Predictors of unplanned readmission in patients undergoing lumbar decompression: multi-institutional analysis of 7016 patients

Clinical article

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Object

Unplanned hospital readmission represents a large financial burden on the Centers for Medicare and Medicaid Services, commercial insurance payers, hospitals, and individual patients, and is a principal target for cost reduction. A large-scale, multi-institutional study that evaluates risk factors for readmission has not been previously performed in patients undergoing lumbar decompression procedures. The goal of this multicenter retrospective study was to find preoperative, intraoperative, and postoperative predictive factors that result in unplanned readmission (UR) after lumbar decompression surgery.

Methods

The National Surgical Quality Improvement Program (NSQIP) database was retrospectively reviewed to identify all patients who received lumbar decompression procedures in 2011. Risk-adjusted multivariate logistic regression analysis was performed to estimate independent predictors of UR.

Results

The overall rate of UR among patients undergoing lumbar decompression was 4.4%. After multivariate logistic regression analysis, anemia (odds ratio [OR] 1.48), dependent functional status (OR 3.03), total operative duration (OR 1.003), and American Society of Anesthesiologists Physical Status Class 4 (OR 3.61) remained as independent predictors of UR. Postoperative complications that were significantly associated with UR included overall complications (OR 5.18), pulmonary embolism (OR 3.72), and unplanned reoperation (OR 56.91).

Conclusions

There were several risk factors for UR after lumbar spine decompression surgery. Identification of high-risk patients and appropriate allocation of resources to reduce postoperative incidence may reduce the readmission rate.

Abbreviations used in this paper:ASA = American Society of Anesthesiologists; BMI = body mass index; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; CPT = Current Procedural Terminology; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; NSQIP = National Surgical Quality Improvement Program; OR = odds ratio; PCI = percutaneous coronary intervention; PPACA = Patient Protection and Accountable Care Act; RVU = relative value unit; SSI = surgical site infection; TIA = transient ischemic attack; UR = unplanned readmission.

Object

Unplanned hospital readmission represents a large financial burden on the Centers for Medicare and Medicaid Services, commercial insurance payers, hospitals, and individual patients, and is a principal target for cost reduction. A large-scale, multi-institutional study that evaluates risk factors for readmission has not been previously performed in patients undergoing lumbar decompression procedures. The goal of this multicenter retrospective study was to find preoperative, intraoperative, and postoperative predictive factors that result in unplanned readmission (UR) after lumbar decompression surgery.

Methods

The National Surgical Quality Improvement Program (NSQIP) database was retrospectively reviewed to identify all patients who received lumbar decompression procedures in 2011. Risk-adjusted multivariate logistic regression analysis was performed to estimate independent predictors of UR.

Results

The overall rate of UR among patients undergoing lumbar decompression was 4.4%. After multivariate logistic regression analysis, anemia (odds ratio [OR] 1.48), dependent functional status (OR 3.03), total operative duration (OR 1.003), and American Society of Anesthesiologists Physical Status Class 4 (OR 3.61) remained as independent predictors of UR. Postoperative complications that were significantly associated with UR included overall complications (OR 5.18), pulmonary embolism (OR 3.72), and unplanned reoperation (OR 56.91).

Conclusions

There were several risk factors for UR after lumbar spine decompression surgery. Identification of high-risk patients and appropriate allocation of resources to reduce postoperative incidence may reduce the readmission rate.

Abbreviations used in this paper:ASA = American Society of Anesthesiologists; BMI = body mass index; CHF = congestive heart failure; COPD = chronic obstructive pulmonary disease; CPT = Current Procedural Terminology; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; NSQIP = National Surgical Quality Improvement Program; OR = odds ratio; PCI = percutaneous coronary intervention; PPACA = Patient Protection and Accountable Care Act; RVU = relative value unit; SSI = surgical site infection; TIA = transient ischemic attack; UR = unplanned readmission.

Unplanned hospital readmissions place a large financial burden on the Center for Medicare and Medicaid Services, commercial insurance payers, hospitals, and individual patients. In addition, with current health care models rapidly gravitating toward improved hospital performance, unplanned readmissions are increasingly surveyed as an indicator for health care quality, hospital performance, and a principal target for cost reduction via mandates of the Patient Protection and Accountable Care Act (PPACA) of 2010.28,29,36,51 Reports describe readmission-related expenditure to exceed $15 billion, which covers 17.6% of the patients who are readmitted within 30 days of discharge.46 In an effort to reduce rising health care costs, the Medicare Payment Advisory Commission selected hospital readmission as a major improvement item for covered patients, yet hospital readmissions remain frequent, costly, and mostly preventable.45

By the recommendation of the Medicare Payment Advisory Commission, the Center for Medicare and Medicaid Services has recently started publicizing standard 30-day readmission rates for certain medical conditions and plans to penalize hospitals with excessive readmission rates.51 Therefore, hospitals are incentivized to obtain readmission data to identify risk factors for readmission, which in turn would allow the hospitals to allocate resources to the patients at greatest risk. To satisfy this mandate, hospitals must develop accurate tools to identify patients with high readmission risk. Currently available readmission models generally have poor predictive power and require further validation in the clinical setting.1,11,29,35,57

There is little data available that evaluates the risk of readmission in spine surgery. In particular, multi-institutional analysis of risk factors for readmission after lumbar decompression has not been previously performed due to limitations in patient data collection capacity.18,58,70 Anticipated changes in the delivery of health care brought on by the PPACA will concentrate focus on outcomes measurement in the practice of medicine; spine surgery is no exception to this rule.

