Comparative Effectiveness and Cost-Effectiveness of Colorectal Cancer Screening With Blood-Based Biomarkers (Liquid Biopsy) vs Fecal Tests or Colonoscopy.

BACKGROUND & AIMS: Colorectal cancer (CRC) screening is highly effective but underused. Blood-based biomarkers (liquid biopsy) could improve screening participation. METHODS: Using our established Markov model, screening every 3 years with a blood-based test that meets minimum Centers for Medicare & Medicaid Services' thresholds (CMSmin) (CRC sensitivity 74%, specificity 90%) was compared with established alternatives. Test attributes were varied in sensitivity analyses. RESULTS: CMSmin reduced CRC incidence by 40% and CRC mortality by 52% vs no screening. These reductions were less profound than the 68%-79% and 73%-81%, respectively, achieved with multi-target stool DNA (Cologuard; Exact Sciences) every 3 years, annual fecal immunochemical testing (FIT), or colonoscopy every 10 years. Assuming the same cost as multi-target stool DNA, CMSmin cost $28,500/quality-adjusted life-year gained vs no screening, but FIT, colonoscopy, and multi-target stool DNA were less costly and more effective. CMSmin would match FIT's clinical outcomes if it achieved 1.4- to 1.8-fold FIT's participation rate. Advanced precancerous lesion (APL) sensitivity was a key determinant of a test's effectiveness. A paradigm-changing blood-based test (sensitivity >90% for CRC and 80% for APL; 90% specificity; cost ≤$120-$140) would be cost-effective vs FIT at comparable participation. CONCLUSIONS: CMSmin could contribute to CRC control by achieving screening in those who will not use established methods. Substituting blood-based testing for established effective CRC screening methods will require higher CRC and APL sensitivities that deliver programmatic benefits matching those of FIT. High APL sensitivity, which can result in CRC prevention, should be a top priority for screening test developers. APL detection should not be penalized by a definition of test specificity that focuses on CRC only.

Left Ventricular Dysfunction Associated With Brain Death: Results From the Donor Heart Study.

BACKGROUND: Left ventricular dysfunction in potential donors meeting brain death criteria often results in nonuse of donor hearts for transplantation, yet little is known about its incidence or pathophysiology. Resolving these unknowns was a primary aim of the DHS (Donor Heart Study), a multisite prospective cohort study. METHODS: The DHS enrolled potential donors by neurologic determination of death (n=4333) at 8 organ procurement organizations across the United States between February 2015 and May 2020. Data included medications administered, serial diagnostic tests, and transthoracic echocardiograms (TTEs) performed: (1) within 48 hours after brain death was formally diagnosed; and (2) 24±6 hours later if left ventricular (LV) dysfunction was initially present. LV dysfunction was defined as an LV ejection fraction <50% and was considered reversible if LV ejection fraction was >50% on the second TTE. TTEs were also examined for presence of LV regional wall motion abnormalities and their reversibility. We assessed associations between LV dysfunction, donor heart acceptance for transplantation, and recipient 1-year survival. RESULTS: An initial TTE was interpreted for 3794 of the 4333 potential donors by neurologic determination of death. A total of 493 (13%) of these TTEs showed LV dysfunction. Among those donors with an initial TTE, LV dysfunction was associated with younger age, underweight, and higher NT-proBNP (N-terminal pro-B-type natriuretic peptide) and troponin levels. A second TTE was performed within 24±6 hours for a subset of donors (n=224) with initial LV dysfunction; within this subset, 130 (58%) demonstrated reversibility. Sixty percent of donor hearts with normal LV function were accepted for transplant compared with 56% of hearts with reversible LV dysfunction and 24% of hearts with nonreversible LV dysfunction. Donor LV dysfunction, whether reversible or not, was not associated with recipient 1-year survival. CONCLUSIONS: LV dysfunction associated with brain death occurs in many potential heart donors and is sometimes reversible. These findings can inform decisions made during donor evaluation and help guide donor heart acceptance for transplantation.

Adjusting Incidence Estimates with Laboratory Test Performances: A Pragmatic Maximum Likelihood Estimation-Based Approach.

