Headache as a COVID-19 onset symptom and post-COVID-19 symptom in hospitalized COVID-19 survivors infected with the Wuhan, Alpha, or Delta SARS-CoV-2 variants

OBJECTIVE: This study looked at differences in the presence of headache as an onset symptom of coronavirus disease 2019 (COVID‐19) and as a post‐COVID‐19 symptom in individuals previously hospitalized owing to infection with the Wuhan, Alpha, or Delta variants of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). BACKGROUND: Headache can be present in up to 50% of individuals during the acute phase of SARS‐CoV‐2 infection and in 10% of subjects during the post‐COVID‐19 phase. There are no data on differences in the occurrence of headache in the acute‐ and post‐COVID‐19 phase according to the SARS‐CoV‐2 variants. METHODS: A cross‐sectional cohort study was conducted. Unvaccinated subjects previously hospitalized for COVID‐19 caused by the Wuhan (n = 201), Alpha (n = 211), or Delta (n = 202) SARS‐CoV‐2 variants were scheduled for a telephone interview 6 months after hospital discharge. Hospitalization data were collected from hospital medical records. RESULTS: The presence of headache as a COVID‐19 onset symptom at hospitalization was higher in subjects with the Delta variant (66/202, 32.7%) than in those infected with the Wuhan (42/201, 20.9%; odds ratio [OR] 1.83, 95% confidence interval [CI] 1.17–2.88) or Alpha (25/211, 11.8%; OR 3.61, 95% CI, 2.16–6.01) variants. The prevalence of post‐COVID‐19 headache 6 months after hospital discharge was higher in individuals infected with the Delta variant (26/202, 12.9%) than in those infected with the Wuhan (11/201, 5.5%; OR 2.52, 95% CI 1.22–5.31) or Alpha (eight of 211, 3.8%; OR 3.74, 95% CI 1.65–8.49) variants. The presence of headache as a COVID‐19 onset symptom was associated with post‐COVID‐19 headache in subjects infected with the Wuhan (OR 7.75, 95% CI 2.15–27.93) and Delta variants (OR 2.78, 95% CI 1.20–6.42) but not with the Alpha variant (OR 2.60, 95% CI 0.49–13.69). CONCLUSION: Headache was a common symptom in both the acute‐ and post‐COVID‐19 phase in subjects infected with the Wuhan, Alpha, and Delta variants but mostly in those infected with the Delta variant

Associated-Onset Symptoms and Post-COVID-19 Symptoms in Hospitalized COVID-19 Survivors Infected with Wuhan, Alpha or Delta SARS-CoV-2 Variant

This study compared associated-symptoms at the acute phase of infection and post-COVID-19 symptoms between individuals hospitalized with the Wuhan, Alpha or Delta SARS-CoV-2 variant. Non-vaccinated individuals hospitalized because of SARS-CoV-2 infection in one hospital during three different waves of the pandemic (Wuhan, Alpha or Delta) were scheduled for a telephone interview. The presence of post-COVID-19 symptoms was systematically assessed. Hospitalization and clinical data were collected from medical records. A total of 201 patients infected with the Wuhan variant, 211 with the Alpha variant and 202 with Delta variant were assessed six months after hospitalization. Patients infected with the Wuhan variant had a greater number of symptoms at hospital admission (higher prevalence of fever, dyspnea or gastrointestinal problems) than those infected with Alpha or Delta variant (p < 0.01). A greater proportion of patients infected with the Delta variant reported headache, anosmia or ageusia as onset symptoms (p < 0.01). The mean number of post-COVID-19 symptoms was higher (p < 0.001) in individuals infected with the Wuhan variant (mean: 2.7 ± 1.3) than in those infected with the Alpha (mean: 1.8 ± 1.1) or Delta (mean: 2.1 ± 1.5) variant. Post-COVID-19 dyspnea was more prevalent (p < 0.001) in people infected with the Wuhan variant, whereas hair loss was higher in those infected with the Delta variant (p = 0.002). No differences in post-COVID-19 fatigue by SARS-CoV-2 variant were found (p = 0.594). Differences in COVID-19 associated onset symptoms and post-COVID-19 dyspnea were observed depending on the SARS-CoV-2 variant. The presence of fatigue was a common post-COVID-19 symptom to all SARS-CoV-2 variants

Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks

[EN] Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolutionThis work has been partially supported by a doctoral grant of the Spanish Ministry of Innovation and Science, with reference FPU17/01993Pellicer-Valero, OJ.; González-Pérez, V.; Casanova Ramón-Borja, JL.; Martín García, I.; Barrios Benito, M.; Pelechano Gómez, P.; Rubio-Briones, J.... (2021). Robust Resolution-Enhanced Prostate Segmentation in Magnetic Resonance and Ultrasound Images through Convolutional Neural Networks. Applied Sciences. 11(2):1-17. https://doi.org/10.3390/app11020844S117112Marra, G., Ploussard, G., Futterer, J., & Valerio, M. (2019). Controversies in MR targeted biopsy: alone or combined, cognitive versus software-based fusion, transrectal versus transperineal approach? World Journal of Urology, 37(2), 277-287. doi:10.1007/s00345-018-02622-5Ahdoot, M., Lebastchi, A. H., Turkbey, B., Wood, B., & Pinto, P. A. (2019). Contemporary treatments in prostate cancer focal therapy. Current Opinion in Oncology, 31(3), 200-206. doi:10.1097/cco.0000000000000515Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. doi:10.1145/3065386Allen, P. D., Graham, J., Williamson, D. C., & Hutchinson, C. E. (s. f.). Differential Segmentation of the Prostate in MR Images Using Combined 3D Shape Modelling and Voxel Classification. 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006. doi:10.1109/isbi.2006.1624940Freedman, D., Radke, R. J., Tao Zhang, Yongwon Jeong, Lovelock, D. M., & Chen, G. T. Y. (2005). Model-based segmentation of medical imagery by matching distributions. IEEE Transactions on Medical Imaging, 24(3), 281-292. doi:10.1109/tmi.2004.841228Klein, S., van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., & Pluim, J. P. W. (2008). Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics, 35(4), 1407-1417. doi:10.1118/1.2842076Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 234-241. doi:10.1007/978-3-319-24574-4_28He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017). Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2017.322Shelhamer, E., Long, J., & Darrell, T. (2017). Fully Convolutional Networks for Semantic Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4), 640-651. doi:10.1109/tpami.2016.2572683He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2016.90Milletari, F., Navab, N., & Ahmadi, S.-A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV). doi:10.1109/3dv.2016.79Zhu, Q., Du, B., Turkbey, B., Choyke, P. L., & Yan, P. (2017). Deeply-supervised CNN for prostate segmentation. 2017 International Joint Conference on Neural Networks (IJCNN). doi:10.1109/ijcnn.2017.7965852To, M. N. N., Vu, D. Q., Turkbey, B., Choyke, P. L., & Kwak, J. T. (2018). Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging. International Journal of Computer Assisted Radiology and Surgery, 13(11), 1687-1696. doi:10.1007/s11548-018-1841-4Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr.2017.243Zhu, Y., Wei, R., Gao, G., Ding, L., Zhang, X., Wang, X., & Zhang, J. (2018). Fully automatic segmentation on prostate MR images based on cascaded fully convolution network. Journal of Magnetic Resonance Imaging, 49(4), 1149-1156. doi:10.1002/jmri.26337Wang, Y., Ni, D., Dou, H., Hu, X., Zhu, L., Yang, X., … Wang, T. (2019). Deep Attentive Features for Prostate Segmentation in 3D Transrectal Ultrasound. IEEE Transactions on Medical Imaging, 38(12), 2768-2778. doi:10.1109/tmi.2019.2913184Lemaître, G., Martí, R., Freixenet, J., Vilanova, J. C., Walker, P. M., & Meriaudeau, F. (2015). Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review. Computers in Biology and Medicine, 60, 8-31. doi:10.1016/j.compbiomed.2015.02.009Litjens, G., Toth, R., van de Ven, W., Hoeks, C., Kerkstra, S., van Ginneken, B., … Madabhushi, A. (2014). Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge. Medical Image Analysis, 18(2), 359-373. doi:10.1016/j.media.2013.12.002Zhu, Q., Du, B., & Yan, P. (2020). Boundary-Weighted Domain Adaptive Neural Network for Prostate MR Image Segmentation. IEEE Transactions on Medical Imaging, 39(3), 753-763. doi:10.1109/tmi.2019.2935018He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. 2015 IEEE International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2015.123Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. doi:10.1109/tkde.2009.191Smith, L. N. (2017). Cyclical Learning Rates for Training Neural Networks. 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). doi:10.1109/wacv.2017.58Abraham, N., & Khan, N. M. (2019). A Novel Focal Tversky Loss Function With Improved Attention U-Net for Lesion Segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). doi:10.1109/isbi.2019.8759329Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P., … Yang, X. (2019). Ultrasound prostate segmentation based on multidirectional deeply supervised V‐Net. Medical Physics, 46(7), 3194-3206. doi:10.1002/mp.13577Orlando, N., Gillies, D. J., Gyacskov, I., Romagnoli, C., D’Souza, D., & Fenster, A. (2020). Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images. Medical Physics, 47(6), 2413-2426. doi:10.1002/mp.14134Karimi, D., Zeng, Q., Mathur, P., Avinash, A., Mahdavi, S., Spadinger, I., … Salcudean, S. E. (2019). Accurate and robust deep learning-based segmentation of the prostate clinical target volume in ultrasound images. Medical Image Analysis, 57, 186-196. doi:10.1016/j.media.2019.07.005PROMISE12 Resultshttps://promise12.grand-challenge.org/Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2020). nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods, 18(2), 203-211. doi:10.1038/s41592-020-01008-

