Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear. METHODS: This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation. RESULTS: Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation. CONCLUSIONS: These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

Discrepancy between predicted and measured exercise intensity for eliciting the maximal rate of lipid oxidation.

BACKGROUND AND AIMS: Ectopic lipid storage is implicated in type 2 diabetes pathogenesis; hence, exercise to deplete stores (i.e., at the intensity that allows for maximal rate of lipid oxidation; MLO) might be optimal for restoring metabolic health. This intensity ("Fatmax") is estimated during incremental exercise ("Fatmax test"). However, in "the field" general recommendations exist regarding a range of percentages of maximal heart rate (HR) to elicit MLO. The degree to which this range is aligned with measured Fatmax has not been investigated. We compared measured HR at Fatmax, with maximal HR percentages within the typically recommended range in a sample of 26 individuals (Female: n = 11, European ancestry: n = 17). METHODS AND RESULTS: Subjects completed a modified Fatmax test with a 5-min warmup, followed by incremental stages starting at 15 W with work rate increased by 15 W every 5 min until termination criteria were reached. Pulmonary gas exchange was recorded and average values for V˙ o2 and V˙ co2 for the final minute of each stage were used to estimate substrate-oxidation rates. We modeled lipid-oxidation kinetics using a sinusoidal model and expressed MLO relative to peak V˙ o2 and HR. Bland-Altman analysis demonstrated lack of concordance between HR at Fatmax and at 50%, 70%, and 80% of age-predicted maximum with a mean difference of 23 b·min-1. CONCLUSION: Our results indicate that estimated "fat-burning" heart rate zones are inappropriate for prescribing exercise to elicit MLO and we recommend direct individual exercise lipid oxidation measurements to elicit these values.

Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4.

INTRODUCTION: End-stage kidney disease (ESKD) is associated with increased morbidity and mortality. Identifying patients with stage 4 CKD (CKD4) at risk of rapid progression to ESKD remains challenging. Accurate prediction of CKD4 progression can improve patient outcomes by improving advanced care planning and optimizing healthcare resource allocation. METHODS: We obtained electronic health record data from patients with CKD4 in a large health system between January 1, 2006, and December 31, 2016. We developed and validated four models, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network (ANN), to predict ESKD at 3 years. We utilized area under the receiver operating characteristic curve (AUROC) to evaluate model performances and utilized Shapley additive explanation (SHAP) values and plots to define feature dependence of the best performance model. RESULTS: We included 3,160 patients with CKD4. ESKD was observed in 538 patients (21%). All approaches had similar AUROCs; ANN yielded the highest AUROC (0.77; 95%CI 0.75 to 0.79) and LASSO regression (0.77; 95%CI 0.75 to 0.79), followed by random forest (0.76; 95% CI 0.74 to 0.79), and XGBoost (0.76; 95% CI 0.74 to 0.78). CONCLUSIONS: We developed and validated several models for near-term prediction of kidney failure in CKD4. ANN, random forest, and XGBoost demonstrated similar predictive performances. Using this suite of models, interventions can be customized based on risk, and population health and resources appropriately allocated.

Digital workflow for creating a coolant channel for direct irrigation through an implant surgical guide.

Heat elicited during the osteotomy for implant placement may have a significant impact on the vitality of surrounding bone and on the healing capacity for osseointegration. This article describes a digital workflow for creating a coolant channel for the direct irrigation of the osteotomy site through an implant surgical guide. This technique can be particularly advantageous when the surgical guide restricts access for direct irrigation of the osteotomy site.

EVIDENCE IS INSUFFICIENT WITH THE ASSOCIATION BETWEEN CANTILEVER TO ANTERIOR-POSTERIOR RATIO AND CLINICAL OUTCOMES OF FIXED IMPLANT-SUPPORTED FULL-ARCH PROSTHESIS.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Walter L, Greenstein G. Utility of measuring anterior-posterior spread to determine distal cantilever length off a fixed implant-supported full-arch prosthesis: A review of the literature. J Am Dent Assoc. 2020 Oct;151(10):790-795. doi:10.1016/j.adaj.2020.06.016. PMID: 32979958. SOURCE OF FUNDING: Unknown. TYPE OF STUDY/DESIGN: Systematic review.

