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Hatipoglu E, Erginoz E, Askar A, Erguney S. Accuracy of the ACS NSQIP Surgical Risk Calculator for Predicting Postoperative Complications in Gastric Cancer Following Open Gastrectomy. Am Surg 2024; 90:640-647. [PMID: 37823864 DOI: 10.1177/00031348231206581] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
INTRODUCTION The prediction of complications before gastric surgery is of utmost importance in shared decision making and proper counseling of the patient in order to minimize postoperative complications. Our aim was to evaluate the predictive validity of American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) risk calculator in gastric cancer patients who underwent gastrectomy. METHODS Preoperative assessment data of 432 patients were retrospectively reviewed and manually entered into the calculator. The accuracy of the calculator was evaluated using Pearson's chi-squared test, C-statistic, Brier score, and Hosmer-Lemeshow test. RESULTS The lowest Brier scores were observed in urinary tract infection, renal failure, venous thromboembolism, pneumonia, and cardiac complications. Best results were obtained for predicting sepsis, discharge to rehabilitation facility, and death (low Brier scores, C-statistic >.7, and Hosmer-Lemeshow P > .05). CONCLUSION The calculator had a strong performance in predicting sepsis, discharge to the rehabilitation facility, and death. However, it performed poor in predicting the most commonly observed events (any or serious complication and surgical site infection).
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Affiliation(s)
- Engin Hatipoglu
- Department of General Surgery, Istanbul University Cerrahpaşa - Cerrahpaşa School of Medicine, Istanbul, Turkey
| | - Ergin Erginoz
- Department of General Surgery, Istanbul University Cerrahpaşa - Cerrahpaşa School of Medicine, Istanbul, Turkey
| | - Ahmet Askar
- Department of General Surgery, Istanbul University Cerrahpaşa - Cerrahpaşa School of Medicine, Istanbul, Turkey
| | - Sabri Erguney
- Department of General Surgery, Istanbul University Cerrahpaşa - Cerrahpaşa School of Medicine, Istanbul, Turkey
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Sakamoto T, Goto T, Fujiogi M, Kawarai Lefor A. Machine learning in gastrointestinal surgery. Surg Today 2021; 52:995-1007. [PMID: 34559310 DOI: 10.1007/s00595-021-02380-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 07/03/2021] [Indexed: 12/11/2022]
Abstract
Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze "big data". In gastrointestinal surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current "big data" era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage "big data" and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.
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Affiliation(s)
- Takashi Sakamoto
- Department of Gastroenterological Surgery, Gastroenterological Center, Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto, Tokyo, 135-8550, Japan. .,Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.
| | - Tadahiro Goto
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan.,TXP Medical Co. Ltd, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 114-8485, Japan
| | - Michimasa Fujiogi
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.,Department of Pediatric Surgery, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Alan Kawarai Lefor
- Department of Surgery, Jichi Medical University, Shimotsuke, Tochigi, 3290498, Japan
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Metzger G, Horwood C, Chen JC, Eaton R, Strassels SA, Tamer RM, Wisler J, Santry H, Rushing A. The Need for Accurate Risk Assessment in a High-Risk Patient Population: A NSQIP Study Evaluating Outcomes of Cholecystectomy in the Patient With Cancer. J Surg Res 2020; 257:519-528. [PMID: 32919342 DOI: 10.1016/j.jss.2020.07.078] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 06/18/2020] [Accepted: 07/11/2020] [Indexed: 12/14/2022]
Abstract
BACKGROUND Cholecystectomy is considered a low-risk procedure with proven safety in many high-risk patient populations. However, the risk of cholecystectomy in patients with active cancer has not been established. METHODS The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database was queried to identify all patients with disseminated cancer who underwent cholecystectomy from 2005 to 2016. Postcholecystectomy outcomes were defined for patients with cancer and those without by comparing several outcomes measures. A multivariate model was used to estimate the odds of 30-d mortality. RESULTS We compared outcomes in 3097 patients with disseminated cancer to a matched cohort of patients without cancer. Patients with cancer had more comorbidities at baseline: dyspnea (10.5% versus 7.0%, P < 0.0001), steroid use (10.1% versus 3.0%, P < 0.0001), and loss of >10% body weight in 6-mo prior (9.3% versus 1.6%, P < 0.0001). Patients with cancer sustained higher rates of wound (2.3% versus 5.6%, P < 0.0001), respiratory (1.4% versus 3.9%, P < 0.0001), and cardiovascular (2.0% versus 6.8%, P < 0.0001) complications. In addition, patients with disseminated cancer experienced a longer length of stay and higher 30-d mortality. Multivariate modeling showed that the odds of 30-d mortality was 3.3 times greater in patients with cancer. CONCLUSIONS Compared to patients without cancer, those with disseminated cancer are at higher risk of complication and mortality following cholecystectomy. Traditional treatment algorithms should be used with caution and care decisions individualized based on the patient's disease status and treatment goals.
