Expanding the perioperative lens: Does the end justify the means?

In 1994, Fischer et al. established the preoperative clinic for the perioperative services at Stanford University Medical Center. By lowering the risk of cancellation and reducing morbidity and mortality against the push to move surgeries to an outpatient, basis, they demonstrated a return on investment. In the 2000s, Aronson et al. designed the prehabilitation clinics at Duke University with the notion that the preoperative process should not only ensure that patients were appropriately risk-stratified, but also clinically optimized before surgery. With a trend towards ambulatory procedures due to current reimbursement structures, hospital administrators should be searching for potential avenues to bolster sagging profits. In this narrative review, we argue that the perioperative services needs to extend beyond the hospital into the postoperative period.

Methodology for estimation of intrinsic dimensions and state variables of microstructures.

According to the manifold hypothesis, real data can be compressed to lie on a low-dimensional manifold. This paper explores the estimation of the dimensionality of this manifold with an interest in identifying independent degrees of freedom and possibly identifying state variables that would govern materials systems. The challenges identified that are specific to materials science are (i) accurate estimation of the number of dimensions of the data, (ii) coping with the intrinsic random and low-bit-depth nature of microstructure samples, and (iii) linking noncompressed domains such as processing to microstructure. Dimensionality estimates are made with the maximum-likelihood-estimation method with the Minkowski p-norms being used as a measure of the distance between microstructural images. It is found that, where dimensionality estimates are required to be accurate, it is necessary to use the Minkowski 1-norm (also known as the L_{1}-norm or Manhattan distance). This effect is found to be due to image quantification and proofs are given regarding the distortion produced by quantization. It is also found that homogenization is an effective way of estimating the dimension of random microstructure image sets. An estimate of 40 dimensions for the fibers of a SiC/SiC fiber composite is obtained. It is also found that, with images generated from a sparse domain (surrogate to the process domain), it is possible to infer the nature of the process manifold from images alone.

Clinical characteristics and health care resource use of patients at risk for wild-type transthyretin amyloid cardiomyopathy identified by machine learning model.

BACKGROUND: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive, life-threatening systemic disorder that is an underrecognized cause of heart failure (HF). When the diagnosis of wild-type ATTR-CM (ATTRwt-CM) is delayed, patients often undergo additional assessments, deferring appropriate management as symptoms potentially worsen. Prompt recognition of patients at risk for ATTRwt-CM is essential to facilitate earlier diagnosis and disease-modifying treatment. A previously developed machine learning model performed well in identifying ATTRwt-CM in patients with HF vs controls with nonamyloid HF using medical claims/electronic health records, providing a systematic framework to raise disease suspicion. OBJECTIVE: To further evaluate this model's performance in identifying ATTRwt-CM using a large claims database of older adults with HF and confirmed ATTRwt-CM or nonamyloid HF; and to explore the characteristics and health care resource utilization (HCRU) of patients with confirmed and suspected ATTRwt-CM. METHODS: In this retrospective study, the prior model was applied using Humana administrative claims for patients diagnosed with ATTRwt-CM (cases) and nonamyloid HF (controls [1:1]). Patients were aged 65-89 years, had at least 2 claims for HF diagnosis (2015-2020), and were continuously enrolled in a Medicare Advantage prescription drug plan for at least 12 months before and at least 6 months after HF diagnosis. For the assessment of characteristics and HCRU, the suspected risk level was categorized based on the predicted probability (PP) from model output (high, moderate, and low risk: PP≥0.70; ≥0.50 and < 0.70; and < 0.50, respectively). RESULTS: Of 267,025 eligible patients, 119 (0.04%) had confirmed ATTRwt-CM; of 266,906 patients with nonamyloid HF, 10,997 (4.1%), 68,174 (25.5%), and 187,735 (70.3%) were categorized as high, moderate, and low risk for ATTRwt-CM, respectively. The model demonstrated sensitivity/specificity/accuracy/receiver operating characteristic area under the concentration-time curve of 88%/65%/77%/0.89, respectively, in differentiating ATTRwt-CM from nonamyloid HF. In patients with confirmed ATTRwt-CM, the mean (SD) time between HF and ATTRwt-CM diagnoses was 751 (528) days; 65% and 48% were hospitalized before and after ATTRwt-CM diagnosis, respectively. Atrial fibrillation was more common in patients with confirmed ATTRwt-CM and high risk (39% and 55%) vs low risk (27%). Hospitalization and emergency department visits after HF diagnosis were reported in 57% and 46% of patients with high ATTRwt-CM risk, respectively. CONCLUSIONS: The ATTRwt-CM predictive model performed well in identifying disease risk in the Humana Research Database. Patients at high risk for ATTRwt-CM had high HCRU and may benefit from the earlier suspicion of ATTRwt-CM. The model may be used as a tool to identify patients with a suspected high risk for the disease to facilitate earlier detection and treatment. DISCLOSURES: This study was sponsored by Pfizer. Medical writing support was provided by Donna McGuire of Engage Scientific Solutions and funded by Pfizer. Drs Bruno and Schepart and Mr Casey are currently employees of Pfizer and equity holders in this publicly traded company. Dr Reed was an employee of Pfizer at the time that this analysis was planned and conducted. Mr Sheer and Dr Simmons are currently employees of Humana, which received research funding from Pfizer. Dr Nair was an employee of Humana at the time that this analysis was planned and conducted.

