A Simple and Interpretable Mortality-Based Value Metric for Condition- or Procedure-Specific Hospital Performance Reporting.

Objective: To develop a simple, interpretable value metric (VM) to assess the value of care of hospitals for specific procedures or conditions by operationalizing the value equation: Value = Quality/Cost. Patients and Methods: The present study was conducted on a retrospective cohort from 2015 to 2018 drawn from the 100% US sample of Medicare inpatient claims. The final cohort comprised 637,341 consecutive inpatient encounters with a cancer-related Medicare Severity-Diagnosis Related Grouping and 13,307 consecutive inpatient encounters with the International Classification of Diseases, Ninth Revision or International Classification of Diseases, Tenth Revision procedure code for partial or total gastrectomy. Claims-based demographic and clinical variables were used for risk adjustment, including age, sex, year, dual eligibility, reason for Medicare entitlement, and binary indicators for each of the Elixhauser comorbidities used in the Elixhauser mortality index. Risk-adjusted 30-day mortality and risk-adjusted encounter-specific costs were combined to form the VM, which was calculated as follows: number needed to treat = 1/(Mortalitynational - Mortalityhospital), and VM = number needed to treat × risk-adjusted cost per encounter. Results: Among hospitals with better-than-average 30-day cancer mortality rates, the cost to prevent 1 excess 30-day mortality for an inpatient cancer encounter ranged from $71,000 (best value) to $1.4 billion (worst value), with a median value of $543,000. Among hospitals with better-than-average 30-day gastrectomy mortality rates, the cost to prevent 1 excess 30-day mortality for an inpatient gastrectomy encounter ranged from $710,000 (best value) to $95 million (worst value), with a median value of $1.8 million. Conclusion: This simple VM may have utility for interpretable reporting of hospitals' value of care for specific conditions or procedures. We found substantial inter- and intrahospital variation in value when defined as the costs of preventing 1 excess cancer or gastrectomy mortality compared with the national average, implying that hospitals with similar quality of care may differ widely in the value of that care.

Rates of Asymptomatic COVID-19 Infection and Associated Factors in Olmsted County, Minnesota, in the Prevaccination Era.

Objective: To estimate rates and identify factors associated with asymptomatic COVID-19 in the population of Olmsted County during the prevaccination era. Patients and Methods: We screened first responders (n=191) and Olmsted County employees (n=564) for antibodies to SARS-CoV-2 from November 1, 2020 to February 28, 2021 to estimate seroprevalence and asymptomatic infection. Second, we retrieved all polymerase chain reaction (PCR)-confirmed COVID-19 diagnoses in Olmsted County from March 2020 through January 2021, abstracted symptom information, estimated rates of asymptomatic infection and examined related factors. Results: Twenty (10.5%; 95% CI, 6.9%-15.6%) first responders and 38 (6.7%; 95% CI, 5.0%-9.1%) county employees had positive antibodies; an additional 5 (2.6%) and 10 (1.8%) had prior positive PCR tests per self-report or medical record, but no antibodies detected. Of persons with symptom information, 4 of 20 (20%; 95% CI, 3.0%-37.0%) first responders and 10 of 39 (26%; 95% CI, 12.6%-40.0%) county employees were asymptomatic. Of 6020 positive PCR tests in Olmsted County with symptom information between March 1, 2020, and January 31, 2021, 6% (n=385; 95% CI, 5.8%-7.1%) were asymptomatic. Factors associated with asymptomatic disease included age (0-18 years [odds ratio {OR}, 2.3; 95% CI, 1.7-3.1] and >65 years [OR, 1.40; 95% CI, 1.0-2.0] compared with ages 19-44 years), body mass index (overweight [OR, 0.58; 95% CI, 0.44-0.77] or obese [OR, 0.48; 95% CI, 0.57-0.62] compared with normal or underweight) and tests after November 20, 2020 ([OR, 1.35; 95% CI, 1.13-1.71] compared with prior dates). Conclusion: Asymptomatic rates in Olmsted County before COVID-19 vaccine rollout ranged from 6% to 25%, and younger age, normal weight, and later tests dates were associated with asymptomatic infection.