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Affiliation(s)
- Gregory Metzger
- Department of General Surgery, The Ohio State University, Columbus, Ohio
| | - Chelsea Horwood
- Department of General Surgery, The Ohio State University, Columbus, Ohio
| | - J C Chen
- Department of General Surgery, The Ohio State University, Columbus, Ohio
| | - Ryan Eaton
- Department of General Surgery, The Ohio State University, Columbus, Ohio
| | - Scott A Strassels
- Department of General Surgery, The Ohio State University, Columbus, Ohio; Division of Critical Care, Trauma and Burn, The Ohio State University, Columbus, Ohio; Center for Surgical Health Assessment, Research, and Policy, The Ohio State University, Columbus, Ohio
| | - Robert M Tamer
- Department of General Surgery, The Ohio State University, Columbus, Ohio; Division of Critical Care, Trauma and Burn, The Ohio State University, Columbus, Ohio; Center for Surgical Health Assessment, Research, and Policy, The Ohio State University, Columbus, Ohio
| | - Jonathan Wisler
- Department of General Surgery, The Ohio State University, Columbus, Ohio; Division of Critical Care, Trauma and Burn, The Ohio State University, Columbus, Ohio
| | - Heena Santry
- Department of General Surgery, The Ohio State University, Columbus, Ohio; Division of Critical Care, Trauma and Burn, The Ohio State University, Columbus, Ohio; Center for Surgical Health Assessment, Research, and Policy, The Ohio State University, Columbus, Ohio
| | - Amy Rushing
- Department of General Surgery, The Ohio State University, Columbus, Ohio; Division of Critical Care, Trauma and Burn, The Ohio State University, Columbus, Ohio.
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Alzahrani SM, Ko CS, Yoo MW. Validation of the ACS NSQIP Surgical Risk Calculator for Patients with Early Gastric Cancer Treated with Laparoscopic Gastrectomy. J Gastric Cancer 2020; 20:267-276. [PMID: 33024583 PMCID: PMC7521978 DOI: 10.5230/jgc.2020.20.e27] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Revised: 07/14/2020] [Accepted: 07/22/2020] [Indexed: 12/16/2022] Open
Abstract
Purpose The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) risk calculator is useful in predicting postoperative adverse events. However, its accuracy in specific disorders is unclear. We validated the ACS NSQIP risk calculator in patients with gastric cancer undergoing curative laparoscopic surgery. Materials and Methods We included 207 consecutive early gastric cancer patients who underwent laparoscopic gastrectomy between January 2018 and January 2019. The preoperative characteristics and risks of the patients were reviewed and entered into the ACS NSQIP calculator. The estimated risks of postoperative outcomes were compared with the observed outcomes using C-statistics and Brier scores. Results Most of the patients underwent distal gastrectomy with Roux-en-Y reconstruction (74.4%). We did not observe any cases of mortality, venous thromboembolism, urinary tract infection, renal failure, or cardiac complications. The other outcomes assessed were complications such as pneumonia, surgical site infections, any complications requiring re-operation or hospital readmission, the rates of discharge to nursing homes/rehabilitation centers, and the length of stay. All C-statistics were <0 and the highest was for pneumonia (0.65; 95% confidence interval: 0.58–0.71). Brier scores ranged from 0.01 for pneumonia to 0.155 for other complications. Overall, the risk calculator was inconsistent in predicting the outcomes. Conclusions The ACS NSQIP surgical risk calculator showed low predictive ability for postoperative adverse events after laparoscopic gastrectomy for patients with early gastric cancer. Further research to adjust the risk calculator for these patients may improve its predictive ability.
