Search

Background: Early diagnostic uncertainty for infection causes delays in antibiotic administration in infected patients and unnecessary antibiotic administration in noninfected patients., Objective: To develop a machine learning model for the early detection of untreated infection (eDENTIFI), with the presence of infection determined by clinician chart review., Derivation Cohort: Three thousand three hundred fifty-seven adult patients hospitalized between 2006 and 2018 at two health systems in Illinois, United States., Validation Cohort: We validated in 1632 patients in a third Illinois health system using area under the receiver operating characteristic curve (AUC)., Prediction Model: Using a longitudinal discrete-time format, we trained a gradient boosted machine model to predict untreated infection in the next 6 hours using routinely available patient demographics, vital signs, and laboratory results., Results: eDENTIFI had an AUC of 0.80 (95% CI, 0.79-0.81) in the validation cohort and outperformed the systemic inflammatory response syndrome criteria with an AUC of 0.64 (95% CI, 0.64-0.65; p < 0.001). The most important features were body mass index, age, temperature, and heart rate. Using a threshold with a 47.6% sensitivity, eDENTIFI detected infection a median 2.0 hours (interquartile range, 0.9-5.2 hr) before antimicrobial administration, with a negative predictive value of 93.6%. Antibiotic administration guided by eDENTIFI could have decreased unnecessary IV antibiotic administration in noninfected patients by 10.8% absolute or 46.4% relative percentage points compared with clinicians., Conclusion: eDENTIFI could both decrease the time to antimicrobial administration in infected patients and unnecessary antibiotic administration in noninfected patients. Further prospective validation is needed., Competing Interests: Dr. Buell is supported by T32 HL007605. Dr. Bhavani is supported by the National Institute of General Medical Sciences GM144867. Dr. Parker is supported by K08HL150291 and R01LM014263. Dr. Churpek is supported by R35GM145330. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Purpose: Sepsis is a heterogeneous syndrome. Identification of sepsis subphenotypes with distinct immune profiles could lead to targeted therapies. This study investigates the immune profiles of patients with sepsis following distinct body temperature patterns (i.e., temperature trajectory subphenotypes)., Methods: Hospitalized patients from four hospitals between 2018 and 2022 with suspicion of infection were included. A previously validated temperature trajectory algorithm was used to classify study patients into temperature trajectory subphenotypes. Microbiological profiles, clinical outcomes, and levels of 31 biomarkers were compared between these subphenotypes., Results: The 3576 study patients were classified into four temperature trajectory subphenotypes: hyperthermic slow resolvers (N = 563, 16%), hyperthermic fast resolvers (N = 805, 23%), normothermic (N = 1693, 47%), hypothermic (N = 515, 14%). The mortality rate was significantly different between subphenotypes, with the highest rate in hypothermics (14.2%), followed by hyperthermic slow resolvers 6%, normothermic 5.5%, and lowest in hyperthermic fast resolvers 3.6% (p < 0.001). After multiple testing correction for the 31 biomarkers tested, 20 biomarkers remained significantly different between temperature trajectories: angiopoietin-1 (Ang-1), C-reactive protein (CRP), feline McDonough sarcoma-like tyrosine kinase 3 ligand (Flt-3l), granulocyte colony stimulating factor (G-CSF), granulocyte-macrophage colony stimulating factor (GM-CSF), interleukin (IL)-15, IL-1 receptor antagonist (RA), IL-2, IL-6, IL-7, interferon gamma-induced protein 10 (IP-10), monocyte chemoattractant protein-1 (MCP-1), human macrophage inflammatory protein 3 alpha (MIP-3a), neutrophil gelatinase-associated lipocalin (NGAL), pentraxin-3, thrombomodulin, tissue factor, soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), and vascular cellular adhesion molecule-1 (vCAM-1).The hyperthermic fast and slow resolvers had the highest levels of most pro- and anti-inflammatory cytokines. Hypothermics had suppressed levels of most cytokines but the highest levels of several coagulation markers (Ang-1, thrombomodulin, tissue factor)., Conclusion: Sepsis subphenotypes identified using the universally available measurement of body temperature had distinct immune profiles. Hypothermic patients, who had the highest mortality rate, also had the lowest levels of most pro- and anti-inflammatory cytokines., (© 2024. Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies., Methods: A consortium of critical care physicians and data scientists from eight US healthcare systems developed the Common Longitudinal Intensive Care Unit (ICU) data Format (CLIF), an open-source database format that harmonizes a minimum set of ICU Data Elements for use in critical illness research. We created a pipeline to process adult ICU EHR data at each site. After development and iteration, we conducted two proof-of-concept studies with a federated research architecture: 1) an external validation of an in-hospital mortality prediction model for critically ill patients and 2) an assessment of 72-hour temperature trajectories and their association with mechanical ventilation and in-hospital mortality using group-based trajectory models., Results: We converted longitudinal data from 94,356 critically ill patients treated in 2020-2021 (mean age 60.6 years [standard deviation 17.2], 30% Black, 7% Hispanic, 45% female) across 8 health systems and 33 hospitals into the CLIF format, The in-hospital mortality prediction model performed well in the health system where it was derived (0.81 AUC, 0.06 Brier score). Performance across CLIF consortium sites varied (AUCs: 0.74-0.83, Brier scores: 0.06-0.01), and demonstrated some degradation in predictive capability. Temperature trajectories were similar across health systems. Hypothermic and hyperthermic-slow-resolver patients consistently had the highest mortality., Conclusions: CLIF facilitates efficient, rigorous, and reproducible critical care research. Our federated case studies showcase CLIF's potential for disease sub-phenotyping and clinical decision-support evaluation. Future applications include pragmatic EHR-based trials, target trial emulations, foundational multi-modal AI models of critical illness, and real-time critical care quality dashboards.

Importance: Intravenous fluids are an essential part of treatment in sepsis, but there remains clinical equipoise on which type of crystalloid fluids to use in sepsis. A previously reported sepsis subphenotype (ie, group D) has demonstrated a substantial mortality benefit from balanced crystalloids compared with normal saline., Objective: To test the hypothesis that targeting balanced crystalloids to patients with group D sepsis through an electronic health record (EHR) alert will reduce 30-day inpatient mortality., Design, Setting, and Participants: The Precision Resuscitation With Crystalloids in Sepsis (PRECISE) trial is a parallel-group, multihospital, single-blind, pragmatic randomized clinical trial to be conducted at 6 hospitals in the Emory Healthcare system. Patients with suspicion of group D infection in whom a clinician initiates an order for normal saline in the emergency department (ED) or intensive care unit (ICU) will be randomized to usual care and intervention arms., Intervention: An EHR alert that appears in the ED and ICUs to nudge clinicians to use balanced crystalloids instead of normal saline., Main Outcomes and Measures: The primary outcome is 30-day inpatient mortality. Secondary outcomes are ICU admission, in-hospital mortality, receipt of vasoactive drugs, receipt of new kidney replacement therapy, and receipt of mechanical ventilation (vasoactive drugs, kidney replacement therapy, and mechanical ventilation are counted if they occur after randomization and within the 30-day study period). Intention-to-treat analysis will be conducted., Discussion: The PRECISE trial may be one of the first precision medicine trials of crystalloid fluids in sepsis. Using routine vital signs (temperature, heart rate, respiratory rate, and blood pressure), available even in low-resource settings, a validated machine learning algorithm will prospectively identify and enroll patients with group D sepsis who may have a substantial mortality reduction from used of balanced crystalloids compared with normal saline., Results: On finalizing participant enrollment and analyzing the data, the study's findings will be shared with the public through publication in a peer-reviewed journal., Conclusions: With use of a validated machine learning algorithm, precision resuscitation in sepsis could fundamentally redefine international standards for intravenous fluid resuscitation., Trial Registration: ClinicalTrials.gov Identifier: NCT06253585.

