Treating Epilepsy Patients with Investigational Anti-COVID-19 Drugs: Recommendations by the Israeli Chapter of the ILAE.

BACKGROUND: The coronavirus disease-2019 (COVID-19) and its management in patients with epilepsy can be complex. Prescribers should consider potential effects of investigational anti-COVID-19 drugs on seizures, immunomodulation by anti-seizure medications (ASMs), changes in ASM pharmacokinetics, and the potential for drug-drug interactions (DDIs). The goal of the Board of the Israeli League Against Epilepsy (the Israeli Chapter of the International League Against Epilepsy, ILAE) was to summarize the main principles of the pharmacological treatment of COVID-19 in patients with epilepsy. This guide was based on current literature, drug labels, and drug interaction resources. We summarized the available data related to the potential implications of anti-COVID-19 co-medication in patients treated with ASMs. Our recommendations refer to drug selection, dosing, and patient monitoring. Given the limited availability of data, some recommendations are based on general pharmacokinetic or pharmacodynamic principles and might apply to additional future drug combinations as novel treatments emerge. They do not replace evidence-based guidelines, should those become available. Awareness to drug characteristics that increase the risk of interactions can help adjust anti-COVID-19 and ASM treatment for patients with epilepsy.

Temporal trends of single-photon emission computed tomography myocardial perfusion imaging in patients without prior coronary artery disease: A 22-year experience at a tertiary academic medical center.

BACKGROUND: Between 1990 and 2006, there was a large national increase in utilization of single-photon emission computed tomography myocardial perfusion imaging (SPECT) for assessment of coronary artery disease (CAD). We aim to examine the trends of SPECT test results and patients' characteristics at Mayo Clinic Rochester. METHODS: Using the Mayo Clinic nuclear cardiology database, we examined all SPECT tests performed between January 1, 1991, and December 31, 2012, in patients without prior CAD. The study cohort was divided into 5 time periods: 1991-1995, 1996-2000, 2001-2005, 2006-2010, and 2011-2012. RESULTS: There were 35,894 eligible SPECT tests (mean age 62.5 ± 12 years, 54% men). Annual utilization of SPECT increased significantly in 1992-2002 but then decreased without evidence of test substitution with stress echocardiography. There were modest changes in CAD risk factors over time. Testing of asymptomatic patients doubled (21.9% in 1991-1995 to 40% in 2006-2010) but later decreased to 33.6% in 2011-2012. Tests on patients with typical angina decreased dramatically (18.3% in 1991-1995 to 6.7% in 2011-2012). Summed stress score, summed difference score, and high-risk SPECT tests all decreased over time in both symptomatic and asymptomatic patients regardless of stress modality (exercise vs pharmacologic). CONCLUSIONS: In Mayo Clinic Rochester, annual SPECT utilization in patients without prior CAD increased in 1992-2002 but then decreased. Despite similar CAD risk factors and decreased utilization after 2003, more tests were low risk; summed stress score, summed difference score, and high-risk tests all decreased. Our findings confirm previous observations that SPECT was increasingly used in patients with a lower prevalence of CAD.

Functional mitral regurgitation: a 30-year unresolved surgical journey from valve replacement to complex valve repairs.

Functional mitral regurgitation remains one of the most complex and controversial aspect--for both clinicians and surgeons--in the management of mitral valve disease in the context of left ventricular dysfunction. Given the current absence of clear guidelines, as well as of results from randomized trials comparing the outcome of different surgical strategies potentially available for this complex scenario, surgical decision making for these high-risk patients poses a real dilemma in the daily practice. The resulting surgical choices often represent a questionable combination of surgeons' personal feeling, local supplies, patients' life expectancy and risk/benefit ratios, opinions and statements of the experts, and so on. This review provides an overview of the present knowledge about the complex pathophysiology underlying functional mitral regurgitation, the different pathophysiology-guided surgical techniques suggested in the last decades, as well as the current results following these different surgical techniques.

Development and validation of a risk quantification index for 30-day postoperative mortality and morbidity in noncardiac surgical patients.

