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      Early Lopinavir/ritonavir does not reduce mortality in COVID-19 patients: Results of a large multicenter study

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          Abstract

          The COVID-19 pandemic, an unprecedented event for current generations of physicians, has stricken hard on society. 1 There is a significant lack of effective drugs for stopping viral replication. Lopinavir/ritonavir (LPV/r) is a well-known combination used in patients with HIV which was included in the arsenal against SARS-CoV-2 early in the pandemic. 2 Its use in COVID-19 was based on inconsistent results from experimental and clinical research that was mostly done while investigating other β-coronaviruses (SARS and MERS). A number of randomized clinical trials have observed no benefit of LPV/r beyond the standard of care.3, 4, 5 However, voices have been raised against interpreting these results as grounds from definitively ruling out LPV/r since some of these studies lacked statistical power, reported encouraging outcomes in secondary endpoints, and included patients with a prolonged period of symptoms before initiation of treatment. 6 , 7 Indeed, there may be a subpopulation of COVID-19 patients – notably those early in the course of the infection – for whom LPV/r may improve their prognosis. In a recent report, Klement-Frutos et al. describe a favorable outcome of patient with COVID-19 after beginning of LPV/r on day 9 of symptoms. 8 Therefore, we aimed to assess the efficacy of LPV/r in a large, multicenter cohort of patients, with special interest in those who received treatment soon after the onset of symptoms. This work belongs to the SEMI-COVID-19 Registry, which is an ongoing, nationwide, retrospective, anonymized cohort of consecutive adult patients hospitalized in Spain for microbiologically confirmed COVID-19. 9 The Registry was approved by the Ethics Committees of the participating centers, and included data on over 300 variables. The primary endpoint was raw-in hospital mortality at 30 days from admission. Patients were considered to have been treated with LPV/r if they had received at least one dose of the drug. Common dosage of LPV/r was 400/100 mg bid. In order to mitigate the effects of possible confounding variables in a non-randomized assessment of treatment with LPV/r, propensity score (PS) was performed. The propensity of receiving LPV/r was estimated using a logistic regression model that included confounding variables which could have affected treatment choice or outcomes as independent variables. The nearest neighbor method with a caliper of 0.1 as used in PS matching and standardized mean differences (SMD) were calculated to evaluate adequacy of propensity matching. Both conditional logit and mixed effects logistic regressions were performed. Furthermore, univariate and multivariate logistic regression models were fitted in order to estimate the treatment effect using all data, as a sensitivity analysis. Multiple imputation was used to handle missing data and model estimates and standard errors were calculated using Rubin's rules. 10 Statistical analyses were performed using R software (v.3.6.2). As of June 1, 2020, the Registry included 9,594 cases, of which 8,553 met the inclusion criteria (Suppl. Fig. 1). Fifty-seven percent were men, median age was 69 years (IQR 56–79), and half of subjects (50.2%) had high blood pressure. Patients were admitted after a median time since symptoms onset of 7 days (IQR 4–9), with median SaO2/FiO2 ratio of 376 (IQR 300–452), C-reactive protein of 58 mg/L (IQR 19–123), and lymphocytes of 940 cells/µL. LPV/r was administered to 5,396 patients (63%) after a median