Mila H. Ju

Underlying Reasons Associated With Hospital Readmission Following Surgery in the United States

IMPORTANCE Financial penalties for readmission have been expanded beyond medical conditions to include surgical procedures. Hospitals are working to reduce readmissions; however, little is known about the reasons for surgical readmission. OBJECTIVE To characterize the reasons, timing, and factors associated with unplanned postoperative readmissions. DESIGN, SETTING, AND PARTICIPANTS Patients undergoing surgery at one of 346 continuously enrolled US hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) between January 1, 2012, and December 31, 2012, had clinically abstracted information examined. Readmission rates and reasons (ascertained by clinical data abstractors at each hospital) were assessed for all surgical procedures and for 6 representative operations: bariatric procedures, colectomy or proctectomy, hysterectomy, total hip or knee arthroplasty, ventral hernia repair, and lower extremity vascular bypass. MAIN OUTCOMES AND MEASURES Unplanned 30-day readmission and reason for readmission. RESULTS The unplanned readmission rate for the 498,875 operations was 5.7%. For the individual procedures, the readmission rate ranged from 3.8% for hysterectomy to 14.9% for lower extremity vascular bypass. The most common reason for unplanned readmission was surgical site infection (SSI) overall (19.5%) and also after colectomy or proctectomy (25.8%), ventral hernia repair (26.5%), hysterectomy (28.8%), arthroplasty (18.8%), and lower extremity vascular bypass (36.4%). Obstruction or ileus was the most common reason for readmission after bariatric surgery (24.5%) and the second most common reason overall (10.3%), after colectomy or proctectomy (18.1%), ventral hernia repair (16.7%), and hysterectomy (13.4%). Only 2.3% of patients were readmitted for the same complication they had experienced during their index hospitalization. Only 3.3% of patients readmitted for SSIs had experienced an SSI during their index hospitalization. There was no time pattern for readmission, and early (≤7 days postdischarge) and late (>7 days postdischarge) readmissions were associated with the same 3 most common reasons: SSI, ileus or obstruction, and bleeding. Patient comorbidities, index surgical admission complications, non-home discharge (hazard ratio [HR], 1.40 [95% CI, 1.35-1.46]), teaching hospital status (HR, 1.14 [95% CI 1.07-1.21]), and higher surgical volume (HR, 1.15 [95% CI, 1.07-1.25]) were associated with a higher risk of hospital readmission. CONCLUSIONS AND RELEVANCE Readmissions after surgery were associated with new postdischarge complications related to the procedure and not exacerbation of prior index hospitalization complications, suggesting that readmissions after surgery are a measure of postdischarge complications. These data should be considered when developing quality indicators and any policies penalizing hospitals for surgical readmission.

Evaluation of Surveillance Bias and the Validity of the Venous Thromboembolism Quality Measure

