Incorporating social determinants of health into transmission modeling of COVID-19 vaccine in the US: a scoping review.

During COVID-19 in the US, social determinants of health (SDH) have driven health disparities. However, the use of SDH in COVID-19 vaccine modeling is unclear. This review aimed to summarize the current landscape of incorporating SDH into COVID-19 vaccine transmission modeling in the US. Medline and Embase were searched up to October 2022. We included studies that used transmission modeling to assess the effects of COVID-19 vaccine strategies in the US. Studies' characteristics, factors incorporated into models, and approaches to incorporate these factors were extracted. Ninety-two studies were included. Of these, 11 studies incorporated SDH factors (alone or combined with demographic factors). Various sets of SDH factors were integrated, with occupation being the most common (8 studies), followed by geographical location (5 studies). The results show that few studies incorporate SDHs into their models, highlighting the need for research on SDH impact and approaches to incorporating SDH into modeling. Funding: This research was funded by the Centers for Disease Control and Prevention (CDC).

Automating detection of diagnostic error of infectious diseases using machine learning.

Diagnostic error, a cause of substantial morbidity and mortality, is largely discovered and evaluated through self-report and manual review, which is costly and not suitable to real-time intervention. Opportunities exist to leverage electronic health record data for automated detection of potential misdiagnosis, executed at scale and generalized across diseases. We propose a novel automated approach to identifying diagnostic divergence considering both diagnosis and risk of mortality. Our objective was to identify cases of emergency department infectious disease misdiagnoses by measuring the deviation between predicted diagnosis and documented diagnosis, weighted by mortality. Two machine learning models were trained for prediction of infectious disease and mortality using the first 24h of data. Charts were manually reviewed by clinicians to determine whether there could have been a more correct or timely diagnosis. The proposed approach was validated against manual reviews and compared using the Spearman rank correlation. We analyzed 6.5 million ED visits and over 700 million associated clinical features from over one hundred emergency departments. The testing set performances of the infectious disease (Macro F1 = 86.7, AUROC 90.6 to 94.7) and mortality model (Macro F1 = 97.6, AUROC 89.1 to 89.1) were in expected ranges. Human reviews and the proposed automated metric demonstrated positive correlations ranging from 0.231 to 0.358. The proposed approach for diagnostic deviation shows promise as a potential tool for clinicians to find diagnostic errors. Given the vast number of clinical features used in this analysis, further improvements likely need to either take greater account of data structure (what occurs before when) or involve natural language processing. Further work is needed to explain the potential reasons for divergence and to refine and validate the approach for implementation in real-world settings.

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.

Diagnosis and Treatment of Pneumonia in Urgent Care Clinics: Opportunities for Improving Care.

Background: Community-acquired pneumonia is a well-studied condition; yet, in the urgent care setting, patient characteristics and adherence to guideline-recommended care are poorly described. Within Intermountain Health, a nonprofit integrated US health care system based in Utah, more patients present to urgent care clinics (UCCs) than emergency departments (EDs) for pneumonia care. Methods: We performed a retrospective cohort study 1 January 2019 through 31 December 2020 in 28 UCCs within Utah. We extracted electronic health record data for patients aged ≥12 years with ICD-10 pneumonia diagnoses entered by the bedside clinician, excluding patients with preceding pneumonia within 30 days or missing vital signs. We compared UCC patients with radiographic pneumonia (n = 4689), without radiographic pneumonia (n = 1053), without chest imaging (n = 1472), and matched controls with acute cough/bronchitis (n = 15 972). Additional outcomes were 30-day mortality and the proportion of patients with ED visits or hospital admission within 7 days after the index encounter. Results: UCC patients diagnosed with pneumonia and possible/likely radiographic pneumonia by radiologist report had a mean age of 40 years and 52% were female. Almost all patients with pneumonia (93%) were treated with antibiotics, including those without radiographic confirmation. Hospital admissions and ED visits within 7 days were more common in patients with radiographic pneumonia vs patients with "unlikely" radiographs (6% vs 2% and 10% vs 6%, respectively). Observed 30-day all-cause mortality was low (0.26%). Patients diagnosed without chest imaging presented similarly to matched patients with cough/acute bronchitis. Most patients admitted to the hospital the same day after the UCC visit (84%) had an interim ED encounter. Pneumonia severity scores (pneumonia severity index, electronic CURB-65, and shock index) overestimated patient need for hospitalization. Conclusions: Most UCC patients with pneumonia were successfully treated as outpatients. Opportunities to improve care include clinical decision support for diagnosing pneumonia with radiographic confirmation and development of pneumonia severity scores tailored to the UCC.

