Population Aging Under Pandemic Conditions

In December 2022, the German Pension Insurance announced an estimated surplus of €2.1 billion for the past year. President Roßbach partly explained this balance was due to the "Corona Pandemic, which has led to an increase in mortality, especially among older people”. Here, the author takes a closer look at the demographics behind that statement and calculates the pandemic-related population deficit among ages 65 and above across Europe. The article also looks at population aging under conditions of continuously reduced life expectancy. © 2023, ZBW – Leibniz Information Center for Economics. All rights reserved.

Angiotensin-Converting Enzyme 2 and Urine Amino Acid Excretion Increase in COVID-19 Patients With AKI

Background: Angiotensin-converting enzyme 2(ACE2), the receptor for SARSCoV-2, is highly expressed in the kidneys. ACE2 also possess a unique function to facilitate amino acid absorption. A persistent elevation in plasma ACE2 during COVID-19 is related to increased mortality. The present study sought to explore the relationship between urine ACE2(uACE2) and renal outcomes in COVID-19 patients. Method(s): In 104 COVID-19 patients without acute kidney injury(AKI), 43 patients with COVID-19-mediated AKI, and 36 non-COVID-19 controls, uACE2, urine tumor necrosis factor receptors I and II(uTNF-RI and uTNF-RII), neutrophil gelatinaseassociated lipocalin(uNGAL), and urine albumin-creatinine ratio were measured. We also assessed ACE2 staining in autopsy kidney samples and generated a propensityscore matched subgroup to perform a targeted urine metabolomic study to describe the characteristic urine signature of COVID-19. Result(s): uACE2 was increased in patients with COVID-19, and further increased in those that developed AKI(Figure 1). After adjusting uACE2 levels for age, sex and previous comorbidities, increased uACE2 was independently associated with over 3-fold higher risk(OR 3.05,95%CI:1.23-7.58, p=0.017) of developing AKI. Increased uACE2 corresponded to a tubular loss of ACE2 in kidney sections and strongly correlated with uTNF-RI and uTNF-RII, suggesting that ADAM17 could be responsible for ACE2 shedding. Urine quantitative metabolome analysis revealed an increased excretion of essential amino acids in COVID-19 patients, including leucine, isoleucine, tryptophan and phenylalanine. Additionally, a strong correlation was observed between urine amino acids and uACE2(Figure 1). Conclusion(s): Elevated uACE2 is related to AKI in patients with COVID-19. The loss of tubular ACE2 during SARS-CoV-2 infection demonstrates a potential link between aminoaciduria and proximal tubular injury.

Use of Receiver Operating Curve Analysis and Machine Learning With an Independent Dose Calculation System Reduces the Number of Physical Dose Measurements Required for Patient-Specific Quality Assurance.

PURPOSE: Our purpose was to assess the use of machine learning methods and Mobius 3D (M3D) dose calculation software to reduce the number of physical ion chamber (IC) dose measurements required for patient-specific quality assurance during corona virus disease 2019. METHODS AND MATERIALS: In this study, 1464 inversely planned treatments using Pinnacle or Raystation treatment planning software (TPS) were delivered using Elekta Versa HD and Varian Truebeam and Truebeam STx linear accelerators between June 2018 and November 2019. For each plan, an independent dose calculation was performed using M3D, and an absolute dose measurement was taken using a Pinpoint IC inside the Mobius phantom. The point dose differences between the TPS and M3D calculation and between TPS and IC measurements were calculated. Agreement between the TPS and IC was used to define the ground truth plan failure. To reduce the on-site personnel during the pandemic, 2 methods of receiver operating characteristic analysis (n = 1464) and machine learning (n = 603) were used to identify patient plans that would require physical dose measurements. RESULTS: In the receiver operating characteristic analysis, a predelivery M3D difference threshold of 3% identified plans that failed an IC measurement at a 4% threshold with 100% sensitivity and 76.3% specificity. This indicates that fewer than 25% of plans required a physical dose measurement. A threshold of 1% on a machine learning model was able to identify plans that failed an IC measurement at a 3% threshold with 100% sensitivity and 54.3% specificity, leading to fewer than 50% of plans that required a physical dose measurement. CONCLUSIONS: It is possible to identify plans that are more likely to fail IC patient-specific quality assurance measurements before delivery. This possibly allows for a reduction of physical measurements taken, freeing up significant clinical resources and reducing the required amount of on-site personnel while maintaining patient safety.