of 0 days since admission (IQR 0–1). Table 1 shows that LPV/r was more likely to be prescribed to patients who presented with more severe clinical condition, including presence of fever, cough, radiological infiltrates (91.7% vs 80.4% p<0.001) and a lower SaO2/FiO2 ratio. On the other hand, LPV/r was less frequent among at-risk subjects in whom toxicity may be more likely: elderly patients presenting with altered mental status, dementia, or other debilitating baseline conditions, as well as patients on immunosuppressive drugs and pregnant women. Table 1 – Baseline characteristics and clinical presentation of all patients according to the administration of LPV/r. Table 1 Unmatched data (n = 8,553) Matched data (n = 5,068) No LPV/r (n = 3,157) LPV/r (n = 5,396) p No LPV/r (n = 2,534) LPV/r (n = 2,534) p Baseline features  Age (years)† 74.9 [60.4;85.2] 66.2 [54.3;75.8] <0.001 70.8 [57.2;81.5] 69.5 [56.6;78.7] <0.001  Sex (female) 1511 (47.9%) 2155 (39.9%) <0.001 1124 (44.4%) 1083 (42.7%) 0.257  Pregnancy 22 (0.70%) 15 (0.28%) 0.008 18 (0.71%) 13 (0.51%) 0.471  Race (Caucasian) 2850 (91.2%) 4647 (87.9%) <0.001 2268 (89.5%) 2279 (89.9%) 0.644  Charlson Comorbidity Index 1.00 [0.00;2.00] 0.00 [0.00;1.00] <0.001 1.00 [0.00;2.00] 1 [0.00;2.00] 0.626  High blood pressure 1817 (57.7%) 2480 (46.1%) <0.001 1357 (53.6%) 1288 (50.8%) 0.056  Immunosuppressive therapy 257 (8.14%) 291 (5.39%) <0.001 205 (8.09%) 186 (7.34%) 0.040  Dementia 628 (20.0%) 199 (3.70%) <0.001 171 (6.75%) 179 (7.06%) 0.009 Clinical presentation  Duration of symptoms (days) † 6.00 [3.00;9.00] 7.00 [4.00;9.00] <0.001 6.00 [3.00;9.00] 7.00 [4.00;9.00] 0.337  Cough 2170 (69.2%) 4281 (79.6%) <0.001 1892 (74.7%) 1918 (75.7%) 0.678  Dyspnea 1787 (56.9%) 3108 (57.9%) 0.370 1439 (56.8%) 1440 (56.8%) 1.000  Altered mental status 574 (18.4%) 351 (6.58%) <0.001 262 (10.3%) 249 (9.83%) 0.576  Temperature 36.9 [36.3;37.6] 37.1 [36.4;37.9] <0.001 36.9 [36.3;37.7] 37.0 [36.4;37.8] 0.013  Heart rate (beats/min) † 86.0 [75.0;98.0] 88.0 [77.0;100] <0.001 86.5 [76.0;99.0] 87.0 [76.0;99.0] 0.816  Respiratory rate > 20 breaths/min 958 (31.2%) 1558 (29.7%) 0.155 728 (28.7%) 735 (29.0%) 0.852 Laboratory  SaO2/FiO2 † 387 [304;452] 372 [297;452] <0.001 392 [307;457] 381 [304;452] 0.008  Lymphocytes (cells/µL) † 990 [700;1330] 910 [700;1260] 0.080 1000 [700;1320] 940 [700;1300] 0.042  C-reactive protein (mg/L) † 55.0 [17.0;120] 59.9 [19.8;125] 0.002 55.7 [17.1;120] 55.9 [18.0;119] 0.391  Creatinine (mg/dL) † 0.93 [0.75;1.23] 0.89 [0.73;1.10] <0.001 0.90 [0.74;1.17] 0.90 [0.74;1.14] 0.718 Other treatments  Interferon-β 52 (1.65%) 1073 (20.1%) <0.001 52 (2.05%) 78 (3.08%) 0.049  Hydroxychloroquine 2299 (72.8%) 4893 (90.7%) <0.001 2084 (82.2%) 2133 (84.2%) 0.071  Remdesivir 17 (0.54%) 26 (0.48%) 0.842 12 (0.47%) 12 (0.47%) 1.000 Mortality 688 (22.8%) 821 (15.7%) <0.001 431 (17.0%) 406 (16.0%) 0.364 † Continuous variables expressed as median and [interquartile range]. LPV/r: lopinavir/ritonavir. Overall, 1,509 patients died (17.6%). The univariate parameters predicting mortality is shown in Table 2 . A PS allowed for comparing two cohorts with similar values on the parameters associated with the prescription of LPV/r (Table 1). Most parameters were adequately matched according to SMD, although some variables had SMD values >0.02 (Suppl. Table 1): In this matched cohort, the adjusted odds ratio (aOR) for mortality for the use of LPV/r was 0.932 (95CI 0.799–1.087; p>0.05) according to both conditional and mixed effects logistic models. Table 2 – Univariate analysis of mortality in all patients and those with duration of symptoms of less than 8 days. Table 2 – All (n = 8,553) Early cohort (n = 6,099) OR (95CI) P OR (95CI) p Sex (female) 0.783 (0.698–0.879) <0.001 0.775 (0.681–0.882) <0.001 Age (per year) 1.090 (1.084–1.096) <0.001 1.082 (1.076–1.089) <0.001 Race (Caucasian) 3.267 (2.499–4.272) <0.001 3.715 (2.734–5.047) <0.001 High blood pressure 2.833 (2.509–3.200) <0.001 2.700 (2.354–3.097) <0.001 Dementia 4.323 (3.715–5.031) <0.001 3.704 (3.137–4.374) <0.001 Immunosuppressive treatment 1.747 (1.431–2.134) <0.001 1.656 (1.328–2.066) <0.001 Charlson Comorbidity Index (per point) 1.334 (1.296–1.373) <0.001 1.307 (1.265–1.349) <0.001 Duration of symptoms (per day) 0.930 (0.918–0.943) <0.001 0.907 (0.886–0.928) <0.001 Cough 0.652 (0.576–0738) <0.001 0.703 (0.612–0.806) <0.001 Temperature (per °C) 1.088 (1.026–1.154) 0.005 1.069 (1.000–1.141) 0.049 Dyspnea 1.865 (1.653–2.104) <0.001 1.828 (1.597–2.091) <0.001 Confusion 5.341 (4.620–6.174) <0.001 4.433 (3.774–5.208) <0.001 Respiratory rate > 20 breaths/min 3.396 (3.020–3.819) <0.001 3.239 (2.838–3.696) <0.001 SaO2/FiO2 (per unit) 0.993 (0.992–0.993) <0.001 0.993 (0.992–0.993) <0.001 Lymphocytes (per cells x 103/µL) 1.000 (1.000–1.000) 0.424 1.000 (1.000–1.000) 0.470 C-reactive protein (per mg/L) 1.005 (1.005–1.006) <0.001 1.006 (1.005–1.006) <0.001 Creatinine (per mg/dL) 1.789 (1.651–1.938) <0.001 1.647 (1.515–1.792) <0.001 Treatment with Hydroxychloroquine 0.447 (0.391–0.512) <0.001 0.483 (0.416–0.562) <0.001 Treatment with Interferon-β 2.094 (1.813–2.420) <0.001 2.098 (1.787–2.463) <0.001 Treatment with Remdesivir 1.096 (0.507–2.366) 0.816 0.871 (0.360–2.109) 0.760 Treatment with LPV/r 0.651 (0.581–0.729) <0.001 0.705 (0.620–0.801) <0.001 LPV/r: lopinavir/ritonavir. OR: odds ratio. 95CI: 95% confidence interval. Of the 6,099 patients who were admitted to hospital within 8 days since onset of symptoms (median time to admission since onset of symptoms 5 days [IQR 3–7]), LPV/r was prescribed to 3,377 (55%). Variables associated with the use of LPV/r were similar to those observed in the cohort as a whole (Suppl. Table 2). In a propensity score matching carried out on this subset of patients, early use of LPV/r was not associated with a lower mortality (conditional logistic regression: aOR 1.110 (95CI 0.944–1.300; p = 0.245); mixed effects logistic regression: aOR 1.105 (95CI 0.944–1.300; p = 0.272)). Consistent with previous studies, our analysis found no overall benefit to the use of LPV/r.3, 4, 5 We have focused on patients who received the antiviral at an earlier stage in the hope of finding greater activity. Indeed, in other viral diseases, the administration of antiviral drugs must be done as soon as possible in order to have a clinically significant activity. 11 Of note, patients included in Rao's clinical trial had a median duration of symptoms of 13 days (IQR 11–16) 4 , and those recruited in the RECOVERY trial presented after 8 days of disease (IQR 4–12). 3 In our sub-analysis, the median duration was 5 days (IQR 3–7), thus allowing us to perform a evaluation on patients who were indeed at a very early stage of disease. However, results were again disappointing, and add another nail in the coffin of LPV/r when considering its use for COVID-19. Our study has some limitations. First, it has the biases inherent to retrospective observational studies. Also, despite the fact that the number of patients included allowed us to perform PS matching, which may have reasonably controlled for many of these biases, the balance of some parameters was not perfect according to SMD values. Still, as a multicenter study involving a large number of hospitals, it has the strength of being rooted in real-life practice, far from strict inclusion and exclusion criteria of clinical trials. Second, we have used mortality as a primary endpoint, as others have done, but we cannot