Aim. To assess the relationship between a fact of being prescribed fluvoxamine around the time of COVID-19 diagnosis and subsequent hospitalizations and mortality in COVID-19 outpatients. Methods. Using administrative data, we identified adult COVID-19 outpatients diagnosed up to August 15, 2021 in Croatia. Subjects prescribed fluvoxamine around the time of COVID-19 diagnosis (Group A), their peers suffering similar psychiatric difficulties but not prescribed with fluvoxamine (Group B) and those free of psychiatric difficulties/treatments (Group C) were mutually exactly matched on a range of pre-COVID covariates. We determined relative risks of COVID-19-related hospitalization, 30-day all-cause hospitaliziation and of COVID-19-related mortality. Results. Out of 416030 outpatients, 1016 were Group A subjects, 749 of whom were matched to 31336/95984 Group B subjects, while 866 were matched to 22792/275804 Group C subjects. Group B and C patients were matched 82323 to 268778. Matched A vs. B relative risks (95%CI/CrI), frequentist and Bayes with skeptical, otpimistic and pesimistic priors, were: COVID-related hospitalization 1.73 (0.56-3.33), 1.15 (0.55-2.11), 1.03 (0.56.1.96) and 1.43 (0.63-2.94), respectively; 30-day all-cause hospitalization 1.88 (0.76-4.67), 1.76 (1.39-2.25), 1.76 (1.39-2.24) and 1.86 (1.43-2.38), respectively; COVID-19 related mortality 0.73 (0.35-1.55), 0.93 (0.53-1.76), 0.79 (0.40-1.54) and 0.88 (0.37-2.11), respectively. Conslusion. COVID-19 outpatients prescribed fluvoxamine around the time of COVID-19 diagnosis were not at a reduced risk of subsequent hospitalizations and mortality compared to COVID-19 outpatients suffering similar psychiatric difficulties but not prescribed with fluvoxamine, or compared to COVID-19 outpatients free of psychiatric difficulties and related treatments
Background: Although concomitant pulmonary vein isolation (PVI) is used more frequently than the Cox-maze procedure, which is currently the gold standard treatment for AF, data on the comparative effectiveness of the two procedures after concomitant mitral valve (MV) surgery are still limited. Objective: We conducted a systematic review to identify randomized controlled trials (RCT) and observational studies comparing the mid-term mortality and recurrence of atrial fibrillation (AF) after concomitant Cox-Maze and PVI in patients with AF undergoing MV surgery based on 12-month follow-up. Methods: Medline, EMBASE databases, and the Cochrane Library were searched from 1987 up to March 2022 for studies comparing concomitant Cox-Maze and PVI. A meta-analysis of RCTs was performed to compare the mid-term clinical outcomes between these two surgical ablation techniques. Results: Three RCTs and 3 observational studies meeting the inclusion criteria were included with 790 patients in total (532 concomitant Cox-Maze and 258 PVI during MV surgery). Regarding AF recurrence, estimate pooled across the 3 RCTs indicated large heterogeneity and high uncertainty. In the largest and highest quality RCT, 12-month AF recurrence was higher in the PVI arm (RR=1.58, 95%CI 0.91-2.73). In 2 out of 3 higher quality observational studies, 12-month AF recurrence was higher in PVI than in Cox-Maze arm (estimated adjusted probabilities 11% vs. 8% and 35% vs. 17%, respectively). RCTs demonstrated comparable 12-month mortality between concomitant Cox-Maze and PVI, while observational studies demonstrated survival benefit of Cox-Maze. Conclusions: Concomitant Cox-Maze in AF patients undergoing MV surgery is associated with better mid-term freedom from AF when compared to PVI with comparable mid-term survival. Large observational studies suggest that there might be a mid-term survival benefit among patients after concomitant Cox-Maze. Further large RCTs with longer standardized follow-up are required in order to clarify benefits of concomitant Cox-Maze in AF patients during MV surgery.
