Postoperative Acute Kidney Injury is Associated with Persistent Renal Dysfunction: A Multicenter Propensity Matched Cohort Study.

Background: The risk of developing a persistent reduction in renal function after postoperative acute kidney injury (pAKI) is not well-established. Objective: Perform a multi-center retrospective propensity matched study evaluating whether patients that develop pAKI have a greater decline in long-term renal function than patients that did not develop postoperative AKI. Design: Multi-center retrospective propensity matched study. Setting: Anesthesia data warehouses at three tertiary care hospitals were queried. Patients: Adult patients undergoing surgery with available preoperative and postoperative creatinine results and without baseline hemodialysis requirements. Measurements: The primary outcome was a decline in follow-up glomerular filtration rate (GFR) of 40% relative to baseline, based on follow-up outpatient visits from 0-36 months after hospital discharge. A propensity score matched sample was used in Kaplan-Meier analysis and in a piecewise Cox model to compare time to first 40% decline in GFR for patients with and without pAKI. Results: A total of 95,208 patients were included. The rate of pAKI ranged from 9.9% to 13.7%. In the piecewise Cox model, pAKI significantly increased the hazard of a 40% decline in GFR. The common effect hazard ratio was 13.35 (95% CI: 10.79 to 16.51, p<0.001) for 0-6 months, 7.07 (5.52 to 9.05, p<0.001) for 6-12 months, 6.02 (4.69 to 7.74, p<0.001) for 12-24 months, and 4.32 (2.65 to 7.05, p<0.001) for 24-36 months. Limitations: Retrospective; Patients undergoing ambulatory surgery without postoperative lab tests drawn before discharge were not captured; certain variables like postoperative urine output were not reliably available. Conclusion: Postoperative AKI significantly increases the risk of a 40% decline in GFR up to 36 months after the index surgery across three institutions.

Machine Learning Predicts Cerebral Vasospasm in Subarachnoid Hemorrhage Patients.

Background: Cerebral vasospasm (CV) is a feared complication occurring in 20-40% of patients following subarachnoid hemorrhage (SAH) and is known to contribute to delayed cerebral ischemia. It is standard practice to admit SAH patients to intensive care for an extended period of vigilant, resource-intensive, clinical monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date. Methods: SAH patients admitted to UCLA from 2013-2022 and a validation cohort from VUMC from 2018-2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or ICU downgrade. At each institution, a light gradient boosting machine (LightGBM) was trained using five- fold cross validation to predict the primary endpoint at various timepoints during hospital admission. Receiver-operator curves (ROC) and precision-recall (PR) curves were generated. Results: A total of 1,750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 an average of over one week in advance, and successfully ruled out 8% of non-verapamil patients with zero false negatives. Minimum leukocyte count, maximum platelet count, and maximum intracranial pressure were the variables with highest predictive accuracy. Our models predicted "no CVRV" vs "CVRV within three days" vs "CVRV after three days" with AUCs=0.88, 0.83, and 0.88, respectively. For external validation at VUMC, 1,654 patients were included, 75 receiving verapamil. Predictive models at VUMC performed very similarly to those at UCLA, averaging 0.01 AUC points lower. Conclusions: We present an accurate (AUC=0.88) and early (>1 week prior) predictor of CVRV using machine learning over two large cohorts of subarachnoid hemorrhage patients at separate institutions. This represents a significant step towards optimized clinical management and improved resource allocation in the intensive care setting of subarachnoid hemorrhage patients.

Norepinephrine versus phenylephrine for treating hypotension during general anaesthesia in adult patients undergoing major noncardiac surgery: a multicentre, open-label, cluster-randomised, crossover, feasibility, and pilot trial.