ICU Utilization After Implementation of Minor Severe Pneumonia Criteria in Real-Time Electronic Clinical Decision Support.

OBJECTIVES: To determine if the implementation of automated clinical decision support (CDS) with embedded minor severe community-acquired pneumonia (sCAP) criteria was associated with improved ICU utilization among emergency department (ED) patients with pneumonia who did not require vasopressors or positive pressure ventilation at admission. DESIGN: Planned secondary analysis of a stepped-wedge, cluster-controlled CDS implementation trial. SETTING: Sixteen hospitals in six geographic clusters from Intermountain Health; a large, integrated, nonprofit health system in Utah and Idaho. PATIENTS: Adults admitted to the hospital from the ED with pneumonia identified by: 1) discharge International Classification of Diseases , 10th Revision codes for pneumonia or sepsis/respiratory failure and 2) ED chest imaging consistent with pneumonia, who did not require vasopressors or positive pressure ventilation at admission. INTERVENTIONS: After implementation, patients were exposed to automated, open-loop, comprehensive CDS that aided disposition decision (ward vs. ICU), based on objective severity scores (sCAP). MEASUREMENTS AND MAIN RESULTS: The analysis included 2747 patients, 1814 before and 933 after implementation. The median age was 71, median Elixhauser index was 17, 48% were female, and 95% were Caucasian. A mixed-effects regression model with cluster as the random effect estimated that implementation of CDS utilizing sCAP increased 30-day ICU-free days by 1.04 days (95% CI, 0.48-1.59; p < 0.001). Among secondary outcomes, the odds of being admitted to the ward, transferring to the ICU within 72 hours, and receiving a critical therapy decreased by 57% (odds ratio [OR], 0.43; 95% CI, 0.26-0.68; p < 0.001) post-implementation; mortality within 72 hours of admission was unchanged (OR, 1.08; 95% CI, 0.56-2.01; p = 0.82) while 30-day all-cause mortality was lower post-implementation (OR, 0.71; 95% CI, 0.52-0.96; p = 0.03). CONCLUSIONS: Implementation of electronic CDS using minor sCAP criteria to guide disposition of patients with pneumonia from the ED was associated with safe reduction in ICU utilization.

Disparities in COVID-19 related outcomes in the United States by race and ethnicity pre-vaccination era: an umbrella review of meta-analyses.

Background: Meta-analyses have investigated associations between race and ethnicity and COVID-19 outcomes. However, there is uncertainty about these associations' existence, magnitude, and level of evidence. We, therefore, aimed to synthesize, quantify, and grade the strength of evidence of race and ethnicity and COVID-19 outcomes in the US. Methods: In this umbrella review, we searched four databases (Pubmed, Embase, the Cochrane Database of Systematic Reviews, and Epistemonikos) from database inception to April 2022. The methodological quality of each meta-analysis was assessed using the Assessment of Multiple Systematic Reviews, version 2 (AMSTAR-2). The strength of evidence of the associations between race and ethnicity with outcomes was ranked according to established criteria as convincing, highly suggestive, suggestive, weak, or non-significant. The study protocol was registered with PROSPERO, CRD42022336805. Results: Of 880 records screened, we selected seven meta-analyses for evidence synthesis, with 42 associations examined. Overall, 10 of 42 associations were statistically significant (p ≤ 0.05). Two associations were highly suggestive, two were suggestive, and two were weak, whereas the remaining 32 associations were non-significant. The risk of COVID-19 infection was higher in Black individuals compared to White individuals (risk ratio, 2.08, 95% Confidence Interval (CI), 1.60-2.71), which was supported by highly suggestive evidence; with the conservative estimates from the sensitivity analyses, this association remained suggestive. Among those infected with COVID-19, Hispanic individuals had a higher risk of COVID-19 hospitalization than non-Hispanic White individuals (odds ratio, 2.08, 95% CI, 1.60-2.70) with highly suggestive evidence which remained after sensitivity analyses. Conclusion: Individuals of Black and Hispanic groups had a higher risk of COVID-19 infection and hospitalization compared to their White counterparts. These associations of race and ethnicity and COVID-19 outcomes existed more obviously in the pre-hospitalization stage. More consideration should be given in this stage for addressing health inequity.

