Search

Current guidelines recommend extubation only if a patient is not receiving vasopressor therapy or is receiving minimal doses of vasopressors. However, recent data indicate that extubation of patients receiving higher vasopressor doses may be safe. This study was undertaken to examine practices regarding extubation of patients receiving vasopressor therapy reported by clinician respondents to a survey by the Michigan Health and Hospital Association Keystone Center. One-third of respondents indicated that they would extubate a patient receiving vasopressors, and one-quarter indicated that it depended on the agent used, but more than half reported that their unit did not have a vasopressor use protocol or they did not know whether it did. Practices regarding extubation of patients receiving vasopressor therapy differed significantly by unit type and by role as a direct care provider. These data indicate that patient and clinician factors may drive practice patterns. Additional research to inform guidelines and local protocols is warranted., (©2023 American Association of Critical-Care Nurses.)

Background: Hospital-specific template matching is a newer method of hospital performance measurement that may be fairer than regression-based benchmarking. However, it has been tested in only limited research settings., Objective: The objective of this study was to test the feasibility of hospital-specific template matching assessments in the Veterans Affairs (VA) health care system and determine power to detect greater-than-expected 30-day mortality., Research Design: Observational cohort study with hospital-specific template matching assessment. For each VA hospital, the 30-day mortality of a representative subset of hospitalizations was compared with the pooled mortality from matched hospitalizations at a set of comparison VA hospitals treating sufficiently similar patients. The simulation was used to determine power to detect greater-than-expected mortality., Subjects: A total of 556,266 hospitalizations at 122 VA hospitals in 2017., Measures: A number of comparison hospitals identified per hospital; 30-day mortality., Results: Each hospital had a median of 38 comparison hospitals (interquartile range: 33, 44) identified, and 116 (95.1%) had at least 20 comparison hospitals. In total, 8 hospitals (6.6%) had a significantly lower 30-day mortality than their benchmark, 5 hospitals (4.1%) had a significantly higher 30-day mortality, and the remaining 109 hospitals (89.3%) were similar to their benchmark. Power to detect a standardized mortality ratio of 2.0 ranged from 72.5% to 79.4% for a hospital with the fewest (6) versus most (64) comparison hospitals., Conclusions: Hospital-specific template matching may be feasible for assessing hospital performance in the diverse VA health care system, but further refinements are needed to optimize the approach before operational use. Our findings are likely applicable to other large and diverse multihospital systems., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Patients are at risk for medication problems after hospital admissions, particularly those with critical illness. Medication problems include continuation of acute medications and discontinuation of chronic medications after discharge. Little is known across a national integrated health care system about the extent of these two medication problems., Objective: To examine the extent of statin medication discontinuation and new antipsychotic medication use after hospital discharge., Design: Retrospective cohort study., Setting: Veterans Affairs healthcare system., Participants: Veterans with an inpatient hospitalization from January 1, 2014-December 31, 2016, survived at least 180 days post-discharge, and received at least one medication through the VA outpatient pharmacy within one year around admission were included. Hospitalizations were grouped into: 1) direct admission to the intensive care unit (ICU) and a diagnosis of sepsis, 2) direct admission to the ICU without sepsis diagnosis, and 3) no ICU stay during the hospitalization., Main Outcome Measures: Statin medication discontinuation and new antipsychotic use at six months post-hospital discharge., Results: A total of 520,187 participants were included in the statin medication and 910,629 in the antipsychotic medication cohorts. Statin discontinuation ranged from 10-15% and new antipsychotic prescription fills from 2-4% across the three hospitalization groups, with highest rates in the ICU admission and sepsis diagnosis group. Statin discontinuation and new antipsychotic use after a hospitalization varied by hospital, with worse performing hospitals having 11% higher odds of discontinuing a statin (median odds ratio at hospital-level, adjusted for patient differences, aMOR: 1.11 (95% CI: 1.09, 1.13)) and 29% higher odds of new antipsychotic use (aMOR, 1.29 (95% CI: 1.24, 1.34)). Risk-adjusted hospital rates of these two medication changes were not correlated (p = 0.49)., Conclusions: Systemic variation in the rates of statin medication continuation and new antipsychotic use were found., Competing Interests: The authors have declared that no competing interests exist.

