Hospital Contributions to Variability in the Use of ICUs Among Elderly Medicare Recipients

Between-hospital variation in indicators of quality of care: a systematic review

BACKGROUND

Efforts to mitigate unwarranted variation in the quality of care require insight into the 'level' (eg, patient, physician, ward, hospital) at which observed variation exists. This systematic literature review aims to synthesise the results of studies that quantify the extent to which hospitals contribute to variation in quality indicator scores.

METHODS

Embase, Medline, Web of Science, Cochrane and Google Scholar were systematically searched from 2010 to November 2023. We included studies that reported a measure of between-hospital variation in quality indicator scores relative to total variation, typically expressed as a variance partition coefficient (VPC). The results were analysed by disease category and quality indicator type.

RESULTS

In total, 8373 studies were reviewed, of which 44 met the inclusion criteria. Casemix adjusted variation was studied for multiple disease categories using 144 indicators, divided over 5 types: intermediate clinical outcomes (n=81), final clinical outcomes (n=35), processes (n=10), patient-reported experiences (n=15) and patient-reported outcomes (n=3). In addition to an analysis of between-hospital variation, eight studies also reported physician-level variation (n=54 estimates). In general, variation that could be attributed to hospitals was limited (median VPC=3%, IQR=1%-9%). Between-hospital variation was highest for process indicators (17.4%, 10.8%-33.5%) and lowest for final clinical outcomes (1.4%, 0.6%-4.2%) and patient-reported outcomes (1.0%, 0.9%-1.5%). No clear pattern could be identified in the degree of between-hospital variation by disease category. Furthermore, the studies exhibited limited attention to the reliability of observed differences in indicator scores.

CONCLUSION

Hospital-level variation in quality indicator scores is generally small relative to residual variation. However, meaningful variation between hospitals does exist for multiple indicators, especially for care processes which can be directly influenced by hospital policy. Quality improvement strategies are likely to generate more impact if preceded by level-specific and indicator-specific analyses of variation, and when absolute variation is also considered.

PROSPERO REGISTRATION NUMBER

CRD42022315850.

Growth In Patient Cost Sharing For Hospitalizations With And Without Intensive Care Among Commercially Insured Patients

Intensive care units (ICUs) are increasingly used for hospital care, yet out-of-pocket spending for ICU hospitalizations remains poorly understood, particularly among the nearly half of the US population with commercial health insurance. Using 2008-19 MarketScan data, we compared 1,441,810 hospitalizations involving ICU services with 13,011,208 hospitalizations that did not involve ICU services. Average cost sharing, adjusted for patient and admission factors, increased from $1,137 per hospitalization in 2008 to $1,539 in 2019, or a 34 percent increase. This was driven by increasing deductibles, which rose by 163 percent. Across twenty clinical conditions whose hospitalizations commonly occurred in both ICU and non-ICU settings, ICU admission was associated with $155 higher cost sharing (13.0 percent higher) relative to cost sharing in non-ICU hospitalizations. Patients with high-deductible plans faced the highest cost sharing relative to those with other plan types. Patients who received out-of-network hospital care encountered higher cost sharing relative to those admitted to in-network hospitals with in-network clinicians.

Intensive Care Unit Utilization Following Major Surgery and the Nurse Work Environment

BACKGROUND

Across hospitals, there is wide variation in ICU utilization after surgery. However, it is unknown whether and to what extent the nurse work environment is associated with a patient's odds of admission to an intensive care unit.

PURPOSE

To estimate the relationship between hospitals' nurse work environment and a patient's likelihood of ICU admission and mortality following surgery.

METHODS

A cross-sectional study of 269 764 adult surgical patients in 453 hospitals was conducted. Logistic regression models were used to estimate the effects of the work environment on the odds of patients' admission to the intensive care unit and mortality.

RESULTS

Patients in hospitals with good work environments had 16% lower odds of intensive care unit admission and 15% lower odds of mortality or intensive care unit admission than patients in hospitals with mixed or poor environments.

