Are social determinants of health associated with an increased length of hospitalization after revision total hip and knee arthroplasty? A comparison study of social deprivation indices.

INTRODUCTION: Length of stay (LOS) has been extensively assessed as a marker for healthcare utilization, functional outcomes, and cost of care for patients undergoing arthroplasty. The notable patient-to-patient variation in LOS following revision hip and knee total joint arthroplasty (TJA) suggests a potential opportunity to reduce preventable discharge delays. Previous studies investigated the impact of social determinants of health (SDoH) on orthopaedic conditions and outcomes using deprivation indices with inconsistent findings. The aim of the study is to compare the association of three publicly available national indices of social deprivation with prolonged LOS in revision TJA patients. MATERIALS AND METHODS: 1,047 consecutive patients who underwent a revision TJA were included in this retrospective study. Patient demographics, comorbidities, and behavioral characteristics were extracted. Area deprivation index (ADI), social deprivation index (SDI), and social vulnerability index (SVI) were recorded for each patient, following which univariate and multivariate logistic regression analyses were performed to determine the relationship between deprivation measures and prolonged LOS (greater than five days postoperatively). RESULTS: 193 patients had a prolonged LOS following surgery. Categorical ADI was significantly associated with prolonged LOS following surgery (OR = 2.14; 95% CI = 1.30-3.54; p = 0.003). No association with LOS was found using SDI and SVI. When accounting for other covariates, only ASA scores (ORrange=3.43-3.45; p < 0.001) and age (ORrange=1.00-1.03; prange=0.025-0.049) were independently associated with prolonged LOS. CONCLUSION: The varying relationship observed between the length of stay and socioeconomic markers in this study indicates that the selection of a deprivation index could significantly impact the outcomes when investigating the association between socioeconomic deprivation and clinical outcomes. These results suggest that ADI is a potential metric of social determinants of health that is applicable both clinically and in future policies related to hospital stays including bundled payment plan following revision TJA.

Effects of Medicaid managed care on early detection of cancer: Evidence from mandatory Medicaid managed care program in Pennsylvania.

OBJECTIVE: To examine changes in late- versus early-stage diagnosis of cancer associated with the introduction of mandatory Medicaid managed care (MMC) in Pennsylvania. DATA SOURCES AND STUDY SETTING: We analyzed data from the Pennsylvania cancer registry (2010-2018) for adult Medicaid beneficiaries aged 21-64 newly diagnosed with a solid tumor. To ascertain Medicaid and managed care status around diagnosis, we linked the cancer registry to statewide hospital-based facility records collected by an independent state agency (Pennsylvania Health Care Cost Containment Council). STUDY DESIGN: We leveraged a natural experiment arising from county-level variation in mandatory MMC in Pennsylvania. Using a stacked difference-in-differences design, we compared changes in the probability of late-stage cancer diagnosis among those residing in counties that newly transitioned to mandatory managed care to contemporaneous changes among those in counties with mature MMC programs. DATA COLLECTION/EXTRACTION METHODS: N/A. PRINCIPAL FINDINGS: Mandatory MMC was associated with a reduced probability of late-stage cancer diagnosis (-3.9 percentage points; 95% CI: -7.2, -0.5; p = 0.02), particularly for screening-amenable cancers (-5.5 percentage points; 95% CI: -10.4, -0.6; p = 0.03). We found no significant changes in late-stage diagnosis among non-screening amenable cancers. CONCLUSIONS: In Pennsylvania, the implementation of mandatory MMC for adult Medicaid beneficiaries was associated with earlier stage of diagnosis among newly diagnosed cancer patients with Medicaid, especially those diagnosed with screening-amenable cancers. Considering that over half of the sample was diagnosed with late-stage cancer even after the transition to mandatory MMC, Medicaid programs and managed care organizations should continue to carefully monitor receipt of cancer screening and design strategies to reduce barriers to guideline-concordant screening or diagnostic procedures.

Volumetric analysis of spontaneous bone formation after segmental mandibulectomy in patients with MRONJ.

