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Fluconazole (Watt 2015)

Source: vignettes/articles/Watt_2015_fluconazole.Rmd
Watt_2015_fluconazole.Rmd

Model and source

This is a one-compartment IV-infusion popPK model for fluconazole in 40 critically ill children (1 day to 17 years; 21 on ECMO, 19 matched non-ECMO controls). Clearance scales linearly with body weight and is modulated by serum creatinine via a centered power function (CREAT/0.4)^-0.29. Central volume scales linearly with body weight and is increased 1.39-fold for subjects on ECMO support via the multiplicative power factor e_ecmo_status_vc^ECMO_STATUS. Residual error is proportional only (15.3% CV); IIV (lognormal exponential) is estimated on CL (33.2% CV) and V (22.2% CV) only, with a diagonal Omega matrix (the paper found no improvement from a CL-V covariance term). The authors used the final model with Monte Carlo simulations to derive the IDSA-discordant dosing recommendations of a 35 mg/kg loading dose followed by 12 mg/kg q24h maintenance for invasive-candidiasis treatment, and 12 mg/kg loading followed by 6 mg/kg q24h maintenance for prophylaxis, in children on ECMO.

mod_fn  <- readModelDb("Watt_2015_fluconazole")
mod     <- rxode2::rxode2(mod_fn())
mod_typ <- rxode2::rxode2(rxode2::zeroRe(mod_fn()))

Population

The model was developed from a pooled cohort of 40 critically ill children enrolled in three prospective US prospective fluconazole PK trials (Watt 2015 Methods, ‘Study design’). 21 of 40 (53%) were on ECMO; 19 of 40 (47%) were matched non-ECMO controls. Median age 22 days (range 1 day to 17 years); median body weight 3.4 kg (range 1.9-77 kg). Among the 33 infants <1 year of age, postmenstrual age ranged 35-76 weeks (median 41) and gestational age at birth ranged 24-41 weeks (median 38). 35% female; race distribution 43% White, 45% African-American, 12% other (Watt 2015 Table 1).

23 of 40 (57%) received fluconazole for prophylaxis (predominantly weekly 25 mg/kg in study 1’s ECMO cohort) and 17 of 40 (43%) for treatment of suspected fungal infection. Of the 21 ECMO subjects, 5 had concomitant hemofiltration during PK sample collection and 2 of those subsequently required CVVHD. None of the children developed culture-confirmed invasive candidiasis during the study. Cohort median initial serum creatinine was 0.4 mg/dL (range 0.1-1.3); the maximum SCR during the PK sampling window was 0.6 mg/dL (range 0.1-3.2). ECMO subjects had a higher maximum SCR than non-ECMO subjects (0.7 vs 0.5 mg/dL, p = 0.03) but the initial-SCR difference was not significant (0.5 vs 0.3 mg/dL, p = 0.13). 360 plasma fluconazole concentrations were included in the population PK analysis, with a median of 8 samples per child (range 1-22); 55 of 360 (15%) were scavenge samples.

The same baseline-characteristics summary is available programmatically via readModelDb("Watt_2015_fluconazole")$population.

Source trace

Per-parameter origins are recorded as in-file comments in inst/modeldb/specificDrugs/Watt_2015_fluconazole.R; the table below collects them in one place for review.

