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Background: Myocardial injury is associated with higher mortality of COVID-19 and more complicated disease course. Little is known about sex differences in COVID-19-associated myocardial injury. We hypothesized that sex modifies the relationship between myocardial injury and severe COVID-19 infection. Methods: This is a cross-sectional study of all hospitalized patients with COVID-19 at a large health system in New York City between 3/2/20 - 5/12/20. We defined severe COVID-19 infection as ICU admission, mechanical ventilation, discharge to hospice, or death. Using multivariable logistic regression, we assessed whether sex modified the effect of peak troponin on the odds of severe COVID-19 infection, adjusting for demographics, co-morbidity, and laboratory values, and did the same for D-dimer. Results: Among 2,746 patients hospitalized with COVID-19, 650 (23.7%) required ICU admission, 648 (23.7%) required mechanical ventilation, and 680 (24.8%) died or were discharged to hospice. Peak troponin was higher in men than women (median 0.06, interquartile range [IQR] 0.01- 0.10 vs. 0.05 [IQR 0.01-0.10], p=0.02). Men were more likely to be admitted to the ICU and to receive mechanical ventilation (Table). After adjustment for demographics, medical history and other laboratory values, peak troponin level was associated with higher odds of severe COVID-19 illness: OR 1.51 per ng/mL, 95% CI 1.27-1.88, p Conclusions: Sex modifies the relationship between myocardial injury and severe COVID-19, with men having higher odds of severe COVID-19 infection associated with increasing peak troponin. Sex differences in myocardial injury may play a role in poorer outcome among men.

Safety-net and teaching hospitals are somewhat more likely to be penalized for excess readmissions, but the association of other hospital characteristics with readmission rates is uncertain and may have relevance for hospital-centered interventions.To examine the independent association of 8 hospital characteristics with hospital-wide 30-day risk-standardized readmission rate (RSRR).This is a retrospective cross-sectional multivariable analysis.US hospitals.Centers for Medicare and Medicaid Services specification of hospital-wide RSRR from July 1, 2013 through June 30, 2014 with race and Medicaid dual-eligibility added.We included 6,789,839 admissions to 4474 hospitals of Medicare fee-for-service beneficiaries aged over 64 years. In multivariable analyses, there was regional variation: hospitals in the mid-Atlantic region had the highest RSRRs [0.98 percentage points higher than hospitals in the Mountain region; 95% confidence interval (CI), 0.84-1.12]. For-profit hospitals had an average RSRR 0.38 percentage points (95% CI, 0.24-0.53) higher than public hospitals. Both urban and rural hospitals had higher RSRRs than those in medium metropolitan areas. Hospitals without advanced cardiac surgery capability had an average RSRR 0.27 percentage points (95% CI, 0.18-0.36) higher than those with. The ratio of registered nurses per hospital bed was not associated with RSRR. Variability in RSRRs among hospitals of similar type was much larger than aggregate differences between types of hospitals.Overall, larger, urban, academic facilities had modestly higher RSRRs than smaller, suburban, community hospitals, although there was a wide range of performance. The strong regional effect suggests that local practice patterns are an important influence. Disproportionately high readmission rates at for-profit hospitals may highlight the role of financial incentives favoring utilization.

Background— Single-site studies have demonstrated inadequate quality of discharge summaries in timeliness, transmission, and content, potentially contributing to adverse outcomes. However, degree of hospital-level variation in discharge summary quality for patients hospitalized with heart failure (HF) is uncertain. Methods and Results— We analyzed discharge summaries of patients enrolled in the Telemonitoring to Improve Heart Failure Outcomes (Tele-HF) study. We assessed hospital-level performance on timeliness (fraction of summaries completed on the day of discharge), documented transmission to the follow-up physician, and content (presence of components suggested by the Transitions of Care Consensus Conference). We obtained 1501 discharge summaries from 1640 (91.5%) patients discharged alive from 46 hospitals. Among hospitals contributing ≥10 summaries, the median hospital dictated 69.2% of discharge summaries on the day of discharge (range, 0.0%–98.0%; P P P Conclusions— Even at the highest performing hospital, discharge summary quality is insufficient in terms of timeliness, transmission, and content. Improvements in all aspects of discharge summary quality are necessary to enable the discharge summary to serve as an effective transitional care tool.