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Affiliation(s)
- Saleh M Alzahrani
- Division of Stomach Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Chang Seok Ko
- Division of Stomach Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Moon-Won Yoo
- Division of Stomach Surgery, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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Schwartz PB, Stahl CC, Ethun C, Marka N, Poultsides GA, Roggin KK, Fields RC, Howard JH, Clarke CN, Votanopoulos KI, Cardona K, Abbott DE. Retroperitoneal sarcoma perioperative risk stratification: A United States Sarcoma Collaborative evaluation of the ACS-NSQIP risk calculator. J Surg Oncol 2020; 122:795-802. [PMID: 32557654 PMCID: PMC7744355 DOI: 10.1002/jso.26071] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 06/06/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND The ACS-NSQIP risk calculator predicts perioperative risk. This study tested the calculator's ability to predict risk for outcomes following retroperitoneal sarcoma (RPS) resection. METHODS The United States Sarcoma Collaborative database was queried for adults who underwent RPS resection. Estimated risk for outcomes was calculated twice in the risk calculator, once using sarcoma-specific CPT codes and once using codes indicative of most comorbid organ resection (eg nephrectomy). ROC curves were generated, with area under the curve (AUC) and Brier scores reported to assess discrimination and calibration. An AUC < 0.6 was considered ineffective discrimination. A negative ▲ Brier indicated improved performance relative to baseline outcome rates. RESULTS In total, 482 patients were identified with a 42.3% 90-day complication rate. Discrimination was poor for all outcomes except "all complications" and "renal failure." Baseline outcome rates were better predictors than calculator estimates except for "discharge to nursing or rehab facility" and "renal failure." Replacing sarcoma-specific CPT codes with resection-specific codes did not improve performance. CONCLUSION The ACS-NSQIP risk calculator poorly predicted outcomes following RPS resection. Changing sarcoma-specific CPT to resection-specific codes did not improve performance. Comorbidities in the calculator may not effectively capture perioperative risk. Future work should evaluate a sarcoma-specific calculator.
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Affiliation(s)
- Patrick B Schwartz
- Department of Surgery, Division of Surgical Oncology, University of Wisconsin, Madison, Wisconsin
| | - Christopher C Stahl
- Department of Surgery, Division of Surgical Oncology, University of Wisconsin, Madison, Wisconsin
| | - Cecilia Ethun
- Department of Surgery, Division of Surgical Oncology, Emory University, Atlanta, Georgia
| | - Nicholas Marka
- Department of Surgery, Division of Surgical Oncology, University of Wisconsin, Madison, Wisconsin
| | - George A Poultsides
- Department of Surgery, Division of Surgical Oncology, Stanford University, Palo Alto, California
| | - Kevin K Roggin
- Department of Surgery, University of Chicago Medicine, Chicago, Illinois
| | - Ryan C Fields
- Department of Surgery, Siteman Cancer Center, Washington University, St. Louis, Missouri
| | - John H Howard
- Department of Surgery, Division of Surgical Oncology, The Ohio State University, Columbus, Ohio
| | - Callisia N Clarke
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | | | - Kenneth Cardona
- Department of Surgery, Division of Surgical Oncology, Emory University, Atlanta, Georgia
| | - Daniel E Abbott
- Department of Surgery, Division of Surgical Oncology, University of Wisconsin, Madison, Wisconsin
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Merath K, Hyer JM, Mehta R, Farooq A, Bagante F, Sahara K, Tsilimigras DI, Beal E, Paredes AZ, Wu L, Ejaz A, Pawlik TM. Use of Machine Learning for Prediction of Patient Risk of Postoperative Complications After Liver, Pancreatic, and Colorectal Surgery. J Gastrointest Surg 2020; 24:1843-1851. [PMID: 31385172 DOI: 10.1007/s11605-019-04338-2] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 07/21/2019] [Indexed: 02/06/2023]
Abstract
