Objective: The median sternotomy remains the most common surgical approach in most centers performing cardiac surgery. Patients are traditionally required to undergo 6 to 8 weeks of postoperative sternal precautions, placing significant limitations on their daily activities. The aim of this study was to determine the safety of an evidence-based movement strategy (Moving Safely) with and without the use of a wearable external sternal support device on patient quality of life after cardiac surgery. Methods: A total of 150 patients at high risk for sternal complications were recruited prospectively between 2017 and 2018 at a single tertiary cardiac center. Patients were divided into 3 groups: standard sternal precautions, Moving Safely, and Moving Safely with a wearable external sternal support device. The primary outcome was patient health-related quality of life, assessed using the EuroQol 5-dimension 5-level and the EuroQoL visual analogue scale, preoperatively, at discharge, and at 8 weeks postsurgery. Simple spirometry was also performed to assess respiratory function at these time points. Results: All patients reported better health-related quality of life on the EuroQol 5-dimension 5-level and EuroQoL visual analogue scale at time of follow-up compared with time of discharge (P < .001, respectively). Similar improvements were seen in forced expiratory volume in 1 second and forced vital capacity (P < .001, respectively). No significant differences were observed in the incidence of sternal complications among the 3 treatment groups. Conclusions: The use of a Moving Safely protocol after cardiac surgery was not associated with adverse events and improved patient health-related quality of life compared with traditional sternal precautions. The support provided by a wearable external sternal support device did not improve patient health-related quality of life after cardiac surgery.

OBJECTIVES/GOALS: Many economic evaluations rely on clinical trial data that may not represent real world populations and intervention effectiveness. We compare risk and cost-effectiveness for the Diabetes Prevention Program (DPP) clinical trial cohort and a real world population eligible for the national DPP to assess the impact of using real world data. METHODS/STUDY POPULATION: To produce real world (US population) representative results, we identified National Health and Nutrition Examination Survey (NHANES) subjects eligible for the national DPP and adjusted projections using survey weights. We used clinical predictive models to estimate individual diabetes risk, and microsimulation to estimate lifetime costs, benefits, and net monetary benefits (NMB) for lifestyle intervention and metformin. We compared results across the DPP clinical trial and NHANES populations. RESULTS/ANTICIPATED RESULTS: Three-year risk of diabetes onset for the DPP trial population (mean of 19.7%, median of 10.3%) exceeded corresponding risk for the NHANES population (mean of 14.6%, median of 4.8%). The proportion of individuals with a three-year diabetes risk < 10% for the DPP trial population (49%) was less than the corresponding proportion for NHANES (67%). Mean NMB for metformin for the DPP trial population ($9,749) exceeded the corresponding value for NHANES ($5,391). The proportion of subjects with negative NMB was 49% for the DPP trial population and 67% for NHANES. Lifestyle intervention had a mean NMB of $34,889 for the DPP trial population and $28,652 for NHANES. Only 20% of the NHANES population eligible for national DPP met inclusion/exclusion criteria for the DPP trial. DISCUSSION/SIGNIFICANCE: Real world populations eligible for the national DPP include a greater proportion of low-risk individuals, and for these people, prevention programs may confer smaller benefits. Technology assessments based on clinical trial data should be revised using real world population and treatment effect data.

BackgroundMajor adverse cardiovascular events (MACE) are a leading cause of morbidity and mortality among adults with type 2 diabetes. Currently, available MACE prediction models have important limitations, including reliance on data that may not be routinely available, narrow focus on primary prevention, limited patient populations, and longtime horizons for risk prediction.ObjectivesThe purpose of this study was to derive and internally validate a claims-based prediction model for 1-year risk of MACE in type 2 diabetes.MethodsUsing medical and pharmacy claims for adults with type 2 diabetes enrolled in commercial, Medicare Advantage, and Medicare fee-for-service plans between 2014 and 2021, we derived and internally validated the annualized claims-based MACE estimator (ACME) model to predict the risk of MACE (nonfatal acute myocardial infarction, nonfatal stroke, and all-cause mortality). The Cox proportional hazards model was composed of 30 covariates, including patient age, sex, comorbidities, and medications.ResultsThe study cohort comprised 6,623,526 adults with type 2 diabetes, mean age 68.1 ± 10.6 years, 49.8% women, and 73.0% Non-Hispanic White. ACME had a concordance index of 0.74 (validation index range: 0.739-0.741). The predicted 1-year risk of the study cohort ranged from 0.4% to 99.9%, with a median risk of 3.4% (IQR: 2.3%-6.5%).ConclusionsACME was derived in a large usual care population, relies on routinely available data, and estimates short-term MACE risk. It can support population risk stratification at the health system and payer levels, participant identification for decentralized clinical trials of cardiovascular disease, and risk-stratified observational studies using real-world data.