In recognition of the need for comprehensive surgical readmission data, the National Surgical Quality Improvement Program (NSQIP) began to capture a 30-day readmission variable starting in January 2011, including planned readmissions, unplanned readmissions (URs), and suspected causes for readmission of surgical patients across a wide variety of procedures. The NSQIP defines UR as any inpatient admission at the same or another hospital occurring within 30 days after discharge from the principal surgical procedure, which was not previously planned at the time of the index discharge.63 The NSQIP is a nationally validated, risk-adjusted surgical outcomes database aimed at measuring and improving the quality of care delivered to surgical patients. The database contains a robust cohort of patients from which a high-powered retrospective study can be performed. The aim of this study was to examine predictors and causes of 30-day UR after a lumbar decompression procedure, using the NSQIP data set.

Methods

Data Source

The 2011 NSQIP database was used to retrospectively identify patients who underwent lumbar decompression procedures. Instituted by the American College of Surgeons in 2004, the NSQIP is a prospectively maintained registry that provides more than 240 clinical variables from over 250 institutions across the nation. Captured variables include surgical patients' preoperative characteristics, comorbidities, intraoperative variables, and 30-day postoperative outcomes in both the inpatient and outpatient setting. The details of the NSQIP database including case inclusion, exclusion, and systematic sampling strategies have been previously described in other studies.8,32,33 In brief, trained surgical reviewers at participating sites capture clinical data from medical records including medical charts, operative logs, anesthesia records, and interviews with the patient and attending surgeon. The recorded data are then de-identified to comply with the NSQIP participant user agreement. To ensure data reliability, clinical reviewers complete comprehensive training programs and the NSQIP conducts interrater reliability audits at participating sites. Most recently, the interobserver disagreement has been calculated as 1.96%.63

Study Population and Outcomes

Patients were identified by the presence of the 3 most common primary Current Procedural Terminology (CPT) codes 63005, 63030, and 63047, which refer to lumbar decompression procedures. Patients were subdivided into Group 1 (no UR) or Group 2 (had UR). Body mass index (BMI) was screened as a potential confounder; therefore, patients with missing height/weight information were excluded.

Descriptive statistics, as well as 30-day complications profiles and 30-day reoperation rates, were calculated for the study group. Patients were stratified by CPT and International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes into specific procedural and diagnostic sets, respectively. Demographic data included age, BMI, obesity, race, and sex. In addition to calculated BMI, the patients were stratified according to the WHO obesity classification. The classification cutoffs include the following: nonobese (BMI < 30 kg/m2), Class I obesity (BMI 30–34.9 kg/m2), Class II obesity (BMI 35–39.9 kg/m2), and Class III obesity (BMI > 40 kg/m2).17,37 Preoperative serum albumin and hematocrit levels were recorded for available patients. Medical comorbidities analyzed included anemia (hematocrit < 36% for women, < 39% for men), diabetes, dyspnea, preoperative functional status, history of chronic obstructive pulmonary disease (COPD), congestive heart failure (CHF) within 30 days of operation, previous percutaneous coronary intervention (PCI) and/or cardiac surgery, history of angina within 30 days of operation, hypertension requiring medication, history of transient ischemic attack (TIA), history of stroke, paraplegia, steroid use for chronic conditions, bleeding disorders, preoperative open wound infection, chemotherapy within 30 days of operation, and history of operation within 30 days of the index operation. Alcohol use (> 2 drinks/day in 2 weeks before admission) and smoking history (within 1 year of operation) were included as lifestyle variables. Recorded operative details included elective surgery, emergency, inpatient/outpatient status, work relative value unit (RVU), wound class, American Society of Anesthesiologists (ASA) Physical Status Classification, and total operative time.

Thirty-day postoperative complications were categorized as overall, surgical, and medical complications. Surgical complications were defined as having surgical site infection (SSI; superficial, deep, and organ/space) and wound dehiscence. Medical complications were defined as complications related to medical conditions that arise from nonsurgical causes and included: 1) cardiovascular (cardiac arrest, myocardial infarction, or stroke); 2) pulmonary (pneumonia, unplanned reintubation, or ventilator-assisted respiration for over 48 hours); 3) coma or peripheral nerve damage (central/peripheral nervous system); 4) renal (progressive renal insufficiency or acute renal failure); 5) thromboembolic (deep venous thrombosis or pulmonary embolism); 6) septic (sepsis or septic shock); 7) urinary tract infection; and 8) requiring blood transfusion of more than 4 units of red blood cells up to 72 hours postoperatively. Reoperation was defined by the NSQIP as returning to the operating room within 30 days of index operation for intervention of any kind. Another variable captured by the 2011 NSQIP included 30-day unplanned reoperation, which is defined as an unplanned return to the operating room for a surgical procedure related to either the index or concurrent procedure.