Shared Decision-Making for Patients Hospitalized with Acute Myocardial Infarction: A Randomized Trial.

Objective: Adherence to guideline-recommended medications after acute myocardial infarction (AMI) is suboptimal. Patient fidelity to treatment regimens may be related to their knowledge of the risk of death following AMI, the pros and cons of medications, and to their involvement in treatment decisions. Shared decision-making may improve both patients' knowledge and involvement in treatment decisions. Methods: In a pilot trial, patients hospitalized with AMI were randomized to the use of the AMI Choice conversation tool or to usual care. AMI Choice includes a pictogram of the patient's estimated risk of mortality at 6 months with and without guideline-recommended medications, ie, aspirin, statins, beta-blockers, and angiotensin-converting enzyme inhibitors. Primary outcomes were patient knowledge and conflict with the decision made assessed via post-encounter surveys. Secondary outcomes were patient involvement in the decision-making process (observer-based OPTION12 scale) and 6-month medication adherence. Results: Patient knowledge of the expected survival benefit from taking medications was significantly higher (62% vs 16%, p<0.0001) in the AMI Choice group (n = 53) compared to the usual care group (n = 53). Both groups reported similarly low levels of conflict with the decision to start the medications (13 (SD 24.2) vs 16 (SD 22) out of 100; p=0.16). The extent to which clinicians in the AMI Choice group involved their patients in the decision-making process was high (OPTION12 score 53 out of 100, SD 12). Medication adherence at 6-months was relatively high in both groups and not different between groups. Conclusion: The AMI Choice conversation tool improved patients' knowledge of their estimated risk of short-term mortality after an AMI and the pros and cons of treatments to reduce this risk. The effect on patient fidelity to recommended medications of using this SDM tool and of SDM in general should be tested in larger trials enrolling patients at high risk for nonadherence. Trial Registration Number: NCT00888537.

A Learning Health System Approach to Hospital Quality Performance Benchmarking: The Composite Hospital Quality Index.

US hospital quality rankings and ratings use disparate methodologies and are weakly correlated. This causes confusion for patients and hospital quality staff. At the authors' institution, a Composite Hospital Quality Index (CHQI) was developed to combine hospital quality ratings. This approach is described and a calculator is shared here for other health systems to explore their performance. Among the US News and World Report Top 50 Hospitals, hospital-specific numeric summary scores were aggregated from the 2021 Centers for Medicare and Medicaid Services (CMS) Hospital Overall Star Rating, the Spring 2021 Leapfrog Safety Grade, and the April 2021 Hospital Consumer Assessment of Healthcare Providers and Systems Star Rating. The CHQI is the hospital-specific sum of the national percentile-rankings across these 3 ratings. In this example, mean (SD) percentiles were as follows: CMS Stars 74 (19), Hospital Consumer Assessment of Healthcare Providers and Systems 63 (19), Leapfrog 65 (24), with mean (SD) CHQI of 202 (49). The CHQI is used at the authors' institution to identify improvement opportunities and ensure that high-quality care is delivered across the health system.

Midwest rural-urban disparities in use of patient online services for COVID-19.

