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.

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.

Joint Modeling of Social Determinants and Clinical Factors to Define Subphenotypes in Out-of-Hospital Cardiac Arrest Survival: Cluster Analysis.

Background: Machine learning clustering offers an unbiased approach to better understand the interactions of complex social and clinical variables via integrative subphenotypes, an approach not studied in out-of-hospital cardiac arrest (OHCA). Objective: We conducted a cluster analysis for a cohort of OHCA survivors to examine the association of clinical and social factors for mortality at 1 year. Methods: We used a retrospective observational OHCA cohort identified from Medicare claims data, including area-level social determinants of health (SDOH) features and hospital-level data sets. We applied k-means clustering algorithms to identify subphenotypes of beneficiaries who had survived an OHCA and examined associations of outcomes by subphenotype. Results: We identified 27,028 unique beneficiaries who survived to discharge after OHCA. We derived 4 distinct subphenotypes. Subphenotype 1 included a distribution of more urban, female, and Black beneficiaries with the least robust area-level SDOH measures and the highest 1-year mortality (2375/4417, 53.8%). Subphenotype 2 was characterized by a greater distribution of male, White beneficiaries and had the strongest zip code-level SDOH measures, with 1-year mortality at 49.9% (4577/9165). Subphenotype 3 had the highest rates of cardiac catheterization at 34.7% (1342/3866) and the greatest distribution with a driving distance to the index OHCA hospital from their primary residence >16.1 km at 85.4% (8179/9580); more were also discharged to a skilled nursing facility after index hospitalization. Subphenotype 4 had moderate median household income at US $51,659.50 (IQR US $41,295 to $67,081) and moderate to high median unemployment at 5.5% (IQR 4.2%-7.1%), with the lowest 1-year mortality (1207/3866, 31.2%). Joint modeling of these features demonstrated an increased hazard of death for subphenotypes 1 to 3 but not for subphenotype 4 when compared to reference. Conclusions: We identified 4 distinct subphenotypes with differences in outcomes by clinical and area-level SDOH features for OHCA. Further work is needed to determine if individual or other SDOH domains are specifically tied to long-term survival after OHCA.

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.

Peripheral Transcriptomics in Acute and Long-Term Kidney Dysfunction in SARS-CoV2 Infection.

Background: Acute kidney injury (AKI) is common in hospitalized patients with SARS-CoV2 infection despite vaccination and leads to long-term kidney dysfunction. However, peripheral blood molecular signatures in AKI from COVID-19 and their association with long-term kidney dysfunction are yet unexplored. Methods: In patients hospitalized with SARS-CoV2, we performed bulk RNA sequencing using peripheral blood mononuclear cells(PBMCs). We applied linear models accounting for technical and biological variability on RNA-Seq data accounting for false discovery rate (FDR) and compared functional enrichment and pathway results to a historical sepsis-AKI cohort. Finally, we evaluated the association of these signatures with long-term trends in kidney function. Results: Of 283 patients, 106 had AKI. After adjustment for sex, age, mechanical ventilation, and chronic kidney disease (CKD), we identified 2635 significant differential gene expressions at FDR<0.05. Top canonical pathways were EIF2 signaling, oxidative phosphorylation, mTOR signaling, and Th17 signaling, indicating mitochondrial dysfunction and endoplasmic reticulum (ER) stress. Comparison with sepsis associated AKI showed considerable overlap of key pathways (48.14%). Using follow-up estimated glomerular filtration rate (eGFR) measurements from 115 patients, we identified 164/2635 (6.2%) of the significantly differentiated genes associated with overall decrease in long-term kidney function. The strongest associations were 'autophagy', 'renal impairment via fibrosis', and 'cardiac structure and function'. Conclusions: We show that AKI in SARS-CoV2 is a multifactorial process with mitochondrial dysfunction driven by ER stress whereas long-term kidney function decline is associated with cardiac structure and function and immune dysregulation. Functional overlap with sepsis-AKI also highlights common signatures, indicating generalizability in therapeutic approaches. SIGNIFICANCE STATEMENT: Peripheral transcriptomic findings in acute and long-term kidney dysfunction after hospitalization for SARS-CoV2 infection are unclear. We evaluated peripheral blood molecular signatures in AKI from COVID-19 (COVID-AKI) and their association with long-term kidney dysfunction using the largest hospitalized cohort with transcriptomic data. Analysis of 283 hospitalized patients of whom 37% had AKI, highlighted the contribution of mitochondrial dysfunction driven by endoplasmic reticulum stress in the acute stages. Subsequently, long-term kidney function decline exhibits significant associations with markers of cardiac structure and function and immune mediated dysregulation. There were similar biomolecular signatures in other inflammatory states, such as sepsis. This enhances the potential for repurposing and generalizability in therapeutic approaches.

