key: cord-0761953-0djfs5fx
authors: Ruge, Max; Gomez, Joanne Michelle D.; du Fay de Lavallaz, Jeanne; Hlepas, Alexander; Rahman, Annas; Patel, Priya; Hoster, Clay; Lavani, Prutha; Nair, Gatha G.; Jahan, Nusrat; Alan Simmons, J.; Rao, Anupama K.; Cotts, William; Williams, Kim; Volgman, Annabelle Santos; Marinescu, Karolina; Suboc, Tisha
title: Impact of pre-existing heart failure on 60-day outcomes in patients hospitalized with COVID-19
date: 2021-06-15
journal: Am Heart J Plus
DOI: 10.1016/j.ahjo.2021.100022
sha: 971854afc3534403df43a304900aa1a2bd6469e9
doc_id: 761953
cord_uid: 0djfs5fx

BACKGROUND: In the coronavirus disease 2019 (COVID-19) global pandemic, patients with cardiovascular disease represent a vulnerable population with higher risk for contracting COVID-19 and worse prognosis with higher case fatality rates. However, the relationship between COVID-19 and heart failure (HF) is unclear, specifically whether HF is an independent risk factor for severe infection or if other accompanying comorbidities are responsible for the increased risk. METHODS: This is a retrospective analysis of 1331 adult patients diagnosed with COVID-19 infection between March and June 2020 admitted at Rush University System for Health (RUSH) in metropolitan Chicago, Illinois, USA. Patients with history of HF were identified by International Classification of Disease, Tenth Revision (ICD-10) code assignments extracted from the electronic medical record. Propensity score matching was utilized to control for the numerous confounders, and univariable logistic regression was performed to assess the relationship between HF and 60-day morbidity and mortality outcomes. RESULTS: The propensity score matched cohort consisted of 188 patients in both the HF and no HF groups. HF patients did not have lower 60-day mortality (OR 0.81; p = 0.43) compared to patients without HF. However, those with HF were more likely to require readmission within 60 days (OR 2.88; p < 0.001) and sustain myocardial injury defined as troponin elevation within 60 days (OR 3.14; p < 0.05). CONCLUSIONS: This study highlights the complex network of confounders present between HF and COVID-19. When balanced for these numerous factors, those with HF appear to be at no higher risk of 60-day mortality from COVID-19 but are at increased risk for morbidity.

Coronavirus disease-2019 (COVID- 19) and has rapidly evolved into a global pandemic responsible for 2.5 million deaths worldwide as of March 2021. 1, 2 Patients with cardiovascular comorbidities are at increased risk for complications and severe COVID-19 infection though it remains unclear whether pre-existing heart failure (HF) is by itself an independent risk factor for severe infection. 1, 3, 4 Studies that have specifically explored the impact of pre-existing HF on COVID-19 morbidity and mortality, each has found that those with HF have a higher in-hospital mortality rate. 3, [5] [6] [7] [8] Some of these studies also found higher rates of other adverse events including acute kidney injury, acute HF, mechanical ventilation, and myocardial injury. 3, 5, 6 However, isolating the effect of HF on COVID-19 outcomes is difficult due to the complex network of confounding variables present. For example, in one study, though HF patients had higher in-hospital mortality rates, after adjustment for comorbidities, HF itself was not an independent risk factor of mortality. 6 Conversely, others have found that HF is an independent risk factor after adjusting for these comorbidities. 3, 8, 9 Isolating and fully understanding the effects of pre-existing HF on COVID-19 infection is crucial as healthcare for patients with HF has been restructured during the pandemic. Therefore, identifying risk factors and high-risk patients is important for appropriate resource allocation and triage. 10 This study of 1331 hospitalized COVID-19 patients sought to compare outcomes between those with and without a previous HF diagnosis. We investigated additional measures of in-hospital J o u r n a l P r e -p r o o f Journal Pre-proof morbidity such as need for renal replacement therapy, intubation, and inotropic agents, as well as rates of readmission, and mortality at 60 days follow-up. We utilized propensity score matching to reduce the effects of any cohort differences other than existing HF to investigate whether HF is an independent risk factor for severe COVID-19 infection or if other accompanying comorbidities are responsible for the increased risk.

This retrospective cohort study consisted of patients ≥18 years old diagnosed with COVID-19 admitted to Rush University System for Health (RUSH) between March to June 2020. Data was collected through a combination of automatic and manual data extraction, and patients were followed through medical record review for a minimum of 60 days after the date of initial Table 1 ).

Baseline characteristics included age, sex, body mass index (BMI), race, and a variety of comorbidities that were included as covariables in propensity score matching (Table 1) .

Covariables were chosen based on clinical relevance with emphasis on including factors with a known association with both HF and COVID-19 in the baseline characteristics.

J o u r n a l P r e -p r o o f Imputation was performed on race (7.6% missing), smoking status (9.2% missing), BMI (1.4% missing) through modeling with multinomial logistic regression, linear regression, and logistic regression, respectively.

