The impact of patient age and comorbidities on the occurrence of “never events” in cerebrovascular surgery: an analysis of the Nationwide Inpatient Sample

Clinical article

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Object

As health care administrators focus on patient safety and cost-effectiveness, methodical assessment of quality outcome measures is critical. In 2008 the Centers for Medicare and Medicaid Services (CMS) published a series of “never events” that included 11 hospital-acquired conditions (HACs) for which related costs of treatment are not reimbursed. Cerebrovascular procedures (CVPs) are complex and are often performed in patients with significant medical comorbidities.

Methods

This study examines the impact of patient age and medical comorbidities on the occurrence of CMS-defined HACs, as well as the effect of these factors on the length of stay (LOS) and hospitalization charges in patients undergoing common CVPs.

Results

The HACs occurred at a frequency of 0.49% (1.33% in the intracranial procedures and 0.33% in the carotid procedures). Falls/trauma (n = 4610, 72.3% HACs, 357 HACs per 100,000 CVPs) and catheter-associated urinary tract infections (n = 714, 11.2% HACs, 55 HACs per 100,000 CVPs) were the most common events. Age and the presence of ≥ 2 comorbidities were strong independent predictors of HACs (p < 0.0001). The occurrence of HACs negatively impacts both LOS and hospital costs. Patients with at least 1 HAC were 10 times more likely to have prolonged LOS (≥ 90th percentile) (p < 0.0001), and 8 times more likely to have high inpatient costs (≥ 90th percentile) (p < 0.0001) when adjusting for patient and hospital factors.

Conclusions

Improved quality protocols focused on individual patient characteristics might help to decrease the frequency of HACs in this high-risk population. These data suggest that risk adjustment according to underlying patient factors may be warranted when considering reimbursement for costs related to HACs in the setting of CVPs.

Abbreviations used in this paper:AHRQ = Agency for Healthcare Research and Quality; AVM = arteriovenous malformation; CAS = carotid artery stenting; CEA = carotid endarterectomy; CMS = Centers for Medicaid and Medicare Services; CVP = cerebrovascular procedure; EC-IC = extracranial-intracranial; HAC = hospital-acquired condition; HCUP = Healthcare Cost and Utilization Project; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; LOS = length of stay; NIS = Nationwide Inpatient Sample; UTI = urinary tract infection.

Object

As health care administrators focus on patient safety and cost-effectiveness, methodical assessment of quality outcome measures is critical. In 2008 the Centers for Medicare and Medicaid Services (CMS) published a series of “never events” that included 11 hospital-acquired conditions (HACs) for which related costs of treatment are not reimbursed. Cerebrovascular procedures (CVPs) are complex and are often performed in patients with significant medical comorbidities.

Methods

This study examines the impact of patient age and medical comorbidities on the occurrence of CMS-defined HACs, as well as the effect of these factors on the length of stay (LOS) and hospitalization charges in patients undergoing common CVPs.

Results

The HACs occurred at a frequency of 0.49% (1.33% in the intracranial procedures and 0.33% in the carotid procedures). Falls/trauma (n = 4610, 72.3% HACs, 357 HACs per 100,000 CVPs) and catheter-associated urinary tract infections (n = 714, 11.2% HACs, 55 HACs per 100,000 CVPs) were the most common events. Age and the presence of ≥ 2 comorbidities were strong independent predictors of HACs (p < 0.0001). The occurrence of HACs negatively impacts both LOS and hospital costs. Patients with at least 1 HAC were 10 times more likely to have prolonged LOS (≥ 90th percentile) (p < 0.0001), and 8 times more likely to have high inpatient costs (≥ 90th percentile) (p < 0.0001) when adjusting for patient and hospital factors.

Conclusions

Improved quality protocols focused on individual patient characteristics might help to decrease the frequency of HACs in this high-risk population. These data suggest that risk adjustment according to underlying patient factors may be warranted when considering reimbursement for costs related to HACs in the setting of CVPs.

