Development and Validation of an Automated Tool to Retrieve and Curate Faculty Publications of Academic Departments.

Introduction Academic departments need to monitor their faculty's academic productivity for various purposes, such as reporting to the medical school dean, assessing the allocation of non-clinical research time, evaluating for rank promotion, and reporting to the Accreditation Council for Graduate Medical Education (ACGME). Our objective was to develop and validate a simple method that automatically generates query strings to identify and process distinct department faculty publications listed in PubMed and Scopus. Methods We created a macro-enabled Excel workbook (Microsoft, Redmond, WA) to automate the retrieval of faculty publications from the PubMed and Scopus bibliometric databases (available at https://bit.ly/get-pubs). Where the returned reference includes the digital object identifier (doi), a link is provided in the workbook. Duplicate publications are removed automatically, and false attributions are managed. Results At the University of Miami, between 2020 and 2021, there were 143 anesthesiology faculty-authored publications with a PubMed identifier (PMID), 95.8% identified by the query and 4.2% missed. At Vanderbilt University Medical Center, between 2019 and 2021, there were 760 anesthesiology faculty-authored publications with a PMID, 94.3% identified by the query and 5.7% missed. Recall, precision, and the F1 score were all above 93% at both medical centers. Conclusions We developed a highly accurate, simple, transportable, scalable method to identify publications in PubMed and Scopus authored by anesthesiology faculty. Manual checking and faculty feedback are required because not all names can be disambiguated, and some references are missed. This process can greatly reduce the burden of curating a list of faculty publications. The methodology applies to other academic departments that track faculty publications.

Patient and Operative Factors Predict Risk of Discretionary Prolonged Postoperative Mechanical Ventilation in a Broad Surgical Cohort.

BACKGROUND: Patients undergoing surgery with general anesthesia and endotracheal intubation are ideally extubated upon case completion, as prolonged postoperative mechanical ventilation (PPMV) has been associated with poor outcomes. However, some patients require PPMV for surgical reasons, such as airway compromise, while others remain intubated at the discretion of the anesthesia provider. Incidence and risk factors for discretionary PPMV (DPPMV) have been described in individual surgical subspecialties and intensive care unit (ICU) populations, but are relatively understudied in a broad surgical cohort. The present study seeks to fill this gap and identify the perioperative risk factors that predict DPPMV. METHODS: After obtaining institutional review board (IRB) exemption, existing electronic health record databases at our large referral center were retrospectively queried for adult surgeries performed between January 2018 and December 2020 with general anesthesia, endotracheal intubation, and by surgical services that do not routinely leave patients intubated for surgical reasons. Patients who arrived to the ICU intubated after surgery were identified as experiencing DPPMV. Selection of candidate risk factors was performed with LASSO-regularized logistic regression, and surviving variables were used to generate a multivariable logistic regression model of DPPMV risk. RESULTS: A total of 32,915 cases met inclusion criteria, of which 415 (1.26%) experienced DPPMV. Compared to extubated patients, those with DPPMV were more likely to have undergone emergency surgery (42.9% versus 3.4%; P < .001), surgery during an existing ICU stay (30.8% versus 2.8%; P < 0.001), and have 20 of the 31 elixhauser comorbidities ( P < .05 for each comparison), among other differences. A risk model with 12 variables, including American Society of Anesthesiologists (ASA) physical classification status, emergency surgery designation, four Elixhauser comorbidities, surgery during an existing ICU stay, surgery duration, estimated number of intraoperative handoffs, and vasopressor, sodium bicarbonate, and albuterol administration, yielded an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.96-0.97) for prediction of DPPMV. CONCLUSIONS: DPPMV was uncommon in this broad surgical cohort but could be accurately predicted using readily available patient-specific and operative factors. These results may be useful for preoperative risk stratification, postoperative resource allocation, and clinical trial planning.

Natural Language Mapping of Electrocardiogram Interpretations to a Standardized Ontology.