rule out any benefits of LPV/r that would have emerged had we analyzed softer outcomes, such as time to improvement or disease duration, as suggested by the report of Klement-Frutos et al. 8 Finally, our analysis has used data from COVID-19 first wave in Spain, when efficacy of corticosteroids or other drugs was not yet proved. Thus, our analysis is not adjusted for these treatments. However, these therapies, at least during the first wave of the pandemic, have usually been reserved for severe patients, and thus may be surrogate predictors of unfavorable progress. In conclusion, we have analyzed a large, multicenter cohort of patients with COVID-19 and have not found any benefits to administering LPV/r, even when it was administered within the first 8 days of symptoms. Our results discourage its use in SARS-CoV-2 infection. Funding This work was supported by the Spanish Society of Internal Medicine (SEMI).

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          A Trial of Lopinavir–Ritonavir in Adults Hospitalized with Severe Covid-19

          Abstract Background No therapeutics have yet been proven effective for the treatment of severe illness caused by SARS-CoV-2. Methods We conducted a randomized, controlled, open-label trial involving hospitalized adult patients with confirmed SARS-CoV-2 infection, which causes the respiratory illness Covid-19, and an oxygen saturation (Sao 2) of 94% or less while they were breathing ambient air or a ratio of the partial pressure of oxygen (Pao 2) to the fraction of inspired oxygen (Fio 2) of less than 300 mm Hg. Patients were randomly assigned in a 1:1 ratio to receive either lopinavir–ritonavir (400 mg and 100 mg, respectively) twice a day for 14 days, in addition to standard care, or standard care alone. The primary end point was the time to clinical improvement, defined as the time from randomization to either an improvement of two points on a seven-category ordinal scale or discharge from the hospital, whichever came first. Results A total of 199 patients with laboratory-confirmed SARS-CoV-2 infection underwent randomization; 99 were assigned to the lopinavir–ritonavir group, and 100 to the standard-care group. Treatment with lopinavir–ritonavir was not associated with a difference from standard care in the time to clinical improvement (hazard ratio for clinical improvement, 1.24; 95% confidence interval [CI], 0.90 to 1.72). Mortality at 28 days was similar in the lopinavir–ritonavir group and the standard-care group (19.2% vs. 25.0%; difference, −5.8 percentage points; 95% CI, −17.3 to 5.7). The percentages of patients with detectable viral RNA at various time points were similar. In a modified intention-to-treat analysis, lopinavir–ritonavir led to a median time to clinical improvement that was shorter by 1 day than that observed with standard care (hazard ratio, 1.39; 95% CI, 1.00 to 1.91). Gastrointestinal adverse events were more common in the lopinavir–ritonavir group, but serious adverse events were more common in the standard-care group. Lopinavir–ritonavir treatment was stopped early in 13 patients (13.8%) because of adverse events. Conclusions In hospitalized adult patients with severe Covid-19, no benefit was observed with lopinavir–ritonavir treatment beyond standard care. Future trials in patients with severe illness may help to confirm or exclude the possibility of a treatment benefit. (Funded by Major Projects of National Science and Technology on New Drug Creation and Development and others; Chinese Clinical Trial Register number, ChiCTR2000029308.)
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            How will country-based mitigation measures influence the course of the COVID-19 epidemic?

