Development & validation of a prognostic model for the early identification of COVID-19 patients at risk of developing common LC symptoms 2022 Deforth

Abstract

Background

The coronavirus disease 2019 (COVID-19) pandemic demands reliable prognostic models for estimating the risk of long COVID. We developed and validated a prediction model to estimate the probability of known common long COVID symptoms at least 60 days after acute COVID-19.

Methods
The prognostic model was built based on data from a multicentre prospective Swiss cohort study. Included were adult patients diagnosed with COVID-19 between February and December 2020 and treated as outpatients, at ward or intensive/intermediate care unit. Perceived long-term health impairments, including reduced exercise tolerance/reduced resilience, shortness of breath and/or tiredness (REST), were assessed after a follow-up time between 60 and 425 days. The data set was split into a derivation and a geographical validation cohort. Predictors were selected out of twelve candidate predictors based on three methods, namely the augmented backward elimination (ABE) method, the adaptive best-subset selection (ABESS) method and model-based recursive partitioning (MBRP) approach. Model performance was assessed with the scaled Brier score, concordance c statistic and calibration plot. The final prognostic model was determined based on best model performance.

Results
In total, 2799 patients were included in the analysis, of which 1588 patients were in the derivation cohort and 1211 patients in the validation cohort. The REST prevalence was similar between the cohorts with 21.6% (n = 343) in the derivation cohort and 22.1% (n = 268) in the validation cohort. The same predictors were selected with the ABE and ABESS approach. The final prognostic model was based on the ABE and ABESS selected predictors. The corresponding scaled Brier score in the validation cohort was 18.74%, model discrimination was 0.78 (95% CI: 0.75 to 0.81), calibration slope was 0.92 (95% CI: 0.78 to 1.06) and calibration intercept was −0.06 (95% CI: −0.22 to 0.09).

Conclusion
The proposed model was validated to identify COVID-19-infected patients at high risk for REST symptoms. Before implementing the prognostic model in daily clinical practice, the conduct of an impact study is recommended.

Open access, https://diagnprognres.biomedcentral.com/articles/10.1186/s41512-022-00135-9

 

Looks very loose, but is somewhat consistent with reality. The concern here would be if such a formula became popular that it would be mindlessly applied, like the "have you recently traveled to China?" stuff that continued for waaaay too long. Several of the numbers used are subjective ratings.

Looks a lot like fine-tuning, frankly (I put them in order), the +number is the risk/weight factor in their formula:

Basically no depth to the number of acute symptoms, as severity is strictly based on ICU/not ICU. Number of symptoms is probably the most relevant factor but they have to be assessed properly and the current medical standard is to ignore symptoms, to not even record them and scold patients for even thinking about them. So this is essentially useless in the real world, as no such data has been recorded so far, on purpose, out of beliefs that doing so is what perpetuates illness, completely indifferent to the obvious fact that this is a delusional fantasy.

I don't think this can be used in the real world. It confirms many prior important factors but uses too many subjective ratings that are relative between people. Frankly this is like making relativity calculations without having the metrics.

At best it can be said that past illness is the most important factor of future illness. Which, duh. Can we freaking move on to something substantial, though?