BACKGROUND Surgical resection is the only potentially curative treatment for patients with colorectal, liver, and pancreatic cancers. Although these procedures are performed with low mortality, rates of complications remain relatively high following hepatopancreatic and colorectal surgery. METHODS The American College of Surgeons (ACS) National Surgical Quality Improvement Program was utilized to identify patients undergoing liver, pancreatic and colorectal surgery from 2014 to 2016. Decision tree models were utilized to predict the occurrence of any complication, as well as specific complications. To assess the variability of the performance of the classification trees, bootstrapping was performed on 50% of the sample. RESULTS Algorithms were derived from a total of 15,657 patients who met inclusion criteria. The algorithm had a good predictive ability for the occurrence of any complication, with a C-statistic of 0.74, outperforming the ASA (C-statistic 0.58) and ACS-Surgical Risk Calculator (C-statistic 0.71). The algorithm was able to predict with high accuracy thirteen out of the seventeen complications analyzed. The best performance was in the prediction of stroke (C-statistic 0.98), followed by wound dehiscence, cardiac arrest, and progressive renal failure (all C-statistic 0.96). The algorithm had a good predictive ability for superficial SSI (C-statistic 0.76), organ space SSI (C-statistic 0.76), sepsis (C-statistic 0.79), and bleeding requiring transfusion (C-statistic 0.79). CONCLUSION Machine learning was used to develop an algorithm that accurately predicted patient risk of developing complications following liver, pancreatic, or colorectal surgery. The algorithm had very good predictive ability to predict specific complications and demonstrated superiority over other established methods.
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Affiliation(s)
- Katiuscha Merath
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - J Madison Hyer
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Rittal Mehta
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Ayesha Farooq
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Fabio Bagante
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Kota Sahara
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Diamantis I Tsilimigras
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Eliza Beal
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Anghela Z Paredes
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Lu Wu
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Aslam Ejaz
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA
| | - Timothy M Pawlik
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH, USA.
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Eichelmann AK, Saidi M, Lindner K, Lenschow C, Palmes D, Pascher A, Hummel R. Impact of preoperative risk factors on outcome after gastrectomy. World J Surg Oncol 2020; 18:17. [PMID: 31980026 PMCID: PMC6982377 DOI: 10.1186/s12957-020-1790-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Accepted: 01/12/2020] [Indexed: 02/07/2023] Open
Abstract
Background Gastrectomy is associated with relevant postoperative morbidity. However, outcome of surgery can be improved by careful selection of patients. The objective of the current study was therefore to identify preoperative risk factors that might impact on patients’ further outcome after surgical resection. Methods Preoperative risk factors having respectively different surgical risk scores for major complex surgery (including Cologne Risk Score, p-/o-POSSUM, and NSQIP risk score) of patients that underwent gastrectomy for AEG II/III tumors and gastric cancer were correlated with complications according to Clavien-Dindo and outcome. Patients who underwent surgery in palliative intention were excluded from further analysis. Results Subtotal gastrectomy was performed in 23%, gastrectomy in 59%, and extended gastrectomy in 18% in a total of 139 patients (mean age: 64 years old). Thirty six percent experienced a minor complication (Dindo I-II) and 24% a major complication (Dindo III-V), which resulted in a prolonged hospital stay (p < 0.001). In-hospital mortality (=Dindo V) was 2.5%. Besides age, type of surgical procedure impacted on complications with extended gastrectomy showing the highest risk (p = 0.005). The o-POSSUM score failed to predict mortality accurately. We observed a highly positive correlation between predicted morbidity respectively mortality and occurrence of complications estimated by p-POSSUM (p = 0.005), Cologne Risk (p = 0.007), and NSQIP scores (p < 0.001). Conclusion The results demonstrate a significant association between different risk scores and occurrence of complications following gastrectomy. The p-POSSUM, Cologne Risk, and NSQIP score exhibited superior performance than the o-POSSUM score. Therefore, these scores might allow identification and selection of high-risk patients and thus might be highly useful for clinical decision making.
