Association Between COVID-19 Diagnosis and In-Hospital Mortality in Patients Hospitalized With ST-Segment Elevation Myocardial Infarction.

IMPORTANCE: There has been limited research on patients with ST-segment elevation myocardial infarction (STEMI) and COVID-19. OBJECTIVE: To compare characteristics, treatment, and outcomes of patients with STEMI with vs without COVID-19 infection. DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort study of consecutive adult patients admitted between January 2019 and December 2020 (end of follow-up in January 2021) with out-of-hospital or in-hospital STEMI at 509 US centers in the Vizient Clinical Database (N = 80 449). EXPOSURES: Active COVID-19 infection present during the same encounter. MAIN OUTCOMES AND MEASURES: The primary outcome was in-hospital mortality. Patients were propensity matched on the likelihood of COVID-19 diagnosis. In the main analysis, patients with COVID-19 were compared with those without COVID-19 during the previous calendar year. RESULTS: The out-of-hospital STEMI group included 76 434 patients (551 with COVID-19 vs 2755 without COVID-19 after matching) from 370 centers (64.1% aged 51-74 years; 70.3% men). The in-hospital STEMI group included 4015 patients (252 with COVID-19 vs 756 without COVID-19 after matching) from 353 centers (58.3% aged 51-74 years; 60.7% men). In patients with out-of-hospital STEMI, there was no significant difference in the likelihood of undergoing primary percutaneous coronary intervention by COVID-19 status; patients with in-hospital STEMI and COVID-19 were significantly less likely to undergo invasive diagnostic or therapeutic coronary procedures than those without COVID-19. Among patients with out-of-hospital STEMI and COVID-19 vs out-of-hospital STEMI without COVID-19, the rates of in-hospital mortality were 15.2% vs 11.2% (absolute difference, 4.1% [95% CI, 1.1%-7.0%]; P = .007). Among patients with in-hospital STEMI and COVID-19 vs in-hospital STEMI without COVID-19, the rates of in-hospital mortality were 78.5% vs 46.1% (absolute difference, 32.4% [95% CI, 29.0%-35.9%]; P < .001). CONCLUSIONS AND RELEVANCE: Among patients with out-of-hospital or in-hospital STEMI, a concomitant diagnosis of COVID-19 was significantly associated with higher rates of in-hospital mortality compared with patients without a diagnosis of COVID-19 from the past year. Further research is required to understand the potential mechanisms underlying this association.

Biomarkers in Hepatic Disease: A Review Focused on Critically Ill Patients.

The ability to make a diagnosis early and appropriately is paramount for the survival of the critically ill ICU patient. Along with the myriad physical examination and imaging modalities available, biomarkers provide a window on the disease process. Herein we review hepatic biomarkers in the context of the critical care patient.

Management of the Potential Organ Donor in the ICU: Society of Critical Care Medicine/American College of Chest Physicians/Association of Organ Procurement Organizations Consensus Statement.

This document was developed through the collaborative efforts of the Society of Critical Care Medicine, the American College of Chest Physicians, and the Association of Organ Procurement Organizations. Under the auspices of these societies, a multidisciplinary, multi-institutional task force was convened, incorporating expertise in critical care medicine, organ donor management, and transplantation. Members of the task force were divided into 13 subcommittees, each focused on one of the following general or organ-specific areas: death determination using neurologic criteria, donation after circulatory death determination, authorization process, general contraindications to donation, hemodynamic management, endocrine dysfunction and hormone replacement therapy, pediatric donor management, cardiac donation, lung donation, liver donation, kidney donation, small bowel donation, and pancreas donation. Subcommittees were charged with generating a series of management-related questions related to their topic. For each question, subcommittees provided a summary of relevant literature and specific recommendations. The specific recommendations were approved by all members of the task force and then assembled into a complete document. Because the available literature was overwhelmingly comprised of observational studies and case series, representing low-quality evidence, a decision was made that the document would assume the form of a consensus statement rather than a formally graded guideline. The goal of this document is to provide critical care practitioners with essential information and practical recommendations related to management of the potential organ donor, based on the available literature and expert consensus.

StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions.

