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Sepsis after major cancer surgery
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Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.JournalofSurgicalResearch.com
Sepsis after major cancer surgery Jesse D. Sammon, DO,a,* Dane E. Klett, MD,a Akshay Sood, MD,a Kola Olugbade Jr., MD,b Marianne Schmid, MD,b Simon P. Kim, MD,c Mani Menon, MD,a and Quoc-Dien Trinh, MDb a
Vattikuti Urology Institute Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, Detroit, Michigan b Center for Surgery and Public Health and Division of Urologic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts c Department of Urology, Yale University, New Haven, Connecticut
article info
abstract
Article history:
Background: Cancer patients undergoing procedures are at increased risk of sepsis. We sought
Received 11 June 2014
to evaluate the incidence of postoperative sepsis following major cancer surgeries (MCS), and
Received in revised form
to describe patient and/or hospital characteristics associated with heightened risk.
14 July 2014
Methods: Patients undergoing 1 of 8 MCS (colectomy, cystectomy, esophagectomy, gas-
Accepted 18 July 2014
trectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy) within the
Available online 24 July 2014
Nationwide Inpatient Sample from 1999e2009 were identified (N ¼ 2,502,710). Logistic regression models fitted with generalized estimating equations were used to estimate
Keywords:
primary predictors (procedure, age, gender, race, insurance, Charlson Comorbidity Index,
Cancer surgery
hospital volume, and hospital bed size) effect on sepsis and sepsis-associated mortality.
Infection
Trends were evaluated with linear regression.
Sepsis
Results: The incidence of MCS-related sepsis increased 2.0% per year (P < 0.001), whereas
Mortality
mortality remained stable. Odds of sepsis were highest among esophagectomy patients
Nationwide Inpatient Sample
(odds ratio [OR]: 3.13, 2.76e3.55) and those with non-private insurance (OR: 1.33, 1.19e1.48 to OR: 1.89, 1.71e2.09). Odds of sepsis-related mortality were highest among lung resection patients (OR: 2.30, 2.00e2.64) and those experiencing perioperative liver failure (OR: 5.68, 4.30e7.52). Increasing hospital volume was associated with lower odds of sepsis and sepsis-related mortality (OR: 0.89, 0.84e0.95 to OR: 0.58, 0.53e0.62 and OR: 0.88, 0.77e0.99 to OR: 0.78, 0.67e0.93). Conclusions: Between 1999 and 2009, the incidence of MCS-related sepsis increased; however, sepsis-related mortality remained stable. Significant disparities exist in patient and hospital characteristics associated with MCS-related sepsis. Hospital volume is an important modifiable risk factor associated with improved sepsis-related outcomes following MCS. ª 2015 Elsevier Inc. All rights reserved.
1.
Introduction
Sepsis is a costly, life-threatening, and multifactorial condition commonly occurring in elderly, immunocompromised,
critically ill, and/or postoperative patients [1]. An estimated 2% of all hospitalizations nationwide are associated with sepsis [1]. In the United States, the direct health-care cost burden of sepsis may be as high as $50,000 per patient per
* Corresponding author. Vattikuti Urology Institute Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, 2799 W. Grand Boulevard, Detroit, MI 48202. Tel.: þ1 207 692 7167; fax: þ1 313 916 4352. E-mail address: [email protected] (J.D. Sammon). 0022-4804/$ e see front matter ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jss.2014.07.046
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discharge and approximately $17 billion annually [2]. The cost burden of sepsis derives from lengthier hospital stays, increased utilization of ventilators, and concurrent management of other comorbidities [3]. Despite a national focus on the prevention and treatment of healthcare-associated infections, mortality remains nearly 20% [1]. As such, sepsis is a significant cause of inpatient death, with surgical patients accounting for approximately one third of all sepsis cases [4]. Patients with an underlying malignant process are often hospitalized for sepsis, and one in six patients with severe sepsis have been shown to have a concurrent neoplastic process increasing their risk of death [3]. Patients with malignancy are at increased risk for developing sepsis given their likelihood of more frequent hospital stays, invasive procedures, and treatment with immunomodulating chemotherapies [1]. Furthermore, patients with malignancy are believed to have baseline immunosuppression due to inflammatory cytokine release, upregulation of immunosuppressive cells (T regulatory cells and myeloid-derived suppressor cells), immunosuppressive cell signaling receptors and/or ligands (programmed cell-death 1), and/or unresponsive and/or decreased T cells [5]. Similarly, opportunistic infections, disorganized neoplastic inflammatory responses, and organ dysfunction secondary to localized invasion place oncologic patients at an increased risk of death [6]. Based on the complex interplay between patient risk factors and the inherent risk of major surgery, we examine postoperative sepsis in patients undergoing one of eight major cancer surgeries (MCS) including: colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy. Large, high-volume academic centers have been associated with a salutary effect on postoperative complications and surgical outcomes across a wide range of surgical interventions [2,7e9], and we hypothesized the same would hold true for rates of sepsis and sepsisrelated mortality following MCS. We explore the relationship between postoperative sepsis and patient demographics, hospital characteristics, and surgeries performed in an effort to identify modifiable risk factors, as well as potential processes or structural changes that may aid in the reduction of postoperative sepsis following MCS.
2.
Materials and methods
2.1.
Data source
Relying on the Nationwide Inpatient Sample (NIS), hospital discharges in the United States between January first, 1999 and December 30th, 2009 were abstracted. The NIS is a set of longitudinal hospital inpatient databases included in the Healthcare Cost and Utilization Project family, created by the Agency for Healthcare Research and Quality through a federal-state partnership [10]. The database includes discharge abstracts from 8 million hospital stays and is the sole hospital database in the United States with discharge information on all patients regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. Each discharge includes up to 15 inpatient diagnostic and 15 procedural codes. All procedures and
789
diagnoses are coded using the International Classification of Disease, ninth Revision, Clinical Modification.
2.2.
Study population
A total of eight major surgical oncological procedures were selected for evaluation of sepsis such as : colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy. These operations include a group of commonly performed complex procedures, which are either associated with the most common cancers or carry a significant risk of morbidity and mortality, as previously examined [11]. Relying on specific International Classification of Disease, ninth Revision, Clinical Modification procedure codes, each surgical procedure was assessed independently and analyses were restricted to cancer diagnoses only, as previously described [12].
2.3.
Patient and hospital characteristics
For all patients, the following variables were available: age, race (White, Black, Hispanic, Asian and/or Pacific Islander, Native American, or other unspecified), insurance status, and Charlson comorbidity index (CCI). Baseline CCI was calculated according to Charlson et al. [13], as adapted by Deyo et al [14]. Insurance categories are combined in general groups, namely private insurance, Medicare, Medicaid, and other (self-pay). Hospital characteristics include hospital volume and number of beds, categorized as small, medium, and large, specific to the hospital’s region and teaching status [15].
2.4.
End points
The primary end points of interest were sepsis, which was identified via billing codes, and perioperative sepsis-related mortality, which was ascertained through discharge record.
2.5.
Statistical analysis
All demographic characteristics were weighted according to the discharge level estimates provided by the Healthcare Cost and Utilization Project [10]. First, descriptive statistics were generated on frequencies and proportions of categorical variables (gender, race, insurance status, CCI, annual hospital volume, hospital location, hospital region, hospital bed size, and hospital teaching status) and stratified according to sepsis occurrence. Medians and interquartile ranges were reported for continuously coded variables (age). Chi-square and KruskaleWallis tests were used to compare the statistical significance of differences within categorical and continuous variables, respectively. Second, temporal trends in rates were quantified by estimated annual percentage change using the linear regression methodology. Third, multivariable logistic regression models, fitted with generalized estimating equations, were used to assess independent predictors of sepsis following an MCS. Covariates composed of age, gender, race, CCI, insurance status, number of hospital beds, and hospital volume, while controlling for the effects for hospital clustering. Finally, in separate logistic regression models (fitted with generalized
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estimating equations), we assessed the relationship between sepsis and perioperative mortality during hospitalization within the entire cohort, and within each surgery. All statistical analyses were performed using the R statistical package system (R Foundation for Statistical Computing, Vienna, Austria), with a two-sided significance level set at P < 0.05. An institutional review board waiver was obtained before conducting this study, in accordance with institutional regulation when dealing with de-identified administrative data.
