key: cord-0755465-l5ck1xpe
authors: Song, X.; Ji, J.; Reva, B.; Joshi, H.; Calinawan, A. P.; Mazumdar, M.; Taioli, E.; Wang, P.; Veluswamy, R.
title: Post-Anticoagulant D-dimer as a Highly Prognostic Biomarker of COVID-19 Mortality
date: 2020-09-03
journal: nan
DOI: 10.1101/2020.09.02.20180984
sha: f04b5c5cec480fb055ee17a553208e9393bd6b14
doc_id: 755465
cord_uid: l5ck1xpe

Importance: Clinical biomarkers that accurately predict mortality are needed for the effective management of patients with severe COVID-19 illness. Objective: To determine whether D-dimer levels after anticoagulation treatment is predictive of in-hospital mortality. Design: Retrospective study using electronic health record data. Setting: A large New York City hospital network serving a diverse, urban patient population. Participants: Adult patients hospitalized for severe COVID-19 infection who received therapeutic anticoagulation for thromboprophylaxis between February 25, 2020 and May 31, 2020. Exposures: Mean and trend of D-dimer levels in the 3 days following the first therapeutic dose of anticoagulation. Main Outcomes: In-hospital mortality versus discharge. Results: 1835 adult patients (median age, 67 years [interquartile range, 57-78]; 58% male) with PCR-confirmed COVID-19 who received therapeutic anticoagulation during hospitalization were included. 74% (1365) of patients were discharged and 26% (430) died in hospital. The study cohort was divided into four groups based on the mean D-dimer levels and its trend following anticoagulation initiation, with significantly different in-hospital mortality rates (p<0.001): 49% for the high mean-increase trend (HI) group; 27% for the high-decrease (HD) group; 21% for the low-increase (LI) group; and 9% for the low-decrease (LD) group. Using penalized logistic regression models to simultaneously analyze 67 variables (baseline demographics, comorbidities, vital signs, laboratory values, D-dimer levels), post-anticoagulant D-dimer groups had the highest adjusted odds ratios (ORadj) for predicting in-hospital mortality. The ORadj of in-hospital death among patients from the HI group was 6.58 folds (95% CI 3.81-11.16) higher compared to the LD group. The LI (ORadj: 4.06, 95% CI 2.23-7.38) and HD (ORadj: 2.37; 95% CI 1.37-4.09) groups were also associated with higher mortality compared to the LD group. Conclusions and Relevance: D-dimer levels and its trend following the initiation of anticoagulation have high and independent predictive value for in-hospital mortality. This novel prognostic biomarker should be incorporated into management protocols to guide resource allocation and prospective studies for emerging treatments in hospitalized COVID-19 patients.

The COVID-19 pandemic, caused by the viral pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in over than 22.2 million confirmed cases and 783,000 deaths worldwide through August, 2020 1 patients are associated with higher mortality, sparking significant interest in understanding the role of D-dimer in these patients [3] [4] [5] [6] . D-dimer levels reflect the underlying hypercoagulable state in COVID-19 patients, and the use of anticoagulant therapy in hospitalized COVID-19 patients with elevated D-dimer levels resulted in a significant mortality benefit 3,7-10 . As a consequence, many guidelines and institutional All rights reserved. No reuse allowed without permission.

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The copyright holder for this preprint this version posted September 3, 2020. . protocols have recommended therapeutic (using intermediate or full doses of anticoagulants) anticoagulation strategies for thromboprophylaxis in patients with severe COVID-19 infection, particularly for those having significantly elevated baseline D-dimer levels [4] [5] [6] . However, while D-dimer measurements generally are followed throughout the hospitalization, there remains no consensus or guidance as to how Ddimer levels should be monitored or interpreted with respect to anticoagulant therapy and outcomes in COVID-19 patients.

In this study, we hypothesized that the D-dimer levels and their trends following therapeutic anticoagulation in patients with severe COVID-19 infections may be predictive of mortality in addition to other known risk factors. To determine the role of Ddimer in this setting, we leveraged a large institutional database of COVID-19 hospitalized patients from the Mount Sinai Health System (MSHS) in New York City, one of the initial epicenters of the COVID-19 pandemic in the United States (US).

