Challenges in interpreting cytokine data in COVID-19 affect patient care and management.

Challenges in using cytokine data are limiting Coronavirus Disease 2019 (COVID-19) patient management and comparison among different disease contexts. We suggest mitigation strategies to improve the accuracy of cytokine data, as we learn from experience gained during the COVID-19 pandemic.

Effect of Androgen-Androgen Receptor Directed Therapy on COVID-19 Outcome in Prostate Cancer Patients.

TMPRSS2 is utilized by SARS-CoV-2 for cellular entry. Androgen-Androgen receptor directed therapy (A/ARDT) downregulates expression of TMPRSS2. We hypothesized A/ARDT might protect prostate cancer (PCa) patients from poor COVID-19 outcome. A retrospective analysis of PCa patients with COVID-19 infection was performed. 146 PCa cases were identified, 17% were on A/ARDT. Hospitalization rates were same 52% (OR = 0.99, 0.41-2.24). Mean hospitalization was 9.2 (Range: 1-25) and 14.9 (Range: 2-47) days in A/ARDT and non-A/ARDT groups, respectively. While definitive conclusions cannot be made regarding outcome differences between groups due to lack of statistical significance, these data generate hypothesis that A/ARDT might shorten hospitalization stay.

Rationale and design of a cluster-randomized pragmatic trial aimed at improving use of guideline directed medical therapy in outpatients with heart failure: PRagmatic trial of messaging to providers about treatment of heart failure (PROMPT-HF).

Heart failure with reduced ejection fraction (HFrEF) is one of the most common chronic illnesses in the United States and carries significant risk of morbidity and mortality. Use of guideline-directed medical therapy (GDMT) for patients with HFrEF has been shown to dramatically improve outcomes, but adoption of these treatments remains generally low. Possible explanations for poor GDMT uptake include lack of knowledge about recommended management strategies and provider reluctance due to uncertainties regarding application of said guidelines to real-world practice. One way to overcome these barriers is by harnessing the electronic health record (EHR) to create patient-centered "best practice alerts" (BPAs) that can guide clinicians to prescribe appropriate medical therapies. If found to be effective, these low-cost interventions can be rapidly applied across large integrated healthcare systems. The PRagmatic Trial Of Messaging to Providers about Treatment of Heart Failure (PROMPT-HF) trial is a pragmatic, cluster randomized controlled trial designed to test the hypothesis that tailored and timely alerting of recommended GDMT in heart failure (HF) will result in greater adherence to guidelines when compared with usual care. PROMPT-HF has completed enrollment of 1,310 ambulatory patients with HFrEF cared for by 100 providers who were randomized to receive a BPA vs usual care. The BPA alerted providers to GDMT recommended for their patients and displayed current left ventricular ejection fraction (LVEF) along with the most recent blood pressure, heart rate, serum potassium and creatinine levels, and estimated glomerular filtration rate. It also linked to an order set customized to the patient that suggests medications within each GDMT class not already prescribed. Our goal is to examine whether tailored EHR-based alerting for outpatients with HFrEF will lead to higher rates of GDMT at 30 days post randomization when compared with usual care. Additionally, we are assessing clinical outcomes such as hospital readmissions and death between the alert versus usual care group. Trial Registration: Clinicaltrials.gov NCT04514458.

Rationale and design of a pragmatic trial aimed at improving treatment of hyperlipidemia in outpatients with very high risk atherosclerotic cardiovascular disease: A pragmatic trial of messaging to providers about treatment of hyperlipidemia (PROMPT-LIPID).

