Early analysis shows that endoscopic flexor hallucis longus transfer has a promising cost-effectiveness profile in the treatment of acute Achilles tendon ruptures.

PURPOSE: Current options for treating an Achilles tendon rupture (ATR) include conservative and surgical approaches. Endoscopic flexor hallucis longus (FHL) transfer has been recently proposed to treat acute ruptures, but its cost-effectiveness potential remains to be evaluated. Therefore, the objective of this study was to perform an early cost-effectiveness analysis of endoscopic FHL transfer for acute ATRs, comparing the costs and benefits of current treatments from a societal perspective. METHODS: A conceptual model was created, with a decision tree, to outline the main health events during the treatment of an acute ATR. The model was parameterized using secondary data. A systematic review of the literature was conducted to gather information on the outcomes of current treatments. Data related to outcomes of endoscopic FHL transfers in acute Achilles ruptures was obtained from a single prospective study. Analysis was limited to the two first years. The incremental cost-effectiveness ratio was the main outcome used to determine the preferred strategy. A willingness-to-pay threshold of $100,000 per quality-adjusted life-year was used. Sensitivity analyses were performed to determine whether changes in input parameters would cause significant deviation from the reference case results. Specifically, a probability sensitivity analysis was conducted using Monte Carlo simulations, and a one-way sensitivity analysis was conducted by sequentially varying each model parameter within a given range. RESULTS: For the reference case, incremental cost-effectiveness ratios exceeded the willingness-to-pay threshold for all the surgical approaches. Overall, primary treatment was the main cost driver. Conservative treatment showed the highest direct costs related to the treatment of complications. In the probabilistic sensitivity analysis, at a willingness-to-pay threshold of $100,000, open surgery was cost-effective in 50.9%, minimally invasive surgery in 55.8%, and endoscopic FHL transfer in 72% of the iterations. The model was most sensitive to parameters related to treatment utilities, followed by the costs of primary treatments. CONCLUSION: Surgical treatments have a moderate likelihood of being cost-effective at a willingness-to-pay threshold of $100,000, with endoscopic FHL transfer showing the highest likelihood. Following injury, interventions to improve health-related quality of life may be better suited for improved cost-effectiveness. LEVEL OF EVIDENCE: Level III.

Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain.

Systematically exploring repurposing effects of antihypertensives.

With availability of voluminous sets of observational data, an empirical paradigm to screen for drug repurposing opportunities (i.e., beneficial effects of drugs on nonindicated outcomes) is feasible. In this article, we use a linked claims and electronic health record database to comprehensively explore repurposing effects of antihypertensive drugs. We follow a target trial emulation framework for causal inference to emulate randomized controlled trials estimating confounding adjusted effects of antihypertensives on each of 262 outcomes of interest. We then fit hierarchical models to the results as a form of postprocessing to account for multiple comparisons and to sift through the results in a principled way. Our motivation is twofold. We seek both to surface genuinely intriguing drug repurposing opportunities and to elucidate through a real application some study design decisions and potential biases that arise in this context.

Early cost estimating model for new bioabsorbable orthopedic implant candidates: A theoretical study.

An early health technology assessment (HTA) study was performed to assess the need for developing a new bioabsorbable implant for the treatment of specific orthopedic injuries. The Anterior Cruciate Ligament Reconstruction (ACLR) procedure was selected based on the need and potential impact of bioabsorbable implants in the treatment of ACL injuries. The economic model considers the possible health events after an ACLR (failures and other complications such as stiffness and pain). A decision tree approach was used, and several sensitivity analyses were performed using a Monte Carlo simulation. A cost estimating model was applied comparatively for currently available metal and bioabsorbable implants against a potential new bioabsorbable implant with improved performance. A reduction in stiffness and pain symptoms were considered as targets for these new implants performance, with reduced inflammation resulting from the use of materials with appropriate biological and mechanical properties. The current study estimates that, under the assumptions made, the introduction of a new bioabsorbable implant in ACLR surgeries may generate yearly cost savings. The model estimates positive cost-benefits of the new implant when it reduces the probability of failure by more than 30%, or reduces at least 14% the probability of complications of an inflammatory nature. The development of a new bioabsorbable orthopedic implant for ACLR is encouraged by this study identifying the need for new bioabsorbable implants with improved biological and mechanical performance. The results of this early HTA have made it possible to anticipate design needs and objectives for the research and development of new orthopedic bioabsorbable implants.

Data-driven curation process for describing the blood glucose management in the intensive care unit.

