Failure to Rescue and Mortality Differences After Appendectomy in a Low-Middle-Income Country and the United States : Anesthesia & Analgesia

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Failure to Rescue and Mortality Differences After Appendectomy in a Low-Middle-Income Country and the United States

Rosero, Eric B. MD, MSc*; Eslava-Schmalbach, Javier MD, MSc, PhD; Garzón-Orjuela, Nathaly MD, MSc; Buitrago, Giancarlo MD, PhD; Joshi, Girish P. MBBS, MD, FFARCS*

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Anesthesia & Analgesia 136(6):p 1030-1038, June 2023. | DOI: 10.1213/ANE.0000000000006336



  • Question: Are there differences in postoperative complications, mortality, and failure to rescue after appendectomy in Colombia, a low-middle–income country, compared to the United States, a high-income country?
  • Findings: Despite lower rates of complications, the adjusted in-hospital mortality rate was about 9× higher, and the failure to rescue rate was, on average, 17× higher in Colombia.
  • Meaning: Mortality after appendectomy was higher in Colombia, despite an observed lower rate of complications, suggesting that higher mortality in Colombia may be driven by higher rates of failure to rescue rather than by higher incidence of complications.

There is wide variation in access to surgical care and postoperative outcomes across nations.1–3 Of >311 million surgical interventions performed annually worldwide, only about 6% occur in low- or middle-income countries (LMICs).4 Compared to high-income countries (HICs), postoperative outcomes are significantly inferior in LMICs,5–7 both for elective and emergency surgeries.7–10 Appendectomy is the emergency abdominal surgical procedure performed most frequently throughout all levels of country income,4 and has been used as a global indicator of access to surgical care. Outcomes after appendectomy have been proposed as a metric to evaluate gaps in the quality of surgical care between LMICs and HICs.11 Studies have found that compared to HICs, postoperative complications and mortality after appendectomy are higher in LMICs.5–7 However, previous research on this topic has been limited to small, single-center studies or to literature reviews using summarized data with inability to produce direct comparisons at the patient level.7–10

It is unclear whether higher mortality after appendectomy in LMICs is due to increased incidence of postoperative complications or to barriers to recognize and treat complications after they occur (ie, failure to rescue [FTR]). FTR has been widely used as a quality metric to compare hospital differences in perioperative care. It is recognized that variations in mortality across hospitals within countries are not necessarily linked to increased incidence of complications but rather to increased rates of FTR.12,13 Seemingly, a similar phenomenon may explain differences in postoperative mortality between countries with different levels of income.14 However, data on surgical outcomes in LMICs from Latin America are scanty.15

The primary aims of the study were to investigate differences in the incidence of FTR, postoperative mortality, and major complications in patients undergoing appendectomy in Colombia, a Latin American LMIC, and the United States, an HIC. We hypothesized that after adjusting for patient-level factors, in-hospital mortality, and FTR rates after appendectomy are higher in Colombia compared to the United States. Investigation of surgical outcomes between countries with different income levels may help to propose interventions to reduce international disparities in perioperative care.


The study was determined to be exempt from full board review by the institutional review board of the University of Texas Southwestern Medical Center at Dallas and the ethics committee of the School of Medicine of Universidad Nacional de Colombia because the data used in the study are deidentified and publicly available. Therefore, written informed consent was not required. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guidelines were followed for this study. The study population consisted of adult patients undergoing appendectomy in Colombia and 2 states of the United States (New York and Florida) during the years 2013 and 2014. These years were selected because 2014 was the most recent year of available data for both countries at the time of study initiation.

