This article is accompanied by the following Invited Commentary:
Søreide K, Story DA, Walder B. Perioperative medicine and mortality after elective and emergency surgery. Eur J Anaesthesiol 2016; 33:314–316.
Over 200 million major surgical procedures are carried out worldwide every year but there are few data describing subsets of populations which may be at particularly high risk of perioperative mortality.1,2 In a recent multicentre international study, the European Surgical Outcomes Study (EuSOS), significant country-to-country variations in mortality were demonstrated throughout Europe, suggesting that some deaths might be preventable.3 Scandinavian countries, including Sweden, had lower in-hospital mortality rates than other countries, which might be attributed to well developed healthcare systems and a generally good quality of care in these countries. The Swedish cohort in this study may also have represented a healthier population or undergone less-complicated procedures.
Low perioperative mortality rates make the detection of high-risk subgroups difficult.2 In the EuSOS, 73% of patients who died were never admitted to the ICU.3 This and other studies highlight the need to understand the role of critical care in perioperative management, and the recognition of the groups of patients in need of ICU care.4 For Swedish patients in the EuSOS, however, the admission rate to the ICU was low compared with other countries which reported higher mortality rates. This goes against the prevailing assumption that access to intensive care improves outcomes.5
The level of care of surgical patients, seen from the postanaesthetic period perspective, might be crucial for outcome.6 The relatively low rate of ICU admissions in Sweden may be because of the fact that most postanaesthesia care units (PACUs) act as lower intensity critical care units. All PACUs within the Swedish EuSOS cohort were capable of invasive blood pressure monitoring, administration of vasopressor and inotropic drugs and noninvasive ventilation for up to 24 h, with high staff-to-patient ratios and frequent rounds by anaesthesiologists. In other countries, a care unit with this care level may be regarded as an ICU. Thus, we hypothesised that length of stay (LOS) in PACU is an important predictor of surgical outcome in Sweden.7 As Sweden may be considered to have a high-quality healthcare system with universal healthcare coverage, we were also interested in studying the contribution of other factors previously shown to be associated with mortality after surgery in this population, such as BMI, the use of the WHO surgical checklist and night surgery.8–19
We believe that analyses of long-term surgical outcomes are as important as short-term outcomes. Such data are rarely published at a national level, apart from procedure or disease-specific reports. In this Swedish Surgical Outcomes Study (SweSOS) we aimed to document both short and long-term mortality rates for a mixed surgical population, and identify perioperative factors influencing them.
Materials and methods
This was an observational cohort study of the Swedish subset of the EuSOS to identify short and long-term predictors of mortality. The study was conducted in accordance with the strengthening the reporting of observational studies in epidemiology (STROBE) statement (www.strobe-statement.org). In Sweden, six university hospitals and two general hospitals took part in the EuSOS in 2011.3 EuSOS was an international multicentre prospective 7-day cohort study designed to assess surgical outcomes throughout Europe, and its primary endpoint was in-hospital mortality. In this study, we extended follow-up of the Swedish cohort to 1 year and also collected data regarding patient height and weight, and PACU LOSs. Ethical approval for this study (Ethical Committee number T526-13) was provided by the Regional Ethical Review Board of Gothenburg (Chairperson Professor Sven Wallerstedt) on 5 July 2013.
During the week of the study conducted between 09.00 a.m. on 4 April 2011 and 08.59 a.m. on 11 April 2011, we collected a sample of 1314 unselected patients who had undergone a variety of surgical procedures. In accordance with the protocol, all patients above 16 years of age who underwent surgery commencing during the 7-day collection period were eligible for inclusion. Patients undergoing planned day-case surgery, cardiac surgery, neurosurgery, radiological or obstetric procedures were excluded. The primary endpoints were 30-day and 1-year mortality rates.
The perioperative characteristics of the patient and the surgical procedure as well as follow-up clinical details until discharge from hospital were registered on paper-based case record forms. We censored hospital discharge data at 60 days after surgery, as in the EuSOS. The information was made anonymous and then transferred to a secure internet-based electronic case record form. We added the date of death by temporarily unlocking key codes from participating centres. These new data were submitted to the Swedish National Coordinator for analyses. Missing data were completed and confirmed to minimise any potential source of bias, and an extensively detailed dataset including 30-day and 1-year mortality data as the dependent outcome variables was constructed for analyses. Hospital nonsurvivors were those who died before discharge but within 60 days after surgery, and their data were used to compare with the EuSOS results.
Thirteen predetermined variables were selected on the basis of clinical relevance, a low rate of missing data and previous evidence of an association with mortality. We kept age, BMI and PACU LOS as continuous variables, and regarded surgical procedure category as a nominal variable. Sex, use of the WHO checklist, use of intraoperative cardiac output monitoring, night surgery and ICU admission were binary variables. The remaining variables were ordinal in their nature: American Society of Anesthesiologists’ (ASA) physical status, number of comorbidities, grade and urgency of surgery.