On September 1, 2008, the Centers for Medicaid and Medicare Services (CMS) introduced a list of 11 complications that were considered high cost and/or high volume and are reasonably preventable through application of evidence-based medicine.33 The events were as follows: air embolism, retained foreign body, blood incompatibility, pressure ulcer, catheter-associated urinary tract infection (UTI), vascular catheter–associated infection, manifestations of poor glycemic control, falls/trauma, deep venous thrombosis or pulmonary embolism after total knee and hip replacements, surgical site infections after coronary artery bypass graft, and surgical site infections after certain orthopedic or bariatric surgeries. These hospital-acquired conditions (HACs), commonly termed “never events,” refer to a policy of zero dollar reimbursement for additional cost of treatment related to these events.35 The mandate was intended to shift the costs of seemingly preventable complications and motivate providers to institute programs focused on patient safety.41 However, health care leaders have questioned how preventable these events truly are and have raised concerns over the lack of adjustments for patient demographics and procedural complexity.33,38 These HACs have significant financial implications for hospitals and providers and important consequences for the quality of health care delivery.

Cerebrovascular procedures (CVPs) are complex and typically require perioperative management in the ICU. Patients often present with significant medical comorbidities and/or disability. These disease-related factors may increase the risk of HACs. The impact of HACs has been examined in general neurosurgical patients at an single institutional level.46 However, no studies to date have described the national frequency of HACs in cerebrovascular patients or assessed potential factors impacting the occurrence of these complications. Our study uses a national discharge database to characterize HACs in a large national cohort of patients undergoing neurovascular surgery. First, we first delineate the prevalence of HACs in 8 common CVPs. Next, we examine the impact of patient age and comorbidities on HAC occurrence. Finally, we determine the impact of HACs on length of stay (LOS) and hospital cost.

Methods

Patient Sample

This study used longitudinal hospital inpatient discharge data from 2002 through 2010 in the Healthcare Cost and Utilization Project (HCUP) Nationwide Inpatient Sample (NIS) database (https://www.hcup-us.ahrq.gov/overview.jsp). The NIS is assembled annually by the Agency for Healthcare Research and Quality (AHRQ) to provide data to track health care utilization, costs, access, and other aspects of health care trends over time. The NIS is the largest publicly available, all-payer inpatient care database in the US, with information for more than 8 million annual hospital admissions. As of 2010, the NIS database contained data from 45 states and more than 1000 hospitals. Additionally, the NIS captures 20% of all US hospital discharges. To obtain national estimates, proper weights were applied as indicated in the HCUP–NIS Calculating NIS Variances Guide.12

Measures Used in the Study

For the purposes of this study, a subset of 1,290,166 patient admissions associated with a CVP (aneurysm clipping, aneurysm coiling, arteriovenous malformation [AVM] resection, AVM embolization, carotid endarterectomy [CEA], carotid artery stenting [CAS], mechanical thrombectomy, or extracranial-intracranial [EC-IC] bypass) between 2002 and 2010 was used. Relevant admissions were identified using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes. Patients who did not have at least 1 of the 8 procedures mentioned were excluded from analysis. Additionally, HACs from 2008 CMS definitions were identified using ICD-9-CM diagnosis and procedural codes. The ICD-9-CM codes for HACs and CVPs were verified by comparison with relevant existing literature.2,6,9,12,15,16,26,28,32 Three HACs were not included in this analysis because they do not pertain to CVPs (deep venous thrombosis or pulmonary embolism after total knee and hip replacements, surgical site infections after coronary artery bypass graft, surgical site infections after certain orthopedic or bariatric surgeries).

Statistical Analyses

Fisher exact tests were conducted to assess the associations between specific HACs and ever having any HAC, based on the variables described above. A multivariate logistic regression model was conducted using survey-adjusted generalized estimating equations to assess the relationship between patient age and medical comorbidities with the outcome of ever having an HAC. Factors hypothesized to affect the frequency of HACs were included in the model as covariates. These models were adjusted for patient (age, number of comorbidities, sex) and hospital (bed size, teaching status, region, CVP volume, and location) factors. A secondary model was adjusted for the same factors (patient and hospital), with age as a continuous variable.

Additionally, we fit multivariate logistic regression models to describe the relationship of the covariates to prolonged LOS and increased costs. Association between at least 1 HAC and prolonged LOS or increased cost was also evaluated. Prolonged LOS was defined as ≥ 90th percentile; that is, ≥ 11 days. Increased cost of treatment was defined as ≥ 90th percentile; that is, ≥ $118,571.44.

The occurrence of HACs was further evaluated among populations with unruptured versus ruptured aneurysms adjusting for patient and hospital factors.