Fluvoxamine for COVID-19 outpatients: for the time being, we might prefer to curb our optimismVladimir TrkuljaRunning head : Fluvoxamin and COVID-19 outpatientsKey words : fluvoxamine, COVID-19, outpatients, hospitalizationsVladimir Trkulja, MD, PhDDepartment of PharmacologyZagreb University School of MedicineŠalata 1110000 Zagreb, Croatiae-mail: email@example.comNumber of words: 613Number of figures/tables: 1To the Editor,A rather elaborate pharmacodynmics rationale 1 and sound pharmacokinetic reasoning 2 support the use of fluvoxamin in early phases of the COVID-19 disease. Two recent meta-analyses, 3, 4 both based on the same three randomized placebo-controlled trials, emphasized the benefit of early fluvoxamine treatment in non-vaccinated adult symptomatic mild COVID-19 outpatients in terms of a reduced risk of disease deterioration over subsequent days. In the first of the meta-analyzed trials, Stop COVID 15, primary outcome was hospitalization or incident hypoxemia needing oxygen treatment within 15 days. The trial was rather small, particularly for a binary outcome (fluvoxamine 2x100 to 3x100 mg/day over 15 days, n=80; placebo n=72) and recorded only 6 events (all with placebo) 5. Stop COVID 2 6followed the same design/outcome, and was stopped at an advanced stage for operational reasons but did not indicate any benefit [incidence 11/272 (4.0%) fluvoxamin vs. 12/275 (4.4%) placebo)]. The meta-analytical pooled estimates 3, 4 were dominated by the results of the TOGETHER trial 7 (fluvoxamine 2x100 mg/day, 10 days) that reported a marked relative reduction in the risk of the primary outcome (emergency room stay of at least 6 hours or hospitalization; over 28 days): 79/741 (11.0%) vs. 119/756 (16.0%), RR=0.69 (95% CrI 0.53-0.90) 7. By far the most events were hospitalizations, but no clear-cut benefit was obvious in this respect [75/741 (10.0%) vs. 97/756 (13.0%), OR=0.77 (0.55-1.05)7]. The meta-analysis by Lee et al.3 focused on hospitalizations and reported a 25% relative risk reduction by a frequentist method (RR=0.75, 95%CI 0.58-0.97), while the Bayesian analysis (weakly informative neutral prior) indicated somewhat more uncertainty (RR=0.78, 95%CrI 0.58-1.08; 81.6% probability of RR ≤0.90) 3. Guo et al.4 employed only frequentist pooling to indicate a marked benefit regarding “study-defined outcomes” (RR=0.69 95%CI 0.54-0.88) and somewhat more uncertainty regarding “hospitalizations” (RR=0.79, 95%CI 0.60-1.03) 4. In the meantime, a report was pubslihed of a randomized placebo-controlled trial conducted in 2020 in Korean outpatients (∼10 days of fluvoxamine 2x100 mg/day)8. It was stopped early for operational reasons8, and the primary outcome (as in Stop COVID trials) was observed in 2/26 treated and 2/26 placebo patients8. Figure 1 depicts meta-analysis of “study-defined primary outcomes” and of “hospitalizations” that uses the same frequentist and Bayesian methodology as used by Lee et al.3 except that (i) it includes the Korean data8 and (ii) employs Hartung-Knapp-Sidik-Jonkman correction shown to yield the least biased confidence interval coverage with small number of trials considerably varying in size9: (a) uncertainty about the benefit regarding “study-defined outcomes” (Figure 1A) is indicated by both the frequentist and Bayesian intervals extending to >1.0 and prediction intervals extending well >1.0. Probability of at least 10% relative risk reduction is 90.0%; (b) uncertainty about the benefit regarding “hospitalizations” (Figure 1B) is even more obvious, with estimate intervals exceding >1.10 (and further extended predictions intervals), with only 73.8% probability of at least 10% relative risk reduction. If one were to disregard two small trials with a few events (and, hence, fragile estimates that could have been by chance, at least in part) 5, 8, for the time being one would be looking at Stop COVID 2 and TOGETHER trial. This means 86/1013 hospitalization events with fluvoxamine vs. 109/1031 events with placebo, and a considerable uncertainty about any practically relevant effect: (i) frequentist RR=0.803 (95%CI 0.422-1.530); (ii) Bayesian RR=0.840 (95%CrI 0.613-1.170) and only 67.4% probablity of at least 10% relative risk reduction. Hopefully, the on-going trials (depicted in ref. 3) will resolve this uncertainty, but presently we might prefer to be cautios rather than overtly optimistic about the actual extent of benefit conveyed by early fluvoxamine treatment in COVID-19 outpatients.