Objectives: To develop and validate machine learning (ML) models to predict high-flow nasal cannula (HFNC) failure in COVID-19, compare their performance to the respiratory rate-oxygenation (ROX) index, and evaluate model accuracy by self-reported race., Design: Retrospective cohort study., Setting: Four Emory University Hospitals in Atlanta, GA., Patients: Adult patients hospitalized with COVID-19 between March 2020 and April 2022 who received HFNC therapy within 24 hours of ICU admission were included., Interventions: None., Measurements and Main Results: Four types of supervised ML models were developed for predicting HFNC failure (defined as intubation or death within 7 d of HFNC initiation), using routine clinical variables from the first 24 hours of ICU admission. Models were trained on the first 60% ( n = 594) of admissions and validated on the latter 40% ( n = 390) of admissions to simulate prospective implementation. Among 984 patients included, 317 patients (32.2%) developed HFNC failure. eXtreme Gradient Boosting (XGB) model had the highest area under the receiver-operator characteristic curve (AUROC) for predicting HFNC failure (0.707), and was the only model with significantly better performance than the ROX index (AUROC 0.616). XGB model had significantly worse performance in Black patients compared with White patients (AUROC 0.663 vs. 0.808, p = 0.02). Racial differences in the XGB model were reduced and no longer statistically significant when restricted to patients with nonmissing arterial blood gas data, and when XGB model was developed to predict mortality (rather than the composite outcome of failure, which could be influenced by biased clinical decisions for intubation)., Conclusions: Our XGB model had better discrimination for predicting HFNC failure in COVID-19 than the ROX index, but had racial differences in accuracy of predictions. Further studies are needed to understand and mitigate potential sources of biases in clinical ML models and to improve their equitability., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine.)

Background: Trajectories of bedside vital signs have been used to identify sepsis subphenotypes with distinct outcomes and treatment responses. The objective of this study was to validate the vitals trajectory model in a multicenter cohort of patients hospitalized with COVID-19 and to evaluate the clinical characteristics and outcomes of the resulting subphenotypes., Research Question: Can the trajectory of routine bedside vital signs identify COVID-19 subphenotypes with distinct clinical characteristics and outcomes?, Study Design and Methods: The study included adult patients admitted with COVID-19 to four academic hospitals in the Emory Healthcare system between March 1, 2020, and May 31, 2022. Using a validated group-based trajectory model, we classified patients into previously defined vital sign trajectories using oral temperature, heart rate, respiratory rate, and systolic and diastolic BP measured in the first 8 h of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes. Heterogeneity of treatment effect to tocilizumab was evaluated., Results: The 7,065 patients with hospitalized COVID-19 were classified into four subphenotypes: group A (n = 1,429, 20%)-high temperature, heart rate, respiratory rate, and hypotensive; group B (1,454, 21%)-high temperature, heart rate, respiratory rate, and hypertensive; group C (2,996, 42%)-low temperature, heart rate, respiratory rate, and normotensive; and group D (1,186, 17%)-low temperature, heart rate, respiratory rate, and hypotensive. Groups A and D had higher ORs of mechanical ventilation, vasopressors, and 30-day inpatient mortality (P < .001). On comparing patients receiving tocilizumab (n = 55) with those who met criteria for tocilizumab but were admitted before its use (n = 461), there was significant heterogeneity of treatment effect across subphenotypes in the association of tocilizumab with 30-day mortality (P = .001)., Interpretation: By using bedside vital signs available in even low-resource settings, we found novel subphenotypes associated with distinct manifestations of COVID-19, which could lead to preemptive and targeted treatments., Competing Interests: Financial/Nonfinancial Disclosures The authors have reported to CHEST the following: S. V. B., C. R., P. A. V., M. M. C., C. M. C., M. M. C. is a named inventor on a patent for a risk stratification algorithm for hospitalized patients (US patent No. 11,410,777)., (Copyright © 2023 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.)