OBJECTIVE: The purpose of this study was to quantify the spontaneous new bone formation and bony bridge formation by 3-dimensional analysis of cone-beam computed tomography (CBCT) after segmental mandibulectomy reconstruction using an R-plate without any graft material in patients with medication-related osteonecrosis of the jaw (MRONJ). STUDY DESIGN: 15 MRONJ patients (13 females and 2 males) were selected based on the inclusion criteria. Data on new bone formation, bony bridge formation, R-plate fracture, patient age, and type and duration of medication were collected. Panoramic and CBCT scans were obtained at 1 day, 6, 12, and 24 months postoperatively. CBCT images of each period were transferred to a personal computer using MIMICS 21.0 for volumetric analysis. After quantifying the volume of new bone formation, we calculated the percentage of the volume of new bone to the segmentally resected necrotic bone volume (%NB). RESULTS: All patients showed spontaneous new bone formation with the average of 20.69% within a year and 28.52% within 2 years, and 80.0% showed bony bridge formation within a year. CONCLUSIONS: The R-plate reconstruction in patients with MRONJ showed significant amount of spontaneous new bone formation within 2 years after segmental mandibulectomy.

Impact of the Affordable Care Act on access to accredited facilities for cancer treatment.

OBJECTIVE: To examine differential changes in receipt of surgery at National Cancer Institute (NCI)-designated comprehensive cancer centers (NCI-CCC) and Commission on Cancer (CoC) accredited hospitals for patients with cancer more likely to be newly eligible for coverage under Affordable Care Act (ACA) insurance expansions, relative to those less likely to have been impacted by the ACA. DATA SOURCES AND STUDY SETTING: Pennsylvania Cancer Registry (PCR) for 2010-2019 linked with discharge records from the Pennsylvania Health Care Cost Containment Council (PHC4). STUDY DESIGN: Outcomes include whether cancer surgery was performed at an NCI-CCC or a CoC-accredited hospital. We conducted a difference-in-differences analysis, estimating linear probability models for each outcome that control for residence in a county with above median county-level pre-ACA uninsurance and the interaction between county-level baseline uninsurance and cancer treatment post-ACA to capture differential changes in access between those more and less likely to become newly eligible for insurance coverage (based on area-level proxy). All models control for age, sex, race and ethnicity, cancer site and stage, census-tract level urban/rural residence, Area Deprivation Index, and year- and county-fixed effects. DATA COLLECTION/EXTRACTION METHODS: We identified adults aged 26-64 in PCR with prostate, lung, or colorectal cancer who received cancer-directed surgery and had a corresponding surgery discharge record in PHC4. PRINCIPAL FINDINGS: We observe a differential increase in receiving care at an NCI-CCC of 6.2 percentage points (95% CI: 2.6-9.8; baseline mean = 9.8%) among patients in high baseline uninsurance areas (p = 0.001). Our estimate of the differential change in care at the larger set of CoC hospitals is positive (3.9 percentage points [95% CI: -0.5-8.2; baseline mean = 73.7%]) but not statistically significant (p = 0.079). CONCLUSIONS: Our findings suggest that insurance expansions under the ACA were associated with increased access to NCI-CCCs.

Neighborhood socioeconomic disadvantages associated with prolonged length of stay and non-home discharge following revision total hip and knee joint arthroplasty.