Statistical Analysis

The statistical program SPSS (version 20, IBM Corp.) was used to perform descriptive statistics and comparisons of demographic data, comorbidities, perioperative details, and outcomes. Comparison of categorical variables was performed using the Pearson chi-square test or Fisher exact test where appropriate. Continuous variables were compared by using 1-way ANOVA or Welch test where needed. Potential risk factors for UR were identified via univariate screening, from which preoperative, intraoperative, and postoperative variables with a p value < 0.2 and 10 or more occurrences were selected. Finally, selected risk factors as well as ASA classification and wound classification were included in the multivariate logistic regression model to estimate independent risk factors for unplanned readmission. To minimize bias in multivariate analysis (because we cannot logically predict from preoperative and/or intraoperative factors which patients will have postoperative complications) and to differentiate predictors from causes of unplanned readmission, risk factors were grouped into preoperative and intraoperative variables, and postoperative variables for separate analysis. For all tests, significance was defined at p < 0.05.

Results

Patient Population

Using the 2011 NSQIP database, 7078 patients were retrieved from the initial query, of whom 62 patients were excluded because of missing weight/height information, leaving a total of 7016 patients for analysis.

Of 314 cases (4.5%) that resulted in readmission, 312 cases (4.4%) were URs. Single interspace laminotomy with decompression was the most common procedure, with a UR rate of 3.7% (Table 1). Single vertebral segment laminectomy with decompression was the second most common procedure and had the highest UR rate (5.7%). Table 2 shows stratification of patients by ICD-9 codes with frequency greater than 100, and demonstrates that displacement of lumbar intervertebral disc without myelopathy (722.1) and spinal stenosis of the lumbar region (724.02) were the two most common diagnoses at index admission with 3.5% and 3.9% UR rates, respectively. Intervertebral disc disorder with myelopathy was the least common diagnosis but had the highest UR rate (7.8%), although this reflects an unadjusted finding that may be influenced by a number of factors.

TABLE 1:

Unplanned readmission rate according to CPT code

CPT CodeProcedureNo. of PatientsUR Rate (%)
63005laminectomy w/ exploration &/or decompression of spinal cord &/or cauda equina, w/o facetectomy foraminotomy, or discectomy (spinal stenosis), 1 or 2 vertebral segments; lumbar, except for spondylolisthesis4693.4
63030laminotomy (hemilaminectomy), w/ decompression of nerve root(s), including partial facetectomy, foraminotomy &/or excision of herniated intervertebral disc; 1 interspace, lumbar38873.7
63047laminectomy, facetectomy, & foraminotomy (unilateral or bilateral w/ decompression of spinal cord, cauda equina &/or nerve roots, [spinal or lateral recess stenosis]), single vertebral segment; lumbar26605.7
TABLE 2:

Unplanned readmission rate by diagnostic indications

ICD-9 CodeDiagnostic IndicationsNo. of PatientsUnplanned Readmission Rate (%)
721.3lumbosacral spondylosis w/o myelopathy3136.1
722.1displacement of lumbar intervertebral disc w/o myelopathy31653.5
722.52degeneration of lumbar or lumbosacral intervertebral disc3505.7
722.73intervertebral disc disorder w/ myelopathy1037.8
724.02spinal stenosis of lumbar region16093.9
724.03spinal stenosis, lumbar region, w/ neurogenic claudication3266.1
724.4lumbosacral neuritis or radiculitis, unspecified2465.7
738.4acquired spondylolisthesis2356.4
noneother6696.3

Preoperative Characteristics

As shown in Table 3, the UR group was significantly older with a mean age of 59.78 years (vs 55.98 years; p < 0.001). For available patients, the UR cohort had significantly lower preoperative hematocrit and serum albumin levels.

TABLE 3:

Population demographics and preoperative characteristics

VariableURp Value
No (%)Yes (%)
no. w/ preop variables6704 (95.6)312 (4.4)
mean age ± SD (yrs)55.98 ± 15.5559.78 ± 16.40<0.001*
mean BMI ± SD (kg/m2)29.75 ± 6.3930.22 ± 6.590.202
obesity2814 (42.0)141 (45.2)0.261
 WHO Classification (kg/m2)0.591
  0 (<30)3890 (58.0)171 (54.8)
  1 (30–34.9)1642 (24.5)78 (25.0)
  2 (35–39.9)729 (10.9)38 (12.2)
  3 (>40)443 (6.6)25 (8.0)
sex0.810
 male3605 (53.8)164 (52.6)
 female3094 (46.2)148 (47.4)
race0.115
 white5486 (81.8)270 (86.5)
 black352 (5.3)18 (5.8)
 Asian123 (1.8)3 (1.0)
 other742 (11.1)21 (6.7)
preop hematocrit ± SD (%)41.04 ± 4.1540.08 ± 4.51<0.001*
preop albumin ± SD (g/dl)4.11 ± 0.473.96 ± 0.510.001*

Statistically significant (p < 0.05).

Hematocrit was recorded if available; n = 5959 and n = 287 for the UR and no UR groups, respectively.

Serum albumin was recorded if available; n = 2148 and n = 127 for UR and no UR groups, respectively.