            Governments will not be able to minimise both deaths from coronavirus disease 2019 (COVID-19) and the economic impact of viral spread. Keeping mortality as low as possible will be the highest priority for individuals; hence governments must put in place measures to ameliorate the inevitable economic downturn. In our view, COVID-19 has developed into a pandemic, with small chains of transmission in many countries and large chains resulting in extensive spread in a few countries, such as Italy, Iran, South Korea, and Japan. 1 Most countries are likely to have spread of COVID-19, at least in the early stages, before any mitigation measures have an impact. What has happened in China shows that quarantine, social distancing, and isolation of infected populations can contain the epidemic. 1 This impact of the COVID-19 response in China is encouraging for the many countries where COVID-19 is beginning to spread. However, it is unclear whether other countries can implement the stringent measures China eventually adopted. Singapore and Hong Kong, both of which had severe acute respiratory syndrome (SARS) epidemics in 2002–03, provide hope and many lessons to other countries. In both places, COVID-19 has been managed well to date, despite early cases, by early government action and through social distancing measures taken by individuals. The course of an epidemic is defined by a series of key factors, some of which are poorly understood at present for COVID-19. The basic reproduction number (R0), which defines the mean number of secondary cases generated by one primary case when the population is largely susceptible to infection, determines the overall number of people who are likely to be infected, or more precisely the area under the epidemic curve. For an epidemic to take hold, the value of R0 must be greater than unity in value. A simple calculation gives the fraction likely to be infected without mitigation. This fraction is roughly 1–1/R0. With R0 values for COVID-19 in China around 2·5 in the early stages of the epidemic, 2 we calculate that approximately 60% of the population would become infected. This is a very worst-case scenario for a number of reasons. We are uncertain about transmission in children, some communities are remote and unlikely to be exposed, voluntary social distancing by individuals and communities will have an impact, and mitigation efforts, such as the measures put in place in China, greatly reduce transmission. As an epidemic progresses, the effective reproduction number (R) declines until it falls below unity in value when the epidemic peaks and then decays, either due to the exhaustion of people susceptible to infection or the impact of control measures. The speed of the initial spread of the epidemic, its doubling time, or the related serial interval (the mean time it takes for an infected person to pass on the infection to others), and the likely duration of the epidemic are determined by factors such as the length of time from infection to when a person is infectious to others and the mean duration of infectiousness. For the 2009 influenza A H1N1 pandemic, in most infected people these epidemiological quantities were short with a day or so to infectiousness and a few days of peak infectiousness to others. 3 By contrast, for COVID-19, the serial interval is estimated at 4·4–7·5 days, which is more similar to SARS. 4 First among the important unknowns about COVID-19 is the case fatality rate (CFR), which requires information on the denominator that defines the number infected. We are unaware of any completed large-scale serology surveys to detect specific antibodies to COVID-19. Best estimates suggest a CFR for COVID-19 of about 0·3–1%, 4 which is higher than the order of 0·1% CFR for a moderate influenza A season. 5 The second unknown is the whether infectiousness starts before onset of symptoms. The incubation period for COVID-19 is about 5–6 days.4, 6 Combining this time with a similar length serial interval suggests there might be considerable presymptomatic infectiousness (appendix 1). For reference, influenza A has a presymptomatic infectiousness of about 1–2 days, whereas SARS had little or no presymptomatic infectiousness. 7 There have been few clinical studies to measure COVID-19 viraemia and how it changes over time in individuals. In one study of 17 patients with COVID-19, peak viraemia seems to be at the end of the incubation period, 8 pointing to the possibility that viraemia might be high enough to trigger transmission for 1–2 days before onset of symptoms. If these patterns are verified by more extensive clinical virological studies, COVID-19 would be expected to be more like influenza A than SARS. For SARS, peak infectiousness took place many days after first symptoms, hence the success of quarantine of patients with SARS soon after symptoms started 7 and the lack of success for this measure for influenza A and possibly for COVID-19. The third uncertainty is whether there are a large number of asymptomatic cases of COVID-19. Estimates suggest that about 80% of people with COVID-19 have mild or asymptomatic disease, 14% have severe disease, and 6% are critically ill, 9 implying that symptom-based control is unlikely to be sufficient unless these cases are only lightly infectious. The fourth uncertainty is the duration of the infectious period for COVID-19. The infectious period is typically short for influenza A, but it seems long for COVID-19 on the basis of the few available clinical virological studies, perhaps lasting for 10 days or more after the incubation period. 