Background: Discharge disposition and length of stay (LOS) are widely recognized markers of healthcare utilization patterns of total hip and knee joint arthroplasty (TJA). These markers are commonly associated with increased postoperative complications, patient dissatisfaction, and higher costs. Area deprivation index (ADI) has been validated as a composite metric of neighborhood-level disadvantage. This study aims to determine the potential association between ADI and discharge disposition or extended LOS following revision TJA. Methods: This study conducted a retrospective analysis of a consecutive series of revision hip and knee TJA patients from a single tertiary institution. Univariate and multivariate regression analysis was used to determine the association between ADI and discharge disposition or LOS, adjusting for patient demographics and comorbidities. Results: 1047 consecutive revision TJA patients were identified across 463 different neighborhoods. 193 (18.4 %) had an extended LOS, and 334 (31.9 %) were discharged to non-home facilities. Compared with Q1 (least deprived cohort), Q2 (odds ratio [OR] = 1.63; p = 0.030) and Q4 (most deprived cohort: OR = 2.04; p = 0.002) cohorts demonstrated higher odds of non-home discharge. Patients in the highest ADI quartile (most deprived cohort) were associated with increased odds of prolonged LOS following revision TJA compared to those in the lowest ADI quartile (OR = 2.63; p < 0.001). Conclusion: This study suggests that higher levels of neighborhood-level disadvantage may be associated with higher odds of non-home discharge and prolonged LOS following revision TJA. Development of interventions based on the area deprivation index may improve discharge planning and reduce unnecessary non-home discharges in patients living in areas of socioeconomic deprivation.

Prediction of 30-Day Mortality Following Revision Total Hip and Knee Arthroplasty: Machine Learning Algorithms Outperform CARDE-B, 5-Item, and 6-Item Modified Frailty Index Risk Scores.

BACKGROUND: Although risk calculators are used to prognosticate postoperative outcomes following revision total hip and knee arthroplasty (total joint arthroplasty [TJA]), machine learning (ML) based predictive tools have emerged as a promising alternative for improved risk stratification. This study aimed to compare the predictive ability of ML models for 30-day mortality following revision TJA to that of traditional risk-assessment indices such as the CARDE-B score (congestive heart failure, albumin (< 3.5 mg/dL), renal failure on dialysis, dependence for daily living, elderly (> 65 years of age), and body mass index (BMI) of < 25 kg/m2), 5-item modified frailty index (5MFI), and 6MFI. METHODS: Adult patients undergoing revision TJA between 2013 and 2020 were selected from the American College of Surgeons National Surgical Quality Improvement Program database and randomly split 80:20 to compose the training and validation cohorts. There were 3 ML models - extreme gradient boosting, random forest, and elastic-net penalized logistic regression (NEPLR) - that were developed and evaluated using discrimination, calibration metrics, and accuracy. The discrimination of CARDE-B, 5MFI, and 6MFI scores was assessed individually and compared to that of ML models. RESULTS: All models were equally accurate (Brier score = 0.005) and demonstrated outstanding discrimination with similar areas under the receiver operating characteristic curve (AUCs, extreme gradient boosting = 0.94, random forest = NEPLR = 0.93). The NEPLR was the best-calibrated model overall (slope = 0.54, intercept = -0.004). The CARDE-B had the highest discrimination among the scores (AUC = 0.89), followed by 6MFI (AUC = 0.80), and 5MFI (AUC = 0.68). Albumin < 3.5 mg/dL and BMI (< 30.15) were the most important predictors of 30-day mortality following revision TJA. CONCLUSIONS: The ML models outperform traditional risk-assessment indices in predicting postoperative 30-day mortality after revision TJA. Our findings highlight the utility of ML for risk stratification in a clinical setting. The identification of hypoalbuminemia and BMI as prognostic markers may allow patient-specific perioperative optimization strategies to improve outcomes following revision TJA.

Comparative proteomics analysis of biofilms and planktonic cells of Enterococcus faecalis and Staphylococcus lugdunensis with contrasting biofilm-forming ability.