Patient comorbidities differed more markedly (Table 4): the UR cohort was more likely to present with anemia (p < 0.001), diabetes (insulin, noninsulin dependent; p < 0.001), dyspnea (at rest, moderate exertion; p = 0.004), dependent functional status (p < 0.001), history of COPD (p = 0.006), history of angina < 30 days of operation (p = 0.036), hypertension requiring medication (p = 0.014), chronic steroid use (p = 0.002), bleeding disorders (p = 0.043), and open wound/wound infection (p = 0.009).

TABLE 4:

Comparison of patient comorbidities

VariableURp Value
No (%)Yes (%)
comorbidities6704 (95.6)312 (4.4)
anemia521 (7.8)47 (15.1)<0.001*
diabetes919 (13.7)66 (21.2)<0.001*
current smoker1485 (22.2)67 (21.5)0.778
EtOH >2 drinks/day75 (1.1)3 (1.0)1.000
dyspnea334 (5.0)27 (8.7)0.004*
dependent functional status128 (1.9)23 (7.4)<0.001*
history of COPD217 (3.2)19 (6.1)0.006*
CHF <30 days12 (0.2)1 (0.3)0.447
previous PCI/cardiac op185 (2.8)15 (4.8)0.034*
history of angina <30 days5 (0.1)2 (0.6)0.036*
hypertension3154 (47.0)169 (54.2)0.014*
history of TIA48 (0.7)1 (0.3)0.725
history of stroke44 (0.7)0 (0.0)0.265
paraplegia43 (0.6)5 (1.6)0.061
chronic steroid use197 (2.9)19 (6.1)0.002*
bleeding disorders91 (1.4)9 (2.9)0.043*
chemotherapy <30 days4 (0.1)0 (0.0)1.000
radiotherapy <90 days0 (0.0)0 (0.0)
prior operation <30 days13 (0.2)1 (0.3)0.471
open wound/wound infection46 (0.7)7 (2.2)0.009*

Statistically significant (p < 0.05). EtOH = ethanol alcohol.

Comparison of operative details is shown in Table 5. The majority of cases were elective operations (90.4%), but patients in the UR group had undergone fewer elective procedures (85.3% vs 90.6%, respectively; p = 0.002). Patients in the UR group were also markedly different in their emergency (3.5% vs 1.8%; p = 0.023) and inpatient (81.1% vs 69.9%; p < 0.001) statuses. A greater proportion of the UR group presented with ASA Classes 3 and 4 compared with the no UR group (p < 0.001). Mean complexity of the procedures as indicated by RVU and total operative time was greater for the UR cohort (p = 0.007 and p < 0.001, respectively).

TABLE 5:

Comparison of operative characteristics

VariableURp Value
No (%)Yes (%)
op variables6704 (95.6)312 (4.4)
elective surgery6076 (90.6)266 (85.3)0.002*
emergency118 (1.8)11 (3.5)0.023*
inpatient4688 (69.9)253 (81.1)<0.001*
RVU ± SD14.22 ± 1.2014.41 ± 1.170.007*
wound class0.456
 clean6641 (99.1)310 (99.4)
 clean-contaminated27 (0.4)0 (0.0)
 contaminated13 (0.2)0 (0.0)
 infected23 (0.3)2 (0.6)
ASA Class<0.001*
 1551 (8.2)14 (4.5)
 23824 (57.0)137 (43.9)
 32229 (33.2)143 (45.8)
 492 (1.4)17 (5.4)
 51 (0.01)0 (0.0)
total op time ± SD (min)117.42 ± 75.67143.22 ± 88.06<0.001*

Statistically significant (p < 0.05).

Univariate Analysis of 30-Day Outcomes

Utilizing univariate analysis, associations of 30-day postoperative outcomes were quantified with respect to UR (Table 6). The overall 30-day morbidity was significantly associated with UR (44.9% vs 6.9%, respectively; p < 0.001), as well as medical complications (31.1% vs 6.2%, respectively; p < 0.001), surgical complications (24.4% vs 0.8%, respectively; p < 0.001), and reoperation (41.3% vs 0.7%, respectively; p < 0.001). Of interest, all of the reoperations performed were unplanned. Surgical complications including SSI were all strongly associated with UR (p < 0.001 and p = 0.002 [wound dehiscence]). Of the medical complications, pneumonia, unplanned re-intubation, pulmonary embolism, urinary tract infection, stroke, cardiac arrest, myocardial infarction, requiring postoperative transfusion of more than 4 units of red blood cells, deep venous thrombosis, and sepsis/septic shock were all significantly associated with UR (p < 0.001). Of particular interest, 77 of the 568 anemic patients received transfusion, and 10 of the 47 anemic patients with UR received transfusion.

