Clinical severity prediction of COVID-19 admitted patients in Spain: SEMI and REDISSEC cohorts

This report addresses, from a machine learning perspective, a multi-class classification problem to predict the first deterioration level of a COVID-19 positive patient at the time of hospital admission. Socio-demographic features, laboratory tests and other measures are taken into account to learn the models. Our output is divided into 4 categories ranging from healthy patients, followed by patients requiring some form of ventilation (divided in 2 cate-gories) and finally patients expected to die. The study is conducted thanks to data provided by Sociedad Española de Medicina Interna (SEMI) and Red de Investigación en Servicios de Salud de Enfermedades Crónicas (REDISSEC). Results show that logistic regression is the best method for identifying patients with clinical deterioration. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research is supported by the Basque Government through the BERC 2022-2025 program, IT1504-22 and Basque Modeling Task Force (BMTF) project, and by the Ministry of Science and Innovation: BCAM Severo Ochoa accreditation CEX2021-001142-S / MICIN / AEI / 10.13039/501100011033 and PID2019-104966GB-I00. The research is developed thanks to data provided by "Sociedad Española de Medicina Interna" (SEMI) and "Red de Investigación en Servicios de Salud de Enfermedades Crónicas" (REDISSEC) groups. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study protocol was approved by the Ethics Committee of the Basque Country (reference PI2020059) I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data used in the present study belongs to "Sociedad Española de Medicina Interna" (SEMI) and "Red de Investigación en Servicios de Salud de Enfermedades Crónicas" (REDISSEC) groups.