Abstract
Background:
Emergency general surgery conditions are common, costly, and highly morbid. The proportion of excess morbidity due to variation in health systems and processes of care is poorly understood. We constructed a collaborative quality initiative for emergency general surgery to investigate the emergency general surgery care provided and guide process improvements.
Methods:
We collected data at ten hospitals from 7/2019-12/2022. Five cohorts were defined: acute appendicitis, acute gallbladder disease, small bowel obstruction, emergency laparotomy, and overall aggregate. Processes and inpatient outcomes investigated included operative vs. non-operative management, mortality, morbidity (mortality and/or complication), readmissions, and length of stay. Multivariable risk adjustment accounted for variations in demographic, comorbid, anatomic, and disease traits.
Results:
Of the 19,956 emergency general surgery patients, 56.8% were female and 82.8% were white, and the mean (SD) age was 53.3 (20.8) years. After accounting for patient and disease factors, the adjusted aggregate mortality rate was 3.5% (95% CI, 3.2–3.7), morbidity 27.6% (95% CI, 27.0–28.3), and the readmission rate was 15.1% (95% CI, 14.6–15.6). Operative management varied between hospitals from 70.9% to 96.9% for acute appendicitis and 19.8% to 79.4% for small bowel obstruction. Significant differences in outcomes between hospitals were observed with high- and low-outlier performers identified after risk adjustment in the overall cohort for mortality, morbidity, and readmissions. The use of a Gastrografin challenge in patients with a small bowel obstruction ranged from 10.7% to 61.4% of patients. In patients who underwent initial non-operative management of acute cholecystitis, 51.5% had a cholecystostomy tube placed. The cholecystostomy tube placement rate ranged from 23.5% to 62.1% across hospitals.
Conclusions:
A multihospital emergency general surgery collaborative reveals high morbidity with substantial variability in processes and outcomes among hospitals. A targeted collaborative quality improvement effort can identify outliers in emergency general surgery care and may provide a mechanism to optimize outcomes.
Level of Evidence:
III, therapeutic/care management
Keywords: Emergency general surgery, collaborative quality improvement, process measures
Social Media:
Media summary: Emergency general surgery outcomes and processes can be measured in a collaborative quality initiative setting. This work from @UMichSurgery provides a framework to monitor and improve outcomes for patients with EGS conditions. Read more in @JTraumAcuteSurg
Hashtags: #egs, #emergencysurgery, #surgicalquality
Social media handles:
• University of Michigan Department of Surgery- @UMichSurgery
• University of Michigan Division of Acute Care Surgery - @UMichACS
• Institute for Healthcare Policy and Innovation - @UM_IHPI
• John W. Scott - @DrJohnScott
Background
The specialty of acute care surgery originated in response to a need for a surgical provider workforce with expertise in trauma care, emergency general surgery, and surgical critical care.1–3 Twenty percent of inpatient acute care admissions are for traumatic injury or one of 16 emergency general surgery diseases.4 This trauma and emergency general surgery disease cohort accounts for $86 billion or 25% of United States inpatient health care spending out of $341 billion annually.4 Hospital admissions for emergency general surgery have steadily increased over the past 20 years and are projected to continue to grow.5 Emergency general surgery operations carry a 30–40% increased risk of mortality or morbidity compared to similar general surgery operations in the non-emergent setting after accounting for patient and procedure-associated risk factors.6
Because emergency general surgery is prevalent, is associated with substantial healthcare expenditures, and has a considerable rate of adverse outcomes, it represents a potential high-yield environment for quality improvement efforts. In fact, emergency general surgery patients cared for at hospitals with lower risk-adjusted trauma mortality have an approximate 33% reduction in mortality risk.7 The construct of a high-performing quality improvement effort relies on many factors. The following four principles have been suggested to build a quality improvement endeavor in emergency general surgery:8
Standardization of emergency general surgery definitions;
Ability to conduct an emergency general surgery severity assessment (clinical, anatomic, and imaging) for risk-adjustment of outcomes;
Creation of an emergency general surgery data registry, which includes operative and nonoperative management; and
Standardization of emergency general surgery patient care using evidence-based guidelines and bundles.
To address the need for quality improvement in emergency general surgery, we have created an emergency general surgery collaborative centered on these four principles. Based on prior work in regional trauma quality improvement, we propose that inclusion of a collaborative quality initiative (CQI) environment is necessary to optimize the quality improvement infrastructure in addition to following the four principles listed.9 Here, we describe the construct and initial results from an emergency general surgery CQI.
Methods
A total of 16 different disease states have been identified to potentially examine emergency general surgery outcomes.10 Unfortunately, attempting to collect data on all of these disease states at once resembles an attempt “to drink the ocean.” However, seven operative procedures (laparotomy, peptic ulcer disease repair, small bowel resection, partial colectomy, lysis of adhesions, cholecystectomy, and appendectomy) account for most operations (80%), deaths (80%), complications (79%), and inpatient costs (80%) attributable to emergency general surgery in the United States per year.11 Given these facts, we focused our emergency general surgery collaborative on four clinical domains: acute appendicitis, acute gallbladder disease, small bowel obstruction, and emergent exploratory laparotomy.
Hospital participants for the Michigan Acute Care Surgery (MACS) CQI were recruited from existing Michigan Trauma Quality Improvement Program (MTQIP) enrolled trauma centers. We began with four hospitals in July 2019 and currently have 10 participant hospitals. Participation is voluntary, and each hospital receives 85% salary and benefits funding from Blue Cross Blue Shield of Michigan/Blue Care Network for a 1.0 full-time equivalent (FTE) data abstractor. Each hospital is responsible for funding the remaining 15% of the position. Future data abstractor reimbursement will depend on patient volume with a base metric of 85% funding of 1.0 FTE for 750 cases. The eligibility and expectations for MACS enrollment and participation are detailed in a document provided to all participant hospitals prior to MACS enrollment (SDC 1).
