Characterization and predictive risk scoring of long-term COVID following successful COVID infection in a South Indian cohort; A prospective single center study. BMC Infectious Diseases


Study the design and setting

This was a single-center hospital-based prospective observational study conducted in a 1350-bed academic tertiary care referral center in South India from October 2021 to December 2021, following the second wave of the pandemic that began in April 2021 in the country. The predominant COVID-19 variant in circulation in India during our study period was Delta, according to data from INSACOG (https://dbtindia.gov.in/insacog). [12], The hospital was a major regional medical center providing care for mild to severe COVID-19 cases with a dedicated isolation facility that included intensive care units and non-intensive care space for inpatients. The hospital also had a special COVID-19 clinic to prepare management plans for cases that required isolation measures only at their residence.

The study was approved by the institutional ethics committee and informed signed consent was obtained from all subjects before enrollment.

study population

The study population included adult patients (>18 years) who had a confirmed COVID-19 diagnosis via positive reverse transcriptase-polymerase chain reaction (RT-PCR) tests and were either receiving inpatient COVID ​​-19 were admitted to the ward and were later discharged. Were admitted to hospital or remained in isolation in their respective homes. These subjects were screened for the inclusion criteria, which specified the administration of at least one dose of vaccination to identify successful COVID-19 infection. Patients under 18 years of age, patients pre/post surgery, patients who were transferred to a non-COVID-19 inpatient setting for continued care for indications other than COVID-19, and end-of-life patients Care was excluded. From study.

data collection

A dedicated health worker asked about post-infection symptoms using a specially designed questionnaire (Additional file 1) at week 2, week 6 and week 12 after the patient’s negative COVID-19 test was confirmed Was trained to interrogate. The prospective survey included patient demographics and pre-existing co-morbidities, neuropsychological manifestations of long-term COVID (fatigue, anxiety, depression, shortness of breath), other somatic manifestations (fever, headache, cough, myalgia , arthralgia, chest pain etc.) were recorded. ), presence of any superseding infection during the period of COVID-19 positivity, which may result in prolonged COVID-19 hospitalization after testing negative for COVID-19, or oxygen deprivation during the period The expression of need, work and functional status can have a complex effect.

Functional status was assessed longitudinally at 2, 6 and 12 weeks using the modified Oswestry scale. [13,14,15,16], Based on the modified Oswestry Disability Index (ODI) score, patients were classified from A (minimal disability) to E (bed bound or exaggerated symptoms) with scoring intervals: A (minimal disability: 0 to 20), B ( Moderate disability: 21 to 40), C (severe disability: 41 to 60), D (crippling, pain affecting all aspects of life: 61 to 80) and E (bed-bound or exaggerated symptoms: 81 to 100).

Vaccination status of the individual was considered complete if the individual had received both of the two scheduled COVID-19 vaccine doses prescribed by the Ministry of Health and Family Welfare (MoHFW), Government of India, while partial vaccination status indicated Was receipt of first dose of vaccine. Booster doses were not available to the general public at the time of the study, and therefore were not considered in determining the subject’s vaccination status.

Data were collected through telephone conversations for consenting patients, using the questionnaire as a template, at the above mentioned time intervals. This was then tabulated into a database, after which the clinical details of each patient were cross-verified from the electronic medical records present in the hospital information system. Once verified, the data was submitted for statistical analysis.

Result

The primary outcome was to estimate the prevalence of long-term COVID in successful infections of our study group. In our study, long-term COVID was defined by the presence of new or persistent symptoms 6 weeks or more after initial SARS-CoV-2 infection. [17, 18], According to NICE guidelines, the term ‘long COVID’ includes both symptomatic (4-12 weeks) and post COVID syndrome (>12 weeks). [19],

Statistical analysis

Baseline characteristics of the study group were summarized using descriptive statistics. Key indicators were expressed in mean and standard deviation for continuous variables and in terms of frequency and percentage for categorical variables. Differences in categorical baseline characteristics between patients who developed post COVID symptoms and those who did develop symptoms were tested by chi square test for independence. Multiple logistic regression was used to estimate the effect of patient characteristics on the likelihood of developing long-term COVID. A propensity score to predict long Covid was developed by backward step-down variable elimination based on AIC values [20], The score is represented by a nomogram [21], To further study the process of change, variance impact factors are calculated for potential predictors.

Average AUC (area under the ROC curve) values ​​were reported from 1000 replicates of the 10-fold cross-validation procedure. The final propensity score was obtained by refitting the model with the most frequently selected variables using the full dataset. Overall model performance was evaluated by Nagelkerke’s R2 and Brier score, discriminatory ability by the C concordance statistic and Somers DXY rank correlation, and model calibration by the Hosmer–Lemeshow test. [22], The significance of differences in individual long COVID symptoms based on propensity score was tested with 2 sample-independent t-tests. The dependence of symptom burden on propensity scores was estimated using linear regression models with natural cubic splines; Similarly, the probability of reduction in functional scores from 2 to 12 weeks was estimated using logistic regression with natural splines. Regression lines with 95% confidence bands were plotted and the significance of these relationships was tested using F-tests. All statistical analyzes for the study were conducted in R version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria. [23], P < 0.05 was considered statistically significant.

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