Pre-specified Target Research Articles

Augmented Depression Therapy for young adults: A mixed methods randomised multiple baseline case series evaluation

Augmented Depression Therapy (ADepT) is an individual psychotherapy for depression, which has been shown to be effective in the general adult population. A randomised multiple baseline case series evaluated the feasibility, acceptability, and effectiveness of ADepT in young adults (aged 20-24). Eleven depressed young adults were recruited from a UK university wellbeing service to receive ADepT during the COVID-19 pandemic, with outcomes evaluated relative to pre-specified continuation targets. All participants received a minimum adequate treatment dose (>60% target); 89% judged ADepT as acceptable and satisfactory and would recommend it to others (>60% target); only 9% showed reliable deterioration for depression or wellbeing (meeting <30% target); and there were no trial- or treatment- related serious adverse events. Qualitative interviews revealed most participants were satisfied with and experienced benefits from ADepT. At post-treatment, reliable improvement was shown by 33% of participants for depression and 67% of participants for wellbeing (not meeting target of both >60%), with medium effect size improvements for depression (g=.78) and large effect size improvement for wellbeing (g=.93; not meeting target of both >.80). ADepT is feasible, acceptable, and safe in young adults but may require modification to maximise effectiveness. Further research outside of the COVID-19 pandemic is warranted.

Phase 2 trial of regorafenib in recurrent or metastatic adenoid cystic carcinoma.

There is a significant need for effective therapies to treat recurrent/metastatic (R/M) adenoid cystic carcinoma (ACC). This study evaluated the multi-targeted, vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitor (TKI) regorafenib in patients with R/M ACC. Patients with progressive R/M ACC were treated with regorafenib until disease progression, consent withdrawal, or excessive toxicity. The primary endpoints were best overall response (BOR) and 6-month progression-free survival (PFS). Genomic and transcriptomic biomarker analyses were performed in tumors from trial participants. Thirty-eight patients were enrolled, including 7 (18%) patients with prior VEGFR-TKIs. No objective responses were observed. Six-month PFS was 45%, and median PFS was 7.2 months (95%CI 5.2-11.9 months). Presence of either activating NOTCH1 (22%) or KDM6A alterations (24%) was associated with decreased PFS (HR 2.6, 95%CI 1.1-6.1, p=0.03). Bulk RNA sequencing of pre-treatment tumors revealed that regorafenib clinical benefit (CB; PFS≥6 months; n=11) was associated with native enrichment of immune-related signatures. Immune deconvolution revealed a greater degree of macrophage and T-cell infiltration in CB tumors. Tumors from patients with no clinical benefit (NCB; PFS<6 months; n=9) had greater expression of signatures related to cell cycle progression (E2F targets, G2/M checkpoint). The trial failed to meet the pre-specified 6-month PFS and BOR targets. We hypothesize that TKI efficacy may be reliant upon an interplay between kinase inhibition and the ACC immune microenvironment, while programs promoting cell cycle progression may contribute to TKI resistance. These observations suggest that trials evaluating CDK4/6 inhibition plus a VEGFR-TKI should be considered.

Phase I/II trial of plinabulin in combination with nivolumab and ipilimumab in patients with recurrent small cell lung cancer (SCLC): Big ten cancer research consortium (BTCRC-LUN17-127) study

