Ursodeoxycholic acid for COVID-19: real-time meta analysis of 13 studies
Abstract
Statistically significant lower risk is seen for recovery and cases. 7 studies from 7 independent teams in 3 countries show statistically significant
improvements.
Meta analysis using the most serious outcome reported shows
17% [5‑29%] lower risk. Results are similar for peer-reviewed studies.
Results are robust — in exclusion sensitivity analysis 6 of 13
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
No treatment or intervention
is 100% effective. All practical, effective, and safe means should be used
based on risk/benefit analysis.
Multiple treatments are typically used
in combination, and other treatments
are significantly more effective.
All data to reproduce this paper and
sources are in the appendix.
Highlights
UDCA reduces
risk for COVID-19 with very high confidence for cases and in pooled analysis, and low confidence for hospitalization and recovery.
Real-time updates and corrections,
transparent analysis with all results in the same format, consistent protocol
for 69
treatments, outcome specific analyses and combined evidence from all studies.
Figure 1.
A. Random effects
meta-analysis. This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
B. Timeline of results in ursodeoxycholic acid studies.
SARS-CoV-2 infection primarily begins in the upper respiratory
tract and may progress to the lower respiratory tract, other tissues, and the
nervous and cardiovascular systems, which may lead to cytokine storm,
pneumonia, ARDS, neurological issues Duloquin, Hampshire, Scardua-Silva, Sodagar, Yang,
cardiovascular complications Eberhardt, organ failure, and death.
Minimizing replication as early as possible is recommended.
SARS-CoV-2 infection and replication involves the complex interplay of 50+
host and viral proteins and other factors Note A, Malone, Murigneux, Lv, Lui, Niarakis, providing many
therapeutic targets for which many existing compounds have known activity.
Scientists have predicted that over 7,000 compounds may
reduce COVID-19 risk c19early.org, either by
directly minimizing infection or replication, by supporting immune system
function, or by minimizing secondary complications.
We analyze all significant
controlled studies of
ursodeoxycholic acid
for COVID-19.
Search methods, inclusion criteria, effect extraction criteria (more serious
outcomes have priority), all individual study data, PRISMA answers, and
statistical methods are detailed in Appendix 1. We present random
effects meta-analysis results for all studies, individual outcomes, and peer-reviewed studies.
Figure 2 shows stages of possible treatment for
COVID-19. Prophylaxis refers to regularly taking medication before
becoming sick, in order to prevent or minimize infection. Early
Treatment refers to treatment immediately or soon after symptoms appear,
while Late Treatment refers to more delayed treatment.
Figure 2. Treatment stages.
An In Vivo animal study supports the efficacy of ursodeoxycholic acid Lee.
Preclinical research is an important part of the development of
treatments, however results may be very different in clinical trials.
Preclinical results are not used in this paper.
Table 1 summarizes the results for all studies, for peer-reviewed studies, and for specific outcomes.
Figure 3, 4, 5, 6, 7, 8, 9, and 10
show forest plots for random effects meta-analysis of
all studies with pooled effects, mortality results, ventilation, ICU admission, hospitalization, recovery, cases, and peer reviewed studies.
| Improvement | Studies | Patients | Authors | |
|---|---|---|---|---|
| All studies | 17% [5‑29%] ** | 13 | 37,170 | 175 |
| Peer-reviewed studiesPeer-reviewed | 15% [0‑28%] * | 11 | 25,758 | 148 |
| Mortality | 4% [-27‑28%] | 5 | 23,627 | 122 |
| ICU admissionICU | 27% [-52‑65%] | 3 | 11,693 | 96 |
| HospitalizationHosp. | 17% [-4‑34%] | 5 | 23,448 | 117 |
| Recovery | 43% [18‑60%] ** | 2 | 302 | 16 |
| Cases | 15% [4‑25%] ** | 8 | 13,456 | 53 |
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Figure 3. Random effects meta-analysis for all studies.
This plot shows pooled effects,
see the specific outcome analyses for individual outcomes.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Effect extraction is pre-specified, using the most serious outcome reported.
For details see the appendix.
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Figure 4. Random effects meta-analysis for mortality results.
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Figure 5. Random effects meta-analysis for ventilation.
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Figure 6. Random effects meta-analysis for ICU admission.
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Figure 7. Random effects meta-analysis for hospitalization.
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Figure 8. Random effects meta-analysis for recovery.
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Figure 9. Random effects meta-analysis for cases.
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Figure 10. Random effects meta-analysis for peer reviewed studies.
Effect extraction is pre-specified, using the most serious outcome reported,
see the appendix for details.
Analysis validating pooled outcomes for
COVID-19 can be found below.
Zeraatkar et al. analyze 356 COVID-19 trials, finding no significant
evidence that preprint results are inconsistent with peer-reviewed studies.