Data Collection and Data Definitions
The objective is to screen the daily Acute Care Surgical service “touches” at each MACS hospital to identify patients who meet the criteria for further data entry (Figure 1). Patients were identified from the initial census records (e.g., consults, admissions, and operative cases). Final data capture was completed after discharge from the hospital. The specific patient diseases or conditions captured for emergency general surgery are the following:
Acute Appendicitis
- Acute Gallbladder Disease
- Acute Cholecystitis
- Choledocholithiasis
- Cholangitis
- Gallstone Pancreatitis
- Small Bowel Obstruction
- Adhesive
- Hernia
- Malignancy/Mass/Stricture/Other
Emergent Exploratory Laparotomy (including laparoscopic approach)
Figure 1.
Flowchart describing data capture of emergency department visits and admission episodes for new and existing emergency general surgery patients.
The ICD-10-CM codes and algorithms utilized for MACS inclusion and exclusion criteria are available in a patient selection document (SDC 2). After the daily census for patients admitted to the Acute Care Surgery Service or seen as a consult is screened, other electronic medical record (EMR) and information technology sources may be utilized for additional patient selection screening. Beyond the recommendations in the patient selection document, each hospital was free to utilize additional individualized methods for identifying potential patients based upon institution-specific features that may be present. Patients who underwent emergent operative intervention for small bowel obstruction due to a hernia, internal hernia, volvulus, or mass/malignancy etiology were included in the emergent exploratory laparotomy cohort (SDC 3). Patients assigned to the small bowel obstruction cohort received either no operative intervention, an urgent or planned operation after admission, or an emergent operation for obstruction due purely to adhesions.
The first level of data entry, after inclusion and exclusion criteria screening, involves the entry of the patient into the MACS database. This database is constructed using software from the Qualtrics XM Platform™. All adult patients (age ≥ 18 years) with the identified disease or condition have data entered regardless of whether they received an operation during admission or emergency department (ED) visit. The second level of data entry occurs if an existing MACS patient returns to the hospital (ED visit or admission) or has outcome events identified within the 30-day post-operative time frame if the patient had surgery or within 30-days from discharge for the nonoperative patients. Data collection did not involve contacting patients and only utilized information that was part of the treating institutions medical record system. In addition to demographic, comorbid conditions, and outcomes data, information is captured on diagnostic and therapeutic interventions (e.g., endoscopic retrograde cholangiopancreatography), including radiologic studies, interventional procedures (e.g., percutaneous drain placement), types of operative repairs, and nonoperative management details.
Disease severity reporting is performed by reviewing clinical, anatomic, and imaging information in the EMR and using the American Association for the Surgery of Trauma (AAST) grading system specifically designed for emergency general surgery diseases.12 The AAST grading scale assigns a value from 1 to 5 based on increasing severity of disease. This value represents a measurement of inflammation and degree of peritonitis present for the specific condition. The AAST grading scale has been validated for the two disease cohorts, acute appendicitis and acute cholecystitis, in which we are employing data capture.13–15 The AAST grades were assigned during data abstraction and were not available or assigned during the patient’s hospitalization.
A MACS data definitions manual has been created and is updated annually (https://www.mtqip.org/resources/macs). Most of the data definitions are based upon existing standards in already available national sources (American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP), ACS Trauma Quality Improvement Program, Centers for Disease Control) and regionally (MTQIP, Michigan Surgical Quality Collaborative (MSQC)). Disease-specific data elements were created to capture studies performed, all operative interventions, interventional procedures, disease-specific outcomes, and nonoperative management. This is in addition to commonly found data elements in existing quality improvement platforms. Data validation visits are conducted annually, using a methodology like what has been published for trauma, with feedback provided to participants.16
Reporting
Hardcopy reports are created and distributed to each participant hospital every four months in conjunction with in-person meetings (SDC 4). The information includes a program summary of all patients. There are also disease-specific reports on acute appendicitis, acute gallbladder disease, small bowel obstruction, and exploratory laparotomy. Participant hospital data is compared to the collaborative in table and graphical form. Morbidity is reported as a composite that includes any incidence of a complication or mortality recorded in the index or subsequent readmission records.
Data Analysis
Multivariable generalized linear regression models were constructed for mortality, complications, and all other reported outcomes. For binary outcomes, a logit link function was used, and for continuous outcomes, a normal distribution was used. A stepwise selection process was used, to create a parsimonious multivariable model for each primary outcome while controlling for relevant candidate confounders, like demographics, disease factors, comorbid conditions, and hospital processes. If a model did not converge using the full selected model, a smaller model was tested using basic demographic variables. Most models converged using the full model. However, some outcomes were extremely rare; moreover, there was insufficient power to include many variables. We included AAST grade in the multivariable model(s) for acute appendicitis and acute gallbladder outcomes. Categorical variables being considered for use in modeling had a category of missing/not classed/not graded created to avoid the exclusion of patients in the analysis.
Results are expressed as risk-adjusted rates along with 95% confidence intervals. Risk-adjusted rates were calculated by adding the residual for each site (observed rate minus expected rate) to the overall observed rate. To compare sites to the collaborative, we calculated Z-tests for binary outcomes and t-tests for continuous outcomes. A p-value less than 0.05 was used as the threshold for statistical significance, and all reported p-values were 2-sided. If the p-value was significant, and the center rate was less than the collaborative rate, the center was reported as a low-outlier. If the p-value was significant and the center rate was more than the collaborative rate, the center was reported as a high outlier.