BACKGROUND: Despite guideline recommendations to optimize low-density lipoprotein cholesterol (LDL-C) reduction with intensification of lipid-lowering therapy (LLT) in patients with atherosclerotic cardiovascular disease (ASCVD), few of these patients achieve LDL-C < 70 mg/dL in practice. PURPOSE: We developed a real-time, targeted electronic health record (EHR) alert with embedded ordering capability to promote intensification of evidence based LLT in outpatients with very high risk ASCVD. METHODS: We designed a pragmatic, multicenter, single-blind, cluster randomized trial to test the effectiveness of an EHR-based LLT intensification alert. The study will enroll about 100 providers who will be randomized to either receive the alert or undergo usual care for outpatients with high risk ASCVD with LDL-C > 70 mg/dL. Total enrollment will include 2,500 patients. The primary outcome will be the proportion of patients with LLT intensification at 90 days. Secondary outcomes include achieved LDL-C at 6 months and the proportion of patients with LDL-C < 70 mg/dL or < 55 mg/dL at 6 months. RESULTS: Enrollment of 1,250 patients (50% of goal) was reached within 47 days (50% women, mean age 72, median LDL-C 91). At baseline, 71%, 9%, and 3% were on statins, ezetimibe, or proprotein convertase subtilisin/kexin type 9 inhibitors, respectively. CONCLUSIONS: PRagmatic Trial of Messaging to Providers about Treatment of HyperLIPIDemia has rapidly reached 50% enrollment of patients with very high risk ASCVD, demonstrating low baseline LLT utilization. This pragmatic, EHR-based trial will determine the effectiveness of a real-time, targeted EHR alert with embedded ordering capability to promote LLT intensification. Findings from this low-cost, widely scalable intervention to improve LDL-C may have important public health implications. TRIAL REGISTRATION: clinicaltrials.gov NCT04394715.

Prognostic Significance of Urinary Biomarkers in Patients Hospitalized With COVID-19.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is common in patients with coronavirus disease 2019 (COVID-19) and associated with poor outcomes. Urinary biomarkers have been associated with adverse kidney outcomes in other settings and may provide additional prognostic information in patients with COVID-19. We investigated the association between urinary biomarkers and adverse kidney outcomes among patients hospitalized with COVID-19. STUDY DESIGN: Prospective cohort study. SETTING & PARTICIPANTS: Patients hospitalized with COVID-19 (n=153) at 2 academic medical centers between April and June 2020. EXPOSURE: 19 urinary biomarkers of injury, inflammation, and repair. OUTCOME: Composite of KDIGO (Kidney Disease: Improving Global Outcomes) stage 3 AKI, requirement for dialysis, or death within 60 days of hospital admission. We also compared various kidney biomarker levels in the setting of COVID-19 versus other common AKI settings. ANALYTICAL APPROACH: Time-varying Cox proportional hazards regression to associate biomarker level with composite outcome. RESULTS: Out of 153 patients, 24 (15.7%) experienced the primary outcome. Twofold higher levels of neutrophil gelatinase-associated lipocalin (NGAL) (HR, 1.34 [95% CI, 1.14-1.57]), monocyte chemoattractant protein (MCP-1) (HR, 1.42 [95% CI, 1.09-1.84]), and kidney injury molecule 1 (KIM-1) (HR, 2.03 [95% CI, 1.38-2.99]) were associated with highest risk of sustaining primary composite outcome. Higher epidermal growth factor (EGF) levels were associated with a lower risk of the primary outcome (HR, 0.61 [95% CI, 0.47-0.79]). Individual biomarkers provided moderate discrimination and biomarker combinations improved discrimination for the primary outcome. The degree of kidney injury by biomarker level in COVID-19 was comparable to other settings of clinical AKI. There was evidence of subclinical AKI in COVID-19 patients based on elevated injury biomarker level in patients without clinical AKI defined by serum creatinine. LIMITATIONS: Small sample size with low number of composite outcome events. CONCLUSIONS: Urinary biomarkers are associated with adverse kidney outcomes in patients hospitalized with COVID-19 and may provide valuable information to monitor kidney disease progression and recovery.

Development and external validation of a diagnostic model for biopsy-proven acute interstitial nephritis using electronic health record data.