Biofilms make it difficult to eradicate bacterial infections through antibiotic treatments and lead to numerous complications. Previously, two periprosthetic infection-related pathogens, Enterococcus faecalis and Staphylococcus lugdunensis were reported to have relatively contrasting biofilm-forming abilities. In this study, we examined the proteomics of the two microorganisms' biofilms using LC-MS/MS. The results showed that each microbe exhibited an overall different profile for differential gene expressions between biofilm and planktonic cells as well as between each other. Of a total of 929 proteins identified in the biofilms of E. faecalis, 870 proteins were shared in biofilm and planktonic cells, and 59 proteins were found only in the biofilm. In S. lugdunensis, a total of 1125 proteins were identified, of which 1072 proteins were found in common in the biofilm and planktonic cells, and 53 proteins were present only in the biofilms. The functional analysis for the proteins identified only in the biofilms using UniProt keywords demonstrated that they were mostly assigned to membrane, transmembrane, and transmembrane helix in both microorganisms, while hydrolase and transferase were found only in E. faecalis. Protein-protein interaction analysis using STRING-db indicated that the resulting networks did not have significantly more interactions than expected. GO term analysis exhibited that the highest number of proteins were assigned to cellular process, catalytic activity, and cellular anatomical entity. KEGG pathway analysis revealed that microbial metabolism in diverse environments was notable for both microorganisms. Taken together, proteomics data discovered in this study present a unique set of biofilm-embedded proteins of each microorganism, providing useful information for diagnostic purposes and the establishment of appropriately tailored treatment strategies. Furthermore, this study has significance in discovering the target candidate molecules to control the biofilm-associated infections of E. faecalis and S. lugdunensis.

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5 days), body mass index (BMI) (> 33.21 kg/m2), and operation time (> 93 min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept = - 0.11; RF, slope = 0.93, intercept = - 0.12; HGB, slope = 0.90, intercept = - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

Conversion of Polyethylene to Low-Molecular-Weight Oil Products at Moderate Temperatures Using Nickel/Zeolite Nanocatalysts.

Polyethylene (PE) is the most widely used plastic, known for its high mechanical strength and affordability, rendering it responsible for ~70% of packaging waste and contributing to microplastic pollution. The cleavage of the carbon chain can induce the conversion of PE wastes into low-molecular-weight hydrocarbons, such as petroleum oils, waxes, and natural gases, but the thermal degradation of PE is challenging and requires high temperatures exceeding 400 °C due to its lack of specific chemical groups. Herein, we prepare metal/zeolite nanocatalysts by incorporating small-sized nickel nanoparticles into zeolite to lower the degradation temperature of PE. With the use of nanocatalysts, the degradation temperature can be lowered to 350 °C under hydrogen conditions, compared to the 400 °C required for non-catalytic pyrolysis. The metal components of the catalysts facilitate hydrogen adsorption, while the zeolite components stabilize the intermediate radicals or carbocations formed during the degradation process. The successful pyrolysis of PE at low temperatures yields valuable low-molecular-weight oil products, offering a promising pathway for the upcycling of PE into higher value-added products.

Association of Medicare eligibility with access to and affordability of care among older cancer survivors.

PURPOSE: Older cancer survivors have substantial needs for ongoing care, but they may encounter difficulties accessing care due to cost concerns. We examined whether near-universal insurance coverage through Medicare-a key source of health insurance coverage in this population-is associated with improvements in care access and affordability among older cancer survivors around age 65. METHODS: In a nationally representative sample of cancer survivors (aged 50-80) from 2006-2018 National Health Interview Survey, we employed a quasi-experimental, regression discontinuity design to estimate changes in insurance coverage, delayed/skipped care due to cost, and worries about or problems paying medical bills at age 65. RESULTS: Medicare coverage sharply increased from 8.3% at age 64 to 98.2% at age 65, ensuring near-universal insurance coverage (99.5%). Medicare eligibility at age 65 was associated with reductions in delayed/skipped care due to cost (discontinuity, - 5.7 percentage points or pp; 95% CI, - 8.1, - 3.3; P < .001), worries about paying for medical bills (- 7.7 pp; 95% CI, - 12.0, - 3.2; P = .001), and problems paying medical bills (- 3.2 pp; 95% CI, - 6.1, - 0.2; P = .036). However, a sizable proportion reported any access or affordability problems (29.7%) between ages 66 and 80. CONCLUSIONS: Near-universal Medicare coverage at age 65 was associated with a reduction-but not elimination-of access and affordability problems among cancer survivors. IMPLICATIONS FOR CANCER SURVIVORS: These findings reaffirm the role of Medicare in improving access and affordability for older cancer survivor and highlight opportunities for reforms to further alleviate financial burden of care in this population.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

The Use of Artificial Intelligence for the Prediction of Periprosthetic Joint Infection Following Aseptic Revision Total Knee Arthroplasty.

