An unexplored ethical issue in clinical research: disclosure of individual findings in the Creando Posibilidades [Creating Possibilities] study.

Individual disclosure refers to the presumptive ethical responsibility of an investigator to communicate to a study participant information that was collected as a part of a research study protocol and is specific to the individual. Currently, there are no federal regulatory guidelines specifying the conditions and management of disclosure of health-related individual-specific information. In this report, the authors discuss the challenges associated with individual disclosure in the context of a longitudinal descriptive study. Arguments favoring disclosure and those challenging disclosure as a general ethical duty are presented. Finally, strategies for addressing individual disclosure are discussed using a research exemplar in which risk behaviors related to health outcomes were measured.

Learning and Collaboration during Crisis: A Novel University-Community Partnership to Manufacture Medical Personal Protective Equipment.

Research on crisis management focuses on pre-planning for disasters in order to understand potential barriers. However, one significant barrier to crisis response is that organizations may come together in unplanned configurations during crisis response. This means that significant opportunities exist for understanding the process by which individuals learn, collaborate, and create new systems during crises. In this case report, we present the case of face shield production by a university, academic medical center, and community partners during the supply chain collapse of the early COVID-19 pandemic with the aim of identifying the relationships that formed during the COVID-19 response, so that this case of relationship formation and participant experiences might inform similar disaster response challenges in the future. Thirteen participants responded to an in-depth questionnaire designed to simulate an asynchronous in-depth interview. Respondents reported on the activities of 80 individuals from 38 units/organizations, providing insight into communication challenges and resolutions. Responses were analyzed using thematic analysis, highlighting roles and relationships among participants. The findings grant insight into the experience of learning from crisis response efforts, responding to recent calls for social scientific work on COVID-19 responses.

Determinants of Variation in Pneumonia Rates After Coronary Artery Bypass Grafting.

BACKGROUND: Although conventional wisdom suggests that differences in patient risk profiles drive variability in postoperative pneumonia rates after coronary artery bypass grafting (CABG), this teaching has yet to be empirically tested. We determined to what extent patient risk factors account for hospital variation in pneumonia rates. METHODS: We studied 324,085 patients undergoing CABG between July 1, 2011, and December 31, 2013, across 998 hospitals using The Society of Thoracic Surgeons Adult Cardiac Database. We developed 5 models to estimate our incremental ability to explain hospital variation in pneumonia rates. Model 1 contained patient demographic characteristics and admission status, while Model 2 added patient risk factors. Model 3 added measures of pulmonary function, Model 4 added measures of cardiac anatomy and function and medications, and Model 5 further added measures of intraoperative and postoperative care. RESULTS: Although 9,175 patients (2.83%) experienced pneumonia, the median estimated distribution of pneumonia rates across hospitals was 2.5% (25th to 75th percentile: 1.5% to 4.0%). Wide variability in pneumonia rates was evident, with some hospitals having rates more than 6 times higher than others (10th to 90th percentile: 1.0% to 6.1%). Among all five models, Model 2 accounted for the most variability at 4.24%. In total, 2.05% of hospital variation in pneumonia rates was explained collectively by traditional patient factors, leaving 97.95% of variation unexplained. CONCLUSIONS: Our findings suggest that patient risk profiles only account for a fraction of hospital variation in pneumonia rates; enhanced understanding of other contributory factors (eg, processes of care) is required to lessen the likelihood of such nosocomial infections.

A Preoperative Risk Model for Postoperative Pneumonia After Coronary Artery Bypass Grafting.

BACKGROUND: Postoperative pneumonia is the most prevalent of all hospital-acquired infections after isolated coronary artery bypass graft surgery (CABG). Accurate prediction of a patient's risk of this morbid complication is hindered by its low relative incidence. In an effort to support clinical decision making and quality improvement, we developed a preoperative prediction model for postoperative pneumonia after CABG. METHODS: We undertook an observational study of 16,084 patients undergoing CABG between the third quarter of 2011 and the second quarter of 2014 across 33 institutions participating in the Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative. Variables related to patient demographics, medical history, admission status, comorbid disease, cardiac anatomy, and the institution performing the procedure were investigated. Logistic regression through forward stepwise selection (p < 0.05 threshold) was utilized to develop a risk prediction model for estimating the occurrence of pneumonia. Traditional methods were used to assess the model's performance. RESULTS: Postoperative pneumonia occurred in 3.30% of patients. Multivariable analysis identified 17 preoperative factors, including demographics, laboratory values, comorbid disease, pulmonary and cardiac function, and operative status. The final model significantly predicted the occurrence of pneumonia, and performed well (C-statistic: 0.74). These findings were confirmed through sensitivity analyses by center and clinically important subgroups. CONCLUSIONS: We identified 17 readily obtainable preoperative variables associated with postoperative pneumonia. This model may be used to provide individualized risk estimation and to identify opportunities to reduce a patient's preoperative risk of pneumonia through prehabilitation.

