Temperature Trajectory Subphenotypes in Oncology Patients with Neutropenia and Suspected Infection.

Rationale: Despite etiologic and severity heterogeneity in neutropenic sepsis, management is often uniform. Understanding host response clinical subphenotypes might inform treatment strategies for neutropenic sepsis. Objectives: In this retrospective two-hospital study, we analyzed whether temperature trajectory modeling could identify distinct, clinically relevant subphenotypes among oncology patients with neutropenia and suspected infection. Methods: Among adult oncologic admissions with neutropenia and blood cultures within 24 hours, a previously validated model classified patients' initial 72-hour temperature trajectories into one of four subphenotypes. We analyzed subphenotypes' independent relationships with hospital mortality and bloodstream infection using multivariable models. Measurements and Main Results: Patients (primary cohort n = 1,145, validation cohort n = 6,564) fit into one of four temperature subphenotypes. "Hyperthermic slow resolvers" (pooled n = 1,140 [14.8%], mortality n = 104 [9.1%]) and "hypothermic" encounters (n = 1,612 [20.9%], mortality n = 138 [8.6%]) had higher mortality than "hyperthermic fast resolvers" (n = 1,314 [17.0%], mortality n = 47 [3.6%]) and "normothermic" (n = 3,643 [47.3%], mortality n = 196 [5.4%]) encounters (P < 0.001). Bloodstream infections were more common among hyperthermic slow resolvers (n = 248 [21.8%]) and hyperthermic fast resolvers (n = 240 [18.3%]) than among hypothermic (n = 188 [11.7%]) or normothermic (n = 418 [11.5%]) encounters (P < 0.001). Adjusted for confounders, hyperthermic slow resolvers had increased adjusted odds for mortality (primary cohort odds ratio, 1.91 [P = 0.03]; validation cohort odds ratio, 2.19 [P < 0.001]) and bloodstream infection (primary odds ratio, 1.54 [P = 0.04]; validation cohort odds ratio, 2.15 [P < 0.001]). Conclusions: Temperature trajectory subphenotypes were independently associated with important outcomes among hospitalized patients with neutropenia in two independent cohorts.

Sildenafil Versus Placebo for Early Pulmonary Vascular Disease in Scleroderma (SEPVADIS): protocol for a randomized controlled trial.

BACKGROUND: Pulmonary hypertension (PH) is a leading cause of death in patients with systemic sclerosis (SSc). An important component of SSc patient management is early detection and treatment of PH. Recently the threshold for the diagnosis of PH has been lowered to a mean pulmonary artery pressure (mPAP) threshold of > 20 mmHg on right heart catheterization (RHC). However, it is unknown if PH-specific therapy is beneficial in SSc patients with mildly elevated pressure (SSc-MEP, mPAP 21-24 mmHg). METHODS: The SEPVADIS trial is a randomized, double-blind, placebo-controlled phase 2 trial of sildenafil in SSc-MEP patients with a target enrollment of 30 patients from two academic sites in the United States. The primary outcome is change in six-minute walk distance after 16 weeks of treatment. Secondary endpoints include change in pulmonary arterial compliance by RHC and right ventricular function by cardiac magnetic resonance imaging at 16 weeks. Echocardiography, serum N-terminal probrain natriuretic peptide, and health-related quality of life is being measured at 16 and 52 weeks. DISCUSSION: The SEPVADIS trial will be the first randomized study of sildenafil in SSc-MEP patients. The results of this trial will be used to inform a phase 3 study to investigate the efficacy of treating patients with mild elevations in mPAP. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT04797286.

Comparison of Machine Learning Methods for Predicting Outcomes After In-Hospital Cardiac Arrest.

