The Emergency Surgery Score (ESS) accurately predicts outcomes in elderly patients undergoing emergency general surgery

Journal Pre-proof The Emergency Surgery Score (ESS) accurately predicts outcomes in elderly patients undergoing emergency general surgery Apostolos Gaitanidis, Sarah Mikdad, Kerry Breen, Napaporn Kongkaewpaisan, April Mendoza, Noelle Saillant, Jason Fawley, Jonathan Parks, George Velmahos, Haytham Kaafarani PII:

S0002-9610(20)30086-6

DOI:

https://doi.org/10.1016/j.amjsurg.2020.02.017

Reference:

AJS 13699

To appear in:

The American Journal of Surgery

Received Date: 8 January 2020 Revised Date:

11 February 2020

Accepted Date: 13 February 2020

Please cite this article as: Gaitanidis A, Mikdad S, Breen K, Kongkaewpaisan N, Mendoza A, Saillant N, Fawley J, Parks J, Velmahos G, Kaafarani H, The Emergency Surgery Score (ESS) accurately predicts outcomes in elderly patients undergoing emergency general surgery, The American Journal of Surgery (2020), doi: https://doi.org/10.1016/j.amjsurg.2020.02.017. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc.

1

The Emergency Surgery Score (ESS) Accurately Predicts Outcomes in Elderly Patients

2

Undergoing Emergency General Surgery

3

Apostolos Gaitanidis, M.D.1,2, Sarah Mikdad, B.S.1, Kerry Breen, B.S.1, Napaporn

4

Kongkaewpaisan, M.D.1, April Mendoza, M.D., MPH1, Noelle Saillant, M.D.1, Jason Fawley,

5

M.D.1, Jonathan Parks, M.D.1, George Velmahos, M.D., PhD1, Haytham M.A. Kaafarani, M.D.,

6

MPH1,2

7

1

8

Hospital, Boston, MA, USA

9

2

10

Division of Trauma, Emergency Surgery and Surgical Critical Care, Massachusetts General

Center for Outcomes & Patient Safety in Surgery (COMPASS), Massachusetts General

Hospital, Boston, MA, USA

11

12

Word count: 1959

13

Running title: ESS in the elderly

14 15

Corresponding author

16

Haytham M.A. Kaafarani, M.D., MPH

17

Associate Professor of Surgery, Harvard Medical School

18

Director of the Center for Outcomes & Patient Safety in Surgery (COMPASS)

19

Director of Clinical Research, Division of Trauma, Emergency Surgery and Surgical Critical

20

Care

21

Massachusetts General Hospital

22

165 Cambridge Street, Suite 810 | MA 02114, Boston

23

E-mail: [email protected] 1

1

2

1

Abstract

2

Background: The performance of the Emergency Surgery Score (ESS), a validated risk

3

calculator, in the elderly emergency general surgery (EGS) patient remains unclear. We

4

hypothesized that ESS accurately predicts outcomes in elderly EGS patients, including

5

octogenarians and nonagenarians.

6

Methods: Using the 2007-2017 National Surgical Quality Improvement Program (NSQIP)

7

database, we included all EGS patients ≥65 years old. The correlation between ESS, mortality

8

and morbidity was assessed in the 3 patient cohorts (>65, octogenarians and nonagenarians),

9

using the area under the curve (AUC).

10

Results: A total of 124,335 patients were included, of which 34,215 (28%) were octogenarians

11

and 7,239 (6%) were nonagenarians. In patients ≥65 years, ESS accurately predicted mortality

12

(AUC 0.81). For octogenarians and nonagenarians, ESS predicted mortality moderately well

13

(AUC 0.77 and 0.69, respectively.

14

Conclusion: ESS accurately predicts mortality and morbidity in the elderly EGS patient, but its

15

accuracy in predicting morbidity decreases for nonagenarians.

16

Keywords: emergency surgery; general surgery; mortality; outcomes; benchmarking

17

3

1

Summary

2

The Emergency Surgery Score is a well-established risk calculator for emergency general

3

surgery (EGS) patients, but its predictive ability in the elderly population is unknown. ESS

4

accurately predicting mortality in EGS patients ≥65 years (AUC 0.81) and moderately-well in

5

octogenarians (AUC 0.77) and nonagenarians (AUC 0.69).

6

4

1

Introduction

2

The global and national burden of Emergency general surgery (EGS) is substantial.