3. 3.1.
Results Baseline descriptives
A weighted estimate of 2,502,710 patients who underwent one of the eight examined procedures was obtained. Baseline sociodemographic characteristics are described in Table 1.
3.2.
Incidence of sepsis
Over the study period, 1.9% of patients experienced sepsis following MCS (Table 1). Of the study population, 37.1% underwent colectomy, 3.2% underwent cystectomy, 0.7% underwent esophagectomy, 3.3% underwent gastrectomy, 9.8% underwent hysterectomy, 14.6% underwent lung resection, 2.3% underwent pancreatectomy, and 29.0% underwent prostatectomy. Of the patients that experienced sepsis, 57.3% underwent colectomy, 6.5% underwent cystectomy, 3.1% underwent esophagectomy, 10.5% underwent gastrectomy, 1.8% underwent hysterectomy, 12.6% underwent lung resection, 7.0% underwent pancreatectomy, and 1.3% underwent prostatectomy (P < 0.001). The overall rate of sepsis increased from 1.25% in 1999 to 2.81% in 2009 with an estimated annual percentage change of 14.06% (P < 0.001, Fig. 1).
3.3.
Predictors of sepsis in MCS
Multivariable logistic regression analyses for predictors of sepsis following MCS are reported in Table 2. Females (odds ratio [OR]: 0.72, 0.69e0.75), and those treated at higher volume hospitals (OR: 0.89, 0.84e0.95 to OR: 0.58, 0.53e0.62) were less likely to experience sepsis postoperatively. In contrast, older age (OR: 1.02, 1.02e1.02), Black and Hispanic races (OR: 1.35, 1.25e1.45; OR: 1.16, 1.05e1.27; respectively), comorbidities 3 (OR: 1.16, 1.09e1.23), non-privately insured individuals (OR: 1.33 1.19e1.48 to OR: 1.89 1.71e2.09), and those treated in urban areas (OR: 1.63, 1.51e1.76) had increased odds of sepsis. Hospital teaching status had no effect on postoperative sepsis rates. Procedure-specific variability of sepsis odds were recorded with esophagectomy patients experiencing the highest odds of sepsis (OR: 3.13, 2.76e3.55).
3.4.
Table 1 e Weighted descriptive characteristics of 2,502,710 patients undergoing a MCS, NIS, 1999e2009. Variables Number of patients, % Age, years Median IQR Gender Male Female Procedure Colectomy Cystectomy Esophagectomy Gastrectomy Hysterectomy Lung resection Pancreatectomy Prostatectomy Race Caucasian Black Hispanic Other Unknown CCI 0 1 2 3 Insurance status Private Medicaid Medicare Uninsured Hospital location Rural Urban Hospital region Northeast Midwest South West Hospital teaching status Non-teaching Teaching Hospital bed size Small Medium Large Hospital volume Median IQR
Overall
Without sepsis
With sepsis
P*
100.0
98.1
1.9
d
66.0 58e74
66.0 58e74
73.0 64e80
60.3 39.7
60.4 39.6
58.4 41.6
37.1 3.2 0.7 3.3 9.8 14.6 2.3 29.0
36.7 3.1 0.7 3.1 10.0 14.7 2.2 29.6
57.3 6.5 3.1 10.5 1.8 12.6 7.0 1.3
60.9 7.1 3.9 3.7 24.4
60.9 7.1 3.9 3.7 24.4
60.0 9.2 5.3 4.7 20.9
62.4 24.9 5.1 7.6
62.6 24.9 5.1 7.4
54.1 23.9 5.1 16.9
42.1 3.2 50.5 4.2
42.6 3.2 50.1 4.2
21.4 5.2 69.5 3.9
10.7 89.3
10.7 89.3
9.5 90.5
21.0 24.3 35.2 18.8
21 24.4 35.1 19.5
21.1 20.9 39.1 18.8
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001 45.3 54.7
45.1 54.9
50.8 49.2
10.2 23.4 66.4
10.2 23.4 66.4
8.9 24.9 66.2
36.0 16.3e69.7
36.3 16.4e70
25.5 10e49.2
<0.001
<0.001
IQR ¼ interquartile range. * ManneWhitney test.
Predictors of mortality in septic patients
Among patients with sepsis, increasing age was associated with a 3% increase in mortality per year of age (confidence interval [CI]: 1.03e1.04, Table 2). Patients who underwent esophagectomy, gastrectomy, lung resection, and pancreatectomy were 1.46- (CI: 1.13e1.90), 1.45 (CI: 1.25e1.67), 2.30 (CI:
2.00e2.64), and 1.10-times (CI: 0.91e1.33) more likely to die during hospitalization for sepsis in the postoperative setting (colectomy as referent). Patients who were treated in intermediate, high, and very high-volume hospitals were 0.88-, 0.89-, and 0.79-times less likely to die during hospitalization
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Fig. 1 e Populations undergoing MCS and sepsis rates. EAPC [ estimated annual percent change.
(CI: 0.77e0.99, CI: 0.78e1.03, and CI: 0.67e0.93, respectively). Mortality in severe sepsis (cases associated with organ failure) was 5.7-, 3.3-, 2.9-, and 2.7-times higher in the case of liver, cardiovascular, kidney, and metabolic organ failure, respectively (CI: 4.30e7.52, CI: 2.99e3.66, CI: 2.60e3.14, and CI: 2.35e3.06, respectively; Table 3) when compared with patients without organ failure. Overall, during the span of the study, mortality was unchanged among patients experiencing sepsis post MCS (from 31.5%e29.4%, P ¼ 0.093, Fig. 2).
4.
Discussion
Sepsis places a considerable burden on the delivery of medical care, manifested by increased hospital stay, increased healthcare costs, and increased mortality [3,6]. Recent literature has shown a significant increase in the incidence of severe sepsisrelated admissions and inpatient sepsis [16,17], specifically after major surgery [18]. To date, most of the epidemiologic studies that describe the risk factors and outcomes associated with sepsis have largely excluded cancer patients [18,19]. The risk of sepsis in cancer patients, however, is significant and challenges surrounding the detection and prevention of sepsis are crucial to overcome, given the increased susceptibility of this population to sepsis [6,17,20e23]. Cancer patients are believed to have baseline immunosuppression due to inflammatory cytokine release, upregulation of immunosuppressive cells (T regulatory cells and myeloid-derived suppressor cells), immunosuppressive cell signaling receptors and/or ligands (programmed cell-death 1), and/or unresponsive and/or decreased T cells. In the septic patient, pro- and anti-inflammatory cytokines reach elevated concentrations, CD4 and CD8 T cells begin to decrease and/or become unresponsive, and T regulatory cells and myeloid-derived suppressor cells increase; the end result of this cascade of change is immunosuppression [5]. In this context, treatment of cancer patients with surgical intervention carries a particularly high risk of sepsis, and few studies to date have specifically explored this relationship [24].