They also show extremely long peer-review delays, with a median of 6 months to
journal publication. A six month delay was equivalent to around 1.5 million
deaths during the first two years of the pandemic. Authors recommend using
preprint evidence, with appropriate checks for potential falsified data, which
provides higher certainty much earlier. Davidson et al. also showed no
important difference between meta analysis results of preprints and
peer-reviewed publications for COVID-19, based on 37 meta analyses including
114 trials.
Heterogeneity in COVID-19 studies arises from many factors including:
The time
between infection or the onset of symptoms and treatment may critically affect
how well a treatment works. For example an antiviral may be very effective
when used early but may not be effective in late stage disease, and may even
be harmful. Oseltamivir, for example, is generally only considered effective
for influenza when used within 0-36 or 0-48 hours McLean, Treanor.
Baloxavir studies for influenza also show that treatment delay is critical
— Ikematsu et al. report an 86% reduction in cases for post-exposure
prophylaxis, Hayden et al. show a 33 hour reduction in the time to
alleviation of symptoms for treatment within 24 hours and a reduction of 13
hours for treatment within 24-48 hours, and Kumar et al. report only 2.5
hours improvement for inpatient treatment.
| Treatment delay | Result |
| Post-exposure prophylaxis | 86% fewer cases Ikematsu |
| <24 hours | -33 hours symptoms Hayden |
| 24-48 hours | -13 hours symptoms Hayden |
| Inpatients | -2.5 hours to improvement Kumar |
Figure 11 shows a mixed-effects meta-regression for efficacy
as a function of treatment delay in COVID-19 studies from 69 treatments, showing
that efficacy declines rapidly with treatment delay. Early treatment is
critical for COVID-19.
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Figure 11. Early treatment is more effective. Meta-regression showing efficacy as a function of treatment delay in COVID-19 studies from 69 treatments.
Details of the patient population including age and comorbidities may
critically affect how well a treatment works. For example, many COVID-19
studies with relatively young low-comorbidity patients show all patients
recovering quickly with or without treatment. In such cases, there is little
room for an effective treatment to improve results, for example as in
López-Medina et al.
Efficacy may
depend critically on the distribution of SARS-CoV-2 variants encountered by
patients. Risk varies significantly across variants Korves, for
example the Gamma variant shows significantly different characteristics
Faria, Karita, Nonaka, Zavascki. Different mechanisms of action may be
more or less effective depending on variants, for example the degree to which
TMPRSS2 contributes to viral entry can differ across variants
Peacock, Willett.
Effectiveness may depend strongly on the dosage and treatment regimen.
The use
of other treatments may significantly affect outcomes, including supplements,
other medications, or other interventions such as prone positioning.
Treatments may be synergistic Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan, therefore
efficacy may depend strongly on combined treatments.
The
quality of medications may vary significantly between manufacturers and
production batches, which may significantly affect efficacy and safety.
Williams et al. analyze ivermectin from 11 different sources, showing
highly variable antiparasitic efficacy across different manufacturers.
Xu et al. analyze a treatment from two different manufacturers, showing 9
different impurities, with significantly different concentrations for each
manufacturer.
Across all
studies there is a strong association between different outcomes, for example
improved recovery is strongly associated with lower mortality. However,
efficacy may differ depending on the effect measured, for example a treatment
may be more effective against secondary complications and have minimal effect
on viral clearance.
The
distribution of studies will alter the outcome of a meta analysis. Consider a
simplified example where everything is equal except for the treatment delay,
and effectiveness decreases to zero or below with increasing delay. If there
are many studies using very late treatment, the outcome may be negative, even
though early treatment is very effective.
All meta analyses combine heterogeneous studies, varying in population,
variants, and potentially all factors above, and therefore may obscure
efficacy by including studies where treatment is less effective. Generally, we
expect the estimated effect size from meta analysis to be less than that for
the optimal case.
Looking at all studies is valuable for providing an overview of all research,
important to avoid cherry-picking, and informative when a positive result is
found despite combining less-optimal situations. However, the resulting
estimate does not apply to specific cases such as
early treatment in high-risk populations.
While we present results for all studies, we also present treatment time and
individual outcome analyses, which may be more informative for specific use
cases.
For COVID-19, delay in clinical results translates into
additional death and morbidity, as well as additional economic and societal
damage. Combining the results of studies reporting different outcomes is
required.
There may be no mortality in a trial with low-risk patients,
however a reduction in severity or improved viral clearance may translate
into lower mortality in a high-risk population.
Different studies may report lower severity, improved recovery, and lower mortality,
and the significance may be very high when combining the results.
"The studies reported different outcomes"
is not a good reason for disregarding results.
We present both specific outcome and pooled analyses.