IMPORTANCE Postoperative venous thromboembolism (VTE) rates are widely reported quality metrics soon to be used in pay-for-performance programs. Surveillance bias occurs when some clinicians use imaging studies to detect VTE more frequently than other clinicians. Because they look more, they find more VTE events, paradoxically worsening their hospitals VTE quality measure performance. A surveillance bias may influence VTE measurement if (1) greater hospital VTE prophylaxis adherence fails to result in lower measured VTE rates, (2) hospitals with characteristics suggestive of higher quality (eg, more accreditations) have greater VTE prophylaxis adherence rates but worse VTE event rates, and (3) higher hospital VTE imaging utilization use rates are associated with higher measured VTE event rates. OBJECTIVE To examine whether a surveillance bias influences the validity of reported VTE rates. DESIGN, SETTING, AND PARTICIPANTS 2010 Hospital Compare and American Hospital Association data from 2838 hospitals were merged. Next, 2009-2010 Medicare claims data for 954,926 surgical patient discharges from 2786 hospitals who were undergoing 1 of 11 major operations were used to calculate VTE imaging (duplex ultrasonography, chest computed tomography/magnetic resonance imaging, and ventilation-perfusion scans) and VTE event rates. MAIN OUTCOMES AND MEASURES The association between hospital VTE prophylaxis adherence and risk-adjusted VTE event rates was examined. The relationship between a summary score of hospital structural characteristics reflecting quality (hospital size, numbers of accreditations/quality initiatives) and performance on VTE prophylaxis and risk-adjusted VTE measures was examined. Hospital-level VTE event rates were compared across VTE diagnostic imaging rate quartiles and with a quantile regression. RESULTS Greater hospital VTE prophylaxis adherence rates were weakly associated with worse risk-adjusted VTE event rates (r2 = 4.2%; P = .03). Hospitals with increasing structural quality scores had higher VTE prophylaxis adherence rates (93.3% vs 95.5%, lowest vs highest quality quartile; P < .001) but worse risk-adjusted VTE rates (4.8 vs 6.4 per 1000, lowest vs highest quality quartile; P < .001). Mean VTE diagnostic imaging rates ranged from 32 studies per 1000 in the lowest imaging use quartile to 167 per 1000 in the highest quartile (P < .001). Risk-adjusted VTE rates increased significantly with VTE imaging use rates in a stepwise fashion, from 5.0 per 1000 in the lowest quartile to 13.5 per 1000 in the highest quartile (P < .001). CONCLUSIONS AND RELEVANCE Hospitals with higher quality scores had higher VTE prophylaxis rates but worse risk-adjusted VTE rates. Increased hospital VTE event rates were associated with increasing hospital VTE imaging use rates. Surveillance bias limits the usefulness of the VTE quality measure for hospitals working to improve quality and patients seeking to identify a high-quality hospital.

A Comparison of 2 Surgical Site Infection Monitoring Systems

IMPORTANCE Surgical site infection (SSI) has emerged as the leading publicly reported surgical outcome and is tied to payment determinations. Many hospitals monitor SSIs using the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP), in addition to mandatory participation (for most states) in the Centers for Disease Control and Preventions National Healthcare Safety Network (NHSN), which has resulted in duplication of effort and incongruent data. OBJECTIVE To identify discrepancies in the implementation of the NHSN and the ACS NSQIP at hospitals that may be affecting the respective SSI rates. DESIGN, SETTING, AND PARTICIPANTS A pilot sample of hospitals that participate in both the NHSN and the ACS NSQIP. INTERVENTIONS For each hospital, observed rates and risk-adjusted observed to expected ratios for year 2012 colon SSIs were collected from both programs. The implementation methods of both programs were identified, including telephone interviews with infection preventionists who collect data for the NHSN at each hospital. MAIN OUTCOMES AND MEASURES Collection methods and colon SSI rates for the NHSN at each hospital were compared with those of the ACS NSQIP. RESULTS Of 16 hospitals, 11 were teaching hospitals with at least 500 beds. The mean observed colon SSI rates were dissimilar between the 2 programs, 5.7% (range, 2.0%-14.5%) for the NHSN vs 13.5% (range, 4.6%-26.7%) for the ACS NSQIP. The mean difference between the NHSN and the ACS NSQIP was 8.3% (range, 1.6%-18.8%), with the ACS NSQIP rate always higher. The correlation between the observed to expected ratios for the 2 programs was nonsignificant (Pearson product moment correlation, ρ = 0.4465; P = .08). The NHSN collection methods were dissimilar among interviewed hospitals. An SSI managed as an outpatient case would usually be missed under the current NHSN practices. CONCLUSIONS AND RELEVANCE Colon SSI rates from the NHSN and the ACS NSQIP cannot be used interchangeably to evaluate hospital performance and determine reimbursement. Hospitals should not use the ACS NSQIP colon SSI rates for the NHSN reports because that would likely result in the hospital being an outlier for performance. It is imperative to reconcile SSI monitoring, develop consistent definitions, and establish one reliable method. The current state hinders hospital improvement efforts by adding unnecessary confusion to the already complex arena of perioperative improvement.