BACKGROUND: Patients with acute interstitial nephritis (AIN) can present without typical clinical features, leading to a delay in diagnosis and treatment. We therefore developed and validated a diagnostic model to identify patients at risk of AIN using variables from the electronic health record. METHODS: In patients who underwent a kidney biopsy at Yale University between 2013 and 2018, we tested the association of >150 variables with AIN, including demographics, comorbidities, vital signs and laboratory tests (training set 70%). We used least absolute shrinkage and selection operator methodology to select prebiopsy features associated with AIN. We performed area under the receiver operating characteristics curve (AUC) analysis with internal (held-out test set 30%) and external validation (Biopsy Biobank Cohort of Indiana). We tested the change in model performance after the addition of urine biomarkers in the Yale AIN study. RESULTS: We included 393 patients (AIN 22%) in the training set, 158 patients (AIN 27%) in the test set, 1118 patients (AIN 11%) in the validation set and 265 patients (AIN 11%) in the Yale AIN study. Variables in the selected model included serum creatinine {adjusted odds ratio [aOR] 2.31 [95% confidence interval (CI) 1.42-3.76]}, blood urea nitrogen:creatinine ratio [aOR 0.40 (95% CI 0.20-0.78)] and urine dipstick specific gravity [aOR 0.95 (95% CI 0.91-0.99)] and protein [aOR 0.39 (95% CI 0.23-0.68)]. This model showed an AUC of 0.73 (95% CI 0.64-0.81) in the test set, which was similar to the AUC in the external validation cohort [0.74 (95% CI 0.69-0.79)]. The AUC improved to 0.84 (95% CI 0.76-0.91) upon the addition of urine interleukin-9 and tumor necrosis factor-α. CONCLUSIONS: We developed and validated a statistical model that showed a modest AUC for AIN diagnosis, which improved upon the addition of urine biomarkers. Future studies could evaluate this model and biomarkers to identify unrecognized cases of AIN.

A distinct association of inflammatory molecules with outcomes of COVID-19 in younger versus older adults.

Aging can alter immunity affecting host defense. COVID-19 has the most devastating clinical outcomes in older adults, raising the implication of immune aging in determining its severity and mortality. We investigated biological predictors for clinical outcomes in a dataset of 13,642 ambulatory and hospitalized adult COVID-19 patients, including younger (age < 65, n = 566) and older (age ≥ 65, n = 717) subjects, with in-depth analyses of inflammatory molecules, cytokines and comorbidities. Disease severity and mortality in younger and older adults were associated with discrete immune mechanisms, including predominant T cell activation in younger adults, as measured by increased soluble IL-2 receptor alpha, and increased IL-10 in older adults although both groups also had shared inflammatory processes, including acute phase reactants, contributing to clinical outcomes. These observations suggest that progression to severe disease and death in COVID-19 may proceed by different immunologic mechanisms in younger versus older subjects and introduce the possibility of age-based immune directed therapies.

Use of Proton Pump Inhibitors Increases Risk of Incident Kidney Stones.

BACKGROUND AND AIMS: Proton pump inhibitors (PPIs) are widely prescribed and have effects on gut ion absorption and urinary ion concentrations. PPIs might therefore protect against or contribute to development of kidney stones. We investigated the association between PPI use and kidney stones. METHODS: We performed a retrospective study using data from the Women's Veteran's Cohort Study, which comprised men and women, from October 1, 1999 through September 30, 2017. We collected data from 465,891 patients on PPI usage over time, demographics, laboratory results, comorbidities, and medication usage. Time-varying Cox proportional hazards and propensity matching analyses determined risk of PPI use and incident development of kidney stones. Use of histamine-2 receptor antagonists (H2RAs) was measured and levothyroxine use was a negative control exposure. RESULTS: PPI use was associated with kidney stones in the unadjusted analysis, with PPI use as a time-varying variable (hazard ratio [HR], 1.74; 95% CI, 1.67-1.82), and persisted in the adjusted analysis (HR, 1.46; CI, 1.38-1.55). The association was maintained in a propensity score-matched subset of PPI users and nonusers (adjusted HR, 1.25; CI 1.19-1.33). Increased dosage of PPI was associated with increased risk of kidney stones (HR, 1.11; CI, 1.09-1.14 for each increase in 30 defined daily doses over a 3-month period). H2RAs were also associated with increased risk (adjusted HR, 1.47; CI 1.31-1.64). We found no association, in adjusted analysis, of levothyroxine use with kidney stones (adjusted HR, 1.06; CI 0.94-1.21). CONCLUSIONS: In a large cohort study of veterans, we found PPI use to be associated with a dose-dependent increase in risk of kidney stones. H2RA use also has an association with risk of kidney stones, so acid suppression might be an involved mechanism. The effect is small and should not change prescribing for most patients.

The Association of COVID-19 With Acute Kidney Injury Independent of Severity of Illness: A Multicenter Cohort Study.