To balance baseline characteristics, we utilized propensity score matching. Whereas prior studies concluded that those with HF have higher mortality rates using multivariable regression to adjust for confounding variables, proponents of propensity score matching argue it is better able to generate a similar distribution of baseline characteristics and comorbidities between the groups, which more closely approximates a quasi-randomized controlled trial. 3, 6, 8, 9, 11, 12 Due to the large number of intertwined comorbidities that exist with HF patients, creating similar groups by optimally balancing covariates is critical. In a simulation study comparing the two statistical methods, propensity score methods were superior at estimating effect sizes than multivariable regression modeling. 11 Propensity scores were calculated using a logistic regression model based on pre-defined covariates. Matching was performed using a 1:1 nearest-neighbor technique with a caliper width of 0.10, an even more stringent caliper than usually recommended for observational studies. 13 The primary outcome was 60-day mortality. Secondary outcomes included need for intubation, need for tracheostomy, need for intensive care unit, vasopressor requirement, and inotrope requirement.

J o u r n a l P r e -p r o o f Journal Pre-proof Patient records were also reviewed for the occurrence of major adverse cardiovascular events (MACE) up to 60 days from hospital admission, which included myocardial injury (defined as cardiac troponin I > 0.09 ng/mL), life-threatening arrhythmia, deep venous thrombosis, symptoms of acute HF, acute renal failure requiring renal replacement therapy, or pulmonary embolism. Data was also collected on readmission and mortality. If there was no documentation of mortality, the patient was assumed to alive.

Baseline characteristics and the incidence of key outcomes were also compared between the matched and non-matched HF patients to better understand why they went unmatched and to determine whether they had significant differences in the frequency of outcomes.

All data analysis, including statistical analyses, was performed using RStudio version 1.3 (Boston, Massachusetts). Propensity score analysis and matching were performed using the MatchIt package. Kaplan-Meier survival estimates were generated and plotted with the survival and survminer packages respectively.

Continuous variables are reported with mean and standard deviation for normally distributed variables and with median and interquartile range for variables not normally distributed.

Categorical variables are reported as counts and proportions. Continuous variables were compared with t-tests, and categorical variables with the Pearson chi-square test.

Journal Pre-proof Univariable logistic regression was performed with presence of HF as the predictor variable for the various outcome variables, both pre-and post-matching. Odds ratios (OR) with 95% confidence intervals (CI) are reported. The threshold for statistical significance was set to a pvalue < 0.05.

Among 1331 patients, 256 (19.2%) had a history of HF. Patients with HF were more likely to be older (p < 0.001), higher BMI (p < 0.01), and African American (p < 0.001) ( Table 1) . Those with HF were also more likely to have a variety of comorbidities including a history of atrial fibrillation, coronary artery disease, hypertension, chronic kidney disease, chronic obstructive pulmonary disorder, diabetes mellitus, asthma, cancer, ventricular arrhythmias, stroke, acute myocardial infarction, and DVT or PE (Table 1) .

Propensity score matching using all the pre-defined variables in table 1 allowed for the matching of 188 in both the heart failure and no heart failure groups. Post-matching, all variables in Table   1 had a standardized mean difference (SMD) less than 0.1, indicating a well-balanced match.

After propensity score matching, patients with HF did not have an increased risk of 60-day mortality versus those without HF (14.4% vs 13.8%; OR 1.04 [CI: 0.58 -1.87]; p = 0.88) ( Figure 1 , Table 2 ). Those with HF also had similar rates of intubation, tracheostomy, need for intensive care unit, and vasopressor or inotropic requirement as those without HF. HF patients J o u r n a l P r e -p r o o f When analyzing only those with HF, the 68 non-matched patients tended to be older (p < 0.01) and were more likely to be female (p < 0.05) than their 188 matched counterparts (Supplemental Table 2 ). In addition, non-matched patients were more likely to have a variety of comorbidities including atrial fibrillation (p < 0.001), coronary artery disease (p < 0.001), hypertension (p < 0.01), chronic kidney disease (p < 0.001), COPD (p < 0.001), diabetes mellitus (p < 0.01), ventricular arrhythmia (p < 0.05), and acute myocardial infarction (p < 0.01). Non-matched HF patients did not have increased risk of the key study outcomes including 60-day mortality, requiring ICU, and overall MACE.

J o u r n a l P r e -p r o o f

Compared to patients without HF, pre-existing HF did not appear to be an independent risk factor for increased 60-day mortality in our cohort. While prior studies have observed higher mortality in HF patients with COVID-19, our study design utilized propensity score matching to reduce bias and create well balanced groups. This reduction in bias is particularly important in isolating the effect of HF on COVID-19 as numerous comorbidities have independent and potentially confounding relationships between these two diseases including diabetes mellitus, chronic kidney disease, chronic respiratory disease, tobacco use, and more. 3, 14 Our findings further echo this importance as prior to matching, an approximately 2-fold higher risk of mortality was present, which approximates that reported in prior studies (Table 2) . 3, 8 However, after balance was achieved through matching, this mirage of an effect was no longer observable.

