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Rationale: Determining when an intensive care unit (ICU) patient is ready for discharge to the ward is a complex daily challenge for any ICU care team. Patients who experience unplanned readmissions to the ICU have increased mortality, length of stay, and cost compared with those not readmitted during their hospital stay. The accuracy of clinician prediction for ICU readmission is unknown. Objectives: To determine the accuracy of ICU physicians and nurses for predicting ICU readmissions Methods: We conducted a prospective study in the medical ICU of an academic hospital from October 2015 to September 2017. After daily rounding for patients being transferred to the ward, ICU clinicians (nurses, residents, fellows, and attendings) were asked to report the likelihood of readmission within 48 hours (using a 1-10 scale, with 10 being "extremely likely"). The accuracy of the clinician prediction score (1-10) was assessed for all clinicians and by clinician type using sensitivity, specificity, and area under the curve (AUC) for the receiver operating characteristic curve for predicting the primary outcome, which was ICU readmission within 48 hours of ICU discharge. Results: A total of 2,833 surveys was collected for 938 ICU-to-ward transfers, of which 40 (4%) were readmitted to the ICU within 48 hours of transfer. The median clinician likelihood of readmission score was 3 (interquartile range, 2-4). When physician and nurse likelihood scores were combined, the median clinician likelihood score had an AUC of 0.70 (95% confidence interval [CI], 0.62-0.78) for predicting ICU readmission within 48 hours. Nurses were significantly more accurate than interns at predicting 48-hour ICU readmission (AUC, 0.73 [95% CI, 0.64-0.82] vs. AUC, 0.60 [95% CI, 0.49-0.71]; P  = 0.03). All other pairwise comparisons were not significantly different for predicting ICU readmission within 48 hours ( P  > 0.05 for all comparisons). Conclusions: We found that all clinicians surveyed in our ICU, regardless of the level of experience or clinician type, had only fair accuracy for predicting ICU readmission. Further research is needed to determine if clinical decision support tools would provide prognostic value above and beyond clinical judgment for determining who is ready for ICU discharge.

Importance: Risk scores used in early warning systems exist for general inpatients and patients with suspected infection outside the intensive care unit (ICU), but their relative performance is incompletely characterized., Objective: To compare the performance of tools used to determine points-based risk scores among all hospitalized patients, including those with and without suspected infection, for identifying those at risk for death and/or ICU transfer., Design, Setting, and Participants: In a cohort design, a retrospective analysis of prospectively collected data was conducted in 21 California and 7 Illinois hospitals between 2006 and 2018 among adult inpatients outside the ICU using points-based scores from 5 commonly used tools: National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), Between the Flags (BTF), Quick Sequential Sepsis-Related Organ Failure Assessment (qSOFA), and Systemic Inflammatory Response Syndrome (SIRS). Data analysis was conducted from February 2019 to January 2020., Main Outcomes and Measures: Risk model discrimination was assessed in each state for predicting in-hospital mortality and the combined outcome of ICU transfer or mortality with area under the receiver operating characteristic curves (AUCs). Stratified analyses were also conducted based on suspected infection., Results: The study included 773 477 hospitalized patients in California (mean [SD] age, 65.1 [17.6] years; 416 605 women [53.9%]) and 713 786 hospitalized patients in Illinois (mean [SD] age, 61.3 [19.9] years; 384 830 women [53.9%]). The NEWS exhibited the highest discrimination for mortality (AUC, 0.87; 95% CI, 0.87-0.87 in California vs AUC, 0.86; 95% CI, 0.85-0.86 in Illinois), followed by the MEWS (AUC, 0.83; 95% CI, 0.83-0.84 in California vs AUC, 0.84; 95% CI, 0.84-0.85 in Illinois), qSOFA (AUC, 0.78; 95% CI, 0.78-0.79 in California vs AUC, 0.78; 95% CI, 0.77-0.78 in Illinois), SIRS (AUC, 0.76; 95% CI, 0.76-0.76 in California vs AUC, 0.76; 95% CI, 0.75-0.76 in Illinois), and BTF (AUC, 0.73; 95% CI, 0.73-0.73 in California vs AUC, 0.74; 95% CI, 0.73-0.74 in Illinois). At specific decision thresholds, the NEWS outperformed the SIRS and qSOFA at all 28 hospitals either by reducing the percentage of at-risk patients who need to be screened by 5% to 20% or increasing the percentage of adverse outcomes identified by 3% to 25%., Conclusions and Relevance: In all hospitalized patients evaluated in this study, including those meeting criteria for suspected infection, the NEWS appeared to display the highest discrimination. Our results suggest that, among commonly used points-based scoring systems, determining the NEWS for inpatient risk stratification could identify patients with and without infection at high risk of mortality.

