Pharyngitis

A total of 144 KD patients from Kaohsiung Chang Gung Memorial Children’s Hospital in Taiwan from 2007 to 2009 participated in this study. They were all children that met the KD criteria [24 (link), 25 (link)] and who were treated with IVIG at the hospital. We also found 50 age-matched febrile control patients that had been admitted to the hospital with a respiratory tract infection, including acute pharyngitis, acute tonsillitis, croup, acute bronchitis, and acute bronchiolitis. Peripheral blood samples were taken at three times: before IVIG treatment (pre-IVIG) and within three days after completing initial IVIG treatment (post-IVIG< 3 days), which served as the acute stage samples, as well as at least three weeks after IVIG treatment, which functioned as the subacute stage samples (post-IVIG> 3 weeks), as described earlier in this paper [26 (link)]. CAL formation is defined as a coronary artery with an internal diameter that measured at least 3 mm (4 mm if the patient was older than five years old) or an internal diameter of a segment that was at least 1.5 times that of an adjacent segment, as observed in an echocardiogram [7 (link), 27 (link)]. IVIG responsiveness is defined as fever reduction within 48 h of completing IVIG treatment without relapse (temperature >38°C) for at least seven days, as well as obvious improvement or normalization of inflammation [3 (link), 7 (link), 28 (link)]. This study was approved by the Chang Gung Memorial Hospital’s Institutional Review Board, and we obtained the written informed consent from the parents or guardians of all the subjects. All of the methods used complied with the approved relevant guidelines.

This work was performed in compliance with the Declaration of Helsinki and was approved by the Ethics Committee of the Affiliated Hospital, Chengdu University of Traditional Chinese Medicine (NO: 2021KL-094). A WeChat mini-program was used to execute questionnaires for selecting volunteers in Chengdu, China. All participants provided written informed consent before the study. MPASD volunteers and healthy controls were recruited by the MPATS and PSQI. The MPATS contains 16 questions, including withdrawal symptoms, salience, social comfort, and mood changes. The inclusion criteria of MPASD subjects were ≥ 40 (MPATS) and ≥ 7 (PSQI) scores (31 ). To minimize the interference of severe sleep disorders upon MPA, those characterized as “poor” sleepers (7 ≥ PSQI ≥ 15 scores) were recruited from a non-clinical population (32 (link)). The PQSI score was made up of questions including sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, daytime dysfunction, and medication use. The exclusion criteria included one of the following conditions: (1) Oral diseases, including periodontitis, bleeding gums or tooth decay. (2) The antibiotic regimen within the last 3 months. (3) Upper respiratory tract infection or rhinitis or pharyngitis whinth 1 month. (4) Smokers and alcohol abusers and other types of addicts. Healthy controls were simultaneously recruited into group N. All enrolled subjects completed the 72-h constant routine.

We investigated the following likely PASC categories based on prior analyses^{17 (link)}, including anemia, thromboembolism, pulmonary embolism, dementia, pulmonary fibrosis, edema, and inflammation, pressure ulcer, diabetes mellitus, malnutrition, fluid disorders, U099/B948, encephalopathy, abnormal heartbeat, chest pain, abdominal pain, constipation, joint pain, cognitive problems, headache, sleep disorders, dyspnea, acute pharyngitis, hair loss, edema, fever, malaise, and fatigue.^{17 (link)} Each condition was defined based on the Clinical Classifications Software Refined (CCSR) v2022.1. Codes that could not be attributed to COVID-19 were removed by clinicians.
We analyzed the risks of newly incident conditions, defined as new documentation of the above mentioned PASC categories in the follow-up period that were not present in the baseline period. Specifically, we compared adjusted hazard ratios (aHR) and adjusted excess burdens of these events occurring 31–180 days after the index date between the SARS-CoV-2 positive group and negative group. For each potential PASC condition, aHR was estimated by a Cox proportional hazard model, and excess burden was defined as the difference in cumulative incidence per 1,000 patients in the positive group and negative group over the follow-up period. For example, an excess burden of 40 for symptom X indicates there were 40 more people per 1,000 with symptom X after COVID-19 infection compared with people not infected with COVID-19. We estimated cumulative incidence by the Aalen-Johansen model¹¹ considering death to be a competing risk for target outcomes. We adjusted for a wide range of baseline covariates by stabilized inverse propensity score re-weighting.^{12 (link)} The standardized mean difference (SMD) was used to quantify the goodness-of-balance of covariates after reweighting. We considered SMD < 0.1 as being balanced in terms of each covariate and required all covariates to be balanced after re-weighting. Both the aHR and excess burden calculations used the same covariates for adjustment.
Baseline covariates included age, gender, race, and ethnicity. The national-level area deprivation index (ADI) was used to assess socioeconomic disadvantage of patients.^{13 (link)} We imputed a missing ADI value with median ADI per site. Healthcare utilization was measured as the number of inpatients, outpatient, and emergency encounters (0, 1–2, 3–4, 5 or more visits for each encounter type). The Body Mass Index (BMI) was categorized according to WHO guidelines. We adopted a tailored list of the Elixhauser comorbidities and related drug categories (e.g., corticosteroid and immunosuppressant prescriptions) to capture comorbidities.¹⁴ Patients were defined as having comorbidity if they had at least two corresponding diagnoses documented during the baseline period.
We reported PASC conditions if they had: adjusted hazard ratio > 1; P-value <3.6 × 10⁻⁴ (corrected by Bonferroni method to control for false discovery) in multiple test settings; and at least 100 patients with the condition. We reported adjusted hazard ratios with a 95% confidence interval. We used Python 3.9, python package lifelines-0.2666 for survival analysis and scikit-learn-0.2318 for machine learning models. Code is available at https://github.com/calvin-zcx/pasc_phenotype.

Daily data on hospitalizations from 1 January 2016 to 31 December 2020 were obtained from the medical database of the largest hospital (the First Affiliated Hospital of Gannan Medical University) in Ganzhou. Patient data acquired from the computerized medical record system included age, gender, date of hospitalization, and principal diagnosis on discharge, which was coded using the International Classification of Diseases, 10th Revision. The codes for respiratory diseases were as follows: total respiratory diseases (J00~J98); asthma (J45~J46); COPD (J40~J44); and URTI (J00~J06) including nasopharyngitis, sinusitis, pharyngitis, tonsillitis, laryngitis, tracheitis, and unspecified URTI (13 (link)); LRTI (J20~J22) including bronchi, bronchioles, and unspecified LRTI; and influenza-pneumonia (J09~J18).