Pediatric mechanical circulatory support (MCS) is considered a strategy for heart failure management as a bridge to recovery and transplantation or as a destination therapy. The final outcome is significantly impacted by the number of complications that may occur during MCS. Children on ventricular assist devices (VADs) and extracorporeal membrane oxygenation (ECMO) are at high risk for bleeding and thrombotic complications that are managed through anticoagulation. The first detailed guideline in pediatric VADs (Edmonton Anticoagulation and Platelet Inhibition Protocol) was based on conventional antithrombotic drugs, such as unfractionated heparin (UFH) and warfarin. UFH is the first-line anticoagulant in pediatric MCS, although its profile is not considered optimal in pediatric setting. The broad variation in heparin doses among children is associated with frequent occurrence of cerebrovascular accidents, bleeding, and thrombocytopenia. Direct thrombin inhibitors (DTIs) have been utilized as alternative strategies to heparin. Since 2018, bivalirudin has become the chosen anticoagulant in the long-term therapy of patients undergoing MCS implantation, according to the most recent protocols shared in North America. This article provides a review of the non-traditional anticoagulation strategies utilized in pediatric MCS, focusing on pharmacodynamics, indications, doses, and monitoring aspects of bivalirudin. Moreover, it exposes the efforts and the collaborations among different specialized centers, which are committed to an ongoing learning in order to minimize major complications in this special pediatric population. Further prospective trials regarding DTIs in a pediatric MCS setting are necessary and in specific well-designed randomized control trials between UFH and bivalirudin. To conclude, based on the reported literature, the clinical use of the bivalirudin in pediatric MCS seems to be a value added in controlling and maybe reducing thromboembolic complications. Further research is necessary to confirm all the results provided by this literature review. Patient population on end-stage heart failure has grown significantly during the last two decades. Independently from the different etiologies of heart failure, unresponsiveness to conventional medical therapy must be an indication for considering mechanical circulatory support (MCS) as a strategy to improve end-organ perfusion, which has resulted in better quality of life and survival for both patients on destination therapy and those bridged to transplant. Additionally, evidence for heart recovery has been even more reported for both adult and pediatric populations. Modulating activation of the coagulation cascade due to exposition to extracorporeal circuit is of basic relevance for a successful strategy of mechanical assistance where hemorrhagic and thrombotic events remain significant complications that negatively influence morbidity and mortality. Peculiarities of coagulation in neonates and congenital heart disease (CHD) patients make handling of coagulation particularly challenging and even more frequently requiring clinicians experienced in hemostasis in this field. The large number of heart centers dealing with MCS results in a fragmentation of clinical experience, which requires an effort for close collaboration in sharing data and protocols for coagulation management on MCS. This article provides a short description of different types of MCS: their differences in terms of nomenclature, indications, short- and long-term strategies, positioning and modality of implantation, technical characteristics, and type of blood flow generated. Particular attention is focused on describing pharmacological and monitoring issues related to both traditional and alternative antithrombotic therapy. Efforts in collaboration between different specialized centers committed to an ongoing learning to minimize major complications in this special pediatric population are outlined. The original contribution of this article includes our lifelong experience in managing anticoagulant therapy for different types of MCS in a setting of heterogeneous patient population for age, weight, and diagnosis. On the basis of the most recent internationally shared protocols, we try to give additional recommendations regarding the antithrombotic therapy for ventricular assistance devices.MCS is a generic acronym that currently describes mechanical devices able to pump blood . SpecifiExtracorporeal membrane oxygenation (ECMO) is generally indicated as a short-term bridge to recovery or to organ transplantation for patients affected by cardiac or pulmonary failure refractory to medical therapy. ECMO can provide respiratory support or combined respiratory and cardiac support.Veno-arterial (VA) ECMO is based on an extracorporeal circuit with a  pump draining blood from the systemic venous pole of circulation , flowing it through a gas exchange (oxygenator), and rewarming and returning it to central systemic arteries . This blood flow through the ECMO circuit results in a more or less effective systemic venous decongestion with an increase in cardiac output. The presence of an oxygenator allows for optimization of hemoglobin oxygenation and replacement of lung function in terms of gas exchange. The final effect on systemic oxygen delivery is the result from residual native cardiac output integrated with required pump flow. The final coronary oxygen delivery will depend on anterograde blood ejected by the left ventricle (whose oxygenation will be influenced by the gas exchange capability of lungs) and the quota of fully oxygenated pump flowing back in the ascending aorta from arterial cannula.According to the site of vessel cannulation, ECMO support may be classified as central  and peripheral (by cannulating veins and arteries from the neck and/or groin).This mechanically driven bypass of the pulmonary circulation will result in left ventricle afterload increase, which could precipitate left ventricle systolic performance and pulmonary edema.Veno-venous ECMO is based on blood draining from and returning into the venous system. The final effect is an augmentation of central venous saturation on the basis of pump flow and oxygenator gas exchange. VV ECMO can provide only an oxygenation function, but not a pump support. Thus, the main prerequisite for establishing the VV ECMO is an adequate cardiac function and the absence of a lung barrage related to high pulmonary vascular resistances.Regarding ventricular assist devices (VADs), the terminology is evolving and varies upon the type of cardiac assistance. VADs can support left (left VAD) or right (right VAD) ventricle or both (bi-VAD) by directly unloading the failing chamber and by returning blood to either the aorta (left VAD) or pulmonary artery (right VAD). The pump can be placed outside  or implanted  inside the body. Different flow types are generated depending on the technical features . According to pump flow dependence from pharmacological manipulation of systemic vascular resistances, left VADs with continuous flow may result in a more complete left ventricle unloading compared to ECMO and pulsatile VAD. Moreover, VADs can be categorized into those which allow for patient discharge (durable devices) and those requiring patient hospitalization , 2. LastThe main characteristics of available MCS, depicted for their main technical features and clinical indications, are described in Endothelial cells produce anticoagulant and procoagulant factors that act together in order to maintain homeostasis  4). Blo. Blo4). Activation of the contact pathway of coagulation provides potential new targets for anticoagulant therapies .Compared to adult population, pediatric MCS is negatively characterized by higher incidence of hemorrhagic and thrombotic events, and this may be explicated in terms of hemostasis alterations related to developmental age and the presence of CHD.Retrospective data from ELSO registry report a 39% prevalence of hemorrhagic complication in the pediatric population .Major bleeding complications and strokes have been, respectively, reported with an incidence until 50 and 29% in the Berlin Heart (BH) Excor® pediatric population . SimilarRegarding continuous-flow durable devices implanted in adolescents, the adverse event rate, especially for neurologic dysfunction, including strokes, was registered relatively low at 10%, with most of the events occurring in the first 3 months after implantation .The developmental hemostasis increases the risk for hemorrhagic complications in neonates and infants and offers additional challenges in titration of standard anticoagulation in pediatrics requiring MCS.The term “developmental hemostasis,” introduced by Maureen Andrew in 1980, describes the physiological changes of the coagulation system related to age , where fNeonates have physiologically low levels of vitamin K-dependent and contact factors  that gradually increase to values approaching adult levels by 6 months of life , 12. PhyThe reduced levels of coagulation factor in infants results in alteration of conventional tests such as the activated partial thromboplastin time (aPTT). Unfortunately, there is no standardization for aPTT because of a wide variability of reagents and analyzing systems from center to center implying different reference ranges . Other gMoreover, acquired coagulation defects have been reported in children with cardiac disease. In children with CHD, a 48-month delay in normalization of coagulation factors has also been documented. Children with cyanotic defects are much more likely to be polycythemic and manifest a hypercoagulability when younger  .In cyanotic older children, chronic hepatic congestion or heart failure may lead to liver dysfunction and a decreased production of coagulation factors and platelets in addition to an increased fibrinolytic activity .Efforts in reducing hemorrhagic and thrombotic events in pediatric MCS population have involved optimization of hemocompatibility of artificial surfaces and pumps, and pharmacologic manipulation of coagulation.Biomaterials or artificial substance of MCS in contact with human body are engineered in order to optimize biocompatibility. Biocompatibility refers to the effects that a biomaterial has on host tissues that are exposed to it. Hemocompatibility, a subset of biocompatibility, describes the effects of a MCS on the coagulation and inflammatory system. The ideal biocompatible surface for blood is the uninjured endothelium. Nowadays, different biomaterials are available for ECMO circuits, and others are still in the process of research and development, in order to target thrombin inhibition and to inhibit platelet adhesion/activation. These biomaterials have been recently classified into three categories: biomimetic surfaces ; bio-passive surfaces , and endothelialization of blood-contacting surfaces .hemolysis caused by the pumps' direct physical injury and the shear stress forces on red blood cells. Hemolysis (Shear stress is a known cause leading to von Willebrand factor (vWF) emergence. vWF is a multimeric glycoprotein containing binding sites for both platelet glycoprotein receptors and collagen , low-molecular weight heparin (LMWH), or warfarin with the addition of anti-platelet agents . However, it has to be remarked that no standardized guidelines for pediatric patients are currently available. DTIs such as bivalirudin are becoming a common alternative to conventional anticoagulation in pediatric patients.The anticoagulation strategy of choice