Society of Critical Care Medicine Guidelines on Recognizing and Responding to Clinical Deterioration Outside the ICU: 2023.

RATIONALE: Clinical deterioration of patients hospitalized outside the ICU is a source of potentially reversible morbidity and mortality. To address this, some acute care hospitals have implemented systems aimed at detecting and responding to such patients. OBJECTIVES: To provide evidence-based recommendations for hospital clinicians and administrators to optimize recognition and response to clinical deterioration in non-ICU patients. PANEL DESIGN: The 25-member panel included representatives from medicine, nursing, respiratory therapy, pharmacy, patient/family partners, and clinician-methodologists with expertise in developing evidence-based Clinical Practice Guidelines. METHODS: We generated actionable questions using the Population, Intervention, Control, and Outcomes (PICO) format and performed a systematic review of the literature to identify and synthesize the best available evidence. We used the Grading of Recommendations Assessment, Development, and Evaluation Approach to determine certainty in the evidence and to formulate recommendations and good practice statements (GPSs). RESULTS: The panel issued 10 statements on recognizing and responding to non-ICU patients with critical illness. Healthcare personnel and institutions should ensure that all vital sign acquisition is timely and accurate (GPS). We make no recommendation on the use of continuous vital sign monitoring among unselected patients. We suggest focused education for bedside clinicians in signs of clinical deterioration, and we also suggest that patient/family/care partners' concerns be included in decisions to obtain additional opinions and help (both conditional recommendations). We recommend hospital-wide deployment of a rapid response team or medical emergency team (RRT/MET) with explicit activation criteria (strong recommendation). We make no recommendation about RRT/MET professional composition or inclusion of palliative care members on the responding team but suggest that the skill set of responders should include eliciting patients' goals of care (conditional recommendation). Finally, quality improvement processes should be part of a rapid response system. CONCLUSIONS: The panel provided guidance to inform clinicians and administrators on effective processes to improve the care of patients at-risk for developing critical illness outside the ICU.

Executive Summary: Society of Critical Care Medicine Guidelines on Recognizing and Responding to Clinical Deterioration Outside the ICU.

RATIONALE: Clinical deterioration of patients hospitalized outside the ICU is a source of potentially reversible morbidity and mortality. To address this, some acute care facilities have implemented systems aimed at detecting and responding to such patients. OBJECTIVES: To provide evidence-based recommendations for hospital clinicians and administrators to optimize recognition and response to clinical deterioration in non-ICU patients. PANEL DESIGN: The 25-member panel included representatives from medicine, nursing, respiratory therapy, pharmacy, patient/family partners, and clinician-methodologists with expertise in developing evidence-based clinical practice guidelines. METHODS: We generated actionable questions using the Population, Intervention, Control, and Outcomes format and performed a systematic review of the literature to identify and synthesize the best available evidence. We used the Grading of Recommendations Assessment, Development, and Evaluation approach to determine certainty in the evidence and to formulate recommendations and good practice statements (GPSs). RESULTS: The panel issued 10 statements on recognizing and responding to non-ICU patients with critical illness. Healthcare personnel and institutions should ensure that all vital sign acquisition is timely and accurate (GPS). We make no recommendation on the use of continuous vital sign monitoring among "unselected" patients due to the absence of data regarding the benefit and the potential harms of false positive alarms, the risk of alarm fatigue, and cost. We suggest focused education for bedside clinicians in signs of clinical deterioration, and we also suggest that patient/family/care partners' concerns be included in decisions to obtain additional opinions and help (both conditional recommendations). We recommend hospital-wide deployment of a rapid response team or medical emergency team (RRT/MET) with explicit activation criteria (strong recommendation). We make no recommendation about RRT/MET professional composition or inclusion of palliative care members on the responding team but suggest that the skill set of responders should include eliciting patients' goals of care (conditional recommendation). Finally, quality improvement processes should be part of a rapid response system (GPS). CONCLUSIONS: The panel provided guidance to inform clinicians and administrators on effective processes to improve the care of patients at-risk for developing critical illness outside the ICU.

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.

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.

The Impact of a Machine Learning Early Warning Score on Hospital Mortality: A Multicenter Clinical Intervention Trial.