Source of Data

Data on patients undergoing appendectomy in NY and FL were extracted from the 2013 and 2014 State Inpatient Databases (SIDs) and Ambulatory Surgery and Services Databases (SASDs). These large administrative databases are developed for the Health Cost and Utilization Project (HCUP) and sponsored by the Agency for Healthcare Research and Quality (AHRQ). The SID collects clinical and nonclinical data from inpatient discharge records from all patients admitted to all community hospitals in a state. Similarly, the SASD consists of patient demographics and clinical and procedural data for ambulatory surgeries and other outpatient procedures performed in free-standing ambulatory centers and outpatient hospital departments within a state. The 2013–2014 HCUP databases contain encounter-level data on up to 25 diagnoses (stored as International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes) and a list of principal and secondary procedures (described as ICD-9 procedure codes in the SID and as Current Procedural Terminology [CPT] codes in the SASD). In addition, the databases contain information on patient demographics, discharge status, hospital charges, and length of hospital stay (LOS). The states of FL and NY were selected because of their high-quality data and large and ethnically diverse population. Two revisit analysis variables allow tracking of patients over time and across different facilities and permit identification of postoperative hospital readmissions.

Data from Colombia were extracted from the 2013 and 2014 administrative Databases of the Contributory Regime (DCR) of the Social Security System in Health (SSSH) of Colombia. The databases are sponsored and administered by the Ministry of Health and Social Protection of Colombia and consist of claims data at the patient encounter level for health care services, including clinic visits, surgical procedures, emergency visits, and hospitalizations. The contributory regime is mandatory for employees and independent workers and covers contributors as well as their dependents. About 209 million people (44% of the Colombian population) were covered by the contributory regime in 2014.16 For each patient encounter, the DCR captures some demographic variables (age and sex), clinical diagnoses in the form of ICD-10 codes, and diagnostic and therapeutic procedures, coded using the Colombian Unique Classification of Healthcare Procedures System (CUPS), which is very similar to the CPT coding system. In addition, the DCR database collects data on dates of service and discharge, type of service setting (outpatient, inpatient, intensive care unit, and hospital ward), and hospital charges.17,18

Selection of Patients

Adult patients (age ≥18 years of age) who had appendectomy for treatment of acute appendicitis (ICD-9-CM codes 540.0–540.9 and ICD-10 codes K35.2–K35.8) were included in the study. Appendectomy cases were extracted from the Colombian DCR using CUPS codes 471100, 471110, 471200, and 471300,17 and from the US databases using ICD-9-CM procedure codes 47.0, 47.01, and 47.09. Cases of incidental appendectomy (removal of the appendix as part of another operation, without evidence of acute appendicitis) or appendectomy secondary to trauma were excluded.


Primary outcomes included in-hospital mortality, FTR, and major complications. In-hospital mortality for the US data was extracted directly from a variable present in the HCUP databases. In-hospital mortality for Colombian patients was determined by linking the DCR database with the death certificate database of the National Administrative Department of Statistics of Colombia. A common variable, not containing patient identifiers, is available in both databases and allows linkage of the data sets. Major postoperative complications were identified using specific ICD-9-CM or ICD-10 diagnosis codes and included respiratory failure, pneumonia, cardiac complications (myocardial infarction, cardiac arrest, or arrhythmias, except for chronic atrial fibrillation), deep venous thrombosis or pulmonary embolism, acute renal failure, postoperative hemorrhage, gastrointestinal bleeding, surgical site infection, sepsis, shock, and postoperative stroke. These complications were selected based on previous studies that have shown good agreement between chart review and ICD coding.19,20 FTR was defined as mortality after any major complication and was calculated as the number of patients who had one or more major complications and died in the hospital (numerator) divided by the total number of patients experiencing major complications. Incidence of 30-day hospital readmissions, hospital LOS, and hospital costs were explored as secondary outcomes. Hospital costs for US records were estimated applying the HCUP cost-to-charge ratio files to the reported hospital charges present in the databases, and then were converted to 2014 US dollars according to the consumer price index. Costs for Colombian cases were adjusted by parity purchasing power (PPP), according the World Bank,21 and reported as 2014 US dollars.


Variables on patient demographics and comorbidities, type of diagnosis, and type of procedure were used to compare baseline characteristics between countries and for adjustment in the multivariable analyses. Appropriate ICD-9-CM or ICD-10 codes were used to create patient comorbidity variables. Comorbid conditions were summarized by calculating a modified Charlson Comorbidity Index (CCI), as described previously.22 Diagnosis severity was classified as acute appendicitis without peritonitis, appendicitis with localized peritonitis, or appendicitis with generalized peritonitis. Appendectomy route was categorized as laparoscopic or open.