Our study has several noteworthy findings. We show the overall rate of sepsis after MCS more than doubled over the study period, increasing from 1.2% of MCS patients in 1999 to 2.8% in 2009 (Fig. 1). Our results are comparable with those of Kumar et al. [25] who used the NIS to demonstrate an overall increase in the rate of sepsis in hospitalized patients from 2000e2007. That study reported that the overall incidence of severe sepsis in hospitalized patients increased from 0.99% in 2000 to 2.38% in 2007, a 160% increase. Our findings are also consistent with a number of previous studies that have evaluated overall rates of sepsis in hospitalized patients [1,24e26]. Despite an increased rate of MCS-related sepsis, the overall rate of sepsis-related mortality remained stable over the study period (Fig. 2). These findings contrast with those of Kumar et al. [25] who noted a steady downward trend in sepsis mortality from 39% in 2000 to 27% in 2007. Our findings of a stable mortality rate are surprising given the advances in modern intensive care and dissemination of sepsis-directed therapies during the past decade [20,27]. The discrepancy in these results may reflect the disparate attributes of the two patient cohorts. Prior studies that have examined sepsis after major surgery have not evaluated a cohort limited to cancer patients or have neglected to assess sepsis outcomes of patients undergoing intervention for malignancy. [4,18]. Contemporary literature has further demonstrated that cancer patients hospitalized with sepsis, regardless of surgical status, are at an increased risk of mortality [17]. In addition to procedure type, factors such as gender, race, age, socioeconomic status, and insurance status were significantly associated with increased odds of perioperative sepsis. Epidemiologic studies have shown increased rates of sepsis in both males and females though females have a lower overall incidence [2,3,24]. We corroborate these findings: female patients were less likely to develop sepsis after MCS after controlling for available confounders. Nonetheless, the unadjusted rate of sepsis is increasing twice as quickly for women as it is for men (Fig. 1). Our analyses also show that Black and Hispanic patients have consistently higher odds of sepsis despite simultaneous adjustment for other confounding patient characteristics (Table 2). Similarly, non-privately insured individuals are
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Table 2 e Multivariable logistic regression analysis, fitted with generalized estimating equations, of predictors of in hospital sepsis and predictors of mortality in patients with sepsis following a MCS, NIS, 1999e2009. Variables
Predictors of sepsis
Age Gender Male Female Procedure Colectomy Cystectomy Esophagectomy Gastrectomy Hysterectomy Lung resection Pancreatectomy Prostatectomy Race White Black Hispanic Other Unknown CCI 0 1 2 3 Insurance status Private Medicaid Medicare Uninsured Hospital location Rural Urban Hospital region Northeast Midwest South West Hospital teaching status Non-teaching Teaching Hospital bed size Small Medium Large Annual hospital volume Low Intermediate High Very high
Predictors of mortality in patients with sepsis
OR (95% CI)
OR (95% CI)
1.02 (1.02e1.02)
1.03 (1.03e1.04)
1.0 (ref) 0.72 (0.69e0.75)
1.0 (ref) 1.04 (0.95e1.14)
1.0 1.22 3.13 2.01 0.16 0.60 1.96 0.03
(ref) (1.12e1.33) (2.76e3.55) (1.87e2.16) (0.13e0.18) (0.56e0.64) (1.79e2.14) (0.03e0.04)
2.0 (ref) 0.77 (0.63e0.95) 1.46 (1.13e1.9) 1.45 (1.25e1.67) 0.76 (0.52e1.13) 2.30 (2.00e2.64) 1.10 (0.91e1.33) 0.28 (0.14e0.53)
1.0 1.35 1.16 1.13 0.94
(ref) (1.25e1.45) (1.05e1.27) (1.02e1.25) (0.89e0.99)
1.0 (ref) 0.87 (0.74e1.03) 0.98 (0.8e1.2) 0.97 (0.78e1.2) 1 (0.89e1.13)
1.0 0.81 0.71 1.16
(ref) (0.77e0.85) (0.64e0.78) (1.09e1.23)
1.0 (ref) 0.87 (0.78e0.97) 0.99 (0.81e1.21) 1.01 (0.89e1.14)
1.0 1.89 1.33 1.33
(ref) (1.71e2.09) (1.25e1.41) (1.19e1.48)
1.0 (ref) 1.19 (0.94e1.5) 1.15 (1.01e1.32) 1.1 (0.85e1.43)
1.0 (ref) 1.63 (1.51e1.76)
1.0 (ref) 1.02 (0.87e1.2)
1.0 0.91 1.13 0.97
1.0 (ref) 0.82 (0.77e0.87) 1.04 (0.99e1.1) 0.81 (0.76e0.86)
(ref) (0.85e0.97) (1.07e1.2) (0.91e1.04)
1.0 (ref) 1.05 (0.99e1.1)
1.0 (ref) 1.08 (0.97e1.2)
1.0 (ref) 1.24 (1.15e1.34) 1.45 (1.35e1.57)
1.0 (ref) 1.17 (0.98e1.38) 1.14 (0.96e1.35)
1.0 0.89 0.72 0.58
1.0 (ref) 0.88 (0.77e0.99) 0.89 (0.78e1.03) 0.79 (0.67e0.93)
(ref) (0.84e0.95) (0.67e0.76) (0.53e0.62)
ref ¼ referent category.
predisposed to higher rates of postoperative sepsis in adjusted analyses. Indeed, previous data have shown that non-private insurance and certain race subgroups are more likely to be diagnosed with advanced disease at presentation, likely as a
Table 3 e The effect of organ failure on the odds of mortality in patients with severe sepsis. Failed organ system Cardiovascular failure Respiratory failure Liver failure Kidney failure Neurologic failure Metabolic organ failure
% of septic patients
OR (95% CI)
P
24.0
3.31 (2.99e3.66)
<0.001
27.3 2.70 31.4 5.30 12.1
2.56 (2.32e2.82) 5.68 (4.30e7.52) 2.86 (2.60e3.14) 1.34 (1.11e1.63) 2.68 (2.35e3.06)
<0.001 <0.001 <0.001 0.003 <0.001
Multivariable logistic regression models, fitted with generalized estimating equations (controlling for age, gender, race, CCI, insurance status, hospital location, hospital region, hospital teaching status, hospital bed size, annual hospital volume, and type of cancer surgery) predicting the odds of mortality in the context of organ failure with severe sepsis following MCS, NIS, 1999e2009.
result of uneven or inequitable access to care [11,28]. These cases may be more complex, requiring longer operative time and subsequent postoperative care, thus increasing the odds of a sepsis event. Moreover, decreased access to care presupposes an implicit disparity in access to high-quality hospitals with subspecialized practice profiles [28]. The evidence supports the hypothesis that certain processes of care related to the prevention of infection are likely to be better implemented at highvolume institutions [29]. In our study, patients who received care at high-volume institutions experienced lower odds of both MCS-related sepsis and sepsis-related mortality. This is an important point as it suggests distinct differences exist between low-volume and high-volume institutions. Begg et al. [7], using the surveillance, epidemiology, and end results-Medicare linked database, looked at the impact of hospital volume on overall mortality following MCS. Their study revealed higher volume institutions had significantly lower 30-d mortality rates, and attributed the difference to surgeon and staff expertise garnered through increased surgical volume. Similarly, Morgan et al. explored the relationship between surgeon and hospital volume on mortality after radical cystectomy. These authors showed that the relationship between surgeon volume and survival after radical cystectomy was mostly driven by hospital volume, thus further suggesting that the structure and process characteristics of high-volume hospitals are responsible for improved long-term outcomes after radical cystectomy [30]. With regard to sepsisrelated mortality, a recent publication showed that hospitals in the highest severe sepsis case-volume quartile had lower rates of hospital sepsis mortality without using more resources, compared with hospitals in the lowest volume quartile [8]. Despite national focus on surgical safety and quality improvement over the last decade, high-volume hospitals continue to have significantly lower mortality rates than low-volume hospitals in the modern era [9]. Treatment of sepsis should involve rapid response team intervention on the floor, early admission to the intensive care unit at the first sign of instability, appropriate antibiotic and fluid administration, and aggressive management of organ dysfunction and/or failure [31]. These methods should be considered standard and have been associated with improved
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Fig. 2 e Failure to rescue following MCS and sepsis. EAPC [ estimated annual percent change.