3

Between 2001 and 2010, EGS accounted for 7.1% of all hospitalizations in the United States,

4

with an annual admission rate of 1,290 per 100,000 people 1. Besides its considerable social and

5

financial impact on the healthcare system, EGS is also independently associated with significant

6

patient morbidity and mortality. Multi-institutional data have demonstrated that EGS patients are

7

at a significantly higher risk for postoperative death and complications compared to patients who

8

undergo elective general surgery procedures

9

outcomes in EGS patients is important to enable sound bedside decision-making, setting patient

10

recovery expectations and conducting meaningful discussions of goals of care with the patients

11

and their families. Recently, the Emergency Surgery Score (ESS) was developed and validated

12

as a scoring system to predict mortality, morbidity and critical illness in EGS patients

13

Eastern Association for the Surgery of Trauma (EAST) multicenter study prospectively

14

confirmed the value of ESS as a strong predictor of mortality, morbidity and need for critical

15

care 8.

2,3

. As such, accurately predicting postoperative

4–7

. An

16

Nonetheless, the performance of ESS has not yet been assessed across different age

17

groups, specifically the elderly. Predicting outcomes in the elderly patient population can be

18

particularly challenging considering the impact of frailty and functional status as well as the

19

complex interaction of multiple co-existent comorbidities

20

United States is also expected to grow in the coming decades 11, so the use of validated tools to

21

predict EGS outcomes in this patient population is needed. In this study, we sought to evaluate

22

the ability of ESS to predict 30-day morbidity and mortality in the elderly patients, with specific

9,10

. The elderly population in the

5

1

focus on octogenarians and nonagenarians. We hypothesized that ESS accurately predicts

2

outcomes in elderly patients.

6

1

Methods

2

Using the ACS-NSQIP 2007-2017 database, we aimed to evaluate the ability of ESS to predict

3

30-day mortality and morbidity for: 1) the elderly patients, defined in our study as older than 65

4

years, 2) octogenarians and 3) nonagenarians. Due to the de-identified nature of the national

5

ACS-NSQIP database, our institution waived the institutional review board (IRB) approval.

6

7

Patient Population

8

All patients that underwent EGS in the 2007 – 2017 ACS-NSQIP database were

9

identified, using the NSQIP variable “emergncy". An emergent operation is defined in ACS-

10

NSQIP as “a case which is operated on within a short time interval from diagnosis; otherwise,

11

the outcome will be potentially threatened”. Patients that underwent elective surgery were

12

excluded. EGS-related procedures were identified using CPT codes for “digestive system”

13

(43020-49999). Patients were divided into the following age groups: 65-79 years, ≥80 years and

14

≥90 years.

15

16

Calculating ESS

17

ESS was calculated for each patient, using pre-operative variables: age, race, transferred

18

from outside emergency department, transferred from an acute care hospital inpatient facility,

19

ascites, BMI <20 kg/m2, disseminated cancer, dyspnea, functional dependence, history of chronic

20

obstructive pulmonary disease (COPD), hypertension, steroid use, ventilator requirement within

21

48 h preoperatively, weight loss >10% in preceding 6 months, albumin <3.0 U/L, alkaline

7

1

phosphatase > 125 U/L, blood urea nitrogen > 40 mg/dL, creatinine > 1.2 mg/dL, international

2

normalized ratio > 1.5, Platelets < 150 x 103/µL, SGOT >40 U/L, Sodium >145 mg/dL and WBC

3

(4.5 < x 103/µL, 15-25 x 103/µL, >25 x 103/µL) [Table 1]. Missing values were assumed to be

4

the null value, thus not resulting in additional ESS points, based on preliminary data by our team

5

suggesting that the predictive ability of ESS does not change by the inclusion of patients with

6

missing data 12.

7

8

Defining 30-day Morbidity and Mortality

9

Morbidity was defined as the occurrence of any of the following complications:

10

superficial surgical site infection (SSI), deep SSI, organ/space SSI, wound dehiscence,

11

pneumonia, unplanned intubation, pulmonary embolism, failure to wean off ventilator >48 h

12

postoperatively, progressive renal insufficiency, acute kidney injury, urinary tract infection,

13

stroke/CVA (cerebrovascular accident), cardiac arrest requiring cardiopulmonary resuscitation,

14

myocardial infarction, bleeding requiring transfusion, deep venous thrombosis, sepsis and septic

15

shock. The variable for mortality in ACS-NSQIP “dopertod” (i.e. Days from Operation to Death)

16

was used to identify patients that died 30 days following surgery.