Statistical Analysis

Univariate analyses were performed to describe baseline characteristics of patients undergoing appendectomy in Colombia or the United States. Discrete variables are presented as frequencies and group percentages and analyzed using χ2 or Fisher tests as appropriate. Continuous data are reported as means (standard deviation) or medians with interquartile range (IQR), as appropriate, and analyzed with t tests or Mann-Whitney tests. Unadjusted differences in outcomes between the 2 countries were assessed with univariate logistic regression and are presented as frequencies (percentage) and odds ratios (ORs) with 95% confidence intervals (CIs). Multivariable logistic regression analyses were conducted to assess the association between country and the outcomes in-hospital mortality, any major complications, and FTR. Country where procedure was performed, clinically relevant factors, including patient age and sex, comorbidity burden expressed as the CCI, appendectomy route, and diagnosis severity were included as independent variables in the models. To assess whether the appendectomy route was a mediator rather than a confounder of the association between country and primary outcomes, a sensitivity analysis was performed using logistic regression models including the same independent variables described above but excluding the appendectomy route. In addition, to confirm that the complication rates were appropriately coded in the Colombian database, a subgroup univariate analysis was performed to assess the complication rates in patients who contributed the most to mortality and FTR (ie, patients >64 years of age with generalized peritonitis). As the study had 3 primary outcomes, the Bonferroni correction method was applied to adjust for multiple comparisons. The significance level was set to a P value <0.0166 (ie, 0.05/3).

The sample size was based on the primary outcome, postoperative mortality. The reported mortality after appendectomy in various countries is 0.1% to 0.5%.23 A relative increase of 20% in odds of mortality between the countries (OR = 1.2) was considered clinically relevant. Assuming a baseline mortality incidence of 0.2% and a logistic regression analysis with 4 covariates, a sample size of 44,500 patients per group was required to detect a difference of 20% in the odds of mortality between the countries, with 80% power, a significance level of 0.016, and 2-sided testing. All the statistical tests were performed using Stata 16.2 (College Station, TX) and SAS 9.4 (SAS Institute) software.


A total of 120,325 appendectomy cases (62,338 in Colombia and 57,987 in the United States) were identified. Table 1 presents baseline characteristics of patients having appendectomy procedures in Colombia and the 2 US states. Compared to the United States, Colombian patients were younger (mean [SD] age, 36.0 [14.7] vs 41.8 [17.3] years; P < .0001) and had lower Charlson comorbidity scores (0.13 [0.58] vs 0.27 [0.76]; P < .0001). Most appendectomies in Colombia (94%) were performed by the open route. In contrast, most appendectomies in the United States (89.4%) were done via laparoscopic approach. There were differences in the severity of appendicitis at the time of diagnosis between the 2 countries. About 78% of patients in the United States and about 73% of patients in Colombia had a diagnosis of appendicitis not complicated by any type of peritonitis, while 7.4% and 13.3% of the patients had generalized peritonitis in Colombia and in the United States, respectively (Table 1). Among patients with peritonitis, 27% in Colombia had generalized peritonitis, and 73% had localized peritonitis. In contrast, among patients with peritonitis in the United States, about 60% had generalized peritonitis, and 40% had localized peritonitis.