Analysis of real-world glucose and insulin clinical data recorded in electronic medical records can provide insights into tailored approaches to clinical care, yet presents many analytic challenges. This work makes publicly available a dataset that contains the curated entries of blood glucose readings and administered insulin on a per-patient basis during ICU admissions in the Medical Information Mart for Intensive Care (MIMIC-III) database version 1.4. Also, the present study details the data curation process used to extract and match glucose values to insulin therapy. The curation process includes the creation of glucose-insulin pairing rules according to clinical expert-defined physiologic and pharmacologic parameters. Through this approach, it was possible to align nearly 76% of insulin events to a preceding blood glucose reading for nearly 9,600 critically ill patients. This work has the potential to reveal trends in real-world practice for the management of blood glucose. This data extraction and processing serve as a framework for future studies of glucose and insulin in the intensive care unit.

Artificial intelligence-based prediction of transfusion in the intensive care unit in patients with gastrointestinal bleeding.

OBJECTIVE: Gastrointestinal (GI) bleeding commonly requires intensive care unit (ICU) in cases of potentialhaemodynamiccompromise or likely urgent intervention. However, manypatientsadmitted to the ICU stop bleeding and do not require further intervention, including blood transfusion. The present work proposes an artificial intelligence (AI) solution for the prediction of rebleeding in patients with GI bleeding admitted to ICU. METHODS: A machine learning algorithm was trained and tested using two publicly available ICU databases, the Medical Information Mart for Intensive Care V.1.4 database and eICU Collaborative Research Database using freedom from transfusion as a proxy for patients who potentially did not require ICU-level care. Multiple initial observation time frames were explored using readily available data including labs, demographics and clinical parameters for a total of 20 covariates. RESULTS: The optimal model used a 5-hour observation period to achieve an area under the curve of the receiving operating curve (ROC-AUC) of greater than 0.80. The model was robust when tested against both ICU databases with a similar ROC-AUC for all. CONCLUSIONS: The potential disruptive impact of AI in healthcare innovation is acknowledge, but awareness of AI-related risk on healthcare applications and current limitations should be considered before implementation and deployment. The proposed algorithm is not meant to replace but to inform clinical decision making. Prospective clinical trial validation as a triage tool is warranted.

State of the Art Causal Inference in the Presence of Extraneous Covariates: A Simulation Study.

The central task of causal inference is to remove (via statistical adjustment) confounding bias that would be present in naive unadjusted comparisons of outcomes in different treatment groups. Statistical adjustment can roughly be broken down into two steps. In the first step, the researcher selects some set of variables to adjust for. In the second step, the researcher implements a causal inference algorithm to adjust for the selected variables and estimate the average treatment effect. In this paper, we use a simulation study to explore the operating characteristics and robustness of state-of-the-art methods for step two (statistical adjustment for selected variables) when step one (variable selection) is performed in a realistically sub-optimal manner. More specifically, we study the robustness of a cross-fit machine learning based causal effect estimator to the presence of extraneous variables in the adjustment set. The take-away for practitioners is that there is value to, if possible, identifying a small sufficient adjustment set using subject matter knowledge even when using machine learning methods for adjustment.

Real-world characterization of blood glucose control and insulin use in the intensive care unit.

The heterogeneity of critical illness complicates both clinical trial design and real-world management. This complexity has resulted in conflicting evidence and opinion regarding the optimal management in many intensive care scenarios. Understanding this heterogeneity is essential to tailoring management to individual patients. Hyperglycaemia is one such complication in the intensive care unit (ICU), accompanied by decades of conflicting evidence around management strategies. We hypothesized that analysis of highly-detailed electronic medical record (EMR) data would demonstrate that patients vary widely in their glycaemic response to critical illness and response to insulin therapy. Due to this variability, we believed that hyper- and hypoglycaemia would remain common in ICU care despite standardised approaches to management. We utilized the Medical Information Mart for Intensive Care III v1.4 (MIMIC) database. We identified 19,694 admissions between 2008 and 2012 with available glucose results and insulin administration data. We demonstrate that hyper- and hypoglycaemia are common at the time of admission and remain so 1 week into an ICU admission. Insulin treatment strategies vary significantly, irrespective of blood glucose level or diabetic status. We reveal a tremendous opportunity for EMR data to guide tailored management. Through this work, we have made available a highly-detailed data source for future investigation.

Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing.