17

18

Statistical analysis

19

The performance of ESS for each patient group was assessed using receiver operator

20

characteristic (ROC) analysis. The area under the curve (AUC) was used as a performance

21

metric for ESS for each patient subgroup. Risk factors for mortality in each patient subgroup

22

were assessed using multivariate logistic regression that included all ESS variables. All statistical 8

1

tests were two-tailed and the threshold of significance was set at 0.05 or less. Statistical analysis

2

was performed using STATA v.15.1 (StataCorp, College Station, Texas).

3

Results

4

A total of 124,335 elderly EGS patients were included. Of these patients, 34,215 (28%)

5

patients were octogenarians and 7,239 (6%) patients were nonagenarians. Detailed demographic

6

and clinical characteristics for the different age groups are displayed in Table 2. Clinical

7

outcomes for each age group are displayed in Table 3. The elderly, octogenarian and

8

nonagenarian patients had mortality rates of 12.4%, 16.3% and 20.4%, respectively, and overall

9

morbidity rates of 41.8%, 45.7% and 45.5%, respectively.

10

11

Performance of ESS in the elderly patients aged ≥65 years old

12

Among all patients aged ≥65 years at the time of admission, ESS performed very well for

13

predicting mortality (AUC 0.81) and moderately well for predicting morbidity (AUC 0.73). ESS

14

scores of 2, 11 and 18 correlated with mortality rates of 0.3%, 36.6% and 75.1%, respectively,

15

and with morbidity rates of 14.6%, 76.6% and 85.6%, respectively (Figure 1). Multivariate

16

logistic regression showed that all ESS variables were independently associated with mortality

17

(Supplementary Table 1).

18

19

Performance of ESS in octogenarians

20

Among patients aged 80-89 years at the time of admission, the AUC for ESS’ prediction

21

of mortality and morbidity were 0.77 and 0.69, respectively. ESS scores of 2, 11 and 18

9

1

correlated with mortality rates of zero, 43.2% and 79%, respectively, and morbidity rates of

2

22.3%, 75.8% and 84.2%, respectively (Figure 2a). Multivariate analyses showed that all ESS

3

variables, except hypertension, were independently associated with mortality (Supplementary

4

Table 2).

5

6

Performance of ESS in nonagenarians

7

Among patients aged ≥90 years at the time of admission, the AUC for ESS’ prediction of

8

mortality was 0.69 and for its prediction of morbidity was 0.63. Mortality was 20.4% and

9

morbidity was 45.5%. ESS scores of 2, 11 and 18 correlated with mortality rates of 8.3%, 47.2%

10

and 100%, respectively, and with morbidity rates of 58.3%, 70.6% and 100%, respectively

11

(Figure 2b). Multivariate analyses demonstrated that 19 of the 22 ESS variables were

12

independently associated with mortality (Supplementary Table 3).

13

Discussion

14

In this study, we demonstrated that ESS accurately predicts postoperative mortality in the

15

elderly and very elderly patient population. ESS also accurately predicted postoperative

16

morbidity in this patient population undergoing EGS, albeit with a decreasing accuracy as the

17

age at admission increases. These findings support the use of ESS as a bedside risk assessment

18

tool that may inform elderly patients and their families with regards to the risks of undergoing

19

EGS. ESS may also function as a benchmarking tool for evaluating quality of care in patients

20

undergoing EGS.

21

Prediction of outcomes in EGS is crucial during clinical decision-making, due to the

22

higher mortality and risk of complications associated with EGS compared to non-emergent 10

1

general surgery, especially in the elderly patients. Havens and colleagues estimated that patients

2

undergoing EGS are at 6 times greater risk of death compared to those undergoing elective

3

general surgery 2. EGS-specific tools are important, since there is a discrepancy in terms of

4

predictive factors between EGS and elective general surgery 13. Due to this discrepancy, general

5

surgery calculators, such as the NSQIP risk calculator, may not predict outcomes after EGS as

6

accurately as ESS does. Another important feature of ESS is that it only includes variables that

7

are available preoperatively. Other EGS-specific predictive tools, such as the AAST anatomic

8

grading systems

9

available preoperatively to utilize in preoperative discussions with the patients and their families.

