Corrigendum: Real-World Treatment Patterns of Antiviral Prophylaxis for Cytomegalovirus Among Adult Kidney Transplant Recipients: A Linked USRDS-Medicare Database Study.

[This corrects the article DOI: 10.3389/ti.2022.10528.][This corrects the article DOI: 10.3389/ti.2023.12367.].

Real-World Treatment Patterns of Antiviral Prophylaxis for Cytomegalovirus Among Adult Kidney Transplant Recipients: A Linked USRDS-Medicare Database Study.

Limited data exist on cytomegalovirus (CMV) antiviral treatment patterns among kidney transplant recipients (KTRs). Using United States Renal Database System registry data and Medicare claims (1 January 2011-31 December 2017), we examined CMV antiviral use in 22,878 KTRs who received their first KT from 2011 to 2016. Three-quarters of KTRs started CMV prophylaxis (85.8% of high-, 82.4% of intermediate-, and 32.1% of low-risk KTRs). Median time to prophylaxis discontinuation was 98, 65, and 61 days for high-, intermediate-, and low-risk KTRs, respectively. Factors associated with receiving CMV prophylaxis were high-risk status, diabetes, receipt of a well-functioning kidney graft, greater time on dialysis before KT, panel reactive antibodies ≥80%, and use of antithymocyte globulin, alemtuzumab, and tacrolimus. KTRs were more likely to discontinue CMV prophylaxis if they developed leukopenia/neutropenia, had cardiovascular disease, or received their kidney from a deceased donor. These findings suggest that adherence to the recommended duration of CMV-prophylaxis for high and intermediate-risk patients is suboptimal, and CMV prophylaxis is overused in low-risk patients.

Process, resource and success factors associated with chimeric antigen receptor T-cell therapy for multiple myeloma.

Background: Chimeric antigen receptor T-cell (CAR-T) therapy represents a new frontier in multiple myeloma. It is important to understand critical success factors (CSFs) that may optimize its use in this therapeutic area. Methods: We estimated the CAR-T process using time-driven activity-based costing. Information was obtained through interviews at four US oncology centers and with payer representatives, and through publicly available data. Results: The CAR-T process comprises 13 steps which take 177 days; it was estimated to include 46 professionals and ten care settings. CSFs included proactive collaboration, streamlined reimbursement and CAR-T administration in alternative settings when possible. Implementing CSFs may reduce episode time and costs by 14.4 and 13.2%, respectively. Conclusion: Our research provides a blueprint for improving efficiencies in CAR-T therapy, thereby increasing its sustainability for multiple myeloma.

Patients with multiple myeloma can now be treated with chimeric antigen receptor T-cell (CAR-T) therapy. We studied how CAR-T therapy is used for multiple myeloma. We also studied things that could help make this therapy easier for doctors to use. The CAR-T process takes 13 steps and 177 days. It begins with the choice to use the therapy and ends about 100 days after it is used. The process uses 46 different healthcare professionals and ten different locations. We found several possible changes that can improve this process. Of these changes, three stand out. First, improved teamwork between members of the care team can help them prepare for and resolve possible problems. Second, reducing insurance red tape will make it easier to provide CAR-T therapy to patients. Third, allowing use of CAR-T therapy in places other than hospitals can help more patients receive this therapy. If applied, these three things may lower the time needed to treat patients by 14.4% and may reduce costs by 13.2%.

Emergency medical services management of ST-segment elevation myocardial infarction in the United States--a report from the American Heart Association Mission: Lifeline Program.