Background: External validations are essential to assess clinical prediction models (CPMs) before deployment. Apart from model misspecification, differences in patient population and other factors influence a model's AUC (c-statistic). We aimed to quantify variation in AUCs across external validation studies and adjust expectations of a model's performance in a new setting. Methods: The Tufts-PACE CPM Registry contains CPMs for cardiovascular disease prognosis. We analyzed the AUCs of 469 CPMs with a total of 1,603 external validations. For each CPM, we performed a random effects meta-analysis to estimate the between-study standard deviation $\tau$ among the AUCs. Since the majority of these meta-analyses has only a handful of validations, this leads to very poor estimates of $\tau$. So, we estimated a log normal distribution of $\tau$ across all CPMs and used this as an empirical prior. We compared this empirical Bayesian approach with frequentist meta-analyses using cross-validation. Results: The 469 CPMs had a median of 2 external validations (IQR: [1-3]). The estimated distribution of $\tau$ had a mean of 0.055 and a standard deviation of 0.015. If $\tau$ = 0.05, the 95% prediction interval for the AUC in a new setting is at least +/- 0.1, regardless of the number of validations. Frequentist methods underestimate the uncertainty about the AUC in a new setting. Accounting for $\tau$ in a Bayesian approach achieved near nominal coverage. Conclusion: Due to large heterogeneity among the validated AUC values of a CPM, there is great irreducible uncertainty in predicting the AUC in a new setting. This uncertainty is underestimated by existing methods. The proposed empirical Bayes approach addresses this problem which merits wide application in judging the validity of prediction models.

IntroductionA major complication of cardiac surgery is bleeding which can require surgical re-exploration and the transfusion of allogeneic blood products. Re-operative procedures for bleeding have been associated with higher rates of mortality and morbidity, therefore an intervention to reduce this complication would be important. Previous investigation has demonstrated that low-cost solutions, such as the use of an intraoperative haemostatic checklist may result in the reduction of bleeding and subsequent transfusion. The goals of this scoping review aim to assess the efficacy of the use of intraoperative haemostatic checklists on blood management in patients undergoing cardiac surgery. Specifically, the objective is to understand if the use of intraoperative haemostatic checklists has been associated with a reduction in bleeding and blood product utilisation in patients undergoing non-emergent cardiac surgery.Methods and analysisA scoping review of literature identifying randomised control and observational trials, reporting on haemostatic checklists in cardiac surgery, will be undertaken. The proposed review will be guided by the methodological framework proposed by Arksey and O’Malley. A search will be conducted for published and unpublished (grey) literature. Published literature will be searched in the following electronic databases: Scopus, MEDLINE, EMBASE and the Cochrane Library. Relevant grey literature will be identified through conference abstracts. Outcomes chosen are patient centred to ensure reduced bleeding and overall positive experience that reduces complications intraoperatively.Ethics and disseminationThis study does not require ethical approval as the data used are from available publications. Our dissemination strategy includes peer-review publication, presentation at conferences and relevant stakeholders.

This Viewpoint discusses the clinical implications of incidentally discovered covert cerebrovascular disease.