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Affiliation(s)
- Ann-Kathrin Eichelmann
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Albert-Schweitzer-Campus 1, W1, 48149, Münster, Germany.
| | - Meltem Saidi
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Albert-Schweitzer-Campus 1, W1, 48149, Münster, Germany
| | - Kirsten Lindner
- Department of Surgery, University Hospital of Schleswig-Holstein, Ratzeburger Allee 160, 23538, Lübeck, Germany
| | - Christina Lenschow
- Department of General, Visceral, Vascular and Pediatric Surgery, University Hospital of Würzburg, Oberdürrbacher Straße 6, 97080, Würzburg, Germany
| | - Daniel Palmes
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Albert-Schweitzer-Campus 1, W1, 48149, Münster, Germany
| | - Andreas Pascher
- Department of General, Visceral and Transplant Surgery, University Hospital Münster, Albert-Schweitzer-Campus 1, W1, 48149, Münster, Germany
| | - Richard Hummel
- Department of Surgery, University Hospital of Schleswig-Holstein, Ratzeburger Allee 160, 23538, Lübeck, Germany
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Dave A, Beal EW, Lopez-Aguiar AG, Poultsides G, Makris E, Rocha FG, Kanji Z, Ronnekleiv-Kelly S, Rendell VR, Fields RC, Krasnick BA, Idrees K, Smith PM, Nathan H, Beems M, Maithel SK, Pawlik TM, Schmidt CR, Dillhoff ME. Evaluating the ACS NSQIP Risk Calculator in Primary Pancreatic Neuroendocrine Tumor: Results from the US Neuroendocrine Tumor Study Group. J Gastrointest Surg 2019; 23:2225-2231. [PMID: 30941685 PMCID: PMC10178764 DOI: 10.1007/s11605-019-04120-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 01/13/2019] [Indexed: 01/31/2023]
Abstract
BACKGROUND In a changing health care environment where patient outcomes will be more closely scrutinized, the ability to predict surgical complications is becoming increasingly important. The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) online risk calculator is a popular tool to predict surgical risk. This paper aims to assess the applicability of the ACS NSQIP calculator to patients undergoing surgery for pancreatic neuroendocrine tumors (PNETs). METHODS Using the US Neuroendocrine Tumor Study Group (USNET-SG), 890 patients who underwent pancreatic procedures between 1/1/2000-12/31/2016 were evaluated. Predicted and actual outcomes were compared using C-statistics and Brier scores. RESULTS The most commonly performed procedure was distal pancreatectomy, followed by standard and pylorus-preserving pancreaticoduodenectomy. For the entire group of patients studied, C-statistics were highest for discharge destination (0.79) and cardiac complications (0.71), and less than 0.7 for all other complications. The Brier scores for surgical site infection (0.1441) and discharge to nursing/rehabilitation facility (0.0279) were below the Brier score cut-off, while the rest were equal to or above and therefore not useful for interpretation. CONCLUSION This work indicates that the ACS NSQIP risk calculator is a valuable tool that should be used with caution and in coordination with clinical assessment for PNET clinical decision-making.
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Affiliation(s)
- Apeksha Dave
- Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Eliza W Beal
- Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Alexandra G Lopez-Aguiar
- Division of Surgical Oncology, Department of Surgery, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | | | | | - Flavio G Rocha
- Department of Surgery, Virginia Mason Medical Center, Seattle, WA, USA
| | - Zaheer Kanji
- Department of Surgery, Virginia Mason Medical Center, Seattle, WA, USA
| | - Sean Ronnekleiv-Kelly
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Victoria R Rendell
- Department of Surgery, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Ryan C Fields
- Department of Surgery, Washington University School of Medicine, St. Louis, MI, USA
| | - Bradley A Krasnick
- Department of Surgery, Washington University School of Medicine, St. Louis, MI, USA
| | - Kamran Idrees
- Division of Surgical Oncology, Department of Surgery, Vanderbilt University, Nashville, TN, USA
| | - Paula Marincola Smith
- Division of Surgical Oncology, Department of Surgery, Vanderbilt University, Nashville, TN, USA
| | - Hari Nathan
- Division of Hepatopancreatobiliary and Advanced Gastrointestinal Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Megan Beems
- Division of Hepatopancreatobiliary and Advanced Gastrointestinal Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, USA
| | - Shishir K Maithel
- Division of Surgical Oncology, Department of Surgery, Winship Cancer Institute, Emory University, Atlanta, GA, USA
| | - Timothy M Pawlik
- Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Carl R Schmidt
- Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Mary E Dillhoff
- Division of Surgical Oncology, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA. .,Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, 320 W. 10th Ave, M256 Starling Loving Hall, Columbus, OH, 43210-1267, USA.
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