Schulte, Phillip J.

382 Background: The natural history of biochemical recurrence (BCR) after radical prostatectomy (RP) is heterogeneous, with most patients not progressing to metastasis. Determining the impact and optimal timing of salvage radiation therapy (SRT) remains challenging, particularly in the absence of prospective randomized trials with a surveillance control arm. Our objective isto quantify the oncologic benefit of SRT among men with BCR after RP. Methods: Patients who underwent RP at Mayo Clinic between 1990 and 2017 and developed BCR (PSA≥0.20ng/mL) were included. Patients treated with SRT were compared to those managed with surveillance using risk-set matching with time-dependent propensity scores, accounting for covariates at BCR and post-BCR. The primary outcome was metastases, analyzed via Kaplan-Meier and Cox proportional hazard models. The number needed to treat (NNT) with SRT to prevent progression was calculated using regression outputs at 5 and 15 years post-BCR. Interaction analyses identified factors modifying SRT’s effect on metastasis. Results: Of 6,881 patients with BCR, 2,109 received SRT. At a median follow-up of 10.2 years, 1,147 patients with BCR developed metastases. After 1:1 matching, SRT was associated with lower metastasis risk than surveillance at 5 years (12.7% vs 19.3%, p<0.0001) and 15 years (28.6% vs. 31.5%, p<0.001). SRT was independently associated with decreased metastasis risk (HR 0.75, 95%CI 0.63–0.90, p=0.002) on multivariable analysis, translating to NNT of 23 and 15 at 5 and 15 years, respectively (Table). Additionally, we noted a significant interaction between PSA at SRT and the association of SRT with metastasis (p-interaction=0.02), such that the reduction in metastatic progression with SRT vs. surveillance was restricted to the cohort of patients who received SRT for PSA>0.40ng/mL (HR 0.66, 95%CI 0.54–0.80, p<0.001). Conclusions: Most patients with BCR post-RP do not develop metastases. Indeed, the NNT with SRT to reduce metastasis versus surveillance indicates a generally indolent disease course. Further, the lack of a significant association of SRT with reduction of metastases in patients with a PSA≤0.40ng/mL underscores the importance of careful patient selection when considering early SRT. Our data establish the rationale for a randomized trial comparing early SRT versus surveillance for appropriately selected patients with BCR after RP. Hazard ratios of SRT after matching by propensity scores, and number needed to treat for systemic progression, prostate cancer specific mortality and overall mortality. DiseaseOutcome Hazard Ratio(95% CI) P value Number Needed to Treat* (95% CI) 5 years 15 years Systemic progression 0.75(0.63 – 0.90) 0.002 23(17 – 41) 15(11 – 27) Prostate cancer mortality 0.81 (0.64 – 1.03) 0.086 103(62 – 494) 26(15 – 124) Overall mortality 0.79 (0.70 – 0.89) 0.0001 38(29 – 62) 12(9 – 20) *Number needed to treat (NNT) calculated using regression based absolute differences in event free survival between salvage radiation therapy (SRT) and observation.

ABSTRACT Understanding clinical trajectories of sepsis patients is crucial for prognostication, resource planning, and to inform digital twin models of critical illness. This study aims to identify common clinical trajectories based on dynamic assessment of cardiorespiratory support using a validated electronic health record data that covers retrospective cohort of 19,177 patients with sepsis admitted to intensive care units (ICUs) of Mayo Clinic Hospitals over 8-year period. Patient trajectories were modeled from ICU admission up to 14 days using an unsupervised machine learning two-stage clustering method based on cardiorespiratory support in ICU and hospital discharge status. Of 19,177 patients, 42% were female with a median age of 65 (interquartile range [IQR], 55–76) years, The Acute Physiology, Age, and Chronic Health Evaluation III score of 70 (IQR, 56–87), hospital length of stay (LOS) of 7 (IQR, 4–12) days, and ICU LOS of 2 (IQR, 1–4) days. Four distinct trajectories were identified: fast recovery (27% with a mortality rate of 3.5% and median hospital LOS of 3 (IQR, 2–15) days), slow recovery (62% with a mortality rate of 3.6% and hospital LOS of 8 (IQR, 6–13) days), fast decline (4% with a mortality rate of 99.7% and hospital LOS of 1 (IQR, 0–1) day), and delayed decline (7% with a mortality rate of 97.9% and hospital LOS of 5 (IQR, 3–8) days). Distinct trajectories remained robust and were distinguished by Charlson Comorbidity Index, The Acute Physiology, Age, and Chronic Health Evaluation III scores, as well as day 1 and day 3 SOFA ( P < 0.001 ANOVA). These findings provide a foundation for developing prediction models and digital twin decision support tools, improving both shared decision making and resource planning.