BACKGROUND: Intraoperative hypotension is associated with postoperative complications. The use of vasopressors is often required to correct hypotension but the best vasopressor is unknown. METHODS: A multicentre, cluster-randomised, crossover, feasibility and pilot trial was conducted across five hospitals in California. Phenylephrine (PE) vs norepinephrine (NE) infusion as the first-line vasopressor in patients under general anaesthesia alternated monthly at each hospital for 6 months. The primary endpoint was first-line vasopressor administration compliance of 80% or higher. Secondary endpoints were acute kidney injury (AKI), 30-day mortality, myocardial injury after noncardiac surgery (MINS), hospital length of stay, and rehospitalisation within 30 days. RESULTS: A total of 3626 patients were enrolled over 6 months; 1809 patients were randomised in the NE group, 1817 in the PE group. Overall, 88.2% received the assigned first-line vasopressor. No drug infiltrations requiring treatment were reported in either group. Patients were median 63 yr old, 50% female, and 58% white. Randomisation in the NE group vs PE group did not reduce readmission within 30 days (adjusted odds ratio=0.92; 95% confidence interval, 0.6-1.39), 30-day mortality (1.01; 0.48-2.09), AKI (1.1; 0.92-1.31), or MINS (1.63; 0.84-3.16). CONCLUSIONS: A large and diverse population undergoing major surgery under general anaesthesia was successfully enrolled and randomised to receive NE or PE infusion. This pilot and feasibility trial was not powered for adverse postoperative outcomes and a follow-up multicentre effectiveness trial is planned. CLINICAL TRIAL REGISTRATION: NCT04789330 (ClinicalTrials.gov).

Early Postoperative Opioid Requirement Is Associated With Later Pain Control Needs After Supratentorial Craniotomies.

BACKGROUND: Despite a renewed focus in recent years on pain management in the inpatient hospital setting, postoperative pain after elective craniotomy remains under investigated. This study aims to identify which perioperative factors associate most strongly with postoperative pain and opioid medication requirements after inpatient craniotomy. MATERIALS AND METHODS: Using an existing dataset, we selected a restricted cohort of patients who underwent elective craniotomy surgery requiring an inpatient postoperative stay during a 7-year period at our institution (n=1832). We examined pain scores and opioid medication usage and analyzed the relative contribution of specific perioperative risk factors to postoperative pain and opioid medication intake (morphine milligram equivalents). RESULTS: Postoperative pain was found to be highest on postoperative day 1 and decreased thereafter (up to day 5). Factors associated with greater postoperative opioid medication requirement were preoperative opioid medication use, duration of anesthesia, degree of pain in the preoperative setting, and patient age. Notably, the most significant factor associated with a higher postoperative pain score and Morphine milligram equivalents requirement was the time elapsed between the end of general anesthesia and a patient's first intravenous opioid medication. CONCLUSION: Postcraniotomy patients are at higher risk for requiring opioid pain medications if they have a history of preoperative opioid use, are of younger age, or undergo a longer surgery. Moreover, early requirement of intravenous opioid medications in the postoperative period should alert treating physicians that a patient's pain may require additional or alternative methods of pain control than routinely administered, to avoid over-reliance on opioid medications.

Intraoperative Hypotension and 30-D Mortality After Liver Transplantation.

Intraoperative hypotension (IOH) is common and associated with mortality in major surgery. Although patients undergoing liver transplantation (LT) have low baseline blood pressure, the relation between blood pressure and mortality in LT is not well studied. We aimed to determine mean arterial pressure (MAP) that was associated with 30-d mortality in LT. Methods: We performed a retrospective cohort study. The data included patient demographics, pertinent preoperative and intraoperative variables, and MAP using various metrics and thresholds. The endpoint was 30-d mortality after LT. Results: One thousand one hundred seventy-eight patients from 2013 to 2020 were included. A majority of patients were exposed to IOH and many for a long period. Eighty-nine patients (7.6%) died within 30 d after LT. The unadjusted analysis showed that predicted mortality was associated with MAP <45 to 60 mm Hg but not MAP <65 mm Hg. The association between MAP and mortality was further tested using adjustment and various duration cutoffs. After adjustment, the shortest durations for MAPs <45, 50, and 55 mm Hg associated with 30-d mortality were 6, 10, and 25 min (odds ratio, 1.911, 1.812, and 1.772; 95% confidence interval, 1.100-3.320, 1.039-3.158, and 1.008-3.114; P = 0.002, 0.036, and 0.047), respectively. Exposure to MAP <60 mm Hg up to 120 min was not associated with increased mortality. Conclusion: In this large retrospective study, we found IOH was common during LT. Intraoperative MAP <55 mm Hg was associated with increased 30-d mortality after LT, and the duration associated with postoperative mortality was shorter with lower MAP than with higher MAP.

A simulation-based evaluation of machine learning models for clinical decision support: application and analysis using hospital readmission.

