International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

Zhang, Harrison G.; Dagliati, Arianna; Abad, Zahra Shakeri Hossein; Xiong, Xin; Bonzel, Clara-Lea; Xia, Zongqi; Tan, Bryce W.Q.; Avillach, Paul; Brat, Gabriel; Hong, Chuan; Morris, Michele; Visweswaran, Shyam; Patel, Lav P.; Gutiérrez-Sacristán, Alba; Hanauer, David A.; Holmes, John H.; Samayamuthu, Malarkodi Jebathilagam; Bourgeois, Florence T.; L'Yi, Sehi; Maidlow, Sarah E.; Moal, Bertrand; Murphy, Shawn N.; Strasser, Zachary H.; Neuraz, Antoine; Ngiam, Kee Yuan; Loh, Ne-Hooi Will; Omenn, Gilbert S.; Prunotto, Andrea; Dalvin, Lauren A; Klann, Jeffrey G.; Schubert, Petra; Vidorreta, Fernando J Sanz; Benoît, Vincent; Verdy, Guillaume; Kavuluru, Ramakanth; Estiri, Hossein; Luo, Yuan; Malovini, Alberto; Tibollo, Valentina; Bellazzi, Riccardo; Cho, Kelly; Ho, Yuk‐Lam; Tan, Amelia L.M.; Tan, Byorn W.L.; Gehlenborg, Nils; Lozano‐Zahonero, Sara; Jouhet, Vianney; Chiovato, Luca; Aronow, Bruce J.; Toh, Emma M. S.; Wong, Wei Gen Scott; Pizzimenti, Sara; Wagholikar, Kavishwar B.; Bucalo, Mauro; Cai, Tianxi; South, Andrew M.; Kohane, Isaac S.; Weber, Griffin M.

doi:10.1038/s41746-022-00623-8

Cited by 18 publications

(20 citation statements)

References 77 publications

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“…Our findings are in accordance with the results provided by Zhang et al [ 18 ] which observed an increased risk of HF in COVID-19 survivors within 89 days, but their results were presented as risk ratio and therefore they were not included in our meta-analysis. Indeed, it would be incorrect to pool risk ratio and hazard ratio together since they evaluate different outcomes from different study designs.…”

Section: Discussionsupporting

confidence: 93%

Risk of incident heart failure after COVID-19 recovery: a systematic review and meta-analysis

Zuin

Rigatelli²,

Roncon³

et al. 2022

Heart Fail Rev

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Patients recovered from COVID-19 have an increased incidence of cardiovascular disease and heart structural changes. The aim of the present manuscript is to assess the risk of incident heart failure (HF) after COVID-19 infection. Data were obtained searching MEDLINE and Scopus for all studies published at any time up to September 1, 2022 reporting the risk of incident HF in COVID-19 recovered patients. The cumulative post-COVID-19 incidence and risk of incident HF were pooled using a random effects model and presented with the corresponding 95% confidence interval (CI). Statistical heterogeneity was measured using the Higgins I 2 statistic. Overall, 21,463,173 patients (mean age 54.5 years, 58.7% males) were analyzed. Among them, 1,628,424 had confirmed COVID-19 infection while the remaining 19,834,749 represented the controls. The mean length of follow-up was 9.2 months. A random effect model revealed a pooled incidence of post COVID-19 HF in 1.1% of cases (95% CI: 0.7–1.6, I 2 : 99.8%). Moreover, recovered COVID-19 patients showed an increased risk of incident HF (HR: 1.90, 95% CI: 1.54–3.24, p < 0.0001, I 2 = 96.5%) in the same follow-up period. Meta-regression showed a direct relationship for the risk of incident HF using age ( p = 0.001) and hypertension (HT) ( p = 0.02) as moderators, while an inverse association was observed when the follow-up length was adopted as moderating variable ( p = 0.01). COVID-19 survivors had an additional 90% risk of developing HF after COVID-19 infection in the long-term period. This risk was directly related with age and previous history of HT especially in the early post-acute phase of the infection. Supplementary Information The online version contains supplementary material available at 10.1007/s10741-022-10292-0.