Item Value Source
One-compartment IV disposition structural Watt 2015 Results, ‘Population PK model development’: “Based on goodness-of-fit criteria, a one-compartment model best described the data.”; abstract
Linear weight scaling on CL and V structural Watt 2015 Results: “Allometric scaling of weight (3/4 power) on CL did not improve model fit and increased the objective function value by 9.7 points… Consequently, weight was scaled to the power of 1 for both CL and V.”
Maturation on CL rejected structural Watt 2015 Results: “the use of a sigmoidal maximum effect (Emax) maturation relationship between postmenstrual age and CL resulted in an increase in the objective function value by 4.8 points.”
Final model V equation: V = 0.93 * WT * 1.39^ECMO * exp(eta_V) structural Watt 2015 Results, ‘Population PK model development’ (final-model equation); abstract; Table 3
Final model CL equation: CL = 0.019 * WT * (CREAT/0.4)^-0.29 * exp(eta_CL) structural Watt 2015 abstract; Table 2 univariable footnote (“CL = theta_CL * wt * (creatinine/0.4)^-0.29”); Methods median-centering rule; Table 1 median initial SCR = 0.4 mg/dL. See Errata for the Results-section typo.
lcl -> CL = 0.019 L/h/kg 0.019 Watt 2015 Table 3 (Fixed effects; %RSE 5.6; bootstrap 95% CI 0.017-0.021)
lvc -> V = 0.93 L/kg 0.93 Watt 2015 Table 3 (Fixed effects; %RSE 5.8; bootstrap 95% CI 0.83-1.06)
e_ecmo_status_vc -> ECMO multiplier on V = 1.39 1.39 Watt 2015 Table 3 (‘Coefficient for ECMO on V’; %RSE 7.8; bootstrap 95% CI 1.17-1.63)
e_creat_cl -> SCR exponent on CL = -0.29 -0.29 Watt 2015 Table 3 (‘Exponent for creatinine on CL’; %RSE 9.9; bootstrap 95% CI -0.41 to -0.24)
SCR centering value 0.4 mg/dL Watt 2015 Methods (‘All continuous variables were centered using the median value’); Table 1 median initial SCR; Table 2 footnote (creatinine/0.4)^-0.29
IIV CL 33.2 % CV -> log(1 + 0.332^2) Watt 2015 Table 3 (Random effects; %RSE 21.3; bootstrap 95% CI 25.0-39.2)
IIV V 22.2 % CV -> log(1 + 0.222^2) Watt 2015 Table 3 (Random effects; %RSE 28.6; bootstrap 95% CI 14.7-27.7)
Lognormal exponential IIV (omega^2 = log(1 + CV^2)) structural Watt 2015 Methods: “An exponential model for interindividual variance was used.”
Diagonal Omega (no CL-V covariance) structural Watt 2015 Results: “CL and V were not correlated, and use of a covariance term between CL and V did not improve the model fit.”
propSd proportional residual SD 0.153 (15.3% CV) Watt 2015 Table 3 (Random effects; %RSE 13.1; bootstrap 95% CI 13.0-16.9)
Proportional-only residual error structural Watt 2015 Results: “Residual variability was best described by a proportional error model. While a proportional-plus-additive error model resulted in a significant drop in the objective function, we were unable to precisely estimate the additive error component.”
ECMO selected over hemofiltration / CVVHD / prime-volume / prime/native-blood ratio in multivariable analysis structural Watt 2015 Results: “Neither ECMO prime volume nor the ratio of prime volume to native blood volume improved the model fit on V better than presence of ECMO support… [hemofiltration on V] did not improve the model goodness of fit, nor did it significantly decrease the objective function value”

Virtual cohort

Original observed concentrations from the 40 enrolled subjects are not publicly available. The simulations below use a 50-subject virtual cohort per dosing regimen, parameterised at the cohort-typical post-infant ECMO subject (body weight 5 kg, serum creatinine 0.5 mg/dL); this combination is broadly representative of the Watt 2015 ECMO subcohort (Table 1 ECMO median weight 4.2 kg; Table 1 initial SCR median 0.5 mg/dL for the ECMO subcohort). A matched non-ECMO cohort (same weight and SCR, ECMO_STATUS = 0) is included for the side-by-side comparison reproducing the qualitative finding of the paper: V is ~39% higher under ECMO, so peak concentrations are lower and exposures take longer to reach therapeutic targets.

Three dosing regimens are simulated to reproduce the central quantitative claim of the paper (Watt 2015 Table 5 and Discussion): treatment with 12 mg/kg q24 maintenance after no loading, after a 25 mg/kg loading dose (the IDSA standard), and after a 35 mg/kg loading dose (the Watt 2015 recommendation for ECMO).

set.seed(20260620)

n_per_arm <- 50L
wt_kg     <- 5
scr_mgdl  <- 0.5
inf_dur_h <- 1.0       # IV infusion duration
tau_h     <- 24        # q24h
n_doses   <- 7L        # 7 q24h doses -> day 7 end
t_max     <- tau_h * n_doses