The interest in applying machine learning in healthcare has grown rapidly in recent years. Most predictive algorithms requiring pathway implementations are evaluated using metrics focused on predictive performance, such as the c statistic. However, these metrics are of limited clinical value, for two reasons: (1) they do not account for the algorithm's role within a provider workflow; and (2) they do not quantify the algorithm's value in terms of patient outcomes and cost savings. We propose a model for simulating the selection of patients over time by a clinician using a machine learning algorithm, and quantifying the expected patient outcomes and cost savings. Using data on unplanned emergency department surgical readmissions, we show that factors such as the provider's schedule and postoperative prediction timing can have major effects on the pathway cohort size and potential cost reductions from preventing hospital readmissions.

Successful Implementation of a Perioperative Data Warehouse Using Another Hospital's Published Specification From Epic's Electronic Health Record System.

BACKGROUND: Many hospitals have replaced their legacy anesthesia information management system with an enterprise-wide electronic health record system. Integrating the anesthesia data within the context of the global hospital information infrastructure has created substantive challenges for many organizations. A process to build a perioperative data warehouse from Epic was recently published from the University of California Los Angeles (UCLA), but the generalizability of that process is unknown. We describe the implementation of their process at the University of Miami (UM). METHODS: The UCLA process was tested at UM, and performance was evaluated following the configuration of a reporting server and transfer of the required Clarity tables to that server. Modifications required for the code to execute correctly in the UM environment were identified and implemented, including the addition of locally specified elements in the database. RESULTS: The UCLA code to build the base tables in the perioperative data warehouse executed correctly after minor modifications to match the local server and database architecture at UM. The 26 stored procedures in the UCLA process all ran correctly using the default settings provided and populated the base tables. After modification of the item lists to reflect the UM implementation of Epic (eg, medications, laboratory tests, physiologic monitors, and anesthesia machine parameters), the UCLA code ran correctly and populated the base tables. The data from those tables were used successfully to populate the existing perioperative data warehouse at UM, which housed data from the legacy anesthesia information management system of the institution. The time to pull data from Epic and populate the perioperative data warehouse was 197 ± 47 minutes (standard deviation [SD]) on weekdays and 260 ± 56 minutes (SD) on weekend days, measured over 100 consecutive days. The longer times on weekends reflect the simultaneous execution of database maintenance tasks on the reporting server. The UCLA extract process has been in production at UM for the past 18 months and has been invaluable for quality assurance, business process, and research activities. CONCLUSIONS: The data schema developed at UCLA proved to be a practical and scalable method to extract information from the Epic electronic health system database into the perioperative data warehouse in use at UM. Implementing the process developed at UCLA to build a comprehensive perioperative data warehouse from Epic is an extensible process that other hospitals seeking more efficient access to their electronic health record data should consider.

Development and validation of a deep neural network model to predict postoperative mortality, acute kidney injury, and reintubation using a single feature set.

During the perioperative period patients often suffer complications, including acute kidney injury (AKI), reintubation, and mortality. In order to effectively prevent these complications, high-risk patients must be readily identified. However, most current risk scores are designed to predict a single postoperative complication and often lack specificity on the patient level. In other fields, machine learning (ML) has been shown to successfully create models to predict multiple end points using a single input feature set. We hypothesized that ML can be used to create models to predict postoperative mortality, AKI, reintubation, and a combined outcome using a single set of features available at the end of surgery. A set of 46 features available at the end of surgery, including drug dosing, blood loss, vital signs, and others were extracted. Additionally, six additional features accounting for total intraoperative hypotension were extracted and trialed for different models. A total of 59,981 surgical procedures met inclusion criteria and the deep neural networks (DNN) were trained on 80% of the data, with 20% reserved for testing. The network performances were then compared to ASA Physical Status. In addition to creating separate models for each outcome, a multitask learning model was trialed that used information on all outcomes to predict the likelihood of each outcome individually. The overall rate of the examined complications in this data set was 0.79% for mortality, 22.3% (of 21,676 patients with creatinine values) for AKI, and 1.1% for reintubation. Overall, there was significant overlap between the various model types for each outcome, with no one modeling technique consistently performing the best. However, the best DNN models did beat the ASA score for all outcomes other than mortality. The highest area under the receiver operating characteristic curve (AUC) models were 0.792 (0.775-0.808) for AKI, 0.879 (0.851-0.905) for reintubation, 0.907 (0.872-0.938) for mortality, and 0.874 (0.864-0.866) for any outcome. The ASA score alone achieved AUCs of 0.652 (0.636-0.669) for AKI, 0.787 (0.757-0.818) for reintubation, 0.839 (0.804-0.875) for mortality, and 0.76 (0.748-0.773) for any outcome. Overall, the DNN architecture was able to create models that outperformed the ASA physical status to predict all outcomes based on a single feature set, consisting of objective data available at the end of surgery. No one model architecture consistently performed the best.