Background: It is unknown if financial pressures to reduce hospital readmission rates following passage of the Affordable Care Act (ACA) have had the unintended effect of increasing mortality rates after hospitalization. We therefore examined correlations between paired changes in hospital 30-day readmission rates and 30-day mortality rates among Medicare fee-for-service beneficiaries hospitalized with heart failure (HF), acute myocardial infarction (AMI), or pneumonia from 2008 to 2014. Methods: We used linear regression to calculate monthly changes in hospitals’ 30-day risk-adjusted readmission rates (RARRs) and 30-day risk-adjusted mortality rates (RAMRs) after discharge for HF, AMI, and pneumonia from 2008 to 2014. Adjustment was made for patient age, sex, comorbidities, hospital length of stay, and season. We then examined the correlation of hospitals’ paired monthly changes in 30-day RARRs and monthly changes in 30-day RAMRs after discharge. Results: From 2008 to 2014, we identified 2,962,554, 1,229,939, and 2,544,530 hospitalizations for HF, AMI, and pneumonia at 5,016, 4,772, and 5,057 hospitals, respectively. Hospital 30-day RARRs declined for all three conditions from 2008 to 2014; the monthly change in RARRs was -0.053 (95% CI -0.055, -0.051) for HF, -0.044 (95% CI -0.047, -0.041) for AMI, and -0.033 (95% CI -0.035, -0.031) for pneumonia. In contrast, the monthly change in hospital 30-day RAMRs after discharge varied by admitting condition and was 0.008 (95% CI 0.007, 0.010) for HF, -0.003 (95% CI -0.006, -0.001) for AMI, and 0.001 (95% CI -0.001, 0.003) for pneumonia. The correlation between monthly changes in hospitals’ 30-day RARRs and 30-day RAMRs after discharge was 0.060 for HF (p Conclusion: Changes in hospital readmission rates for HF, AMI, and pneumonia were poorly correlated with changes in mortality rates after hospitalization between 2008 and 2014. These findings suggest that financial incentives to improve hospitals’ readmission performance have not increased mortality after hospitalization.

Reducing readmissions is a national priority, but many hospitals lack practical tools to identify patients at increased risk of unplanned readmission.To estimate the association between a composite measure of patient condition at discharge, the Rothman Index (RI), and unplanned readmission within 30 days of discharge.Adult medical and surgical patients in a major teaching hospital in 2011.The RI is a composite measure updated regularly from the electronic medical record based on changes in vital signs, nursing assessments, Braden score, cardiac rhythms, and laboratory test results. We developed 4 categories of RI and tested its association with readmission within 30 days, using logistic regression, adjusted for patient age, sex, insurance status, service assignment (medical or surgical), and primary discharge diagnosis.Sixteen percent of the sample patients (N=2730) had an unplanned readmission within 30 days of discharge. The risk of readmission for a patient in the highest risk category (RI70) was1 in 5 while the risk of readmission for patients in the lowest risk category was about 1 in 10. In multivariable analysis, patients with an RI70 (the highest risk category) or 70-79 (medium risk category) had 2.65 (95% confidence interval, 1.72-4.07) and 2.40 (95% confidence interval, 1.57-3.67) times higher odds of unplanned readmission, respectively, compared with patients in the lowest risk category.Clinicians can use the RI to help target hospital programs and supports to patients at highest risk of readmission.

In 2010, the Accreditation Council for Graduate Medical Education released its resident duty hours restrictions, requiring that faculty monitor their residents’ patient handoffs to ensure that residents are competent in handoff communications. Although studies have reported the need to improve the effectiveness of the handoff and a variety of curricula have been suggested and implemented, a common method for teaching and evaluating handoff skills has not been developed. Also in 2010, engineers, informaticians, and physicians interested in patient handoffs attended a symposium in Savannah, Georgia, hosted by the Association for Computing Machinery, entitled Handovers and Handoffs: Collaborating in Turns. As a result of this symposium, a workgroup formed to develop practical and readily implementable educational materials for medical educators involved in teaching patient handoffs to residents. In this article, the result of that yearlong collaboration, the authors aim to provide clarity on the definition of the patient handoff, to review the barriers to performing effective handoffs in academic health centers, to identify available solutions to improve handoffs, and to provide a structured approach to educating residents on handoffs via a curricular blueprint. The authors’ blueprint was developed to guide educators in customizing handoff education programs to fit their specific, local needs. Hopefully, it also will provide a starting point for future research into improving the patient handoff. Increasingly complex patient care environments require both innovations in handoff education and improvements in patient care systems to improve continuity of care.

Failure to rescue (FTR), the rate of death in patients suffering 1 of 6 in-hospital complications, is an Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator calculated from administrative data.: We sought to assess the accuracy of the AHRQ FTR algorithm.We undertook a retrospective chart review of 60 denominator cases of FTR identified by the algorithm at each of 40 University HealthSystem Consortium institutions. The primary outcome was the overall accuracy of the algorithm compared with chart review. We also assessed accuracy by complication type, patient characteristics, institution, service assignment, and mortality.Of 2354 cases, 1193 (50.7%) were accurately identified by the algorithm as having had at least one of the FTR-qualifying complications during hospitalization. Of the 3073 complications identified in these patients, 1497 (48.7%) were correctly flagged by the algorithm, 907 (29.5%) were present on admission, 419 (13.6%) were not confirmed by chart review, and 250 (8.1%) met a predefined complication-specific criterion for exclusion. The case accuracy rate varied significantly by institution (mean, 50.7%; range, 18.3-100%; P0.001), service assignment (surgical service, 62.9% vs. nonsurgical service, 42.9%; P0.001), and mortality (alive, 43.9% vs. dead, 67.5%; P0.001) but was not affected by patients' age, gender, race, or insurance status.As currently calculated from administrative data, the FTR algorithm misidentifies half of the cases on average, is least accurate for nonsurgical cases, and is widely variable across institutions. This indicator may be useful internally to flag possible cases of quality failure but has limitations for external institutional comparisons. Improvements in coding quality and consistency across institutions are needed.