Tocilizumab for reduction of mortality in severe COVID-19 patients: how should we GRADE it?Vladimir TrkuljaVladimir Trkulja, MD, PhDDepartment of PharmacologyZagreb University School of MedicineŠalata 1110000 Zagreb, Croatiae-mail: firstname.lastname@example.orgNumber of words: 799Number of figures/tables: 1To the Editor,A recent systematic review/meta-analysis 1 of randomized trials (RCTs) of tocilizumab (plus standard of care [SoC] vs. SoC w/wo placebo) in severe COVID-19 patients was a pleasure to read owing to a clear presentation of a thorough approach to data (e.g., sensitivity analyses, accounting for corticosteroid use, need for mechanical ventilation [MV] at baseline). Authors assigned high quality (certainty) GRADE levels to the evidence of efficacy in reduction of mortality overall (10 RCTs) and in patients without MV at baseline (data from 9 RCTs), and reduction of incident MV (10 RCTs). The grading was based on fixed-effect pooling, likely owing to low inconsistency index (I2) and closely similar fixed-effect and random-effects estimates1. It is this point that deserves a few comments. Conceptually, fixed-effect meta-analysis of RCTs in medicine is rarely justified, since the underlying assumption is practically inevitably violated due to variety of elements contributing to clinical heterogeneity2. The authors1 presented a range of differences in trial designs (e.g., one or repeated tocilizumab dose, more or less use of concomitant corticosteroids, differences in proportion of subjects on MV). When variance across trials is low, fixed and random-effects estimates are numerically close/identical, but the conceptual differences remain. Again, conceptually, the random-effects method is a preferred approach2 (regardless of numerical closeness of fixed/random estimates) and the choice (fixed/random) should not be based on the heterogeneity estimates2. At this point, the issue of the choice of the variance (τ2) estimator should be mentioned. A number of estimators have been explored: performance depends on the nature of the outcome, may vary across trial sizes, depends on the differences in size of included trials, and is problematic when the number of studies is lowe.g.,2-5. Variance reflects on the assigned trial weights and measures of uncertainty about the pooled estimate. While no τ2 estimator is ideal 2-5, it has been suggested that the Paule-Mandel (PM) estimator performs better than the common DerSimonian-Laird estimator for binary outcomes3.Another point to consider is the method to calculate confidence intervals (CIs) around the pooled estimate. While not without certain limitations 6, the Hartung-Knapp-Sidik-Jonkman (HKSJ) method has been repeatedly shown (under variety of scenarios) to result in more adequate coverage probability than the standard method4,7. Figure 1A re-creates meta-analysis (data presented by the authors1) on mortality across the 10 RCTs (all subjects) – it is only that it uses PM variance estimator and HKSJ correction: random-effects estimate suggests that the mean of the distribution of the effects is 0.88 (as reported1), but the CIs extend to 1.04, suggesting that it includes also effects that are somewhat above unity. It also provides prediction intervals (wider) - the best illustration of heterogeneity2,8. When viewed from the present standpoint, data indicate a non-trivial level of imprecision and heterogeneity. The authors themselves reported apparent differences (mortality reduction vs. no reduction) between estimates based on RCTs with a high proportion vs. low proportion of patients concomitantly treated with corticosteroids 1(or those generated accounting only for corticosteroid-treated vs. not treated patients, but such data were very scarce1): so, there is apparent inconsistency of the estimates across clinical settings. As re-created in Figure 1B-C, there was a tendency of reduced mortality in trials with a high proportion of patients co-treated with corticosteroids (corticosteroid treatment regimen likely variable), but with quite some imprecision and heterogeneity; and no such tendency with “low corticosteroid use”. Similarly, in patients not on MV at baseline, there was a consistent reduction in mortality risk across trials with a high proportion of steroid co-treated patients, but not in trials with a low proportion of co-treated patients (Figure 1D-E). There was also a consistent reduction of risk of incident MV in trials with a high proportion of corticosteroid co-treated patients (Figure 1F), whereas the estimate in trials with “low steroid use” is burdened with heterogeneity and imprecision (Figure 1G).Considering the above, if one were to assign a GRADE level9 to evidence of benefit of tocilizumab in severe COVID-19 patients based on the 10 RCTs addressed in the published meta-analysis1, then the following seems reasonable: a) considering (indiscriminately) all 10 RCTs (and all patients), certainty about reduced mortality is closer to “low/moderate” then to “high” due to imprecision (CIs 0.75-1.04) and heterogeneity/inconsistency; b) data on the effect of tocilizumab+corticosteroid combination that could be extracted from the 10 RCTs are scarce. Trials with high vs. low concomitant use of corticosteroids could be perceived as a proxy, but this is indirect, suggestive and not conclusive evidence. Therefore, while the effects of tocilizumab on the risk of incident MV and mortality in patients not on MV at baseline in trials with a high proportion of corticosteroid co-treated patients were consistent and reasonably precisely estimated, certainty about the benefit of tocilizumab (on top of corticosteroids; regimen?) in this setting is at best moderate/low.ReferencesVela D, Vela-Gaxha Z, Rexhepi M, Olloni R, Hyseni V, nallbani R. Efficacy and safety of tocilizumab versus standard of care/placebo in patients with COVID-19; a systematic review and meta-analysis of randomized controlled trials. Br J Clin Pharmacol . 