BACKGROUND: Optimal risk adjustment is a requisite precondition for monitoring quality of care and interpreting public reports of hospital outcomes. Current risk-adjustment measures have been criticized for including baseline variables that are difficult to obtain and inadequately adjusting for high-risk patients. The authors sought to develop highly predictive risk-adjustment models for 30-day mortality and morbidity based only on a small number of preoperative baseline characteristics. They included the Current Procedural Terminology code corresponding to the patient's primary procedure (American Medical Association), American Society of Anesthesiologists Physical Status, and age (for mortality) or hospitalization (inpatient vs. outpatient, for morbidity). METHODS: Data from 635,265 noncardiac surgical patients participating in the American College of Surgeons National Surgical Quality Improvement Program between 2005 and 2008 were analyzed. The authors developed a novel algorithm to aggregate sparsely represented Current Procedural Terminology codes into logical groups and estimated univariable Procedural Severity Scores-one for mortality and morbidity, respectively-for each aggregated group. These scores were then used as predictors in developing respective risk quantification models. Models were validated with c-statistics, and calibration was assessed using observed-to-expected ratios of event frequencies for clinically relevant strata of risk. RESULTS: The risk quantification models demonstrated excellent predictive accuracy for 30-day postoperative mortality (c-statistic [95% CI] 0.915 [0.906-0.924]) and morbidity (0.867 [0.858-0.876]). Even in high-risk patients, observed rates calibrated well with estimated probabilities for mortality (observed-to-expected ratio: 0.93 [0.81-1.06]) and morbidity (0.99 [0.93-1.05]). CONCLUSION: The authors developed simple risk-adjustment models, each based on three easily obtained variables, that allow for objective quality-of-care monitoring among hospitals.

Risk-based centralized data monitoring of clinical trials at the time of COVID-19 pandemic.

OBJECTIVES: COVID-19 pandemic caused several alarming challenges for clinical trials. On-site source data verification (SDV) in the multicenter clinical trial became difficult due to travel ban and social distancing. For multicenter clinical trials, centralized data monitoring is an efficient and cost-effective method of data monitoring. Centralized data monitoring reduces the risk of COVID-19 infections and provides additional capabilities compared to on-site monitoring. The key steps for on-site monitoring include identifying key risk factors and thresholds for the risk factors, developing a monitoring plan, following up the risk factors, and providing a management plan to mitigate the risk. METHODS: For analysis purposes, we simulated data similar to our clinical trial data. We classified the data monitoring process into two groups, such as the Supervised analysis process, to follow each patient remotely by creating a dashboard and an Unsupervised analysis process to identify data discrepancy, data error, or data fraud. We conducted several risk-based statistical analysis techniques to avoid on-site source data verification to reduce time and cost, followed up with each patient remotely to maintain social distancing, and created a centralized data monitoring dashboard to ensure patient safety and maintain the data quality. CONCLUSION: Data monitoring in clinical trials is a mandatory process. A risk-based centralized data review process is cost-effective and helpful to ignore on-site data monitoring at the time of the pandemic. We summarized how different statistical methods could be implemented and explained in SAS to identify various data error or fabrication issues in multicenter clinical trials.

Major cardiac events for adult survivors of childhood cancer diagnosed between 1970 and 1999: report from the Childhood Cancer Survivor Study cohort.

OBJECTIVE: To investigate the impact of modifications to contemporary cancer protocols, which minimize exposures to cardiotoxic treatments and preserve long term health, on serious cardiac outcomes among adult survivors of childhood cancer. DESIGN: Retrospective cohort study. SETTING: 27 institutions participating in the Childhood Cancer Survivor Study. PARTICIPANTS: 23 462 five year survivors (6193 (26.4%) treated in the 1970s, 9363 (39.9%) treated in the 1980s, and 7906 (33.6%) treated in the 1990s) of leukemia, brain cancer, Hodgkin lymphoma, non-Hodgkin lymphoma, renal tumors, neuroblastoma, soft tissue sarcomas, and bone sarcomas diagnosed prior to age 21 years between 1 January 1970 and 31 December 1999. Median age at diagnosis was 6.1 years (range 0-20.9) and 27.7 years (8.2-58.3) at last follow-up. A comparison group of 5057 siblings of cancer survivors were also included. MAIN OUTCOME MEASURES: Cumulative incidence and 95% confidence intervals of reported heart failure, coronary artery disease, valvular heart disease, pericardial disease, and arrhythmias by treatment decade. Events were graded according to the National Cancer Institute's Common Terminology Criteria for Adverse Events. Multivariable subdistribution hazard models were used to estimate hazard ratios by decade, and mediation analysis examined risks with and without exposure to cardiotoxic treatments. RESULTS: The 20 year cumulative incidence of heart failure (0.69% for those treated in the 1970s, 0.74% for those treated in the 1980s, 0.54% for those treated in the 1990s) and coronary artery disease (0.38%, 0.24%, 0.19%, respectively), decreased in more recent eras (P<0.01), though not for valvular disease (0.06%, 0.06%, 0.05%), pericardial disease (0.04%, 0.02%, 0.03%), or arrhythmias (0.08%, 0.09%, 0.13%). Compared with survivors with a diagnosis in the 1970s, the risk of heart failure, coronary artery disease, and valvular heart disease decreased in the 1980s and 1990s but only significantly for coronary artery disease (hazard ratio 0.65, 95% confidence interval 0.45 to 0.92 and 0.53, 0.36 to 0.77, respectively). The overall risk of coronary artery disease was attenuated by adjustment for cardiac radiation (0.90, 0.78 to 1.05), particularly among survivors of Hodgkin lymphoma (unadjusted for radiation: 0.77, 0.66 to 0.89; adjusted for radiation: 0.87, 0.69 to 1.10). CONCLUSIONS: Historical reductions in exposure to cardiac radiation have been associated with a reduced risk of coronary artery disease among adult survivors of childhood cancer. Additional follow-up is needed to investigate risk reductions for other cardiac outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT01120353.

