key: cord-0684326-t4m664zb authors: Levine-Tiefenbrun, Matan; Yelin, Idan; Uriel, Hedva; Kuint, Jacob; Schreiber, Licita; Herzel, Esma; Katz, Rachel; Ben-Tov, Amir; Gazit, Sivan; Patalon, Tal; Chodick, Gabriel; Kishony, Roy title: SARS-CoV-2 RT-qPCR test detection rates are associated with patient age, sex, and time since diagnosis date: 2021-11-23 journal: J Mol Diagn DOI: 10.1016/j.jmoldx.2021.10.010 sha: 8dd65f087ed479a9528df00bd09a2ea6698bb9a4 doc_id: 684326 cord_uid: t4m664zb Quantifying the detection rate of the widely used SARS-CoV-2 RT-qPCR test and its dependence on patient demographics and disease progression is key in designing epidemiological strategies. Analyzing 843,917 test results of 521,696 patients, for each patient, a ‘positivity period’ was defined between COVID-19 diagnosis and last positive test result. The fraction of positive tests within this period was then used to estimate detection rate. Regression analyses were used to determine associations of detection with time of sampling after diagnosis, patient demographics and viral RNA copy number based on RT-qPCR Ct values of the next positive tests. The overall detection rate in tests carried within 14 days following diagnosis was 83.1%. This rate was higher at days 0-5 following diagnosis (89.3%). Furthermore, detection rate was strongly associated with demographics, with odds ratio of 0.58 (95% CI: 0.53-0.63) for women over men and 0.74 (95% CI: 0.72-0.75) for 10 years younger patients. Finally, detection rate with the Allplex 2019-nCoV RT-qPCR kit was associated, at the single-patient level, with viral RNA copy number (p-value [Formula: see text]). These results thereby show that the reliability of the test result is reduced in later days as well as for women and younger patients, where the viral loads are typically lower. The ongoing COVID-19 pandemic has already infected tens of millions of people worldwide. A major tool in combating the pandemic is testing for viral carriage, which is used for both diagnostic and epidemiologic purposes. The most commonly used viral detection tests are based on the reverse transcription quantitative polymerase chain reaction of viral genes (RT-qPCR). This nucleic acid test is of high specificity, i.e. very low false-positive rate [1] [2] [3] [4] . In contrast, the false-negative rate of these tests was often reported as high [5] [6] [7] [8] [9] . High falsenegative rates may impede local and global efforts to slow down disease spread, as patients incorrectly diagnosed as non-carriers might pose an obstacle for efforts such as contact tracing [10] [11] [12] . Systematically quantifying the rate of detection and its dependencies on disease progression and patient demographics is therefore critical for disease spread modeling and public health policy-making. Various approaches have been taken to estimate the false-negative rate of COVID-19 RT-qPCR tests. Measuring the rate of false-negative results in a population of patients with highly specific pathologies (e.g. chest CT) has initially alerted physicians and epidemiologists of the high false-negative rate, estimated at approximately 30% [3] [4] [5] [6] 8, [13] [14] [15] [16] . A meta-analysis of multiple such studies found that the reported rates were highly variable with a mean false-negative rate of 11% 17 . However, and as previously noted 17, 18 , these meta-analysis studies were necessarily based on a combination of variable studies of non-uniform origins and methodologies, using different kits with inherently different limits of detection and typically involving small groups of patients. Another approach compared initial RT-qPCR test results to post-hoc convalescent serologic tests finding a false-negative rate of 14% 19 . A more recent systematic approach was based on 'longitudinal testing' in which the accuracy of each test is determined based on later tests of the same patient: a negative test which is soon followed by a positive one is deemed false negative 20, 21 . Application of this approach in a hospital setting resulted in an estimation of a false-negative rate of 17.8% 18 . Beyond the average false-negative rate, meta-analysis studies showed a strong association of false-negative results with time since exposure 22 or time since onset of symptoms 23 . These associations suggest that negative test results obtained during a 'positivity period' reflect waning infections and viral loads declining towards the test limit of detection. Therefore, such negative results do not necessarily indicate a false-negative result, especially as later positive results may be of high Ct values and may result from fragmented RNA and not from infectious viral particles 24 . Moreover, at the patient-specific level, as the viral load is associated with time since onset of symptoms, sex and age [25] [26] [27] [28] [29] [30] [31] [32] [33] , it has been proposed that detection rates might also depend on demographics, but current studies lacked statistical power for quantifying such dependencies 18, [34] [35] [36] [37] [38] [39] . Here, utilizing