and its titration and monitoring constitute additional challenges due to the peculiar pharmacokinetics and pharmacodynamics in children, with supplementary difficulty in neonates.Heparin is a sulfated mucopolysaccharide derived from porcine intestine. It is a heterogeneous molecule regarding size, anticoagulant activity, and pharmacokinetic properties. Heparin molecules range in molecular weight from 3,000 to 30,000 kDa. Just one-third of the heparin molecules has the unique pentasaccharide sequence, which is responsible for the anticoagulant effect of heparin. The heparin/antithrombin (AT) complex inactivates thrombin, factor IIa, and factors Xa, IXa, XIa, and XIIa. Heparin catalyzes AT-mediated thrombin inhibition by a ternary heparin/AT/thrombin complex. Instead, in order to inhibit the factor Xa, it is necessary to bind only AT. In both cases, the unique pentasaccharide sequence, found within some heparin molecules, is needed. Heparin prevents fibrin formation and inhibits thrombin-induced activation of platelets and factors V, VIII, and XI, by inactivating or decreasing thrombin generation . UFH inhHeparin has various significant limitations, particularly evident in infants.First, heparin has a high degree of inter- and intra-patient variability in dosing, which leads to poor maintenance of standard therapeutic levels . This isSecond, monitoring heparin with the aPTT or anti-factor Xa are problematic, due to the limitations of these assays. The aPTT, in particular, is frequently impacted by these variables: pre-analytic (blood sampling), analytic variables (reagent and/or coagulometer used), and biological variables  .Third, heparin can induce thrombocytopenia. However, the incidence of heparin-induced thrombocytopenia (HIT) in neonates, particularly those with CHD, is about 1%, which is not insignificant considering that this complication can lead to devastating consequences including death .Fourth, heparin is a biologic compound; it is subject to potential contamination .Finally, heparin inhibits osteoblast formation promoting bone loss and osteoporosis .Problems related to balance coagulation on MCS are associated to the accuracy in monitoring heparin administration.Different monitoring tests are used to measure heparin effect.The activated clotting time (ACT) is a very basic test that measures clotting time in seconds in whole blood, by using one of two activators (kaolin or celite) . ACT is The safety and efficacy of ACT to monitor heparin anticoagulation in children have not been really evaluated by well-designed trials.The aPTT is a standard global clotting assay that reflects the function of contact factors  and FII, FVIII, FX, up to the conversion of fibrinogen to fibrin. aPTT evaluates the intrinsic and common pathways of the coagulation cascade. It is performed by adding an activator and phospholipid to citrated platelet-poor plasma and then adding calcium to measure clotting time in seconds . This teThe anti-factor Xa assay is a test to monitor heparin effect that has recently gained popularity. Patient plasma is added to a test reagent that contains excess of factor Xa. UFH from the patient's plasma binds AT and inhibits Xa. In this way, it measures the residual amount of Xa linked to a chromophore substrate, which will be inversely proportional to the UFH concentration. The amount of residual Xa is used to calculate the anti Xa level .Regarding heparin monitoring in pediatric ECMO, several limitations of each test have been evidenced as well as a poor correlation among them. ACT is a bedside, rapid, and widely used test to monitor heparin titration on ECMO. Currently, 94% of ELSO-reporting centers check aPTT at least once per day . NeverthIn adult ECMO patients, studies relating ACT and aPTT values to heparin concentrations evidenced a poor or moderate correlations between, respectively, ACT, aPTT, and heparin doses. Even a poor correlation between ACT and aPTT values has been reported .r = 0.02) and this was explained with frequent thrombocytopenia and coagulation factor deficiencies characterizing a pediatric patient population. aPTT was also weakly correlated with anti-Xa, although somewhat better than ACT, even in neonatal populations  function tests, such as Platelet mapping, MultiplateThe viscoelastic tests provide a point-of-care assessment of the entire hemostatic pathway in whole blood, from clot initiation to fibrinolysis. A variety of activators are employed for each device to examine different aspects of the coagulation pathway . A graphRegarding viscoelastic tests, normal values in healthy neonates and older infants have been published . In addiTherefore, viscoelastic tests and platelet function assays are recommended during MCS to reduce the risk of thrombosis and bleeding . Even thEven the relevance of PLT function monitoring with aggregometry needs further investigation.The progressive increase of heparin dose based on anti-Xa, aPTT, or ACT is defined “heparin resistance.” The mechanism can be dependent on or independent of AT. The mechanism AT mediated includes reduced synthesis, increased clearance consequent to nephropathy, and accelerated consumption . The AT-independent heparin resistance may be related to increased heparin binding to proteins and platelets , medications (such as nitroglycerin), or increased factor VIII . Traditin = 14) and a historical case–control design. There was a lack of close examination regarding the relationship between the AT activity and global hemostatic function. This implies the necessity of a research about interactions between AT and other pro- and anticoagulant factors in order to identify clinical indications for the administration of AT.Acquired AT deficiency is more common than congenital AT deficiency, which is the only in-label application for AT concentrate administration. The US Pediatric Health Information System provided a review and concluded that 97% of children who were treated with AT received it off-label, particularly in neonates. Congenital heart/lung problems (36%) represented the most common diagnosis associated with off-label use, and the most common setting was ECMO . Anyway,Thrombin-inhibiting drugs can block the action of thrombin by binding to its three domains: the catalytic and two exosites. Exosite 1 binds fibrin while exosite 2 binds the AT–heparin complex . BivalenThe safety of DTIs, which are predominantly cleared by the kidneys, remains to be established in patients with renal dysfunction. The half-life of bivalirudin is prolonged in subjects with severe renal impairment, and decreased doses are needed. Hepatic metabolism and proteolysis at other sites also contribute to bivalirudin metabolism. The use of aspirin does not affect the plasma concentrations of DTIs .Further studies on bivalirudin pharmacodynamics and pharmacokinetics in pediatrics would be necessary.The use of bivalirudin in pediatric ECMO has been poorly evaluated, and currently, no prospective studies on bivalirudin use in MCS exist. This is due to the fact that most of the studies include heparin as first-line therapy. In literature, different indications for bivalirudin have been reported, but the most frequent reasons are heparin resistance or diagnosis of HIT.Ranucci et al. reported that bivalirudin use in postcardiotomy ECMO was characterized by significantly higher ACT, aPTT, and R values at TEG, compared to heparin. The authors described patterns of fluctuation in heparin responsiveness requiring dose adjustments accordingly to low AT and correction-related modification of AT levels. The AT-independent bivalirudin antithrombotic action would be responsible for stably higher values of anticoagulation tests. Moreover, patients treated with bivalirudin received lower amount of allogeneic transfusions. The initial bivalirudin infusion rate reported was 0.03–0.05 mg/kg/h, with a medium maintenance rate of 0.05–0.1 mg/kg/h .Nagle et al. analyzed the bivalirudin use in pediatric VV and VA ECMO. In order to maintain a therapeutic aPTT, initial bivalirudin infusion doses ranged from 0.05 to 0.3 mg/kg/h, while maintenance infusion rate needed ranged from 0.045 to 0.48 mg/kg/h. This strategy was associated to an average percentage of time on therapeutic aPTT range of 47.5% and no cerebrovascular bleeding events occurred .Ezetendu et al. described a case report of shift from heparin to bivalirudin to manage anticoagulation because of VV ECMO-related hemolysis and hyperbilirubinemia precluding accurate monitoring of heparin therapy (aPTT >150 s with anti-Xa level was < 0.1 IU/ml). Bivalirudin was successfully used for anticoagulation in this infant, with an initial dose of 0.3 mg/kg/h. No bolus was needed .Preston et al. reported replacement of heparin with bivalirudin in a pediatric patient requiring VV-ECMO who developed HIT. The bivalirudin loading doses were between 0.4 and 1.6 mg/kg and the maintenance dose ranged from 1.2 to 1.8 mg/kg/h. The authors described the efficacy of continuous bivalirudin infusion in case of plasma exchange during VV ECMO .Pollak et al. described the use of bivalirudin in a newborn, which showed HIT-related thrombosis after 3 days of VA-ECMO support for congenital diaphragmatic hernia. Bivalirudin was administered at a loading dose of 0.4 mg/kg. Then, the initial infusion rate of 0.15 mg/kg/h was titrated to maintain the ACT in the target range of 180–200 s, requiring a dose ranging between 0.06 and 0.17 mg/kg/h. Of note, while the initial efficacious infusion rate was very low, an escalation dose to 1.1–1.6 mg/kg/h was necessary to maintain a target level .Campbell et al. retrospectively reviewed 34 pediatric patients receiving bivalirudin for ECMO/VAD. The average dose of bivalirudin infusion was 0.37 mg/kg/h, with a maximum dose of 0.62 mg/kg/h. This is the first study to report that even though bivalirudin dosing in ECMO and VAD patients is consistent with a previously described dosing, bivalirudin requirement may be higher in VAD patients .In a recent case series of continuous-flow left VADs, Sylvia et al. demonstrated that bivalirudin seems to be associated with a lower risk of major bleeding .Hamzah et al. first reported a different starting dose of bivalirudin according to renal clearance and described a strategy to briefly reach a therapeutic goal in anticoagulation level. To reach the aPTT goal target, an initial dose of bivalirudin was 0.3 mg/kg/h in case of creatinine clearance > 60 ml/min, instead of 0.15 mg/kg/h for subjects with renal dysfunction. Infusion rate was titrated by 0.05–0.1 mg/kg/h dose increment. Bleeding events were fewer in the bivalirudin group and there was no difference in the rate of thrombotic events between the two groups. Furthermore, the authors demonstrated how the requirement of bivalirudin needed to maintain target anticoagulation increased over time according to increasing creatinine clearance and plasmatic fibrinogen levels. The authors concluded that the use of bivalirudin in pediatric MCS is feasible, safe, reliable, and cost-effective in comparison to heparin , paracorporeal continuous-flow devices (44%), and combination of devices (7%). Initial infusion rate ranged between 0.1 and 0.4 mg/kg/h, with an average of 0.3 mg/kg/h. The maximum dose ranged between 0.1 and 3.9 mg/kg/h with a median value of 1 mg/kg/h. The target aPTT value was variable between 50 and 100 s. Major bleeding was reported in 16% of patients and stroke in 12% of cases . The overall survival to transplantation was 88% .Finally, Bates et al. highlighted the potential use of bivalirudin, in a