Early Outcomes of SARS-CoV-2 Infection in a Multisite Prospective Cohort of Inpatient Veterans.

Background: Over 870 000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections have occurred among Veterans Health Administration users, and 24 000 have resulted in death. We examined early outcomes of SARS-CoV-2 infection in hospitalized veterans. Methods: In an ongoing, prospective cohort study, we enrolled veterans age ≥18 tested for SARS-CoV-2 and hospitalized at 15 Department of Veterans Affairs medical centers between February 2021 and June 2022. We estimated adjusted odds ratios (aORs), adjusted incidence rate ratios (aIRRs), and adjusted hazard ratios (aHRs) for maximum illness severity within 30 days of study entry (defined using the 4-category VA Severity Index for coronavirus disease 2019 [COVID-19]), as well as length of hospitalization and rehospitalization within 60 days, in relationship with demographic characteristics, Charlson comorbidity index (CCI), COVID-19 vaccination, and calendar period of enrollment. Results: The 542 participants included 329 (61%) who completed a primary vaccine series (with or without booster; "vaccinated"), 292 (54%) enrolled as SARS-CoV-2-positive, and 503 (93%) men, with a mean age of 64.4 years. High CCI scores (≥5) occurred in 61 (44%) vaccinated and 29 (19%) unvaccinated SARS-CoV-2-positive participants. Severe illness or death occurred in 29 (21%; 6% died) vaccinated and 31 (20%; 2% died) unvaccinated SARS-CoV-2-positive participants. SARS-CoV-2-positive inpatients per unit increase in CCI had greater multivariable-adjusted odds of severe illness (aOR, 1.21; 95% CI, 1.01-1.45), more hospitalization days (aIRR, 1.06; 95% CI, 1.03-1.10), and rehospitalization (aHR, 1.07; 95% CI, 1.01-1.12). Conclusions: In a cohort of hospitalized US veterans with SARS-CoV-2 infection, those with a higher CCI had more severe COVID-19 illness, more hospital days, and rehospitalization, after adjusting for vaccination status, age, sex, and calendar period.

Incidence and Outcomes of Non-Ventilator-Associated Hospital-Acquired Pneumonia in 284 US Hospitals Using Electronic Surveillance Criteria.

