Background To cope with shortages of equipment during the COVID-19 pandemic, we established a nonprofit end-to-end system to identify, validate, regulate, manufacture, and distribute 3D-printed medical equipment. Here we describe the local and global impact of this system. Methods Together with critical care experts, we identified potentially lacking medical equipment and proposed solutions based on 3D printing. Validation was based on the ISO 13485 quality standard for the manufacturing of customized medical devices. We posted the design files for each device on our website together with their technical and printing specifications and created a supply chain so that hospitals from our region could request them. We analyzed the number/type of items, petitioners, manufacturers, and catalogue views. Results Among 33 devices analyzed, 26 (78·8%) were validated. Of these, 23 (88·5%) were airway consumables and 3 (11·5%) were personal protective equipment. Orders came from 19 (76%) hospitals and 6 (24%) other healthcare institutions. Peak production was reached 10 days after the catalogue was published. A total of 22,135 items were manufactured by 59 companies in 18 sectors; 19,212 items were distributed to requesting sites during the busiest days of the pandemic. Our online catalogue was also viewed by 27,861 individuals from 113 countries. Conclusions 3D printing helped mitigate shortages of medical devices due to problems in the global supply chain.
Background: To cope with shortages of equipment during the COVID-19 pandemic, we established a nonprofit end-to-end system to identify, validate, regulate, manufacture, and distribute 3D-printed medical equipment. Here we describe the local and global impact of this system. Methods: Together with critical care experts, we identified potentially lacking medical equipment and proposed solutions based on 3D printing. Validation was based on the ISO 13485 quality standard for the manufacturing of customized medical devices. We posted the design files for each device on our website together with their technical and printing specifications and created a supply chain so that hospitals from our region could request them. We analyzed the number/type of items, petitioners, manufacturers, and catalogue views. Results: Among 33 devices analyzed, 26 (78·8%) were validated. Of these, 23 (88·5%) were airway consumables and 3 (11·5%) were personal protective equipment. Orders came from 19 (76%) hospitals and 6 (24%) other healthcare institutions. Peak production was reached 10 days after the catalogue was published. A total of 22,135 items were manufactured by 59 companies in 18 sectors; 19,212 items were distributed to requesting sites during the busiest days of the pandemic. Our online catalogue was also viewed by 27,861 individuals from 113 countries.Conclusions: 3D printing helped mitigate shortages of medical devices due to problems in the global supply chain.
Background: To cope with shortages of equipment during the COVID-19 pandemic, we established a nonprofit end-to-end system to identify, validate, regulate, manufacture, and distribute 3D-printed medical equipment. Here we describe the local and global impact of this system. Methods: Together with critical care experts, we identified potentially lacking medical equipment and proposed solutions based on 3D printing. Validation was based on the ISO 13485 quality standard for the manufacturing of customized medical devices. We posted the design files for each device on our website together with their technical and printing specifications and created a supply chain so that hospitals from our region could request them. We analyzed the number/type of items, petitioners, manufacturers, and catalogue views.Results: Among 33 devices analyzed, 26 (78·8%) were validated. Of these, 23 (88·5%) were airway consumables and 3 (11·5%) were personal protective equipment. Orders came from 19 (76%) hospitals and 6 (24%) other healthcare institutions. Peak production was reached 10 days after the catalogue was published. A total of 22,135 items were manufactured by 59 companies in 18 sectors; 19,212 items were distributed to requesting sites during the busiest days of the pandemic. Our online catalogue was also viewed by 27,861 individuals from 113 countries.Conclutions: 3D printing helped mitigate shortages of medical devices due to problems in the global supply chain.
Background and Aims The native arteriovenous fistula is considered the vascular access of preference, since it is directly related to major survival, reduced complications, mortality, and costs. Still, its proper maintenance remains a challenge for nephrologists. A previous study from our group, which used the data of 117 arteriovenous fistulas, led to identify in the multivariable analysis age and vein diameters as predictive factors for early failure. On the other hand, Artificial Intelligence has been established as a tool to identify relationships between variables at deep levels, which might be unseen with more conservative methods like classic statistics. Therefore, through a Machine Learning technique known as Random Forest, the aim is to evaluate the same comorbidity, biological and Doppler ultrasound variables data to identify those with a major relation with the early failure of the native arteriovenous fistula. Method Retrospective cohort study, gathering the same data of the previous study (from 2011 to 2015): survival, ultrasound mapping (morphology and hemodynamics), comorbidities (blood pressure, severe arteriopathy, diabetes, Charlson’s Index), and laboratory (haemoglobin, calcium, phosphorus, PTH, ferritin, PCR). Different Artificial Intelligence algorithms were tested, but the most suited one for the study's aim turned out to be Random Forest. A model was trained, dividing the data in two sets, training and validation, with an 80/20 ratio. The algorithm used 100 decision trees, with a maximum individual depth of 3 levels. The training was made with the variables that represented the 100%, 95%, 90% and 85% of impact in the fistula's maturation from a theresold according to Gini’s Index. Results Age 65.7 (32-88) years, male 59.8%. Hypertension 86.7%, diabetes 50.7% and vascular disease 41.3%. The trained model obtained provided the following results in the evaluation: accuracy 0.82, precision 0.86, AUROC 0.85, F1 0.86 (balance between precision and predictive value). The most relevant variables by decision order were age, phosphorous, PTH, ferritin and calcium. Morphologic and hemodynamic variables such as the vessels diameter, Peak Systolic Velocity, Charlson’s Index or PCR, were also found to be relevant, but in a minor level. Conclusion In comparison with classic statistics, Machine Learning techniques might create a change of paradigm in predictive models for the patency of the vascular access for Haemodialysis. Even though the Artificial Intelligence-based model provided the same relevance for age in maturation failure as traditional models, other findings stand out, like the major participation of variables related with mineral metabolism or inflammation, rather than ultrasound based ones.
Background: To cope with shortages of equipment during the COVID-19 pandemic, we established a nonprofit end-to-end system to identify, validate, regulate, manufacture, and distribute 3D-printed medical equipment. Here we describe the local and global impact of this system. Methods: Together with critical care experts, we identified potentially lacking medical equipment and proposed solutions based on 3D printing. Validation was based on the ISO 13485 quality standard for the manufacturing of customized medical devices. We posted the design files for each device on our website together with their technical and printing specifications and created a supply chain so that hospitals from our region could request them. We analyzed the number/type of items, petitioners, manufacturers, and catalogue views.Results: Among 33 devices analyzed, 26 (78·8%) were validated. Of these, 23 (88·5%) were airway consumables and 3 (11·5%) were personal protective equipment. Orders came from 19 (76%) hospitals and 6 (24%) other healthcare institutions. Peak production was reached 10 days after the catalogue was published. A total of 22,135 items were manufactured by 59 companies in 18 sectors; 19,212 items were distributed to requesting sites during the busiest days of the pandemic. Our online catalogue was also viewed by 27,861 individuals from 113 countries.Conclusions: 3D printing helped mitigate shortages of medical devices due to problems in the global supply chain.
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