Faculty Bibliography

BACKGROUND:There is concern about the potential of an increased risk related to medications that act on the renin-angiotensin-aldosterone system in patients exposed to coronavirus disease 2019 (Covid-19), because the viral receptor is angiotensin-converting enzyme 2 (ACE2). METHODS:We assessed the relation between previous treatment with ACE inhibitors, angiotensin-receptor blockers, beta-blockers, calcium-channel blockers, or thiazide diuretics and the likelihood of a positive or negative result on Covid-19 testing as well as the likelihood of severe illness (defined as intensive care, mechanical ventilation, or death) among patients who tested positive. Using Bayesian methods, we compared outcomes in patients who had been treated with these medications and in untreated patients, overall and in those with hypertension, after propensity-score matching for receipt of each medication class. A difference of at least 10 percentage points was prespecified as a substantial difference. RESULTS:Among 12,594 patients who were tested for Covid-19, a total of 5894 (46.8%) were positive; 1002 of these patients (17.0%) had severe illness. A history of hypertension was present in 4357 patients (34.6%), among whom 2573 (59.1%) had a positive test; 634 of these patients (24.6%) had severe illness. There was no association between any single medication class and an increased likelihood of a positive test. None of the medications examined was associated with a substantial increase in the risk of severe illness among patients who tested positive. CONCLUSIONS:We found no substantial increase in the likelihood of a positive test for Covid-19 or in the risk of severe Covid-19 among patients who tested positive in association with five common classes of antihypertensive medications.

BACKGROUND:Although clinical decision support (CDS) alerts are effective reminders of best practices, their effectiveness is blunted by clinicians who fail to respond to an overabundance of inappropriate alerts. An electronic health record (EHR)-integrated machine learning (ML) algorithm is a potentially powerful tool to increase the signal-to-noise ratio of CDS alerts and positively impact the clinician's interaction with these alerts in general. OBJECTIVE:This study aimed to describe the development and implementation of an ML-based signal-to-noise optimization system (SmartCDS) to increase the signal of alerts by decreasing the volume of low-value herpes zoster (shingles) vaccination alerts. METHODS:We built and deployed SmartCDS, which builds personalized user activity profiles to suppress shingles vaccination alerts unlikely to yield a clinician's interaction. We extracted all records of shingles alerts from January 2017 to March 2019 from our EHR system, including 327,737 encounters, 780 providers, and 144,438 patients. RESULTS:During the 6 weeks of pilot deployment, the SmartCDS system suppressed an average of 43.67% (15,425/35,315) potential shingles alerts (appointments) and maintained stable counts of weekly shingles vaccination orders (326.3 with system active vs 331.3 in the control group; P=.38) and weekly user-alert interactions (1118.3 with system active vs 1166.3 in the control group; P=.20). CONCLUSIONS:All key statistics remained stable while the system was turned on. Although the results are promising, the characteristics of the system can be subject to future data shifts, which require automated logging and monitoring. We demonstrated that an automated, ML-based method and data architecture to suppress alerts are feasible without detriment to overall order rates. This work is the first alert suppression ML-based model deployed in practice and serves as foundational work in encounter-level customization of alert display to maximize effectiveness.

