Incidence and predictors of 30-day readmission for patients discharged home after craniotomy for malignant supratentorial tumors in California (1995–2010)

Clinical article

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Object

Hospital readmission within 30 days of discharge is a major contributor to the high cost of health care in the US and is also a major indicator of patient care quality. The purpose of this study was to investigate the incidence, causes, and predictors of 30-day readmission following craniotomy for malignant supratentorial tumor resection.

Methods

The longitudinal California Office of Statewide Health Planning & Development inpatient-discharge administrative database is a data set that consists of 100% of all inpatient hospitalizations within the state of California and allows each patient to be followed throughout multiple inpatient hospital stays, across multiple institutions, and over multiple years (from 1995 to 2010). This database was used to identify patients who underwent a craniotomy for resection of primary malignant brain tumors. Causes for unplanned 30-day readmission were identified by principle ICD-9 diagnosis code and multivariate analysis was used to determine the independent effect of various patient factors on 30-day readmissions.

Results

A total of 18,506 patients received a craniotomy for the treatment of primary malignant brain tumors within the state of California between 1995 and 2010. Four hundred ten patients (2.2%) died during the index surgical admission, 13,586 patients (73.4%) were discharged home, and 4510 patients (24.4%) were transferred to another facility. Among patients discharged home, 1790 patients (13.2%) were readmitted at least once within 30 days of discharge, with 27% of readmissions occurring at a different hospital than the initial surgical institution. The most common reasons for readmission were new onset seizure and convulsive disorder (20.9%), surgical infection of the CNS (14.5%), and new onset of a motor deficit (12.8%). Medi-Cal beneficiaries were at increased odds for readmission relative to privately insured patients (OR 1.52, 95% CI 1.20–1.93). Patients with a history of prior myocardial infarction were at an increased risk of readmission (OR 1.64, 95% CI 1.06–2.54) as were patients who developed hydrocephalus (OR 1.58, 95% CI 1.20–2.07) or venous complications during index surgical admission (OR 3.88, 95% CI 1.84–8.18).

Conclusions

Using administrative data, this study demonstrates a baseline glioma surgery 30-day readmission rate of 13.2% in California for patients who are initially discharged home. This paper highlights the medical histories, perioperative complications, and patient demographic groups that are at an increased risk for readmission within 30 days of home discharge. An analysis of conditions present on readmission that were not present at the index surgical admission, such as infection and seizures, suggests that some readmissions may be preventable. Discharge planning strategies aimed at reducing readmission rates in neurosurgical practice should focus on patient groups at high risk for readmission and comprehensive discharge planning protocols should be implemented to specifically target the mitigation of potentially preventable conditions that are highly associated with readmission.

Abbreviations used in this paper:DVT = deep venous thrombosis; LOS = length of stay; OSHPD = Office of Statewide Health Planning & Development; PE = pulmonary embolism.

Object

Hospital readmission within 30 days of discharge is a major contributor to the high cost of health care in the US and is also a major indicator of patient care quality. The purpose of this study was to investigate the incidence, causes, and predictors of 30-day readmission following craniotomy for malignant supratentorial tumor resection.

Methods

The longitudinal California Office of Statewide Health Planning & Development inpatient-discharge administrative database is a data set that consists of 100% of all inpatient hospitalizations within the state of California and allows each patient to be followed throughout multiple inpatient hospital stays, across multiple institutions, and over multiple years (from 1995 to 2010). This database was used to identify patients who underwent a craniotomy for resection of primary malignant brain tumors. Causes for unplanned 30-day readmission were identified by principle ICD-9 diagnosis code and multivariate analysis was used to determine the independent effect of various patient factors on 30-day readmissions.

Results

A total of 18,506 patients received a craniotomy for the treatment of primary malignant brain tumors within the state of California between 1995 and 2010. Four hundred ten patients (2.2%) died during the index surgical admission, 13,586 patients (73.4%) were discharged home, and 4510 patients (24.4%) were transferred to another facility. Among patients discharged home, 1790 patients (13.2%) were readmitted at least once within 30 days of discharge, with 27% of readmissions occurring at a different hospital than the initial surgical institution. The most common reasons for readmission were new onset seizure and convulsive disorder (20.9%), surgical infection of the CNS (14.5%), and new onset of a motor deficit (12.8%). Medi-Cal beneficiaries were at increased odds for readmission relative to privately insured patients (OR 1.52, 95% CI 1.20–1.93). Patients with a history of prior myocardial infarction were at an increased risk of readmission (OR 1.64, 95% CI 1.06–2.54) as were patients who developed hydrocephalus (OR 1.58, 95% CI 1.20–2.07) or venous complications during index surgical admission (OR 3.88, 95% CI 1.84–8.18).

Conclusions

Using administrative data, this study demonstrates a baseline glioma surgery 30-day readmission rate of 13.2% in California for patients who are initially discharged home. This paper highlights the medical histories, perioperative complications, and patient demographic groups that are at an increased risk for readmission within 30 days of home discharge. An analysis of conditions present on readmission that were not present at the index surgical admission, such as infection and seizures, suggests that some readmissions may be preventable. Discharge planning strategies aimed at reducing readmission rates in neurosurgical practice should focus on patient groups at high risk for readmission and comprehensive discharge planning protocols should be implemented to specifically target the mitigation of potentially preventable conditions that are highly associated with readmission.

Abbreviations used in this paper:DVT = deep venous thrombosis; LOS = length of stay; OSHPD = Office of Statewide Health Planning & Development; PE = pulmonary embolism.

Hospital readmission within 30 days of discharge is a major contributor to the high cost of health care in the US. Medicare payments for unplanned 30-day readmission episodes were responsible for $17.4 billion or roughly 17% of the total Medicare hospital payments for 2004.10,14 As a result, 30-day readmissions have become an important metric for measuring the quality of patient care. The Patient Protection and Affordable Care Act of 2010 authorized Medicare to use financial penalties to incentivize hospitals to reduce 30-day readmissions. Institutions have begun implementing programs to reduce hospital readmissions in both medical and surgical specialties. Despite coordinated efforts from physicians, nurses, pharmacists, and lower-level care providers, many hospitals have struggled to understand and improve upon the factors that underlie high readmission rates.15

Few studies have examined the rehospitalization of patients after the neurosurgical care of brain malignancy. The purpose of this study was to determine the 30-day readmission rate for patients undergoing the resection of primary brain tumors and to identify which factors predispose certain patient groups to rehospitalization.