Treatment of velopharyngeal dysfunction: A clinical report.

Current tumor therapies can impair velopharyngeal function and lead to the development of hypernasality and nasal regurgitation. This article describes a prosthetic restoration of velopharyngeal dysfunction with a multidisciplinary approach including a speech pathologist and videofluoroscopic and nasal endoscopic procedures.

Digital Workflow for Retrofitting a Surveyed Crown Using a Removable Partial Denture as an Antagonist.

Digital workflow expedites the procedure of retrofitting a surveyed crown against an existing removable partial denture (RPD). This article describes a simple and straightforward technique of digital workflow where an existing RPD is scanned as an antagonist to design the rest seat, guide plane, and height of contour of a surveyed crown.

Cement Type (Adhesive vs. Conventional) May Not Affect Clinical Performance of Zirconia and Lithium Disilicate Tooth-Supported Crowns.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Effect of cement type on the clinical performance and complications of zirconia and lithium disilicate tooth-supported crowns: A systematic review. Report of the Committee on Research in Fixed Prosthodontics of the American Academy of Fixed Prosthodontics. Maroulakos G, Thompson GA, Kontogiorgos ED. J Prosthet Dent 2019;121:754-65. SOURCE OF FUNDING: Unknown. TYPE OF STUDY/DESIGN: Systematic review.

Recording an accurate maxillomandibular relationship by adding vertical stops to the occlusal rims.

Complete denture fabrication includes accurate recording of the maxillomandibular relationship to ensure the functional requirements of occlusion and minimize the traumatic effects on the residual alveolar ridges. However, errors may occur when the occlusal rims have uneven and nonuniform occlusal contacts. The technique described is a straightforward method for recording an accurate silicone centric relationship record with dome-shaped baseplate wax added as vertical stops on the occlusal surface of the mandibular occlusal rim.

A technique for verifying the accuracy of the virtual mounting of digital scans against the actual occlusal contacts.

The digital scans of dentate arches can be mounted from a virtual interocclusal record to expedite the fabrication of dental prostheses. However, the virtual mounting may develop an occlusal error when combined with less than ideally scanned data and an algorithm that matches poorly. This article describes a method of verifying the accuracy of virtual mounting against the actual occlusal contacts marked with colored articulating paper.

Recording and reproducing cast orientation by using an implant impression coping and implant analog: A dental technique.

For the fabrication of a removable partial denture, the orientation of a definitive cast should be recorded and reproduced to indicate the most desirable path of placement and undercut areas. This article describes a straightforward and accurate method of recording and reproducing the cast orientation by using an implant impression coping and an implant analog.

Multiple Disconnections/Reconnections of Implant Abutment May Induce Recession of Peri-implant Mucosa.

ARTICLE TITLE AND BIBLIOGRAPHIC INFORMATION: Abutment Disconnection/Reconnection Affects Peri-implant Marginal Bone Levels: A Meta-Analysis. Koutouzis T, Gholami F, Reynolds J, Lundgren T, Kotsakis GA. Int J Oral Maxillofac Implants 2017;32(3):575-81. SOURCE OF FUNDING: The authors declared no conflict of interest TYPE OF STUDY/DESIGN: Systematic review with meta-analysis of data.

Personalized long-term prediction of cognitive function: Using sequential assessments to improve model performance.

Prediction of onset and progression of cognitive decline and dementia is important both for understanding the underlying disease processes and for planning health care for populations at risk. Predictors identified in research studies are typically accessed at one point in time. In this manuscript, we argue that an accurate model for predicting cognitive status over relatively long periods requires inclusion of time-varying components that are sequentially assessed at multiple time points (e.g., in multiple follow-up visits). We developed a pilot model to test the feasibility of using either estimated or observed risk factors to predict cognitive status. We developed two models, the first using a sequential estimation of risk factors originally obtained from 8 years prior, then improved by optimization. This model can predict how cognition will change over relatively long time periods. The second model uses observed rather than estimated time-varying risk factors and, as expected, results in better prediction. This model can predict when newly observed data are acquired in a follow-up visit. Performances of both models that are evaluated in10-fold cross-validation and various patient subgroups show supporting evidence for these pilot models. Each model consists of multiple base prediction units (BPUs), which were trained using the same set of data. The difference in usage and function between the two models is the source of input data: either estimated or observed data. In the next step of model refinement, we plan to integrate the two types of data together to flexibly predict dementia status and changes over time, when some time-varying predictors are measured only once and others are measured repeatedly. Computationally, both data provide upper and lower bounds for predictive performance.