CONCLUSIONS

Patients in hospitals with better nurse work environments were less likely to be admitted to an intensive care unit and less likely to die. Hospitals with better nurse work environments may be better equipped to provide postoperative patient care on lower acuity units.

Prediction of Acute Respiratory Failure Requiring Advanced Respiratory Support in Advance of Interventions and Treatment: A Multivariable Prediction Model From Electronic Medical Record Data

Background

Acute respiratory failure occurs frequently in hospitalized patients and often begins outside the ICU, associated with increased length of stay, cost, and mortality. Delays in decompensation recognition are associated with worse outcomes.

Objectives

The objective of this study is to predict acute respiratory failure requiring any advanced respiratory support (including noninvasive ventilation). With the advent of the coronavirus disease pandemic, concern regarding acute respiratory failure has increased.

Derivation Cohort

All admission encounters from January 2014 to June 2017 from three hospitals in the Emory Healthcare network (82,699).

Validation Cohort

External validation cohort: all admission encounters from January 2014 to June 2017 from a fourth hospital in the Emory Healthcare network (40,143). Temporal validation cohort: all admission encounters from February to April 2020 from four hospitals in the Emory Healthcare network coronavirus disease tested (2,564) and coronavirus disease positive (389).

Prediction Model

All admission encounters had vital signs, laboratory, and demographic data extracted. Exclusion criteria included invasive mechanical ventilation started within the operating room or advanced respiratory support within the first 8 hours of admission. Encounters were discretized into hour intervals from 8 hours after admission to discharge or advanced respiratory support initiation and binary labeled for advanced respiratory support. Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment, our eXtreme Gradient Boosting-based algorithm, was compared against Modified Early Warning Score.

Results

Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment had significantly better discrimination than Modified Early Warning Score (area under the receiver operating characteristic curve 0.85 vs 0.57 [test], 0.84 vs 0.61 [external validation]). Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment maintained a positive predictive value (0.31-0.21) similar to that of Modified Early Warning Score greater than 4 (0.29-0.25) while identifying 6.62 (validation) to 9.58 (test) times more true positives. Furthermore, Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment performed more effectively in temporal validation (area under the receiver operating characteristic curve 0.86 [coronavirus disease tested], 0.93 [coronavirus disease positive]), while achieving identifying 4.25-4.51× more true positives.

Conclusions

Prediction of Acute Respiratory Failure requiring advanced respiratory support in Advance of Interventions and Treatment is more effective than Modified Early Warning Score in predicting respiratory failure requiring advanced respiratory support at external validation and in coronavirus disease 2019 patients. Silent prospective validation necessary before local deployment.

Association of Hospital-Level Intensive Care Unit Use and Outcomes in Older Patients With Isolated Rib Fractures

IMPORTANCE

The optimal level of care for older patients with rib fractures as an isolated injury is unknown.

OBJECTIVES

To characterize interhospital variability in intensive care unit (ICU) vs non-ICU admission of older patients with isolated rib fractures and to evaluate whether greater hospital-level use of ICU admission is associated with improved outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study included trauma patients aged 65 years and older with isolated rib fractures who were admitted to US trauma centers participating in the National Trauma Data Bank between January 1, 2015, and December 31, 2016. Patients were excluded if they had other significant injuries, were intubated or had assisted respirations in the emergency department (ED), or had a Glasgow Coma Scale (GCS) score of less than 9 in the ED. Hospitals with fewer than 10 eligible patients were excluded. Data analysis was conducted from May 2019 through September 2020.

EXPOSURES

Admission to the ICU.

MAIN OUTCOMES AND MEASURES

Composite of unplanned intubation, pneumonia, or death during hospitalization.