RATIONALE & OBJECTIVE: Although coronavirus disease 2019 (COVID-19) has been associated with acute kidney injury (AKI), it is unclear whether this association is independent of traditional risk factors such as hypotension, nephrotoxin exposure, and inflammation. We tested the independent association of COVID-19 with AKI. STUDY DESIGN: Multicenter, observational, cohort study. SETTING & PARTICIPANTS: Patients admitted to 1 of 6 hospitals within the Yale New Haven Health System between March 10, 2020, and August 31, 2020, with results for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing via polymerase chain reaction of a nasopharyngeal sample. EXPOSURE: Positive test for SARS-CoV-2. OUTCOME: AKI by KDIGO (Kidney Disease: Improving Global Outcomes) criteria. ANALYTICAL APPROACH: Evaluated the association of COVID-19 with AKI after controlling for time-invariant factors at admission (eg, demographic characteristics, comorbidities) and time-varying factors updated continuously during hospitalization (eg, vital signs, medications, laboratory results, respiratory failure) using time-updated Cox proportional hazard models. RESULTS: Of the 22,122 patients hospitalized, 2,600 tested positive and 19,522 tested negative for SARS-CoV-2. Compared with patients who tested negative, patients with COVID-19 had more AKI (30.6% vs 18.2%; absolute risk difference, 12.5% [95% CI, 10.6%-14.3%]) and dialysis-requiring AKI (8.5% vs 3.6%) and lower rates of recovery from AKI (58% vs 69.8%). Compared with patients without COVID-19, patients with COVID-19 had higher inflammatory marker levels (C-reactive protein, ferritin) and greater use of vasopressors and diuretic agents. Compared with patients without COVID-19, patients with COVID-19 had a higher rate of AKI in univariable analysis (hazard ratio, 1.84 [95% CI, 1.73-1.95]). In a fully adjusted model controlling for demographic variables, comorbidities, vital signs, medications, and laboratory results, COVID-19 remained associated with a high rate of AKI (adjusted hazard ratio, 1.40 [95% CI, 1.29-1.53]). LIMITATIONS: Possibility of residual confounding. CONCLUSIONS: COVID-19 is associated with high rates of AKI not fully explained by adjustment for known risk factors. This suggests the presence of mechanisms of AKI not accounted for in this analysis, which may include a direct effect of COVID-19 on the kidney or other unmeasured mediators. Future studies should evaluate the possible unique pathways by which COVID-19 may cause AKI.

Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify ("phenotype") patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients. METHODS: We specified criteria using structured data elements to create rules for identifying patients and also developed multiple natural language processing (NLP)-based approaches for automated phenotyping of patients, tested them with tenfold cross-validation for 10 iterations (n = 7144) and external validation (n = 2988) and compared them with a standard method to identify patient conditions, the Systematized Nomenclature of Medicine. The gold standard for GIB diagnosis was the independent dual manual review of medical records. The primary outcome was the positive predictive value. RESULTS: A decision rule using GIB-specific terms from ED triage and ED review-of-systems assessment performed better than the Systematized Nomenclature of Medicine on internal validation and external validation (positive predictive value = 85% confidence interval:83%-87% vs 69% confidence interval:66%-72%; P < 0.001). The syntax-based NLP algorithm and Bidirectional Encoder Representation from Transformers neural network-based NLP algorithm had similar performance to the structured-data fields decision rule. CONCLUSIONS: An automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision making in real time for patients with acute GIB presenting to the ED.

A Time-Updated, Parsimonious Model to Predict AKI in Hospitalized Children.