Background. Pneumonia (PNU) is the second most common nosocomial infection in the United States and is associated with substantial morbidity and mortality. While definitions from CDC were developed to increase the reliability of surveillance data, reduce the burden of surveillance in healthcare facilities, and enhance the utility of surveillance data for improving patient safety - the algorithm is still laborious. We propose an implementation of a refined algorithm script which combines two CDC definitions with the use of natural language processing (NLP), a tool which relies on pattern matching to determine whether a condition of interest is reported as present or absent in a report, to automate PNU surveillance. Methods. Using SAS v9.4 to write a query, we used a combination of National Healthcare Safety Network's (NHSN) PNU and ventilator-associated event (VAE) definitions that use discrete fields found in electronic medical records (EMR) and trained an NLP tool to determine whether chest x-ray report was indicative of PNU (Fig1). To validate, we assessed sensitivity/specificity of NLP tool results compared with clinicians' interpretations. Results. The NLP tool was highly accurate in classifying the presence of PNU in chest x-rays. After training the NLP tool, there were only 4% discrepancies between NLP tool and clinicians interpretations of 223 x-ray reports - sensitivity 92.2% (81.1-97.8), specificity 97.1% (93.4-99.1), PPV 90.4% (79.0-96.8), NPV 97.7% (94.1- 99.4). Combining the automated use of discrete EMR fields with NLP tool significantly reduces the time spent manually reviewing EMRs. A manual review for PNU without automation requires approximately 10 minutes each day per admission. With a monthly average of 2,350 adult admissions at our hospital and 16,170 patient-days for admissions with at least 2 days, the algorithm saves approximately 2,695 review hours. Conclusion. The use of discrete EMR fields with an NLP tool proves to be a timelier, cost-effective yet accurate alternative to manual PNU surveillance review. By allowing an automated algorithm to review PNU, timely reports can be sent to units about individual cases. Compared with traditional CDC surveillance definitions, an automated tool allows real-time critical review for infection and prevention activities

Introduction/UNASSIGNED:Emergency department (ED) patient care often hinges on the result of a diagnostic test. Frequently there is a lag time between a test result becoming available for review and physician decision-making or disposition based on that result. We implemented a system that electronically alerts ED providers when test results are available for review via a smartphone- and smartwatch-push notification. We hypothesized this would reduce the time from result to clinical decision-making. Methods/UNASSIGNED:We retrospectively assessed the impact of the implementation of a push notification system at three EDs on time-to-disposition or time-to-follow-up order in six clinical scenarios of interest: chest radiograph (CXR) to disposition, basic metabolic panel (BMP) to disposition, urinalysis (UA) to disposition, respiratory pathogen panel (RPP) to disposition, hemoglobin (Hb) to blood transfusion order, and abnormal D-dimer to computed tomography pulmonary angiography (CTPA) order. All ED patients during a one-year period of push-notification availability were included in the study. The primary outcome was median time in each scenario from result availability to either disposition order or defined follow-up order. The secondary outcome was the overall usage rate of the opt-in push notification system by providers. Results/UNASSIGNED:During the study period there were 6115 push notifications from 4183 ED encounters (2.7% of all encounters). Of the six clinical scenarios examined in this study, five were associated with a decrease in median time from test result availability to patient disposition or follow-up order when push notifications were employed: CXR to disposition, 80 minutes (interquartile range [IQR] 32-162 minutes) vs 56 minutes (IQR 18-141 minutes), difference 24 minutes (p<0.01); BMP to disposition, 128 minutes (IQR 62-225 minutes) vs 116 minutes (IQR 33-226 minutes), difference 12 minutes (p<0.01); UA to disposition, 105 minutes (IQR 43-200 minutes) vs 55 minutes (IQR 16-144 minutes), difference 50 minutes (p<0.01); RPP to disposition, 80 minutes (IQR 28-181 minutes) vs 37 minutes (IQR 10-116 minutes), difference 43 minutes (p<0.01); and D-dimer to CTPA, 14 minutes (IQR 6-30 minutes) vs 6 minutes (IQR 2.5-17.5 minutes), difference 8 minutes (p<0.01). The sixth scenario, Hb to blood transfusion (difference 19 minutes, p=0.73), did not meet statistical significance. Conclusion/UNASSIGNED:Implementation of a push notification system for test result availability in the ED was associated with a decrease in lag time between test result and physician decision-making in the examined clinical scenarios. Push notifications were used in only a minority of ED patient encounters.

