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.

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.

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.

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.

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.

Recent innovations in intensive care unit risk-prediction models.

During the past 20 years, ICU risk-prediction models have undergone significant development, validation, and refinement. Among the general ICU severity of illness scoring systems, the Acute Physiology and Chronic Health Evaluation (APACHE), Mortality Prediction Model (MPM), and the Simplified Acute Physiology Score (SAPS) have become the most accepted and used. To risk-adjust patients with longer, more severe illnesses like sepsis and acute respiratory distress syndrome, several models of organ dysfunction or failure have become available, including the Multiple Organ Dysfunction Score (MODS), the Sequential Organ Failure Assessment (SOFA), and the Logistic Organ Dysfunction Score (LODS). Recent innovations in risk adjustment include automatic physiology and diagnostic variable retrieval and the use of artificial intelligence. These innovations have the potential of extending the uses of case-mix and severity-of-illness adjustment in the areas of clinical research, patient care, and administration. The challenges facing intensivists in the next few years are to further develop these models so that they can be used throughout the IUC stay to assess quality of care and to extend them to more specific patient groups such as the elderly and patients with chronic ICU courses.

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.

Growing importance of capitation in Switzerland.

Since its liberalization the Swiss health insurance market has shown risk selection activities of the insurance funds, which call for risk adjustment. Because risk selection continues to be profitable under the current risk adjustment formula, fast growing HMO and PPO plans are (mis)used to attract good risks rather than to contain costs. For fear of being replaced by one centralised fund, social health insurers are themselves proposing improvements of the risk adjustment formula, to be applied to funds. The revised formula proposed in this paper, applicable among funds for risk adjustment and to gate-keeping models to calculate fair capitation, explains 12.4% of the variance of health care expenditure, halves profits from risk selection, and uses only the (few) data that are available in Switzerland.

[The future of public health]. / Zur Zukunft des Gesundheitswesens.

As the results of the structural reform policy regulating the health care sector, the paper presents the possible path of increasing intrasectoral risk selection and intersectoral risk externalization (especially between the law of health care insurance, the law of statutory nursing insurance, and the German poverty law). Particularly the new developments in the hospital sector will be analyzed regarding the dynamics of risk selection and risk externalization. The new economic ways of financing the hospital sector are inducing the need of systematic integration of the different branches of social law, especially more developed networks of the health care, the nursing system, and the social care services.