Background The use of fidaxomicin is recommended as first-line therapy for all patients with Clostridioides difficile infection (CDI). However, real-world studies have shown conflicting evidence of superiority.Methods We conducted a retrospective single-center study of patients diagnosed with CDI between 2011 and 2021. A primary composite outcome of clinical failure, 30-day relapse, or CDI-related death was used. A multivariable cause-specific Cox proportional hazards model was used to evaluate fidaxomicin compared to vancomycin in preventing the composite outcome. A separate model was fit on a subset of patients with C. difficile ribotypes adjusting for ribotype.Results There were 598 patients included, of whom 84 received fidaxomicin. The primary outcome occurred in 8 (9.5%) in the fidaxomicin group compared to 111 (21.6%) in the vancomycin group. The adjusted multivariable model showed fidaxomicin was associated with 63% reduction in the risk of the composite outcome compared to vancomycin (hazard ratio [HR] = 0.37; 95% confidence interval [CI], .17-.80). In the 337 patients with ribotype data after adjusting for ribotype 027, the results showing superiority of fidaxomicin were maintained (HR = 0.19; 95% CI, .05-.77).Conclusions In the treatment of CDI, we showed that real-world use of fidaxomicin is associated with lower risk of a composite end point of treatment failure. This study evaluated the treatment effectiveness of fidaxomicin compared to vancomycin in a single-center real-world study. Our results showed that fidaxomicin was associated with a reduced risk of Clostridioides difficile infection recurrence even after adjusting for ribotype 027.

Background: Delirium is prevalent and underdetected among cardiac surgery pa-tients on the postoperative ward. This study aimed to validate the 4 A's Test delirium screening tool and evaluate its accuracy both when used by research as-sistants and when subsequently implemented by nursing staff on the ward.Methods: This single-center, prospective observational study evaluated the perfor-mance of the 4 A's Test administered by research assistants (phase 1) and nursing staff (phase 2). Assessments were undertaken during the patients' first 3 postoper-ative days on the postcardiac surgery ward along with previous routine nurse-led Confusion Assessment Method assessments. These index tests were compared with a reference standard diagnosis of delirium based on Diagnostic and Statistical Manual of Mental Disorders 5th Edition criteria. Surveys regarding delirium screening were administered to nurses pre-and postimplementation of the 4 A's Test in phase 2 of the study.Results: In phase 1, a total of 137 patients were enrolled, of whom 24.8% experi-enced delirium on the postoperative cardiac ward. The 4 A's Test had a sensitivity of 85% (95% confidence interval, 73-93) and a specificity of 90% (95% confidence interval, 85-93) compared with the reference standard. The nurse-assessed Confu-sion Assessment Method had a sensitivity of 23% (95% confidence interval, 13-37) and specificity of 100% (95% confidence interval, 99-100). In phase 2, nurses (n 1/4 51) screened 179 patients for delirium using the 4 A's Test. Compared with the reference rater, the 4 A's Test had a sensitivity of 58% (95% confidence inter -val, 28-85) and specificity of 94% (95% confidence interval, 85-98). Postimplemen-tation, 64% of nurses thought that the 4 A's Test improved their confidence in delirium detection, and 76% of nurses would consider routine 4 A's Test use.Conclusions: The 4 A's Test demonstrated moderate sensitivity and high specificity to detect delirium in a real-world setting after cardiac surgery on the postoperative ward. A modified model of use with less frequent administration, along with increased engagement of the postoperative team, is recommended to improve early delirium detection on the cardiac surgery postoperative ward. (J Thorac Car-diovasc Surg 2023;165:1151-60)

At present, there is a lack of information on patient and caregiver values, and perceived priorities and barriers, to guide successful post-discharge recovery. This was a single center, multiple methods study that investigated patient, caregiver, and health care provider perceptions of the discharge process after cardiac surgery. Themes emerging from focus group discussions with patients and caregivers were used to develop surveys relating to values, barriers, and challenges relating to the discharge process. Thirty-two patients (n = 16) and caregivers (n = 16) participated in four separate focus groups. Four themes emerged from these discussions: (1) a lack of understanding about what the discharge process entails and when discharge is appropriate, (2) issues relating to the information provided to patients at the time of discharge, (3) participant experiences with the health care system, and (4) the experiences of caregivers. Seventy-eight patients, 34 caregivers, 53 nurses and/or other allied health professionals, and 8 surgeons completed the cross-sectional surveys. The most important component of the discharge process for patients and caregivers was "knowing what to do in an emergency." Health care providers less accurately identified what caregivers perceived as the most important aspects of the discharge process.Statements relating to informational barriers to discharge were the most discordant among patient and caregiver respondents. After discharge, patients and caregivers identified the need for longer-term follow up with the surgeon and more support in the community. Incorporation of patient and caregiver values to guide the post-cardiac surgery discharge process is essential to promote successful recovery.