AbstractBackgroundEvaluations of critical bleeding and massive transfusion have focused on traumatic hemorrhage. However, most critical bleeding in hospitalized patients occurs outside trauma. The purpose of this study was to provide an in‐depth description examining the critical administration threshold (CAT; ≥3 units red blood cells (RBCs) in a 1‐h period) occurrences in nontraumatic hemorrhage. This will assist in establishing the framework for future investigations in nontraumatic hemorrhage.MethodsThis is an observational cohort study of adults experiencing critical bleeding defined as being CAT+ during hospitalization from 2016 to 2021 at a single academic institution. A CAT episode started with administration of the first qualifying RBC unit and ended at the time of completion of the last allogeneic unit prior to a ≥4‐h gap without subsequent transfusion. The primary goal was to describe demographic, clinical and transfusion characteristics of participants with nontraumatic critical bleeding.Results2433 patients suffered critical bleeding, most often occurring in the operating room (71.1%) followed by the intensive care unit (20.8%). 57% occurred on the initial day of hospitalization, with a median duration of 138 (36, 303) minutes. The median number of RBCs transfused during the episode was 5 (4, 8), with median total allogeneic units of 9 (4, 9). Hospital mortality was 19.2%. The most common cause of death was multi‐organ failure (50.3%), however death within 24 h was due to exsanguination (72.7%).DiscussionThe critical administration threshold may be employed to identify critical bleeding in non‐trauma settings of life‐threatening hemorrhage, with a mortality rate of approximately 20%.

Background: Hospitalization represents a major access point for prescription opioids, yet little is known regarding patterns and outcomes of opioid exposures before and after hospitalization for critical illness. Methods: This is an observational, population-based cohort study of adults (≥18 years) hospitalized for critical illness from 2010 to 2019. Multivariable models assess associations between opioid exposures prior to hospitalization, classified according to the Consortium to Study Opioid Risks and Trends, and posthospitalization opioid exposures and clinical outcomes through 1-year posthospitalization. Results: Of 11 496 patients, 6318 (55%) were men with a median age of 66 (51, 79) years and 40% (n = 4623) surgical admissions. Prehospitalization opioid availability included 8449 (73%) none, 2117 (18%) short-term, 471 (4%) episodic, and 459 (4%) long-term. Thirty-nine percent (4144/10 708) of hospital survivors were discharged with opioids, with higher prescribing rates for surgical admissions (55%). Greater preadmission opioid exposures were associated with higher prevalent opioid availability at 1 year (odds ratio [95% confidence interval] 24.1 [18.3-31.8], 9.42 [7.18-12.3], and 2.55 [2.08-3.12] for long-term, episodic, and short-term exposures, respectively, vs none, P < .001). Greater preadmission opioid exposures were associated with longer hospitalizations (1.13 [1.04-1.23], 1.15 [1.06-1.25], and 1.08 [1.04-1.13] multiplicative increase in geometric mean, P < .001), more readmissions (hazard ratio [HR] 2.08 [1.74-2.49], 1.88 [1.56-2.26], and 1.48 [1.33-1.64], P < .001), and higher 1-year mortality (HR 1.59 [1.28-1.98], 1.75 [1.41-2.18], and 1.49 [1.32-1.69], P < .001). Similar associations were observed across surgical and nonsurgical admissions. Conclusions: Prehospitalization opioid exposures in survivors of critical illness are associated with clinical outcomes through 1 year and may serve as important prognostic markers.