Patient predictors, such as age (≤60 years of age, 61–70 years, 71–80 years, > 80 years), LOS, number of comorbidities (0, 1, ≥ 2), and sex were treated as categorical variables. Age was analyzed as both a categorical and continuous variable. Categorical cutoffs were chosen on patient decade of life. Hospital-level variables such as bed size (small, ≤ 200; medium, 201–400; large, <400 beds); teaching status (nonteaching, teaching); hospital region (Northeast, Midwest, South, West); volume (low, ≤ 72; medium, 73–152; high, > 152 procedures); and location (urban, rural) were already coded as categorical variables. Inpatient charges were adjusted for inflation and are reported in 2012 dollars. Because all variables were analyzed categorically, there was no need to normalize the data via transformations.

Statistical significance was defined as p < 0.05. All descriptive and regression analyses were performed using SAS version 9.3.

Results

Frequency of HACs

Between 2002 and 2010, there were 1,290,166 patient admissions associated with 1,299,613 CVPs, with 6321 admissions for patients who had at least 1 HAC. Of the 6321 admissions for patients with at least 1 HAC, 6269 had only 1 HAC and 52 had multiple HACs. The overall frequency of HACs was 0.49%. The frequency of HACs in the intracranial procedures (aneurysm clipping, aneurysm coiling, AVM embolization, AVM resection, mechanical thrombectomy, EC-IC bypass) was 1.33%. The HACs occurring most often were falls/trauma (n = 4610, 72.3% of HACs, 357 HACs per 100,000 CVPs) and catheter-associated UTIs (n = 714, 11.2% of HACs, 55 HACs per 100,000 CVPs). Tables 1 and 2 show complete data for each model covariate.

TABLE 1:

Frequency of HACs in patients with CVPs

CVPNo. (%) of All CVPsNo. w/ HACs (%)
aneurysm clipping88,551 (6.8)1157 (1.3)
AVM resection10,816 (0.8)185 (1.7)
CEA992,595 (76.4)3158 (0.3)
EC-IC bypass1649 (0.1)25 (1.5)
aneurysm coiling88,593 (6.8)1189 (1.3)
AVM embolization14,919 (1.1)107 (0.7)
CAS91,958 (7.1)396 (0.4)
mechanical thrombectomy10,532 (0.8)236 (2.2)
total ops*1,299,613

Total number of procedures is higher than total number of patient admissions because patients can undergo > 1 procedure in a given admission.

TABLE 2:

Number and type of HACs in patients with CVPs*

HACNo. of HACs (%)Incidence/100,000
air embolism10 (0.2)0.8
foreign objects151 (2.4)12
blood incompatibility5 (0.1)0.4
pressure ulcers216 (3.4)17
catheter-associated UTI714 (11.2)55
vascular catheter–associated infection516 (8.1)40
poor glycemic control150 (2.4)12
falls/trauma4610 (72.3)357
total6372 (0.49)

Overal total of CVPs = 1,299,613.

The most common CVP was CEA (n = 992,595; 76.4% of all CVPs). The CVPs with the highest percent HAC were mechanical thrombectomy (n = 236; 2.2% with HAC) and AVM resection (n = 185; 1.7% with HAC). The CVP with the lowest percent HAC was CEA (n = 3158; 0.3% with HAC) (Table 1).

Impact of Age and Comorbidities on HAC Frequency

Multivariate logistic regression analysis adjusting for patient- and hospital-level covariates revealed that individuals between 71 and 80 years of age (OR 0.53, p < 0.0001, 95% CI 0.45–0.62), those 61–70 years old (OR 0.41, p < 0.0001, 95% CI 0.34–0.48), or ≤ 60 years old (OR 0.74, p < 0.0001, 95% CI 0.63–0.86) all had lower odds of developing an HAC than patients > 80 years. Similarly, when adjusting for covariates, the presence of ≥ 2 medical comorbidities (OR 2.24, p < 0.0001, 95% CI 1.79–2.81) was associated with a higher HAC frequency, when compared with no comorbidities (Table 3).

TABLE 3:

Patient predictors of HACs*

PredictorOR95% CIp Value
comorbidities
 0reference
 11.120.86–1.450.3986
 ≥22.241.79–2.81<0.0001
age category (yrs)
 ≤600.740.63–0.86<0.0001
 61–700.410.34–0.48<0.0001
 71–800.530.45–0.62<0.0001
 >80reference
female
 yes1.501.34–1.69<0.0001
 noreference
endo procedure
 yes, combination including endo2.461.49–4.070.0004
 yes, only endo1.891.66–2.16<0.0001
 noreference

Endo = endovascular.