OBJECTIVE: ST-segment elevation myocardial infarction (STEMI) is a major cause of morbidity and mortality in the United States. Emergency medical services (EMS) agencies play a critical role in its initial identification and treatment. We conducted this study to assess EMS management of STEMI care in the United States. METHODS: A structured questionnaire was administered to leaders of EMS agencies to define the elements of STEMI care related to 4 core measures: (1) electrocardiogram (ECG) capability at the scene, (2) destination protocols, (3) catheterization laboratory activation before hospital arrival, and (4) 12-lead ECG quality review. Geographic areas were grouped into large metropolitan, small metropolitan, micropolitan, and noncore (or rural) by using Urban Influence Codes, with a stratified analysis. RESULTS: Data were included based on responses from 5296 EMS agencies (36% of those in the United States) serving 91% of the US population, with at least 1 valid response from each of the 50 states and the District of Columbia. Approximately 63% of agencies obtained ECGs at the scene using providers trained in ECG acquisition and interpretation. A total of 46% of EMS systems used protocols to determine hospital destination, cardiac catheterization laboratory activation, and communications with the receiving hospital. More than 75% of EMS systems used their own agency funds to purchase equipment, train personnel, and provide administrative oversight. A total of 49% of agencies have quality review programs in place. In general, EMS systems covering higher population densities had easier access to resources needed to maintain STEMI systems of care. Emergency medical services systems that have adopted all 4 core elements cover 14% of the US population. CONCLUSIONS: There are large differences in EMS systems of STEMI care in the United States. Most EMS agencies have implemented at least 1 of the 4 core elements of STEMI care, with many having implemented multiple elements.

Estimating Changes in Weight and Metabolic Parameters Before and After Treatment With Cariprazine: A Retrospective Study of Electronic Health Records.

PURPOSE: Weight gain and associated negative cardiometabolic effects can occur as a result of mental illness or treatment with second-generation antipsychotics (SGAs), leading to increased rates of morbidity and mortality. In this analysis, we evaluated the effect of the SGA cariprazine on weight and metabolic parameters in a real-world, retrospective, observational dataset. METHODS: Electronic health records from the Optum Humedica database (October 1, 2014-December 31, 2020) were analyzed during the 12-month period before starting cariprazine (baseline) and for up to 12 months following cariprazine initiation; approved and off-label indications were included. Body weight trajectories were estimated in the overall patient cohort and at 3-, 6-, and 12-month timepoints (primary objective). Changes in hemoglobin A1c (HbA1c), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglycerides were also evaluated (secondary objectives). Percentages of patients with clinically relevant shifts in body weight, total cholesterol, and fasting triglycerides were also determined. Discontinuation rates for metabolic regulating medications were calculated. Average predicted values were estimated by linear mixed-effects regression models. FINDINGS: A total of 2,301 patients were included; average duration of follow-up was 133.7 days. Average predicted weight change for patients during the cariprazine overall follow-up period was +2.4 kg, with predicted weight changes of +0.8 kg (n = 811), +1.1 kg (n = 350), and +1.4 kg (n = 107) at months 3, 6, and 12, respectively. Overall, the majority of patients did not experience clinically significant (≥7%) weight gain (82.8%) or loss (90.5%) after starting cariprazine. Average predicted HbA1c levels (n = 189) increased during baseline (0.15%/year) and decreased during cariprazine treatment (-0.2%/year). Average predicted triglyceride levels (n = 257) increased during baseline (15.0 mg/dL/year) and decreased during cariprazine treatment (-0.7 mg/dL/year). Predicted LDL (n = 247) and HDL (n = 255) values decreased during baseline (-7.3 and -1.1 mg/dL/year, respectively); during cariprazine treatment, LDL increased by 5.6 mg/dL/year and HDL decreased by -0.6 mg/dL/year. During follow-up, most patients did not shift from normal/borderline to high total cholesterol (<240 to ≥240 mg/dL; 522 [90.2%]) or fasting triglyceride (<200 to ≥200 mg/dL; 143 [88.8%] patients) levels; shifts from high to normal/borderline levels occurred in 44 (61.1%) patients for total cholesterol and 38 (57.6%) patients for fasting triglycerides. After starting cariprazine, the discontinuation rate per 100 patient-years was 60.4 for antihyperglycemic medication and 87.4 for hyperlipidemia medication. IMPLICATIONS: These real-world results support short-term clinical trial findings describing a neutral weight and metabolic profile associated with cariprazine treatment and they expand the dataset to include long-term follow-up.

Uncompensated care cost: a pilot study using hospitals in a Texas county.

The financial ramifications of uncompensated care cost (UCC) on the healthcare industry have been difficult to quantify. With the lack of a standardized definition of uncompensated care and the need to account for the uninsured, indigent, and immigrant populations, the authors identified $190 million of UCC from Southwestern border hospitals for emergency room treatment of undocumented immigrants and $934 million of uncompensated care charges for 23 hospitals in a Texas county, which translated to $353 million of UCC. Although lawmakers passed the Medicare Prescription Drug Improvement and Modernization Act (2003) to address the growing imbalance, the shortfall of funds highlights the growing crisis and need for policy intervention.