OBJECTIVES: Prognostication of neurologic status among survivors of in-hospital cardiac arrests remains a challenging task for physicians. Although models such as the Cardiac Arrest Survival Post-Resuscitation In-hospital score are useful for predicting neurologic outcomes, they were developed using traditional statistical techniques. In this study, we derive and compare the performance of several machine learning models with each other and with the Cardiac Arrest Survival Post-Resuscitation In-hospital score for predicting the likelihood of favorable neurologic outcomes among survivors of resuscitation. DESIGN: Analysis of the Get With The Guidelines-Resuscitation registry. SETTING: Seven-hundred fifty-five hospitals participating in Get With The Guidelines-Resuscitation from January 1, 2001, to January 28, 2017. PATIENTS: Adult in-hospital cardiac arrest survivors. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 117,674 patients in our cohort, 28,409 (24%) had a favorable neurologic outcome, as defined as survival with a Cerebral Performance Category score of less than or equal to 2 at discharge. Using patient characteristics, pre-existing conditions, prearrest interventions, and periarrest variables, we constructed logistic regression, support vector machines, random forests, gradient boosted machines, and neural network machine learning models to predict favorable neurologic outcome. Events prior to October 20, 2009, were used for model derivation, and all subsequent events were used for validation. The gradient boosted machine predicted favorable neurologic status at discharge significantly better than the Cardiac Arrest Survival Post-Resuscitation In-hospital score (C-statistic: 0.81 vs 0.73; p < 0.001) and outperformed all other machine learning models in terms of discrimination, calibration, and accuracy measures. Variables that were consistently most important for prediction across all models were duration of arrest, initial cardiac arrest rhythm, admission Cerebral Performance Category score, and age. CONCLUSIONS: The gradient boosted machine algorithm was the most accurate for predicting favorable neurologic outcomes in in-hospital cardiac arrest survivors. Our results highlight the utility of machine learning for predicting neurologic outcomes in resuscitated patients.

Comparison of early warning scores for predicting clinical deterioration and infection in obstetric patients.

BACKGROUND: Early warning scores are designed to identify hospitalized patients who are at high risk of clinical deterioration. Although many general scores have been developed for the medical-surgical wards, specific scores have also been developed for obstetric patients due to differences in normal vital sign ranges and potential complications in this unique population. The comparative performance of general and obstetric early warning scores for predicting deterioration and infection on the maternal wards is not known. METHODS: This was an observational cohort study at the University of Chicago that included patients hospitalized on obstetric wards from November 2008 to December 2018. Obstetric scores (modified early obstetric warning system (MEOWS), maternal early warning criteria (MEWC), and maternal early warning trigger (MEWT)), paper-based general scores (Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS), and a general score developed using machine learning (electronic Cardiac Arrest Risk Triage (eCART) score) were compared using the area under the receiver operating characteristic score (AUC) for predicting ward to intensive care unit (ICU) transfer and/or death and new infection. RESULTS: A total of 19,611 patients were included, with 43 (0.2%) experiencing deterioration (ICU transfer and/or death) and 88 (0.4%) experiencing an infection. eCART had the highest discrimination for deterioration (p < 0.05 for all comparisons), with an AUC of 0.86, followed by MEOWS (0.74), NEWS (0.72), MEWC (0.71), MEWS (0.70), and MEWT (0.65). MEWC, MEWT, and MEOWS had higher accuracy than MEWS and NEWS but lower accuracy than eCART at specific cut-off thresholds. For predicting infection, eCART (AUC 0.77) had the highest discrimination. CONCLUSIONS: Within the limitations of our retrospective study, eCART had the highest accuracy for predicting deterioration and infection in our ante- and postpartum patient population. Maternal early warning scores were more accurate than MEWS and NEWS. While institutional choice of an early warning system is complex, our results have important implications for the risk stratification of maternal ward patients, especially since the low prevalence of events means that small improvements in accuracy can lead to large decreases in false alarms.

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithms using electronic health records for identifying ICU transfer within 12 hours indicative of a child's condition. DESIGN: Observational cohort study. SETTING: Two urban, tertiary-care, academic hospitals (sites 1 and 2). PATIENTS: Pediatric inpatients (age <18 yr). INTERVENTIONS: None. MEASUREMENT AND MAIN RESULTS: Our primary outcome was direct ward to ICU transfer. Using age, vital signs, and laboratory results, we derived logistic regression with regularization, restricted cubic spline regression, random forest, and gradient boosted machine learning models. Among 50,830 admissions at site 1 and 88,970 admissions at site 2, 1,993 (3.92%) and 2,317 (2.60%) experienced the primary outcome, respectively. Site 1 data were split longitudinally into derivation (2009-2017) and validation (2018-2019), whereas site 2 constituted the external test cohort. Across both sites, the gradient boosted machine was the most accurate model and outperformed a modified version of the Bedside Pediatric Early Warning Score that only used physiologic variables in terms of discrimination ( C -statistic site 1: 0.84 vs 0.71, p < 0.001; site 2: 0.80 vs 0.74, p < 0.001), sensitivity, specificity, and number needed to alert. CONCLUSIONS: We developed and externally validated a novel machine learning model that identifies ICU transfers in hospitalized children more accurately than current tools. Our model enables early detection of children at risk for deterioration, thereby creating opportunities for intervention and improvement in outcomes.