outcomes. The ability of high-volume hospitals to provide superior outcomes is likely related to these factors but we were surprised that academic institutions and larger institutions were not associated with improved odds of sepsis and sepsis-associated mortality. A recent report highlighted several investigational sepsis therapies [5]. The most intriguing category, immunomodulators, target specific aspects of the immune system and work to reverse the effects of immune suppression and invigorate the natural immune system. The most successful immunomodulator studied to date, granulocyte macrophage-colony stimulating factor, works by inducing monocyte differentiation and activating neutrophils and macrophages. In several small trials, it has been shown to restore monocyte human leukocyte antigen-DR, a surrogate marker of positive immune response, and it has been associated with a decreased length of ventilator use, a decreased intensive care unit stay, and a decreased overall hospital stay. It is important to recognize the role these agents may play in the treatment of septic cancer patients, a population stricken with baseline immune suppression, as they may receive great benefit from the immune boosting properties of immunomodulators especially when combined with the standard treatment regimen. Finally, we found risk factors associated with higher odds of sepsis-related mortality following MCS include procedure type and organ failure. Procedures associated with increased odds of sepsis-related mortality are esophagectomy, gastrectomy, and lung resection with, respectively, 1.46-, 1.45-, and 2.30-fold increased odds (Table 2). Moreover, organ failure, notably liver (OR: 5.68) and cardiovascular failure (OR: 3.31), is associated with significantly increased odds of sepsis-related mortality (Table 3). These findings are consistent with prior studies that have demonstrated patients with organ dysfunction to be at an increased risk of severe complications and mortality [1,24]. There are several limitations of our study, and our findings must be interpreted within this context. One of the significant limitations of the NIS is that events of MCS-related sepsis and sepsis-related mortality were captured through claims data, a process with well-described limitations. Also, we were unable to control for coding or data collection errors. Furthermore, administrative billing and/or coding practices may change
over time [32]. The increase in the diagnosis of sepsis, for example, may be partially attributable to changes in hospital reimbursement rates and/or broadening clinical guidelines. Alternatively, mortality is more reliably captured in claims data. Another important limitation of the NIS data includes a lack of consistent surgeon identification, precluding adjustment for the effect of surgeon volume, or individual surgeon practice patterns. Finally, in this study, we examined race as a risk factor for sepsis and controlled for it when assessing the odds of postoperative mortality; however, we were unable to assess the effect of race on cancer incidence.
5.
Conclusions
In conclusion, we provide new insight into the effect of sepsis on surgical oncology. We observed consistent increasing rates of sepsis over the last decade, yet no change in sepsis-related mortality, within the context of MCS. We noted the existence of significant disparities in hospital, insurance, and demographic attributes associated with MCS-related sepsis. Most importantly, high-volume institutions are associated with significantly decreased odds of sepsis-related mortality. This study highlights the need for improved access to quality care, at institutions regularly performing complex oncologic surgery, to avoid potentially devastating outcomes related to the diagnosis of sepsis and its detrimental link to inpatient mortality.
Acknowledgment Authors’ contributions: All authors made substantial contributions to conception and design, acquisition of data, and/or analysis and interpretation of data. All authors participated in drafting the article and/or revising it critically for important intellectual content. Finally, all authors gave final approval of the version to be submitted.
Disclosure The authors declare no conflicts of interest.
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references
[1] Martin GS. Sepsis, severe sepsis and septic shock: changes in incidence, pathogens and outcomes. Expert Rev Anti Infect Ther 2012;10:701. [2] Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 2003;348:1546. [3] Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001;29:1303. [4] Vogel TR, Dombrovskiy VY, Carson JL, Graham AM, Lowry SF. Postoperative sepsis in the United States. Ann Surg 2010;252: 1065. [5] Hotchkiss RS, Monneret G, Payen D. Immunosuppression in sepsis: a novel understanding of the disorder and a new therapeutic approach. Lancet Infect Dis 2013;13:260. [6] Williams MD, Braun LA, Cooper LM, et al. Hospitalized cancer patients with severe sepsis: analysis of incidence, mortality, and associated costs of care. Crit Care 2004;8:R291. [7] Begg CB, Cramer LD, Hoskins WJ, Brennan MF. Impact of hospital volume on operative mortality for major cancer surgery. JAMA 1998;280:1747. [8] Walkey AJ, Wiener RS. Hospital case volume and outcomes among patients hospitalized with severe sepsis. Am J Respir Crit Care Med 2014;189:548. [9] Reames BN, Ghaferi AA, Birkmeyer JD, Dimick JB. Hospital volume and operative mortality in the modern era. Ann Surg 2014;260:244. [10] HCUP Nationwide Inpatient Sample (NIS). Healthcare cost and utilization project (HCUP). 2007-2009. Rockville, MD: Quality AfHRa; 2009. [11] LaPar DJ, Bhamidipati CM, Mery CM, et al. Primary payer status affects mortality for major surgical operations. Ann Surg 2010;252:544. discussion 550e541. [12] Sammon J, Trinh VQ, Ravi P, et al. Health care-associated infections after major cancer surgery: temporal trends, patterns of care, and effect on mortality. Cancer 2013;119:2317. [13] Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373. [14] Deyo RA, Cherkin D, Ciol M. Adapting a clinical comorbidity index for use with ICD-9-CM administrative database. J Clin Epidemiol 1992;45:613. [15] HCUP NIS. NIS Description of data Elements. Rockville, MD: H-CUP e; 2011. [16] Dombrovskiy VY, Martin AA, Sunderram J, Paz HL. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med 2007;35:1244.
[17] Rosolem MM, Rabello LS, Lisboa T, et al. Critically ill patients with cancer and sepsis: clinical course and prognostic factors. J Crit Care 2012;27:301. [18] Vogel TR, Dombrovskiy VY, Lowry SF. Impact of infectious complications after elective surgery on hospital readmission and late deaths in the U.S. Medicare population. Surg Infect (Larchmt) 2012;13:307. [19] Kisat M, Villegas CV, Onguti S, et al. Predictors of sepsis in moderately severely injured patients: an analysis of the National Trauma Data Bank. Surg Infect (Larchmt) 2013; 14:62. [20] Pene F, Percheron S, Lemiale V, et al. Temporal changes in management and outcome of septic shock in patients with malignancies in the intensive care unit. Crit Care Med 2008; 36:690. [21] Regazzoni CJ, Irrazabal C, Luna CM, Poderoso JJ. Cancer patients with septic shock: mortality predictors and neutropenia. Support Care Cancer 2004;12:833. [22] Vandijck DM, Benoit DD, Depuydt PO, et al. Impact of recent intravenous chemotherapy on outcome in severe sepsis and septic shock patients with hematological malignancies. Intensive Care Med 2008;34:847. [23] Taccone FS, Artigas AA, Sprung CL, Moreno R, Sakr Y, Vincent JL. Characteristics and outcomes of cancer patients in European ICUs. Crit Care 2009;13:R15. [24] Mokart D, Leone M, Sannini A, et al. Predictive perioperative factors for developing severe sepsis after major surgery. Br J Anaesth 2005;95:776. [25] Kumar G, Kumar N, Taneja A, et al. Nationwide trends of severe sepsis in the 21st century (2000-2007). Chest 2011;140: 1223. [26] Peake SL, Bailey M, Bellomo R, et al. Australasian resuscitation of sepsis evaluation (ARISE): a multi-centre, prospective, inception cohort study. Resuscitation 2009; 80:811. [27] Blot S, Afonso E, Labeau S. Insights and advances in multidisciplinary critical care: a review of recent research. Am J Crit Care 2014;23:70. [28] Sherman ER, Heydon KH, St John KH, et al. Administrative data fail to accurately identify cases of healthcare-associated infection. Infect Control Hosp Epidemiol 2006;27:332. [29] Kohn L. To err is human: an interview with the Institute of Medicine’s Linda Kohn. Jt Comm J Qual Improv 2000;26:227. [30] Morgan TM, Barocas DA, Keegan KA, et al. Volume outcomes of cystectomydis it the surgeon or the setting? J Urol 2012; 188:2139. [31] Kostakou E, Rovina N, Kyriakopoulou M, Koulouris NG, Koutsoukou A. Critically ill cancer patient in intensive care unit: issues that arise. J Crit Care 2014;29:817. [32] Lindenauer PK, Lagu T, Shieh MS, Pekow PS, Rothberg MB. Association of diagnostic coding with trends in hospitalizations and mortality of patients with pneumonia, 2003-2009. JAMA 2012;307:1405.
