Risk stratification models to predict perioperative mortality in pediatric surgical populations are based on patient comorbidities, but do not take into consideration the intrinsic risk of the surgical procedures.
Surgical procedures identified by specialty are not independent risk factors for perioperative mortality in pediatric patients. However, in multivariable predictive algorithms, the interaction of patient comorbidities with the intrinsic risk of the surgical procedure strongly predicts 30-day mortality.
Recently developed risk stratification models for perioperative mortality incorporate patient comorbidities as predictors but fail to consider the intrinsic risk of surgical procedures. In this study, the authors used the American College of Surgeons National Surgical Quality Improvement Program Pediatric database to demonstrate the relationship between the intrinsic surgical risk and 30-day mortality and develop and validate an accessible risk stratification model that includes the surgical procedures in addition to the patient comorbidities and physical status.
A retrospective analysis of the American College of Surgeons National Surgical Quality Improvement Program Pediatric database was performed. The incidence of 30-day mortality was the primary outcome. Surgical Current Procedural Terminology codes with at least 25 occurrences were included. Multivariable logistic regression model was used to determine the predictors for mortality including patient comorbidities and intrinsic surgical risk. An internal validation using bootstrap resampling, and an external validation of the model were performed.
The authors analyzed 367,065 surgical cases encompassing 659 unique Current Procedural Terminology codes with an incidence of overall 30-day mortality of 0.34%. Intrinsic risk of surgical procedures represented by Current Procedural Terminology risk quartiles instead of broad categorization was significantly associated with 30-day mortality (P < 0.001). Predicted risk of 30-day mortality ranges from 0% with no comorbidities to 4.7% when all comorbidities are present among low-risk surgical procedures and from 0.07 to 46.7% among high-risk surgical procedures. Using an external validation cohort of 110,474 observations, the multivariable predictive risk model displayed good calibration and excellent discrimination with area under curve (c-index) equals 0.95 (95% CI, 0.94 to 0.96; P < 0.001).
Understanding and accurately estimating perioperative risk by accounting for the intrinsic risk of surgical procedures and patient comorbidities will lead to a more comprehensive discussion between patients, families, and providers and could potentially be used to conduct cost analysis and allocate resources.
The global incidence of perioperative mortality in the pediatric surgical population is extremely low. However, the incidence of 30-day mortality can vary from 0.1 to 15% dependent on the patient’s comorbidities and physical status at the time of surgery.1–3 During the past decade, several groups have developed risk stratification models to improve prediction of perioperative major event (including death) in adults and enhance perioperative discussion of risk among physicians and the family, as well as improve resource allocation.4–7 The development of comparable risk stratification models has been undertaken in the pediatric surgical population as well.1,8
In a recent study, we developed the Pediatric Risk Assessment score to predict perioperative mortality in neonates, infants, and children undergoing noncardiac surgery.1 The score includes patient’s age (e.g., less than 12 months), the presence of a neoplasm, the degree of emergency of the surgical procedure, the presence of at least one comorbidity (e.g., respiratory disease, congenital heart disease, kidney insufficiency, neurologic or hematologic disease), and characteristics of critical illness (e.g., mechanical ventilation, inotropic support, preoperative cardiopulmonary resuscitation). The score’s internal validation in a large cohort demonstrated an excellent accuracy in predicting perioperative mortality in children undergoing noncardiac surgery; however, the intrinsic risk of the surgical procedure was not included into our predictive model.
In adults, the intrinsic risk of surgical procedures for the occurrence of perioperative adverse cardiac events was recently stratified into three risk categories (e.g., low, intermediate, and high).6 In this study, the analysis demonstrated a wide variation in the intrinsic risk of particular surgical procedures despite the procedures being categorized within the same location (i.e., intrathoracic, intraperitoneal, urologic). To date, no such analysis of the intrinsic risk of individual pediatric surgical procedures has been published. The American College of Surgeons National Surgical Quality Improvement Program Pediatric Surgical Risk Calculator is a tool capable of estimating the risk of multiple complications and mortality for a wide variety of surgical procedures and concurrent patient comorbidities.2 However, the algorithm used by the American College of Surgeons National Surgical Quality Improvement Program Pediatric Surgical Risk Calculator to calculate the risk is invisible to the user and has never been publicly validated.
