Limitations in the use of automated mental status detection for clinical decision support.

BACKGROUND: Clinical decision support (CDS) tools improve adherence to evidence-based practices but are dependent upon data quality in the electronic health record (EHR). Mental status is an integral component of many risk stratification scores, but it is not known whether EHR-measures of altered mental status are reliable. The Glasgow Coma Scale (GCS) is a measure of altered mentation that is widely adopted and entered in the EHR in structured format. We sought to determine the accuracy GCS < 15 as an EHR-measure of altered mentation compared to ED provider documentation. METHODS: In patients presenting to an academic Emergency Department (ED) with pneumonia we abstracted GCS values entered by nurses during routine care and in a randomly selected subset manually reviewed provider documentation for evidence of altered mental status. We defined eConfusion as present if GCS < 15 at any point during the ED encounter. We then calculated the CURB-65 score and corresponding suggested disposition using each method. Performance of eConfusion and corresponding CURB-65 compared to manual versions was measured using agreement (Cohen's K), sensitivity, and specificity. RESULTS: Among 300 randomly selected encounters, 47 (16 %) had eConfusion present and 46 (15 %) had evidence of altered mental status in provider documentation with Cohen's K 0.73. eConfusion had 78 % sensitivity and 96 % specificity for provider documented altered mental status. When input into CURB-65 to recommend inpatient disposition, eConfusion had 95 % sensitivity, and recommended discordant disposition for 3 %. CONCLUSIONS: There was modest agreement between eConfusion and provider documentation of altered mental status. eConfusion had good specificity but low sensitivity which resulted in under-estimation of the CURB-65 score and occasional inappropriate disposition recommendations compared to provider documentation. These data do not support the use of GCS as a measure for altered mentation for use in CDS tools in the ED.

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.

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.

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.

Simulated Adoption of 2019 Community-Acquired Pneumonia Guidelines Across 114 Veterans Affairs Medical Centers: Estimated Impact on Culturing and Antibiotic Selection in Hospitalized Patients.

BACKGROUND: The 2019 American Thoracic Society/Infectious Diseases Society of America guidelines for community-acquired pneumonia (CAP) revised recommendations for culturing and empiric broad-spectrum antibiotics. We simulated guideline adoption in Veterans Affairs (VA) inpatients. METHODS: For all VA acute hospitalizations for CAP from 2006-2016 nationwide, we compared observed with guideline-expected proportions of hospitalizations with initial blood and respiratory cultures obtained, empiric antibiotic therapy with activity against methicillin-resistant Staphylococcus aureus (anti-MRSA) or Pseudomonas aeruginosa (antipseudomonal), empiric "overcoverage" (receipt of anti-MRSA/antipseudomonal therapy without eventual detection of MRSA/P. aeruginosa on culture), and empiric "undercoverage" (lack of anti-MRSA/antipseudomonal therapy with eventual detection on culture). RESULTS: Of 115 036 CAP hospitalizations over 11 years, 17 877 (16%) were admitted to an intensive care unit (ICU). Guideline adoption would slightly increase respiratory culture (30% to 36%) and decrease blood culture proportions (93% to 36%) in hospital wards and increase both respiratory (40% to 100%) and blood (95% to 100%) cultures in ICUs. Adoption would decrease empiric selection of anti-MRSA (ward: 27% to 1%; ICU: 61% to 8%) and antipseudomonal (ward: 25% to 1%; ICU: 54% to 9%) therapies. This would correspond to greatly decreased MRSA overcoverage (ward: 27% to 1%; ICU: 56% to 8%), slightly increased MRSA undercoverage (ward: 0.6% to 1.3%; ICU: 0.5% to 3.3%), with similar findings for P. aeruginosa. For all comparisons, P < .001. CONCLUSIONS: Adoption of the 2019 CAP guidelines in this population would substantially change culturing and empiric antibiotic selection practices, with a decrease in overcoverage and slight increase in undercoverage for MRSA and P. aeruginosa.

