Herasevich, Vitaly

2054 Background: Systematic reviews that summarize the toxicity of Immune checkpoint inhibitors (ICIs) become outdated very soon after publication. Therefore, we reported results of a toxicity meta-analysis at 2019 ASCO meeting and informed the intent to create a living systematic review (LSR). LSRs combine human and machine effort and support rapid evidence synthesis and living clinical practice guidelines. Now, we report our experience maintaining a LSR on toxicity of ICIs. Methods: Steps include quarterly literature searches to identify new clinical trials reporting ICI-associated adverse events (AEs), AI-enabled screening of new citations which meet the inclusion criteria, automated cumulative meta-analysis and an online reporting platform. Standard data formats and protocols were designed for inputting text, tables and graphics. Software was written to interpret these data and output the information in the appropriate format, such as a forest plot and summary tables. Finally, a dynamic interface that enables user inputs and displays the associated output was designed. Results: The LSR is continuously updated incorporating toxicity data from new clinical trials as it becomes available. We have screened 8000 relevant citations and summarized the odds of Grade 3 or higher AEs and AEs of special interest in patient receiving ICIs. The results are updated on quarterly basis and are available online. The results are updated on quarterly basis and will be available on a website at the time of publication. Prototype with dummy data is available at this link . This interface can also be manipulated via user input to organize and sort data tables and forest plots by type of cancer, name or mechanism (PD-1 or PD-L1) of ICI agent, single agent or combination, type of control arm, line of treatment and several other clinically relevant filters. For example, a user can instantaneously generate a meta-analysis summarizing the risk of colitis or pneumonitis in metastatic lung cancer trials with pembrolizmuab. Conclusions: This LSR engine can prospectively synthesize toxicity data from ICI trials in an efficient manner providing accurate and timely information for advanced clinical decision support at point-of-care. Efforts are ongoing to improve efficiency of screening, improve AI-enabled processes for automated screening and data abstraction, and test across multiple clinical questions.

Background Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards. Methods A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review. Results In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards. Conclusions A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.

Objectives: In 2014, the Tele-ICU Committee of the Society of Critical Care Medicine published an article regarding the state of ICU telemedicine, one better defined today as tele-critical care. Given the rapid evolution in the field, the authors now provide an updated review. Data Sources and Study Selection: We searched PubMed and OVID for peer-reviewed literature published between 2010 and 2018 related to significant developments in tele-critical care, including its prevalence, function, activity, and technologies. Search terms included electronic ICU, tele-ICU, critical care telemedicine, and ICU telemedicine with appropriate descriptors relevant to each sub-section. Additionally, information from surveys done by the Society of Critical Care Medicine was included given the relevance to the discussion and was referenced accordingly. Data Extraction and Data Synthesis: Tele-critical care continues to evolve in multiple domains, including organizational structure, technologies, expanded-use case scenarios, and novel applications. Insights have been gained in economic impact and human and organizational factors affecting tele-critical care delivery. Legislation and credentialing continue to significantly influence the pace of tele-critical care growth and adoption. Conclusions: Tele-critical care is an established mechanism to leverage critical care expertise to ICUs and beyond, but systematic research comparing different models, approaches, and technologies is still needed.

241 Background: In a rapidly moving field, such as cancer immunotherapy, where immune checkpoint inhibitors (ICIs) are used across 14 different tumor types, patients may receive suboptimal treatment or even be harmed if information on toxicity is not readily translated for use in clinical practice. Every single systematic review and meta-analysis which attempted to summarize toxicity of immune checkpoint inhibitors (ICIs) quickly became outdated. A living systematic review, which is defined as a systematic review that is continually updated to incorporate relevant new evidence as it becomes available, is necessary in this situation. Methods: The process of creating a living systematic review started with the creation of a comprehensive search designed by a librarian experienced in systematic reviews in collaboration with the study’s principle investigator. Search was constantly updated every 3 months and evidence is synthesized in a series of steps (microtasks) using a combination of human and augmented intelligence. A complete infrastructure is being developed and it includes automated cumulative meta-analysis and an online reporting platform which will constantly update information for clinicians and patients in a live manner. Results: We screened 6746 studies during Sep 2018-March 2019 and identified 6746 studies and we were able to successfully maintain up-to-date toxicity estimates for immune mediated adverse events over this period while maintaining the rigor of a conventional systematic review. Eventually, we will integrate the steps of LSR into one, user-friendly, semi-automated format which can independently provide accurate estimates and feed into and support a living guidelines platform through shared Application Programing Interface (APIs). Conclusions: LSRs are feasible, efficient, and when fully developed can reduce redundancy and waste in medical research, improve the quality of evidence, reduce human effort and support living and dynamic guidelines to facilitate truly informed shared decision making.