Table 1. - Baseline Characteristics of Patients Having Appendectomy in Colombia and the United States (States of New York and Florida), 2013–2014
Characteristic Colombia, n = 62,338 United States, n = 57,987 P value
Age group, y
 18–39 42,737 (68.56) 29,236 (50.42) <.0001
 40–64 16,150 (25.91) 21,642 (37.32)
 65–74 2075 (3.33) 4556 (7.86)
 ≥75 1376 (2.21) 2553 (4.40)
 Female 30,820 (49.44) 28,705 (49.50) .8289
 Male 31,518 (50.56) 29,282 (50.50)
 2013 33,552 (53.82) 29,463 (50.81) <.0001
 2014 28,786 (46.18) 28,524 (49.19)
Route of appendectomy
 Open 58,657 (94.10) 6169 (10.64) <.0001
 Laparoscopic 3681 (5.90) 51,818 (89.36)
Type of appendicitis
 Appendicitis no peritonitis 45,335 (72.72) 45,126 (77.82) .0214
 Appendicitis with localized peritonitis 12,409 (19.91) 5126 (8.84)
 Appendicitis with generalized peritonitis 4594 (7.37) 7735 (13.34)
Charlson Comorbidity Index
 0 57,245 (91.83) 47,596 (80.08) <.0001
 1 to 2 4377 (7.02) 9232 (15.92)
 3 or more 716 (1.15) 1159 (2.00)
Hypertension 3924 (6.29) 12,050 (20.78) <.0001
Myocardial infarction 45 (0.07) 587 (1.01) <.0001
Congestive heart failure 623 (1.00) 545 (0.94) .2927
Vascular disease 18 (0.03) 619 (1.07) <.0001
Peripheral vascular disease 38 (0.06) 433 (0.75) <.0001
Cerebrovascular disease 120 (0.19) 209 (0.36) <.0001
Coagulopathy 33 (0.05) 553 (0.95) <.0001
Dementia 56 (0.09) 61 (0.11) .3929
Pulmonary circulatory disease 37 (0.06) 159 (0.27) <.0001
Chronic pulmonary disease 2567 (4.12) 4485 (7.73) <.0001
Rheumatic arthritis/collagenous disease 277 (0.44) 510 (0.88) <.0001
Peptic ulcer disease 33 (0.05) a (0.01) <.0001
Chronic blood loss anemia (0.00) a 139 (0.24) <.0001
Deficiency anemia 501 (0.80) 2067 (3.56) <.0001
Liver disease 74 (0.12) 646 (1.11) <.0001
Diabetes without complications 1150 (1.84) 3668 (6.34) <.0001
Diabetes with complications 151 (0.24) 310 (0.53) <.0001
Obesity 92 (0.15) 4503 (7.77) <.0001
Paraplegia 10 (0.02) 147 (0.25) <.0001
Chronic renal failure 478 (0.77) 817 (1.41) <.0001
Metastatic carcinoma 39 (0.06) 122 (0.21) <.0001
Solid tumor without metastasis 534 (0.86) 263 (0.45) <.0001
Hypothyroidism 1359 (2.18) 2799 (4.83) <.0001
Paralysis 10 (0.02) 147 (0.25) <.0001
Other neurological disorders 56 (0.09) 861 (1.48) <.0001
Psychoses 18 (0.03) 721 (1.24) <.0001
Depression 23 (0.04) 2400 (4.14) <.0001
AIDS/HIV 143 (0.23) 51 (0.09) <.0001
Data are n (%) and P values are Mantel-Haenszel χ2 values.
Abbreviations: AIDS/HIV, acquired immunodeficiency syndrome/human immunodeficiency virus; HCUP, Health Cost and Utilization Project.
aCounts <10 not reported per HCUP data agreement.

Overall, the combined major complications rate was significantly lower in Colombia (2.8% vs 6.6%; OR, 0.41; 95% CI, 0.39–0.44; Table 2). Furthermore, the incidence of most individual postoperative complications (except for gastrointestinal bleeding and stroke) was significantly lower in Colombia. However, subgroup univariate analysis in patients >64 years of age with generalized peritonitis revealed nonsignificant differences in combined complication rates between the countries. Furthermore, the incidence of most individual complications was not lower in Colombia compared to the United States (Supplemental Digital Content 1, Table 1,