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Section: Discussionsupporting

confidence: 93%

Risk of incident heart failure after COVID-19 recovery: a systematic review and meta-analysis

Zuin

Rigatelli²,

Roncon³

et al. 2022

Heart Fail Rev

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“…This is because PASC appears to be a clinical syndrome that encompasses a constellation of sometimes non-specific symptoms (e.g., pain, fatigue, brain fog) that are not well represented or not often coded using administrative coding schemas. 5,10 We investigated whether or not textual data extracted using natural language processing pipelines can better capture the symptomatology associated with PASC for a patient that was extracted in chart review. For seven common symptoms associated with PASC, we examined the data capture of each symptom by (1) ICD-10 codes alone, (2) natural language processing (NLP) of clinical narratives (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…clinician notes and discharge summaries) alone, (3) both, or (4) either codes or NLP. 5,8,9,[11][12][13][14] Diagnosis codes and concept unique identifiers (CUIs) that were mapped to each of the 7 PASC symptoms are listed in Supplementary Table 6.…”

Section: Methodsmentioning

confidence: 99%

“…[1][2][3][4] Real-world data such as electronic health records (EHR) data has the potential to capture the wide spectrum of clinical features attributed to PASC in diverse patient populations. 5 To this end, the International Classification of Diseases (ICD)-10 codes is a system used by healthcare providers to code all diagnoses for claims processing and is also commonly used for EHR research purposes to capture outcomes and define cohorts in clinical studies. 6 The World Health Organization (WHO) introduced an ICD-10 code specifically for long COVID (U09.9) in October of 2021.…”

Section: Introductionmentioning

confidence: 99%

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Characterizing the use of the ICD-10 Code for Long COVID in 3 US Healthcare Systems

Zhang

Maripuri²,

Samayamuthu

et al. 2023

Preprint

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The International Classification of Diseases (ICD)-10 code (U09.9) for post-acute sequelae of COVID-19 (PASC) was introduced in October of 2021. As researchers seek to leverage this billing code for research purposes in large scale real-world studies of PASC, it is of utmost importance to understand the functional use of the code by healthcare providers and the clinical characteristics of patients who have been assigned this code. To this end, we operationalized clinical case definitions of PASC using World Health Organization and Centers for Disease Control guidelines. We then chart reviewed 300 patients with COVID-19 from three participating healthcare systems of the 4CE Consortium who were assigned the U09.9 code. Chart review results showed the average positive predictive value (PPV) of the U09.9 code ranged from 40.2% to 65.4% depending on which definition of PASC was used in the evaluation. The PPV of the U09.9 code also fluctuated significantly between calendar time periods. We demonstrated the potential utility of textual data extracted from natural language processing techniques to more comprehensively capture symptoms associated with PASC from electronic health records data. Finally, we investigated the utilization of long COVID clinics in the cohort of patients. We observed that only an average of 24.0% of patients with the U09.9 code visited a long COVID clinic. Among patients who met the WHO PASC definition, only an average of 35.6% visited a long COVID clinic.

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“…/2022 between SARS-CoV-2 infection and PASC symptoms and conditions [7,[16][17][18][19][20][21]. Research on PASC among children is emerging, with few studies using large cohorts or comparing hospitalized and non-hospitalized patients [22][23][24]. Symptoms and conditions following SARS-CoV-2 infection among children and non-hospitalized adults also have not been well characterized with large samples.…”

Section: Introductionmentioning

confidence: 99%

Association between SARS-CoV-2 Infection and Select Symptoms and Conditions 31 to 150 Days After Testing among Children and Adults