Objective . To examine whether heart rate interval based rapid alert (HIRA) score derived from a combination model of heart rate variability (HRV) and modified early warning score (MEWS) is a surrogate for the detection of acute respiratory failure (ARF) in critically ill sepsis patients. Approach . Retrospective HRV analysis of sepsis patients admitted to Emory healthcare intensive care unit (ICU) was performed between sepsis-related ARF and sepsis controls without ARF. HRV measures such as time domain, frequency domain, and nonlinear measures were analyzed up to 24 h after patient admission, 1 h before the onset of ARF, and a random event time in the sepsis controls. Statistical significance was computed by the Wilcoxon Rank Sum test. Machine learning algorithms such as eXtreme Gradient Boosting and logistic regression were developed to validate the HIRA score model. The performance of HIRA and early warning score models were evaluated using the area under the receiver operating characteristic (AUROC). Main Results . A total of 89 (ICU) patients with sepsis were included in this retrospective cohort study, of whom 31 (34%) developed sepsis-related ARF and 58 (65%) were sepsis controls without ARF. Time-domain HRV for Electrocardiogram (ECG) Beat-to-Beat RR intervals strongly distinguished ARF patients from controls. HRV measures for nonlinear and frequency domains were significantly altered ( p < 0.05) among ARF compared to controls. The HIRA score AUC: 0.93; 95% confidence interval (CI): 0.88-0.98) showed a higher predictive ability to detect ARF when compared to MEWS (AUC: 0.71; 95% CI: 0.50-0.90). Significance . HRV was significantly impaired across patients who developed ARF when compared to controls. The HIRA score uses non-invasively derived HRV and may be used to inform diagnostic and therapeutic decisions regarding the severity of sepsis and earlier identification of the need for mechanical ventilation., (Creative Commons Attribution license.)

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: Robotic bronchoscopy (RB) aims to increase the diagnostic yield of guided bronchoscopy by providing improved navigation, farther reach, and stability during lesion sampling., Methods: We reviewed data on consecutive cases in which RB was used to diagnose lung lesions from June 15, 2018, to December 15, 2019, at the University of Chicago Medical Center., Results: The median lesion size was 20.5 mm. All patients had at least 12 months of follow-up. The overall diagnostic accuracy was 77% (95 of 124). The diagnostic accuracy was 85%, 84%, and 38% for concentric, eccentric, and absent radial endobronchial ultrasound (r-EBUS) views, respectively (P < .001). A positive r-EBUS view and lesions size of 20 to 30 mm had higher odds of achieving a diagnosis on multivariate analysis. The 12-month diagnostic accuracy, sensitivity, specificity, and positive and negative predictive value for malignancy were 77%, 69%, 100%, 100%, and 58%, respectively. Pneumothorax was noted in 1.6% (n = 2) patients with bleeding reported in 3.2% (n = 4). No postprocedure respiratory failure was noted., Conclusions: The overall diagnostic accuracy using RB for pulmonary lesion sampling in our cohort with 12-month follow-up compared favorably with established guided bronchoscopy technologies. Lesion size ≥20 mm and confirmation by r-EBUS predicted higher accuracy independent of concentric or eccentric r-EBUS patterns., (Copyright © 2023 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Objective: Severe infection can lead to organ dysfunction and sepsis. Identifying subphenotypes of infected patients is essential for personalized management. It is unknown how different time series clustering algorithms compare in identifying these subphenotypes., Materials and Methods: Patients with suspected infection admitted between 2014 and 2019 to 4 hospitals in Emory healthcare were included, split into separate training and validation cohorts. Dynamic time warping (DTW) was applied to vital signs from the first 8 h of hospitalization, and hierarchical clustering (DTW-HC) and partition around medoids (DTW-PAM) were used to cluster patients into subphenotypes. DTW-HC, DTW-PAM, and a previously published group-based trajectory model (GBTM) were evaluated for agreement in subphenotype clusters, trajectory patterns, and subphenotype associations with clinical outcomes and treatment responses., Results: There were 12 473 patients in training and 8256 patients in validation cohorts. DTW-HC, DTW-PAM, and GBTM models resulted in 4 consistent vitals trajectory patterns with significant agreement in clustering (71-80% agreement, P < .001): group A was hyperthermic, tachycardic, tachypneic, and hypotensive. Group B was hyperthermic, tachycardic, tachypneic, and hypertensive. Groups C and D had lower temperatures, heart rates, and respiratory rates, with group C normotensive and group D hypotensive. Group A had higher odds ratio of 30-day inpatient mortality (P < .01) and group D had significant mortality benefit from balanced crystalloids compared to saline (P < .01) in all 3 models., Discussion: DTW- and GBTM-based clustering algorithms applied to vital signs in infected patients identified consistent subphenotypes with distinct clinical outcomes and treatment responses., Conclusion: Time series clustering with distinct computational approaches demonstrate similar performance and significant agreement in the resulting subphenotypes., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

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.