Genetic Association between ACE2 (rs2285666 and rs2074192) and TMPRSS2 (rs12329760 and rs2070788) Polymorphisms with Post-COVID Symptoms in Previously Hospitalized COVID-19 Survivors

The aim of the study was to identify the association between four selected COVID-19 polymorphisms of ACE2 and TMPRSS2 receptors genes with the presence of long-COVID symptomatology in COVID-19 survivors. These genes were selected as they associate with the entry of the SARS-CoV-2 virus into the cells, so polymorphisms could be important for the prognoses of long-COVID symptoms. Two hundred and ninety-three (n = 293, 49.5% female, mean age: 55.6 ± 12.9 years) individuals who had been previously hospitalized due to COVID-19 were included. Three potential genotypes of the following single nucleotide polymorphisms (SNPs) were obtained from non-stimulated saliva samples of participants: ACE2 (rs2285666), ACE2 (rs2074192), TMPRSS2 (rs12329760), TMPRSS2 (rs2070788). Participants were asked to self-report the presence of any post-COVID defined as a symptom that started no later than one month after SARS-CoV-2 acute infection and whether the symptom persisted at the time of the study. At the time of the study (mean: 17.8, SD: 5.2 months after hospital discharge), 87.7% patients reported at least one symptom. Fatigue (62.8%), pain (39.9%) or memory loss (32.1%) were the most prevalent post-COVID symptoms. Overall, no differences in long-COVID symptoms were dependent on ACE2 rs2285666, ACE2 rs2074192, TMPRSS2 rs12329760, or TMPRSS2 rs2070788 genotypes. The four SNPs assessed, albeit previously associated with COVID-19 severity, do not predispose for developing long-COVID symptoms in people who were previously hospitalized due to COVID-19 during the first wave of the pandemic

Prevalence of Musculoskeletal Post-COVID Pain in Hospitalized COVID-19 Survivors Depending on Infection with the Historical, Alpha or Delta SARS-CoV-2 Variant

We compared the prevalence of musculoskeletal post-COVID pain between previously hospitalized COVID-19 survivors infected with the historical, Alpha or Delta SARS-CoV-2 variant. Data about musculoskeletal post-COVID pain were systematically collected through a telephone interview involving 201 patients who had survived the historical variant, 211 who had survived the Alpha variant and 202 who had survived the Delta variant six months after hospital discharge. Participants were recruited from non-vaccinated individuals hospitalized due to SARS-CoV-2 infection in one hospital of Madrid (Spain) during three different waves of the pandemic (historical, Alpha or Delta variant). Hospitalization and clinical data were collected from hospital medical records. In addition, anxiety/depressive levels and sleep quality were also assessed. The prevalence of musculoskeletal post-COVID pain was higher (p = 0.003) in patients infected with the historical variant (47.7%) than in those infected with the Alpha (38.3%) or Delta (41%) variants. A significantly (p = 0.002) higher proportion of individuals infected with the historical variant reported generalized pain (20.5%) when compared with those infected with the other variants. The prevalence of new-onset post-COVID musculoskeletal pain reached 80.1%, 75.2% and 79.5% of patients infected with the historical, Alpha or Delta variants, respectively. No specific risk factors for developing post-COVID pain were identified depending on the SARS-CoV-2 variant. In conclusion, this study found that musculoskeletal post-COVID pain is highly prevalent in COVID-19 survivors six months after hospital discharge, with the highest prevalence and most generalized pain symptoms in individuals infected with the historical variant. Approximately 50% developed “de novo” post-COVID musculoskeletal pain symptoms

Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

[EN] The development of accurate real-time models of the biomechanical behavior of different organs and tissues still poses a challenge in the field of biomechanical engineering. In the case of the liver, specifically, such a model would constitute a great leap forward in the implementation of complex applications such as surgical simulators, computed-assisted surgery or guided tumor irradiation. In this work, a relatively novel approach for developing such a model is presented. It consists in the use of a machine learning algorithm, which provides real-time inference, trained on tens of thousands of simulations of the biomechanical behavior of the liver carried out by the finite element method on more than 100 different liver geometries. Considering a target accuracy threshold of 3 mm for the Euclidean Error, four different scenarios were modeled and assessed: a single liver with an arbitrary force applied (99.96% of samples within the accepted error range), a single liver with two simultaneous forces applied (99.84% samples in range), a single liver with different material properties and an arbitrary force applied (98.46% samples in range), and a much more general model capable of modeling the behavior of any liver with an arbitrary force applied (99.01% samples in range for the median liver). The results show that the Machine Learning models perform extremely well on all the scenarios, managing to keep the Mean Euclidean Error under 1 mm in all cases. Furthermore, the proposed model achieves working frequencies above 100Hz on modest hardware (with frequencies above 1000Hz being easily achievable on more powerful GPUs) thus fulfilling the real-time requirements. These results constitute a remarkable improvement in this field and may involve a prompt implementation in clinical practice.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R, also supported by European FEDER funds.Pellicer-Valero, OJ.; Rupérez Moreno, MJ.; Martinez-Sanchis, S.; Martín-Guerrero, JD. (2020). Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations. Expert Systems with Applications. 143:1-12. https://doi.org/10.1016/j.eswa.2019.113083S112143Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. arXiv:1605.08695.Brunon, A., Bruyère-Garnier, K., & Coret, M. (2010). Mechanical characterization of liver capsule through uniaxial quasi-static tensile tests until failure. Journal of Biomechanics, 43(11), 2221-2227. doi:10.1016/j.jbiomech.2010.03.038Chinesta, F., Leygue, A., Bordeu, F., Aguado, J. V., Cueto, E., Gonzalez, D., … Huerta, A. (2013). PGD-Based Computational Vademecum for Efficient Design, Optimization and Control. Archives of Computational Methods in Engineering, 20(1), 31-59. doi:10.1007/s11831-013-9080-xClifford, M. A., Banovac, F., Levy, E., & Cleary, K. (2002). Assessment of Hepatic Motion Secondary to Respiration for Computer Assisted Interventions. Computer Aided Surgery, 7(5), 291-299. doi:10.3109/10929080209146038Cotin, S., Delingette, H., & Ayache, N. (2000). A hybrid elastic model for real-time cutting, deformations, and force feedback for surgery training and simulation. The Visual Computer, 16(8), 437-452. doi:10.1007/pl00007215Duysak, A., Zhang, J. J., & Ilankovan, V. (2003). Efficient modelling and simulation of soft tissue deformation using mass-spring systems. International Congress Series, 1256, 337-342. doi:10.1016/s0531-5131(03)00423-0Fung, Y. C., & Skalak, R. (1981). Biomechanics: Mechanical Properties of Living Tissues. Journal of Biomechanical Engineering, 103(4), 231-298. doi:10.1115/1.3138285González, D., Aguado, J. V., Cueto, E., Abisset-Chavanne, E., & Chinesta, F. (2016). kPCA-Based Parametric Solutions Within the PGD Framework. Archives of Computational Methods in Engineering, 25(1), 69-86. doi:10.1007/s11831-016-9173-4González, D., Cueto, E., & Chinesta, F. (2015). Computational Patient Avatars for Surgery Planning. Annals of Biomedical Engineering, 44(1), 35-45. doi:10.1007/s10439-015-1362-zJahya, A., Herink, M., & Misra, S. (2013). A framework for predicting three-dimensional prostate deformation in real time. The International Journal of Medical Robotics and Computer Assisted Surgery, 9(4), e52-e60. doi:10.1002/rcs.1493Lister, K., Gao, Z., & Desai, J. P. (2010). Development of In Vivo Constitutive Models for Liver: Application to Surgical Simulation. Annals of Biomedical Engineering, 39(3), 1060-1073. doi:10.1007/s10439-010-0227-8Lorente, D., Martínez-Martínez, F., Rupérez, M. J., Lago, M. A., Martínez-Sober, M., Escandell-Montero, P., … Martín-Guerrero, J. D. (2017). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications, 71, 342-357. doi:10.1016/j.eswa.2016.11.037Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Myronenko, A., & Xubo Song. (2010). Point Set Registration: Coherent Point Drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), 2262-2275. doi:10.1109/tpami.2010.46Niroomandi, S., Alfaro, I., Cueto, E., & Chinesta, F. (2012). Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models. Computer Methods and Programs in Biomedicine, 105(1), 1-12. doi:10.1016/j.cmpb.2010.06.012Plantefève, R., Peterlik, I., Haouchine, N., & Cotin, S. (2015). Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery. Annals of Biomedical Engineering, 44(1), 139-153. doi:10.1007/s10439-015-1419-zLarge elastic deformations of isotropic materials. I. Fundamental concepts. (1948). Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 240(822), 459-490. doi:10.1098/rsta.1948.0002Large elastic deformations of isotropic materials IV. further developments of the general theory. (1948). Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 241(835), 379-397. doi:10.1098/rsta.1948.0024Ruder, S. (2016). An overview of gradient descent optimization algorithms. (pp. 1–14). arXiv: 1609.04747.Untaroiu, C. D., & Lu, Y.-C. (2013). Material characterization of liver parenchyma using specimen-specific finite element models. Journal of the Mechanical Behavior of Biomedical Materials, 26, 11-22. doi:10.1016/j.jmbbm.2013.05.013Valanis, K. C., & Landel, R. F. (1967). The Strain‐Energy Function of a Hyperelastic Material in Terms of the Extension Ratios. Journal of Applied Physics, 38(7), 2997-3002. doi:10.1063/1.171003