BACKGROUND: Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a computable representation of the findings. Challenges include misspellings, nonstandard abbreviations, jargon, and equivocation in diagnostic interpretations. OBJECTIVES: Our objective was to develop an algorithm to reliably and efficiently extract such information and map it to the standardized ECG ontology developed jointly by the American Heart Association, the American College of Cardiology Foundation, and the Heart Rhythm Society. The algorithm was to be designed to be easily modifiable for use with EHRs and ECG reporting systems other than the ones studied. METHODS: An algorithm using natural language processing techniques was developed in structured query language to extract and map quantitative and diagnostic information from ECG narrative reports to the cardiology societies' standardized ECG ontology. The algorithm was developed using a training dataset of 43,861 ECG reports and applied to a test dataset of 46,873 reports. RESULTS: Accuracy, precision, recall, and the F1-measure were all 100% in the test dataset for the extraction of quantitative data (e.g., PR and QTc interval, atrial and ventricular heart rate). Performances for matches in each diagnostic category in the standardized ECG ontology were all above 99% in the test dataset. The processing speed was approximately 20,000 reports per minute. We externally validated the algorithm from another institution that used a different ECG reporting system and found similar performance. CONCLUSION: The developed algorithm had high performance for creating a computable representation of ECG interpretations. Software and lookup tables are provided that can easily be modified for local customization and for use with other EHR and ECG reporting systems. This algorithm has utility for research and in clinical decision-support where incorporation of ECG findings is desired.

Etiology and Timing of Postoperative Rapid Response Team Activations.

In this retrospective cohort study we sought to evaluate the association between the etiology and timing of rapid response team (RRT) activations in postoperative patients at a tertiary care hospital in the southeastern United States. From 2010 to 2016, there were 2,390 adult surgical inpatients with RRT activations within seven days of surgery. Using multivariable linear regression, we modeled the correlation between etiology of RRT and timing of the RRT call, as measured from the conclusion of the surgical procedure. We found that respiratory triggers were associated with an increase in time after surgical procedure to RRT of 10.6 h compared to activations due to general concern (95% CI 3.9 - 17.3) (p = 0.002). These findings may have an impact on monitoring of postoperative patients, as well as focusing interventions to better respond to clinically deteriorating patients.

Reliability of the ASA Physical Status Classification System in Predicting Surgical Morbidity: a Retrospective Analysis.

The American Society of Anesthesiologists (ASA) Physical Status Classification System has been used to assess pre-anesthesia comorbid conditions for over 60 years. However, the ASA Physical Status Classification System has been criticized for its subjective nature. In this study, we aimed to assess the correlation between the ASA physical status assignment and more objective measures of overall illness. This is a single medical center, retrospective cohort study of adult patients who underwent surgery between November 2, 2017 and April 22, 2020. A multivariable ordinal logistic regression model was developed to examine the relationship between the ASA physical status and Elixhauser comorbidity groups. A secondary analysis was then conducted to evaluate the capability of the model to predict 30-day postoperative mortality. A total of 56,820 cases meeting inclusion criteria were analyzed. Twenty-seven Elixhauser comorbidities were independently associated with ASA physical status. Older patient (adjusted odds ratio, 1.39 [per 10 years of age]; 95% CI 1.37 to 1.41), male patient (adjusted odds ratio, 1.24; 95% CI 1.20 to 1.29), higher body weight (adjusted odds ratio, 1.08 [per 10 kg]; 95% CI 1.07 to 1.09), and ASA emergency status (adjusted odds ratio, 2.11; 95% CI 2.00 to 2.23) were also independently associated with higher ASA physical status assignments. Furthermore, the model derived from the primary analysis was a better predictor of 30-day mortality than the models including either single ASA physical status or comorbidity indices in isolation (p < 0.001). We found significant correlation between ASA physical status and 27 of the 31 Elixhauser comorbidities, as well other demographic characteristics. This demonstrates the reliability of ASA scoring and its potential ability to predict postoperative outcomes. Additionally, compared to ASA physical status and individual comorbidity indices, the derived model offered better predictive power in terms of short-term postoperative mortality.