Objectives: Under "Rory's Regulations," New York State Article 28 acute care hospitals were mandated to implement sepsis protocols and report patient-level data. This study sought to determine how well cases reported under state mandate align with discharge records in a statewide administrative database., Design: Observational cohort study., Setting: First 27 months of mandated sepsis reporting (April 1, 2014, to June 30, 2016)., Patients: Hospitalizations with sepsis at New York State Article 28 acute care hospitals., Intervention: Sepsis regulations with mandated reporting., Measurements and Main Results: We compared cases reported to the New York State Department of Health Sepsis Clinical Database with discharge records in the Statewide Planning and Research Cooperative System database. We classified discharges as 1) "coded sepsis discharges"-a diagnosis code for severe sepsis or septic shock and 2) "possible sepsis discharges," using Dombrovskiy and Angus criteria. Of 111,816 sepsis cases reported to the New York State Department of Health Sepsis Clinical Database, 105,722 (94.5%) were matched to discharge records in Statewide Planning and Research Cooperative System. The percentage of coded sepsis discharges reported increased from 67.5% in the first quarter to 81.3% in the final quarter of the study period (mean, 77.7%). Accounting for unmatched cases, as many as 82.7% of coded sepsis discharges were potentially reported, whereas at least 17.3% were unreported. Compared with unreported discharges, reported discharges had higher rates of acute organ dysfunction (e.g., cardiovascular dysfunction 63.0% vs 51.8%; p < 0.001) and higher in-hospital mortality (30.2% vs 26.1%; p < 0.001). Hospital characteristics (e.g., number of beds, teaching status, volume of sepsis cases) were similar between hospitals with a higher versus lower percent of discharges reported, p values greater than 0.05 for all. Hospitals' percent of discharges reported was not correlated with risk-adjusted mortality of their submitted cases (Pearson correlation coefficient 0.11; p = 0.17)., Conclusions: Approximately four of five discharges with a diagnosis code of severe sepsis or septic shock in the Statewide Planning and Research Cooperative System data were reported in the New York State Department of Health Sepsis Clinical Database. Incomplete reporting appears to be driven more by underrecognition than attempts to game the system, with minimal bias to risk-adjusted hospital performance measurement.

Objectives: Acute renal replacement therapy in patients with sepsis has increased dramatically with substantial costs. However, the extent of variability in use across hospitals-and whether greater use is associated with better outcomes-is unknown., Design: Retrospective cohort study., Setting: Nationwide Inpatient Sample in 2011., Patients: Eighteen years old and older with sepsis and acute kidney injury admitted to hospitals sampled by the Nationwide Inpatient Sample in 2011., Interventions: We estimated the risk- and reliability-adjusted rate of acute renal replacement therapy use for patients with sepsis and acute kidney injury at each hospital. We examined the association between hospital-specific renal replacement therapy rate and in-hospital mortality and hospital costs after adjusting for patient and hospital characteristics., Measurements and Main Results: We identified 293,899 hospitalizations with sepsis and acute kidney injury at 440 hospitals, of which 6.4% (n = 18,885) received renal replacement therapy. After risk and reliability adjustment, the median hospital renal replacement therapy rate for patients with sepsis and acute kidney injury was 3.6% (interquartile range, 2.9-4.5%). However, hospitals in the top quintile of renal replacement therapy use had rates ranging from 4.8% to 13.4%. There was no significant association between hospital-specific renal replacement therapy rate and in-hospital mortality (odds ratio per 1% increase in renal replacement therapy rate: 1.03; 95% CI, 0.99-1.07; p = 0.10). Hospital costs were significantly higher with increasing renal replacement therapy rates (absolute cost increase per 1% increase in renal replacement therapy rate: $1,316; 95% CI, $157-$2,475; p = 0.03)., Conclusions: Use of renal replacement therapy in sepsis varied widely among nationally sampled hospitals without associated differences in mortality. Improving renal replacement standards for the initiation of therapy for sepsis may reduce healthcare costs without increasing mortality.

