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Sepsis remains a leading and preventable cause of hospital utilization and mortality in the United States. Despite updated guidelines, the optimal definition of sepsis as well as optimal timing of bundled treatment remain uncertain. Identifying patients with infection who benefit from early treatment is a necessary step for tailored interventions. In this study, we aimed to illustrate clinical predictors of time-to-antibiotics among patients with severe bacterial infection and model the effect of delay on risk-adjusted outcomes across different sepsis definitions.A multicenter retrospective observational study.A seven-hospital network including academic tertiary care center.Eighteen thousand three hundred fifteen patients admitted with severe bacterial illness with or without sepsis by either acute organ dysfunction (AOD) or systemic inflammatory response syndrome positivity.The primary exposure was time to antibiotics. We identified patient predictors of time-to-antibiotics including demographics, chronic diagnoses, vitals, and laboratory results and determined the impact of delay on a composite of inhospital death or length of stay over 10 days. Distribution of time-to-antibiotics was similar across patients with and without sepsis. For all patients, a J-curve relationship between time-to-antibiotics and outcomes was observed, primarily driven by length of stay among patients without AOD. Patient characteristics provided good to excellent prediction of time-to-antibiotics irrespective of the presence of sepsis. Reduced time-to-antibiotics was associated with improved outcomes for all time points beyond 2.5 hours from presentation across sepsis definitions.Antibiotic timing is a function of patient factors regardless of sepsis criteria. Similarly, we show that early administration of antibiotics is associated with improved outcomes in all patients with severe bacterial illness. Our findings suggest identifying infection is a rate-limiting and actionable step that can improve outcomes in septic and nonseptic patients.

BACKGROUND: Predicting death in a cohort of clinically diverse, multi-condition hospitalized patients is difficult. This frequently hinders timely serious illness care conversations. Prognostic models that can determine 6-month death risk at the time of hospital admission can improve access to serious illness care conversations. OBJECTIVE: The objective is to determine if the demographic, vital sign, and laboratory data from the first 48 h of a hospitalization can be used to accurately quantify 6-month mortality risk. DESIGN: This is a retrospective study using electronic medical record data linked with the state death registry. PARTICIPANTS: Participants were 158,323 hospitalized patients within a 6-hospital network over a 6-year period. MAIN MEASURES: Main measures are the following: the first set of vital signs, complete blood count, basic and complete metabolic panel, serum lactate, pro-BNP, troponin-I, INR, aPTT, demographic information, and associated ICD codes. The outcome of interest was death within 6 months. KEY RESULTS: Model performance was measured on the validation dataset. A random forest model—mini serious illness algorithm—used 8 variables from the initial 48 h of hospitalization and predicted death within 6 months with an AUC of 0.92 (0.91–0.93). Red cell distribution width was the most important prognostic variable. min-SIA (mini serious illness algorithm) was very well calibrated and estimated the probability of death to within 10% of the actual value. The discriminative ability of the min-SIA was significantly better than historical estimates of clinician performance. CONCLUSION: min-SIA algorithm can identify patients at high risk of 6-month mortality at the time of hospital admission. It can be used to improved access to timely, serious illness care conversations in high-risk patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-020-05733-1) contains supplementary material, which is available to authorized users.