Identifying High-Risk Subphenotypes and Associated Harms From Delayed Antibiotic Orders and Delivery.

OBJECTIVES: Early antibiotic administration is a central component of sepsis guidelines, and delays may increase mortality. However, prior studies have examined the delay to first antibiotic administration as a single time period even though it contains two distinct processes: antibiotic ordering and antibiotic delivery, which can each be targeted for improvement through different interventions. The objective of this study was to characterize and compare patients who experienced order or delivery delays, investigate the association of each delay type with mortality, and identify novel patient subphenotypes with elevated risk of harm from delays. DESIGN: Retrospective analysis of multicenter inpatient data. SETTING: Two tertiary care medical centers (2008-2018, 2006-2017) and four community-based hospitals (2008-2017). PATIENTS: All patients admitted through the emergency department who met clinical criteria for infection. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Patient demographics, vitals, laboratory values, medication order and administration times, and in-hospital survival data were obtained from the electronic health record. Order and delivery delays were calculated for each admission. Adjusted logistic regression models were used to examine the relationship between each delay and in-hospital mortality. Causal forests, a machine learning method, was used to identify a high-risk subgroup. A total of 60,817 admissions were included, and delays occurred in 58% of patients. Each additional hour of order delay (odds ratio, 1.04; 95% CI, 1.03-1.05) and delivery delay (odds ratio, 1.05; 95% CI, 1.02-1.08) was associated with increased mortality. A patient subgroup identified by causal forests with higher comorbidity burden, greater organ dysfunction, and abnormal initial lactate measurements had a higher risk of death associated with delays (odds ratio, 1.07; 95% CI, 1.06-1.09 vs odds ratio, 1.02; 95% CI, 1.01-1.03). CONCLUSIONS: Delays in antibiotic ordering and drug delivery are both associated with a similar increase in mortality. A distinct subgroup of high-risk patients exist who could be targeted for more timely therapy.

Determining the Electronic Signature of Infection in Electronic Health Record Data.

OBJECTIVES: Recent sepsis studies have defined patients as "infected" using a combination of culture and antibiotic orders rather than billing data. However, the accuracy of these definitions is unclear. We aimed to compare the accuracy of different established criteria for identifying infected patients using detailed chart review. DESIGN: Retrospective observational study. SETTING: Six hospitals from three health systems in Illinois. PATIENTS: Adult admissions with blood culture or antibiotic orders, or Angus International Classification of Diseases infection codes and death were eligible for study inclusion as potentially infected patients. Nine-hundred to 1,000 of these admissions were randomly selected from each health system for chart review, and a proportional number of patients who did not meet chart review eligibility criteria were also included and deemed not infected. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The accuracy of published billing code criteria by Angus et al and electronic health record criteria by Rhee et al and Seymour et al (Sepsis-3) was determined using the manual chart review results as the gold standard. A total of 5,215 patients were included, with 2,874 encounters analyzed via chart review and a proportional 2,341 added who did not meet chart review eligibility criteria. In the study cohort, 27.5% of admissions had at least one infection. This was most similar to the percentage of admissions with blood culture orders (26.8%), Angus infection criteria (28.7%), and the Sepsis-3 criteria (30.4%). Sepsis-3 criteria was the most sensitive (81%), followed by Angus (77%) and Rhee (52%), while Rhee (97%) and Angus (90%) were more specific than the Sepsis-3 criteria (89%). Results were similar for patients with organ dysfunction during their admission. CONCLUSIONS: Published criteria have a wide range of accuracy for identifying infected patients, with the Sepsis-3 criteria being the most sensitive and Rhee criteria being the most specific. These findings have important implications for studies investigating the burden of sepsis on a local and national level.

Multicenter Validation of the Neonatal Sequential Organ Failure Assessment Score for Prognosis in the Neonatal Intensive Care Unit.

Infants in the neonatal intensive care unit are at risk of life-threatening organ dysfunction, but few objective tools with utility exist. In a multicenter cohort of 20 152 infants, we show the neonatal sequential organ failure assessment score had good-to-excellent discrimination of mortality across centers, birth weights, and time points after admission.