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.

The Influence of Urban Gardening Activities on Participants' Perceived Restorativeness, Resilience, Sense of Community and Stress.

This study aimed to investigate the influence of urban garden activities on participants' perceived restorativeness, resilience, sense of community, and stress reduction. A total of ninety individuals who agreed to participate in the experiment were divided into experimental and control groups. To collect data, 16 sessions of urban garden activities were conducted every two weeks from May to November 2022. Perceived Restorativeness Scale, Connor-Davidson Resilience Scale, Sense of Community Index, and Brief Encounter Psychosocial Instrument were employed to measure participants' psychological effects. To evaluate physiological effects, salivary cortisol tests were performed. The results of the study revealed that urban gardening activities influenced on participants' physiological and psychological reactions in positive ways.

Joint Modeling of Clinical and Biomarker Data in Acute Kidney Injury Defines Unique Subphenotypes with Differing Outcomes.

BACKGROUND: AKI is a heterogeneous syndrome. Current subphenotyping approaches have only used limited laboratory data to understand a much more complex condition. METHODS: We focused on patients with AKI from the Assessment, Serial Evaluation, and Subsequent Sequelae in AKI (ASSESS-AKI). We used hierarchical clustering with Ward linkage on biomarkers of inflammation, injury, and repair/health. We then evaluated clinical differences between subphenotypes and examined their associations with cardiorenal events and death using Cox proportional hazard models. RESULTS: We included 748 patients with AKI: 543 (73%) of them had AKI stage 1, 112 (15%) had AKI stage 2, and 93 (12%) had AKI stage 3. The mean age (±SD) was 64 (13) years; 508 (68%) were men; and the median follow-up was 4.7 (Q1: 2.9, Q3: 5.7) years. Patients with AKI subphenotype 1 ( N =181) had the highest kidney injury molecule (KIM-1) and troponin T levels. Subphenotype 2 ( N =250) had the highest levels of uromodulin. AKI subphenotype 3 ( N =159) comprised patients with markedly high pro-brain natriuretic peptide and plasma tumor necrosis factor receptor-1 and -2 and low concentrations of KIM-1 and neutrophil gelatinase-associated lipocalin. Finally, patients with subphenotype 4 ( N =158) predominantly had sepsis-AKI and the highest levels of vascular/kidney inflammation (YKL-40, MCP-1) and injury (neutrophil gelatinase-associated lipocalin, KIM-1). AKI subphenotypes 3 and 4 were independently associated with a higher risk of death compared with subphenotype 2 and had adjusted hazard ratios of 2.9 (95% confidence interval, 1.8 to 4.6) and 1.6 (95% confidence interval, 1.01 to 2.6, P = 0.04), respectively. Subphenotype 3 was also independently associated with a three-fold risk of CKD and cardiovascular events. CONCLUSIONS: We discovered four AKI subphenotypes with differing clinical features and biomarker profiles that are associated with longitudinal clinical outcomes.