Improving Risk Adjustment for Mortality After Pediatric Cardiac Surgery: The UK PRAiS2 Model.

BACKGROUND: Partial Risk Adjustment in Surgery (PRAiS), a risk model for 30-day mortality after children's heart surgery, has been used by the UK National Congenital Heart Disease Audit to report expected risk-adjusted survival since 2013. This study aimed to improve the model by incorporating additional comorbidity and diagnostic information. METHODS: The model development dataset was all procedures performed between 2009 and 2014 in all UK and Ireland congenital cardiac centers. The outcome measure was death within each 30-day surgical episode. Model development followed an iterative process of clinical discussion and development and assessment of models using logistic regression under 25 × 5 cross-validation. Performance was measured using Akaike information criterion, the area under the receiver-operating characteristic curve (AUC), and calibration. The final model was assessed in an external 2014 to 2015 validation dataset. RESULTS: The development dataset comprised 21,838 30-day surgical episodes, with 539 deaths (mortality, 2.5%). The validation dataset comprised 4,207 episodes, with 97 deaths (mortality, 2.3%). The updated risk model included 15 procedural, 11 diagnostic, and 4 comorbidity groupings, and nonlinear functions of age and weight. Performance under cross-validation was: median AUC of 0.83 (range, 0.82 to 0.83), median calibration slope and intercept of 0.92 (range, 0.64 to 1.25) and -0.23 (range, -1.08 to 0.85) respectively. In the validation dataset, the AUC was 0.86 (95% confidence interval [CI], 0.82 to 0.89), and the calibration slope and intercept were 1.01 (95% CI, 0.83 to 1.18) and 0.11 (95% CI, -0.45 to 0.67), respectively, showing excellent performance. CONCLUSIONS: A more sophisticated PRAiS2 risk model for UK use was developed with additional comorbidity and diagnostic information, alongside age and weight as nonlinear variables.

Current Risk Adjustment and Comorbidity Index Underperformance in Predicting Post-Acute Utilization and Hospital Readmissions After Joint Replacements: Implications for Comprehensive Care for Joint Replacement Model.

OBJECTIVE: To compare the performances of 3 comorbidity indices, the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, and the Centers for Medicare & Medicaid Services (CMS) risk adjustment model, Hierarchical Condition Category (HCC), in predicting post-acute discharge settings and hospital readmission for patients after joint replacement. METHODS: A retrospective study of Medicare beneficiaries with total knee replacement (TKR) or total hip replacement (THR) discharged from hospitals in 2009-2011 (n = 607,349) was performed. Study outcomes were post-acute discharge setting and unplanned 30-, 60-, and 90-day hospital readmissions. Logistic regression models were built to compare the performance of the 3 comorbidity indices using C statistics. The base model included patient demographics and hospital use. Subsequent models included 1 of the 3 comorbidity indices. Additional multivariable logistic regression models were built to identify individual comorbid conditions associated with high risk of hospital readmissions. RESULTS: The 30-, 60-, and 90-day unplanned hospital readmission rates were 5.3%, 7.2%, and 8.5%, respectively. Patients were most frequently discharged to home health (46.3%), followed by skilled nursing facility (40.9%) and inpatient rehabilitation facility (12.7%). The C statistics for the base model in predicting post-acute discharge setting and 30-, 60-, and 90-day readmission in TKR and THR were between 0.63 and 0.67. Adding the Charlson Comorbidity Index, the Elixhauser Comorbidity Index, or HCC increased the C statistic minimally from the base model for predicting both discharge settings and hospital readmission. The health conditions most frequently associated with hospital readmission were diabetes mellitus, pulmonary disease, arrhythmias, and heart disease. CONCLUSION: The comorbidity indices and CMS-HCC demonstrated weak discriminatory ability to predict post-acute discharge settings and hospital readmission following joint replacement.