Empirical Anti-MRSA vs Standard Antibiotic Therapy and Risk of 30-Day Mortality in Patients Hospitalized for Pneumonia.

Importance: Use of empirical broad-spectrum antibiotics for pneumonia has increased owing to concern for resistant organisms, including methicillin-resistant Staphylococcus aureus (MRSA). The association of empirical anti-MRSA therapy with outcomes among patients with pneumonia is unknown, even for high-risk patients. Objective: To compare 30-day mortality among patients hospitalized for pneumonia receiving empirical anti-MRSA therapy vs standard empirical antibiotic regimens. Design, Setting, and Participants: Retrospective multicenter cohort study was conducted of all hospitalizations in which patients received either anti-MRSA or standard therapy for community-onset pneumonia in the Veterans Health Administration health care system from January 1, 2008, to December 31, 2013. Subgroups of patients analyzed were those with initial intensive care unit admission, MRSA risk factors, positive results of a MRSA surveillance test, and positive results of a MRSA admission culture. Primary analysis was an inverse probability of treatment-weighted propensity score analysis using generalized estimating equation regression; secondary analyses included an instrumental variable analysis. Statistical analysis was conducted from June 14 to November 20, 2019. Exposures: Empirical anti-MRSA therapy plus standard pneumonia therapy vs standard therapy alone within the first day of hospitalization. Main Outcomes and Measures: Risk of 30-day all-cause mortality after adjustment for patient comorbidities, vital signs, and laboratory results. Secondary outcomes included the development of kidney injury and secondary infections with Clostridioides difficile, vancomycin-resistant Enterococcus species, or gram-negative bacilli. Results: Among 88 605 hospitalized patients (86 851 men; median age, 70 years [interquartile range, 62-81 years]), empirical anti-MRSA therapy was administered to 33 632 (38%); 8929 patients (10%) died within 30 days. Compared with standard therapy alone, in weighted propensity score analysis, empirical anti-MRSA therapy plus standard therapy was significantly associated with an increased adjusted risk of death (adjusted risk ratio [aRR], 1.4 [95% CI, 1.3-1.5]), kidney injury (aRR, 1.4 [95% CI, 1.3-1.5]), and secondary C difficile infections (aRR, 1.6 [95% CI, 1.3-1.9]), vancomycin-resistant Enterococcus spp infections (aRR, 1.6 [95% CI, 1.0-2.3]), and secondary gram-negative rod infections (aRR, 1.5 [95% CI, 1.2-1.8]). Similar associations between anti-MRSA therapy use and 30-day mortality were found by instrumental variable analysis (aRR, 1.6 [95% CI, 1.4-1.9]) and among patients admitted to the intensive care unit (aRR, 1.3 [95% CI, 1.2-1.5]), those with a high risk for MRSA (aRR, 1.2 [95% CI, 1.1-1.4]), and those with MRSA detected on surveillance testing (aRR, 1.6 [95% CI, 1.3-1.9]). No significant favorable association was found between empirical anti-MRSA therapy and death among patients with MRSA detected on culture (aRR, 1.1 [95% CI, 0.8-1.4]). Conclusions and Relevance: This study suggests that empirical anti-MRSA therapy was not associated with reduced mortality for any group of patients hospitalized for pneumonia. These results contribute to a growing body of evidence that questions the value of empirical use of anti-MRSA therapy using existing risk approaches.

Cardiorespiratory fitness, visceral fat, and body fat, but not dietary inflammatory index, are related to C-reactive protein in cancer survivors.