Potentially Preventable Hospitalizations and the Burden of Healthcare-Associated Infections.

BACKGROUND: An estimated 4% of hospital admissions acquired healthcare-associated infections (HAIs) and accounted for $9.8 (USD) billion in direct cost during 2011. In 2010, nearly 140 000 of the 3.5 million potentially preventable hospitalizations (PPHs) may have acquired an HAI. There is a knowledge gap regarding the co-occurrence of these events. AIMS: To estimate the period occurrences and likelihood of acquiring an HAI for the PPH population. METHODS: Retrospective, cross-sectional study using logistic regression analysis of 2011 Texas Inpatient Discharge Public Use Data File including 2.6 million admissions from 576 acute care hospitals. Agency for Healthcare Research and Quality Prevention Quality Indicator software identified PPH, and existing administrative data identification methodologies were refined for Clostridium difficile infection, central line-associated bloodstream infection, catheter-associated urinary tract infection, and ventilator-associated pneumonia. Odds of acquiring HAIs when admitted with PPH were adjusted for demographic, health status, hospital, and community characteristics. FINDINGS: We identified 272 923 PPH, 14 219 HAI, and 986 admissions with PPH and HAI. Odds of acquiring an HAI for diabetic patients admitted for lower extremity amputation demonstrated significantly increased odds ratio of 2.9 (95% confidence interval: 2.16-3.91) for Clostridium difficile infection. Other PPH patients had lower odds of acquiring HAI compared to non-PPH patients, and results were frequently significant. CONCLUSIONS: Clinical implications include increased risk of HAI among diabetic patients admitted for lower extremity amputation. Methodological implications include identification of rare events for inpatient subpopulations and the need for improved codification of HAIs to improve cost and policy analyses regarding allocation of resources toward clinical improvements.

Leveraging Administrative Data for Program Evaluations: A Method for Linking Data Sets Without Unique Identifiers.

In community-based wellness programs, Social Security Numbers (SSNs) are rarely collected to encourage participation and protect participant privacy. One measure of program effectiveness includes changes in health care utilization. For the 65 and over population, health care utilization is captured in Medicare administrative claims data. Therefore, methods as described in this article for linking participant information to administrative data are useful for program evaluations where unique identifiers such as SSN are not available. Following fuzzy matching methodologies, participant information from the National Study of the Chronic Disease Self-Management Program was linked to Medicare administrative data. Linking variables included participant name, date of birth, gender, address, and ZIP code. Seventy-eight percent of participants were linked to their Medicare claims data. Linking program participant information to Medicare administrative data where unique identifiers are not available provides researchers with the ability to leverage claims data to better understand program effects.

Concordance between Self-Reports and Medicare Claims among Participants in a National Study of Chronic Disease Self-Management Program.

OBJECTIVES: To evaluate the concordance between self-reported data and variables obtained from Medicare administrative data in terms of chronic conditions and health care utilization. DESIGN: Retrospective observational study. PARTICIPANTS: We analyzed data from a sample of Medicare beneficiaries who were part of the National Study of Chronic Disease Self-Management Program (CDSMP) and were eligible for the Centers for Medicare and Medicaid Services (CMS) pilot evaluation of CDSMP (n = 119). METHODS: Self-reported and Medicare claims-based chronic conditions and health care utilization were examined. Percent of consistent numbers, kappa statistic (κ), and Pearson's correlation coefficient were used to evaluate concordance. RESULTS: The two data sources had substantial agreement for diabetes and chronic obstructive pulmonary disease (COPD) (κ = 0.75 and κ = 0.60, respectively), moderate agreement for cancer and heart disease (κ = 0.50 and κ = 0.47, respectively), and fair agreement for depression (κ = 0.26). With respect to health care utilization, the two data sources had almost perfect or substantial concordance for number of hospitalizations (κ = 0.69-0.79), moderate concordance for ED care utilization (κ = 0.45-0.61), and generally low agreement for number of physician visits (κ ≤ 0.31). CONCLUSION: Either self-reports or claim-based administrative data for diabetes, COPD, and hospitalizations can be used to analyze Medicare beneficiaries in the US. Yet, caution must be taken when only one data source is available for other types of chronic conditions and health care utilization.