Disease-Specific Trends of Comorbidity Coding and Implications for Risk Adjustment in Hospital Administrative Data.

OBJECTIVE: To investigate changes in comorbidity coding after the introduction of diagnosis related groups (DRGs) based prospective payment and whether trends differ regarding specific comorbidities. DATA SOURCES: Nationwide administrative data (DRG statistics) from German acute care hospitals from 2005 to 2012. STUDY DESIGN: Observational study to analyze trends in comorbidity coding in patients hospitalized for common primary diseases and the effects on comorbidity-related risk of in-hospital death. EXTRACTION METHODS: Comorbidity coding was operationalized by Elixhauser diagnosis groups. The analyses focused on adult patients hospitalized for the primary diseases of heart failure, stroke, and pneumonia, as well as hip fracture. PRINCIPAL FINDINGS: When focusing the total frequency of diagnosis groups per record, an increase in depth of coding was observed. Between-hospital variations in depth of coding were present throughout the observation period. Specific comorbidity increases were observed in 15 of the 31 diagnosis groups, and decreases in comorbidity were observed for 11 groups. In patients hospitalized for heart failure, shifts of comorbidity-related risk of in-hospital death occurred in nine diagnosis groups, in which eight groups were directed toward the null. CONCLUSIONS: Comorbidity-adjusted outcomes in longitudinal administrative data analyses may be biased by nonconstant risk over time, changes in completeness of coding, and between-hospital variations in coding. Accounting for such issues is important when the respective observation period coincides with changes in the reimbursement system or other conditions that are likely to alter clinical coding practice.

As the health insurance underwriting cycle turns: what next?

After twenty-five years of a consistent health insurance underwriting cycle, the pattern of insurer profitability changed greatly in the 1990s, raising speculation about the future. We conclude from interviews with industry experts that health plan competition and limits on plans' ability to predict costs will continue to drive a cycle, albeit one even more muted than it was in the 1990s because of changes in industry structure and forecasting improvements. Plans will price closer to cost trends and forego the more heated price competition that drove major losses in the past, reducing premium volatility but possibly leading to higher average premiums.

The underwriting cycle: the rule of six.

The underwriting cycle is a thing of the past for most health insurance companies. There were six primary factors that caused the six-year pattern of the underwriting cycle for 1965-1991. These factors were claims payment cycle time, renewal dates and process, growth versus profit objectives, role of the actuary, rate regulation, and reimbursement methods. Most companies have made major changes to influence these factors, which will prevent a recurrence of the underwriting cycles of the past.

Identifying patient preferences for communicating risk estimates: a descriptive pilot study.

BACKGROUND: Patients increasingly seek more active involvement in health care decisions, but little is known about how to communicate complex risk information to patients. The objective of this study was to elicit patient preferences for the presentation and framing of complex risk information. METHOD: To accomplish this, eight focus group discussions and 15 one-on-one interviews were conducted, where women were presented with risk data in a variety of different graphical formats, metrics, and time horizons. Risk data were based on a hypothetical woman's risk for coronary heart disease, hip fracture, and breast cancer, with and without hormone replacement therapy. Participants' preferences were assessed using likert scales, ranking, and abstractions of focus group discussions. RESULTS: Forty peri- and postmenopausal women were recruited through hospital fliers (n = 25) and a community health fair (n = 15). Mean age was 51 years, 50% were non-Caucasian, and all had completed high school. Bar graphs were preferred by 83% of participants over line graphs, thermometer graphs, 100 representative faces, and survival curves. Lifetime risk estimates were preferred over 10 or 20-year horizons, and absolute risks were preferred over relative risks and number needed to treat. CONCLUSION: Although there are many different formats for presenting and framing risk information, simple bar charts depicting absolute lifetime risk were rated and ranked highest overall for patient preferences for format.

Decreased usage and increased effectiveness of percutaneous coronary intervention in complex older patients with acute coronary syndromes.

