Incorporation of Drone Technology Into the Chain of Survival for OHCA: Estimation of Time Needed for Bystander Treatment of OHCA and CPR Performance.

BACKGROUND: Drone-delivered automated external defibrillators (AEDs) hold promises in the treatment of out-of-hospital cardiac arrest. Our objective was to estimate the time needed to perform resuscitation with a drone-delivered AED and to measure cardiopulmonary resuscitation (CPR) quality. METHODS: Mock out-of-hospital cardiac arrest simulations that included a 9-1-1 call, CPR, and drone-delivered AED were conducted. Each simulation was timed and video-recorded. CPR performance metrics were recorded by a Laerdal Resusci Anne Quality Feedback System. Multivariable regression modeling examined factors associated with time from 9-1-1 call to AED shock and CPR quality metrics (compression rate, depth, recoil, and chest compression fraction). Comparisons were made among those with recent CPR training (≤2 years) versus no recent (>2 years) or prior CPR training. RESULTS: We recruited 51 research participants between September 2019 and March 2020. The median age was 34 (Q1-Q3, 23-54) years, 56.9% were female, and 41.2% had recent CPR training. The median time from 9-1-1 call to initiation of CPR was 1:19 (Q1-Q3, 1:06-1:26) minutes. A median time of 1:59 (Q1-Q3, 01:50-02:20) minutes was needed to retrieve a drone-delivered AED and deliver a shock. The median CPR compression rate was 115 (Q1-Q3, 109-124) beats per minute, the correct compression depth percentage was 92% (Q1-Q3, 25-98), and the chest compression fraction was 46.7% (Q1-Q3, 39.9%-50.6%). Recent CPR training was not associated with CPR quality or time from 9-1-1 call to AED shock. Younger age (per 10-year increase; ß, 9.97 [95% CI, 4.63-15.31] s; P<0.001) and prior experience with AED (ß, -30.0 [95% CI, -50.1 to -10.0] s; P=0.004) were associated with more rapid time from 9-1-1 call to AED shock. Prior AED use (ß, 6.71 [95% CI, 1.62-11.79]; P=0.011) was associated with improved chest compression fraction percentage. CONCLUSION: Research participants were able to rapidly retrieve an AED from a drone while largely maintaining CPR quality according to American Heart Association guidelines. Chest compression fraction was lower than expected.

Causal diagramming for assessing human system risk in spaceflight.

For over a decade, the National Aeronautics and Space Administration (NASA) has tracked and configuration-managed approximately 30 risks that affect astronaut health and performance before, during and after spaceflight. The Human System Risk Board (HSRB) at NASA Johnson Space Center is responsible for setting the official risk posture for each of the human system risks and determining-based on evaluation of the available evidence-when that risk posture changes. The ultimate purpose of tracking and researching these risks is to find ways to reduce spaceflight-induced risk to astronauts. The adverse effects of spaceflight begin at launch and continue throughout the duration of the mission, and in some cases, across the lifetime of the astronaut. Historically, research has been conducted in individual risk "silos" to characterize risk, however, astronauts are exposed to all risks simultaneously. In January of 2020, the HSRB at NASA began assessing the potential value of causal diagramming as a tool to facilitate understanding of the complex causes and effects that contribute to spaceflight-induced human system risk. Causal diagrams in the form of directed acyclic graphs (DAGs) are used to provide HSRB stakeholders with a shared mental model of the causal flow of risk. While primarily improving communication among those stakeholders, DAGs also allow a composite risk network to be created that can be tracked and configuration managed. This paper outlines the HSRB's pilot process for this effort, the lessons learned, and future goals for data-driven risk management approaches.

Adequacy of in-mission training to treat tibial shaft fractures in mars analogue testing.

Long bone fractures are a concern in long-duration exploration missions (LDEM) where crew autonomy will exceed the current Low Earth Orbit paradigm. Current crew selection assumptions require extensive complete training and competency testing prior to flight for off-nominal situations. Analogue astronauts (n = 6) can be quickly trained to address a single fracture pattern and then competently perform the repair procedure. An easy-to-use external fixation (EZExFix) was employed to repair artificial tibial shaft fractures during an inhabited mission at the Mars Desert Research Station (Utah, USA). Bone repair safety zones were respected (23/24), participants achieved 79.2% repair success, and median completion time was 50.04 min. Just-in-time training in-mission was sufficient to become autonomous without pre-mission medical/surgical/mechanical education, regardless of learning conditions (p > 0.05). Similar techniques could be used in LDEM to increase astronauts' autonomy in traumatic injury treatment and lower skill competency requirements used in crew selection.