Hospital size, urban location, and teaching status were associated with increased odds of developing an HAC (p < 0.0001). Hospitals with high (OR 0.68, p < 0.0001, 95% CI 0.57–0.82) and medium (OR 0.74, p < 0.001, 95% CI 0.63–0.87) annual CVP volume had lower frequencies of HACs when compared with hospitals with low annual volume (Table 4).

TABLE 4:

Hospital predictors of HACs

PredictorOR95% CIp Value
hospital region
 Northeastreferece
 Midwest0.850.68–1.070.1605
 South1.210.97–1.500.0892
 West1.361.07–1.740.0130
hospital teaching status
 nonteachingreference
 teaching1.501.28–1.76<0.0001
hospital bed size
 smallreference
 medium1.621.17–2.260.0041
 large1.991.44–2.70<0.0001
annual CVP vol
 lowreference
 medium0.740.63–0.870.0003
 high0.680.57–0.82<0.0001
hospital location
 ruralreference
 urban1.851.66–2.16<0.0001

Finally, our secondary analysis assessing the association between aneurysm type (ruptured vs unruptured) did not reveal a significant effect (OR 0.68, p = 0.2606, 95% CI 0.34–1.34). Additionally, age as a continuous variable was not significantly associated with the likelihood of HAC occurrence (p = 0.5086).

Length of Stay and Inpatient Costs

The average LOS for patients with no HACs was approximately 4 days, with a total charge of $47,805, whereas patients with at least 1 HAC had an average LOS of 16 days at a total charge of $159,856 (Table 5).

TABLE 5:

Length of stay and inpatient costs in patients with CVPs*

FactorPts w/o HACsPts w/ HACsp Value
mean LOS (days)4.2116.54<0.0001
mean total inpatient costs$47,805$159,856<0.0001

Pts = patients.

Multivariate analysis demonstrated that patients with at least 1 HAC were 10 times more likely to have prolonged LOS when adjusting for patient and hospital factors (OR 10.80, p < 0.0001, 95% CI 9.46–12.33 vs no HACs), whereas patients with at least 1 HAC were 8 times more likely to have high inpatient costs (OR 8.12, p < 0.0001, 95% CI 7.09–9.31 vs no HACs) (Table 6).

TABLE 6:

Occurrence of HACs as predictor of prolonged LOS and high costs, 2002–2010

HACProlonged LOSHigh Inpatient Costs
OR95% CIp ValueOR95% CIp Value
yes10.809.46–12.33<0.00018.127.09–9.31<0.0001
noreferencereference

Discussion

The current study examines the impact of patient age and comorbidities on CMS-defined HACs, as well as the effects of HACs on the LOS and hospitalization charges for patients undergoing common CVPs. As health care administrators, payers, and the public focus on patient safety and cost-effectiveness, assessment of quality outcome measures is critical. Evaluating data on a national level establishes benchmarks and allows for ongoing monitoring of trends and performance. In an effort to document hospital practices, shift payer costs, and ultimately affect delivery of care, the CMS published and instituted a universal series of “never events” in 2008, of which 11 HACs were examined. Additional costs of treatment related to these codes are not currently reimbursed.

The current study establishes a baseline HAC incidence of 0.49% in a cohort of patients undergoing 8 common CVPs between 2002 and 2010. The frequency of HACs was 1.33% in the intracranial procedures and 0.33% in the carotid procedures. Although the vast majority of these patients had a single HAC, there were patients who experienced multiple events. Falls/trauma (72.3%) and catheter-associated UTIs (11.2%) were the most common occurrences. Prior studies have demonstrated an increased risk of falls in hospitalized stroke patients21,25,34,43,48 and in individuals admitted to Neuroscience and Neurosurgery services.21,25,34,36,40,43,48 The high proportion of UTIs probably reflects intraoperative use of urinary catheters secondary to procedural length and inherent need for osmotic diuresis. A recent study by Titsworth et al. suggests that comprehensive UTI prevention efforts along with continuous quality improvement programs can significantly reduce the duration of urinary catheterization and the rate of catheter-associated UTIs in a neurological ICU.47

