Tag: long covid methodology

A Mechanistic Model for Long COVID Dynamics

Abstract:

Long COVID, a long-lasting disorder following an acute infection of COVID-19, represents a significant public health burden at present. In this paper, we propose a new mechanistic model based on differential equations to investigate the population dynamics of long COVID. By connecting long COVID with acute infection at the population level, our modeling framework emphasizes the interplay between COVID-19 transmission, vaccination, and long COVID dynamics. We conducted a detailed mathematical analysis of the model. We also validated the model using numerical simulation with real data from the US state of Tennessee and the UK.

Source: Derrick J, Patterson B, Bai J, Wang J. A Mechanistic Model for Long COVID Dynamics. Mathematics (Basel). 2023 Nov;11(21):4541. doi: 10.3390/math11214541. Epub 2023 Nov 3. PMID: 38111916; PMCID: PMC10727852. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10727852/ (Full text)

Data-driven prognosis of long COVID in patients using machine learning

Abstract:

Long-COVID is a health condition in which individuals experience persisting, returning or new symptoms longer than 4 weeks after they have recovered from COVID-19 and this condition can even last for months. It can cause multi-organ failure and in some cases, it can even lead to death. The effects and symptoms of Long COVID can vary from person to person. Even though it’s rising globally, there is a limited understanding about its prediction, risk factors and whether its prognosis can be predicted in the initial first week of acute COVID-19. Artificial Intelligence (AI) and Machine Learning (ML) have aided the medical industry in a variety of ways including the diagnosis, prediction, and prognosis of many diseases.

This paper introduces a novel method to determine Long COVID in the early or first week of acute COVID-19 by considering the basic demographics, and symptoms during COVID-19, along with the clinical lab results of the patients hospitalized. In comparison with different ML models such as Logistic Regression, Support Vector Machine (SVM), XGBoost and Artificial Neural Network (ANN) to predict and classify the patients as Long COVID or Short COVID during the first week of COVID-19, ANN has outperformed the other models with an accuracy of 81% when considering the symptoms of COVID-19 and a 79% for the clinical test data. The predictive factors and the significant clinical tests for the Long COVID are also determined by using different methods like Chi-square Test and Pearson Correlation.

Source: S. S. Parvathy, Nagesh Subbanna, Sethuraman Rao, Rahul Krishnan Pathinarupothi, T. S. Dipu, Merlin Moni, Chithira V. Nair; Data-driven prognosis of long COVID in patients using machine learning. AIP Conf. Proc. 15 December 2023; 2901 (1): 060014. https://doi.org/10.1063/5.0178561 https://pubs.aip.org/aip/acp/article/2901/1/060014/2930006 (Full text available as PDF file)

Predictive models of long COVID

Abstract:

Background: The cause and symptoms of long COVID are poorly understood. It is challenging to predict whether a given COVID-19 patient will develop long COVID in the future.

Methods: We used electronic health record (EHR) data from the National COVID Cohort Collaborative to predict the incidence of long COVID. We trained two machine learning (ML) models – logistic regression (LR) and random forest (RF). Features used to train predictors included symptoms and drugs ordered during acute infection, measures of COVID-19 treatment, pre-COVID comorbidities, and demographic information. We assigned the ‘long COVID’ label to patients diagnosed with the U09.9 ICD10-CM code. The cohorts included patients with (a) EHRs reported from data partners using U09.9 ICD10-CM code and (b) at least one EHR in each feature category. We analysed three cohorts: all patients (n = 2,190,579; diagnosed with long COVID = 17,036), inpatients (149,319; 3,295), and outpatients (2,041,260; 13,741).

Findings: LR and RF models yielded median AUROC of 0.76 and 0.75, respectively. Ablation study revealed that drugs had the highest influence on the prediction task. The SHAP method identified age, gender, cough, fatigue, albuterol, obesity, diabetes, and chronic lung disease as explanatory features. Models trained on data from one N3C partner and tested on data from the other partners had average AUROC of 0.75.

Interpretation: ML-based classification using EHR information from the acute infection period is effective in predicting long COVID. SHAP methods identified important features for prediction. Cross-site analysis demonstrated the generalizability of the proposed methodology.