Prosthodontic treatment of a retrognathic edentulous maxilla demonstrating limited interarch distance: 3.5-year results with fixed and removable implant prostheses.

The prosthodontic treatment of patients with a retrognathic edentulous maxilla should consider the restoration of the lower facial profile and access for oral hygiene. This clinical report describes prosthodontic treatments of a patient with edentulism who presented with repeated fractures of the denture teeth of a maxillary implant-supported complete fixed dental prosthesis (ICFDP) and a mandibular implant-supported overdenture. Considerable plaque accumulation was noted on the ICFDP, which was replaced with an open palatal design implant-supported overdenture. However, the patient experienced difficulty managing the 2 removable prostheses. The patient's mandible was eventually restored with a milled titanium alloy framework ICFDP with metal occlusal surfaces. This combined approach of fixed and removable prostheses was stable at 3.5-year follow-up appointment, without compromising the patient's oral hygiene or comfort.

Impact of implant support on mandibular free-end base removable partial denture: theoretical study.

OBJECTIVES: This study investigated the impact of implant support on the development of shear force and bending moment in mandibular free-end base removable partial dentures (RPDs). MATERIAL AND METHODS: Three theoretical test models of unilateral mandibular free-end base RPDs were constructed to represent the base of tooth replacement, as follows: Model 1: first and second molars (M1 and M2); Model 2: second premolar (P2), M1, and M2; and Model 3: first premolar (P1), P2, M1, and M2. The implant support located either at M1 or M2 sites. The occlusal loading was concentrated at each replacement tooth to calculate the stress resultants developed in the RPD models using the free-body diagrams of shear force and bending moment. RESULTS: There was a trend of reduction in the peak shear force and bending moment when the base was supported by implant. However, the degree of reduction varied with the location of implant support. The moment reduced by 76% in Model 1, 58% in Model 2, and 42% in Model 3, when the implant location shifted from M1 to M2 sites. CONCLUSIONS: The shear forces and bending moments subjected to mandibular free-end base RPDs were found to decrease with the addition of implant support. However, the impact of implant support varied with the location of implant in this theoretical study.

Expedited Record Base Fabrication Using an Irreversible Hydrocolloid Cast.

The registration of a maxillomandibular relationship requires additional clinical and laboratory procedures when the mouth presents with loss of occlusal support. This procedure can be a challenge for a patient who needs urgent care or resides in a remote area. This article describes a procedure for expediting the mounting of a master cast for the fabrication of a maxillary immediate complete denture. The technique presented describes the use of a silicone record base made on an irreversible hydrocolloid cast generated from the final impression.

Indirect method of base adaptation against supporting element of tooth root for a partial overdenture prosthesis.

The base of a partial overdenture prosthesis should be fitted intraorally against the supporting element of a tooth root. Chairside relining is a common method; however, an autopolymerizing acrylic resin presents high porosity when polymerized intraorally. This article describes an indirect method where an impression is made with a silicone occlusion registration material to create a replica of the supporting elements of the residual ridge and the tooth root in a high-viscosity polyvinyl siloxane impression material.

A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.

Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.

Federated Learning in Health care Using Structured Medical Data.

The success of machine learning-based studies is largely subjected to accessing a large amount of data. However, accessing such data is typically not feasible within a single health system/hospital. Although multicenter studies are the most effective way to access a vast amount of data, sharing data outside the institutes involves legal, business, and technical challenges. Federated learning (FL) is a newly proposed machine learning framework for multicenter studies, tackling data-sharing issues across participant institutes. The promise of FL is simple. FL facilitates multicenter studies without losing data access control and allows the construction of a global model by aggregating local models trained from participant institutes. This article reviewed recently published studies that utilized FL in clinical studies with structured medical data. In addition, challenges and open questions in FL in clinical studies with structured medical data were discussed.