Effect of post-discharge venous thromboembolism on hospital quality comparisons following hip and knee arthroplasty

BACKGROUND Symptomatic pre-discharge venous thromboembolism (VTE) rates after total or partial hip or knee arthroplasty have been proposed as patient safety indicators. However, assessing only pre-discharge VTE rates may be suboptimal for quality measurement as the duration of stay is relatively short and the VTE risk extends beyond the inpatient setting. METHODS Patients who underwent total or partial hip or knee arthroplasty were identified in the 2008 through 2010 American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database. Outcomes of interest were the deep venous thrombosis (DVT), pulmonary embolism (PE), and overall VTE rates within thirty days after surgery and the rates during the pre-discharge and post-discharge portions of this time period. Risk-adjusted hospital rankings based on only pre-discharge (inpatient) events were compared with those based on both pre-discharge and post-discharge events within thirty days of surgery. RESULTS A total of 23,924 patients underwent total or partial hip arthroplasty (8499) or knee arthroplasty (15,425) at ninety-five hospitals. For hip arthroplasty, the VTE rate was 0.9%, with 57.9% of the events occurring after discharge. For knee arthroplasty, the VTE rate was 1.9%, with 38.3% of the events occurring after discharge. The median time of VTE occurrence was eleven days postoperatively for hip arthroplasty and three days for knee arthroplasty. The median duration of stay was three days for both hip and knee arthroplasty. When hospitals were ranked according to VTE rates, hospital outlier status designations changed when post-discharge events were included (κ = 0.386; 44% false-positive rate for low outliers). The median change in hospital quality ranking was 7 (interquartile range, 2 to 17), with a rank correlation of r = 0.82. CONCLUSIONS Nearly twice as many VTE complications were captured if both pre-discharge and post-discharge events were considered, and inclusion of post-discharge events changed hospital quality rankings. These data suggest that inclusion of post-discharge events should be considered when comparing the quality of hospitals on the basis of postoperative VTE rates. LEVEL OF EVIDENCE Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.

National evaluation of hospital readmission after pulmonary resection

OBJECTIVES Our objectives were to (1) assess readmission rates and timing after pulmonary resection, (2) report the most common reasons for rehospitalization, and (3) identify risk factors for unplanned readmission after pulmonary resection. METHODS Patients who underwent pulmonary resection were identified from the 2011 American College of Surgeons National Surgical Quality Improvement Program database. We examined readmission within 30 days of surgery for all resections and 3 subgroups: open lobectomy, video-assisted thoracoscopic lobectomy, and pneumonectomy. Regression models were developed to identify factors associated with readmission. RESULTS In 1847 patients, there were 899 open lobectomies (49%), 724 video-assisted thoracoscopic lobectomies (39%), and 85 pneumonectomies (5%). The overall readmission rate was 9.3% with no significant difference found among patients undergoing open lobectomy (9.1%), video-assisted thoracoscopic lobectomy (8.4%), or pneumonectomy (11.8%) (P = .576). The median time from operation to readmission was similar among patients undergoing open (14 days) or video-assisted thoracoscopic lobectomy (13 days). The most common cause of readmission for all groups examined was pulmonary related. In multivariable analyses, the strongest factor associated with readmission was an inpatient complication after the initial surgery in all resections (hazard ratio [HR], 4.29; 95% confidence interval [CI], 3.05-6.04), open lobectomy (HR, 4.36; 95% CI, 2.75-6.94), and video-assisted thoracoscopic lobectomy (HR, 4.60; 95% CI, 2.65-7.97). Surgical approach was not associated with readmission (video-assisted thoracoscopic vs open lobectomy: HR, 1.07; 95% CI, 0.75-1.52). CONCLUSIONS Experiencing a postoperative complication was strongly associated with unplanned readmission. Increased attention toward reducing postoperative complications and earlier outpatient follow-up in these patients may be a viable strategy for decreasing readmissions after pulmonary resection.

Association between hospital imaging use and venous thromboembolism events rates based on clinical data.

