Incidence of Intraoperative Vasoplegic Syndrome in Lung Transplantation.

OBJECTIVES: Severe hypotension and low systemic vascular resistance in the setting of adequate cardiac output, known as "vasoplegic syndrome" (VS), is a physiologic disturbance reported in 9% to 44% of cardiac surgery patients. Although this phenomenon is well-documented in cardiac surgery, there are few studies on its occurrence in lung transplantation. The goal of this study was to characterize the incidence of VS in lung transplantation, as well as identify associated risk factors and outcomes. DESIGN: Retrospective study of single and bilateral lung transplants from April 2013 to September 2021. SETTING: The study was conducted at an academic hospital. PARTICIPANTS: Patients ≥18 years of age who underwent lung transplantation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The authors defined VS as mean arterial pressure <65 mmHg, cardiac index ≥2.2 L/min/m2, and ≥30 minutes of vasopressor administration after organ reperfusion. The association between VS and risk factors or outcomes was assessed using t tests, Mann-Whitney U, and chi-square tests. The authors ran multivariate logistic regression models to determine factors independently associated with VS. The incidence of VS was 13.9% (CI 10.4%-18.4%). In the multivariate model, male sex (odds ratio 2.85, CI 1.07-7.58, p = 0.04) and cystic fibrosis (odds ratio 5.76, CI 1.43-23.09, p = 0.01) were associated with VS. CONCLUSIONS: The incidence of VS in lung transplantation is comparable to that of cardiac surgery. Interestingly, male sex and cystic fibrosis are strong risk factors. Identifying lung transplant recipients at increased risk of VS may be crucial to anticipating intraoperative complications.

A Retrospective Analysis Demonstrates That a Failure to Document Key Comorbid Diseases in the Anesthesia Preoperative Evaluation Associates With Increased Length of Stay and Mortality.

BACKGROUND: The introduction of electronic health records (EHRs) has helped physicians access relevant medical information on their patients. However, the design of EHRs can make it hard for clinicians to easily find, review, and document all of the relevant data, leading to documentation that is not fully reflective of the complete history. We hypothesized that the incidence of undocumented key comorbid diseases (atrial fibrillation [afib], congestive heart failure [CHF], chronic obstructive pulmonary disease [COPD], diabetes, and chronic kidney disease [CKD]) in the anesthesia preoperative evaluation was associated with increased postoperative length of stay (LOS) and mortality. METHODS: Charts of patients >18 years who received anesthesia in an inpatient facility were reviewed in this retrospective study. For each disease, a precise algorithm was developed to look for key structured data (medications, lab results, structured medical history, etc) in the EHR. Additionally, the checkboxes from the anesthesia preoperative evaluation were queried to determine the presence or absence of the documentation of the disease. Differences in mortality were modeled with logistic regression, and LOS was analyzed using linear regression. RESULTS: A total of 91,011 cases met inclusion criteria (age 18-89 years; 52% women, 48% men; 70% admitted from home). Agreement between the algorithms and the preoperative note was >84% for all comorbidities other than chronic pain (63.5%). The algorithm-detected disease not documented by the anesthesia team in 34.5% of cases for chronic pain (vs 1.9% of cases where chronic pain was documented but not detected by the algorithm), 4.0% of cases for diabetes (vs 2.1%), 4.3% of cases for CHF (vs 0.7%), 4.3% of cases for COPD (vs 1.1%), 7.7% of cases for afib (vs 0.3%), and 10.8% of cases for CKD (vs 1.7%). To assess the association of missed documentation with outcomes, we compared patients where the disease was detected by the algorithm but not documented (A+/P-) with patients where the disease was documented (A+/P+). For all diseases except chronic pain, the missed documentation was associated with a longer LOS. For mortality, the discrepancy was associated with increased mortality for afib, while the differences were insignificant for the other diseases. For each missed disease, the odds of mortality increased 1.52 (95% confidence interval [CI], 1.42-1.63) and the LOS increased by approximately 11%, geometric mean ratio of 1.11 (95% CI, 1.10-1.12). CONCLUSIONS: Anesthesia preoperative evaluations not infrequently fail to document disease for which there is evidence of disease in the EHR data. This missed documentation is associated with an increased LOS and mortality in perioperative patients.