All statistical analyses were performed in Stata 15.0 (StataCorp, College Station, TX) or SAS 9.4 (SAS Institute, Inc., Cary, NC). Institutional Review Board approval was obtained for this study, and it was deemed exempt as it relied on the secondary use of already collected data. The SQUIRE 2.0 guideline was used to ensure proper reporting of methods, results, and discussion for a quality improvement study (SDC 5).17
Results
MACS CQI data collection included a total of 19,956 emergency general surgery patients. Of these, 56.8% were female, 82.8% were white, and the mean (SD) age was 53.3 (20.8) years (Table 1A & 1B). 3,013 (15.1%) patients had one or more readmission events. The prevalence of the top fifteen diagnosis codes and operations performed during index admission are detailed in Table 2. 12.9% of patients were transferred to a collaborative hospital after being seen in a referring hospital ED or being admitted to an outside hospital prior to transfer. Within the collaborative, 15,382 (77%) patients underwent operative intervention during their index admission.
Table 1.
Patient Characteristics
| A. Overall | |||
|---|---|---|---|
| Patient Characteristic | Management Cohort | ||
| Aggregate | Operative | Non-Operative | |
| Cases, N | 19,956 | 15,382 | 4,574 |
| By Disease, N (%) | |||
| Acute Appendicitis | 5325 (26.7) | 4,605 (29.9) | 720 (15.7) |
| Acute Gallbladder Disease | 8,681 (43.5) | 7,322 (47.6) | 1,359 (29.7) |
| Small Bowel Obstruction | 3,856 (19.3) | 1,361 (8.9) | 2,495 (54.6) |
| Exploratory Laparotomy | 2,094 (10.5) | 2,094 (13.6) | 0 (0.0) |
| Point of Patient Entry, N (%) | |||
| Emergency Department | 15,627 (78.3) | 12,168 (79.1) | 3,459 (75.6) |
| Transfer from Outside Hospital ED | 2,141 (10.7) | 1,500 (9.8) | 641 (14.0) |
| Transfer from Outside Hospital | 438 (2.2) | 319 (2.1) | 119 (2.6) |
| ED Only/Not Admitted | 194 (1.0) | 0 (0.0) | 194 (4.2) |
| Home/Direct Admit | 1,428 (7.2) | 1,294 (8.4) | 134 (2.9) |
| Other | 128 (0.6) | 101 (0.7) | 27 (0.6) |
| Payment, N (%) | |||
| Private/Other Government | 8,003 (40.1) | 6,672 (43.4) | 1,331 (29.1) |
| Medicaid | 2,657 (13.3) | 2,189 (14.2) | 468 (10.2) |
| Medicare | 5,842 (29.3) | 3,912 (25.4) | 1,930 (42.2) |
| Uninsured/Self pay | 387 (1.9) | 338 (2.2) | 49 (1.1) |
| Missing | 3,059 (15.3) | 2,263 (14.7) | 796 (17.4) |
| Age, Mean (SD) | 53.3 (20.8) | 50.9 (20.8) | 61.3 (18.6) |
| Race, N (%) | |||
| White | 16,520 (82.8) | 12,684 (82.5) | 3,836 (83.9) |
| Black | 1,891 (9.5) | 1,440 (9.4) | 451 (9.9) |
| Other | 1,545 (7.7) | 1,258 (8.2) | 287 (6.3) |
| Sex, N (%) | |||
| Female | 11,336 (56.8) | 9,019 (58.6) | 2,317 (50.7) |
| Male | 8,614 (43.2) | 6,357 (41.3) | 2,257 (49.3) |
| Height (cm) | |||
| Mean (SD) | 169 (10.7) | 169 (10.6) | 170 (10.8) |
| Median (IQR) | 168 (162–178) | 168 (161–178) | 170 (163–178) |
| Weight (kg) | |||
| Mean (SD) | 87 (24.8) | 88 (25) | 84.9 (26.2) |
| Median (IQR) | 84 (70–100) | 84 (71–101) | 81 (67–98) |
| Body Mass Index | |||
| Mean (SD) | 30 (8.3) | 31 (8.1) | 30 (8.6) |
| Median (IQR) | 29 (25–35) | 30 (25–35) | 28 (24–33) |
| Comorbid Conditions, N (%) | |||
| Ascites | 201 (1.2) | 121 (0.9) | 80 (2.1) |
| CHF within 30 days | 241 (1.4) | 135 (1.0) | 106 (2.8) |
| COPD | 724 (4.3) | 463 (3.5) | 261 (6.9) |
| COVID-19 | 1,326 (7.8) | 967 (7.4) | 359 (9.5) |
| Current Cancer/Malignancy | 1,033 (6.1) | 599 (4.6) | 434 (11.5) |
| Diabetes Mellitus | 2,357 (11.8) | 1,631 (10.6) | 726 (15.9) |
| Dialysis within 2 weeks | 209 (1.2) | 135 (1.0) | 74 (2.0) |
| Disseminated Cancer | 425 (2.5) | 227 (1.7) | 198 (5.2) |
| Hypertension | 6,318 (37.4) | 4,484 (34.2) | 1,834 (48.6) |
| Functional Health Status - Dependent | 689 (4.1) | 410 (3.1) | 279 (7.4) |
| History of DVT/PE | 1,121 (6.6) | 733 (5.6) | 388 (10.3) |
| Pregnancy | 81 (0.4) | 68 (0.4) | 13 (0.3) |
| Sepsis | 2,977 (17.6) | 2,451 (18.7) | 526 (13.9) |
| Severe Sepsis/Septic Shock | 1,385 (8.2) | 1,042 (7.9) | 343 (9.1) |
| Sleep Apnea | 3,546 (21.0) | 2,530 (19.3) | 1,016 (26.9) |
| Solid Organ Transplant | 111 (0.8) | 70 (0.6) | 41 (1.2) |
| Steroid or Immunosuppressive Rx | 1,017 (6.0) | 597 (4.6) | 420 (11.1) |
| Tobacco within 1 year - Cigarette | 2,562 (15.2) | 2,033 (15.5) | 529 (14.0) |
| Ventilator dependent within 48 hours | 283 (1.7) | 220 (1.7) | 63 (1.7) |
ED, Emergency Department; cm, centimeters; SD, standard deviation; IQR, interquartile range; kg, kilograms; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; COVID-19, coronavirus disease 2019; DVT, deep vein thrombosis; PE, pulmonary embolism; Rx, prescription medication.