Methods

Data Source

The data source for this study was the California Office of Statewide Health Planning & Development (OSHPD) longitudinal inpatient-discharge administrative database for the years 1995 to 2010, obtained from the State of California OSHPD (http://www.oshpd.ca.gov/HID/Products/PatDischargeData/PublicDataSet/). The California inpatient discharge database is an administrative, longitudinal database that represents a 100% sample of all inpatient discharges from California licensed hospitals. Each patient within the database is given a unique, masked patient identifier so that each patient may be followed throughout multiple inpatient hospital stays, across multiple institutions, and over multiple years within the state of California during the study period.

Inclusion and Exclusion Criteria and Definition of End Points

An index admission for surgical treatment of a primary brain tumor was defined using a combination of ICD-9-CM diagnosis and procedure codes. Patients were included in this study if they were given both a diagnosis of supratentorial malignant brain tumor (191.0–191.5, 191.8, 198.9) while also receiving a procedure code for lobectomy (01.53), excision or destruction of tissue or lesion of brain (01.59), or open brain biopsy (01.14) during the same hospital stay.2,4 Patients who received a previous craniotomy for any diagnosis prior to their index craniotomy for supratentorial malignant brain tumor were excluded from this study. The primary end point examined in this study was an unplanned hospital readmission less than or equal to 30 days after inpatient discharge for surgical treatment of a primary brain tumor. Within the OSHPD longitudinal inpatient-discharge administrative database, unplanned hospital readmissions are defined as inpatient stays that are unscheduled at the hospital 24 hours prior to patient admission (http://www.oshpd.ca.gov/HID/Products/PatDischargeData/PublicDataSet/). Planned rehospitalizations were excluded from our analysis of readmission. Patients who were discharged to a location other than home after surgery or readmitted to the hospital from a location other than home were not included in our readmission analysis so as to eliminate the effect of inter- and/or intrahospital transfer on 30-day readmissions. However, all patients undergoing an index admission regardless of discharge location were included in our analysis of index surgical episode outcomes (Table 1).

TABLE 1:

Patient characteristics and surgical episode outcome among 18,506 patients undergoing craniotomy for primary malignant brain tumor in California (1995–2010)

VariableValue
mean age in yrs (median)51.6 (54.0)
race/ethnicity (%)n = 18,324
 non-Hispanic white13,546 (74.0)
 African American626 (3.4)
 Hispanic2772 (15.1)
 Asian931 (5.1)
 Native American/other449 (2.5)
sex (%)n = 18,506
 male10,684 (57.7)
 female7822 (42.3)
medical history prior to surgical episode (%)n = 18,506
 hypertension5988 (32.4)
 tobacco use disorder3513 (19.0)
 seizure & convulsive disorder2449 (13.2)
 cerebrovascular disease2160 (11.7)
 chronic pulmonary disease1771 (9.6)
 diabetes mellitus1672 (9.0)
 lipid disorder1118 (8.0)
 obesity & overweight1061 (5.7)
 speech & language disorder893 (4.8)
 motor deficit857 (4.6)
 moderate or severe liver disease779 (4.2)
 myocardial infarction598 (3.2)
 congestive heart failure474 (2.6)
 cerebral edema & compression of the brain432 (2.3)
 hydrocephalus375 (2.0)
 gait & coordination dysfunction372 (2.0)
 peripheral vascular disease220 (1.2)
 vision & optic disorder186 (1.0)
 malaise & fatigue171 (0.9)
 peptic ulcer disease110 (0.6)
 renal disease61 (0.3)
 mild liver disease55 (0.3)
mean Charlson comorbidity index (median)2.7 (2.0)
expected primary payer (%)n = 15,996
 Medicare3903 (24.4)
 Medi-Cal1562 (9.8)
 private insurance10,132 (63.3)
 uninsured369 (2.3)
hospital teaching status (%)n = 18,506
 neurosurgical residency program5881 (31.8)
 no neurosurgical residency program12,625 (68.2)
hospital pediatric status (%)n = 18,506
 nonpediatric hospital17,918 (96.8)
 pediatric hospital588 (3.2)
mean LOS on surgical admission in days (median)8.2 (6.0)
mean charges for surgical admission in $ (median)95,906 (72,029)
surgical admission mortality (%)410 (2.2)
patients discharged home (%)13,586 (73.4)

Patient Characteristics

Patient age, self-declared race/ethnicity, sex, expected primary payer (Medicare, Medi-Cal, private insurance, or uninsured), admission source (home, residential care facility, jail/prison, and others), type of admission (elective or nonelective), discharge disposition (home, died, residential care facility, jail/prison, and others), and calendar year were coded in the California inpatient discharge database. Due to a small sample size of certain ethnic groups within the database, patients of Native American race/ethnicity were combined with “other” race/ethnicity to avoid unstable coefficients in the multivariate model. To assess the effect of general medical comorbidities, the Charlson comorbidity index was calculated for each patient using the method described by Romano and colleagues.17

The medical history for each patient was defined using ICD-9-CM diagnosis and procedure codes. A patient was determined to have a specific condition if he or she carried a specified ICD-9-CM code on the index surgical admission or during any inpatient episode prior to the index surgical admission episode from 1995 to 2010 (Table 2).

