The Impact of Leg Length and Offset Change on Dislocation Risk Following Primary Total Hip Arthroplasty.

INTRODUCTION: Soft tissue management in total hip arthroplasty (THA) includes appropriate restoration and/or alteration of leg length and offset to re-establish natural hip biomechanics. The purpose of this study was to evaluate the effect of leg length and offset-derived variables in a multivariable survival model for dislocation. METHODS: Clinical, surgical, and radiographic data was retrospectively acquired for 12,582 patients undergoing primary THA at a single institution from 1998 to 2018. There were twelve variables derived from preoperative and postoperative radiographs related to leg length and offset that were measured using a validated automated algorithm. These measurements, as well as other modifiable and non-modifiable surgical, clinical, and demographic factors, were used to determine hazard ratios (HR) for dislocation risk. RESULTS: None of the leg length or offset variables conferred significant risk or protective benefit for dislocation risk. By contrast, all other variables included in the multivariable model demonstrated a statistically significant effect on dislocation risk with a minimum effect size of 28% (range 0.72 to 1.54) (sex, surgical approach, acetabular liner type, femoral head size, neurologic disease, spine disease, and prior spine surgery). CONCLUSION: Contrary to traditional teaching and our hypothesis, operative changes in leg length and offset did not demonstrate any clinically or statistically significant effect in this large and well-characterized cohort. This does not imply that these variables are not important in individual cases, but rather suggests the overall impact of leg length and offset changes is relatively minor for dislocation risk compared to other variables that were found to be highly clinically and statistically significant in this population. These results may also suggest that surgeons do a good job of restoring native leg length and offset for patients, which may mitigate their analyzed impact.

2024 Egyptian consensus statement on the role of non-statin therapies for LDL cholesterol lowering in different patient risk categories.

BACKGROUND: The new millennium has witnessed increased understanding of cardiovascular (CV) risk factors and improvement in atherosclerotic cardiovascular disease (ASCVD) management. The role of LDL cholesterol and other atherogenic lipid particles in the development of atherosclerosis is now beyond doubt. MAIN BODY: Statins have been widely used and recommended in guidelines for preventing and managing ischemic events. However, statins have side effects, and many patients do not achieve their low-density lipoprotein cholesterol (LDL-C) goals. In recent years, non-statin lipid-lowering agents have gained increasing use as adjuncts to statins or as alternatives in patients who cannot tolerate statins. This consensus proposes a simple approach for initiating non-statin lipid-lowering therapy and provides evidence-based recommendations. Our key advancements include the identification of patients at extreme risk for CV events, the consideration of initial combination therapy of statin and ezetimibe in very high-risk and extreme-risk groups and the extended use of bempedoic acid in patients not reaching LDL-C targets especially in resource-limited settings. CONCLUSIONS: Overall, this consensus statement provides valuable insights into the expanding field of non-statin therapies and offers practical recommendations to enhance CV care, specifically focusing on improving LDL-C control in Egypt. While these recommendations hold promise, further research and real-world data are needed for validation and refinement.

Effect of Digital Early Warning Scores on Hospital Vital Sign Observation Protocol Adherence: Stepped-Wedge Evaluation.

BACKGROUND: Early warning scores (EWS) are routinely used in hospitals to assess a patient's risk of deterioration. EWS are traditionally recorded on paper observation charts but are increasingly recorded digitally. In either case, evidence for the clinical effectiveness of such scores is mixed, and previous studies have not considered whether EWS leads to changes in how deteriorating patients are managed. OBJECTIVE: This study aims to examine whether the introduction of a digital EWS system was associated with more frequent observation of patients with abnormal vital signs, a precursor to earlier clinical intervention. METHODS: We conducted a 2-armed stepped-wedge study from February 2015 to December 2016, over 4 hospitals in 1 UK hospital trust. In the control arm, vital signs were recorded using paper observation charts. In the intervention arm, a digital EWS system was used. The primary outcome measure was time to next observation (TTNO), defined as the time between a patient's first elevated EWS (EWS ≥3) and subsequent observations set. Secondary outcomes were time to death in the hospital, length of stay, and time to unplanned intensive care unit admission. Differences between the 2 arms were analyzed using a mixed-effects Cox model. The usability of the system was assessed using the system usability score survey. RESULTS: We included 12,802 admissions, 1084 in the paper (control) arm and 11,718 in the digital EWS (intervention) arm. The system usability score was 77.6, indicating good usability. The median TTNO in the control and intervention arms were 128 (IQR 73-218) minutes and 131 (IQR 73-223) minutes, respectively. The corresponding hazard ratio for TTNO was 0.99 (95% CI 0.91-1.07; P=.73). CONCLUSIONS: We demonstrated strong clinical engagement with the system. We found no difference in any of the predefined patient outcomes, suggesting that the introduction of a highly usable electronic system can be achieved without impacting clinical care. Our findings contrast with previous claims that digital EWS systems are associated with improvement in clinical outcomes. Future research should investigate how digital EWS systems can be integrated with new clinical pathways adjusting staff behaviors to improve patient outcomes.