Age and baseline comorbidities were clear risk factors for HACs in our patient cohort. Individuals > 80 years of age were significantly more susceptible than younger patients. Furthermore, patients with multiple comorbidities were more than twice as likely to develop HACs. It is well known that both age and medical comorbidities impact outcome after CVPs.13,24,29,37 Older age is associated with poor outcome in the setting of surgical and endovascular treatment of intracranial aneurysms4,7,37,51 and carotid stenosis.10,39 Patient age is also an independent predictor of death following endovascular mechanical thrombectomy procedures for acute ischemic stroke.2 Medical comorbidities are a risk factor for CEA,20,52 and for angioplasty and stenting procedures.42 Recent studies show that hyperglycemia is strongly associated with hospital complications, death, and disability after aneurysmal subarachnoid hemorrhage.19 Latorre et al. demonstrated that aggressive hyperglycemia management can reduce the odds of unfavorable outcome.30

Published literature also indicates that advanced age and/or the presence of medical comorbidities are associated with increased HAC frequency following radical cervical lymph node dissection and radical cystectomy procedures.27,31 Joice et al. reported that each decade of age increased the risk of HACs by as much as 20% following radical cystectomy.27 These data suggest a potential financial liability for centers that treat older patients with multiple medical comorbidities. Unfortunately, this is the typical population subset that is affected by carotid disease and ischemic stroke.3,11,22

Our analysis controlled for hospital and procedure-related factors that might impact the occurrence of HACs. The effect size point estimates for these variables are generally smaller than those of patient age and comorbidities. The data suggest that large hospitals, teaching facilities, and centers in urban areas have a high incidence of HACs. Although somewhat counterintuitive, this may reflect the complexity of cerebrovascular cases referred to and treated at these facilities. By contrast, increased case volume is independently associated with a lower frequency of HACs. Prior reports suggest that higher provider volume correlates with more favorable outcomes in the setting of aneurysm surgery,8,14 CEA,1,2,23 and mechanical thrombectomy procedures.49 These findings are generalizable. In a meta-analysis performed in 2000, 123 of 128 studies involving 40 surgical procedures demonstrated lower mortality rates at high-volume hospitals.35 Our results indicate that endovascular procedures have a higher rate of HACs than open cerebrovascular surgeries. This finding is largely driven by the carotid disease subgroup (carotid artery angioplasty and stenting vs CEA). The CAS patient population is, by definition, a high-risk cohort for which CMS requires documentation of increased surgical risk to qualify for reimbursement. When the patients with CEA and CAS are removed from analysis, a difference in HAC frequency no longer exists between the open surgical and endovascular groups.

After adjusting for relevant hospital and procedure-related factors, HACs were associated with increased LOS (10 times more likely to have prolonged LOS) and greater hospitalization charges (8 times more likely to have high inpatient costs). In a prior study of a tertiary neurosurgical facility, LOS was significantly higher in patients with HACs than in those without.46 These findings are consistent with reports from other surgical subspecialties and substantiate the outcomes anticipated by the CMS.5,18,31,44,50 Population-level data will help determine whether the CMS HAC regulation ultimately translates to decreased inpatient admission durations and cost reduction over time.

This study has several limitations associated with the use of an administrative dataset. Classification of HACs depends on accurate reporting of ICD-9-CM codes that match conditions qualifying under the CMS and AHRQ guidelines. Misidentification may lead to information bias. The HACs and “never events” were first classified by the CMS in 2008, and our analysis includes data and appropriate definitions from 2002 to 2010. These definitions change over time. Consistent information could be accurately queried and synthesized from the relevant ICD-9 codes as a summation of the study years, but analysis across the years is problematic due to the changing definitions of ICD-9 complications and procedural codes.