TABLE 2:

Diagnosis and procedure codes (ICD-9-CM) used to define patient comorbidities and complications

CategoryCode*
hypertension401.0, 401.1, 401.9
tobacco use disorder305.1, V15.82
seizure & convulsive disorder345.10, 345.11, 345.2, 345.3, 345.40, 345.41, 345.50, 345.51, 345.70, 345.71, 345.8, 345.9, 780.39
cerebrovascular disease430, 431, 432.0, 432.1, 432.9, 433.00, 433.01, 433.10, 433.11, 433.20, 433.21, 433.30, 433.31, 433.80, 433.81, 433.90, 433.91, 434.00, 434.01, 434.10, 434.11, 434.90, 434.91, 435.0, 435.1, 435.2, 435.3, 435.8, 435.9, 436, 437.0, 437.1, 437.2, 437.3, 437.7, 437.8, 437.9, 438.0, 438.89, 438.9
chronic pulmonary disease
lipid disorder272.0, 272.1, 272.2
diabetes mellitus
obesity & overweight278.00, 278.01, 278.02, V85.36, V85.37, V85.38, V85.39, V85.4
speech & language disorder784.3, 784.41, 784.42, 784.5, 784.51, 784.52, 784.59, 784.60, 784.61, 784.69, 937.5, 937.2, 438.10, 438.11, 438.12, 438.19
moderate or severe liver disease
motor deficit342.0, 342.1, 342.8, 342.9, 344.0, 344.1, 344.3, 344.4, 344.6, 344.81, 344.89, 344.9, 781.4, 781.94, 799.3, 438.20, 438.30, 438.40, 438.50, 438.82, 438.83
myocardial infarction
cerebral edema & compression of brain348.4, 348.5
hydrocephalus331.3, 331.4, 02.2, 02.34
congestive heart failure
gait & coordination dysfunction781.2, 438.81, 438.84, 781.3, 93.22
peripheral vascular disease
vision & optic disorder368.46, 368.47, 377.49, 377.75, 378.81, 379.50, 379.56, 438.7
malaise & fatigue780.7, 780.71, 780.79
peptic ulcer disease
renal disease
mild liver disease
nonspecific CNS surgical complication996.2, 996.75, 997.00, 997.01, 997.09
general infection039.1, 039.8, 041.00, 041.01, 041.02, 041.03, 041.04, 041.09, 041.10, 041.11, 041.12, 041.19, 041.2, 041.3, 041.4, 041.5, 041.6, 041.7, 041.81, 041.82, 041.83, 041.84, 041.85, 041.86, 041.89, 041.9, 999.31, 999.39
sepsis & septicemia003.1, 038.0, 038.10, 038.11, 038.12, 038.19, 038.2, 038.3, 038.40, 038.41, 038.42, 038.43, 038.44, 038.49, 038.8, 038.9, 790.7, 995.91, 995.92
urinary tract infection590.10, 590.11, 590.80, 595.0, 595.3, 595.4, 599.0, 997.5
DVT, PE, & venous complications415.10, 415.11, 415.19, 416.2, 451.0, 451.11, 451.19, 451.2, 451.82, 451.83, 451.84, 451.89, 451.9, 453.1, 453.2, 453.40, 453.41, 453.42, 453.51, 453.52, 453.6, 453.80, 453.81, 453.82, 453.9
surgical infection of the CNS324.0, 326, 998.51, 998.59, 01.31, 01.25, 01.24, 01.59, 03.4, 036.0, 047.9, 049.9, 320.0, 320.1, 320.2, 320.3, 320.7, 320.81, 320.82, 320.89, 320.9, 321.0, 321.1, 321.2, 322.0, 322.1, 322.2, 322.9, 323.01, 323.02, 323.9, 324.1, 324.9

All codes are ICD-9-CM codes unless otherwise indicated.

ICD-9 code categorical classification from Romano and colleagues.17

ICD-9 procedure code.

Hospital Characteristics

Hospital identifier, county location of each hospital, and total charges for each inpatient episode were coded within the California inpatient discharge database. The teaching status of each hospital was determined according to whether the hospital was affiliated with a neurosurgical residency training program (http://www.societyns.org/match_information.html). Pediatric hospitals were defined as those hospitals solely dedicated to the treatment of pediatric patients.

Reasons for 30-Day Readmission

To identify the reasons for readmission, a cross-sectional analysis of ICD-9-CM principal diagnosis codes present on readmission was conducted for all patients readmitted within 30 days of discharge. Each ICD-9-CM principal diagnosis code with more than 1% prevalence in readmitted patients was placed into 1 of 15 disease categories (Table 2). The ICD-9-CM codes comprising each of the 15 disease categories were used to tabulate the number of readmitted patients who belonged in each disease category. Because of the longitudinal nature of the data used in this study, we were able to determine which diagnoses were present on both discharge and readmission for each patient. We established the reason for each patient's readmission as a diagnosis that was not present on discharge but was present as a primary diagnosis on readmission.

Statistical Analysis

Bivariate analysis of mean patient age, surgical length of stay (LOS), Charlson score, and readmission status was performed using the Welch t-test. Bivariate analysis of patient factors and readmission status was performed using the Pearson chi-square test. Multivariate analyses were performed using logistic regression models to determine the odds of hospital readmission within 30 days of surgical discharge while adjusting for age, demographics, surgical admission LOS, race/ethnicity, sex, expected primary payer, hospital teaching status, California county, calendar year of surgery, medical history prior to surgical admission, and complications arising during surgical admission.

Expected 30-day readmission rates were calculated for each patient using a multivariate logistic regression model for 30-day readmission. The expected likelihoods of 30-day readmission were then aggregated by hospital, and the ratio of observed to expected readmissions for each hospital was calculated. Statistical analysis was performed using commercially available software (STATA/AMP 10, Stata Corp. LP). All tests were 2-sided, and p values < 0.05 were considered statistically significant.

Results

There were 18,506 inpatient admissions for resection of primary brain tumors in California between 1995 and 2010. Four hundred ten patients (2.2%) died during the index surgical admission, 13,586 patients (73.4%) were discharged home, and 4510 patients (24.4%) were transferred to another facility (Table 1).

Among the home discharge patients, 1790 (13.2%) had at least 1 unplanned readmission within 30 days of discharge, and 483 (27%) of these readmitted patients were readmitted to a different hospital than the hospital from which they were discharged following the index surgical episode (Table 3). There were also 377 patients excluded from our analysis who had a planned readmission within 30 days of discharge. A majority of these planned readmissions were for brain excision (28.6%), chemotherapy (22.0%), and radiotherapy (9.3%).