Statistical analysis was conducted using SPSS version 20 (SPSS Inc., Chicago, Illinois, USA). A Kaplan–Meier curve was constructed for the study population in relation to a matched background population. Survival was evaluated with standardised mortality ratio calculated as the ratio between the observed number of deceased patients in the study population and the expected number of deaths in an age and sex-matched general population during the corresponding time period (2011). Age and sex-specific mortality rates for the background population were obtained from Statistics Sweden (www.scb.se). Data are presented as percentages, mean [± standard deviation], median [interquartile range, (IQR)] or odds ratio [95% confidence interval (CI)].
Binary logistic regression was used in the univariate analyses for the 13 variables chosen to identify those having probable association with short and long-term mortalities. A P value of less than 0.05 was considered significant. We tested two models: in the first model, we made no assumptions and entered each variable in a simple regression analysis; in the second model, we entered age and sex as covariates. The second model did not improve the stability of the regression so we chose to continue with the analysis based on the first model. A multivariate model was constructed including all significant variables from univariate analyses for long-term mortality. We did not undertake a multivariate analysis of short-term mortality because the outcome value was too low to allow for robust analysis. The Hosmer–Lemeshow test was used to test for goodness of fit of the regression, and Nagelkerke R2 for effect size.20
For sensitivity analysis, we assessed the basic model with two further samples: one excluding patients with missing BMI data and one excluding patients with preexisting metastatic cancer. These two groups were chosen to test for any possible bias effect. The first group was chosen because there was missing BMI data in 15.6% of the patients, with a possible statistical effect on the results. The second group of patients was chosen because 5.9% of the cohort had metastatic cancer, raising the possibility that they might have been cared for in a different way perioperatively.
The Swedish subset of EuSOS consisted of 1314 patients. Three hundred and three patients were lost to follow-up after discharge. One hospital was unable to unlock the key code because of administrative reasons beyond the control of the study group, and its 292 patients could not be included in SweSOS. Eleven patients from other centres either emigrated from the country or were unregistered immigrants or tourists, with no possibility of follow-up. The final dataset consisted of 1011 patients (Fig. 1).
Characteristics of the SweSOS cohort are shown in Table 1. Mortality increased over 12 months following the study week, increasing by nearly five-fold at 1 year compared with 30 days after surgery. Thirty-day, 3-month, 6-month and 1-year mortalities with their 95% CIs were 1.8% (1.0–2.6), 3.9% (2.7–5.0), 5% (3.7–6.4) and 8.5% (6.8–10.2), respectively. Standardised mortality rates (95% CI) for 30-day and 1-year mortalities for the Swedish cohort compared with an age and sex-matched Swedish population in 2011 were 10.0 (5.9–15.8) and 3.9 (3.1–4.8), respectively. Kaplan–Meier survival curves for these populations up to 1 year of follow-up are shown in Fig. 2.
Thirty-six patients (3.6%) in the SweSOS cohort were admitted to an ICU postoperatively. Twenty of them (56%) were regarded as requiring routine postoperative care. Four patients admitted to ICU (11.1%) died within 30 days; two of them died in ICU after decisions to limit therapy. Ten ICU patients (27.8%) died within 1 year, nine of them with an ASA physical status of 3 or 4 and the tenth had cancer; most of them underwent major and emergency surgery. Six patients out of 11 hospital nonsurvivors (54.5%) died without having been admitted to ICU; four had metastatic cancer, one had four chronic diseases and one had an ASA physical status of 4.
LOS in PACUs varied, with a median of about 3 h (175 min, IQR 110–270 min). One hundred and fifty patients (14.8%) stayed for more than 6 h in PACU, and 67 of them (6.6%) stayed for more than 12 h. When the LOS in PACU exceeded 24 h, further care was regarded as ICU admission.
Univariate logistic regression analysis identified age, ASA physical status, number of comorbidities, urgency of surgery and ICU admission as predictors of short-term mortality (Table 2).
The following factors were associated with long-term mortality in univariate analyses: age, ASA physical status, BMI, number of comorbidities, grade and urgency of surgery, PACU LOS and ICU admission (Table 3).
In multivariate analysis, only age, number of comorbidities and urgency of surgery were independent predictive factors for long-term outcome (Table 4). The P value of the Hosmer–Lemeshow test was 0.83, and Nagelkerke R2 was 0.33. Notably, ASA physical status covaried with number of comorbidities, and was not an independent predictor of 1-year mortality. Grade of surgery, BMI, PACU LOS and ICU admission were also not identified as significant predictors.
We also tested a three-level model with patient factors at the first level, surgical factors at the second level and postoperative factors at the third level. These models yielded the same results as the one-level model. We also tested for hospital site and found no differences.
