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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 7  |  Issue : 3  |  Page : 119-127

Predictors of postoperative complications in retroperitoneal sarcoma surgery


Department of Surgery, King Abdulaziz University, Jeddah, Saudi Arabia

Date of Web Publication4-Nov-2019

Correspondence Address:
Mohammed O Nassif
Department of Surgery, King Abdulaziz University, Jeddah
Saudi Arabia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ssj.ssj_28_19

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  Abstract 

Introduction: Retroperitoneal sarcomas (RPSs) are large in size and often involve adjacent organs or vital structures. Completeness of resection is critical for long-term survival; however, this often involves extensive surgeries. This study aimed to identify predictors of early severe postoperative complications after RPS surgery.
Methodology: In patients who underwent surgery for RPS, intraoperative variables and patient characteristics were assessed to determine predictors for severe postoperative complications.
Results: Two hundred and thirty-three patients were included. In comparison to patients who had no comorbidity, those with one or more comorbidities were more likely to have postoperative complications (odds ratio [OR]: 2.38; confidence interval [CI]: 1.03–5.48). Patients who avoided admission to the intensive care unit (ICU) within 24 h of surgery had less complications postoperatively (OR: 0.08; CI: 0.02–0.30). Multiple organ resection during surgery and patients' age had no impact on the occurrence of severe complications.
Conclusion: This study showed that a high patient comorbidity index, male gender, and early admission to the ICU were independently associated with an increased risk of postoperative severe complications. However, the age of the patient and degree of surgical resection had no impact on this occurrence. These findings suggest that age and extent of resection should not be used as a sole determinant of patient's eligibility for curative surgery.

Keywords: Elderly, postoperative morbidity and mortality, retroperitoneal sarcoma


How to cite this article:
Nassif MO. Predictors of postoperative complications in retroperitoneal sarcoma surgery. Saudi Surg J 2019;7:119-27

How to cite this URL:
Nassif MO. Predictors of postoperative complications in retroperitoneal sarcoma surgery. Saudi Surg J [serial online] 2019 [cited 2019 Nov 12];7:119-27. Available from: http://www.saudisurgj.org/text.asp?2019/7/3/119/270241


  Introduction Top


Sarcomas are rare neoplasms that account for <1% of all adult cancers, and retroperitoneal sarcomas (RPSs) represent only 9% of all soft-tissue malignancies.[1] Based on the data collected from 1973 to 2008 by the Surveillance, Epidemiology, and End Results program of the National Cancer Institute,[2] the annual incidence of RPS was approximately 2.7 cases/million of population. The mean age of diagnosis was 64 years with women and men having similar incidence.

Due to the rareness of this disease, there is a lack of established evidence-based treatment guidelines. That being said, there are many aspects that are extrapolated from large trials on extremity sarcomas and are universally agreed upon. The National Comprehensive Cancer Network summarizes these in its yearly guidelines.[3] At present, surgical resection with negative margins is the only potentially curative treatment for RPS.[4] Obtaining a negative margin at the time of surgery is the most important survival prognostic facto.[4],[5],[6],[7],[8],[9],[10],[11],[12],[13],[14] These surgeries are often extensive and require multiple organ resections.[15],[16],[17]

Given the lack of preoperative guidelines for patient selection criteria and studies concerning postoperative complications, it is important to study population-based RPS perioperative data as opposed to institution based. The Province of Quebec has a universal health insurance program (Regie de l'assurance-maladie du Quebec [RAMQ]) and maintains administrative database on every insured resident including data from all claims for medical services rendered. Of the population of Quebec (7.75 million), 97.8% have provincial health insurance.[18],[19] This supplies us with a convenient chance to study predictive factors associated with postoperative complications for rare diseases. The objective of this study was to identify risk factors that predict the occurrence of early postoperative severe complications after RPS surgery in the Province of Quebec over the period limited by the database available between 1993 and 2007. All-cause mortality and return to the operating room (OR) within 30 days were also examined.