Impact of Vaccination on Major Adverse Cardiovascular Events in Patients With COVID-19 Infection.

Taken from the largest U.S. cohort of patients with SARS-CoV2, our results demonstrate the association of even partial vaccination with lower risk of MACE after SARS-CoV-2 infection.

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.

High physical activity alleviates the adverse effect of higher sedentary time on the incidence of chronic kidney disease.

BACKGROUND: Low physical activity (PA) increases the prevalence of chronic kidney disease (CKD). This study aimed to investigate the effects of PA and sedentary time (ST) on the changes in renal function and the development of CKD in the middle-aged Korean population. METHODS: From the Korean Genome and Epidemiology Study Database, 7988 participants in their 40s and 60s were identified and stratified by (1) PA: high-PA (>24 MET-h/day), moderate-PA (9-24 MET-h/day) and low-PA (<9 MET-h/day); and (2) ST: high-ST (>6 h/day), moderate-ST (3-6 h/day) and low-ST (<3 h/day). Incident CKD was defined as estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 following the Chronic Kidney Disease Epidemiology Collaboration equation. RESULTS: The mean age of the participants was 52.0 years. The overall incidence of CKD was 16.8 per 1000 person-years over a median of 12 years. The lower the PA and the higher the ST, the lower the baseline eGFR. Relative to the high-PA, the coefficients of the annual eGFR decline were -0.12 (95% confidence interval [CI]: -0.26 to 0.001, P = 0.081) and -0.13 (95% CI: -0.27 to 0.01, P = 0.078) in the moderate- and low-PA groups, respectively. Similarly, relative to the low-ST, the coefficients of annual eGFR decline were -0.07 (59% CI: -0.19 to 0.05, P = 0.236) and -0.14 (95% CI: -0.28 to -0.01, P = 0.039) in the moderate- and high-ST groups, respectively. Incident CKD was higher with lower PA (hazard ratio: high-PA 1.00, moderate-PA 1.13 [1.00, 1.28, P = 0.056] and low-PA 1.25 [1.11, 1.24, P < 0.001]) and higher ST (hazard ratio: low-ST 1.00, moderate-ST 1.04 [0.94, 1.16, P = 0.440] and high-ST 1.19 [1.05, 1.34, P = 0.007]). The high-PA reduced the risk for the CKD development irrespective of the amount of ST. CONCLUSIONS: Low-PA and high-ST are risk factors for the development of CKD in the middle-aged Korean population. High-PA recovers high-ST, inducing a harmful effect on the occurrence of CKD.

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.

Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit.

OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) has heterogenous clinical courses, indicating that there might be distinct subphenotypes in critically ill patients. Although prior research has identified these subphenotypes, the temporal pattern of multiple clinical features has not been considered in cluster models. We aimed to identify temporal subphenotypes in critically ill patients with COVID-19 using a novel sequence cluster analysis and associate them with clinically relevant outcomes. MATERIALS AND METHODS: We analyzed 1036 confirmed critically ill patients with laboratory-confirmed SARS-COV-2 infection admitted to the Mount Sinai Health System in New York city. The agglomerative hierarchical clustering method was used with Levenshtein distance and Ward's minimum variance linkage. RESULTS: We identified four subphenotypes. Subphenotype I (N = 233 [22.5%]) included patients with rapid respirations and a rapid heartbeat but less need for invasive interventions within the first 24 hours, along with a relatively good prognosis. Subphenotype II (N = 418 [40.3%]) represented patients with the least degree of ailments, relatively low mortality, and the highest probability of discharge from the hospital. Subphenotype III (N = 259 [25.0%]) represented patients who experienced clinical deterioration during the first 24 hours of intensive care unit admission, leading to poor outcomes. Subphenotype IV (N = 126 [12.2%]) represented an acute respiratory distress syndrome trajectory with an almost universal need for mechanical ventilation. CONCLUSION: We utilized the sequence cluster analysis to identify clinical subphenotypes in critically ill COVID-19 patients who had distinct temporal patterns and different clinical outcomes. This study points toward the utility of including temporal information in subphenotyping approaches.