Measuring the performance of models that predict individualized treatment effect is challenging because the outcomes of two alternative treatments are inherently unobservable in one patient. The C-for-benefit was proposed to measure discriminative ability. However, measures of calibration and overall performance are still lacking. We aimed to propose metrics of calibration and overall performance for models predicting treatment effect in randomized clinical trials (RCTs). Similar to the previously proposed C-for-benefit, we defined observed pairwise treatment effect as the difference between outcomes in pairs of matched patients with different treatment assignment. We match each untreated patient with the nearest treated patient based on the Mahalanobis distance between patient characteristics. Then, we define the Eavg-for-benefit, E50-for-benefit, and E90-for-benefit as the average, median, and 90th quantile of the absolute distance between the predicted pairwise treatment effects and local-regression-smoothed observed pairwise treatment effects. Furthermore, we define the cross-entropy-for-benefit and Brier-for-benefit as the logarithmic and average squared distance between predicted and observed pairwise treatment effects. In a simulation study, the metric values of deliberately “perturbed models” were compared to those of the data-generating model, i.e., “optimal model”. To illustrate these performance metrics, different modeling approaches for predicting treatment effect are applied to the data of the Diabetes Prevention Program: 1) a risk modelling approach with restricted cubic splines; 2) an effect modelling approach including penalized treatment interactions; and 3) the causal forest. As desired, performance metric values of “perturbed models” were consistently worse than those of the “optimal model” (Eavg-for-benefit ≥ 0.043 versus 0.002, E50-for-benefit ≥ 0.032 versus 0.001, E90-for-benefit ≥ 0.084 versus 0.004, cross-entropy-for-benefit ≥ 0.765 versus 0.750, Brier-for-benefit ≥ 0.220 versus 0.218). Calibration, discriminative ability, and overall performance of three different models were similar in the case study. The proposed metrics were implemented in a publicly available R-package “HTEPredictionMetrics”. The proposed metrics are useful to assess the calibration and overall performance of models predicting treatment effect in RCTs.

Abstract Background Patients with immunocompromising conditions are at increased risk of C.difficile infection (CDI) and recurrence. Fidaxomicin reduces the risk of recurrence in immunocompetent hosts. However, there is limited data on fidaxomicin effectiveness in hosts with immunocompromising conditions. Methods We retrospectively assessed the treatment of CDI among patients with immunocompromising conditions who were diagnosed between 2011 and 2021 at Tufts Medical Center. Patients were considered to be treated by fidaxomicin or vancomycin if they received ≥72 hours of the agent. Patients less than 18 years, those who did not receive treatment for CDI, and those treated with metronidazole only were excluded. The study outcome was a composite of failure to achieve clinical cure within 72 hours of treatment initiation, relapse within 30 days following completion of initial treatment and death due to CDI. Time to event analysis used a cause specific Cox proportional hazards to compare the rate of the composite outcome in the two groups, accounting for the competing risk of death from other causes. Multiple imputation was used for missing variables but not the outcome. Results A total of 238 patients with immunocompromising conditions received vancomycin (n= 38) or fidaxomicin (n= 200) for treatment of CDI. Patients who received vancomycin were significantly more likely to be male (51.5% vs 26.2%, p 0.005) and to have had community acquired infection (31.1% vs 9.5%, p 0.03) compared to fidaxomicin (Table 1). The composite outcome occurred in 48 (24%) patients in the vancomycin group compared to 5 (13.2%) in the fidaxomicin group. In the multivariable model adjusted for sex, number of antecedent antibiotics, antibiotics during treatment, severity and type of immunosuppression, fidaxomicin was associated with 65% reduction in the hazard of the composite outcome compared with vancomycin (HR 0.35, 95% CI 0.12-0.99) (Table 2). Conclusion The use of fidaxomicin was more effective than vancomycin in preventing poor CDI outcomes among patients with immunocompromising conditions. The study may have residual confounding by indication and limited generalizability based on a single site. Future studies should confirm our findings. Disclosures Jennifer K. Chow, MD, MS, Kamada: Grant/Research Support|Merck: Grant/Research Support|Moderna: DSMB David Kent, MD, W.L. Gore: Grant/Research Support David R. Snydman, MD, Merck: Advisor/Consultant|Merck: Grant/Research Support|Prolacta: Advisor/Consultant|Prolacta: Grant/Research Support|Seres therapeutics: Advisor/Consultant|Seres Therapeutics: Grant/Research Support|Summit Therapeutics: Grant/Research Support