Patient stratification based on the risk of severe illness in emergency departments through collaborative machine learning models.

OBJECTIVES: Emergency department (ED) overcrowding presents a global challenge that inhibits prompt care for critically ill patients. Traditional 5-level triage system that heavily rely on the judgment of the triage staff could fail to detect subtle symptoms in critical patients, thus leading to delayed treatment. Unlike previous rivalry-focused approaches, our study aimed to establish a collaborative machine learning (ML) model that renders risk scores for severe illness, which may assist the triage staff to provide a better patient stratification for timely critical cares. METHODS: This retrospective study was conducted at a tertiary teaching hospital. Data were collected from January 2015 to October 2022. Demographic and clinical information were collected at triage. The study focused on severe illness as the outcome. We developed artificial neural network (ANN) models, with or without utilizing the Taiwan Triage and Acuity Scale (TTAS) score as one of the predictors. The model using the TTAS score is termed a machine-human collaborative model (ANN-MH), while the model without it is referred to as a machine-only model (ANN-MO). The predictive power of these models was assessed using the area under the receiver-operating-characteristic (AUROC) and the precision-recall curves (AUPRC); their sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score were compared. RESULTS: The study analyzed 668,602 ED visits from 2015 to 2022. Among them, 278,724 visits from 2015 to 2018 were used for model training and validation, while 320,201 visits from 2019 to 2022 were for testing model performance. Approximately 2.6% of visits were by severely ill patients, whose TTAS scores ranged from 1 to 5. The ANN-MH model achieved a testing AUROC of 0.918 and AUPRC of 0.369, while for the ANN-MO model the AUROC and AUPRC were 0.909 and 0.339, respectively. Based on these metrics, the ANN-MH model outperformed the ANN-MO model, and both surpassed human triage classification. Subgroup analyses further highlighted the models' capability to identify higher-risk patients within the same triage level. CONCLUSIONS: The traditional 5-level triage system often falls short, leading to under-triage of critical patients. Our models include a score-based differentiation within a triage level to offer advanced risk stratification, thereby promoting patient safety.

Perspectives of Healthcare Professionals on Clinician-Patient Communication of Cardiovascular Disease Risk.

When the consultation is predominantly verbal, existing research in clinician-patient communication indicates that many patients struggle to understand and recall medical consultations or may not understand the extent of their illness or the purpose of their treatment plan. When the clinician-patient discussion centers around the risk of a repeated cardiovascular disease (CVD) related event, qualitatively assessing what factors affect the communication of this risk may guide the creation of effective communication solutions. Semi-structured interviews were conducted with 17 clinicians treating patients at stages along the cardiac rehabilitation patients' journey. Thematic analysis identified factors that prevent patients from understanding the risk they face of experiencing a repeated cardiac event. Results indicate a clearer understanding of the cardiac rehabilitation patient journey by means of a patient journey map; an overview of how CVD risk is currently communicated; and the factors that affect communication of these risks in the form of themes and sub-themes. Findings shape the proposal of an evidence informed model of opportunities for enhanced digital media supported communication in cardiac rehabilitation.

Leveraging Artificial Intelligence and Machine Learning to Optimize Enhanced Recovery After Surgery (ERAS) Protocols.