Optimizing a Drone Network to Respond to Opioid Overdoses.

Introduction:Effective out-of-hospital administration of naloxone in opioid overdoses is dependent on timely arrival of naloxone. Delays in emergency medical services (EMS) response time could potentially be overcome with drones to deliver naloxone efficiently to the scene for bystander use. Our objective was to evaluate a mathematical optimization simulation for geographical placement of drone bases in reducing response time to opioid overdose. Methods: Using retrospective data from a single EMS system from January 2016-February 2019, we created a geospatial drone-network model based on current technological specifications and potential base locations. Genetic optimization was then used to maximize county coverage by drones and the number of overdoses covered per drone base. From this model, we identified base locations that minimize response time and the number of drone bases required. Results: In a drone network model with 2,327 opioid overdoses, as the number of modeled drone bases increased the calculated response time decreased. In a geospatially optimized drone network with four drone bases, response time compared to ambulance arrival was reduced by 4 minutes 38 seconds and covered 64.2% of the county. Conclusion: In our analysis we found that in a mathematical model for geospatial optimization, implementing four drone bases could reduce response time of 9-1-1 calls for opioid overdoses. Therefore, drones could theoretically improve time to naloxone delivery.

FedScore: A privacy-preserving framework for federated scoring system development.

OBJECTIVE: We propose FedScore, a privacy-preserving federated learning framework for scoring system generation across multiple sites to facilitate cross-institutional collaborations. MATERIALS AND METHODS: The FedScore framework includes five modules: federated variable ranking, federated variable transformation, federated score derivation, federated model selection and federated model evaluation. To illustrate usage and assess FedScore's performance, we built a hypothetical global scoring system for mortality prediction within 30 days after a visit to an emergency department using 10 simulated sites divided from a tertiary hospital in Singapore. We employed a pre-existing score generator to construct 10 local scoring systems independently at each site and we also developed a scoring system using centralized data for comparison. RESULTS: We compared the acquired FedScore model's performance with that of other scoring models using the receiver operating characteristic (ROC) analysis. The FedScore model achieved an average area under the curve (AUC) value of 0.763 across all sites, with a standard deviation (SD) of 0.020. We also calculated the average AUC values and SDs for each local model, and the FedScore model showed promising accuracy and stability with a high average AUC value which was closest to the one of the pooled model and SD which was lower than that of most local models. CONCLUSION: This study demonstrates that FedScore is a privacy-preserving scoring system generator with potentially good generalizability.

Updates to the NASA human system risk management process for space exploration.

This paper describes updates to NASA's approach for assessing and mitigating spaceflight-induced risks to human health and performance. This approach continues to evolve to meet dynamically changing risk environments: lunar missions are currently being designed and the ultimate destination will be Mars. Understanding the risks that astronauts will face during a Mars mission will depend on building an evidence base that informs not only how the humans respond to the challenges of the spaceflight environment, but also how systems and vehicles can be designed to support human capabilities and limitations. This publication documents updates to the risk management process used by the Human System Risk Board at NASA and includes changes to the likelihood and consequence matrix used by the board, the design reference mission categories and parameters, and the standardized evaluation of the levels of evidence that the board accepts when setting risk posture. Causal diagramming, using directed acyclic graphs, provides all stakeholders with the current understanding of how each risk proceeds from a spaceflight hazard to a mission-level outcome. This standardized approach enables improved communication among stakeholders and delineates how and where more knowledge can improve perspective of human system risks and which countermeasures can best mitigate these risks.

Development, Deployment, and Implementation of a Machine Learning Surgical Case Length Prediction Model and Prospective Evaluation.