In this study, we used the American College of Surgeons National Surgical Quality Improvement Program Pediatric database to (1) demonstrate the relationship between the intrinsic surgical risk and 30-day mortality in neonates, infants, and children undergoing noncardiac surgery; and (2) develop and validate a risk stratification model that include patient comorbidities and physical status, as well as the surgical procedures after stratification for their intrinsic risk. Our objective is to develop an accessible risk stratification model.
Materials and Methods
Participating hospitals in the American College of Surgeons National Surgical Quality Improvement Program are not identified and Institutional Review Board approval was not required for this study. The data source and study population described below are similar to the development of the Pediatric Risk Assessment score.1
The American College of Surgeons National Surgical Quality Improvement Program Pediatric collects de-identified data on children less than 18 yr of age undergoing noncardiac surgery. It includes 129 variables, including preoperative risk factors, intraoperative characteristics, and 30-day postoperative mortality and morbidity outcomes in both the inpatient and outpatient settings.9 A site’s trained and certified Surgical Clinical Reviewer captures these data using a variety of methods including medical chart review. Adverse events and comorbidities reported in the database are determined by strict inclusion criteria. A systematic sampling strategy with an 8-day cycle is used to avoid bias in case selection and to ensure a diverse surgical case mix independent from the day of the week. In addition, to ensure the quality of the data collected, the American College of Surgeons National Surgical Quality Improvement Program Pediatric conducts inter-rater reliability audits of selected participating sites.10
The results of the audits completed to date reveal an overall disagreement rate of approximately 2% for all assessed program variables. For the database, exclusion criteria included: patients 18 yr or older, trauma cases, solid organ transplantation, cardiac surgery, and cases coming from hospitals with an inter-rater reliability audit disagreement rate greater than 5%, or a 30-day follow-up rate less than 80%.
A total of 187 unique Current Procedural Terminology codes were excluded due to occurring less than 25 times, corresponding to 1,703 cases. Missing data were present on one or more of the variables used in the analysis in only 0.7% (2,513 of 369,176) of the cases in the 2012 to 2016 American College of Surgeons National Surgical Quality Improvement Program Pediatric database. Due to this extremely low rate of missing data, no missing data method was implemented.
We performed a retrospective analysis of the 2012 to 2016 Pediatric databases of the American College of Surgeons National Surgical Quality Improvement Program database. The primary outcome variable for our analysis was the incidence of 30-day mortality. Current Procedural Terminology codes with fewer than 25 occurrences were excluded.11
The following characteristics were considered: age, body weight, height, gender, American Society of Anesthesiologists (ASA) Physical Status classification, prematurity (fewer than 24, 24 to 36, and more than 36 weeks of gestation), type of procedure (elective vs. urgent surgery), preoperative respiratory disease (e.g., asthma, chronic lung or airway diseases, cystic fibrosis), preoperative oxygen supplementation, tracheostomy, liver and pancreatic diseases, diabetes, congenital heart disease, acute or chronic kidney disease, neurologic disease (e.g., mental retardation, cerebral palsy, central nervous system disease, intracerebral hemorrhage, seizure), immune disease, preoperative use of steroids, neoplasm, chemotherapy, preoperative inotropic support, preoperative mechanical ventilation, preoperative cardiopulmonary resuscitation, and preoperative transfusion (defined as transfusion of whole blood or erythrocytes during the 48 h before surgery).
Surgical type was categorized based on Current Procedural Terminology codes. Intrinsic surgical risk was determined by utilizing surgery Current Procedural Terminology codes with at least 25 occurrences in the sample (659 unique Current Procedural Terminology codes). Current Procedural Terminology risk quartiles were built utilizing the empirical 30-day mortality rates for each Current Procedural Terminology code and creating four groups of Current Procedural Terminology codes corresponding to increasing intrinsic surgical risk. The range for 30-day mortality rate for Current Procedural Terminology risk quartile 1 was 0%, risk quartile 2 was greater than 0% to less than 0.14%, risk quartile 3 was greater than or equal to 0.14% to less than 1.15%, and risk quartile 4 was greater than or equal to 1.15%. Current Procedural Terminology risk quartiles 1 and 2 comprised the low-risk procedure category, and quartiles 3 and 4 the high-risk procedures. The cut-offs of 30-day mortality rate to define the Current Procedural Terminology risk quartiles were determined by examining the distribution on the case level of mortality rates based on Current Procedural Terminology codes. Since the 30-day mortality rate in the National Surgical Quality Improvement Program database is very low, the four risk quartiles are not equal in size in terms of number of surgical cases.