Objective:The objective was to assess the presence and extent of venous thromboembolic (VTE) surveillance bias using high-quality clinical data. Background:Hospital VTE rates are publicly reported and used in pay-for-performance programs. Prior work suggested surveillance bias: hospitals that look more for VTE with imaging studies find more VTE, thereby incorrectly seem to have worse performance. However, these results have been questioned as the risk adjustment and VTE measurement relied on administrative data. Methods:Data (2009–2010) from 208 hospitals were available for analysis. Hospitals were divided into quartiles according to VTE imaging use rates (Medicare claims). Observed and risk-adjusted postoperative VTE event rates (regression models using American College of Surgeons National Surgical Quality Improvement Project data) were examined across VTE imaging use rate quartiles. Multivariable linear regression models were developed to assess the impact of hospital characteristics (American Hospital Association) and hospital imaging use rates on VTE event rates. Results:The mean risk-adjusted VTE event rates at 30 days after surgery increased across VTE imaging use rate quartiles: 1.13% in the lowest quartile to 1.92% in the highest quartile (P < 0.001). This statistically significant trend remained when examining only the inpatient period. Hospital VTE imaging use rate was the dominant driver of hospital VTE event rates (P < 0.001), as no other hospital characteristics had significant associations. Conclusions:Even when examined with clinically ascertained outcomes and detailed risk adjustment, VTE rates reflect hospital imaging use and perhaps signify vigilant, high-quality care. The VTE outcome measure may not be an accurate quality indicator and should likely not be used in public reporting or pay-for-performance programs.

Evaluation of hospital factors associated with hospital postoperative venous thromboembolism imaging utilisation practices

Background Recent research suggests that hospital rates of postoperative venous thromboembolism (VTE) are subject to surveillance bias: the more hospitals ‘look for’ VTE, the more VTE they find. However, little is known about what drives variation in hospital VTE imaging rates. We conducted an observational study to examine hospital and market characteristics that were associated with hospital-level rates of postoperative VTE imaging, focusing on hospitals with particularly high rates. Methods For Medicare beneficiaries undergoing 11 major operations (2009–2010) at 2820 hospitals, hospital-level postoperative VTE imaging use rates were calculated. Hospital characteristics associated with hospital VTE imaging use rates were examined including case severity, size, ownership, VTE process measure adherence, accreditations, staffing, malpractice environment, and county market factors. Associations between explanatory variables and VTE imaging rates were assessed using quantile regressions at the 25th, median, 75th and 90th quantiles. Results Mean postoperative VTE imaging rates ranged from 85.26 (SD=67.38) per 1000 discharges in the lowest quartile of hospitals ranked by VTE imaging rates to 168.86 (SD=76.70) in the highest quartile. Drivers of high imaging rates at the 90th quantile were high resident-to-bed ratio (coefficient=51.35, p<0.01), Joint Commission accreditation (coefficient=19.05, p<0.01), presence of other hospitals in the same market with high imaging rates (coefficient=15.29, p<0.01), average case severity (coefficient=11.97, p<0.01), local malpractice costs (coefficient=11.29, p<0.01), and market competition (coefficient=11.03, p<0.01). Conclusions Hospital teaching status, resident-to-bed ratio, malpractice environment and local market factors drive hospital postoperative VTE imaging use, suggesting that non-clinical forces predominantly drive hospital VTE imaging practices.

Stepwise age-related outcomes of elective endovascular abdominal aortic aneurysm repair: 11-year institutional review.

OBJECTIVE Endovascular repair of abdominal aortic aneurysms (EVAR) has largely supplanted open surgery over the past 2 decades. Faced with an aging population, the outcomes of EVAR among various age groups were examined. METHOD Retrospective review of elective EVAR cases was performed at a single institution from 1998 to 2009. Patients were separated into 4 age groups for easy comparison. Perioperative data were analyzed using Fishers exact test. RESULTS Demographics were similar among the groups except for sex, BMI, and smoking status. The 30-day morbidity and mortality data were not statistically different among groups. From EVAR to end of the study, there was a 10.9% all-cause mortality rate (with no difference among groups) and an 8.0% reintervention rate (with the oldest age group having a lower reintervention rate; P < .03). CONCLUSIONS EVAR remains a good treatment option for elective aneurysm repair despite advanced age, which alone does not appear to be an independent predictor of outcome.

