Dataset Information

External Validation of Surgical Risk Preoperative Assessment System in Pulmonary Resection.

ABSTRACT:

Background

Accurate preoperative risk assessment is necessary for informed decision making for patients and surgeons. Several preoperative risk calculators are available but few have been examined in the general thoracic surgical patient population. The Surgical Risk Preoperative Assessment System (SURPAS), a risk-assessment tool applicable to a wide spectrum of surgical procedures, was developed to predict the risks of common adverse postoperative outcomes using a parsimonious set of preoperative input variables. We sought to externally validate the performance of SURPAS for postoperative complications in patients undergoing pulmonary resection.

Methods

Between January 2016 and December 2018, 2514 patients underwent pulmonary resection at our center. Using data from our institution's prospectively maintained database, we calculated the predicted risks of 12 categories of postoperative outcomes using the latest version of SURPAS. Performance of SURPAS against observed patient outcomes was assessed by discrimination (concordance index) and calibration (calibration curves).

Results

The discrimination ability of SURPAS was moderate across all outcomes (concordance indices, 0.640 to 0.788). Calibration curves indicated good calibration for all outcomes except infectious and cardiac complications, discharge to a location other than home, and mortality (all overestimated by SURPAS).

Conclusions

SURPAS demonstrates outcomes for pulmonary resections with reasonable predictive ability. Discretion should be applied when assessing risk for postoperative infectious and cardiac complications, discharge to a location other than home, and mortality. Although the parsimonious nature of SURPAS is one of its strengths, its performance might be improved by including additional factors known to influence outcomes after pulmonary resection, such as sex and pulmonary function.

SUBMITTER: Chudgar NP

PROVIDER: S-EPMC8100919 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Publications

External Validation of Surgical Risk Preoperative Assessment System in Pulmonary Resection.

Chudgar Neel P NP Yan Shi S Hsu Meier M Tan Kay See KS Gray Katherine D KD Nobel Tamar T Molena Daniela D Sihag Smita S Bott Matthew M Jones David R DR Rusch Valerie W VW Rocco Gaetano G Isbell James M JM

The Annals of thoracic surgery 20201017 1

<h4>Background</h4>Accurate preoperative risk assessment is necessary for informed decision making for patients and surgeons. Several preoperative risk calculators are available but few have been examined in the general thoracic surgical patient population. The Surgical Risk Preoperative Assessment System (SURPAS), a risk-assessment tool applicable to a wide spectrum of surgical procedures, was developed to predict the risks of common adverse postoperative outcomes using a parsimonious set of pr ...[more]

PMID: 33075325

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Project description:BackgroundThe 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.MethodsThe 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.ResultsModel 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.ConclusionRefinements 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.

Project description:IntroductionAmong industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning-based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages.MethodsThe ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures.ResultsThe area under the curve was 0.84 (95% confidence interval [CI], 0.77-0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79-0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1-2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages.ConclusionsOur data confirmed ENDORISK model's laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.

Project description:BACKGROUND:This study aimed to externally validate and upgrade the recent difficulty scoring system (DSS) proposed by Halls et al. to predict intraoperative complications (IOC) during laparoscopic liver resection (LLR). METHODS:The DSS was validated in a cohort of 128 consecutive patients undergoing pure LLRs between 2008 and 2019 at a single tertiary referral center. The validated DSS includes four difficulty levels based on five risk factors (neoadjuvant chemotherapy, previous open liver resection, lesion type, lesion size and classification of resection). As established by the validated DSS, IOC was defined as excessive blood loss (>?775?mL), conversion to an open approach and unintentional damage to surrounding structures. Additionally, intra- and postoperative outcomes were compared according to the difficulty levels with usual statistic methods. The same five risk factors were used for validation done by linear and advanced nonlinear (artificial neural network) models. The study was supported by mathematical computations to obtain a mean risk curve predicting the probability of IOC for every difficulty score. RESULTS:The difficulty level of LLR was rated as low, moderate, high and extremely high in 36 (28.1%), 63 (49.2%), 27 (21.1%) and 2 (1.6%) patients, respectively. IOC was present in 23 (17.9%) patients. Blood loss of >775?mL occurred in 8 (6.2%) patients. Conversion to open approach was required in 18 (14.0%) patients. No patients suffered from unintentional damage to surrounding structures. Rates of IOC (0, 9.5, 55.5 and 100%) increased gradually with statistically significant value among difficulty levels (P?<?0.001). The relations between the difficulty level, need for transfusion, operative time, hepatic pedicle clamping, and major postoperative morbidity were statistically significant (P?<?0.05). Linear and nonlinear validation models showed a strong correlation (correlation coefficients 0.914 and 0.948, respectively) with the validated DSS. The Weibull cumulative distribution function was used for predicting the mean risk probability curve of IOC. CONCLUSION:This external validation proved this DSS based on patient's, tumor and surgical factors enables us to estimate the risk of intra- and postoperative complications. A surgeon should be aware of an increased risk of complications before starting with more complex procedures.