TABLE 3:

Thirty-day outcomes among 13,586 patients discharged home following craniotomy for primary malignant brain tumor

VariableValue
30-day mortality (%)
 inpatient81 (0.6)
 outpatient235 (1.7)
 total316 (2.3)
30-day readmission (%)1790 (13.2)
 patients w/ 1 readmission episode1577
 patients w/ 2 readmission episodes196
 patients w/ 3 readmission episodes15
 patients w/ 4 readmission episodes2
 total 30-day readmission episodes2022
 patients w/ readmission occurring at a different hospital from surgical site (%)483 (27.0)
 mean days to first 30-day readmission (median)12.3 (11.0)
 mean LOS during 30-day readmission episode in days (median)5.9 (4.0)
 mean charges ($) per 30-day readmission episode (median)44,249 (20,296)
reason for readmission by diagnosis (%)
 seizure & convulsive disorder20.9
 surgical infection of the CNS14.5
 motor deficit12.8
 DVT, PE, & venous complications11.3
 general infection11.1
 cerebral edema & compression of the brain10.1
 hydrocephalus9.6
 speech & language disorder8.5
 urinary tract infection8.5
 cerebrovascular disorder6.3
 nonspecific CNS surgical complication6.3
 sepsis & septicemia4.9
 malaise & fatigue2.6
 gait & coordination dysfunction2.0
 vision & optic disorder1.3

Index Admission Episodes

The median age of patients undergoing tumor resection was 54 years. The majority of patients were privately insured non-Hispanic white patients who received neurosurgical care at nonacademic medical centers (Table 1). The median LOS for index surgical admission was 6 days, and the median total charges per index surgical admission were $72,029 (Table 1). The most prevalent diseases in patients' medical histories prior to surgery were hypertension (32.4%), tobacco use disorder (19.0%), and seizure and convulsive disorder (13.2%).

Thirty-Day Outcomes and 30-Day Readmissions

Three hundred sixteen patients (2.3%) died within 30 days of home discharge (Table 3). A total of 1790 patients were rehospitalized within 30 days of discharge, and the median time to first readmission was 11 days. Although a majority of readmitted patients had only 1 readmission episode, 11.8% of readmitted patients had multiple readmissions and there were a total of 2022 30-day readmission episodes. The median LOS during a readmission was 4 days, and the median hospital charges per 30-day readmission episode were $20,296. The diagnoses that were most often established as reasons of readmission were seizure and convulsive disorder (20.9%), surgical infection of the CNS (14.5%), and motor deficit (12.8%).

Differences Between Readmitted and Nonreadmitted Patients

Patients readmitted within 30 days had a 2-day longer median LOS on their index surgical episode than nonreadmitted patients. There were significant racial/ethnic differences noted between the 2 groups. Readmitted patients were more frequently African American or Hispanic than nonreadmitted patients. Patients with at least one 30-day hospital readmission were more often Medicare and Medi-Cal beneficiaries and less often privately insured. Other observed differences between readmitted and nonreadmitted patients are noted in Table 4.

TABLE 4:

Comparative analysis of nonreadmitted patients and patients with at least one 30-day readmission after home discharge

Variable30-Day Readmissionsp Value*
None (%)At Least 1 (%)
mean age in yrs (median)48.4 (50.0)47.2 (51.0)0.031
mean LOS on surgical admission in days (median)6.2 (4.0)9.2 (6.0)<0.001
mean Charlson comorbidity index (median)2.4 (2.0)2.6 (2.0)<0.001
race/ethnicity<0.001
 non-Hispanic white75.069.3
 African American2.93.8
 Hispanic14.719.5
 Asian5.04.8
 Native American/other2.62.6
sex0.032
 male58.561.2
 female41.538.8
medical history
 hypertension26.530.10.001
 tobacco use disorder18.217.30.345
 cerebrovascular disease8.59.30.257
 chronic pulmonary disease8.19.50.037
 lipid disorder7.79.80.003
 diabetes mellitus7.09.10.001
 obesity & overweight4.96.20.022
 moderate or severe liver disease3.24.30.016
 myocardial infarction2.33.7<0.001
 congestive heart failure1.42.8<0.001
 peripheral vascular disease0.81.10.287
 peptic ulcer disease0.40.60.236
 renal disease0.20.30.517
 mild liver disease0.20.40.038
complications during surgical admission
 seizure & convulsive disorder21.619.40.046
 speech & language disorder8.99.70.275
 motor deficit8.09.60.029
 cerebral edema & compression of the brain3.64.00.362
 hydrocephalus5.211.6<0.001
 nonspecific CNS surgical complication1.02.5<0.001
 gait & coordination dysfunction2.02.90.019
 vision & optic disorder2.63.10.246
 malaise & fatigue0.50.50.760
 general infection1.53.7<0.001
 sepsis & septicemia0.31.3<0.001
 urinary tract infection2.53.9<0.001
 DVT, PE, & venous complications0.41.2<0.001
 cerebrovascular disorder3.23.30.827
 surgical infection of the CNS0.82.4<0.001
expected primary payer<0.001
 Medicare17.920.7
 Medi-Cal9.216.3
 private insurance70.261.0
 uninsured2.72.0
hospital teaching status0.443
 neurosurgical residency program34.733.8
 no neurosurgical residency program65.366.2
hospital pediatric status
 nonpediatric hospital87.673.5<0.001
 pediatric hospital12.426.5

Values in bold are statistically significant.

The results of the multivariate analyses are presented in Table 5. In the multivariate regression model, longer LOSs were associated with greater odds of subsequent readmission. Medi-Cal beneficiaries were at an increased likelihood of readmission relative to the privately insured (OR 1.52, 95% CI 1.20–1.93) as were patients with histories of prior myocardial infarctions (OR 1.64, 95% CI 1.06–2.54).