Source: Antony B, Blau H, Casiraghi E, Loomba JJ, Callahan TJ, Laraway BJ, Wilkins KJ, Antonescu CC, Valentini G, Williams AE, Robinson PN, Reese JT, Murali TM; N3C consortium. Predictive models of long COVID. EBioMedicine. 2023 Oct;96:104777. doi: 10.1016/j.ebiom.2023.104777. Epub 2023 Sep 4. PMID: 37672869; PMCID: PMC10494314. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10494314/ (Full text)

Core outcome measurement instruments for use in clinical and research settings for adults with post-COVID-19 condition: an international Delphi consensus study

Abstract:

Post-COVID-19 condition (also known as long COVID) is a new, complex, and poorly understood disorder. A core outcome set (COS) for post-COVID-19 condition in adults has been developed and agreement is now required on the most appropriate measurement instruments for these core outcomes.

We conducted an international consensus study involving multidisciplinary experts and people with lived experience of long COVID. The study comprised a literature review to identify measurement instruments for the core outcomes, a three-round online modified Delphi process, and an online consensus meeting to generate a core outcome measurement set (COMS). 594 individuals from 58 countries participated.

The number of potential instruments for the 12 core outcomes was reduced from 319 to 19. Consensus was reached for inclusion of the modified Medical Research Council Dyspnoea Scale for respiratory outcomes. Measures for two relevant outcomes from a previously published COS for acute COVID-19 were also included: time until death, for survival, and the Recovery Scale for COVID-19, for recovery. Instruments were suggested for consideration for the remaining nine core outcomes: fatigue or exhaustion, pain, post-exertion symptoms, work or occupational and study changes, and cardiovascular, nervous system, cognitive, mental health, and physical outcomes; however, consensus was not achieved for instruments for these outcomes.

The recommended COMS and instruments for consideration provide a foundation for the evaluation of post-COVID-19 condition in adults, which should help to optimise clinical care and accelerate research worldwide. Further assessment of this COMS is warranted as new data emerge on existing and novel measurement instruments.

Source: Gorst SL, Seylanova N, Dodd SR, Harman NL, O’Hara M, Terwee CB, Williamson PR, Needham DM, Munblit D, Nicholson TR; PC-COS study group. Core outcome measurement instruments for use in clinical and research settings for adults with post-COVID-19 condition: an international Delphi consensus study. Lancet Respir Med. 2023 Nov 2:S2213-2600(23)00370-3. doi: 10.1016/S2213-2600(23)00370-3. Epub ahead of print. PMID: 37926103. https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(23)00370-3/fulltext (Full text)

Accelerating discovery: A novel flow cytometric method for detecting fibrin(ogen) amyloid microclots using long COVID as a model

Abstract:

Long COVID has become a significant global health and economic burden, yet there are currently no established methods or diagnostic tools to identify which patients might benefit from specific treatments. One of the major pathophysiological factors contributing to Long COVID is the presence of hypercoagulability; this results in insoluble amyloid microclots that are resistant to fibrinolysis. Our previous research using fluorescence microscopy has demonstrated a significant amyloid microclot load in Long COVID patients. However, this approach lacked the elements of statistical robustness, objectivity, and rapid throughput.

In the current study, we have used imaging flow cytometry for the first time to show a significantly increased concentration and size of these microclots. We identified notable variations in size and fluorescence between microclots in Long COVID and those of controls even using a 20× objective. By combining cell imaging and the high-event-rate and full-sample analysis nature of a conventional flow cytometer, imaging flow cytometry can eliminate erroneous results and increase accuracy in gating and analysis beyond what pure quantitative measurements from conventional flow cytometry can provide.

Although imaging flow cytometry was used in our study, our results suggest that the signals indicating the presence of microclots should be easily detectable using a conventional flow cytometer. Flow cytometry is a more widely available technique than fluorescence microscopy and has been used in pathology laboratories for decades, rendering it a potentially more suitable and accessible method for detecting microclots in individuals suffering from Long COVID or conditions with similar pathology, such as myalgic encephalomyelitis.

