). Asking parents about whether their child kicks their legs excessively in sleep had sensitivity 50%, specificity 51%, positive predictive value (PPV) 10%, negative predictive value (NPV) 90% and positive likelihood ratio (LR+) 1.02 when compared to objective analysis. Asking parents about whether their child is restless in sleep had sensitivity 70%, specificity 26%, PPV 9%, NPV 89% and LR+ 0.95., Conclusions: Asking parents about their child's symptoms is not an accurate predictor of raised PLMS index. We recommend that leg electromyography be used in all pediatric sleep studies to record PLMS.

Objective: To determine whether unrecognized obstructive sleep apnoea (OSA) is present in some children diagnosed with normal variant short stature., Methods: One hundred and fifty-eight children aged less than 15 years and previously diagnosed with familial short stature or constitutional delay of growth were identified from the endocrine clinic database. A validated, standardized questionnaire designed to screen for symptoms of sleep disorders in children was mailed to the parents of eligible children., Results: Fifty-three questionnaires were returned. Fifteen of these had an abnormal score (greater than the mean + three standard deviations in 1157 normal control children). Of these, 10 agreed to a sleep study. Overnight polysomnography showed no evidence of OSA or other sleep/breathing disorders. However, five (half) children showed frequent periodic leg movements of 6.3, 9.2, 9.4, 10.2 and 15.4 per h (adult normal <5 per h)., Conclusions: We did not find OSA among a group of children with normal variant short stature. However, we found frequent periodic limb movements during sleep in a large proportion of the subjects, the significance of which remains to be determined.

To generate DNA deletions, a tandem array of class IIS restriction enzyme recognition sites was cloned into a plasmid. The recognition sites were arranged so that each enzyme cleaves at a different site within an adjacent target sequence. Digestion with both enzymes followed by end repair and ligation resulted in the deletion of DNA between the two sites of cleavage. Because both recognition sites are preserved following deletion, it was found that sequential deletions could be generated using cycles of restriction enzyme digestion, end repair and ligation. Therefore, this system represents a valuable tool in the definition of functional DNA sequences.