Objective: Hospitals vary widely in ICU admission rates across numerous medical diagnoses. The extent to which variability in ICU use is specific to individual diagnoses or is a function of the hospital, regardless of disease, is unknown., Design: Retrospective cohort study., Setting: A total of 1,120 acute care hospitals with ICU capabilities., Patients: Medicare beneficiaries 65 years old or older admitted for five medical diagnoses (acute myocardial infarction, congestive heart failure, stroke, pneumonia, and chronic obstructive pulmonary disease) and a surgical diagnosis (hip fracture treated with arthroplasty) in 2010., Interventions: None., Measurements and Main Results: We used multilevel models to calculate risk- and reliability-adjusted ICU admission rates, examined the correlation in ICU admission rates across diagnosis and calculated intraclass correlation coefficients and median odds ratios to quantify the variability in ICU admission rate that was attributable to hospitals. We also examined the ability of a high ICU-use hospital for one condition to predict high ICU use for other conditions. We identified 348,462 patients with one of the eligible conditions. ICU admission rates were positively correlated within hospitals for included medical diagnoses (r range, 0.38-0.59; p < 0.01). The top hospital quartile of ICU use for congestive heart failure had a sensitivity of 50-60% and specificity of 79-81% for detecting top quartile hospitals for each other conditions. After adjustment for patient and hospital characteristics, hospitals accounted for 17.6% (95% CI, 16.2-19.1%) of variability in ICU admission, corresponding to a median odds ratio of 2.3, compared to 25.8% (95% CI, 24.5-27.1%) and median odds ratio 2.8 for diagnosis. This suggests a patient with median baseline risk of ICU admission would more than double his/her odds of ICU admission if moving to a higher utilizing hospital., Conclusions: Hospitals account for a significant proportion of variation independent of measured patient and hospital characteristics, suggesting the need for further work to evaluate the causes of variation at the hospital level and potential consequences of variation across hospitals.

Importance: Although survival of patients with in-hospital cardiac arrest varies markedly among hospitals, specific resuscitation practices that distinguish sites with higher cardiac arrest survival rates remain unknown., Objective: To identify resuscitation practices associated with higher rates of in-hospital cardiac arrest survival., Design, Setting, and Participants: Nationwide survey of resuscitation practices at hospitals participating in the Get With the Guidelines-Resuscitation registry and with 20 or more adult in-hospital cardiac arrest cases from January 1, 2012, through December 31, 2013. Data analysis was performed from June 10 to December 22, 2015., Main Outcomes and Measures: Risk-standardized survival rates for cardiac arrest were calculated at each hospital and were then used to categorize hospitals into quintiles of performance. The association between resuscitation practices and quintiles of survival was evaluated using hierarchical proportional odds logistic regression models., Results: Overall, 150 (78.1%) of 192 eligible hospitals completed the study survey, and 131 facilities with 20 or more adult in-hospital cardiac arrest cases comprised the final study cohort. Risk-standardized survival rates after in-hospital cardiac arrest varied substantially (median, 23.7%; range, 9.2%-37.5%). Several resuscitation practices were associated with survival on bivariate analysis, although only 3 were significant after multivariable adjustment: monitoring for interruptions in chest compressions (adjusted odds ratio [OR] for being in a higher survival quintile category, 2.71; 95% CI, 1.24-5.93; P = .01), reviewing cardiac arrest cases monthly (adjusted OR for being in a higher survival quintile category, 8.55; 95% CI, 1.79-40.00) or quarterly (OR, 6.85; 95% CI, 1.49-31.30; P = .03), and adequate resuscitation training (adjusted OR, 3.23; 95% CI, 1.21-8.33; P = .02)., Conclusions and Relevance: Using survey information from acute care hospitals participating in a national quality improvement registry, we identified 3 resuscitation strategies associated with higher hospital rates of survival for patients with in-hospital cardiac arrest. These strategies can form the foundation for best practices for resuscitation care at hospitals given the high incidence and variation in survival for in-hospital cardiac arrest.