Understanding the incidence of disease is often crucial for public policy decision-making, as observed during the COVID-19 pandemic. Estimating incidence is challenging, however, when the definition of incidence relies on tests that imperfectly measure disease, as in the case when assays with variable performance are used to detect the SARS-CoV-2 virus. To our knowledge, there are no pragmatic methods to address the bias introduced by the performance of labs in testing for the virus. In the setting of a longitudinal study, we developed a maximum likelihood estimation-based approach to estimate laboratory performance-adjusted incidence using the expectation-maximization algorithm. We constructed confidence intervals (CIs) using both bootstrapped-based and large-sample interval estimator approaches. We evaluated our methods through extensive simulation and applied them to a real-world study (TrackCOVID), where the primary goal was to determine the incidence of and risk factors for SARS-CoV-2 infection in the San Francisco Bay Area from July 2020 to March 2021. Our simulations demonstrated that our method converged rapidly with accurate estimates under a variety of scenarios. Bootstrapped-based CIs were comparable to the large-sample estimator CIs with a reasonable number of incident cases, shown via a simulation scenario based on the real TrackCOVID study. In more extreme simulated scenarios, the coverage of large-sample interval estimation outperformed the bootstrapped-based approach. Results from the application to the TrackCOVID study suggested that assuming perfect laboratory test performance can lead to an inaccurate inference of the incidence. Our flexible, pragmatic method can be extended to a variety of disease and study settings.

Quality improvement project to reduce medicare 1-day write-offs due to inappropriate admission orders.

BACKGROUND: We identified that Stanford Health Care had a significant number of patients who after discharge are found by the utilization review committee not to meet Center for Mediare and Medicaid Services (CMS) 2-midnight benchmark for inpatient status. Some of the charges incurred during the care of these patients are written-off and known as Medicare 1-day write-offs. This study which aims to evaluate the use of a Best Practice Alert (BPA) feature on the electronic medical record, EPIC, to ensure appropriate designation of a patient's hospitalization status as either inpatient or outpatient in accordance with Center for Medicare and Medicaid services (CMS) 2 midnight length of stay benchmark thereby reducing the number of associated write-offs. METHOD: We incorporated a best practice alert (BPA) into the Epic Electronic Medical Record (EMR) that would prompt the discharging provider and the case manager to review the patients' inpatient designation prior to discharge and change the patient's designation to observation when deemed appropriate. Patients who met the inclusion criteria (Patients must have Medicare fee-for-service insurance, inpatient length of stay (LOS) less than 2 midnights, inpatient designation as hospitalization status at time of discharge, was hospitalized to an acute level of care and belonged to one of 37 listed hospital services at the time of signing of the discharge order) were randomized to have the BPA either silent or active over a three-month period from July 18, 2019, to October 18, 2019. RESULT: A total of 88 patients were included in this study: 40 in the control arm and 48 in the intervention arm. In the intervention arm, 8 (8/48, 16.7%) had an inpatient status designation despite potentially meeting Medicare guidelines for an observation stay, comparing to 23 patients (23/40, 57.5%) patients in the control group (p = 0.001). The estimated number of write-offs in the control arm was 17 (73.9%, out of 23 inpatient patients) while in the intervention arm was 1 (12.5%, out of 8 inpatient patient) after accounting for patients who may have met inpatient criteria for other reasons based on case manager note review. CONCLUSION: This is the first time to our knowledge that a BPA has been used in this manner to reduce the number of Medicare 1-day write-offs.

A parental communication assessment initiative in the paediatric cardiovascular ICU.