OBJECTIVE: To implement a machine learning model using only the restricted data available at case creation time to predict surgical case length for multiple services at different locations. BACKGROUND: The operating room is one of the most expensive resources in a health system, estimated to cost $22 to $133 per minute and generate about 40% of hospital revenue. Accurate prediction of surgical case length is necessary for efficient scheduling and cost-effective utilization of the operating room and other resources. METHODS: We introduced a similarity cascade to capture the complexity of cases and surgeon influence on the case length and incorporated that into a gradient-boosting machine learning model. The model loss function was customized to improve the balance between over- and under-prediction of the case length. A production pipeline was created to seamlessly deploy and implement the model across our institution. RESULTS: The prospective analysis showed that the model output was gradually adopted by the schedulers and outperformed the scheduler-predicted case length from August to December 2022. In 33,815 surgical cases across outpatient and inpatient platforms, the operational implementation predicted 11.2% fewer underpredicted cases and 5.9% more cases within 20% of the actual case length compared with the schedulers and only overpredicted 5.3% more. The model assisted schedulers to predict 3.4% more cases within 20% of the actual case length and 4.3% fewer underpredicted cases. CONCLUSIONS: We created a unique framework that is being leveraged every day to predict surgical case length more accurately at case posting time and could be potentially utilized to deploy future machine learning models.

Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study.

The scheduling of operating room (OR) slots requires the accurate prediction of surgery duration. We evaluated the performance of existing Moving Average (MA) based estimates with novel machine learning (ML)-based models of surgery durations across two sites in the US and Singapore. We used the Duke Protected Analytics Computing Environment (PACE) to facilitate data-sharing and big data analytics across the US and Singapore. Data from all colorectal surgery patients between 1 January 2012 and 31 December 2017 in Singapore and, 1 January 2015 to 31 December 2019 in the US were used, and 7585 cases and 3597 single and multiple procedure cases from Singapore and US were included. The ML models were based on categorical gradient boosting (CatBoost) models trained on common data fields shared by both institutions. The procedure codes were based on the Table of Surgical Procedure (TOSP) (Singapore) and the Current Procedural Terminology (CPT) codes (US). The two types of codes were mapped by surgical experts. The CPT codes were then transformed into the relative value unit (RVU). The ML models outperformed the baseline MA models. The MA, scheduled durations and procedure codes were found to have higher loadings as compared to surgeon factors. We further demonstrated the use of the Duke PACE in facilitating data-sharing and big data analytics.

Estimating medical risk in human spaceflight.

NASA and commercial spaceflight companies will soon be retuning humans to the Moon and then eventually sending them on to Mars. These distant planetary destinations will pose new risks-in particular for the health of the astronaut crews. The bulk of the evidence characterizing human health and performance in spaceflight has come from missions in Low Earth Orbit. As missions last longer and travel farther from Earth, medical risk is expected to contribute an increasing proportion of total mission risk. To date, there have been no reliable estimates of how much. The Integrated Medical Model (IMM) is a Probabilistic Risk Assessment (PRA) Monte-Carlo simulation tool developed by NASA for medical risk assessment. This paper uses the IMM to provide an evidence-based, quantified medical risk estimate comparison across different spaceflight mission durations. We discuss model limitations and unimplemented capabilities providing insight into the complexity of medical risk estimation for human spaceflight. The results enable prioritization of medical needs in the context of other mission risks. These findings provide a reasonable bounding estimate for medical risk in missions to the Moon and Mars and hold value for risk managers and mission planners in performing cost-benefit trades for mission capability and research investments.

Creation of an Automated Fluorescence Guided Tumor Ablation System.

OBJECTIVE: Create a device that improves the identification and extent of resection at the interface between healthy and tumor tissue; ultimately, using this device would improve surgical outcomes for patients and increase survival. METHODS: We have created a contactless tumor removal system that utilizes endogenous fluorescence feedback to inform a laser ablation system to execute autonomous removal of phantom tumor tissue. RESULTS: This completely non-contact surgical system is capable of resecting the tumor boundary of a tissue phantom with an average root mean square error (RMSE) of approximately 1.55 mm and an average max error of approximately 2.15 mm. There is no difference in the performance of the system when changing the size of the internal tumor from 7.5-12.5 mm in diameter. DISCUSSION: Future research steps include creating a more intelligent spectral search strategy to increase the density of points around the resection boundary, and to develop a more sophisticated classifier to predict pathologic diagnosis and tissue subtypes located regionally around the tumor boundaries. We envision this device being used to resect the boundaries of tumors identified by exogenously delivered tumor-labeling fluorophores, such as fluorescein or 5-ALA, in addition to approaches relying on autofluorescence of endogenous fluorophores.