Safety and efficacy of catheter-directed therapy versus anticoagulation alone in a higher-risk acute pulmonary embolism population.

There is little data comparing safety and efficacy outcomes in patients with pulmonary embolism (PE) receiving catheter directed therapies (CDT) compared to a similar-risk cohort of PE patients receiving anticoagulation alone. 1094 patients with acute PE were studied. CDT and conservatively-managed patients were compared using propensity score matching to assess safety outcomes, which included bleeding and acute kidney injury at 2 and 7 days after PE diagnosis. Efficacy outcomes included change in vital signs over 72 h and in-hospital mortality. PE patients with RV strain who underwent CDT (n = 76) had more bleeding at 2 days (additional 1.04 g/dL loss, 95% CI - 1.48 to - 0.60, p < 0.001) and 7 days (additional 1.36 g/dL loss, 95% CI - 1.88 to - 0.84, p < 0.001) compared to those receiving anticoagulation alone (n = 303). There was a significant increase in creatinine at 2 days (additional 0.22 mg/dL elevation, 95% CI 0.02 to 0.42, p = 0.03), but not at 7 days (additional 0.12 mg/dL elevation, 95% CI - 0.11 to 0.35, p = 0.30). In-hospital mortality for patients receiving CDT versus anticoagulation alone was similar (OR 1.21, 95% CI 0.53 to 2.77; p = 0.65). In patients with baseline abnormal vital signs who received CDT versus anticoagulation alone, heart rate, respiratory rate and oxygen requirement improved significantly faster and to levels closer to normal (p ≤ 0.001). CDT was associated with a small but increased risk of bleeding, but no significant worsening of renal function. CDT may be associated with more rapid improvements in heart rate, respiratory rate, and oxygen requirement.

The Development and Validation of a Machine Learning Model to Predict Bacteremia and Fungemia in Hospitalized Patients Using Electronic Health Record Data.

OBJECTIVES: Bacteremia and fungemia can cause life-threatening illness with high mortality rates, which increase with delays in antimicrobial therapy. The objective of this study is to develop machine learning models to predict blood culture results at the time of the blood culture order using routine data in the electronic health record. DESIGN: Retrospective analysis of a large, multicenter inpatient data. SETTING: Two academic tertiary medical centers between the years 2007 and 2018. SUBJECTS: All hospitalized patients who received a blood culture during hospitalization. INTERVENTIONS: The dataset was partitioned temporally into development and validation cohorts: the logistic regression and gradient boosting machine models were trained on the earliest 80% of hospital admissions and validated on the most recent 20%. MEASUREMENTS AND MAIN RESULTS: There were 252,569 blood culture days-defined as nonoverlapping 24-hour periods in which one or more blood cultures were ordered. In the validation cohort, there were 50,514 blood culture days, with 3,762 cases of bacteremia (7.5%) and 370 cases of fungemia (0.7%). The gradient boosting machine model for bacteremia had significantly higher area under the receiver operating characteristic curve (0.78 [95% CI 0.77-0.78]) than the logistic regression model (0.73 [0.72-0.74]) (p < 0.001). The model identified a high-risk group with over 30 times the occurrence rate of bacteremia in the low-risk group (27.4% vs 0.9%; p < 0.001). Using the low-risk cut-off, the model identifies bacteremia with 98.7% sensitivity. The gradient boosting machine model for fungemia had high discrimination (area under the receiver operating characteristic curve 0.88 [95% CI 0.86-0.90]). The high-risk fungemia group had 252 fungemic cultures compared with one fungemic culture in the low-risk group (5.0% vs 0.02%; p < 0.001). Further, the high-risk group had a mortality rate 60 times higher than the low-risk group (28.2% vs 0.4%; p < 0.001). CONCLUSIONS: Our novel models identified patients at low and high-risk for bacteremia and fungemia using routinely collected electronic health record data. Further research is needed to evaluate the cost-effectiveness and impact of model implementation in clinical practice.

Temperature Trajectory Subphenotypes Correlate With Immune Responses in Patients With Sepsis.