BackgroundPlinabulin is a GEF-H1 releasing agent with an immune-enhancing function. We report results from a multicenter Phase I/II study (NCT03575793) assessing plinabulin in combination with nivolumab and ipilimumab for the treatment of recurrent SCLC. MethodsIn Phase I, patients were enrolled using a 3 + 3 design to determine dose-limiting toxicities (DLTs) and recommended Phase 2 dose (RP2D). Patients received nivolumab (1 mg/kg), ipilimumab (3 mg/kg), and plinabulin (in escalating doses) on day 1 of each 21-day cycle for 4 cycles followed by maintenance with plinabulin and nivolumab. In phase II, patients with recurrent PD(L)1 inhibitor resistant SCLC were enrolled. The primary objective was median progression-free survival (PFS). ResultsBetween 9/2018 and 2/2023, 39 patients were enrolled, and 36 patients received study treatment and were evaluable for safety (16 in Phase I; 20 in Phase II). In the phase I dose-escalation, there were 2 DLTs; grade 3 altered mental status lasting <24 h and grade 3 infusion reaction. The Plinabulin RP2D was determined to be 30 mg/m2. Common TRAEs were vomiting (44 %), nausea (42 %), and infusion reaction (36 %); 6 % of patients had a ≥grade 3 TRAE. Five patients (14 %) had ≥grade 3 irAEs; there were no cases of immune-related pneumonitis. In the efficacy analysis in 27 patients, the median PFS was 1.6 months (95 % CI 1.2 to 2.7) and the trial did not meet the pre-specified target median PFS of 3.5 months. Four patients treated at 30 mg/m2 had PR (confirmed 1, unconfirmed 3); 5 patients had SD with a CBR of 33 %. Two of 8 patients treated in phase I at the lower 20 mg/m2 dose had confirmed PR, with 1 patient on the drug regimen for >90 cycles. The median OS and follow-up time were 5.5 months and 2.5 months respectively. ConclusionsPlinabulin in combination with nivolumab and ipilimumab was tolerable at the dose of 30 mg/m2. While the clinical responses in PD-1 resistant SCLC were limited, some patients had a long duration of response. The number of ≥grade 3 irAE with the combination were lower than expected.

An evaluation of sample size requirements for developing risk prediction models with binary outcomes

BackgroundRisk prediction models are routinely used to assist in clinical decision making. A small sample size for model development can compromise model performance when the model is applied to new patients. For binary outcomes, the calibration slope (CS) and the mean absolute prediction error (MAPE) are two key measures on which sample size calculations for the development of risk models have been based. CS quantifies the degree of model overfitting while MAPE assesses the accuracy of individual predictions.MethodsRecently, two formulae were proposed to calculate the sample size required, given anticipated features of the development data such as the outcome prevalence and c-statistic, to ensure that the expectation of the CS and MAPE (over repeated samples) in models fitted using MLE will meet prespecified target values. In this article, we use a simulation study to evaluate the performance of these formulae.ResultsWe found that both formulae work reasonably well when the anticipated model strength is not too high (c-statistic < 0.8), regardless of the outcome prevalence. However, for higher model strengths the CS formula underestimates the sample size substantially. For example, for c-statistic = 0.85 and 0.9, the sample size needed to be increased by at least 50% and 100%, respectively, to meet the target expected CS. On the other hand, the MAPE formula tends to overestimate the sample size for high model strengths. These conclusions were more pronounced for higher prevalence than for lower prevalence. Similar results were drawn when the outcome was time to event with censoring. Given these findings, we propose a simulation-based approach, implemented in the new R package ‘samplesizedev’, to correctly estimate the sample size even for high model strengths. The software can also calculate the variability in CS and MAPE, thus allowing for assessment of model stability.ConclusionsThe calibration and MAPE formulae suggest sample sizes that are generally appropriate for use when the model strength is not too high. However, they tend to be biased for higher model strengths, which are not uncommon in clinical risk prediction studies. On those occasions, our proposed adjustments to the sample size calculations will be relevant.

Joint Statistical Inference for the Area under the ROC Curve and Youden Index under a Density Ratio Model

The receiver operating characteristic (ROC) curve is a valuable statistical tool in medical research. It assesses a biomarker’s ability to distinguish between diseased and healthy individuals. The area under the ROC curve (AUC) and the Youden index (J) are common summary indices used to evaluate a biomarker’s diagnostic accuracy. Simultaneously examining AUC and J offers a more comprehensive understanding of the ROC curve’s characteristics. In this paper, we utilize a semiparametric density ratio model to link the distributions of a biomarker for healthy and diseased individuals. Under this model, we establish the joint asymptotic normality of the maximum empirical likelihood estimator of (AUC,J) and construct an asymptotically valid confidence region for (AUC,J). Furthermore, we propose a new test to determine whether a biomarker simultaneously exceeds prespecified target values of AUC0 and J0 with the null hypothesis H0:AUC≤AUC0 or J≤J0 against the alternative hypothesis Ha:AUC&gt;AUC0 and J&gt;J0. Simulation studies and a real data example on Duchenne Muscular Dystrophy are used to demonstrate the effectiveness of our proposed method and highlight its advantages over existing methods.