MSHS includes the Mount Sinai Hospital and 7 other tertiary and community hospitals throughout New York City, serving a diverse patient population with a high representation of low-income minorities. This study utilized a comprehensive COVID-19 database created by the Mount Sinai Data Warehouse, which includes de-identified clinical data extracted from the electronic medical records of all patients tested for and/or diagnosed with COVID-19 until May 31, 2020. We included all adult (≥18 years All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . https://doi.org/10.1101/2020.09.02.20180984 doi: medRxiv preprint of age) patients who were hospitalized for a new COVID-19 infection (based on RT-PCR COVID-19 assay using nasopharyngeal swabs), and were treated with therapeutic anticoagulation for thromboprophylaxis. Of those, patients having follow up data for at least 3 days after the first anticoagulant dose and information on their hospitalization outcome (discharged vs. deceased) were included. We then excluded patients who : 1) had absolute contraindications for therapeutic anticoagulation due to either low platelet counts (<50,000/uL) or elevated international normalization ratio (INR>1.5); 2) were given therapeutic anticoagulation or tissue plasminogen activator (TPA) for a newly diagnosed VTE as large vessel thrombosis could affect post-anticoagulant D-dimer levels; and 3) were discharged and readmitted into the hospital as baseline information for each admission could not be uniquely identified.

For each patient, we obtained baseline sociodemographic data (i.e., age, sex, selfreported race and ethnicity), smoking status, body mass index (BMI), and 18 common comorbidities (Supplemental Table 1 ). Baseline vital signs (temperature, systolic blood pressure, diastolic blood pressure, oxygen saturation, heart rate, respiratory rate) and laboratory tests obtained within 24 hours of admission and prior to receiving anticoagulation were collected. In cases where multiple vital signs were recorded during the first 24 hours of admission, we used the most clinically abnormal measurement concerning for systemic inflammatory response syndrome (SIRS) 11 . A preprocessing procedure was performed to exclude laboratory tests that were missing in > 50% of All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . https://doi.org/10.1101/2020.09.02.20180984 doi: medRxiv preprint patients. The remaining 35 laboratory tests that were used for analysis included complete blood count (CBC) with differential, complete metabolic panel (CMP), inflammatory markers (i.e., ferritin, C-reactive protein [CRP], Lactate dehydrogenase

[LDH]) liver function tests and baseline D-dimer. All together, 65 baseline variables were considered for each patient (Supplemental Table 1 ).

The MSHS, alongside many other high acuity hospitals, has developed a standardized 

We recorded post-anticoagulant D-dimer levels as all measurements collected within the first 3 days after therapeutic anticoagulation was started. As the number of D-dimer All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . measurements during this period varied dramatically (from 0 to 18 measurements) for each patient, we calculated both the mean and trend to summarize the data. The trend was defined, if at least 2 measurements were available, as the slope of a linear regression model characterizing the dependence of the post-anticoagulant D-dimer values on the test collection time from anticoagulation. Using 2.5 ug/ml as a cutoff for the post-anticoagulant D-dimer mean value, and 0 as a cutoff for the post-anticoagulant D-dimer trend, we divided patients into four groups with similar sample sizes: HI---high mean value (>=2.5 ug/ml) and increase trend (trend>=0); HD---high mean value and decrease trend; LI ---low mean value and increase trend; and LD ---low mean value and decrease trend.

The study endpoint is a binary indicator of in-hospital mortality, defined as patients who died during their admission vs. patients who were discharged alive from the hospital, usually to home, nursing facility, acute/sub-acute rehab or long-term care facility.

To test the associations between baseline and post-anticoagulant D-dimer variables with in-hospital mortality, ߯ 2 tests and two-sample Wilcoxon tests were used for categorical and continuous variables, respectively. Bonferroni correction for multiple testing provided p<0.001 (=0.1/65) as the cutoff to determine significant associations with in-hospital mortality. Missing values in categorical variables were treated as a All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . We assessed whether the in-hospital mortality and the baseline characteristics of patients differed across the four D-dimer groups described above. We assessed the predictivity of D-dimers for in-hospital mortality conditional on baseline characteristics of patients. To better estimate effect sizes of predictors, we randomly split the samples into a discovery and validation subset with equal sizes and performed variable selection on the discovery subset while inference of effect sizes on the All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . Table 1 ). For the variables selected by the penalized regressions, we performed an ordinary logistic regression using the validation subset to estimate odds ratios (ORs) and the corresponding 95% CIs. For the variables that confirmed to be statistically significant in the validation subset, we calculated AUC differences between leave-one-predictor-out models and the full model to assess the relative importance of each predictor.

Moreover, we performed parallel analyses using only baseline variables, and compared the predictive performance of these models with the above ones using post- Table 3 ).

