Comparison of post-acute sequelae following hospitalization for COVID-19 and influenza

The present study used the TriNetX COVID-19 Network, an international collaboration of health research platforms that compiles de-identified patient data from electronic health records (EHRs). These records encompass a wide variety of patient information, including demographic details, medical diagnoses, procedures, medication records, laboratory results, genomic data, and types of healthcare organization visits. Over 120 healthcare organizations (HCOs) worldwide, predominantly academic health centers, have contributed data from their main hospitals, affiliated institutions, and outpatient clinics to TriNetX. For this specific analysis, we utilized the COVID-19 network, encompassing data from more than 114 million patients from 87 HCOs. TriNetX offers integrated tools for patient-level data analysis and delivers aggregated results to the researchers. Detailed information on the database can be accessed online [24]. Written informed consent was not required because TriNetX contains anonymized data. The Institutional Review Board of the Chi Mei Medical Center approved the study protocol (no. 11202–002).

In the patient selection process, the TriNetX database was used, which contains 86 HCOs as of July 4, 2023. The initial patient pool consisted of individuals who had visited these HCOs at least twice between March 1, 2020, and January 1, 2023. Patients who tested positive for SARS-CoV-2 or were diagnosed with COVID-19 between January 1, 2022, and January 1, 2023, and those who were prescribed antiviral agents and were initially hospitalized were identified from this pool. The prescribed antiviral medications included molnupiravir, remdesivir, and nirmatrelvir plus ritonavir. The selection process was identical for all patients diagnosed with influenza within the same timeframe. The analysis was restricted to patients aged ≥ 18.

Subsequently, several exclusion criteria were used. For the COVID-19 group, patients who were also diagnosed with influenza and those with long-term COVID-related symptoms one year before the index date were excluded. Similarly, for the influenza group, patients who tested positive for SARS-CoV-2 or were diagnosed with COVID-19 and those with long-term COVID-related symptoms one year before the index date were excluded.

Finally, patient selection involved propensity score matching on a 1:1 basis for age at index, race, sex, adverse socioeconomic determinants of health, and comorbid medical conditions. This resulted in two comparable groups for this study: a COVID-19 and an influenza group (Tables S1 and S2).

We considered 47 variables to adjust for imbalances in baseline characteristics between the COVID-19 and influenza groups. The list included both confirmed and suspected risk factors for COVID-19 and more severe cases of the illness, such as demographics (eg, age, sex, and ethnicity), adverse socioeconomic determinants of health (including "problems related to education and literacy," "problems related to employment and unemployment," and "problems related to housing and economic circumstances," as defined by ICD-10), and comorbidities (such as obesity, hyperlipidemia, hypertension, diabetes mellitus, chronic kidney disease, asthma, chronic lower respiratory diseases, ischemic heart disease, neoplasm, chronic liver diseases, stroke, dementia, rheumatoid arthritis, lupus, psoriasis, human immunodeficiency virus infection, mood disorders, and psychotic disorders).

The primary outcome of this study was a composite outcome consisting of 12 clinical features of post-COVID conditions, observed 90–180 days after the index event. These features include chest/throat pain, abnormal breathing, abdominal symptoms, fatigue/malaise, anxiety/depression, pain, headache, cognitive dysfunction, myalgia, loss of taste or smell, sleep disturbances, coughing, and palpitations [25‐27].

We investigated the secondary outcomes of all-cause hospitalization, all-cause emergency department (ED) visits, and deaths during the follow-up period. Table S3 provides additional details regarding the diagnostic, visiting, and procedural codes used to define these outcomes [21, 28, 29].

We used the built-in propensity score-matching (PSM) function of the TriNetX platform to ensure a 1:1 match between the participants in the COVID-19 and influenza groups. This was achieved using a nearest-neighbor greedy matching algorithm with a caliper width of 0.1 pooled standard deviation. Standard differences were then computed to assess the balance between groups, with differences in absolute values < 0.1, indicating a good match between groups [30].

Subsequently, we performed Kaplan–Meier analysis, followed by log-rank tests and calculation of hazard ratio (HR) with 95% confidence intervals (CI) to compare the two groups. Statistical significance was set at p < 0.05. The HR was used to describe the relative risk of post-COVID conditions based on a comparison of time-to-event rates calculated using a proportional hazard model, which is a built-in function in TriNetX.

For subgroup analysis, we compared the primary and secondary outcomes between the two groups, stratified by age (18–64 and ≥ 65 years), sex, vaccine status (unvaccinated, 1 or 2 doses of vaccine, boosted), and race (Caucasian and non-Caucasian). The vaccine type used was Pfizer with CPT code 91,307 (0051A, 0052A, 0071A, 0072A), Janssen 91,303 (0031A), Novavax (0041A, 0042A), and Moderna 91,301 (0011A, 0012A, 0013A, 0111A).