Importance Anthracycline-containing regimens are highly effective for diffuse large B-cell lymphoma (DLBCL); however, patients with preexisting heart failure (HF) may be less likely to receive anthracyclines and may be at higher risk of lymphoma mortality. Objective To assess the prevalence of preexisting HF in older patients with DLBCL and its association with treatment patterns and outcomes. Design, Setting, and Participants This longitudinal cohort study used data from the Surveillance, Epidemiology, and End Results (SEER)-Medicare registry from 1999 to 2016. The SEER registry is a system of population-based cancer registries, capturing more than 25% of the US population. Linkage to Medicare offers additional information from billing claims. This study included individuals 65 years and older with newly diagnosed DLBCL from 2000 to 2015 with Medicare Part A or B continuously in the year prior to lymphoma diagnosis. Data were analyzed from September 2020 to December 2022. Exposures Preexisting HF in the year prior to DLBCL diagnosis ascertained from billing codes required one of the following: (1) 1 primary inpatient discharge diagnosis, (2) 2 outpatient diagnoses, (3) 3 secondary inpatient discharge diagnoses, (4) 3 emergency department diagnoses, or (5) 2 secondary inpatient discharge diagnoses plus 1 outpatient diagnosis. Main Outcomes and Measures The primary outcome was anthracycline-based treatment. The secondary outcomes were (1) cardioprotective medications and (2) cause-specific mortality. The associations between preexisting HF and cancer treatment were estimated using multivariable logistic regression. The associations between preexisting HF and cause-specific mortality were evaluated using cause-specific Cox proportional hazards models with adjustment for comorbidities and cancer treatment. Results Of 30 728 included patients with DLBCL, 15 474 (50.4%) were female, and the mean (SD) age was 77.8 (7.2) years. Preexisting HF at lymphoma diagnosis was present in 4266 patients (13.9%). Patients with preexisting HF were less likely to be treated with an anthracycline (odds ratio, 0.55; 95% CI, 0.49-0.61). Among patients with preexisting HF who received an anthracycline, dexrazoxane or liposomal doxorubicin were used in 78 of 1119 patients (7.0%). One-year lymphoma mortality was 41.8% (95% CI, 40.5-43.2) with preexisting HF and 29.6% (95% CI, 29.0%-30.1%) without preexisting HF. Preexisting HF was associated with higher lymphoma mortality in models adjusting for baseline and time-varying treatment factors (hazard ratio, 1.24; 95% CI, 1.18-1.31). Conclusions and Relevance In this study, preexisting HF in patients with newly diagnosed DLBCL was common and was associated with lower use of anthracyclines and lower use of any chemotherapy. Trials are needed for this high-risk population.

