Background The unprecedented public health impact of the COVID-19 pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear. Methods and Findings We develop a mathematical model of SARS-CoV-2 transmission, COVID-19 disease and clinical care to explore the potential public-health impact of a range of different potential therapeutics, under a range of different scenarios varying: i) healthcare capacity, ii) epidemic trajectories; and iii) drug efficacy in the absence of supportive care. In each case, the outcome of interest was the number of COVID-19 deaths averted in scenarios with the therapeutic compared to scenarios without. We find the impact of drugs like dexamethasone (which are delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R=1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalisation) could have much greater benefits, particularly in resource-poor settings facing large epidemics. Conclusions There is a global asymmetry in who is likely to benefit from advances in the treatment of COVID-19 to date, which have been focussed on hospitalised-patients and predicated on an assumption of adequate access to supportive care. Therapeutics that can feasibly be delivered to those earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

Background: A decrease in lactate concentration over time during septic shock is associated with favourable outcomes. However, if this applies to hourly intervals during the initial time period in the ICU is unknown. The aim of this study was to investigate whether there is an early hourly reduction rate of lactate that is related to clinical outcome in septic shock patients treated in the ICU. Methods:A cohort of adult septic shock patients admitted to the ICU with an initial lactate level >2 mmol/L and receiving vasopressor was retrospectively analysed.Mean hourly reduction rate of lactate (ΔLact/h) was calculated individually from all lactate concentrations measured from inclusion until normalization of lactate (≤1.5 mmol/L) within 24 hours. The mortality at 30 days following ICU admission was evaluated.Results: Among 1405 ICU admissions during 2 years, 104 patients were eligible.Mortality rate at 30 days was 34%. The optimal cut-off values of baseline lactate and ΔLact/h for 30-day mortality were 4 mmol/L and 2.5%/h. When stratifying the patients by these cut-points, those with baseline lactate > 4 mmol/L and ΔLact/h < 2.5%/h had lowest probability of survival (27%). Multivariable logistic regression showed that ΔLact/h <2.5%/h, baseline lactate >4 mmol/L and high Simplified Acute Physiology Score III were independent risk factors of 30-day mortality. Conclusions:In this retrospective pilot cohort, a mean reduction rate of lactate <2.5%/h within the first 24 hours of ICU stay was associated with an increased risk of 30-day mortality in septic shock patients.

Background: Plasma lactate concentrations and their trends over time are used for clinical prognosis, and to guide treatment, in critically ill patients. Although heavily relied upon for clinical decision-making, lactate kinetics of these patients is sparsely studied. Aim: To establish and validate a feasible method to study lactate kinetics in critically ill patients. Methods: Healthy volunteers (n = 6) received a bolus dose of 13 C-labeled lactate (20 μmol/kg body weight), and 43 blood samples were drawn over 2 h to determine the decay in labeled lactate. Data was analyzed using noncompartmental modeling calculating rates of appearance (R a) and clearance of lactate. The area under the curve (AUC) was calculated using a linear-up log-down trapezoidal approach with extrapolation beyond 120 min using the terminal slope to obtain the whole AUC. After evaluation, the same protocol was used in an unselected group of critically ill patients (n = 10). Results: R a for healthy volunteers and ICU patients were 12.8 ± 3.9 vs 22.7 ± 11.1 μmol/kg/min and metabolic clearance 1.56 ± 0.39 vs 1.12 ± 0.43 L/min, respectively. ICU patients with normal lactate concentrations showed kinetics very similar to healthy volunteers. Simulations showed that reducing the number of samples from 43 to 14 gave the same results. Our protocol yielded results on lactate kinetics very similar to previously published data using other techniques. Conclusion: This simple and user-friendly protocol using an isotopically labeled bolus dose of lactate was accurate and feasible for studying lactate kinetics in critically ill ICU patients.