OBJECTIVES: To determine the impact of a machine learning early warning risk score, electronic Cardiac Arrest Risk Triage (eCART), on mortality for elevated-risk adult inpatients. DESIGN: A pragmatic pre- and post-intervention study conducted over the same 10-month period in 2 consecutive years. SETTING: Four-hospital community-academic health system. PATIENTS: All adult patients admitted to a medical-surgical ward. INTERVENTIONS: During the baseline period, clinicians were blinded to eCART scores. During the intervention period, scores were presented to providers. Scores greater than or equal to 95th percentile were designated high risk prompting a physician assessment for ICU admission. Scores between the 89th and 95th percentiles were designated intermediate risk, triggering a nurse-directed workflow that included measuring vital signs every 2 hours and contacting a physician to review the treatment plan. MEASUREMENTS AND MAIN RESULTS: The primary outcome was all-cause inhospital mortality. Secondary measures included vital sign assessment within 2 hours, ICU transfer rate, and time to ICU transfer. A total of 60,261 patients were admitted during the study period, of which 6,681 (11.1%) met inclusion criteria (baseline period n = 3,191, intervention period n = 3,490). The intervention period was associated with a significant decrease in hospital mortality for the main cohort (8.8% vs 13.9%; p < 0.0001; adjusted odds ratio [OR], 0.60 [95% CI, 0.52-0.71]). A significant decrease in mortality was also seen for the average-risk cohort not subject to the intervention (0.49% vs 0.26%; p < 0.05; adjusted OR, 0.53 [95% CI, 0.41-0.74]). In subgroup analysis, the benefit was seen in both high- (17.9% vs 23.9%; p = 0.001) and intermediate-risk (2.0% vs 4.0 %; p = 0.005) patients. The intervention period was also associated with a significant increase in ICU transfers, decrease in time to ICU transfer, and increase in vital sign reassessment within 2 hours. CONCLUSIONS: Implementation of a machine learning early warning score-driven protocol was associated with reduced inhospital mortality, likely driven by earlier and more frequent ICU transfer.

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.

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.

A Vital Sign-Based Model to Predict Clinical Deterioration in Hospitalized Children.

OBJECTIVES: Clinical deterioration in hospitalized children is associated with increased risk of mortality and morbidity. A prediction model capable of accurate and early identification of pediatric patients at risk of deterioration can facilitate timely assessment and intervention, potentially improving survival and long-term outcomes. The objective of this study was to develop a model utilizing vital signs from electronic health record data for predicting clinical deterioration in pediatric ward patients. DESIGN: Observational cohort study. SETTING: An urban, tertiary-care medical center. PATIENTS: Patients less than 18 years admitted to the general ward during years 2009-2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome of clinical deterioration was defined as a direct ward-to-ICU transfer. A discrete-time logistic regression model utilizing six vital signs along with patient characteristics was developed to predict ICU transfers several hours in advance. Among 31,899 pediatric admissions, 1,375 (3.7%) experienced the outcome. Data were split into independent derivation (yr 2009-2014) and prospective validation (yr 2015-2018) cohorts. In the prospective validation cohort, the vital sign model significantly outperformed a modified version of the Bedside Pediatric Early Warning System score in predicting ICU transfers 12 hours prior to the event (C-statistic 0.78 vs 0.72; p < 0.01). CONCLUSIONS: We developed a model utilizing six commonly used vital signs to predict risk of deterioration in hospitalized children. Our model demonstrated greater accuracy in predicting ICU transfers than the modified Bedside Pediatric Early Warning System. Our model may promote opportunities for timelier intervention and risk mitigation, thereby decreasing preventable death and improving long-term health.

Validating the Electronic Cardiac Arrest Risk Triage (eCART) Score for Risk Stratification of Surgical Inpatients in the Postoperative Setting: Retrospective Cohort Study.

