A Novel Algorithm for Analysis of Multiple Endpoints Using Risk–Benefit Profiles

Often it is necessary to evaluate effectiveness of an intervention on the basis of multiple event outcomes of variable benefit and harm, which may develop over time. An attractive approach is to order combinations of these events based on desirability of the overall outcome (e.g. from cure without any adverse events to death), and then determine whether the intervention shifts the distribution of these ordered outcomes towards more desirable (Evans, Follmann 2016). The win ratio introduced in Pocock et al 2012 was an earlier implementation of this approach. More recently Claggett et al 2015 proposed a more comprehensive method allowing nonparametric and regression-based inference in presence of competing risks. Key to the method is weighting observations by inverse probability of censoring (IPC) processes specific to participants and event types. The method has seemingly great practical utility, but computation of weights is a non-trivial challenge with real-life data when each event can have its own censoring time. We present a novel recursive algorithm solving this problem for an arbitrary number of events ordered by clinical importance or desirability. The algorithm can be implemented in SAS or R software, and computes IPC weights, as well as nonparametric or parametric estimates and resampling-based measures of uncertainty. We illustrate the approach using data from the SPRINT trial of antihypertensive intervention, comparing risk-benefit profiles for robust, pre-frail, and frail subpopulations, and in analysis of fall as a function of progressive risk factors. More general use of the software tools deploying the method is described.

Baseline hypocapnia is associated with intubation in COVID-19 diagnosed patients

ABSTRACT Introduction Hypocapnia may be one of the several factors predefining the need for intubation of patients needing hospitalization for COVID-19 pneumonia. Methods A retrospective evaluation of patient files hospitalized for COVID-19 pneumonia from October 2020 until January 2021. Univariate and multivariate regression was used, as well as a multinomial regression to account for multiple endpoints (discharge, intubation, death). Results Hypocapnia was strongly associated with intubation (OR: 0.86, 95% CI: 0.76, 0.97). Additionally, last pCO2 (OR: 1.08, 95% CI: 1.01, 1.16), baseline FiO2 (OR: 1.05, 95% CI: 1.03, 1.07) as well as last FiO2 (OR: 1.21, 95% CI: 1.11, 1.46), total severity score on admission (OR: 1.18, 95% CI: 1.03, 1.37) and last pO2 (OR: 0.89, 95% CI: 0.85, 0.92) were found to have a significant impact on intubation. Incorporation of deceased patients withheld the negative association with pCO2 levels (OR: 0.88, 95% CI: 0.78, 0.98). Conclusion The dissociation between respiratory failure and a clinically comfortable patient is partly due to decreased carbon dioxide levels and clinicians should bare it in mind when handling patients with COVID-19 pneumonia. Hypocapnia seems to be a determinant factor of intubation in patients with COVID-19 pneumonia in this study.

Utilization and Efficacy of Resuscitation Endpoints in Trauma and Burn Patients: A Review Article

Shock is a sequelae in trauma and burn patients that substantially increases the risk for morbidity and mortality. The use of resuscitation endpoints allows for improved management of these patients, with the potential to prevent further morbidity/mortality. We conducted a review of the current literature on the efficacy of hemodynamic, metabolic, and regional resuscitation endpoints for use in trauma and burn patients. Hemodynamic endpoints included mean arterial pressure (MAP), heart rate (HR), urinary output (UO), compensatory reserve index (CRI), intrathoracic blood volume, and stroke volume variation (SVV). Metabolic endpoints measure cellular responses to decreased oxygen delivery and include serum lactic acid (LA), base deficit (BD), bicarbonate, anion gap, apparent strong ion difference, and serum pH. Mean arterial pressure, HR, UO, and LA are the most established markers of trauma and burn resuscitation. The evidence suggests LA is a superior metabolic endpoint marker. Newer resuscitation endpoint technologies such as point-of-care ultrasound (PoCUS), thromboelastography (TEG), and rotational thromboelastometry (ROTEM) may improve patient outcomes; however, additional research is needed to establish the efficacy in trauma and burn patients. The endpoints discussed have situational strengths and weaknesses and no single universal resuscitation endpoint has yet emerged. This review may increase knowledge and aid in guideline development. We recommend clinicians continue to integrate multiple endpoints with emphasis on MAP, HR, UO, LA, and BD. Future investigation should aim to standardize endpoints for each clinical presentation. The search for universal and novel resuscitation parameters in trauma and burns should also continue.

Bayesian Design for Identifying Cohort-Specific Optimal Dose Combinations Based on Multiple Endpoints: Application to a Phase I Trial in Non-Small Cell Lung Cancer

Immunotherapy and chemotherapy combinations have proven to be a safe and efficacious treatment approach in multiple settings. However, it is not clear whether approved doses of chemotherapy developed to achieve a maximum tolerated dose are the ideal dose when combining cytotoxic chemotherapy with immunotherapy to induce immune responses. This trial of a modulated dose chemotherapy and Pembrolizumab, with or without a second immunomodulatory agent, uses a Bayesian design to select the optimal treatment combination by balancing both safety and efficacy of the chemotherapy and immunotherapy agents within each of two cohorts. The simulation study provides evidence that the proposed Bayesian design successfully addresses the primary study aim to identify the optimal dose combination for each of the two independent patient cohorts. This conclusion is supported by the high percentage of simulated trials which select a treatment combination that is both safe and highly efficacious. The proposed trial was funded and was being finalized when the sponsoring company decided not to proceed due to negative findings in another patient population. The proposed trial design will continue to be relevant as multiple chemotherapy and immunotherapy combinations become the standard of care and future research will require evaluating the appropriate doses of various components of multiple drug regimens.

Cyber Threat Hunting

Security and Information Event Management (SIEM) systems require significant manual input; SIEM tools with machine learning minimizes this effort but are reactive and only effective if known attack patterns are captured by the configured rules and queries. Cyber threat hunting, a proactive method of detecting cyber threats without necessarily knowing the rules or pre-defined knowledge of threats, still requires significant manual effort and is largely missing the required machine intelligence to deploy autonomous analysis. This paper proposes a novel and interactive cognitive and predictive threat-hunting prototype tool to minimize manual configuration tasks by using machine intelligence and autonomous analytical capabilities. This tool adds proactive threat-hunting capabilities by extracting unique network communication behaviors from multiple endpoints autonomously while also providing an interactive UI with minimal configuration requirements and various cognitive visualization techniques to help cyber experts quickly spot events of cyber significance from high-dimensional data.