Automated Assessment of Existing Patient's Revised Cardiac Risk Index Using Algorithmic Software.

BACKGROUND: Previous work in the field of medical informatics has shown that rules-based algorithms can be created to identify patients with various medical conditions; however, these techniques have not been compared to actual clinician notes nor has the ability to predict complications been tested. We hypothesize that a rules-based algorithm can successfully identify patients with the diseases in the Revised Cardiac Risk Index (RCRI). METHODS: Patients undergoing surgery at the University of California, Los Angeles Health System between April 1, 2013 and July 1, 2016 and who had at least 2 previous office visits were included. For each disease in the RCRI except renal failure-congestive heart failure, ischemic heart disease, cerebrovascular disease, and diabetes mellitus-diagnosis algorithms were created based on diagnostic and standard clinical treatment criteria. For each disease state, the prevalence of the disease as determined by the algorithm, International Classification of Disease (ICD) code, and anesthesiologist's preoperative note were determined. Additionally, 400 American Society of Anesthesiologists classes III and IV cases were randomly chosen for manual review by an anesthesiologist. The sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve were determined using the manual review as a gold standard. Last, the ability of the RCRI as calculated by each of the methods to predict in-hospital mortality was determined, and the time necessary to run the algorithms was calculated. RESULTS: A total of 64,151 patients met inclusion criteria for the study. In general, the incidence of definite or likely disease determined by the algorithms was higher than that detected by the anesthesiologist. Additionally, in all disease states, the prevalence of disease was always lowest for the ICD codes, followed by the preoperative note, followed by the algorithms. In the subset of patients for whom the records were manually reviewed, the algorithms were generally the most sensitive and the ICD codes the most specific. When computing the modified RCRI using each of the methods, the modified RCRI from the algorithms predicted in-hospital mortality with an area under the receiver operating characteristic curve of 0.70 (0.67-0.73), which compared to 0.70 (0.67-0.72) for ICD codes and 0.64 (0.61-0.67) for the preoperative note. On average, the algorithms took 12.64 ± 1.20 minutes to run on 1.4 million patients. CONCLUSIONS: Rules-based algorithms for disease in the RCRI can be created that perform with a similar discriminative ability as compared to physician notes and ICD codes but with significantly increased economies of scale.

Risk factor stratification for postoperative delirium: A retrospective database study.

METHODS: The EHR of 32734 patients >18 years of age undergoing surgery and had POD assessment were reviewed. Patient characteristics and study variables were summarized between delirium groups. We constructed univariate logistic regression models for POD using each study variable to estimate odds ratios (OR) and constructed a multivariable logistic regression model with stepwise variable selection. In order to create a clinically useful/implementable tool we created a nomogram to predict risk of delirium. RESULTS: Overall, we found a rate of POD of 3.7% across our study population. The Model achieved an AUC of the ROC curve of 0.83 (95% CI 0.82-0.84). We found that age, increased American Society of Anesthesiologists (ASA) score (ASA 3-4 OR 2.81, CI 1.49-5.28, P < .001), depression (OR 1.28, CI 1.12-1.47, P < .001), postoperative benzodiazepine use (OR 3.52, CI 3.06-4.06, P < .001) and urgent cases (Urgent OR 3.51, CI 2.92-4.21, P < .001; Emergent OR 3.99, CI 3.21-4.96, P < .001; Critically Emergent OR 5.30, CI 3.53-7.96, P < .001) were associated with POD. DISCUSSION: We were able to distinguish the contribution of individual risk factors to the development of POD. We created a clinically useful easy-to-use tool that has the potential to accurately identify those at high-risk of delirium, a first step to prevent POD.

Intraoperative High Tidal Volume Ventilation and Postoperative Acute Respiratory Distress Syndrome in Liver Transplant.