2021; doi: 10.1111/bcp.15124.Higgins JPT, Thomson SG, Spiegelhalter DJ. A re-evaluation of random-effects meta-analysis. J R Statist Soc A . 2009; 172(Pt1):137-159.Veroniki AA, Jackson D, Viechtbauer W, Bender R, Bowden J, Knapp G, Kuss O, Higgins JPT, Langan D, Salanti G. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res Synth Methods . 2016;7(1): 55-79.Langan D, Higgins JPT, Jakson D, Bowden J. Veroniki AA, Kontopantelis E, Viechtbauer W, Simmonds M. A comparison of heterogeneity variance estimators in simulated random-effects meta-analyses. Res Synth Methods. 2019; 10(1):83-98.IntHout J, Ioannidis JPA, Borm GF, Goeman JJ. Small studies are more heterogeneous than large ones: a meta-meta-analysis. J Clin Epidemiol . 2015; 68(8):860-869.Jakson D, Law M, Rucker G, Schwarzer G. The Hartung-Knapp modification for random-effects meta-analysis: a useful refinement but are there any residual concerns? Stat Med . 2017; 36(25):3923-3934.IntHout J, Ioannidis JPA, Borm GF. The Hartung-Knapp-Sidik-Jonkman method for random effects meta-analysis is straightforward and considerably outperforms the standard DerSimonian-Laird method.BMC Med Res Methodol . 2014; 14:25 doi:10.1186/1471-2288-14-25.IntHout J, Ioannidis JPA, Rovers MM, Goeman JJ. Plea for routinely presenting prediction intervals in meta-analysis. BMJ Open . 2016; 6:e010247 doi: 10.1136/bmjopen-2015-010247Guyatt GH, Oxman, AD, Vist GE, Kurz R, Falck-Ytter Y, Schunemann HJ. GRADE: what is “quality of evidence” and why is it important to clinicians. BMJ . 2008;336(7651):995-998.Balduzzi S, Rucker G, Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health . 2019; 22(4):153-160.Figure 1 . Re-creation of the published meta-analysis1 using data provided in the published figures: the difference is in that the present estimates are generated using the Paule-Mandel variance estimator (Q-profile method for variance estimate confidence intervals) instead of the DerSimonian-Laired method available in the RevMan software used by the authors1, and Hartung Knapp Sidik Jonkman correction for random effects (see text for explanation). Panel A corresponds to published1Figure 1, panels B and C correspond to published1supplemental Figure S4. Published meta-analysis1 does not include figures that would correspond to panels D-G. Panels E and G are reduced to summaries for brevity. Note that in all meta-analyses point-estimates of I2 and τ2 were low, but the upper limits of their confidence intervals were rather high, particularly when only 4 RCTs were included (except in panel F with highly consistent results across trials). “High%” or “low %” steroid use refers to trials (as presented in the published meta-analysis1) in which >50% or <50% of the patients were co-treated with corticosteroids. Meta-analyses were performed using packagemeta 10 in R.MV – mechanical ventilation; RCT – randomized controlled trial; SoC – standard of care
Objectives: Veno-venous extracorporeal membrane oxygenation (VV-ECMO) is increasingly being used in acutely deteriorating patients with end-stage lung disease as a bridge to transplantation (BTT). It can allow critically ill recipients to remain eligible for lung transplant (LTx) while reducing pretransplant deconditioning. We analyzed early and mid-term postoperative outcomes of patients on VV-ECMO as a BTT and the impact of preoperative VV-ECMO on posttransplant survival outcomes. Methods: All consecutive LTx performed at our institution between January 2012 and December 2018 were analyzed. After matching, BTT patients were compared with non-bridged LTx recipients. Results: Out of 297 transplanted patients, 21 (7.1%) were placed on VV-ECMO as a BTT. After matching, we observed a similar 30-day mortality between BTT and non-BTT patients (4.6% vs. 6.6%, p=0.083) despite a higher incidence of early postoperative complications (need for ECMO, delayed chest closure, acute kidney injury). Furthermore, preoperative VV-ECMO did not appear associated with 30-day or 1-year mortality in both frequentist and Bayesian analysis (OR 0.35, 95%CI 0.03-3.49, p=0.369; OR 0.27, 95%CrI 0.01-3.82, P=84.7%, respectively). In sensitivity analysis, both subgroups were similar in respect to 30-day (7.8% vs. 6.5%, p=0.048) and 1-year mortality (12.5% vs. 18%, p=0.154). Conclusions: Patients with acute refractory respiratory failure while waiting for LTx represent a high-risk cohort of patients. We observed that these patients can be successfully bridged to LTx with VV-ECMO with post-transplant mortality comparable to non-BTT patients.