Zhang¹,

Romieu-Hernandez²,

Boehmer³

et al. 2022

Preprint

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Background An increasing number of studies have described new and persistent symptoms and conditions as potential post-acute sequelae of SARS-CoV-2 infection (PASC). However, it remains unclear whether certain symptoms or conditions occur more frequently among persons with SARS-CoV-2 infection compared with those never infected with SARS-CoV-2. We compared the occurrence of specific COVID-associated symptoms and conditions as potential PASC 31 to 150 days following a SARS-CoV-2 test among adults (≥20 years) and children (<20 years) with positive and negative test results documented in the electronic health records (EHRs) of institutions participating in PCORnet, the National Patient-Centered Clinical Research Network. Methods and Findings This study included 3,091,580 adults (316,249 SARS-CoV-2 positive; 2,775,331 negative) and 675,643 children (62,131 positive; 613,512 negative) who had a SARS-CoV-2 laboratory test (nucleic acid amplification or rapid antigen) during March 1, 2020–May 31, 2021 documented in their EHR. We identified hospitalization status in the day prior through the 16 days following the SARS-CoV-2 test as a proxy for the severity of COVID-19. We used logistic regression to calculate the odds of receiving a diagnostic code for each symptom outcome and Cox proportional hazard models to calculate the risk of being newly diagnosed with each condition outcome, comparing those with a SARS-CoV-2 positive test to those with a negative test. After adjustment for baseline covariates, hospitalized adults and children with a positive test had increased odds of being diagnosed with ≥1 symptom (adults: adjusted odds ratio[aOR], 1.17[95% CI, 1.11-1.23]; children: aOR, 1.18[95% CI, 1.08-1.28]) and shortness of breath (adults: aOR, 1.50[95% CI, 1.38-1.63]; children: aOR, 1.40[95% CI, 1.15-1.70]) 31-150 days following a SARS-CoV-2 test compared with hospitalized individuals with a negative test. Hospitalized adults with a positive test also had increased odds of being diagnosed with ≥3 symptoms (aOR, 1.16[95% CI, 1.08 – 1.26]) and fatigue (aOR, 1.12[95% CI, 1.05 – 1.18]) compared with those testing negative. The risks of being newly diagnosed with type 1 or type 2 diabetes (aHR, 1.25[95% CI, 1.17-1.33]), hematologic disorders (aHR, 1.19[95% CI, 1.11-1.28]), and respiratory disease (aHR, 1.44[95% CI, 1.30-1.60]) were higher among hospitalized adults with a positive test compared with those with a negative test. Non-hospitalized adults with a positive SARS-CoV-2 test had higher odds of being diagnosed with fatigue (aOR, 1.11[95% CI, 1.05-1.16]) and shortness of breath (aOR, 1.22[95% CI, 1.15-1.29]), and had an increased risk (aHR, 1.12[95% CI, 1.02-1.23]) of being newly diagnosed with hematologic disorders (i.e., venous thromboembolism and pulmonary embolism) 31-150 days following SARS-CoV-2 test compared with those testing negative. The risk of being newly diagnosed with certain conditions, such as mental health conditions and neurological disorders, was lower among patients with a positive viral test relative to those with a negative viral test. Conclusions Patients with SARS-CoV-2 infection were at higher risk of being diagnosed with certain symptoms and conditions, particularly fatigue, respiratory symptoms, and hematological abnormalities, after acute infection. The risk was highest among adults hospitalized after SARS-CoV-2 infection.

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International electronic health record-derived post-acute sequelae profiles of COVID-19 patients

Cited by 18 publications

References 77 publications

Risk of incident heart failure after COVID-19 recovery: a systematic review and meta-analysis

Risk of incident heart failure after COVID-19 recovery: a systematic review and meta-analysis

Characterizing the use of the ICD-10 Code for Long COVID in 3 US Healthcare Systems

Association between SARS-CoV-2 Infection and Select Symptoms and Conditions 31 to 150 Days After Testing among Children and Adults

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