TABLE 5:

Odds of 30-day readmission among patients discharged home following craniotomy for primary brain tumor in California (1995–2010)*

VariableAdjusted OR (95% CI)p Value
LOS on surgical admission (days)
 ≤11.12 (0.71–1.78)0.620
 20.82 (0.60–1.11)0.203
 31.10 (0.85–1.41)0.480
 41.00 (reference)
 51.16 (0.86–1.55)0.329
 61.25 (0.92–1.69)0.149
 7–131.64 (1.29–2.07)<0.001
 14–202.17 (1.53–3.09)<0.001
race/ethnicity
 non-Hispanic white1.00 (reference)
 African American1.26 (0.85–1.87)0.252
 Hispanic0.86 (0.69–1.06)0.150
 Asian0.85 (0.61–1.18)0.328
 Native American/other1.00 (0.67–1.53)0.987
sex
 male1 (reference)
 female0.93 (0.80–1.07)0.282
medical history
 hypertension0.97 (0.81–1.17)0.777
 tobacco use disorder0.89 (0.73–1.09)0.258
 cerebrovascular disease1.73 (0.43–7.01)0.438
 chronic pulmonary disease0.99 (0.76–1.29)0.949
 lipid disorder1.17 (0.90–1.52)0.233
 diabetes mellitus1.21 (0.92–1.59)0.175
 obesity & overweight1.00 (0.71–1.41)0.989
 moderate or severe liver disease1.25 (0.84–1.86)0.275
 myocardial infarction1.64 (1.06–2.54)0.026
 congestive heart failure1.06 (0.57–1.97)0.863
 peptic ulcer disease1.03 (0.29–3.74)0.956
 mild liver disease2.89 (0.71–11.75)0.137
complications during surgical admission
 seizure & convulsive disorder0.90 (0.76–1.08)0.255
 speech & language disorder1.06 (0.84–1.34)0.608
 motor deficit0.93 (0.73–1.21)0.603
 cerebral edema & compression of the brain1.01 (0.72–1.43)0.940
 hydrocephalus1.58 (1.20–2.07)0.001
 nonspecific CNS surgical complication1.03 (0.52–2.03)0.931
 gait & coordination dysfunction0.98 (0.62–1.54)0.913
 vision & optic disorder1.22 (0.84–1.79)0.297
 malaise & fatigue0.84 (0.29–2.43)0.752
 general infection1.82 (0.91–3.64)0.089
 sepsis & septicemia0.80 (0.24–2.70)0.720
 urinary tract infection0.52 (0.29–0.94)0.032
 DVT, PE, & venous complications3.88 (1.84–8.18)<0.001
 cerebrovascular disorder0.41 (0.10–1.71)0.221
 surgical infection of the CNS0.63 (0.26–1.55)0.316
expected primary payer
 Medicare1.00 (0.72–1.39)0.992
 Medi-Cal1.52 (1.20–1.93)0.001
 private insurance1.00 (reference)
 uninsured0.69 (0.44–1.11)0.125
hospital teaching status
 no neurosurgical residency program1.00 (reference)
 neurosurgical residency program1.07 (0.89–1.29)0.448
hospital pediatric status
 nonpediatric hospital1.00 (reference)
 pediatric hospital1.64 (1.00–2.70)0.051

Other covariates not included in Table 5 are patient age, California county, and calendar year.

Values in bold are statistically significant.

The development of hydrocephalus during the index surgical admission was associated with a higher likelihood of 30-day readmission (OR 1.58, 95% CI 1.20–2.07) as was the development of a deep venous thrombosis (DVT), pulmonary embolism (PE), or other venous complication (OR 3.88, 95% CI 1.84–8.18). Urinary tract infections during the index surgical episode were associated with lower odds of 30-day readmission (OR 0.52, 95% CI 0.29–0.94).

Readmission Rates by Hospital

Forty hospitals (20.7%) had higher than expected 30-day readmission rates after craniotomy for resection of primary malignant brain tumors during the study period (Fig. 1). Among patients discharged home during the study period, 501 craniotomies were performed at a pediatric hospital within California and 133 (26.6%) of these patients had at least one 30-day readmission, while 13,085 craniotomies were performed at a nonpediatric hospital and 1657 (12.7%) of these patients had at least 1 readmission. Additionally, among home-discharged patients, 8885 craniotomies were performed at nonteaching hospitals with a readmission rate of 13.3%, while 4701 craniotomies were performed at teaching hospitals with a readmission rate of 12.9%.

Fig. 1.
Fig. 1.

Observed/expected 30-day readmission rates by hospital. The triangles represent each California (CA) hospital's observed readmission rate divided by the expected readmission rate for that hospital as calculated from our multivariate model of readmissions. A hospital that has an observed rate equal to the predicted/expected rate has a triangle at 1 (red line). The error bars represent the 95% CI around each triangle. Forty (20.7%) of 193 hospitals had observed/expected 30-day readmission rates that were significantly greater than 1 (p ≤ 0.05).

Discussion

Neurosurgical Cost Burden of 30-Day Readmission

In this study it was found that 13.2% of all patients discharged home after craniotomy for tumor resection were rehospitalized within 30 days of discharge. Each readmission represented an additional $20,296 in median hospital charges on top of the $72,029 in charges for the index neurosurgical admission. While it is expected that a small fraction of all operative patients will be rehospitalized due to the new onset of unpreventable medical conditions following any surgical procedure, reducing the number of preventable unplanned 30-day readmissions could create significant cost savings for health care payers. It was determined that a 40% reduction in the number of 30-day readmission episodes for brain tumor patients undergoing craniotomy within the state of California would eliminate 606 hospitalizations and create more than $12 million in cost savings.

Lowering readmission rates could also create cost savings by preventing redundant medical charges. In this study, it was observed that 27.0% of readmitted patients were rehospitalized at a different location than the site that provided initial surgical care. Because providers within different hospital systems typically operate in isolation from one another, it is reasonable to expect that the care of these readmitted patients included significant unnecessary health care costs in the form of redundant imaging, workup, and care.

In spite of the significant cost and morbidity burden that 30-day readmission places on patients, payers, and providers, it is important to note that most surgical centers in California have observed readmission rates similar to those expected by multivariate modeling. In the present study, it was shown that 20.7% of hospitals had an observed to expected ratio of 30-day readmission rates that was significantly greater than 1. This implies that almost 80% of the hospitals in California performing craniotomy for tumor resection operated at an expected level of care quality during the study period, and only one-fifth of hospitals would be financially penalized in the future according to Centers for Medicare & Medicaid Studies guidelines for 30-day readmission.3 While the scope of this study focused on patient-level factors, it is believed that future investigation into specific hospital-level factors associated with higher than expected readmission rates is warranted.