10

As a result, such grading systems cannot be used for preoperative bedside counseling. In

11

addition, these grading systems do not emphasize the acuity of disease and physiological

12

derangements in their risk calculations, as ESS does. Risk prediction calculators, such as ESS,

13

may assist surgeons better counsel their elderly patients and their families. In addition, ESS may

14

prove useful in efforts at optimizing resource utilization by helping triage the appropriate high-

15

risk patients to the ICU.

14–16

, rely heavily on intraoperative and pathologic findings, which are not

16

The elderly population in the United States is expected to continue to grow in the next

17

decade 11. EGS is more commonly performed in elderly patients 17, with some reports suggesting

18

that nearly half of them do not survive 30 days postoperatively 18,19. Mortality and morbidity are

19

higher among the elderly even for low-risk procedures such as appendectomy

20

used risk assessment tools such as the ACS-NSQIP risk calculator, the American Society of

21

Anesthesiologists (ASA) classification and P-POSSUM underperform in patients of advancing

22

age

10,21

20

. Commonly

, which is why validating ESS for this patient population is crucial. Our study suggests

11

1

that the predictive ability of each of these risk assessment tools is lower than ESS in the elderly

2

(e.g. ESS: AUC 0.81 vs. AUC 0.771-0.784 for P-POSSUM, and ASA classification).

3

However, in this study, we uncovered a trend towards decreasing ESS accuracy as age

4

increased. This may be due to several reasons. First, the development of ESS was based on

5

patients of all ages undergoing EGS. Octogenarian and nonagenarian patients constituted a

6

minority of the initial patient sample, so the weight of each individual risk factor for these age

7

groups may not be as adequately reflected in ESS. As a result, although most ESS variables were

8

independent predictors of mortality in octogenarians and nonagenarians, the weight of ESS

9

variables differed in different age groups. Another contributing factor to the decreasing

10

predictive ability of ESS in these age groups is that the initial development of ESS consisted of

11

patients undergoing all EGS procedures. As expected, a significant portion of that cohort

12

consisted of patients undergoing laparoscopic appendectomies and cholecystectomies. However,

13

these procedures are less frequently performed in octogenarians and nonagenarians compared to

14

younger patients. Shah and colleagues identified gastrointestinal bleeding as the most common

15

admission diagnosis in octogenarians and nonagenarians undergoing EGS using the Nationwide

16

Inpatient Sample

17

the most common causes of nonagenarians undergoing EGS, which is consistent with our

18

findings

19

that correlate best with postoperative outcomes in procedures commonly performed in the elderly

20

(e.g. surgery for small bowel obstruction) may be underrepresented in ESS.

23

22

. Another study found that incarcerated hernias and bowel obstruction were

. With these different procedure patterns, it should be expected that the risk factors

21

Our study has a few limitations. First, it is a retrospective analysis incorporating data

22

from multiple institutions across the United States that follow different protocols for patient care.

23

Second, we equated missing values to the low-risk group for each variable rather than excluded 12

1

these patients. By doing this, the power of the study increased, since all eligible patients were

2

included regardless of the presence of missing variables. This also helped avoid selection bias of

3

only including patients that had all ESS variables available. Certain laboratory tests, such as INR

4

and SGOT, may only be obtained in high-risk patients. By including all patients regardless of

5

missing variables, we avoided the selection bias of excluding lower-risk patients, which makes

6

the results of the study more reliable. Third, we did not examine meaningful survival, as the

7

database did not have any quality of life variables. Fourth, our database only included patients

8

that underwent surgery, with potential selection bias in our outcomes not reflecting the outcome

9

of the elderly and very elderly whose goals of care discussions led to nonoperative or “comfort-

10

measures only” management strategies.

11

Conclusion

12

In conclusion, ESS accurately predicts outcomes in elderly patients undergoing

13

emergency general surgery, including octogenarians and nonagenarians, albeit with decreasing

14

accuracy with advancing age. These findings further support the use of ESS for bedside decision-

15

making, counselling of patients and their families, resource allocation and for quality

16

benchmarking of surgical care in the elderly.