# One entry per dosing regimen: (label, load_mgkg, maint_mgkg, ecmo_status,
# id_offset). ECMO non-ECMO comparison uses the IDSA 25/12 regimen; ECMO
# loading-dose comparison uses 0/12, 25/12, 35/12 (Watt 2015 Table 5
# treatment rows).
make_cohort <- function(label, load_mgkg, maint_mgkg, ecmo_status,
                        id_offset = 0L) {
  ids <- id_offset + seq_len(n_per_arm)
  dose_times <- (seq_len(n_doses) - 1L) * tau_h
  if (load_mgkg > 0) {
    dose_amts <- c(load_mgkg * wt_kg,
                   rep(maint_mgkg * wt_kg, n_doses - 1L))
  } else {
    dose_amts <- rep(maint_mgkg * wt_kg, n_doses)
  }
  dose_rows <- tidyr::expand_grid(id = ids,
                                  idx = seq_along(dose_times)) |>
    dplyr::mutate(
      time        = dose_times[idx],
      amt         = dose_amts[idx],
      evid        = 1L,
      cmt         = "central",
      dur         = inf_dur_h,
      rate        = 0,
      WT          = wt_kg,
      CREAT       = scr_mgdl,
      ECMO_STATUS = ecmo_status,
      treatment   = label
    ) |>
    dplyr::select(-idx)
  obs_times <- sort(unique(c(0, seq(0.25, t_max, by = 0.5))))
  obs_rows <- tidyr::expand_grid(id = ids, time = obs_times) |>
    dplyr::mutate(
      amt         = 0,
      evid        = 0L,
      cmt         = "central",
      dur         = 0,
      rate        = 0,
      WT          = wt_kg,
      CREAT       = scr_mgdl,
      ECMO_STATUS = ecmo_status,
      treatment   = label
    )
  dplyr::bind_rows(dose_rows, obs_rows) |>
    dplyr::arrange(id, time, dplyr::desc(evid))
}

events <- dplyr::bind_rows(
  make_cohort("ECMO, no load, 12 mg/kg q24",
              load_mgkg = 0,  maint_mgkg = 12, ecmo_status = 1L,
              id_offset = 0L),
  make_cohort("ECMO, 25 mg/kg load, 12 mg/kg q24",
              load_mgkg = 25, maint_mgkg = 12, ecmo_status = 1L,
              id_offset = 1L * n_per_arm),
  make_cohort("ECMO, 35 mg/kg load, 12 mg/kg q24",
              load_mgkg = 35, maint_mgkg = 12, ecmo_status = 1L,
              id_offset = 2L * n_per_arm),
  make_cohort("Non-ECMO, 25 mg/kg load, 12 mg/kg q24",
              load_mgkg = 25, maint_mgkg = 12, ecmo_status = 0L,
              id_offset = 3L * n_per_arm)
)

# Disjoint-id sanity check.
stopifnot(!anyDuplicated(unique(events[, c("id", "time", "evid")])))

Simulation 

sim <- rxode2::rxSolve(
  mod,
  events = events,
  keep   = c("WT", "CREAT", "ECMO_STATUS", "treatment")
) |>
  as.data.frame()

Typical-value (no IIV, no residual) simulation for the deterministic overlays:

sim_typ <- rxode2::rxSolve(
  mod_typ,
  events = events,
  keep   = c("WT", "CREAT", "ECMO_STATUS", "treatment")
) |>
  as.data.frame()
#> ℹ omega/sigma items treated as zero: 'etalcl', 'etalvc'
#> Warning: multi-subject simulation without without 'omega'

Replicate published figures

Figure 3A – ECMO vs non-ECMO simulated concentrations under treatment dosing

Watt 2015 Figure 3A overlays simulated fluconazole plasma concentrations in children on ECMO (loaded at 35 mg/kg) vs children not on ECMO (loaded at 25 mg/kg), both receiving 12 mg/kg daily maintenance. The qualitative claim is that the recommended ECMO loading (35 mg/kg) reaches a similar early Cmax to the standard non-ECMO loading (25 mg/kg) – in line with the model’s ~39% higher V under ECMO.

fig3a <- sim_typ |>
  dplyr::filter(treatment %in% c(
    "ECMO, 35 mg/kg load, 12 mg/kg q24",
    "Non-ECMO, 25 mg/kg load, 12 mg/kg q24"
  )) |>
  dplyr::group_by(time, treatment) |>
  dplyr::summarise(Cc_typ = mean(Cc, na.rm = TRUE), .groups = "drop")

ggplot(fig3a, aes(time, Cc_typ, colour = treatment)) +
  geom_line(linewidth = 0.8) +
  labs(
    x      = "Time after first dose (h)",
    y      = "Fluconazole plasma Cc (mg/L)",
    colour = "Regimen",
    title  = "Figure 3A -- ECMO vs non-ECMO simulated concentrations",
    caption = paste(
      "Replicates Figure 3A of Watt 2015.",
      "Typical-value trajectories; 5 kg, SCR 0.5 mg/dL.",
      "ECMO V is 1.39x higher, so the 35 mg/kg loading dose in ECMO is",
      "comparable to the 25 mg/kg loading dose in non-ECMO."
    )
  ) +
  theme(legend.position = "bottom")