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.

A Computational Method for Learning Disease Trajectories From Partially Observable EHR Data.

Diseases can show different courses of progression even when patients share the same risk factors. Recent studies have revealed that the use of trajectories, the order in which diseases manifest throughout life, can be predictive of the course of progression. In this study, we propose a novel computational method for learning disease trajectories from EHR data. The proposed method consists of three parts: first, we propose an algorithm for extracting trajectories from EHR data; second, three criteria for filtering trajectories; and third, a likelihood function for assessing the risk of developing a set of outcomes given a trajectory set. We applied our methods to extract a set of disease trajectories from Mayo Clinic EHR data and evaluated it internally based on log-likelihood, which can be interpreted as the trajectories' ability to explain the observed (partial) disease progressions. We then externally evaluated the trajectories on EHR data from an independent health system, M Health Fairview. The proposed algorithm extracted a comprehensive set of disease trajectories that can explain the observed outcomes substantially better than competing methods and the proposed filtering criteria selected a small subset of disease trajectories that are highly interpretable and suffered only a minimal (relative 5%) loss of the ability to explain disease progression in both the internal and external validation.

Loss of abutment teeth for double crown-retained removable partial dentures compared to clasp-retained removable partial dentures: a systematic review and meta-analysis.

Objective: To analyze the loss of abutment teeth for double crown-retained removable partial dentures (DC-RPDs) compared to clasp-retained removable partial dentures (C-RPDs). Method and materials: A search was conducted in the Ovid MEDLINE, Embase, Web of Science databases, and a manual search. The search was conducted based on the PICO framework with inclusion and exclusion criteria. After extracting the data of selected studies, a meta-analysis was performed to estimate abutment loss with 95% confidence interval (CI). The statistical significance was defined as P < .05, and the heterogeneity of the data was assessed based on the chi-squared test and I2 statistics. Risk of bias assessment was conducted using Cochrane Risk of Bias tool and Newcastle-Ottawa Scale. Results: A total of 4,692 records were identified from electronic databases and 38 studies were included for quantitative synthesis of 3,393 subjects with 13,552 abutment teeth. A total of 1,226 abutment teeth were lost with > 4,016 RPDs. Seven studies were compounded for > 668 C-RPDs (mean follow-up time ≤ 5 years) and six studies for 893 C-RPDs (mean follow-up time < 5 years), where the estimates of abutment loss were 5% (95% CI 2% to 8%) and 8% (95% CI 5% to 13%), respectively. The data were not significantly different (P = .1), and were heterogenous between the studies (τ2 ≥ 0.34, I2 ≥ 87.38%). Thirteen studies were compounded for 1,223 DC-RPDs (mean follow-up time ≤ 5 years) and eight studies for 1,033 DC-RPDs (mean follow-up time > 5 years), where the estimates of abutment loss were 6% (95% CI 5% to 8%) and 12% (95% CI 8% to 18%), respectively. The data were heterogenous (τ2 ≥ 0.17, I2 ≥ 75.86%), and were significantly different between the studies (P = .005). Overall, C-RPDs were not significantly different from DC-RPDs in abutment loss (P ≥ .3). A significant predictor for abutment loss was follow-up time with DC-RPDs (P = .005), where the risk of abutment loss per year was 18% (P = .0001). In contrast, follow-up time was not a significant factor for C-RPDs (P = .1). None of the included studies were at high risk of bias. Conclusion: Within the limitations of the current systematic review and meta-analysis, abutment loss was not significantly different between C-RPDs and DC-RPDs. A significant predictor was follow-up time for DC-RPDs, whereas this factor was not significant for C-RPDs. Further research is needed to investigate critical factors for abutment loss with RPDs.