Purpose: Predicting surgical outcome could improve individualizing treatment strategies for patients with advanced ovarian cancer. It has been suggested earlier that gene expression signatures (GES) might harbor the potential to predict surgical outcome. Experimental Design: Data derived from high-grade serous tumor tissue of FIGO stage IIIC/IV patients of AGO-OVAR11 trial were used to generate a transcriptome profiling. Previously identified molecular signatures were tested. A theoretical model was implemented to evaluate the impact of medically associated factors for residual disease (RD) on the performance of GES that predicts RD status. Results: A total of 266 patients met inclusion criteria, of those, 39.1% underwent complete resection. Previously reported GES did not predict RD in this cohort. Similarly, The Cancer Genome Atlas molecular subtypes, an independent de novo signature and the total gene expression dataset using all 21,000 genes were not able to predict RD status. Medical reasons for RD were identified as potential limiting factors that impact the ability to use GES to predict RD. In a center with high complete resection rates, a GES which would perfectly predict tumor biological RD would have a performance of only AUC 0.83, due to reasons other than tumor biology. Conclusions: Previously identified GES cannot be generalized. Medically associated factors for RD may be the main obstacle to predict surgical outcome in an all-comer population of patients with advanced ovarian cancer. If biomarkers derived from tumor tissue are used to predict outcome of patients with cancer, selection bias should be focused on to prevent overestimation of the power of such a biomarker. See related commentary by Handley and Sood, p. 9

Objective: A tiered trauma team activation system allocates resources proportional to patients' needs based upon injury burden. Previous trauma hospital-triage models are limited to predicting Injury Severity Score which is based on > 10% all-cause in-hospital mortality, rather than need for emergent intervention within 6 hours (NEI-6). Our aim was to develop a novel prediction model for hospital-triage that utilizes criteria available to the EMS provider to predict NEI-6 and the need for a trauma team activation.Methods: A regional trauma quality collaborative was used to identify all trauma patients ≥ 16 years from the American College of Surgeons-Committee on Trauma verified Level 1 and 2 trauma centers. Logistic regression and random forest were used to construct two predictive models for NEI-6 based on clinically relevant variables. Restricted cubic splines were used to model nonlinear predictors. The accuracy of the prediction model was assessed in terms of discrimination.Results: Using data from 12,624 patients for the training dataset (62.6% male; median age 61 years; median ISS 9) and 9,445 patients for the validation dataset (62.6% male; median age 59 years; median ISS 9), the following significant predictors were selected for the prediction models: age, gender, field GCS, vital signs, intentionality, and mechanism of injury. The final boosted tree model showed an AUC of 0.85 in the validation cohort for predicting NEI-6.Conclusions: The NEI-6 trauma triage prediction model used prehospital metrics to predict need for highest level of trauma activation. Prehospital prediction of major trauma may reduce undertriage mortality and improve resource utilization.

Objective Ensuring an efficient response to COVID-19 requires a degree of inter-system coordination and capacity management coupled with an accurate assessment of hospital utilization including length of stay (LOS). We aimed to establish optimal practices in inter-system data sharing and LOS modeling to support patient care and regional hospital operations. Materials and Methods We completed a retrospective observational study of patients admitted with COVID-19 followed by 12-week prospective validation, involving 36 hospitals covering the upper Midwest. We developed a method for sharing de-identified patient data across systems for analysis. From this, we compared 3 approaches, generalized linear model (GLM) and random forest (RF), and aggregated system level averages to identify features associated with LOS. We compared model performance by area under the ROC curve (AUROC). Results A total of 2068 patients were included and used for model derivation and 597 patients for validation. LOS overall had a median of 5.0 days and mean of 8.2 days. Consistent predictors of LOS included age, critical illness, oxygen requirement, weight loss, and nursing home admission. In the validation cohort, the RF model (AUROC 0.890) and GLM model (AUROC 0.864) achieved good to excellent prediction of LOS, but only marginally better than system averages in practice. Conclusion Regional sharing of patient data allowed for effective prediction of LOS across systems; however, this only provided marginal improvement over hospital averages at the aggregate level. A federated approach of sharing aggregated system capacity and average LOS will likely allow for effective capacity management at the regional level.