Available online at www.sciencedirect.com
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Sepsis after major cancer surgery Jesse D. Sammon, DO,a,* Dane E. Klett, MD,a Akshay Sood, MD,a Kola Olugbade Jr., MD,b Marianne Schmid, MD,b Simon P. Kim, MD,c Mani Menon, MD,a and Quoc-Dien Trinh, MDb a
Vattikuti Urology Institute Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, Detroit, Michigan b Center for Surgery and Public Health and Division of Urologic Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts c Department of Urology, Yale University, New Haven, Connecticut
article info
abstract
Article history:
Background: Cancer patients undergoing procedures are at increased risk of sepsis. We sought
Received 11 June 2014
to evaluate the incidence of postoperative sepsis following major cancer surgeries (MCS), and
Received in revised form
to describe patient and/or hospital characteristics associated with heightened risk.
14 July 2014
Methods: Patients undergoing 1 of 8 MCS (colectomy, cystectomy, esophagectomy, gas-
Accepted 18 July 2014
trectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy) within the
Available online 24 July 2014
Nationwide Inpatient Sample from 1999e2009 were identified (N ¼ 2,502,710). Logistic regression models fitted with generalized estimating equations were used to estimate
Keywords:
primary predictors (procedure, age, gender, race, insurance, Charlson Comorbidity Index,
Cancer surgery
hospital volume, and hospital bed size) effect on sepsis and sepsis-associated mortality.
Infection
Trends were evaluated with linear regression.
Sepsis
Results: The incidence of MCS-related sepsis increased 2.0% per year (P < 0.001), whereas
Mortality
mortality remained stable. Odds of sepsis were highest among esophagectomy patients
Nationwide Inpatient Sample
(odds ratio [OR]: 3.13, 2.76e3.55) and those with non-private insurance (OR: 1.33, 1.19e1.48 to OR: 1.89, 1.71e2.09). Odds of sepsis-related mortality were highest among lung resection patients (OR: 2.30, 2.00e2.64) and those experiencing perioperative liver failure (OR: 5.68, 4.30e7.52). Increasing hospital volume was associated with lower odds of sepsis and sepsis-related mortality (OR: 0.89, 0.84e0.95 to OR: 0.58, 0.53e0.62 and OR: 0.88, 0.77e0.99 to OR: 0.78, 0.67e0.93). Conclusions: Between 1999 and 2009, the incidence of MCS-related sepsis increased; however, sepsis-related mortality remained stable. Significant disparities exist in patient and hospital characteristics associated with MCS-related sepsis. Hospital volume is an important modifiable risk factor associated with improved sepsis-related outcomes following MCS. ª 2015 Elsevier Inc. All rights reserved.
1.
Introduction
Sepsis is a costly, life-threatening, and multifactorial condition commonly occurring in elderly, immunocompromised,
critically ill, and/or postoperative patients [1]. An estimated 2% of all hospitalizations nationwide are associated with sepsis [1]. In the United States, the direct health-care cost burden of sepsis may be as high as $50,000 per patient per
* Corresponding author. Vattikuti Urology Institute Center for Outcomes Research Analytics and Evaluation, Henry Ford Health System, 2799 W. Grand Boulevard, Detroit, MI 48202. Tel.: þ1 207 692 7167; fax: þ1 313 916 4352. E-mail address: [email protected] (J.D. Sammon). 0022-4804/$ e see front matter ª 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jss.2014.07.046
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discharge and approximately $17 billion annually [2]. The cost burden of sepsis derives from lengthier hospital stays, increased utilization of ventilators, and concurrent management of other comorbidities [3]. Despite a national focus on the prevention and treatment of healthcare-associated infections, mortality remains nearly 20% [1]. As such, sepsis is a significant cause of inpatient death, with surgical patients accounting for approximately one third of all sepsis cases [4]. Patients with an underlying malignant process are often hospitalized for sepsis, and one in six patients with severe sepsis have been shown to have a concurrent neoplastic process increasing their risk of death [3]. Patients with malignancy are at increased risk for developing sepsis given their likelihood of more frequent hospital stays, invasive procedures, and treatment with immunomodulating chemotherapies [1]. Furthermore, patients with malignancy are believed to have baseline immunosuppression due to inflammatory cytokine release, upregulation of immunosuppressive cells (T regulatory cells and myeloid-derived suppressor cells), immunosuppressive cell signaling receptors and/or ligands (programmed cell-death 1), and/or unresponsive and/or decreased T cells [5]. Similarly, opportunistic infections, disorganized neoplastic inflammatory responses, and organ dysfunction secondary to localized invasion place oncologic patients at an increased risk of death [6]. Based on the complex interplay between patient risk factors and the inherent risk of major surgery, we examine postoperative sepsis in patients undergoing one of eight major cancer surgeries (MCS) including: colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy. Large, high-volume academic centers have been associated with a salutary effect on postoperative complications and surgical outcomes across a wide range of surgical interventions [2,7e9], and we hypothesized the same would hold true for rates of sepsis and sepsisrelated mortality following MCS. We explore the relationship between postoperative sepsis and patient demographics, hospital characteristics, and surgeries performed in an effort to identify modifiable risk factors, as well as potential processes or structural changes that may aid in the reduction of postoperative sepsis following MCS.
2.
Materials and methods
2.1.
Data source
Relying on the Nationwide Inpatient Sample (NIS), hospital discharges in the United States between January first, 1999 and December 30th, 2009 were abstracted. The NIS is a set of longitudinal hospital inpatient databases included in the Healthcare Cost and Utilization Project family, created by the Agency for Healthcare Research and Quality through a federal-state partnership [10]. The database includes discharge abstracts from 8 million hospital stays and is the sole hospital database in the United States with discharge information on all patients regardless of payer, including persons covered by Medicare, Medicaid, private insurance, and the uninsured. Each discharge includes up to 15 inpatient diagnostic and 15 procedural codes. All procedures and
789
diagnoses are coded using the International Classification of Disease, ninth Revision, Clinical Modification.
2.2.
Study population
A total of eight major surgical oncological procedures were selected for evaluation of sepsis such as : colectomy, cystectomy, esophagectomy, gastrectomy, hysterectomy, lung resection, pancreatectomy, and prostatectomy. These operations include a group of commonly performed complex procedures, which are either associated with the most common cancers or carry a significant risk of morbidity and mortality, as previously examined [11]. Relying on specific International Classification of Disease, ninth Revision, Clinical Modification procedure codes, each surgical procedure was assessed independently and analyses were restricted to cancer diagnoses only, as previously described [12].
2.3.
Patient and hospital characteristics
For all patients, the following variables were available: age, race (White, Black, Hispanic, Asian and/or Pacific Islander, Native American, or other unspecified), insurance status, and Charlson comorbidity index (CCI). Baseline CCI was calculated according to Charlson et al. [13], as adapted by Deyo et al [14]. Insurance categories are combined in general groups, namely private insurance, Medicare, Medicaid, and other (self-pay). Hospital characteristics include hospital volume and number of beds, categorized as small, medium, and large, specific to the hospital’s region and teaching status [15].
2.4.
End points
The primary end points of interest were sepsis, which was identified via billing codes, and perioperative sepsis-related mortality, which was ascertained through discharge record.
2.5.
Statistical analysis
All demographic characteristics were weighted according to the discharge level estimates provided by the Healthcare Cost and Utilization Project [10]. First, descriptive statistics were generated on frequencies and proportions of categorical variables (gender, race, insurance status, CCI, annual hospital volume, hospital location, hospital region, hospital bed size, and hospital teaching status) and stratified according to sepsis occurrence. Medians and interquartile ranges were reported for continuously coded variables (age). Chi-square and KruskaleWallis tests were used to compare the statistical significance of differences within categorical and continuous variables, respectively. Second, temporal trends in rates were quantified by estimated annual percentage change using the linear regression methodology. Third, multivariable logistic regression models, fitted with generalized estimating equations, were used to assess independent predictors of sepsis following an MCS. Covariates composed of age, gender, race, CCI, insurance status, number of hospital beds, and hospital volume, while controlling for the effects for hospital clustering. Finally, in separate logistic regression models (fitted with generalized
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estimating equations), we assessed the relationship between sepsis and perioperative mortality during hospitalization within the entire cohort, and within each surgery. All statistical analyses were performed using the R statistical package system (R Foundation for Statistical Computing, Vienna, Austria), with a two-sided significance level set at P < 0.05. An institutional review board waiver was obtained before conducting this study, in accordance with institutional regulation when dealing with de-identified administrative data.
