Operative intervention for a central pulmonary artery pseudoaneurysm.

Pulmonary artery (PA) pseudoaneurysms are a rare but potentially lethal diagnosis. They can be further categorized by etiology or location and are typically successfully treated with endovascular therapies. However, they occasionally require operative intervention. Here, we present a case of a patient who presented with a central PA pseudoaneurysm on computed tomography scan with unclear etiology that was initially treated with conservative management. However, this was noted to have rapid enlargement on interval imaging necessitating urgent surgical intervention. The patient underwent a median sternotomy, anterior PA arteriotomy for exposure, exclusion of the posterior artery pseudoaneurysm with a bovine pericardial patch, and closure of the anterior arteriotomy with a bovine pericardial patch. The patient did well and was discharged on postoperative day 11 with repeat imaging showing resolution.

The value of the "Surgical Risk Preoperative Assessment System" (SURPAS) in preoperative consultation for elective surgery: a pilot study.

BACKGROUND: Risk assessment is essential to informed decision making in surgery. Preoperative use of the Surgical Risk Preoperative Assessment System (SURPAS) providing individualized risk assessment, may enhance informed consent. We assessed patient and provider perceptions of SURPAS as a risk assessment tool. METHODS: A convergent mixed-methods study assessed SURPAS's trial implementation, concurrently collecting quantitative and qualitative data, separately analyzing it, and integrating the results. Patients and providers were surveyed and interviewed on their opinion of how SURPAS impacted the preoperative encounter. Relationships between patient risk and patient and provider assessment of SURPAS were examined. RESULTS: A total of 197 patients were provided their SURPAS postoperative risk estimates in nine surgeon's clinics. Of the total patients, 98.8% reported they understood their surgical risks very or quite well after exposure to SURPAS; 92.7% reported SURPAS was very helpful or helpful. Providers shared that 83.4% of the time they reported SURPAS was very or somewhat helpful; 44.7% of the time the providers reported it changed their interaction with the patient and this change was beneficial 94.3% of the time. As patient risk increased, providers reported that SURPAS was increasingly helpful (p < 0.0001). CONCLUSIONS: Patients and providers reported the use of SURPAS helpful and informative during the preoperative risk assessment of patients, thus improving the surgical decision making process. Patients thought that SURPAS was helpful regardless of their risk level, whereas providers thought that SURPAS was more helpful in higher risk patients.

Use of Surgical Risk Preoperative Assessment System (SURPAS) and Patient Satisfaction During Informed Consent for Surgery.

BACKGROUND: The objective of this study was to determine the effects of using the Surgical Risk Preoperative Assessment System (SURPAS) on patient satisfaction and surgeon efficiency in the surgical informed consent process, as compared to surgeons' "usual" consent process. STUDY DESIGN: Patient perception of the consent process was assessed via survey in 2 cohorts: 10 surgeons in different specialties used their "usual" consent process for 10 patients; these surgeons were then taught to use SURPAS, and they used it during the informed consent process of 10 additional patients. The data were compared using Fisher's exact test and the Cochran-Mantel-Haenszel test. RESULTS: One hundred patients underwent the "usual" consent process (USUAL), and 93 underwent SURPAS-guided consent (SURPAS). Eighty-two percent of SURPAS were "very satisfied" and 18% were "satisfied" with risk discussion vs 16% and 72% of USUAL, respectively. Of those who used SURPAS, 75.3% reported the risk discussion made them "more comfortable" with surgery vs 19% of USUAL, and 90.3% of SURPAS users reported "somewhat" or "greatly decreased" anxiety vs 20% of USUAL. All p values were <0.0001. Among SURPAS patients, 97.9% reported "enough time spent discussing risks" vs 72.0% of USUAL patients. CONCLUSIONS: The SURPAS tool improved the informed consent process for patients compared with the "usual" consent process, in terms of patient satisfaction, ie making patients feel more comfortable and less anxious about their impending operations. Providers should consider integrating the SURPAS tool into their preoperative consent process.

Systematic Review of Preoperative Risk Discussion in Practice.

BACKGROUND: Informed consent is an ethical imperative of surgical practice. This requires effective communication of procedural risks to patients and is learned during residency. No systematic review has yet examined current risk disclosure. This systematic review aims to use existing published information to assess preoperative provision of risk information by surgeons. METHODS: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses as a guide, a standardized search in Ovid MEDLINE, Embase, CINHAL, and PubMed was performed. Three reviewers performed the study screening, with 2-reviewer consensus required at each stage. Studies containing objective information concerning preoperative risk provision in adult surgical patients were selected for inclusion. Studies exclusively addressing interventions for pediatric patients or trauma were excluded, as were studies addressing risks of anesthesia. RESULTS: The initial search returned 12,988 papers after deduplication, 33 of which met inclusion criteria. These studies primarily evaluated consent through surveys of providers, record reviews and consent recordings. The most ubiquitous finding of all study types was high levels of intra-surgeon variation in what risk information is provided to patients preoperatively. Studies recording consents found the lowest rates of risk disclosure. Studies using multiple forms of investigation corroborated this, finding disparity between verbally provided information vs chart documentation. CONCLUSIONS: The wide variance in what information is provided to patients preoperatively inhibits the realization of the ethical and practical components of informed consent. The findings of this review indicate that significant opportunities exist for practice improvement. Future development of surgical communication tools and techniques should emphasize standardizing what risks are shared with patients.

Identification of postoperative complications using electronic health record data and machine learning.