Postoperative Venous Thromboembolism Outcomes Measure – Analytic Exploration of Potential Misclassification of Hospital Quality Due to Surveillance Bias

Several studies have demonstrated the presence of surveillance bias in the Agency for Healthcare Research and Quality Patient Safety Indicator #12 (PSI12), Postoperative Venous Thromboembolism (VTE), in which hospitals with higher rates of VTE-related diagnostic imaging also have disproportionately higher PSI12 rates.1 Surveillance bias in PSI12 raises a subsequent question that has received less attention: how accurate is PSI12 in (a) identifying truly poor-quality hospitals (“true positives” for poor VTE outcomes) versus those that only appear to be poor-quality hospitals due to high VTE-imaging rates (“false positives”); and (b) identifying truly high-quality hospitals with low VTE rates (“true negatives”) versus those that only appear to be high-quality hospitals because of inappropriately low VTE imaging rates (“false negatives”). Because incentives are tied to PSI12, it is important to understand the PSI12s potential for hospital misclassification with respect to quality. In the absence of a universal screening protocol, VTE-imaging rates are generally below 100%. Imaging is not performed at random, but targeted at patients who are symptomatic or at greater risk for VTE such that patients are prioritized for imaging based on observable signs and symptoms of VTE and factors. This implies that there are diminishing returns to VTE-imaging. Because of this targeting/prioritization, imaging will reveal clinically-significant VTE until imaging rates exceed the true incidence of clinically-significant VTE. At that point, additional imaging will detect non-clinically-significant clots2 for which treatment may not have a favorable cost-effectiveness profile. Because the PSI12 numerator counts all diagnosed VTE events as identified on the basis of International Classification of Diseases, 9th Revision (ICD-9-CM) codes,3 higher VTE-imaging rates can inflate the numerator because ICD-9-CM codes do not differentiate between clinically-significant and subclinical VTE. If subclinical VTE could be reliably distinguished from clinically-significant VTE, subclinical events could be excluded from the numerator to eliminate this source of surveillance bias. There is, however, another problem with PSI12—one which lurks in the denominator: “all surgical discharges age 18 and older defined by specific DRGs or MS-DRGs and an ICD-9-CM code for an operating room procedure.”3 For any rate measure, all observations in the denominator should be at risk for experiencing the numerator event. In PSI2, the numerator counts patients diagnosed with VTE, but VTE diagnosis depends on imaging. Although all surgical patients are at risk for postoperative VTE, not all patients are at equal risk for imaging due to practice variations, differences in organizational/institutional characteristics (e.g. technological capacity and radiology staffing) and/or heterogeneity in hospital culture. The PSI12 denominator represents the actual population at risk of VTE diagnosis only under 100% screening. To illustrate how this denominator problem along with the inability to differentiate and exclude subclinical VTE from clinically-significant events in the numerator can jointly lead to inaccurate conclusions about hospital quality, consider two hypothetical hospitals, Hospital-X and Hospital-Z. The x-axes in Figure 1 show the number of patients in Hospital-X and Hospital-Z that are in the PSI12 denominator. The primary y- axis shows the true, underlying incidence of VTE in each hospital. In practice, this true rate is unobservable, but for the purposes of this hypothetical illustration, we assume it is known. Dark-shaded regions depict the proportion of each hospitals denominator that develops clinically-significant VTE, while light-shaded regions depict the proportion developing subclinical VTE. Hospital-X has better VTE-related quality of care: its underlying clinically-significant VTE incidence is 20%. Hospital-Z has poorer quality-of-care: its clinically-significant VTE incidence is 40%. Both hospitals have a 30% incidence of subclinical VTE. Figure 1 Hypothetical Illustration of Hospital Misclassification Due to Surveillance Bias in PSI12 The secondary y-axis shows the number of patients that receive VTE-imaging. At 10% surveillance (Line A), both hospitals have identical PSI12 rates of 10%, although Hospital-X has higher quality than Hospital-Z. In Hospital-X, 50% of clinically-significant VTE went undetected, compared to 75% in Hospital-Z. Based on PSI12, both hospitals not only appear the same, but they appear to have better VTE outcomes than they actually do. At 20% surveillance (Line 2), both hospitals have PSI12 rates of 20%. This captures all clinically-significant VTE in Hospital-X, but 50% of clinically-significant VTE remain undetected in Hospital-Z. At 40% surveillance (Line 3), both hospitals have PSI12 rates of 40%. However, in Hospital-X, this is comprised of 20 clinically-significant VTE plus an additional 20 subclinical VTE. In Hospital-Z all 40 cases were clinically-significant. At 100% screening (Line 4), the observed PSI12 rates of Hospital X and Z are 50% and 70%, respectively. The relative ordering is accurate (Hospital-X has lower rates of VTE than Hospital-Z), although VTE rates are inflated due to inclusion of both clinically-significant and subclinical VTE. By means of stylized construction, Figure 1 reveals that, holding underlying clinical quality constant, PSI12 may not reflect true clinical quality due to variation in VTE imaging rates. Furthermore, holding surveillance rates constant, PSI12 rates can still fail to reflect true levels of clinical quality due to unobserved heterogeneity in underlying VTE incidence across hospitals. The analysis of surveillance bias in PSI12 is not simply an empirical or theoretical exercise in measurement science. The potential for PSI12 to misclassify hospitals with respect to quality of care can lead to unintended consequences on multiple levels. Consumers may unwittingly choose “the wrong” hospital based on ostensibly low PSI12 rates if those rates are low because of inadequate surveillance. By the same token, consumers may reject higher quality hospitals because of ostensibly higher PSI12 rates that are high because of more intense surveillance. Payers may misdirect financial rewards towards “false negative” hospitals (worse outcomes than reflected in PSI12) away from “false positive” (better outcomes than reflected in PSI12). Finally, inefficiency in hospital resource allocation may ensue if hospital quality improvement (QI) leaders/teams use PSI12 to guide investment in QI activities. Developing an empirical approach to quantify the magnitude of misclassification in PSI12 due to surveillance bias hinges on the existence of a gold standard for measuring true underlying VTE incidence that is independent of VTE surveillance imaging. To our knowledge, no such gold standard exists. Nevertheless, PSI12 can be improved if administrative codes are developed and implemented that enable reliable identification and exclusion of subclinical VTE from the measure numerator. Regardless, stakeholders should consider the limitations of this measure when using PSI12 as a basis for evaluating hospital quality.