Predictors of Readmission

Many studies in the surgical literature regarding 30-day readmissions establish postoperative complications as a common risk factor for readmission across multiple specialties.5,6,8,11,16 The present study similarly showed that certain complications arising during the index surgical admission increase the odds of readmission for patients after the resection of brain malignancy. Specifically, patients who developed hydrocephalus during the surgical admission had 58% greater odds of readmission than those who did not develop hydrocephalus. Furthermore, patients who experienced a DVT, PE, or venous complication during surgical admission were nearly 4 times more likely to be readmitted within 30 days of discharge. It is thus proposed that more astute monitoring and management of both hydrocephalus and venous complications in the perioperative period may reduce the frequency of 30-day patient rehospitalizations.

Previous studies have found that longer LOSs during surgical hospitalization are associated with an increased risk of readmission, with the assertion being that LOS serves as a surrogate for postoperative complications.11,18 The results of the present study showed a similar trend of increasing odds of readmission for LOSs greater than 1 week. We propose that longer LOSs are inherently associated with a greater level of postoperative complications and greater risk of acquiring nosocomial infections, which may increase the risk of readmission.

Certain components of a patient's medical history were also shown to be associated with readmission. While other studies have shown an association between comorbidities and readmission, these studies have focused solely on comorbidities present at admission as a predictor of readmission. The present study, in contrast, used a longitudinal database and was able to characterize the presence of pathologies throughout the portion of a patient's medical history occurring during the study period rather than being limited to those pathologies present on initial surgical admission. Patients who had any history of myocardial infarction were observed to have a 64% greater likelihood of 30-day readmission than those patients without a history of myocardial infarction. This is the first study (to our knowledge) that examined patients' medical histories rather than comorbidities present on admission, and we propose that additional investigation be undertaken to better understand the relationship between medical history and readmission.

Lastly, it was found that Medi-Cal beneficiaries had increased odds for readmission relative to privately insured patients (OR = 1.52). Initially, it was believed that perhaps Medi-Cal patients were at increased odds of readmission because of premature discharge after surgery. However, on further analysis it was shown that the median LOS during surgical admission was 4 days for privately insured patients and 8 days for Medi-Cal beneficiaries. As a result, it is proposed that certain factors within the population of Medi-Cal patients predispose them to longer LOSs and greater odds of rehospitalization. We believe that a randomized clinical trial is necessary to better understand the factors that increase the likelihood of readmission among Medi-Cal patients. The results of such future studies could prove to be essential to our understanding of 30-day readmissions in neurosurgery. Additionally, it is possible that there may be differences in both the surgical technologies used and the index surgical procedures performed on Medi-Cal beneficiaries versus privately insured patients that increase the likelihood of readmission in the Medi-Cal group. Early application of technologies such as intraoperative MRI, image-guided surgery platforms, and cranial tractography may create differences in neurooncological care based on patient insurance status. The current population-based study, however, is unable to resolve the differences that may exist in the types of procedures performed between insurance groups. We hope that future investigations will better detail the disparities in surgical care provided to Medi-Cal beneficiaries compared with their privately insured counterparts.

Clinical Applications

A better understanding of the factors that influence the likelihood of readmission for neurosurgical patients will allow providers to institute programs for reducing 30-day readmissions. Previous studies have begun to define the types of systematized action that hospitals may take to reduce readmission rates, and we believe that the results of the present study highlight potential interventions that require further investigation.

Because the two most common diagnoses present on readmission were seizure and CNS infection, we suggest that better and more aggressive seizure prophylaxis and antibiotic regimens could potentially reduce readmission rates after the resection of brain malignancy. Furthermore, there is evidence to suggest that better patient education and medication counseling as part of a discharge bundle approach decreases unplanned acute care utilization at 30 days.9,12 A discharge bundle for neurosurgical patients after tumor resection could include education about the symptoms of surgical site infection and seizure disorder that warrant a return to the clinic. This intervention would allow early identification and treatment of developing postoperative morbidity in an outpatient setting rather than through inpatient readmission. A neurosurgical discharge bundle could also include patient counseling from a clinical pharmacist about correct dosing schedules and durations of pharmacotherapies. While the time and resources required for the implementation of this discharge bundle approach may not be feasible for all patients, the patient groups at risk for readmission highlighted in this study may benefit from this intervention.

Postdischarge interventions directed at these high-risk patient groups could also prove to be of benefit in reducing 30-day readmissions.7 The Agency for Healthcare Research and Quality has established timely follow-up appointment scheduling as an important component of the discharge workflow that may reduce readmission.1 The time course for scheduling follow-up appointments in current neurosurgical practice varies between institutions; however, the results of this study establish 11 days as the median time to 30-day readmission. Implicit in this finding is that half of all readmissions will occur after 11 days postdischarge, and we believe that this is an important time point for surgeons to identify patients with developing pathologies that have the potential to cause rehospitalization.

Strengths and Limitations

This is the first investigation to analyze 30-day readmissions in neurosurgical practice, and it is important to address both the strengths and the limitations in the present study. First, analyses that use large administrative databases rely on the use of ICD-9 diagnosis codes to identify patients and their outcomes. These ICD-9 diagnosis codes are used for hospital billing, and lack clinical data such as histological grades, radiological diagnoses, and physiological parameters. As a consequence, the current study was not able to investigate the epidemiology of readmission for specific types of intracranial malignancy. Nevertheless, we believe that this investigation represents an important step toward a better macro understanding of the readmission process following craniotomy for intracranial malignancy. We expect that future studies will be more granular (that is, specifically tailored to each unique tumor classification) by drawing upon the conclusions of this report to better elucidate the differences in readmission between distinct classifications of brain tumors.

Second, ICD-9 diagnosis coding is often performed by lower level clinical professionals rather than by experienced clinicians and surgeons. As such, inaccuracies in diagnostic and procedural coding may be introduced to the data set. While the use of ICD-9 coding may be associated with potential inaccuracies, it is unlikely that these inaccuracies would be biased or systematic in a manner that would affect our conclusions. Instead, the use of administrative data in this study increased the statistical power of our analysis by allowing for a more robust sample size and a reduction in Type II errors.