Purpose: Sepsis is a heterogeneous syndrome and identification of sub-phenotypes is essential. This study used trajectories of vital signs to develop and validate sub-phenotypes and investigated the interaction of sub-phenotypes with treatment using randomized controlled trial data., Methods: All patients with suspected infection admitted to four academic hospitals in Emory Healthcare between 2014-2017 (training cohort) and 2018-2019 (validation cohort) were included. Group-based trajectory modeling was applied to vital signs from the first 8 h of hospitalization to develop and validate vitals trajectory sub-phenotypes. The associations between sub-phenotypes and outcomes were evaluated in patients with sepsis. The interaction between sub-phenotype and treatment with balanced crystalloids versus saline was tested in a secondary analysis of SMART (Isotonic Solutions and Major Adverse Renal Events Trial)., Results: There were 12,473 patients with suspected infection in training and 8256 patients in validation cohorts, and 4 vitals trajectory sub-phenotypes were found. Group A (N = 3483, 28%) were hyperthermic, tachycardic, tachypneic, and hypotensive. Group B (N = 1578, 13%) were hyperthermic, tachycardic, tachypneic (not as pronounced as Group A) and hypertensive. Groups C (N = 4044, 32%) and D (N = 3368, 27%) had lower temperatures, heart rates, and respiratory rates, with Group C normotensive and Group D hypotensive. In the 6,919 patients with sepsis, Groups A and B were younger while Groups C and D were older. Group A had the lowest prevalence of congestive heart failure, hypertension, diabetes mellitus, and chronic kidney disease, while Group B had the highest prevalence. Groups A and D had the highest vasopressor use (p < 0.001 for all analyses above). In logistic regression, 30-day mortality was significantly higher in Groups A and D (p < 0.001 and p = 0.03, respectively). In the SMART trial, sub-phenotype significantly modified treatment effect (p = 0.03). Group D had significantly lower odds of mortality with balanced crystalloids compared to saline (odds ratio (OR) 0.39, 95% confidence interval (CI) 0.23-0.67, p < 0.001)., Conclusion: Sepsis sub-phenotypes based on vital sign trajectory were consistent across cohorts, had distinct outcomes, and different responses to treatment with balanced crystalloids versus saline., (© 2022. Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients., Methods: This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers., Findings: Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses., Interpretation: SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated., Funding: This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS)., Competing Interests: Declaration of interests PGL- Present work: efforts were supported by, BJC HealthCare Healthcare Innovation Lab “Big Ideas” award, Doris Duke Charitable Foundation, and Fund to Retain Clinical Scientists, # 2015215. Outside scope of present work- CDC MMC- Outside scope of present work, grants to institution: DOD PRMRP W81XWH-21-1-0009, NIH/ NIDDK R01-DK126933A -01, NIH/ NIGMS R35-13362546, NIH/NIGMS, R01-GM123193. Patent pending ARCD. P0535US.P2. APM- Outside scope of present work: Pfizer personal stock ownership. TK- Outside scope of present work: Grants: NIA, AHRQ, NLM, NCATS, NIMH; Licenses: Springer, Elsevier; Consultant: Pfizer, Inc; Presentations and Events: Department of Defense. CCS- Outside scope of present work: Grants: NIH, Department of Defense, Roche-Genentech, Quantum Leap Healthcare Collaborative; Consultant: Vasomune, Gen1e Life Sciences, Cellenkos, Janssen. SLW. AD, KD, TMP, SVB, AB, PS, EHG, BNR, MK, and AM have no conflicts of interest to report., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Objectives: Early identification of infection improves outcomes, but developing models for early identification requires determining infection status with manual chart review, limiting sample size. Therefore, we aimed to compare semi-supervised and transfer learning algorithms with algorithms based solely on manual chart review for identifying infection in hospitalized