Rationale: Intermediate care (i.e., step-down or progressive care) is an alternative to the intensive care unit (ICU) for patients with moderate severity of illness. The adoption and current use of intermediate care is unknown., Objectives: To characterize trends in intermediate care use among U.S. hospitals., Methods: We examined 135 million acute care hospitalizations among elderly individuals (≥65 yr) enrolled in fee-for-service Medicare (U.S. federal health insurance program) from 1996 to 2010. We identified patients receiving intermediate care as those with intensive care or coronary care room and board charges labeled intermediate ICU., Measurements and Main Results: In 1996, a total of 960 of the 3,425 hospitals providing critical care billed for intermediate care (28%), and this increased to 1,643 of 2,783 hospitals (59%) in 2010 (P < 0.01). Only 8.2% of Medicare hospitalizations in 1996 were billed for intermediate care, but billing steadily increased to 22.8% by 2010 (P < 0.01), whereas the percentage billed for ICU care and ward-only care declined. Patients billed for intermediate care had more acute organ failures diagnoses codes compared with general ward patients (22.4% vs. 15.8%). When compared with patients billed for ICU care, those billed for intermediate care had fewer organ failures (22.4% vs. 43.4%), less mechanical ventilation (0.9% vs. 16.7%), lower mean Medicare spending ($8,514 vs. $18,150), and lower 30-day mortality (5.6% vs. 16.5%) (P < 0.01 for all comparisons)., Conclusions: Intermediate care billing increased markedly between 1996 and 2010. These findings highlight the need to better define the value, specific practices, and effective use of intermediate care for patients and hospitals.

Background: Research on the quality and cost of care traditionally focuses on individual physicians or medical groups. Social network theory suggests that the care a patient receives also depends on the network of physicians with whom a patient's physician is connected., Objectives: The objectives of the study are: (1) identify physician networks; (2) determine whether the rate of ambulatory care-sensitive hospital admissions (ACSAs) varies across networks--even different networks at the same hospital; and (3) determine the relationship between ACSA rates and network characteristics., Research Design: We identified networks by applying network detection algorithms to Medicare 2008 claims for 987,000 beneficiaries in 5 states. We estimated a fixed-effects model to determine the relationship between networks and ACSAs and a multivariable model to determine the relationship between network characteristics and ACSAs., Results: We identified 417 networks. Mean size: 129 physicians; range, 26-963. In the fixed-effects model, ACSA rates varied significantly across networks: there was a 46% difference in rates between networks at the 25th and 75th performance percentiles. At 95% of hospitals with admissions from 2 networks, the networks had significantly different ACSA rates; the mean difference was 36% of the mean ACSA rate. Networks with a higher percentage of primary-care physicians and networks in which patients received care from a larger number of physicians had higher ACSA rates., Conclusions: Physician networks have a relationship with ACSAs that is independent of the physicians in the network. Physician networks could be an important focus for understanding variations in medical care and for intervening to improve care.

Background: Understanding how hospitals functioned during the 2009 influenza A(H1N1)pdm09 pandemic may improve future public health emergency response, but information about its impact on US hospitals remains largely unknown., Research Design: We matched hospital and emergency department (ED) discharge data from the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project with community-level influenza-like illness activity during each hospital's pandemic period in fall 2009 compared with a corresponding calendar baseline period. We compared inpatient mortality for sentinel conditions at high-surge versus nonsurge hospitals., Results: US hospitals experienced a doubling of pneumonia and influenza ED visits during fall 2009 compared with prior years, along with an 18% increase in overall ED visits. Although no significant increase in total inpatient admissions occurred overall, approximately 10% of all study hospitals experienced high surge, associated with higher acute myocardial infarction and stroke case fatality rates. These hospitals had similar characteristics to other US hospitals except that they had higher mortality for acute cardiac illnesses before the pandemic. After adjusting for 2008 case fatality rates, the association between high-surge hospitals and increased mortality for acute myocardial infarction and stroke patients persisted., Conclusions: The fall 2009 pandemic period substantially impacted US hospitals, mostly through increased ED visits. For a small proportion of hospitals that experienced a high surge in inpatient admissions, increased mortality from selected clinical conditions was associated with both prepandemic outcomes and surge, highlighting the linkage between daily hospital operations and disaster preparedness.