Effect of language interpretation modality on throughput and mortality for critical care patients: A retrospective observational study.

OBJECTIVE: Limited data exist describing possible delays in patient transfer from the emergency department (ED) as a result of language barriers and the effects of interpretation services. We described the differences in ED length of stay (LOS) before intensive care unit (ICU) arrival and mortality based on availability of telephone or in-person interpretation services. METHODS: Using an ICU database from an urban academic tertiary care hospital, ED patients entering the ICU were divided into groups based on primary language and available interpretation services (in-person vs telephone). Non-parametric tests were used to compare ED LOS and mortality between groups. RESULTS: Among 22,422 included encounters, English was recorded as the primary language for 51% of patients (11,427), and 9% of patients (2042) had a primary language other than English. Language was not documented for 40% of patients (8953). Among encounters with patients with non-English primary languages, in-person interpretation was available for 63% (1278) and telephone interpretation was available for 37% (764). In the English-language group, median ED LOS was 292 minutes (interquartile range [IQR], 205-412) compared with 309 minutes (IQR, 214-453) for patients speaking languages with in-person interpretation available and 327 minutes (IQR, 225-463) for patients speaking languages with telephone interpretation available. Mortality was higher among patients with telephone (15%) or in-person (11%) interpretation available compared with patients who primarily spoke English (9%). CONCLUSIONS: Patients with primary languages other than English who were critically ill spent a median of 17 to 35 more minutes in the ED before ICU arrival and experienced higher mortality rates compared with patients who spoke English as a primary language.

CPT to RVU conversion improves model performance in the prediction of surgical case length.

Methods used to predict surgical case time often rely upon the current procedural terminology (CPT) code as a nominal variable to train machine-learned models, however this limits the ability of the model to incorporate new procedures and adds complexity as the number of unique procedures increases. The relative value unit (RVU, a consensus-derived billing indicator) can serve as a proxy for procedure workload and could replace the CPT code as a primary feature for models that predict surgical case length. Using 11,696 surgical cases from Duke University Health System electronic health records data, we compared boosted decision tree models that predict individual case length, changing the method by which the model coded procedure type; CPT, RVU, and CPT-RVU combined. Performance of each model was assessed by inference time, MAE, and RMSE compared to the actual case length on a test set. Models were compared to each other and to the manual scheduler method that currently exists. RMSE for the RVU model (60.8 min) was similar to the CPT model (61.9 min), both of which were lower than scheduler (90.2 min). 65.2% of our RVU model's predictions (compared to 43.2% from the current human scheduler method) fell within 20% of actual case time. Using RVUs reduced model prediction time by ninefold and reduced the number of training features from 485 to 44. Replacing pre-operative CPT codes with RVUs maintains model performance while decreasing overall model complexity in the prediction of surgical case length.

Touch-Point Detection Using Thermal Video With Applications to Prevent Indirect Virus Spread.

Viral and bacterial pathogens can be transmitted through direct contact with contaminated surfaces. Efficient decontamination of contaminated surfaces could lead to decreased disease transmission, if optimized methods for detecting contaminated surfaces can be developed. Here we describe such a method whereby thermal tracking technology is utilized to detect thermal signatures incurred by surfaces through direct contact. This is applicable in public places to assist with targeted sanitation and cleaning efforts to potentially reduce chance of disease transmission. In this study, we refer to the touched region of the surface as a "touch-point" and examine how the touch-point regions can be automatically localized with a computer vision pipeline of a thermal image sequence. The pipeline mainly comprises two components: a single-frame and a multi-frame analysis. The single-frame analysis consists of a Background subtraction method for image pre-processing and a U-net deep learning model for segmenting the touch-point regions. The multi-frame analysis performs a summation of the outputs from the single-frame analysis and creates a cumulative map of touch-points. Results show that the touch-point detection pipeline can achieve 75.0% precision and 81.5% F1-score for the testing experiments of predicting the touch-point regions. This preliminary study shows potential applications of preventing indirect pathogen spread in public spaces and improving the efficiency of cleaning sanitation.

Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.

STUDY OBJECTIVE: This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models. METHODS: Data were curated from 468,167 adult patient encounters in 3 EDs (1 academic and 2 community-based EDs) of a large academic health system from August 1, 2015, to October 31, 2018. The models were validated using encounter data from January 1, 2019, to December 31, 2019. An operational user dashboard was developed, and the models were run on real-time encounter data. RESULTS: For the intermediate admission model, the area under the receiver operating characteristic curve was 0.873 and the area under the precision-recall curve was 0.636. For the ICU admission model, the area under the receiver operating characteristic curve was 0.951 and the area under the precision-recall curve was 0.461. The models had similar performance in both the academic- and community-based settings as well as across the 2019 and real-time encounter data. CONCLUSION: Machine learning models were developed to accurately make predictions regarding the probability of inpatient or ICU admission throughout the entire duration of a patient's encounter in ED and not just at the time of triage. These models remained accurate for a patient cohort beyond the time period of the initial training data and were integrated to run on live electronic health record data, with similar performance.

Ketamine in the Prehospital Environment: A National Survey of Paramedics in the United States.

BACKGROUND: Use of ketamine in the prehospital setting may be advantageous due to its potent analgesic and sedative properties and favorable risk profile. Use in the military setting has demonstrated both efficacy and safety for pain relief. The purpose of this study was to assess ketamine training, use, and perceptions in the civilian setting among nationally certified paramedics (NRPs) in the United States. METHODS: A cross-sectional survey of NRPs was performed. The electronic questionnaire assessed paramedic training, authorization, use, and perceptions of ketamine. Included in the analysis were completed surveys of paramedics who held one or more state paramedic credentials, indicated "patient care provider" as their primary role, and worked in non-military settings. Descriptive statistics were calculated. RESULTS: A total of 14,739 responses were obtained (response rate=23%), of which 10,737 (73%) met inclusion criteria and constituted the study cohort. Over one-half (53%) of paramedics reported learning about ketamine during their initial paramedic training. Meanwhile, 42% reported seeking ketamine-related education on their own. Of all respondents, only 33% (3,421/10,737) were authorized by protocol to use ketamine. Most commonly authorized uses included pain management (55%), rapid sequence intubation (RSI; 72%), and chemical restraint/sedation (72%). One-third of authorized providers (1,107/3,350) had never administered ketamine, with another 32% (1,070/3,350) having administered ketamine less than five times in their career. Ketamine was perceived to be safe and effective as the vast majority reported that they were comfortable with the use of ketamine (94%) and would, in similar situations (95%), use it again. CONCLUSION: This was the first large, national survey to assess ketamine training, use, and perceptions among paramedics in the civilian prehospital setting. While training related to ketamine use was commonly reported among paramedics, few were authorized to administer the drug by their agency's protocols. Of those authorized to use ketamine, most paramedics had limited experience administering the drug. Future research is needed to determine why the prevalence of ketamine use is low and to assess the safety and efficacy of ketamine use in the prehospital setting. Buckland DM , Crowe RP , Cash RE , Gondek S , Maluso P , Sirajuddin S , Smith ER , Dangerfield P , Shapiro G , Wanka C , Panchal AR , Sarani B . Ketamine in the prehospital environment: a national survey of paramedics in the United States. Prehosp Disaster Med. 2018;33(1):23-28.

Emergency Medical Considerations in a Space-Suited Patient.

INTRODUCTION: The Stratex Project is a high altitude balloon flight that culminated in a freefall from 41,422 m (135,890 ft), breaking the record for the highest freefall to date. Crew recovery operations required an innovative approach due to the unique nature of the event as well as the equipment involved. The parachutist donned a custom space suit similar to a NASA Extravehicular Mobility Unit (EMU), with life support system mounted to the front and a parachute on the back. This space suit had a metal structure around the torso, which, in conjunction with the parachute and life support assembly, created a significant barrier to extraction from the suit in the event of a medical emergency. For this reason the Medical Support Team coordinated with the pressure suit assembly engineer team for integration, training in suit removal, definition of a priori contingency leadership on site, creation of color-coded extraction scenarios, and extraction drills with a suit mock-up that provided insight into limitations to immediate access. This paper discusses novel extraction processes and contrasts the required medical preparation for this type of equipment with the needs of the prior record-holding jump that used a different space suit with easier immediate access. Garbino A, Nusbaum DM, Buckland DM, Menon AS, Clark JB, Antonsen EL. Emergency medical considerations in a space-suited patient. Aerosp Med Hum Perform. 2016; 87(11):958-962.