After applying the selection criteria to the COVID-19 database (n=65,501 patients), the final study cohort consisted of 1835 laboratory-confirmed COVID-19 positive adult patients who were hospitalized in the MSHS between February 25 and May 31, 2020 All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . https://doi.org/10.1101/2020.09.02.20180984 doi: medRxiv preprint (Figure 1) . Among them, 470 (26%) study patients died during hospitalization and 1365 (74%) were discharged alive. Patients who died during hospitalization were generally older, had more comorbidities, presented with signs of more severe respiratory distress (higher respiratory rates and lower minimum oxygen saturation), had lower kidney function, higher levels of inflammatory markers (ferritin, CRP, LDH) and higher baseline D-dimers (p<0.001 for all comparisons after Bonferroni correction). Although not statistically significant, patients who died during hospitalization also experienced a longer time between admission and the start of therapeutic anticoagulation (Tables 1 and S1).

Following therapeutic anticoagulation, the mean D-dimer was significantly higher for patients who died vs. those who were discharged from the hospital (median 3.71 ug/ml; (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. Table 2 ). Among patients within the high or low mean post-anticoagulant D-dimer groups, only lower baseline D-dimer was associated with increasing D-dimer trends (p<0.001) (Figure 3B ).

Jointly modeling post-anticoagulant D-dimer groups and 65 baseline covariates with penalized logistic regressions, 12 variables were selected to be predictive of in-hospital mortality through 10-fold cross validation based on the discovery subset. Among these, 10 variables were confirmed to be significantly associated with in-hospital mortality based on the validation subset ( Figure 4A ). Compared to patients in the LD postanticoagulant D-dimer group, patients in the HI post-anticoagulant D-dimer group were All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. (Figure 4A) . The baseline D-dimer value was not a significant predictor of mortality and was not selected in the final model. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. (starting at >0.5 ug/ml) to be predictive of in-hospital mortality 3,6,14 5 . However, while Ddimer at the time of admission may help stratify all COVID-19 patients according to mortality risk, it was not a significant predictor of mortality in our hospitalized cohort of COVID-19 patients with severe illness and receiving therapeutic anticoagulation for thromboprophylaxis. Instead, we found post-anticoagulant D-dimer levels to be highly predictive of in-hospital mortality in this group, with patients in the HI group 6.58 times more likely to die during the admission than patients in the LD group. Interestingly, All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . 1 6 patients in the LI post-anticoagulant D-dimer group had a higher risk of dying than those in the HD group, suggesting that the trend of the D-dimer following anticoagulation is more important than the three-day mean. As there were limited specific baseline patient characteristics associated with post-anticoagulant D-dimer trends, going forward it is critical that serial measurements are collected for more accurate prediction of in-hospital mortality.

While on anticoagulation, D-dimer and other coagulation parameters are commonly measured throughout the hospitalization. However, there is no consensus on how to incorporate such data to guide management decisions. Persistently elevated or rising Ddimer levels following anticoagulation may signify continued risk of large vessel and micro thrombotic events 15,16 . Our findings provide a novel prognostic biomarker that can be widely incorporated into the treatment decision protocols for severe COVID-19 patients on therapeutic anticoagulation. We highlight an important subset of patients associated with especially poor outcomes (i.e., HI post-anticoagulant D-dimer) that can help guide resource allocation and prospective studies for emerging treatments. If proven effective in this setting, additional anticoagulants (i.e. antiplatelet therapy; TPA) may be considered to manage the potential worsening of coagulopathy in these patients. Conversely, future studies may explore if patients in the LD group can be changed to prophylactic doses of anticoagulant and whether they require continued thromboprophylaxis after hospital discharge.

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(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. .

There are limitations to this study worth discussing. Patients treated at a single tertiary hospital network in New York City may not be representative of the general population in the US and worldwide. There may be imprecisions of laboratory assays, which can alter the assessment of D-dimer. In addition, we were unable to account for unmeasured confounders that may affect D-dimer levels, a particular limitation inherent to all observational studies. Ongoing randomized controlled trials assessing the impact of therapeutic anticoagulation on COVID-19 outcomes should validate the ability of post-anticoagulant D-dimer levels to predict mortality. However, it may take significant time for the results of these trials to be reported. Therefore, our findings provide useful and immediate information to help guide management decisions in patients with severe COVID-19 illness.

D-dimer levels and trends following initiation of anticoagulation have high and independent predictive value for in-hospital mortality for COVID-19 patients, and should be considered in management decisions for these patients. All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . https://doi.org/10.1101/2020.09.02.20180984 doi: medRxiv preprint

We would like to express our sincerest condolences to the patients and their families who were affected by the COVID-19 outbreak. We greatly appreciate all the health care providers who contributed to the care of these patients. The Mount Sinai de-identified COVID-19 database was supported through the computational and data resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai.

The datasets analyzed during the current study are not publicly available due to United States Federal Health Insurance Portability and Accountability Act (HIPAA) compliance.

A de-identified dataset may be available from the corresponding authors on reasonable request. All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. . (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint this version posted September 3, 2020. All rights reserved. No reuse allowed without permission.

(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 

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