Table 2. - Unadjusted Postoperative Outcomes After Appendectomy in Colombia and the United States (States of New York and Florida), 2013–2014
Outcome Colombia United States OR (95% CI) P value
Died during hospitalization 275 (0.44) 44 (0.08) 5.83 (4.24–8.02) <.0001
Failure to rescue 240 (13.6) 41 (1.03) 14.59 (10.42–20.43) <.0001
Any major complication 1769 (2.84) 3853(6.64) 0.41 (0.39–0.44) <.0001
Respiratory complications 306 (0.49) 907 (1.56) 0.31 (0.27–0.35) <.0001
 Respiratory failure 126 (0.20) 563 (0.97) 0.21 (0.17–0.25) <.0001
 Pneumonia 192 (0.31) 511 (0.88) 0.35 (0.29–0.41) <.0001
Cardiac complications 111 (0.18) 228 (0.39) 0.45 (0.36–0.57) <.0001
 Acute myocardial infarction 68 (0.11) 73 (0.13) 0.87 (0.62–1.21) .3945
 Cardiac arrest 13 (0.02) 23 0.04) 0.53 (0.27–1.04) .0594
 Arrhythmia 32 (0.05) 149 (0.26) 0.20 (0.14–0.29) <.0001
Pulmonary embolism or deep vein thrombosis 37 (0.06) 78 (0.13) 0.44 (0.30–0.65) <.0001
Acute renal failure 72 (0.12) 1250 (2.16) 0.05 (0.04–0.07) <.0001
Gastrointestinal bleeding 770 (1.24) 261 (0.45) 2.77 (2.40–3.18) <.0001
Postoperative hemorrhage 78 (0.13) 223 (0.38) 0.33 (0.25–0.42) <.0001
Surgical site infection 98 (0.16) 322 (0.56) 0.28 (0.23–0.35) <.0001
Sepsis 373 (0.60) 1309 (2.26) 0.26 (0.23–0.29) <.0001
Shock 92 (0.15) 645 (1.11) 0.13 (0.11–0.16) <.0001
Postoperative stroke a (0.01) (0.01) a 1.40 (0.39–4.94) .6041
Readmission within 30 days 3335 (5.35) 2490 (4.29) 1.26 (1.19–1.33) <.0001
Length of hospital stay, days
 Mean (SD) 6.3 (2.6) 2.3 (2.9) NA <.0001
 Median (IQR) 2.0 (1.0–6.0) 1.0 (1.0–3.0) NA <.0001
Hospital costs, US dollars b NA
 Mean (SD) 1682 (3783) 9013 (6972) NA <.0001
 Median (IQR) 984 (712–1536) 7674 (6035–9974) NA <.0001
Data are n (%). P values are Mantel-Haenszel chi-square values.
Abbreviations: CI, confidence interval; HCUP, Health Cost and Utilization Project; IQR, interquartile range; NA, not applicable; OR, odds ratio.
aCounts <10 not reported per HCUP data use agreement.
bCost calculated as 2014 power parity purchase adjusted US dollars.

The proportion of patients who died and had an associated major complication was 87.3% in Colombia and 93.2% in the United States. This proportion was not significantly different between the countries (P = .261). The univariable analyses revealed a higher incidence of postoperative mortality (0.44% vs 0.08%; OR, 5.83; 95% CI, 4.24–8.02) and FTR (13.6% vs 1.0%; OR, 14.59; 95% CI, 10.42–20.43) in Colombia compared to the United States. The rate of FTR was higher for more serious complications. For example, mortality rates after myocardial infarction were higher than those for surgical site infection (35% and 0%, respectively, in Colombia, and 5.5% and 0.9% in the United States) (Supplemental Digital Content 1, Table 2, In addition, compared to the United States, the hospital LOS was significantly longer, the rates of 30-day readmission were higher, and the hospital costs were significantly lower in Colombia.