8 The reports of a few super-spreading events are a routine feature of all infectious diseases and should not be overinterpreted. 10 What do these comparisons with influenza A and SARS imply for the COVID-19 epidemic and its control? First, we think that the epidemic in any given country will initially spread more slowly than is typical for a new influenza A strain. COVID-19 had a doubling time in China of about 4–5 days in the early phases. 3 Second, the COVID-19 epidemic could be more drawn out than seasonal influenza A, which has relevance for its potential economic impact. Third, the effect of seasons on transmission of COVID-19 is unknown; 11 however, with an R0 of 2–3, the warm months of summer in the northern hemisphere might not necessarily reduce transmission below the value of unity as they do for influenza A, which typically has an R0 of around 1·1–1·5. 12 Closely linked to these factors and their epidemiological determinants is the impact of different mitigation policies on the course of the COVID-19 epidemic. A key issue for epidemiologists is helping policy makers decide the main objectives of mitigation—eg, minimising morbidity and associated mortality, avoiding an epidemic peak that overwhelms health-care services, keeping the effects on the economy within manageable levels, and flattening the epidemic curve to wait for vaccine development and manufacture on scale and antiviral drug therapies. Such mitigation objectives are difficult to achieve by the same interventions, so choices must be made about priorities. 13 For COVID-19, the potential economic impact of self-isolation or mandated quarantine could be substantial, as occurred in China. No vaccine or effective antiviral drug is likely to be available soon. Vaccine development is underway, but the key issues are not if a vaccine can be developed but where phase 3 trials will be done and who will manufacture vaccine at scale. The number of cases of COVID-19 are falling quickly in China, 4 but a site for phase 3 vaccine trials needs to be in a location where there is ongoing transmission of the disease. Manufacturing at scale requires one or more of the big vaccine manufacturers to take up the challenge and work closely with the biotechnology companies who are developing vaccine candidates. This process will take time and we are probably a least 1 year to 18 months away from substantial vaccine production. So what is left at present for mitigation is voluntary plus mandated quarantine, stopping mass gatherings, closure of educational institutes or places of work where infection has been identified, and isolation of households, towns, or cities. Some of the lessons from analyses of influenza A apply for COVID-19, but there are also differences. Social distancing measures reduce the value of the effective reproduction number R. With an early epidemic value of R0 of 2·5, social distancing would have to reduce transmission by about 60% or less, if the intrinsic transmission potential declines in the warm summer months in the northern hemisphere. This reduction is a big ask, but it did happen in China. School closure, a major pillar of the response to pandemic influenza A, 14 is unlikely to be effective given the apparent low rate of infection among children, although data are scarce. Avoiding large gatherings of people will reduce the number of super-spreading events; however, if prolonged contact is required for transmission, this measure might only reduce a small proportion of transmissions. Therefore, broader-scale social distancing is likely to be needed, as was put in place in China. This measure prevents transmission from symptomatic and non-symptomatic cases, hence flattening the epidemic and pushing the peak further into the future. Broader-scale social distancing provides time for the health services to treat cases and increase capacity, and, in the longer term, for vaccines and treatments to be developed. Containment could be targeted to particular areas, schools, or mass gatherings. This approach underway in northern Italy will provide valuable data on the effectiveness of such measures. The greater the reduction in transmission, the longer and flatter the epidemic curve (figure ), with the risk of resurgence when interventions are lifted perhaps to mitigate economic impact. Figure Illustrative simulations of a transmission model of COVID-19 A baseline simulation with case isolation only (red); a simulation with social distancing in place throughout the epidemic, flattening the curve (green), and a simulation with more effective social distancing in place for a limited period only, typically followed by a resurgent epidemic when social distancing is halted (blue). These are not quantitative predictions but robust qualitative illustrations for a range of model choices. The key epidemiological issues that determine the impact of social distancing measures are what