Crew Recovery and Contingency Planning for a Manned Stratospheric Balloon Flight - the StratEx Program.

The StratEx program used a self-contained space suit and balloon system to loft pilot Alan Eustace to a record-breaking altitude and skydive from 135,897 feet (41,422 m). After releasing from the balloon and a stabilized freefall, the pilot safely landed using a parachute system based on a modified tandem parachute rig. A custom spacesuit provided life support using a similar system to NASA's (National Aeronautics and Space Administration; Washington, DC USA) Extravehicular Mobility Unit. It also provided tracking, communications, and connection to the parachute system. A recovery support team, including at least two medical personnel and two spacesuit technicians, was charged with reaching the pilot within five minutes of touchdown to extract him from the suit and provide treatment for any injuries. The team had to track the flight at all times, be prepared to respond in case of premature release, and to operate in any terrain. Crew recovery operations were planned and tailored to anticipate outcomes during this novel event in a systematic fashion, through scenario and risk analysis, in order to minimize the probability and impact of injury. This analysis, detailed here, helped the team configure recovery assets, refine navigation and tracking systems, develop procedures, and conduct training. An extensive period of testing and practice culminated in three manned flights leading to a successful mission and setting the record for exit altitude, distance of fall with stabilizing device, and vertical speed with a stabilizing device. During this mission, recovery teams reached the landing spot within one minute, extracted the pilot, and confirmed that he was not injured. This strategy is presented as an approach to prehospital planning and care for improved safety during crew recovery in novel, extreme events. Menon AS , Jourdan D , Nusbaum DM , Garbino A , Buckland DM , Norton S , Clark JB , Antonsen EL . Crew recovery and contingency planning for a manned stratospheric balloon flight - the StratEx program. Prehosp Disaster Med. 2016;31(5):524-531.

Long-term biomechanical outcomes after Achilles tendon ruptures.

PURPOSE: The ideal treatment for Achilles tendon ruptures is still unknown. Biomechanical were correlated to radiological and clinical parameters to study outcomes. METHODS: In this retrospective, assessor-blinded multi-centre cohort study, 52 patients with unilateral Achilles tendon rupture were assessed, each at least 3 years after injury. Patients underwent open surgery, percutaneous surgery or non-surgical treatment of Achilles tendon rupture. Both legs underwent plantar pressure distribution and isokinetic measures. Demographic parameters, maximum calf circumference (MCC) and clinical scores (American Orthopaedic Foot and Ankle Society, Achilles tendon rupture score, Hannover) were also evaluated. Complications were not assessed. RESULTS: Peak plantar flexion torque (PPFT) was significantly weaker on the treated side compared to the untreated leg [80.4 ± 29.7 Nm (mean ± SD) vs. 92.1 ± 27.4 Nm, p < 0.0001]. PPFT and push-off force (POFF) were not different between treatment groups nor was there a leg difference in POFF alone. There was only a weak correlation of clinical scores and PPFT or POFF, respectively. MCC correlated significantly with both PPFT (R (2) = 0.21, p = 0.01) and POFF (R (2) = 0.29, p < 0.0001). POFF appeared to be a predictor of PPFT (R (2) = 0.31, p < 0.0001). Open surgery outperformed non-surgical treatment in terms of centre-of-pressure line (p = 0.007), torque per muscle volume (p = 0.04) and relative POFF per body weight (p = 0.02) and relative in side comparison (p = 0.03). CONCLUSIONS: Clinical scores do not predict biomechanical outcomes. Clinically measured MCC is a good predictor of PPFT and POFF and can easily be used in clinical practice. Relative POFF in side comparison as well as per body weight favours surgical treatment.