OBJECTIVES: We recently found that distinct body temperature trajectories of infected patients correlated with survival. Understanding the relationship between the temperature trajectories and the host immune response to infection could allow us to immunophenotype patients at the bedside using temperature. The objective was to identify whether temperature trajectories have consistent associations with specific cytokine responses in two distinct cohorts of infected patients. DESIGN: Prospective observational study. SETTING: Large academic medical center between 2013 and 2019. SUBJECTS: Two cohorts of infected patients: 1) patients in the ICU with septic shock and 2) hospitalized patients with Staphylococcus aureus bacteremia. INTERVENTIONS: Clinical data (including body temperature) and plasma cytokine concentrations were measured. Patients were classified into four temperature trajectory subphenotypes using their temperature measurements in the first 72 hours from the onset of infection. Log-transformed cytokine levels were standardized to the mean and compared with the subphenotypes in both cohorts. MEASUREMENTS AND MAIN RESULTS: The cohorts consisted of 120 patients with septic shock (cohort 1) and 88 patients with S. aureus bacteremia (cohort 2). Patients from both cohorts were classified into one of four previously validated temperature subphenotypes: "hyperthermic, slow resolvers" (n = 19 cohort 1; n = 13 cohort 2), "hyperthermic, fast resolvers" (n = 18 C1; n = 24 C2), "normothermic" (n = 54 C1; n = 31 C2), and "hypothermic" (n = 29 C1; n = 20 C2). Both "hyperthermic, slow resolvers" and "hyperthermic, fast resolvers" had high levels of G-CSF, CCL2, and interleukin-10 compared with the "hypothermic" group when controlling for cohort and timing of cytokine measurement (p < 0.05). In contrast to the "hyperthermic, slow resolvers," the "hyperthermic, fast resolvers" showed significant decreases in the levels of several cytokines over a 24-hour period, including interleukin-1RA, interleukin-6, interleukin-8, G-CSF, and M-CSF (p < 0.001). CONCLUSIONS: Temperature trajectory subphenotypes are associated with consistent cytokine profiles in two distinct cohorts of infected patients. These subphenotypes could play a role in the bedside identification of cytokine profiles in patients with sepsis.

Identifying Novel Sepsis Subphenotypes Using Temperature Trajectories.

Rationale: Sepsis is a heterogeneous syndrome, and identifying clinically relevant subphenotypes is essential.Objectives: To identify novel subphenotypes in hospitalized patients with infection using longitudinal temperature trajectories.Methods: In the model development cohort, inpatient admissions meeting criteria for infection in the emergency department and receiving antibiotics within 24 hours of presentation were included. Temperature measurements within the first 72 hours were compared between survivors and nonsurvivors. Group-based trajectory modeling was performed to identify temperature trajectory groups, and patient characteristics and outcomes were compared between the groups. The model was then externally validated at a second hospital using the same inclusion criteria.Measurements and Main Results: A total of 12,413 admissions were included in the development cohort, and 19,053 were included in the validation cohort. In the development cohort, four temperature trajectory groups were identified: "hyperthermic, slow resolvers" (n = 1,855; 14.9% of the cohort); "hyperthermic, fast resolvers" (n = 2,877; 23.2%); "normothermic" (n = 4,067; 32.8%); and "hypothermic" (n = 3,614; 29.1%). The hypothermic subjects were the oldest and had the most comorbidities, the lowest levels of inflammatory markers, and the highest in-hospital mortality rate (9.5%). The hyperthermic, slow resolvers were the youngest and had the fewest comorbidities, the highest levels of inflammatory markers, and a mortality rate of 5.1%. The hyperthermic, fast resolvers had the lowest mortality rate (2.9%). Similar trajectory groups, patient characteristics, and outcomes were found in the validation cohort.Conclusions: We identified and validated four novel subphenotypes of patients with infection, with significant variability in inflammatory markers and outcomes.

Evaluating the Need to Address Digital Literacy Among Hospitalized Patients: Cross-Sectional Observational Study.