Hydromorphone Unit Dose Affects Intraoperative Dosing: An Observational Study.

BACKGROUND: Although clinical factors related to intraoperative opioid administration have been described, there is little research evaluating whether administration is influenced by drug formulation and, specifically, the unit dose of the drug. The authors hypothesized that the unit dose of hydromorphone is an independent determinant of the quantity of hydromorphone administered to patients intraoperatively. METHODS: This observational cohort study included 15,010 patients who received intraoperative hydromorphone as part of an anesthetic at the University of California, Los Angeles hospitals from February 2016 to March 2018. Before July 2017, hydromorphone was available as a 2-mg unit dose. From July 1, 2017 to November 20, 2017, hydromorphone was only available in a 1-mg unit dose. On November 21, 2017, hydromorphone was reintroduced in the 2-mg unit dose. An interrupted time series analysis was performed using segmented Poisson regression with two change-points, the first representing the switch from a 2-mg to 1-mg unit dose, and the second representing the reintroduction of the 2-mg dose. RESULTS: The 2-mg to 1-mg unit dose change was associated with a 49% relative decrease in the probability of receiving a hydromorphone dose greater than 1 mg (risk ratio, 0.51; 95% CI, 0.40-0.66; P < 0.0001). The reintroduction of a 2-mg unit dose was associated with a 48% relative increase in the probability of administering a dose greater than 1 mg (risk ratio, 1.48; 95% CI, 1.11-1.98; P = 0.008). CONCLUSIONS: This observational study using an interrupted time series analysis demonstrates that unit dose of hydromorphone (2 mg vs. 1 mg) is an independent determinant of the quantity of hydromorphone administered to patients in the intraoperative period.

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.

BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge. METHODS: Using a cohort of surgical patients at a tertiary care academic medical center, surgical, demographic, lab, medication, care team, and current procedural terminology data were extracted from the electronic health record. The primary outcome was whether there existed a future hospital readmission originating from the emergency department within 30 days of surgery. Secondarily, the time interval from surgery to the prediction was analyzed at 0, 12, 24, 36, 48, and 60 h. Different machine learning models for predicting the primary outcome were evaluated with respect to the area under the receiver-operator characteristic curve metric using different permutations of the available features. RESULTS: Surgical hospital admissions (N = 34,532) from April 2013 to December 2016 were included in the analysis. Surgical and demographic features led to moderate discrimination for prediction after discharge (area under the curve: 0.74 to 0.76), whereas medication, consulting team, and current procedural terminology features did not improve the discrimination. Lab features improved discrimination, with gradient-boosted trees attaining the best performance (area under the curve: 0.866, SD 0.006). This performance was sustained during temporal validation with 2017 to 2018 data (area under the curve: 0.85 to 0.88). Lastly, the discrimination of the predictions calculated 36 h after surgery (area under the curve: 0.88 to 0.89) nearly matched those from time of discharge. CONCLUSIONS: A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department. Moreover, these predictions can be confidently calculated at 36 h after surgery without consideration of discharge-level data.

An automated machine learning-based model predicts postoperative mortality using readily-extractable preoperative electronic health record data.

BACKGROUND: Rapid, preoperative identification of patients with the highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level or utilise the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. METHODS: We report on the use of machine learning algorithms, specifically random forests, to create a fully automated score that predicts postoperative in-hospital mortality based solely on structured data available at the time of surgery. Electronic health record data from 53 097 surgical patients (2.01% mortality rate) who underwent general anaesthesia between April 1, 2013 and December 10, 2018 in a large US academic medical centre were used to extract 58 preoperative features. RESULTS: Using a random forest classifier we found that automatically obtained preoperative features (area under the curve [AUC] of 0.932, 95% confidence interval [CI] 0.910-0.951) outperforms Preoperative Score to Predict Postoperative Mortality (POSPOM) scores (AUC of 0.660, 95% CI 0.598-0.722), Charlson comorbidity scores (AUC of 0.742, 95% CI 0.658-0.812), and ASA physical status (AUC of 0.866, 95% CI 0.829-0.897). Including the ASA physical status with the preoperative features achieves an AUC of 0.936 (95% CI 0.917-0.955). CONCLUSIONS: This automated score outperforms the ASA physical status score, the Charlson comorbidity score, and the POSPOM score for predicting in-hospital mortality. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

Digital Quality Improvement Approach Reduces the Need for Rescue Antiemetics in High-Risk Patients: A Comparative Effectiveness Study Using Interrupted Time Series and Propensity Score Matching Analysis.