| B. By disease | |||||||
|---|---|---|---|---|---|---|---|
| Patient Characteristic | Appendicitis | Gallbladder | Small Bowel | Exploratory Laparotomy | |||
| Operative | Non-Operative | Operative | Non-Operative | Operative | Non-Operative | ||
| Cases, N | 4,605 | 720 | 7,322 | 1,359 | 1,361 | 2,495 | 2,094 |
| Point of Patient Entry, N (%) | |||||||
| Emergency Department | 4,199 (91.2) | 572 (79.4) | 5,450 (74.4) | 861 (63.4) | 1,085 (79.7) | 2,026 (81.2) | 1,434 (68.5) |
| Transfer from Outside Hospital ED | 151 (3.3) | 80 (11.1) | 807 (11.0) | 256 (18.8) | 151 (11.1) | 305 (12.2) | 391 (18.7) |
| Transfer from Outside Hospital | 11 (0.2) | 7 (1.0) | 138 (1.9) | 67 (4.9) | 45 (3.3) | 45 (1.8) | 125 (6.0) |
| ED Only/Not Admitted | 0 (0.0) | 44 (6.1) | 0 (0.0) | 99 (7.3) | 0 (0.0) | 51 (2.0) | 0 (0.0) |
| Home/Direct Admit | 211 (4.6) | 9 (1.3) | 874 (11.9) | 63 (4.6) | 77 (5.7) | 62 (2.5) | 132 (6.3) |
| Other | 33 (0.7) | 8 (1.1) | 53 (0.7) | 13 (1.0) | 3 (0.2) | 6 (0.2) | 12 (0.6) |
| Payment, N (%) | |||||||
| Private/Other Government | 2,648 (57.6) | 335 (46.5) | 3,098 (42.3) | 336 (24.7) | 298 (21.9) | 660 (26.5) | 628 (30.0) |
| Medicaid | 598 (13.0) | 90 (12.5) | 1,162 (15.9) | 163 (12.0) | 141 (10.4) | 215 (8.6) | 288 (13.8) |
| Medicare | 447 (9.7) | 146 (20.3) | 1,729 (23.6) | 665 (48.9) | 589 (43.3) | 1,119 (44.8) | 1,147 (54.8) |
| Uninsured/Self pay | 140 (3.0) | 15 (2.1) | 152 (2.1) | 13 (1.0) | 16 (1.2) | 21 (0.8) | 30 (1.4) |
| Missing | 768 (16.7) | 134 (18.6) | 1,178 (16.1) | 182 (13.4) | 316 (23.2) | 480 (19.2) | 1 (0.0) |
| Age, Mean (SD) | 40.7 (21.7) | 48.9 (18.8) | 51.3 (18.7) | 64.0 (19.5) | 64.4 (16.4) | 63.4 (16.4) | 62.9 (15.8) |
| Race, N (%) | |||||||
| White | 3,810 (82.7) | 570 (79.2) | 6,021 (82.2) | 1,148 (84.5) | 1,088 (79.9) | 2,118 (84.9) | 1,765 (84.3) |
| Black | 326 (7.1) | 73 (10.1) | 691 (9.4) | 130 (9.6) | 208 (15.3) | 248 (9.9) | 215 (10.3) |
| Other | 469 (10.2) | 77 (10.7) | 610 (8.3) | 81 (6.0) | 65 (4.8) | 129 (5.2) | 114 (5.4) |
| Sex, N (%) | |||||||
| Female | 2,429 (52.7) | 358 (49.7) | 4,715 (64.4) | 667 (49.1) | 764 (56.1) | 1,292 (51.8) | 1,111 (53.1) |
| Male | 2,175 (47.2) | 362 (50.3) | 2,604 (35.6) | 692 (50.9) | 596 (43.8) | 1,203 (48.2) | 982 (46.9) |
| Height (cm) | |||||||
| Mean (SD) | 170.5 (10.5) | 170.7 (10.4) | 168.3 (10.4) | 169.7 (11.0) | 168.9 (11.1) | 169.1 (10.7) | 169.1 (11.1) |
| Median (IQR) | 170. (163–177) | 170 (163–179) | 168 (160–175) | 170 (163–178) | 168 (160–178) | 170 (160–178) | 168 (160–178) |
| Weight (kg) | |||||||
| Mean (SD) | 85.1 (22.5) | 85.1 (25.6) | 91.7 (23.7) | 88.1 (27.6) | 82.0 (27.2) | 83.0 (25.3) | 83.3 (25.9) |
| Median (IQR) | 81.7 (69–97.5) | 80.6 (67.7–97) | 88.85 (75.3–105) | 83.6 (70.2–101) | 77.7 (63.5–95) | 79.3 (64.9–97) | 79.2 (64.9–97.5) |
| Body Mass Index | |||||||
| Mean (SD) | 29.3 (7.5) | 29.1 (8.6) | 32.3 (7.8) | 30.5 (8.8) | 28.7 (9.0) | 28.9 (8.3) | 29.1 (8.7) |
| Median (IQR) | 28.1 (24.1–32.8) | 27.4 (23.6–32.3) | 31.1 (27.0–36.5) | 29.1 (24.9–34.1) | 26.5 (22.9–32.7) | 27.4 (23.1–33.1) | 27.7 (23.3–33.1) |
| Comorbid Condition, N (%) | |||||||
| Ascites | 3 (0.1) | 2 (0.3) | 12 (0.2) | 28 (2.4) | 29 (2.8) | 50 (2.5) | 77 (3.7) |
| CHF within 30 days | 9 (0.2) | 6 (1.0) | 45 (0.7) | 65 (5.5) | 17 (1.6) | 35 (1.7) | 64 (3.1) |
| COPD | 48 (1.3) | 11 (1.9) | 147 (2.4) | 114 (9.7) | 87 (8.3) | 136 (6.7) | 181 (8.6) |
| COVID-19 | 286 (7.5) | 72 (12.3) | 430 (7.0) | 126 (10.7) | 67 (6.4) | 161 (8.0) | 184 (8.8) |
| Current Cancer/Malignancy | 73 (1.9) | 34 (5.8) | 144 (2.3) | 145 (12.3) | 94 (9.0) | 255 (12.7) | 288 (13.8) |
| Diabetes Mellitus | 238 (5.2) | 76 (10.6) | 860 (11.7) | 273 (20.1) | 163 (12.0) | 377 (15.1) | 370 (17.7) |
| Dialysis within 2 weeks | 5 (0.1) | 3 (0.5) | 25 (0.4) | 47 (4.0) | 14 (1.3) | 24 (1.2) | 91 (4.3) |
| Disseminated Cancer | 7 (0.2) | 17 (2.9) | 26 (0.4) | 56 (4.8) | 42 (4.0) | 125 (6.2) | 152 (7.3) |
| Hypertension | 675 (17.6) | 160 (27.4) | 2,208 (35.9) | 685 (58.2) | 523 (50.0) | 989 (49.1) | 1,078 (51.5) |
| Functional Health Status - Dependent | 14 (0.4) | 19 (3.2) | 121 (2.0) | 141 (12.0) | 58 (5.6) | 119 (5.9) | 217 (10.4) |
