BACKGROUND
Hydroxychloroquine (HCQ) has proved ineffective in treating patients hospitalised with Coronavirus Disease 2019 (COVID-19), but uncertainty remains over its safety and efficacy in chemoprevention. Previous chemoprevention randomised controlled trials (RCTs) did not individually show benefit of HCQ against COVID-19 and, although meta-analysis did suggest clinical benefit, guidelines recommend against its use.
METHODS AND FINDINGS
Healthy adult participants from the healthcare setting, and later from the community, were enrolled in 26 centres in 11 countries to a double-blind, placebo-controlled, randomised trial of COVID-19 chemoprevention. HCQ was evaluated in Europe and Africa, and chloroquine (CQ) was evaluated in Asia, (both base equivalent of 155 mg once daily). The primary endpoint was symptomatic COVID-19, confirmed by PCR or seroconversion during the 3-month follow-up period. The secondary and tertiary endpoints were: asymptomatic laboratory-confirmed Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection; severity of COVID-19 symptoms; all-cause PCR-confirmed symptomatic acute respiratory illness (including SARS-CoV-2 infection); participant reported number of workdays lost; genetic and baseline biochemical markers associated with symptomatic COVID-19, respiratory illness and disease severity (not reported here); and health economic analyses of HCQ and CQ prophylaxis on costs and quality of life measures (not reported here). The primary and safety analyses were conducted in the intention-to-treat (ITT) population. Recruitment of 40,000 (20,000 HCQ arm, 20,000 CQ arm) participants was planned but was not possible because of protracted delays resulting from controversies over efficacy and adverse events with HCQ use, vaccine rollout in some countries, and other factors. Between 29 April 2020 and 10 March 2022, 4,652 participants (46% females) were enrolled (HCQ/CQ n = 2,320; placebo n = 2,332). The median (IQR) age was 29 (23 to 39) years. SARS-CoV-2 infections (symptomatic and asymptomatic) occurred in 1,071 (23%) participants. For the primary endpoint the incidence of symptomatic COVID-19 was 240/2,320 in the HCQ/CQ versus 284/2,332 in the placebo arms (risk ratio (RR) 0.85 [95% confidence interval, 0.72 to 1.00; p = 0.05]). For the secondary and tertiary outcomes asymptomatic SARS-CoV-2 infections occurred in 11.5% of HCQ/CQ recipients and 12.0% of placebo recipients: RR: 0.96 (95% CI, 0.82 to 1.12; p = 0.6). There were no differences in the severity of symptoms between the groups and no severe illnesses. HCQ/CQ chemoprevention was associated with fewer PCR-confirmed all-cause respiratory infections (predominantly SARS-CoV-2): RR 0.61 (95% CI, 0.42 to 0.88; p = 0.009) and fewer days lost to work because of illness: 104 days per 1,000 participants over 90 days (95% CI, 12 to 199 days; p < 0.001). The prespecified meta-analysis of all published pre-exposure RCTs indicates that HCQ/CQ prophylaxis provided a moderate protective benefit against symptomatic COVID-19: RR 0.80 (95% CI, 0.71 to 0.91). Both drugs were well tolerated with no drug-related serious adverse events (SAEs). Study limitations include the smaller than planned study size, the relatively low number of PCR-confirmed infections, and the lower comparative accuracy of serology endpoints (in particular, the adapted dried blood spot method) compared to the PCR endpoint. The COPCOV trial was registered with ClinicalTrials.gov; number NCT04303507.
INTERPRETATION
In this large placebo-controlled, double-blind randomised trial, HCQ and CQ were safe and well tolerated in COVID-19 chemoprevention, and there was evidence of moderate protective benefit in a meta-analysis including this trial and similar RCTs.
In locations where few people have received Covid-19 vaccines, health systems remain vulnerable to spikes in SARS-CoV-2 infections. Triage tools, which could include biomarkers, to identify patients with moderate Covid-19 infection suitable for community-based management would be useful in the event of surges. In consultation with FIND (Geneva, Switzerland) we shortlisted seven biomarkers for evaluation, all measurable using point-of-care tests, and either currently available or in late-stage development.
METHODS
We prospectively recruited unvaccinated adults with laboratory-confirmed Covid-19 presenting to two hospitals in India with moderate symptoms, in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. Moderate disease was defined as oxygen saturation (SpO2) ≥ 94% and respiratory rate < 30 breaths per minute (bpm), in the context of systemic symptoms (breathlessness or fever and chest pain, abdominal pain, diarrhoea, or severe myalgia). All patients had clinical observations and blood collected at presentation, and were followed up for 14 days for the primary outcome, defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/fraction of inspired oxygen (FiO2) < 400; or death. We specified a priori that each model would contain three easily ascertained clinical parameters (age, sex, and SpO2) and one of the seven biomarkers (C-reactive protein (CRP), D-dimer, interleukin-6 (IL-6), neutrophil-to-lymphocyte ratio (NLR), procalcitonin (PCT), soluble triggering receptor expressed on myeloid cells-1 (sTREM-1), or soluble urokinase plasminogen activator receptor (suPAR)), to ensure the models would be implementable in high patient-throughput, low-resource settings. We evaluated the models’ discrimination, calibration, and clinical utility in a held-out external temporal validation cohort.