In order to combine the results of studies reporting different outcomes we use
the most serious outcome reported in each study, based on the thesis that
improvement in the most serious outcome provides comparable measures of
efficacy for a treatment. A critical advantage of this approach is
simplicity and transparency.
There are many other ways to combine evidence for different outcomes, along
with additional evidence such as dose-response relationships, however these
increase complexity.
Another way to view pooled analysis is that we are using more of
the available information. Logically we should, and do, use additional
information. For example dose-response and
treatment delay-response relationships provide significant additional evidence
of efficacy that is considered when reviewing the evidence for a
treatment.
Trials with high-risk patients may be restricted due to ethics for treatments
that are known or expected to be effective, and they increase difficulty for
recruiting. Using less severe outcomes as a proxy for more serious outcomes
allows faster collection of evidence.
For many COVID-19 treatments, a reduction in mortality logically
follows from a reduction in hospitalization, which follows from a reduction in
symptomatic cases, which follows from a reduction in PCR positivity. We can
directly test this for COVID-19.
Analysis of the the association between different outcomes across studies from
all 69
treatments we cover confirms the validity of pooled outcome analysis for COVID-19.
Figure 12 shows that lower hospitalization is very strongly associated
with lower mortality (p < 0.000000000001).
Similarly, Figure 13 shows that improved recovery is very strongly associated
with lower mortality (p < 0.000000000001).
Considering the extremes, Singh et al. show an association between viral clearance and
hospitalization or death, with p = 0.003 after excluding one large
outlier from a mutagenic treatment, and based on 44 RCTs including 52,384
patients.
Figure 14 shows that improved viral clearance is strongly associated
with fewer serious outcomes. The association is very similar to
Singh et al., with higher confidence due to the larger number of
studies. As with Singh et al., the confidence increases
when excluding the outlier treatment, from p = 0.0000031 to p = 0.0000000067.
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Figure 12. Lower hospitalization is associated with lower mortality, supporting pooled outcome analysis.
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Figure 13. Improved recovery is associated with lower mortality, supporting pooled outcome analysis.
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Figure 12. Improved viral clearance is associated with fewer serious outcomes, supporting pooled outcome analysis.
Currently, 44 of the treatments we analyze show statistically significant efficacy or harm, defined as ≥10% decreased risk or >0% increased risk from ≥3 studies. 88% of these have been confirmed with one or more specific outcomes, with a mean delay of 4.7 months. When restricting to RCTs only, 54% of treatments showing statistically significant efficacy/harm with pooled effects have been confirmed with one or more specific outcomes, with a mean delay of 5.5 months.
Figure 15 shows when treatments were found effective during the
pandemic. Pooled outcomes often resulted in earlier detection of efficacy.
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Figure 15. The time when studies showed that
treatments were effective, defined as statistically significant improvement of
≥10% from ≥3 studies.
Pooled results typically show efficacy earlier than specific
outcome results. Results from all studies often shows efficacy much earlier
than when restricting to RCTs.
Results reflect conditions as used in trials to date, these depend on the
population treated, treatment delay, and treatment regimen.
Pooled analysis could hide efficacy, for example a treatment that is
beneficial for late stage patients but has no effect on viral clearance may
show no efficacy if most studies only examine viral clearance. In practice, it
is rare for a non-antiviral treatment to report viral clearance and to not
report clinical outcomes; and in practice other sources of heterogeneity such
as difference in treatment delay is more likely to hide efficacy.
Analysis validates the use of pooled effects and shows significantly faster
detection of efficacy on average.
However, as with all meta analyses, it is important to review the different
studies included. We also present individual outcome analyses, which may be
more informative for specific use cases.
Publishing is often biased
towards positive results, however evidence suggests that there may be a negative bias for
inexpensive treatments for COVID-19. Both negative and positive results are
very important for COVID-19, media in many countries prioritizes negative
results for inexpensive treatments (inverting the typical incentive for
scientists that value media recognition), and there are many reports of
difficulty publishing positive results
Boulware, Meeus, Meneguesso, twitter.com.
For ursodeoxycholic acid, there is currently not
enough data to evaluate publication bias with high confidence.
Funnel
plots have traditionally been used for analyzing publication bias. This is
invalid for COVID-19 acute treatment trials — the underlying assumptions
are invalid, which we can demonstrate with a simple example. Consider a set of
hypothetical perfect trials with no bias. Figure 16 plot A
shows a funnel plot for a simulation of 80 perfect trials, with random group
sizes, and each patient's outcome randomly sampled (10% control event
probability, and a 30% effect size for treatment). Analysis shows no asymmetry
(p > 0.05). In plot B, we add a single typical variation in COVID-19 treatment
trials — treatment delay. Consider that efficacy varies from 90% for
treatment within 24 hours, reducing to 10% when treatment is delayed 3 days.