patients., Materials and Methods: This multicenter retrospective study of admissions to 6 hospitals included "gold-standard" labels of infection from manual chart review and "silver-standard" labels from nonchart-reviewed patients using the Sepsis-3 infection criteria based on antibiotic and culture orders. "Gold-standard" labeled admissions were randomly allocated to training (70%) and testing (30%) datasets. Using patient characteristics, vital signs, and laboratory data from the first 24 hours of admission, we derived deep learning and non-deep learning models using transfer learning and semi-supervised methods. Performance was compared in the gold-standard test set using discrimination and calibration metrics., Results: The study comprised 432 965 admissions, of which 2724 underwent chart review. In the test set, deep learning and non-deep learning approaches had similar discrimination (area under the receiver operating characteristic curve of 0.82). Semi-supervised and transfer learning approaches did not improve discrimination over models fit using only silver- or gold-standard data. Transfer learning had the best calibration (unreliability index P value: .997, Brier score: 0.173), followed by self-learning gradient boosted machine (P value: .67, Brier score: 0.170)., Discussion: Deep learning and non-deep learning models performed similarly for identifying infection, as did models developed using Sepsis-3 and manual chart review labels., Conclusion: In a multicenter study of almost 3000 chart-reviewed patients, semi-supervised and transfer learning models showed similar performance for model discrimination as baseline XGBoost, while transfer learning improved calibration., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objectives: Body temperature trajectories of infected patients are associated with specific immune profiles and survival. We determined the association between temperature trajectories and distinct manifestations of coronavirus disease 2019., Design: Retrospective observational study., Setting: Four hospitals within an academic healthcare system from March 2020 to February 2021., Patients: All adult patients hospitalized with coronavirus disease 2019., Interventions: Using a validated group-based trajectory model, we classified patients into four previously defined temperature trajectory subphenotypes using oral temperature measurements from the first 72 hours of hospitalization. Clinical characteristics, biomarkers, and outcomes were compared between subphenotypes., Measurements and Main Results: The 5,903 hospitalized coronavirus disease 2019 patients were classified into four subphenotypes: hyperthermic slow resolvers (n = 1,452, 25%), hyperthermic fast resolvers (1,469, 25%), normothermics (2,126, 36%), and hypothermics (856, 15%). Hypothermics had abnormal coagulation markers, with the highest d-dimer and fibrin monomers (p < 0.001) and the highest prevalence of cerebrovascular accidents (10%, p = 0.001). The prevalence of venous thromboembolism was significantly different between subphenotypes (p = 0.005), with the highest rate in hypothermics (8.5%) and lowest in hyperthermic slow resolvers (5.1%). Hyperthermic slow resolvers had abnormal inflammatory markers, with the highest C-reactive protein, ferritin, and interleukin-6 (p < 0.001). Hyperthermic slow resolvers had increased odds of mechanical ventilation, vasopressors, and 30-day inpatient mortality (odds ratio, 1.58; 95% CI, 1.13-2.19) compared with hyperthermic fast resolvers. Over the course of the pandemic, we observed a drastic decrease in the prevalence of hyperthermic slow resolvers, from representing 53% of admissions in March 2020 to less than 15% by 2021. We found that dexamethasone use was associated with significant reduction in probability of hyperthermic slow resolvers membership (27% reduction; 95% CI, 23-31%; p < 0.001)., Conclusions: Hypothermics had abnormal coagulation markers, suggesting a hypercoagulable subphenotype. Hyperthermic slow resolvers had elevated inflammatory markers and the highest odds of mortality, suggesting a hyperinflammatory subphenotype. Future work should investigate