BACKGROUND: Mechanical ventilation plays an important role in perioperative management and patient outcomes. Although mechanical ventilation with high tidal volume (HTV) is injurious in patients in the intensive care unit, the effects of HTV ventilation in patients undergoing liver transplant (LT) has not been reported. The aim of this study was to determine if intraoperative HTV ventilation was associated with the development of acute respiratory distress syndrome (ARDS). METHODS: Patients undergoing LT between 2013 and 2018 at a tertiary medical center were reviewed. The tidal volume was recorded at 3 time points: after anesthesia induction, before liver reperfusion, and at the end of surgery. Patients were divided into 2 groups: HTV (>10 mL/kg predicted body weight [pBW]) and non-HTV (≤10 mL/kg pBW). The 2 groups were compared. Independent risk factors were identified by multivariable logistic models. RESULTS: Of 780 LT patients, 85 (10.9%) received HTV ventilation. Female sex and greater difference between actual body weight and pBW were independent risk factors for HTV ventilation. Patients who received HTV ventilation had a significantly higher incidence of ARDS (10.3% vs 3.9%; P = .01) than those who received non-HTV ventilation. CONCLUSIONS: In this retrospective study, we showed that HTV ventilation during LT was common and was associated with a higher incidence of ARDS. Therefore, tidal volume should be carefully selected during LT surgery. More studies using a prospective randomized controlled design are needed.

Evaluating Surgeons on Intraoperative Disposable Supply Costs: Details Matter.

BACKGROUND: Cost report cards have demonstrated variation in intraoperative supply costs and may allow comparisons between surgeons. However, cost data are complex and, if not properly vetted, may be inaccurate. METHODS: A retrospective assessment of intraoperative supply costs for consecutive laparoscopic cholecystectomies (2013-2017) at a 4-facility academic center was performed. Using unadjusted data (akin to an auto-generated report card), surgeons were ranked and highest to lowest-cost ratios were calculated. Then, four stepwise adjustments were performed: (1) excluded non-comparable operations and low volume (< 10 cases) surgeons, (2) eliminated outlier cases based on instrument profiles, (3) stratified by facility, and (4) adjusted prices (assigned one price; corrected aberrant/missing prices). Surgeon rank and highest to lowest-cost ratios were then re-calculated. RESULTS: The unadjusted data identified 1392 cases for 33 surgeons (range, 1-317 cases). The ratio between the highest cost and lowest cost surgeon was 4.13. Steps 1 and 2 excluded 272 cases and 15 surgeons. Facility sample sizes ranged from 144 to 621 (step 3). Adjusting prices (step 4) required manual review of 472 unique items: 45% had > 1 price and 16 had missing prices. After all adjustments, surgeons had different rankings and highest to lowest-cost ratios within sites were smaller (ratio range, 1.17-2.10). CONCLUSIONS: Evaluating surgeons based on intraoperative supply costs is sensitive to analytic methods. Surgeons who were initially considered cost outliers became the least expensive within a given site. Auto-generated cost report cards may require additional analyses to produce accurate comparative assessments.

Anesthesiology Point of Care project.

We are developing a dynamic prototype visual communication system for the operating room environs. This has classically been viewed as an isolated and impenetrable workplace. All medical experiences and all teaching remain in a one to one closed loop with no recall or subsequent sharing for the training and education of other colleagues. The "Anesthesia Point of Care" (APOC) concept embraces the sharing of, recording of, and presentation of various physiological and pharmacological events so that real time memory can be shared at a later time for the edification of other colleagues who were not present at the time of the primary learning event. In addition it also provides a remarkably rapid tool for fellow faculty to respond to obvious stress and crisis events that can be broadcast instantly at the time of happening. Finally, it also serves as an efficient and effective means of paging and general communication throughout the daily routines among various healthcare providers in anesthesiology who work as a team unit; these include the staff, residents, CRNAs, physician assistants, and technicians. This system offers a unique opportunity to eventually develop future advanced ideas that can include training exercises, presurgical evaluations, surgical scheduling and improvements in efficiency based upon earlier than expected case completion or conversely later than expected case completion and even as a unique window to development of improved billing itemization and coordination.