RESULTS

Among 23 951 patients (11 066 [46.2%] women; mean [SD] age, 77.0 [7.2] years) at 573 hospitals, the median (interquartile range) proportion of ICU use was 16.7% (7.4%-32.0%), but this varied from a low of 0% to a high of 91.9%. The composite outcome occurred in 787 patients (3.3%), with unplanned intubation in 317 (1.3%), pneumonia in 180 (0.8%), and death in 451 (1.9%). Accounting for the hierarchical nature of the data and adjusting for propensity scores reflecting factors associated with ICU admission, receiving care at a hospital with the greatest ICU use (quartile 4), compared with a hospital with the lowest ICU use, was associated with decreased likelihood of the composite outcome (adjusted odds ratio, 0.71; 95% CI, 0.55-0.92).

CONCLUSIONS AND RELEVANCE

In this study, admission location of older patients with isolated rib fractures was variable across hospitals, but hospitalization at a center with greater ICU use was associated with improved outcomes. It may be warranted for hospitals with low ICU use to admit more such patients to an ICU.

Medicaid Expansion and Mechanical Ventilation in Asthma, Chronic Obstructive Pulmonary Disease, and Heart Failure

Rationale: The Affordable Care Act's Medicaid expansion has led to increased access to chronic disease care among newly insured adults. Despite this, its effects on clinical outcomes, particularly for patients with asthma, chronic obstructive pulmonary disease, and heart failure, are uncertain. Objectives: To assess whether Medicaid expansion was associated with changes in mechanical ventilation rates among hospitalized patients with heart failure, asthma, and chronic obstructive pulmonary disease. Methods: Difference-in-differences analysis comparing discharge data from four states that expanded Medicaid in 2014 (Arizona, Iowa, New Jersey, and Washington) and three comparison states that did not (North Carolina, Nebraska, and Wisconsin) was performed. Models were adjusted for patient and hospital factors. Results: Mechanical ventilation rates at baseline were 7.2% in nonexpansion states and 8.8% in expansion states. Medicaid expansion was associated with a decline in mechanical ventilation rates at -0.2% per quarter (95% confidence interval [CI], -0.3% to 0.0%; P = 0.010). We did not observe a change in the rate of ICU admission (-0.4% per quarter; 95% CI, -0.8% to 0.1%; P = 0.10) or in-hospital mortality (0.1% per quarter; 95% CI, 0.0% to 0.1%; P = 0.30). In a negative control among adults aged 65 years or older, changes in mechanical ventilation rates were similar, though the CIs crossed zero (-0.1%; 95% CI, -0.2% to 0.0%; P = 0.08). Conclusions: Medicaid expansion may have been associated with a decline in mechanical ventilation rates among uninsured and Medicaid-covered patients admitted with heart failure, chronic obstructive pulmonary disease, and asthma.

ICU Utilization for Patients With Acute Exacerbation of Chronic Obstructive Pulmonary Disease Receiving Noninvasive Ventilation

OBJECTIVES

We investigated whether patients with chronic obstructive pulmonary disease could safely receive noninvasive ventilation outside of the ICU.

DESIGN

Retrospective cohort study.

SETTING

Twelve states with ICU utilization flag from the State Inpatient Database from 2014.

PATIENTS

Patients greater than or equal to 18 years old with primary diagnosis of acute exacerbation of chronic obstructive pulmonary disease and secondary diagnosis of respiratory failure who received noninvasive ventilation.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Multilevel logistic regression models were used to obtain hospital-level ICU utilization rates. We risk-adjusted using both patient/hospital characteristics. The primary outcome was in-hospital mortality; secondary outcomes were invasive monitoring (arterial/central catheters), hospital length of stay, and cost. We examined 5,081 hospitalizations from 424 hospitals with ICU utilization ranging from 0.05 to 0.98. The overall median in-hospital mortality was 2.62% (interquartile range, 1.72-3.88%). ICU utilization was not significantly associated with in-hospital mortality (β = 0.01; p = 0.05) or length of stay (β = 0.18; p = 0.41), which was confirmed by Spearman correlation (ρ = 0.06; p = 0.20 and ρ = 0.02; p = 0.64, respectively). However, lower ICU utilization was associated with lower rates of invasive monitor placement by linear regression (β = 0.05; p < 0.001) and Spearman correlation (ρ = 0.28; p < 0.001). Lower ICU utilization was also associated with significantly lower cost by linear regression (β = 14.91; p = 0.02) but not by Spearman correlation (ρ = 0.09; p = 0.07).