Enhanced recovery after surgery (ERAS) protocols have transformed perioperative care by implementing evidence-based strategies to hasten patient recovery, decrease complications, and shorten hospital stays. However, challenges such as inconsistent adherence and the need for personalized adjustments persist, prompting exploration into innovative solutions. The emergence of artificial intelligence (AI) and machine learning (ML) offers a promising avenue for optimizing ERAS protocols. While ERAS emphasizes preoperative optimization, minimally invasive surgery (MIS), and standardized postoperative care, challenges such as adherence variability and resource constraints impede its effectiveness. AI/ML technologies offer opportunities to overcome these challenges by enabling real-time risk prediction, personalized interventions, and efficient resource allocation. AI/ML applications in ERAS extend to patient risk stratification, personalized care plans, and outcome prediction. By analyzing extensive patient datasets, AI/ML algorithms can predict individual patient risks and tailor interventions accordingly. Moreover, AI/ML facilitates proactive interventions through predictive modeling of postoperative outcomes, optimizing resource allocation, and enhancing patient care. Despite the potential benefits, integrating AI and ML into ERAS protocols faces obstacles such as data access, ethical considerations, and healthcare professional training. Overcoming these challenges requires a human-centered approach, fostering collaboration among clinicians, data scientists, and patients. Transparent communication, robust cybersecurity measures, and ethical model validation are crucial for successful integration. It is essential to ensure that AI and ML complement rather than replace human expertise, with clinicians maintaining oversight and accountability.

What are the common factors that lead to the failure to achieve minimal clinically important difference after shoulder surgery? A scoping review.

INTRODUCTION: The goal of this research is to identify the factors that negatively impact the achievement of the minimum clinically significant change (MCID) for the American Shoulder and Elbow Surgeons (ASES) score within the realm of various orthopedic shoulder procedures. METHODS: We conducted a comprehensive review of studies published from 2002 to 2023, utilizing OvidMedline and PubMed databases. Our search criteria included terms such as "minimal clinically important difference" or "MCID" along with associated MeSH terms, in addition to "American shoulder and elbow surgeon" or "ASES." We selectively included primary investigations that assessed factors linked to the failure to achieve MCID for the ASES score subsequent to orthopedic shoulder procedures, while excluding papers addressing anatomical, surgical, or injury-related aspects. RESULTS: Our analysis identified 149 full-text articles, leading to the inclusion of 12 studies for detailed analysis. The selected studies investigated outcomes following various orthopedic shoulder procedures, encompassing biceps tenodesis, total shoulder arthroplasty, and rotator cuff repair. Notably, factors, such as gender, body mass index, diabetes, smoking habits, opioid usage, depression, anxiety, workers' compensation, occupational satisfaction, and the preoperative ASES score, were all associated with the inability to attain MCID. CONCLUSION: In summary, numerous factors exert a negative influence on the attainment of MCID following shoulder procedures, and these factors appear to be irrespective of the specific surgical technique employed. Patients presenting with these factors may perceive their surgical outcomes as less successful when compared to those without these factors. Identifying these factors can enable healthcare providers to provide more effective counseling to patients regarding their expected outcomes and rehabilitation course. Furthermore, these findings can aid in the development of a screening tool to better identify these risk factors and optimize them before surgery.

Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs.

Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.

[Patient education during outpatient procedures]. / Patientenaufklärung bei ambulanten Eingriffen.

Patient education is a key obligation for doctors under the treatment contract. The expansion of the AOP catalogue (catalogue of operations that can be performed on an outpatient basis in accordance with Section 115b of the Fifth Book of the German Social Code (SGB V)) from January 2023 opens up new outpatient treatment options that tend to involve higher risks. This risk profile must be taken into account when informing patients.In any case, the timing of the information should be chosen so that the patient can give their consent in a well-considered manner. There is no fixed "blocking period" between information and consent, so the patient can consent immediately. In the case of high-risk procedures, the patient should be informed several days in advance. Criteria for determining the right time are the type and severity of the procedure, urgency and individual circumstances of the patient. The information provided should be complete and comprehensible, including the diagnosis, need for treatment, risks and alternatives. Comprehensive documentation of the information provided goes without saying.Telemedical counselling is possible in suitable cases, but the risk of timely and complete counselling remains with the doctor performing the procedure. In view of outpatient procedures that require follow-up care at home, the safety information should be more comprehensive.The expansion of the AOP catalogue opens up new opportunities for outpatient procedures, but harbours legal risks. Adapted risk and safety information is required, whereby telemedicine can optimise practice organisation.

[Patient education during outpatient procedures]. / Patientenaufklärung bei ambulanten Eingriffen.