Of note, Rey at al. similarly demonstrated a higher mortality rate in HF patients, but when adjusted for comorbidities, they found that HF was not an independent risk factor for mortality. 6 Our findings suggest that the variables controlled for during our propensity score matching are the drivers of the higher mortality in HF patients with COVID-19 rather than the HF itself. In examining prior studies asking this same research question, each has adjusted for different comorbidities, which could in part be responsible for differences in results (Supplemental Table   3 ). For example, the largest study of COVID-19 patients with pre-existing HF did not control for coronary artery disease, which is associated with HF and worse COVID-19 outcomes. 9 To our J o u r n a l P r e -p r o o f knowledge, our study provides the most comprehensive attempt, to date, at controlling for confounders between HF and COVID-19.

Importantly, pre-existing HF was a significant predictor for readmission and the occurrence of an acute HF exacerbation within 60 days. Prior studies assessing the association between HF and increased readmissions have shown mixed results, but many of these studies have been limited by smaller sample sizes and shorter follow-up durations. [15] [16] [17] In our study cohort, 159 total patients (pre-matching) were readmitted during 60-days follow-up, which provided a robust sample for analysis. With the continued spread of COVID-19, high rates of readmission in patients with COVID-19 and history of HF requires more structured remote care for HF patients. 10

While myocardial injury is known to occur in up to 60% of hospitalized COVID-19 patients, our data suggest that those with pre-existing HF are at increased risk for this complication. Several mechanisms of COVID-19 induced acute myocardial injury have been proposed including myocyte damage from hypoxia, inflammation, downregulation of angiotensin converting enzyme 2 (ACE2), hypercoagulability leading to development of coronary microvascular thrombi, and supply-demand mismatch leading to plaque rupture and myocardial infarction. 18, 19 It is unclear why HF patients are at increased risk, but troponin elevation is prevalent in patients with chronic HF and present in nearly all patients presenting with acute decompensated HF. 20 Therefore, this suggests that the increased risk for myocardial injury could be due to a combination of both J o u r n a l P r e -p r o o f COVID-19 effects on myocardium and a generalized increased risk for troponin elevation in HF patients.

While many COVID-19 studies focus on in-hospital outcomes, our follow up of 60 days provides critical information surrounding both in-hospital and post-discharge events. While COVID-19 has significantly changed health care delivery across virtually all specialties, special consideration must be given to HF patients due to their baseline increased risk for complications, readmission, and overall mortality. In fact, the most common cause of readmission in Medicare patients is chronic heart failure. 21 Therefore, our data provide a longitudinal view of adverse outcomes from COVID-19 in patient population known to be at risk for medical complications.

We also analyzed the non-matched HF patients in comparison to matched HF patients to provide insight into why these patients went unmatched and whether they may represent a sicker subpopulation of HF patients that could limit generalizability of our study. At baseline, the nonmatched patients were older and had increased frequency of a variety of comorbidities. However, there was no difference in the frequency of key outcomes between the matched and non-matched HF patients, indicating that this is not a limitation of this study.

In our cohort, approximately 1 of every 5 patients admitted with COVID-19 had a pre-existing diagnosis of HF, which is considerably higher than prior HF studies. 3, 6, 8, 9 Our mortality rate for both the entire cohort (9.2%) and heart failure patients (18.8%) was also lower than these prior J o u r n a l P r e -p r o o f Journal Pre-proof HF studies. This difference is, in part, attributable to differences in city-specific disease burdens at the time of the study. For example, 3 of the major HF studies took place in New York City from late February to June 2020, Italy from March to April 2020, and Madrid, Spain from March to April 2020. Each of these areas were profoundly strained by disease burden during the time of the study, which therefore limits each's external validity. In contrast, Chicago was experiencing a more manageable disease burden at this time. As of April 13, 2020, New York City had the most reported cases of any city in the United States at 106,764, which included 7,154 deaths. 22 Chicago on the other hand, while fifth overall, had only 15,474 reported cases and 543 deaths. 22 Internationally, the United States' 7-day rolling average of COVID-19 confirmed deaths per million people on April 1, 2020 was 2.29 compared to 13.55 and 17.54 in Italy and Spain, respectively. 23 Because Chicago as a whole was less strained at the time these data were collected, our data are likely more applicable to other populations.

The use of electronic health records has inherent sources for error. In this case, identification of diagnosis of HF was done with the use of ICD-10 codes, which have been shown to be specific (96.8%) but not sensitive (75.3%) for identifying a prior HF diagnosis. 24 Another potential source of error is in the method of COVID-19 diagnosis. Confirmed COVID-19 patients were included based on either positive polymerase chain reaction (PCR) testing or point-of-care rapid testing, with each test subject to their own sensitivity and specificities. Based on data using multiple cohort studies, the overall sensitivity and specificity of COVID-19 testing has been estimated to be 71% and 100%, respectively. 25 Lastly, readmission rates were only captured for J o u r n a l P r e -p r o o f patients who had a subsequent hospitalization to a health care system in or around Chicago that also use the EPIC electronic medical record.

In a propensity-matched population of hospitalized COVID-19 patients in metropolitan Chicago, a pre-existing HF history was not associated with increased risk of 60-day mortality. However, those with HF were found to be at higher risk for a complicated course including myocardial injury, as well as need for structured hospital follow up to avoid hospital readmission.

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