Periprosthetic joint infection (PJI) following revision total knee arthroplasty (TKA) for aseptic failure is associated with poor outcomes, patient morbidity, and high health care expenditures. The aim of this study was to develop novel machine learning algorithms for the prediction of PJI following revision TKA for patients with aseptic indications for revision surgery. A single-institution database consisting of 1,432 consecutive revision TKA patients with aseptic etiologies was retrospectively identified. The patient cohort included 208 patients (14.5%) who underwent re-revision surgery for PJI. Three machine learning algorithms (artificial neural networks, support vector machines, k-nearest neighbors) were developed to predict this outcome and these models were assessed by discrimination, calibration, and decision curve analysis. This is a retrospective study. Among the three machine learning models, the neural network model achieved the best performance across discrimination (area under the receiver operating characteristic curve = 0.78), calibration, and decision curve analysis. The strongest predictors for PJI following revision TKA for aseptic reasons were prior open procedure prior to revision surgery, drug abuse, obesity, and diabetes. This study utilized machine learning as a tool for the prediction of PJI following revision TKA for aseptic failure with excellent performance. The validated machine learning models can aid surgeons in patient-specific risk stratifying to assist in preoperative counseling and clinical decision making for patients undergoing aseptic revision TKA.

[ 11 C]-PBR28 positron emission tomography signal as an imaging marker of joint inflammation in knee osteoarthritis.

ABSTRACT: Although inflammation is known to play a role in knee osteoarthritis (KOA), inflammation-specific imaging is not routinely performed. In this article, we evaluate the role of joint inflammation, measured using [ 11 C]-PBR28, a radioligand for the inflammatory marker 18-kDa translocator protein (TSPO), in KOA. Twenty-one KOA patients and 11 healthy controls (HC) underwent positron emission tomography/magnetic resonance imaging (PET/MRI) knee imaging with the TSPO ligand [ 11 C]-PBR28. Standardized uptake values were extracted from regions-of-interest (ROIs) semiautomatically segmented from MRI data, and compared across groups (HC, KOA) and subgroups (unilateral/bilateral KOA symptoms), across knees (most vs least painful), and against clinical variables (eg, pain and Kellgren-Lawrence [KL] grades). Overall, KOA patients demonstrated elevated [ 11 C]-PBR28 binding across all knee ROIs, compared with HC (all P 's < 0.005). Specifically, PET signal was significantly elevated in both knees in patients with bilateral KOA symptoms (both P 's < 0.01), and in the symptomatic knee ( P < 0.05), but not the asymptomatic knee ( P = 0.95) of patients with unilateral KOA symptoms. Positron emission tomography signal was higher in the most vs least painful knee ( P < 0.001), and the difference in pain ratings across knees was proportional to the difference in PET signal ( r = 0.74, P < 0.001). Kellgren-Lawrence grades neither correlated with PET signal (left knee r = 0.32, P = 0.19; right knee r = 0.18, P = 0.45) nor pain ( r = 0.39, P = 0.07). The current results support further exploration of [ 11 C]-PBR28 PET signal as an imaging marker candidate for KOA and a link between joint inflammation and osteoarthritis-related pain severity.

Analysis of dental traumatic patterns in standing electric scooter-related accidents.

BACKGROUND: There have been several studies about the increasing accident risks and injuries of standing electric scooters, but there is no study about the dental traumatic injuries related with standing electric scooter so far. OBJECTIVES: The purpose of this study is to report the overall dental traumatic patterns, and compare the patterns of standing electric scooter-related dental trauma with other traumatic causes. Also, considerations about minimizing the risks of electric scooter-related trauma will be discussed. METHODS: Data on patients who visited Region Trauma Center of Wonju Severance Christian Hospital with dental emergency from January 2020 to December 2022 were collected. RESULTS: The crown-root fracture and avulsion occurred significantly higher in electric scooter-related accidents than others. Furthermore, relatively minor dental injuries including concussion and subluxation showed higher percentage to be occurred as combined injuries in electric scooter-related accidents. The prevalence of traumatized posterior teeth was significantly higher in electric scooter-related trauma than others. Most of patients were teenagers and twenties. Also, the electric scooter-related accidents mostly occurred at evening and night. Furthermore, the number of patients wearing a helmet in electric scooter accidents was 1 out of 33. CONCLUSION: The standing electric scooter-related dental trauma resulted in an increased prevalence of relatively severe dental trauma. Supplementation and reinforcement of the related policies as well as strict enforcement of the laws on electric scooter users will be needed to prevent severe dental and craniofacial trauma.