Intraoperative Hypotension in Patients Having Major Noncardiac Surgery Under General Anesthesia: A Systematic Review of Blood Pressure Optimization Strategies.

Intraoperative hypotension is associated with increased risks of postoperative complications. Consequently, a variety of blood pressure optimization strategies have been tested to prevent or promptly treat intraoperative hypotension. We performed a systematic review to summarize randomized controlled trials that evaluated the efficacy of blood pressure optimization interventions in either mitigating exposure to intraoperative hypotension or reducing risks of postoperative complications. Medline, Embase, PubMed, and Cochrane Controlled Register of Trials were searched from database inception to August 2, 2023, for randomized controlled trials (without language restriction) that evaluated the impact of any blood pressure optimization intervention on intraoperative hypotension and/or postoperative outcomes. The review included 48 studies (N = 46,377), which evaluated 10 classes of blood pressure optimization interventions. Commonly assessed interventions included hemodynamic protocols using arterial waveform analysis, preoperative withholding of antihypertensive medications, continuous blood pressure monitoring, and adjuvant agents (vasopressors, anticholinergics, anticonvulsants). These same interventions reduced intraoperative exposure to hypotension. Conversely, low blood pressure alarms had an inconsistent impact on exposure to hypotension. Aside from limited evidence that higher prespecified intraoperative blood pressure targets led to a reduced risk of complications, there were few data suggesting that these interventions prevented postoperative complications. Heterogeneity in interventions and outcomes precluded meta-analysis. Several different blood pressure optimization interventions show promise in reducing exposure to intraoperative hypotension. Nonetheless, the impact of these interventions on clinical outcomes remains unclear. Future trials should assess promising interventions in samples sufficiently large to identify clinically plausible treatment effects on important outcomes.

A model-free variable screening method for optimal treatment regimes with high-dimensional survival data

Summary We propose a model-free variable screening method for the optimal treatment regime with high-dimensional survival data. The proposed screening method provides a unified framework to select the active variables in a prespecified target population, including the treated group as a special case. Based on this framework, the optimal treatment regime is exactly the optimal classifier that minimizes a weighted misclassification error rate, with weights associated with survival outcome variables, the censoring distribution and a prespecified target population. Our main contribution involves reformulating the weighted classification problem into a classification problem within a hypothetical population, where the observed data can be viewed as a sample obtained from outcome-dependent sampling, with the selection probability inversely proportional to the weights. Consequently, we introduce the weighted Kolmogorov–Smirnov approach for selecting active variables in the optimal treatment regime, extending the conventional Kolmogorov–Smirnov method for binary classification. Additionally, the proposed screening method exhibits two levels of robustness. The first level of robustness is achieved because the proposed method does not require any model assumptions for the survival outcome on treatment and covariates, whereas the other is attained as the form of treatment regimes is allowed to be unspecified even without requiring convex surrogate loss, such as logit loss or hinge loss. As a result, the proposed screening method is robust to model misspecifications, and nonparametric learning methods such as random forests and boosting can be applied to those selected variables for further analysis. The theoretical properties of the proposed method are established. The performance of the proposed method is examined through simulation studies and illustrated by a lung cancer dataset.

Fast saccades to faces during the feedforward sweep.