In plot B, each trial's treatment delay is randomly selected. Analysis now
shows highly significant asymmetry, p < 0.0001, with six variants of
Egger's test all showing p < 0.05
Egger, Harbord, Macaskill, Moreno, Peters, Rothstein, Rücker, Stanley.
Note that these tests fail even though treatment delay is uniformly
distributed. In reality treatment delay is more complex — each trial has
a different distribution of delays across patients, and the distribution
across trials may be biased (e.g., late treatment trials may be more common).
Similarly, many other variations in trials may produce asymmetry, including
dose, administration, duration of treatment, differences in SOC,
comorbidities, age, variants, and bias in design, implementation, analysis,
and reporting.
Figure 16. Example funnel plot analysis for simulated perfect trials.
Pharmaceutical drug
trials often have conflicts of interest whereby sponsors or trial staff have a
financial interest in the outcome being positive. UDCA for COVID-19
lacks this because it is off-patent, has multiple manufacturers, and is very low cost.
In contrast, most COVID-19 ursodeoxycholic acid trials have been run by
physicians on the front lines with the primary goal of finding the best
methods to save human lives and minimize the collateral damage caused by
COVID-19. While pharmaceutical companies are careful to run trials under
optimal conditions (for example, restricting patients to those most likely to
benefit, only including patients that can be treated soon after onset when
necessary, and ensuring accurate dosing), not all ursodeoxycholic acid trials
represent the optimal conditions for efficacy.
Summary statistics from
meta analysis necessarily lose information. As with all meta analyses, studies
are heterogeneous, with differences
in treatment delay, treatment regimen, patient demographics, variants,
conflicts of interest, standard of care, and other factors. We provide analyses for specific
outcomes and by treatment delay, and we aim to identify key characteristics in
the forest plots and summaries. Results should be viewed in the context of
study characteristics.
Some analyses classify treatment based on early or late
administration, as done here, while others distinguish between mild, moderate,
and severe cases. Viral load does not indicate degree of symptoms — for
example patients may have a high viral load while being asymptomatic. With
regard to treatments that have antiviral properties, timing of treatment is
critical — late administration may be less helpful regardless of
severity.
Details of treatment delay per patient is often not available.
For example, a study may treat 90% of patients relatively early, but the
events driving the outcome may come from 10% of patients treated very late.
Our 5 day cutoff for early treatment may be too conservative, 5 days may be too late in many cases.
Comparison across treatments is confounded by differences in
the studies performed, for example dose, variants, and conflicts of interest.
Trials with conflicts of interest may use designs better suited to the
preferred outcome.
In some cases, the most serious outcome has very few events,
resulting in lower confidence results being used in pooled analysis, however
the method is simpler and more transparent. This is less critical as the
number of studies increases. Restriction to outcomes with sufficient power may
be beneficial in pooled analysis and improve accuracy when there are few
studies, however we maintain our pre-specified method to avoid any
retrospective changes.
Studies show that combinations of treatments can be highly
synergistic and may result in many times greater efficacy than individual
treatments alone Alsaidi, Andreani, De Forni, Fiaschi, Jeffreys, Jitobaom, Jitobaom (B), Ostrov, Said, Thairu, Wan.
Therefore standard of care may be critical and benefits may diminish or
disappear if standard of care does not include certain treatments.
This real-time analysis is constantly updated based on
submissions. Accuracy benefits from widespread review and submission of
updates and corrections from reviewers. Less popular treatments may receive
fewer reviews.
No treatment or intervention is 100% available and
effective for all current and future variants. Efficacy may vary significantly
with different variants and within different populations. All treatments have
potential side effects. Propensity to experience side effects may be predicted
in advance by qualified physicians. We do not provide medical advice. Before
taking any medication, consult a qualified physician who can compare all
options, provide personalized advice, and provide details of risks and
benefits based on individual medical history and situations.
SARS-CoV-2 infection and replication involves a complex interplay of 50+ host
and viral proteins and other factors Lui, Lv, Malone, Murigneux, Niarakis,
providing many therapeutic targets.
Over 7,000 compounds have been predicted to reduce COVID-19
risk c19early.org, either by directly
minimizing infection or replication, by supporting immune system function, or
by minimizing secondary complications.
Figure 17 shows an overview of the results for ursodeoxycholic acid
in the context of multiple COVID-19 treatments, and Figure 18 shows a plot
of efficacy vs. cost for COVID-19 treatments.
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Figure 17.
Scatter plot showing results within the context of multiple COVID-19 treatments.
Diamonds shows the results of random effects meta-analysis.
0.6% of 7,000+ proposed treatments show efficacy
c19early.org (B).
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Figure 18. Efficacy vs. cost for COVID-19 treatments.
Statistically significant lower risk is seen for recovery and cases. 7 studies from 7 independent teams in 3 countries show statistically significant
improvements.
