Tissue perfusion pressure enables continuous hemodynamic evaluation and risk prediction in the intensive care unit.

Treatment of circulatory shock in critically ill patients requires management of blood pressure using invasive monitoring, but uncertainty remains as to optimal individual blood pressure targets. Critical closing pressure, which refers to the arterial pressure when blood flow stops, can provide a fundamental measure of vascular tone in response to disease and therapy, but it has not previously been possible to measure this parameter routinely in clinical care. Here we describe a method to continuously measure critical closing pressure in the systemic circulation using readily available blood pressure monitors and then show that tissue perfusion pressure (TPP), defined as the difference between mean arterial pressure and critical closing pressure, provides unique information compared to other hemodynamic parameters. Using analyses of 5,988 admissions to a modern cardiac intensive care unit, and externally validated with 864 admissions to another institution, we show that TPP can predict the risk of mortality, length of hospital stay and peak blood lactate levels. These results indicate that TPP may provide an additional target for blood pressure optimization in patients with circulatory shock.

Prediction of operative mortality for patients undergoing cardiac surgical procedures without established risk scores.

OBJECTIVE: Current cardiac surgery risk models do not address a substantial fraction of procedures. We sought to create models to predict the risk of operative mortality for an expanded set of cases. METHODS: Four supervised machine learning models were trained using preoperative variables present in the Society of Thoracic Surgeons (STS) data set of the Massachusetts General Hospital to predict and classify operative mortality in procedures without STS risk scores. A total of 424 (5.5%) mortality events occurred out of 7745 cases. Models included logistic regression with elastic net regularization (LogReg), support vector machine, random forest (RF), and extreme gradient boosted trees (XGBoost). Model discrimination was assessed via area under the receiver operating characteristic curve (AUC), and calibration was assessed via calibration slope and expected-to-observed event ratio. External validation was performed using STS data sets from Brigham and Women's Hospital (BWH) and the Johns Hopkins Hospital (JHH). RESULTS: Models performed comparably with the highest mean AUC of 0.83 (RF) and expected-to-observed event ratio of 1.00. On external validation, the AUC was 0.81 in BWH (RF) and 0.79 in JHH (LogReg/RF). Models trained and applied on the same institution's data achieved AUCs of 0.81 (BWH: LogReg/RF/XGBoost) and 0.82 (JHH: LogReg/RF/XGBoost). CONCLUSIONS: Machine learning models trained on preoperative patient data can predict operative mortality at a high level of accuracy for cardiac surgical procedures without established risk scores. Such procedures comprise 23% of all cardiac surgical procedures nationwide. This work also highlights the value of using local institutional data to train new prediction models that account for institution-specific practices.

Deep Learning to Predict Mortality After Cardiothoracic Surgery Using Preoperative Chest Radiographs.

BACKGROUND: The Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) estimates mortality risk only for certain common procedures (eg, coronary artery bypass or valve surgery) and is cumbersome, requiring greater than 60 inputs. We hypothesized that deep learning can estimate postoperative mortality risk based on a preoperative chest radiograph for cardiac surgeries in which STS-PROM scores were available (STS index procedures) or unavailable (non-STS index procedures). METHODS: We developed a deep learning model (CXR-CTSurgery) to predict postoperative mortality based on preoperative chest radiographs in 9283 patients at Massachusetts General Hospital (MGH) having cardiac surgery before April 8, 2014. CXR-CTSurgery was tested on 3615 different MGH patients and externally tested on 2840 patients from Brigham and Women's Hospital (BWH) having surgery after April 8, 2014. Discrimination for mortality was compared with the STS-PROM using the C-statistic. Calibration was assessed using the observed-to-expected ratio (O/E ratio). RESULTS: For STS index procedures, CXR-CTSurgery had a C-statistic similar to STS-PROM at MGH (CXR-CTSurgery: 0.83 vs STS-PROM: 0.88; P = .20) and BWH (0.74 vs 0.80; P = .14) testing cohorts. The CXR-CTSurgery C-statistic for non-STS index procedures was similar to STS index procedures in the MGH (0.87 vs 0.83) and BWH (0.73 vs 0.74) testing cohorts. For STS index procedures, CXR-CTSurgery had better calibration than the STS-PROM in the MGH (O/E ratio: 0.74 vs 0.52) and BWH (O/E ratio: 0.91 vs 0.73) testing cohorts. CONCLUSIONS: CXR-CTSurgery predicts postoperative mortality based on a preoperative CXR with similar discrimination and better calibration than the STS-PROM. This may be useful when the STS-PROM cannot be calculated or for non-STS index procedures.