Due to the infrequency of events, temporal trends were not assessed. Furthermore, the database only accounts for events that occurred during the patients' hospital admissions and cannot control for local and regional quality control policies. Additionally, due to the administrative nature of the NIS, it is difficult to determine if documented HACs were preexisting or occurred during admission. Of note, one of the most common HACs (falls/trauma) is assumed to occur and be recorded in a hospital setting. It is further possible that patients may have presented at admission with HACs that were mistakenly identified as new events.17 Finally, the NIS does not include financial stakes such as professional fees, which may result in underestimation of total hospital costs.45 It is difficult to compare “never event” and HAC rates across published studies because investigators use somewhat varying definitions and different ICD-9 codes to classify these “never events.”27,31

Conclusions

Age and associated medical comorbidities are robust predictors of HAC occurrence in the patient population with cerebrovascular conditions. Overall, these factors appear to have a stronger impact on the frequency of HACs than do admitting hospital characteristics or selection of surgical procedure. These data suggest that risk adjustment according to underlying patient factors may be warranted when considering reimbursement for costs related to HACs in the setting of CVPs. Increases in LOS and higher hospitalization charges secondary to event occurrence are a burden to the health care system. The results of this study, while providing a baseline of the quality of cerebrovascular care, suggest that increased attention paid to a patient's age and comorbidity profile can reduce complications. Improved processes and protocol implementation in the elderly population and in those with comorbidities might help to decrease the frequency of these events and, by extension, decrease the total costs and LOS for this patient population.

Disclosure

This project and publication were supported in part by the AANS Medical Student Summer Research Fellowship. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the AANS. The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Author contributions to the study and manuscript preparation include the following. Conception and design: He, Wen, Adamcyzk, Mack. Acquisition of data: Wen, Cen, Mack. Analysis and interpretation of data: He, Wen, Cen, Mack. Drafting the article: He. Critically revising the article: all authors. Reviewed submitted version of manuscript: all authors. Approved the final version of the manuscript on behalf of all authors: He. Statistical analysis: Wen, Cen. Administrative/technical/material support: Attenello, Adamcyzk, Amar. Study supervision: Mack.

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    Nakai AAkeda MKawabata I: Incidence and risk factors for inpatient falls in an academic acute-care hospital. J Nippon Med Sch 73:2652702006

  • 37

    Ogilvy CSCarter BS: Stratification of outcome for surgically treated unruptured intracranial aneurysms. Neurosurgery 52:82882003

  • 38

    Pronovost PJGoeschel CAWachter RM: The wisdom and justice of not paying for “preventable complications”. JAMA 299:219721992008

  • 39

    Rajamani KKennedy KFRuggiero NJRosenfield KSpertus JChaturvedi S: Outcomes of carotid endarterectomy in the elderly: report from the National Cardiovascular Data Registry. Stroke 44:117211742013

  • 40

    Rohde JMMyers AHVlahov D: Variation in risk for falls by clinical department: implications for prevention. Infect Control Hosp Epidemiol 11:5215241990

  • 41

    Rosenthal MB: Nonpayment for performance? Medicare's new reimbursement rule. N Engl J Med 357:157315752007

  • 42

    Setacci Fde Donato GChisci ESirignano PGalzerano GCastriota F: Economic impact of endarterectomy vs carotid artery stenting: a one year, single centre study. EuroIntervention 3:3403442007

  • 43

    Siracuse JJOdell DDGondek SPOdom SRKasper EMHauser CJ: Health care and socioeconomic impact of falls in the elderly. Am J Surg 203:3353382012

  • 44

    Stansbury JPJia HWilliams LSVogel WBDuncan PW: Ethnic disparities in stroke: epidemiology, acute care, and postacute outcomes. Stroke 36:3743862005

  • 45

    Sun BCEmond JACamargo CA Jr: Direct medical costs of syncope-related hospitalizations in the United States. Am J Cardiol 95:6686712005

  • 46

    Teufack SGCampbell PJabbour PMaltenfort MEvans JRatliff JK: Potential financial impact of restriction in “never event” and periprocedural hospital-acquired condition reimbursement at a tertiary neurosurgical center: a single-institution prospective study. Clinical article. J Neurosurg 112:2492562010

  • 47

    Titsworth WLHester JCorreia TReed RGuin PArchibald L: The effect of increased mobility on morbidity in the neurointensive care unit. Clinical article. J Neurosurg 116:137913882012

  • 48

    Tutuarima JAvan der Meulen JHde Haan RJvan Straten ALimburg M: Risk factors for falls of hospitalized stroke patients. Stroke 28:2973011997

  • 49

    Vogel TRDombrovskiy VYGraham AM: Carotid artery stenting in the nation: the influence of hospital and physician volume on outcomes. Vasc Endovascular Surg 44:89942010

  • 50

    Whitehouse JDFriedman NDKirkland KBRichardson WJSexton DJ: The impact of surgical-site infections following orthopedic surgery at a community hospital and a university hospital: adverse quality of life, excess length of stay, and extra cost. Infect Control Hosp Epidemiol 23:1831892002