Importance: Non-ventilator-associated hospital-acquired pneumonia (NV-HAP) is a common and deadly hospital-acquired infection. However, inconsistent surveillance methods and unclear estimates of attributable mortality challenge prevention. Objective: To estimate the incidence, variability, outcomes, and population attributable mortality of NV-HAP. Design, Setting, and Participants: This cohort study retrospectively applied clinical surveillance criteria for NV-HAP to electronic health record data from 284 US hospitals. Adult patients admitted to the Veterans Health Administration hospital from 2015 to 2020 and HCA Healthcare hospitals from 2018 to 2020 were included. The medical records of 250 patients who met the surveillance criteria were reviewed for accuracy. Exposures: NV-HAP, defined as sustained deterioration in oxygenation for 2 or more days in a patient who was not ventilated concurrent with abnormal temperature or white blood cell count, performance of chest imaging, and 3 or more days of new antibiotics. Main Outcomes and Measures: NV-HAP incidence, length-of-stay, and crude inpatient mortality. Attributable inpatient mortality by 60 days follow-up was estimated using inverse probability weighting, accounting for both baseline and time-varying confounding. Results: Among 6 022 185 hospitalizations (median [IQR] age, 66 [54-75] years; 1 829 475 [26.1%] female), there were 32 797 NV-HAP events (0.55 per 100 admissions [95% CI, 0.54-0.55] per 100 admissions and 0.96 per 1000 patient-days [95% CI, 0.95-0.97] per 1000 patient-days). Patients with NV-HAP had multiple comorbidities (median [IQR], 6 [4-7]), including congestive heart failure (9680 [29.5%]), neurologic conditions (8255 [25.2%]), chronic lung disease (6439 [19.6%]), and cancer (5,467 [16.7%]); 24 568 cases (74.9%) occurred outside intensive care units. Crude inpatient mortality was 22.4% (7361 of 32 797) for NV-HAP vs 1.9% (115 530 of 6 022 185) for all hospitalizations; 12 449 (8.0%) were discharged to hospice. Median [IQR] length-of-stay was 16 (11-26) days vs 4 (3-6) days. On medical record review, pneumonia was confirmed by reviewers or bedside clinicians in 202 of 250 patients (81%). It was estimated that NV-HAP accounted for 7.3% (95% CI, 7.1%-7.5%) of all hospital deaths (total hospital population inpatient death risk of 1.87% with NV-HAP events included vs 1.73% with NV-HAP events excluded; risk ratio, 0.927; 95% CI, 0.925-0.929). Conclusions and Relevance: In this cohort study, NV-HAP, which was defined using electronic surveillance criteria, was present in approximately 1 in 200 hospitalizations, of whom 1 in 5 died in the hospital. NV-HAP may account for up to 7% of all hospital deaths. These findings underscore the need to systematically monitor NV-HAP, define best practices for prevention, and track their impact.

Immunocompromised Host Pneumonia: Definitions and Diagnostic Criteria: An Official American Thoracic Society Workshop Report.

Pneumonia imposes a significant clinical burden on people with immunocompromising conditions. Millions of individuals live with compromised immunity because of cytotoxic cancer treatments, biological therapies, organ transplants, inherited and acquired immunodeficiencies, and other immune disorders. Despite broad awareness among clinicians that these patients are at increased risk for developing infectious pneumonia, immunocompromised people are often excluded from pneumonia clinical guidelines and treatment trials. The absence of a widely accepted definition for immunocompromised host pneumonia is a significant knowledge gap that hampers consistent clinical care and research for infectious pneumonia in these vulnerable populations. To address this gap, the American Thoracic Society convened a workshop whose participants had expertise in pulmonary disease, infectious diseases, immunology, genetics, and laboratory medicine, with the goal of defining the entity of immunocompromised host pneumonia and its diagnostic criteria.

Electronic surveillance criteria for non-ventilator-associated hospital-acquired pneumonia: Assessment of reliability and validity.