For sensitivity analyses, we conducted two further models of the multivariate analyses: one excluding 158 patients with missing BMI data (n = 853, 84.4% of the whole cohort) and the other excluding 60 patients with metastatic cancer (n = 951, 94.1% of the whole cohort) (Fig. 1). Both models confirmed the results of the basic model. In the model excluding patients with metastatic cancer, ICU admission became a significant predictor of 1-year mortality (P = 0.012), whereas it was not so in the basic model (P = 0.093). The results of these sensitivity analyses are presented as a supplemental data file, http://links.lww.com/EJA/A79, together with detailed results for the logistic regression.
In this study, we showed that although short-term postsurgical mortality in Sweden was low, long-term mortality was substantially higher. Both mortality rates were also higher when compared with an age and sex-matched background population in Sweden. Mortality rates were driven by known risk factors such as age, number of comorbidities and surgical urgency. ICU admission was a predictor of both short and long-term mortality, but only in univariate analyses. Contrary to our hypothesis, LOS in PACU was not independently predictive of mortality.
Short-term mortality in our study is consistent with those reported in earlier Scandinavian studies.21–26 However, mortality rates in these reports vary considerably, from 0.002 to 11%, and are mostly procedure specific. These studies identified almost identical perioperative factors predictive of early postsurgical mortality as those seen in the current study.
Longer-term mortalities are generally poorly reported in perioperative literature. The current study reports an overall mortality for an unselected population and includes those undergoing unplanned or emergency surgery but excludes day-case procedures. However, other Scandinavian studies and speciality registries report 1-year mortality rates that vary greatly as their figures come from selected surgical populations.27–31 For example, the 1-year mortality after colorectal surgery might be as low as 3%, but could be as high as 61% after lower limb amputation.27,30 In an epidemiological study from the USA, Khuri et al.31 showed that 30-day postsurgery mortality was 0.8% if no complication was registered and 13.3% for patients with postoperative complications. One-year mortality rates were 6.9 and 28.1%, respectively. The results from the present Swedish cohort are in line with these findings, with short and long-term mortalities nearer the lower limit of the wide ranges found in the American study. This may be explained partly by the fact that only patients undergoing major surgery were included in the study by Khuri et al.,31 whereas our study included significantly larger proportions of minor and intermediate grade surgical procedures.
We noted that patients with metastatic cancer made up 5.9% of the Swedish cohort. One logical question is whether deaths at 1 year could be overrepresented in this group of patients, but in our sensitivity analysis, we found little evidence of this.
Some factors that have previously been identified as possible determinants of postoperative mortality did not seem to be important in this limited sample of the Swedish surgical population.8–19 Univariate analyses of the short-term outcome did not show any statistical significance for BMI, night surgery, the use of the WHO checklist or the use of cardiac output monitoring. These may be truly negative findings but could also be related to the sample size and small number of outcome parameters. There were only 18 deaths within 30 days in this cohort, precluding robust multivariate analyses.
Both ASA physical status and number of comorbidities were determined as significant factors in univariate analyses of both short and long-term mortalities. As expected, ASA physical status and number of comorbidities covaried, and in multivariate analyses of long-term mortality, only the latter was an independent predictor of mortality.
Admissions to an ICU after surgery in our Swedish cohort were fewer than in other participating European countries in the EuSOS (3.6% in SweSOS compared with 7.9% in the remaining EuSOS patients).3 Patients admitted to ICU (n = 36) had higher mortality rates (11.1% at 30 days and 27.8% at 1 year) compared with the total cohort (1.8 and 8.5%, respectively). These mortality rates are in line with a recent study from an Austrian population wherein the hospital mortality of surgical ICU patients was reported as 6.4% for elective cases and 20.8% for emergency cases.4 In our study, 10 patients admitted to ICU died within 12 months, and half were in-hospital deaths. Although the numbers are small, we can report that nine of these 10 patients were from ASA classes 3 and 4; most underwent major and emergency surgery.
Notably, 54.5% (n = 6) of patients who died in hospital did so without having been admitted to ICU. The comparable figure in the EuSOS was 73%.3 Four of these six patients suffered from metastatic cancer, one patient had four chronic diseases and all patients had ASA physical status 3 or 4. Thus, one possible reason for low ICU admission rates in Sweden may be that patients, who it is felt are unlikely to obtain benefit, are excluded from critical care. We do not have data documenting advance directives, and therefore cannot study their impact on ICU admission and mortality. Similarly, we do not have data on how high-risk patients are identified, or how routine ICU admissions are determined. In general, however, most routine admissions to ICU in Sweden are procedure specific. Another possible reason for low ICU admission rates is that PACUs may act as high-dependency units with the ability to provide critical care services such as invasive blood pressure monitoring, noninvasive ventilation, vasopressor and inotrope therapy and increased monitoring possibilities. This possibility is supported by the fact that if the number of patients with a LOS in PACU > 12 hours are added to those transferred to ICU, the difference in ICU admission rates disappears. In fact, seven of nine patients with unplanned ICU admission (data not shown) also had long PACU stays after surgery, suggesting an ongoing need for critical care services beyond the PACU period. We found no evidence that a prolonged PACU stay was associated with mortality, regardless of whether the time in PACU was treated as a continuous variable or dichotomised around the median time of 175 min. We analysed LOS over 12 h in PACU as a separate variable and as a combined variable with ICU admission. Neither of these variables was associated with increased mortality in the multivariate analysis.