PURPOSE: Rural populations are disproportionately affected by the COVID-19 pandemic. We characterized urban-rural disparities in patient portal messaging utilization for COVID-19, and, of those who used the portal during its early stage in the Midwest. METHODS: We collected over 1 million portal messages generated by midwestern Mayo Clinic patients from February to August 2020. We analyzed patient-generated messages (PGMs) on COVID-19 by urban-rural locality and incorporated patients' sociodemographic factors into the analysis. FINDINGS: The urban-rural ratio of portal users, message senders, and COVID-19 message senders was 1.18, 1.31, and 1.79, indicating greater use among urban patients. The urban-rural ratio (1.69) of PGMs on COVID-19 was higher than that (1.43) of general PGMs. The urban-rural ratios of messaging were 1.72-1.85 for COVID-19-related care and 1.43-1.66 for other health care issues on COVID-19. Compared with urban patients, rural patients sent fewer messages for COVID-19 diagnosis and treatment but more messages for other reasons related to COVID-19-related health care (eg, isolation and anxiety). The frequent senders of COVID-19-related messages among rural patients were 40+ years old, women, married, and White. CONCLUSIONS: In this Midwest health system, rural patients were less likely to use patient online services during a pandemic and their reasons for its use differ from urban patients. Results suggest opportunities for increasing equity in rural patient engagement in patient portals (in particular, minority populations) for COVID-19. Public health intervention strategies could target reasons why rural patients might seek health care in a pandemic, such as social isolation and anxiety.

SARS-CoV-2 Testing Before International Airline Travel, December 2020 to May 2021.

Although there have been several case reports and simulation models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission associated with air travel, there are limited data to guide testing strategy to minimize the risk of SARS-CoV-2 exposure and transmission onboard commercial aircraft. Among 9853 passengers with a negative SARS-CoV-2 polymerase chain reaction test performed within 72 hours of departure from December 2020 through May 2021, five (0.05%) passengers with active SARS-CoV-2 infection were identified with rapid antigen tests and confirmed with rapid molecular test performed before and after an international flight from the United States to Italy. This translates to a case detection rate of 1 per 1970 travelers during a time of high prevalence of active infection in the United States. A negative molecular test for SARS-CoV-2 within 72 hours of international airline departure results in a low probability of active infection identified on antigen testing during commercial airline flight.

Prevalence of SARS-CoV-2 Antibodies in a Multistate Academic Medical Center.

OBJECTIVE: To estimate the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies in health care personnel. METHODS: The Mayo Clinic Serology Screening Program was created to provide a voluntary, two-stage testing program for SARS-CoV-2 antibodies to health care personnel. The first stage used a dried blood spot screening test initiated on June 15, 2020. Those participants identified as reactive were advised to have confirmatory testing via a venipuncture. Venipuncture results through August 8, 2020, were considered. Consent and authorization for testing was required to participate in the screening program. This report, which was conducted under an institutional review board-approved protocol, only includes employees who have further authorized their records for use in research. RESULTS: A total of 81,113 health care personnel were eligible for the program, and of these 29,606 participated in the screening program. A total of 4284 (14.5%) of the dried blood spot test results were "reactive" and warranted confirmatory testing. Confirmatory testing was completed on 4094 (95.6%) of the screen reactive with an overall seroprevalence rate of 0.60% (95% CI, 0.52% to 0.69%). Significant variation in seroprevalence was observed by region of the country and age group. CONCLUSION: The seroprevalence for SARS-CoV-2 antibodies through August 8, 2020, was found to be lower than previously reported in other health care organizations. There was an observation that seroprevalence may be associated with community disease burden.

Providing safe care for patients in the coronavirus disease 2019 (COVID-19) era: A case series evaluating risk for hospital-associated COVID-19.

OBJECTIVE: We evaluated the risk of patients contracting coronavirus disease 2019 (COVID-19) during their hospital stay to inform the safety of hospitalization for a non-COVID-19 indication during this pandemic. METHODS: A case series of adult patients hospitalized for 2 or more nights from May 15 to June 15, 2020 at large tertiary-care hospital in the midwestern United States was reviewed. All patients were screened at admission with the severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) test. Selected adult patients were also tested by IgG serology. After dismissal, patients with negative serology and PCR at admission were asked to undergo repeat serologic testing at 14-21 days after discharge. The primary outcome was healthcare-associated COVID-19 defined as a new positive SARS-CoV-2 PCR test on or after day 4 of hospital stay or within 7 days of hospital dismissal, or seroconversion in patients previously established as seronegative. RESULTS: Of the 2,068 eligible adult patients, 1,778 (86.0%) completed admission PCR testing, while 1,339 (64.7%) also completed admission serology testing. Of the 1,310 (97.8%) who were both PCR and seronegative, 445 (34.0%) repeated postdischarge serology testing. No healthcare-associated COVID-19 cases were detected during the study period. Of 1,310 eligible PCR and seronegative adults, no patients tested PCR positive during hospital admission (95% confidence interval [CI], 0.0%-0.3%). Of the 445 (34.0%) who completed postdischarge serology testing, no patients seroconverted (0.0%; 95% CI, 0.0%-0.9%). CONCLUSION: We found low likelihood of hospital-associated COVID-19 with strict adherence to universal masking, physical distancing, and hand hygiene along with limited visitors and screening of admissions with PCR.