Meta analysis using the most serious outcome reported shows
17% [5‑29%] lower risk. Results are similar for peer-reviewed studies.
Results are robust — in exclusion sensitivity analysis 6 of 13
studies must be excluded to avoid finding statistically significant efficacy
in pooled analysis.
Brevini:
Retrospective study from two registries of 1,096 COVID-19 patients with chronic liver disease, including 31 treated with ursodeoxycholic acid (UDCA). Propensity score matching was used to compare outcomes between UDCA-treated and untreated patients. The analysis found that UDCA treatment was associated with reduced hospitalization, ICU admission, ventilation, and death from COVID-19. The authors suggest that UDCA may decrease susceptibility to SARS-CoV-2 infection by downregulating the host receptor ACE2 through inhibition of the farnesoid X receptor.
Authors also show that UDCA-mediated downregulation of ACE2 reduces susceptibility to SARS-CoV-2 infection in vitro, in vivo and in human lungs and livers perfused ex situ; and that UDCA reduces the expression of ACE2 in the nasal epithelium in humans.
Authors also show that UDCA-mediated downregulation of ACE2 reduces susceptibility to SARS-CoV-2 infection in vitro, in vivo and in human lungs and livers perfused ex situ; and that UDCA reduces the expression of ACE2 in the nasal epithelium in humans.
Corpechot:
Retrospective cohort study of 10,147 chronic liver disease patients in France, with 1,322 exposed to ursodeoxycholic acid (UDCA), showing lower risk of hospitalization for COVID-19 with UDCA exposure, without statistical significance (adjusted OR 0.48, 95% CI 0.20-1.19). A case-control analysis of 88 hospitalized patients and 840 matched controls showed no significant difference, and there was no significant difference for ICU admission and mortality. The study is underpowered due to the low number of COVID-19 hospitalizations.
Costello:
OpenSAFELY retrospective 11,320 primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) patients showing lower risk of COVID-19 hospitalization or death with ursodeoxycholic acid (UDCA) treatment.
Cui:
Retrospective 215 patients with chronic hepatitis B in China, showing lower risk of COVID-19 infection, milder symptoms, and faster recovery with ursodeoxycholic acid (UDCA) treatment.
Gao:
Retrospective 393 hospitalized patients with hematologic disorders in China, showing lower risk of COVID-19 with UDCA use.
John:
Retrospective 3,214 veterans with cirrhosis comparing 1,607 participants taking ursodeoxycholic acid (UDCA) to 1,607 propensity score matched controls not taking UDCA. UDCA use was associated with significantly lower odds of SARS-CoV-2 infection (aOR 0.54), symptomatic COVID-19 (aOR 0.54), moderate or worse COVID-19 (aOR 0.51), and severe/critical COVID-19 (aOR 0.48). While retrospective, the results suggest UDCA may reduce COVID-19 susceptibility and severity in patients with cirrhosis through downregulation of ACE2 receptors.
Li:
Retrospective propensity score matched cohort study of 225 chronic liver disease patients on UDCA therapy matched to 225 controls without UDCA in China. UDCA use was associated with lower COVID-19 infection rate (85% vs 94%), lower maximum temperature, less severe symptoms, shorter recovery time (5 vs 7 days median), and lower risk of infection on regression (OR 0.32). The results rely on patient self-report rather than lab confirmed COVID-19 diagnosis.
Liu:
Retrospective 280 Chinese families with children previously seen in a liver clinic assessing whether ursodeoxycholic acid (UDCA) reduced SARS-CoV-2 infection risk. Among infected families, the study found no significant difference in confirmed or suspected SARS-CoV-2 infections between children taking UDCA (80.9%) and those not taking it (77.6%) (p=0.843).
Marrone:
PSM retrospective 629 hospitalized COVID-19 patients showing no significant difference in survival between 108 patients taking UDCA prior to infection compared to 521 matched controls not taking the drug. The lack of observed benefit in this retrospective inpatient cohort does not preclude potential protective effects of UDCA against infection or illness severe enough to require hospitalization.
Ming:
Retrospective 8,964 primary care patients prescribed ursodeoxycholic acid (UDCA) in the UK. Higher categorized UDCA adherence (≥80%) was associated with lower COVID-19 incidence (OR 0.86), whereas adherence as a continuous variable was not significant. However, adherence was measured indirectly via prescription records which may not reflect actual usage. Additionally, more adherent patients may differ systematically on unmeasured confounders (e.g., health behaviors) that influence COVID-19 risk.
Ojeda‐Fernández:
Retrospective cohort study of 9,617 patients with liver disease in Italy, divided into UDCA users and non-users. UDCA exposure was not associated with reduced SARS-CoV-2 infection or improved COVID-19 outcomes including death, hospitalization, and ICU admission in this unvaccinated cohort. The large sample size provides power, but administrative data limitations include lack of important confounders like BMI and hypertension.
