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medrxiv; 2021.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2021.09.26.21264135


BackgroundNon-invasive oxygen saturation (SpO2) measurement is a central vital sign that supports the management of COVID-19 patients. However, reports on SpO2 characteristics (patterns and dynamics) are scarce and none, to our knowledge, has analysed high resolution continuous SpO2 in COVID-19. MethodsSpO2 signal sampled at 1Hz and clinical data were collected from COVID-19 departments at the Rambam Health Care Campus (Haifa, Israel) between May 1st, 2020 and February 1st, 2021. Data from a total of 367 COVID-19 patients, totalling 27K hours of continuous SpO2 recording, could be retrieved, including 205 non-critical and 162 critical cases. Desaturations based on different SpO2 threshold definitions and oximetry derived digital biomarkers (OBMs) were extracted and compared across severity and support levels. FindingsAn absolute SpO2 threshold at 93% was the most efficient in discriminating between critical and non-critical patients without support or under oxygen support. Under no support, the non-critical group depicted a fold change (FC) of 1 {middle dot}8 times more frequent desaturations compared to the critical group. However, the hypoxic burden was 1 {middle dot}6 times more important in critical versus non-critical patients. Other OBMs depicted significant differences, notably the percentage of time below 93% SpO2 (CT93) was the most discriminating OBM. Mechanical ventilation depicted a strong effect on SpO2 by significantly reducing the frequency (1 {middle dot}85 FC) and depth (1 {middle dot}21 FC) of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected up to several hours before the initiation of the mechanical ventilation. InterpretationThis is the first report investigating continuous SpO2 measurements in hospitalized patients affected with COVID-19. SpO2 characteristics differ between critical and non-critical patients and are impacted by the level of support. OBMs from high resolution SpO2 signal may enable to anticipate clinically relevant events, monitoring of treatment response and may be indicative of future deterioration. FundingThe Milner Foundation, The Placide Nicod fundation and the Technion Machine Learning and Intelligent Systems center (MLIS).

medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.11.19.20235077


BackgroundCOVID-19 is a newly recognized illness with a predominantly respiratory presentation. As winter approaches in the northern hemisphere, it is important to characterize the differences in disease presentation and trajectory between COVID-19 patients and other patients with common respiratory illnesses. These differences can enhance knowledge of pathogenesis and help in guiding treatment. MethodsData from electronic medical records were obtained from individuals admitted with respiratory illnesses to Rambam Health Care Campus, Haifa, Israel, between October 1st, 2014 and September 1st, 2020. Four groups of patients were defined: COVID-19 (693), influenza (1,612), severe acute respiratory infection (SARI) (2,292) and Others (4,054). The variable analyzed include demographics (7), vital signs (8), lab tests (38), and comorbidities (15) from a total of 8,651 hospitalized adult patients. Statistical analysis was performed on biomarkers measured at admission and for their disease trajectory in the first 48 hours of hospitalization, and on comorobidity prevalence. ResultsCOVID-19 patients were overall younger in age and had higher body mass index, compared to influenza and SARI. Comorbidity burden was lower in the COVID-19 group compared to influenza and SARI. Severely- and moderately-ill COVID-19 patients older than 65 years of age suffered higher rate of in-hospital mortality compared to hospitalized influenza patients. At admission, white blood cells and neutrophils were lower among COVID-19 patients compared to influenza and SARI patients, while pulse rate and lymphoctye percentage were higher. Trajectories of variables during the first two days of hospitalization revealed that white blood count, neutrophils percentage and glucose in blood increased among COVID-19 patients, while decreasing among other patients. ConclusionsThe intrinsic virulence of COVID-19 appeared higher than influenza. In addition, several critical functions, such as immune response, coagulation, heart and respiratory function and metabolism were uniquely affected by COVID-19.

medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.09.04.20185645


Importance: The spread of COVID-19 has led to a severe strain on hospital capacity in many countries. There is a need for a model to help planners assess expected COVID-19 hospital resource utilization. Objective: Provide publicly available tools for predicting future hospital-bed utilization given a succinct characterization of the status of currently hospitalized patients and scenarios for future incoming patients. Design: Retrospective cohort study following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1st to May 2nd, 2020. Patient clinical course was modelled with a machine learning approach based on a set of multistate Cox regression-based models with adjustments for right censoring, recurrent events, competing events, left truncation, and time-dependent covariates. The model predicts the patient's entire disease course in terms of clinical states, from which we derive the patient's hospital length-of-stay, length-of-stay in critical state, risk of in-hospital mortality, and overall hospital-bed utilization. Accuracy assessed over 8 cross-validation cohorts of size 330, using per-day Mean Absolute Error (MAE) of predicted hospital utilization over time; and area under the receiver operating characteristics (AUROC) for individual risk of critical illness and in-hospital mortality, assessed on the first day of hospitalization. We present predicted hospital utilization under hypothetical incoming patient scenarios. Setting: 27 Israeli hospitals. Participants: During the study period, 2,703 confirmed COVID-19 patients were hospitalized in Israel for 1 day or more; 28 were excluded due to missing age or sex; the remaining 2,675 patients were included in the analysis. Main Outcomes and Measures: Primary outcome: per-day estimate of total number of hospitalized patients and number of patients in critical state; secondary outcome: risk of a single patient experiencing critical illness or in-hospital mortality. Results: For random validation samples of 330 patients, the per-day MAEs for total hospital-bed utilization and critical-bed utilization, averaged over 64 days, were 4.72 {+/-} 1.07 and $1.68 {+/-} 0.40 respectively; the AUROCs for prediction of the probabilities of critical illness and in-hospital mortality were 0.88 {+/-} 0.04 and 0.96 {+/-} 0.04, respectively. We further present the impact of several scenarios of patient influx on healthcare system utilization, demonstrating the ability to accurately plan ahead how to allocate healthcare resources. Conclusions and Relevance: We developed a model that, given basic easily obtained data as input, accurately predicts total and critical care hospital utilization. The model enables evaluating the impact of various patient influx scenarios on hospital utilization. Accurate predictions are also given for individual patients' probability of in-hospital mortality and critical illness. We further provide an R software package and a web-application for the model.