Dataset Information

Refining the predictive variables in the "Surgical Risk Preoperative Assessment System" (SURPAS): a descriptive analysis.

ABSTRACT:

Background

The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious set of models providing accurate preoperative prediction of common adverse outcomes for individual patients. However, focus groups with surgeons and patients have developed a list of questions about and recommendations for how to further improve SURPAS's usability and usefulness. Eight issues were systematically evaluated to improve SURPAS.

Methods

The eight issues were divided into three groups: concerns to be addressed through further analysis of the database; addition of features to the SURPAS tool; and the collection of additional outcomes. Standard multiple logistic regression analysis was performed using the 2005-2015 American College of Surgeons National Surgical Quality Improvement Participant Use File (ACS NSQIP PUF) to refine models: substitution of the preoperative sepsis variable with a procedure-related risk variable; testing of an indicator variable for multiple concurrent procedure codes in complex operations; and addition of outcomes to increase clinical applicability. Automated risk documentation in the electronic health record and a patient handout and supporting documentation were developed. Long term functional outcomes were considered.

Results

Model discrimination and calibration improved when preoperative sepsis was replaced with a procedure-related risk variable. Addition of an indicator variable for multiple concurrent procedures did not significantly improve the models. Models were developed for a revised set of eleven adverse postoperative outcomes that separated bleeding/transfusion from the cardiac outcomes, UTI from the other infection outcomes, and added a predictive model for unplanned readmission. Automated documentation of risk assessment in the electronic health record, visual displays of risk for providers and patients and an "About" section describing the development of the tool were developed and implemented. Long term functional outcomes were considered to be beyond the scope of the current SURPAS tool.

Conclusion

Refinements to SURPAS were successful in improving the accuracy of the models, while reducing manual entry to five of the eight variables. Adding a predictor variable to indicate a complex operation with multiple current procedure codes did not improve the accuracy of the models. We developed graphical displays of risk for providers and patients, including a take-home handout and automated documentation of risk in the electronic health record. These improvements should facilitate easier implementation of SURPAS.

SUBMITTER: Henderson WG

PROVIDER: S-EPMC6702720 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Refining the predictive variables in the "Surgical Risk Preoperative Assessment System" (SURPAS): a descriptive analysis.

Henderson William G WG Bronsert Michael R MR Hammermeister Karl E KE Lambert-Kerzner Anne A Meguid Robert A RA

Patient safety in surgery 20190820

<h4>Background</h4>The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious set of models providing accurate preoperative prediction of common adverse outcomes for individual patients. However, focus groups with surgeons and patients have developed a list of questions about and recommendations for how to further improve SURPAS's usability and usefulness. Eight issues were systematically evaluated to improve SURPAS.<h4>Methods</h4>The eight issues were divided into three groups ...[more]

PMID: 31452684

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Project description:Study objectivePreoperative assessment is a standard evaluation, traditionally done in-person in a preanesthesia clinic, for patients who will be undergoing a procedure involving anesthesia. Given the increased adoption of virtual care during the coronavirus disease 2019 (COVID-19) pandemic, the purpose of this systematic review and meta-analysis is to review the effectiveness of virtual preoperative assessment for the evaluation of surgical patients.DesignSystematic review and meta-analysis.SettingMEDLINE (Ovid), MEDLINE InProcess/ePubs, Embase, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and ClinicalTrials.gov were searched from the initial coverage of the respective database to May 2021. A manual citation search of Google Scholar and PubMed was conducted to identify missed articles. Continued literature surveillance was done through July 2021.PatientsPatients aged 18 years and older undergoing virtual preoperative anesthesia assessment.InterventionsVirtual preoperative assessment.MeasurementsSurgery cancellation rates, patient experience, resources saved, staff experience, success in using the data collected to diagnose and manage patients.Main resultsFifteen studies (n = 31,496 patients) were included in this review. The average age of patients was 58 ± 15 years, and 47% were male. Virtual preoperative assessment resulted in similar surgery cancellation rates compared to in-person evaluation, with a pooled cancellation rate of 2% (95% confidence interval [CI]: 1-3%). Most studies reported a positive patient experience, with a pooled estimate of 90% (95% CI, 81-95%). There was a high success rate in using the information collected with virtual care, in the range of 92-100%, to diagnose and manage patients resulting in time and cost savings in the range of 24-137 min and $60-67 per patient.ConclusionsThis systematic review and meta-analysis demonstrates the utility of virtual care for preoperative assessment of surgical patients. Virtual preanesthesia evaluation had similar surgery cancellation rates, high patient satisfaction, and reduced costs compared to in-person evaluation.

Project description:BackgroundCurrently, in the specialized literature there are no substantiated clinical and radiological indications for differentiated use of anterior cervical discectomy and fusion (ACDF) and anterior cervical corpectomy and fusion (ACCF) in the treatment of patients with two-segmental cervical degenerative diseases. The objectives of this study were to (I) identify risk factors that were associated with unsatisfactory results of two-level ACDF and one-level ACCF in the treatment of patients with cervical degenerative diseases despite current perioperative management, and (II) develop a clinical and radiological algorithm for personalized surgical tactics.MethodsWe retrospectively identified risk factors for the development of unsatisfactory clinical postoperative results after two-level ACDF (n=81) and one-level ACCF (n=78), operated in the period of 2009-2019 for two-segmental cervical degenerative disease.ResultsSatisfactory clinical results after two-level ACDF were noted in cases with total kyphotic deformity of less than 15°; local kyphotic deformity less than 10˚; the absence of circumferential spondylotic cervical stenosis; the absence of a myelopathic lesion at the level of the vertebral body; absence of migrating intervertebral disk (IVD) hernia more than 1/3 of the vertebral body; T1 slope vertebra less than 15°; IVD degeneration according to Suzuki A. 0-II; facet joint (FJ) degeneration according to Okamoto A. I-III; interbody height (IH) more than 2 mm. Satisfactory clinical results after single-level ACCF were registered in cases with IVD degeneration according to Suzuki A. III; FJ degeneration according to Okamoto A. IV-V; IH 3 mm or less; regardless of the cervical lordosis, the angle of local kyphotic deformity and T1 slope, the presence of circumferential spondylotic cervical stenosis, the localization of the myelopathic lesion and the distance of migration IVD herniation.ConclusionsIndividual planning and differentiated implementation of ACDF and ACCF in patients with two-segmental cervical degenerative disease, taking into account a comprehensive preoperative clinical and radiological assessment, contributes to the effective elimination of existing neurological symptoms, reducing the intensity of neck pain and upper limbs pain, restoring the functional state and quality of patients' lives in the minimum 24 months postoperative period, as well as reducing the number of postoperative complications and reoperations.

Dataset Information

Refining the predictive variables in the "Surgical Risk Preoperative Assessment System" (SURPAS): a descriptive analysis.

Background

Methods

Results

Conclusion

Publications

Refining the predictive variables in the "Surgical Risk Preoperative Assessment System" (SURPAS): a descriptive analysis.

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