proportion of infected individuals have mild symptoms and whether these individuals will self-isolate and to what effectiveness; how quickly symptomatic individuals take to isolate themselves after the onset of symptoms; and the duration of any non-symptomatic infectious period before clear symptoms occur with the linked issue of how transmissible COVID-19 is during this phase. Individual behaviour will be crucial to control the spread of COVID-19. Personal, rather than government action, in western democracies might be the most important issue. Early self-isolation, seeking medical advice remotely unless symptoms are severe, and social distancing are key. Government actions to ban mass gatherings are important, as are good diagnostic facilities and remotely accessed health advice, together with specialised treatment for people with severe disease. Isolating towns or even cities is not yet part of the UK Government action plan. 15 This plan is light on detail, given the early stages of the COVID-19 epidemic and the many uncertainties, but it outlines four phases of action entitled contain, delay, research, and mitigate. 15 The UK has just moved from contain to delay, which aims to flatten the epidemic and lower peak morbidity and mortality. If measures are relaxed after a few months to avoid severe economic impact, a further peak is likely to occur in the autumn (figure). Italy, South Korea, Japan, and Iran are at the mitigate phase and trying to provide the best care possible for a rapidly growing number of people with COVID-19. The known epidemiological characteristics of COVID-19 point to urgent priorities. Shortening the time from symptom onset to isolation is vital as it will reduce transmission and is likely to slow the epidemic (appendices 2, 3) However, strategies are also needed for reducing household transmission, supporting home treatment and diagnosis, and dealing with the economic consequences of absence from work. Peak demand for health services could still be high and the extent and duration of presymptomatic or asymptomatic transmission—if this turns out to be a feature of COVID-19 infection—will determine the success of this strategy. 16 Contact tracing is of high importance in the early stages to contain spread, and model-based estimates suggest, with an R0 value of 2·5, that about 70% of contacts will have to be successfully traced to control early spread. 17 Analysis of individual contact patterns suggests that contact tracing can be a successful strategy in the early stages of an outbreak, but that the logistics of timely tracing on average 36 contacts per case will be challenging. 17 Super-spreading events are inevitable, and could overwhelm the contact tracing system, leading to the need for broader-scale social distancing interventions. Data from China, South Korea, Italy, and Iran suggest that the CFR increases sharply with age and is higher in people with COVID-19 and underlying comorbidities. 18 Targeted social distancing for these groups could be the most effective way to reduce morbidity and concomitant mortality. During the outbreak of Ebola virus disease in west Africa in 2014–16, deaths from other causes increased because of a saturated health-care system and deaths of health-care workers. 19 These events underline the importance of enhanced support for health-care infrastructure and effective procedures for protecting staff from infection. In northern countries, there is speculation that changing contact patterns and warmer weather might slow the spread of the virus in the summer. 11 With an R0 of 2·5 or higher, reductions in transmission by social distancing would have to be large; and much of the changes in transmission of pandemic influenza in the summer of 2009 within Europe were thought to be due to school closures, but children are not thought to be driving transmission of COVID-19. Data from the southern hemisphere will assist in evaluating how much seasonality will influence COVID-19 transmission. Model-based predictions can help policy makers make the right decisions in a timely way, even with the uncertainties about COVID-19. Indicating what level of transmission reduction is required for social distancing interventions to mitigate the epidemic is a key activity (figure). However, it is easy to suggest a 60% reduction in transmission will do it or quarantining within 1 day from symptom onset will control transmission, but it is unclear what communication strategies or social distancing actions individuals and governments must put in place to achieve these desired outcomes. A degree of pragmatism will be needed for the implementation of social distancing and quarantine measures. Ongoing data collection and epidemiological analysis are therefore essential parts of assessing the impacts of mitigation strategies, alongside clinical research on how to best manage seriously ill patients with COVID-19. There are difficult decisions ahead for governments. How individuals respond to advice on how best to prevent transmission will be as important as government actions, if not more important. Government communication strategies to keep the public informed of how best to avoid infection are vital, as is extra support to manage the economic downturn.
