ABSTRACT
BACKGROUND: We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO2) signals. METHODS: We recorded the BF and SpO2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S24 severity score that represented the patient's severity over the previous 24 h; the validity of MS24, the maximum S24 score, was checked against rates of intubation risk and prolonged ICU stay. RESULTS: Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87). CONCLUSIONS: Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.
Subject(s)
COVID-19 , Triage , Critical Care , Humans , Retrospective Studies , SARS-CoV-2 , Unsupervised Machine LearningABSTRACT
STUDY DESIGN: Observational study. PURPOSE: The actual sanitary crisis led to a massive mobilization of the sanitary system toward intensive care units and management of coronavirus disease 2019 (COVID-19) patients. However, some patients still require spinal interventions. The present study aimed to assess the impact of the COVID-19 pandemic on spine surgical in a moderate COVID-19 cluster region. OVERVIEW OF LITERATURE: Previous studies have reported screening and management of patients with spinal conditions during the COVID-19 pandemic. However, to date, knowledge, no observational study on spine surgeries during the pandemic has not been reported. METHODS: Between March 17, 2020 and April 17, 2020, information on spine surgical activity was prospectively collected at our institution. This surgical activity related to the first month of confinement in France was compared to the activity during the same period in 2019 to evaluate the impact of the COVID-19 pandemic on surgical activities. RESULTS: In order to reduce the contamination rate of patients and medical staff during hospitalization, the spine department was completely reorganized. Non-urgent elective spine surgeries were cancelled. When considering the global amount of surgeries procedures during the first month of confinement, a decrease of almost 50% was observed in the number of surgical procedures. During the study period, 62 patients were eligible for spine surgery. The numbers of patients managed for tumor and infectious cases were stable, while a considerable reduction was observed in the number of trauma and degenerative cases. During the follow-up period, two patients were tested as COVID+ during the postoperative course, and no cases of medical or paramedical staff contamination were reported using polymerase chain reaction-testing. CONCLUSIONS: During the COVID-19 pandemic, it is possible to maintain spine surgical activity. Each surgical procedure must be discussed and organized with all the caregivers involved. Indications for surgery must be in line with the scientific guidelines and adapted to each healthcare facility.