The ability to accurately measure and assess current and potential health care system capacities is an issue of local and national significance. Recent joint statements by the Institute of Medicine and the Agency for Healthcare Research and Quality have emphasized the need to apply industrial and systems engineering principles to improving health care quality and patient safety outcomes. To address this need, a decision support tool was developed for planning and budgeting of current and future bed capacity, and evaluating potential process improvement efforts. The Strategic Bed Analysis Model (StratBAM) is a discrete-event simulation model created after a thorough analysis of patient flow and data from Geisinger Health System's (GHS) electronic health records. Key inputs include: timing, quantity and category of patient arrivals and discharges; unit-level length of care; patient paths; and projected patient volume and length of stay. Key outputs include: admission wait time by arrival source and receiving unit, and occupancy rates. Electronic health records were used to estimate parameters for probability distributions and to build empirical distributions for unit-level length of care and for patient paths. Validation of the simulation model against GHS operational data confirmed its ability to model real-world data consistently and accurately. StratBAM was successfully used to evaluate the system impact of forecasted patient volumes and length of stay in terms of patient wait times, occupancy rates, and cost. The model is generalizable and can be appropriately scaled for larger and smaller health care settings.

Risk stratification in acute pulmonary embolism: frequency and impact on treatment decisions and outcomes.

OBJECTIVES: Guidelines have recommended that risk stratification be performed in patients diagnosed with an acute pulmonary embolism (PE). No study has described the use of risk stratification in routine clinical practice. The purpose of this study was to measure the frequency and impact of risk stratification on treatment decisions and outcomes in patients admitted with acute PE. METHODS: A retrospective cohort study was conducted of all of the patients admitted with acute PE at two Geisinger community-based teaching hospitals between 2006 and 2011. Baseline demographics, vital signs, and relevant clinical variables were recorded. The Pulmonary Embolism Severity Index was calculated for each patient. Risk stratification was defined as the measurement of either a biomarker or an echocardiogram within 24 hours of admission. The outcomes measured were short-term adverse events (in-hospital mortality or need for intensive care) and 30-day mortality. RESULTS: The mean age for the study cohort (n = 889) was 61 ± 17 years and 52% were men. Overall, 59% of study subjects were risk stratified. The frequency of risk stratification did not change over time. Risk stratification was associated with assignment to a higher acuity of care and increased use of thrombolysis and inferior vena cava filter placement. When controlling for severity of illness, risk stratification was a significant predictor of worsened short-term adverse outcome (odds ratio 3.43, 95% confidence interval 1.75-6.74, P < 0.001) but was not associated with improved 30-day mortality (odds ratio 1.14, 95% confidence interval 0.66-1.95, P = 0.64). CONCLUSIONS: Risk stratification is frequently performed in patients admitted with acute PE and has had a stable prevalence during a 5-year period. The use of risk stratification in acute PE is associated with assignment to higher levels of care and with more advanced treatments. Despite more intense treatment, risk stratification does not improve either short-term outcomes or 30-day mortality.

Clinical characteristics, sepsis interventions and outcomes in the obese patients with septic shock: an international multicenter cohort study.

INTRODUCTION: Data are sparse as to whether obesity influences the risk of death in critically ill patients with septic shock. We sought to examine the possible impact of obesity, as assessed by body mass index (BMI), on hospital mortality in septic shock patients. METHODS: We performed a nested cohort study within a retrospective database of patients with septic shock conducted in 28 medical centers in Canada, United States and Saudi Arabia between 1996 and 2008. Patients were classified according to the World Health Organization criteria for BMI. Multivariate logistic regression analysis was performed to evaluate the association between obesity and hospital mortality. RESULTS: Of the 8,670 patients with septic shock, 2,882 (33.2%) had height and weight data recorded at ICU admission and constituted the study group. Obese patients were more likely to have skin and soft tissue infections and less likely to have pneumonia with predominantly Gram-positive microorganisms. Crystalloid and colloid resuscitation fluids in the first six hours were given at significantly lower volumes per kg in the obese and very obese patients compared to underweight and normal weight patients (for crystalloids: 55.0 ± 40.1 ml/kg for underweight, 43.2 ± 33.4 for normal BMI, 37.1 ± 30.8 for obese and 27.7 ± 22.0 for very obese). Antimicrobial doses per kg were also different among BMI groups. Crude analysis showed that obese and very obese patients had lower hospital mortality compared to normal weight patients (odds ratio (OR) 0.80, 95% confidence interval (CI) 0.66 to 0.97 for obese and OR 0.61, 95% CI 0.44 to 0.85 for very obese patients). After adjusting for baseline characteristics and sepsis interventions, the association became non-significant (OR 0.80, 95% CI 0.62 to 1.02 for obese and OR 0.69, 95% CI 0.45 to 1.04 for very obese). CONCLUSIONS: The obesity paradox (lower mortality in the obese) documented in other populations is also observed in septic shock. This may be related in part to differences in patient characteristics. However, the true paradox may lie in the variations in the sepsis interventions, such as the administration of resuscitation fluids and antimicrobial therapy. Considering the obesity epidemic and its impact on critical care, further studies are warranted to examine whether a weight-based approach to common therapeutic interventions in septic shock influences outcome.