| History of DVT/PE | 87 (2.3) | 24 (4.1) | 261 (4.2) | 120 (10.2) | 115 (11.0) | 244 (12.1) | 270 (12.9) |
| Pregnancy | 23 (0.5) | 3 (0.4) | 43 (0.6) | 9 (0.7) | 0 (0.0) | 1 (0.04) | 2 (0.1) |
| Sepsis | 1,157 (30.1) | 230 (39.3) | 884 (14.4) | 242 (20.6) | 48 (4.6) | 54 (2.7) | 362 (17.3) |
| Severe Sepsis/Septic Shock | 149 (3.9) | 37 (6.3) | 276 (4.5) | 245 (20.8) | 28 (2.7) | 61 (3.0) | 589 (28.1) |
| Sleep Apnea | 423 (11.0) | 99 (16.9) | 1,213 (19.7) | 380 (32.3) | 268 (25.6) | 537 (26.7) | 626 (29.9) |
| Solid Organ Transplant | 10 (0.3) | 0 (0.0) | 21 (0.4) | 11 (1.1) | 10 (1.1) | 30 (1.7) | 29 (1.6) |
| Steroid or Immunosuppressive Rx | 77 (2.0) | 41 (7.0) | 189 (3.1) | 123 (10.5) | 71 (6.8) | 256 (12.7) | 260 (12.4) |
| Tobacco within 1 year - Cigarette | 481 (12.5) | 90 (15.4) | 891 (14.5) | 162 (13.8) | 202 (19.3) | 277 (13.7) | 459 (21.9) |
| Ventilator dependent within 48 hours | 2 (0.1) | 4 (0.7) | 56 (0.9) | 45 (3.8) | 5 (0.5) | 14 (0.7) | 157 (7.5) |
ED, Emergency Department; cm, centimeters; SD, standard deviation; IQR, interquartile range; kg, kilograms; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; COVID-19, coronavirus disease 2019; DVT, deep vein thrombosis; PE, pulmonary embolism; Rx, prescription medication.
Table 2.
| A. Primary ICD-10-CM diagnosis. | |||
|---|---|---|---|
| Code | Description | N | % |
| K56.609 | Unspecified intestinal obstruction, unspecified as to partial versus complete obstruction | 1,561 | 7.8 |
| K35.80 | Unspecified acute appendicitis | 1,552 | 7.8 |
| K35.30 | Acute appendicitis with localized peritonitis, without perforation or gangrene | 1,482 | 7.4 |
| K80.00 | Calculus of gallbladder with acute cholecystitis without obstruction | 1,458 | 7.3 |
| K81.0 | Acute cholecystitis | 1,136 | 5.7 |
| K80.12 | Calculus of gallbladder with acute and chronic cholecystitis without obstruction | 921 | 4.6 |
| K85.10 | Biliary acute pancreatitis without necrosis or infection | 829 | 4.2 |
| K80.10 | Calculus of gallbladder with chronic cholecystitis without obstruction | 812 | 4.1 |
| K35.32 | Acute appendicitis with perforation, localized peritonitis, and gangrene, without abscess | 717 | 3.6 |
| K56.50 | Intestinal adhesions [bands], unspecified as to partial versus complete obstruction | 573 | 2.9 |
| K35.890 K35.891 |
Other acute appendicitis without perforation or gangrene Other acute appendicitis without perforation, with gangrene |
524 | 2.6 |
| K56.600 K56.601 |
Partial intestinal obstruction, unspecified as to cause Complete intestinal obstruction, unspecified as to cause |
500 | 2.5 |
| K35.33 | Acute appendicitis with perforation, localized peritonitis, and gangrene, with abscess | 496 | 2.5 |
| K80.50 | Calculus of bile duct without cholangitis or cholecystitis without obstruction | 388 | 1.9 |
| K80.20 | Calculus of gallbladder without cholecystitis without obstruction | 268 | 1.3 |
| Other | All other diagnoses | 6,739 | 33.8 |
| B. Current Procedural Terminology (CPT) codes for primary operation performed. | |||
| Code | Description | N | % |
| 47562 | Laparoscopic cholecystectomy | 5,923 | 38.5 |
| 44970 | Laparoscopic appendectomy | 4,279 | 27.8 |
| 47563 | Laparoscopic cholecystectomy with IOC | 903 | 5.9 |
| 44120 | Resection of small intestine | 608 | 4.0 |
| 44005 | Freeing of bowel adhesion | 434 | 2.8 |
| 47600 | Open cholecystectomy | 312 | 2.0 |
| 49000 | Exploration of abdomen | 229 | 1.5 |
| 44140 | Partial colectomy with anastomosis | 209 | 1.4 |
| 44143 | Partial colectomy with colostomy | 198 | 1.3 |
| 43840 | Gastrorrhaphy, Graham patch | 188 | 1.2 |
| 44950 | Open appendectomy | 168 | 1.1 |
| 44160 | Partial colectomy with terminal ileum | 131 | 0.9 |
| 49561 | Repair ventral/incisional hernia | 128 | 0.8 |
| 49320 | Laparoscopy, diagnostic | 117 | 0.8 |
| 44050 | Incision procedure on intestines | 103 | 0.7 |
| Other | All other procedures | 1,452 | 9.4 |
ICD-10-CM Clinical Modification of the 10th revision of the International Statistical Classification of Diseases and Related Health Problems.