Deployment of an Interdisciplinary Predictive Analytics Task Force to Inform Hospital Operational Decision-Making During the COVID-19 Pandemic.

In March 2020, our institution developed an interdisciplinary predictive analytics task force to provide coronavirus disease 2019 (COVID-19) hospital census forecasting to help clinical leaders understand the potential impacts on hospital operations. As the situation unfolded into a pandemic, our task force provided predictive insights through a structured set of visualizations and key messages that have helped the practice to anticipate and react to changing operational needs and opportunities. The framework shared here for the deployment of a COVID-19 predictive analytics task force could be adapted for effective implementation at other institutions to provide evidence-based messaging for operational decision-making. For hospitals without such a structure, immediate consideration may be warranted in light of the devastating COVID-19 third-wave which has arrived for winter 2020-2021.

Analysis of the Changing Economics of US Hospital Transcatheter Aortic Valve Replacement Programs.

New technologies in medicine, even if they are promising medically, are often expensive and logistically difficult to implement at the hospital level. Transcatheter aortic valve replacement (TAVR) is a model technology that is revolutionary in treating aortic stenosis, but has been plagued with significant challenges with financial sustainability. In this article, a margin analysis at the hospital level was performed using literature data. A TAVR industry analysis was performed using Porter's Five Forces framework. The data indicate that TAVR is more expensive than surgical aortic valve replacement, although the cost of TAVR is declining with the use of an optimized minimalist protocol. The overall industry is growing as its clinical indications expand, and it will likely undergo significant reduction of costs when new valves enter the US market. As such, TAVR is a growing industry, with financial sustainability currently dependent on operational efficiency. A concluding list of specific program interventions is provided to help TAVR programs improve operational efficiency and clinical outcomes, as well as help decide whether to create, expand, or redirect funding for TAVR programs. Importantly, the frameworks used to analyze this rapidly evolving technology can be applied to other new technologies to determine financial sustainability.

Correlations Between COVID-19 Cases and Google Trends Data in the United States: A State-by-State Analysis.

OBJECTIVE: To evaluate whether a digital surveillance model using Google Trends is feasible for obtaining accurate data on coronavirus disease 2019 and whether accurate predictions can be made regarding new cases. METHODS: Data on total and daily new cases in each US state were collected from January 22, 2020, to April 6, 2020. Information regarding 10 keywords was collected from Google Trends, and correlation analyses were performed for individual states as well as for the United States overall. RESULTS: Among the 10 keywords analyzed from Google Trends, face mask, Lysol, and COVID stimulus check had the strongest correlations when looking at the United States as a whole, with R values of 0.88, 0.82, and 0.79, respectively. Lag and lead Pearson correlations were assessed for every state and all 10 keywords from 16 days before the first case in each state to 16 days after the first case. Strong correlations were seen up to 16 days prior to the first reported cases in some states. CONCLUSION: This study documents the feasibility of syndromic surveillance of internet search terms to monitor new infectious diseases such as coronavirus disease 2019. This information could enable better preparation and planning of health care systems.

Association of Do-Not-Resuscitate Patient Case Mix With Publicly Reported Risk-Standardized Hospital Mortality and Readmission Rates.