Saccadic choice tasks use eye movements as a response method, typically in a task where observers are asked to saccade as quickly as possible to an image of a prespecified target category. Using this approach, face-selective saccades have been observed within 100ms poststimulus. When taking into account oculomotor processing, this suggests that faces can be detected in as little as 70 to 80ms. It has therefore been suggested that face detection must occur during the initial feedforward sweep, since this latency leaves little time for feedback processing. In the current experiment, we tested this hypothesis using backward masking-a technique shown to primarily disrupt feedback processing while leaving feedforward activation relatively intact. Based on minimum saccadic reaction time, we found that face detection benefited from ultra-fast, accurate saccades within 110 to 160ms and that these eye movements are obtainable even under extreme masking conditions that limit perceptual awareness. However, masking did significantly increase the median SRT for faces. In the manual responses, we found remarkable detection accuracy for faces and houses, even when participants indicated having no visual experience of the test images. These results provide evidence for the view that the saccadic bias to faces is initiated by coarse information used to categorize faces in the feedforward sweep but that, in most cases, additional processing is required to quickly reach the threshold for saccade initiation.

Colorimetric CO2 Detector to Improve Effective Mask Ventilations in Very Preterm Infants: A Pilot Randomized Controlled Study

Introduction: End-tidal CO2 (ETCO2) detector is currently recommended for confirmation of endotracheal tube placement during neonatal resuscitation. Whether it is feasible to use ETCO2 detectors during mask ventilation to reduce risk of bradycardia and desaturations, which are associated with increased risk of death in preterm babies, is unknown. Methods: This is a pilot randomized controlled trial (NCT04287907) involving newborns 24 + 0/7 to 32 + 0/7 weeks gestation who required mask ventilation at birth. Infants were randomized into groups with or without colorimetric ETCO2 detectors. Combined duration of any bradycardia (<100 bpm) and time below prespecified target oxygen saturation (SpO2) as measured by pulse oximetry were compared. Results: Fifty participants were randomized, 47 with outcomes analysed (2 incomplete data, 1 postnatal diagnosis of trachea-oesophageal fistula). Mean gestational age and birthweight were 28.5 ± 1.9 versus 29.4 ± 1.6 weeks (p = 0.1) and 1,252.7 ± 409.7 g versus 1,334.6 ± 369.1 g (p = 0.5) in the intervention and control arm, respectively. Mean combined duration of bradycardia and desaturation was 276.7 ± 197.7 s (intervention) and 322.7 ± 277.7 s (control) (p = 0.6). Proportion of participants with any bradycardia or desaturation at 5 min were 38.1% (intervention) and 56.5% (control) (p = 0.2). No chest compressions, epinephrine administration, or death occurred in the delivery room. Conclusion: This pilot study demonstrates the feasibility of a trial to evaluate colorimetric ETCO2 detectors during mask ventilation of very preterm infants to reduce bradycardia and low SpO2. Further assessment with a larger population will be required to determine if ETCO2 detector usage at resuscitation reduces risk of adverse outcomes, including death and disability, in very preterm infants.

Serum Urate as a Surrogate Outcome for Gout Flares: Where Do We Stand Today?

In gout research, serum urate has been widely accepted as the primary endpoint in clinical trials of urate-lowering therapies by both the FDA and EMA for many years. However, for serum urate to be a meaningful outcome measure, it should reflect at least one important patient-centered clinical outcome, such as gout flares. The relationship between achieving a pre-specified “target” serum urate and a corresponding improvement in patient-centered outcomes has been difficult to show due to variation in reporting of both serum urate and gout flares in clinical trials; a paradoxical rise in gout flares after starting urate-lowering therapy and a delay after achieving the pre-specified target serum urate before gout flares settle coupled with the relatively short duration of the trials. However, recent evidence from individual-level patient data from two, two-year randomized controlled trials clearly shows that achieving target urate is associated with a subsequent reduction and cessation of gout flares. In this review, we examine the evidence supporting serum urate as a surrogate outcome for gout flares, the methods, and the challenges of showing the validity of surrogacy.