Okushin:
Retrospective 94 outpatients attending a university hospital gastroenterology clinic in Japan showing no significant difference in SARS-CoV-2 infection rates between ursodeoxycholic acid (UDCA)-treated patients and control groups without UDCA treatment. However, UDCA-treated patients tended to have higher rates of subclinical infection based on serology, suggesting UDCA may reduce COVID-19 severity.
Yu:
Retrospective 115 COVID-19 patients hospitalized during an Omicron outbreak in China, of which 65 received ursodeoxycholic acid (UDCA) treatment and 50 received standard care. It found that UDCA was associated with faster body temperature recovery, with a hazard ratio of 1.62 (95% CI 0.99-2.60, p=0.053) compared to standard care after adjusting for covariates. Patients receiving higher UDCA doses (≥300mg daily) had significantly faster recovery than the standard care group, with a hazard ratio of 1.82 (95% CI 1.07-3.10, p=0.028). To further analyze the exposure-response relationship, the authors developed an AI model called VirtualBody that accurately predicted individualized UDCA pharmacokinetic profiles. Additional analysis using VirtualBody-generated data found UDCA AUC, indicating total exposure over time, had a stronger correlation with clinical outcome than cumulative dose. Overall, the study suggests UDCA may shorten recovery time from COVID-19, especially at higher doses, warranting further investigation.
We perform ongoing searches of PubMed, medRxiv, Europe PMC,
ClinicalTrials.gov, The Cochrane Library, Google Scholar, Research
Square, ScienceDirect, Oxford University Press, the reference lists of other
studies and meta-analyses, and submissions to the site c19early.org.
Search terms are ursodeoxycholic acid and COVID-19 or SARS-CoV-2. Automated searches are performed twice daily, with all matches reviewed for inclusion.
All studies regarding the use of ursodeoxycholic acid for COVID-19 that report
a comparison with a control group are included in the main analysis.
This is a living analysis and is updated regularly.
We extracted effect sizes and associated data from all studies.
If studies report multiple kinds of effects then the most serious
outcome is used in pooled analysis, while other outcomes are included in the
outcome specific analyses. For example, if effects for mortality and cases are
both reported, the effect for mortality is used, this may be different to the
effect that a study focused on.
If symptomatic
results are reported at multiple times, we used the latest time, for example
if mortality results are provided at 14 days and 28 days, the results at 28
days have preference. Mortality alone is preferred over combined outcomes.
Outcomes with zero events in both arms are not used, the next most serious
outcome with one or more events is used. For example, in low-risk populations
with no mortality, a reduction in mortality with treatment is not possible,
however a reduction in hospitalization, for example, is still valuable.
Clinical outcomes are considered more important than viral test status. When
basically all patients recover in both treatment and control groups,
preference for viral clearance and recovery is given to results mid-recovery
where available. After most or all patients have recovered there is little or
no room for an effective treatment to do better, however faster recovery is
valuable.
If only individual symptom data is available, the most serious symptom has
priority, for example difficulty breathing or low SpO2 is more
important than cough.
When results provide an odds ratio, we compute the relative risk when
possible, or convert to a relative risk according to Zhang.
Reported confidence intervals and p-values were used when available,
using adjusted values when provided. If multiple types of adjustments are
reported propensity score matching and multivariable regression has preference
over propensity score matching or weighting, which has preference over
multivariable regression. Adjusted results have preference over unadjusted
results for a more serious outcome when the adjustments significantly alter
results. When needed, conversion between reported p-values and
confidence intervals followed Altman, Altman (B), and Fisher's exact
test was used to calculate p-values for event data. If continuity
correction for zero values is required, we use the reciprocal of the opposite
arm with the sum of the correction factors equal to 1 Sweeting.
Results are expressed with RR < 1.0 favoring treatment, and using the risk of
a negative outcome when applicable (for example, the risk of death rather than
the risk of survival). If studies only report relative continuous values such
as relative times, the ratio of the time for the treatment group versus the
time for the control group is used. Calculations are done in Python
(3.12.3) with
scipy (1.13.0), pythonmeta (1.26), numpy (1.26.4), statsmodels (0.14.2), and plotly (5.21.0).
Forest plots are computed using PythonMeta Deng
with the DerSimonian and Laird random effects model (the fixed effect
assumption is not plausible in this case) and inverse variance weighting.
Results are presented with 95% confidence intervals. Heterogeneity among studies was
assessed using the I2 statistic.
Mixed-effects meta-regression results are computed with R (4.1.2) using the metafor
(3.0-2) and rms (6.2-0) packages, and using the most serious sufficiently powered outcome.