BACKGROUND: Using the American College of Surgeons National Surgical Quality Improvement Program (NSQIP) complication status of patients who underwent an operation at the University of Colorado Hospital, we developed a machine learning algorithm for identifying patients with one or more complications using data from the electronic health record (EHR). METHODS: We used an elastic-net model to estimate regression coefficients and carry out variable selection. International classification of disease codes (ICD-9), common procedural terminology (CPT) codes, medications, and CPT-specific complication event rate were included as predictors. RESULTS: Of 6840 patients, 922 (13.5%) had at least one of the 18 complications tracked by NSQIP. The model achieved 88% specificity, 83% sensitivity, 97% negative predictive value, 52% positive predictive value, and an area under the curve of 0.93. CONCLUSIONS: Using machine learning on EHR postoperative data linked to NSQIP outcomes data, a model with 163 predictors from the EHR identified complications well at our institution.

Accurate Preoperative Prediction of Discharge Destination Using 8 Predictor Variables: A NSQIP Analysis.

BACKGROUND: With inpatient length of stay decreasing, discharge destination after surgery can serve as an important metric for quality of care. Additionally, patients desire information on possible discharge destination. Adequate planning requires a multidisciplinary approach, can reduce healthcare costs and ensure patient needs are met. The Surgical Risk Preoperative Assessment System (SURPAS) is a parsimonious risk assessment tool using 8 predictor variables developed from the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) dataset. SURPAS is applicable to more than 3,000 operations in adults in 9 surgical specialties, predicts important adverse outcomes, and is incorporated into our electronic health record. We sought to determine whether SURPAS can accurately predict discharge destination. STUDY DESIGN: A "full model" for risk of postoperative "discharge not to home" was developed from 28 nonlaboratory preoperative variables from ACS NSQIP 2012-2017 dataset using logistic regression. This was compared with the 8-variable SURPAS model using the C index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots testing calibration, and the Brier score, a combined metric of discrimination and calibration. RESULTS: Of 5,303,519 patients, 447,153 (8.67%) experienced a discharge not to home. The SURPAS model's C index, 0.914, was 99.24% of that of the full model's (0.921); the Hosmer-Lemeshow plots indicated good calibration and the Brier score was 0.0537 and 0.0514 for the SUPAS and full model, respectively. CONCLUSIONS: The 8-variable SURPAS model preoperatively predicts risk of postoperative discharge to a destination other than home as accurately as the 28 nonlaboratory variable ACS NSQIP full model. Therefore, discharge destination can be integrated into the existing SURPAS tool, providing accurate outcomes to guide decision-making and help prepare patients for their postoperative recovery.

Accurate preoperative prediction of unplanned 30-day postoperative readmission using 8 predictor variables.

BACKGROUND: Unplanned postoperative readmissions are associated with high costs, may indicate poor care quality, and present a substantial opportunity for healthcare quality improvement. Patients want to know their risk of unplanned readmission, and surgeons need to know the risk to adequately counsel their patients. The Surgical Risk Preoperative Assessment System tool was developed from the American College of Surgeons National Surgical Quality Improvement Program dataset and is a parsimonious model using 8 predictor variables. Surgical Risk Preoperative Assessment System is applicable to >3,000 operations in 9 surgical specialties, predicts 30-day postoperative mortality and morbidity, and is incorporated into our electronic health record. METHODS: A Surgical Risk Preoperative Assessment System model was developed using logistic regression. It was compared to the 28 nonlaboratory variables model from the American College of Surgeons National Surgical Quality Improvement Program 2012 to 2017 dataset using the c-index as a measure of discrimination, the Hosmer-Lemeshow observed-to-expected plots testing calibration, and the Brier score, a combined metric of discrimination and calibration. RESULTS: Of 4,861,370 patients, 188,150 (3.98%) experienced unplanned readmission related to the index operation. The Surgical Risk Preoperative Assessment System model's c-index, 0.728, was 99.3% of that of the full model's, 0.733; the Hosmer-Lemeshow plots indicated good calibration; and the Brier score was 0.0372 for Surgical Risk Preoperative Assessment System and 0.0371 for the full model. CONCLUSION: The 8 variable Surgical Risk Preoperative Assessment System model detects patients at risk for postoperative unplanned, related readmission as accurately as the full model developed from all 28 nonlaboratory preoperative variables in the American College of Surgeons National Surgical Quality Improvement Program dataset. Therefore, unplanned readmission can be integrated into the existing Surgical Risk Preoperative Assessment System tool providing moderately accurate prediction of postoperative readmission.

Identification of urinary tract infections using electronic health record data.

BACKGROUND: Population ascertainment of postoperative urinary tract infections (UTIs) is time-consuming and expensive, as it often requires manual chart review. Using the American College of Surgeons National Surgical Quality Improvement Program UTI status of patients who underwent an operation at the University of Colorado Hospital, we sought to develop an algorithm for identifying UTIs using data from the electronic health record. METHODS: Data were split into training (operations occurring between 2013-2015) and test (operations in 2016) sets. A binomial generalized linear model with an elastic-net penalty was used to fit the model and carry out variables selection. International classification of disease codes, common procedural terminology codes, antibiotics, catheterization, and common procedural terminology-specific UTI event rates were included as predictors. The Youden's J statistic was used to determine the optimal classification threshold. RESULTS: Of 6,840 patients, 134 (2.0%) had a UTI. The model achieved 92% specificity, 80% sensitivity, 100% negative predictive value, 16% positive predictive value, and an area under the curve of 0.94 using a decision threshold of 0.03. CONCLUSIONS: A model with 14 predictors from the electronic health record identifies UTIs well, and it could be used to scale up UTI surveillance or to estimate the impact of large-scale interventions on UTI rates.