BACKGROUND: Timely prediction of AKI in children can allow for targeted interventions, but the wealth of data in the electronic health record poses unique modeling challenges. METHODS: We retrospectively reviewed the electronic medical records of all children younger than 18 years old who had at least two creatinine values measured during a hospital admission from January 2014 through January 2018. We divided the study population into derivation, and internal and external validation cohorts, and used five feature selection techniques to select 10 of 720 potentially predictive variables from the electronic health records. Model performance was assessed by the area under the receiver operating characteristic curve in the validation cohorts. The primary outcome was development of AKI (per the Kidney Disease Improving Global Outcomes creatinine definition) within a moving 48-hour window. Secondary outcomes included severe AKI (stage 2 or 3), inpatient mortality, and length of stay. RESULTS: Among 8473 encounters studied, AKI occurred in 516 (10.2%), 207 (9%), and 27 (2.5%) encounters in the derivation, and internal and external validation cohorts, respectively. The highest-performing model used a machine learning-based genetic algorithm, with an overall receiver operating characteristic curve in the internal validation cohort of 0.76 [95% confidence interval (CI), 0.72 to 0.79] for AKI, 0.79 (95% CI, 0.74 to 0.83) for severe AKI, and 0.81 (95% CI, 0.77 to 0.86) for neonatal AKI. To translate this prediction model into a clinical risk-stratification tool, we identified high- and low-risk threshold points. CONCLUSIONS: Using various machine learning algorithms, we identified and validated a time-updated prediction model of ten readily available electronic health record variables to accurately predict imminent AKI in hospitalized children.

Real-Time Prediction of Acute Kidney Injury in Hospitalized Adults: Implementation and Proof of Concept.

RATIONALE & OBJECTIVE: Acute kidney injury (AKI) is diagnosed based on changes in serum creatinine concentration, a late marker of this syndrome. Algorithms that predict elevated risk for AKI are of great interest, but no studies have incorporated such an algorithm into the electronic health record to assist with clinical care. We describe the experience of implementing such an algorithm. STUDY DESIGN: Prospective observational cohort study. SETTING & PARTICIPANTS: 2,856 hospitalized adults in a single urban tertiary-care hospital with an algorithm-predicted risk for AKI in the next 24 hours>15%. Alerts were also used to target a convenience sample of 100 patients for measurement of 16 urine and 6 blood biomarkers. EXPOSURE: Clinical characteristics at the time of pre-AKI alert. OUTCOME: AKI within 24 hours of pre-AKI alert (AKI24). ANALYTICAL APPROACH: Descriptive statistics and univariable associations. RESULTS: At enrollment, mean predicted probability of AKI24 was 19.1%; 18.9% of patients went on to develop AKI24. Outcomes were generally poor among this population, with 29% inpatient mortality among those who developed AKI24 and 14% among those who did not (P<0.001). Systolic blood pressure<100mm Hg (28% of patients with AKI24 vs 18% without), heart rate>100 beats/min (32% of patients with AKI24 vs 24% without), and oxygen saturation<92% (15% of patients with AKI24 vs 6% without) were all more common among those who developed AKI24. Of all biomarkers measured, only hyaline casts on urine microscopy (72% of patients with AKI24 vs 25% without) and fractional excretion of urea nitrogen (20% [IQR, 12%-36%] among patients with AKI24 vs 34% [IQR, 25%-44%] without) differed between those who did and did not develop AKI24. LIMITATIONS: Single-center study, reliance on serum creatinine level for AKI diagnosis, small number of patients undergoing biomarker evaluation. CONCLUSIONS: A real-time AKI risk model was successfully integrated into the EHR.

A simple real-time model for predicting acute kidney injury in hospitalized patients in the US: A descriptive modeling study.