OBJECTIVE: Challenges to communication between families and care providers of paediatric patients in intensive care units (ICU) include variability of communication preferences, mismatched goals of care, and difficulties carrying forward family preferences from provider to provider. Our objectives were to develop and test an assessment tool that queries parents of children requiring cardiac intensive care about their communication preferences and to determine if this tool facilitates patient-centred care and improves families' ICU experience. DESIGN: In this quality improvement initiative, a novel tool was developed, the Parental Communication Assessment (PCA), which asked parents with children hospitalised in the cardiac ICU about their communication preferences. Participants were prospectively randomised to the intervention group, which received the PCA, or to standard care. All participants completed a follow-up survey evaluating satisfaction with communication. MAIN RESULTS: One hundred thirteen participants enrolled and 56 were randomised to the intervention group. Participants who received the PCA preferred detail-oriented communication over big picture. Most parents understood the daily discussions on rounds (64%) and felt comfortable expressing concerns (68%). Eighty-six percent reported the PCA was worthwhile. Parents were generally satisfied with communication. However, an important proportion felt unprepared for difficult decisions or setbacks, inadequately included or supported in decision-making, and that they lacked control over their child's care. There were no significant differences between the intervention and control groups in their communication satisfaction results. CONCLUSIONS: Parents with children hospitalised in the paediatric ICU demonstrated diverse communication preferences. Most participants felt overall satisfied with communication, but individualising communication with patients' families according to their preferences may improve their experience.

Stir-up Regimen After General Anesthesia in the Postanesthesia Care Unit: A Nurse Led Stepped Wedge Cluster Randomized Control Trial.

PURPOSE: To implement a standardized Stir-up Regimen (deep breathing, coughing, repositioning, mobilization [moving arms/legs], assessing and managing pain and nausea) within the first 30 minutes of arrival in the postanesthesia care unit (PACU), with a goal of decreasing recovery time in the immediate postanesthesia period (Phase I). DESIGN: A pragmatic stepped wedge cluster randomized control trial. Initially, data were collected on time in Phase I in three PACUs (control). Subsequently, the same three units were randomized to sequentially transition to the Stir-up Regimen (intervention). METHODS: A stepped wedge cluster randomized control trial design was used to implement a standardized Stir-up Regimen in three PACUs in an academic hospital for adult patients who received at least 30 minutes of general anesthesia. The measured outcome was the PACU time in minutes from patient arrival to when the patient met Phase I discharge criteria. Differences between intervention and control groups were evaluated using a generalized mixed-effects model. Nurses were educated about the Stir-up Regimen in team huddles, in-services, video demonstrations, email notifications and reminders, and immediate feedback at the bedside. Implementation science principles were used to assess the adoption of the Stir-up Regimen through a presurvey, postsurvey and spot-check observations in all three PACUs. FINDINGS: A total of 5,809 PACU adult patient admissions were included: control group (n = 2,860); intervention group (n = 2,949); males (n = 2,602), and females (n = 3,206). The intervention was associated with a reduction in overall mean Phase I recovery time of 4.9 minutes (95% CI: -8.4 to -1.4, P = .007). One PACU decreased time by 9.6 minutes (95% CI: -15.3 to -4.0, P < .001). The other units also reduced Phase I recovery time, but this did not reach statistical significance. The spot-check observations confirmed the intervention was adopted by the nurses, as most interventions were nurse-initiated versus patient-initiated during the first 30 minutes in PACU. CONCLUSIONS: Standardization of a Stir-up Regimen within 30 minutes of patient PACU arrival resulted in decreased Phase I recovery time.

Incidence and Prevalence of Coronavirus Disease 2019 Within a Healthcare Worker Cohort During the First Year of the Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic.

BACKGROUND: Preventing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2_ infections in healthcare workers (HCWs) is critical for healthcare delivery. We aimed to estimate and characterize the prevalence and incidence of coronavirus disease 2019 (COVID-19) in a US HCW cohort and to identify risk factors associated with infection. METHODS: We conducted a longitudinal cohort study of HCWs at 3 Bay Area medical centers using serial surveys and SARS-CoV-2 viral and orthogonal serological testing, including measurement of neutralizing antibodies. We estimated baseline prevalence and cumulative incidence of COVID-19. We performed multivariable Cox proportional hazards models to estimate associations of baseline factors with incident infections and evaluated the impact of time-varying exposures on time to COVID-19 using marginal structural models. RESULTS: A total of 2435 HCWs contributed 768 person-years of follow-up time. We identified 21 of 2435 individuals with prevalent infection, resulting in a baseline prevalence of 0.86% (95% confidence interval [CI], .53%-1.32%). We identified 70 of 2414 incident infections (2.9%), yielding a cumulative incidence rate of 9.11 cases per 100 person-years (95% CI, 7.11-11.52). Community contact with a known COVID-19 case was most strongly correlated with increased hazard for infection (hazard ratio, 8.1 [95% CI, 3.8-17.5]). High-risk work-related exposures (ie, breach in protective measures) drove an association between work exposure and infection (hazard ratio, 2.5 [95% CI, 1.3-4.8). More cases were identified in HCWs when community case rates were high. CONCLUSIONS: We observed modest COVID-19 incidence despite consistent exposure at work. Community contact was strongly associated with infections, but contact at work was not unless accompanied by high-risk exposure.