QTNet: Deep Learning for Estimating QT Intervals Using a Single Lead ECG.

QT prolongation often leads to fatal arrhythmia and sudden cardiac death. Antiarrhythmic drugs can increase the risk of QT prolongation and therefore require strict post-administration monitoring and dosage control. Measurement of the QT interval from the 12-lead electrocardiogram (ECG) by a trained expert, in a clinical setting, is the accepted method for tracking QT prolongation. Recent advances in wearable ECG technology, however, raise the possibility of automated out-of-hospital QT tracking. Applications of Deep Learning (DL) - a subfield within Machine Learning - in ECG analysis holds the promise of automation for a variety of classification and regression tasks. In this work, we propose a residual neural network, QTNet, for the regression of QT intervals from a single lead (Lead-I) ECG. QTNet is trained in a supervised manner on a large ECG dataset from a U.S. hospital. We demonstrate the robustness and generalizability of QTNet on four test-sets; one from the same hospital, one from another U.S. hospital, and two public datasets. Over all four datasets, the mean absolute error (MAE) in the estimated QT interval ranges between 9ms and 15.8ms. Pearson correlation coefficients vary between 0.899 and 0.914. By contrast, QT interval estimation on these datasets with a standard method for automated ECG analysis (NeuroKit2) yields MAEs between 22.29ms and 90.79ms, and Pearson correlation coefficients 0.345 and 0.620. These results demonstrate the utility of QTNet across distinct datasets and patient populations, thereby highlighting the potential utility of DL models for ubiquitous QT tracking.Clinical Relevance- QTNet can be applied to inpatient or ambulatory Lead-I ECG signals to track QT intervals. The method facilitates ambulatory monitoring of patients at risk of QT prolongation.

Timing of diuretic administration effects on urine volume in hospitalized patients.

Importance: Some medications have effects that depend on the time of day they are given. Current knowledge of the time-of-day effects of specific medications in hospitalized patients with cardiovascular disease is very limited. In hospitalized patients, increased medication efficiency might reduce dose (and associated side effects) and/or the length of time in the Intensive Care Unit (ICU) or hospital-potentially improving patient outcomes and patient and family quality of life and reducing financial costs. We studied whether the time of day or night patients in Cardiac or Intensive Care Units receive a diuretic affects urine volume. Methods: In this observational study, data were collected from 7,685 patients (63% male, 18 to 98 years old) admitted to one hospital's Acute Care Cardiac units, Cardiac ICUs, Cardiac Surgery ICUs, and/or Non-cardiac ICUs who received intravenous furosemide (a diuretic), had measurements of urine volume, were hospitalized for ≥3 days between January 2016 to July 2021 and were older than 18 years. The outcomes of interest were urine volume normalized by the most recent (not older than 24 h) weight or body mass index (BMI), (i) in the hour after the time of diuretic administration, and (ii) when no diuretics were administered for the previous 3 h. Results: We identified diuretic medication administration time 23:00-04:59 as a predictor of higher urine volume response. For patients without recent diuretic medication, higher urine volume was predicted 11:00-16:59 and 17:00-22:59. Other factors that affected urine volume response to the diuretic were sex, age, medication dose, creatinine concentration, diagnoses, and hospital unit. Discussion: Time-of-day of medication administration may be a factor associated with increased medication efficiency. Randomized controlled trials should be conducted to quantify the relative effect of modifiable factors, such as time of medication administration, that may affect short- and longer-term outcomes.