For all statistical tests, a p-value less than 0.05 was considered statistically significant.
Grobid 0.8.0 is used to parse PDF documents.
We have classified studies as early treatment if most patients
are not already at a severe stage at the time of treatment (for example based
on oxygen status or lung involvement), and treatment started within 5 days of
the onset of symptoms. If studies contain a mix of early treatment and late
treatment patients, we consider the treatment time of patients contributing
most to the events (for example, consider a study where most patients are
treated early but late treatment patients are included, and all mortality
events were observed with late treatment patients).
We note that a shorter time may be preferable. Antivirals are typically only
considered effective when used within a shorter timeframe, for example 0-36 or
0-48 hours for oseltamivir, with longer delays not being effective
McLean, Treanor.
We received no funding, this research is done in our spare
time. We have no affiliations with any pharmaceutical companies or political
parties.
A summary of study results is below. Please submit
updates and corrections at https://c19early.org/udcameta.html.
Effect extraction follows pre-specified rules as detailed above
and gives priority to more serious outcomes.
For pooled analyses, the first (most serious) outcome is used, which may
differ from the effect a paper focuses on.
Other outcomes are used in outcome specific analyses.
| Brevini, 12/5/2022, retrospective, United Kingdom, peer-reviewed, 80 authors. | risk of death, 94.4% lower, RR 0.06, p = 0.13, treatment 0 of 31 (0.0%), control 14 of 155 (9.0%), NNT 11, relative risk is not 0 because of continuity correction due to zero events (with reciprocal of the contrasting arm), propensity score matching. |
| risk of mechanical ventilation, 66.7% lower, RR 0.33, p = 0.48, treatment 1 of 31 (3.2%), control 15 of 155 (9.7%), NNT 16, propensity score matching. | |
| risk of ICU admission, 75.0% lower, RR 0.25, p = 0.21, treatment 1 of 31 (3.2%), control 20 of 155 (12.9%), NNT 10, propensity score matching. | |
| risk of hospitalization, 39.6% lower, RR 0.60, p = 0.03, treatment 11 of 31 (35.5%), control 91 of 155 (58.7%), NNT 4.3, propensity score matching. | |
| Corpechot, 1/19/2024, retrospective, France, peer-reviewed, 5 authors, study period 1 March, 2020 - 31 December, 2020. | risk of death, 54.0% higher, RR 1.54, p = 0.45, treatment 3 of 1,322 (0.2%), control 13 of 8,825 (0.1%). |
| risk of ICU admission, 19.1% lower, RR 0.81, p = 1.00, treatment 4 of 1,322 (0.3%), control 33 of 8,825 (0.4%), NNT 1401. | |
| risk of hospitalization, 40.2% lower, RR 0.60, p = 0.17, treatment 1,322, control 8,825, combination of cohort and case control analyses. | |
| risk of hospitalization, 51.8% lower, RR 0.48, p = 0.11, treatment 6 of 1,322 (0.5%), control 80 of 8,825 (0.9%), NNT 221, adjusted per study, odds ratio converted to relative risk, multivariable. | |
| risk of hospitalization, 7.0% lower, OR 0.93, p = 0.92, treatment 7 of 88 (8.0%) cases, 58 of 840 (6.9%) controls, adjusted per study, case control OR. | |
| Costello, 12/13/2023, retrospective, United Kingdom, preprint, 16 authors, study period 1 March, 2020 - 31 December, 2022. | risk of death, 24.0% lower, HR 0.76, p = 0.13, treatment 7,225, control 4,080. |
| risk of hospitalization, 19.0% lower, HR 0.81, p = 0.02, treatment 7,225, control 4,080. | |
| risk of death/hospitalization, 21.0% lower, HR 0.79, p = 0.005, treatment 7,225, control 4,080. | |
| Cui, 3/20/2024, retrospective, China, peer-reviewed, 5 authors. | recovery time >7 days, 47.8% lower, RR 0.52, p = 0.010, treatment 13 of 64 (20.3%), control 51 of 131 (38.9%), NNT 5.4, propensity score matching. |
| risk of case, 83.0% lower, OR 0.17, p = 0.001, treatment 78, control 137, adjusted per study, propensity score matching, multivariable, RR approximated with OR. | |
| Gao, 11/28/2023, retrospective, China, peer-reviewed, 5 authors, study period December 2022 - January 2023. | risk of case, 12.1% lower, RR 0.88, p = 0.03, treatment 114 of 163 (69.9%), control 183 of 230 (79.6%), NNT 10, odds ratio converted to relative risk. |