Patient education is a key obligation for doctors under the treatment contract. The expansion of the AOP catalogue (catalogue of operations that can be performed on an outpatient basis in accordance with Section 115b of the Fifth Book of the German Social Code (SGB V)) from January 2023 opens up new outpatient treatment options that tend to involve higher risks. This risk profile must be taken into account when informing patients.In any case, the timing of the information should be chosen so that the patient can give their consent in a well-considered manner. There is no fixed "blocking period" between information and consent, so the patient can consent immediately. In the case of high-risk procedures, the patient should be informed several days in advance. Criteria for determining the right time are the type and severity of the procedure, urgency and individual circumstances of the patient. The information provided should be complete and comprehensible, including the diagnosis, need for treatment, risks and alternatives. Comprehensive documentation of the information provided goes without saying.Telemedical counselling is possible in suitable cases, but the risk of timely and complete counselling remains with the doctor performing the procedure. In view of outpatient procedures that require follow-up care at home, the safety information should be more comprehensive.The expansion of the AOP catalogue opens up new opportunities for outpatient procedures, but harbours legal risks. Adapted risk and safety information is required, whereby telemedicine can optimise practice organisation.

A proposed cleaning classification system for reusable medical devices to complement the Spaulding classification.

A central tenet in infection prevention is application of the Spaulding classification system for the safe use of medical devices. Initially defined in the 1950s, this system defines devices and surfaces as being critical, semi-critical or non-critical depending on how they will be used on a patient. Different levels of antimicrobial treatment, defined as various levels of disinfection or sterilization, are deemed appropriate to reduce patient risk of infection. However, a focus on microbial inactivation is insufficient to address this concern, which has been particularly highlighted in routine healthcare facility practices, emphasizing the underappreciated importance of cleaning and achieving acceptable levels of cleanliness. A deeper understanding of microbiology has evolved since the 1950s, which has led to re-evaluation of the Spaulding classification along with a commensurate emphasis on achieving appropriate cleaning. Albeit underappreciated, cleaning has always been important as the presence of residual materials on surfaces can interfere with the efficacy of the antimicrobial process to inactivate micro-organisms, as well as other risks to patients including device damage, malfunction and biocompatibility concerns. Unfortunately, this continues to be relevant, as attested by reports in the literature on the occurrence of device-related infections and outbreaks due to failures in processing expectations. This reflects, in part, increasing sophistication in device features and reuse, along with commensurate manufacturer's instructions for use. Consequently, this constitutes the first description and recommendation of a new cleaning classification system to complement use of the traditional Spaulding definitions to help address these modern-day technical and patient risk challenges. This quantitative risk-based classification system highlights the challenge of efficient cleaning based on the complexity of device features present, as an isolated variable impacting cleaning. This cleaning classification can be used in combination with the Spaulding classification to improve communication of cleaning risk of a reusable medical device between manufacturers and healthcare facilities, and improve established cleaning practices. This new cleaning classification system will also inform future creation, design thinking and commensurate innovations for the sustainable safe reuse of important medical devices.

Factors associated with dental implant loss/complications in the Veterans Health Administration, 2015-2019.

OBJECTIVES: Twelve percent of the U.S. population has a dental implant. Although rare, implant loss/complications can impact quality of life. This study evaluated indicators for implant loss/complications. METHODS: Veterans with dental implants placed between 2015 and 2019 were included. Implant loss/complications were defined as implant removal or peri-implant defect treatment within 90 days. Binomial logistic regression identified factors associated with implant loss/complications. RESULTS: From 2015 to 2019, 48,811 dental implants were placed in 38,246 Veterans. Implant loss/complications was identified for 202 (0.4%) implants. In adjusted analyses, Veterans aged 50-64 years (OR = 1.92 (95% confidence interval (CI): 1.06, 3.46)) and ≥65 (OR = 2.01 (95% CI: 1.14, 3.53)) were more likely to have implant loss/complications. History of oral infection, tooth location, and number of implants placed all significantly increased the odds of loss/complications. CONCLUSION: Dental implant loss/complications are rare outcomes. Older age, location of implant, and the number of implants placed during a visit were significant predictors of loss/complication.

The association between care integration and care quality.