TABLE 6:

Comparison of 30-day outcomes

VariableURp Value
No (%)Yes (%)
30-day postop variables6704 (95.6)312 (4.4)
overall complications464 (6.9)140 (44.9)<0.001*
surgical complications54 (0.8)76 (24.4)<0.001*
 superficial SSI37 (0.6)28 (9.0)<0.001*
 deep SSI5 (0.1)32 (10.3)<0.001*
 organ/space SSI3 (0.0)13 (4.2)<0.001*
 wound dehiscence9 (0.1)4 (1.3)0.002*
medical complications418 (6.2)97 (31.1)<0.001*
 pneumonia15 (0.2)6 (1.9)<0.001*
 unplanned re-intubation11 (0.2)5 (1.6)<0.001*
 pulmonary embolism12 (0.2)15 (4.8)<0.001*
 mechanical ventilation >48 hrs10 (0.1)2 (0.6)0.097
 renal insufficiency5 (0.1)1 (0.3)0.239
 acute renal failure3 (0.0)0 (0.0)1.000
 urinary tract infection70 (1.0)16 (5.1)<0.001*
 stroke1 (0.0)4 (1.3)<0.001*
 coma >24 hrs0 (0.0)0 (0.0)
 peripheral nerve injury2 (0.0)0 (0.0)1.000
 cardiac arrest4 (0.1)5 (1.6)<0.001*
 myocardial infarction6 (0.1)5 (1.6)<0.001*
 postop transfusion292 (4.4)36 (11.5)<0.001*
 deep venous thrombosis26 (0.4)18 (5.8)<0.001*
 sepsis/septic shock20 (0.3)23 (7.4)<0.001*
reoperation49 (0.7)129 (41.3)<0.001*

Statistically significant (p < 0.05).

Multivariate Analysis

To estimate risk-adjusted independent predictors and causes of UR, multivariate logistic regression models were used that controlled for confounding variables. After adjustment, independent predictors of UR that remained significant included anemia (OR 1.48, 95% CI 1.04–2.10; p = 0.029), dependent functional status (OR 3.03, 95% CI 1.86–4.92; p < 0.001), total operative duration (OR 1.003, 95% CI 1.001–1.004; p < 0.001), and ASA Class 4 (OR 3.61, 95% CI 1.58–8.25; p = 0.002; Table 7). The model exhibited adequate discrimination with a C-index of 0.665. With respect to postoperative complications, overall complications (OR 5.18, 95% CI 1.26–21.41; p = 0.023), pulmonary embolism (OR 3.72, 95% CI 1.37–10.10; p = 0.01), postoperative transfusion (OR 0.27, 95% CI 0.12–0.60; p = 0.001), and unplanned reoperation (OR 56.91, 95% CI 37.90–85.46; p < 0.001) remained significantly associated with UR (Table 8). This model also exhibited an adequate discrimination with C-index of 0.793.

TABLE 7:

Predictors of UR after risk-adjusted multivariate logistic regression analysis

VariableOR95% CIp Value
anemia1.481.04–2.100.029*
diabetes1.270.93–1.720.129
dyspnea1.280.81–2.000.288
dependent functional status3.031.86–4.92<0.001*
COPD1.260.74–2.130.389
previous PCI/PCS1.190.68–2.080.552
hypertension0.820.62–1.080.156
chronic steroid use1.600.96–2.640.069
emergency1.820.92–3.610.085
elective surgery0.770.54–1.100.146
inpatient vs outpatient inpatient1.250.91–1.730.168
RVU0.980.88–1.090.735
age1.011.00–1.020.191
total op time1.0031.001–1.004<0.001*
wound class vs clean dirty/infected0.390.08–1.920.248
ASA Physical Status vs Class 1
 ASA Class 21.200.68–2.130.530
 ASA Class 31.660.90–3.060.107
 ASA Class 43.611.58–8.250.002*

Statistically significant (p < 0.05). C-index = 0.665. PCS = previous cardiac surgery.

TABLE 8:

Causes of UR after risk-adjusted multivariate logistic regression analysis

VariableOR95% CIp Value
overall complications5.181.26–21.410.023*
surgical complications0.560.05–5.950.634
 superficial SSI4.650.55–39.440.159
 deep SSI5.960.52–67.830.150
 organ/space SSI2.640.18–38.020.475
medical complications1.750.38–8.080.471
 pulmonary embolism3.721.37–10.100.01*
 urinary tract infection0.670.28–1.610.370
 postop transfusion0.270.12–0.600.001*
 deep venous thrombosis1.940.79–4.800.149
 sepsis/septic shock1.840.65–5.210.248
unplanned reoperation56.9137.90–85.46<0.001*

Statistically significant (p < 0.05). C-index = 0.793.

Discussion

Study Rationale

Enactment of the PPACA has elicited national efforts to reduce readmissions, yet readmissions remain frequent and extremely costly across all medical and surgical specialties.7,12,16,19,21,31,42–44,52,56 In attempting to analyze the factors occurring with readmission, the hindering factor may be the readmission data itself, which stems from inconsistent methods of data collection.2,24,43 In addition, studies have shown mixed results in defining causes of readmission.5,26,31,48,55,59 Although surgical patients are inherently different from medical patients, a useful starting point would be the ability to differentiate planned from unplanned readmissions.5,21,25,41 We queried the 2011 NSQIP dataset to find predictors for unplanned readmissions in lumbar decompression procedures, with the goal of finding clues to preventable versus nonpreventable readmissions at a hospital level. Recently, the reliability and accuracy of the readmission variable in NSQIP has been validated.62

To our knowledge, this is the first multicenter review of over 7,000 patients who underwent lumbar decompression during 2011. These were predominantly elective procedures. In our analysis, we identified several independent risk factors associated with unplanned readmission; these risk factors were present in all stages of the operation (pre-, intra-, and postoperative setting), thereby warranting closer inspection.