CONCLUSIONS

There is wide variability in the rate of ICU utilization for noninvasive ventilation across hospitals. Chronic obstructive pulmonary disease patients receiving noninvasive ventilation had similar in-hospital mortality across the ICU utilization spectrum but a lower rate of receiving invasive monitors and probably lower cost when treated in lower ICU-utilizing hospitals. Although the results suggest that noninvasive ventilation can be delivered safely outside of the ICU, we advocate for hospital-specific risk assessment if a hospital were considering changing its noninvasive ventilation delivery policy.

Intensivists in U.S. Acute Care Hospitals

OBJECTIVES

To determine the total numbers of privileged and full-time equivalent intensivists in acute care hospitals with intensivists and compare the characteristics of hospitals with and without intensivists.

DESIGN

Retrospective analysis of the American Hospital Association Annual Survey Database (Fiscal Year 2015).

SETTING

Two-thousand eight-hundred fourteen acute care hospitals with ICU beds.

PATIENTS

None.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Of the 2,814 acute care hospitals studied, 1,469 (52%) had intensivists and 1,345 (48%) had no intensivists. There were 28,808 privileged and 19,996 full-time equivalent intensivists in the 1,469 hospitals with intensivists. In these hospitals, the median (25-75th percentile) numbers of privileged and full-time equivalent intensivists were 11 (5-24) and 7 (2-17), respectively. Compared with hospitals without intensivists, hospitals with privileged intensivists were primarily located in metropolitan areas (91% vs 50%; p < 0.001) and at the aggregate level had nearly thrice the number of hospital beds (403,522 [75%] vs 137,146 [25%]), 3.6 times the number of ICU beds (74,222 [78%] vs 20,615 [22%]), and almost twice as many ICUs (3,383 [65%] vs 1,846 [35%]). At the hospital level, hospitals with privileged intensivists had significantly more hospital beds (median, 213 vs 68; p < 0.0001), ICU beds (median, 32 vs 8; p < 0.0001), a higher ratio of ICU to hospital beds (15.6% vs 12.6%; p < 0.0001), and a higher number of ICUs per hospital (2 vs 1; p < 0.0001) than hospitals without intensivists.

CONCLUSIONS

Analyzing the intensivist section of the American Hospital Association Annual Survey database is a novel approach to estimating the numbers of privileged and full-time equivalent intensivists in acute care hospitals with ICU beds in the United States. This methodology opens the door to an enhanced understanding of the current supply and distribution of intensivists as well as future research into the intensivist workforce.

Goldilocks, the Three Bears and Intensive Care Unit Utilization: Delivering Enough Intensive Care But Not Too Much. A Narrative Review

Professional societies have developed recommendations for patient triage protocols, but wide variations in triage patterns for many acute conditions exist among hospitals in the United States. Differences in hospitals’ triage patterns can be attributed to factors such as physician behavior, hospital policy and real-time conditions such as intensive care unit capacity. The patient safety concern is that patients evaluated for admission to the intensive care unit during times of high intensive care unit capacity may have adverse outcomes related to delays in care. Because standardization of a national triage policy is not feasible due to differing resources available at each hospital, local guidelines should prevail that take into account hospitals’ local resources. The goal would be to better match intensive care unit bed supply with demand.