Background: Patients with acute myocardial infarction (AMI) who are admitted to hospitals without coronary revascularization are frequently transferred to hospitals with this capability, yet we know little about the basis for how such revascularization hospitals are selected., Methods and Results: We examined interhospital transfer patterns in 71 336 AMI patients admitted to hospitals without revascularization capabilities in the 2006 Medicare claims using network analysis and regression models. A total of 31 607 (44.3%) AMI patients were transferred from 1684 nonrevascularization hospitals to 1104 revascularization hospitals. Median time to transfer was 2 days. Median transfer distance was 26.7 miles, with 96.1% within 100 miles. In 45.8% of cases, patients bypassed a closer hospital to go to a farther hospital that had a better 30-day risk standardized mortality rates. However, in 36.8% of cases, another revascularization hospital with lower 30-day risk-standardized mortality was actually closer to the original admitting nonrevascularization hospital than the observed transfer destination. Adjusted regression models demonstrated that shorter transfer distances were more common than transfers to the hospitals with lowest 30-day mortality rates. Simulations suggest that an optimized system that prioritized the transfer of AMI patients to a nearby hospital with the lowest 30-day mortality rate might produce clinically meaningful reductions in mortality., Conclusions: More than 40% of AMI patients admitted to nonrevascularization hospitals are transferred to revascularization hospitals. Many patients are not directed to nearby hospitals with the lowest 30-day risk-standardized mortality, and this may represent an opportunity for improvement.

Context: Long-term acute care hospitals have emerged as a novel approach for the care of patients recovering from severe acute illness, but the extent and increases in their activity at the national level are unknown., Objective: To examine temporal trends in long-term acute care hospital utilization after an episode of critical illness among fee-for-service Medicare beneficiaries aged 65 years or older., Design, Setting, and Patients: Retrospective cohort study using the Medicare Provider Analysis and Review files from 1997 to 2006. We included all Medicare hospitalizations involving admission to an intensive care unit of an acute care, nonfederal hospital within the continental United States., Main Outcome Measures: Overall long-term acute care utilization, associated costs, and survival following transfer., Results: The number of long-term acute care hospitals in the United States increased at a mean rate of 8.8% per year, from 192 in 1997 to 408 in 2006. During that time, the annual number of long-term acute care admissions after critical illness increased from 13,732 to 40,353, with annual costs increasing from $484 million to $1.325 billion. The age-standardized population incidence of long-term acute care utilization after critical illness increased from 38.1 per 100,000 in 1997 to 99.7 per 100,000 in 2006, with greater use among male individuals and black individuals in all periods. Over time, transferred patients had higher numbers of comorbidities (5.0 in 1997-2000 vs 5.8 in 2004-2006, P < .001) and were more likely to receive mechanical ventilation at the long-term acute care hospital (16.4% in 1997-2000 vs 29.8% in 2004-2006, P < .001). One-year mortality after long-term acute care hospital admission was high throughout the study period: 50.7% in 1997-2000 and 52.2% in 2004-2006., Conclusions: Long-term acute care hospital utilization after critical illness is common and increasing. Survival among Medicare beneficiaries transferred to long-term acute care after critical illness is poor.

Wide variation between hospitals in the quality of critical care lead to many potentially avoidable deaths. Regionalization of critical care is a possible solution; regionalization has been implemented for trauma and neonatal intensive care, and it is under active discussion for medical and cardiac critical care. However, regionalization is only one possible approach to reorganizing critical care services. This commentary introduces the technique of network analysis as a framework for the following: (1) understanding how critically ill patients move between hospitals, (2) defining the roles hospitals play in regional care delivery, and (3) suggesting systematic improvements that may benefit population health. We examined transfers of critically ill Medicare patients in Connecticut in 2005 as a model system. We found that patients are systematically transferred to more capable hospitals. However, we find the standard distinction of hospitals into either "secondary hospitals" or "tertiary hospitals" poorly explains observed transfer patterns; instead, hospitals show a continuum of roles. We further examine the implications of the network pattern in a simulation of quarantine of a hospital to incoming transfers, as occurred during the severe acute respiratory syndrome epidemic. Network perspectives offer new ways to study systems to care for critically ill patients and provide additional tools for addressing pragmatic problems in triage and bed management, regionalization, quality improvement, and disaster preparedness.