BACKGROUND: Acute kidney injury (AKI) is an adverse event that carries significant morbidity. Given that interventions after AKI occurrence have poor performance, there is substantial interest in prediction of AKI prior to its diagnosis. However, integration of real-time prognostic modeling into the electronic health record (EHR) has been challenging, as complex models increase the risk of error and complicate deployment. Our goal in this study was to create an implementable predictive model to accurately predict AKI in hospitalized patients and could be easily integrated within an existing EHR system. METHODS AND FINDINGS: We performed a retrospective analysis looking at data of 169,859 hospitalized adults admitted to one of three study hospitals in the United States (in New Haven and Bridgeport, Connecticut) from December 2012 to February 2016. Demographics, medical comorbidities, hospital procedures, medications, and laboratory data were used to develop a model to predict AKI within 24 hours of a given observation. Outcomes of AKI severity, requirement for renal replacement therapy, and mortality were also measured and predicted. Models were trained using discrete-time logistic regression in a subset of Hospital 1, internally validated in the remainder of Hospital 1, and externally validated in Hospital 2 and Hospital 3. Model performance was assessed via the area under the receiver-operator characteristic (ROC) curve (AUC). The training set cohort contained 60,701 patients, and the internal validation set contained 30,599 patients. External validation data sets contained 43,534 and 35,025 patients. Patients in the overall cohort were generally older (median age ranging from 61 to 68 across hospitals); 44%-49% were male, 16%-20% were black, and 23%-29% were admitted to surgical wards. In the training set and external validation set, 19.1% and 18.9% of patients, respectively, developed AKI. The full model, including all covariates, had good ability to predict imminent AKI for the validation set, sustained AKI, dialysis, and death with AUCs of 0.74 (95% CI 0.73-0.74), 0.77 (95% CI 0.76-0.78), 0.79 (95% CI 0.73-0.85), and 0.69 (95% CI 0.67-0.72), respectively. A simple model using only readily available, time-updated laboratory values had very similar predictive performance to the complete model. The main limitation of this study is that it is observational in nature; thus, we are unable to conclude a causal relationship between covariates and AKI and do not provide an optimal treatment strategy for those predicted to develop AKI. CONCLUSIONS: In this study, we observed that a simple model using readily available laboratory data could be developed to predict imminent AKI with good discrimination. This model may lend itself well to integration into the EHR without sacrificing the performance seen in more complex models.

Machine Learning to Predict Outcomes in Patients with Acute Gastrointestinal Bleeding: A Systematic Review.

Risk stratification of patients with gastrointestinal bleeding (GIB) is recommended, but current risk assessment tools have variable performance. Machine learning (ML) has promise to improve risk assessment. We performed a systematic review to evaluate studies utilizing ML techniques for GIB. Bibliographic databases and conference abstracts were searched for studies with a population of overt GIB that used an ML algorithm with outcomes of mortality, rebleeding, hemostatic intervention, and/or hospital stay. Two independent reviewers screened titles and abstracts, reviewed full-text studies, and extracted data from included studies. Risk of bias was assessed with an adapted Quality in Prognosis Studies tool. Area under receiver operating characteristic curves (AUCs) were the primary assessment of performance with AUC ≥ 0.80 predefined as an acceptable threshold of good performance. Fourteen studies with 30 assessments of ML models met inclusion criteria. No study had low risk of bias. Median AUC reported in validation datasets for predefined outcomes of mortality, intervention, or rebleeding was 0.84 (range 0.40-0.98). AUCs were higher with artificial neural networks (median 0.93, range 0.78-0.98) than other ML models (0.81, range 0.40-0.92). ML performed better than clinical risk scores (Glasgow-Blatchford, Rockall, Child-Pugh, MELD) for mortality in upper GIB. Limitations include heterogeneity of ML models, inconsistent comparisons of ML models with clinical risk scores, and high risk of bias. ML generally provided good-excellent prognostic performance in patients with GIB, and artificial neural networks tended to outperform other ML models. ML was better than clinical risk scores for mortality in upper GIB.

Pragmatic Trial of Messaging to Providers About Treatment of Hyperlipidemia (PROMPT-LIPID): A Randomized Clinical Trial.