BACKGROUND: The control of chronic inflammation has emerged as a target for improving the health of cancer survivors (CS). AIM: To examine differences in fitness and dietary characteristics of CS when grouped by low vs. moderate to high serum C-reactive protein (CRP). METHODS: CS (N = 26, mean age = 68 ± 12 years) were evaluated for body mass index (BMI), body composition, cardiorespiratory fitness, dietary intake, dietary inflammatory index (DII), and serum CRP. Participants were assigned to one of two groups based on serum CRP concentrations: low CRP (≤1 mg/L) (LWC; n = 13) or moderate to high (CRP > 1 mg/L) (MHC; n = 13) and t-tests compared them. Data are presented as mean ± SD. RESULTS: LWC had higher VO2peak values (mL/kg/min) (p = 0.0003), and lower visceral fat area (cm2) (p = 0.02) and body fat mass (kg) (p = 0.04). Secondary analysis using Pearson's correlation coefficients, including all current study participant data, found significant negative relationships between CRP and total dietary fat intake (p = 0.02), saturated fat (p = 0.03), and polyunsaturated fat (p = 0.03). CONCLUSION: CS with moderate to high serum CRP concentrations had higher fat mass, visceral fat mass, and lower cardiorespiratory fitness. There was a significant negative relationship between dietary, fat, polyunsaturated and saturated fat, and CRP. However, these dietary fat related findings warrant further investigation. To summarize, improving cardiorespiratory fitness, maintaining lower body fat, may be helpful in altering chronic inflammation in CS.

Sleep characteristics that predict atrial fibrillation.

BACKGROUND: The relationship between sleep disruption, independent of obstructive sleep apnea (OSA), and atrial fibrillation (AF) is unknown. OBJECTIVE: The purpose of this study was to determine whether poor sleep itself is a risk factor for AF. METHODS: We first performed an analysis of participants in the Health eHeart Study and validated those findings in the longitudinal Cardiovascular Health Study, including a subset of patients undergoing polysomnography. To determine whether the observed relationships readily translated to medical practice, we examined 2005-2009 data from the California Healthcare Cost and Utilization Project. RESULTS: Among 4553 Health eHeart participants, the 526 with AF exhibited more frequent nighttime awakening (odd ratio [OR] 1.47; 95% confidence interval [CI] 1.14-1.89; P = .003). In 5703 Cardiovascular Health Study participants followed for a median 11.6 years, frequent nighttime awakening predicted a 33% greater risk of AF (hazard ratio [HR] 1.33; 95% CI 1.17-1.51; P <.001). In patients with polysomnography (N = 1127), every standard deviation percentage decrease in rapid eye movement (REM) sleep was associated with a 18% higher risk of developing AF (HR 1.18; 95% CI 1.00-1.38; P = .047). Among 14,330,651 California residents followed for a median 3.9 years, an insomnia diagnosis predicted a 36% increased risk of new AF (HR 1.36; 95% CI 1.30-1.42; P <.001). CONCLUSION: Sleep disruption consistently predicted AF before and after adjustment for OSA and other potential confounders across several different populations. Sleep quality itself may be important in the pathogenesis of AF, potentially representing a novel target for prevention.

Atrial ectopy as a mediator of the association between race and atrial fibrillation.

BACKGROUND: Blacks have a lower risk of atrial fibrillation (AF) despite having more AF risk factors, but the mechanism remains unknown. Premature atrial contraction (PAC) burden is a recently identified risk factor for AF. OBJECTIVE: The purpose of this study was to determine whether the burden of PACs explains racial differences in AF risk. METHODS: PAC burden (number per hour) was assessed by 24-hour ambulatory electrocardiographic (ECG) monitoring in a randomly selected subset of patients in the Cardiovascular Health Study. Participants were followed prospectively for the development of AF, diagnosed by study ECG and hospital admission records. RESULTS: Among 938 participants (median age 73 years; 34% black; 58% female), 206 (22%) developed AF over a median follow-up of 11.0 years (interquartile range 6.1-13.4). After adjusting for age, sex, body mass index, coronary disease, congestive heart failure, diabetes, hypertension, alcohol consumption, smoking status, and study site, black race was associated with a 42% lower risk of AF (hazard ratio 0.58, 95% confidence interval [CI] 0.40-0.85; P = .005). The baseline PAC burden was 2.10 times (95% CI 1.57-2.83; P <.001) higher in whites than blacks. There was no detectable difference in premature ventricular contraction (PVC) burden by race. PAC burden mediated 19.5% (95% CI 6.3-52.5) of the adjusted association between race and AF. CONCLUSION: On average, whites exhibited more PACs than blacks, and this difference statistically explains a modest proportion of the differential risk of AF by race. The differential PAC burden, without differences in PVCs, by race suggests that identifiable common exposures or genetic influences might be important to atrial pathophysiology.