3. 3.1.
Results Baseline descriptives
A weighted estimate of 2,502,710 patients who underwent one of the eight examined procedures was obtained. Baseline sociodemographic characteristics are described in Table 1.
3.2.
Incidence of sepsis
Over the study period, 1.9% of patients experienced sepsis following MCS (Table 1). Of the study population, 37.1% underwent colectomy, 3.2% underwent cystectomy, 0.7% underwent esophagectomy, 3.3% underwent gastrectomy, 9.8% underwent hysterectomy, 14.6% underwent lung resection, 2.3% underwent pancreatectomy, and 29.0% underwent prostatectomy. Of the patients that experienced sepsis, 57.3% underwent colectomy, 6.5% underwent cystectomy, 3.1% underwent esophagectomy, 10.5% underwent gastrectomy, 1.8% underwent hysterectomy, 12.6% underwent lung resection, 7.0% underwent pancreatectomy, and 1.3% underwent prostatectomy (P < 0.001). The overall rate of sepsis increased from 1.25% in 1999 to 2.81% in 2009 with an estimated annual percentage change of 14.06% (P < 0.001, Fig. 1).
3.3.
Predictors of sepsis in MCS
Multivariable logistic regression analyses for predictors of sepsis following MCS are reported in Table 2. Females (odds ratio [OR]: 0.72, 0.69e0.75), and those treated at higher volume hospitals (OR: 0.89, 0.84e0.95 to OR: 0.58, 0.53e0.62) were less likely to experience sepsis postoperatively. In contrast, older age (OR: 1.02, 1.02e1.02), Black and Hispanic races (OR: 1.35, 1.25e1.45; OR: 1.16, 1.05e1.27; respectively), comorbidities 3 (OR: 1.16, 1.09e1.23), non-privately insured individuals (OR: 1.33 1.19e1.48 to OR: 1.89 1.71e2.09), and those treated in urban areas (OR: 1.63, 1.51e1.76) had increased odds of sepsis. Hospital teaching status had no effect on postoperative sepsis rates. Procedure-specific variability of sepsis odds were recorded with esophagectomy patients experiencing the highest odds of sepsis (OR: 3.13, 2.76e3.55).
3.4.
Table 1 e Weighted descriptive characteristics of 2,502,710 patients undergoing a MCS, NIS, 1999e2009. Variables Number of patients, % Age, years Median IQR Gender Male Female Procedure Colectomy Cystectomy Esophagectomy Gastrectomy Hysterectomy Lung resection Pancreatectomy Prostatectomy Race Caucasian Black Hispanic Other Unknown CCI 0 1 2 3 Insurance status Private Medicaid Medicare Uninsured Hospital location Rural Urban Hospital region Northeast Midwest South West Hospital teaching status Non-teaching Teaching Hospital bed size Small Medium Large Hospital volume Median IQR
Overall
Without sepsis
With sepsis
P*
100.0
98.1
1.9
d
66.0 58e74
66.0 58e74
73.0 64e80
60.3 39.7
60.4 39.6
58.4 41.6
37.1 3.2 0.7 3.3 9.8 14.6 2.3 29.0
36.7 3.1 0.7 3.1 10.0 14.7 2.2 29.6
57.3 6.5 3.1 10.5 1.8 12.6 7.0 1.3
60.9 7.1 3.9 3.7 24.4
60.9 7.1 3.9 3.7 24.4
60.0 9.2 5.3 4.7 20.9
62.4 24.9 5.1 7.6
62.6 24.9 5.1 7.4
54.1 23.9 5.1 16.9
42.1 3.2 50.5 4.2
42.6 3.2 50.1 4.2
21.4 5.2 69.5 3.9
10.7 89.3
10.7 89.3
9.5 90.5
21.0 24.3 35.2 18.8
21 24.4 35.1 19.5
21.1 20.9 39.1 18.8
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001 45.3 54.7
45.1 54.9
50.8 49.2
10.2 23.4 66.4
10.2 23.4 66.4
8.9 24.9 66.2
36.0 16.3e69.7
36.3 16.4e70
25.5 10e49.2
<0.001
<0.001
IQR ¼ interquartile range. * ManneWhitney test.
Predictors of mortality in septic patients
Among patients with sepsis, increasing age was associated with a 3% increase in mortality per year of age (confidence interval [CI]: 1.03e1.04, Table 2). Patients who underwent esophagectomy, gastrectomy, lung resection, and pancreatectomy were 1.46- (CI: 1.13e1.90), 1.45 (CI: 1.25e1.67), 2.30 (CI:
2.00e2.64), and 1.10-times (CI: 0.91e1.33) more likely to die during hospitalization for sepsis in the postoperative setting (colectomy as referent). Patients who were treated in intermediate, high, and very high-volume hospitals were 0.88-, 0.89-, and 0.79-times less likely to die during hospitalization
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Fig. 1 e Populations undergoing MCS and sepsis rates. EAPC [ estimated annual percent change.
(CI: 0.77e0.99, CI: 0.78e1.03, and CI: 0.67e0.93, respectively). Mortality in severe sepsis (cases associated with organ failure) was 5.7-, 3.3-, 2.9-, and 2.7-times higher in the case of liver, cardiovascular, kidney, and metabolic organ failure, respectively (CI: 4.30e7.52, CI: 2.99e3.66, CI: 2.60e3.14, and CI: 2.35e3.06, respectively; Table 3) when compared with patients without organ failure. Overall, during the span of the study, mortality was unchanged among patients experiencing sepsis post MCS (from 31.5%e29.4%, P ¼ 0.093, Fig. 2).
4.
Discussion
Sepsis places a considerable burden on the delivery of medical care, manifested by increased hospital stay, increased healthcare costs, and increased mortality [3,6]. Recent literature has shown a significant increase in the incidence of severe sepsisrelated admissions and inpatient sepsis [16,17], specifically after major surgery [18]. To date, most of the epidemiologic studies that describe the risk factors and outcomes associated with sepsis have largely excluded cancer patients [18,19]. The risk of sepsis in cancer patients, however, is significant and challenges surrounding the detection and prevention of sepsis are crucial to overcome, given the increased susceptibility of this population to sepsis [6,17,20e23]. Cancer patients are believed to have baseline immunosuppression due to inflammatory cytokine release, upregulation of immunosuppressive cells (T regulatory cells and myeloid-derived suppressor cells), immunosuppressive cell signaling receptors and/or ligands (programmed cell-death 1), and/or unresponsive and/or decreased T cells. In the septic patient, pro- and anti-inflammatory cytokines reach elevated concentrations, CD4 and CD8 T cells begin to decrease and/or become unresponsive, and T regulatory cells and myeloid-derived suppressor cells increase; the end result of this cascade of change is immunosuppression [5]. In this context, treatment of cancer patients with surgical intervention carries a particularly high risk of sepsis, and few studies to date have specifically explored this relationship [24].