BACKGROUND: Technology is a potentially powerful tool to assist patients with transitions of care during and after hospitalization. Patients with low health literacy who are predisposed to poor health outcomes are particularly poised to benefit from such interventions. However, this population may lack the ability to effectively engage with technology. Although prior research studied the role of health literacy in technology access/use among outpatients, hospitalized patient populations have not been investigated in this context. Further, with the rapid uptake of technology, access may no longer be pertinent, and differences in technological capabilities may drive the current digital divide. Thus, characterizing the digital literacy of hospitalized patients across health literacy levels is paramount. OBJECTIVE: We sought to determine the relationship between health literacy level and technological access, use, and capability among hospitalized patients. METHODS: Adult inpatients completed a technology survey that asked about technology access/use and online capabilities as part of an ongoing quality of care study. Participants' health literacy level was assessed utilizing the 3-question Brief Health Literacy Screen. Descriptive statistics, bivariate chi-squared analyses, and multivariate logistic regression analyses (adjusting for age, race, gender, and education level) were performed. Using Bonferroni correction for the 18 tests, the threshold P value for significance was <.003. RESULTS: Among 502 enrolled participants, the mean age was 51 years, 71.3% (358/502) were African American, half (265/502, 52.8%) were female, and half (253/502, 50.4%) had at least some college education. Over one-third (191/502, 38.0%) of participants had low health literacy. The majority of participants owned devices (owned a smartphone: 116/173, 67.1% low health literacy versus 235/300, 78.3% adequate health literacy, P=.007) and had used the Internet previously (143/189, 75.7% low health literacy versus 281/309, 90.9% adequate health literacy, P<.001). Participants with low health literacy were more likely to report needing help performing online tasks (133/189, 70.4% low health literacy versus 135/303, 44.6% adequate health literacy, P<.001). In the multivariate analysis, when adjusting for age, race, gender, and education level, we found that low health literacy was not significantly associated with a lower likelihood of owning smartphones (OR: 0.8, 95% CI 0.5-1.4; P=.52) or using the internet ever (OR: 0.5, 95% CI 0.2-0.9; P=.02). However, low health literacy remained significantly associated with a higher likelihood of needing help performing any online task (OR: 2.2, 95% CI 1.3-3.6; P=.002). CONCLUSIONS: The majority of participants with low health literacy had access to technological devices and had used the internet previously, but they were unable to perform online tasks without assistance. The barriers patients face in using online health information and other health information technology may be more related to online capabilities rather than to technology access. When designing and implementing technological tools for hospitalized patients, it is important to ensure that patients across digital literacy levels can both understand and use them.

Allergic Immune Diseases and the Risk of Mortality Among Patients Hospitalized for Acute Infection.

OBJECTIVES: The immune response during sepsis remains poorly understood and is likely influenced by the host's preexisting immunologic comorbidities. Although more than 20% of the U.S. population has an allergic-atopic disease, the type 2 immune response that is overactive in these diseases can also mediate beneficial pro-resolving, tissue-repair functions. Thus, the presence of allergic immunologic comorbidities may be advantageous for patients suffering from sepsis. The objective of this study was to test the hypothesis that comorbid type 2 immune diseases confer protection against morbidity and mortality due to acute infection. DESIGN: Retrospective cohort study of patients hospitalized with an acute infection between November 2008 and January 2016 using electronic health record data. SETTING: Single tertiary-care academic medical center. PATIENTS: Admissions to the hospital through the emergency department with likely infection at the time of admission who may or may not have had a type 2 immune-mediated disease, defined as asthma, allergic rhinitis, atopic dermatitis, or food allergy, as determined by International Classification of Diseases, 9th Revision, Clinical Modification codes. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Of 10,789 admissions for infection, 2,578 (24%) had a type 2 disease; these patients were more likely to be female, black, and younger than patients without type 2 diseases. In unadjusted analyses, type 2 patients had decreased odds of dying during the hospitalization (0.47; 95% CI, 0.38-0.59, p < 0.001), while having more than one type 2 disease conferred a dose-dependent reduction in the risk of mortality (p < 0.001). When adjusting for demographics, medications, types of infection, and illness severity, the presence of a type 2 disease remained protective (odds ratio, 0.55; 95% CI, 0.43-0.70; p < 0.001). Similar results were found using a propensity score analysis (odds ratio, 0.57; 95% CI, 0.45-0.71; p < 0.001). CONCLUSIONS: Patients with type 2 diseases admitted with acute infections have reduced mortality, implying that the type 2 immune response is protective in sepsis.

Validation of Early Warning Scores at Two Long-Term Acute Care Hospitals.