OBJECTIVE: The study aims to analyze the relationship between care integration and care quality, and to examine if the relationship varies by patient risk. DATA SOURCES AND STUDY SETTING: The key independent variables used validated measures derived from a provider survey of functional (i.e., administrative and clinical systems) and social (i.e., patient integration, professional cooperation, professional coordination) integration. Survey responses represented data from a stratified sample of 59 practice sites from 17 health systems. Dependent variables included three quality measures constructed from patient-level Medicare data: colorectal cancer screening among patients at risk, patient-level 30-day readmission, and a practice-level Healthcare Effectiveness Data and Information Set (HEDIS) composite measure of publicly reported, individual measures of ambulatory clinical quality performance. DATA COLLECTION/EXTRACTION METHOD: We obtained quality- and beneficiary-level covariate data for the 41,966 Medicare beneficiaries served by the 59 practices in our survey sample. STUDY DESIGN: We estimated hierarchical linear models to examine the association between care integration and care quality and the moderating effect of patients' clinical risk score. We graphically visualized the moderating effects at ±1 standard deviation of our z-standardized independent and moderating variables and performed simple slope tests. PRINCIPAL FINDINGS: Our analyses uncovered a strong positive relationship between social integration, specifically patient integration, and the quality of care a patient receives (e.g., a 1-point increase in a practice's patient integration was associated with 0.31-point higher HEDIS composite score, p < 0.01). Further, we documented positive and significant associations between aspects of social and functional integration on quality of care based on patient risk. CONCLUSIONS: The findings suggest social integration matters for improving the quality of care and that the relationship of integration to quality is not uniform for all patients. Policymakers and practitioners considering structural integrations of health systems should direct attention beyond structure to consider the potential for social integration to impact outcomes and how that might be achieved.

Identifying the patient harms to include in an in silico clinical trial.

BACKGROUND AND OBJECTIVE: Clinical trials represent a crucial step in the development and approval of medical devices. These trials involve evaluating the safety and efficacy of the device in a controlled setting with human subjects. However, traditional clinical trials can be expensive, time-consuming, and ethically challenging. Augmenting clinical trials with data from computer simulations, so called in silico clinical trials (ISCT), has the potential to address these challenges while satisfying regulatory requirements. However, determination of the patient harms in scope of an ISCT is necessary to ensure all harms are sufficiently addressed while maximizing the utility of the ISCT. This topic is currently lacking guidance. The objective of this work is to propose a general method to determine which patient harms should be included in an ISCT for a regulatory submission. METHODS: The proposed method considers the risk associated with the harm, the impact of the device on the likelihood of occurrence of the harm and the technical feasibility of evaluating the harm via ISCT. Consideration of the risk associated with the harm provides maximum clinical impact of the ISCT, in terms of focusing on those failure modes which are most relevant to the patient population. Consideration of the impact of the device on a particular harm, and the technical feasibility of modeling a particular harm supports that the technical effort is devoted to a problem that (1) is relevant to the device in question, and (2) can be solved with contemporary modeling techniques. RESULTS AND CONCLUSIONS: As a case study, the proposed method is applied to a total shoulder replacement humeral system. With this framework, it is hoped that a consistent approach to scoping an ISCT can be adopted, supporting investment in ISCT by the industry, enabling consistent review of the ISCT approach across device disciplines by regulators, and providing maximum impact of modeling technologies in support of devices to improve patient outcomes.

Pancreas Rejection in the Artificial Intelligence Era: New Tool for Signal Patients at Risk.

INTRODUCTION: Pancreas transplantation is currently the only treatment that can re-establish normal endocrine pancreatic function. Despite all efforts, pancreas allograft survival and rejection remain major clinical problems. The purpose of this study was to identify features that could signal patients at risk of pancreas allograft rejection. METHODS: We collected 74 features from 79 patients who underwent simultaneous pancreas-kidney transplantation (SPK) and used two widely-applicable classification methods, the Naive Bayesian Classifier and Support Vector Machine, to build predictive models. We used the area under the receiver operating characteristic curve and classification accuracy to evaluate the predictive performance via leave-one-out cross-validation. RESULTS: Rejection events were identified in 13 SPK patients (17.8%). In feature selection approach, it was possible to identify 10 features, namely: previous treatment for diabetes mellitus with long-term Insulin (U/I/day), type of dialysis (peritoneal dialysis, hemodialysis, or pre-emptive), de novo DSA, vPRA_Pre-Transplant (%), donor blood glucose, pancreas donor risk index (pDRI), recipient height, dialysis time (days), warm ischemia (minutes), recipient of intensive care (days). The results showed that the Naive Bayes and Support Vector Machine classifiers prediction performed very well, with an AUROC and classification accuracy of 0.97 and 0.87, respectively, in the first model and 0.96 and 0.94 in the second model. CONCLUSION: Our results indicated that it is feasible to develop successful classifiers for the prediction of graft rejection. The Naive Bayesian generated nomogram can be used for rejection probability prediction, thus supporting clinical decision making.