Table 3. - Logistic Regression Analysis of Factors Affecting In-Hospital Mortality, Failure to Rescue, and Complications After Appendectomy in Colombia and the United States (States of New York and Florida), 2013–2014
Variables In-hospital mortality In-hospital FTR Any major complication
OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value
Country <.0001 <.0001 <.0001
 United States Reference Reference Reference
 Colombia 8.92 (5.69–13.98) 17.01 (10.66–27.16) 0.32 (0.30–0.35)
Age, per 1 y increase 1.09 (1.08–1.10) <.0001 1.06 (1.05–1.07) <.0001 1.03 (1.03–1.03) <.0001
Sex .102 .497 <.0001
 Female Reference Reference Reference
 Male 1.22 (0.96–1.54) 1.10 (0.83–1.46) 1.22 (1.15–1.29)
Charlson comorbidity index 1.34 (1.25–1.43) <.0001 1.28 (1.17–1.40) <.0001 1.43 (1.40–1.46) <.0001
Appendectomy route .014 .014 <.0001
 Laparoscopic Reference Reference Reference
 Open 1.84 (1.63–3.00) 1.93 (1.14–3.26) 1.85 (1.71–2.01)
Type of appendicitis <.0001 <.0001 <.0001
 No peritonitis Reference Reference Reference
 Localized peritonitis 1.34 (0.91–1.97) 1.09 (0.71–1.68) 2.47 (2.29–2.66)
 Generalized peritonitis 8.02 (6.09–10.54) 2.80 (2.02–3.87) 3.27 (3.06–3.51)
C statistic 0.960 0.915 0.776
Hosmer and Lemeshow goodness-of-fit test, P value 0.686 0.396 <.001
The multivariable model simultaneously included all the listed independent variables.
Abbreviations: CI, confidence interval; FTR, failure to rescue; OR, odds ratio.

Table 4. - Logistic Regression Analysis of Factors Affecting In-Hospital Mortality, Failure to Rescue, and Complications, Excluding Appendectomy Route From Independent Variables
Variables In-hospital mortality In-hospital FTR Any major complication
OR (95% CI) P value OR (95% CI) P value OR (95% CI) P value
Country <.0001 <.0001 <.0001
 United States Reference Reference Reference
 Colombia 13.52 (9.74–18.77) 26.14 (18.12–37.72) 0.52 (0.49–0.55)
Age, per 1 y increase 1.09 (1.08–1.10) <.0001 1.06 (1.05–1.07) <.0001 1.03 (1.03–1.03) <.0001
Sex .112 .497 <.0001
 Female Reference Reference Reference
 Male 1.21 (0.95–1.53) 1.09 (0.82–1.45) 1.22 (1.16–1.30)
Charlson Comorbidity Index 1.34 (1.25–1.43) <.0001 1.28 (1.17–1.40) <.0001 1.44 (1.40–1.47) <.0001
Type of appendicitis <.0001 <.0001 <.0001
 No peritonitis Reference Reference Reference
 Localized peritonitis 1.40 (0.96–2.05) 1.14 (0.75–1.76) 2.74 (2.55–2.94)
 Generalized peritonitis 8.33 (6.33–10.94) 2.87 (2.08–3.97) 3.45 (3.22–3.69)
C statistics 0.959 0.914 0.774
Hosmer and Lemeshow goodness-of-fit test, P value 0.682 0.451 <.001
The multivariable model simultaneously included all the listed independent variables.
Abbreviations: CI, confidence interval, FTR, failure to rescue; OR, odds ratio.

The multivariable logistic regression analyses revealed that the odds of postoperative complications were significantly lower in Colombia compared to the United States (OR, 0.32; 95% CI, 0.30–0.35). However, the adjusted odds of in-hospital mortality were about 9× higher in Colombia (OR, 8.92; 95% CI, 5.69–13.98), and the odds of FTR were about 17× higher in Colombia compared to the United States (OR, 17.01; 95% CI, 10.66–27.16; Table 3). The sensitivity analysis, excluding appendectomy route from the independent variables, revealed larger ORs for the association between country and the 3 primary outcomes, compared to the original full logistic regression models (Table 4).


This study identified gaps on outcomes after appendectomy between Colombia and the United States. The rates of postoperative complications were lower in Colombia compared to the United States. However, after adjusting for patient and surgical factors, the likelihood of in-hospital mortality after major complications was disproportionately higher in Colombia, revealing that differences in mortality between the 2 countries may be driven by differences in FTR more than by differences in occurrence of complications. This suggests that hospitals in Colombia may have lower ability to adequately recognize and treat serious postoperative complications.