17

13

1

Figure legend Figure 1

Observed 30-day mortality by Emergency Surgery Score (ESS) score for patients aged ≥65 years

Figure 2

Observed 30-day mortality by Emergency Surgery Score (ESS) score for: a) Octogenarians and b) Nonagenarians

2

14

1

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1

Table 1. The Emergency Surgery Score (ESS)

2

Variable Points Demographics Age >60 years 2 White race 1 Transfer from outside emergency department 1 Transfer from an acute care hospital inpatient 1 facility Comorbidities Ascites BMI <20 kg/m2 1 Disseminated cancer 1 Dyspnea 3 Functional dependence 1 History of COPD 1 Hypertension 1 Steroid use 1 Ventilator requirement within 48 hrs 3 preoperatively Weight loss >10% in the preceding 6 months 1 Laboratory values Albumin <3.0 U/L 1 Alkaline phosphatase >125 U/L 1 Blood urea nitrogen >40 mg/dL 1 Creatinine >1.2 mg/dL 2 International normalized ratio >1.5 1 Platelets <150 × 103 /µL 1 SGOT >40 U/L 1 Sodium >145 mg/dL 1 WBC × 103 /µL <4.5 1 >15 and ≤25 1 >25 2 Maximum Score 29 BMI: body mass index, COPD: chronic obstructive pulmonary disease, WBC: white blood cell

3

17

1

Table 2. Patient characteristics by age group Variable Demographics Female sex White race Admitted from home Transferred from outside emergency department Transferred from other acute care hospital (inpatient) Baseline comorbidities Functionally dependent BMI <20 kg/m2 BMI >35 kg/m2 >10% weight loss in past 6 mo Bleeding disorder Transfusion of more than 4 units of pRBCs up to 72 hours before surgery Dyspnea History of COPD Ascites Steroid use CHF up to 30 d before surgery HTN requiring medications Diabetes requiring oral agents or insulin Smoking up to 12 months before surgery History of disseminated cancer Ventilator dependence Laboratory tests Albumin <3.0 Alkaline phosphatase >125 Total bilirubin >1.0 BUN >40.0 Creatinine >1.2 Ht <38.0 INR >1.5

Age 65-79 years (n=82,881)

Age 80-89 years (n=34,215)

Age ≥90 years (n= 7,239)

P-value

43,672 (52.7%) 60,432 (72.9%) 71,501 (86.3%) 5,515 (6.7%)

20,173 (59%) 25,623 (74.9%) 29,783 (87.1%) 2,171 (6.4%)

4,801 (66.3%) 5,550 (76.7%) 6,464 (89.3%) 370 (5.1%)

<0.001 <0.001

5,758 (7%)

2,231 (6.5%)

397 (5.5%)

<0.001

10,150 (12.3%) 10,297 (12.4%) 14,338 (17.3%) 3,319 (4%) 11,759 (14.2%) 4,468 (5.4%)

7,064 (20.7%) 6,384 (18.7%) 4,446 (13%) 1,280 (3.7%) 6,495 (19%) 1,969 (5.8%)

2,056 (28.4%) 1,897 (26.2%) 898 (12.4%) 204 (2.8%) 1,224 (16.9%) 339 (4.7%)

<0.001 <0.001 <0.001 <0.001 <0.001 0.001

9,343 (11.3%) 10,164 (12.3%) 3,148 (3.8%) 6,440 (7.8%) 2,681 (3.2%) 54,136 (65.3%) 17,692 (21.4%)

4,263 (12.5%) 4,309 (12.6%) 1,178 (3.4%) 2,268 (6.6%) 1,683 (4.9%) 25,565 (74.7%) 6,082 (17.8%)

691 (9.6%) 624 (8.6%) 210 (2.9%) 304 (4.2%) 374 (5.2%) 5,479 (75.7%) 808 (11.2%)

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

13,035 (15.7%)

1,909 (5.6%)

160 (2.2%)

<0.001

4,423 (5.3%) 4,905 (5.9%)

1,365 (4%) 1,694 (5%)

197 (2.7%) 169 (2.3%)

<0.001 <0.001

18,898 (22.8%) 10,817 (13%) 19,004 (22.9%) 9,957 (12%) 24,726 (29.8%) 39,967 (48.2%) 6,949 (8.4%)

8,476 (24.8%) 4,016 (11.7%) 7,849 (22.9%) 5,597 (16.4%) 12,790 (37.4%) 19,094 (55.8%) 3,678 (10.8%)

1,594 (22%) 743 (10.3%) 1,502 (20.8%) 1,243 (17.2%) 2,719 (37.6%) 4,174 (57.7%) 593 (8.2%)

<0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001

<0.001 <0.001

18

PT >35 Plt <150,000 SGOT >40 Sodium >145 WBC <4,500 WBC 15,000–25,000 WBC > 25,000 Procedure performed or admission diagnosis Laparoscopic appendectomy Laparoscopic cholecystectomy Colectomy for acute diverticulitis Small bowel obstruction Incarcerated hernia repair 1 2 3 4