  Methodology Top


Settings and data sources

A random population-based sample of over 5 million insured persons has been developed by the Clinical and Health Informatics Research Group, McGill University, Montreal Canada, to assess various components of health-care quality and physician competency. This resource has already supported a number of high-impact studies.[20],[21],[22],[23],[24] It includes information collected on each patient from the following data sources maintained by the RAMQ.

  • Registrant database – Includes date of birth, gender, first three digits of the postal code, insurance eligibility coverage periods, and if applicable, date of death
  • Med-Echo database – Includes hospitalization dates, principal diagnosis and up to 10 secondary diagnoses, and intramural procedures and transfers.


Medical service claims database

It includes information on all services provided by physicians under the fee-for-service payment model. This includes information on diagnosis (International Classification of Diseases-9th edition [ICD]-9), procedure (service code), date, location, physician specialty, physician identification, and patient identification. This database was used as the main pool that the cohort in this study was selected from and has been validated in a previous study. It has been found that if a fee-for-service billing is the prevalent method of payment, data collected form billing claims by the physicians are reliable and valid. It could be reliably used to be study prevalence of an occurrence and its associated risk factors.[25] Additional demographic information was obtained from census data from Statistics Canada and linked using postal codes.[26] RPS diagnosis was ascertained using the ICD codes, ICD-9, and the RPS surgery service codes. The index date was the date of the RPS surgery. Appropriate ethical clearance was obtained.

Cohort selection

From the random population-based cohort described above, a subcohort was built, based on the following inclusion criteria:

  • Adults – Patients over the age of 18 years were identified by their date of birth and the index date (the index date used was the first RPS excision surgery done)
  • Incidental RPS – Newly diagnosed RPS, recognized by a claim with an ICD-9 codes for RPS and verified [Appendix A]
  • Diagnosis date – All diagnoses between 1993 and 2007
  • Underwent RPS surgery – Identified through claim codes that were verified by specialized surgeons who operate on RPS regularly
  • With 1 year of medical history – Availability of administrative claims data for 365 days before the index date. Reason was to rule out any previous diagnosis with RPS
  • With 1-month follow-up – Availability of administrative claims data for a minimum of 30 days following the index date. Reason is to find all complications within a month of the index date. Patients who die before the completion of 30 days are also included.



Two groups of patients were excluded from the cohort:

  • Pregnant patients – Patients who had pregnancy ICD-9 codes in claims within 9 months before the index date
  • Patients with nonrelated diseases – These include repeated ICD-9 codes of sarcomas elsewhere in the body, retroperitoneal cancers of solid organs, and other retroperitoneal benign diseases. Reason was to validate the cohort by having a high specificity cutoff.


A stepwise approach was taken when building the cohort [Figure 1]. Each step was established after extracting multiple random patient samples before the exclusion and manually searching their codes to verify that the path was correct.
Figure 1: Cohort selection algorithm

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Independent analytic variables

Patient related

  • Age – Age at the index date calculated using the date of birth from the registrant database
  • Sex – Sex of the patient, male or female, from the registrant database
  • Place of residence – The municipalities of Quebec were divided into rural and nonrural according to Statistics Canada's definition of rural census as “the population outside settlements with 1,000 or more population with a population density of 400 or more inhabitants per square kilometer,” Statistics Canada, 2007[27]
  • Income and education estimates – Census data were used to measure the mean family income and educational level percentage in the patients' residential area as described by Wilkins.[20],[28] Ten (4.3%) patients had missing data on one or more demographic variables (postal code, income, or education) at the time of their index date. By carrying forward or backward of the nonmissing values (i.e., using single imputation methods [29]) for adjacent years for each patient, data were imputed for five patients. For the remaining five patients, values for adjacent years were not available and thus the mean of the most frequently appearing value and the year and area was used to replace the missing data
  • Distance to cancer center with sarcoma program – The mean latitude and longitude was calculated for each patient's forward sortation area (FSA) (postal code area) and used to define the center of the FSA. The data on the geographical coordinates of all postal codes were obtained from Statistics Canada's 2006 postal code conversion file.[30] These centers were then used to calculate the distance between it and the coordinates of cancer centers with a sarcoma program. These are centers that treat sarcoma patients, have multidisciplinary teams, and deliver chemo/radiation therapy
  • Charlson Comorbidity Index (CCI) – The Charlson score was determined using the enhanced ICD-9-CM coding algorithms for Charlson comorbidities [31] with original Charlson weights [32] in the 365 days before the index date. Our database only contained four-digit diagnostic codes and all the 5th digits from the diagnostic codes used by the enhanced algorithm were ignored. However, similar to the Dartmouth–Manitoba algorithm, severity of comorbidities was adjusted to avoid double-counting one chronic condition that may be characterized using multiple diagnosis codes.[33] For example, a patient with metastatic solid tumor had the code for metastasis marked, while no code was inserted for the specific primary organ malignancy. Moderate-to-severe liver disease and complicated diabetes were treated the same way. Since every patient in the cohort had sarcoma, the malignancy points were given only to patients that had other previous malignancies. The patients were then divided according to their score into groups: 0 score (no comorbidity, healthy), 1–2 score (low CCI), and 3 or more score (high CCI).[34]