Phenotypic clustering of heart failure with preserved ejection fraction reveals different rates of hospitalization.

AIMS: Approximately 50% of patients with heart failure have preserved (≥50%) ejection fraction (HFpEF). Improved understanding of the phenotypic heterogeneity of HFpEF might facilitate development of targeted therapies and interventions. METHODS: This retrospective study characterized a cohort of patients with HFpEF based on similar clinical profiles and evaluated 1-year heart failure related hospitalization. Enrolment, medical and pharmacy data were used to identify patients newly diagnosed with heart failure enrolled in a Medicare Advantage Prescription Drug or commercial healthcare plan. To identify only those patients with HFpEF, we used natural language processing techniques of ejection fraction values abstracted from a linked free-text clinical notes data source. The study population comprised 1515 patients newly identified with HFpEF between 1 January 2011 and 31 December 2015. RESULTS: Using unsupervised machine learning, we identified three distinguishable patient clusters representing different phenotypes: cluster-1 patients had the lowest prevalence of heart failure comorbidities and highest mean age; cluster-2 patients had higher prevalence of metabolic syndrome and pulmonary disease, despite younger mean age; and cluster-3 patients had higher prevalence of cardiac arrhythmia and renal disease. Cluster-3 had the highest 1-year heart failure related hospitalization rates. Within-cluster analysis, prior use of diuretics (cluster-1 and cluster-2) and age (cluster-2 and cluster-3) was associated with 1-year heart failure related hospitalization. Combination therapy was associated with decreased 1-year heart failure related hospitalization in cluster-1. CONCLUSION: This study demonstrated that clustering can be used to characterize subgroups of patients with newly identified HFpEF, assess differences in heart failure related hospitalization rates at 1 year and suggest patient subtypes may respond differently to treatments or interventions.

Real-World Analysis of Guideline-Based Therapy After Hospitalization for Heart Failure.

Background Patients hospitalized with heart failure (HF) with reduced ejection fraction have high risk of rehospitalization or death. Despite guideline recommendations based on high-quality evidence, a substantial proportion of patients with HF with reduced ejection fraction receive suboptimal care and/or do not comply with optimal care following hospitalization. Methods and Results This retrospective observational study identified 17 106 patients with HF with reduced ejection fraction with an incident HF-related hospitalization using the Humana Medicare Advantage database (2008-2016). HF medication classes (beta-blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, angiotensin receptor neprilysin inhibitors, or mineralocorticoid receptor antagonists) received in the year after hospitalization were recorded, and categorized by treatment intensity (ie, number of concomitant medication classes received: none [23% of patients; n=3987], monotherapy [22%; n=3777], dual therapy [41%; n=7056], or triple therapy [13%; n=2286]). Compared with no medication, risk of primary outcome (composite of death or rehospitalization) was significantly reduced (hazard ratio [95% CI]) with monotherapy (0.68 [0.64-0.71]), dual therapy (0.56 [0.53-0.59]), and triple therapy (0.45 [0.41-0.50]). Nearly half (46%) of patients who received post-discharge medication had no dose escalation. Overall, 59% of patients had follow-up with a primary care physician within 14 days of discharge, and 23% had follow-up with a cardiologist. Conclusions In real-world clinical practice, increasing treatment intensity reduced risk of death and rehospitalization among patients hospitalized for HF, though the use of guideline-recommended dual and triple HF therapy remained low. There are opportunities to improve post-discharge medical management for patients with HF with reduced ejection fraction such as optimizing dose titration and improving post-discharge follow-up with providers.