heterogeneous VAD population, in case of suspected or confirmed pump thrombosis. The authors reported the safety of the drug  and a good management of this fearsome adverse event with bivalirudin. Only one case of major bleeding occurred and the patient made a full recovery .In general, the results of all the above mentioned studies are encouraging and promising. Nevertheless, further specific well-designed, randomized, prospective control trials between the standard of care with UFH and DTIs in pediatric MCS setting are needed.Finally, some form of DTI resistance has been suspected, probably related to the possibility to develop neutralizing antibodies as well as peptides. However, at the moment, we do not exactly know the mechanism by which sensitivity to DTIs could be reduced or even lost over time .Recently, conventional antithrombotic regimens in patients requiring VAD or ECMO are progressively changing, contextually to the changes in antithrombotic international protocols.Regarding the common antithrombotic strategies for MCS, the Edmonton Anticoagulation and Platelet Inhibition Protocol was the first detailed guideline that provides the mainstay antithrombotic regimen in pediatric paracorporeal VADs for a long time . It was The investigational Device Exemption (IDE) evaluated the Edmonton protocol outcomes and related events. This analysis, published by Steiner et al., showed an incidence of 24% for major bleeding events and 9% for neurologic complications. Most patients suffering from major events were inside the therapeutic range proposed by the Edmonton protocol . Major bRegarding modifications of antiplatelet therapy, Rosenthal et al. evaluated an anti-thrombotic regimen based on greater platelet inhibition with less reliance on platelet function testing.In this study , childreThe targeted doses of aspirin, clopidogrel, and dipyridamole were higher and less variable in the Stanford cohort . The incRecently, the use of DTIs has become increasingly common in antithrombotic strategies revealing encouraging findings. The first and largest multi-center experience of DTI use in pediatric Excor VAD support, described by VanderPluym et al. and previously cited, was based on an antithrombotic strategy using bivalirudin or argatroban associated with adjuvant antiplatelet therapy. A lower rate of stroke compared to the Edmonton guideline was reported with risk for adverse events approximating those reported for intracorporeal continuous-flow VADs in children with older age , 66. FurTaking into consideration the various logistical, ethical, and financial challenges of completing a randomized controlled trial of anticoagulation agents in the pediatric VAD population, MCS centers worldwide are seeking learning collaborations.In April 2017, the Advanced Cardiac Therapies Improving Outcomes Network (ACTION) was created . This ne®).North America data on approaches to issues, such as pre-implant evaluations and adverse events occurring after implantation, were “harmonized,” offering a protocol for pulsatile paracorporeal VADs , are described.In case of high bleeding risk, as in the early period post-implantation, the aPTT goal of 50–60 s is recommended. For standard risk of thrombosis, bivalirudin titration should be adjusted to maintain aPTT between 60 and 80 s; for high risk of thrombosis, an aPTT goal between 70 and 90 s is suggested . Any adjMoreover, this protocol provides recommendations for adjuvant antiplatelet therapy, indicating doses and adverse effects, how to titrate the therapy, how to modify it on the basis of clinical signs (fibrin/thrombin deposition in the circuit and bleeding), and responsiveness testing.The topic of MCS remains a hot issue in the pediatric setting and is the center of many debates. There are many reasons behind this complexity: first, the difficulty to manage the adverse events of pediatric patients supported with ECMO or assisted with temporary or durable devices. Second, even though there are advancements in biocompatibility, the prevention or treatment of complications, such as bleeding and thrombosis, remains challenging.Nowadays, the efforts of collaboration between various centers with the aim to harmonize different practices have the potential to result in changes in clinical management. Collaborative ACTION could have a positive impact on the outcome and could bring a significant contribution in supporting clinicians on anticoagulation policies. Without undermining the need for prospective randomized controlled trials able to compare anticoagulation agents in pediatric MCS, it is fundamental to highlight the importance of current observational reports. Undoubtedly, there are immense difficulties to complete these studies, in terms of logistical, ethical, and financial challenges.Based on the literature presented above, this review concludes that, aligning with the protocols of ACTION, the use of bivalirudin seems safe with a simple learning curve. In case it is handled according to anticoagulation therapeutic range, it facilitates the uniformity of treatment protocols in different centers. To sum up, the use of bivalirudin appears to be effective to control thromboembolic complications even in younger infants who underwent different types of MCS.CG, AR, and LDC wrote 100% of the manuscript. IF, AA, FC, SGP, ML, and GDF revised the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The fifth-generation (5G) network is presented as one of the main options for Industry 4.0 connectivity. To comply with critical messages, 5G offers the Ultra-Reliable and Low latency Communications (URLLC) service category with a millisecond end-to-end delay and reduced probability of failure. There are several approaches to achieve these requirements; however, these come at a cost in terms of redundancy, particularly the solutions based on multi-connectivity, such as Packet Duplication (PD). Specifically, this paper proposes a Machine Learning (ML) method to predict whether PD is required at a specific data transmission to successfully send a URLLC message. This paper is focused on reducing the resource usage with respect to pure static PD. The concept was evaluated on a 5G simulator, comparing between single connection, static PD and PD with the proposed prediction model. The evaluation results show that the prediction model reduced the number of packets sent with PD by 81% while maintaining the same level of latency as a static PD technique, which derives from a more efficient usage of the network resources. Wired communications have been widely used in industrial scenarios as new applications of automation and Artificial Intelligence (AI) with high mobility are rolled out. However, wired communications are costly in terms of installation and maintenance and cannot cover new use cases, such as mobility in factories. As a result, the whole industry is shifting towards more flexible and adaptable scenarios, resulting in the Industry 4.0 paradigm. Industry 4.0 is the fourth industrial revolution to improve the flexibility of production and distribution processes where wireless networks have an important part.The advances in the fields of robotics, AI and Machine Learning (ML) converge in Industry 4.0 to adapt production to new customer demands, such as an increased customization, reduced costs and lower environmental impact . WirelesEnhanced Mobile BroadBand (eMBB): this service category is an evolution of traditional mobile broadband, with higher data rates (up to 20 Gbps) and bandwidth. It is similar to the traditional use of networks by users, such as web browsing or streaming multimedia content.Massive Machine-Type Communications (mMTC): this service category covers massive connection of devices, with a sporadic and lower volume of data exchange over the network. It is mainly focused on the Internet of Things (IoT).Ultra-Reliable and Low Latency Communications (URLLC): this service category aims to cover critical communications, where short messages are exchanged with requirements of lower latency and higher reliability. The latency requirement varies from 1 to 15 ms, depending on the application itself. However, in 5G, it is expected to reach a maximum latency of 1 ms with a reliability target of The fifth-generation (5G) radio technology, which has been standardized by the 3rd Generation Partnership Project (3GPP) members, aims to provide more flexibility to support new services and applications. Unlike previous technologies, that were focused on traditional mobile broadband, in 5G new services categories have been defined according to their requirements:URLLC can support use cases, such as closed loop control, hazard sensors, robot automatization, augmented/virtual reality, drone communications and mobile eHealth. The latency requirement varies from 1 to 15 ms, while the reliability targets can range between d 1−10−9 . These ud 1−10−9 ,5 and byd 1−10−9 .Another solution consists of eliminating steps in the connection protocols to reduce the access time—known as Grant-free transmission . Multi-cIn , eMBB/URMoreover, in  increasiIn the second method, the duplication is only performed when the primary base station receives a negative acknowledgement (NACK) associated with the transmission. A Deep Reinforcement Learning method was proposed in  to decidAlthough these techniques have shown their effectiveness in reducing latency and packet loss, they come at an additional cost, which derives from a less efficient usage of the network resources. The usage of these techniques does not add any benefit if the baseline network can guarantee the requirements of the services/applications at a given moment. Prediction may allow the base station to decide if a redundant technique is required.This paper proposes a method to predict the End-to-End (E2E) latency as a Service Key Performance Indicator (S-KPI)  by obserIn particular, the authors in  applied The advantage of this approach is that RAN KPIs can be easily measured by the network terminals and infrastructure elements. In this paper, the precision of the estimator is evaluated. The dynamic PD approach using the predictor was evaluated, comparing the E2E latency performance with single connection and a static PD  techniques and the resource consumption when using a static vs. a dynamic PD approach (based on the proposed latency predictor).The remainder of this paper is organized as follows. In Wired connections have been widely used in industrial networks, such as ProfiNET, EtherCAT and the set of Time Sensitive Networks (TSN) protocols. Nevertheless, wired infrastructures are costly in terms of installation and maintenance. These are also not suitable for novel Industry 4.0 use cases, such as mobile robots.In wireless technologies, there is a division between two types of networks that enables different applications. The division is regarding Local Area Networks (LAN) and Wide Area Networks (WAN). LANs have a coverage range of up to 100 m; covering areas, such as rooms or even full factories. The main wireless LAN technologies are based on the IEEE 802.11 family—commonly named WiFi. Moreover, there exist customized solutions for factories, based on IEEE 802.15.1 and 802.15.4, such as Wireless Interface to Sensors and Actuators (WISA) and WirelessHART. These technologies operate in an unlicensed spectrum and suffer from poor scalability .On the other hand, WANs provide a higher coverage range, from distances of a few kilometers up to whole countries. The most extended and known wireless WANs are the 3GPP-based technologies . As cellular networks