BACKGROUND: Lipid-lowering therapy (LLT) is underutilized for very high-risk atherosclerotic cardiovascular disease. PROMPT-LIPID (PRagmatic Trial of Messaging to Providers about Treatment of HyperLIPIDemia) sought to determine whether electronic health record (EHR) alerts improve 90-day LLT intensification in patients with very high-risk atherosclerotic cardiovascular disease. METHODS: PROMPT-LIPID was a pragmatic trial in which cardiovascular and internal medicine clinicians within Yale New Haven Health (New Haven, CT) were cluster-randomized to receive an EHR alert with individualized LLT recommendations or no alert for outpatients with very high-risk atherosclerotic cardiovascular disease and LDL-C (low-density lipoprotein cholesterol), ≥70 mg/dL. The primary outcome was 90-day LLT intensification (change to high-intensity statin and addition of ezetimibe or PCSK9i [proprotein subtilisin/kexin type 9 inhibitors]). Secondary outcomes included LDL-C level, proportion of patients with LDL-C of <70 or < 55 mg/dL, rate of major adverse cardiovascular events, ED visit incidence, and 6-month mortality. Results were analyzed using logistic and linear regression clustered at the provider level. RESULTS: The no-alert group included 47 clinicians and 1370 patients (median age, 71 years; 50.1% female, median LDL-C, 93 mg/dL); the alert group included 49 clinicians and 1130 patients (median age, 72 years; 47% female, median LDL-C 91, mg/dL). The primary outcome was observed in 14.1% of patients in the alert group as compared with 10.4% in the no-alert group. There were no differences in any secondary outcomes at 6 months. Among 542 patients whose clinicians (n=46) did not dismiss the EHR alert recommendations, LLT intensification was significantly greater (21.2% versus 10.4%, odds ratio, 2.33 [95% CI, 1.48-3.66]). CONCLUSIONS: With a real-time, targeted, individualized EHR alert as compared with usual care, the proportion of patients with atherosclerotic cardiovascular disease with LLT intensification was numerically higher but not statistically significant. Among clinicians who did not dismiss the alert, there was a > 2-fold increase in LLT intensification. EHR alerts, coupled with strategies to reduce clinician dismissal, may help address persistent gaps in LDL-C management. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04394715, https://www.clinicaltrials.gov/ct2/show/study/NCT04394715.

Effect of intravenous antihypertensives on outcomes of severe hypertension in hospitalized patients without acute target organ damage.

BACKGROUND: Treatment of severe inpatient hypertension (HTN) that develops during hospitalization is not informed by guidelines. Intravenous (i.v.) antihypertensives are used to manage severe HTN even in the absence of acute target organ damage; however they may result in unpredictable blood pressure (BP) reduction and cardiovascular events. Our goal was to assess the association between i.v. antihypertensives and clinical outcomes in this population. METHODS: This is a multihospital retrospective study of adults admitted for reasons other than HTN who develop severe HTN during hospitalization without acute target end organ damage. We defined severe HTN as BP elevation of systolic >180 or diastolic >110 mmHg. Treatment was defined as receiving i.v. antihypertensives within 3 h of BP elevation. We used overlap propensity score weighted Cox models to study the association between treatment and clinical outcomes during index hospitalization. RESULTS: Of 224 265 unique, nonintensive care unit hospitalizations, 20 383 (9%) developed severe HTN, of which 5% received i.v. antihypertensives and 79% were untreated within 3 h of severe BP elevation. In the overlap propensity weighted population, patients who received i.v. antihypertensives were more likely to develop myocardial injury (5.9% in treated versus 3.6% in untreated; hazard ratio [HR]: 1.6 [1.13, 2.24]). Treatment was not associated with increased risk of stroke (HR: 0.7 [0.3, 1.62]), acute kidney injury (HR: 0.97 [0.81, 1.17]), or death (HR: 0.86 [0.49, 1.51]). CONCLUSIONS: Intravenous antihypertensives were associated with increased risk of myocardial injury in patients who develop severe HTN during hospitalization. These results suggest that i.v. antihypertensives should be used with caution in patients without acute target organ damage.

Risk of COVID-19 breakthrough infection and hospitalization in individuals with comorbidities.

BACKGROUND: The successful development of multiple COVID-19 vaccines has led to a global vaccination effort to reduce severe COVID-19 infection and mortality. However, the effectiveness of the COVID-19 vaccines wane over time leading to breakthrough infections where vaccinated individuals experience a COVID-19 infection. Here we estimate the risks of breakthrough infection and subsequent hospitalization in individuals with common comorbidities who had completed an initial vaccination series. METHODS: Our study population included vaccinated patients between January 1, 2021 to March 31, 2022 who are present in the Truveta patient population. Models were developed to describe 1) time from completing primary vaccination series till breakthrough infection; and 2) if a patient was hospitalized within 14 days of breakthrough infection. We adjusted for age, race, ethnicity, sex, and year-month of vaccination. RESULTS: Of 1,218,630 patients in the Truveta Platform who had completed an initial vaccination sequence between January 1, 2021 and March 31, 2022, 2.85, 3.42, 2.75, and 2.88 percent of patients with CKD, chronic lung disease, diabetes, or are in an immunocompromised state experienced breakthrough infection, respectively, compared to 1.46 percent of the population without any of these four comorbidities. We found an increased risk of breakthrough infection and subsequent hospitalization in individuals with any of the four comorbidities when compared to individuals without these four comorbidities. CONCLUSIONS: Vaccinated individuals with any of the studied comorbidities experienced an increased risk of breakthrough COVID-19 infection and subsequent hospitalizations compared to the people without any of the studied comorbidities. Individuals with immunocompromising conditions and chronic lung disease were most at risk of breakthrough infection, while people with CKD were most at risk of hospitalization following breakthrough infection. Patients with multiple comorbidities have an even greater risk of breakthrough infection or hospitalization compared to patients with none of the studied comorbidities. Individuals with common comorbidities should remain vigilant against infection even if vaccinated.