Importance: The Centers for Medicare and Medicaid Services's (CMS's) 30-day risk-standardized mortality rate (RSMR) and risk-standardized readmission rate (RSRR) models do not adjust for do-not-resuscitate (DNR) status of hospitalized patients and may bias Hospital Readmissions Reduction Program (HRRP) financial penalties and Overall Hospital Quality Star Ratings. Objective: To identify the association between hospital-level DNR prevalence and condition-specific 30-day RSMR and RSRR and the implications of this association for HRRP financial penalty. Design, Setting, and Participants: This cross-sectional study obtained patient-level data from the Medicare Limited Data Set Inpatient Standard Analytical File and hospital-level data from the CMS Hospital Compare website for all consecutive Medicare inpatient encounters from July 1, 2015, to June 30, 2018, in 4484 US hospitals. Hospitalized patients had a principal diagnosis of acute myocardial infarction (AMI), heart failure (HF), stroke, pneumonia, or chronic obstructive pulmonary disease (COPD). Incoming acute care transfers, discharges against medical advice, and patients coming from or discharged to hospice were among those excluded from the analysis. Exposures: Present-on-admission (POA) DNR status was defined as an International Classification of Diseases, Ninth Revision diagnosis code of V49.86 (before October 1, 2015) or as an International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of Z66 (beginning October 1, 2015). Hospital-level prevalence of POA DNR status was calculated for each of the 5 conditions. Main Outcomes and Measures: Hospital-level 30-day RSMRs and RSRRs for 5 condition-specific cohorts (mortality cohorts: AMI, HF, stroke, pneumonia, and COPD; readmission cohorts: AMI, HF, pneumonia, and COPD) and HRRP financial penalty status (yes or no). Results: Included in the study were 4 884 237 inpatient encounters across condition-specific 30-day mortality cohorts (patient mean [SD] age, 78.8 [8.5] years; 2 608 182 women [53.4%]) and 4 450 378 inpatient encounters across condition-specific 30-day readmission cohorts (patient mean [SD] age, 78.6 [8.5] years; 2 349 799 women [52.8%]). Hospital-level median (interquartile range [IQR]) prevalence of POA DNR status in the mortality cohorts varied: 11% (7%-16%) for AMI, 13% (7%-23%) for HF, 14% (9%-22%) for stroke, 17% (9%-26%) for pneumonia, and 10% (5%-18%) for COPD. For the readmission cohorts, the hospital-level median (IQR) POA DNR prevalence was 9% (6%-15%) for AMI, 12% (6%-22%) for HF, 16% (8%-24%) for pneumonia, and 9% (4%-17%) for COPD. The 30-day RSMRs were significantly higher for hospitals in the highest quintiles vs the lowest quintiles of DNR prevalence (eg, AMI: 12.9 [95% CI, 12.8-13.1] vs 12.5 [95% CI, 12.4-12.7]; P < .001). The inverse was true among the readmission cohorts, with the highest quintiles of DNR prevalence exhibiting the lowest RSRRs (eg, AMI: 15.3 [95% CI, 15.1-15.5] vs 15.9 [95% CI, 15.7-16.0]; P < .001). A 1% absolute increase in risk-adjusted hospital-level DNR prevalence was associated with greater odds of avoiding HRRP financial penalty (odds ratio, 1.06; 95% CI, 1.04-1.08; P < .001). Conclusions and Relevance: This cross-sectional study found that the lack of adjustment in CMS 30-day RSMR and RSRR models for POA DNR status of hospitalized patients may be associated with biased readmission penalization and hospital-level performance.

Transcatheter Aortic Valve Replacement Programs: Clinical Outcomes and Developments.

Transcatheter aortic valve replacement is a relatively recent revolutionary treatment that has now become a standard procedure for treating severe aortic stenosis. In this article, the authors review the clinical history of transcatheter aortic valve replacement, summarize the major clinical trials, and describe the evolution of the technique over time. In doing so, the authors hope to provide a clear and concise review of the history and clinical evidence behind transcatheter aortic valve replacement.