Potentially Preventable Intensive Care Unit Admissions in the United States, 2006-2015

Rationale: Increasing intensive care unit (ICU) beds and the critical care workforce are often advocated to address an aging and increasingly medically complex population. However, reducing potentially preventable ICU stays may be an alternative to ensure adequate capacity.Objectives: To determine the proportions of ICU admissions meeting two definitions of being potentially preventable using nationally representative U.S. claims databases.Methods: We analyzed claims from 2006 to 2015 from all Medicare Fee-for-Service (FFS) beneficiaries and from a large national payer offering a private insurance (PI) plan and a Medicare Advantage (MA) plan. Potentially preventable hospitalizations were identified using existing definitions for ambulatory care sensitive conditions (ACSCs) and life-limiting malignancies (LLMs).Results: We analyzed 420,369,434 person-years of insurance coverage, during which there were 99,793,416 acute inpatient hospitalizations, of which 16,646,977 (16.7%) were associated with an ICU admission. Of these, the proportions with an ACSC were 12.9%, 12.7%, and 15.8%, and with an LLM were 5.2%, 5.4%, and 6.4%, among those with PI, MA, and FFS, respectively. Over 10 years, the absolute percentages of ACSC-associated ICU stays declined (PI = -1.1%, MA -6.4%, FFS -6.4%; all P < 0.001 for all trends). Smaller changes were noted among LLM-associated ICU stays, declining in the MA cohort (-0.8%) and increasing in the FFS (+0.3%) and PI (+0.2%) populations (P < 0.001 for all trends).Conclusions: An appreciable proportion of U.S. ICU admissions may be preventable with community-based interventions. Investment in the outpatient infrastructure required to prevent these ICU admissions should be considered as a complementary, if not alternative, strategy to expanding ICU capacity to meet future demand.

Multicenter derivation and validation of an early warning score for acute respiratory failure or death in the hospital

BACKGROUND

Acute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model-Accurate Prediction of Prolonged Ventilation (APPROVE)-to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.

METHODS

This was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort.

RESULTS

There were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85-0.88) in 2013 and 0.90 (95% CI 0.84-0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%).

CONCLUSIONS

An automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02488174 . Registered on 18 March 2015.

Intensive care utilization following major noncardiac surgical procedures in Ontario, Canada: a population-based study

PURPOSE

Patients are sometimes admitted to intensive care units (ICU) after elective noncardiac surgery for advanced monitoring and treatments not available on a general postsurgical ward. However, patterns of ICU utilization are poorly understood. Our aims were to assess the incidence and determinants of ICU utilization after elective noncardiac surgical procedures.

METHODS

Population-based cohort study included adult patients who underwent 13 types of major elective noncardiac surgical procedures between 2006 and 2014 in Ontario, Canada. Primary outcome was early admission to ICU within 24 h after surgery. A prespecified analysis using multilevel logistic regression modeling separately examined patient- and hospital-level factors associated with early ICU admission within distinct groups of surgical procedures.

RESULTS

Early ICU admission occurred in 9.6% of the included 541,524 patients. Patients admitted early to ICU showed higher median age (68 vs. 65 years), burden of prehospital comorbidities (Charlson comorbidity index score ≥ 2, 33.1 vs. 10.4%), 30-day mortality rates (2.4 vs. 0.3%), and longer median postoperative hospital stays (6 vs. 4 days) than patients admitted to a ward. There was wide variation in proportions of patients admitted early to ICU across different surgery types (0.9% for hysterectomy to 90.8% for open abdominal aortic aneurysm repair) with generally low 30-day mortality across procedures (0.1-2.8%). Within individual procedures, there was wide interhospital variation in the range of early ICU admission rates (hysterectomy 0.07-14.4%, lower gastrointestinal resection 1.3-95%, endovascular aortic aneurysm 1.3-95.2%). The individual hospital where surgery was performed accounted for a large proportion of the variation in early ICU admission rates, with the median odds ratio ranging from 2.3 for hysterectomy to 21.5 for endovascular aortic aneurysm.

CONCLUSIONS

There is a wide variation in early ICU admission across and within surgical procedures. The individual hospital accounts for a large proportion of this variation. Further research is required to identify the basis for this variation and to develop better methods for allocating ICU resources for postoperative management of surgical patients.