80 Background: Several previous systematic reviews and meta-analyses have attempted to summarize toxicity of Immune Checkpoint inhibitors (ICIs). However, very soon after each one of these reviews has been published, it became outdated. ICIs are currently used in 14 different cancers and data is rapidly evolving from new clinical trials. A living Systematic review, which is defined as a systematic review that is continually updated to incorporate relevant new evidence as it becomes available, is necessary in this situations. Methods: In order to dynamically display content -- including textual data, tables, charts, and graphs – standard data formats and protocols were designed for various components of the systematic reviews carried out by researchers. Given these standardized formats, software was written to interpret this data and output the information in the appropriate format, such as a forest plot or data table. Finally, a dynamic interface that enables user inputs (such as which aspect of the meta-analysis to view) and displays the associated output was designed. The overall living Systematic Review infrastructure includes monthly literature searches, cumulative meta-analysis, and an online reporting platform. Results: We have developed a Living Systematic Review engine that can take in standardized data and reflect this data in an interactive, intuitive manner in the form of text, tables, and plots. This interface can also be manipulated via user input to organize and sort data tables or omit/highlight specific pieces of data. Conclusions: We have initiated the first living systematic review in immune-oncology that will be continuously updated, incorporating relevant new evidence as it becomes available, and will provide accurate and up to date toxicity estimates to support clinical decision making. The Living Systematic Review interface allows users to easily and intuitively access the most recent available clinical evidence on toxicity of ICIs.

6596 Background: Several previous systematic reviews and meta-analyses have attempted to summarize toxicity of Immune checkpoint inhibitors (ICIs). However, very soon after each one of these reviews has been published, it became outdated. ICIs are currently used in 14 different cancers and data is rapidly evolving from new clinical trials. A living Systematic review, which is defined as a systematic review that is continually updated to incorporate relevant new evidence as it becomes available, is necessary in this situations. Therefore, we performed an updated systematic review and a meta-analysis which will serve as a foundation of a living Systematic review. Methods: MEDLINE, EMBASE and Cochrane were searched to identify phase 2 and 3 RCTs of PD-1/PD-L1 ICIs. Included studies compared either immunotherapy alone or combination with existing standard of care treatment and reported data for AE’s of interest. DerSimonian-Laird random effects Meta-Analysis was performed to derive pooled odds Ratio (OR) estimates for AE’s of interest. An infrastructure of a living systematic review is being developed and it includes monthly literature searches, cumulative meta-analysis and an online reporting platform. Results: We screened 6746 studies and 31 phase 3 and 2 phase 2 RCTs (n = 21,421) were included in the analysis. 22 RCTs used PD-1/PD-L1 ICIs as a single agent and 11 as a combination therapy. Selected toxicity estimates are summarized in a table. Conclusions: The meta-analysis updates previously published toxicity estimates and provides additional information about the risk of toxicities in single versus combination regimens. We have initiated the first living systematic review in oncology that will be continuously updated, incorporating relevant new evidence as it becomes available, and will provide accurate and up to date toxicity estimates to support clinical decision making. [Table: see text]

Abstract Objective Despite increased use of electronic health records (EHRs), the clinical impact of system downtime is unknown. Materials and Methods This retrospective matched cohort study evaluated the impact of EHR downtime episodes lasting more than 60 minutes over a 6-year study period. Patients age 18 years or older who underwent surgical procedures at least 60 minutes in duration with an inpatient stay exceeding 24 hours within the study period were eligible for inclusion. Out of 4115 patients exposed to 1 of 176 EHR downtime episodes, 4103 patients were matched to an unexposed cohort in a 1:1 ratio. Multivariable regression analysis, as well as trend analysis for effect of duration of downtime on outcomes, was performed. Results Downtime-exposed patients had operating room duration 1.1 times longer (p < .001) and postoperative length of stay 1.04 times longer (p = .007) compared to unexposed patients. The 30-day mortality rates were similar between these groups (odds ratio 1.26, p > .05). In trend analysis, there was no association between duration of downtime with respect to evaluated outcomes, postoperative length of stay, and 30-day mortality. Conclusion EHR downtime had no impact on 30-day mortality. Potential associations for increased postoperative length of stay and duration of time spent in the operating room were observed among downtime-exposed patients. No trend effect was observed with respect to duration of downtime and postoperative length of stay and 30-day mortality rates.