Smartphone-Based Geofencing to Ascertain Hospitalizations.

BACKGROUND: Ascertainment of hospitalizations is critical to assess quality of care and the effectiveness and adverse effects of various therapies. Smartphones, mobile geolocators that are ubiquitous, have not been leveraged to ascertain hospitalizations. Therefore, we evaluated the use of smartphone-based geofencing to track hospitalizations. METHODS AND RESULTS: Participants aged ≥18 years installed a mobile application programmed to geofence all hospitals using global positioning systems and cell phone tower triangulation and to trigger a smartphone-based questionnaire when located in a hospital for ≥4 hours. An in-person study included consecutive consenting patients scheduled for electrophysiology and cardiac catheterization procedures. A remote arm invited Health eHeart Study participants who consented and engaged with the study via the internet only. The accuracy of application-detected hospitalizations was confirmed by medical record review as the reference standard. Of 22 eligible in-person patients, 17 hospitalizations were detected (sensitivity 77%; 95% confidence interval, 55%-92%). The length of stay according to the application was positively correlated with the length of stay ascertained via the electronic medical record (r=0.53; P=0.03). In the remote arm, the application was downloaded by 3443 participants residing in all 50 US states; 243 hospital visits at 119 different hospitals were detected through the application. The positive predictive value for an application-reported hospitalization was 65% (95% confidence interval, 57%-72%). CONCLUSIONS: Mobile application-based ascertainment of hospitalizations can be achieved with modest accuracy. This first proof of concept may ultimately be applicable to geofencing other types of prespecified locations to facilitate healthcare research and patient care.

Direct Measurements of Smartphone Screen-Time: Relationships with Demographics and Sleep.

BACKGROUND: Smartphones are increasingly integrated into everyday life, but frequency of use has not yet been objectively measured and compared to demographics, health information, and in particular, sleep quality. AIMS: The aim of this study was to characterize smartphone use by measuring screen-time directly, determine factors that are associated with increased screen-time, and to test the hypothesis that increased screen-time is associated with poor sleep. METHODS: We performed a cross-sectional analysis in a subset of 653 participants enrolled in the Health eHeart Study, an internet-based longitudinal cohort study open to any interested adult (≥ 18 years). Smartphone screen-time (the number of minutes in each hour the screen was on) was measured continuously via smartphone application. For each participant, total and average screen-time were computed over 30-day windows. Average screen-time specifically during self-reported bedtime hours and sleeping period was also computed. Demographics, medical information, and sleep habits (Pittsburgh Sleep Quality Index-PSQI) were obtained by survey. Linear regression was used to obtain effect estimates. RESULTS: Total screen-time over 30 days was a median 38.4 hours (IQR 21.4 to 61.3) and average screen-time over 30 days was a median 3.7 minutes per hour (IQR 2.2 to 5.5). Younger age, self-reported race/ethnicity of Black and "Other" were associated with longer average screen-time after adjustment for potential confounders. Longer average screen-time was associated with shorter sleep duration and worse sleep-efficiency. Longer average screen-times during bedtime and the sleeping period were associated with poor sleep quality, decreased sleep efficiency, and longer sleep onset latency. CONCLUSIONS: These findings on actual smartphone screen-time build upon prior work based on self-report and confirm that adults spend a substantial amount of time using their smartphones. Screen-time differs across age and race, but is similar across socio-economic strata suggesting that cultural factors may drive smartphone use. Screen-time is associated with poor sleep. These findings cannot support conclusions on causation. Effect-cause remains a possibility: poor sleep may lead to increased screen-time. However, exposure to smartphone screens, particularly around bedtime, may negatively impact sleep.