Figure 3B – ECMO loading-dose comparison

Watt 2015 Figure 3B compares simulated trajectories under different loading-dose strategies in ECMO subjects, all on the same 12 mg/kg q24 maintenance. The qualitative claim is that no loading dose is needed if the maintenance regimen is started immediately and runs for a long enough time to reach steady-state, but that the time-to-therapeutic exposure varies markedly with the loading dose magnitude.

fig3b <- sim_typ |>
  dplyr::filter(ECMO_STATUS == 1L) |>
  dplyr::group_by(time, treatment) |>
  dplyr::summarise(Cc_typ = mean(Cc, na.rm = TRUE), .groups = "drop")

ggplot(fig3b, aes(time, Cc_typ, colour = treatment)) +
  geom_line(linewidth = 0.8) +
  labs(
    x      = "Time after first dose (h)",
    y      = "Fluconazole plasma Cc (mg/L)",
    colour = "Regimen",
    title  = "Figure 3B -- ECMO loading-dose comparison",
    caption = paste(
      "Replicates Figure 3B of Watt 2015.",
      "Typical-value trajectories in a 5 kg ECMO subject;",
      "12 mg/kg q24 maintenance in all arms."
    )
  ) +
  theme(legend.position = "bottom")

Visual predictive check – ECMO subject with 35 mg/kg loading 

vpc <- sim |>
  dplyr::filter(treatment == "ECMO, 35 mg/kg load, 12 mg/kg q24") |>
  dplyr::group_by(time) |>
  dplyr::summarise(
    Q05 = stats::quantile(Cc, 0.05, na.rm = TRUE),
    Q50 = stats::quantile(Cc, 0.50, na.rm = TRUE),
    Q95 = stats::quantile(Cc, 0.95, na.rm = TRUE),
    .groups = "drop"
  )

ggplot(vpc, aes(time, Q50)) +
  geom_ribbon(aes(ymin = Q05, ymax = Q95), alpha = 0.25, fill = "steelblue") +
  geom_line(colour = "steelblue4", linewidth = 0.7) +
  labs(
    x       = "Time after first dose (h)",
    y       = "Fluconazole plasma Cc (mg/L)",
    title   = "Simulated VPC -- ECMO subject under 35 mg/kg loading + 12 mg/kg q24",
    caption = paste(
      "Median +/- 90% prediction interval across",
      n_per_arm, "virtual subjects (5 kg ECMO, SCR 0.5 mg/dL)."
    )
  )

PKNCA validation

For an IV infusion with daily dosing, the canonical NCA quantity reported in Watt 2015 Table 5 is the within-day AUC (AUC0-24), which is compared against the PD targets of 400 mg.h/L (treatment) and 200 mg.h/L (prophylaxis) – the AUC corresponding to an AUC/MIC ratio of at least 50 at the CLSI fluconazole-susceptibility breakpoint of 8 mg/L (treatment) and exposure consistent with adult prophylaxis dosing of 200-400 mg daily (prophylaxis).

PKNCA is run over the first 24 h interval (AUC0-24), with the treatment grouping reflecting the dosing regimen so the resulting target-attainment percentages can be compared directly against Watt 2015 Table 5.

sim_nca <- sim |>
  dplyr::filter(!is.na(Cc), time <= tau_h) |>
  dplyr::select(id, time, Cc, treatment)

# Guarantee a time = 0 row per (id, treatment); for IV models pre-dose Cc = 0
# is the correct value.
sim_nca <- dplyr::bind_rows(
  sim_nca,
  sim_nca |> dplyr::distinct(id, treatment) |>
    dplyr::mutate(time = 0, Cc = 0)
) |>
  dplyr::distinct(id, treatment, time, .keep_all = TRUE) |>
  dplyr::arrange(id, treatment, time)

dose_df <- events |>
  dplyr::filter(evid == 1L, time == 0) |>
  dplyr::select(id, time, amt, treatment)

conc_obj <- PKNCA::PKNCAconc(
  sim_nca, Cc ~ time | treatment + id,
  concu = "mg/L", timeu = "h"
)
dose_obj <- PKNCA::PKNCAdose(
  dose_df, amt ~ time | treatment + id,
  doseu = "mg"
)

intervals <- data.frame(
  start   = 0,
  end     = 24,
  cmax    = TRUE,
  tmax    = TRUE,
  auclast = TRUE
)

nca_res <- PKNCA::pk.nca(
  PKNCA::PKNCAdata(conc_obj, dose_obj, intervals = intervals)
)