Differences in Mortality Among Patients With Asthma and COPD Hospitalized With COVID-19.

BACKGROUND: It remains unclear whether patients with asthma and/or chronic obstructive pulmonary disease (COPD) are at increased risk for severe coronavirus disease 2019 (COVID-19). OBJECTIVE: Compare in-hospital COVID-19 outcomes among patients with asthma, COPD, and no airway disease. METHODS: A retrospective cohort study was conducted on 8,395 patients admitted with COVID-19 between March 2020 and April 2021. Airway disease diagnoses were defined using International Classification of Diseases, 10th Revision codes. Mortality and sequential organ failure assessment (SOFA) scores were compared among groups. Logistic regression analysis was used to identify and adjust for confounding clinical features associated with mortality. RESULTS: The median SOFA score in patients without airway disease was 0.32 and mortality was 11%. In comparison, asthma patients had lower SOFA scores (median 0.15; P < .01) and decreased mortality, even after adjusting for age, diabetes, and other confounders (odds ratio 0.65; P = .01). Patients with COPD had higher SOFA scores (median 0.86; P < .01) and increased adjusted odds of mortality (odds ratio 1.40; P < .01). Blood eosinophil count of 200 cells/µL or greater, a marker of type 2 inflammation, was associated with lower mortality across all groups. Importantly, patients with asthma showed improved outcomes even after adjusting for eosinophilia, indicating that noneosinophilic asthma was associated with protection as well. CONCLUSIONS: COVID-19 severity was increased in patients with COPD and decreased in those with asthma, eosinophilia, and noneosinophilic asthma, independent of clinical confounders. These findings suggest that COVID-19 severity may be influenced by intrinsic immunological factors in patients with airway diseases, such as type 2 inflammation.

Excessive Blood Pressure Response to Clonidine in Hospitalized Patients With Asymptomatic Severe Hypertension.

BACKGROUND: There are limited and nonconcordant data on the rapidity and safety of blood pressure response to clonidine in the setting of asymptomatic severe hypertension. We evaluated the blood pressure response to clonidine in hospitalized patients with asymptomatic severe hypertension. METHODS: We performed a review of hospitalized, noncritically ill patients receiving clonidine within 6 hours of developing asymptomatic severe hypertension (systolic blood pressure [SBP] >180 or diastolic blood pressure [DBP] >110 mm Hg in the absence of acute hypertension-mediated target organ damage). The incidence of mean arterial pressure (MAP) reduction by ≥30% at 4 hours after clonidine was the primary endpoint. RESULTS: We identified 200 relevant patient encounters (median age 63 years, 48.5% women). Median time to clonidine following asymptomatic severe hypertension was 2.8 hours. A total of 20 (10%) patients had ≥30% MAP reduction within 4 hours after clonidine, and 32 (16%) patients had ≥30% reduction in either SBP, DBP, or MAP. Older age, female sex, and preexisting vascular disease were associated with ≥30% MAP reductions (P < 0.05). Only patient sex and clonidine dose of 0.3 mg were significant in multivariable models. There were 14 adverse events observed within 24 hours of administration of clonidine; most (9) were acute kidney injury. There were no ischemic (myocardial, cerebrovascular) events. CONCLUSIONS: A substantial minority of hospitalized patients with asymptomatic severe hypertension experience precipitous blood pressure decline with clonidine, and though blood pressure declines more precipitously in women and those receiving higher doses (0.3 mg specifically), the response to clonidine is generally not predictable on clinical grounds.