Our results are in agreement with studies that have reported higher mortality rates after appendectomy in LMICs.23–25 A study on >50,000 appendectomies in Brazil reported a mortality rate of 0.47%.23 Analysis of about 40,000 appendectomies performed in >100 institutions in the United States found a 30-day mortality rate of 0.07% to 0.17%.24 Reports comparing FTR between LMICs and HICs are extremely scarce. A study in 474 hospitals from 27 countries with diverse income levels found significant international variations in FTR after surgery.14 However, the study was focused on elective cases and included only 8 LIMCs mostly from Asia and Africa with minimal representation of Latin American countries.

Evidence suggests that FTR is associated more with hospital characteristics than with patient-level factors.12,26 Analysis of our data revealed that the variability in FTR could be explained by country-level factors, which may be related to binational differences in hospital resources and health care behaviors. This variability did not decrease after adjustment for patient and procedure factors. Hospitals in HICs have availability of more advanced physical resources and technology, more intensive care beds, higher nurse-to-patient ratios, and larger proportions of board-certified specialists.27–29 According to the World Bank, the specialist surgical workforce (surgeons, obstetricians, and anesthesiologists) in Colombia and the United States in 2015 was 22 and 55 per 100,000 population, respectively.30 Although hospital resources are difficult to modify in LMICs, some behavioral factors could be actionable even in hospitals with few resources, causing an impact on FTR rates. These include improved institutional safety culture, coordinated teamwork, efficient interprofessional communication, and health care personnel attitudes that may be directed to early recognition of clinical deterioration, implementation of rapid response initiatives, and timely escalation of care.31,32 Unfortunately, the data used for the study do not include variables on the characteristics of the health systems of both countries. Therefore, we were not able to adjust for those variables in our regression models.

The rates of combined complications and most of the individual postoperative complications were lower in Colombia. This finding may be explained by the younger and healthier sample of patients from Colombia, who had lower incidence of chronic comorbidities but also lower incidence of generalized peritonitis. Undercoding of complications in Colombia, the country reporting lower complication rates, would be an alternative explanation. However, our sensitivity analysis in patients who contributed the most to mortality and FTR (ie, patients >64 years of age with generalized peritonitis) revealed similar rates of complication coding between the countries (Supplemental Digital Content 1, Table 1,, suggesting that undercoding of complications is not a concern.

Another finding of this study was the large difference in the use of laparoscopy between the 2 countries. Others have also found discrepancies in utilization of laparoscopic appendectomy in LMICs compared to HICs.7,23 Our multivariable analyses revealed that open appendectomy was significantly associated with increased odds of mortality, FTR, and complications. Based on a sensitivity analysis when appendectomy route was excluded from the logistic models, the appendectomy route may be a mediator rather than a confounder of the significant differences in outcomes observed between the 2 countries (Table 4). Underutilization of laparoscopy may also help explain the significantly longer LOS observed in Colombia. Implementation of enhanced recovery after surgery pathways, of which minimally invasive surgical approach is one of the key components, has been shown to reduce postoperative complications and hospital LOS as well as 30-day readmission rates.33 Underutilization of laparoscopic approach in LMICs may be related to economic limitations that prevent acquisition of laparoscopic equipment, expensive disposable instruments and supplies, barriers to training surgeons, and barriers to reimbursement.34 Third-party payers in Colombia appear to be reluctant to reimburse for laparoscopic approach, as those are more expensive than the corresponding open surgeries.35 As expected, hospital costs for appendectomy were lower in Colombia.

