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Obstructive sleep apnea may increase the risk of perioperative complications and is more prevalent among candidates for surgery than in the general population. With more than 40 million surgical procedures performed annually, the costs are high. According to the Centers for Disease Control and Prevention, the rate of sleep disorders is reaching epidemic proportions, with as many as 70 million people in the United States affected by these conditions. It is estimated that 1 in 4 men and 1 in 10 women in this country have obstructive sleep apnea (OSA). 1 This disease complex places a burden on society and the health care system because of its association with adverse events ranging from loss of productivity to increased risk of cardiopulmonary illness and related death. OSA may also increase the risk of perioperative complications and is more . . .

Objectives To evaluate the impact of total knee replacement on quality of life in people with knee osteoarthritis and to estimate associated differences in lifetime costs and quality adjusted life years (QALYs) according to use by level of symptoms.Design Marginal structural modeling and cost effectiveness analysis based on lifetime predictions for total knee replacement and death from population based cohort data.Setting Data from two studies—Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST)—within the US health system.Participants 4498 participants with or at high risk for knee osteoarthritis aged 45-79 from the OAI with no previous knee replacement (confirmed by baseline radiography) followed up for nine years. Validation cohort comprised 2907 patients from MOST with two year follow-up.Intervention Scenarios ranging from current practice, defined as total knee replacement practice as performed in the OAI (with procedural rates estimated by a prediction model), to practice limited to patients with severe symptoms to no surgery.Main outcome measures Generic (SF-12) and osteoarthritis specific quality of life measured over 96 months, model based QALYs, costs, and incremental cost effectiveness ratios over a lifetime horizon.Results In the OAI, total knee replacement showed improvements in quality of life with small absolute changes when averaged across levels of confounding variables: 1.70 (95% uncertainty interval 0.26 to 3.57) for SF-12 physical component summary (PCS); −10.69 (−13.39 to −8.01) for Western Ontario and McMaster Universities arthritis index (WOMAC); and 9.16 (6.35 to 12.49) for knee injury and osteoarthritis outcome score (KOOS) quality of life subscale. These improvements became larger with decreasing functional status at baseline. Provision of total knee replacement to patients with SF-12 PCS scores <35 was the optimal scenario given a cost effectiveness threshold of $200 000/QALY, with cost savings of $6974 ($5789 to $8269) and a minimal loss of 0.008 (−0.056 to 0.043) QALYs compared with current practice. These findings were reproduced among patients with knee osteoarthritis from the MOST cohort and were robust against various scenarios including increased rates of total knee replacement and mortality and inclusion of non-healthcare costs but were sensitive to increased deterioration in quality of life without surgery. In a threshold analysis, total knee replacement would become cost effective in patients with SF-12 PCS scores ≤40 if the associated hospital admission costs fell below $14 000 given a cost effectiveness threshold of $200 000/QALY.Conclusion Current practice of total knee replacement as performed in a recent US cohort of patients with knee osteoarthritis had minimal effects on quality of life and QALYs at the group level. If the procedure were restricted to more severely affected patients, its effectiveness would rise, with practice becoming economically more attractive than its current use.

Background The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes. Methods We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods. Results Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD. Conclusions We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.

Objectives: Approximately 20–30% of patients with COVID-19 require hospitalization, and 5–12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers’ efforts and help hospitals plan their flow of operations. Methods: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. Results: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2–81.1%) sensitivity, 76.3% (95% CI: 74.7–77.9%) specificity, 76.2% (95% CI: 74.6–77.7%) accuracy, and 79.9% (95% CI: 75.2–84.6%) area under the receiver operating characteristics curve. Conclusions: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.

The rationale for using small molecule inhibitors of oncogenic proteins as cancer therapies depends, at least in part, on the assumption that metastatic tumors are primarily clonal with respect to mutant oncogene. With the emergence of BRAF(V600E) as a therapeutic target, we investigated intra- and inter-tumor heterogeneity in melanoma using detection of the BRAF(V600E) mutation as a marker of clonality. BRAF mutant-specific PCR (MS-PCR) and conventional sequencing were performed on 112 tumors from 73 patients, including patients with matched primary and metastatic specimens (n = 18). Nineteen patients had tissues available from multiple metastatic sites. Mutations were detected in 36/112 (32%) melanomas using conventional sequencing, and 85/112 (76%) using MS-PCR. The better sensitivity of the MS-PCR to detect the mutant BRAF(V600E) allele was not due to the presence of contaminating normal tissue, suggesting that the tumor was comprised of subclones of differing BRAF genotypes. To determine if tumor subclones were present in individual primary melanomas, we performed laser microdissection and mutation detection via sequencing and BRAF(V600E)-specific SNaPshot analysis in 9 cases. Six of these cases demonstrated differing proportions of BRAF(V600E)and BRAF(wild-type) cells in distinct microdissected regions within individual tumors. Additional analyses of multiple metastatic samples from individual patients using the highly sensitive MS-PCR without microdissection revealed that 5/19 (26%) patients had metastases that were discordant for the BRAF(V600E) mutation. In conclusion, we used highly sensitive BRAF mutation detection methods and observed substantial evidence for heterogeneity of the BRAF(V600E) mutation within individual melanoma tumor specimens, and among multiple specimens from individual patients. Given the varied clinical responses of patients to BRAF inhibitor therapy, these data suggest that additional studies to determine possible associations between clinical outcomes and intra- and inter-tumor heterogeneity could prove fruitful.