Inspection policy optimization for hierarchical multistate systems under uncertain mission scenarios: A risk-averse perspective

Most engineered systems intend to perform missions with a pre-specified target success probability to reduce undesirable failure risks. Before executing the next mission, inspection activities are conducted across various physical levels for assessing the probability of mission success. However, due to the randomness of a system’s degradation behavior and the presence of measurement errors, inspection results inevitably contain uncertainty. Meanwhile, mission durations and acceptable system states may also be uncertain, due to uncontrollable factors, such as random operating environments and mission demands. In such a circumstance, it is of great significance to identify the optimal multilevel inspection policy to answer, as great confident as possible, the question that the system can complete the next mission with a target mission success probability. This paper develops a novel metric to gauge the effectiveness of a multilevel inspection policy to assess if the system can complete the next mission with a pre-specified target success probability from a risk-averse perspective, based on which an optimization method is proposed to seek an inspection policy under uncertain scenarios with the aim of minimizing the maximum regret of the proposed metric. A stochastic fractal search algorithm, along with two tailored local search rules, is designed to efficiently resolve the resulting optimization problem. Two cases, including a three-component system and a rocket fueling mechanism’s control system, are used to illustrate the efficacy of the proposed approach, which is capable of effectively identifying the risk of mission failures by inspection policies.

Imperceptible Attacks on Fault Detection and Diagnosis Systems in Smart Buildings

Automated Fault Detection and Diagnosis (AFDD) systems are critical to safe and efficient operation of smart buildings. A significant amount of building data can be collected and analyzed to detect building compo-nent failures. Attacks against such data that are contami-nated with small additive disturbances (i.e., adversarial per-turbation attacks) could dreadfully impact the performance of such systems while maintaining a high level of imper-ceptibility. The vulnerability studies of such data attacks is lacking. Specifically, most existing detection and clas-sification models have flat structures, regarded as Single-Stage Classifiers (SSCs), are prone to adversarial data perturbation attacks. In this paper, we present a coarse-to-fine Hierarchical Fault Detection and multi-level Diagnosis (HFDD) model, and formulate a mathematical program to derive targeted attacks on the model with respect to a pre-specified target diagnosis level. Two algorithms are developed based on convex relaxations of the formulated program for non-targeted attacks. An Alternating Direction Method of Multipliers (ADMM)-based solver is developed for the convex programs. Extensive experiments are con-ducted using two real-world datasets of measurements from air handling units and chillers, demonstrating the fea-sibility of the proposed attacks with regard to misclassifi-cation rate and imperceptibility of the attack. We also show that the HFDD is more robust to disturbances than SSC-based fault detection and multi-level diagnosis systems.

Noninvasive hemoglobin monitoring for maintaining hemoglobin concentration within the target range during major noncardiac surgery: A randomized controlled trial

Study objectiveThe effect of noninvasive CO-oximetry hemoglobin (SpHb) monitoring on the clinical outcomes of patients undergoing surgery remains unclear. This trial aimed to evaluate whether SpHb monitoring helps maintain hemoglobin levels within a predefined target range during major noncardiac surgeries with a potential risk of intraoperative hemorrhage. DesignA single-center, prospective, randomized controlled trial. SettingUniversity hospital. PatientsOne hundred and thirty patients undergoing elective noncardiac surgery with a potential risk of hemorrhage. InterventionsPatients were randomly allocated to undergo either SpHb-guided management (SpHb group) or usual care (control group). MeasurementsThe primary outcome was the rate of deviation of the total hemoglobin concentration (determined from laboratory testing) from a pre-specified target range (8–14 g/dL). This was defined as the number of laboratory tests revealing such deviations divided by the total number of laboratory tests performed during the surgery. Main resultsThe primary outcome occurred significantly less frequently in the SpHb group as compared to that in the control group (15/555 [2.7%]) vs. 68/598 [11.4%]; relative risk, 0.24; 95% confidence interval, 0.13–0.41; P < 0.001). Fewer point-of-care blood tests were performed in the SpHb group than in the control group (median [interquartile range], 2 [1–4] vs. 4 [2–5]; P < 0.001). There were no significant intergroup differences in the number of patients who received red blood cell transfusions during surgery (SpHb vs. control, 33.8% vs. 46.2%; P = 0.201). The incidence of unnecessary red blood cell preparation (>2 units) was lower in the SpHb group than in the control group (3.1% vs. 16.9%; P = 0.024). ConclusionsCompared with routine care, SpHb-guided management resulted in significantly lower rates of hemoglobin deviation outside the target range intraoperatively in patients undergoing major noncardiac surgeries with a potential risk of hemorrhage. Clinical trial registrationClinicalTrials.gov (identifier: NCT03816514).

