Statistical Analysis
We used the open-source platform R for statistical analyses (R
Foundation for Statistical Computing, Vienna, Austria), including
appropriate packages such as rms, Boruta, and compare
groups.7–9 We present descriptive statistics as
medians and interquartile ranges for continuous variables that violate
normality, and present normally distributed continuous variables as
means. We present categorical variables as frequencies and percentages.
All univariate statistics were two-sided.
We selected appropriate tests for univariate comparisons depending on
the nature of the variable. Accordingly, we used Student’s t-test or the
Kruskall-Wallis test for continuous variables and chi-squared or Fisher
exact tests for categorical variables.
Time to event analysis and cumulative incidences were plotted by the
survminer package. The failure event was intubation, and the date
variable was the time elapsed between hospitalization and intubation.
Statistical significance was estimated by the log-rank test.
We constructed a full model by including variables important in
univariate comparisons (at a significance level of p<0.05), as
well as variables important based on medical knowledge. We then selected
relevant variables using the Boruta package. We used a random forest
algorithm with default attributes in the elimination procedure and used
a multiple imputation algorithm to impute missing observations. More
specifically, we imputed missing observations five times and then
combined effect estimates via the Hmisc package. We also tested
medically important variables one by one in the final model and decided
on their inclusion according to the calibration plot and performance
indices. We internally validated the final model using bootstrap
resampling evaluated according to Somers’ Dxy rank correlation and the
C-index (the concordance probability).
Finally, we also constructed a nomogram using the linear predictions of
the final model for the intubation outcome.
RESULTS
During the study period, 161 and 114 patients were followed in the
Lop/Dox cohort and the Others cohort, respectively. Table 1 displays the
baseline characteristics of the cohorts.
Briefly, most severity factors were more pronounced in the Lop/Dox
cohort. For example, age was significantly higher (55∙0 [46∙0, 64∙8]
vs 61∙0 [48∙0, 72∙0], years) in the Lop/Dox cohort, which is an
important factor associated with adverse outcomes. Respiration rate per
minute and ACE II inhibitor usages were likewise all high in the Lop/Dox
cohort, while O2 saturation was lower. However, the
number of patients that needed intubation did not differ between the
cohorts (Lop/Dox, 25 (15∙5%); Others, 19 (16∙7%)).
The overall fatality rates in the Lop/Dox and Others cohorts were 12∙4%
(20/161) and 8∙7% (10/114), respectively. The fatality rates among
intubated patients in the Lop/Dox and Others cohorts were 68% (17/25)
and 52.6% (10/19), respectively (p -value = 0.79). Neither
fatality rate comparison was statistically significant.
A univariate comparison of baseline risk factors between patients who
needed intubation to maintain O2 saturation and those
not requiring intubation is displayed in Table 2.
Among the evaluated factors, age, white blood cell (WBC) count,
lymphocyte to WBC ratio, O2 saturation, respiration rate
per minute, the elapsed time between the onset of symptoms to
hospitalization, and hypertension were statistically significant. Figure
1 displays the cumulative incidence of intubated patients in the Lop/Dox
and Others cohort.
The numbers of intubated patients did not differ between the two
cohorts, although severity parameters were more unfavorable in the
Lop/Dox cohort.
Estimated effects from multivariate models are displayed in Table 3.
Briefly, three variables were retained in the final model when
evaluating fatality as an outcome, while four variables were retained
for the intubation outcomes. Specifically, age, oxygen saturation at
hospital admission, and elapsed time between the onset of symptoms and
hospitalization were the covariates associated with COVID-19 fatality.
Age was not associated with intubation, in contrast to the lymphocyte to
WBC count ratio, which was associated with intubation. We also
constructed a nomogram that displays the relative importance of
predictive covariates and the estimated risk of intubation, as shown in
Figure 2.
DISCUSSION
We conducted a comparative study between two medical centers, which
demonstrated that lopinavir in combination with doxycycline is as
effective as the FVP, HCQ, and azithromycin combination regimen.