The rationale for using small molecule inhibitors of oncogenic proteins as cancer therapies depends, at least in part, on the assumption that metastatic tumors are primarily clonal with respect to mutant oncogene. With the emergence of BRAFV600E as a therapeutic target, we investigated intra- and inter-tumor heterogeneity in melanoma using detection of the BRAFV600E mutation as a marker of clonality. BRAF mutant-specific PCR (MS-PCR) and conventional sequencing were performed on 112 tumors from 73 patients, including patients with matched primary and metastatic specimens (n = 18). Nineteen patients had tissues available from multiple metastatic sites. Mutations were detected in 36/112 (32%) melanomas using conventional sequencing, and 85/112 (76%) using MS-PCR. The better sensitivity of the MS-PCR to detect the mutant BRAFV600E allele was not due to the presence of contaminating normal tissue, suggesting that the tumor was comprised of subclones of differing BRAF genotypes. To determine if tumor subclones were present in individual primary melanomas, we performed laser microdissection and mutation detection via sequencing and BRAFV600E-specific SNaPshot analysis in 9 cases. Six of these cases demonstrated differing proportions of BRAFV600Eand BRAFwild-type cells in distinct microdissected regions within individual tumors. Additional analyses of multiple metastatic samples from individual patients using the highly sensitive MS-PCR without microdissection revealed that 5/19 (26%) patients had metastases that were discordant for the BRAFV600E mutation. In conclusion, we used highly sensitive BRAF mutation detection methods and observed substantial evidence for heterogeneity of the BRAFV600E mutation within individual melanoma tumor specimens, and among multiple specimens from individual patients. Given the varied clinical responses of patients to BRAF inhibitor therapy, these data suggest that additional studies to determine possible associations between clinical outcomes and intra- and inter-tumor heterogeneity could prove fruitful.

Background The area under the ROC curve (AUC) of risk models is known to be influenced by differences in case-mix and effect size of predictors. The impact of heterogeneity in correlation among predictors has however been under investigated. We sought to evaluate how correlation among predictors affects the AUC in development and external populations. Methods We simulated hypothetical populations using two different methods based on means, standard deviations, and correlation of two continuous predictors. In the first approach, the distribution and correlation of predictors were assumed for the total population. In the second approach, these parameters were modeled conditional on disease status. In both approaches, multivariable logistic regression models were fitted to predict disease risk in individuals. Each risk model developed in a population was validated in the remaining populations to investigate external validity. Results For both approaches, we observed that the magnitude of the AUC in the development and external populations depends on the correlation among predictors. Lower AUCs were estimated in scenarios of both strong positive and negative correlation, depending on the direction of predictor effects and the simulation method. However, when adjusted effect sizes of predictors were specified in the opposite directions, increasingly negative correlation consistently improved the AUC. AUCs in external validation populations were higher or lower than in the derivation cohort, even in the presence of similar predictor effects. Conclusions Discrimination of risk prediction models should be assessed in various external populations with different correlation structures to make better inferences about model generalizability.