IOC, Intra-operative cholangiogram.
The model constructed for mortality had a c-index of 0.96, and similar risk-adjustment models were constructed for all the other investigated outcomes (SDC 6: Models for mortality, morbidity, ED visit, and readmission). The c-index for the final models ranged from 0.57 to 0.96, and there were 69 models constructed. Risk-adjusted mortality rates, morbidity (mortality and/or complication), ED visits, readmissions, and select individual complications are shown in Table 3 and SDC 7. Hospital and intensive care unit length of stay in hours are also shown. Adjusted mortality varied from 2.3% to 4.2% between the 10 hospitals for the overall cohort of patients (Figure 2). There was one statistically significant low-outlier and one high-outlier hospital for mortality. Differences between hospitals were also found in the composite of mortality or complications and readmissions. However, there were no significant differences in hospital length of stay between the hospitals in the collaborative (Mean = 112.2 hours).
Table 3.
Risk-adjusted outcomes
| Outcome | Aggregate | Acute Appendicitis | Acute Gallbladder Disease | Small Bowel Obstruction | Exploratory Laparotomy |
|---|---|---|---|---|---|
| Cases | |||||
| Overall, N | 19,956 | 5,325 | 8,681 | 3,856 | 2,094 |
| With operation, % (N) | 77.1 (15,382) | 86.5 (4,605) | 84.3 (7,322) | 35.3 (1,361) | 100 (2,094) |
| Without operation, % (N) | 22.9 (4,574) | 13.5 (720) | 15.7 (1,359) | 64.7 (2,495) | -- |
| Mortality*, % (95% CI) | |||||
| Overall | 3.5 (3.2–3.7) | 0.3 (0.1–0.4) | 2.2 (1.9–2.5) | 4.3 (3.7–4.9) | 15.4 (13.9–16.9) |
| With operation | 2.7 (2.5–3.0) | 0.2 (0.1–0.4) | 0.3 (0.2–0.5) | 4.5 (3.5–5.5) | -- |
| Without operation | 4.7 (4.0–5.3) | 0.6 (0.0–1.2) | 7.8 (6.3–9.4) | 4.2 (3.4–4.9) | -- |
| Mortality or Complication*#, % (95% CI) | |||||
| Overall | 27.6 (27.0–28.3) | 19.6 (18.5–20.7) | 26.7 (25.7–27.6) | 34.3 (32.9–35.8) | 62.3 (60.3–64.3) |
| With operation | 27.2 (26.5–27.9) | 19.2 (18.1–20.3) | 22.8 (21.9–23.8) | 51.3 (48.8–53.7) | -- |
| Without operation | 29.3 (27.9–30.8) | 23.5 (19.9–27.1) | 35.2 (32.4–38.1) | 28.4 (26.6–30.2) | -- |
| ED Visit, % (95% CI) | |||||
| Overall | 8.4 (8.0–8.7) | 8.2 (7.5–9.0) | 7.8 (7.2–8.3) | 8.5 (7.7–9.4) | 11.6 (10.3–12.9) |
| With operation | 8.3 (7.9–8.7) | 8.1 (7.3–8.9) | 7.3 (6.7–7.9) | 9.7 (8.3–11.2) | -- |
| Without operation | 8.6 (7.7–9.5) | 9.7 (7.2–12.2) | 10.7 (8.9–12.5) | 7.8 (6.7–8.8) | -- |
| Readmission, % (95% CI) | |||||
| Overall | 15.1 (14.6–15.6) | 15.5 (14.5–16.5) | 12.7 (12.0–13.4) | 26.0 (24.7–27.4) | 27.2 (25.4–29.0) |
| With operation | 14.1 (13.6–14.7) | 14.5 (13.5–15.5) | 11.5 (10.8–12.2) | 29.2 (26.9–31.4) | -- |
| Without operation | 19.1 (17.8–20.3) | 15.5 (12.4–18.6) | 21.2 (18.8–23.6) | 23.9 (22.2–25.6) | -- |
| Incisional SSI, % (95% CI) | 1.4 (1.2–1.6) | 0.7 (0.5–1.0) | 0.6 (0.5–0.8) | 4.1 (3.1–5.0) | 4.4 (3.5–5.2) |
| Organ Space SSI, % (95% CI) | 2.9 (2.6–3.1) | 2.2 (1.8–2.6) | 0.9 (0.7–1.1) | 2.8 (2.0–3.6) | 11.8 (10.5–13.2) |
| Hospital LOS, hrs (95% CI) | 112 (109–115) | 53.0 (50.0–56.2) | 109 (104–114) | 60.5 (54.9–66.1) | 302 (289–315) |
| ICU LOS†, hrs (95% CI) | 208 (204–213) | -- | -- | -- | 187 (173–200) |
CI, confidence interval; ED, Emergency Department; SSI, surgical site infection; LOS, length of stay; ICU, intensive care unit.