Comorbidity and case complexity data are presented as median and interquartile range and number and percentage for categorical data. Univariate statistical testing was done using Wilcoxon rank sum tests and chi-square tests, as appropriate. Multivariable logistic regression modeling building using stepwise backward elimination with removal criteria of P > 0.05 was applied to identify independent predictors of 30-day mortality and to develop a multivariable algorithm combining both patient comorbidities and intrinsic surgical risk to predict the risk of 30-day mortality.12
Patient comorbidities and intrinsic surgical risk were considered as predictors of 30-day mortality. Using the likelihood ratio test to assess significance, five variables were included in the final model: body weight (kg), ASA Physical Status classification, preoperative sepsis, preoperative inotropic support, and preoperative ventilator dependence (all within 24 h before surgery). For the risk algorithm, body weight was dichotomized to less than or greater than 5 kg, and ASA Physical Status classification was dichotomized to create a binary indicator of high ASA (ASA Physical Status III or higher). The dichotomization of continuous risk factors was based on clinical experience and the ability to provide high sensitivity and specificity for discrimination of cases with and without 30-day mortality.
Dichotomous patient comorbidities and intrinsic surgical risk variables were utilized in the full multivariable predictive algorithm of 30-day mortality. A simplified multivariable predictive algorithm was created considering the number of comorbidity risk factors present.
Multivariable logistic regression results are presented as adjusted odds ratios, 95% CIs, and P values. The predictive algorithms for the probability of 30-day mortality are presented as empirical probabilities, model-based predicted probabilities, and 95% CIs as a measure of precision of the model-based estimates, stratified by intrinsic surgical risk.
A two-tailed α level of 0.05 was used as the threshold for statistical significance. Stata 15.0 was utilized for all statistical analyses (StataCorp, USA).
Internal validation was performed for our final multivariable model utilizing 500 bootstrap resamples.13,14 In our internal bootstrap validation, we assessed model performance using the c-index (area under the curve), the bias-corrected Somers D rank correlation, Nagelkerke R2, the slope and intercept of the logistic calibration equation, the maximum absolute difference in predicted and calibrated probabilities (Emax), the discrimination index D, the unreliability index U, and the Brier quadratic probability score B. Internal bootstrap validation was performed by re-fitting the multivariable model in 500 bootstrap resample dataset with replacement produced by the 2012 to 2016 National Surgical Quality Improvement Program Pediatric database.
Furthermore, external model validation was performed using the 2017 National Surgical Quality Improvement Program Pediatric database in order to assess the generalizability of our model in an external cohort with a similar patient mix. Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test, where a nonsignificant P value indicates that the prognostic multivariable model generalizes well to the new cohort. Model discrimination was assessed using the area under the receiving operating characteristic curve. An area under the receiver operating characteristic curve of 0.800 to 0.899 will be considered to demonstrate acceptable model discrimination, and values greater than or equal to 0.900 will be considered outstanding model discrimination.12 Observed probabilities of 30-day mortality were compared to the fitted probabilities produced in our multivariable algorithm using this external cohort.
Power Analysis and Sample Size Considerations
The sample sizes that were analyzed in this study based on 2012 to 2016 from the American College of Surgeons National Surgical Quality Improvement Program Pediatric database among patients with at least three of the identified comorbidities provide more than 90% statistical power for detecting a difference in 30-day mortality between low and high intrinsic surgical risk procedures of 10%, based on logistic regression modeling. Power analyses were performed using nQuery Advisor 8.2.2 (Statistical Solutions Ltd., Ireland).
A final sample of 367,065 surgical cases encompassing 659 unique Current Procedural Terminology codes was obtained for analysis. All Current Procedural Terminology codes with fewer than 25 occurrences were excluded. Among these cases, 1,252 (0.34%) involved 30-day mortality. All Current Procedural Terminology codes were categorized into four intrinsic risk quartiles. The complete list of all procedures is listed in the appendix.
Nonsurvivors were more often neonates (41.8% vs. 4.2%), had low body weight (less than 5 kg; 65% vs. 9%), had higher ASA Physical Status classification greater than III (96.7% vs. 25.5%), higher rates of preoperative sepsis (30.8% vs. 7.9%), inotropic support (32.4% vs. 0.6%), congenital heart disease (50.2% vs. 10.0%), and ventilator dependence within 48 h before surgery (65.5% vs. 2.9%; all P < 0.001). Intrinsic risk of surgical procedures represented by Current Procedural Terminology risk quartiles was significantly associated with 30-day mortality (P < 0.001; table 1). Multivariable logistic regression analysis revealed the following factors as being independent predictors of 30-day mortality: weight, ASA Physical Status classification, preoperative sepsis, inotropic support, ventilator dependence, and risk quartile. Using stepwise backward elimination with removal criteria of P > 0.05, neonatal status, sex, and congenital heart disease were eliminated. Table 2 displays the adjusted model of 30-day mortality based on all comorbidity and case complexity risk factors.