Do preoperative PROMIS scores independently predict 90-day readmission following primary total knee arthroplasty?

INTRODUCTION: The rising demand for total knee arthroplasty (TKA) is expected to increase the total number of TKA-related readmissions, presenting significant public health and economic burden. With the increasing use of Patient-Reported Outcomes Measurement Information System (PROMIS) scores to inform clinical decision-making, this study aimed to investigate whether preoperative PROMIS scores are predictive of 90-day readmissions following primary TKA. MATERIALS AND METHODS: We retrospectively reviewed a consecutive series of 10,196 patients with preoperative PROMIS scores who underwent primary TKA. Two comparison groups, readmissions (n = 79; 3.6%) and non-readmissions (n = 2091; 96.4%) were established. Univariate and multivariate logistic regression analyses were then performed with readmission as the outcome variable to determine whether preoperative PROMIS scores could predict 90-day readmission. RESULTS: The study cohort consisted of 2170 patients overall. Non-white patients (OR = 3.53, 95% CI [1.16, 10.71], p = 0.026) and patients with cardiovascular or cerebrovascular disease (CVD) (OR = 1.66, 95% CI [1.01, 2.71], p = 0.042) were found to have significantly higher odds of 90-day readmission after TKA. Preoperative PROMIS-PF10a (p = 0.25), PROMIS-GPH (p = 0.38), and PROMIS-GMH (p = 0.07) scores were not significantly associated with 90-day readmission. CONCLUSION: This study demonstrates that preoperative PROMIS scores may not be used to predict 90-day readmission following primary TKA. Non-white patients and patients with CVD are 3.53 and 1.66 times more likely to be readmitted, highlighting existing racial disparities and medical comorbidities contributing to readmission in patients undergoing TKA.

Patient-Reported Outcomes Measurement Information System Physical Function and Global Physical Health Subscale Strongly Correlate and Perform Similarly to the Knee Injury and Osteoarthritis Outcome Score-Physical Function Short Form.

BACKGROUND: Arthroplasty surgeons use a variety of patient-reported outcome measures (PROMs) to assess functional well-being, including the Knee Injury and Osteoarthritis Outcome Score (KOOS) Physical Function short form (KOOS-PS), Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function Short Form 10a (PROMIS PF SF 10a), and PROMIS Global-10 Physical Health subscale. However, there is a paucity of literature assessing their concurrent validity and performance. METHODS: Between June 2016 and December 2020, patient visits at an arthroplasty clinic for knee concerns were identified. Patients who completed KOOS-PS, PROMIS PF SF 10a, and PROMIS Global-10, including its physical and mental health subscales, at the same visit were identified. Spearman rho (ρ) correlations were calculated and ceiling and floor effects identified. Overall, 5,303 patient encounters were included. RESULTS: Among physical function domains, strong correlation existed between the KOOS-PS and PROMIS PF SF 10a (ρ = 0.76, P < .001), KOOS-PS and PROMIS Global Physical Health (ρ = 0.71, P < .001), and PROMIS PF SF 10a and PROMIS Global Physical Health (ρ = 0.78, P < .001). No physical function-focused PROM had an appreciable floor effect (ie, at or more than 1%). The KOOS-PS had a small but measurable ceiling effect (n = 105 [2.0%]). CONCLUSIONS: All of the examined PROMs are acceptable to measure the functional status of patients with knee pathology, with the PROMIS Global-10 also being able to capture elements of mental health too. The PROMIS Global-10 may be of most value of the PROMs assessed, as the United States Centers for Medicare and Medicaid Services already incorporate the mental health component into new alternative payment models.