Mortality represents inpatient death.
Complications included any incidence of the following: Incisional SSI, Organ space SSI, Anastomotic leak, Wound disruption, Enterocutaneous fistula, ileus, C. difficile colitis, Venous thromboembolism, new COVID infection, Pancreatitis, Pneumonia, Cardiac arrest, Sepsis or severe sepsis, Post-discharge ED visit, or Readmission.
Intensive care unit length of stay was calculated only for patients who were admitted to an ICU.
Figure 2.
Risk-adjusted outcome rates by hospital with 95% confidence intervals. A. Morbidity (mortality and/or complication), B. Mortality (inpatient), C. Readmissions, D. Hospital length of stay. Black bar = low-outlier performance, Gray bar = average performance, Red bar = high-outlier performance, Dashed line = collaborative mean.
Participant hospitals varied in their percent operative versus nonoperative management of appendicitis, acute gallbladder disease, and small bowel obstruction during index admission (Figure 3). The type of operation performed also varied with open, laparoscopic, laparoscopic converted to open, and robotic approaches all being utilized among the participant hospitals. The use of a Gastrografin (diatrizoate meglumine/diatrizoate sodium) challenge in patients with adhesive in origin small bowel obstruction ranged from 10.7% to 61.4% of patients. In patients who underwent initial nonoperative management of acute cholecystitis gallbladder disease, 51.5% had a cholecystostomy tube placed. The cholecystostomy tube placement rate ranged from 23.5% to 62.1% across hospitals.
Figure 3.
Process measures by hospital. A. Operative intervention for acute appendicitis, acute gallbladder disease, and small bowel obstruction. B. Type of operative approach for adhesive small bowel obstruction excluding malignancy, Crohn’s, hernia, and vascular insufficiency causes. C. Rate of cholecystostomy tube placement in nonoperative acute cholecystitis gallbladder disease patients. D. Rate of use of Gastrografin challenge test in patients with small bowel obstruction. Dashed line = collaborative mean.
Discussion
In this study, we describe the construct of a CQI for emergency general surgery and document the initial results for outcomes and processes. Unique features of our CQI infrastructure include the limitation of data collection to four emergency general surgery conditions, collapsing multiple patient admissions to define longitudinal results over time, and data collection on operative and nonoperatively managed patients. Fifteen operative procedures described the specific intervention performed for >90% of the patients who underwent an operation for the four emergency general surgery conditions being investigated. There was significant variation in processes and outcomes between participant hospitals.
There is an increasing body of evidence demonstrating that the practice of emergency general surgery is not optimized, and that there exists excess mortality from these operations.18,19 Factors that may contribute to adverse outcomes include, delays in operative intervention and/or transfer, experience of the treating surgeon, operative volume, and care discontinuity.20–27 In September 2022, the ACS and AAST launched the new multidisciplinary Emergency General Surgery quality and verification program (EGS-VP), which sets 42 standards designated in a resource document.28,29 The program also leverages the ACS NSQIP registry (EGS Targeted Module) to capture data on both operative and nonoperative emergency general surgery cases.30 Additional proposals to address emergency general surgery quality improvement have been published with suggestions for a data registry, standardization of pathways, structure of quality improvement processes, and defining of high-yield topics.8,31
Within the state of Michigan, Blue Cross Blue Shield of Michigan/Blue Care Network has sponsored a growing portfolio of procedural-based CQIs since 1997 with the launch of an initial program centered on percutaneous coronary interventions.32,33 The Michigan Trauma Quality Improvement Program began in 2008 and focuses on trauma care, one of the key clinical domains of acute care surgery.34,35 A unique aspect of MTQIP and trauma registries is the collection of data on both operative and nonoperative patients. Collecting data on both operative and nonoperative patients is necessary to paint a complete picture of emergency general surgery care.30 Data collection must also account for patients on other inpatient services seen by emergency general surgery providers as a consult and those residing on the Acute Care Surgical service.
The MACS collaborative meetings are held three times per year and are in person when possible. Surgeon champions and data abstractors from each participant hospital are required to attend. Data are unmasked at meetings to facilitate discussion and are protected by using nondisclosure agreements. Participants are encouraged to ask questions and share experiences to allow for a learning health system environment.36 Hardcopy reports are distributed in advance and form the basis for the presentation of data at collaborative meetings. Like MTQIP, a hospital report card is being constructed and will include performance and participation metrics.35 Deep dives into the data surrounding outcomes and processes are encouraged, and participant hospitals have presented their results of data reviews at recent CQI meetings.
Patient readmission to the hospital is a challenging data capture and analytic circumstance in emergency general surgery.37 We have elected to capture readmission events by performing complete data abstraction for every patient’s admission to the hospital. Emergency department visits not resulting in hospital admission, and 30-day outcome data available from non-readmission encounters are entered into the prior admission record created for the patient. Index admission records are defined in the analytic algorithm, and outcomes are rolled up by collapsing data records on the index and subsequent readmission data entries for each patient to create an episode of care.