OBJECTIVES: Early warning scores were developed to identify high-risk patients on the hospital wards. Research on early warning scores has focused on patients in short-term acute care hospitals, but there are other settings, such as long-term acute care hospitals, where these tools could be useful. However, the accuracy of early warning scores in long-term acute care hospitals is unknown. DESIGN: Observational cohort study. SETTING: Two long-term acute care hospitals in Illinois from January 2002 to September 2017. PATIENTS: Admitted adult long-term acute care hospital patients. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Demographic characteristics, vital signs, laboratory values, nursing flowsheet data, and outcomes data were collected from the electronic health record. The accuracy of individual variables, the Modified Early Warning Score, the National Early Warning Score version 2, and our previously developed electronic Cardiac Arrest Risk Triage score were compared for predicting the need for acute hospital transfer or death using the area under the receiver operating characteristic curve. A total of 12,497 patient admissions were included, with 3,550 experiencing the composite outcome. The median age was 65 (interquartile range, 54-74), 46% were female, and the median length of stay in the long-term acute care hospital was 27 days (interquartile range, 17-40 d), with an 8% in-hospital mortality. Laboratory values were the best predictors, with blood urea nitrogen being the most accurate (area under the receiver operating characteristic curve, 0.63) followed by albumin, bilirubin, and WBC count (area under the receiver operating characteristic curve, 0.61). Systolic blood pressure was the most accurate vital sign (area under the receiver operating characteristic curve, 0.60). Electronic Cardiac Arrest Risk Triage (area under the receiver operating characteristic curve, 0.72) was significantly more accurate than National Early Warning Score version 2 (area under the receiver operating characteristic curve, 0.66) and Modified Early Warning Score (area under the receiver operating characteristic curve, 0.65; p < 0.01 for all pairwise comparisons). CONCLUSIONS: In this retrospective cohort study, we found that the electronic Cardiac Arrest Risk Triage score was significantly more accurate than Modified Early Warning Score and National Early Warning Score version 2 for predicting acute hospital transfer and mortality. Because laboratory values were more predictive than vital signs and the average length of stay in an long-term acute care hospital is much longer than short-term acute hospitals, developing a score specific to the long-term acute care hospital population would likely further improve accuracy, thus allowing earlier identification of high-risk patients for potentially life-saving interventions.

Characteristics of Rapid Response Calls in the United States: An Analysis of the First 402,023 Adult Cases From the Get With the Guidelines Resuscitation-Medical Emergency Team Registry.

OBJECTIVES: To characterize the rapid response team activations, and the patients receiving them, in the American Heart Association-sponsored Get With The Guidelines Resuscitation-Medical Emergency Team cohort between 2005 and 2015. DESIGN: Retrospective multicenter cohort study. SETTING: Three hundred sixty U.S. hospitals. PATIENTS: Consecutive adult patients experiencing rapid response team activation. INTERVENTIONS: Rapid response team activation. MEASUREMENTS AND MAIN RESULTS: The cohort included 402,023 rapid response team activations from 347,401 unique healthcare encounters. Respiratory triggers (38.0%) and cardiac triggers (37.4%) were most common. The most frequent interventions-pulse oximetry (66.5%), other monitoring (59.6%), and supplemental oxygen (62.0%)-were noninvasive. Fluids were the most common medication ordered (19.3%), but new antibiotic orders were rare (1.2%). More than 10% of rapid response teams resulted in code status changes. Hospital mortality was over 14% and increased with subsequent rapid response activations. CONCLUSIONS: Although patients requiring rapid response team activation have high inpatient mortality, most rapid response team activations involve relatively few interventions, which may limit these teams' ability to improve patient outcomes.

The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model.

OBJECTIVES: To develop an acute kidney injury risk prediction model using electronic health record data for longitudinal use in hospitalized patients. DESIGN: Observational cohort study. SETTING: Tertiary, urban, academic medical center from November 2008 to January 2016. PATIENTS: All adult inpatients without pre-existing renal failure at admission, defined as first serum creatinine greater than or equal to 3.0 mg/dL, International Classification of Diseases, 9th Edition, code for chronic kidney disease stage 4 or higher or having received renal replacement therapy within 48 hours of first serum creatinine measurement. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Demographics, vital signs, diagnostics, and interventions were used in a Gradient Boosting Machine algorithm to predict serum creatinine-based Kidney Disease Improving Global Outcomes stage 2 acute kidney injury, with 60% of the data used for derivation and 40% for validation. Area under the receiver operator characteristic curve (AUC) was calculated in the validation cohort, and subgroup analyses were conducted across admission serum creatinine, acute kidney injury severity, and hospital location. Among the 121,158 included patients, 17,482 (14.4%) developed any Kidney Disease Improving Global Outcomes acute kidney injury, with 4,251 (3.5%) developing stage 2. The AUC (95% CI) was 0.90 (0.90-0.90) for predicting stage 2 acute kidney injury within 24 hours and 0.87 (0.87-0.87) within 48 hours. The AUC was 0.96 (0.96-0.96) for receipt of renal replacement therapy (n = 821) in the next 48 hours. Accuracy was similar across hospital settings (ICU, wards, and emergency department) and admitting serum creatinine groupings. At a probability threshold of greater than or equal to 0.022, the algorithm had a sensitivity of 84% and a specificity of 85% for stage 2 acute kidney injury and predicted the development of stage 2 a median of 41 hours (interquartile range, 12-141 hr) prior to the development of stage 2 acute kidney injury. CONCLUSIONS: Readily available electronic health record data can be used to predict impending acute kidney injury prior to changes in serum creatinine with excellent accuracy across different patient locations and admission serum creatinine. Real-time use of this model would allow early interventions for those at high risk of acute kidney injury.