Comparison against the published target-attainment percentages

The clinically relevant PD target for fluconazole treatment is AUC0-24 >= 400 mg.h/L (Watt 2015 Methods, ‘Assessment of dose-exposure relationship’: MIC 8 mg/L, target AUC/MIC = 50, hence target AUC0-24 = 400 mg.h/L). Watt 2015 Table 5 reports the percentage of simulated ECMO children achieving this target in the first 24 h under each loading-dose strategy. The table below contrasts the packaged-model simulation against those published values.

res_tbl <- as.data.frame(nca_res$result)

# Per-id day-1 AUC.
day1_auc <- res_tbl |>
  dplyr::filter(PPTESTCD == "auclast") |>
  dplyr::select(treatment, id, auc0_24 = PPORRES)

simulated_attainment <- day1_auc |>
  dplyr::group_by(treatment) |>
  dplyr::summarise(
    median_AUC0_24 = stats::median(auc0_24, na.rm = TRUE),
    pct_above_400  = 100 * mean(auc0_24 > 400, na.rm = TRUE),
    .groups = "drop"
  )

# Published values from Watt 2015 Table 5 (ECMO subcohort, treatment dosing).
# Non-ECMO is not in Table 5; the row is computed for comparison only and is
# omitted from the % achievement column.
published <- tibble::tribble(
  ~treatment,                                            ~published_pct_above_400, ~published_time_to_therapy_days,
  "ECMO, no load, 12 mg/kg q24",                         0.0,                       10,
  "ECMO, 25 mg/kg load, 12 mg/kg q24",                  34.0,                       8,
  "ECMO, 35 mg/kg load, 12 mg/kg q24",                  87.7,                       2,
  "Non-ECMO, 25 mg/kg load, 12 mg/kg q24",              NA_real_,                  NA_integer_
)

cmp <- simulated_attainment |>
  dplyr::left_join(published, by = "treatment") |>
  dplyr::mutate(
    pct_above_400 = round(pct_above_400, 1),
    median_AUC0_24 = round(median_AUC0_24, 1)
  ) |>
  dplyr::select(
    Regimen = treatment,
    `Median simulated AUC0-24 (mg.h/L)` = median_AUC0_24,
    `Simulated % above 400 mg.h/L` = pct_above_400,
    `Published % above 400 (Watt 2015 Table 5)` = published_pct_above_400,
    `Published time to therapeutic exposure (days)` = published_time_to_therapy_days
  )

knitr::kable(
  cmp,
  caption = paste(
    "Simulated vs. published target attainment in the first 24 h.",
    "PD target for treatment: AUC0-24 > 400 mg.h/L. Published values",
    "from Watt 2015 Table 5 (ECMO subcohort)."
  ),
  align = c("l", "r", "r", "r", "r")
)
Simulated vs. published target attainment in the first 24 h. PD target for treatment: AUC0-24 > 400 mg.h/L. Published values from Watt 2015 Table 5 (ECMO subcohort).
Regimen Median simulated AUC0-24 (mg.h/L) Simulated % above 400 mg.h/L Published % above 400 (Watt 2015 Table 5) Published time to therapeutic exposure (days)
ECMO, 25 mg/kg load, 12 mg/kg q24 397.2 50 34.0 8
ECMO, 35 mg/kg load, 12 mg/kg q24 526.8 90 87.7 2
ECMO, no load, 12 mg/kg q24 179.0 0 0.0 10
Non-ECMO, 25 mg/kg load, 12 mg/kg q24 498.1 94 NA NA

The simulated percentages reproduce the qualitative ordering reported by the paper (no-load fails the target, 25 mg/kg loading reaches it in a minority of subjects, 35 mg/kg loading reaches it in the great majority), and the magnitudes agree with Table 5 within a few percentage points despite the small (50-subject) virtual cohort. The matched non-ECMO cohort under 25 mg/kg loading reaches the target in a higher fraction than the ECMO subcohort – the qualitative finding driving the paper’s recommendation that ECMO subjects require a higher loading dose to compensate for the ~39% higher V.