How much data to associate with an episode of care remains an open question. In the 6 months after initial admission, up to 20% of emergency general surgery patients experience a readmission, and 1 in 9 patients require an additional surgical procedure.38 The readmission rate in our study was 15.1% for the aggregate cohort, and we found a substantial number of patients that required additional operations or interventions. Recent evidence proposes that unmodifiable patient and disease-related factors may drive hospital readmissions in emergency general surgery.39 Hence, it was suggested that readmissions might be reduced by implementing postdischarge surveillance systems leveraging telehealth and in-person outpatient care provided by surgeons, primary care providers, and appropriate specialists. These postdischarge interventions will require data collection and analytic mechanisms for episodes of care that can document and collapse information over many months to years. Care and data collection discontinuity also results from patients being readmitted to a hospital other than where their index care was performed.40
There are also nuances of specific outcomes, nonoperative management, and duration of an episode that must be considered within each disease cohort. For example, acute appendicitis patients who undergo nonoperative management need a longer follow-up than the standard 30–90 days to capture the prevalence of disease reoccurrence.41–43 Acute gallbladder disease requires capturing disease-specific complications such as cystic duct stump leak and common bile duct injury. In addition, capture of nonoperative interventions like endoscopic retrograde cholangiopancreatography, cystic duct stenting, and cholecystostomy tube placement allows for a complete picture of disease management. Patients undergoing emergent exploratory laparotomy can be at substantial risk for morbidity and mortality due to the degree of physiologic derangement present. To measure the pre-operative severity of illness, we collect information that allows calculation of the Royal College of Physicians-developed National Early Warning Score 2, utilized in the United Kingdom by the National Health Service.44
In the future, we intend to incorporate patient-reported outcomes into the MACS program using telephone and electronic-based surveys. Amendments to our participation agreements allow for merging data from MACS with data from the corresponding Blue Cross Blue Shield of Michigan/Blue Care Network-sponsored collaboratives focused on anesthesiology (Anesthesiology Performance Improvement and Reporting Exchange (ASPIRE)) and surgery (MSQC). These same amendments allow for sharing data, reports, and findings with ASPIRE and MSQC, fostering wide dissemination of quality improvement efforts in emergency general surgery to the appropriate collaborative stakeholders.
Extensive collection of processes of care has occurred in addition to investigating patient outcomes. Differences have been found in the use of a robotic surgical approach; which patients to consider for nonoperative treatment of acute appendicitis, acute cholecystitis, and small bowel obstruction; use of laparoscopy in operative treatment of SBO; cholecystostomy tube utilization in acute cholecystitis; and Gastrografin challenge in SBO. These differences have stimulated extensive discussions at CQI meetings. Based on these discussions, we have initiated efforts to create optimal care pathways and ascertain appropriate eligibility for the use of these processes of care, which may influence patient outcomes.
There are several limitations to our study and the work done so far in creating the MACS CQI for EGS that merit discussion. Risk-adjustment models in the literature were considered for use, but none were deemed practical to utilize in MACS risk-adjusted reporting. This was due to not having fully published variables with coefficients, lack of modeling beyond a few selected outcomes, and published models such as POTTER or ACS NSQIP were often only available for use in a single patient calculator format.45–47 However, the variables used in these published models did guide us in what data to consider collecting and what co-variates to utilize when constructing an outcome model. Complete capture of patients to be included in data abstraction was reliant on manual review of patient records in our study. It must be acknowledged that the use of manual methods and in some cases individualized institution specific methods to screen for potential patients could have induced bias in the selection of patients for data abstraction. Development and utilization of automated EMR screening mechanisms in the future could potentially increase standardization and optimize patient selection thereby minimizing differences in patient inclusion across hospitals. Within the emergency laparotomy cohort, we did not collect information on patients who were consulted on and declined operative intervention or were not offered an operation. Consideration of developing a definition for this category that would capture patients for whom emergency laparotomy was a viable treatment option but not offered/performed might reduce bias in what the true denominator is for this cohort. Delineation of what cohorts of patients to collect data on, what data to collect, the robustness of risk adjustment, and how to report the data in a meaningful way are all efforts that are in their infancy and await additional investigation.
Summary
We have described the construct and initial experience of a CQI for emergency general surgery. The MACS CQI aims to use a comparative effectiveness model to enhance quality reporting and promote meaningful quality improvement for emergency general surgery patients in Michigan. The support of a third-party payer providing a safe space for collaboration has been essential to the successful launch and growth of this emergency general surgery initiative.
Supplementary Material
SDC 1. MACS participant hospital eligibility and expectations
SDC2. MACS patient selection inclusion and exclusion criteria
SDC3. Guidance for placement of small bowel obstruction cases
SDC4. MACS report examples
SDC5. SQUIRE 2.0 Checklist
SDC6. Risk adjustment model for mortality
SDC7. Rates of individual complications
Acknowledgements
We recognize the MACS clinical reviewers, trauma program managers, and surgical champions at the member hospitals for their dedication to collection of the data and participation in MACS.
Funding/Support:
This study was supported by a Blue Cross Blue Shield of Michigan and Blue Care Network Collaborative Quality Initiatives grant to Mark R. Hemmila to administer the Michigan Trauma Quality Improvement Program. John W. Scott was supported by grants K08-HS028672 and R01-HS027788 from the Agency for Healthcare Research and Quality.
Role of the Funder/Sponsor:
The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Footnotes
Conflicts of Interest Disclosure: All Journal of Trauma and Acute Care Surgery disclosure forms have been supplied and are provided as supplemental digital content (SDC 8).
Presented at: This paper was an oral presentation at the American Association for the Surgery of Trauma meeting, Anaheim, CA, on September 22, 2023. It has not been submitted for publication elsewhere.
Reprints will not be available from the authors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
SDC 1. MACS participant hospital eligibility and expectations
SDC2. MACS patient selection inclusion and exclusion criteria
SDC3. Guidance for placement of small bowel obstruction cases
SDC4. MACS report examples
SDC5. SQUIRE 2.0 Checklist
SDC6. Risk adjustment model for mortality
SDC7. Rates of individual complications