Third, unlike a prospective study, the nature and processes related to readmission are inferred. We took several steps to clarify the types of patients being studied in this report. First, we focused on patients who were discharged home. While there are likely preventable readmissions in patients who are discharged to rehabilitation or nursing facilities, we wished to focus on patients who had achieved the best initial outcomes following craniotomy and were discharged home. We also sought to avoid potentially confounding effects of patients who were transferred from one facility to another facility for a higher level of care in this report. We plan to report on patterns of transfer of care in a future report. Second, we chose to analyze readmission episodes that were unplanned. In some patients, a practice pattern of biopsy followed by readmission for definitive craniotomy may be performed. As a result, we excluded all planned readmissions from our analysis to avoid characterizing this practice pattern as a preventable 30-day readmission. Our exclusion of planned readmissions is also in accordance with the Centers for Medicare and Medicaid Services recommendations for defining preventable readmission.3 Lastly, we chose to exclude patients who received a previous craniotomy from our analysis to ensure that we captured surgical episodes for primary brain tumors.

We believe that an additional advantage of the longitudinal California inpatient discharge database used in this study was the ability to identify readmissions to different hospitals. Most investigations looking at 30-day readmission after surgery rely on same-hospital readmission data found in single institutional electronic medical records or databases such as the National Surgical Quality Improvement Program. However, Nasir and colleagues have shown that same-hospital readmission rates may not be effective surrogates for true readmission rates.13 The use of a longitudinal data set that follows each patient across multiple hospitals and across multiple years allowed us to avoid the underestimation of true readmission rates noted in other studies.

Conclusions

In this report, we determined the current 30-day readmission rate in patients undergoing brain tumor surgery who were discharged home in California. Certain medical conditions that are highly prevalent on readmission such as seizure may highlight the need for targeted interventions to improve patient education and therapy surrounding seizure prevention. In addition, this work lays the foundational design for targeted prospective studies of readmission and targeted interventions to reduce 30-day readmission. We also demonstrated the potential cost savings associated with reduction in readmission rates following brain tumor surgery.

Disclosure

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: Marcus, Chen, Chang. Acquisition of data: Chang. Analysis and interpretation of data: all authors. Drafting the article: Marcus. 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: Carter. Statistical analysis: Carter, Marcus, McCutcheon, Noorbakhsh, Parina, Gonda, Chang. Administrative/technical/material support: Noorbakhsh, Chang. Study supervision: Carter, Gonda, Chen, Chang.

This article contains some figures that are displayed in color online but in black-and-white in the print edition.

References

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    Agency for Healthcare Research Quality: Re-engineered discharge project dramatically reduces return trips to the hospital Rockville, MDAgency for Healthcare Research and Quality2011. (http://www.ahrq.gov/research/mar11/0311RA1.htm) [Accessed January 21 2014]

    • Search Google Scholar
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  • 2

    Barker FG IICurry WT JrCarter BS: Surgery for primary supratentorial brain tumors in the United States, 1988 to 2000: the effect of provider caseload and centralization of care. Neuro Oncol 7:49632005

    • Search Google Scholar
    • Export Citation
  • 3

    Centers for Medicare and Medicaid Services: Readmissions Reduction Program BaltimoreCenters for Medicare and Medicaid Services2013. (http://cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html) [Accessed January 22 2014]

    • Search Google Scholar
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  • 4

    Curry WT JrCarter BSBarker FG II: Racial, ethnic, and socioeconomic disparities in patient outcomes after craniotomy for tumor in adult patients in the United States, 1988–2004. Neurosurgery 66:4274382010

    • Search Google Scholar
    • Export Citation
  • 5

    Hannan ELRacz MJWalford GRyan TJIsom OWBennett E: Predictors of readmission for complications of coronary artery bypass graft surgery. JAMA 290:7737802003

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    • Export Citation
  • 6

    Hannan ELZhong YLahey SJCulliford ATGold JPSmith CR: 30-day readmissions after coronary artery bypass graft surgery in New York state. JACC Cardiovasc Interv 4:5695762011

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    • Export Citation
  • 7

    Hansen LOYoung RSHinami KLeung AWilliams MV: Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med 155:5205282011

    • Search Google Scholar
    • Export Citation
  • 8

    Hendren SMorris AMZhang WDimick J: Early discharge and hospital readmission after colectomy for cancer. Dis Colon Rectum 54:136213672011

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    • Export Citation
  • 9

    Jack BWChetty VKAnthony DGreenwald JLSanchez GMJohnson AE: A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 150:1781872009

    • Search Google Scholar
    • Export Citation
  • 10

    Jencks SFWilliams MVColeman EA: Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 360:141814282009

    • Search Google Scholar
    • Export Citation
  • 11

    Kassin MTOwen RMPerez SDLeeds ICox JCSchnier K: Risk factors for 30-day hospital readmission among general surgery patients. J Am Coll Surg 215:3223302012

    • Search Google Scholar
    • Export Citation
  • 12

    Koehler BERichter KMYoungblood LCohen BAPrengler IDCheng D: Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med 4:2112182009

    • Search Google Scholar
    • Export Citation
  • 13

    Nasir KLin ZBueno HNormand SLDrye EEKeenan PS: Is same-hospital readmission rate a good surrogate for all-hospital readmission rate?. Med Care 48:4774812010

    • Search Google Scholar
    • Export Citation
  • 14

    Patient Protection Affordable Care Act of 2010 Pub Law 111–148 124 Stat. 127 Sec. 6301 (March 23 2010) (http://www.gpo.gov/fdsys/pkg/PLAW-111publ148/pdf/PLAW-111publ148.pdf) [Accessed January 21 2014]

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    Rau J: Medicare to penalize 2,217 hospitals for excess readmissions. Kaiser Health News August132012. (http://www.kaiserhealthnews.org/stories/2012/august/13/medicare-hospitalsreadmissions-penalties.aspx) [Accessed January 14 2014]