operate at a licensed spectrum, a better performance can be obtained. Cellular networks provide ubiquitous connectivity beyond the limits of the factory.This means that production that is geographically distributed can be monitored and managed using a single network. As WANs may be provided as a service by cellular network operators, they do not imply the acquisition, installation and maintenance of infrastructure, allowing multi-tenancy and, therefore, resulting in lower costs  and imprWireless networks generally suffer from a higher latency and packet loss probability than wired networks. For this reason, the recent generations of cellular networks have introduced different optimizations for reducing the latency and also improving reliability.For 5G, Industry 4.0 is one of the main development verticals, where its applications have been explored for a special network design. One of the main novelties in 5G is the introduction of MEC . MEC conRearrangeable modules in production lines : traditiAutomated Guided Vehicle (AGV) : it is cDrones : drones Autonomous robots : robots Connected workers solutions : the devIn the Industry 4.0 paradigm, agility is a key objective in the design of factories. Agility means the flexibility of the system to changing requirements by replacing or improving separated modules. Some of the main technologies that allow such agility in factories are the following:All of these technologies rely on wireless connectivity. Some of them, such as production lines, are already mature technologies where a mobility component is added in the Industry 4.0 paradigm. In that cases, wired connections need to be changed to wireless .Multi-connectivity in 5G New Radio (NR) inherits from the Long Term Evolution (LTE) Dual Connectivity (DC) concept. LTE DC was first specified in Release 12  and alloIn Release 15, multi-RAT DC was specified for DC operation with NR and LTE nodes . Not onlMoreover, NR-NR DC for standalone deployments was standardized in Release 16 , in whicPD is a multi-connectivity solution that improves reliability by increasing redundancy of the transmission. When PD is activated, the PDCP entity in the MN is responsible for PD, whereas the PDCP entity in the receiver is responsible for detecting and removing duplicated packets. The PDCP entity duplicates the packet data unit (PDU) to avoid twice performing functions, such as ciphering, header compression, integrity protection etc. This PDCP PDU has the same sequence number in both.Then, the packet is forwarded by MN to the SN via Xn-U interface for transmission to the UE. The packet will undergo through independent Radio Link Control (RLC), Medium Access Control (MAC) and physical layer processing at each gNB. This implies that the packet can be transmitted at different time intervals and over different frequency resources and that physical transmission aspects, such as beamforming, MCS, ACK/NACK signaling and the Hybrid Automatic Repeat Request (HARQ) mechanism, are independent.On the receiver side, multiple copies of the packet are received, and the UE will forward the first successfully received packet to the higher layers and remove duplicated packets received later, based on the PDCP sequence number. As PD improves reliability, it becomes a suitable mechanism for URLLC, which demands a higher reliability and low latency. Not only to improve reliability but also to reduce latency, as independent transmissions are performed by two different gNBs, as mentioned before. In this case, latency reduction will be dependent on the best link. Reliability and latency for URLLC are dependent on each other, that is, high reliability is consequential only if packets are received within the latency constraint. As a consequence, PD may be used to improve both the reliability and latency.Nevertheless, PD operates at the cost of wasting resources on SN. Thus, it is not efficient to always send packets duplicated, as there could be situations where the conditions are fulfilled by the primary node. For that reason, it is important to provide a dynamic mechanism for MN that controls the activation/deactivation of PD for URLLC devices, in order to reduce the resource costs at SN, which may affect other UEs attached on SN.In this paper, we propose the use of a ML predictor, which, based on channel conditions, predicts the E2E latency for URLLC devices in order to activate/deactivate duplication for a packet transmission.This section describes the proposed solution in order to assess reliability for URLLC communications by using a dynamic packet duplication algorithm based on ML to reduce the resource consumption.The objective of the system described in this paper is to provide a prediction of the E2E latency to assess the need for using PD solution. The proposed system provides an estimator for downlink transmissions that is trained offline in a server running in the network edge to reduce the computing workload and memory requirements at the master node.The end devices may be installed in production line elements, AGVs, drones etc. These terminals may need to receive messages with a certain guarantee of latency and reliability. The reliability and latency can be improved by using multi-connectivity, that is, duplicating the packet via two paths, with the cost of dramatically increasing the amount of radio resources spent per transmission.An alternative way, as presented in this paper, is to first predict whether the network will be able to provide that guarantee without using PD. The MN, right before transmitting, can estimate if a regular transmission, that is, without extra resources, will be sufficient or if PD technique needs to be established. When URLLC devices are not involved in any processor-intensive task, they will measure the latency and report the measurements along with the KPIs to the system where a ML method will update the estimation model.The server will collect the data gathered by the devices and generate the ML parameters (prediction model) for the estimator. Finally, the MN uses the estimator to predict the E2E latency of URLLC devices. Based on the prediction, MN will activate/deactivate PD for the current packet transmission.KPI monitor: collects the KPIs from the radio interface at regular intervals.S-KPI monitor: reads the information from the URLLC device and measures the latency.Training data collector: joins the data generated by the KPI monitor and the S-KPI monitor. The data joined is used as input to train the ML model.Estimator: performs the task of estimating the S-KPI. The primary inputs are the current KPIs  as measured by the MN. The output is the estimation of the E2E latency. This module also has a secondary input that consist in the estimation model extracted from the ML.The functions of the different modules of the system are explained below:In cellular networks, performance monitoring (PM) indicators are collected from different points of the network: gNBs, radio interfaces, core network etc. UE traces can also be collected, representing the radio PM indicators collected by the terminals. The PMs are sent to a centralized location where they are analyzed.Monitoring the network provides many alternatives for detection of problems, analysis of the performance, among others. Nevertheless, these KPIs contains information from lower layers of the network, but not on the performance at application layer in the UEs. Since the development of 5G is centered on the E2E Quality of Service (QoS), this approach has gained importance in recent years. S-KPIs  measure The main inconvenience of S-KPI is that they are difficult to measure. They require special procedures at the application layer of the device. The E2E latency can only be measured a posteriori, thus, becoming useless to decide whether a special transmission, such as PD can be performed at a given time.To address this, a KPI to S-KPI estimator can be used . This teSignal to Interference plus Noise Ratio (SINR): includes all the usable signals in the computation. It is used by some vendors to better determine the CQI to adapt the modulation.Modulation index: indicates the modulation index used from the table of the MCS when performing a packet transmission. A higher index selects a more efficient modulation, with a higher spectral efficiency and code rate. Otherwise, a lower modulation index selects a more robust modulation, with a lower spectral efficiency and code rate.Reception Success: indicates if a packet has been decoded successfully at the receiver or not. This is used to determine if a packet has suffered a HARQ retransmission, since the NACK message is indicated by the receiver to the base station.In this paper, the hypothesis is that, through this mapping, the E2E latency can be obtained a priori by monitoring the following KPIs:The hypothesis assumes that the RAN network is the main contributor to the variable part of the latency. This is due to the jitter being higher at Radio Access Technologies (RATs)  as opposSince the relations between the measured KPIs and the factors that determine the latency are complex, ML is commonly used to find and exploit such relations. In this paper, ML generates the prediction model that encompasses the complex relations among the cited KPIs and the latency.In this paper, random forests  were selRandom forests are an ensemble method commonly used to resolve several types of ML learning problems, such as classification and regression. A random forest consists of a set of decision trees. Each decision tree takes the input to the forest and returns an estimated value. The structure of the tree is created in the training process. On each tree, a decision is performed by comparing the input with a threshold. Based on the output of the comparison, a new comparison is performed with a different input and threshold, which determines the prediction of the tree.To improve the accuracy, each decision tree output is aggregated. For regression, the aggregation method is typically the average of the output of all the trees. A summarized scheme of the random forests prediction is shown in Number of decision trees: this establishes the number of trees that constitutes the forest. This must be chosen in relation to the input dataset to avoid overhead.Bootstrap: this parameter decides how each tree is built independently. If it is not activated, the complete dataset is used for each tree. Otherwise, the initial dataset is divided into subsets of dataset for each tree.Division criterion: this defines the quality of a split according to the condition set in the node. The most used criteria for regression are the squared error and absolute error.Maximum leaves per tree: this sets the maximum depth of the tree in the forest.Maximum samples to split: this determines the maximum number of samples to consider in order to choose the condition that determines the split.Minimum samples to split: this determines the minimum samples needed to consider a new split.Moreover, several parameters must be adjusted before starting the execution, related to the performance of both the decision trees and the complete set that builds the forest. Some of the most important configuration parameters are the following:A training method is applied over a set of labeled samples, (vectors with inputs to the system and the expected output) to obtain the trees. The training process  consistsIn the ML scheme proposed in this paper, the labeled samples are collected in the UEs and transmitted to a server located at the network edge, where different datasets