Background:
Concerns exist regarding exacerbation of existing disparities in health care access with the rapid implementation of telemedicine during the coronavirus disease 2019 (COVID-19) pandemic. ...However, data on pre-existing disparities in telemedicine utilization is currently lacking.
Objective:
We aimed to study: (1) the prevalence of outpatient telemedicine visits before the COVID-19 pandemic by patient subgroups based on age, comorbidity burden, residence rurality, and median household income; and (2) associated diagnosis categories.
Research Design:
This was a retrospective cohort study.
Subject:
Commercial claims data from the Truven MarketScan database (2014−2018) representing n=846,461,609 outpatient visits.
Measures:
We studied characteristics and utilization of outpatient telemedicine services before the COVID-19 pandemic by patient subgroups based on age, comorbidity burden, residence rurality, and median household income. Disparities were assessed in unadjusted and adjusted (regression) analyses.
Results:
With overall telemedicine uptake of 0.12% (n=1,018,092/846,461,609 outpatient visits) we found that pre-COVID-19 disparities in telemedicine use became more pronounced over time with lower use in patients who were older, had more comorbidities, were in rural areas, and had lower median household incomes (all trends and effect estimates
P
<0.001).
Conclusion:
These results contextualize pre-existing disparities in telemedicine use and are crucial in the monitoring of potential disparities in telemedicine access and subsequent outcomes after the rapid expansion of telemedicine during the COVID-19 pandemic.
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.
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.
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.
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.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Given that randomized trials exploring adjuvant chemotherapy for bladder cancer have been underpowered and/or terminated prematurely, yielding inconsistent results and creating an evidence gap, we ...sought to compare the effectiveness of cystectomy versus cystectomy plus adjuvant chemotherapy in real-world patients.
We conducted an observational study to compare the effectiveness of adjuvant chemotherapy versus observation postcystectomy in patients with pathologic T3-4 and/or pathologic node-positive bladder cancer using the National Cancer Data Base. We compared overall survival using propensity score (-adjusted, -stratified, -weighted, and -matched) analyses based on patient-, facility-, and tumor-level characteristics. A sensitivity analysis was performed to examine the impact of performance status.
A total of 5,653 patients met study inclusion criteria; 23% received adjuvant chemotherapy postcystectomy. Chemotherapy-treated patients were younger and more likely to have private insurance, live in areas with a higher median income and higher percentage of high school-educated residents, and have lymph node involvement and positive surgical margins (P < .05 for all comparisons). Stratified analyses adjusted for propensity score demonstrated an improvement in overall survival with adjuvant chemotherapy (hazard ratio, 0.70; 95% CI, 0.64 to 0.76), and similar results were achieved with propensity score matching and weighting. The association between adjuvant chemotherapy and improved survival was consistent in subset analyses and was robust to the effects of poor performance status.
In this observational study, adjuvant chemotherapy was associated with improved survival in patients with locally advanced bladder cancer. Although neoadjuvant chemotherapy remains the preferred approach based on level I evidence, these data lend further support for the use of adjuvant chemotherapy in patients with locally advanced bladder cancer postcystectomy who did not receive chemotherapy preoperatively.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
BACKGROUND:Despite the concern that sleep apnea (SA) is associated with increased risk for postoperative complications, a paucity of information is available regarding the effect of this disorder on ...postoperative complications and resource utilization in the orthopedic population. With an increasing number of surgical patients suffering from SA, this information is important to physicians, patients, policymakers, and administrators alike.
METHODS:We analyzed hospital discharge data of patients who underwent total hip or knee arthroplasty in approximately 400 U.S. Hospitals between 2006 and 2010. Patient, procedure, and health care system-related demographics and outcomes such as mortality, complications, and resource utilization were compared among groups. Multivariable logistic regression models were fit to assess the association between SA and various outcomes.
RESULTS:We identified 530,089 entries for patients undergoing total hip and knee arthroplasty. Of those, 8.4% had a diagnosis code for SA. In the multivariate analysis, the diagnosis of SA emerged as an independent risk factor for major postoperative complications (OR 1.47; 95% confidence interval CI, 1.39–1.55). Pulmonary complications were 1.86 (95% CI, 1.65–2.09) times more likely and cardiac complications 1.59 (95% CI, 1.48–1.71) times more likely to occur in patients with SA. In addition, SA patients were more likely to receive ventilatory support, use more intensive care, stepdown and telemetry services, consume more economic resources, and have longer lengths of hospitalization.
CONCLUSIONS:The presence of SA is a major clinical and economic challenge in the postoperative period. More research is needed to identify SA patients at risk for complications and develop evidence-based practices to aid in the allocation of clinical and economic resources.
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 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.
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.
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.
Discrimination of risk prediction models should be assessed in various external populations with different correlation structures to make better inferences about model generalizability.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Abstract There is a paucity of data available on perioperative outcomes of patients undergoing total knee arthroplasty (TKA) for rheumatoid arthritis (RA). We determined differences in demographics ...and risk for perioperative adverse events between patients suffering from osteoarthritis (OA) versus RA using a population-based approach. Of 351,103 entries for patients who underwent TKA, 3.4% had a diagnosis of RA. RA patients were on average younger RA: 64.3 years vs OA: 66.6 years; P < 0.001 and more likely female RA: 79.2% vs OA: 63.2%; P < 0. 001. The unadjusted rates of mortality and most major perioperative adverse events were similar in both groups, with the exception of infection RA: 4.5% vs. OA: 3.8%; P < 0.001. RA was not associated with increased adjusted odds for combined adverse events.
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