key: cord-0778727-gl8xzzdv
authors: Bhavani, Sivasubramanium V.; Huang, Elbert S.; Verhoef, Philip A.; Churpek, Matthew M.
title: Novel Temperature Trajectory Subphenotypes in COVID-19
date: 2020-07-21
journal: Chest
DOI: 10.1016/j.chest.2020.07.027
sha: 9890c2ff03a1292e1d1bfede096905d1d81a9c10
doc_id: 778727
cord_uid: gl8xzzdv

nan

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resulting illness, coronavirus disease 2019 (COVID-19), has affected over 9 million people globally, with over 470,000 deaths. The highest number of cases and fatalities have occurred in the United States, with over 120,000 deaths as of the end of June 1 . In contrast to other common viral infections, COVID-19 presents unique challenges with high rates of hypoxemic respiratory failure, hyperinflammatory cytokine storm, coagulation abnormalities, and cardiac and renal dysfunction 2, 3 . Identification of COVID-19 subphenotypes could lead to better understanding of the diverse host responses resulting in these heterogeneous presentations.

Fever is a common presenting symptom in COVID-19, and the thermoregulatory response to infection operates at the intersection of the immunological, neurological, cardiovascular, and other body systems 4, 5 . Thus, longitudinal temperature trajectories could provide unique insights into the multi-organ dysfunction seen with COVID-19. We have previously published a novel method of identifying subphenotypes in hospitalized patients with all-cause infection using longitudinal body temperature measurements 6 . These temperature trajectory subphenotypes had distinct demographics, physiological and immune markers, and outcomes. We hypothesize that using a similar approach specific to COVID-19 patients would identify subphenotypes with unique clinical characteristics and inflammatory and coagulation abnormalities. Importantly we hypothesize that these temperature trajectory subphenotypes will have distinct outcomes, with the primary outcome of interest being 30-day inpatient mortality.

We included all adult patients admitted to University of Chicago Medicine between March 1 st and June 24 th , 2020 who tested positive for SARS-CoV-2. We excluded patients tested for SARS-CoV-2 more than three days after admission to exclude potential hospital-acquired cases. We also excluded patients who were discharged or died within 24 hours of hospitalization, since these patients may not have adequate temperature data to be classified by the algorithm. We included temperature measurements taken in the first 72 hours of hospitalization in the algorithm. The temperature data from hour 0 to hour 72 were split into onehour blocks of time. For patients with multiple temperature measurements in a one-hour block, the earliest measurement was used. We applied group based trajectory modeling (GBTM) to identify the temperature trajectory subphenotypes. GBTM is a finite mixture model used to identify clusters of patients following similar trajectories of a variable of interest (i.e., temperature) 7 . We selected the four-group quadratic model based on our prior work 6 . The GBTM algorithm computes a unique quadratic equation of temperature as a function of time for each of the four subphenotypes. Patients are classified into the temperature trajectory subphenotype whose quadratic function most closely matches their temperature measurements.

Once patients were classified into subphenotypes, the differences in clinical characteristics between the subphenotypes were compared using analysis of rank (ANOVA) or chi-squared tests, as appropriate. The primary outcome was 30-day inpatient mortality, which was modeled on subphenotype using Cox regression analysis, controlling for demographics, comorbidities, and severity of illness. Patients who were discharged from the hospital before 30 days were assumed to be alive at 30 days for the regression analysis. GBTM was performed using the traj package in Stata.

Our final cohort consisted of 696 hospitalized patients positive for SARS-CoV-2. The median age was 61 years (IQR 47-73 years), with 51% males, and a predominantly African American patient population (85%). The intensive care unit (ICU) admission rate was 35%, and the 30-day inpatient mortality rate was 8.8%.