The independent predictors were multiplexed to create a multivariable predictive algorithm for the risk of 30-day mortality. A predicted probability of 30-day mortality was calculated for all covariate patterns of comorbidities and stratified by intrinsic surgical risk. Supplemental Digital Content 1 (http://links.lww.com/ALN/B891) displays the full risk algorithm, with empirical mortality rates as well as model-based mortality probability and 95% CIs. Among low-risk surgical procedures, the risk of 30-day mortality ranged from 0.00% (95% CI, 0.00 to 0.01%) when no comorbidities are present, to 4.74% (95% CI, 3.17 to 7.03%) when all comorbidities are present. This association is magnified among high-risk surgical procedures, where the risk of 30-day mortality ranged from 0.07% (95% CI, 0.05 to 0.09%) when no comorbidities are present, to 46.72% (95% CI, 43.04 to 50.44%) when all comorbidities are present.
The bootstrapped results of the internal validation suggest that our predictive model has an excellent internal validity and model performance with a c-index or area under the curve of 0.961, and a bias-corrected Somers D rank correlation of 0.922. The Nagelkerke R2 measure was 0.395. The intercept and slope of an overall logistic calibration equation were 0.020 and 1.007. The maximum absolute difference in predicted and calibrated probabilities, or Emax, was 0.006. The discrimination index D was 0.018 and the unreliability index U was 0, resulting in an overall quality index or logarithmic probability score Q equals 0.018. The Brier quadratic probability score B was 0.003.
In addition to an internal bootstrap validation, we performed an external model validation using the 2017 National Surgical Quality Improvement Program Pediatric database. The results of the observed and the fitted (expected) probabilities of 30-day mortality are found in table 3. Using this external validation cohort of 110,474 observations, our multivariable predictive risk model displayed good calibration to the data (Hosmer–Lemeshow goodness-of-fit P = 0.116) and outstanding model discrimination (area under curve [c-index] equals 0.953; 95% CI, 0.944 to 0.961; P < 0.001).
A simplified algorithm was created using the number of comorbidity risk factors rather than the specified combinations. The results of this model are found in Supplemental Digital Content 2 (http://links.lww.com/ALN/B892). In this simplified algorithm the interaction between comorbidities and case complexity remains. Within the high-risk surgical procedure category, the risk of mortality is exacerbated. The association between Current Procedural Terminology risk quartile and risk of mortality is modified by patient comorbidity profile (fig. 1). In this simplified model, the risk of 30-day mortality ranges from 0 to 5.2% among low-risk procedures, whereas it ranges from 0.05 to 39.3% among high-risk procedures.
This study demonstrates the relationship between the intrinsic surgical risk and 30-day mortality for 659 specific pediatric surgical procedures. Consequently, procedures typically characterized by procedure location (i.e., intrathoracic, intraperitoneal) or surgical specialty (i.e., plastics, urology) are now grouped by intrinsic risk. When surgical procedures are identified by specialty, the relationship between mortality and a specific procedure is not possible. In fact, pediatric postoperative mortality has been shown to be caused primarily by patient- and anesthesia-related factors when the surgical procedures are grouped by specialty.15 The granularity is important because when intrinsic operative risk is analyzed in conjunction with patient comorbidities it becomes clear that the interaction of these two variables strongly predicts perioperative mortality. As demonstrated in figure 1, procedures with low intrinsic risk can be performed on children with three or fewer concurrent comorbidities with low mortality with a steady increase in mortality seen when four and five comorbidities are present. In contrast, an exponential increase in mortality associated with each additional comorbidity above two was observed for procedures with high intrinsic risk. Specifically, when no comorbidities are present, the probability of 30-day mortality is similar between the low and high intrinsic surgical risk groups (0% vs. 0.05%). In contrast, when all five comorbidities are present, the probability of 30-day mortality is much lower in the low intrinsic surgical risk compared to the high intrinsic surgical risk group (5.2% vs. 39.3%).