Use of EHR-Based Pediatric Quality Measures: Views of Health System Leaders and Parents.

Measures of health care quality are produced from a variety of data sources, but often, physicians do not believe these measures reflect the quality of provided care. The aim was to assess the value to health system leaders (HSLs) and parents of benchmarking on health care quality measures using data mined from the electronic health record (EHR). Using in-context interviews with HSLs and parents, the authors investigated what new decisions and actions benchmarking using data mined from the EHR may enable and how benchmarking information should be presented to be most informative. Results demonstrate that although parents may have little experience using data on health care quality for decision making, they affirmed its potential value. HSLs expressed the need for high-confidence, validated metrics. They also perceived barriers to achieving meaningful metrics but recognized that mining data directly from the EHR could overcome those barriers. Parents and HSLs need high-confidence health care quality data to support decision making.

A Dictionary Approach to Electron Backscatter Diffraction Indexing.

We propose a framework for indexing of grain and subgrain structures in electron backscatter diffraction patterns of polycrystalline materials. We discretize the domain of a dynamical forward model onto a dense grid of orientations, producing a dictionary of patterns. For each measured pattern, we identify the most similar patterns in the dictionary, and identify boundaries, detect anomalies, and index crystal orientations. The statistical distribution of these closest matches is used in an unsupervised binary decision tree (DT) classifier to identify grain boundaries and anomalous regions. The DT classifies a pattern as an anomaly if it has an abnormally low similarity to any pattern in the dictionary. It classifies a pixel as being near a grain boundary if the highly ranked patterns in the dictionary differ significantly over the pixel's neighborhood. Indexing is accomplished by computing the mean orientation of the closest matches to each pattern. The mean orientation is estimated using a maximum likelihood approach that models the orientation distribution as a mixture of Von Mises-Fisher distributions over the quaternionic three sphere. The proposed dictionary matching approach permits segmentation, anomaly detection, and indexing to be performed in a unified manner with the additional benefit of uncertainty quantification.

The coagulopathy of acute sepsis.

PURPOSE OF REVIEW: Sepsis, defined by the presence of infection and host inflammation, is a lethal clinical syndrome with an increasing mortality rate worldwide. In severe disease, the coagulation system becomes diffusely activated, with consumption of multiple clotting factors resulting in disseminated intravascular coagulation (DIC). When present, DIC portends a higher mortality rate. Understanding the mechanisms that tie inflammation and diffuse thrombosis will allow therapeutic interventions to be developed. The coagulopathy of acute sepsis is a dynamic process that is time and disease burden specific. Whole-blood testing of coagulation may provide more clinically useful information than the classical tests. Natural anticoagulants that regulate thrombosis are downregulated in sepsis. Patients may benefit from the modulation of the coagulation system when systemic inflammation and hypercoagulopathy exist. Proper timing of anticoagulant therapy may ultimately lead to decreased incidence of multisystem organ dysfunction. RECENT FINDINGS: The pathogenesis of coagulopathy in sepsis is driven by an upregulation of procoagulant mechanisms and simultaneous downregulation of natural anticoagulants. Inflammation caused by the invading organism is a natural host defense that cannot be eliminated during treatment. Successful strategies to prevent multisystem organ dysfunction center on stratifying patients at high risk for DIC and restoring the balance of inflammation and coagulation. SUMMARY: The prevention of DIC in septic patients is a key therapeutic target in preventing death from multisystem organ failure. Stratifying patients for therapy using thromboelastometry, specific markers for DIC, and composite scoring systems is an area of growing research.