are collected. The collection of different datasets helps the KPI to S-KPI mapping by capturing the effects of contextual variables. The ML process is executed in a server, and the estimator is used by the MN to predict the S-KPI. In particular, the inputs of the algorithm are the SINR, the modulation index and the reception success, as previously indicated in While the measurements in this paper were performed on simulations, it is important to discuss the issues that arise when this implementation is ported to the real world, in a real MN and stock UEs. Specifically, in this subsection, the aspects on data collection requirements, the need for retraining and the hardware and software requirements both on the MN and the UE are discussed.Regarding the data, the ML algorithm requires data on KPIs and S-KPIs. KPIs are easily collected by the Network Monitoring System (NMS), which is part of the maintenance systems of the MN. The S-KPIs are harder to obtain, since they require the collection of data in the UEs. Therefore, to obtain real values, a measurement campaign must be done, including UEs with the appropriate software for capturing and transmitting the S-KPIs.Another important aspect is the need for retraining the model to adapt it to changes in the environment. The validity of the model depends on variations in the behavior of the features that are used in it, which determine the relation between the independent and dependent variables. Second-order effects, such as contextual factors that are not included in the model . These effects are out of the scope of the tests in this paper; however, on real tests, the required frequency of retraining would be an important parameter to study.Implement the data collection and ML stages in the network core. The main advantage is the availability of large datasets that add diversity to the final model. Another advantage of this method is that cloud computing resources can be used better. This is even more important when looking ahead to future 6G networks, where network elements in the core network for ML and AI are envisioned.Implement everything in the network edge. In this case, to gain diversity, a Federated Learning (FL) mechanism can be used to share model parameters between different agents. FL is the collaborative learning, which trains and updates the model through the joint effort of multiple servers that are deployed in a decentralized manner within the network.To implement the proposed functionality in reality, some hardware and software requirements are posed over both the MN elements and the UEs. Specifically, in the MN, the software for data collection (KPIs and S-KPIs), as well as the ML algorithm and the estimator . While the requirement is quite low in terms of hardware and software, it has some complications in the form of privacy and confidentiality issues. Therefore, it is expected that the use of UEs with the appropriate software is limited to a reduced set of devices acting over a predefined period of time.Ownership of the network will also play a central role for this aspect; if the user  is also the owner of the network equipment where the data is collected (edge or core), then it is likely that the rate of penetration of the S-KPI probes is higher in the installed base of UEs and more stable in time. Therefore, this would also be an advantage of the system architecture where the data collection and machine learning is done in the edge, and the model parameters are then shared with FL.The proposed scheme was tested in a simulated 5G network, using ns-3, which is a free and open-source network simulator that is very popular in research . In partIn this section, the simulation scenario along with the KPI recollection phase to train the model are described.2 and a height of 10 meters. The scenario is based on the one proposed in 3GPP 38.901 (Table 7.8-7) [The scenario consists in an indoor factory, with an area of 4800 mAttending to the RAN part, there are two picocells with a height of 8 m, which are interconnected via Xn interface, with a base station distance of 50 m. Both gNBs operate with a frequency of 3.7 GHz and a bandwidth of 20 MHz. One transmission/reception omnidirectional antenna was used in both, picocells and UEs, with 23 dBm as downlink transmission power. The packet processing from MAC to PHY layer is fixed at two slots. This is a delay between the control/data acquisition from the RLC layer by the MAC layer and the moment at which the data is available to go over the air.The transport block decode latency is set to 100 microseconds at UE and gNB. It is a delay between the data acquisition from the air by the PHY layer and the moment at which the data block is available to process at the MAC layer.The processing delay needed to decode Downlink Control Information (DCI) and decode downlink data is set to 0 slots.The processing delay needed from the end of downlink data reception to the earliest possible start of the corresponding ACK/NACK transmission is set to 1 slot.The slot length configured is set to 0.25 ms, which corresponds to numerology 2, as defined in the standard ; whereasOn each base station, there are 15 UEs attached, whose positions are fixed and randomly selected at the beginning of the simulation. For each UE, a Constant Bit Rate (CBR) flow of 1 Mbps is sent by the gNB in downlink direction.On the other hand, there is a URLLC device that is connected to both gNBs simultaneously, one acting as a MN and the other one as a SN. The initial position of the URLLC device is random over the scenario, using a uniform distribution. Then, when the simulation begins, the URLLC device selects a random direction  over 360 degrees and maintains that direction during 10 s with a speed of 2 m/s.When the timer expires, a new direction is selected. The URLLC device represents an AGV that is remotely controlled by the network. To do this, the network sends short commands, in this case, UDP packets with a periodicity of 10 ms and size of 64 bytes.In this paper, a MEC  is consiThe first part of the test consists in obtaining a dataset from URLLC devices—that is, a collection of KPIs in order to train the ML part. To do this, a simulation was performed, where an URLLC device moves from the entire scenario and recollects different KPIs. This movement along with the scenario setup is shown in Upon a packet reception at UE, it knows the SINR received if the packet was not decoded successfully and the modulation index used in the transmission. These KPIs along with the latency measured are the inputs to train the ML model. Based on these samples, it is possible to train the model when there are sufficient samples.v is the speed), which is why HARQ feedback from the previous packet was selected as an input.Once the ML model was trained, the latency predictor is used by the MN. That is, based on the actual conditions, such as SINR, modulation index and HARQ feedback of the previous packet, the estimator predicts the E2E latency. Since URLLC transmission intervals are very short, we assume that the channel conditions are the same between the current packet and the previous , always duplicating the packet and the dynamic packet duplication based on the prediction model .This effect is clearly visible in the latency of the modulation index, where robust modulation decrease the probability of HARQ retransmission. Nevertheless, the higher the modulation index is, the higher the probability of retransmission is, as SINR drops occurs. Finally, the latency when the packet decodification was not successful at the PHY layer at UE  is higher, whereas when the reception was successful when the latency was lower as expected.The false positive rate indicates the proportion of times where the actual latency was lower than the threshold but the estimator predicted it would be higher. In this case, the false positive rate is 0.0041%. The false negative rate indicates the proportion of times where the actual latency was higher than the threshold and the estimator failed at predicting this. Ideally, this value should be close to zero. In the results, the latency estimator has a 0.0615% of error rate. The success rate indicates the percent of times where the value of latency was correctly predicted to be either below or above the threshold. In the results, the latency estimator has a 99.9849% of success rate.This subsection presents the results obtained when the ML algorithm was trained and the PD approach is used. In particular, a latency comparison between not using PD (single connection), always duplicating the packet  and the proposed dynamic PD algorithm is performed. Furthermore, for the PD techniques, a comparison of the resource consumption and latency obtained is performed.The latency below the threshold rate when using PD and not using PD is shown in The latency gain distribution when PD is activated by the prediction model is shown in Taking into account the number of packets duplicated, it is remarkable that, when using random forest, the number of packets duplicated is lower, specifically, the PD reduction rate is 81.0211%. Both techniques present a similar latency below the threshold rate, with 95.7891% for always PD and 95.7541% when using random forest. That clearly indicates that a dynamic PD is more suitable, guaranteeing the same level of low latency but without wasting extra resources.The average (packet) latency reduction rate measures the proportion of times where latency was reduced when duplicating the packet. In the results, the average (packet) latency reduction rate is better when duplicating via random forest than always duplicating, obtaining a latency reduction rate of 86.5506% for the system proposed and 25.0917% for a static duplication. The lower rate when always duplicating is due to sending the packet via two path when conditions are favorable at MN—that is, the latency constraint is fulfilled by MN link.Many solutions have been proposed for URLLC connectivity in order to achieve high reliability and low latency; however, these typically come at a cost in terms of resource usage. This paper proposes a scheme to predict the E2E latency in order to determine if the PD technique is required for downlink transmissions (dynamic PD) and, thus, reduce the resource wasting.The proposed algorithm uses a ML approach, where an estimator is running in the MN. The algorithm was implemented and tested in a 5G simulator, showing high accuracy for determining whether or not to activate PD for a packet transmission.Moreover, the PD technique was evaluated using the predictor . The results obtained show that the proposed dynamic PD based on the E2E latency prediction is more efficient than always duplicating, obtaining a high PD reduction rate (81%) and maintaining the same level of latency below the threshold.In addition, when activating PD based on the predictor, the latency reduction is higher than when using always duplicating technique, that is, there are so many unnecessary packets duplicated, where the latency constraint can be fulfilled by a single connection. A comparison between PD techniques and a single connection was performed, where PD techniques demonstrate that help to achieve URLLC latency constraint.As a future line of work, some of the implementation aspects will be studied. A major milestone toward the real implementation of the system will be the study of FL for the creation and enrichment of a model. Another important aspect will be the study of the required model update frequency, which may also be reduced due to FL.


