Development and validation of the long covid symptom and impact tools, a set of patient-reported instruments constructed from patients' lived experience

ObjectivesTo develop and validate patient-reported instruments, based on patients lived experiences, for monitoring the symptoms and impact of long covid. DesignThe long covid Symptom and Impact Tools (ST and IT) were constructed from the answers to a survey with open-ended questions to 492 patients with long covid. Validation of the tools involved adult patients with suspected or confirmed covid-19 and symptoms extending over three weeks after onset. Construct validity was assessed by examining the relations of the ST and IT scores with health related quality of life (EQ-5D-5L), function (PCFS, post-covid functional scale), and perceived health (MYMOP2). Reliability was determined by a test-retest. The "patient acceptable symptomatic state" (PASS) was determined by the percentile method. ResultsValidation involved 1022 participants (55% with confirmed covid-19, 79% female and 12.5% hospitalised for covid-19). The long covid ST and IT scores were strongly correlated with the EQ-5D-5L (rs = -0.45 and rs = -0.59 respectively), the PCFS (rs = -0.39 and rs = -0.55), and the MYMOP2 (rs = -0.40 and rs = -0.59). Reproducibility was excellent with an interclass correlation coefficient of 0.83 (95% confidence interval 0.80 to 0.86) for the ST score and 0.84 (0.80 to 0.87) for the IT score. In total, 793 (77.5%) patients reported an unacceptable symptomatic state, thereby setting the PASS for the long covid IT score at 30 (28 to 33). ConclusionsThe long covid ST and IT tools, constructed from patients lived experiences, provide the first validated and reliable instruments for monitoring the symptoms and impact of long covid. Short summaryWe developed the long covid Symptom and Impact Tools (ST and IT) from the experiences of 492 patients, captured during a survey with open-ended questions, and assessed their validity and reliability in a sample of 1022 patients with long covid.

Predictive usefulness of PCR testing in different patterns of Covid-19 symptomatology - Analysis of a French cohort of 12,810 outpatients

Polymerase Chain reaction (PCR) is a key tool to diagnose Covid-19. Yet access to PCR is often limited. In this paper, we develop a clinical strategy for prescribing PCR to patients based on data from COVIDOM, a French cohort of 54,000 patients with clinically suspected Covid-19 including 12,810 patients tested by PCR. Using a machine learning algorithm (a decision tree), we show that symptoms alone are sufficient to predict PCR outcome with a mean average precision of 86%. We identify combinations of symptoms that are predictive of PCR positivity (90% for anosmia/ageusia) or negativity (only 30% of PCR+ for a subgroup with cardiopulmonary symptoms): in both cases, PCR provides little added diagnostic value. We deduce a prescribing strategy based on clinical presentation that can improve the global efficiency of PCR testing.