Fluvoxamine for COVID-19 ICU patients?Vladimir TrkuljaDepartment of PharmacologyZagreb University School of MedicineŠalata 11, Zagreb, Croativladimir.email@example.comNumber of words: 800Number of figures/tables: 1To the Editor,I read with interest a recently BJCP-accepted manuscript on the use of fluvoxamine in COVID-19 patients who needed admission to an intensive care unit (ICU) 1. It was instructive to read about the pre-existing clinical experience and about possible mechanisms of presumed benefits of fluvoxamine in COVID-19. However, attention needs to be drawn to the suggested effect of fluvoxamine quantified as a 40% reduction in instantaneous risk of death. The authors report1 on a cohort (n=51) of patients who, upon ICU admission, were treated with oral fluvoxamine added to the standard of care (SoC) (3x100 mg/day over 15 days, then 2x50 mg/day over 7 days), and who were compared to a cohort (n=51) of SoC-only patients. The two cohorts were said to be matched 1. Based on reported data 1, it appears that the patients were matched exactly in respect to gender and COVID-19 vaccination status, and, seemingly, on a rather narrow age-caliper, but the matching method was not reported 1; not reported was also a measure of matching adequacy – standardized difference (d ), a preferred method of balance assessment (adequate if d <0.1) since independent of the sample size 2. Based on the reported data1, for example, the fluvoxamine – SoC d regarding body mass index was -0.30 (-0.31 in women and -0.29 in men); also, d=-0.122 regarding history of diabetes,d= -0.350 regarding history of treated hypertension,d=-0.11 regarding on-admission APACHE score – all suggesting a considerable imbalance between the two cohorts (lower values in the fluvoxamine cohort). The authors provide Kaplan-Meier curves of time-to-death (or ICU discharge) but without the numbers at risk1. Still, data could be read from the graphs and curves reconstructed (Figure 1A):(i) the first marked difference between the treated and controls occurs during the first 7 days of observation – 3 patients died and 3 were censored in the former, and 11 died and 4 were censored the latter cohort (Figure 1A). This difference in deaths (3 vs. 11) did not change over the entire later period since the overall difference in the number of deaths was 9 (30/51 in treated vs. 39/51 in controls). This would indicate a very rapid-onset (and subsequently “lost”) effect of fluvoxamine, which does not seem pharmacologically plausible. The assumed fluvoxamine mechanisms1 are not of the immediate-onset type; with a 3x100 mg/day dosing, elimination half-life is likely to extend well beyond 30 hours, hence steady-state would be achieved only after 7-10 days 3. Combined with the baseline imbalance between groups, this indicates that the initial separation of the two curves – more or less preserved throughout the entire subsequent period - was likely not attributable to fluvoxamine; (ii) after day 21, and particularly after day 28, the numbers at risk were very low, and after day 35 there were no further events (Figure 1A), hence accounting for the entire curve is likely misleading 4; (iii) although the curves do not cross (Figure 1A), they indicate a possibility that hazard ratio varied over time. Hazard ratio as generated in a Cox proportional hazard model (as done by the authors) is an average of values that can change over time5; it is also inherently prone to selection bias and, even in absence of confounding its interpretation is not straightforward5. This holds for randomized and particularly for non-randomized settings 5. Reconstructed data depicted in Figure 1A were used to fit a complementary log-log model for continuous time process taking into account the first 35 days (no events after that point): the method treats time as a continuous but more “coarsely” measured variable, in intervals of identical length (in this case 7-day intervals, i.e., weeks); based on assumption of constant hazard within the interval, the method provides period-specific (for weeks 1-5) hazard ratios 6, which is likely a preferable option 5. Figure 1B depicts estimated probabilities of death and HRs: it is only during week 1 that the hazard appeared lower in treated – a period during which, as elaborated, fluvoxamine most likely had no effect. Finally, authors fitted a multivariable Cox model 1to