Meta-analysis on biomechanical properties of meniscus repairs: are devices better than sutures?

PURPOSE: Meniscal repair devices have been extensively tested during the past decades as reported in the literature. Reviewing the different meniscal repair devices and sutures with their respective biomechanical properties. METHODS: For this meta-analysis, we conducted a systematic online search using PubMed, EMBASE, CCTR, and CINAHL using the search terms Meniscus OR Meniscal AND Biomechanics AND Repair). Load-to-failure (LtF), stiffness, and cyclic outcome measures were extracted independently and in duplicate. The systematic search revealed 841 manuscripts in total. After exclusion of duplicates and irrelevant publications, 41 studies remained for final analysis. The studies were published in English and German from 1995 to 2013. Due to differing cyclic force protocols, cyclic outcomes had to be excluded. RESULTS: Overall, sutures had a higher LtF [suture: 87.7 ± 0.3 N (weighted mean ± standard error), device: 56.3 ± 0.1 N] and stiffness (suture: 8.9 ± 0.04 N/mm, device: 8.6 ± 0.04 N/mm) than devices, both p < 0.05. In LfT testing, PDS 0 Vertical (145.0 ± 8.1 N), OrthoCord 2-0 (143.6 ± 11.3 N), and Ethibond No 0 Vertical (133.4 ± 7.7 N) were the strongest sutures and Meniscal Viper (140.9 ± 5.1 N), MaxFire Vertical (136.2 ± 11.3 N), and FasT-Fix Vertical (115.2 ± 1.6 N) were the strongest devices. Second-generation devices were significantly stronger and stiffer than first-generation devices (p < 0.001). CLINICAL RELEVANCE: Suture repair remains the gold standard with a vertically oriented suture configuration showing superior LtF values compared to a horizontal configuration. Nevertheless, some meniscal repair devices have similar biomechanical properties to suture repairs. Both suture repairs and devices have a place in meniscal restoration. LEVEL OF EVIDENCE: None, meta-analysis of controlled laboratory studies.

All-inside meniscal repair devices compared with their matched inside-out vertical mattress suture repair: introducing 10,000 and 100,000 loading cycles.

BACKGROUND: All-inside arthroscopic meniscal repairs are favored by most clinicians because of their lower complication rate and decreased morbidity compared with inside-out techniques. Until now, only 1000 cycles have been used for biomechanical testing. HYPOTHESIS: All-inside meniscal repairs will show inferior biomechanical response to cyclic loading (up to 100,000 cycles) and load-to-failure testing compared with inside-out suture controls. STUDY DESIGN: Controlled laboratory study. METHODS: Bucket-handle tears in 72 porcine menisci were repaired using the Omnispan and Fast-Fix 360 (all-inside devices) and Orthocord 2-0 and Ultrabraid 2-0 sutures (matched controls). Initial displacement, displacement after cyclic loading (100, 500, 1000, 2000, 5000, 10,000, and 100,000 cycles) between 5 and 20 N, ultimate load to failure, and mode of failure were recorded, as well as stiffness. RESULTS: Initial displacement and displacement after cyclic loading were not different between the groups. The Omnispan repair demonstrated the highest load-to-failure force (mean ± SD, 151.3 ± 21.5 N) and was significantly stronger than all the other constructs (Orthocord 2-0, 105.5 ± 20.4 N; Ultrabraid 2-0, 93.4 ± 22.5 N; Fast-Fix 360, 76.6 ± 14.2 N) (P < .0001 for all). The Orthocord vertical inside-out mattress repair was significantly stronger than the Fast-Fix 360 repair (P = .003). The Omnispan (30.8 ± 3.5 N/mm) showed significantly higher stiffness compared with the Ultrabraid 2-0 (22.9 ± 6.9 N/mm, P < .0001) and Fast-Fix 360 (23.7 ± 3.9 N/mm, P = .001). The predominant mode of failure was suture failure. CONCLUSION: All-inside meniscal devices show comparable biomechanical properties compared with inside-out suture repair in cyclic loading, even after 100,000 cycles. CLINICAL RELEVANCE: Eight to 10 weeks of rehabilitation might not pose a problem for all repairs in this worst-case scenario.