Acute Pain and Development of Opioid Use Disorder: Patient Risk Factors.

PURPOSE OF REVIEW: Pharmacological therapy for acute pain carries the risk of opioid misuse, with opioid use disorder (OUD) reaching epidemic proportions worldwide in recent years. This narrative review covers the latest research on patient risk factors for opioid misuse in the treatment of acute pain. In particular, we emphasize newer findings and evidence-based strategies to reduce the prevalence of OUD. RECENT FINDINGS: This narrative review captures a subset of recent advances in the field targeting the literature on patients' risk factors for OUD in the treatment for acute pain. Besides well-recognized risk factors such as younger age, male sex, lower socioeconomic status, White race, psychiatric comorbidities, and prior substance use, additional challenges such as COVID-19 further aggravated the opioid crisis due to associated stress, unemployment, loneliness, or depression. To reduce OUD, providers should evaluate both the individual patient's risk factors and preferences for adequate timing and dosing of opioid prescriptions. Short-term prescription should be considered and patients at-risk closely monitored. The integration of non-opioid analgesics and regional anesthesia to create multimodal, personalized analgesic plans is important. In the management of acute pain, routine prescription of long-acting opioids should be avoided, with implementation of a close monitoring and cessation plan.

Physical restraint within the prehospital Emergency Medical Care Environment: A scoping review.

Background: Psychomotor agitation and aggressive behaviour (AAB) have the potential to occur in any healthcare setting, including those in which Emergency Medical Services (EMS) operate. This scoping review aimed to examine the available literature on physical restraint of patients within the prehospital setting and to identify guidelines and their effectiveness, safety to patients and health care practitioners and strategies relating to physical restraint when used by EMS. Methods: We performed our scoping review using the methodological framework described by Arksey and O'Malley augmented by that of Sucharew and Macaluso. Several steps guided the review process: identification of the research question, eligibility criteria, information sources (CINAHL, Medline, Cochrane and Scopus), search, selection and data collection, ethical approval, collation, summarizing and reporting on the results. Results: The population of interest, in this scoping review was prehospital physically restrained patients, however, there was a reduced research focus on this population in comparison to the larger emergency department. Conclusion: The limitation of informed consent from incapacitated patients may relate to the lack of prospective real-world research from previous and future studies. Future research should focus on patient management, adverse events, practitioner risk, policy, and education within the prehospital setting.

A review of Spaulding's classification system for effective cleaning, disinfection and sterilization of reusable medical devices: Viewed through a modern-day lens that will inform and enable future sustainability.

Despite advances in medicine and innovations in many underpinning fields including disease prevention and control, the Spaulding classification system, originally proposed in 1957, remains widely used for defining the disinfection and sterilization of contaminated re-usable medical devices and surgical instruments. Screening PubMed and Scopus databases using a PRISMA guiding framework generated 272 relevant publications that were used in this review. Findings revealed that there is a need to evolve how medical devices are designed, and processed by cleaning, disinfection (and/or sterilization) to mitigate patient risks, including acquiring an infection. This Spaulding Classification remains in use as it is logical, easily applied and understood by users (microbiologists, epidemiologists, manufacturers, industry) and by regulators. However, substantial changes have occurred over the past 65 years that challenge interpretation and application of this system that includes inter alia emergence of new pathogens (viruses, mycobacteria, protozoa, fungi), a greater understanding of innate and adaptive microbial tolerance to disinfection, toxicity risks, increased number of vulnerable patients and associated patient procedures, and greater complexity in design and use of medical devices. Common cited examples include endoscopes that enable non- or minimal invasive procedures but are highly sophisticated with various types of materials (polymers, electronic components etc), long narrow channels, right angle and heat-sensitive components and various accessories (e.g., values) that can be contaminated with high levels of microbial bioburden and patient tissues after use. Contaminated flexible duodenoscopes have been a source of several significant infection outbreaks, where at least 9 reported cases were caused by multidrug resistant organisms [MDROs] with no obvious breach in processing detected. Despite this, there is evidence of the lack of attention to cleaning and maintenance of these devices and associated equipment. Over the last few decades there is increasing genomic evidence of innate and adaptive resistance to chemical disinfectant methods along with adaptive tolerance to environmental stresses. To reduce these risks, it has been proposed to elevate classification of higher-risk flexible endoscopes (such as duodenoscopes) from semi-critical [contact with mucous membrane and intact skin] to critical use [contact with sterile tissue and blood] that entails a transition to using low-temperature sterilization modalities instead of routinely using high-level disinfection; thus, increasing the margin of safety for endoscope processing. This timely review addresses important issues surrounding use of the Spaulding classification system to meet modern-day needs. It specifically addresses the need for automated, robust cleaning and drying methods combined with using real-time monitoring of device processing. There is a need to understand entire end-to-end processing of devices instead of adopting silo approaches that in the future will be informed by artificial intelligence and deep-learning/machine learning. For example, combinational solutions that address the formation of complex biofilms that harbour pathogenic and opportunistic microorganisms on the surfaces of processed devices. Emerging trends are addressed including future sustainability for the medical devices sector that can be enabled via a new Quintuple Helix Hub approach that combines academia, industry, healthcare, regulators, and society to unlock real world solutions.