Lopinavir is a broad-spectrum protease inhibitor that was successfully
implemented during the SARS and MERS outbreaks.10 In
silico docking studies have also indicated that lopinavir can inhibit
SARS-CoV-2 protease.11 Therefore, lopinavir is a
widely recognized experimental alternatives to more established COVID-19
medications. Recently, a randomized controlled study compared lopinavir
to standard-care therapy among COVID-19 patients with a median of 13
days of delay from the onset of symptoms.12 Although
the lopinavir arm displayed an apparent benefit at 14 days, the
difference did not maintain statistical significance by 28 days.
However, this study included patients at the late stage of the disease.
Pulmonary damage resulting from excessive inflammation independent of
viral activity in the later stages of the disease reveals the importance
of effective early intervention using antivirals.13,14
Postmortem studies indicate that pulmonary damage might be related to
dysregulated inflammation (rather than viral activity) in the alveoli
caused by accumulated highly cytotoxic lymphocytes and inflammatory
cells.15 Therefore, supporting the regulation of
cytokine production is thought to be important in COVID-19 treatment.
Doxycycline is a strong inducer of suppressors of cytokine signaling
proteins (SOCS)16 and is successfully used among
dengue hemorrhagic fever patients.17 We supplemented
lopinavir with doxycycline for its immunomodulatory effect as well as
its antibacterial activity, especially against pulmonary pathogens like
mycoplasma. We note that, before test results become available, making a
differential diagnosis between COVID-19 pneumonia and similarly
presenting illnesses might be challenging in some cases, such as
Mycoplasma pneumonia.
The patients included in our study were hospitalized after a median of
five days from the onset of initial signs and symptoms; this was true in
both medical centers comprising our study. Interestingly, the elapsed
time between the onset of symptoms and hospitalization was inversely
correlated with adverse outcomes. This inverse correlation probably
reflects the speed of the disease trajectory from the onset of symptoms
to deterioration. This finding is surprising because the intuitive
perception is that a patient admitted to the hospital and receiving
medications early would be more likely to have an advantageous outcome.
However, in real life, most patients with mild symptoms do not seek an
immediate medical investigation, and this may skew the observed results.
We found that advanced age was one of the most critical risk factors for
fatality regardless of the treatment regimen. This finding is consistent
with previously published case series from China and the United
States.18 Sex (i.e. male gender) previously considered
an important predictive factor for COVID-19 mortality, was not
associated with mortality based on the univariate and multivariate
analyses in our cohort. In the literature, elevated inflammation
biomarkers such as C-reactive protein, ferritin, and increased
neutrophil-to-lymphocyte ratio have been associated with death from the
COVID-19 disease.19 Secondary bacterial infections are
inevitable in COVID-19 patients with severely damaged bronchial mucosa,
especially after intubation; this is true among patients with a normal
respiratory system or among patients with nosocomial flora pathogens.
For instance, in three of our patients, Acinetobacter baumanniirelated ventilator-associated pneumonia was developed in an ICU that
treated COVID-19 with antibiotics. In such a case, nonspecific
inflammation biomarkers generally increase.
In the face of this new and challenging disease, our goal is to develop
a risk assessment tool that strengthens the clinician’s resources in
treating patients. However, the lack of optimal selection of multiple
parameters make risk assessment tools difficult to use. We therefore
recommend our nomogram, which consists of symptom duration, vital signs,
and blood parameters to ensure rapid triage in the management of
patients at risk.
Our study has limitations, including its retrospective nature and some
violations of comparability at the two hospital settings. The compliance
of outpatient treatments could not be followed up in patients discharged
early. Unrecorded data about the patients who remained hospitalized,
especially at the ICU, after the study was complete may cause the
results to be biased.
In conclusion, although there are different treatment protocols between
centers, we identified classic risk factors, such as age and oxygen
saturation, as the determinants of COVID-19 related pneumonia prognosis.
Hence, until a drug or drug combination is available that is rigorously
evidence-based, lopinavir in combination doxycycline therapy seems
effective, especially soon after hospital admission for COVID-19.
1.4.1. Acknowledgments
During the epidemic of COVID-19, we would like to thank all our
healthcare professionals, governors for their supports, and the Turkish
people who comply with our warnings during the national lockdown.
1.5.1. Declarations of interest : None
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Figure legends:
Figure 1. Cumulative incidence plot of Lop/Dox and Others cohorts.
Events are shown over the x axis of the plot.
Figure 2. A nomogram for outcome intubated. Total points obtained by the
sum of the individual points will predict the risk of intubation.