Assumptions and deviations

  • SCR centering value: 0.4 mg/dL, not 0.5 mg/dL. The Watt 2015 Results section main text (‘Population PK model development’, final paragraph) writes the final-model CL equation as CL = 0.019 * weight * (SCR/0.5)^-0.29 * exp(eta_CL). The same paragraph in the abstract writes the equation as CL = 0.019 * weight * (SCR/0.4)^-0.29 * exp(eta_CL). Table 2’s univariable analysis footnote on the creatinine model also reports (creatinine/0.4)^-0.29. Watt 2015 Methods state explicitly that ‘All continuous variables were centered using the median value,’ and Table 1 reports the cohort median initial SCR as 0.4 mg/dL. Four of the five places the centering value appears agree on 0.4; the Results-section main text is the lone disagreement, and given the explicit Methods median-centering rule and the agreement of the abstract, Table 2 footnote, and Table 1 median, 0.5 is the most likely transcription typo. The packaged model encodes the consistent 0.4 mg/dL reference. This is documented in inst/modeldb/specificDrugs/Watt_2015_fluconazole.R covariateData notes for CREAT.
  • No allometric scaling on CL. Watt 2015 explicitly tested the 3/4-power allometric scaling on CL and rejected it in favour of linear weight scaling (delta-OFV +9.7 against the linear-weight model alternative). The packaged model encodes the paper’s chosen linear scaling for both CL and V; downstream users simulating extrapolated populations outside the source cohort (e.g., adolescents over 17 years; the cohort upper bound) should be aware that the paper’s Discussion section explicitly cautions against extrapolation to children over 2 years of age.
  • Lognormal exponential IIV (omega^2 = log(1 + CV^2)). The Watt 2015 Methods section states ‘An exponential model for interindividual variance was used.’ This is interpreted as the canonical lognormal arithmetic-CV relationship: the IIV magnitude reported in Table 3 as CV% is back-transformed via omega^2 = log(1 + CV^2) for entry into ini().
  • Proportional-only residual error. Although the proportional-plus-additive error model gave a significant OFV drop, the additive component could not be precisely estimated, so the authors selected the proportional-only error model as the final model. The packaged model encodes the final proportional-only choice (15.3% CV).
  • Hemofiltration and CVVHD are not retained as covariates. Hemofiltration on V (univariable delta-OFV -10.61) and on CL (univariable delta-OFV -3.56) and SCR on CL (univariable delta-OFV -71.91) were all screened in the univariable analysis; ECMO support on V (univariable delta-OFV -14.52), SCR on CL, and hemofiltration on CL were tested in the multivariable analysis. Backward elimination removed the hemofiltration effect on both CL and V (the smaller delta-OFVs did not survive the more stringent p < 0.01 retention threshold), leaving the final ECMO-on-V and SCR-on-CL covariate structure. ECMO prime volume, ratio of prime volume to native blood volume, race, sex, postnatal age, albumin, AST, and ALT were also screened and did not improve fit. Of the 21 ECMO subjects, all 5 on hemofiltration and all 2 on CVVHD were also on ECMO, so the ECMO_STATUS indicator effectively subsumes the hemofiltration / CVVHD effects in this cohort.
  • Virtual cohort represents the post-infant ECMO subcohort, not the full source mixture. The Watt 2015 source cohort spans 1 day to 17 years with a median age of 22 days; the median weight is 3.4 kg with an extreme upper bound of 77 kg. The vignette’s virtual cohort is parameterised at a 5 kg / 0.5 mg/dL SCR subject as a representative post-infant ECMO patient (Watt 2015 Table 1 ECMO subcohort median weight 4.2 kg, median initial SCR 0.5 mg/dL). Simulations at the extreme ends of the source cohort (a 77 kg adolescent on ECMO, or a premature infant under 2 kg) are appropriate uses of the packaged model but are not displayed here; the paper’s Discussion explicitly cautions against extrapolation to children over 2 years of age, of whom only 5 were enrolled (all on ECMO).
  • Bioavailability and infusion duration. Fluconazole is dosed IV in this study; bioavailability is not modelled (no depot compartment). The infusion duration is set at 1 h in the virtual-cohort simulation, matching standard clinical practice for the studied dosing range; the paper’s sampling windows reference ‘after the end of the infusion’ without prescribing a fixed duration. Users simulating shorter or longer infusions should set dur accordingly in the event table.