Neutrophils incite and macrophages avert electrical storm after myocardial infarction.

Sudden cardiac death, arising from abnormal electrical conduction, occurs frequently in patients with coronary heart disease. Myocardial ischemia simultaneously induces arrhythmia and massive myocardial leukocyte changes. In this study, we optimized a mouse model in which hypokalemia combined with myocardial infarction triggered spontaneous ventricular tachycardia in ambulatory mice, and we showed that major leukocyte subsets have opposing effects on cardiac conduction. Neutrophils increased ventricular tachycardia via lipocalin-2 in mice, whereas neutrophilia associated with ventricular tachycardia in patients. In contrast, macrophages protected against arrhythmia. Depleting recruited macrophages in Ccr2 -/- mice or all macrophage subsets with Csf1 receptor inhibition increased both ventricular tachycardia and fibrillation. Higher arrhythmia burden and mortality in Cd36 -/- and Mertk -/- mice, viewed together with reduced mitochondrial integrity and accelerated cardiomyocyte death in the absence of macrophages, indicated that receptor-mediated phagocytosis protects against lethal electrical storm. Thus, modulation of leukocyte function provides a potential therapeutic pathway for reducing the risk of sudden cardiac death.

Human acute inflammatory recovery is defined by co-regulatory dynamics of white blood cell and platelet populations.

Inflammation is the physiologic reaction to cellular and tissue damage caused by trauma, ischemia, infection, and other pathologic conditions. Elevation of white blood cell count (WBC) and altered levels of other acute phase reactants are cardinal signs of inflammation, but the dynamics of these changes and their resolution are not well established. Here we studied inflammatory recovery from trauma, ischemia, and infection by tracking longitudinal dynamics of clinical laboratory measurements in hospitalized patients. We identified a universal recovery trajectory defined by exponential WBC decay and delayed linear growth of platelet count (PLT). Co-regulation of WBC-PLT dynamics is a fundamental mechanism of acute inflammatory recovery and provides a generic approach for identifying high-risk patients: 32x relative risk (RR) of adverse outcomes for cardiac surgery, 9x RR of death from COVID-19, 9x RR of death from sepsis, and 5x RR of death from myocardial infarction.

Remote monitoring in the use of extracorporeal membrane oxygenation and acute mechanical circulatory support.

PURPOSE OF REVIEW: To provide an overview of the role of remote monitoring tools in management of critically-ill patients requiring acute mechanical circulatory support (MCS). RECENT FINDINGS: Tele-critical care systems have received new interest during the COVID-19 pandemic, which has stretched the capacity of health systems everywhere. At the same time, utilization of MCS and extracorporeal membrane oxygenation (ECMO) technologies has increased during the pandemic. The opportunity for remote monitoring and clinical decision support for ECMO and acute MCS devices has been recognized by industry partners, with several major platforms implementing technology infrastructure for it in available products. Healthcare systems face challenges interfacing multiple devices from multiple manufacturers with each other and with their designated electronic health records. Furthermore, the availability of data must be combined with algorithms for alerting on clinical events and with implementation systems to act upon these alerts. Studies are not yet published validating remote monitoring platforms for ECMO and MCS in clinical care. SUMMARY: Remote monitoring for MCS devices represents a major opportunity for further investigation to improve the utilization of these devices and better serve patients.

Optical coherence tomography in coronary atherosclerosis assessment and intervention.