OBJECTIVE: Assess the accuracy of 3 early warning scores for predicting severe adverse events in postoperative inpatients. SUMMARY OF BACKGROUND DATA: Postoperative clinical deterioration on inpatient hospital services is associated with increased morbidity, mortality, and cost. Early warning scores have been developed to detect inpatient clinical deterioration and trigger rapid response activation, but knowledge regarding the application of early warning scores to postoperative inpatients is limited. METHODS: This was a retrospective cohort study of adult patients hospitalized on the wards after surgical procedures at an urban academic medical center from November, 2008 to January, 2016. The accuracies of the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS), and the electronic cardiac arrest risk triage (eCART) score were compared in predicting severe adverse events (ICU transfer, ward cardiac arrest, or ward death) in the postoperative period using the area under the receiver operating characteristic curve (AUC). RESULTS: Of the 32,537 patient admissions included in the study, 3.8% (n = 1243) experienced a severe adverse outcome after the procedure. The accuracy for predicting the composite outcome was highest for eCART [AUC 0.79 (95% CI: 0.78-0.81)], followed by NEWS [AUC 0.76 (95% CI: 0.75-0.78)], and MEWS [AUC 0.75 (95% CI: 0.73-0.76)]. Of the individual vital signs and labs, maximum respiratory rate was the most predictive (AUC 0.67) and maximum temperature was an inverse predictor (AUC 0.46). CONCLUSION: Early warning scores are predictive of severe adverse events in postoperative patients. eCART is significantly more accurate in this patient population than both NEWS and MEWS.

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.

Predictors of In-Hospital Mortality After Rapid Response Team Calls in a 274 Hospital Nationwide Sample.

OBJECTIVES: Despite wide adoption of rapid response teams across the United States, predictors of in-hospital mortality for patients receiving rapid response team calls are poorly characterized. Identification of patients at high risk of death during hospitalization could improve triage to intensive care units and prompt timely reevaluations of goals of care. We sought to identify predictors of in-hospital mortality in patients who are subjects of rapid response team calls and to develop and validate a predictive model for death after rapid response team call. DESIGN: Analysis of data from the national Get with the Guidelines-Medical Emergency Team event registry. SETTING: Two-hundred seventy four hospitals participating in Get with the Guidelines-Medical Emergency Team from June 2005 to February 2015. PATIENTS: 282,710 hospitalized adults on surgical or medical wards who were subjects of a rapid response team call. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome was death during hospitalization; candidate predictors included patient demographic- and event-level characteristics. Patients who died after rapid response team were older (median age 72 vs 66 yr), were more likely to be admitted for noncardiac medical illness (70% vs 58%), and had greater median length of stay prior to rapid response team (81 vs 47 hr) (p < 0.001 for all comparisons). The prediction model had an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.78-0.79), with systolic blood pressure, time since admission, and respiratory rate being the most important variables. CONCLUSIONS: Patients who die following rapid response team calls differ significantly from surviving peers. Recognition of these factors could improve postrapid response team triage decisions and prompt timely goals of care discussions.

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.

Quick Sepsis-related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores for Detecting Clinical Deterioration in Infected Patients outside the Intensive Care Unit.

RATIONALE: The 2016 definitions of sepsis included the quick Sepsis-related Organ Failure Assessment (qSOFA) score to identify high-risk patients outside the intensive care unit (ICU). OBJECTIVES: We sought to compare qSOFA with other commonly used early warning scores. METHODS: All admitted patients who first met the criteria for suspicion of infection in the emergency department (ED) or hospital wards from November 2008 until January 2016 were included. The qSOFA, Systemic Inflammatory Response Syndrome (SIRS), Modified Early Warning Score (MEWS), and the National Early Warning Score (NEWS) were compared for predicting death and ICU transfer. MEASUREMENTS AND MAIN RESULTS: Of the 30,677 included patients, 1,649 (5.4%) died and 7,385 (24%) experienced the composite outcome (death or ICU transfer). Sixty percent (n = 18,523) first met the suspicion criteria in the ED. Discrimination for in-hospital mortality was highest for NEWS (area under the curve [AUC], 0.77; 95% confidence interval [CI], 0.76-0.79), followed by MEWS (AUC, 0.73; 95% CI, 0.71-0.74), qSOFA (AUC, 0.69; 95% CI, 0.67-0.70), and SIRS (AUC, 0.65; 95% CI, 0.63-0.66) (P < 0.01 for all pairwise comparisons). Using the highest non-ICU score of patients, ≥2 SIRS had a sensitivity of 91% and specificity of 13% for the composite outcome compared with 54% and 67% for qSOFA ≥2, 59% and 70% for MEWS ≥5, and 67% and 66% for NEWS ≥8, respectively. Most patients met ≥2 SIRS criteria 17 hours before the combined outcome compared with 5 hours for ≥2 and 17 hours for ≥1 qSOFA criteria. CONCLUSIONS: Commonly used early warning scores are more accurate than the qSOFA score for predicting death and ICU transfer in non-ICU patients. These results suggest that the qSOFA score should not replace general early warning scores when risk-stratifying patients with suspected infection.