OBJECTIVE: Surveillance of non-ventilator-associated hospital-acquired pneumonia (NV-HAP) is complicated by subjectivity and variability in diagnosing pneumonia. We compared a fully automatable surveillance definition using routine electronic health record data to manual determinations of NV-HAP according to surveillance criteria and clinical diagnoses. METHODS: We retrospectively applied an electronic surveillance definition for NV-HAP to all adults admitted to Veterans' Affairs (VA) hospitals from January 1, 2015, to November 30, 2020. We randomly selected 250 hospitalizations meeting NV-HAP surveillance criteria for independent review by 2 clinicians and calculated the percent of hospitalizations with (1) clinical deterioration, (2) CDC National Healthcare Safety Network (CDC-NHSN) criteria, (3) NV-HAP according to a reviewer, (4) NV-HAP according to a treating clinician, (5) pneumonia diagnosis in discharge summary; and (6) discharge diagnosis codes for HAP. We assessed interrater reliability by calculating simple agreement and the Cohen κ (kappa). RESULTS: Among 3.1 million hospitalizations, 14,023 met NV-HAP electronic surveillance criteria. Among reviewed cases, 98% had a confirmed clinical deterioration; 67% met CDC-NHSN criteria; 71% had NV-HAP according to a reviewer; 60% had NV-HAP according to a treating clinician; 49% had a discharge summary diagnosis of pneumonia; and 82% had NV-HAP according to any definition according to at least 1 reviewer. Only 8% had diagnosis codes for HAP. Interrater agreement was 75% (κ = 0.50) for CDC-NHSN criteria and 78% (κ = 0.55) for reviewer diagnosis of NV-HAP. CONCLUSIONS: Electronic NV-HAP surveillance criteria correlated moderately with existing manual surveillance criteria. Reviewer variability for all manual assessments was high. Electronic surveillance using clinical data may therefore allow for more consistent and efficient surveillance with similar accuracy compared to manual assessments or diagnosis codes.

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.

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.

Deploying an Electronic Clinical Decision Support Tool for Diagnosis and Treatment of Pneumonia Into Rural and Critical Access Hospitals: Utilization, Effect on Processes of Care, and Clinician Satisfaction.

PURPOSE: Electronic clinical decision support (CDS) for treatment of community-acquired pneumonia (ePNa) is associated with improved guideline adherence and decreased mortality. How rural providers respond to CDS developed for urban hospitals could shed light on extending CDS to resource-limited settings. METHODS: ePNa was deployed into 10 rural and critical access hospital emergency departments (EDs) in Utah and Idaho in 2018. We reviewed pneumonia cases identified through ICD-10 codes after local deployment to measure ePNa utilization and guideline adherence. ED providers were surveyed to assess quantitative and qualitative aspects of satisfaction. FINDINGS: ePNa was used in 109/301 patients with pneumonia (36%, range 0%-67% across hospitals) and was associated with appropriate antibiotic selection (93% vs 65%, P < .001). Fifty percent of survey recipients responded, 87% were physicians, 87% were men, and the median ED experience was 10 years. Mean satisfaction with ePNa was 3.3 (range 1.7-4.8) on a 5-point Likert scale. Providers with a favorable opinion of ePNa were more likely to favor implementation of additional CDS (P = .005). Satisfaction was not associated with provider type, age, years of experience or experience with ePNa. Ninety percent of respondents provided qualitative feedback. The most common theme in high and low utilization hospitals was concern about usability. Compared to high utilization hospitals, low utilization hospitals more frequently identified concerns about adaptation for local needs. CONCLUSIONS: ePNa deployment to rural and critical access EDs was moderately successful and associated with improved antibiotic use. Concerns about usability and adapting ePNa for local use predominated the qualitative feedback.

Positive Predictive Value of COVID-19 ICD-10 Diagnosis Codes Across Calendar Time and Clinical Setting.

PURPOSE: To estimate the positive predictive value (PPV) of International Classification of Diseases, Tenth Revision (ICD-10) code U07.1, COVID-19 virus identified, in the Department of Veterans of Affairs (VA). PATIENTS AND METHODS: Records of ICD-10 code U07.1 from inpatient, outpatient, and emergency/urgent care settings were extracted from VA medical record data from 4/01/2020 to 3/31/2021. A weighted, random sample of 1500 records from each quarter of the one-year observation period was reviewed by study personnel to confirm active COVID-19 infection at the time of diagnosis and classify reasons for false positive records. PPV was estimated overall and compared across clinical setting and quarters. RESULTS: We identified 664,406 records of U07.1. Among the 1500 reviewed, 237 were false positives (PPV: 84.2%, 95% CI: 82.4-86.0). PPV ranged from 77.7% in outpatient settings to 93.8% in inpatient settings and was 83.3% in quarter 1, 80.5% in quarter 2, 86.1% in quarter 3, and 83.6% in quarter 4. The most common reasons for false positive records were history of COVID-19 (44.3%) and orders for laboratory tests (21.5%). CONCLUSION: The PPV of ICD-10 code U07.1 is low, especially in outpatient settings. Directed training may improve accuracy of coding to levels that are deemed adequate for future use in surveillance efforts.