Source: Turner S, Laubscher GJ, Khan MA, Kell DB, Pretorius E. Accelerating discovery: A novel flow cytometric method for detecting fibrin(ogen) amyloid microclots using long COVID as a model. Heliyon. 2023 Aug 29;9(9):e19605. doi: 10.1016/j.heliyon.2023.e19605. PMID: 37809592; PMCID: PMC10558872. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10558872/ (Full text)

Integrating patient-reported physical, mental, and social impacts to classify long COVID experiences

Abstract:

Long COVID was originally identified through patient-reported experiences of prolonged symptoms. Many studies have begun to describe long COVID; however, this work typically focuses on medical records, instead of patient experiences, and lacks a comprehensive view of physical, mental, and social impacts.

As part of our larger My COVID Diary (MCD) study, we captured patient experiences using a prospective and longitudinal patient-reported outcomes survey (PROMIS-10) and free-text narrative submissions. From this study population, we selected individuals who were still engaged in the MCD study and reporting poor health (PROMIS-10 scores < 3) at 6 months (n = 634). We used their PROMIS-10 and narrative data to describe and classify their long COVID experiences.

Using Latent Class Analysis of the PROMIS-10 data, we identified four classifications of long COVID experiences: a few lingering issues (n = 107), significant physical symptoms (n = 113), ongoing mental and cognitive struggles (n = 235), and numerous compounding challenges (n = 179); each classification included a mix of physical, mental, and social health struggles with varying levels of impairment. The classifications were reinforced and further explained by patient narratives. These results provide a new understanding of the varying ways that long COVID presents to help identify and care for patients.

Source: Vartanian K, Fish D, Kenton N, Gronowski B, Wright B, Robicsek A. Integrating patient-reported physical, mental, and social impacts to classify long COVID experiences. Sci Rep. 2023 Sep 28;13(1):16288. doi: 10.1038/s41598-023-43615-8. PMID: 37770554; PMCID: PMC10539528. https://www.nature.com/articles/s41598-023-43615-8 (Full text)

How methodological pitfalls have created widespread misunderstanding about long COVID

Key messages:

  • The existing epidemiological research on long COVID has suffered from overly broad case definitions and a striking absence of control groups, which have led to distortion of risk.

  • The unintended consequences of this may include, but are not limited to, increased societal anxiety and healthcare spending, a failure to diagnose other treatable conditions misdiagnosed as long COVID and diversion of funds and attention from those who truly suffer from chronic conditions secondary to COVID-19.

  • Future research should include properly matched control groups, sufficient follow-up time after infection and internationally-established diagnostic or inclusion and exclusion criteria.

Source: Høeg TB, Ladhani S, Prasad V. How methodological pitfalls have created widespread misunderstanding about long COVID. BMJ Evid Based Med. 2023 Sep 25:bmjebm-2023-112338. doi: 10.1136/bmjebm-2023-112338. Epub ahead of print. PMID: 37748921. https://ebm.bmj.com/content/early/2023/08/10/bmjebm-2023-112338 (Full text)

Cocreation of Assistive Technologies for Patients With Long COVID: Qualitative Analysis of a Literature Review on the Challenges of Patient Involvement in Health and Nursing Sciences

Abstract:

Background: Digital assistive technologies have the potential to address the pressing need for adequate therapy options for patients with long COVID (also known as post-COVID-19 condition) by enabling the implementation of individual and independent rehabilitation programs. However, the involvement of the target patient group is necessary to develop digital devices that are closely aligned to the needs of this particular patient group.

Objective: Participatory design approaches, such as cocreation, may be a solution for achieving usability and user acceptance. However, there are currently no set methods for implementing cocreative development processes incorporating patients. This study addresses the following research questions: what are the tasks and challenges associated with the involvement of patient groups? What lessons can be learned regarding the adequate involvement of patients with long COVID?

Methods: First, a literature review based on a 3-stage snowball process was conducted to identify the tasks and challenges emerging in the context of the cocreation of digital assistive devices and services with patient groups. Second, a qualitative analysis was conducted in an attempt to extract relevant findings and criteria from the identified studies. Third, using the method of theory adaptation, this paper presents recommendations for the further development of the existing concepts of cocreation in relation to patients with long COVID.

Results: The challenges of an active involvement of patients in cocreative development in health care include hierarchical barriers and differences in the levels of specific knowledge between professionals and patients. In the case of long COVID, patients themselves are still inexperienced in dealing with their symptoms and are hardly organized into established groups. This amplifies general hurdles and leads to questions of group identity, power structure, and knowledge creation, which are not sufficiently addressed by the current methods of cocreation.

