Association of Molnupiravir and Nirmatrelvir-Ritonavir with reduced mortality and sepsis in hospitalized omicron patients: a territory-wide study.

This study evaluates the association between antivirals (Molnupiravir and Nirmatrelvir-Ritonavir) and all-cause and respiratory mortality and organ dysfunction among high-risk COVID-19 patients during an Omicron outbreak. Two cohorts, Nirmatrelvir-Ritonavir versus control and Molnupiravir versus control, were constructed with inverse probability treatment weighting to balance baseline characteristics. Cox proportional hazards models evaluated the association of their use with all-cause mortality, respiratory mortality, and all-cause sepsis (a composite of circulatory shock, respiratory failure, acute liver injury, coagulopathy, and acute liver impairment). Patients recruited were hospitalized and diagnosed with the COVID-19 Omicron variant between February 22, 2022 and April 15, 2022, and followed up until May 15, 2022. The study included 17,704 patients. There were 4.67 and 22.7 total mortalities per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio, - 18.1 [95% CI - 23.0 to - 13.2]; hazard ratio, 0.18 [95% CI, 0.11-0.29]). There were 6.64 and 25.9 total mortalities per 1000 person-days in the Molnupiravir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, - 19.3 [95% CI - 22.6 to - 15.9]; hazard ratio, 0.23 [95% CI 0.18-0.30]). In all-cause sepsis, there were 13.7 and 35.4 organ dysfunction events per 1000 person-days in the Nirmatrelvir-Ritonavir and control groups respectively before adjustment (weighted incidence rate ratio per 1000 person-days, - 21.7 [95% CI - 26.3 to - 17.1]; hazard ratio, 0.44 [95% CI 0.38-0.52]). There were 23.7 and 40.8 organ dysfunction events in the Molnupiravir and control groups respectively before adjustment (weighted incidence ratio per 1000 person-days, - 17.1 [95% CI, - 20.6 to - 13.6]; hazard ratio, 0.63 [95% CI 0.58-0.69]). Among COVID-19 hospitalized patients, use of either Nirmatrelvir-Ritonavir or Molnupiravir compared with no antiviral use was associated with a significantly lower incidence of 28-days all-cause and respiratory mortality and sepsis.

Association of Molnupiravir and Nirmatrelvir-Ritonavir with preventable mortality, hospital admissions and related avoidable healthcare system cost among high-risk patients with mild to moderate COVID-19.

Background: Real-world data is currently limited on the association between oral antiviral therapy and healthcare system burden in patients with mild-to-moderate COVID-19. This study aims to evaluate the clinical and cost effectiveness of Molnupiravir and Nirmatrelvir-ritonavir use in reducing mortality in this population. Methods: This is a retrospective cohort study involving 54,355 COVID-19 patients during February 22-March 31,2022 in Hong Kong. Inverse probability of treatment weighting (IPTW) was used to adjust patient characteristics. Our exposure of interest was Molnupiravir/Nirmatrelvir-Ritonavir prescription, with all-cause mortality as the primary outcome. IPTW-adjusted multivariate regressions were used to estimate treatment impact on clinic re-attendance and unplanned admissions. Finally, attributed cost and incremental cost-effectiveness ratios (ICER) were estimated. Findings: In the outpatient cohort (N = 33,217, 61.1%), 16.1% used Molnupiravir and 13.4% used Nirmatrelvir-Ritonavir, while in the inpatient cohort (N = 21,138, 38.9%), 3.8% used Molnupiravir and 1.3% used Nirmatrelvir-Ritonavir. IPTW-adjusted Cox model estimated that Molnupiravir (hazard ratio (HR)(95%CI)=0.31 (0.24-0.40), P< 0.0001) and Nirmatrelvir-Ritonavir (HR=0.10 (95%CI 0.05-0.21), P< 0.0001) were significantly associated with a reduced mortality hazard. In the outpatient cohort, both antiviral prescriptions were associated with reduced odds for unplanned hospital admissions (Molnupiravir: odds ratio (OR) =0.72 (0.52-0.98), P=0.039; Nirmatrelvir-Ritonavir: OR=0.37 (0.23-0.60), P<0.0001). Among hospitalised patients, both antiviral prescriptions were associated with significant reductions in the odds ratios for 28-days readmission (Molnupiravir: OR=0.71 (0.52-0.97), P=0.031; Nirmatrelvir-Ritonavir: OR=0.47 (0.24-0.93), P=0.030). ICERs for death averted for Molnupiravir stood at USD493,345.09 in outpatient settings and USD2,629.08 in inpatient settings. In outpatient settings, Nirmatrelvir-ritonavir cost USD331,105.27 to avert one death, but saved USD5,502.53 to avert one death in comparison with standard care. Interpretation: In high-risk patients in Hong Kong with mild-to-moderate COVID-19, Molnupiravir and Nirmatrelvir-Ritonavir prescriptions were associated with reduced all-cause mortality and significant cost savings. Funding: Centre for Health Systems & Policy Research is funded by The Tung's Foundation; and The Laboratory of Data Discovery for Health Limited(D24H) is funded the AIR@InnoHK platform administered by the Innovation and Technology Commission of Hong Kong. Funders did not have any role in study design, data collection, data analysis, interpretation and writing of this manuscript.

An artificial intelligence-enabled smartphone app for real-time pressure injury assessment.

The management of chronic wounds in the elderly such as pressure injury (also known as bedsore or pressure ulcer) is increasingly important in an ageing population. Accurate classification of the stage of pressure injury is important for wound care planning. Nonetheless, the expertise required for staging is often not available in a residential care home setting. Artificial-intelligence (AI)-based computer vision techniques have opened up opportunities to harness the inbuilt camera in modern smartphones to support pressure injury staging by nursing home carers. In this paper, we summarise the recent development of smartphone or tablet-based applications for wound assessment. Furthermore, we present a new smartphone application (app) to perform real-time detection and staging classification of pressure injury wounds using a deep learning-based object detection system, YOLOv4. Based on our validation set of 144 photos, our app obtained an overall prediction accuracy of 63.2%. The per-class prediction specificity is generally high (85.1%-100%), but have variable sensitivity: 73.3% (stage 1 vs. others), 37% (stage 2 vs. others), 76.7 (stage 3 vs. others), 70% (stage 4 vs. others), and 55.6% (unstageable vs. others). Using another independent test set, 8 out of 10 images were predicted correctly by the YOLOv4 model. When deployed in a real-life setting with two different ambient brightness levels with three different Android phone models, the prediction accuracy of the 10 test images ranges from 80 to 90%, which highlight the importance of evaluation of mobile health (mHealth) application in a simulated real-life setting. This study details the development and evaluation process and demonstrates the feasibility of applying such a real-time staging app in wound care management.