Active surveillance of serious adverse events following transfusion of COVID-19 convalescent plasma.

BACKGROUND: The reported incidence of adverse reactions following Coronavirus disease 2019 (COVID-19) convalescent plasma (CCP) transfusion has generally been lower than expected based on the incidence of transfusion reactions that have been observed in studies of conventional plasma transfusion. This raises the concern for under-reporting of adverse events in studies of CCP that rely on passive surveillance strategies. MATERIALS AND METHODS: Our institution implemented a protocol to actively identify possible adverse reactions to CCP transfusion. In addition, we retrospectively reviewed the charts of inpatients who received CCP at Stanford Hospital between May 13, 2020 and January 31, 2021. We determined the incidence of adverse events following CCP transfusion. RESULTS: A total of 49 patients received CCP. Seven patients (14%) had an increased supplemental oxygen requirement within 4 h of transfusion completion, including one patient who was intubated during the transfusion. An additional 11 patients (total of 18, 37%) had increased oxygen requirements within 24 h of transfusion, including 3 patients who were intubated. Six patients (12%) fulfilled criteria for transfusion-associated circulatory overload (TACO). CONCLUSION: Using an active surveillance strategy, we commonly observed adverse events following the transfusion of CCP to hospitalized patients. It was not possible to definitively determine whether or not these adverse events are related to CCP transfusion. TACO was likely over-diagnosed given overlap with the manifestations of COVID-19. Nevertheless, these results suggest that the potential adverse effects of CCP transfusion may be underestimated by reports from passive surveillance studies.

Lung Ultrasound Findings in Patients Hospitalized With COVID-19.

OBJECTIVES: Lung ultrasound (LUS) can accurately diagnose several pulmonary diseases, including pneumothorax, effusion, and pneumonia. LUS may be useful in the diagnosis and management of COVID-19. METHODS: This study was conducted at two United States hospitals from 3/21/2020 to 6/01/2020. Our inclusion criteria included hospitalized adults with COVID-19 (based on symptomatology and a confirmatory RT-PCR for SARS-CoV-2) who received a LUS. Providers used a 12-zone LUS scanning protocol. The images were interpreted by the researchers based on a pre-developed consensus document. Patients were stratified by clinical deterioration (defined as either ICU admission, invasive mechanical ventilation, or death within 28 days from the initial symptom onset) and time from symptom onset to their scan. RESULTS: N = 22 patients (N = 36 scans) were included. Eleven (50%) patients experienced clinical deterioration. Among N = 36 scans, only 3 (8%) were classified as normal. The remaining scans demonstrated B-lines (89%), consolidations (56%), pleural thickening (47%), and pleural effusion (11%). Scans from patients with clinical deterioration demonstrated higher percentages of bilateral consolidations (50 versus 15%; P = .033), anterior consolidations (47 versus 11%; P = .047), lateral consolidations (71 versus 29%; P = .030), pleural thickening (69 versus 30%; P = .045), but not B-lines (100 versus 80%; P = .11). Abnormal findings had similar prevalences between scans collected 0-6 days and 14-28 days from symptom onset. DISCUSSION: Certain LUS findings may be common in hospitalized COVID-19 patients, especially for those that experience clinical deterioration. These findings may occur anytime throughout the first 28 days of illness. Future efforts should investigate the predictive utility of these findings on clinical outcomes.