The discrepancy between short and long-term outcomes is notable. Possible reasons for the good short-term outcome seen in this study may be good nursing care, early and appropriate use of analgesics, widespread implementation of early warning systems and the early use of computerised tomography, all recently identified as predictors of short-term survival.32–34 Some clinics have routine multiprofessional perioperative conferences involving a senior surgeon, senior anaesthetist and nurses from theatre, anaesthesia and PACU to plan procedures. In contrast, there are few such systems in place for longer-term care. Thus, the occurrence and lack of timely detection of late complications after surgery as well as the absence of structured communication systems between in-hospital and out-of-hospital care givers may be an explanatory factor for the increased long-term mortality.
The extent to which longer-term mortality can be influenced by events in the perioperative period is poorly studied. However, the higher incidence of longer-term mortality shown in our study indicates that follow-up is important. Mortality rate increased nearly five-fold at 1 year compared with 30 days after surgery, a finding which we did not expect. It is difficult to compare these results with earlier findings because little data is available on longer-term outcomes after surgery. Data from the newly established Swedish Peri-Operative Registry (www.periop.se) indicate similar trends in long-term outcomes and will hopefully help shed more light on this issue. Our hope is that analysis of large datasets from the Swedish Peri-Operative Registry will allow the identification of modifiable risk factors such as the occurrence of untoward events and complications intraoperatively and postoperatively, as well as management factors such as clinical guidelines and risk-stratification protocols. In this regard, we draw from the experience of the American College of Surgeons’ National Surgical Quality Improvement Programme wherein studies in excess of 80 000 procedures are generally required to identify important modifiable risk factors.31,35–36
It is very important to recognise some major limitations of this study even if it is, to our knowledge, the first study describing longer-term outcomes for an unselected surgical population in Sweden. First, there is a lack of data regarding postoperative complications, although studies indicate that the occurrence of complications in the perioperative period or unplanned reoperations outweigh patient and surgical characteristics as determinants of both short and long-term outcome.31,35–37 Second, we did not follow patients for more than 12 months. Longer-term follow-up may have revealed trends not seen within a year of surgery. Furthermore, in the comparison with an age and sex-matched background population, we could not account for other factors that may have influenced mortality. We also had a large number of dropouts which could have had an impact on the results. In addition, we cannot rule out the possibility of other significant confounding factors that could have been registered but were not measured or tested for in our univariate analyses, and thus not included in the multivariate analysis.38 As our sample size was small and limited to the available cohort and the short-term mortality rate was also small, we refrained from conducting multivariate analyses for 30-day mortality to avoid getting false-positive findings. Small sample size may also explain the wide CIs seen for many variables in the multivariate analysis (e.g. number of comorbidities and urgency of surgery) and this may also have caused us to miss a ‘dose–response’ effect with mortality.
Although short-term postoperative mortality in Sweden was low, long-term mortality was substantially higher with nearly five-fold increases at 1 year compared with 30 days. This cohort had lower survival compared with an age and sex-matched population in the same year in Sweden, demonstrating a significant and sustained risk of death over time, in this surgical population. Both short and long-term mortalities were driven by well known factors such as age, comorbidities and surgical urgency. ASA class, ICU admission and PACU LOS were not independent predictive factors of long-term mortality. Taken together, these results highlight the need for follow-up beyond the in-hospital period to fully assess the consequences of surgery.
Acknowledgements relating to this article
Assistance with the study: we gratefully acknowledge the contribution of the SweSOS Study Group: H. Björne, J. Wernerman, A. Hedin, E. Merisson, L. Layous, S. Söndergaard, A. Oscarsson Tibblin, B. Klarin, H. Seeman Lodding, M. Jawad and MS Chew.
Financial support and sponsorship: this work was supported by the Research Council of Halland County Council, Sweden.
Conflicts of interest: none.
Presentation: preliminary data from this study were presented as a poster presentation at the annual meeting of the European Society of Anaesthesiology (Euroanaesthesia 2014), 31 May to 3 June 2014, Stockholm, Sweden.
Comment from the editor: MSC is an Associate Editor of the European Journal of Anaesthesiology
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