Collaborative quantitative scientists, including biostatisticians, epidemiologists, bioinformaticists, and data‐related professionals, play vital roles in research, from study design to data analysis and dissemination. It is imperative that academic health care centers (AHCs) establish an environment that provides opportunities for the quantitative scientists who are hired as staff to develop and advance their careers. With the rapid growth of clinical and translational research, AHCs are charged with establishing organizational methods, training tools, best practices, and guidelines to accelerate and support hiring, training, and retaining this staff workforce. This paper describes three essential elements for building and maintaining a successful unit of collaborative staff quantitative scientists in academic health care centers: (1) organizational infrastructure and management, (2) recruitment, and (3) career development and retention. Specific strategies are provided as examples of how AHCs can excel in these areas.

BACKGROUND: Preoperative anemia is associated with adverse outcomes in cardiac surgery, yet it remains unclear what proportion of this association is mediated through red blood cell (RBC) transfusions. METHODS: This is a historical observational cohort study of adults undergoing coronary artery bypass grafting or valve surgery on cardiopulmonary bypass at an academic medical center between May 1, 2008, and May 1, 2018. A mediation analysis framework was used to evaluate the associations between preoperative anemia and postoperative outcomes, including a primary outcome of acute kidney injury (AKI). Intraoperative RBC transfusions were evaluated as mediators of preoperative anemia and outcome relationships. The estimated total effect, average direct effect of preoperative anemia, and percent of the total effect mediated through transfusions are presented with 95% confidence intervals and P -values. RESULTS: A total of 4117 patients were included, including 1234 (30%) with preoperative anemia. Overall, 437 of 4117 (11%) patients went on to develop AKI, with a greater proportion of patients having preoperative anemia (219 of 1234 [18%] vs 218 of 2883 [8%]). In multivariable analyses, the presence of preoperative anemia was associated with increased postoperative AKI (6.4% [4.2%–8.7%] absolute difference in percent with AKI, P < .001), with incremental decreases in preoperative hemoglobin concentrations displaying greater AKI risk (eg, 11.9% [6.9%–17.5%] absolute increase in probability of AKI for preoperative hemoglobin of 9 g/dL compared to a reference of 14 g/dL, P < .001). The association between preoperative anemia and postoperative AKI was primarily due to direct effects of preoperative anemia (5.9% [3.6%–8.3%] absolute difference, P < .001) rather than mediated through intraoperative RBC transfusions (7.5% [−4.3% to 21.1%] of the total effect mediated by transfusions, P = .220). Preoperative anemia was also associated with longer hospital durations (1.07 [1.05–1.10] ratio of geometric mean length of stay, P < .001). Of this total effect, 38% (22%, 62%; P < .001) was estimated to be mediated through subsequent intraoperative RBC transfusion. Preoperative anemia was not associated with reoperation or vascular complications. CONCLUSIONS: Preoperative anemia was associated with higher odds of AKI and longer hospitalizations in cardiac surgery. The attributable effects of anemia and transfusion on postoperative complications are likely to differ across outcomes. Future studies are necessary to further evaluate mechanisms of anemia-associated postoperative organ injury and treatment strategies.

