Coronavirus Disease 2019 Temperature Trajectories Correlate With Hyperinflammatory and Hypercoagulable Subphenotypes

Distinct immune profiles and clinical outcomes in sepsis subphenotypes based on temperature trajectories

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.

A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research

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.

Using Trajectories of Bedside Vital Signs to Identify COVID-19 Subphenotypes

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.

Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care

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.

Impact of changing case definitions for coronavirus disease 2019 (COVID-19) hospitalization on pandemic metrics

OBJECTIVE

To examine the impact of commonly used case definitions for coronavirus disease 2019 (COVID-19) hospitalizations on case counts and outcomes.

DESIGN, PATIENTS, AND SETTING

Retrospective analysis of all adults hospitalized between March 1, 2020, and March 1, 2022, at 5 Massachusetts acute-care hospitals.

INTERVENTIONS

We applied 6 commonly used definitions of COVID-19 hospitalization: positive severe acute respiratory coronavirus virus 2 (SARS-CoV-2) polymerase chain reaction (PCR) assay within 14 days of admission, PCR plus dexamethasone administration, PCR plus remdesivir, PCR plus hypoxemia, institutional COVID-19 flag, or COVID-19 International Classification of Disease, Tenth Revision (ICD-10) codes. Outcomes included case counts and in-hospital mortality. Overall, 100 PCR-positive cases were reviewed to determine each definition's accuracy for distinguishing primary or contributing versus incidental COVID-19 hospitalizations.

RESULTS

Of 306,387 hospital encounters, 15,436 (5.0%) met the PCR-based definition. COVID-19 hospitalization counts varied substantially between definitions: 4,628 (1.5% of all encounters) for PCR plus dexamethasone, 5,757 (1.9%) for PCR plus remdesivir, 11,801 (3.9%) for PCR plus hypoxemia, 15,673 (5.1%) for institutional flags, and 15,868 (5.2%) for ICD-10 codes. Definitions requiring dexamethasone, hypoxemia, or remdesivir selected sicker patients compared to PCR alone (mortality rates 12.2%, 10.7%, and 8.8% vs 8.3%, respectively). Definitions requiring PCR plus remdesivir or dexamethasone did not detect a reduction in in-hospital mortality associated with the SARS-CoV-2 Omicron variant. ICD-10 codes had the highest sensitivity (98.4%) but low specificity (39.5%) for distinguishing primary or contributing versus incidental COVID-19 hospitalizations. PCR plus dexamethasone had the highest specificity (92.1%) but low sensitivity (35.5%).

CONCLUSIONS

Commonly used definitions for COVID-19 hospitalizations generate variable case counts and outcomes and differentiate poorly between primary or contributing versus incidental COVID-19 hospitalizations. Surveillance definitions that better capture and delineate COVID-19-associated hospitalizations are needed.

Clinical Sepsis Phenotypes in Critically Ill Patients

Sepsis, defined as the life-threatening dysregulated host response to an infection leading to organ dysfunction, is considered as one of the leading causes of mortality worldwide, especially in intensive care units (ICU). Moreover, sepsis remains an enigmatic clinical syndrome, with complex pathophysiology incompletely understood and a great heterogeneity both in terms of clinical expression, patient response to currently available therapeutic interventions and outcomes. This heterogeneity proves to be a major obstacle in our quest to deliver improved treatment in septic critical care patients; thus, identification of clinical phenotypes is absolutely necessary. Although this might be seen as an extremely difficult task, nowadays, artificial intelligence and machine learning techniques can be recruited to quantify similarities between individuals within sepsis population and differentiate them into distinct phenotypes regarding not only temperature, hemodynamics or type of organ dysfunction, but also fluid status/responsiveness, trajectories in ICU and outcome. Hopefully, we will eventually manage to determine both the subgroup of septic patients that will benefit from a therapeutic intervention and the correct timing of applying the intervention during the disease process.