After excluding ineligible participants, the final cohort comprised 7,187 individuals hospitalized for COVID-19 and 11,266 individuals who survived hospitalization for influenza, serving as the comparator cohorts (Fig. 1). Baseline characteristics differed significantly between the COVID-19 and influenza groups. Patients with COVID-19 were, on average, older compared to those with influenza (63.9 ± 16.7 vs. 55.4 ± 21.2). Furthermore, there were variations in the distribution of sex and race between the two groups (Table 1). The study group also exhibited a higher prevalence of being overweight and obese than the control group (16.1% vs. 11.2%). Conversely, the prevalence of asthma was higher in the influenza group than that in the COVID-19 group (11.0% vs. 5.8%). However, after propensity score matching, 6,614 patients were identified in each group, resulting in well-balanced baseline characteristics. No significant differences were observed between the COVID-19 and influenza groups (Table 1).

Overall, the COVID-19 group had a higher incidence of any post-COVID-19 condition when compared with the influenza group (17.9% vs. 13.0%), with an HR of 1.398 (95% CI, 1.251–1.562) (Table S2). During the follow-up period, the study group had a higher risk of developing post-COVID conditions when compared with the control group (log-rank p < 0.05; Fig. 2). Specifically, anxiety/depression was the most common post-COVID-19 condition, followed by abnormal breathing, abdominal symptoms, fatigue, chest/throat pain, coughing, cognitive symptoms, sleep disturbance, headache, palpitations, myalgia, and loss of taste or smell (Fig. 3). Compared to the influenza group, the COVID-19 group had a significantly higher incidence of abnormal breathing (HR, 1.506; 95% CI, 1.246–1.822), abdominal symptoms (HR, 1.313; HR, 1.034–1.664), fatigue (HR, 1.486; 95% CI, 1.158–1.907), and cognitive symptoms (HR, 1.815; 95% CI, 1.235–2.668) (Table S4).

During the follow-up period, the COVID-19 group had a significantly higher risk of composite outcomes of all-cause ED visits, hospitalizations, and deaths when compared with the influenza group (27.5% vs. 21.7; HR, 1.303; 95% CI, 1.194–1.422) (Table S2). Specifically, the COVID-19 group had a higher risk of all-cause ED visits (14.8% vs. 12.1%; HR, 1.237; 95% CI, 1.098–1.393), hospitalizations (17.0% vs. 13.1%; HR, 1.302; 95% CI, 1.163–1.457), and mortality (1.5% vs. 0.4%; HR, 4.378; 95% CI, 2.573–7.449) (Table S2).

In most subgroup analyses, the COVID-19 group was associated with a consistently and significantly higher risk of post-COVID conditions (Fig. 4) and composite clinical outcomes of all-cause ED visits, hospitalizations, and deaths (Fig. 5). Exceptions were those who had received the COVID-19 vaccination and non-Caucasian populations, in which only a non-significantly higher risk of post-acute sequelae in COVID-19 was found.

This multinational and multicenter retrospective study included all adults hospitalized for COVID-19 or influenza and used a PSM approach to minimize confounding between the groups. The present findings were consistent across almost all subgroup analyses. In addition, this real-world study focused on the Omicron waves after 2022. Finally, it is difficult to determine whether the diagnosis of post-COVID conditions is related to preexisting underlying conditions or viral infections. To overcome this difficulty, we excluded patients with a history of post-COVID-19 symptoms prior to SARS-CoV-2 infection, which may have helped to make sure post-COVID conditions were newly developed following hospitalization. Therefore, this study provides updated information with robust evidence and high generalizability.

This study had several limitations. First, as an EHR-based study, we could not exclude residual errors, missing information, and inconsistencies. Incomplete documentation, coding variations, and data entry errors can introduce bias. EHR data may not represent the entire population as they primarily include individuals who seek medical care and have access to healthcare facilities. Additionally, it is important to understand that the absence of symptom reporting does not necessarily mean that symptoms have not occurred. However, this study focused on a specific group of patients who survived hospitalization. Thus, the associated selection bias was minimized. Second, although this study tried to adjust for most of the confounding variables between the study and control groups, residual factors that are not captured in the EHR, such as lifestyle or unmeasured comorbidities, may impact the outcomes of interest and potentially lead to biased results. Here, we did not assess the confounding effects of anti-COVID-19 treatments, such as anti-viral, systemic corticosteroid, or interleukin-6 blockade; however, these factors may affect the development of post-acute sequelae of SARS-CoV-2 infections [36‐38]. Third, it is noteworthy that some post-COVID conditions may be mentioned more frequently in patients with COVID-19 compared to those with influenza, even if they are equally common, potentially due to heightened anticipation and awareness by both the patient and the healthcare provider. Forth, TrinetX is a database that encompasses a vast geographic span, including the Americas, Europe, the Middle East, Africa, and the Asia–Pacific regions, however, we cannot access to specific data on the contribution of individuals and HCOs from each country due to their privacy policy. We just knew that a significant portion of these collaborations is concentrated in the United States. Finally, this study focused on hospitalized patients with COVID-19 or influenza; however, most patients with COVID-19 or influenza did not require admission for treatment. Further studies are required to clarify these issues.