Abstract 12956: Novel Digital Strategy for Improving Diversity in Cardiovascular Clinical Trials

Background: Diversity of trial participants is crucial for the generalizability of clinical trial findings. Cardiovascular trials often underrepresent non-white participants, women, and older individuals. Investigators for the Apple Heart Study (AHS) and the Project Baseline Health Study (PBHS) used a digital platform or ‘waiting room’ to invite interested individuals in the United States to learn about the study, sign an electronic consent, and provide some baseline information including assessment of key inclusion/exclusion criteria. For AHS, the direct-to-participant approach led to the recruitment of participants that were comparably aligned with the US census. For PBHS, participants were enrolled from a ‘waiting room’ using specific parameters to meet the pre-specified target of mimicking the US census for race and ethnicity. Methods: We describe the sex, age, and race/ethnicity of the population enrolled in AHS and PBHS. AHS was a completely siteless/virtual trial, while PBHS did traditional assessments at physical locations while also having virtual components. Results: A total of 419,297 participants were enrolled in just 8 months in the AHS and 2502 were enrolled in PBHS. The sex, age, and race/ethnicity of the study populations are shown (see Table). 42-55% were women, 11.6% were Hispanic, 8-16% were black, 6-10% were Asian and 63-68% were white. AHS enrolled from all 50 states, while PBHS enrolled from 4 locations in California and North Carolina. Conclusion: In two direct-to-participant clinical trials, we were able to recruit participants that were comparably aligned with the US census. The digital strategies used were successful in both siteless and traditional site-based research studies. The direct-to-participant recruitment approach and digital waiting room are potentially scalable methods for digital recruitment and enrollment to improve diversity in pivotal cardiovascular studies.

Integrating production, replenishment and fulfillment decisions for supply chains: a target-based robust optimisation approach

In this paper, a three-echelon supply chain problem under demand uncertainty is considered. The problem is formulated as a multiperiod two-stage stochastic optimisation model. The first stage, consisting of production and replenishment decisions, is integrated with the second stage, which comprises reactive fulfillment decisions, allowing seamless determination as demands are revealed over time. The demand in each period is characterised by an uncertainty set based on the nominal value and demand bounds. We propose a target-based robust optimisation (TRO) approach to determine the most robust planning with respect to a pre-specified cost target. The proposed TRO approach can trade off the total cost (performance) and model feasibility in the presence of demand perturbation (robustness) by fine-tuning the cost target. The robust counterpart is converted to a quadratically constrained linear programming (QCLP) problem, which can be solved by commercial solvers. Numerical experiments demonstrate that the TRO approach can outperform traditional robust optimisation methods in terms of both cost and feasibility against demand uncertainty by enabling precise adjustment of the cost target. Importantly, the TRO approach provides a flexible means to strike a balance between performance and robustness metrics, making it a valuable tool for supply chain planning under uncertain conditions.

Isoniazid and rifampicin exposure during treatment in drug-susceptible TB.