a large dataset of quantitative patient-level test series done with a single type of measurement equipment with characterized limit of detection 16, 40 , with linked demographics and electronic health records (843,917 tests for 521,969 patients), we apply a longitudinaltesting based approach to quantify the detection rate of COVID-19 test results at the community and its associations with age, sex and time since diagnosis. Finally, we test whether this rate is associated with viral RNA copy number at the single-patient level. The study protocol was approved by the ethics committee of Maccabi Healthcare Services, Tel-Aviv, Israel. IRB number: 0066-20-MHS. Anonymized clinical records of SARS- "positive" (7.4%), "negative" (92%) or "borderline-positive" (0.6%, which were considered as positive in the analysis). Patients for whom two tests with different results were recorded on the same day were excluded from the analysis (274 patients, 0.05%). For each patient, the earliest date of COVID-19 symptoms and/or epidemiologically-based referral was considered as "date of diagnosis". When both symptom-based diagnosis and epidemiological-based referrals were available, they were usually recorded on the same day. For simplicity, a small number of patients for whom both a diagnosis and a referral were available, but were more than a day apart were excluded (5.2% of diagnosed patients). For any patient with at least one positive test, a 'positive period' was defined as the period between their date of diagnosis and their last positive test. Negative test reports during this J o u r n a l P r e -p r o o f period were regarded as undetectable, while positive test reports during this period were regarded as detectable. Detection rate was calculated as + . Logistic regression of an undetectable result versus detectable result was performed using Python's statsmodels library. The probability of a detectable result was fitted to the test result (detectable: 1, undetectable: 0) for all tests within the positive period. Linear regression of Ct values for each fluorescence channel was performed using Python's statsmodels library. Odds ratios (OR) were calculated from the coefficients of the above logistic regression. For the binary variable sex (male: 1, female: 0), OR was defined as: Table S1 ). Undetectable and detectable test results were defined based on their context within a patient series of test results. For each patient, the series of test results following diagnosis were considered (Fig. 1A,B) (Fig. 1A) . To avoid bias for detectable results, the last positive result, used for defining the end of the positive period, was not counted towards detectable results. The rate of negative results increased over time, and at 20 days after diagnosis, the number of negative tests first surpassed the number of positive or undetectable results (Fig. 1B,C) . Relative to time of first negative test, undetected test results were extremely rare during the two preceding days, but were otherwise relatively equally distributed indicating that undetectable test results were not restricted to the short time period of the end of carriage (Supplemental Figure S1A, Following the observed association between time after diagnosis and detectable result, the detection rate during disease progression was characterised. Calculating detection rate per day after diagnosis (Methods: 'calculating detection rate'), that detection rate during the first few days was found to be fairly constant and high (89.3%, days 0-5) and to then gradually decreased over days 6-14 ( Fig. 2A) . We next focused on the earlier days after diagnosis (days 0-5), in which detection rate was relatively high, and in which a precise diagnosis is most critical for epidemiological needs and contact tracing. Multivariate logistic regression analysis of test results for these days alone identified an association of undetectable results during these days with sex and age. Table S4 ). An opposite association was found with the IC gene, in agreement with within-tube competition for reagents between the multiplexed reactions (Supplemental Figure S2C ) 48 . The viral RNA copy number association with demographics and time, therefore, mirrored the associations of the detection rate with these same parameters. Figure S4 ). The above analysis of a large dataset of electronic health records of COVID-19 patients showed that while on average the detection rate is about 83%, this rate varies strongly with age, sex and time after diagnosis. At the first few days following diagnosis, the detection rate is only 90% on average and even higher for men and older patients. Combining these data with Ct values of RT-qPCR tests provides evidence that detection rates possibly stem from low viral loads at the single-patient level. Negative tests within this individually determined period were regarded as 'undetectable' (orange). Similarly, Positive tests within the 'positive period' were regarded as detectable (blue). All test series end with a