Trends in Illness Severity, Hospitalization, and Mortality for Community-Onset Pneumonia at 118 US Veterans Affairs Medical Centers.

BACKGROUND: Deaths from pneumonia were decreasing globally prior to the COVID-19 pandemic, but it is unclear whether this was due to changes in patient populations, illness severity, diagnosis, hospitalization thresholds, or treatment. Using clinical data from the electronic health record among a national cohort of patients initially diagnosed with pneumonia, we examined temporal trends in severity of illness, hospitalization, and short- and long-term deaths. DESIGN: Retrospective cohort PARTICIPANTS: All patients >18 years presenting to emergency departments (EDs) at 118 VA Medical Centers between 1/1/2006 and 12/31/2016 with an initial clinical diagnosis of pneumonia and confirmed by chest imaging report. EXPOSURES: Year of encounter. MAIN MEASURES: Hospitalization and 30-day and 90-day mortality. Illness severity was defined as the probability of each outcome predicted by machine learning predictive models using age, sex, comorbidities, vital signs, and laboratory data from encounters during years 2006-2007, and similar models trained on encounters from years 2015 to 2016. We estimated the changes in hospitalizations and 30-day and 90-day mortality between the first and the last 2 years of the study period accounted for by illness severity using time covariate decompositions with model estimates. RESULTS: Among 196,899 encounters across the study period, hospitalization decreased from 71 to 63%, 30-day mortality 10 to 7%, 90-day mortality 16 to 12%, and 1-year mortality 29 to 24%. Comorbidity risk increased, but illness severity decreased. Decreases in illness severity accounted for 21-31% of the decrease in hospitalizations, and 45-47%, 32-24%, and 17-19% of the decrease in 30-day, 90-day, and 1-year mortality. Findings were similar among underrepresented patients and those with only hospital discharge diagnosis codes. CONCLUSIONS: Outcomes for community-onset pneumonia have improved across the VA healthcare system after accounting for illness severity, despite an increase in cases and comorbidity burden.

Voltage-gated potassium channel KCNV2 (Kv8.2) contributes to epilepsy susceptibility.

Mutations in voltage-gated ion channels are responsible for several types of epilepsy. Genetic epilepsies often exhibit variable severity in individuals with the same mutation, which may be due to variation in genetic modifiers. The Scn2a(Q54) transgenic mouse model has a sodium channel mutation and exhibits epilepsy with strain-dependent severity. We previously mapped modifier loci that influence Scn2a(Q54) phenotype severity and identified Kcnv2, encoding the voltage-gated potassium channel subunit Kv8.2, as a candidate modifier. In this study, we demonstrate a threefold increase in hippocampal Kcnv2 expression associated with more severe epilepsy. In vivo exacerbation of the phenotype by Kcnv2 transgenes supports its identification as an epilepsy modifier. The contribution of KCNV2 to human epilepsy susceptibility is supported by identification of two nonsynonymous variants in epilepsy patients that alter function of Kv2.1/Kv8.2 heterotetrameric potassium channels. Our results demonstrate that altered potassium subunit function influences epilepsy susceptibility and implicate Kcnv2 as an epilepsy gene.

A Linguistic Analysis Examining the Impact of COVID-19 on Pneumonia Diagnosis and Disease Models.

Written clinical language embodies and reflects the clinician's mental models of disease. Prior to the COVID-19 pandemic, pneumonia was shifting away from concern for healthcare-associated pneumonia and toward recognition of heterogeneity of pathogens and host response. How these models are reflected in clinical language or whether they were impacted by the pandemic has not been studied. We aimed to assess changes in the language used to describe pneumonia following the COVID-19 pandemic.

Pneumonia diagnosis performance in the emergency department: a mixed-methods study about clinicians' experiences and exploration of individual differences and response to diagnostic performance feedback.