Rationale: Regionalization has been proposed as a method to improve outcomes for medical patients receiving mechanical ventilation in the intensive care unit., Objectives: To determine the number of patients who would be affected by regionalization and the potential mortality reduction under a regionalized system of care., Methods: We performed a retrospective cohort study with Monte Carlo simulation, using 2001 state discharge data from eight states representing 42% of the U.S. population. Adult medical patients undergoing invasive mechanical ventilation were identified. Patient location and hospital mortality rates were obtained from the discharge data; estimates of the relative risk reduction in hospital mortality for high-volume hospitals compared with low-volume hospitals were obtained from the published literature and applied to the cohort., Measurements and Main Results: Of 180,976 adult medical patients who underwent mechanical ventilation at 1,170 nonfederal hospitals, 83,050 (46%) received mechanical ventilation at 887 (76%) hospitals with low annual volumes (fewer than 275 patients per year). Using published risk estimates, approximately 4,720 lives per year (95% range, 2,522-6,744) could potentially be saved in the 8 states by routinely transferring patients from low- to high-volume hospitals, representing a number needed to treat of 15.7. The median distance that patients would need to travel to reach a high-volume hospital was 8.5 miles (interquartile range, 4.0-21.2 mi)., Conclusions: Regionalization of intensive care could potentially improve survival for patients undergoing mechanical ventilation. Transfer distances are modest for most patients.

Despite suggestive evidence, there has been no adequately powered systematic study of the ways in which marital status influences health care consumption. Using a novel data set of 609016 newly diagnosed, seriously ill elderly individuals in the USA, and employing hierarchical linear models, we look at differences in the experience of hospitalization as a function of marital status. We find that the married consistently use higher quality hospitals and have shorter lengths of stay. On the other hand, the married and the widowed appear to receive similar quality care once they are in the hospital. Marital status thus has a substantial impact on the health care obtained by the elderly. We suggest that these patterns are most consistent with spouses exerting their benefits by functioning as higher-order decision-makers than as home health assistants.

There appears to be significant heterogeneity across diseases in their patterns of health care use at the end of life. We use a new, nationally representative sample of patients diagnosed in 1993 with 13 serious diseases to demonstrate this variation in rates of inpatient, outpatient, and hospice utilization. The diseases are: cancer of the lung, colon, pancreas, urinary tract, liver or biliary tract, head or neck, or central nervous system, as well as leukemia or lymphoma, stroke, congestive heart failure, hip fracture, or myocardial infarction. We present disease-specific rates of: length of stay, interhospital transfer, outpatient visits in the year before and 3 years after diagnosis, death within 4 years, and gender-specific hospice use rates among decedents. Among decedents with noncancer diagnoses, rates of hospice use vary from 5.9% to 8.7%. Among decedents with cancer diagnoses, rates vary from 15.2% to 35.2%. For the cohort overall, 14.2% of male decedents and 12.4% of female decedents used hospice. Patterns of end-of-life care vary substantially according to diagnosis.

Objectives: To examine racial and ethnic differences in place of death, adjusting for likely confounders., Design: A retrospective cohort analyzed using multinomial logistic regression., Setting: United States in 1993., Participants: A nationally representative sample of 22,658 deaths in 1993 from the National Mortality Followback Survey., Measurements: Place of death as determined on the death certificate, with controls for age, sex, income, education, and cause of death. The outcomes of interest were death in a hospital during an inpatient stay, death in a nursing home, death in a private residence, or death in some other place., Results: After adjustment, 43% of whites die after an inpatient hospital stay, as do 50% of blacks and 56% of Mexican Americans. Twenty percent of whites, 22% of Mexican Americans, and 14% of blacks die in nursing homes. Twenty-two percent of whites, 18% of blacks, and 9% of Mexicans die in a private residence., Conclusions: There are substantial differences between whites, blacks, and Mexican Americans in place of death that cannot be explained by differences in age, sex, income, education, and causes of death between the groups.