Conclusions: The adaptation of transdisciplinary methods to cocreative development approaches focusing on collaborative and inclusive communication can address the recurring challenges of actively integrating patients with long COVID into development processes.

Source: Dalko K, Kraft B, Jahn P, Schildmann J, Hofstetter S. Cocreation of Assistive Technologies for Patients With Long COVID: Qualitative Analysis of a Literature Review on the Challenges of Patient Involvement in Health and Nursing Sciences. J Med Internet Res. 2023 Aug 15;25:e46297. doi: 10.2196/46297. PMID: 37581906. https://www.jmir.org/2023/1/e46297 (Full text)

Modeling Long Covid Disease Network in Pediatric Population

Abstract:

The effects of COVID-19 have had a tremendous impact on the quality of life, work, and society. This has been exacerbated by the progression of COVID-19 into Long COVID. Long COVID is not a specific disease or symptom but a set of wide-ranging conditions that linger in COVID-19 patients for four weeks or beyond post-initial COVID-19 detection. This relatively new condition is challenging due to a lack of prior research and data specific to the pediatric population, comprising 25.24% of all Long COVID cases under study.

Besides, there is a lack of deeper understanding about who may develop Long COVID. Various comorbidities could provide insights into the path leading toward a patient’s Long COVID detection, as referenced in Berg et al. (2022). Thus, we address two research questions in our study. First, what chronic co-morbidities are prevalent in pediatric patients exhibiting Long COVID symptoms? Second, what nonchronic conditions are  associated with pediatric patients diagnosed with Long COVID?

To delve into the research questions, we use 80,000 Long COVID pediatric patients N3C (National COVID Cohort Collaboration) data across 72 healthcare units located in the US. The model we developed has 3 stages – First, we apply network analytics techniques to identify pre-existing chronic and non-chronic conditions among those diagnosed with Long COVID. Second, using CDC’s definition for Long COVID, we develop a bi-partite network representing a large pediatric population diagnosed with COVID-19 who subsequently developed Long-COVID. This bipartite network has patients on one side and diseases on the other with no connection among the patients and among the diseases. We take projection on the disease side to create disease-disease projection graph. Third, the projected disease-disease graph is processed such that we create bipartite network comprising pre-COVID diseases on one side and Long COVID diseases on the other side. We take the projection of both sides to carry out analysis regarding chronic and non-chronic pre-COVID conditions leading to Long COVID.

The above model was implemented using 0.5 million pediatric COVID patient dataset from the N3C (2020). Besides using Spark SQL and PySpark to analyze the data, we used graphical tools such as Gephi to integrate Community Detection algorithms and create visualizations. Since the size of the overall patient record is large, it necessitated implementation of various code optimization techniques for faster processing. This study provides critical building blocks for developing Long COVID prediction and recommendation systems models

Source: Kushagra, Kushagra; joghataee, mohammad; Gupta, Ashish; Kalgotra, Pankush; and Qin, Xiao, “Modeling Long Covid Disease Network in Pediatric Population” (2023). AMCIS 2023 TREOs. 107. https://aisel.aisnet.org/treos_amcis2023/107

Use Of Total-Body Pet Imaging To Identify Deep-Tissue Sars-Cov-2 Viral Reservoirs And T Cell Responses In Patients With Long Covid

Project Summary:

This study is the first in the world to use advanced imaging technologies to identify deep tissue SARS-CoV-2 reservoirs and T cell activity in LongCovid study participants. Specifically the team will use longitudinal ImmunoPET-CT imaging of radiolabeled SARS-CoV-2-specific monoclonal antibodies (mAbs) to identify SARS-CoV-2 tissue reservoirs in individuals with Long COVID. The project team is also using ImmunoPET-CT imaging to identify the spatial and temporal dynamics of tissue-based T cell activity in Long COVID study participants.

Tissue biopsy samples from the lymph node and gut will also be collected from Long COVID study participants undergoing imaging. These tissue samples will be analyzed for SARS-CoV-2 RNA, spike, and nucleocapsid proteins, other chronic viruses (e.g., Epstein-Barr virus and cytomegalovirus), and cellular immune responses. Data collected on the tissue samples will be correlated with the imaging data, so that potential viral reservoirs and T cell activity in study participants can be validated by overlapping methods.

Read full article HERE.