A spreadsheet tool for designing statistical quality control programs based on patient risk parameters.

BACKGROUND: Quality control (QC) in the laboratory aims to reduce the risk of harm to a patient due to erroneous results, as highlighted by the Clinical Laboratory Standards Institute (CLSI) guidance for Statistical Quality Control (SQC) (C24-Ed4). To effectively reduce patient risk, a convenient spreadsheet tool was developed to assist laboratories in SQC design based on patient risk parameters. METHODS: In accordance with Parvin's patient risk model and the mathematical formula for calculating the expected number of unreliable final patient results [E(Nuf)], the function is edited using Excel software, and the maximum E(Nuf) [MaxE(Nuf)] value and other risk parameters based on the current QC strategy are calculated to assess the risk of the QC strategy. RESULTS: A convenient spreadsheet tool is proposed in this study. After the quality requirements, performance parameters, practical run size, QC rules and the number of QC results of test items are input, the laboratory is enabled to quickly obtain MaxE(Nuf) value, maximum run size and other data based on the strategy. The QC strategy conforming to the risk requirements can be developed by changing the QC rules or the quantity of run size. Moreover, the Power Function Graph of the QC strategy and two risk diagrams are presented simultaneously. CONCLUSIONS: Convenient spreadsheet tools can be adopted by laboratories to assess the risks of QC strategies and design appropriate risk-based SQC strategies to reduce patient risk to acceptable levels.

Coronary artery disease, left ventricular function and cardiac biomarkers determine all-cause mortality in cancer patients-a large monocenter cohort study.

Cancer patients are at risk of suffering from cardiovascular diseases (CVD). Nevertheless, the impact of cardiovascular comorbidity on all-cause mortality (ACM) in large clinical cohorts is not well investigated. In this retrospective cohort study, we collected data from 40,329 patients who were subjected to cardiac catherization from 01/2006 to 12/2017 at University Hospital Heidelberg. The study population included 3666 patients with a diagnosis of cancer prior to catherization and 3666 propensity-score matched non-cancer patients according to age, gender, diabetes and hypertension. 5-year ACM in cancer patients was higher with a reduced left ventricular function (LVEF < 50%; 68.0% vs 50.9%) or cardiac biomarker elevation (high-sensitivity cardiac troponin T (hs-cTnT; 64.6% vs 44.6%) and N-terminal brain natriuretic peptide (NT-proBNP; 62.9% vs 41.4%) compared to cancer patients without cardiac risk. Compared to non-cancer patients, NT-proBNP was found to be significantly higher (median NT-proBNP cancer: 881 ng/L, IQR [254; 3983 ng/L] vs non-cancer: 668 ng/L, IQR [179; 2704 ng/L]; p < 0.001, Wilcoxon-rank sum test) and turned out to predict ACM more accurately than hs-cTnT (NT-proBNP: AUC: 0.74; hs-cTnT: AUC: 0.63; p < 0.001, DeLong's test) in cancer patients. Risk factors for atherosclerosis, such as diabetes and age (> 65 years) were significant predictors for increased ACM in cancer patients in a multivariate analysis (OR diabetes: 1.96 (1.39-2.75); p < 0.001; OR age > 65 years: 2.95 (1.68-5.4); p < 0.001, logistic regression). Our data support the notion, that overall outcome in cancer patients who underwent cardiac catherization depends on cardiovascular comorbidities. Therefore, particularly cancer patients may benefit from standardized cardiac care.