'Who's Covering This Patient?' Developing a First-Contact Provider (FCP) Designation in an Electronic Health Record.

BACKGROUND: Safe and efficient inpatient care depends on accurate identification of the licensed independent practitioner (LIP) primarily responsible for each admitted patient. The inability to do so has far-reaching consequences, including poor communication among care teams, delays in patient care (including critical result reporting), and significant threats to patient safety. METHODS: At the University of Chicago Medical Center, an 800-bed academic hospital, a new Epic feature, called First-Contact Provider (FCP), was developed to identify the responsible LIP for each inpatient. The number of patients with only one designated FCP at a given time was audited daily. To ensure correct technical function, the number of Best Practice Advisories (BPAs) alerting of no documented FCP was measured. The number of inpatient critical lab values reported directly to LIPs was measured as a proxy for the accuracy of FCP in identifying the correct LIP. RESULTS: During the nine-month study period, the average daily inpatient census was 568 and the average monthly critical lab volume was 1,727. By the end of the study, the weekly mean percentage of patients with one FCP documented at noon reached 98.6%. The weekly mean number of BPAs dropped from 5,313/day to less than 50/day. The monthly mean percentage of critical results reported directly to LIPs increased from a pre-FCP baseline of 18.0% to 87.8%. CONCLUSION: FCP largely solved the far-reaching problem of accurate LIP identification for hospitalized patients. This, in turn, significantly improved the ability to report inpatient critical lab values directly to LIPs.

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.

Investigating the Impact of Different Suspicion of Infection Criteria on the Accuracy of Quick Sepsis-Related Organ Failure Assessment, Systemic Inflammatory Response Syndrome, and Early Warning Scores.

OBJECTIVE: Studies in sepsis are limited by heterogeneity regarding what constitutes suspicion of infection. We sought to compare potential suspicion criteria using antibiotic and culture order combinations in terms of patient characteristics and outcomes. We further sought to determine the impact of differing criteria on the accuracy of sepsis screening tools and early warning scores. DESIGN: Observational cohort study. SETTING: Academic center from November 2008 to January 2016. PATIENTS: Hospitalized patients outside the ICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Six criteria were investigated: 1) any culture, 2) blood culture, 3) any culture plus IV antibiotics, 4) blood culture plus IV antibiotics, 5) any culture plus IV antibiotics for at least 4 of 7 days, and 6) blood culture plus IV antibiotics for at least 4 of 7 days. Accuracy of the quick Sepsis-related Organ Failure Assessment score, Sepsis-related Organ Failure Assessment score, systemic inflammatory response syndrome criteria, the National and Modified Early Warning Score, and the electronic Cardiac Arrest Risk Triage score were calculated for predicting ICU transfer or death within 48 hours of meeting suspicion criteria. A total of 53,849 patients met at least one infection criteria. Mortality increased from 3% for group 1 to 9% for group 6 and percentage meeting Angus sepsis criteria increased from 20% to 40%. Across all criteria, score discrimination was lowest for systemic inflammatory response syndrome (median area under the receiver operating characteristic curve, 0.60) and Sepsis-related Organ Failure Assessment score (median area under the receiver operating characteristic curve, 0.62), intermediate for quick Sepsis-related Organ Failure Assessment (median area under the receiver operating characteristic curve, 0.65) and Modified Early Warning Score (median area under the receiver operating characteristic curve 0.67), and highest for National Early Warning Score (median area under the receiver operating characteristic curve 0.71) and electronic Cardiac Arrest Risk Triage (median area under the receiver operating characteristic curve 0.73). CONCLUSIONS: The choice of criteria to define a potentially infected population significantly impacts prevalence of mortality but has little impact on accuracy. Systemic inflammatory response syndrome was the least predictive and electronic Cardiac Arrest Risk Triage the most predictive regardless of how infection was defined.