Postsurgical hospital acquired infections (HAIs) are viewed as a quality benchmark in healthcare due to their association with morbidity, mortality and high cost. The prediction of HAIs allows for implementing prevention strategies at early stages to reduce postsurgical complications. In the United States, the National Surgical Quality Improvement Program (NSQIP) maintains a registry considered a “gold-standard” for HAIs outcome reporting, but it relies heavily on costly manual chart review and therefore only includes a small percent of surgery cases from each participating site. Most HAI prediction models rely on a wide range of weak risk factors, which are combined into models with many parameters and require larger sample sizes than available from NSQIP at a single health system. In this study, we propose an alternative approach to develop a robust prediction model, using the few NSQIP cases efficiently. Rather than training the HAIs prediction models directly on the small number of NSQIP patients, we leverage a simple detection model which detects HAIs after the fact on postoperative data and use this detection model to label a large non-NSQIP perioperative dataset on which prediction models are constructed. Detection models rely on strong signals requiring fewer samples to learn. We evaluate this approach in a single academic health system with 115,202 surgeries (10,354 in NSQIP). The prediction models were evaluated on the NSQIP “gold-standard” labels. While organ-space surgical site infection showed comparable performance, the proposed model demonstrated better performance for prediction of superficial surgical site infection, sepsis or septic shock, pneumonia, and urinary tract infection.

The adoption of predictive modeling for clinical decision support is accelerating in healthcare, however, the need for large sample sizes puts smaller health systems at a disadvantage. Small health systems have insufficient positive cases to build models are left with three choices. First, they can obtain already trained models, which are often too generic. Second, they can participate in research networks, building a model through a network-wide data set. Since small hospitals can only contribute small amounts of data influencing the resulting shared model minimally, this approach yields only minimal specialization. The third option is transfer learning, where a model previously trained on a large population is refined to the specific population, which carries the danger of over-specializing to the idiosyncrasies of the small data set. In this paper, we present a novel paradigm, consensus modeling, that allows a small health system to collaborate with a larger system to build a model specific to the smaller system without sharing any data instances. The method is similar to transfer learning in that it refines models from the larger system to be specific to the small system, but through iterative refinement, the larger system alleviates the risk of over-specializing to the small system. We evaluated the approach on predicting postoperative complications at two health systems with 9,044 and 38,545 patients. The model obtained from the proposed consensus modeling paradigm achieved a predictive performance on the small system that is as good as the transfer learning approach (AUC 0.71 vs 0.71) but significantly outperformed the transfer learning approach on the large dataset (AUC 0.80 vs 0.65) suggesting significantly reduced over-specializing.