Since optical coherence tomography (OCT) was first performed in humans two decades ago, this imaging modality has been widely adopted in research on coronary atherosclerosis and adopted clinically for the optimization of percutaneous coronary intervention. In the past 10 years, substantial advances have been made in the understanding of in vivo vascular biology using OCT. Identification by OCT of culprit plaque pathology could potentially lead to a major shift in the management of patients with acute coronary syndromes. Detection by OCT of healed coronary plaque has been important in our understanding of the mechanisms involved in plaque destabilization and healing with the rapid progression of atherosclerosis. Accurate detection by OCT of sequelae from percutaneous coronary interventions that might be missed by angiography could improve clinical outcomes. In addition, OCT has become an essential diagnostic modality for myocardial infarction with non-obstructive coronary arteries. Insight into neoatherosclerosis from OCT could improve our understanding of the mechanisms of very late stent thrombosis. The appropriate use of OCT depends on accurate interpretation and understanding of the clinical significance of OCT findings. In this Review, we summarize the state of the art in cardiac OCT and facilitate the uniform use of this modality in coronary atherosclerosis. Contributions have been made by clinicians and investigators worldwide with extensive experience in OCT, with the aim that this document will serve as a standard reference for future research and clinical application.

Intravital Microscopy in Atherosclerosis Research.

Atherosclerosis is a lipid-driven inflammatory disorder that narrows the arterial lumen and can induce life-threatening complications from coronary artery disease, cerebrovascular disease, and peripheral artery disease. On a mechanistic level, the development of novel cellular-resolution intravital microscopy imaging approaches has recently enabled in vivo studies of underlying biological processes governing disease onset and progress. In particular, multiphoton microscopy has emerged as a promising intravital imaging tool utilizing two-photon-excited fluorescence and second-harmonic generation that provides subcellular resolution and increased imaging depths beyond confocal and epifluorescence microscopy. In this chapter, we describe the state-of-the-art multiphoton microscopy applied to the study of murine atherosclerosis.

Patient contrastive learning: A performant, expressive, and practical approach to electrocardiogram modeling.

Supervised machine learning applications in health care are often limited due to a scarcity of labeled training data. To mitigate the effect of small sample size, we introduce a pre-training approach, Patient Contrastive Learning of Representations (PCLR), which creates latent representations of electrocardiograms (ECGs) from a large number of unlabeled examples using contrastive learning. The resulting representations are expressive, performant, and practical across a wide spectrum of clinical tasks. We develop PCLR using a large health care system with over 3.2 million 12-lead ECGs and demonstrate that training linear models on PCLR representations achieves a 51% performance increase, on average, over six training set sizes and four tasks (sex classification, age regression, and the detection of left ventricular hypertrophy and atrial fibrillation), relative to training neural network models from scratch. We also compared PCLR to three other ECG pre-training approaches (supervised pre-training, unsupervised pre-training with an autoencoder, and pre-training using a contrastive multi ECG-segment approach), and show significant performance benefits in three out of four tasks. We found an average performance benefit of 47% over the other models and an average of a 9% performance benefit compared to best model for each task. We release PCLR to enable others to extract ECG representations at https://github.com/broadinstitute/ml4h/tree/master/model_zoo/PCLR.

Late onset atrial fibrillation in patients undergoing surgical aortic valve replacement.

OBJECTIVE: Aortic valve disease is a risk factor for atrial fibrillation (AF), and AF is associated with increased late mortality and morbidity after cardiac surgery. The evolution of alternative approaches to AF prophylaxis, including less invasive technologies and medical therapies, has altered the balance between risk and potential benefit for prophylactic intervention at the time of surgical aortic valve replacement (SAVR). Such interventions impose incremental risk, however, making an understanding of predictors of new onset AF that persists beyond the perioperative episode relevant. METHODS: We conducted a retrospective single-institution cohort analysis of patients undergoing SAVR with no history of preoperative AF (n = 1014). These patients were cross-referenced against an institutional electrocardiogram (ECG) database to identify those with ECGs 3-12 months after surgery. Logistic regression was used to identify predictors of late AF. RESULTS: Among the 401 patients (40%), who had ECGs in our institution 3-12 months after surgery, 16 (4%) had late AF. Patients with late AF were older than patients without late AF (73 vs. 65, p = .025), and underwent procedures that were more urgent/emergent (38% vs. 15%, p = .015), with higher predicted risk of mortality (2.2% vs. 1.3%, p = .012). Predictors associated with the development of late AF were advanced age, higher preoperative creatinine level and urgent/emergent surgery. CONCLUSIONS: The incidence of late AF 3-12 months after SAVR, is low. Prophylactic AF interventions at the time of SAVR may not be warranted.