Our study has important strengths, including a large sample size, and the use of perioperative data from 2 different nations with patient-level variables. However, the study has several limitations, most of them related to the retrospective nature of the research and use of administrative data. The definition of comorbidities and complications was based on ICD diagnostic codes reported in the databases. Possible variation in coding practices between the countries and incomplete or inaccurate coding of comorbid conditions may have led to imprecise risk adjustment for clinical characteristics. Similar inaccuracies may arise from the use of ICD codes to define postoperative complications. However, we attempted to overcome this shortcoming by selecting our list of major complications based on studies showing good agreement between ICD-9-CM coding and data derived from patient chart review. We attempted to decrease confounding by adjusting for patient comorbidities summarized as the CCI. However, residual confounding is possible because the composite CCI score may not be able to fully account for differences in comorbidity burden. The proportion of patients who died and had an associated major complication was 87.3% and 93.2% in Colombia and the United States, respectively. However, some patients may not have died as a result of the complications they experienced. Our study does not include random samples of patients from each country. All eligible appendectomies done in the states of NY and FL during the study period were used as representative of the United States. However, the cases from these 2 large states may not appropriately represent the whole US population. Similarly, the Colombian database includes only patients in contributory regime who have full access to health care, which covers about 44% of the population, but excludes 50% of the population, consisting of patients with the lowest income covered by the subsidized health care system, and 6% of the population who pay for health care out-of-pocket. It is possible that this may have contributed to the imbalance between the samples from United States and Colombia. However, the key finding of this article of FTR should also be applicable to the 50% low-income group because the postoperative care is not expected to be superior to our study group, which had health care insurance. Finally, our study was limited to assessing in-hospital instead of 30-day postoperative mortality. This is because the HCUP databases do not collect postoperative outcomes after hospital discharge and cannot be linked to the National Vital Statistics System. However, because difficulty collecting patient data after discharge is recognized, the Lancet Commission on Global Surgery has recommended inpatient perioperative mortality as a key surgical system indicator.4,36

In conclusion, this study revealed that despite lower rates of major postoperative complications after appendectomy, in-hospital mortality in Colombia was significantly higher than that observed in the United States. These results could not be completely explained by differences in patient demographic and clinical factors but were associated with a higher incidence of FTR. Our findings suggest that LMICs, such as Colombia, may benefit from quality improvement efforts directed at early detection and treatment of postoperative complications.


Name: Eric B. Rosero, MD.

Contribution: This author helped with study conception and design‚ data analysis and interpretation‚ drafting the manuscript‚ critical revision of the manuscript for important intellectual content‚ statistical analysis‚ and final approval of the version to be published.

Conflicts of Interest: None.

Name: Javier Eslava-Schmalbach, PhD.

Contribution: This author helped with study conception and design‚ data analysis and interpretation‚ drafting the manuscript‚ critical revision of the manuscript for important intellectual content‚ statistical analysis‚ and final approval of the version to be published.

Conflicts of Interest: None.

Name: Nathaly Garzón-Orjuela, MSc.

Contribution: This author helped with study conception and design‚ data analysis and interpretation‚ drafting the manuscript‚ critical revision of the manuscript for important intellectual content‚ statistical analysis‚ and final approval of the version to be published.

Conflicts of Interest: None.

Name: Giancarlo Buitrago, PhD.

Contribution: This author helped with study conception and design‚ data analysis and interpretation‚ drafting the manuscript‚ critical revision of the manuscript for important intellectual content‚ statistical analysis‚ and final approval of the version to be published.

Conflicts of Interest: None.

Name: Girish P. Joshi, MBBS, MD, FFARCS.

Contribution: This author helped study conception and design‚ data analysis and interpretation‚ drafting the manuscript‚ critical revision of the manuscript for important intellectual content‚ statistical analysis‚ and final approval of the version to be published.

Conflicts of Interest: G. P. Joshi has received honoraria from Baxter Pharmaceuticals.

This manuscript was handled by: Angela Enright, MB, FRCPC.


Agency for Healthcare Research and Quality
Charlson Comorbidity Index
confidence interval
Current Procedural Terminology
Colombian Unique Classification of Health care Procedures System
Databases of the Contributory Regime
failure to rescue
Health Cost and Utilization Project
high-income country
intraclass correlation coefficient
International Classification of Diseases, Ninth Revision, Clinical Modification
International Classification of Diseases, Tenth Revision, Clinical Modification
interquartile range
low- or middle-income country
length of hospital stay
odds ratio
parity purchasing power
Ambulatory Surgery and Services Databases
State Inpatient Databases
Social Security System in Health
Strengthening the Reporting of Observational Studies in Epidemiology


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