BackgroundNeoadjuvant chemotherapy (NAC) is the standard of care for locally advanced HER2 + breast cancer (BC). Optimal sequencing of treatment (NAC vs. surgery first) is less clear cut in stage I (T1N0) HER2 + BC, where information from surgical pathology could impact adjuvant treatment decisions. Utilizing the NCDB, we evaluated the trend of NAC use compared to upfront surgery in patients with small HER2 + BC.MethodsWe identified NCDB female patients diagnosed with T1 N0 HER2 + BC from 2010 through 2015. Prevalence ratios (PR) using multivariable robust Poisson regression models were calculated to measure the association between baseline characteristics and the receipt of NAC. Analysis of trends over time was denoted by annual percent change (APC) of NAC versus surgery upfront.ResultsOf the 14,949 that received chemotherapy and anti-HER2 therapy during the study period, overall 1281 (8.6%) received NAC and 13,668 (91.4%) received adjuvant treatment. Patients receiving NAC increased annually from 4.2% in 2010 to 17.3% in 2015, with the most rapid increase occurring between years 2013 (8.5%) and 2014 (14.2%). The greatest increase was seen in patients with cT1c tumors with an APC of 37.8% over the study period (95% CI 29.0, 47.3%, p < 0.01), although a significant trend was likewise seen in patients with cT1a (APC = 26.1%,95% CI 1.59, 56.6%), and cT1b (APC = 27.4%, 95% CI 18.0, 37.7%) tumors. Predictors of neoadjuvant therapy receipt were age younger than 50 (PR = 1.69, 95% CI 1.52, 1.89), Mountain/Pacific area (PR = 1.24, 95% CI 1.05, 1.46), and estrogen receptor negativity (ER− PR + : PR = 2.01, 95% CI 1.51, 2.68; ER− PR− : PR = 1.49, 95% CI 1.32, 1.69).ConclusionsNeoadjuvant therapy for T1 N0 HER2 + BC increased over the study period and was mostly due increased use in clinical T1c tumors. This may be consistent with secular change in Pertuzumab treatment following FDA approval in 2013.

Background The Oncology Care Model (OCM) was developed as a payment model to encourage participating practices to provide better-quality care for cancer patients at a lower cost. The risk-adjustment model used in OCM is a Gamma generalized linear model (Gamma GLM) with log-link. The predicted value of expense for the episodes identified for our academic medical center (AMC), based on the model fitted to the national data, did not correlate well with our observed expense. This motivated us to fit the Gamma GLM to our AMC data and compare it with two other flexible modeling methods: Random Forest (RF) and Partially Linear Additive Quantile Regression (PLAQR). We also performed a simulation study to assess comparative performance of these methods and examined the impact of non-linearity and interaction effects, two understudied aspects in the field of cost prediction. Methods The simulation was designed with an outcome of cost generated from four distributions: Gamma, Weibull, Log-normal with a heteroscedastic error term, and heavy-tailed. Simulation parameters both similar to and different from OCM data were considered. The performance metrics considered were the root mean square error (RMSE), mean absolute prediction error (MAPE), and cost accuracy (CA). Bootstrap resampling was utilized to estimate the operating characteristics of the performance metrics, which were described by boxplots. Results RF attained the best performance with lowest RMSE, MAPE, and highest CA for most of the scenarios. When the models were misspecified, their performance was further differentiated. Model performance differed more for non-exponential than exponential outcome distributions. Conclusions RF outperformed Gamma GLM and PLAQR in predicting overall and top decile costs. RF demonstrated improved prediction under various scenarios common in healthcare cost modeling. Additionally, RF did not require prespecification of outcome distribution, nonlinearity effect, or interaction terms. Therefore, RF appears to be the best tool to predict average cost. However, when the goal is to estimate extreme expenses, e.g., high cost episodes, the accuracy gained by RF versus its computational costs may need to be considered.

Background The effectiveness of prophylactic central neck dissection (pCND) in the treatment of patients with papillary thyroid carcinoma (PTC) to prevent local recurrence is controversial. We performed a meta-analysis to assess the effect of pCND on local recurrence in PTC. Methods Exhaustive search of online search engines identified five retrospective studies that compared the local recurrence rates of PTC in patients without clinically detectable nodal disease in patients undergoing thyroidectomy + pCND (group A) to those undergoing thyroidectomy alone (group B). A meta-analysis was performed by the fixed effects method. Recurrence was documented by imaging, thyroglobulin detection, or reoperation. Location of recurrence was identified in either the central or lateral neck compartment. Results A total of 1264 patients were included, 396 in group A and 868 in group B. Follow-up ranged from 6 months to 27 years. The overall recurrence rate was 2.02% in group A versus 3.92% in group B (odds ratio [OR] = 1.05, 95% confidence interval [95% CI] 0.48–2.31). The recurrence rate in the central neck compartment in group A was 1.86% compared to 1.68% in group B (OR = 1.31, 95% CI 0.44–3.91). The recurrence rate in the lateral neck compartment in group A was 3.73% compared to 3.79% in group B (OR = 1.21, 95% CI 0.52–2.75). There was no statistically significant difference in the OR in the local recurrence between the two groups. Conclusions This meta-analysis indicates that pCND does not greatly reduce local recurrence in thyroid cancer. However, the available studies have substantial limitations and a prospective multicenter study to determine the indications for pCND is warranted.