Optical Coherence Tomography of Plaque Vulnerability and Rupture: JACC Focus Seminar Part 1/3.

Plaque rupture is the most common cause of acute coronary syndromes and sudden cardiac death. Characteristics and pathobiology of vulnerable plaques prone to plaque rupture have been studied extensively over 2 decades in humans using optical coherence tomography (OCT), an intravascular imaging technique with micron scale resolution. OCT studies have identified key features of plaque vulnerability and described the in vivo characteristics and spatial distribution of thin cap fibroatheromas as major precursors to plaque rupture. In addition, OCT data supports the evolving understanding of coronary heart disease as a panvascular process associated with inflammation. In the setting of high atherosclerotic burden, plaque ruptures often occur at multiple sites in the coronary arteries, and plaque progression and healing are dynamic processes modulated by systemic risk factors. This review details major investigations with intravascular OCT into the biology and clinical implications of plaque vulnerability and plaque rupture.

White Blood Cell and Platelet Dynamics Define Human Inflammatory Recovery.

Inflammation is the physiologic reaction to cellular and tissue damage caused by pathologic processes including trauma, infection, and ischemia 1 . Effective inflammatory responses integrate molecular and cellular functions to prevent further tissue damage, initiate repair, and restore homeostasis, while futile or dysfunctional responses allow escalating injury, delay recovery, and may hasten death 2 . Elevation of white blood cell count (WBC) and altered levels of other acute phase reactants are cardinal signs of inflammation, but the dynamics of these changes and their resolution are not established 3,4 . Patient responses appear to vary dramatically with no clearly defined signs of good prognosis, leaving physicians reliant on qualitative interpretations of laboratory trends 4,5 . We retrospectively, observationally studied the human acute inflammatory response to trauma, ischemia, and infection by tracking the longitudinal dynamics of cellular and serum markers in hospitalized patients. Unexpectedly, we identified a conserved pattern of recovery defined by co-regulation of WBC and platelet (PLT) populations. Across all inflammatory conditions studied, recovering patients followed a consistent WBC-PLT trajectory shape that is well-approximated by exponential WBC decay and delayed linear PLT growth. This recovery trajectory shape may represent a fundamental archetype of human physiologic response at the cellular population scale, and provides a generic approach for identifying high-risk patients: 32x relative risk of adverse outcomes for cardiac surgery patients, 9x relative risk of death for COVID-19, and 5x relative risk of death for myocardial infarction.

SiglecF(HI) Marks Late-Stage Neutrophils of the Infarcted Heart: A Single-Cell Transcriptomic Analysis of Neutrophil Diversification.

Background Neutrophils are thought to be short-lived first responders to tissue injuries such as myocardial infarction (MI), but little is known about their diversification or dynamics. Methods and Results We permanently ligated the left anterior descending coronary arteries of mice and performed single-cell RNA sequencing and analysis of >28 000 neutrophil transcriptomes isolated from the heart, peripheral blood, and bone marrow of mice on days 1 to 4 after MI or at steady-state. Unsupervised clustering of cardiac neutrophils revealed 5 major subsets, 3 of which originated in the bone marrow, including a late-emerging granulocyte expressing SiglecF, a marker classically used to define eosinophils. SiglecFHI neutrophils represented ≈25% of neutrophils on day 1 and grew to account for >50% of neutrophils by day 4 post-MI. Validation studies using quantitative polymerase chain reaction of fluorescent-activated cell sorter sorted Ly6G+SiglecFHI and Ly6G+SiglecFLO neutrophils confirmed the distinct nature of these populations. To confirm that the cells were neutrophils rather than eosinophils, we infarcted GATA-deficient mice (∆dblGATA) and observed similar quantities of infiltrating Ly6G+SiglecFHI cells despite marked reductions of conventional eosinophils. In contrast to other neutrophil subsets, Ly6G+SiglecFHI neutrophils expressed high levels of Myc-regulated genes, which are associated with longevity and are consistent with the persistence of this population on day 4 after MI. Conclusions Overall, our data provide a spatial and temporal atlas of neutrophil specialization in response to MI and reveal a dynamic proinflammatory cardiac Ly6G+SigF+(Myc+NFÏ°B+) neutrophil that has been overlooked because of negative selection.