Analysis of 3.7 million adult patients between 1991 and 2005 suggested that the most robust predictor of postoperative mortality should be a model containing patient demographics, comorbidities, and surgical procedures categorized by anatomic location into 36 subcategories. There was a 256-fold difference in mortality between the lowest (nucleus pulposus surgery) and highest (liver transplant) risk surgical procedures.16 A recent investigation in adults further expanded on the concept of intrinsic surgical risk by analyzing 1,880 Current Procedural Terminology codes to categorize 202 specific surgical procedures into low, intermediate, and high intrinsic risk. Intrinsic risk, thus determined, proved to be a more robust predictor of perioperative adverse cardiac events than surgical procedures grouped by anatomic location.6
The physiologic responses initiated in the cardiovascular, pulmonary, endocrine, coagulation, and immune systems by direct surgical tissue injury in addition to the responses initiated by mechanical deformation of organs, blood loss, core temperature variations, and fluid shifts vary tremendously depending on the invasiveness and the duration of the surgical procedure. The greater the physiologic response to a surgical procedure, the greater is the intrinsic surgical risk. This is consistent with the finding that the physiologic compromise and intrinsic surgical risk associated with an open colectomy is greater than that of excision of a skin lesion and with the finding that procedures in the same body cavity and on the same organ (partial splenectomy, risk quartile 1 and total splenectomy, risk quartile 4) would be associated with substantially different intrinsic risk dependent on the complexity of the procedure.
It is interesting that the presence of congenital heart disease did not warrant retention in our multivariable model in light of previous work demonstrating that in children undergoing noncardiac surgery major and severe congenital heart disease, as defined by functional status and residual lesion burden, is associated with increased mortality.8,17 This is likely due to the fact that the presence of congenital heart disease was considered as a binary variable in this analysis and the severity of congenital heart disease was not considered. It is also likely that children with major and severe congenital heart disease underwent procedures with high intrinsic risk less frequently than children without major or severe congenital heart disease.
During the informed consent process, parents are interested in receiving comprehensive information regarding their child’s surgical procedure including delineation of possible complications and provision of this additional information does not increase parental anxiety.18 Use of this simple and easily applicable risk categorization will provide parents with a more comprehensive overview of the risk associated with a particular surgical procedure during the informed consent process. In addition, categorization of the risk based on specific surgical procedures and patient comorbidities has the potential to improve preoperative optimization and allocation of resources.19
This study has several strengths and limitations. The limitations include the retrospective nature of the study design. The use of a large multi-institutional database may include missing data, miscoded diagnoses, or procedures. However, the American College of Surgeons National Surgical Quality Improvement Program is well designed and undergoes a thorough audit that makes it more accurate and informative than other administrative databases. While the American College of Surgeons National Surgical Quality Improvement Program database provides some granularity as regards the severity of comorbidities it does not provide the type of detailed information (e.g., creatinine clearance, pulmonary function tests, blood gas analysis) necessary to further sub-categorize disease severity. In addition, because the American College of Surgeons National Surgical Quality Improvement Program database does not contain geographic or site-specific identification it was impossible to analyze the impact of hospital setting and anesthesia provider on outcome as was done in the European Anaesthesia PRactice In Children Observational Trial (APRICOT) study.20 Assessing generalizability in an external cohort with a similar patient case mix is important to assess model performance.14 A major strength of this study is the external validation with a separate 2017 National Surgical Quality Improvement Program cohort that revealed very strong model performance and generalizability. Demonstrating generalizability of the predictive algorithm in institutions remains needed to confirm utility in clinical practice.
In conclusion, this study demonstrates that the combination of intrinsic surgical risk and patient comorbidities accurately estimates the risk of 30-day mortality in children and allows stratification of this risk. High-intrinsic surgical risk in children contributes significantly to 30-day mortality across the full range of patient comorbidities and is particularly impactful in patients presenting with several of the five identified comorbidities.
The authors thank the American College of Surgeons National Surgical Quality Improvement Program and the hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program, the source of data used herein. The American College of Surgeons National Surgical Quality Improvement Program has not verified, and is not responsible for, the statistical validity of the data analysis or the conclusions derived by the authors.
This study was solely supported by the Department of Anesthesiology, Critical Care and Pain Medicine at Boston Children’s Hospital, Harvard Medical School, Boston, Massachusetts.
The authors declare no competing interests.
This Appendix describes the procedures in each risk quartile (RQ) with 87 procedures in RQ1, 15 in RQ2, 46 in RQ3, and 33 in RQ4.