Management of hemothorax after thoracic endovascular aortic repair for ruptured aneurysms

Background Thoracic aortic aneurysm rupture is often a fatal condition. Emergent thoracic endovascular aortic repair (TEVAR) has emerged as a suitable treatment option. Unfortunately, respiratory complications from hemothorax continue to be an important cause of morbidity and mortality even after successful management of the aortic rupture. We hypothesize that early hemothorax decompression after TEVAR for ruptured aneurysms decreases the rate of postoperative respiratory complications. Methods Single-center, retrospective eight-year review of ruptured thoracic aneurysms treated with TEVAR. Results Seventeen patients presented with ruptured degenerative thoracic aortic aneurysms, all of which were successfully treated emergently with TEVAR. The mean age was 74 years among the 12 (70.6%) men and 5 (29.4%) women treated. Inpatient and 30-day mortality rates for the entire cohort were both 17.6% (three patients). The 90-day mortality rate was 47.1% (eight patients). Thirty-day morbidities of the entire cohort included stroke (n = 1, 5.9%), spinal cord ischemia (n = 3, 17.6%; only one was temporary), cardiac arrest (n = 4, 23.5%; 3 were fatal), respiratory failure (n = 5, 29.4%), and renal failure (n = 5, 29.4%). A large hemothorax was identified in the majority of patients (n = 14, 82.4%). While six (42.9% of 14) patients had immediate chest tube decompression on the day of index procedure, three (21.4% of 14) patients had decompression on postoperative day 1, 4, and 7, respectively. Although not statistically significant, there were trends toward higher rates of respiratory failure (50.0% vs. 16.7%, P = 0.198) and 90-day mortality (62.5% vs. 33.3%, P = 0.280) for patients with delayed or no hemothorax decompression when compared to patients with immediate hemothorax decompression. Conclusions The morbidity and mortality of ruptured degenerative thoracic aortic aneurysms remains high despite the introduction of TEVAR. In this single-center experience, there was a trend toward decreased respiratory complications and increased survival with early chest decompression of hemothorax after TEVAR.