OBJECTIVES: We sought to (1) characterize the process of diagnosing pneumonia in an emergency department (ED) and (2) examine clinician reactions to a clinician-facing diagnostic discordance feedback tool. MATERIALS AND METHODS: We designed a diagnostic feedback tool, using electronic health record data from ED clinicians' patients to establish concordance or discordance between ED diagnosis, radiology reports, and hospital discharge diagnosis for pneumonia. We conducted semistructured interviews with 11 ED clinicians about pneumonia diagnosis and reactions to the feedback tool. We administered surveys measuring individual differences in mindset beliefs, comfort with feedback, and feedback tool usability. We qualitatively analyzed interview transcripts and descriptively analyzed survey data. RESULTS: Thematic results revealed: (1) the diagnostic process for pneumonia in the ED is characterized by diagnostic uncertainty and may be secondary to goals to treat and dispose the patient; (2) clinician diagnostic self-evaluation is a fragmented, inconsistent process of case review and follow-up that a feedback tool could fill; (3) the feedback tool was described favorably, with task and normative feedback harnessing clinician values of high-quality patient care and personal excellence; and (4) strong reactions to diagnostic feedback varied from implicit trust to profound skepticism about the validity of the concordance metric. Survey results suggested a relationship between clinicians' individual differences in learning and failure beliefs, feedback experience, and usability ratings. DISCUSSION AND CONCLUSION: Clinicians value feedback on pneumonia diagnoses. Our results highlight the importance of feedback about diagnostic performance and suggest directions for considering individual differences in feedback tool design and implementation.

Hospital admission decisions for older Veterans with community-onset pneumonia: An analysis of 118 U.S. Veterans Affairs Medical Centers.

OBJECTIVES: Age is important for prognosis in community-onset pneumonia, but how it influences admission decisions in the emergency department (ED) is not well characterized. Using clinical data from the electronic health record in a national cohort, we examined pneumonia hospitalization patterns, variation, and relationships with mortality among older versus younger Veterans. METHODS: In a retrospective cohort of patients ≥ 18 years presenting to EDs with a diagnosis of pneumonia at 118 VA Medical Centers January 1, 2006, to December 31, 2016, we compared observed, predicted, and residual hospitalization risk for Veterans < 70, 70-79, and ≥ 80 years of age using generalized estimating equations and machine learning models with 71 patient factors. We examined facility variation in residual hospitalization across facilities and explored whether facility differences in hospitalization risk correlated with differences in 30-day mortality. RESULTS: Among 297,498 encounters, 165,003 (55%) were for Veterans < 70 years, 61,076 (21%) 70-80, and 71,419 (24%) ≥ 80. Hospitalization rates were 52%, 67%, and 76%, respectively. After other patient factors were adjusting for, age 70-79 had an odds ratio (OR) of 1.39 (95% confidence interval [CI] 1.34-1.44) and ≥ 80 had an OR of 2.1 (95% CI 2.0-2.2) compared to age < 70. There was substantial variation in hospitalization across facilities among Veterans < 70 (<35% hospitalization at the lowest decile of facilities vs. > 66% at the highest decile) that was similar but with higher risk for patients 70-79 years (54% vs. 82%) and ≥ 80 years (59% vs. 85%) and remained after accounting for patient factors, with no consistently positive or negative associations with facility-level 30-day mortality. CONCLUSIONS: Older Veterans with community-onset pneumonia experience high risk of hospitalization, with widespread facility variation that has no clear relationship to short-term mortality.

Development and evaluation of an interoperable natural language processing system for identifying pneumonia across clinical settings of care and institutions.

Objective: To evaluate the feasibility, accuracy, and interoperability of a natural language processing (NLP) system that extracts diagnostic assertions of pneumonia in different clinical notes and institutions. Materials and Methods: A rule-based NLP system was designed to identify assertions of pneumonia in 3 types of clinical notes from electronic health records (EHRs): emergency department notes, radiology reports, and discharge summaries. The lexicon and classification logic were tailored for each note type. The system was first developed and evaluated using annotated notes from the Department of Veterans Affairs (VA). Interoperability was assessed using data from the University of Utah (UU). Results: The NLP system was comprised of 782 rules and achieved moderate-to-high performance in all 3 note types in VA (precision/recall/f1: emergency = 88.1/86.0/87.1; radiology = 71.4/96.2/82.0; discharge = 88.3/93.0/90.1). When applied to UU data, performance was maintained in emergency and radiology but decreased in discharge summaries (emergency = 84.7/94.3/89.3; radiology = 79.7/100.0/87.9; discharge = 65.5/92.7/76.8). Customization with 34 additional rules increased performance for all note types (emergency = 89.3/94.3/91.7; radiology = 87.0/100.0/93.1; discharge = 75.0/95.1/83.4). Conclusion: NLP can be used to accurately identify the diagnosis of pneumonia across different clinical settings and institutions. A limited amount of customization to account for differences in lexicon, clinical definition of pneumonia, and EHR structure can achieve high accuracy without substantial modification.

Computerized Mortality Prediction for Community-acquired Pneumonia at 117 Veterans Affairs Medical Centers.