ETHICS
Ethical approval was given by the ethics committees of AIIMS and CMC, India, the Oxford Tropical Research Ethics Committee, UK; and by the MSF Ethics Review Board.
ClinicalTrials.gov number, NCT04441372.
RESULTS
426 participants were recruited, of which 89 (21.0%) met the primary outcome. 257 participants comprised the development, and 166 the validation, cohorts. The three models containing NLR, suPAR, or IL-6 demonstrated promising discrimination (c-statistics: 0.72 to 0.74) and calibration (calibration slopes: 1.01 to 1.05) in the held-out validation cohort. Furthermore, they provided greater utility than a model containing the clinical parameters alone (c-statistic = 0.66; calibration slope = 0.68). The inclusion of either NLR or suPAR improved predictive performance such that the ratio of correctly to incorrectly discharged patients increased from 10:1 to 23:1 or 25:1 respectively. Including IL-6 resulted in a similar proportion (~21%) of correctly discharged patients as the clinical model, but without missing any patients requiring supplemental oxygen.
CONCLUSION
We present three clinical prediction models that could help clinicians identify patients with moderate Covid-19 suitable for community-based management. These models are readily implementable and, if validated, could be of particular relevance for resource-limited settings.
CONFLICTS OF INTEREST
None declared.
In locations where few people have received COVID-19 vaccines, health systems remain vulnerable to surges in SARS-CoV-2 infections. Tools to identify patients suitable for community-based management are urgently needed.
METHODS
We prospectively recruited adults presenting to two hospitals in India with moderate symptoms of laboratory-confirmed COVID-19 in order to develop and validate a clinical prediction model to rule-out progression to supplemental oxygen requirement. The primary outcome was defined as any of the following: SpO2 < 94%; respiratory rate > 30 bpm; SpO2/FiO2 < 400; or death. We specified a priori that each model would contain three clinical parameters (age, sex and SpO2) and one of seven shortlisted biochemical biomarkers measurable using commercially-available rapid tests (CRP, D-dimer, IL-6, NLR, PCT, sTREM-1 or suPAR), to ensure the models would be suitable for resource-limited settings. We evaluated discrimination, calibration and clinical utility of the models in a held-out temporal external validation cohort.
RESULTS
426 participants were recruited, of whom 89 (21.0%) met the primary outcome. 257 participants comprised the development cohort and 166 comprised the validation cohort. The three models containing NLR, suPAR or IL-6 demonstrated promising discrimination (c-statistics: 0.72 to 0.74) and calibration (calibration slopes: 1.01 to 1.05) in the validation cohort, and provided greater utility than a model containing the clinical parameters alone.
CONCLUSIONS
We present three clinical prediction models that could help clinicians identify patients with moderate COVID-19 suitable for community-based management. The models are readily implementable and of particular relevance for locations with limited resources.
In rural and difficult-to-access settings, early and accurate recognition of febrile children at risk of progressing to serious illness could contribute to improved patient outcomes and better resource allocation. This study aims to develop a prognostic clinical prediction tool to assist community healthcare providers identify febrile children who might benefit from referral or admission for facility-based medical care.
METHODS AND ANALYSIS
This prospective observational study will recruit at least 4900 paediatric inpatients and outpatients under the age of 5 years presenting with an acute febrile illness to seven hospitals in six countries across Asia. A venous blood sample and nasopharyngeal swab is collected from each participant and detailed clinical data recorded at presentation, and each day for the first 48 hours of admission for inpatients. Multianalyte assays are performed at reference laboratories to measure a panel of host biomarkers, as well as targeted aetiological investigations for common bacterial and viral pathogens. Clinical outcome is ascertained on day 2 and day 28.Presenting syndromes, clinical outcomes and aetiology of acute febrile illness will be described and compared across sites. Following the latest guidance in prediction model building, a prognostic clinical prediction model, combining simple clinical features and measurements of host biomarkers, will be derived and geographically externally validated. The performance of the model will be evaluated in specific presenting clinical syndromes and fever aetiologies.
ETHICS AND DISSEMINATION
The study has received approval from all relevant international, national and institutional ethics committees. Written informed consent is provided by the caretaker of all participants. Results will be shared with local and national stakeholders, and disseminated via peer-reviewed open-access journals and scientific meetings.
TRIAL REGISTRATION NUMBER NCT04285021.
Artemisinin-resistant Plasmodium falciparum malaria parasites are now present across much of mainland Southeast Asia, where ongoing surveys are measuring and mapping their spatial distribution. These efforts require substantial resources. Here we propose a generic 'smart surveillance' methodology to identify optimal candidate sites for future sampling and thus map the distribution of artemisinin resistance most efficiently.
METHODS
The approach uses the 'uncertainty' map generated iteratively by a geostatistical model to determine optimal locations for subsequent sampling.
RESULTS
The methodology is illustrated using recent data on the prevalence of the K13-propeller polymorphism (a genetic marker of artemisinin resistance) in the Greater Mekong Subregion.
CONCLUSION
This methodology, which has broader application to geostatistical mapping in general, could improve the quality and efficiency of drug resistance mapping and thereby guide practical operations to eliminate malaria in affected areas.