Clinical trials in a COVID-19 pandemic: Shared infrastructure for continuous learning in a rapidly changing landscape.

BACKGROUND: Clinical trials, conducted efficiently and with the utmost integrity, are a key component in identifying effective vaccines, therapies, and other interventions urgently needed to solve the COVID-19 crisis. Yet launching and implementing trials with the rigor necessary to produce convincing results is a complicated and time-consuming process. Balancing rigor and efficiency involves relying on designs that employ flexible features to respond to a fast-changing landscape, measuring valid endpoints that result in translational actions and disseminating findings in a timely manner. We describe the challenges involved in creating infrastructure with potential utility for shared learning. METHODS: We have established a shared infrastructure that borrows strength across multiple trials. The infrastructure includes an endpoint registry to aid in selecting appropriate endpoints, a registry to facilitate establishing a Data & Safety Monitoring Board, common data collection instruments, a COVID-19 dedicated design and analysis team, and a pragmatic platform protocol, among other elements. RESULTS: The authors have relied on the shared infrastructure for six clinical trials for which they serve as the Data Coordinating Center and have a design and analysis team comprising 15 members who are dedicated to COVID-19. The authors established a pragmatic platform to simultaneously investigate multiple treatments for the outpatient with adaptive features to add or drop treatment arms. CONCLUSION: The shared infrastructure provides appealing opportunities to evaluate disease in a more robust manner with fewer resources and is especially valued during a pandemic where efficiency in time and resources is crucial. The most important element of the shared infrastructure is the pragmatic platform. While it may be the most challenging of the elements to establish, it may provide the greatest benefit to both patients and researchers.

Impact of Modified Anesthesia Management for Pediatric Patients With Williams Syndrome.

OBJECTIVE: This study compared the percent change in systolic blood pressure and the incidence of adverse cardiac events (ACEs; defined as cardiac arrest, cardiopulmonary resuscitation, arrhythmias, or ST-segment changes) during anesthesia induction in patients with Williams syndrome (WS) before and after implementation of a perioperative management strategy. DESIGN: Retrospective observational cohort study. SETTING: Single quaternary academic referral center. PARTICIPANTS: The authors reviewed the records of all children with WS at the authors' institution who underwent general anesthesia for cardiac catheterization, diagnostic imaging, or any type of surgery between November 2008 and August 2019. The authors identified 142 patients with WS, 48 of whom underwent 118 general anesthesia administrations. A historic group (HG) was compared with the intervention group (IG). INTERVENTIONS: Change in perioperative management (three-stage risk stratification: preoperative intravenous hydration, intravenous anesthesia induction, and early use of vasoactives). MEASUREMENTS AND MAIN RESULTS: The authors determined event rates within 60 minutes of anesthesia induction. Standardized mean difference (SMD) was calculated (SMD >0.2 suggests clinically meaningful difference). Sixty-seven general anesthesia encounters were recorded in the HG (mean age, 4.8 years; mean weight, 16.3 kg) and 51 in the IG (mean age, 6.0 years; mean weight, 18.2 kg). The change in systolic blood pressure was -17.5% (-30.0, -5.0) in the HG versus -9% (-18.0, 5.0) in the IG (p = 0.015; SMD = 0.419), and the incidence of ACEs was 6% in the HG and 2% in the IG (p = 0.542; SMD = 0.207). CONCLUSIONS: Preoperative risk stratification, preoperative intravenous hydration, intravenous induction, and early use of continuous vasoactives resulted in greater hemodynamic stability, with a 2% incidence of ACEs.

Interobserver Agreement of Lung Ultrasound Findings of COVID-19.