sequence of one or more negative results (red). (B) Longitudinal SARS-CoV-2 test results for the study population (Table 1; Difference in detection rate between two age groups (<40 and ≥40, dark and light grey, respectively) calculated separately for early and late days after diagnosis (hatched and empty, respectively). Fisher exact test (Methods: 'Differences in detection rate between age groups'). ** p-value<0.01, *** p-value<0.001. Error bars indicate SD. Specificity and Predictive Values of Molecular and Serological Tests for COVID-19: A Longitudinal Study in Emergency Room Evaluation of a quantitative RT-PCR assay for the detection of the emerging coronavirus SARS-CoV-2 using a high throughput system Substantial underestimation of SARS-CoV-2 infection in the United States Virological assessment of hospitalized patients with COVID-2019 Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing Correlation of Chest CT and RT-PCR Testing for Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases Laboratory Diagnosis and Monitoring the Viral Shedding of SARS-CoV-2 Infection SARS-CoV-2 turned positive in a discharged patient with COVID-19 arouses concern regarding the present standards for discharge False Negative Tests for SARS-CoV-2 Infection-Challenges and Implications False negative results and tolerance limits of SARS-CoV-2 laboratory tests Interpreting a covid-19 test result Does CT help in reducing RT-PCR false negative rate for COVID-19? More accurate estimates of the accuracy of RT-PCR and chest CT tests for COVID-19 Diagnostic accuracy of RT-PCR for detection of SARS-CoV-2 compared to a "composite reference standard Evaluation of Four Commercial Kits for SARS-CoV-2 Real-Time Reverse-Transcription Polymerase Chain Reaction Approved by Emergency-Use-Authorization in Korea Diagnostic Performance of CT and Reverse Transcriptase Polymerase Chain Reaction for Coronavirus Disease 2019: A Meta-Analysis Sensitivity of RT-PCR testing of upper respiratory tract samples for SARS-CoV-2 in hospitalised patients: a retrospective cohort study The clinical sensitivity of a single SARS-CoV-2 upper respiratory tract RT-PCR test for diagnosing COVID-19 using convalescent antibody as a comparator Repeated testing necessary: Assessing negative predictive value of SARS-CoV-2 qPCR in a population of young adults False negative rate of COVID-19 PCR testing: a discordant testing analysis Variation in False-Negative Rate of Reverse Transcriptase Polymerase Chain Reaction-Based SARS-CoV-2 Tests by Time Since Exposure Estimating the false-negative test probability of SARS-CoV-2 by RT-PCR Viral cultures for COVID-19 infectious potential assessment-a systematic review Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study Temporal dynamics in viral shedding and transmissibility of COVID-19 Pediatric SARS-CoV-2: Clinical Presentation, Infectivity, and Immune Responses Estimating infectiousness throughout SARS-CoV-2 infection course Comparison of Viral Loads in Individuals With or Without Symptoms At Time of COVID-19 Testing Among 32,480 Residents and Staff of Nursing Homes and Assisted Living Facilities in Massachusetts SARS-CoV-2 detection, viral load and infectivity over the course of an infection Association of viral load with serum biomakers among COVID-19 cases Dynamics of SARS-CoV-2: A Single-Center Cohort Study. Diagnostics, 2021 Association of Viral Load in SARS-CoV-2 Patients With Age and Gender Dynamic profile of RT-PCR findings from 301 COVID-19 patients in Wuhan, China: A descriptive study False-negative results of initial RT-PCR assays for COVID-19: A systematic review Distinct characteristics of COVID-19 patients with initial rRT-PCR-positive and rRT-PCRnegative results for SARS-CoV-2 Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19 False negative of RT-PCR and prolonged nucleic acid conversion in COVID-19: Rather than recurrence Allplex 2019-nCoV Assay -Instructions for Use Viral load of SARS-CoV-2 in clinical samples SARS-CoV-2 Viral Load in Upper Respiratory Specimens of Infected Patients SARS-CoV-2 Viral Load in Clinical Samples from Critically Ill Patients Prolonged virus shedding even after seroconversion in a patient with COVID-19 Viral load dynamics and disease severity in patients infected with SARS-CoV-2 in Zhejiang province Predicting infectious SARS-CoV-2 from diagnostic samples Virology, transmission, and pathogenesis of SARS-CoV-2 Practical considerations in design of internal amplification controls for diagnostic PCR assays Initial real world evidence for lower viral load of individuals who have been vaccinated by BNT162b2 Initial report of decreased SARS-CoV-2 viral load after inoculation with the BNT162b2 vaccine patients (age ≥40, light grey) than for younger patients (age <40, dark grey). Error bars indicate SE Difference between Ct values of N gene of positive results following FN and TP. Positive test results which followed an undetectable test result had a higher Ct value than positive test results following undetectable test results. * p-value<10 −10