  • 51

    Wiebers DOWhisnant JPHuston J IIIMeissner IBrown RD JrPiepgras DG: Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment. Lancet 362:1031102003

  • 52

    Wimmer NJYeh RWCutlip DEMauri L: Risk prediction for adverse events after carotid artery stenting in higher surgical risk patients. Stroke 43:321832242012

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Article Information

Address correspondence to: Shuhan He, B.S., Keck School of Medicine of USC, 1200 North State St., Ste. 5046, Los Angeles, CA 90089. email: Shuhanhe@usc.edu.

Please include this information when citing this paper: published online June 27, 2014; DOI: 10.3171/2014.4.JNS131253.

© AANS, except where prohibited by US copyright law.

Headings

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    Morse BCBoland BNBlackhurst DWRoettger RH: Analysis of Centers for Medicaid and Medicare Services ‘never events' in elderly patients undergoing bowel operations. Am Surg 76:8418452010

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    Nakai AAkeda MKawabata I: Incidence and risk factors for inpatient falls in an academic acute-care hospital. J Nippon Med Sch 73:2652702006

  • 37

    Ogilvy CSCarter BS: Stratification of outcome for surgically treated unruptured intracranial aneurysms. Neurosurgery 52:82882003

  • 38

    Pronovost PJGoeschel CAWachter RM: The wisdom and justice of not paying for “preventable complications”. JAMA 299:219721992008

  • 39

    Rajamani KKennedy KFRuggiero NJRosenfield KSpertus JChaturvedi S: Outcomes of carotid endarterectomy in the elderly: report from the National Cardiovascular Data Registry. Stroke 44:117211742013

  • 40

    Rohde JMMyers AHVlahov D: Variation in risk for falls by clinical department: implications for prevention. Infect Control Hosp Epidemiol 11:5215241990

  • 41

    Rosenthal MB: Nonpayment for performance? Medicare's new reimbursement rule. N Engl J Med 357:157315752007

  • 42

    Setacci Fde Donato GChisci ESirignano PGalzerano GCastriota F: Economic impact of endarterectomy vs carotid artery stenting: a one year, single centre study. EuroIntervention 3:3403442007

  • 43

    Siracuse JJOdell DDGondek SPOdom SRKasper EMHauser CJ: Health care and socioeconomic impact of falls in the elderly. Am J Surg 203:3353382012

  • 44

    Stansbury JPJia HWilliams LSVogel WBDuncan PW: Ethnic disparities in stroke: epidemiology, acute care, and postacute outcomes. Stroke 36:3743862005

  • 45

    Sun BCEmond JACamargo CA Jr: Direct medical costs of syncope-related hospitalizations in the United States. Am J Cardiol 95:6686712005

  • 46

    Teufack SGCampbell PJabbour PMaltenfort MEvans JRatliff JK: Potential financial impact of restriction in “never event” and periprocedural hospital-acquired condition reimbursement at a tertiary neurosurgical center: a single-institution prospective study. Clinical article. J Neurosurg 112:2492562010

  • 47

    Titsworth WLHester JCorreia TReed RGuin PArchibald L: The effect of increased mobility on morbidity in the neurointensive care unit. Clinical article. J Neurosurg 116:137913882012

  • 48

    Tutuarima JAvan der Meulen JHde Haan RJvan Straten ALimburg M: Risk factors for falls of hospitalized stroke patients. Stroke 28:2973011997

  • 49

    Vogel TRDombrovskiy VYGraham AM: Carotid artery stenting in the nation: the influence of hospital and physician volume on outcomes. Vasc Endovascular Surg 44:89942010

  • 50

    Whitehouse JDFriedman NDKirkland KBRichardson WJSexton DJ: The impact of surgical-site infections following orthopedic surgery at a community hospital and a university hospital: adverse quality of life, excess length of stay, and extra cost. Infect Control Hosp Epidemiol 23:1831892002

  • 51

    Wiebers DOWhisnant JPHuston J IIIMeissner IBrown RD JrPiepgras DG: Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment. Lancet 362:1031102003

  • 52

    Wimmer NJYeh RWCutlip DEMauri L: Risk prediction for adverse events after carotid artery stenting in higher surgical risk patients. Stroke 43:321832242012

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