Supplemental Digital Content is available in the text, Stagnation in hospitals’ adoption of data integration functionalities coupled with reduction in the number of operational health information exchanges could become a significant impediment to hospitals’ adoption of 3 critical capabilities: electronic health information exchange, interoperability, and medication reconciliation, in which electronic systems are used to assist with resolving medication discrepancies and improving patient safety. Against this backdrop, we assessed the relationships between the 3 capabilities. We conducted an observational study applying partial least squares-structural equation modeling technique to 27 variables obtained from the 2013 American Hospital Association annual survey Information Technology (IT) supplement, which describes health IT capabilities. We included 1330 hospitals. In confirmatory factor analysis, out of the 27 variables, 15 achieved loading values greater than 0.548 at P

Background: After hospitalization for heart failure (HF) and acute myocardial infarction (AMI), patients experience increased risk of death and hospital readmission. Defining the trajectory and timing of this period of risk may help guide interventions to improve post-discharge outcomes. Methods: We used 2008-10 Medicare data to identify patients ≥65 years discharged alive after HF or AMI hospitalization. Using hazard rates, we characterized the risks of death and first readmission on each day after discharge to describe (1) the maximum daily risks of death and readmission after discharge; (2) risks of death and readmission 1 year after discharge; (3) the time in days after discharge for the risks of death and readmission to reach their maximum daily rates and 50% of their maximum daily rates to characterize the rapidity of decline in risk. We created separate survival models for death and first readmission. Data were censored after 1 year follow up. The readmission model also censored for death prior to readmission. Results: Of 878,963 HF hospitalizations, 367,542 (41.8%) died and 618,283 (70.3%) were readmitted in 1 year. Of 350,509 AMI hospitalizations, 90,623 (25.9%) died and 177,031(50.5%) were readmitted in 1 year. The Figure shows hazard rates by time after discharge. For HF, daily risk of death was 0.0056 maximally and 0.0011 at 1 year (19% of maximum). Daily risk of readmission was 0.013 maximally and 0.002 at 1 year (16% of maximum). Daily risk of death was highest 1 day after discharge and 50% less 11 days after discharge. Daily risk of readmission was highest 4 days after discharge and 50% less 49 days after discharge. For AMI, daily risk of death was 0.010 maximally and 0.0004 at 1 year (4% of maximum). Daily risk of readmission was 0.015 maximally and 0.0011 at 1 year (7% of maximum). Daily risk of death was highest 1 day after discharge and 50% less 6 days after discharge. Daily risk of readmission was highest 2 days after discharge and 50% less 13 days after discharge. Conclusions: After hospitalization for HF and AMI, risk of death is highest on day 1 after discharge and then declines rapidly. In contrast, risk of readmission peaks later and declines more slowly. This extended period of risk for readmission may justify continued vigilance beyond the 30-day period used by Medicare to evaluate hospital readmission performance.

Administration of Intravenous Fluids in Patients Hospitalized with Heart Failure Background: Administration of intravenous (IV) fluids to patients with acute decompensated heart failure (HF) may worsen pulmonary congestion or hemodynamic status. However, little is known about the use of IV fluids in clinical practice. We examined the frequency and volume with which commonly used IV fluid preparations are used among inpatients with HF as well as the association of these treatment patterns with diuretic use and length of stay (LOS). Methods: We identified all hospitalizations in 2009-2010 with a principal discharge diagnosis of heart failure from the Premier, Inc. hospitals. In order to capture inappropriate therapy with intravenous fluids, we limited our study period to the initial two days, a period during which most HF patients undergo diuresis for fluid overload. We excluded patients with a potential indication for fluid resuscitation (concomitant therapy with vasopressors or inotropes, undergoing invasive procedures, or having secondary diagnosis of bleeding, sepsis or anaphylaxis). We also excluded hospitalizations with age < 18 yrs, < 3 day LOS, transfer-ins, and hospitals with < 25 HF cases. We calculated the administration frequency and volume of commonly used IV fluids (normal saline, half normal saline, 5% dextrose, and Lactated Ringer’s), the percentage of patients receiving concomitant therapy with loop diuretics, and the average LOS associated with administration of each of the four fluids. Results: Among 195,652 hospitalizations, 25% (48,611) were administered at least one of the four IV fluids. Normal saline was the most commonly administered fluid (19.4% of hospitalizations), followed by 5% dextrose (6%), half normal saline (2.2%), and Lactated Ringer’s (0.2%). The median volume of fluid administration over the first two days was 1,000 ml (IQR: 250, 1250). Eighty-nine percent of patients receiving fluid therapy received concomitant loop diuretics. Increasing median LOS was noted with increasing fluid volumes across all four individual IV fluid categories (see Table). Conclusion: A substantial proportion of patients hospitalized with HF receive a considerable volume of intravenous fluid during their first 2 hospital days; normal saline was the most commonly administered formulation. Loop diuretic use was very common in those receiving IV fluids. Greater understanding of the concomitant use of intravenous fluids and diuretics and their relation to length of stay may provide an opportunity to improve quality of care.