165 (0.2%) 11,335 (13.7%) 12,334 (14.9%) 2,083 (2.5%) 4,469 (5.4%) 17,656 (21.3%) 3,655 (4.4%)

83 (0.2%) 5,174 (15.1%) 4,757 (13.9%) 1,210 (3.5%) 1,832 (5.4%) 6,826 (20%) 1,567 (4.6%)

13 (0.2%) 954 (13.2%) 805 (11.1%) 268 (3.7%) 328 (4.5%) 1,433 (19.8%) 329 (4.5%)

0.283 <0.001 <0.001 <0.001 0.007 <0.001 0.416

12,432 (15%) 6,469 (7.8%)

2,300 (6.7%) 2,105 (6.2%) 1,528 (4.5%)

371 (5.1%) 357 (4.9%) 223 (3.1%)

<0.001 <0.001 <0.001

4,261 (5.1%)

10,687 (12.9%) 6,073 (17.8%) 1,518 (21%) <0.001 5,619 (6.8%) 2,547 (7.4%) 696 (9.6%) <0.001 BMI: Body mass index, pRBC: Packed red blood cells, COPD: Chronic obstructive pulmonary disease, CHF: Congestive heart failure, HTN: Hypertension, Ht: Hematocrit, INR: International normalized ratio, PT: Prothrombin time, Plt: Platelet, SGOT: Serum glutamic-oxaloacetic transaminase, WBC: White blood cell

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1

2 3

Table 3. Outcomes for different age groups Outcome

Age 65-79 years (n=82,881)

Age 80-89 years (n=34,215)

Age ≥90 years (n= 7,239)

P-value

Superficial SSI Deep SSI Organ/Space SSI Wound Dehiscence Pneumonia Unplanned Intubation DVT PE Progressive Renal Insufficiency Acute Kidney Injury UTI Stroke/CVA Cardiac arrest with CPR Myocardial infarction Bleeding requiring transfusion Sepsis Septic shock Failure to wean off ventilator >48 h postop

3,561 (4.3%) 1,117 (1.4%) 4,162 (5%) 1,475 (1.8%) 5,987 (7.2%) 4,591 (5.5%) 1,927 (2.3%) 829 (1%) 934 (1.1%)

1,348 (3.9%) 370 (1.1%) 1,389 (4.1%) 554 (1.6%) 3,270 (9.6%) 2,370 (6.9%) 943 (2.8%) 337 (1%) 439 (1.3%)

264 (3.7%) 64 (0.9%) 218 (3%) 99 (1.4%) 771 (10.7%) 435 (6%) 179 (2.5%) 39 (0.5%) 83 (1.2%)

0.002 <0.001 <0.001 0.011 <0.001 <0.001 <0.001 0.001 0.076

4,278 (5.2%) 2,511 (3%) 533 (0.6%) 1,736 (2.1%)

1,776 (5.2%) 1,508 (4.4%) 322 (0.9%) 923 (2.7%)

235 (3.3%) 350 (4.8%) 61 (0.8%) 181 (2.5%)

<0.001 <0.001 <0.001 <0.001

1,304 (1.6%) 10,942 (13.2%)

854 (2.5%) 4,903 (14.3%)

222 (3.1%) 954 (13.2%)

<0.001 <0.001

6,502 (7.8%) 8,438 (10.2%) 10,654 (12.9%)

2,695 (7.9%) 3,985 (11.7%) 4,788 (14%)

577 (8%) 708 (9.8%) 778 (10.8%)

0.923 <0.001 <0.001

Morbidity 33,087 (39.9%) 15,624 (45.7%) 3,293 (45.5%) <0.001 Mortality 8,354 (10.1%) 5,564 (16.3%) 1,479 (20.4%) <0.001 SSI: Surgical site infection, DVT: Deep venous thrombosis, PE: Pulmonary embolism, UTI: Urinary tract infection, CVA: Cerebrovascular accident, CPR: Cardiopulmonary resuscitation

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Highlights •

The Emergency Surgery Score is a well-established risk calculator for emergency general surgery (EGS) patients, but its predictive ability in the elderly population is unknown.

•

ESS accurately predicts mortality in EGS patients ≥65 years (AUC 0.81).

•

ESS predicted mortality moderately well in octogenarians (AUC 0.77) and nonagenarians (AUC 0.69).