Predictive model for long COVID in children 3 months after a SARS-CoV-2 PCR test 2022 Nugawela, Crawley et al

Abstract

Background
To update and internally validate a model to predict children and young people (CYP) most likely to experience long COVID (i.e. at least one impairing symptom) 3 months after SARS-CoV-2 PCR testing and to determine whether the impact of predictors differed by SARS-CoV-2 status.

Methods
Data from a nationally matched cohort of SARS-CoV-2 test-positive and test-negative CYP aged 11–17 years was used. The main outcome measure, long COVID, was defined as one or more impairing symptoms 3 months after PCR testing. Potential pre-specified predictors included SARS-CoV-2 status, sex, age, ethnicity, deprivation, quality of life/functioning (five EQ-5D-Y items), physical and mental health and loneliness (prior to testing) and number of symptoms at testing. The model was developed using logistic regression; performance was assessed using calibration and discrimination measures; internal validation was performed via bootstrapping and the final model was adjusted for overfitting.

Results
A total of 7139 (3246 test-positives, 3893 test-negatives) completing a questionnaire 3 months post-test were included. 25.2% (817/3246) of SARS-CoV-2 PCR-positives and 18.5% (719/3893) of SARS-CoV-2 PCR-negatives had one or more impairing symptoms 3 months post-test. The final model contained SARS-CoV-2 status, number of symptoms at testing, sex, age, ethnicity, physical and mental health, loneliness and four EQ-5D-Y items before testing. Internal validation showed minimal overfitting with excellent calibration and discrimination measures (optimism-adjusted calibration slope: 0.96575; C-statistic: 0.83130).

Conclusions
We updated a risk prediction equation to identify those most at risk of long COVID 3 months after a SARS-CoV-2 PCR test which could serve as a useful triage and management tool for CYP during the ongoing pandemic. External validation is required before large-scale implementation.

Open access, https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-022-02664-y

 

I suspect if they add to their list of pre-specified predictors watching TV, playing games, and eating cereal, it may enhance the predictive value.

 

This is obviously not a predictive model as it cannot be used to predict anything. Statistical analysis of associative pooled data is completely useless at predicting individual prognosis. This entire approach is a waste of time whose only effect is creating confidently wrong outcomes. It's Meyers-Briggs medicine, except somehow even worse.

Probabilistic models have utility at the population level but cannot be used individually. This is the old pre-science model with a veneer of lies, damned lies and statistics. Of course it fails miserably in practice, it's delusional to pretend that this is serious.