Incremental Effect of Aging on Obesity-Related Incident Chronic Kidney Disease in the Korean General Population.

OBJECTIVES: Obesity may increase the risk of kidney function decline. However, few studies have addressed how age modifies obesity-associated risk of chronic kidney disease (CKD) in the Asian general population. DESIGN: A community-based prospective cohort study. SETTING AND PARTICIPANTS: 6538 Korean general population with normal kidney function from the Korean Genome and Epidemiology Study Database. METHODS: Participants were stratified according to age (40-49, 50-59, and 60-69 years old) and body mass index [≥18.5-<23 (normal-weight), ≥23-<27.5 (overweight), and ≥27.5 (obese)]. We conducted Kaplan-Meier and multivariable-adjusted Cox regression analyses to investigate the association of aging and obesity with incident CKD. RESULTS: During the 12-year follow-up, an overall incidence rate of CKD was 6.1 cases per 1000 person-years. Obese, but not overweight, people had an increased risk of incident CKD compared with normal-weight people in multivariable models adjusted for metabolic factors. When analyzed by 10-year increments, this association was significant only in 60-69-year-old individuals. Kaplan-Meier analysis showed that the incidence of CKD associated with overweight or obesity showed an accentuated increase with age. With reference to normal-weight individuals aged 40-49 years, the adjusted hazard ratio of CKD increased with age regardless of body mass index, and the positive association between obesity and incident CKD was more prominent with increasing age. CONCLUSIONS AND IMPLICATIONS: Obesity-associated risk of incident CKD was accentuated in older people, and this association was independent of metabolic abnormalities.

Loss of nonvital abutment teeth for removable partial dentures: a systematic review and meta-analysis.

OBJECTIVE: To analyze the loss of nonvital abutment teeth compared to vital abutment teeth supporting removable partial dentures (RPDs). METHOD AND MATERIALS: An electronic search was conducted in the Ovid MEDLINE, Embase, and Web of Science databases, and supplemented by a manual search. The search was done to identify clinical studies reporting the loss of nonvital abutment teeth compared to vital abutment teeth for RPDs. The data were extracted from each selected article and meta-analysis was performed using a random effects model to estimate adjusted proportion of abutment loss with RPDs along with 95% confidence interval (CI). Risk of bias assessment was conducted using Cochrane Risk of Bias tools and Newcastle-Ottawa Scale. The statistical significance was set as P value < .05. RESULTS: A total of 3,898 records were identified from electronic databases and 8 studies were included for quantitative synthesis of 834 subjects with 1,036 RPDs. No additional records were identified through manual search. Among 1,152 nonvital abutment teeth, 123 teeth were lost. The estimate of nonvital abutment loss was 13% (95% CI 9-18%]. The data were statistically significant (P < .0001), and were heterogenous between the studies (χ2 [df = 7] = 35.9, P < .0001; τ2 = 0.25, I2 = 76.46%). A total of 2,186 vital abutment teeth were compounded where 114 teeth were lost. The estimate of vital abutment loss was 4% (95% CI 2-7%). The data were statistically significant (P < .0001), and were heterogenous between the studies (χ2 [df = 7] = 23.7, P = .01; τ2 = 0.56, I2 = 86.31%). The risk of abutment loss was approximately three times greater with the loss of vitality (odds ratio = 3.04, 95% CI 1.53-6.05; P = .001). In addition, abutment loss was significantly greater with increasing follow-up time (P = .01). None of the included studies were considered to be at high risk of bias. CONCLUSION: Within the limitations of this systematic review and meta-analysis, the loss of nonvital abutment teeth was significantly greater than that of vital abutment teeth for RPDs. Further research is needed to identify critical factors associated with the loss of nonvital abutment teeth.

Open Proximal Contact with Implant-Supported Fixed Prostheses Compared with Tooth-Supported Fixed Prostheses: A Systematic Review and Meta-analysis.