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              Repurposed Antiviral Drugs for Covid-19 — Interim WHO Solidarity Trial Results

              Abstract Background World Health Organization expert groups recommended mortality trials of four repurposed antiviral drugs — remdesivir, hydroxychloroquine, lopinavir, and interferon beta-1a — in patients hospitalized with coronavirus disease 2019 (Covid-19). Methods We randomly assigned inpatients with Covid-19 equally between one of the trial drug regimens that was locally available and open control (up to five options, four active and the local standard of care). The intention-to-treat primary analyses examined in-hospital mortality in the four pairwise comparisons of each trial drug and its control (drug available but patient assigned to the same care without that drug). Rate ratios for death were calculated with stratification according to age and status regarding mechanical ventilation at trial entry. Results At 405 hospitals in 30 countries, 11,330 adults underwent randomization; 2750 were assigned to receive remdesivir, 954 to hydroxychloroquine, 1411 to lopinavir (without interferon), 2063 to interferon (including 651 to interferon plus lopinavir), and 4088 to no trial drug. Adherence was 94 to 96% midway through treatment, with 2 to 6% crossover. In total, 1253 deaths were reported (median day of death, day 8; interquartile range, 4 to 14). The Kaplan–Meier 28-day mortality was 11.8% (39.0% if the patient was already receiving ventilation at randomization and 9.5% otherwise). Death occurred in 301 of 2743 patients receiving remdesivir and in 303 of 2708 receiving its control (rate ratio, 0.95; 95% confidence interval [CI], 0.81 to 1.11; P=0.50), in 104 of 947 patients receiving hydroxychloroquine and in 84 of 906 receiving its control (rate ratio, 1.19; 95% CI, 0.89 to 1.59; P=0.23), in 148 of 1399 patients receiving lopinavir and in 146 of 1372 receiving its control (rate ratio, 1.00; 95% CI, 0.79 to 1.25; P=0.97), and in 243 of 2050 patients receiving interferon and in 216 of 2050 receiving its control (rate ratio, 1.16; 95% CI, 0.96 to 1.39; P=0.11). No drug definitely reduced mortality, overall or in any subgroup, or reduced initiation of ventilation or hospitalization duration. Conclusions These remdesivir, hydroxychloroquine, lopinavir, and interferon regimens had little or no effect on hospitalized patients with Covid-19, as indicated by overall mortality, initiation of ventilation, and duration of hospital stay. (Funded by the World Health Organization; ISRCTN Registry number, ISRCTN83971151; ClinicalTrials.gov number, NCT04315948.)
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                Author and article information

                Journal
                J Infect
                J Infect
                The Journal of Infection
                Published by Elsevier Ltd on behalf of The British Infection Association.
                0163-4453
                1532-2742
                11 February 2021
                11 February 2021
                Affiliations
                [a ]Internal Medicine Department, 12 de Octubre University Hospital, Research Institute Hospital 12 de Octubre “ i+12 Institute”, Madrid, Spain
                [b ]Internal Medicine Department, Bellvitge University Hospital-IDIBELL, L'Hospitalet de Llobregat (Barcelona), Spain
                [c ]Internal Medicine Department, Gregorio Marañon University Hospital, Madrid, Spain
                [d ]Internal Medicine Department, La Paz University Hospital, Madrid, Spain
                [e ]Internal Medicine Department, Albacete University Hospital Complex, Albacete, Spain
                [f ]Internal Medicine Department, Puerta de Hierro University Hospital, Majadahonda (Madrid), Spain
                [g ]Internal Medicine Department, Miguel Servet Hospital, Zaragoza, Spain
                Author notes
                [* ]Corresponding author at : Hospital Univ. 12 de Octubre. Servicio de Medicina Interna (Planta 13). Avda. Córdoba s/n. 28041 Madrid, Spain.
                Article
                S0163-4453(21)00077-3
                10.1016/j.jinf.2021.02.011
                7877892
                33582204
                0303430b-ce7c-4ef1-a4a5-7a66bdc83350
                © 2021 Published by Elsevier Ltd on behalf of The British Infection Association.

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

                History
                : 8 February 2021
                Categories
                Letter to the Editor

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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