Substantial literature has highlighted the importance of patient-reported outcome and experience measures  to collect clinically relevant information to better understand and address what matters to patients. The purpose of this systematic review is to synthesize the evidence about how healthcare providers implement individual-level PROMs and PREMs data into daily practice.TM.This mixed methods systematic review protocol describes the design of our synthesis of the peer-reviewed research evidence , systematic reviews, organizational implementation projects, expert opinion, and grey literature. Keyword synonyms for “PROMs,” PREMs,” and “implementation” will be used to search eight databases ) with limiters of English from 2009 onwards. Study selection criteria include implementation at the point-of-care by healthcare providers in any practice setting. Eligible studies will be critically appraised using validated tools . Guided by the review questions, data extraction and synthesis will occur simultaneously to identify biographical information and methodological characteristics as well as classify study findings related to implementation processes and strategies. As part of the narrative synthesis approach, two frameworks will be utilized: (a) Consolidated Framework for Implementation Research (CFIR) to identify influential factors of PROMs and PREMs implementation and (b) Expert Recommendations for Implementing Change (ERIC) to illicit strategies. Data management will be undertaken using NVivo 12Data from PROMs and PREMs are critical to adopt a person-centered approach to healthcare. Findings from this review will guide subsequent phases of a larger project that includes interviews and a consensus-building forum with end users to create guidelines for implementing PROMs and PREMs at the point of care.CRD42020182904.PROSPERO The online version contains supplementary material available at 10.1186/s13643-021-01725-2. Shifting from traditional, biomedical, disease-focused, and scientific models of healthcare toward patients being drivers of their care is essential to improve health and clinical outcomes . Person-The use of PCM data in healthcare has been a robust area of research at individual, organizational, and system levels around the world. Considerable emphasis has been placed on the use of aggregated PCM data to inform program evaluation, quality improvement, benchmarking, value-based healthcare, and to some degree managerial decision-making , 12. OvePopulation, Phenomenon of Interest, Context (PIC) format  that influence PCM implementation by HCPs at the point of care?This protocol builds on the work of the aforementioned reviews and will be informed by knowledge translation and implementation science theory. To this end, our aim is to conduct a comprehensive mixed methods systematic review (MMSR) to synthesize a wide range of evidence about the daily use of individual-level PCM data by HCPs in all practice settings or health services. The The protocol for this systematic review is part of a larger integrated knowledge translation (IKT), three-phase study to create user guidelines that support HPC use of PCM for clinical decision-making and care planning, wherein HCP interviews and a deliberative dialog, consensus-building forum with key stakeholders will also contribute to guideline development. The PCM implementation guidelines for HCPs will support routine collection, interpretation, and integration of these data in daily practice, ultimately contributing to effective, quality healthcare.population, phenomenon of interest, and context (PIC) format has been applied to formulate the review questions, devise a search strategy, and guide study selection criteria. The project will utilize an iterative process undertaken primarily by AW with input from members of the research staff and larger research team based on areas of expertise. A written record will be organized according to the matrix method [The present protocol has been registered within the PROSPERO database  and is being reported in accordance with the reporting guidance provided in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) statement  (see cheThe proposed search will be conducted in accordance with the checklist for Peer Review of Electronic Search Strategies (PRESS) Guideline for systematic reviews  to achieThe evidence to answer our question will be retrieved by searching for the published literature between January 2009 onwards using eight databases with the EBSCO platform that covers the subjects of nursing, allied health, health sciences, psychological literature, physical therapy, occupational health, nutrition, kinesiology, and evidence-based reviews ). We established a 10-year date range after a preliminary search of the literature noted that research about PCMs use at the individual level was first published around 2009. Upon further investigation, we determined that 71% of the relevant records were published from 2016 onwards. This confirmed our decision to use this search date. Limiters being used include: (a) scholarly/peer reviewed citations, (b) English language, and (c) date range. In the keyword search, we will used appropriate truncation  and in some circumstances the boolean “NEAR” or proximity locators to link terms that may not be adjacent  . All ideOur exploration will be supplemented by using other searching strategies to carry out a comprehensive search and counterbalance the limits of keyword database searching . This inwhat” type of evidence) but also evidence related to subjective experiences, attitude, behaviors, and/or the accepted discourse at the time of practice (“knowing how” type of evidence) [The MMSR methodology combines an assortment of evidence to create a breadth and depth of understanding of the review questions posed to inform practice and policy. The inclusion of all available evidence, regardless of type, allows for the degree of agreement or discrepancies between sources of evidence as well as validating or triangulating the findings. Various aspects of a phenomenon of interest can be examined and the available data can contextualize the findings . Furthervidence) . Opinionvidence) . InclusiThe next step to finding relevant evidence for inclusion in this review is to define the selection criteria. To be included in the review, the literature needs to meet the following eligibility criteria   and duplCritical appraisal of included studies will determine the level of evidence and methodological quality as a basis for our confidence to act on the recommendations from our synthesis. Two independent reviewers, blinded to each other’s assessments, will retrieve all included citations, and applicable supplemental files, in full-text format for assessment. AW will provide a robust orientation to primary reviewers  of the process and appraisal checklists to ensure rigor. Authors will be contacted to request missing or additional data for clarification, where required. Any disagreements that arise between the reviewers will be resolved through consensus discussions among select team members, or a blinded third reviewer.We will use the following standardized JBI critical appraisal instruments for assessing quality Expert opinion and literature reviewAll checklists contain a series of criteria (range 8 to 15 questions) scored as being “met” or “not met” or “unclear” and, in some instances, as “not applicable.” Following critical appraisal, all studies will be given a percentage score with higher scores indicating a greater percentage of the quality criteria were met. The research team decided not to set a quality threshold to exclude evidence. Rather, once the data are synthesized, we will determine the confidence to act based on the quality and level of the evidence. A modified version of the JBI levels of evidence , 53 for Given the evidence in this review is explorative, descriptive, and interpretative in nature, the JBI Grades of Recommendation will be the criteria used to define the overall strength of the recommendation  , 54.TM (Version 12.6) [TM file classification function. The NVivoTM codes function will be used to identify the column topic response for items that have more than one response option . These data will offer contextual and methodological data to support the data synthesis results [The data extraction step provides the means by which the most pertinent information about the topic  can be summarized and culled from the primary studies. All source documents will be loaded into the data management software NVivoon 12.6) . Using ton 12.6) , p. 150)on 12.6) . Column  results . Select TM [The next step will be the extraction of the pertinent study findings, specifically