substantiate the fluvoxamine effect. With a total of 15 independents in a study with 102 subjects, the model was likely overfitted and susceptible to bias arising from over(unnecessary)-adjustments 7. But more importantly, it included adjustment for renal replacement therapy (RRT), which was actually one of the outcomes. Inadequacy of adjustments for post-exposure outcomes as if they were baseline covariates has been extensively elaborated 8 and almost inevitably results in a considerable bias, regardless of whether the respective variable was actually a mediator or a collider 8. Such adjustments require implementation of marginal structural models or some of the g-estimation methods 9.Overall, the reported difference between the two cohorts of patients is more likely bias arising from design and analysis than evidence supporting a causal effect of fluvoxamine.ReferencesČalušić M, Marčec R, Lukša L et al. Safety and efficacy of fluvoxamine in COVID-19 ICU patients: an open label, prospective cohort trial with matched controls. Br J Clin Pharmacol . 2021; doi: 10.1111/bcp.15126.Stuart EA. Matching methods for causal inference: a review and a look forward. Stat Sci . 2010; 25(1):1-21.Hiemke C, Hartter S. Pharmacokinetics of selective serotonin reuptake inhibitors. Pharmacol Ther . 2000; 85 (1):11-28.Machin D, Cheung YB, Parmar MKB, eds. Survival analysis: a practical approach . 2nd ed. Chichester, West Sussex: John Wiley & Sons Ltd; 2006. p.38.Hernan MA. The hazards of hazard ratios. Epidemiology 2010; 21(1):13-15.Prentice RL, Gloecker LA. Regression analysis of grouped survival data with application to breast cancer data. Biometrics 1978; 34(1):57-67.Schisterman EF, Core SF, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiological studies. Epidemiology 2009; 20(4):488-495.Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology 2003; 14(4):300-306.Hernan MA, Robins JM, eds. Causal inference: What if . 1st ed. CRC Press LLC; 2019.Figure 1 . Summary of re-analysis of survival data published in ref. 1. A . Reconstructed curves of Kaplan-Meier product-limit estimates. Data1 were read using a digitizing software, and were re-analyzed and curves were drawn using JMP 13 software (SAS Institute Inc., Cary, NC). Upward oriented ticks indicate censorings, downward oriented ticks indicate failures. ICU – intensive care unit. B . Estimated probabilities of death during weeks 1 to 5 by treatment (Fluvox – fluvoxamine) and period-specific hazard ratios (HR) with confidence intervals. A complementary log-log model was fitted to reconstituted data using SAS 9.4 for Windows (SAS Inc., Cary, NC).
Aim. Cancer patients with reduced dihydropyrimidine dehydrogenase (DPD) activity are at increased risk of severe fluoropyrimidine (FP)-related adverse events (AE). Guidelines recommend FP dosing adjusted to genotype-predicted DPD activity based on four DPYD variants (rs3918290, rs55886062, rs67376798, rs56038477). We evaluated relationship between three further DPYD polymorphisms [c.496A>G (rs2297595), *6 c.2194G>A (rs1801160) and *9A c.85T>C (rs1801265)] and the risk of severe AEs. Methods. Consecutive FP-treated adult patients were genotyped for “standard” and tested DPYD variants, and for UGT1A1*28 if irinotecan was included, and were monitored for the occurrence of grade ≥3 (National Cancer Institute Common Terminology Criteria) vs. grade 0-2 AEs. For each of the tested polymorphisms, variant allele carriers were matched to respective wild type controls (optimal full matching combined with exact matching, in respect to: age, sex, type of cancer, type of FP, DPYD activity score, use of irinotecan/UGT1A1, adjuvant therapy, radiotherapy, biological therapy and genotype on the remaining three tested polymorphisms). Results. Of the 503 included patients (82.3% colorectal cancer), 283 (56.3%) developed grade ≥3 AEs, mostly diarrhea and neutropenia. Odds of grade ≥3 AEs were higher in c.496A>G variant carriers (n=127) than in controls (n=376) [OR=5.20 (95%CI 1.88-14.3), Bayesian OR=5.24 (95% CrI 3.06-9.12)]. Odds tended to be higher in *6 c.2194G>A variant carries (n=58) than in controls (n=432) [OR=1.88 (0.95-3.73), Bayesian OR=1.90 (1.03-3.56)]. *9A c.85T>G did not appear associated with grade ≥3 AEs (206 variant carriers vs. 284 controls). Conclusion. DPYD c.496A>G variant might need to be considered for inclusion in the DPYD genotyping panel.