Introduction/Background Predicting surgical outcome could improve individualizing treatment strategies for pts with AOC. Several earlier papers have proposed that debulking signatures using RNA expression analyses might help in this regard. Methodology FFPE tumor tissue of FIGO stage IIIC/IV pts of AGO-OVAR 11 were used to generate whole-exome data. Previously identified molecular signatures (MS), including TCGA molecular subtype and three other published MPG, were tested. We used state-of the art biostatistical approaches for analyses. A theoretical model using data of a tertiary gynecologic cancer center was implemented to evaluate the impact of predictive factors for RD not directly related to tumor biology on the performance of a debulking signature model that predicts RD status (RD0 and RD Results Of the 266 pts that met inclusion criteria, 104 (39.1%) underwent complete resection. Previously reported MPG did not predict RD in this cohort. Best Area under the Curve (AUC) was generated using LR: 0.53±0.04. Similarly, TCGA molecular subtypes (AUC=0.56±0.04), an independent de novo signature (AUC=0.51±0.04), and the total gene expression data set using all 21,000 genes (AUC=0.55±0.03) were not able to predict RD status. We identified reasons unrelated to tumor biology that serve as potential limiting factors in the ability to use MS for predicting RD. Even in a centre with a complete resection rate higher than 65% in all comers, a debulking signature which perfectly predicts RD (100%) would have a predictive performance of only AUC 0.83, due to UTB. Conclusion Previously identified MPG cannot be generalized. Our theoretical model showed that factors unrelated to tumor biology limit the ability to identify a molecular debulking signature for RD. UTB may be the main obstacle to predict surgical outcome in an all comer Population. Disclosure FH: Advisory board: Roche, Tesaro, Honoraria: AstraZeneca, Roche, Tesaro, Clovis, travel/accommodation expenses: PharmaMar; Tesaro; SK: none ; RT: none; AG: none; LU: none; CA: none; AB:none; CW: none; UC: Advisory board/honoraria: Roche, Astra Zeneca; JW:none; AB: none; SP: none; LH:none; SM: none; BA: Advisory board: Roche, Tesaro, Amgen; honoraria: Celgene, Clovis, Astra Zeneca; travel/accommodation expenses: PharmaMar; FH: Honoraria: Roche, Tesaro, Clovis, Astra Zeneca, PharmaMar travel/accommodations/expenses: Astra Zeneca, Pharmamar; SS: none; JS:; RK: Advisory Role: Medtronic, Roche, Teva; Honoraria: AstraZeneca, Intuitive Surgical, Prostrakan, RIEMSER; travel/accommodations/expenses: Cambridge Medical Robotics; CK: none; BS: none; IB: Honoraria: Roche Pharma, CLOVIS, Tesaro, AstraZeneca, Seattle Genetics, Amgen; PH: Advisory Board: Astra Zeneca, Roche, Tesaro, Lilly, Clovis, Immunogen, MSD/Merck; Honoraria: Astra Zeneca, Roche, Sotio, Tesaro, Stryker, ASCO, Zai Lab, MSD; Research funding (Inst): Astra Zeneca, Roche, GSK, Boehringer Ingelheim, Medac, DFG, European Union, DKH, Tesaro, Genmab; SD: none; BW: none; JP: Advisory board: Clovis, Roche, AstraZeneca, Tesaro; Honoraria: Roche, AstraZeneca, Tesaro, Amgen, Clovis, MSD; travel/accommodations/expenses: Roche, Tesaro; AdB: advisory board: Roche, Astra Zeneca, Tesaro, Clovis, Pfizer, Biocad, Genmab, Seattle Genetics, MSD; Honoraria: Roche, Astra Zeneca, Tesaro, Clovis, Pfizer, Biocad, Genmab, Seattle Genetics, MSD.

Surgical procedures carry the risk of postoperative infectious complications, which can be severe, expensive, and morbid. A growing body of evidence indicates that high-resolution intraoperative data can be predictive of these complications. However, these studies are often contradictory in their findings as well as difficult to replicate, suggesting that these predictive models may be capturing institutional artifacts. In this work, data and models from two independent institutions, Mayo Clinic and University of Minnesota-affiliated Fairview Health Services, were directly compared using a common set of definitions for the variables and outcomes. We built perioperative risk models for seven infectious post-surgical complications at each site to assess the value of intraoperative variables. Models were internally validated. We found that including intraoperative variables significantly improved the models’ predictive performance at both sites for five out of seven complications. We also found that significant intraoperative variables were similar between the two sites for four of the seven complications. Our results suggest that intraoperative variables can be related to the underlying physiology for some infectious complications.

We report recent progress in the development of a precision test for individualized use of the VEGF-A targeting drug bevacizumab for treating ovarian cancer. We discuss the discovery model stage (i.e., past feasibility modeling and before conversion to the production test). Main results: (a) Informatics modeling plays a critical role in supporting driving clinical and health economic requirements. (b) The novel computational models support the creation of a precision test with sufficient predictivity to reduce healthcare system costs up to $30 billion over 10 years, and make the use of bevacizumab affordable without loss of length or quality of life. (c) Connecting development of precision medicine tests to Randomized Clinical Trials enhances the robustness of computational modeling, accelerates development and validation of the precision test by 5-10 years, is highly generalizable and scalable, and should thus be considered as a high-priority design for similar precision medicine efforts.

5520Background: We previously demonstrated that bevacizumab may differentially improve ovarian cancer progression-free survival (PFS) depending on TCGA molecular subtypes. Despite recent progress i...

5550Background: Complete resection (CR) at primary debulking surgery is the single most important prognostic factor in AOC. It has been demonstrated, that suboptimal debulking (residual disease > 1...