Abstract Background Clostridioides difficile infection (CDI) recurs in up to 25% of patients who achieve initial response to treatment. Therefore, the goal of treatment is to establish initial cure and prevent relapses. In this study, we examine the clinical effectiveness of fidaxomicin compared to vancomycin in a real-world setting. Methods This was a retrospective study conducted between 2011 and 2021 among patients who were treated with vancomycin or fidaxomicin for CDI at Tufts Medical Center. Patients were allocated to each treatment group if they received 72 hours of the agent. Patients less than 18 years, those who did not receive any treatment for CDI those treated with metronidazole only or fecal microbiota transplant were excluded. The primary study outcome was a composite of failure to achieve clinical cure within 72 hours of treatment initiation, relapse within 30 days following completion of initial treatment or death related to CDI. A secondary outcome of combined 30 and 90 day relapse was evaluated. A time to event analysis using a cause specific Cox proportional hazards model comparing the rate of the composite outcome in the two groups was conducted, accounting for the competing risk of death from other causes. Multiple imputation was used for missing variables but not the outcome. Results A total of 637 patients were diagnosed with CDI and able to be analyzed. There were 550 patients who received vancomycin compared to 87 patients who received fidaxomicin. Patients who received fidaxomicin were more likely to be female, have history of prior CDI and were on dialysis compared to those who received vancomycin (Table 1). The composite outcome occurred in 13 (14.9%) patients in the fidaxomicin group compared to 139 (25.3%) in the vancomycin group (Table 2). The adjusted hazard ratio for the composite outcome was significantly lower in the fidaxomicin group compared to vancomycin (HR 0.5, 95% CI 0.3-0.9) (Table 3). The adjusted risk of the secondary outcome, 30 and 90 day relapse, was reduced by 34% in the fidaxomicin group compared to vancomycin (HR 0.34, 95% CI 0.15-0.77). Conclusion Our analysis demonstrates that the use of fidaxomicin was associated with reduced hazard of CDI poor treatment outcomes. This study describes real world experience using fidaxomicin in patients with CDI. Disclosures Jennifer K. Chow, MD, MS, Kamada: Grant/Research Support|Merck: Grant/Research Support|Moderna: DSMB David Kent, M.D., M.S., W.L. Gore: Grant/Research Support David R. Snydman, MD, Merck: Advisor/Consultant|Merck: Grant/Research Support|Prolacta: Advisor/Consultant|Prolacta: Grant/Research Support|Seres therapeutics: Advisor/Consultant|Seres Therapeutics: Grant/Research Support|Summit Therapeutics: Grant/Research Support

Multiple sclerosis (MS) is heterogeneous with respect to outcomes, and evaluating possible heterogeneity of treatment effect (HTE) is of high interest. HTE is non-random variation in the magnitude of a treatment effect on a clinical outcome across levels of a covariate (i.e. a patient attribute or set of attributes). Multiple statistical techniques can evaluate HTE. The simplest but most bias-prone is conventional one variable-at-a-time subgroup analysis. Recently, multivariable predictive approaches have been promoted to provide more patient-centered results, by accounting for multiple relevant attributes simultaneously. We review approaches used to estimate HTE in clinical trials of MS.

Objective(s): In light of the absence of patient and caregiver input in Enhanced Re-covery After Surgery Cardiac Surgery guideline development, we conducted a scoping review to identify patient and caregiver preferences and prioritized out-comes related to perioperative care in cardiac surgery and its lifelong impact. Methods: Five electronic databases were searched to retrieve studies investigating patient or caregiver preferences and prioritized outcomes. Information was charted in duplicate and analyzed using descriptive statistics or thematic analysis. A patient and caregiver consultation workshop validated scoping review findings and solicited novel preferences and outcomes.Results: Of the 5292 articles retrieved, 43 met inclusion criteria. Most were from Europe (n = 19, 44%) or North America (n = 15, 35%) and qualitative and quan-titative designs were represented in equal proportions. Fifty-two methods were used to obtain stakeholder preferences and prioritized outcomes, the majority be-ing qualitative in nature (n = 32, 61%). Based on the collective preferences of 3772 patients and caregivers from the review and 17 from the consultation workshop, a total of 108 patient preferences, 32 caregiver preferences, and 19 prioritized out-comes were identified. The most commonly identified theme was "information and education." Improved quality of life was the most common patient-prioritized outcome, and all caregiver-prioritized outcomes were derived from the consultation workshop.Conclusions: Patient and caregiver preferences overlap with Enhanced Recovery After Surgery Cardiac Surgery recommendations targeting preoperative risk reduc-tion strategies, prehabilitation, patient engagement technology, and intra-and post-operative strategies to reduce discomfort. To support clinical practice, future research should investigate associations with key surgical outcomes. (J Thorac Car-diovasc Surg 2023

Practitioners building classifiers often start with a smaller pilot dataset and plan to grow to larger data in the near future. Such projects need a toolkit for extrapolating how much classifier accuracy may improve from a 2x, 10x, or 50x increase in data size. While existing work has focused on finding a single "best-fit" curve using various functional forms like power laws, we argue that modeling and assessing the uncertainty of predictions is critical yet has seen less attention. In this paper, we propose a Gaussian process model to obtain probabilistic extrapolations of accuracy or similar performance metrics as dataset size increases. We evaluate our approach in terms of error, likelihood, and coverage across six datasets. Though we focus on medical tasks and image modalities, our open source approach generalizes to any kind of classifier.