Predictors of Unplanned Readmission in Pre- and Intraoperative Setting

We found that anemia predicted UR in patients undergoing lumbar decompression, which substantiates the current consensus in both surgical and medical settings. Fischer et al.16 conducted a retrospective review of 10,699 plastic surgery procedures from 2011 NSQIP and determined anemia was a strong predictor of readmission (OR 1.8, p = 0.003). In addition, in studies of 289,077 pulmonary patients and 1,491 internal medicine patients, anemia was a prognostic factor for early readmission.39,49 This intuitively makes sense because in numerous studies, anemia has been reported as a risk factor for postoperative complications across various surgical specialties.14,22,38,40,60,61 Because postoperative complications are shown to be associated with readmission,5,31,48,50,55,59 it is likely that anemic patients' predisposition to postoperative complications led to UR.

General functional status is known to be associated with postoperative morbidity and mortality, particularly in cancer patients.3,47 Our data suggest patients with dependent functional status are more likely to be readmitted (Table 7). In support of our finding, Kariv et al. demonstrated that worse functional capacity class remained a predictor of readmission even after controlling for selected peri-operative variables.30 Several studies have proposed measures to improve functional status and pulmonary function prior to operation to decrease postoperative incidence.4,10,20 Of the remaining predictors of UR, increased operative time, as well as ASA class 4 assignments were associated with an elevated risk of UR. Longer operative times are shown to correlate with both increased rates of surgical and medical complications, as well as longer hospital lengths of stay (Kim JY, Mioton LM, Rambachan A, unpublished data).53 Higher ASA classifications are also known to be associated with adverse complications and longer hospital stay.9,13,68 In addition, ASA classification has been shown to incrementally increase the risk of readmission in concordance with increasing scores.31 These findings converge on the concept that risk factors may indirectly attribute to UR by increasing postoperative incidence, further reinforcing currently proposed causes of readmission, that is postoperative complications.6,67

Causes of Unplanned Readmission in Postoperative Setting

As expected, postoperative complications found to be associated with UR on univariate analysis included overall, surgical, medical complications, and unplanned reoperation. After multivariate analysis, overall complications, pulmonary embolism, and unplanned reoperation still remained significant risk factors for readmission. Of note, patients undergoing unplanned reoperation were 56.91 times more likely to experience unplanned readmission. However, this must be viewed in light of the fact that NSQIP does not provide timing data regarding reoperation and readmission, making it difficult to assess whether reoperation was performed to treat complications during index hospital admission or during readmission. Discharged surgical patients may develop complications related to the index procedure, necessitating unplanned readmission and subsequent reoperation. The incidence of reoperation included this way will likely inflate the risk associated with unplanned readmission. These findings should also be viewed in light of the fact that reducing postoperative incidence is already a paramount goal for spinal surgeons. Therefore, focused efforts to reduce readmission with respect to postoperative outcomes may not be necessary, as these measures are already taking place in practice.

Enhanced treatment at the time of the index procedure/admission may result in immediate diagnosis and treatment of a specific postoperative complication, ultimately resulting in the avoidance of an unplanned, preventable readmission. We observed that recipients of > 4 units of postoperative transfusions were in decreased risk of UR (OR 0.27, p = 0.001) (Table 8). However, our data indicate that, of the 47 anemic patients who experienced UR, only 10 received blood transfusion; therefore, 37 patients with UR may still have been anemic at the time of discharge. This explains why transfusion, although considered a surrogate for morbidity, is likely to exercise a protective effect. Transfused patients are recognized to carry high risks for complications, leading to enhanced inpatient treatment and definitive management of complications. The protective effect of transfusion on readmission was seen in free flap and pediatric sickle cell patients;34,64,65 however, in general surgery patients undergoing colon resection, postoperative transfusion is shown to be a risk factor for readmission.31,59

Enhancement Measures

To date, implementation of hospital-based pay-for-performance programs and benchmarks has produced conflicting results in improving patient outcomes such as mortality.27,66 In lumbar decompression patients, prevention of readmission for an acutely ill postoperative patient could delay appropriate care and potentially be harmful. Accordingly, resources allocated toward preventing surgical readmissions should be redirected toward individual and systems-based practices for preventing complications. Recent endeavors to accomplish this include the Comprehensive Unit-Based Safety Program (CUSP)69 and the Enhanced Recovery After Surgery (ERAS) program by the UK National Health Service,54 both of which are intended to decrease perioperative complication rates, facilitate discharge, and reduce hospital readmissions. Though not universally applicable, some of the adoptable recommendations include standardization of skin preparation, administration of preoperative chlorhexidine showers, warming of patients in the preanesthesia area, adoption of enhanced sterile techniques for “dirty” versus “clean” portions of surgical cases, and addressing lapses in prophylactic antibiotics.