Emergency department triage is the first point in time when a patient's acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome-mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients' chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency-inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Ten-fold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model-a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients' age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical variables and the chief complaint presented higher recall for patients assigned MTS-3 and can identify those who are at risk of the composite outcome.

Predicting Intensive Care Unit admission among patients presenting to the emergency department using machine learning and natural language processing.

The risk stratification of patients in the emergency department begins at triage. It is vital to stratify patients early based on their severity, since undertriage can lead to increased morbidity, mortality and costs. Our aim was to present a new approach to assist healthcare professionals at triage in the stratification of patients and in identifying those with higher risk of ICU admission. Adult patients assigned Manchester Triage System (MTS) or Emergency Severity Index (ESI) 1 to 3 from a Portuguese and a United States Emergency Departments were analyzed. Variables routinely collected at triage were used and natural language processing was applied to the patient chief complaint. Stratified random sampling was applied to split the data in train (70%) and test (30%) sets and 10-fold cross validation was performed for model training. Logistic regression, random forests, and a random undersampling boosting algorithm were used. We compared the performance obtained with the reference model-using only triage priorities-with the models using additional variables. For both hospitals, a logistic regression model achieved higher overall performance, yielding areas under the receiver operating characteristic and precision-recall curves of 0.91 (95% CI 0.90-0.92) and 0.30 (95% CI 0.27-0.33) for the United States hospital and of 0.85 (95% CI 0.83-0.86) and 0.06 (95% CI 0.05-0.07) for the Portuguese hospital. Heart rate, pulse oximetry, respiratory rate and systolic blood pressure were the most important predictors of ICU admission. Compared to the reference models, the models using clinical variables and the chief complaint presented higher recall for patients assigned MTS/ESI 3 and can identify patients assigned MTS/ESI 3 who are at risk for ICU admission.

Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review.

MOTIVATION: Emergency Departments' (ED) modern triage systems implemented worldwide are solely based upon medical knowledge and experience. This is a limitation of these systems, since there might be hidden patterns that can be explored in big volumes of clinical historical data. Intelligent techniques can be applied to these data to develop clinical decision support systems (CDSS) thereby providing the health professionals with objective criteria. Therefore, it is of foremost importance to identify what has been hampering the application of such systems for ED triage. OBJECTIVES: The objective of this paper is to assess how intelligent CDSS for triage have been contributing to the improvement of quality of care in the ED as well as to identify the challenges they have been facing regarding implementation. METHODS: We applied a standard scoping review method with the manual search of 6 digital libraries, namely: ScienceDirect, IEEE Xplore, Google Scholar, Springer, MedlinePlus and Web of Knowledge. Search queries were created and customized for each digital library in order to acquire the information. The core search consisted of searching in the papers' title, abstract and key words for the topics "triage", "emergency department"/"emergency room" and concepts within the field of intelligent systems. RESULTS: From the review search, we found that logistic regression was the most frequently used technique for model design and the area under the receiver operating curve (AUC) the most frequently used performance measure. Beside triage priority, the most frequently used variables for modelling were patients' age, gender, vital signs and chief complaints. The main contributions of the selected papers consisted in the improvement of a patient's prioritization, prediction of need for critical care, hospital or Intensive Care Unit (ICU) admission, ED Length of Stay (LOS) and mortality from information available at the triage. CONCLUSIONS: In the papers where CDSS were validated in the ED, the authors found that there was an improvement in the health professionals' decision-making thereby leading to better clinical management and patients' outcomes. However, we found that more than half of the studies lacked this implementation phase. We concluded that for these studies, it is necessary to validate the CDSS and to define key performance measures in order to demonstrate the extent to which incorporation of CDSS at triage can actually improve care.

A Systems Approach to Analyzing and Preventing Hospital Adverse Events.

OBJECTIVE: This study aimed to demonstrate the use of a systems theory-based accident analysis technique in health care applications as a more powerful alternative to the chain-of-event accident models currently underpinning root cause analysis methods. METHOD: A new accident analysis technique, CAST [Causal Analysis based on Systems Theory], is described and illustrated on a set of adverse cardiovascular surgery events at a large medical center. The lessons that can be learned from the analysis are compared with those that can be derived from the typical root cause analysis techniques used today. RESULTS: The analysis of the 30 cardiovascular surgery adverse events using CAST revealed the reasons behind unsafe individual behavior, which were related to the design of the system involved and not negligence or incompetence on the part of individuals. With the use of the system-theoretic analysis results, recommendations can be generated to change the context in which decisions are made and thus improve decision making and reduce the risk of an accident. CONCLUSIONS: The use of a systems-theoretic accident analysis technique can assist in identifying causal factors at all levels of the system without simply assigning blame to either the frontline clinicians or technicians involved. Identification of these causal factors in accidents will help health care systems learn from mistakes and design system-level changes to prevent them in the future.