BACKGROUND: Observational real-world studies on therapeutic drug monitoring (TDM) in relation to pharmacokinetic (PK) target values are lacking. This study aims to describe the PK of rifampicin (RIF) and isoniazid (INH) in a real-world setting of patients with drug-susceptible TB in relation to frequently used threshold values.METHODS: A total of 116 patients with TB using standard doses of RIF and INH and who had TDM as part of clinical care were included. Maximum plasma concentration (Cmax) and 24 h area under the concentration time curve (AUC24) at standard and revised doses were described in relation to the threshold values (Cmax ≥8 mg/L for RIF and ≥3 mg/L for INH).RESULTS: For RIF (100 patients), median Cmax and median AUC24 were respectively 7.9 mg/L (IQR 6.0-11.0) and 35.8 mg*h/L (IQR 27.4-57.3) at the first TDM measurement after a standard dose of 600 mg. For INH (90 patients), median Cmax and median AUC24 were respectively 2.9 mg/L (IQR 1.3-2.5) and 12.5 mg*h/L (IQR 8.7-18.9) at the first TDM after a standard dose 300 mg. Overall, more than 50% of study participants had drug exposure below threshold values at the first TDM.CONCLUSION: Our study shows that the measured Cmax values for both RIF and INH were frequently below the pre-specified targets, emphasising the need for better justification of drug exposure targets. These TDM results highlight the need for validating PK targets of anti-TB drugs associated with clinically relevant outcomes.

Performance of closed-loop systems for intravenous drug administration: a systematic review and meta-analysis of randomised controlled trials.

Closed-loop drug delivery systems are autonomous computers able to administer medication in response to changes in physiological parameters (controlled variables). While limited evidence suggested that closed-loop systems can perform better than manual drug administration in certain settings, this technology remains a research tool with an uncertain risk/benefit profile. Our aim was comparing the performance of closed-loop systems with manual intravenous drug administration in adults. We searched MEDLINE, CENTRAL, and Embase from inception until November 2022, without restriction to language. We assessed for inclusion randomised controlled trials comparing closed-loop and manual administration of intravenous drugs in adults, intraoperatively or in the Intensive Care Unit. We identified 32 studies on closed-loop administration of propofol, noradrenaline, phenylephrine, insulin, neuromuscular blockers, and vasodilators. Most studies were at moderate or high risk of bias. The results showed that closed-loop systems reduced the duration of blood pressure outside prespecified targets during noradrenaline (MD 14.9%, 95% CI 9.6-20.2%, I2 = 66.6%) and vasodilators administration (MD 7.4%, 95% CI 5.2-9.7%, I2 = 62.3%). Closed-loop systems also decreased the duration of recovery after propofol (MD 1.3min, 95% CI 0.4-2.1min, I2 = 58.6%) and neuromuscular blockers (MD 9.0min, 95% CI 7.9-10.0min, I2 = 0%). The certainty of the evidence was low or very low for most outcomes. Automatic technology may be used to improve the hemodynamic profile during noradrenaline and vasodilators administration and reduce the duration of postanaesthetic recovery.Registration: This systematic review was registered with PROSPERO (CRD42022336950) on the 7th of June 2022.

The empirical modelling of house prices and debt revisited: a policy-oriented perspective

The recent boom in house prices in many countries during the COVID-19 pandemic and the possibility of household financial distress are of concern among some central banks. We revisit the empirical modelling of house prices and household debt with a policy-oriented perspective using Norwegian data over the last four decades within the cointegrated VAR model. Our findings suggest, in line with previous work, a long-run mutually reinforcing relationship between these financial magnitudes, and thus the potential for the build-up of financial instabilities and spillover effects to the real economy. Applying a policy control analysis, we find that both house prices and debt are controllable magnitudes to some pre-specified target levels through the mortgage interest rate, which enables the central bank to reduce large fluctuations and bubble tendencies in the housing market. The present control analysis thus provides some useful policy implications from empirically relevant representations of two important financial factors entering the decision process of the policy maker.