337 Background: Prostate cancer remains the most common solid organ malignancy among males in the United States. Commercial biomarkers and multiparametric MRI have been utilized to better triage men with elevated PSA and determine patients most likely to have clinically significant prostate cancer (csPCa) upon prostate biopsy. We studied the combination of MRI with PSA-based and novel extracellular vesicle (EV)-based biomarkers to determine the optimal pre-biopsy testing to reduce the number of unnecessary biopsies. Methods: Men presenting to our clinic with elevated PSA (≥ 2 ng/ml) were prospectively enrolled from October 2019 to March 2022. All men underwent multiparametric MRI and blood draws to determine prostate health index (PHI) scoring, EV density, and PSA density. MRI-fusion biopsy was performed with both systematic and targeted prostate sampling, with clinicians blinded to prior blood testing results. Blood concentrations of prostate EV (ProsEVs) were measured by nanoscale flow cytometry using antibodies against PSMA and STEAP1. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated, and receiver operator characteristic (ROC) curves were constructed separately for all patients, those without prior diagnosis of PCa, and biopsy-naïve patients. Bootstrapping analysis was performed to provide more accurate assessment of the predictive ability of all combinations. Results: Ultimately, 175 men enrolled and underwent MRI, blood testing, and prostate biopsy. Among the 175 patients included, 101 (58%) had never undergone biopsy, 38 (22%) had a previous negative biopsy, and 36 (21%) were on active surveillance (AS) for GS6 disease. AUC of mpMRI (PI-RADS≥3) to predict clinically significant PCa was 0.66 with a sensitivity, specificity, NPV and PPV of 95.2%, 37.6%, 76.0% and 79.26%. MRI combined with STEAP1-EV density had an AUC of 0.77 with a sensitivity, specificity, NPV and PPV of 90.1%, 46.7%, 64.4% and 80.9%. A multivariable model including MRI, STEAP1-EV density, PSA density and PHI density outperformed other combinations with an AUC, sensitivity, specificity, NPV and PPV of 0.90, 89.2%, 68.7%, 72.8% and 88.1%. Conclusions: Combination of mpMRI with ProsEV and PSA derivatives demonstrated better predictive ability for csPCa on biopsy compared to imaging or serum testing alone. Inclusion of prostate volume with biomarkers further increased predictive ability which could lead to a decrease in number of unnecessary biopsies for men with benign conditions and indolent cancer.

Abstract Rationale Pulmonary complications contribute significantly to nonrelapse mortality following hematopoietic stem cell transplantation (HCT). Identifying patients at high risk can help enroll such patients into clinical studies to better understand, prevent, and treat posttransplantation respiratory failure syndromes. Objectives To develop and validate a prediction model to identify those at increased risk of acute respiratory failure after HCT. Methods Patients underwent HCT between January 1, 2019, and December 31, 2021, at one of three institutions. Those treated in Rochester, MN, formed the derivation cohort, and those treated in Scottsdale, AZ, or Jacksonville, FL, formed the validation cohort. The primary outcome was the development of acute respiratory distress syndrome (ARDS), with secondary outcomes including the need for invasive mechanical ventilation (IMV) and/or noninvasive ventilation (NIV). Predictors were based on prior case-control studies. Measurements and Main Results Of 2,450 patients undergoing stem cell transplantation, there were 1,718 hospitalizations (888 patients) in the training cohort and 1,005 hospitalizations (470 patients) in the test cohort. A 22-point model was developed, with 11 points from prehospital predictors and 11 points from posttransplantation or early (&lt;24-h) in-hospital predictors. The model performed well in predicting ARDS (C-statistic, 0.905; 95% confidence interval [CI], 0.870–0.941) and the need for IMV and/or NIV (C-statistic, 0.863; 95% CI, 0.828–0.898). The test cohort differed markedly in demographic, medical, and hematologic characteristics. The model also performed well in this setting in predicting ARDS (C-statistic, 0.841; 95% CI, 0.782–0.900) and the need for IMV and/or NIV (C-statistic, 0.872; 95% CI, 0.831–0.914). Conclusions A novel prediction model incorporating data elements from the pretransplantation, posttransplantation, and early in-hospital domains can reliably predict the development of post-HCT acute respiratory failure.