Perioperative

  • Hospital setting – Defined as academic or nonacademic, based on assignations from the Ministry of Health
  • Specialty of treating surgeon – Specialty codes were provided from billing claims data. In the case of more than one surgeon having billed for the index procedure, the principal role was assigned to the one who was remunerated the maximum amount as opposed to others who received differential pay, based on the assistant's roles. Recognized medical specialties were identified from service claims at the day of the surgery and they were grouped into general surgery or other
  • Planned intensive care unit (ICU) admission – Patients who were admitted to the ICU were identified by billing codes with the ICU as a location for the billing. If these patients were admitted within the first 24 h window after surgery, they were considered an elective ICU admission
  • Additional organs resected at surgery – Additional claims billed on the index date were reviewed. Those that corresponded to surgical procedures were classified according to the system/organ involved. Any surgery from one of the groups was counted as one additional organ resected
  • Length of stay (LOS) – The Med-Echo hospitalization database was used to identify the number of days the patient was admitted to the hospital after the index date.


Outcome

  • All-cause mortality – Date of death was captured from the registrant database for patient who dies within 30 days of the index date
  • Return to the OR – Patients who returned to the OR were identified by the occurrence of a surgical billing claim in the operative room within 30 days of the index date
  • Severe postoperative complications – Severe complications were defined as those that were grades III, IV, and V according to the Clavien morbidity and mortality classification.[35] Grade III included patients who had a complication requiring surgery, endoscopy, or interventional radiology with or without general anesthesia within 30 days of surgery. In addition to patients who returned to the OR “see above,” specific codes for endoscopic procedures and interventional radiology were identified within 30 days of the index date. Grade IV were patients who were admitted to the ICU after 24 h from the index date and within 30 days after. They were identified by claims that occurred in the ICU or coronary care unit during that period. Grade V were patients who died within 30 days of surgery “see above.”


Assessment of outcomes

Two main outcomes were identified from the databases: all-cause mortality and severe complications within 30 days of RPS. Severe complications were defined as those that were grades III, IV, and V according to the Clavien morbidity and mortality classification.[35]

Statistical analysis

Age, income, education, distance, Charlson index, distance, and intraoperative variables were categorized on the basis of their frequency distributions in the cohort. Univariate analysis was used to assess the unadjusted correlations between each predictor and the outcome (postoperative complications). Subsequently, a multivariate logistic regression was constructed. Because all patients had unique surgeons, adjusting for clustering of patients within physicians was not necessary. Collinearity between variables was assessed via a Pearson's correlation matrix, and subsequently, distance, income, and ICU admissions were left out of the model due to their correlation (coefficient of ≥0.25) with residence, secondary education, and Charlson index, respectively. Selected interaction terms were introduced in the model one at a time, but none was found significant. All P values were for two-tailed tests with statistical significance defined as P ≤ 0.05. SAS software (SAS version 9.2, Institute, Inc., Cary, North Carolina, USA) was used for all analyses.[36]


  Results Top


From an initial 5,212,853 patients in the database, 233 patients met our inclusion/exclusion criteria [Figure 1]. These patients were over the age of 18 years and who have undergone surgery for verified newly diagnosed RPS between January 1993 and March 2007. One patient was excluded from the cohort due to pregnancy.