Focusing on information context for ITS using a spatial age of information model

New technologies for sensing and communication act as enablers for cooperative driving applications. Sensors are able to detect objects in the surrounding environment and information such as their current location is exchanged among vehicles. In order to cope with the vehicles’ mobility, such information is required to be as fresh as possible for proper operation of cooperative driving applications. The age of information (AoI) has been proposed as a metric for evaluating freshness of information; recently also within the context of intelligent transportation systems (ITS). We investigate mechanisms to reduce the AoI of data transported in form of beacon messages while controlling their emission rate. We aim to balance packet collision probability and beacon frequency using the average peak age of information (PAoI) as a metric. This metric, however, only accounts for the generation time of the data but not for application-specific aspects, such as the location of the transmitting vehicle. We thus propose a new way of interpreting the AoI by considering information context, thereby incorporating vehicles’ locations. As an example, we characterize such importance using the orientation and the distance of the involved vehicles. In particular, we introduce a weighting coefficient used in combination with the PAoI to evaluate the information freshness, thus emphasizing on information from more important neighbors. We further design the beaconing approach in a way to meet a given AoI requirement, thus, saving resources on the wireless channel while keeping the AoI minimal. We illustrate the effectiveness of our approach in Manhattan-like urban scenarios, reaching pre-specified targets for the AoI of beacon messages.

A phase 2 study of neoadjuvant lenvatinib in locally advanced invasive thyroid cancer.

TPS6105 Background: Lenvatinib is a multiple receptor tyrosine kinase (RTK) inhibitor that has demonstrated meaningful benefit in radioiodine (RAI)-refractory differentiated thyroid cancer (DTC). In SELECT, lenvatinib significantly prolonged progression-free survival (PFS) and was associated with an overall response rate (ORR) of 65% in patients with RAI-refractory DTC, with the greatest degree of tumor shrinkage occurring in the first 2 cycles. The robust activity of lenvatinib in DTC suggests that neoadjuvant treatment in patients presenting with bulky neck disease may make surgery more feasible, allows for optimal surgical outcomes and less aggressive surgical management. Neoadjuvant lenvatinib to improve surgical outcomes in locally advanced DTC has not been studied to date. This phase 2 study will examine the efficacy and safety of lenvatinib prior to thyroidectomy, with the goal of achieving more favorable surgical outcomes. Methods: This is a Simon two-stage Phase 2 open-label multicenter clinical trial for adult patients who meet the following criteria: have been diagnosed with locally advanced or persistent/recurrent thyroid and/or cervical nodal DTC, deemed at risk for R2 resection (surgical margins with macroscopic tumor) (as evident by vocal cord paralysis by laryngoscopic exam, imaging documenting extrathyroidal/extranodal extension, or major vessels involvement), have measurable disease per RECIST v1.1, have an ECOG performance status ≤ 1, and no contraindication to surgery. A total of 28 patients will be enrolled from three centers (Massachusetts General Hospital/Massachusetts Eye &amp; Ear Infirmary (MGH/MEEI), Memorial Sloan-Kettering Cancer Center (MSKCC), and MD Anderson Cancer Center (MDACC)). Participants will receive up to 6 cycles of lenvatinib prior to surgery depending on response. Participants will undergo restaging at the end of cycle 2, 4, and 6. Participants with sufficient response to treatment who projected to achieve an R0 or R1 resection will stop lenvatinib and proceed to surgery within 7-10 days from the last dose. Participants who do not have a sufficient response who are tolerating treatment, will receive an additional 2-4 cycles of lenvatinib, followed by surgery. The primary objective is the R0 (surgical margins free from tumor)/R1 (surgical margins free from macroscopic tumor) resection rate. Secondary outcome measures include R0/R1 resection rates in each of 5 pre-specified anatomic target interfaces (perithyroid muscles, cartilage, esophagus, recurrent laryngeal nerve, major vessels), evaluation of the change in surgical morbidity complexity MGH/MEEI-MSKCC-MDACC (MMM) score and the Invasive Thyroid Class, ORR prior to surgery per RECIST v1.1, and the safety of lenvatinib in this patient population. The study is currently accruing with 11 patients enrolled at time of submission. Clinical trial information: NCT04321954 .