Conservative Approach for Traumatic Anterior Crown-Root Fractured Teeth by Orthodontic Extrusion using Customized Mini-Tube Appliance: A Clinical Review.

A complicated crown-root fracture is a fracture involving enamel, dentin, cementum, and pulp. Because crown fracture generally extends below the gingival margin, several options may be indicated to expose the margins before permanent restoration. Among them, orthodontic extrusion is the most non-invasive treatment option. In this case report, a case of traumatic crown-root fracture of the maxillary incisor was managed by root canal treatment with fiber-reinforced ceramic post-placement followed by orthodontic extrusion using a customized mini-tube appliance technique. Then, the porcelain fused zirconia crown was restored. Traumatized orthodontic extruded teeth have shown a reliable prognosis without inflammatory signs nor complications after a 15-month follow-up.

Comparison of primary payer in cancer registry and discharge data.

OBJECTIVES: To determine agreement between variables capturing the primary payer at cancer diagnosis across the Pennsylvania Cancer Registry (PCR) and statewide facility discharge records (Pennsylvania Health Care Cost Containment Council [PHC4]) for adults younger than 65 years, and to specifically examine factors associated with misclassification of Medicaid status in the registry given the role of managed care. STUDY DESIGN: Cross-sectional analysis of the primary cancer cases among adults aged 21 to 64 years in the PCR from 2010 to 2016 linked to the PHC4 facility visit records. METHODS: We assessed agreement of payer at diagnosis (Medicare, Medicaid, private, other, uninsured, unknown) across data sources, including positive predictive value (PPV) and sensitivity, using the PHC4 records as the gold standard. The probability of misclassifying Medicaid in registry was estimated using multivariate logit models. RESULTS: Agreement of payers was high for private insurance (PPV, 89.7%; sensitivity, 83.6%), but there was misclassification and/or underreporting of Medicaid in the registry (PPV, 80%; sensitivity, 58%). Among cases with "other" and "unknown" insurance, 73.8% and 62.1%, respectively, had private insurance according to the PHC4 records. Medicaid managed care was associated with a statistically significant increase of 12.6 percentage points (95% CI, 9.4-15.8) in the probability of misclassifying Medicaid enrollment as private insurance in the registry. CONCLUSIONS: Findings suggest caution in conducting and interpreting research using insurance variables in cancer registries.

Can machine learning models predict prolonged length of hospital stay following primary total knee arthroplasty based on a national patient cohort data?

INTRODUCTION: The total length of stay (LOS) is one of the biggest determinators of overall care costs associated with total knee arthroplasty (TKA). An accurate prediction of LOS could aid in optimizing discharge strategy for patients in need and diminishing healthcare expenditure. The aim of this study was to predict LOS following TKA using machine learning models developed on a national-scale patient cohort. METHODS: The ACS-NSQIP database was queried to acquire 267,966 TKA cases from 2013 to 2020. Four machine learning models-artificial neural network (ANN), random forest, histogram-based gradient boosting, and k-nearest neighbor were trained and tested on the dataset for the prediction of prolonged LOS (LOS exceeded the 75th of all values in the cohort). The model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration, and clinical utility. RESULTS: ANN delivered the best performance among the four models. ANN distinguished prolonged LOS in the study cohort with an AUC of 0.71 and accurately predicted the probability of prolonged LOS for individual patients (calibration slope: 0.82; calibration intercept: 0.03; Brier score: 0.089). All models demonstrated clinical utility by generating positive net benefits in decision curve analyses. Operation time, pre-operative transfusion, pre-operative laboratory tests (hematocrit, platelet count, and white blood cell count), and BMI were the strongest predictors of prolonged LOS. CONCLUSION: ANN demonstrated modest discrimination capacity and excellent performance in calibration and clinical utility for the prediction of prolonged LOS following TKA. Clinical application of the machine learning models has the potential to improve care coordination and discharge planning for patients at high risk of extended hospitalization after surgery. Incorporating more relevant patient factors may further increase the models' prediction strength.