Medication Safety in Two Intensive Care Units of a Community Teaching Hospital After Electronic Health Record Implementation: Sociotechnical and Human Factors Engineering Considerations.

OBJECTIVE: The aim of the study was to assess the impact of Electronic Health Record (EHR) implementation on medication safety in two intensive care units (ICUs). METHODS: Using a prospective pre-post design, we assessed 1254 consecutive admissions to two ICUs before and after an EHR implementation. Each medication event was evaluated with regard to medication error (error type, medication-management stage) and impact on patient (severity of potential or actual harm). RESULTS: We identified 4063 medication-related events either pre-implementation (2074 events) or post-implementation (1989 events). Although the overall potential for harm due to medication errors decreased post-implementation only 2 of the 3 error rates were significantly lower post-implementation. After EHR implementation, we observed reductions in rates of medication errors per admission at the stages of transcription (0.13-0, P < 0.001), dispensing (0.49-0.16, P < 0.001), and administration (0.83-0.56, P = 0.011). Within the ordering stage, 4 error types decreased post-implementation (orders with omitted information, error-prone abbreviations, illegible orders, failure to renew orders) and 4 error types increased post-implementation (orders of wrong drug, orders containing a wrong start or stop time, duplicate orders, orders with inappropriate or wrong information). Within the administration stage, we observed a reduction of late administrations and increases in omitted administrations and incorrect documentation. CONCLUSIONS: Electronic Health Record implementation in two ICUs was associated with both improvement and worsening in rates of specific error types. Further safety improvements require a nuanced understanding of how various error types are influenced by the technology and the sociotechnical work system of the technology implementation. Recommendations based on human factors engineering principles are provided for reducing medication errors.

Patient safety - the role of human factors and systems engineering.

Patient safety is a global challenge that requires knowledge and skills in multiple areas, including human factors and systems engineering. In this chapter, numerous conceptual approaches and methods for analyzing, preventing and mitigating medical errors are described. Given the complexity of healthcare work systems and processes, we emphasize the need for increasing partnerships between the health sciences and human factors and systems engineering to improve patient safety. Those partnerships will be able to develop and implement the system redesigns that are necessary to improve healthcare work systems and processes for patient safety.

Integrating a safety smart list into the electronic health record decreases intensive care unit length of stay and cost.

PURPOSE: To measure how an integrated smartlist developed for critically ill patients would change intensive care units (ICUs) length of stay (LOS), mortality, and charges. MATERIALS AND METHODS: Propensity-score analysis of adult patients admitted to one of 14 surgical and medical ICUs between June 2017 and May 2018. The smart list aimed to certain preventative measures for all critical patients (e.g., removing unneeded catheters, starting thromboembolic prophylaxis, etc.) and was integrated into the electronic health record workflows at the hospitals under study. RESULTS: During the study period, 11,979 patients were treated in the 14 participating ICUs by 518 unique providers. Patients who had the smart list used during ≥60% of their ICU stay (N = 432 patients, 3.6%) were significantly more likely to have a shorter ICU LOS (HR = 1.20, 95% CI:1.0 to 1.4, p = 0.015) with an average decrease of -$1218 (95% CI: -$1830 to -$607, P < 0.001) in the amount charged per day. The intervention cohort had fewer average ventilator days (3.05 vent days, SD = 2.55) compared to propensity score matched controls (3.99, SD = 4.68, p = 0.015), but no changes in mortality (16.7% vs 16.0%, p = 0.78). CONCLUSIONS: An integrated smart list shortened LOS and lowered charges in a diverse cohort of critically ill patients.