BACKGROUND: Affecting nearly 30% of all surgical patients, postoperative nausea and vomiting (PONV) can lead to patient dissatisfaction, prolonged recovery times, and unanticipated hospital admissions. There are well-established, evidence-based guidelines for the prevention of PONV; yet physicians inconsistently adhere to them. We hypothesized that an electronic medical record-based clinical decision support (CDS) approach that incorporates a new PONV pathway, education initiative, and personalized feedback reporting system can decrease the incidence of PONV. METHODS: Two years of data, from February 17, 2015 to February 16, 2016, was acquired from our customized University of California Los Angeles Anesthesiology perioperative data warehouse. We queried the entire subpopulation of surgical cases that received general anesthesia with volatile anesthetics, were ≥12 years of age, and spent time recovering in any of the postanesthesia care units (PACUs). We then defined PONV as the administration of an antiemetic medication during the aforementioned PACU recovery. Our CDS system incorporated additional PONV-specific questions to the preoperative evaluation form, creation of a real-time intraoperative pathway compliance indicator, initiation of preoperative PONV risk alerts, and individualized emailed reports sent weekly to clinical providers. The association between the intervention and PONV was assessed by comparing the slopes from the incidence of PONV pre/postintervention as well as comparing observed incidences in the postintervention period to what we expected if the preintervention slope would have continued using interrupted time series analysis regression models after matching the groups on PONV-specific risk factors. RESULTS: After executing the PONV risk-balancing algorithm, the final cohort contained 36,796 cases, down from the 40,831 that met inclusion criteria. The incidence of PONV before the intervention was estimated to be 19.1% (95% confidence interval [CI], 17.9%-20.2%) the week before the intervention. Directly after implementation of the CDS, the total incidence decreased to 16.9% (95% CI, 15.2%-18.5%; P = .007). Within the high-risk population, the decrease in the incidence of PONV went from 29.3% (95% CI, 27.6%-31.1%) to 23.5% (95% CI, 20.5%-26.5%; P < .001). There was no significant difference in the PONV incidence slopes over the entire pre/postintervention periods in the high- or low-risk groups, despite an abrupt decline in the PONV incidence for high-risk patients within the first month of the CDS implementation. CONCLUSIONS: We demonstrate an approach to reduce PONV using individualized emails and anesthesia-specific CDS tools integrated directly into a commercial electronic medical record. We found an associated decrease in the PACU administration of rescue antiemetics for our high-risk patient population.

BayesCCE: a Bayesian framework for estimating cell-type composition from DNA methylation without the need for methylation reference.

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.

Rigid Esophagoscopy-Induced Aortic Rupture During Oral Gastric Tube Placement in Patient With Esophageal Stricture: A Case Report.

This is the first account of significant aortic injury during diagnostic rigid esophagoscopy in an adult with an esophageal stricture. We describe the resultant hemothorax and hemodynamic collapse and the successful treatment with massive volume resuscitation, vasopressors, and timely surgical intervention including thoracic endovascular aortic repair. We discuss the importance of rapid diagnosis, relevant anatomy, treatment modalities, and communication as cornerstones for learning.

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality. METHODS: The data used to train and validate the algorithm consists of 59,985 patients with 87 features extracted at the end of surgery. Feed-forward networks with a logistic output were trained using stochastic gradient descent with momentum. The deep neural networks were trained on 80% of the data, with 20% reserved for testing. The authors assessed improvement of the deep neural network by adding American Society of Anesthesiologists (ASA) Physical Status Classification and robustness of the deep neural network to a reduced feature set. The networks were then compared to ASA Physical Status, logistic regression, and other published clinical scores including the Surgical Apgar, Preoperative Score to Predict Postoperative Mortality, Risk Quantification Index, and the Risk Stratification Index. RESULTS: In-hospital mortality in the training and test sets were 0.81% and 0.73%. The deep neural network with a reduced feature set and ASA Physical Status classification had the highest area under the receiver operating characteristics curve, 0.91 (95% CI, 0.88 to 0.93). The highest logistic regression area under the curve was found with a reduced feature set and ASA Physical Status (0.90, 95% CI, 0.87 to 0.93). The Risk Stratification Index had the highest area under the receiver operating characteristics curve, at 0.97 (95% CI, 0.94 to 0.99). CONCLUSIONS: Deep neural networks can predict in-hospital mortality based on automatically extractable intraoperative data, but are not (yet) superior to existing methods.