Imaging Cardiovascular and Lung Macrophages With the Positron Emission Tomography Sensor 64Cu-Macrin in Mice, Rabbits, and Pigs.

BACKGROUND: Macrophages, innate immune cells that reside in all organs, defend the host against infection and injury. In the heart and vasculature, inflammatory macrophages also enhance tissue damage and propel cardiovascular diseases. METHODS: We here use in vivo positron emission tomography (PET) imaging, flow cytometry, and confocal microscopy to evaluate quantitative noninvasive assessment of cardiac, arterial, and pulmonary macrophages using the nanotracer 64Cu-Macrin-a 20-nm spherical dextran nanoparticle assembled from nontoxic polyglucose. RESULTS: PET imaging using 64Cu-Macrin faithfully reported accumulation of macrophages in the heart and lung of mice with myocardial infarction, sepsis, or pneumonia. Flow cytometry and confocal microscopy detected the near-infrared fluorescent version of the nanoparticle (VT680Macrin) primarily in tissue macrophages. In 5-day-old mice, 64Cu-Macrin PET imaging quantified physiologically more numerous cardiac macrophages. Upon intravenous administration of 64Cu-Macrin in rabbits and pigs, we detected heightened macrophage numbers in the infarcted myocardium, inflamed lung regions, and atherosclerotic plaques using a clinical PET/magnetic resonance imaging scanner. Toxicity studies in rats and human dosimetry estimates suggest that 64Cu-Macrin is safe for use in humans. CONCLUSIONS: Taken together, these results indicate 64Cu-Macrin could serve as a facile PET nanotracer to survey spatiotemporal macrophage dynamics during various physiological and pathological conditions. 64Cu-Macrin PET imaging could stage inflammatory cardiovascular disease activity, assist disease management, and serve as an imaging biomarker for emerging macrophage-targeted therapeutics.

The myeloid type I interferon response to myocardial infarction begins in bone marrow and is regulated by Nrf2-activated macrophages.

Sterile tissue injury is thought to locally activate innate immune responses via damage-associated molecular patterns (DAMPs). Whether innate immune pathways are remotely activated remains relatively unexplored. Here, by analyzing ~145,000 single-cell transcriptomes at steady state and after myocardial infarction (MI) in mice and humans, we show that the type I interferon (IFN) response, characterized by expression of IFN-stimulated genes (ISGs), begins far from the site of injury, in neutrophil and monocyte progenitors within the bone marrow. In the peripheral blood of patients, we observed defined subsets of ISG-expressing neutrophils and monocytes. In the bone marrow and blood of mice, ISG expression was detected in neutrophils and monocytes and their progenitors, intensified with maturation at steady-state and after MI, and was controlled by Tet2 and Irf3 transcriptional regulators. Within the infarcted heart, ISG-expressing cells were negatively regulated by Nrf2 activation in Ccr2- steady-state cardiac macrophages. Our results show that IFN signaling begins in the bone marrow, implicate multiple transcriptional regulators (Tet2, Irf3, and Nrf2) in governing ISG expression, and provide a clinical biomarker (ISG score) for studying IFN signaling in patients.

Association of Red Blood Cell Distribution Width With Mortality Risk in Hospitalized Adults With SARS-CoV-2 Infection.