    • Search Google Scholar
    • Export Citation
  • 16

    Reddy DMTownsend CM JrKuo YFFreeman JLGoodwin JSRiall TS: Readmission after pancreatectomy for pancreatic cancer in Medicare patients. J Gastrointest Surg 13:196319752009

    • Search Google Scholar
    • Export Citation
  • 17

    Romano PSRoos LLJollis JG: Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol 46:107510901993

    • Search Google Scholar
    • Export Citation
  • 18

    Schneider EBHyder OBrooke BSEfron JCameron JLEdil BH: Patient readmission and mortality after colorectal surgery for colon cancer: impact of length of stay relative to other clinical factors. J Am Coll Surg 214:3903992012

    • Search Google Scholar
    • Export Citation

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Article Information

Contributor Notes

Address correspondence to: Bob S. Carter, M.D., Ph.D., UC San Diego Division of Neurosurgery, 200 W. Arbor Dr., San Diego, CA 92103-8893. email: bobcarter@gmail.com.Please include this information when citing this paper: published online March 7, 2014; DOI: 10.3171/2014.1.JNS131264.
Headings
Figures
  • View in gallery

    Observed/expected 30-day readmission rates by hospital. The triangles represent each California (CA) hospital's observed readmission rate divided by the expected readmission rate for that hospital as calculated from our multivariate model of readmissions. A hospital that has an observed rate equal to the predicted/expected rate has a triangle at 1 (red line). The error bars represent the 95% CI around each triangle. Forty (20.7%) of 193 hospitals had observed/expected 30-day readmission rates that were significantly greater than 1 (p ≤ 0.05).

References
  • 1

    Agency for Healthcare Research Quality: Re-engineered discharge project dramatically reduces return trips to the hospital Rockville, MDAgency for Healthcare Research and Quality2011. (http://www.ahrq.gov/research/mar11/0311RA1.htm) [Accessed January 21 2014]

    • Search Google Scholar
    • Export Citation
  • 2

    Barker FG IICurry WT JrCarter BS: Surgery for primary supratentorial brain tumors in the United States, 1988 to 2000: the effect of provider caseload and centralization of care. Neuro Oncol 7:49632005

    • Search Google Scholar
    • Export Citation
  • 3

    Centers for Medicare and Medicaid Services: Readmissions Reduction Program BaltimoreCenters for Medicare and Medicaid Services2013. (http://cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html) [Accessed January 22 2014]

    • Search Google Scholar
    • Export Citation
  • 4

    Curry WT JrCarter BSBarker FG II: Racial, ethnic, and socioeconomic disparities in patient outcomes after craniotomy for tumor in adult patients in the United States, 1988–2004. Neurosurgery 66:4274382010

    • Search Google Scholar
    • Export Citation
  • 5

    Hannan ELRacz MJWalford GRyan TJIsom OWBennett E: Predictors of readmission for complications of coronary artery bypass graft surgery. JAMA 290:7737802003

    • Search Google Scholar
    • Export Citation
  • 6

    Hannan ELZhong YLahey SJCulliford ATGold JPSmith CR: 30-day readmissions after coronary artery bypass graft surgery in New York state. JACC Cardiovasc Interv 4:5695762011

    • Search Google Scholar
    • Export Citation
  • 7

    Hansen LOYoung RSHinami KLeung AWilliams MV: Interventions to reduce 30-day rehospitalization: a systematic review. Ann Intern Med 155:5205282011

    • Search Google Scholar
    • Export Citation
  • 8

    Hendren SMorris AMZhang WDimick J: Early discharge and hospital readmission after colectomy for cancer. Dis Colon Rectum 54:136213672011

    • Search Google Scholar
    • Export Citation
  • 9

    Jack BWChetty VKAnthony DGreenwald JLSanchez GMJohnson AE: A reengineered hospital discharge program to decrease rehospitalization: a randomized trial. Ann Intern Med 150:1781872009

    • Search Google Scholar
    • Export Citation
  • 10

    Jencks SFWilliams MVColeman EA: Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 360:141814282009

    • Search Google Scholar
    • Export Citation
  • 11

    Kassin MTOwen RMPerez SDLeeds ICox JCSchnier K: Risk factors for 30-day hospital readmission among general surgery patients. J Am Coll Surg 215:3223302012

    • Search Google Scholar
    • Export Citation
  • 12

    Koehler BERichter KMYoungblood LCohen BAPrengler IDCheng D: Reduction of 30-day postdischarge hospital readmission or emergency department (ED) visit rates in high-risk elderly medical patients through delivery of a targeted care bundle. J Hosp Med 4:2112182009

    • Search Google Scholar
    • Export Citation
  • 13

    Nasir KLin ZBueno HNormand SLDrye EEKeenan PS: Is same-hospital readmission rate a good surrogate for all-hospital readmission rate?. Med Care 48:4774812010

    • Search Google Scholar
    • Export Citation
  • 14

    Patient Protection Affordable Care Act of 2010 Pub Law 111–148 124 Stat. 127 Sec. 6301 (March 23 2010) (http://www.gpo.gov/fdsys/pkg/PLAW-111publ148/pdf/PLAW-111publ148.pdf) [Accessed January 21 2014]

  • 15

    Rau J: Medicare to penalize 2,217 hospitals for excess readmissions. Kaiser Health News August132012. (http://www.kaiserhealthnews.org/stories/2012/august/13/medicare-hospitalsreadmissions-penalties.aspx) [Accessed January 14 2014]

    • Search Google Scholar
    • Export Citation
  • 16

    Reddy DMTownsend CM JrKuo YFFreeman JLGoodwin JSRiall TS: Readmission after pancreatectomy for pancreatic cancer in Medicare patients. J Gastrointest Surg 13:196319752009

    • Search Google Scholar
    • Export Citation
  • 17

    Romano PSRoos LLJollis JG: Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol 46:107510901993

    • Search Google Scholar
    • Export Citation
  • 18

    Schneider EBHyder OBrooke BSEfron JCameron JLEdil BH: Patient readmission and mortality after colorectal surgery for colon cancer: impact of length of stay relative to other clinical factors. J Am Coll Surg 214:3903992012

    • Search Google Scholar
    • Export Citation
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