Project description:BackgroundEuroLung Risk scores were established to predict postoperative morbidity and mortality in patients undergoing anatomic lung resections. We aimed to perform an external validation of the EuroLung scores, which were calculated from data of the European Society of Thoracic Surgeons database, in our video-assisted thoracoscopic surgery cohort.MethodsAll available EuroLung scores were calculated for 718 patients scheduled for anatomic video-assisted thoracoscopic surgery resections between 2009 and 2019. Morbidity and mortality according to the definitions of the EuroLung scores were analyzed in a prospectively maintained database.ResultsOverall observed complication rate was 10.45%. Scores showed weak individual correlation (η = 0.155-0.174). The EuroLung1 app score showed the biggest area under the receiver operative characteristic (ROC) curve with 0.660. Binary logistic regression analysis showed that predicted postoperative forced expiratory volume in 1 s was associated with increased complications in both EuroLung1 and parsimonious EuroLung1 scores. Thirty-day mortality was 0.7% (predicted 1.10-1.40%) and was associated with predicted postoperative forced expiratory volume in 1 s for both EuroLung2 and parsimonious EuroLung2 scores. The EuroLung2 (2016) showed the biggest area under the ROC curve with 0.673. Only a very weak eta correlation between predicted and observed mortality was found for both aggregate EuroLung2, EuroLung2 (2016), EuroLung2 (2019), and parsimonious EuroLung2 (2016) (η = 0.025/0.015/0.011/0.009).ConclusionEuroLung scores help to estimate postoperative morbidity. However, even with the highest aggregate EuroLung scores possible only 50% suffer from postoperative morbidity. Although calibration of the scores was acceptable, discrimination between predicted and observed events was poor. Therefore, individual correlation between predicted and observed events is weak. Therefore, EuroLung scores may be best used to compare institutional quality of care to the European Society of Thoracic Surgeons database but should not be used to preclude patients from surgical treatment.

Project description:ObjectivesPulmonary complications remain a frequent cause of morbidity in patients undergoing oesophagectomy. Risk screening tools assist in patient stratification. Ferguson proposed a risk score system to predict major pulmonary complications after oesophagectomy. Our objective was to externally validate this risk score system.MethodsWe analysed our institutional database for patients undergoing oesophagectomy for cancer from August 2009 to December 2012. We analysed patients who had complete documentation of variables used in the Ferguson risk score calculation: forced expiratory volume in the 1 s, diffusion capacity of the lung for carbon monoxide, performance status and age. One hundred and thirty-six patients qualified for analysis in the validation study. Outcome variables measured included major pulmonary complications, defined as need for reintubation for respiratory failure and pneumonia. The risk score was then calculated for each individual based on the model. Incidence of major pulmonary events was assessed in the five risk class groupings to assess the discriminative ability of the Ferguson score.ResultsMajor pulmonary complications occurred in 35% of patients (47/136). Overall mortality was 6% (8/136). Patients were grouped into five risk categories according to their Ferguson pulmonary risk score: 0-2, 8 patients (6%); 3-4, 24 patients (18%); 5-6, 49 patients (36%); 29 patients (21%); 9-14, 26 patients (19%). The incidence of major pulmonary complications in these categories was 0, 17, 20, 41 and 77%, respectively. The accuracy of the risk score system for predicting major pulmonary complications was 76% (P < 0.0001).ConclusionsThis pulmonary risk scoring system is a reliable instrument to be used during the preoperative phase to differentiate patients who may be at higher risk for pulmonary complications after oesophagectomy. These data can assist in patient selection, and in patient education/informed consent and can guide postoperative management.

Dataset Information

External Validation of Surgical Risk Preoperative Assessment System in Pulmonary Resection.

Background

Methods

Results

Conclusions

Publications

External Validation of Surgical Risk Preoperative Assessment System in Pulmonary Resection.

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