Four subphenotypes were identified: Group 1 (n=139, 20%) had normal presenting temperatures that increased over the subsequent 72 hours; Group 2 (n=97, 14%) had higher presenting temperatures but decreased over time; Group 3 (n=277, 40%) had normal body temperatures throughout without significant changes; Group 4 (n=183, 26%) had low body temperatures (Figure 1) . Age was significantly different between the subphenotypes: Group 1 was the youngest, while Group 4 was the oldest (57 vs 64 years, p=0.04). Group 4 had the highest prevalence of congestive heart failure (CHF) (32%, p=0.002) and chronic pulmonary disease (31%, p=0.01). Body Mass Index (BMI) was highest in Group 1 and lowest in Group 4 (34 vs 29, p<0.001). There were significant differences in laboratory results, with Group 4 having the highest d-dimer, troponin, lactic acid, creatinine, and total bilirubin ( Table 1) .

Although Tylenol use was significantly different between the subphenotypes, the pattern of Tylenol administration over time did not suggest antipyretic medications playing a role in the divergent temperature patterns of Group 1 and Group 2. Specifically, Group 1 consistently got more Tylenol over time compared to Group 2, suggesting that the increase in temperature over time was not due solely to inadequate antipyretic therapy.

Group 1 had the highest 30-day inpatient mortality rate at 12.2%, while Group 2 had the lowest mortality rate at 3.1%. On Cox regression controlling for demographics, comorbidities, and Sequential Organ Failure Assessment (SOFA) score, patients' subphenotype was significantly associated with mortality (p<0.05). Group 1 had almost five times higher hazard ratio of mortality than patients in Group 2 (HR 4.98, 95% CI 1.41-17.6; p=0.01). Group 3 and 4 had higher hazard ratio of mortality compared to Group 2 but did not reach statistical significance (Group 3 -HR 3.12; p=0.07; Group 4 -HR 2.38; p=0.2).

We report the use of longitudinal temperature measurements to identify novel subphenotypes in COVID-19 illness. Group 1 were the youngest subphenotype with the highest BMI, while Group 4 were the oldest with high prevalence of pulmonary disease and CHF. Group 1 had the highest mortality rate, while Group 4 had the most significant lab abnormalities with elevated creatinine, total bilirubin and lactic acid. The high mortality rate seen in Group 1 and the organ dysfunction seen in Group 4 suggest that both subphenotypes have a dysregulated response to COVID-19.

Conversely, Group 2 had the lowest mortality rate, suggesting a potentially favorable host response to infection.

Given the heterogeneity of COVID-19, and the diversity of potential therapeutics, identification of clinically relevant subphenotypes is necessary for a precision approach to treatment 8 . For instance, it is unclear which patients benefit from therapies that block elements of the cytokine storm response 8, 9 . Fever is a hallmark of cytokine storm, and thus Group 1 with rising body temperatures and elevated CRP levels may benefit from this type of therapy.

Similarly, prolonged time to normalization of body temperature correlates with SARS-2-COV shedding 10 . Therefore, Group 1 may have prolonged viral shedding and could require extended antiviral therapy. Group 4 were the oldest patients and had 32% prevalence of CHF; given the age, high-risk comorbidity, and elevated d-dimer levels, Group 4 may be at higher risk for venous thromboembolism and may benefit from aggressive screening or treatment, hypotheses that should be tested in future work. The limitations of the study include that it is retrospective and single center. Further research is needed using multicenter data to investigate the prognostic and phenotypic potential of temperature trajectory subphenotypes in COVID-19.

In conclusion, we found four distinct subphenotypes of COVID-19 patients with markedly different clinical characteristics and mortality rates. Our results suggest that patients in these subphenotypes may have differing risks for developing cytokine storm, coagulopathy, cardiac and renal injury, and may require targeted management. Group 4 (n=183, 26%) had low body temperatures. Group 1 had almost five times higher hazard ratio of death compared to Group 2, when controlling for demographics, comorbidities, and severity of illness.

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Abbreviation list: 1. severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) 2. coronavirus disease 2019 (COVID-19) 3. group based trajectory modeling (GBTM) 4. analysis of rank (ANOVA) 5

Body Mass Index (BMI)

C-reactive protein (CRP) 9. sequential organ failure assessment (SOFA) score