Electronic cardiac arrest triage score best predicts mortality after intervention in patients with massive and submassive pulmonary embolism.

OBJECTIVE: To determine if the cardiac arrest triage (CART) Score would better predict poor outcomes after pharmacomechanical therapy (PMT) for massive and submassive pulmonary embolism (PE) than traditional risk scores BACKGROUND: PMT for massive and submassive PE allows for clot lysis with minimal doses of fibrinolytics. Although PMT results in improved right ventricular function, and reduced pulmonary pressures and thrombus burden, predictors of poor outcome are not well-studied. METHODS: We conducted a retrospective analysis of all patients who underwent PMT for massive or submassive PE at a single institution from 2010 to 2016. The CART score and electronic CART (eCART) score, derived previously as early warning scores for hospitalized patients, were compared to pulmonary embolism severity index (PESI) comparing the area under the receiver-operator characteristic curve (AUC) for predicting 30-day mortality. RESULTS: We studied 61 patients (56 ±17 years, 44.0% male, 29.5% massive PE, mean PESI 114.6 ± 42.7, mean CART 13.5 ± 1.39, mean eCART 108.5 ± 28.6). Thirty-day mortality was 24.6%. Treatments included rheolytic thrombectomy (32.7%), catheter-directed thrombolysis (50.8%), ultrasound-assisted thrombolysis (32.7%), and mechanical thrombectomy (4.9%). There were no differences in outcome based on technique. The eCART and CART scores had higher AUCs compared to PESI in predicting 30-day mortality (0.84 vs 0.72 vs 0.69, P = .010). We found troponin I and pro-BNP were higher in higher eCART tertiles, however AUCs were 0.51 and 0.63, respectively for 30-day mortality when used as stand-alone predictors. CONCLUSION: Compared to PESI score, CART and eCART scores better predict mortality in massive or submassive PE patients undergoing PMT.

Association Between Survival and Time of Day for Rapid Response Team Calls in a National Registry.

OBJECTIVES: Decreased staffing at nighttime is associated with worse outcomes in hospitalized patients. Rapid response teams were developed to decrease preventable harm by providing additional critical care resources to patients with clinical deterioration. We sought to determine whether rapid response team call frequency suffers from decreased utilization at night and how this is associated with patient outcomes. DESIGN: Retrospective analysis of a prospectively collected registry database. SETTING: National registry database of inpatient rapid response team calls. PATIENTS: Index rapid response team calls occurring on the general wards in the American Heart Association Get With The Guidelines-Medical Emergency Team database between 2005 and 2015 were analyzed. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome was inhospital mortality. Patient and event characteristics between the hours with the highest and lowest mortality were compared, and multivariable models adjusting for patient characteristics were fit. A total of 282,710 rapid response team calls from 274 hospitals were included. The lowest frequency of calls occurred in the consecutive 1 AM to 6:59 AM period, with 266 of 274 (97%) hospitals having lower than expected call volumes during those hours. Mortality was highest during the 7 AM hour and lowest during the noon hour (18.8% vs 13.8%; adjusted odds ratio, 1.41 [1.31-1.52]; p < 0.001). Compared with calls at the noon hour, those during the 7 AM hour had more deranged vital signs, were more likely to have a respiratory trigger, and were more likely to have greater than two simultaneous triggers. CONCLUSIONS: Rapid response team activation is less frequent during the early morning and is followed by a spike in mortality in the 7 AM hour. These findings suggest that failure to rescue deteriorating patients is more common overnight. Strategies aimed at improving rapid response team utilization during these vulnerable hours may improve patient outcomes.