Relative Bradycardia and Tachycardia and Their Associations with Adverse Outcomes in Hospitalized COVID-19 Patients

Background

Relative-tachycardia (RT), a phenomenon of unproportionately high heart-rate elevation in response to fever, has been previously attributed to unfavourable outcomes in severe-inflammatory-response-syndrome (SIRS). Relative heart-rate to body-temperature ratio (RHR) and its prognostic associations in patients with severe and critical coronavirus disease 2019 (COVID-19) have not been investigated.

Methods

We retrospectively analyzed heart-rate and body-temperature data at admission in patients who were hospitalized due to COVID-19 at a tertiary center from March 2020 to June 2021. After excluding patients with known heart rate affecting medications (beta-blockers and other antiarrhythmics) and atrial fibrillation, a total of 3490 patients were analyzed. Patients were divided into quartiles based on RHR on admission, with patients belonging to the 1st quartile designated as having relative-bradycardia (RB) and patients belonging to 4th quartile designated as having RT. Comparisons with baseline clinical characteristics and the course of treatment were done.

Results

There were 57.5% male patients. Median age was 69 years. Most patients had severe or critical COVID-19 at admission. Median heart-rate at the time of hospital admission was 90/min, median body-temperature was 38 °C, and median RHR was 2.36 with interquartile-range 2.07-2.65. RB in comparison to middle-range RHR was significantly associated with older age, higher comorbidity burden, less severe COVID-19 and less pronounced inflammatory profile, and in comparison to RT additionally with higher frequency of hyperlipoproteinemia but lower frequency of obesity. RT in comparison to middle-range RHR was significantly associated with younger age, more severe COVID-19, lower comorbidity burden, lower frequency of arterial hypertension, higher frequency of diabetes mellitus, and more pronounced inflammatory profile. In multivariate analyses adjusted for clinically meaningful parameters, RB patients experienced more favorable survival compared to RT, whereas RT patients experienced higher mortality in comparison to RB and middle-range RHR patients, independently of older age, male sex, higher comorbidity burden and higher COVID-19 severity.

Conclusions

Heart rate and axillary temperature are an indispensable part of a clinical exam, easy to measure, at effectively no cost. RT at admission, as a sign of excessive activation of the sympathetic nervous system, is independently associated with fatal outcomes in COVID-19 patients.

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.

Simulation of COVID-19 symptoms in a genetically engineered mouse model: implications for the long haulers

The ongoing pandemic (also known as coronavirus disease-19; COVID-19) by a constantly emerging viral agent commonly referred as the severe acute respiratory syndrome corona virus 2 or SARS-CoV-2 has revealed unique pathological findings from infected human beings, and the postmortem observations. The list of disease symptoms, and postmortem observations is too long to mention; however, SARS-CoV-2 has brought with it a whole new clinical syndrome in "long haulers" including dyspnea, chest pain, tachycardia, brain fog, exercise intolerance, and extreme fatigue. We opine that further improvement in delivering effective treatment, and preventive strategies would be benefited from validated animal disease models. In this context, we designed a study, and show that a genetically engineered mouse expressing the human angiotensin converting enzyme 2; ACE-2 (the receptor used by SARS-CoV-2 agent to enter host cells) represents an excellent investigative resource in simulating important clinical features of the COVID-19. The ACE-2 mouse model (which is susceptible to SARS-CoV-2) when administered with a recombinant SARS-CoV-2 spike protein (SP) intranasally exhibited a profound cytokine storm capable of altering the physiological parameters including significant changes in cardiac function along with multi-organ damage that was further confirmed via histological findings. More importantly, visceral organs from SP treated mice revealed thrombotic blood clots as seen during postmortem examination. Thus, the ACE-2 engineered mouse appears to be a suitable model for studying intimate viral pathogenesis thus paving the way for identification, and characterization of appropriate prophylactics as well as therapeutics for COVID-19 management.

Development and validation of novel sepsis subphenotypes using trajectories of vital signs

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.