Background: Emergency department (ED) care decisions often hinge on the result of a diagnostic test. Frequently there is a lag time between a test result becoming available for review, and physician decision-making based on that result. Push notifications to physician smartphones have demonstrated improvement in this lag time in chest pain patients, but have not been studied in other ED patients. We implemented a system by which ED providers can subscribe to electronic alerts when test results are available for review via a smartphone or smartwatch push notification, and hypothesized that this would reduce the time to make clinical decisions. Method(s): This was a retrospective, multicenter, observational study in three emergency departments of an urban health system. We assessed push notification impact on time to disposition or time to follow-up order in six clinical scenarios of interest: chest x-ray (CXR) to disposition, basic metabolic panel (BMP) to disposition, urinalysis (UA) to disposition, respiratory pathogen panel (RPP) to disposition, hemoglobin (Hb) to blood transfusion order, and D-dimer to computed tomography pulmonary angiography (CTPA) order. All adult ED patients during a one-year period of push notification availability were included in the study. The primary outcome was median time from result availability to disposition order or defined follow-up order. Median times with interquartile ranges were determined in each scenario and the Mann Whitney (Wilcoxon) test for unpaired data was used to determine statistical significance. Result(s): During the study period there were 6,115 push notifications from 4,183 eligible ED encounters (2.7% of all ED encounters). All six scenarios studied were associated with a decrease in median time from test result availability to patient disposition, or from test result availability to follow-up order, when push notifications were employed: CXR to disposition (24 minutes, p<0.01), BMP to disposition (12 minutes, p<0.01), UA to disposition (50 minutes, p<0.01), RPP to disposition (43 minutes, p<0.01), D-dimer to CTPA (8 minutes, p<0.01), Hb to blood transfusion (19 minutes, p=0.73). Conclusion(s): Implementation of a push notification system for test result availability in the ED was associated with a decrease in lag time between test result availability and physician decision-making

PURPOSE/OBJECTIVE:To develop natural language processing (NLP) to identify incidental lung nodules (ILNs) in radiology reports for assessment of management recommendations. METHOD AND MATERIALS/METHODS:We searched the electronic health records for patients who underwent chest CT during 2014 and 2017, before and after implementation of a department-wide dictation macro of the Fleischner Society recommendations. We randomly selected 950 unstructured chest CT reports and reviewed manually for ILNs. An NLP tool was trained and validated against the manually reviewed set, for the task of automated detection of ILNs with exclusion of previously known or definitively benign nodules. For ILNs found in the training and validation sets, we assessed whether reported management recommendations agreed with Fleischner Society guidelines. The guideline concordance of management recommendations was compared between 2014 and 2017. RESULTS:The NLP tool identified ILNs with sensitivity and specificity of 91.1% and 82.2%, respectively, in the validation set. Positive and negative predictive values were 59.7% and 97.0%. In reports of ILNs in the training and validation sets before versus after introduction of a Fleischner reporting macro, there was no difference in the proportion of reports with ILNs (108 of 500 [21.6%] versus 101 of 450 [22.4%]; P = .8), or in the proportion of reports with ILNs containing follow-up recommendations (75 of 108 [69.4%] versus 80 of 101 [79.2%]; P = .2]. Rates of recommendation guideline concordance were not significantly different before and after implementation of the standardized macro (52 of 75 [69.3%] versus 60 of 80 [75.0%]; P = .43). CONCLUSION/CONCLUSIONS:NLP reliably automates identification of ILNs in unstructured reports, pertinent to quality improvement efforts for ILN management.

BACKGROUND: Testing for venous thromboembolism (VTE) is associated with cost and risk to patients (e.g. radiation). To assess the appropriateness of imaging utilization at the provider level, it is important to know that provider's diagnostic yield (percentage of tests positive for the diagnostic entity of interest). However, determining diagnostic yield typically requires either time-consuming, manual review of radiology reports or the use of complex and/or proprietary natural language processing software. OBJECTIVES: The objectives of this study were twofold: 1) to develop and implement a simple, user-configurable, and open-source natural language processing tool to classify radiology reports with high accuracy and 2) to use the results of the tool to design a provider-specific VTE imaging dashboard, consisting of both utilization rate and diagnostic yield. METHODS: Two physicians reviewed a training set of 400 lower extremity ultrasound (UTZ) and computed tomography pulmonary angiogram (CTPA) reports to understand the language used in VTE-positive and VTE-negative reports. The insights from this review informed the arguments to the five modifiable parameters of the NLP tool. A validation set of 2,000 studies was then independently classified by the reviewers and by the tool; the classifications were compared and the performance of the tool was calculated. RESULTS: The tool was highly accurate in classifying the presence and absence of VTE for both the UTZ (sensitivity 95.7%; 95% CI 91.5-99.8, specificity 100%; 95% CI 100-100) and CTPA reports (sensitivity 97.1%; 95% CI 94.3-99.9, specificity 98.6%; 95% CI 97.8-99.4). The diagnostic yield was then calculated at the individual provider level and the imaging dashboard was created. CONCLUSIONS: We have created a novel NLP tool designed for users without a background in computer programming, which has been used to classify venous thromboembolism reports with a high degree of accuracy. The tool is open-source and available for download at http://iturrate.com/simpleNLP. Results obtained using this tool can be applied to enhance quality by presenting information about utilization and yield to providers via an imaging dashboard.

