Kari Kraemer1, Mark E Cohen2, Yaoming Liu2, Douglas C Barnhart3, Shawn J Rangel4, Jacqueline M Saito5, Karl Y Bilimoria6, Clifford Y Ko7, Bruce L Hall8. 1. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL. Electronic address: kkraemer@facs.org. 2. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL. 3. Division of Pediatric Surgery, Primary Children's Medical Center, University of Utah, Salt Lake City, UT. 4. Department of Surgery, Boston Children's Hospital, Harvard Medical School, Boston, MA. 5. Department of Surgery, Washington University in St Louis, St Louis, MO. 6. Surgical Outcomes and Quality Improvement Center (SOQIC), Department of Surgery and Center for Healthcare Studies, Feinberg School of Medicine, Chicago, IL; Northwestern Medicine, Northwestern University, Chicago, IL. 7. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL; Department of Surgery, University of California Los Angeles David Geffen School of Medicine and the VA Greater Los Angeles Healthcare System, Los Angeles, CA. 8. Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL; Department of Surgery, Washington University in St Louis, St Louis, MO; Center for Health Policy and the Olin Business School at Washington University in St Louis; John Cochran Veterans Affairs Medical Center; and BJC Healthcare, St Louis, MO.
Abstract
BACKGROUND: There is an increased desire among patients and families to be involved in the surgical decision-making process. A surgeon's ability to provide patients and families with patient-specific estimates of postoperative complications is critical for shared decision making and informed consent. Surgeons can also use patient-specific risk estimates to decide whether or not to operate and what options to offer patients. Our objective was to develop and evaluate a publicly available risk estimation tool that would cover many common pediatric surgical procedures across all specialties. STUDY DESIGN: American College of Surgeons NSQIP Pediatric standardized data from 67 hospitals were used to develop a risk estimation tool. Surgeons enter 18 preoperative variables (demographics, comorbidities, procedure) that are used in a logistic regression model to predict 9 postoperative outcomes. A surgeon adjustment score is also incorporated to adjust for any additional risk not accounted for in the 18 risk factors. RESULTS: A pediatric surgical risk calculator was developed based on 181,353 cases covering 382 CPT codes across all specialties. It had excellent discrimination for mortality (c-statistic = 0.98), morbidity (c-statistic = 0.81), and 7 additional complications (c-statistic > 0.77). The Hosmer-Lemeshow statistic and graphic representations also showed excellent calibration. CONCLUSIONS: The ACS NSQIP Pediatric Surgical Risk Calculator was developed using standardized and audited multi-institutional data from the ACS NSQIP Pediatric, and it provides empirically derived, patient-specific postoperative risks. It can be used as a tool in the shared decision-making process by providing clinicians, families, and patients with useful information for many of the most common operations performed on pediatric patients in the US.
BACKGROUND: There is an increased desire among patients and families to be involved in the surgical decision-making process. A surgeon's ability to provide patients and families with patient-specific estimates of postoperative complications is critical for shared decision making and informed consent. Surgeons can also use patient-specific risk estimates to decide whether or not to operate and what options to offer patients. Our objective was to develop and evaluate a publicly available risk estimation tool that would cover many common pediatric surgical procedures across all specialties. STUDY DESIGN: American College of Surgeons NSQIP Pediatric standardized data from 67 hospitals were used to develop a risk estimation tool. Surgeons enter 18 preoperative variables (demographics, comorbidities, procedure) that are used in a logistic regression model to predict 9 postoperative outcomes. A surgeon adjustment score is also incorporated to adjust for any additional risk not accounted for in the 18 risk factors. RESULTS: A pediatric surgical risk calculator was developed based on 181,353 cases covering 382 CPT codes across all specialties. It had excellent discrimination for mortality (c-statistic = 0.98), morbidity (c-statistic = 0.81), and 7 additional complications (c-statistic > 0.77). The Hosmer-Lemeshow statistic and graphic representations also showed excellent calibration. CONCLUSIONS: The ACS NSQIP Pediatric Surgical Risk Calculator was developed using standardized and audited multi-institutional data from the ACS NSQIP Pediatric, and it provides empirically derived, patient-specific postoperative risks. It can be used as a tool in the shared decision-making process by providing clinicians, families, and patients with useful information for many of the most common operations performed on pediatric patients in the US.
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