ABSTRACT
Background: Droplets and aerosol cloud generating procedures in dentistry can increase the risk of airborne transmission of diseases such as COVID-19. To gain insight into the diffusion of spatters and possible preventive measures, we measured the particle spatial-temporal distribution characteristic and evaluated the effectiveness of the control measures.Methods: We conducted an experiment to observe the emitted spatters obtained during the simulated dental preparation by using high-speed videography. We measured the particle size distributions by laser diffraction and preliminarily estimated its velocity. We qualitatively and quantitatively described the spatial-temporal distributions of spatters and their control measure effects. Results: Majority of the dental spatters were small droplets (diameter less than 50 μm). A large number of smallest droplets (diameter less than 10 μm) were generated by high-speed air turbine handpiece. At the oral outlet, the speed of large droplets could exceed 2.63 m/s, and the speed of aerosol clouds ranged from 0.31–2.37 m/s. The evolution of the spatters showed that the more fully developed the state, the greater the number of spatters and the wider the contamination range. When the operation mode was moved from the central incisor to the first molar, the spatter direction became increasingly concentrated, and the velocities were enhanced. Larger droplets randomly moved along trajectories and rapidly settled. The aerosol cloud tended to float as a mass that interacted with the surrounding air. The high-volume evacuation could effectively clear away most of the dental spatters. The suction air purifier could change the diffusion direction of the spatters, compress the contamination range, and control aerosol escape into surrounding air. Conclusions: Our view is that we should combine the ‘point’ control measure (high-volume evacuation) and ‘area’ control measure (suction air purifier) to reduce the scope of pollution and prevent the aerosol escape into the surroundings. The study contributes to devising more accurate infection control guidelines, establishing appropriate interventions for different oral treatments, and minimizing the spread of respiratory diseases so that we can reduce cost and achieve the best results when medical resources are limited.
Subject(s)
COVID-19 , Respiratory Tract DiseasesABSTRACT
OBJECTIVETo develop and validate a prognostic model for in-hospital mortality in COVID-19 patients using routinely collected demographic and clinical characteristics. DESIGNMulticenter, retrospective cohort study. SETTINGJinyintan Hospital, Union Hospital, and Tongji Hosptial in Wuhan, China. PARTICIPANTSA pooled derivation cohort of 1008 COVID-19 patients from Jinyintan Hospital, Union Hospital in Wuhan and an external validation cohort of 1031 patients from Tongji Hospital in Wuhan, China. MAIN OUTCOME MEASURESOutcome of interest was in-hospital mortality, treating discharged alive from hospital as the competing event. Fine-Gray models, using backward elimination for inclusion of predictor variables and allowing non-linear effects of continuous variables, were used to derive a prognostic model for predicting in-hospital mortality among COVID-19 patients. Internal validation was implemented to check model overfitting using bootstrap approach. External validation to a separate hospital was implemented to evaluate the generalizability of the model. RESULTSThe derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (n=1008, 43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (n=1031, 47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted survival curves were close to the observed survival curves across patients with different risk profiles. CONCLUSIONSThe PLANS model based on the five routinely collected demographic and clinical characteristics (platelet count, lymphocyte count, age, neutrophil count, and sex) showed excellent discriminative and calibration accuracy in predicting in-hospital mortality in COVID-19 patients. This prognostic model would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.