Differences in Long-COVID Symptoms between Vaccinated and Non-Vaccinated (BNT162b2 Vaccine) Hospitalized COVID-19 Survivors Infected with the Delta Variant

This study compared differences in the presence of post-COVID symptoms among vaccinated and non-vaccinated COVID-19 survivors requiring hospitalization due to the Delta (B.1.617.2) variant. This cohort study included hospitalized subjects who had survived SARS-CoV-2 infection (Delta variant) from July to August 2021 in an urban hospital in Madrid, Spain. Individuals were classified as vaccinated if they received full administration (i.e., two doses) of BNT162b2 (&ldquo;Pfizer-BioNTech&rdquo;) vaccines. Other vaccines were excluded. Those with just one dose of the BNT162b2 vaccine were considered as non-vaccinated. Patients were scheduled for a telephone interview at a follow-up around six months after infection for assessing the presence of post-COVID symptoms with particular attention to those symptoms starting after acute infection and hospitalization. Anxiety/depressive levels and sleep quality were likely assessed. Hospitalization and clinical data were collected from medical records. A total comprising 109 vaccinated and 92 non-vaccinated COVID-19 survivors was included. Vaccinated patients were older and presented a higher number of medical comorbidities, particular cardiorespiratory conditions, than non-vaccinated patients. No differences in COVID-19 onset symptoms at hospitalization and post-COVID symptoms six months after hospital discharge were found between vaccinated and non-vaccinated groups. No specific risk factor for any post-COVID symptom was identified in either group. This study observed that COVID-19 onset-associated symptoms and post-COVID symptoms six-months after hospitalization were similar between previously hospitalized COVID-19 survivors vaccinated and those non-vaccinated. Current data can be applied to the Delta variant and those vaccinated with BNT162b2 (Pfizer-BioNTech) vaccine

Clustering analysis reveals different profiles associating long-term post-COVID symptoms, COVID-19 symptoms at hospital admission and previous medical co-morbidities in previously hospitalized COVID-19 survivors

PURPOSE: To identify subgroups of COVID-19 survivors exhibiting long-term post-COVID symptoms according to clinical/hospitalization data by using cluster analysis in order to foresee the illness progress and facilitate subsequent prognosis. METHODS: Age, gender, height, weight, pre-existing medical comorbidities, Internal Care Unit (ICU) admission, days at hospital, and presence of COVID-19 symptoms at hospital admission were collected from hospital records in a sample of patients recovered from COVID-19 at five hospitals in Madrid (Spain). A predefined list of post-COVID symptoms was systematically assessed a mean of 8.4 months (SD 15.5) after hospital discharge. Anxiety/depressive levels and sleep quality were assessed with the Hospital Anxiety and Depression Scale and Pittsburgh Sleep Quality Index, respectively. Cluster analysis was used to identify groupings of COVID-19 patients without introducing any previous assumptions, yielding three different clusters associating post-COVID symptoms with acute COVID-19 symptoms at hospital admission. RESULTS: Cluster 2 grouped subjects with lower prevalence of medical co-morbidities, lower number of COVID-19 symptoms at hospital admission, lower number of post-COVID symptoms, and almost no limitations with daily living activities when compared to the others. In contrast, individuals in cluster 0 and 1 exhibited higher number of pre-existing medical co-morbidities, higher number of COVID-19 symptoms at hospital admission, higher number of long-term post-COVID symptoms (particularly fatigue, dyspnea and pain), more limitations on daily living activities, higher anxiety and depressive levels, and worse sleep quality than those in cluster 2. CONCLUSIONS: The identified subgrouping may reflect different mechanisms which should be considered in therapeutic interventions