Understanding the Host in the Management of Pneumonia. An Official American Thoracic Society Workshop Report.

Pneumonia causes a significant burden of disease worldwide. Although all populations are at risk of pneumonia, those at extremes of age and those with immunosuppressive disorders, underlying respiratory disease, and critical illness are particularly vulnerable. Although clinical practice guidelines addressing the management and treatment of pneumonia exist, few of the supporting studies focus on the crucial contributions of the host in pneumonia pathogenesis and recovery. Such essential considerations include the host risk factors that lead to susceptibility to lung infections; biomarkers reflecting the host response and the means to pursue host-directed pneumonia therapy; systemic effects of pneumonia on the host; and long-term health outcomes after pneumonia. To address these gaps, the Pneumonia Working Group of the Assembly on Pulmonary Infection and Tuberculosis led a workshop held at the American Thoracic Society meeting in May 2018 with overarching objectives to foster attention, stimulate research, and promote funding for short-term and long-term investigations into the host contributions to pneumonia. The workshop involved participants from various disciplines with expertise in lung infection, pneumonia, sepsis, immunocompromised patients, translational biology, data science, genomics, systems biology, and clinical trials. This workshop report summarizes the presentations and discussions and important recommendations for future clinical pneumonia studies. These recommendations include establishing consensus disease and outcome definitions, improved phenotyping, development of clinical study networks, standardized data and biospecimen collection and protocols, and development of innovative trial designs.

Nonventilator hospital-acquired pneumonia: A call to action.

In 2020 a group of U.S. healthcare leaders formed the National Organization to Prevent Hospital-Acquired Pneumonia (NOHAP) to issue a call to action to address non-ventilator-associated hospital-acquired pneumonia (NVHAP). NVHAP is one of the most common and morbid healthcare-associated infections, but it is not tracked, reported, or actively prevented by most hospitals. This national call to action includes (1) launching a national healthcare conversation about NVHAP prevention; (2) adding NVHAP prevention measures to education for patients, healthcare professionals, and students; (3) challenging healthcare systems and insurers to implement and support NVHAP prevention; and (4) encouraging researchers to develop new strategies for NVHAP surveillance and prevention. The purpose of this document is to outline research needs to support the NVHAP call to action. Primary needs include the development of better models to estimate the economic cost of NVHAP, to elucidate the pathophysiology of NVHAP and identify the most promising pathways for prevention, to develop objective and efficient surveillance methods to track NVHAP, to rigorously test the impact of prevention strategies proposed to prevent NVHAP, and to identify the policy levers that will best engage hospitals in NVHAP surveillance and prevention. A joint task force developed this document including stakeholders from the Veterans' Health Administration (VHA), the U.S. Centers for Disease Control and Prevention (CDC), The Joint Commission, the American Dental Association, the Patient Safety Movement Foundation, Oral Health Nursing Education and Practice (OHNEP), Teaching Oral-Systemic Health (TOSH), industry partners and academia.

Angiotensin II receptor blocker or angiotensin-converting enzyme inhibitor use and COVID-19-related outcomes among US Veterans.