Economics of Beta-Cell Replacement Therapy.

PURPOSE OF REVIEW: Type 1 diabetes impacts 1.3 million people in the USA with a total direct lifetime medical cost of $133.7 billion. Management requires a mix of daily exogenous insulin administration and frequent glucose monitoring. Decision-making by the individual can be burdensome. RECENT FINDINGS: Beta-cell replacement, which involves devices protecting cells from autoimmunity and allo-rejection, aims at restoring physiological glucose regulation and improving clinical outcomes in patients. Given the significant burden of T1D in the healthcare systems, cost-effectiveness analyses can drive innovation and policymaking in the area. This review presents the health economics analyses performed for donor-derived islet transplantation and the possible outcomes of stem cell-derived beta cells. Long-term cost-effectiveness of islet transplantation depends on the engraftment of these transplants, and the expenses and thresholds assumed by healthcare systems in different countries. Early health technology assessment analyses for stem cell-derived beta-cell replacement suggest manufacturing optimization is necessary to reduce upfront costs.

Bringing Stem Cell-Based Therapies for Type 1 Diabetes to the Clinic: Early Insights from Bioprocess Economics and Cost-Effectiveness Analysis.

Differentiation of pluripotent stem cells (PSCs) into ß cells could provide insulin independence for type 1 diabetes (T1D) patients. This approach would reduce the clinical complications that most patients managed on intensive insulin therapy (IIT) face. However, bottlenecks of PSC manufacturing and limited engraftment of encapsulated cells hinder the long-term effectiveness of these therapies. A bioprocess decision-support tool is combined with a disease state-transition model to evaluate the cost-effectiveness of the stem cell-based therapy against IIT. Clinical effectiveness is assessed in quality-adjusted life years (QALYs). Manufacturing costs per patient reduce from $430 000 to $160 000 with optimization of batch size and annual demand. For 96% of the patients, cell therapy improves the quality of life compared to IIT. Cost savings are achieved for 2% of the population through prevention of renal disease. The therapy is cost-effective for 3.4% of patients when a willingness to pay (WTP) of up to $150 000 per QALY is considered. A 75% cost reduction in the cell therapy price increases cost-effectiveness likelihood to 51% at $100 000 per QALY. This study highlights the need for scalable manufacturing platforms for stem cell therapies, as well as to prioritizing access to the therapy to patients with an increased likelihood of costly complications.

Modeling biological and economic uncertainty on cell therapy manufacturing: the choice of culture media supplementation.

AIM: To evaluate the cost-effectiveness of autologous cell therapy manufacturing in xeno-free conditions. MATERIALS & METHODS: Published data on the isolation and expansion of mesenchymal stem/stromal cells introduced donor, multipassage and culture media variability on cell yields and process times on adherent culture flasks to drive cost simulation of a scale-out campaign of 1000 doses of 75 million cells each in a 400 square meter Good Manufacturing Practices facility. RESULTS & CONCLUSION: Passage numbers in the expansion step are strongly associated with isolation cell yield and drive cost increases per donor of $1970 and 2802 for fetal bovine serum and human platelet lysate. Human platelet lysate decreases passage numbers and process costs in 94.5 and 97% of donors through lower facility and labor costs. Cost savings are maintained with full equipment depreciation and higher numbers of cells per dose, highlighting the number of cells per passage step as the key cost driver.

Nosocomial amplification of MERS-coronavirus in South Korea, 2015.

Background: Nosocomial amplification resulted in nearly 200 cases of Middle East respiratory syndrome (MERS) during the 2015 South Korean MERS-coronavirus outbreak. It remains unclear whether certain types of cases were more likely to cause secondary infections than others, and if so, why. Methods: Publicly available demographic and transmission network data for all cases were collected from the Ministry of Health and Welfare. Statistical analyses were conducted to determine the relationship between demographic characteristics and the likelihood of human-to-human transmission. Findings from the statistical analyses were used to inform a hypothesis-directed literature review, through which mechanistic explanations for nosocomial amplification were developed. Results: Cases that failed to recover from MERS were more likely to cause secondary infections than those that did. Increased probability of direct, human-to-human transmission due to clinical manifestations associated with death, as well as indirect transmission via environmental contamination (e.g., fomites and indoor ventilation systems), may serve as mechanistic explanations for nosocomial amplification of MERS-coronavirus in South Korea. Conclusions: In addition to closely monitoring contacts of MERS cases that fail to recover during future nosocomial outbreaks, potential fomites with which they may have had contact should be sanitized. Furthermore, indoor ventilation systems that minimize recirculation of pathogen-bearing droplets should be implemented whenever possible.