PURPOSE: To investigate the odds of developing open proximal contact (OPC) between implant-supported fixed prostheses and adjacent teeth compared with that between tooth-supported fixed prostheses and adjacent teeth. MATERIALS AND METHODS: A search was conducted in the Ovid MEDLINE, Embase, and Web of Science databases for clinical studies on OPC developed with implant-supported prostheses or tooth-supported prostheses. A meta-analysis was performed using statistical software to estimate the odds of developing OPC with implant-supported prostheses compared with tooth-supported prostheses, with a 95% confidence interval (CI). The statistical significance was defined as P < .05. RESULTS: The odds ratio (OR) of developing OPC with implant-supported prostheses was 2.46 compared with tooth-supported prostheses (95% CI [1.21 to 5.01]; P = .013]. However, the data were highly heterogenous (τ2 = 0.40, I2 = 95.67%). The total estimates of developing OPC were 41% (95% CI [30% to 54%]) with implant-supported prostheses and 22% (95% CI [18% to 26%]) with tooth-supported prostheses. OPC was more prevalent at the mesial side than at the distal side of implant-supported prostheses (OR = 2.38, 95% CI [0.94 to 6]; P = .066), whereas OPC was more prevalent at the distal side than at the mesial side of tooth-supported prostheses (OR = 1.94, 95% CI [1.09 to 3.45]; P = .024). There was no statistically significant difference in developing OPC with covariates of sex, age, arch, splinting of implants or adjacent teeth, region, and vitality of adjacent teeth, retention type, opposing dentition, occlusal force, parafunctional activities, and follow-up time. OPC was found to increase 9% per year with implant-supported prostheses (OR = 1.09). The estimate of interproximal gap next to implant-supported prostheses was 245.8 µm (95% CI [86.4 to 405.3 µm]). CONCLUSION: Within the limitations of this systematic review and meta-analysis, the odds of developing OPC were significantly higher with implant-supported prostheses than with tooth-supported prostheses. However, the data were highly heterogenous, and thus, a well-designed randomized clinical study needs to be conducted to validate the results of this systematic review.

Bone loss in the anterior edentulous maxilla opposing two-implant-supported overdentures vs complete dentures: a systematic review and meta-analysis.

OBJECTIVE: To investigate bone loss in the anterior edentulous maxilla restored with maxillary complete dentures and opposed by mandibular two-implant-supported overdentures (2-IODs) or complete dentures. METHOD AND MATERIALS: A systematic search was conducted using the Ovid MEDLINE, Embase, Web of Science, CINAHL, and Cochrane databases for studies investigating bone loss in the anterior edentulous maxilla with mandibular 2-IODs or complete dentures. Two reviewers assessed the eligibility of studies and risk of bias assessment was conducted according to the Newcastle-Ottawa Scale. A meta-analysis was performed using statistical software to estimate weighted mean difference in bone loss with 95% confidence interval (CI). The level of significance was defined as P value (< .05). RESULTS: A total of 2,510 studies were identified through electronic and manual searching. Six studies were selected and compounded for quantitative synthesis of 163 patients. Bone loss in the anterior edentulous maxilla was greater with 2-IODs than with complete dentures. The total estimate of weighted mean difference between 2-IODs and complete dentures was -1.40 (95% CI -3.12 to 0.31). However, the difference was not statistically significant (P = .11). The data were heterogenous across the studies based on chi-square statistics (χ2 [df = 7] = 52.75, P < .0001; τ2 = 5.53, I2 = 95.21%). In addition, the impact of implant splinting on bone loss was not significant (P > .29). None of the included studies were considered to be at high risk of bias. CONCLUSION: Within the limitations of the current systematic review and meta-analysis, the estimate of bone loss in the anterior edentulous maxilla was greater with 2-IODs than with complete dentures. However, the difference was not statistically significant. A well-designed randomized clinical study needs to be conducted to validate the results of this systematic review.