| John, 4/5/2023, retrospective, USA, peer-reviewed, 15 authors. | risk of case, 46.0% lower, OR 0.54, p < 0.001, treatment 1,607, control 1,607, RR approximated with OR. |
| Li, 5/3/2023, retrospective, China, peer-reviewed, mean age 53.0, 5 authors, study period January 2022 - December 2022. | risk of hospitalization, 40.0% lower, RR 0.60, p = 0.72, treatment 3 of 225 (1.3%), control 5 of 225 (2.2%), NNT 112. |
| hospitalization time, 28.6% lower, relative time 0.71, p < 0.001, treatment median 5.0 IQR 3.0 n=225, control median 7.0 IQR 3.0 n=225. | |
| risk of severe case, 80.0% lower, RR 0.20, p = 0.22, treatment 1 of 225 (0.4%), control 5 of 225 (2.2%), NNT 56. | |
| risk of moderate/severe case, 80.0% lower, RR 0.20, p < 0.001, treatment 10 of 225 (4.4%), control 50 of 225 (22.2%), NNT 5.6. | |
| risk of case, 10.9% lower, RR 0.89, p = 0.05, treatment 192 of 225 (85.3%), control 212 of 225 (94.2%), NNT 11, odds ratio converted to relative risk, propensity score matching. | |
| Liu, 6/29/2023, retrospective, China, peer-reviewed, survey, 2 authors. | risk of case, 2.1% higher, RR 1.02, p = 0.88, treatment 95 of 146 (65.1%), control 51 of 80 (63.7%), confirmed. |
| risk of case, 4.3% higher, RR 1.04, p = 0.61, treatment 118 of 146 (80.8%), control 62 of 80 (77.5%), confirmed + suspected. | |
| Marrone, 9/21/2023, retrospective, Italy, peer-reviewed, 10 authors, study period 1 March, 2020 - 31 December, 2022. | risk of death, 7.0% higher, HR 1.07, p = 0.77, treatment 26 of 108 (24.1%), control 118 of 521 (22.6%), adjusted per study, multivariable, Cox proportional hazards. |
| Ming, 11/16/2023, retrospective, United Kingdom, peer-reviewed, 3 authors, high vs. low. | risk of case, 12.9% lower, RR 0.87, p = 0.03, treatment 185 of 3,804 (4.9%), control 297 of 5,060 (5.9%), NNT 99, odds ratio converted to relative risk, high vs. low adherence. |
| Ojeda‐Fernández, 8/22/2023, retrospective, Italy, peer-reviewed, 11 authors, study period 1 March, 2020 - 31 May, 2021, trial NCT05659654 (history). | risk of death, 7.0% higher, HR 1.07, p = 0.67, treatment 54 of 219 (24.7%), control 259 of 1,141 (22.7%), adjusted per study, multivariable, Cox proportional hazards. |
| risk of ICU admission, 4.0% lower, HR 0.96, p = 0.96, treatment 3 of 219 (1.4%), control 15 of 1,141 (1.3%), adjusted per study, multivariable, Cox proportional hazards. | |
| risk of hospitalization, 6.0% higher, HR 1.06, p = 0.66, treatment 77 of 219 (35.2%), control 393 of 1,141 (34.4%), adjusted per study, multivariable, Cox proportional hazards. | |
| risk of case, 2.8% lower, OR 0.97, p = 0.77, wave 1 and wave 2 combined, RR approximated with OR. | |
| risk of case, 0.9% higher, RR 1.01, p = 0.94, treatment 83 of 1,687 (4.9%), control 399 of 7,930 (5.0%), NNT 896, odds ratio converted to relative risk, wave 1. | |
| risk of case, 8.7% lower, RR 0.91, p = 0.56, treatment 43 of 1,125 (3.8%), control 273 of 6,731 (4.1%), NNT 428, odds ratio converted to relative risk, wave 2. | |
| Okushin, 7/27/2023, retrospective, Japan, peer-reviewed, 7 authors, study period 1 January, 2023 - 10 February, 2023. | risk of symptomatic case, 16.7% lower, RR 0.83, p = 0.81, treatment 10 of 47 (21.3%), control 12 of 47 (25.5%), NNT 24, viral hepatitis, propensity score matching. |
| risk of case, no change, RR 1.00, p = 1.00, treatment 16 of 47 (34.0%), control 16 of 47 (34.0%), viral hepatitis, propensity score matching. | |
| risk of symptomatic case, no change, RR 1.00, p = 1.00, treatment 10 of 47 (21.3%), control 10 of 47 (21.3%), all, propensity score matching. | |
| risk of case, 6.7% higher, RR 1.07, p = 1.00, treatment 16 of 47 (34.0%), control 15 of 47 (31.9%), all, propensity score matching. | |
| Yu, 5/4/2023, retrospective, China, preprint, 11 authors, study period 10 December, 2022 - 30 December, 2022. | risk of no recovery, 38.3% lower, HR 0.62, p = 0.05, treatment 62, control 45, inverted to make HR<1 favor treatment. |
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