There are reports of effective outpatient care-coordination models in medical patients that reduce readmission; these include structured discharge planning, medication reconciliation, and timely outpatient follow-up.15,23,24 Similar efforts can be implemented in lumbar decompression patients, which in conjunction with postoperative complication prophylaxis, may reduce the fairly high rate of 30-day readmission seen in spine patients.12

Study Limitations

Although our study used the NSQIP database which captures a broad range of clinical variables and patient populations across the nation, there are several limitations that are inherent in the use of the NSQIP database. First, NSQIP only captures unplanned readmission occurring within 30 days of the principal surgical procedure, thus under-representing the overall unplanned readmission rate that occurs after 30 days. Another limitation of this NSQIP-derived study is that the impact of readmission relative to total length of hospital stay cannot be directly assessed (e.g. total length of stay or an exact etiology of readmission cannot be elucidated).16 Second, temporal relationship of occurrence of complication to index procedure cannot always be determined. Specifically, it is not always possible to determine whether a patient developed a complication/s while in inpatient service and was readmitted due to inadequate management of the complication/s, or whether a patient developed a complication/s as an outpatient and was “readmitted” to the inpatient setting.16 Lastly, there are no established guidelines in the literature for determining preventable or clinically unnecessary hospital readmissions in surgical patients, nor does the database define patients' insurance or socioeconomic status, which may affect resource allocation and discharge services. Also, the NSQIP data are not specialty specific; therefore, some clinical information that is pertinent to spine surgeons is absent. Regardless, the current study aims to contribute to currently available readmission literature, by increasing provider awareness of estimated predictors of unplanned readmission, thus reducing preventable and unwanted readmissions.

Conclusions

Readmissions remain a fiscal threat to hospitals due to proposed penalties under PPACA and adversely reflect on patients' functional improvement. With full implementation of the health care reform, readmissions continue to be an important area of quality improvement effort. Reducing postoperative incidence is a primary goal for spine surgeons; as a result, indirect measures to reduce readmissions are already taking place. Unifying data collection methods to reduce differences observed in readmission data, allocating resources to identify high-risk patients, as well as improving patient education at discharge can further reduce readmission in lumbar decompression patients.

Disclosure

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper. De-identified patient information is freely available to all institutional members who comply with the American College of Surgeons (ACS)-NSQIP Data Use Agreement. The Data Use Agreement implements the protections afforded by the Health Insurance Portability and Accountability Act of 1996 and the ACS-NSQIP Hospital Participation Agreement. The ACS NSQIP and the hospitals participating in the ACS NSQIP are the source of the data used herein; they have not verified and are not responsible for the statistical validity of the data analysis or the conclusions derived by the authors.

Author contributions to the study and manuscript preparation include the following. Conception and design: BD Kim, Smith. Acquisition of data: BD Kim. Analysis and interpretation of data: BD Kim, Lim. Drafting the article: BD Kim. 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: JYS Kim. Statistical analysis: BD Kim, Lim. Administrative/technical/material support: Study supervision: JYS Kim, Smith.

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Article Information

Contributor Notes

Address correspondence to: John Y. S. Kim, M.D., Department of Surgery, Northwestern University, Feinberg School of Medicine, 675 N. St. Clair St., Galter Ste. 19-250, Chicago, IL 60611. email: jokim@nmh.org.Please include this information when citing this paper: published online April 11, 2014; DOI: 10.3171/2014.3.SPINE13699.

© AANS, except where prohibited by US copyright law.

Headings
References
  • 1

    Allaudeen NSchnipper JLOrav EJWachter RMVidyarthi AR: Inability of providers to predict unplanned readmissions. J Gen Intern Med 26:7717762011

    • Search Google Scholar
    • Export Citation
  • 2

    Amin BYTu THSchairer WWNa LTakemoto SBerven S: Pitfalls of calculating hospital readmission rates based on nonvalidated administrative data sets. Clinical article. J Neurosurg Spine 18:1341382013

    • Search Google Scholar
    • Export Citation
  • 3

    Arozullah AMKhuri SFHenderson WGDaley J: Development and validation of a multifactorial risk index for predicting postoperative pneumonia after major noncardiac surgery. Ann Intern Med 135:8478572001

    • Search Google Scholar
    • Export Citation
  • 4

    Arthur HMDaniels CMcKelvie RHirsh JRush B: Effect of a preoperative intervention on preoperative and postoperative outcomes in low-risk patients awaiting elective coronary artery bypass graft surgery. A randomized, controlled trial. Ann Intern Med 133:2532622000

    • Search Google Scholar
    • Export Citation
  • 5

    Azimuddin KRosen LReed JF IIIStasik JJRiether RDKhubchandani IT: Readmissions after colorectal surgery cannot be predicted. Dis Colon Rectum 44:9429462001

    • Search Google Scholar
    • Export Citation
  • 6

    Ball CGPitt HAKilbane MEDixon ESutherland FRLillemoe KD: Peri-operative blood transfusion and operative time are quality indicators for pancreatoduodenectomy. HPB (Oxford) 12:4654712010

    • Search Google Scholar
    • Export Citation
  • 7

    Ben-Chetrit EChen-Shuali CZimran EMunter GNesher G: A simplified scoring tool for prediction of readmission in elderly patients hospitalized in internal medicine departments. Isr Med Assoc J 14:7527562012