from the results and discussion sections of each citation. Using NVivoTM , the proTM , code naTM . The CFITM . A furthTM , 59. CodTM . In summTM .The role of theory in evidence synthesis. Contributing to knowledge translation theory on how PCM implementation works, why, and for whom we will use the CFIR [TM for extraction, the process of synthesis begins as the theory contributes to the interpretation of study findings and determines how widely applicable the findings may be. Study data will be grouped into discrete constructs according to the CFIR domain about the characteristics of the end-users; NVivoTM refers to these as codes. In this way, theory building and theory testing can be incorporated as a key aspect of the proposed systematic review [Element 1: the CFIR  and ERICthe CFIR , 59 thatc review .Developing a preliminary synthesis. A preliminary synthesis is conducted to understand the codes identified and summarize the results of included studies. This will be achieved by defining patterns of findings simultaneously across all the studies based on our primary and secondary questions. An initial description of the findings will evolve based on similarity in meaning to produce an integrated synthesis. One tool used is grouping and clustering [TM to code the data within each citation will subsequently allow us to visualize prominent theoretical constructs. As per the narrative synthesis approach, we next identify the main, recurrent, and/or most important themes across the aggregated data from multiple studies. This will be done in a staged, iterative approach starting with the highest to lowest level of evidence. For example, we will code all systematic review studies to create NVivoTM coding summary reports for each construct. Reading the aggregated data report allows us to identify descriptions of salient themes between and within each theoretical construct from this group of studies. Next, we will code the quantitative studies, read the coding summary report to identify salient themes and add the cumulative description. After that, we move to the next level of evidence to repeat the process. An outcome of this element is a summary of the salient themes across studies.Element 2: ustering . Using NExploring relationships of the data between and within the studies. The purpose of the third element is to identify reasons that might explain any differences in the findings regarding the experiences of HCPs. The emerging patterns identified in the pooled data will be further analyzed to identify factors, study characteristics, and context explaining differences. Comparing and contrasting relationships across studies is important to this stage of the synthesis as a means to explore the influence of heterogeneity. Possible tools for consideration are subgroup analysis and mind mapping [TM relationship and query features will aid in our exploration of associations.Element 3:  mapping . This wiAssessing the robustness of the synthesis. This element allows for the integration of the quality assessments to determine the strength of the evidence and support with the trustworthiness of the synthesis products . Using NVivoTM, the included studies will be assigned both a level of evidence and a quality score that will be cross-linked to the products of the synthesis. From this, a final determination of the strength of the evidence to support conclusions draw from the synthesis process can be made [Element 4:  be made .The synthesis will follow a convergent integrated approach as per the JBI methodology for MMSR. In this manner, data from all types of evidence will be simultaneously extracted and synthesized into meaningful codes. Furthermore, this integrated approach means that the transformed “qualitized” data will be combined to identify patterns across all the studies as well as explore relationships of the data between and within the studies . The intUsing an IKT approach, our 30-member team includes researchers, knowledge users (KU), and patient partners from four practice settings to represent various degrees of PCM implementation and use by diverse HPCs. The described MMSR products (phase 1) will be integrated with findings from HCP interviews (phase 2) conducted to illicit “real world” experiences in the four KU practice settings. These data will comprise a research brief for use in the final phase consisting of a deliberative dialog, consensus-building forum with key stakeholders to triangulate and reach consensus about the topic of study. Taken together, these three phases support the robust development of an evidence-based guideline on how to interpret and “act on” PCM data to inform clinical decision-making and care planning that is patient-centered. The guideline will be applicable to individual-level PMC data use in all practice settings  for adaptation to the particular . This approach is used to ensure that the resultant SR findings, and subsequent guideline, are relevant and applicable to audiences at various levels: healthcare providers (micro); healthcare managers/leaders (meso) and decision-makers (meso or macro) responsible for PCM implementation; researchers (meso); and educators of entry-level health professional programs (meso).Reporting for this study will follow the PRISMA statement . Any ameThe aim of the proposed MMSR using narrative synthesis is to address an existing gap about the needs, capabilities, motivations, and individual factors influencing HCPs adoption behavior . The evidence on implementing PCM focuses predominantly on the CFIR domains about the intervention characteristics e.g., , 61, 64), 6461, 6Building on existing evidence, strengths of the proposed project are the inclusion of all practice settings and all HCP disciplines. Furthermore, the protocol methods were selected to mitigate the limitations of past reviews and broaden our understanding of this phenomenon , 31, 41.While there are several strengths to this systemtatic review, we also anticipate various challenges and limitations. One particular challenge will be determining whether the CFIR domain “characterises of individuals” has sufficient constructs to capture the scope of HCP experiences, needs, and influential factors found in the literature. Based on the anticipated large body of eligible literature, the narrative synthesis will be complex and arduous, particularly element 3. A further challenge will be managing the larger yield in a timely manner to ensure it is current while triangulating it with the interviews and deliberative dialog before developing the final guideline. The study is confronted with selection bias resulting from the following: (a) the restriction of the literature search from January 2009 onwards; (b) the inclusion of evidence published only in the English language; and (c) the exclusion of search terms for the names of specific patient-reported instruments or tools . To mitigate selection bias, we created explicit selection criteria that were based on the review questions to determine eligible literature . Most ofIn summary, the protocol for this MMSR meets an internationally clinically driven need to conduct a comprehensive synthesis of various types of evidence on (a) experiences of HCPs applying these tools, (b) effective methods for HCP interpretation and integration of individual-level PCM data, and (c) identification of relevant factors influencing PCM implementation. This rigorous systematic review is one part of a larger three-phased project that will be followed by HCP interviews and a consensus-building forum to elicit input from the end-users who are asked to change their practice behaviors. The wealth of evidence obtained from this review will inform the implementation of PCM as a complex intervention requiring synergy among the five CFIR domains to support optimal implementation and sustainability in the use of PCM data. The outcome of this review will provide knowledge users with practical, actionable, and evidence-based information. Overall, this project contributes to a larger study with the goal to develop of an evidence-informed guideline that supports the use of effective implementation methods/strategies to enhance the widespread incorporation of PCM data into HCPs’ daily practice. Overall, this project is intended to promote “shared ownership” of individual-level PCM data to better understand and address what matters to patients.Additional file 1. Summary of Published Systematic and Scoping Reviews about Healthcare Providers Using PCM in Routine Practice.Additional file 2. PRISMA-P Checklist.Additional file 3. Master List of Synonyms for Key Concepts in the Search Strategy.Additional file 4. Example of Search Strategy in Bibliographic Database.


