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
1. Weiser TG, Regenbogen SE, Thompson KD, et al. An estimation of the global volume of surgery: a modelling strategy based on available data. Lancet
2. Pearse RM, Harrison DA, James P, et al. Identification and characterisation of the high-risk surgical population in the United Kingdom. Crit Care
3. Pearse RM, Moreno RP, Bauer P, et al. Mortality after surgery in Europe: a 7 day cohort study. Lancet
4. Rhodes A, Moreno RP, Metnitz B, et al. Epidemiology and outcome following postsurgical admission to critical care. Int Care Med
5. Clarke A, Murdoch H, Thomas MJ, et al. Mortality and postoperative care after emergency laparotomy. Eur J Anaesthesiol
6. Walder B. Improvement of perioperative care for better outcomes after surgery. Eur J Anaesthesiol
7. Eichenberger AS, Haller G, Cheseaux N, et al. A clinical pathway in a postanaesthesia care unit to reduce length of stay, mortality and unplanned intensive care unit admission. Eur J Anaesthesiol
8. Donati A, Ruzzi M, Adrario E, et al. A new and feasible model for predicting operative risk. Br J Anaesth
9. Stelzner S, Hellmich G, Koch R, et al. Perioperative risk assessment in surgery – an analysis in 10395 patients. Zentralbl Chir
10. Sabaté A, Gil-Bona J, Pi A, et al. Perioperative mortality: retrospective cross-sectional study of surgical patients who died between 2004 and 2008 in a tertiary care hospital. Rev Esp Anestesiol Reanim
11. Ingraham AM, Cohen ME, Raval MV, et al. Comparison of hospital performance in emergency versus elective general surgery operations at 198 hospitals. J Am Coll Surg
12. Valentijn TM, Galal W, Hoeks SE, et al. Impact of obesity on postoperative and long-term outcomes in a general surgery population: a retrospective cohort study. World J Surg
13. Mullen JT, Moorman DW, Davenport DL. The obesity paradox: body mass index and outcomes in patients undergoing nonbariatric general surgery. Ann Surg
14. Haynes AB, Weiser TG, Berry WR, et al. A surgical safety checklist to reduce morbidity and mortality in a global population. N Engl J Med
15. van Klei WA1, Hoff RG, van Aarnhem EE, et al. Effects of the introduction of the WHO ‘Surgical Safety Checklist’ on in-hospital mortality: a cohort study. Ann Surg
16. Hamilton MA, Cecconi M, Rhodes A. A systematic review and meta-analysis on the use of preemptive hemodynamic intervention to improve postoperative outcomes in moderate and high-risk surgical patients. Anesth Analg
17. Pearse RM, Harrison DA, MacDonald N, et al. Effect of a perioperative, cardiac output-guided hemodynamic therapy algorithm on outcomes following major gastrointestinal surgery: a randomized clinical trial and systematic review. J Am Med Assoc
18. Pestaña D, Espinosa E, Eden A, et al. Perioperative goal-directed hemodynamic optimization using noninvasive cardiac output monitoring in major abdominal surgery: a prospective, randomized, multicenter, pragmatic trial: POEMAS study. Anesth Analg
19. Turrentine FE, Wang H, Young JS, Calland JF. What is the safety of nonemergent operative procedures performed at night? J Trauma
20. Bewick V, Cheek L, Ball J. Statistics review 14: Logistic regression. Crit Care
21. Ostvoll E, Sunnergren O, Ericsson E, et al. Mortality after tonsil surgery, a population study, covering eight years and 82,527 operations in Sweden. Eur Arch Otorhinolaryngol
22. Sandblom G, Videhult P, Crona Guterstam Y, et al. Mortality after a cholecystectomy: a population-based study. HPB (Oxford)
23. Nguyen NT, Nguyen B, Smith B, et al. Proposal for a bariatric mortality risk classification system for patients undergoing bariatric surgery. Surg Obes Rela Dis
24. Nilsson H, Nilsson E, Angerås U, Nordin P. Mortality after groin surgery: delay of treatment and cause of death. Hernia
25. Rutegård M, Haapamäki M, Matthiessen P, Rutegård J. Early postoperative mortality after surgery for rectal cancer in Sweden. Colorectal Dis
26. Iversen LH. Aspects of survival from colorectal cancer in Denmark. Dan Med J
27. Nordenvall C, Ekbom A, Bottai M, et al. Mortality after total colectomy in 3084 patients with inflammatory bowel disease: a population-based cohort study. Aliment Pharmacol Ther
28. Robinson TN, Wu DS, Sauaia A, et al. Slower walking speed forecasts increased postoperative morbidity and 1-year mortality across surgical specialties. Ann Surg
29. Hommel A, Ulander K, Bjorkelund KB, et al. Influence of optimised treatment of people with hip fractures on time to operation, length of hospital stay, reoperations and mortality within 1 year. Injury
30. Juul AB, Wetterslev J, Kofoed-Enevoldsen A. Long-term postoperative mortality in diabetic patients undergoing major noncardiac surgery. Eur J Anaesthesiol
31. Khuri SF, Henderson WG, DePalma RG, et al. Determinants of long-term survival after major surgery and the adverse effect of postoperative complications. Ann Surg
32. Huddart S, Peden CJ, Swart M, et al. Use of a pathway quality improvement care bundle to reduce mortality after emergency laprotomy. Br J Surg
33. Vester-Andersen M, Lundstrøm LH, Møller MH, et al. Mortality and postoperative care pathways after emergency gastrointestinal surgery in 2904 patients: a population-based cohort study. Br J Anaesth
34. Symons NRA, Moorthy K, Almoudaris AM, et al. Mortality in high-risk emergency general surgical admissions. Br J Surg
35. Almoudaris AM, Burns EM, Mamidanna R, et al. Value of failure to rescue as a marker of the standard of care following reoperation for complications after colorectal resection. Br J Surg
36. Ghaferi AA, Birkmeyer JD, Dimick JB. Variation in hospital mortality associated with inpatient surgery. N Engl J Med
37. Rama-Maceiras P, Rey-Rilo T, Moreno-Lopez E, et al. Unplanned surgical reoperations in a tertiary hospital: perioperative mortality and associated risk factors. Eur J Anaesthesiol
38. De Hert S, Imberger G, Carlisle J, et al. Preoperative evaluation of the adult patient undergoing noncardiac surgery: guidelines from the European Society of Anaesthesiology. Eur J Anaesthesiol
Supplemental Digital Content
© 2016 European Society of Anaesthesiology