Background: Deep learning algorithms have achieved human-equivalent performance in image recognition. However, the majority of clinical data within electronic health records is inherently in a non-image format. Therefore, creating visual representations of clinical data could facilitate using cutting-edge deep learning models for predicting outcomes such as in-hospital mortality, while enabling clinician interpretability. The objective of this study was to develop a framework that first transforms longitudinal patient data into visual timelines and then utilizes deep learning to predict in-hospital mortality., Methods and Findings: All adult consecutive patient admissions from 2008-2016 at a tertiary care center were included in this retrospective study. Two-dimensional visual representations for each patient were created with clinical variables on one dimension and time on the other. Predictors included vital signs, laboratory results, medications, interventions, nurse examinations, and diagnostic tests collected over the first 48 hours of the hospital stay. These visual timelines were utilized by a convolutional neural network with a recurrent layer model to predict in-hospital mortality. Seventy percent of the cohort was used for model derivation and 30% for independent validation. Of 115,825 hospital admissions, 2,926 (2.5%) suffered in-hospital mortality. Our model predicted in-hospital mortality significantly better than the Modified Early Warning Score (area under the receiver operating characteristic curve [AUC]: 0.91 vs. 0.76, P < 0.001) and the Sequential Organ Failure Assessment score (AUC: 0.91 vs. 0.57, P < 0.001) in the independent validation set. Class-activation heatmaps were utilized to highlight areas of the picture that were most important for making the prediction, thereby providing clinicians with insight into each individual patient's prediction., Conclusions: We converted longitudinal patient data into visual timelines and applied a deep neural network for predicting in-hospital mortality more accurately than current standard clinical models, while allowing for interpretation. Our framework holds promise for predicting several important outcomes in clinical medicine., Competing Interests: Dr. Churpek is supported by a career development award from the NHLBI (K08 HL121080), an R01 from NIGMS (R01 GM123193), and has a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients. This does not alter our adherence to PLOS ONE policies on sharing data and materials

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.

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: This retrospective two-hospital study analyzed whether temperature trajectory modeling could identify distinct, clinically relevant subphenotypes among neutropenic oncology patients with 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=1145, validation cohort n=6564) fit into one of four temperature subphenotypes. "Hyperthermic slow resolvers" (pooled n=1140 [14.8%], mortality n=104 [9.1%]) and "hypothermic" encounters (n=1612 [20.9%], mortality n=138 [8.6%]) had higher mortality than "hyperthermic fast resolvers" (n=1314 [17.0%], mortality n=47 [3.6%]) and "normothermic" (n=3643 [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 encounters (n=418 [11.5%]), 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 neutropenic patients in two independent cohorts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Early warning tools have been widely implemented without evidence to guide (a) recognition and (b) response team expertise optimisation. With growing databases from MET-calls and digital hospitals, we now have access to guiding information. The Queensland Adult-Deterioration-Detection-System (Q-ADDS) is widely used and requires validation.Aim: Compare the accuracy of Q-ADDS to National Early Warning Score (NEWS), Between-the-Flags (BTF) and the electronic Cardiac Arrest Risk Triage Score (eCART)).Methods: Data from the Chicago University hospital database were used. Clinical deterioration was defined as unplanned admission to ICU or death. Currently used NEWS, BTF and eCART trigger thresholds were compared with a clinically endorsed Q-ADDS variant.Results: Of 224,912 admissions, 11,706 (5%) experienced clinical deterioration. Q-ADDS (AUC 0.71) and NEWS (AUC 0.72) had similar predictive accuracy, BTF (AUC 0.64) had the lowest, and eCART (AUC 0.76) the highest. Early warning alert (advising ward MO review) had similar NPV (99.2-99.3%), for all the four tools however sensitivity varied (%: Q-ADDS = 47/NEWS = 49/BTF = 66/eCART = 40), as did alerting rate (% vitals sets: Q-ADDS = 1.4/NEWS = 3.5/BTF = 4.1/eCART = 3.4). MET alert (advising MET/critical-care review) had similar NPV for all the four tools (99.1-99.2%), however sensitivity varied (%: Q-ADDS = 14/NEWS = 24/BTF = 19/eCART = 29), as did MET alerting rate (%: Q-ADDS = 1.4/NEWS = 3.5/BTF = 4.1/eCART = 3.4). High-severity alert (advising advanced ward review, Q-ADDS only): NPV = 99.1%, sensitivity = 26%, alerting rate = 3.5%.Conclusion: The accuracy of Q-ADDS is comparable to NEWS, and higher than BTF, with eCART being the most accurate. Q-ADDS provides an additional high-severity ward alert, and generated significantly fewer MET alerts. Impacts of increased ward awareness and fewer MET alerts on actual MET call numbers and patient outcomes requires further evaluation. [ABSTRACT FROM AUTHOR]