OBJECTIVE: To determine the clinical significance of tachycardia in the postoperative period. PATIENTS AND METHODS: Individuals 18 years or older undergoing hip and knee arthroplasty were included in the study. Two data sets were collected from different time periods: development data set from January 1, 2011, through December 31, 2011, and validation data set from December 1, 2012, through September 1, 2014. We used the development data set to identify the optimal definition of tachycardia with the strongest association with the vascular composite outcome (pulmonary embolism and myocardial necrosis and infarction). The predictive value of this definition was assessed in the validation data set for each outcome of interest, pulmonary embolism, myocardial necrosis and infarction, and infection using multiple logistic regression to control for known risk factors. RESULTS: In 1755 patients in the development data set, a maximum heart rate (HR) greater than 110 beats/min was found to be the best cutoff as a correlate of the composite vascular outcome. Of the 4621 patients who underwent arthroplasty in the validation data set, 40 (0.9%) had pulmonary embolism. The maximum HR greater than 110 beats/min had an odds ratio (OR) of 9.39 (95% CI, 4.67-18.87; sensitivity, 72.5%; specificity, 78.0%; positive predictive value, 2.8%; negative predictive value, 99.7%) for pulmonary embolism. Ninety-seven patients (2.1%) had myocardial necrosis (elevated troponin). The maximum HR greater than 110 beats/min had an OR of 4.71 (95% CI, 3.06-7.24; sensitivity, 47.4%; specificity, 78.1%; positive predictive value, 4.4%; negative predictive value, 98.6%) for this outcome. Thirteen (.3%) patients had myocardial infarction according to our predetermined definition, and the maximum HR greater than 110 beats/min had an OR of 1.72 (95% CI, 0.47-6.27). CONCLUSION: Postoperative tachycardia within the first 4 days of surgery should not be dismissed as a postoperative variation in HR, but may precede clinically significant adverse outcomes.

BACKGROUND: Computed tomography pulmonary angiography (CTA) is the gold standard for diagnosing pulmonary embolism (PE) but involves radiation and iodinated contrast exposure. Of orthopedic patients evaluated for PE, a minority have a positive CTA study. Herein, we evaluate end tidal carbon dioxide (ETCO2) as a method to identify patients at low risk for PE and may not require a CTA. We hypothesize that ETCO2 will be useful for predicting the absence of PE in postoperative orthopedic patients. METHODS: In this prospective study, all patients older than 18 years who were admitted for orthopedic surgery and who had a CTA performed for PE were eligible. These patients underwent an ETCO2 measurement. Patients were determined to have PE if they had a positive PE-protocol CT. RESULTS: Between May 2014 and April 2015, 121 patients met the inclusion criteria for the study. Of these patients, 84 had a negative CTA examination, 25 had a positive examination, and 12 had a nondiagnostic examination. We found a statistically significant difference (P = .03) when comparing the average ETCO2 values for the positive and negative CTA groups. An ETCO2 cutoff value of 43 mm Hg was 100% sensitive with a negative predictive value of 100% for absence of PE on CTA. CONCLUSION: This study demonstrates a significant difference in ETCO2 measurements between postoperative orthopedic patients with and without CTA-detected PE. A cutoff value of >43 mm Hg may be useful in excluding patients from undergoing CTA.