Importance: Coronavirus disease 2019 (COVID-19) is an acute respiratory illness with a high rate of hospitalization and mortality. Biomarkers are urgently needed for patient risk stratification. Red blood cell distribution width (RDW), a component of complete blood counts that reflects cellular volume variation, has been shown to be associated with elevated risk for morbidity and mortality in a wide range of diseases. Objective: To investigate whether an association between mortality risk and elevated RDW at hospital admission and during hospitalization exists in patients with COVID-19. Design, Setting, and Participants: This cohort study included adults diagnosed with SARS-CoV-2 infection and admitted to 1 of 4 hospitals in the Boston, Massachusetts area (Massachusetts General Hospital, Brigham and Women's Hospital, North Shore Medical Center, and Newton-Wellesley Hospital) between March 4, 2020, and April 28, 2020. Main Outcomes and Measures: The main outcome was patient survival during hospitalization. Measures included RDW at admission and during hospitalization, with an elevated RDW defined as greater than 14.5%. Relative risk (RR) of mortality was estimated by dividing the mortality of those with an elevated RDW by the mortality of those without an elevated RDW. Mortality hazard ratios (HRs) and 95% CIs were estimated using a Cox proportional hazards model. Results: A total of 1641 patients were included in the study (mean [SD] age, 62[18] years; 886 men [54%]; 740 White individuals [45%] and 497 Hispanic individuals [30%]; 276 nonsurvivors [17%]). Elevated RDW (>14.5%) was associated with an increased mortality risk in patients of all ages. The RR for the entire cohort was 2.73, with a mortality rate of 11% in patients with normal RDW (1173) and 31% in those with an elevated RDW (468). The RR in patients younger than 50 years was 5.25 (normal RDW, 1% [n = 341]; elevated RDW, 8% [n = 65]); 2.90 in the 50- to 59-year age group (normal RDW, 8% [n = 256]; elevated RDW, 24% [n = 63]); 3.96 in the 60- to 69-year age group (normal RDW, 8% [n = 226]; elevated RDW, 30% [104]); 1.45 in the 70- to 79-year age group (normal RDW, 23% [n = 182]; elevated RDW, 33% [n = 113]); and 1.59 in those ≥80 years (normal RDW, 29% [n = 168]; elevated RDW, 46% [n = 123]). RDW was associated with mortality risk in Cox proportional hazards models adjusted for age, D-dimer (dimerized plasmin fragment D) level, absolute lymphocyte count, and common comorbidities such as diabetes and hypertension (hazard ratio of 1.09 per 0.5% RDW increase and 2.01 for an RDW >14.5% vs ≤14.5%; P < .001). Patients whose RDW increased during hospitalization had higher mortality compared with those whose RDW did not change; for those with normal RDW, mortality increased from 6% to 24%, and for those with an elevated RDW at admission, mortality increased from 22% to 40%. Conclusions and Relevance: Elevated RDW at the time of hospital admission and an increase in RDW during hospitalization were associated with increased mortality risk for patients with COVID-19 who received treatment at 4 hospitals in a large academic medical center network.

An activatable PET imaging radioprobe is a dynamic reporter of myeloperoxidase activity in vivo.

Myeloperoxidase (MPO) is a critical proinflammatory enzyme implicated in cardiovascular, neurological, and rheumatological diseases. Emerging therapies targeting inflammation have raised interest in tracking MPO activity in patients. We describe 18F-MAPP, an activatable MPO activity radioprobe for positron emission tomography (PET) imaging. The activated radioprobe binds to proteins and accumulates at sites of MPO activity. The radioprobe 18F-MAPP has a short blood half-life, remains stable in plasma, does not demonstrate cytotoxicity, and crosses the intact blood-brain barrier. The 18F-MAPP imaging detected sites of elevated MPO activity in living mice embedded with human MPO and in mice induced with chemical inflammation or myocardial infarction. The 18F-MAPP PET imaging noninvasively differentiated varying amounts of MPO activity, competitive inhibition, and MPO deficiency in living animals, confirming specificity and showing that the radioprobe can quantify changes in in vivo MPO activity. The radiosynthesis has been optimized and automated, an important step in translation. These data indicate that 18F-MAPP is a promising translational candidate to noninvasively monitor MPO activity and inflammation in patients.