Deaths induced by compassionate use of hydroxychloroquine during the first COVID-19 wave: an estimate

BACKGROUND

During the first wave of COVID-19, hydroxychloroquine (HCQ) was used off-label despite the absence of evidence documenting its clinical benefits. Since then, a meta-analysis of randomised trials showed that HCQ use was associated with an 11% increase in the mortality rate. We aimed to estimate the number of HCQ-related deaths worldwide.

METHODS AND FINDINGS

We estimated the worldwide in-hospital mortality attributable to HCQ use by combining the mortality rate, HCQ exposure, number of hospitalised patients, and the increased relative risk of death with HCQ. The mortality rate in hospitalised patients for each country was calculated using pooled prevalence estimated by a meta-analysis of published cohorts. The HCQ exposure was estimated using median and extreme estimates from the same systematic review. The number of hospitalised patients during the first wave was extracted from dedicated databases. The systematic review included 44 cohort studies (Belgium: k = 1, France: k = 2, Italy: k = 12, Spain: k = 6, Turkey: k = 3, USA: k = 20). HCQ prescription rates varied greatly from one country to another (range 16-84%). Overall, using median estimates of HCQ use in each country, we estimated that 16,990 HCQ-related in-hospital deaths (range 6267-19256) occurred in the countries with available data. The median number of HCQ-related deaths in Belgium, Turkey, France, Italy, Spain, and the USA was 240 (range not estimable), 95 (range 92-128), 199 (range not estimable), 1822 (range 1170-2063), 1895 (range 1475-2094) and 12739 (3244- 15570), respectively.

CONCLUSIONS

Although our estimates are limited by their imprecision, these findings illustrate the hazard of drug repurposing with low-level evidence.

Off-label use of orthopedical trauma implants in a low-income country

Purpose

Lack of resources, severe injuries, and logistical flaws force surgeons in low-income countries (LIC) to improvise during surgery and use implants “off-label.” These off-label treatments are specific for the work of trauma surgeons in non-governmental (NGO) hospitals in LIC. The aim of this study is to show the need of off-label surgery in an environment of low resources by means of typical examples.

Methods

Off-label treated fractures, the implant used instead, and the reason for off-label treatment were investigated in 367 injuries over a three month period in an NGO hospital in Sierra Leone.

Results

Twenty-seven fractures were treated off-label with mostly K-wires (88.89%) and external fixators (51.85%). Three reasons for off-label use could be defined: no suitable implants (N = 14), the condition of soft tissues that did not allow internal osteosyntheses (N = 10), and implants not ready for surgery due to logistic flaws (N = 3). The implants needed were mostly locking plates.

Conclusion

Surgeons in similar settings must use K-wires and external fixators to treat complex fractures. Using implants off-label can help surgeons to treat fractures otherwise left untreated.

Proof-of-concept study: Homomorphically encrypted data can support real-time learning in personalized cancer medicine

BACKGROUND

The successful introduction of homomorphic encryption (HE) in clinical research holds promise for improving acceptance of data-sharing protocols, increasing sample sizes, and accelerating learning from real-world data (RWD). A well-scoped use case for HE would pave the way for more widespread adoption in healthcare applications. Determining the efficacy of targeted cancer treatments used off-label for a variety of genetically defined conditions is an excellent candidate for introduction of HE-based learning systems because of a significant unmet need to share and combine confidential data, the use of relatively simple algorithms, and an opportunity to reach large numbers of willing study participants.

METHODS

We used published literature to estimate the numbers of patients who might be eligible to receive treatments approved for other indications based on molecular profiles. We then estimated the sample size and number of variables that would be required for a successful system to detect exceptional responses with sufficient power. We generated an appropriately sized, simulated dataset (n = 5000) and used an established HE algorithm to detect exceptional responses and calculate total drug exposure, while the data remained encrypted.

RESULTS

Our results demonstrated the feasibility of using an HE-based system to identify exceptional responders and perform calculations on patient data during a hypothetical 3-year study. Although homomorphically encrypted computations are time consuming, the required basic computations (i.e., addition) do not pose a critical bottleneck to the analysis.

CONCLUSION

In this proof-of-concept study, based on simulated data, we demonstrate that identifying exceptional responders to targeted cancer treatments represents a valuable and feasible use case. Past solutions to either completely anonymize data or restrict access through stringent data use agreements have limited the utility of abundant and valuable data. Because of its privacy protections, we believe that an HE-based learning system for real-world cancer treatment would entice thousands more patients to voluntarily contribute data through participation in research studies beyond the currently available secondary data populated from hospital electronic health records and administrative claims. Forming collaborations between technical experts, physicians, patient advocates, payers, and researchers, and testing the system on existing RWD are critical next steps to making HE-based learning a reality in healthcare.

Knowledge and attitudes of patients in primary care on off-label treatment

OBJECTIVE

To examine primary care patients' knowledge and attitudes on off-label treatment.

METHODS

In 47 primary care offices in Osijek-Baranja County, 1300 patients were given questionnaires about their knowledge and attitudes on off-label treatment. Questionnaires were completed voluntarily and anonymously.

RESULTS

Majority of patients (96.3%) had no knowledge about off-label treatment, and upon having it explained, 42.3% would not accept such treatment under any circumstances, while 54.5% would never accept such treatment for their children. Patients place more trust in primary care doctors than in hospital doctors; 69.6% of patients strongly believe that primary care doctors would not expose them to off-label treatment while only 41.2% of them think the same in relation to hospital doctors. In case of serious consequences following off-label therapy 49.9% of patients would sue doctors who prescribed it.

CONCLUSION

Patients in primary care possess limited knowledge about off-label treatment. Most patients do not wish to be exposed to such treatment, and are particularly reluctant to have their children exposed to it. In case of serious side effects, half of the patients would sue doctors who had prescribed the off-label therapy.