Descriptive statistics of this cohort are listed in [Table 1]. The mean age was 57.6 years (standard deviation, 14.7), of whom 52.8% were male. The majority of patients (60.5%) were living in an urban setting, mainly in the metropolitan areas of Montreal and Quebec city. Two-thirds of the patients lived in the areas where the average individual overall income was between 30,000 and 60,000 Canadian Dollars (CAD) per year, whereas only 9.4% lived in impoverished areas with an average household income of <30,000 CAD per year. The majority of patients (47.6%) lived in the areas that had a population consisting of 60%–80% who were at least high school graduates. Only 1.3% of patients lived in the areas that had an average of <40% high school graduates. Two-thirds of the patients lived within 50 km of a cancer center with a sarcoma program and only 23.2% had to travel more than 100 km to seek treatment. In terms of comorbidity, 33.1% of patients were found to have a CCI score of 0 at the time of surgery, whereas 27.9% had a score of 1–2 and 39.1% had a score of 3 or more.
Table 1: Patient characteristics

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As for perioperative findings, it was found that the university hospitals were the center of choice for the majority of patients (87.5%) and only 12.5% were treated in nonacademic hospitals [Table 2]. The primary surgeon was a general surgeon in 76.47% of cases, with urologist staffing 9.27% of cases. Two-thirds of the patients had only a RPS resection without any additional organs removed during surgery, 26.6% had 1–2 organs resected, and 6.9% had three or more. An elective ICU admission during the first 24 h postoperatively was arranged for 24 (10%) patients. The average LOS in the hospital postoperatively was 12.6 days, median 9 days, and the mode 7 days. LOS ranged between 1 day (death) and 135 days and only 17 patients had LOS above 30 days, and if excluded, the average of the remaining was 9.5 days.
Table 2: Perioperative findings

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Severe postoperative complications within 30 days [Table 3] occurred in a third of the patients, of these 7 (3%) patients died. However, 52 (22%) patients had a Grade IV complication and were required to have an emergency admission to the ICU. Only 19 (8%) patients had a Grade III complication without an ICU admission. The OR was revisited by 35 (15%) patients within 30 days postoperatively and was considered a complication.
Table 3: Postoperative outcomes

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Multivariate analysis using a generalized estimating equation [Table 4] showed that, in comparison to patients who had no comorbidity, those with a CCI score of ≥3 were more likely to have early severe postoperative complications (odds ratio [OR]: 2.58; confidence interval [CI]: 1.05–6.35). Those with a CCI score of 0 or a low score of 1–2 showed no significant relationship. In addition, patients who lived in the areas with an average higher income had a less change of having complications (OR: 0.82; CI: 0.67–0.99). Furthermore, the distance between patients address and the nearest cancer center with a sarcoma program had no effect on complication rate.
Table 4: Multivariate predictors of severe postoperative complications

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Patients who avoided admission to the ICU within 24 h of surgery had less complications postoperatively (OR: 0.07; CI: 0.02–0.25). Male patients had a higher risk of complications (OR: 2.4; CI: 1.05–5.48). Multiple organs resected during the RPS surgery had no significant effect on the occurrence of complications (OR: 2.09; CI: 0.97–4.5). Surprisingly, the increase in patients' age as well had no impact on the occurrence of early severe complications postoperatively (OR: 0.89; CI: 0.68–1.16).


  Discussion Top


There is an increased demand for reliable and informative studies on postoperative outcome and prognosis, both by the health professionals and by the public. A better quality of health care delivered with a lower cost is a shared goal by everyone. This study addresses early severe postoperative complications and mortality after patients underwent curative RPS surgery. Severe postoperative complications occurred in a third of the patients and 7 (3%) patients died. This percentage is similar to previous studies of patients treated for RPS, which showed that 30%–35% had early postoperative severe complications and 5%–6% died within a month.[37]

Predictors of risk factors on early postoperative complications after RPS were also identified. Overall, it was found that high patient comorbidity index, admission to the ICU within 24 h of surgery, male sex, and low average house hold income predicted a higher rate of postoperative severe complications. However, the age of the patient, the distance from a cancer center with a sarcoma program, low CCI, and degree of surgical resection had no impact on this occurrence.