BACKGROUND: Lung ultrasound (LUS) has received considerable interest in the clinical evaluation of patients with COVID-19. Previously described LUS manifestations for COVID-19 include B-lines, consolidations, and pleural thickening. The interrater reliability (IRR) of these findings for COVID-19 is unknown. METHODS: This study was conducted between March and June 2020. Nine physicians (hospitalists: n = 4; emergency medicine: n = 5) from 3 medical centers independently evaluated n = 20 LUS scans (n = 180 independent observations) collected from patients with COVID-19, diagnosed via RT-PCR. These studies were randomly selected from an image database consisting of COVID-19 patients evaluated in the emergency department with portable ultrasound devices. Physicians were blinded to any patient information or previous LUS interpretation. Kappa values (κ) were used to calculate IRR. RESULTS: There was substantial IRR on the following items: normal LUS scan (κ = 0.79 [95% CI: 0.72-0.87]), presence of B-lines (κ = 0.79 [95% CI: 0.72-0.87]), ≥3 B-lines observed (κ = 0.72 [95% CI: 0.64-0.79]). Moderate IRR was observed for the presence of any consolidation (κ = 0.57 [95% CI: 0.50-0.64]), subpleural consolidation (κ = 0.49 [95% CI: 0.42-0.56]), and presence of effusion (κ = 0.49 [95% CI: 0.41-0.56]). Fair IRR was observed for pleural thickening (κ = 0.23 [95% CI: 0.15-0.30]). DISCUSSION: Many LUS manifestations for COVID-19 appear to have moderate to substantial IRR across providers from multiple specialties utilizing differing portable devices. The most reliable LUS findings with COVID-19 may include the presence/count of B-lines or determining if a scan is normal. Clinical protocols for LUS with COVID-19 may require additional observers for the confirmation of less reliable findings such as consolidations.

Development and validation of a predictive model for American Society of Anesthesiologists Physical Status.

BACKGROUND: The American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. Increasingly, the ASA-PS has been applied to other uses including justification of inpatient admission. Our objectives were to develop and cross-validate a statistical model for predicting ASA-PS; and 2) assess the concurrent and predictive validity of the model by assessing associations between model-derived ASA-PS, observed ASA-PS, and a diverse set of 30-day outcomes. METHODS: Using the 2014 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use Data File, we developed and internally cross-validated multinomial regression models to predict ASA-PS using preoperative NSQIP data. Accuracy was assessed with C-Statistics and calibration plots. We assessed both concurrent and predictive validity of model-derived ASA-PS relative to observed ASA-PS and 30-day outcomes. To aid further research and use of the ASA-PS model, we implemented it into an online calculator. RESULTS: Of the 566,797 elective procedures in the final analytic dataset, 8.9% were ASA-PS 1, 48.9% were ASA-PS 2, 39.1% were ASA-PS 3, and 3.2% were ASA-PS 4. The accuracy of the 21-variable model to predict ASA-PS was C = 0.77 +/- 0.0025. The model-derived ASA-PS had stronger association with key indicators of preoperative status including comorbidities and higher BMI (concurrent validity) compared to observed ASA-PS, but less strong associations with postoperative complications (predictive validity). The online ASA-PS calculator may be accessed at https://s-spire-clintools.shinyapps.io/ASA_PS_Estimator/ CONCLUSIONS: Model-derived ASA-PS better tracked key indicators of preoperative status compared to observed ASA-PS. The ability to have an electronically derived measure of ASA-PS can potentially be useful in research, quality measurement, and clinical applications.

Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty?