Red Blood Cell Transfusions Impact Pneumonia Rates After Coronary Artery Bypass Grafting.

BACKGROUND: Pneumonia, a known complication of coronary artery bypass grafting (CABG), significantly increases a patient's risk of morbidity and mortality. Although not well characterized, red blood cell (RBC) transfusions may increase a patient's risk of pneumonia. We describe the relationship between RBC transfusion and postoperative pneumonia after CABG. METHODS: A total of 16,182 consecutive patients underwent isolated CABG between 2011 and 2013 at 1 of 33 hospitals in the state of Michigan. We used multivariable logistic regression to estimate the relative odds of pneumonia associated with the use or number of RBC units (0, 1, 2, 3, 4, 5, and ≥ 6). We adjusted for predicted risk of mortality, preoperative hematocrit values, history of pneumonia, cardiopulmonary bypass duration, and medical center. We confirmed the strength and direction of these relationships among selected clinical subgroups in a secondary analysis. RESULTS: Five hundred seventy-six (3.6%) patients had pneumonia and 6,451 (39.9%) received RBC transfusions. There was a significant association between any RBC transfusion and pneumonia (adjusted odds ratio [ORadj], 3.4; p < 0.001). There was a dose response between number of units and odds of pneumonia, with a ptrend less than 0.001. Patients receiving only 2 units of RBCs had a 2-fold (ORadj, 2.1; p < 0.001) increased odds of developing pneumonia. These findings were consistent across clinical subgroups. CONCLUSIONS: We found a significant volume-dependent association between an increasing number of RBCs and the odds of pneumonia, which persisted after risk adjustment. Clinical teams should explore opportunities for preventing a patient's risk of RBC transfusions, including reducing hemodilution or adopting a lower transfusion threshold in a stable patient.

Sources of Variation in Hospital-Level Infection Rates After Coronary Artery Bypass Grafting: An Analysis of The Society of Thoracic Surgeons Adult Heart Surgery Database.

BACKGROUND: Patients undergoing coronary artery bypass grafting (CABG) are at risk for a variety of infections. Investigators have focused on predictors of these adverse sequelae, but less attention has been focused on characterizing hospital-level variability in these outcomes. METHODS: Between July 2011 and December 2013, The Society of Thoracic Surgeons Adult Cardiac Surgery Database shows 365,686 patients underwent isolated CABG in 1,084 hospitals. Hospital-acquired infections (HAIs) were defined as pneumonia, sepsis/septicemia, deep sternal wound infection/mediastinitis, vein harvest/cannulation site infection, or thoracotomy infection. Hospitals were ranked by their HAI rate as low (≤ 10th percentile), medium (10th to 90th percentile), and high (>90th percentile). Differences in perioperative factors and composite morbidity and mortality end points across these groups were determined using the Wilcoxon rank sum and χ(2) tests. RESULTS: HAIs occurred among 3.97% of patients overall, but rates varied across hospital groups (low: <0.84%, medium: 0.84% to 8.41%, high: >8.41%). Pneumonia (2.98%) was the most common HAI, followed by sepsis/septicemia (0.84%). Patients at high-rate hospitals more often smoked, had diabetes, chronic lung disease, New York Heart Association Functional Classification III to IV, and received blood products (p < 0.001); however, they less often were prescribed the appropriate antibiotics (p < 0.001). Major morbidity and mortality occurred among 12.3% of patients, although this varied by hospital group (low: 8.6%, medium: 12.3%, high: 17.9%; p < 0.001). CONCLUSIONS: Substantial hospital-level variation exists in postoperative HAIs among patients undergoing CABG, driven predominantly by pneumonia. Given the relatively small absolute differences in comorbidities across hospital groups, our findings suggest factors other than case mix may explain the observed variation in HAI rates.