Rationale: Computerized severity assessment for community-acquired pneumonia could improve consistency and reduce clinician burden. Objectives: To develop and compare 30-day mortality-prediction models using electronic health record data, including a computerized score with all variables from the original Pneumonia Severity Index (PSI) except confusion and pleural effusion ("ePSI score") versus models with additional variables. Methods: Among adults with community-acquired pneumonia presenting to emergency departments at 117 Veterans Affairs Medical Centers between January 1, 2006, and December 31, 2016, we compared an ePSI score with 10 novel models employing logistic regression, spline, and machine learning methods using PSI variables, age, sex and 26 physiologic variables as well as all 69 PSI variables. Models were trained using encounters before January 1, 2015; tested on encounters during and after January 1, 2015; and compared using the areas under the receiver operating characteristic curve, confidence intervals, and patient event rates at a threshold PSI score of 970. Results: Among 297,498 encounters, 7% resulted in death within 30 days. When compared using the ePSI score (confidence interval [CI] for the area under the receiver operating characteristic curve, 0.77-0.78), performance increased with model complexity (CI for the logistic regression PSI model, 0.79-0.80; CI for the boosted decision-tree algorithm machine learning PSI model using the Extreme Gradient Boosting algorithm [mlPSI] with the 19 original PSI factors, 0.83-0.85) and the number of variables (CI for the logistic regression PSI model using all 69 variables, 0.84-085; CI for the mlPSI with all 69 variables, 0.86-0.87). Models limited to age, sex, and physiologic variables also demonstrated high performance (CI for the mlPSI with age, sex, and 26 physiologic factors, 0.84-0.85). At an ePSI score of 970 and a mortality-risk cutoff of <2.7%, the ePSI score identified 31% of all patients as being at "low risk"; the mlPSI with age, sex, and 26 physiologic factors identified 53% of all patients as being at low risk; and the mlPSI with all 69 variables identified 56% of all patients as being at low risk, with similar rates of mortality, hospitalization, and 7-day secondary hospitalization being determined. Conclusions: Computerized versions of the PSI accurately identified patients with pneumonia who were at low risk of death. More complex models classified more patients as being at low risk of death and as having similar adverse outcomes.

Fine mapping of an epilepsy modifier gene on mouse Chromosome 19.

Mutations in voltage-gated sodium channels are associated with several types of human epilepsy. Variable expressivity and penetrance are common features of inherited epilepsy caused by sodium channel mutations, suggesting that genetic modifiers may influence clinical severity. The mouse model Scn2a(Q54) has an epilepsy phenotype due to a mutation in Scn2a that results in elevated persistent sodium current. Phenotype severity in Scn2a(Q54) mice is dependent on the genetic background. Congenic C57BL/6J.Q54 mice have delayed onset and low seizure frequency compared to (C57BL/6J x SJL/J)F1.Q54 mice. Previously, we identified two modifier loci that influence the Scn2a(Q54) epilepsy phenotype: Moe1 (modifier of epilepsy 1) on Chromosome 11 and Moe2 on Chromosome 19. We have constructed interval-specific congenic strains to further refine the position of Moe2 on Chromosome 19 to a 5-Mb region. Sequencing and expression analyses of genes in the critical interval suggested two potential modifier candidates: (1) voltage-gated potassium channel subunit subfamily V, member 2 (Kcnv2), and (2) SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a, member 2 (Smarca2). Based on its biological role in regulating membrane excitability and the association between ion channel variants and seizures, Kcnv2 is a strong functional candidate for Moe2. Modifier genes affecting the epilepsy phenotype of Scn2a(Q54) mice may contribute to variable expressivity and penetrance in human epilepsy patients with sodium channel mutations.

Abdominal aortic aneurysm with inferior vena cava compression in association with deep venous thrombosis.

Deep venous thrombosis (DVT) of the lower extremity may be caused by external compression of the inferior vena cava (IVC) by a neighboring mass. A 55-year-old male presented with 8 months of intermittent left lower extremity swelling and signs of chronic venous stasis. Duplex ultrasound showed extensive DVT in the left leg. Subsequent imaging revealed a 4.2 cm infrarenal abdominal aortic aneurysm (AAA) compressing the IVC. In cases of chronic, unilateral DVT, work-up should include imaging for an anatomic cause. AAA is a rare cause of IVC compression with DVT, and is most commonly related to inflammatory AAAs. Previously reported cases of IVC compression by non-inflammatory AAAs have been secondary to large aneurysms (greater than 6 cm). This case illustrates that smaller AAAs lacking hemodynamically significant IVC obstruction may be found in association with DVT.