Tranexamic acid: from trauma to routine perioperative use.

PURPOSE OF REVIEW: Optimizing hemostasis with antifibrinolytics is becoming a common surgical practice. Large clinical studies have demonstrated efficacy and safety of tranexamic acid (TXA) in the trauma population to reduce blood loss and transfusions. Its use in patients without pre-existing coagulopathies is debated, as thromboembolic events are a concern. In this review, perioperative administration of TXA is examined in nontrauma surgical populations. Additionally, risk of thromboembolism, dosing regimens, and timing of dosing are assessed. RECENT FINDINGS: Perioperative use of TXA is associated with reduced blood loss and transfusions. Thromboembolic effects do not appear to be increased. However, optimal dosing and timing of TXA administration is still under investigation for nontrauma surgical populations. SUMMARY: As part of a perioperative blood management programme, TXA can be used to help reduce blood loss and mitigate exposure to blood transfusion.

3D Materials image segmentation by 2D propagation: a graph-cut approach considering homomorphism.

Segmentation propagation, similar to tracking, is the problem of transferring a segmentation of an image to a neighboring image in a sequence. This problem is of particular importance to materials science, where the accurate segmentation of a series of 2D serial-sectioned images of multiple, contiguous 3D structures has important applications. Such structures may have distinct shape, appearance, and topology, which can be considered to improve segmentation accuracy. For example, some materials images may have structures with a specific shape or appearance in each serial section slice, which only changes minimally from slice to slice, and some materials may exhibit specific inter-structure topology that constrains their neighboring relations. Some of these properties have been individually incorporated to segment specific materials images in prior work. In this paper, we develop a propagation framework for materials image segmentation where each propagation is formulated as an optimal labeling problem that can be efficiently solved using the graph-cut algorithm. Our framework makes three key contributions: 1) a homomorphic propagation approach, which considers the consistency of region adjacency in the propagation; 2) incorporation of shape and appearance consistency in the propagation; and 3) a local non-homomorphism strategy to handle newly appearing and disappearing substructures during this propagation. To show the effectiveness of our framework, we conduct experiments on various 3D materials images, and compare the performance against several existing image segmentation methods.

A model based iterative reconstruction algorithm for high angle annular dark field-scanning transmission electron microscope (HAADF-STEM) tomography.

High angle annular dark field (HAADF)-scanning transmission electron microscope (STEM) data is increasingly being used in the physical sciences to research materials in 3D because it reduces the effects of Bragg diffraction seen in bright field TEM data. Typically, tomographic reconstructions are performed by directly applying either filtered back projection (FBP) or the simultaneous iterative reconstruction technique (SIRT) to the data. Since HAADF-STEM tomography is a limited angle tomography modality with low signal to noise ratio, these methods can result in significant artifacts in the reconstructed volume. In this paper, we develop a model based iterative reconstruction algorithm for HAADF-STEM tomography. We combine a model for image formation in HAADF-STEM tomography along with a prior model to formulate the tomographic reconstruction as a maximum a posteriori probability (MAP) estimation problem. Our formulation also accounts for certain missing measurements by treating them as nuisance parameters in the MAP estimation framework. We adapt the iterative coordinate descent algorithm to develop an efficient method to minimize the corresponding MAP cost function. Reconstructions of simulated as well as experimental data sets show results that are superior to FBP and SIRT reconstructions, significantly suppressing artifacts and enhancing contrast.

Adherence to oral tyrosine kinase inhibitor therapies in chronic myeloid leukemia.

Ensuring adherence to therapy is a challenge in chronic diseases, particularly in cancers such as chronic myeloid leukemia (CML), where there has been increased availability and use of oral formulations. A conceptual model of adherence was developed based on findings from a comprehensive literature review, to inform strategies for improving adherence to oral CML therapies. A complex interplay of factors (including clinical, psychological and behavioural) influence adherence to such therapies. Healthcare professionals have a key role in promoting and facilitating adherence and future strategies should place greater emphasis on understanding patient-level experiences in order to create personalized solutions.