Preferences and attitudes of young Chinese clinicians about using a shared decision making tools for communicating cardiovascular risk.

OBJECTIVE: This study assesses the attitudes and preferences of Chinese clinicians toward their involvement in shared decision making (SDM). METHODS: From May 2014 to May 2015, 200 Chinese clinicians from two hospitals were enrolled to complete a survey on their attitude towards SDM. We conducted the survey via face-to-face interviews before and after an educational intervention on SDM among young Chinese clinicians. The clinicians were asked to give the extent of agreement to SDM. They also gave the extent of difficulty in using decision aids (DAs) during the SDM process. The variation in the range of responses to each question before and after the SDM intervention was recorded. The frequency of changed responses was analyzed by using JMP 6.0 software. Data were statistically analyzed using Chi-square and Mann-Whitney U tests, as appropriate to the data type. Multiple logistic regressions were used to test for those factors significantly and independently associated with preference for an approach for each scenario. RESULTS: Of the 200 young Chinese clinicians sampled, 59.0% indicated a preference for SDM and a desire to participate in SDM before receiving education or seeing the DA, and this number increased to 69.0% after seeing the DA with the sample video of the SDM process on Statin Choice. However, 28.5% of the respondents still reported that, in their current practice, they make clinical decisions on behalf of their patients. The clinicians who denied a desire to use the DA stated that the main barriers to implement SDM or DA use in China are lack of time and knowledge of SDM. CONCLUSIONS: Most young Chinese clinicians want to participate in SDM. However, they state the main barriers to perform SDM are lack of experience and time. The educational intervention about SDM that exposes clinicians to DAs was found to increase their receptivity.

Developing a toolkit to implement the Statin Choice Conversation Aid at scale: application of a work reduction model.

BACKGROUND: Guidelines recommend shared decision making (SDM) for determining whether to use statins to prevent cardiovascular events in at-risk patients. We sought to develop a toolkit to facilitate the cross-organizational spread and scale of a SDM intervention called the Statin Choice Conversation Aid (SCCA) by (i) assessing the work stakeholders must do to implement the tool; and (ii) orienting the resulting toolkit's components to communicate and mitigate this work. METHODS: We conducted multi-level and mixed methods (survey, interview, observation, focus group) characterizations of the contexts of 3 health systems (n = 86, 84, and 26 primary care clinicians) as they pertained to the impending implementation of the SCCA. We merged the data within implementation outcome domains of feasibility, appropriateness, and acceptability. Using Normalization Process Theory, we then characterized and categorized the work stakeholders did to implement the tool. We used clinician surveys and IP address-based tracking to calculate SCCA usage over time and judged how stakeholder effort was allocated to influence outcomes at 6 and 18 months. After assessing the types and impact of the work, we developed a multi-component toolkit. RESULTS: At baseline, the three contexts differed regarding feasibility, acceptability, and appropriateness of implementation. The work of adopting the tool was allocated across many strategies in complex and interdependent ways to optimize these domains. The two systems that allocated the work strategically had higher uptake (5.2 and 2.9 vs. 1.1 uses per clinician per month at 6 months; 3.8 and 2.1 vs. 0.4 at 18 months, respectively) than the system that did not. The resulting toolkit included context self-assessments intended to guide stakeholders in considering the early work of SCCA implementation; and webinars, EMR integration guides, video demonstrations, and an implementation team manual aimed at supporting this work. CONCLUSIONS: We developed a multi-component toolkit for facilitating the scale-up and spread of a tool to promote SDM across clinical settings. The theory-based approach we employed aimed to distinguish systems primed for adoption and support the work they must do to achieve implementation. Our approach may have value in orienting the development of multi-component toolkits and other strategies aimed at facilitating the efficient scale up of interventions. TRIAL REGISTRATION: ClinicalTrials.gov NCT02375815 .

Shared decision-making in atrial fibrillation: navigating complex issues in partnership with the patient.