Our study has several noteworthy findings. We show the overall rate of sepsis after MCS more than doubled over the study period, increasing from 1.2% of MCS patients in 1999 to 2.8% in 2009 (Fig. 1). Our results are comparable with those of Kumar et al. [25] who used the NIS to demonstrate an overall increase in the rate of sepsis in hospitalized patients from 2000e2007. That study reported that the overall incidence of severe sepsis in hospitalized patients increased from 0.99% in 2000 to 2.38% in 2007, a 160% increase. Our findings are also consistent with a number of previous studies that have evaluated overall rates of sepsis in hospitalized patients [1,24e26]. Despite an increased rate of MCS-related sepsis, the overall rate of sepsis-related mortality remained stable over the study period (Fig. 2). These findings contrast with those of Kumar et al. [25] who noted a steady downward trend in sepsis mortality from 39% in 2000 to 27% in 2007. Our findings of a stable mortality rate are surprising given the advances in modern intensive care and dissemination of sepsis-directed therapies during the past decade [20,27]. The discrepancy in these results may reflect the disparate attributes of the two patient cohorts. Prior studies that have examined sepsis after major surgery have not evaluated a cohort limited to cancer patients or have neglected to assess sepsis outcomes of patients undergoing intervention for malignancy. [4,18]. Contemporary literature has further demonstrated that cancer patients hospitalized with sepsis, regardless of surgical status, are at an increased risk of mortality [17]. In addition to procedure type, factors such as gender, race, age, socioeconomic status, and insurance status were significantly associated with increased odds of perioperative sepsis. Epidemiologic studies have shown increased rates of sepsis in both males and females though females have a lower overall incidence [2,3,24]. We corroborate these findings: female patients were less likely to develop sepsis after MCS after controlling for available confounders. Nonetheless, the unadjusted rate of sepsis is increasing twice as quickly for women as it is for men (Fig. 1). Our analyses also show that Black and Hispanic patients have consistently higher odds of sepsis despite simultaneous adjustment for other confounding patient characteristics (Table 2). Similarly, non-privately insured individuals are
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Table 2 e Multivariable logistic regression analysis, fitted with generalized estimating equations, of predictors of in hospital sepsis and predictors of mortality in patients with sepsis following a MCS, NIS, 1999e2009. Variables
Predictors of sepsis
Age Gender Male Female Procedure Colectomy Cystectomy Esophagectomy Gastrectomy Hysterectomy Lung resection Pancreatectomy Prostatectomy Race White Black Hispanic Other Unknown CCI 0 1 2 3 Insurance status Private Medicaid Medicare Uninsured Hospital location Rural Urban Hospital region Northeast Midwest South West Hospital teaching status Non-teaching Teaching Hospital bed size Small Medium Large Annual hospital volume Low Intermediate High Very high
Predictors of mortality in patients with sepsis
OR (95% CI)
OR (95% CI)
1.02 (1.02e1.02)
1.03 (1.03e1.04)
1.0 (ref) 0.72 (0.69e0.75)
1.0 (ref) 1.04 (0.95e1.14)
1.0 1.22 3.13 2.01 0.16 0.60 1.96 0.03
(ref) (1.12e1.33) (2.76e3.55) (1.87e2.16) (0.13e0.18) (0.56e0.64) (1.79e2.14) (0.03e0.04)
2.0 (ref) 0.77 (0.63e0.95) 1.46 (1.13e1.9) 1.45 (1.25e1.67) 0.76 (0.52e1.13) 2.30 (2.00e2.64) 1.10 (0.91e1.33) 0.28 (0.14e0.53)
1.0 1.35 1.16 1.13 0.94
(ref) (1.25e1.45) (1.05e1.27) (1.02e1.25) (0.89e0.99)
1.0 (ref) 0.87 (0.74e1.03) 0.98 (0.8e1.2) 0.97 (0.78e1.2) 1 (0.89e1.13)
1.0 0.81 0.71 1.16
(ref) (0.77e0.85) (0.64e0.78) (1.09e1.23)
1.0 (ref) 0.87 (0.78e0.97) 0.99 (0.81e1.21) 1.01 (0.89e1.14)
1.0 1.89 1.33 1.33
(ref) (1.71e2.09) (1.25e1.41) (1.19e1.48)
1.0 (ref) 1.19 (0.94e1.5) 1.15 (1.01e1.32) 1.1 (0.85e1.43)
1.0 (ref) 1.63 (1.51e1.76)
1.0 (ref) 1.02 (0.87e1.2)
1.0 0.91 1.13 0.97
1.0 (ref) 0.82 (0.77e0.87) 1.04 (0.99e1.1) 0.81 (0.76e0.86)
(ref) (0.85e0.97) (1.07e1.2) (0.91e1.04)
1.0 (ref) 1.05 (0.99e1.1)
1.0 (ref) 1.08 (0.97e1.2)
1.0 (ref) 1.24 (1.15e1.34) 1.45 (1.35e1.57)
1.0 (ref) 1.17 (0.98e1.38) 1.14 (0.96e1.35)
1.0 0.89 0.72 0.58
1.0 (ref) 0.88 (0.77e0.99) 0.89 (0.78e1.03) 0.79 (0.67e0.93)
(ref) (0.84e0.95) (0.67e0.76) (0.53e0.62)
ref ¼ referent category.
predisposed to higher rates of postoperative sepsis in adjusted analyses. Indeed, previous data have shown that non-private insurance and certain race subgroups are more likely to be diagnosed with advanced disease at presentation, likely as a
Table 3 e The effect of organ failure on the odds of mortality in patients with severe sepsis. Failed organ system Cardiovascular failure Respiratory failure Liver failure Kidney failure Neurologic failure Metabolic organ failure
% of septic patients
OR (95% CI)
P
24.0
3.31 (2.99e3.66)
<0.001
27.3 2.70 31.4 5.30 12.1
2.56 (2.32e2.82) 5.68 (4.30e7.52) 2.86 (2.60e3.14) 1.34 (1.11e1.63) 2.68 (2.35e3.06)
<0.001 <0.001 <0.001 0.003 <0.001
Multivariable logistic regression models, fitted with generalized estimating equations (controlling for age, gender, race, CCI, insurance status, hospital location, hospital region, hospital teaching status, hospital bed size, annual hospital volume, and type of cancer surgery) predicting the odds of mortality in the context of organ failure with severe sepsis following MCS, NIS, 1999e2009.
result of uneven or inequitable access to care [11,28]. These cases may be more complex, requiring longer operative time and subsequent postoperative care, thus increasing the odds of a sepsis event. Moreover, decreased access to care presupposes an implicit disparity in access to high-quality hospitals with subspecialized practice profiles [28]. The evidence supports the hypothesis that certain processes of care related to the prevention of infection are likely to be better implemented at highvolume institutions [29]. In our study, patients who received care at high-volume institutions experienced lower odds of both MCS-related sepsis and sepsis-related mortality. This is an important point as it suggests distinct differences exist between low-volume and high-volume institutions. Begg et al. [7], using the surveillance, epidemiology, and end results-Medicare linked database, looked at the impact of hospital volume on overall mortality following MCS. Their study revealed higher volume institutions had significantly lower 30-d mortality rates, and attributed the difference to surgeon and staff expertise garnered through increased surgical volume. Similarly, Morgan et al. explored the relationship between surgeon and hospital volume on mortality after radical cystectomy. These authors showed that the relationship between surgeon volume and survival after radical cystectomy was mostly driven by hospital volume, thus further suggesting that the structure and process characteristics of high-volume hospitals are responsible for improved long-term outcomes after radical cystectomy [30]. With regard to sepsisrelated mortality, a recent publication showed that hospitals in the highest severe sepsis case-volume quartile had lower rates of hospital sepsis mortality without using more resources, compared with hospitals in the lowest volume quartile [8]. Despite national focus on surgical safety and quality improvement over the last decade, high-volume hospitals continue to have significantly lower mortality rates than low-volume hospitals in the modern era [9]. Treatment of sepsis should involve rapid response team intervention on the floor, early admission to the intensive care unit at the first sign of instability, appropriate antibiotic and fluid administration, and aggressive management of organ dysfunction and/or failure [31]. These methods should be considered standard and have been associated with improved
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Fig. 2 e Failure to rescue following MCS and sepsis. EAPC [ estimated annual percent change.