    • Search Google Scholar
    • Export Citation
  • 8

    Birkmeyer JDShahian DMDimick JBFinlayson SRFlum DRKo CY: Blueprint for a new American College of Surgeons: National Surgical Quality Improvement Program. J Am Coll Surg 207:7777822008

    • Search Google Scholar
    • Export Citation
  • 9

    Caperell KPitetti R: Is higher ASA class associated with an increased incidence of adverse events during procedural sedation in a pediatric emergency department?. Pediatr Emerg Care 25:6616642009

    • Search Google Scholar
    • Export Citation
  • 10

    Carli FZavorsky GS: Optimizing functional exercise capacity in the elderly surgical population. Curr Opin Clin Nutr Metab Care 8:23322005

    • Search Google Scholar
    • Export Citation
  • 11

    Cima RRLackore KANehring SACassivi SDDonohue JHDeschamps C: How best to measure surgical quality? Comparison of the Agency for Healthcare Research and Quality Patient Safety Indicators (AHRQ-PSI) and the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) postoperative adverse events at a single institution. Surgery 150:9439492011

    • Search Google Scholar
    • Export Citation
  • 12

    Deyo RAMirza SKMartin BIKreuter WGoodman DCJarvik JG: Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA 303:125912652010

    • Search Google Scholar
    • Export Citation
  • 13

    Dietrich CGKottmann TDiedrich ADrouven FM: Sedation-associated complications in endoscopy are not reduced significantly by implementation of the German S-3-guideline and occur in a severe manner only in patients with ASA class III and higher. Scan J Gastroenterol 48:108210872013

    • Search Google Scholar
    • Export Citation
  • 14

    Dunne JRMalone DTracy JKGannon CNapolitano LM: Perioperative anemia: an independent risk factor for infection, mortality, and resource utilization in surgery. J Surg Res 102:2372442002

    • Search Google Scholar
    • Export Citation
  • 15

    Feigenbaum PNeuwirth ETrowbridge LTeplitsky SBarnes CAFireman E: Factors contributing to all-cause 30-day readmissions: a structured case series across 18 hospitals. Med Care 50:5996052012

    • Search Google Scholar
    • Export Citation
  • 16

    Fischer JPWes AMNelson JASerletti JMKovach SJ: Factors associated with readmission following plastic surgery-a review of 10,669 procedures from 2011 American College of Surgeons National Surgical Quality Improvement Project dataset. Plast Reconstr Surg 132:6646742013

    • Search Google Scholar
    • Export Citation
  • 17

    Flegal KMCarroll MDKuczmarski RJJohnson CL: Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord 22:39471998

    • Search Google Scholar
    • Export Citation
  • 18

    Fung KTeknos TNVandenberg CDLyden THBradford CRHogikyan ND: Prevention of wound complications following salvage laryngectomy using free vascularized tissue. Head Neck 29:4254302007

    • Search Google Scholar
    • Export Citation
  • 19

    Garrison GMMansukhani MPBohn B: Predictors of thirty-day readmission among hospitalized family medicine patients. J Am Board Fam Med 26:71772013

    • Search Google Scholar
    • Export Citation
  • 20

    Gracey DRDivertie MBDidier EP: Preoperative pulmonary preparation of patients with chronic obstructive pulmonary disease: a prospective study. Chest 76:1231291979

    • Search Google Scholar
    • Export Citation
  • 21

    Guinier DMantion GAAlves AKwiatkowski FSlim KPanis Y: Risk factors of unplanned readmission after colorectal surgery: a prospective, multicenter study. Dis Colon Rectum 50:131613232007

    • Search Google Scholar
    • Export Citation
  • 22

    Gupta PKSundaram AMactaggart JNJohanning JMGupta HFang X: Preoperative anemia is an independent predictor of postoperative mortality and adverse cardiac events in elderly patients undergoing elective vascular operations. Ann Surg 258:109611022013

    • Search Google Scholar
    • Export Citation
  • 23

    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
  • 24

    Hechenbleikner EMMakary MASamarov DVBennett JLGearhart SLEfron JE: Hospital readmission by method of data collection. J Am Coll Surg 216:115011582013

    • Search Google Scholar
    • Export Citation
  • 25

    Jackson BMNathan DPDoctor LWang GJWoo EYFairman RM: Low rehospitalization rate for vascular surgery patients. J Vasc Surg 54:7677722011

    • Search Google Scholar
    • Export Citation
  • 26

    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
  • 27

    Jha AKJoynt KEOrav EJEpstein AM: The long-term effect of premier pay for performance on patient outcomes. N Engl J Med 366:160616152012

    • Search Google Scholar
    • Export Citation
  • 28

    Jha AKOrav EJEpstein AM: Public reporting of discharge planning and rates of readmissions. N Engl J Med 361:263726452009

  • 29

    Kansagara DEnglander HSalanitro AKagen DTheobald CFreeman M: Risk prediction models for hospital readmission: a systematic review. JAMA 306:168816982011

    • Search Google Scholar
    • Export Citation
  • 30

    Kariv YWang WSenagore AJHammel JPFazio VWDelaney CP: Multivariable analysis of factors associated with hospital readmission after intestinal surgery. Am J Surg 191:3643712006

    • Search Google Scholar
    • Export Citation
  • 31

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

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