The relationship between preoperative comorbidity levels and postoperative complications is expected and has been well documented in previous studies with other surgeries.[38],[39],[40],[41] Although we have found no statistical significance between patients who had a CCI score of 1–2 and postoperative risk, the cutoff between this group and ≥3 CCI is not very big. Any comorbidity is believed to affect outcomes in surgery and CCI of 3 should not be taken as a magic number.

ICU requirement during the early postoperative period has been linked to early postoperative deaths.[42] Many major surgeries are initially observed in the ICU postoperatively as a precaution. A study of 148 patients admitted postliver resection in the ICU showed that 88.1% of patients only received closed monitoring during their stay and the majority of patients who were treated in the ICU had either bled significantly in the OR or underwent an extended resection of the liver.[43] Many patients are being admitted to the ICU unnecessarily causing an increased demand of beds and a large waste resources,[44],[45] not to mention the effects on the patients.[46] Many surgeries have selective criteria for admission to the ICU;[47],[48],[49] this can be done for RPS surgery as well.

In regard to male sex having a higher risk of severe postoperative complications, previous studies have shown associations between male sex and worse outcome in other surgeries (neurosurgery interventions, bile duct exploration, and colorectal surgery [50],[51],[52],[53]). The reasons are still unknown, although some speculate that the protective role of estrogen might contribute to faster recovery and less complications.[50],[54]

Another point to add is that, even in a health-care system that is universally available and free for all residents, there was a difference in complication rates between patients living in low and high income areas. While we consider the health system in Quebec tiered-free, this could be due to other factors not accounted for in this study (e.g., social well-being, family support, and general knowledge of the disease). That being said, many studies have shown an association between low socioeconomic status and worse outcomes in cancer patients' status.[55],[56],[57],[58]

What comes as a surprise is that the age of the patients and number of organs resected did not predict a higher postoperative severe complication rate. Assuming that many oncologists would put plenty of weight on the patients' age and the expected extent of resection when making their treatment decision,[59],[60] it is fair to conclude that some patients who were not candidates for surgery might have benefited from it and vice versa.


  Limitations Top


To begin, this database was derived from a registration data obtained from patients' charts and designed for administrative purposes. In addition, the clerical personnel who are responsible for entering these data are usually not physicians themselves. As a result, medical conditions might have been overlooked, and consequently, the relative contribution of these factors to postoperative death might have been underestimated.[61] However, given the rarity of this disease, having such a large number of cases to review is beneficial.

Second, there were limitations to the amount of information on each patient collected and available in this dataset. There might have been additional (potential) confounding factors that were not available in the database, including the intent to cure versus debulking, the clinical condition of the patient before the procedure, and pharmacotherapy. Therefore, residual confounding of other factors not available to us may still exist.

Third, as per the design, analyses were restricted to patients who underwent surgery. No information was included from patients who were screened, but did not undergo surgery because their mortality risk was perceived as prohibitive. Obviously, exclusion of patients at risk of adverse outcomes might have diluted estimates of relative risk.

Fourth, the database from 1993 to 2007 would be considered somewhat old, but the conclusion still stands the same as the surgical approach and management of RPS has not differed since then.


  Conclusion Top


Regardless of a surgeon's capabilities and experience with RPS, many will face postoperative complications routinely in their everyday management of these patients. These complications have a significant impact on patients and health systems, yet our knowledge on appropriate patient selection and incidence of early postoperative complications is still limited. This study shines the light and some of the risk factors of these complications. Better understanding of them can be an aid to decision-makers in their selection of candidates, preoperative preparation, meticulous surgeries, and postoperative readiness in anticipation of any postoperative complication.

Acknowledgments

The Canadian Cancer Society Research Institute, Canadian Institutes of Health Research, and Fonds de la recherche en Santé du Québec provided funding for the database used for this study. King Abdulaziz University provided funds for training at McGill University. My research was conducted during training at Dr. Ari Megurditchian's research lab and analysis was conducted by Mrs. Stanimira Krotneva.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

 
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