Risk Quartile 1
Anterior neck procedures (thyroid/thyroglossal duct)
Arteriovenous malformation supratentorial
Craniotomy: bone flap/cyst
Cystoscopy and ureteroscopy
Dislocation of hip and femur
Drainage of neck abscess
Ear procedures (tympanoplasty, mastoidectomy, others)
External fixation (bone)
Excision of parotid tumor/gland
Facial bone reconstruction
Foot division of joint capsule, ligament, or cartilage
Fracture/dislocation of humerus/tibia/foot
Incision and drainage of submandibular/submental gland
Implantation/revision/repositioning of tunneled intrathecal or epidural
Laminectomy (with or without neoplasm)
Laparoscopic cyst aspiration
Lower gastrointestinal procedures/fistula/anoplasty
Lymphadenectomy (except deep cervical)
Mediastinal tumor resection
Neuroendoscopic replacement of ventricular catheter
Osteotomy (limb) excluding hip osteotomy
Ovarian cyst drainage/resection
Palatoplasty, secondary repair of cleft palate/lip
Partial excision of bone tumor
Procedure on tendons and/or muscles
Procedures related to the bile duct
Replacement of cranial nerve stimulator
Reconstruction pectus excavatum
Repair of syndactyly
Rhinoplasty with/without revision to nasal tip
Simple diaphragm repair
Sinus endoscopy (partial ethmoidectomy)
Sinus endoscopy: sphenoidotomy
Skin lesion excision
Spine fusion reinsertion or removal, exploration
Subdural implantation of electrodes
Thoracoscopic thymus resection
Urinary tract procedures
Upper gastrointestinal procedure
Varicocele excision or ligation
Ventral hernia (omphalocele)
Risk Quartile 2
Craniotomy: bone tumor resection
Laminectomy with release of spinal cord
Lymphadenectomy (deep cervical)
Neuro: Implantation of cranial nerve neurostimulator
Orchidopexy (abdominal approach)
Primary plastic cleft lip/palate
Sinus surgery: ethmoidectomy
Sinus surgery: maxillary antrostomy
Risk Quartile 3
Arthrotomy (hip infection)
Brain tumor resection/open or endoscopy
Bronchoscopy (foreign body removal)
Colectomy for congenital megacolon
Craniectomy with cervical laminectomy
Craniotomy: electrode placement for seizure monitoring
Cystoscopy and ureteroscopy with stent placement
Cystostomy with drainage
Diagnostic thoracoscopy (mediastinum)
Excision of submandibular gland
Fracture of femoral shaft
Implantation or replacement of drug infusion device
Laparoscopic colostomy or cecostomy
Laparoscopic esophageal procedure
Laparoscopic small intestine resection
Laparoscopic proctectomy with pull-through
Laparoscopic proctectomy and colectomy
Large omphalocele/final reduction
Laryngoscopy with operative procedure
Nephrectomy with rib resection, ureterectomy
Osteotomy (hip) with fixation
Placement of enterostomy/rev of complicated enterostomy
Pulmonary wedge resection
Repair of low imperforate anus
Replacement or revision ventriculoperitoneal shunt/ventriculocisternostomy
Rhinoplasty including any of the following septal repair/choanal/polyp removal/sinus endoscopy
Small intestine resection (no tapering)
Small omphalocele with primary closure
Thoracotomy for lobectomy/pneumonectomy/segmentectomy/wedge resection
Transplantation of ureter to skin
Risk Quartile 4
Burr holes for implanting ventricular catheter, cerebral electrodes
Complicated nephrectomy from prior surgery
Creation of ventriculoperitoneal, atrial, jugular, or others shunt
Diagnostic thoracoscopy (mediastinal/pericardial)
Exploratory laparotomy (neoplasm)
Gastric bypass Roux-en-Y
Hartmann procedure colectomy
Imbrication of diaphragm for eventration
Intraperitoneal catheter for dialysis
Liver wedge biopsy
Malrotation correction and/or reduction of midgut volvulus.
Mediastinotomy/foreign body removal
Open colostomy or cecostomy/gastrostomy
Parietal pleurectomy (thoracoscopic)
Peritoneal abscess drainage
Sinus surgery: sphenoidectomy
Small intestine resection (with tapering)
Suture for perforated ulcer/wound/injury to the gastrointestinal tract
Thoracic approach for esophageal surgery
Thoracoscopic with foreign body removal (intrapleural)
Thoracoscopy/thoracotomy for lung biopsy nodule/mass/infiltrate/cyst removal
Tracheal stenosis resection
Tracheoscopy and laryngoscopy with biopsy/newborn