Preoperative risk stratification of critically ill patients.

STUDY OBJECTIVE: Risk assessment historically emphasized cardiac morbidity and mortality in elective, outpatient, non-cardiac surgery. However, critically ill patients increasingly present for therapeutic interventions. Our study investigated the relationship of American Society of Anesthesiologists (ASA) class, revised cardiac risk index (RCRI), and sequential organ failure assessment (SOFA) score with survival to discharge in critically ill patients with respiratory failure. DESIGN: Retrospective cohort analysis over a 21-month period. SETTING: Five adult intensive care units (ICUs) at a single tertiary medical center. PATIENTS: Three hundred fifty ICU patients in respiratory failure, who underwent 501 procedures with general anesthesia. MEASUREMENTS: Demographic, clinical, and surgical variables were collected from the pre-anesthesia evaluation forms and preoperative ICU charts. The primary outcome was survival to discharge. MAIN RESULTS: Ninety-six patients (27%) did not survive to discharge. There were significant differences between survivors and non-survivors for ASA (3.7 vs. 3.9, p=0.001), RCRI (1.6 vs. 2.0, p=0.003), and SOFA score (8.1 vs. 11.2, p<0.001). Based on the area under the receiver operating characteristic curve for these relationships, there was only modest discrimination between the groups, ranging from the most useful SOFA (0.68) to less useful RCRI (0.60) and ASA (0.59). CONCLUSIONS: This single center retrospective study quantified a high perioperative risk for critically ill patients with advanced airways: one in four did not survive to discharge. Preoperative ASA score, RCRI, and SOFA score only partially delineated survivors and non-survivors. Given the existing limitations, future research may identify assessment tools more relevant to discriminating survival outcomes for critically ill patients in the perioperative environment.

Creation and Validation of an Automated Algorithm to Determine Postoperative Ventilator Requirements After Cardiac Surgery.

BACKGROUND: In medical practice today, clinical data registries have become a powerful tool for measuring and driving quality improvement, especially among multicenter projects. Registries face the known problem of trying to create dependable and clear metrics from electronic medical records data, which are typically scattered and often based on unreliable data sources. The Society for Thoracic Surgery (STS) is one such example, and it supports manually collected data by trained clinical staff in an effort to obtain the highest-fidelity data possible. As a possible alternative, our team designed an algorithm to test the feasibility of producing computer-derived data for the case of postoperative mechanical ventilation hours. In this article, we study and compare the accuracy of algorithm-derived mechanical ventilation data with manual data extraction. METHODS: We created a novel algorithm that is able to calculate mechanical ventilation duration for any postoperative patient using raw data from our EPIC electronic medical record. Utilizing nursing documentation of airway devices, documentation of lines, drains, and airways, and respiratory therapist ventilator settings, the algorithm produced results that were then validated against the STS registry. This enabled us to compare our algorithm results with data collected by human chart review. Any discrepancies were then resolved with manual calculation by a research team member. RESULTS: The STS registry contained a total of 439 University of California Los Angeles cardiac cases from April 1, 2013, to March 31, 2014. After excluding 201 patients for not remaining intubated, tracheostomy use, or for having 2 surgeries on the same day, 238 cases met inclusion criteria. Comparing the postoperative ventilation durations between the 2 data sources resulted in 158 (66%) ventilation durations agreeing within 1 hour, indicating a probable correct value for both sources. Among the discrepant cases, the algorithm yielded results that were exclusively correct in 75 (93.8%) cases, whereas the STS results were exclusively correct once (1.3%). The remaining 4 cases had inconclusive results after manual review because of a prolonged documentation gap between mechanical and spontaneous ventilation. In these cases, STS and algorithm results were different from one another but were both within the transition timespan. This yields an overall accuracy of 99.6% (95% confidence interval, 98.7%-100%) for the algorithm when compared with 68.5% (95% confidence interval, 62.6%-74.4%) for the STS data (P < .001). CONCLUSIONS: There is a significant appeal to having a computer algorithm capable of calculating metrics such as total ventilator times, especially because it is labor intensive and prone to human error. By incorporating 3 different sources into our algorithm and by using preprogrammed clinical judgment to overcome common errors with data entry, our results proved to be more comprehensive and more accurate, and they required a fraction of the computation time compared with manual review.