BACKGROUND: Existing universal and procedure-specific surgical risk prediction models of death and major complications after elective total joint arthroplasty (TJA) have limitations including poor transparency, poor to modest accuracy, and insufficient validation to establish performance across diverse settings. Thus, the need remains for accurate and validated prediction models for use in preoperative management, informed consent, shared decision-making, and risk adjustment for reimbursement. QUESTIONS/PURPOSES: The purpose of this study was to use machine learning methods and large national databases to develop and validate (both internally and externally) parsimonious risk-prediction models for mortality and complications after TJA. METHODS: Preoperative demographic and clinical variables from all 107,792 nonemergent primary THAs and TKAs in the 2013 to 2014 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) were evaluated as predictors of 30-day death and major complications. The NSQIP database was chosen for its high-quality data on important outcomes and rich characterization of preoperative demographic and clinical predictors for demographically and geographically diverse patients. Least absolute shrinkage and selection operator (LASSO) regression, a type of machine learning that optimizes accuracy and parsimony, was used for model development. Tenfold validation was used to produce C-statistics, a measure of how well models discriminate patients who experience an outcome from those who do not. External validation, which evaluates the generalizability of the models to new data sources and patient groups, was accomplished using data from the Veterans Affairs Surgical Quality Improvement Program (VASQIP). Models previously developed from VASQIP data were also externally validated using NSQIP data to examine the generalizability of their performance with a different group of patients outside the VASQIP context. RESULTS: The models, developed using LASSO regression with diverse clinical (for example, American Society of Anesthesiologists classification, comorbidities) and demographic (for example, age, gender) inputs, had good accuracy in terms of discriminating the likelihood a patient would experience, within 30 days of arthroplasty, a renal complication (C-statistic, 0.78; 95% confidence interval [CI], 0.76-0.80), death (0.73; 95% CI, 0.70-0.76), or a cardiac complication (0.73; 95% CI, 0.71-0.75) from one who would not. By contrast, the models demonstrated poor accuracy for venous thromboembolism (C-statistic, 0.61; 95% CI, 0.60-0.62) and any complication (C-statistic, 0.64; 95% CI, 0.63-0.65). External validation of the NSQIP- derived models using VASQIP data found them to be robust in terms of predictions about mortality and cardiac complications, but not for predicting renal complications. Models previously developed with VASQIP data had poor accuracy when externally validated with NSQIP data, suggesting they should not be used outside the context of the Veterans Health Administration. CONCLUSIONS: Moderately accurate predictive models of 30-day mortality and cardiac complications after elective primary TJA were developed as well as internally and externally validated. To our knowledge, these are the most accurate and rigorously validated TJA-specific prediction models currently available (http://med.stanford.edu/s-spire/Resources/clinical-tools-.html). Methods to improve these models, including the addition of nonstandard inputs such as natural language processing of preoperative clinical progress notes or radiographs, should be pursued as should the development and validation of models to predict longer term improvements in pain and function. LEVEL OF EVIDENCE: Level III, diagnostic study.

NOS3 895G>T and CBR3 730G>A Are Associated with Recurrence Risk in Non-Muscle-Invasive Bladder Cancer with Intravesical Instillations of THP.

OBJECTIVES: To analyze the correlation between pharmacogenomic biomarkers and the efficacy of pirarubicin (THP, also named 4'-O-tetrahydropyranyl-adriamycin) and to explore potential associations of individual genetic backgrounds with the clinical outcomes of non-muscle-invasive bladder cancer (NMIBC) patients. METHODS: Between July 2003 and June 2011, a total of 91 patients were treated with transurethral resection (TUR) of the bladder tumor and were histopathologically confirmed to have NMIBC. Patients received an immediate instillation and maintenance therapy with THP. All patients underwent follow-up for recurrence. We genotyped 13 single nucleotide polymorphisms (SNPs) from blood and saliva DNA samples of all patients. RESULTS: The associations of patients' genotypes with tumor recurrence risks were analyzed by survival analysis. A total of 16 (17.6%) of the 91 patients with NMIBC had tumor recurrences with a median follow-up of 17 months (range, 2-83 months). We confirmed the effect of the European Organization for Research and Treatment of Cancer (EORTC) risk score for predicting tumor recurrence (p = 0.002, log-rank test). We adjusted for the EORTC score and found that 2 SNPs, NOS3 895G>T (rs1799983) (p = 0.02, HR = 4.32, 95% CI, 1.30-14.39, GT+TT vs. GG) and CBR3 730G>A (rs1056892) (p = 0.04, HR = 2.57, 95% CI, 1.07-6.18, GA+AA vs. GG), were significantly associated with a higher recurrence risk after TUR and instillations of THP in NMIBC patients. CONCLUSIONS: Our results suggest that NOS3 895G>T and CBR3 730G>A are genetic markers that can be used to predict tumor recurrence in NMIBC patients receiving intravesical instillations of THP. The effects of those 2 SNPs are independent of the EORTC scores. Further studies with larger sample sizes and longer follow-ups are needed to confirm our results.