Medical Students Who Pursue a Joint MD/MBA Degree: Who Are They and Where Are They Heading?

Increasingly, health care is being delivered in large, complex organizations, and physicians must learn to function effectively in them. As a result, several medical and business schools have developed joint programs to train physician leaders who receive both medical degree (MD) and master of business administration (MBA) degrees. We examined several themes in relation to these programs, revolving around concerns about who is attracted to them and whether exposure to the differing cultures of medicine and business have an impact on the professional identities of their graduates as manifested in their motivations, aspirations, and careers. We addressed these issues by studying students in the joint MD/MBA program at Harvard Medical School (HMS) and Harvard Business School (HBS). Our data came from several internal sources and a survey of all students enrolled in the joint program in spring 2013. We found relatively few differences between joint program students and equivalent cohorts of HMS students in terms of personal characteristics, preadmission performance, and performance at HMS and HBS. Contrary to the concerns that such programs may draw students away from medicine, the vast majority embraced careers involving extensive postgraduate medical training, with long-term plans that leveraged their new perspectives and skills to improve health care delivery.

When a checklist is not enough: How to improve them and what else is needed.

OBJECTIVES: Checklists are being introduced to enhance patient safety, but the results have been mixed. The goal of this research is to understand why time-outs and checklists are sometimes not effective in preventing surgical adverse events and to identify additional measures needed to reduce these events. METHODS: A total of 380 consecutive patients underwent complex cardiac surgery over a 24-month period between November 2011 and November 2013 at an academic medical center, out of a total of 529 cardiac cases. Elective isolated aortic valve replacements, mitral valve repairs, and coronary artery bypass graft surgical procedures (N = 149) were excluded. A time-out was conducted in a standard fashion in all patients in accordance with the World Health Organization surgical checklist protocol. Adverse events were classified as anything that resulted in an operative delay, nonavailability of equipment, failure of drug administration, or unexpected adverse clinical outcome. These events and their details were collected every week and analyzed using a systemic causal analysis technique using a technique called CAST (causal analysis based on systems theory). This analytic technique evaluated the sociotechnical system to identify the set of causal factors involved in the adverse events and the causal factors explored to identify reasons. Recommendations were made for the improvement of checklists and the use of system design changes that could prevent such events in the future. RESULTS: Thirty events were identified. The causal analysis of these 30 adverse events was carried out and actionable events classified. There were important limitations in the use of standard checklists as a stand-alone patient safety measure in the operating room setting, because of multiple factors. Major categories included miscommunication between staff, medication errors, missing instrumentation, missing implants, and improper handling of equipment or instruments. An average of 3.9 recommendations were generated for each adverse event scenario. CONCLUSIONS: Time-outs and checklists can prevent some types of adverse events, but they need to be carefully designed. Additional interventions aimed at improving safety controls in the system design are needed to augment the use of checklists. Customization of checklists for specialized surgical procedures may reduce adverse events.

Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology.

Left ventricular ejection fraction (LVEF) constitutes an important physiological parameter for the assessment of cardiac function, particularly in the settings of coronary artery disease and heart failure. This study explores the use of routinely and easily acquired variables in the intensive care unit (ICU) to predict severely depressed LVEF following ICU admission. A retrospective study was conducted. We extracted clinical physiological variables derived from ICU monitoring and available within the MIMIC II database and developed a fuzzy model using sequential feature selection and compared it with the conventional logistic regression (LR) model. Maximum predictive performance was observed using easily acquired ICU variables within 6 hours after admission and satisfactory predictive performance was achieved using variables acquired as early as one hour after admission. The fuzzy model is able to predict LVEF ≤ 25% with an AUC of 0.71 ± 0.07, outperforming the LR model, with an AUC of 0.67 ± 0.07. To the best of the authors' knowledge, this is the first study predicting severely impaired LVEF using multivariate analysis of routinely collected data in the ICU. We recommend inclusion of these findings into triaged management plans that balance urgency with resources and clinical status, particularly for reducing the time of echocardiographic examination.