Atrial fibrillation (AF) is an important risk factor for stroke. Although anticoagulation is effective in mitigating this risk, many high-risk patients are not anticoagulated in routine practice. Furthermore, as many as 50% of those who are prescribed an anticoagulant stop treatment within a year. This under treatment may be due, in part, to difficulty in navigating difficult decisions about initiating potentially lifelong therapy with significant costs, potential risks, and impact on daily life. To address these challenges, the most recent American guidelines issued a class I recommendation to use shared decision-making (SDM) to individualize patients' antithrombotic care. The call by the major cardiovascular organizations for SDM is in an effort to improve quality of care by promoting decisions that reflect what is best for an individual patient based on their stroke and bleeding risks, as well as their comorbid conditions and socio-personal context. SDM is readily applicable to current cardiovascular practice, but ongoing work will be needed to determine whether brief, evidence-based, and patient-oriented tools are able to support thoughtful, patient-centered decision-making and, ultimately, improve the rates of appropriate treatment initiation and adherence.

The success of opening single chronic total occlusion lesions to improve myocardialviabilitytrial (SOS-COMEDY): Study protocol of a prospective multicenter study.

AIMS: Success of opening single (SOS)-comedy is a prospective multicenter study to compare the improvement in the decrease of myocardial viability by percutaneous coronary intervention (PCI) with that by optimal medical therapy (OMT) alone in patients with chronic total occlusion (CTO) of a single coronary artery. METHODS AND RESULTS: The risks and the benefits of both options (PCI and OMT) were listed in a CTO decision aid (DA). Eligible participants detected by invasive coronary angiography (ICA) or coronary computed tomography angiography (CCTA) were divided into PCI or OMT groups according to patients' choice after shared-decision making process with DA. Participants will undergo positron emission tomography/computed tomography (PET/CT), cardiac magnetic resonance (CMR) and transthoracic echocardiography (TTE), and proceed to ICA and revascularization if possible. Blinded core laboratory interpretation will be performed for ICA, CCTA, PET/CT, CMR, and TTE. All participants will be followed up for 12 months. The primary endpoint is the improvement to the decrease of myocardial viability from baseline assessed with the use of PET/CT after 12-month follow-up. CONCLUSIONS: All of the patients are appropriately consented before enrolling in this study, which has been approved by the Ethics Committee. Results of SOS-COMEDY will be helpful to develop a strategy for single CTO patients.

How guiding coalitions promote positive culture change in hospitals: a longitudinal mixed methods interventional study.

BACKGROUND: Quality collaboratives are widely endorsed as a potentially effective method for translating and spreading best practices for acute myocardial infarction (AMI) care. Nevertheless, hospital success in improving performance through participation in collaboratives varies markedly. We sought to understand what distinguished hospitals that succeeded in shifting culture and reducing 30-day risk-standardised mortality rate (RSMR) after AMI through their participation in the Leadership Saves Lives (LSL) collaborative. PROCEDURES: We conducted a longitudinal, mixed methods intervention study of 10 hospitals over a 2-year period; data included surveys of 223 individuals (response rates 83%-94% depending on wave) and 393 in-depth interviews with clinical and management staff most engaged with the LSL intervention in the 10 hospitals. We measured change in culture and RSMR, and key aspects of working related to team membership, turnover, level of participation and approaches to conflict management. MAIN FINDINGS: The six hospitals that experienced substantial culture change and greater reductions in RSMR demonstrated distinctions in: (1) effective inclusion of staff from different disciplines and levels in the organisational hierarchy in the team guiding improvement efforts (referred to as the 'guiding coalition' in each hospital); (2) authentic participation in the work of the guiding coalition; and (3) distinct patterns of managing conflict. Guiding coalition size and turnover were not associated with success (p values>0.05). In the six hospitals that experienced substantial positive culture change, staff indicated that the LSL learnings were already being applied to other improvement efforts. PRINCIPAL CONCLUSIONS: Hospitals that were most successful in a national quality collaborative to shift hospital culture and reduce RSMR showed distinct patterns in membership diversity, authentic participation and capacity for conflict management.