BACKGROUND: Application of percutaneous coronary intervention (PCI) in patients with acute coronary syndromes (ACS) is suboptimal in older frail individuals. This study was conducted to verify if background risk is a risk factor for underuse and diminished effectiveness of PCI in older patients. METHODS: An observational cohort study was conducted using data from the Acute Myocardial Infarction in Florence 2 registry, including all ACS hospitalised in 1 year in the area of Florence, Italy. Patients aged 75+ years were selected, whose background risk was stratified with the Silver Code (SC), a validated tool predicting mortality based upon administrative data. Multivariable OR for PCI application and HR for 1-year mortality by PCI usage were calculated. RESULTS: In 698 patients (358 women, mean age 83 years), of whom 176 had ST-segment elevation myocardial infarction (STEMI), for each point increase in SC score the odds for application of PCI decreased by 11%, whereas the hazard of 1-year mortality increased by 10%, adjusting for positive and negative predictors. PCI reduced 1-year mortality progressively more with increasing SC, with HR (95% CI) of 0.8 (0.19 to 1.21), 0.41 (0.18 to 0.45), 0.41 (0.23 to 0.74) and 0.26 (0.14 to 0.48) for SC of 0-3, 4-6, 7-10 and 11+. CONCLUSIONS: Application of PCI in older ACS patients decreased with increasing background risk. This therapeutic attitude could not be justified by decreasing effectiveness of PCI in more compromised patients: conversely, application of PCI was associated with a long-term survival advantage that increased progressively with background risk, as expressed by SC.

Optimizing ACS NSQIP modeling for evaluation of surgical quality and risk: patient risk adjustment, procedure mix adjustment, shrinkage adjustment, and surgical focus.

The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) collects detailed clinical data from participating hospitals using standardized data definitions, analyzes these data, and provides participating hospitals with reports that permit risk-adjusted comparisons with a surgical quality standard. Since its inception, the ACS NSQIP has worked to refine surgical outcomes measurements and enhance statistical methods to improve the reliability and validity of this hospital profiling. From an original focus on controlling for between-hospital differences in patient risk factors with logistic regression, ACS NSQIP has added a variable to better adjust for the complexity and risk profile of surgical procedures (procedure mix adjustment) and stabilized estimates derived from small samples by using a hierarchical model with shrinkage adjustment. New models have been developed focusing on specific surgical procedures (eg, "Procedure Targeted" models), which provide opportunities to incorporate indication and other procedure-specific variables and outcomes to improve risk adjustment. In addition, comparative benchmark reports given to participating hospitals have been expanded considerably to allow more detailed evaluations of performance. Finally, procedures have been developed to estimate surgical risk for individual patients. This article describes the development of, and justification for, these new statistical methods and reporting strategies in ACS NSQIP.

Improving American College of Surgeons National Surgical Quality Improvement Program risk adjustment: incorporation of a novel procedure risk score.

BACKGROUND: Risk-adjusted evaluation is a key component of the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP). The purpose of this study was to improve standard ACS NSQIP risk adjustment using a novel procedure risk score. STUDY DESIGN: Current Procedural Terminology codes (CPTs) represented in ACS NSQIP data were assigned to 136 procedure groups. Log odds predicted risk from preliminary logistic regression modeling generated a continuous risk score for each procedure group, used in subsequent modeling. Appropriate subsets of 271,368 patients in the 2008 ACS NSQIP were evaluated using logistic models for overall 30-day morbidity, 30-day mortality, and surgical site infection (SSI). Models were compared when including either work Relative Value Unit (RVU), RVU and the standard ACS NSQIP CPT range variable (CPT range), or RVU and the newly constructed CPT risk score (CPT risk), plus routine ACS NSQIP predictors. RESULTS: When comparing the CPT risk models with the CPT range models for morbidity in the overall general and vascular surgery dataset, CPT risk models provided better discrimination through higher c statistics at earlier steps (0.81 by step 3 vs 0.81 by step 46), more information through lower Akaike's information criterion (127,139 vs 130,019), and improved calibration through a smaller Hosmer-Lemeshow chi-square statistic (48.76 vs 116.79). Improved model characteristics of CPT risk over CPT range were most apparent for broader patient populations and outcomes. The CPT risk and standard CPT range models were moderately consistent in identification of outliers as well as assignment of hospitals to quality deciles (weighted kappa ≥ 0.870). CONCLUSIONS: Information from focused, clinically meaningful CPT procedure groups improves the risk estimation of ACS NSQIP models.