The importance of patient-reported outcome measures (PROMs) for rheumatoid arthritis (RA) clinical studies has been recognised for many years. The current study aims to describe the RA PROMs used over the past 20 years, and their performance metrics, to underpin appropriate tool selection.The study included a systematic search for PROMs that have been in use over the period 2000–2019, with detailed documentation of their psychometric properties, and a user-friendly presentation of the extensive evidence base.125 PROMs were identified with psychometric evidence available. The domains of pain, fatigue, emotional functions, mobility, physical functioning and work dominated, with self-efficacy and coping as personal factors. Domains such as stiffness and sleep were poorly served. The most frequently used PROMs included the Health Assessment Questionnaire Disability Index (HAQ), the Short Form 36 (SF-36), the EuroQoL and the Modified HAQ which, between them, appeared in more than 3500 papers. Strong psychometric evidence was found for the HAQ, and the SF-36 Physical Functioning and Vitality (fatigue) domains. Otherwise, all domains except stiffness, sleep, education and health utility, had at least one PROM with moderate level of psychometric evidence.There is a broad range of PROMs for measuring RA outcomes, but the quality of psychometric evidence varies widely. This work identifies gaps in key RA domains according to the biopsychosocial model. A wide range of patient-reported outcome measures (PROMs) are commonly used for rheumatoid arthritis (RA) outcomes.Some PROMs, for example, Health Assessment Questionnaire Disability Index, are well known.This is the first comprehensive review of all RA PROMs in published studies, including trials.This work provides a detailed psychometric analysis of RA PROMs, and highlights deficits in measurement of several domains.Existing PROMs encompass a wide range of domains, and some lesser-known outcomes that may be useful for clinicians.This work enables appropriate selection of PROMs, based on their performance for assessing particular domains.The impact of rheumatoid arthritis (RA) on the health status of the individual has long been understood.7 10–12Various systematic reviews have been published to help select the most appropriate PROM with the best evidence, however, these are frequently associated with a single domain or specific joints.The study set out to identify the PROMs that have been in use over a 20-year period (2000–2019), and to systematically catalogue their psychometric properties. This is a sister study of the earlier paper on PROMs used in osteoarthritis, using the same methodology which is described later.17In this study we define PROM as any patient-(or proxy-) completed questionnaire where a set of items are summated to give a total score, or a series of subscale scores, or both.Electronic searches were performed in databases indexing health-related journals using Medline via PubMed and Scopus. Three different searches were used; the first to identify PROMs in use during the specified period (2000–2019); the second to identify papers for a specific PROM where some form of psychometric evidence was present; the third to count the number of times a PROM was used during the search period. An example of search 1, as PubMed search criteria, is presented in 10.1136/rmdopen-2021-001707.supp1Supplementary dataPotential papers with a candidate PROM identified in search 1 were then screened by two independent reviewers . This included independent screening of the titles and abstracts. For search 2, having added the name of the PROM to the search criteria without date restriction, papers were included if they met the following criteria: (1) the subjects related to the evidence had RA and the evidence was RA specific; (2) one or more of the chosen psychometric criteria specific to the PROM  in question  were reported in the article; (3) the article was in English and (4) it was available in full text. These selected papers were again reviewed by two independent reviewers and any disagreements were discussed and resolved with a third reviewer (AT).The results are reported in a series of hierarchically structured tables and spreadsheets: (i) overall summary table—main paper; (ii) PROM-specific summaries—10.1136/rmdopen-2021-001707.supp2Supplementary data10.1136/rmdopen-2021-001707.supp3Supplementary data10.1136/rmdopen-2021-001707.supp4Supplementary data10.1136/rmdopen-2021-001707.supp5Supplementary dataAn independent full-text review of each paper identified the psychometric evidence. This was collated in accord with the domains of the COnsensus-based Standards for the selection of health Measurement INstruments checklistDiscrimination is evaluated by the magnitude of internal consistency reliability, and whether or not some form of Minimally Important Difference (MID)/Minimal Clinically Important Difference (MCID) and Standardised Response Mean (SRM) is presented. For validity, certain PROMs may have been developed originally, for example, for arthritis in general, and subsequently validated for RA, then we designate them as a ‘hybrid’ disease-specific PROM  dominated the measured domains . The domThe majority of (sub)scales in use were disease-specific, either originally designed as such, or subsequently validated for RA. The lack of ‘strong’ evidence for the PROMs, would render the classification ‘moderate’ (yellow), although this may have been affected by proprietary status. There were only two domains, fatigue and physical functioning , that had PROMs with strong evidence. Almost all domains had a range of PROMs fulfilling ‘moderate’ criteria on the OMERACT filter. Likewise, emotional functions  were also represented with a high number of PROMs (n=23), ten of which had moderate evidence. The domain ‘work’ was served by 14 PROMs, three of which with moderate level of evidence. In contrast, the domains of stiffness, sleep, education and health utility were poorly served with respect to psychometric evidence.Domain-specific assessments are presented in tables 1–18 in In papers published from 2000 to 2019, 125 PROMs were found with some psychometric evidence, and these were categorised based on the variety of commonly used domains. Almost all domains included at least one PROM rated as at least ‘moderate’ (yellow) on the OMERACT filter summary.The most dominant domains were those of pain (ICF-b280), fatigue , emotional functions (b152), physical functioning (d4–d6) either as a composite or as its domains, for example, mobility (d4), along with work (d8451). This is not surprising as these are the common aspects of RA which patients report, and include potentially modifying factors relevant for intervention.23 24There are particular issues related to the inclusion of PROMs in Randomised Controlled Trials (RCTs), routine clinical practice and observational epidemiological studies. Regarding the RCTs, it has been argued that the selection of PROs for trials depends on the study objective as well as the viewpoint of the stakeholder.For the future, combining PROs with technology, such as computerised adaptive testing, electronic patient-reported systems, web-based platforms and patient dashboards, could further help PROM integration into routine rheumatology clinical practice.32 33The results from this review provide a domain-specific catalogue which can help in consideration of the choice of PROMs to be used. PROMs that have either yellow or green indicators will be worth considering, conditional on the year of publication. Yellow may indicate a propriety status if the feasibility indicator is also yellow, and it will be essential to check the status of any PROM to ascertain its current propriety status. If necessary, the relevant published papers listed in The lack of adequate PROMs to evaluate and define symptoms such as stiffness and sleep problems, is of concern, as both of these are common in RA.15This study was one of several EULAR funded initiatives to catalogue the available PROMs across several rheumatic diseases, leading to the EULAR Outcome Measures Library.Some confusion also arose concerning the SF-36. It was originally developed by the RAND corporation and known as the RAND-Short Form 36 (RAND-36), being a short form of much longer survey. It subsequently became known as the SF-36, with items identical to the RAND-36, but where the scoring of the general health and pain scales differed.In conclusion, a significant array of PROMs is available to populate RCTs; routine clinical data collection and observational studies in RA. The evidence presented here provides a domain-specific catalogue of those PROMs available until 2019, but readers are encouraged to check post-2019 for any new scale or evidence emerging in their domain of interest for those PROMs already catalogued. Initiatives such as the EULAR Outcome Measures Library will further facilitate knowledge about available PROMs, and initiatives such as that from OMERACT should facilitate the development of new PROMs where shortfalls have been shown to exist.


This survey study assesses the rate at which US academic medical centers have adopted evidenced-based guidelines for the management of COVID-19 into practice. COVID-19 management has evolved rapidly, creating challenges for implementation. This study was conducted to assess the fidelity with which academic medical centers (AMCs) adopted evidence into practice.AAPOR) reporting guideline.This survey study was deemed exempt from review and informed consent by the University of California, San Francisco, because it does not involve human participants. Response rates were computed according to the American Association for Public Opinion Research   responded. Hospitalist leaders involved in the direct care of patients with COVID-19 provided responses. A total of 49 sites (94%) self-identified as AMCs; the remaining 3 sites (6%) identified as AMC-affiliated teaching hospitals. Fifty-one sites (98%) issued internal COVID-19 management guidance. Guidance at 48 sites (94%) was generated by multidisciplinary committees, including infectious disease (47 sites [98%]), infection control (43 sites [90%]), hospital medicine (42 sites [88%]), and critical care (40 sites [83%]). Of 51 sites with internal COVID-19 management guidance, recommendations were disseminated most commonly through email (43 sites [84%]), institutional websites (42 sites [82%]), and integration into the electronic health record as COVID-19-specific order sets (37 sites [73%]) and note templates (33 sites [65%]). The percentage of institutions recommending each studied intervention is shown in In this survey study, 3 themes emerged from our analysis. First, translation from evidence to practice guidelines was remarkably complete for interventions supported by aligned national guidelines and high-quality studies. A striking example is the near universal adoption of dexamethasone among patients requiring at least 4 L of supplementary oxygen only 6 to 8 months after the RECOVERY trial demonstrated a survival benefit. The lone exception to this trend was baricitinib; however, new evidence and guidelines were released within 1 week of survey distribution. Practice convergence was also observed when evidence and guidelines aligned against interventions, as seen in the infrequent recommendation of dexamethasone for patients with oxygen saturation greater than 94%, as measured by pulse oximetry. However, clear opportunity for improvement still exists.Second, institutions favored treatment over not treatment, particularly when guidelines diverged from each other or from the underlying evidence, as exemplified by 69% to 81% of sites recommending remdesivir or dexamethasone, respectively, when evidence or guidelines conflicted. We suspect this finding reflects systemic biases to do something rather than nothing when faced with uncertainty, likely exacerbated by inconsistent definitions of disease severity across studies and guidelines.Finally, AMCs demonstrated a willingness to innovate across a range of interventions. Novel interventions, such as awake proning, phase-of-illness–restricted remdesivir, and D-dimer–based therapeutic anticoagulation, varied widely with respect to patient selection and procedural specifics, but collectively, and with 17% to 67% of sites recommending 1 or more of such novel interventions, they demonstrate that AMCs were sophisticated consumers of information, willing to bridge knowledge gaps with expert opinion. Additional research is needed to understand how AMCs monitor innovation outcomes and deimplement practices when negative evidence emerges.Limitations of this study include a 37% nonresponse rate, reliance on self-reporting, lack of longitudinal follow-up, and lack of data on actual clinical practice and outcomes. Nonetheless, our findings demonstrate that AMCs were capable of responding nimbly to emerging data and shifting guidelines, although both overtreatment and experimentation were observed where significant uncertainty persisted. While factors unique to the early pandemic likely shaped this performance, we hope some strategies, such as use of focused multidisciplinary teams and novel information sharing tools, can be harnessed to accelerate the translation of evidence to bedside for COVID-19 and beyond.