whether temperature subphenotypes benefit from targeted antithrombotic and anti-inflammatory strategies., Competing Interests: Dr. Bhavani is supported by the American Thoracic Society and GlaxoSmithKline research grant in coronavirus disease 2019 and by National Institutes of Health (NIH)/National Institute of General Medical Sciences (NIGMS) K23GM144867. Dr. Maier is supported by NIH/National Center for Advancing Translational Sciences UL1TR002378. Dr. Churpek is supported by NIGMS (R01GM123193), Department of Defense (W81XWH-21-1-0009), National Institute on Aging (R21 AG068720), and National Institute on Alcohol Abuse and Alcoholism (R01 DA051464-01); he has a patent pending (ARCD. P0535US.P2) for risk stratification algorithms for hospitalized patients and has received research support from EarlySense (Tel Aviv, Israel). Dr. Coopersmith is supported by funding from the NIH (GM072808, GM104323, AA027396). Drs. Bhavani, Parker, Holder, Kamaleswaran, Churpek, and Coopersmith received support for article research from the NIH. Drs. Parker’s, Kamaleswaran’s, Wang’s, Churpek’s, and Coopersmith’s institutions received funding from the NIH. Dr. Holder received funding from Baxter International. Dr. Wang’s institution received funding from The Petit Institute Faculty Fellow Fund, the Amazon Faculty Research Fellowship, The Wallace H. Coulter Distinguished Faculty Fellow Award, the Georgia Institution of Technology, and the National Science Foundation; she received support for article research from The Petit Institute Faculty Fellow, The Wallace H. Coulter Distinguished Faculty Fellow, and The Amazon Faculty Research Fellowship. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2022 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Background: Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches., Methods: PRoVENT-COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov, NCT04346342., Findings: Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent class analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO 2 /FiO 2 , pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0·10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0-15 vs 5, 0-17; p=0·016) and more frequent venous thrombotic events (109 [32%] of 336 patients vs 176 [26%] of 671 patients; p=0·048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day mortality (OR 1·64, 95% CI 1·17-2·29 for ventilatory ratio; 1·82, 1·24-2·66 for mechanical power). The association between upward ventilatory ratio trajectories (trajectory B) and 28-day mortality was confirmed in the replication cohorts (OR 4·65, 95% CI 1·87-11·6 for ventilatory ratio in replication cohort 1; 1·89, 1·05-3·37 for ventilatory ratio in replication cohort 2)., Interpretation: At baseline, COVID-19-related ARDS has no consistent respiratory subphenotype. Patients diverged from a fairly homogenous to a more heterogeneous population, with trajectories of ventilatory ratio and mechanical power being the most discriminatory. Modelling these parameters alone provided prognostic value for duration of mechanical ventilation and mortality., Funding: Amsterdam UMC., Competing Interests: Declaration of interests LDJB reports grants from the Dutch Lung Foundation (Young Investigator grant), grants from the Dutch Lung Foundation and Health Holland (Public–Private Partnership grant), grants from the Dutch Lung Foundation (Dirkje Postma Award), grants from the IMI COVID19 iniative, and grants from Amsterdam UMC fellowship, outside the submitted work. All other authors declare no competing interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Drs. Churpek and Edelson received funding from National Institute of General Medical Sciences R01 GM123193; disclosed a patent pending (ARCD. P0535US. P2) for risk stratification algorithms for hospitalized patients and have received research support from EarlySense (Tel Aviv, Israel). Drs. Churpek, Winslow, Shah, and Afshar received support for article research from the National Institutes of Health. Dr. Afshar also received support from a K23 from National Institute on Alcohol Abuse and Alcoholism K23 AA024503. Dr. Edelson received support for article research from the American Heart Association (Dallas, TX) and Laerdal Medical (Stavanger, Norway), and she received research support from Philips Healthcare (Andover, MA). She disclosed ownership interest in Quant HC (Chicago, IL), which is developing products for risk stratification of hospitalized patients; she is president/co-founder and shareholder of AgileMD with licensing agreements with Philips Healthcare and EarlySense; and she is the chair for the American Heart Association Get with The Guidelines adult research task force. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

Background and Objective: IPC in patients with MPE are removed within 3 months in 30-58% of cases, usually due to decreased pleural fluid output as a result of pleurodesis. Disease control can also account for the lack of fluid output, potentially explaining why 4-14% of patients undergo repeat pleural intervention for fluid re-accumulation (at the time of disease recurrence or progression). The aim of our pilot study is to determine the accuracy of thoracic ultrasound (TUS) in predicting pleurodesis success in patients with MPE at the time of IPC removal., Methods: This is a single-centre, prospective observational cohort study that enrolled consecutive patients with confirmed MPE treated with IPC at the time of IPC removal. TUS was performed to calculate a PAS. Patients were followed up for a minimum of 3 months. Failure was defined as pleural fluid recurrence within 3 months., Results: Twenty-seven patients were screened and 25 were included in the final analysis. Pleurodesis success was observed in 88% (n = 22) and failure in 12% (n = 3) of patients. The mean PAS was higher in patients with pleurodesis success (22.0 vs 9.3, P = 0.01). A PAS greater than 10 predicted pleurodesis success with a sensitivity of 100% and specificity of 86%., Conclusion: This pilot study suggests that TUS at the time of IPC removal accurately identifies patients who have achieved pleurodesis and therefore will not have re-accumulation of pleural effusion or require an ipsilateral pleural intervention for at least 3 months post-IPC removal., (© 2020 Asian Pacific Society of Respirology.)

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.

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.

Background: The Robotic Endoscopic System (Auris Health, Inc., Redwood City, CA) has the potential to overcome several limitations of contemporary guided-bronchoscopic technologies for the diagnosis of lung lesions. Our objective is to report on the initial post-marketing feasibility, safety and diagnostic yield of this technology., Methods: We retrospectively reviewed data on consecutive cases in which robot-assisted bronchoscopy was used to sample lung lesions at four centers in the US (academic and community) from June 15th, 2018 to December 15th, 2018., Results: One hundred and sixty-seven lesions in 165 patients were included in the analysis, with an average follow-up of 185 ± 55 days. The average size of target lesions was 25.0 ± 15.0 mm. Seventy-one percent were located in the peripheral third of the lung. Pneumothorax and airway bleeding occurred in 3.6 and 2.4% cases, respectively. Navigation was successful in 88.6% of cases. Tissue samples were successfully obtained in 98.8%. The diagnostic yield estimates ranged from 69.1 to 77% assuming the cases of biopsy-proven inflammation without any follow-up information (N = 13) were non-diagnostic and diagnostic, respectively. The yield was 81.5, 71.7 and 26.9% for concentric, eccentric and absent r-EBUS views, respectively. Diagnostic yield was not affected by lesion size, density, lobar location or centrality., Conclusions: RAB implementation in community and academic centers is safe and feasible, with an initial diagnostic yield of 69.1-77% in patients with lung lesions that require diagnostic bronchoscopy. Comparative trials with the existing bronchoscopic technologies are needed to determine cost-effectiveness of this technology.

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.

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.