BACKGROUND: Angiotensin II receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs) may positively or negatively impact outcomes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We investigated the association of ARB or ACEI use with coronavirus disease 2019 (COVID-19)-related outcomes in US Veterans with treated hypertension using an active comparator design, appropriate covariate adjustment, and negative control analyses. METHODS AND FINDINGS: In this retrospective cohort study of Veterans with treated hypertension in the Veterans Health Administration (01/19/2020-08/28/2020), we compared users of (A) ARB/ACEI vs. non-ARB/ACEI (excluding Veterans with compelling indications to reduce confounding by indication) and (B) ARB vs. ACEI among (1) SARS-CoV-2+ outpatients and (2) COVID-19 hospitalized inpatients. The primary outcome was all-cause hospitalization or mortality (outpatients) and all-cause mortality (inpatients). We estimated hazard ratios (HR) using propensity score-weighted Cox regression. Baseline characteristics were well-balanced between exposure groups after weighting. Among outpatients, there were 5.0 and 6.0 primary outcomes per 100 person-months for ARB/ACEI (n = 2,482) vs. non-ARB/ACEI (n = 2,487) users (HR 0.85, 95% confidence interval [CI] 0.73-0.99, median follow-up 87 days). Among outpatients who were ARB (n = 4,877) vs. ACEI (n = 8,704) users, there were 13.2 and 14.8 primary outcomes per 100 person-months (HR 0.91, 95%CI 0.86-0.97, median follow-up 85 days). Among inpatients who were ARB/ACEI (n = 210) vs. non-ARB/ACEI (n = 275) users, there were 3.4 and 2.0 all-cause deaths per 100 person months (HR 1.25, 95%CI 0.30-5.13, median follow-up 30 days). Among inpatients, ARB (n = 1,164) and ACEI (n = 2,014) users had 21.0 vs. 17.7 all-cause deaths, per 100 person-months (HR 1.13, 95%CI 0.93-1.38, median follow-up 30 days). CONCLUSIONS: This observational analysis supports continued ARB or ACEI use for patients already using these medications before SARS-CoV-2 infection. The novel beneficial association observed among outpatients between users of ARBs vs. ACEIs on hospitalization or mortality should be confirmed with randomized trials.

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.

Discharge and Role of GABA Pontomesencephalic Neurons in Cortical Activity and Sleep-Wake States Examined by Optogenetics and Juxtacellular Recordings in Mice.

The cholinergic neurons in the pontomesencephalic tegmentum have been shown to discharge in association with and promote cortical activation during active or attentive waking and paradoxical or rapid eye movement sleep. However, GABA neurons lie intermingled with the cholinergic neurons and may contribute to or oppose this activity and role. Here we investigated in vitro and in vivo the properties, activities, and role of GABA neurons within the laterodorsal tegmental and sublaterodorsal tegmental nuclei (LDT/SubLDT) using male and female transgenic mice expressing channelrhodopsin-(ChR2)-EYFP in vesicular GABA transporter (VGAT)-expressing neurons. Presumed GABA (pGABA) neurons were identified by response to photostimulation and verified by immunohistochemical staining following juxtacellular labeling in vivo pGABA neurons were found to be fast-firing neurons with the capacity to burst when depolarized from a hyperpolarized membrane potential. When stimulated in vivo in urethane-anesthetized or unanesthetized mice, the pGABA neurons fired repetitively at relatively fast rates (∼40 Hz) during a continuous light pulse or phasically in bursts (>100 Hz) when driven by rhythmic light pulses at theta (4 or 8 Hz) frequencies. pNon-GABA, which likely included cholinergic, neurons were inhibited during each light pulse to discharge rhythmically in antiphase to the pGABA neurons. The reciprocal rhythmic bursting by the pGABA and pNon-GABA neurons drove rhythmic theta activity in the EEG. Such phasic bursting by GABA neurons also occurred in WT mice in association with theta activity during attentive waking and paradoxical sleep.SIGNIFICANCE STATEMENT Neurons in the pontomesencephalic tegmentum, particularly cholinergic neurons, play an important role in cortical activation, which occurs during active or attentive waking and paradoxical or rapid eye movement sleep. Yet the cholinergic neurons lie intermingled with GABA neurons, which could play a similar or opposing role. Optogenetic stimulation and recording of these GABA neurons in mice revealed that they can discharge in rhythmic bursts at theta frequencies and drive theta activity in limbic cortex. Such phasic burst firing also occurs during natural attentive waking and paradoxical sleep in association with theta activity and could serve to enhance sensory-motor processing and memory consolidation during these states.