outcomes. The ability of high-volume hospitals to provide superior outcomes is likely related to these factors but we were surprised that academic institutions and larger institutions were not associated with improved odds of sepsis and sepsis-associated mortality. A recent report highlighted several investigational sepsis therapies [5]. The most intriguing category, immunomodulators, target specific aspects of the immune system and work to reverse the effects of immune suppression and invigorate the natural immune system. The most successful immunomodulator studied to date, granulocyte macrophage-colony stimulating factor, works by inducing monocyte differentiation and activating neutrophils and macrophages. In several small trials, it has been shown to restore monocyte human leukocyte antigen-DR, a surrogate marker of positive immune response, and it has been associated with a decreased length of ventilator use, a decreased intensive care unit stay, and a decreased overall hospital stay. It is important to recognize the role these agents may play in the treatment of septic cancer patients, a population stricken with baseline immune suppression, as they may receive great benefit from the immune boosting properties of immunomodulators especially when combined with the standard treatment regimen. Finally, we found risk factors associated with higher odds of sepsis-related mortality following MCS include procedure type and organ failure. Procedures associated with increased odds of sepsis-related mortality are esophagectomy, gastrectomy, and lung resection with, respectively, 1.46-, 1.45-, and 2.30-fold increased odds (Table 2). Moreover, organ failure, notably liver (OR: 5.68) and cardiovascular failure (OR: 3.31), is associated with significantly increased odds of sepsis-related mortality (Table 3). These findings are consistent with prior studies that have demonstrated patients with organ dysfunction to be at an increased risk of severe complications and mortality [1,24]. There are several limitations of our study, and our findings must be interpreted within this context. One of the significant limitations of the NIS is that events of MCS-related sepsis and sepsis-related mortality were captured through claims data, a process with well-described limitations. Also, we were unable to control for coding or data collection errors. Furthermore, administrative billing and/or coding practices may change
over time [32]. The increase in the diagnosis of sepsis, for example, may be partially attributable to changes in hospital reimbursement rates and/or broadening clinical guidelines. Alternatively, mortality is more reliably captured in claims data. Another important limitation of the NIS data includes a lack of consistent surgeon identification, precluding adjustment for the effect of surgeon volume, or individual surgeon practice patterns. Finally, in this study, we examined race as a risk factor for sepsis and controlled for it when assessing the odds of postoperative mortality; however, we were unable to assess the effect of race on cancer incidence.
5.
Conclusions
In conclusion, we provide new insight into the effect of sepsis on surgical oncology. We observed consistent increasing rates of sepsis over the last decade, yet no change in sepsis-related mortality, within the context of MCS. We noted the existence of significant disparities in hospital, insurance, and demographic attributes associated with MCS-related sepsis. Most importantly, high-volume institutions are associated with significantly decreased odds of sepsis-related mortality. This study highlights the need for improved access to quality care, at institutions regularly performing complex oncologic surgery, to avoid potentially devastating outcomes related to the diagnosis of sepsis and its detrimental link to inpatient mortality.
Acknowledgment Authors’ contributions: All authors made substantial contributions to conception and design, acquisition of data, and/or analysis and interpretation of data. All authors participated in drafting the article and/or revising it critically for important intellectual content. Finally, all authors gave final approval of the version to be submitted.
Disclosure The authors declare no conflicts of interest.
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references
[1] Martin GS. Sepsis, severe sepsis and septic shock: changes in incidence, pathogens and outcomes. Expert Rev Anti Infect Ther 2012;10:701. [2] Martin GS, Mannino DM, Eaton S, Moss M. The epidemiology of sepsis in the United States from 1979 through 2000. N Engl J Med 2003;348:1546. [3] Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001;29:1303. [4] Vogel TR, Dombrovskiy VY, Carson JL, Graham AM, Lowry SF. Postoperative sepsis in the United States. Ann Surg 2010;252: 1065. [5] Hotchkiss RS, Monneret G, Payen D. Immunosuppression in sepsis: a novel understanding of the disorder and a new therapeutic approach. Lancet Infect Dis 2013;13:260. [6] Williams MD, Braun LA, Cooper LM, et al. Hospitalized cancer patients with severe sepsis: analysis of incidence, mortality, and associated costs of care. Crit Care 2004;8:R291. [7] Begg CB, Cramer LD, Hoskins WJ, Brennan MF. Impact of hospital volume on operative mortality for major cancer surgery. JAMA 1998;280:1747. [8] Walkey AJ, Wiener RS. Hospital case volume and outcomes among patients hospitalized with severe sepsis. Am J Respir Crit Care Med 2014;189:548. [9] Reames BN, Ghaferi AA, Birkmeyer JD, Dimick JB. Hospital volume and operative mortality in the modern era. Ann Surg 2014;260:244. [10] HCUP Nationwide Inpatient Sample (NIS). Healthcare cost and utilization project (HCUP). 2007-2009. Rockville, MD: Quality AfHRa; 2009. [11] LaPar DJ, Bhamidipati CM, Mery CM, et al. Primary payer status affects mortality for major surgical operations. Ann Surg 2010;252:544. discussion 550e541. [12] Sammon J, Trinh VQ, Ravi P, et al. Health care-associated infections after major cancer surgery: temporal trends, patterns of care, and effect on mortality. Cancer 2013;119:2317. [13] Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373. [14] Deyo RA, Cherkin D, Ciol M. Adapting a clinical comorbidity index for use with ICD-9-CM administrative database. J Clin Epidemiol 1992;45:613. [15] HCUP NIS. NIS Description of data Elements. Rockville, MD: H-CUP e; 2011. [16] Dombrovskiy VY, Martin AA, Sunderram J, Paz HL. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: a trend analysis from 1993 to 2003. Crit Care Med 2007;35:1244.
[17] Rosolem MM, Rabello LS, Lisboa T, et al. Critically ill patients with cancer and sepsis: clinical course and prognostic factors. J Crit Care 2012;27:301. [18] Vogel TR, Dombrovskiy VY, Lowry SF. Impact of infectious complications after elective surgery on hospital readmission and late deaths in the U.S. Medicare population. Surg Infect (Larchmt) 2012;13:307. [19] Kisat M, Villegas CV, Onguti S, et al. Predictors of sepsis in moderately severely injured patients: an analysis of the National Trauma Data Bank. Surg Infect (Larchmt) 2013; 14:62. [20] Pene F, Percheron S, Lemiale V, et al. Temporal changes in management and outcome of septic shock in patients with malignancies in the intensive care unit. Crit Care Med 2008; 36:690. [21] Regazzoni CJ, Irrazabal C, Luna CM, Poderoso JJ. Cancer patients with septic shock: mortality predictors and neutropenia. Support Care Cancer 2004;12:833. [22] Vandijck DM, Benoit DD, Depuydt PO, et al. Impact of recent intravenous chemotherapy on outcome in severe sepsis and septic shock patients with hematological malignancies. Intensive Care Med 2008;34:847. [23] Taccone FS, Artigas AA, Sprung CL, Moreno R, Sakr Y, Vincent JL. Characteristics and outcomes of cancer patients in European ICUs. Crit Care 2009;13:R15. [24] Mokart D, Leone M, Sannini A, et al. Predictive perioperative factors for developing severe sepsis after major surgery. Br J Anaesth 2005;95:776. [25] Kumar G, Kumar N, Taneja A, et al. Nationwide trends of severe sepsis in the 21st century (2000-2007). Chest 2011;140: 1223. [26] Peake SL, Bailey M, Bellomo R, et al. Australasian resuscitation of sepsis evaluation (ARISE): a multi-centre, prospective, inception cohort study. Resuscitation 2009; 80:811. [27] Blot S, Afonso E, Labeau S. Insights and advances in multidisciplinary critical care: a review of recent research. Am J Crit Care 2014;23:70. [28] Sherman ER, Heydon KH, St John KH, et al. Administrative data fail to accurately identify cases of healthcare-associated infection. Infect Control Hosp Epidemiol 2006;27:332. [29] Kohn L. To err is human: an interview with the Institute of Medicine’s Linda Kohn. Jt Comm J Qual Improv 2000;26:227. [30] Morgan TM, Barocas DA, Keegan KA, et al. Volume outcomes of cystectomydis it the surgeon or the setting? J Urol 2012; 188:2139. [31] Kostakou E, Rovina N, Kyriakopoulou M, Koulouris NG, Koutsoukou A. Critically ill cancer patient in intensive care unit: issues that arise. J Crit Care 2014;29:817. [32] Lindenauer PK, Lagu T, Shieh MS, Pekow PS, Rothberg MB. Association of diagnostic coding with trends in hospitalizations and mortality of patients with pneumonia, 2003-2009. JAMA 2012;307:1405.