Healthcare Utilization in Different Stages among Patients with Dementia: A Nationwide Population-Based Study

To evaluate the trend of healthcare utilization among patients with dementia (PwD) in different post-diagnosis periods, Taiwan’s nationwide population database was used in this study. PwD were identified on the basis of dementia diagnoses during 2002–2011. We further subdivided the cases into 10 groups from the index year to the 10th year after diagnosis. The frequency of emergency department visits and hospitalizations, the length of stay, outpatient and department visits, and the number of medications used were retrieved. The Joinpoint regression approach was used to estimate the annual percent change (APC) of healthcare utilization. The overall trend of healthcare utilization increased with the progression of dementia, with a significant APC during the first to second year after diagnosis (p < 0.01), except that the frequency of outpatient visits showed a decreasing trend with a significant APC from the first to fifth year. All sex- and age-stratified analyses revealed that male gender and old age contributed to greater use of healthcare services but did not change the overall trend. This study provides a better understanding of medical resource utilization across the full spectrum of dementia, which can allow policymakers, physicians, and caregivers to devise better care plans for PwD.

Development and Validation of a Nomogram for Determining Patients Requiring Prolonged Postanesthesia Care Unit Length of Stay After Laparoscopic Cholecystectomy

Background: Laparoscopic Cholecystectomy (LC) is a common surgical procedure for managing gallbladder disease. Prolonged length of stay (LOS) in the postanesthesia care unit (PACU) may lead to overcrowding and a decline in medical resource utilization. In this work, we aimed to develop and validate a predictive nomogram for identifying patients who require prolonged PACU LOS.Methods: Data from 913 patients undergoing LC at a single institution in China between 2018 and 2019 were collected, and grouped into a training set (cases during 2018) and a test set (cases during 2019). Using the least absolute shrinkage and selection operator regression model, the optimal feature was selected, and multivariable logistic regression analysis was used to build the prolonged PACU LOS risk model. The C-index, calibration plot, and decision curve analysis were used in assessing the model calibration, discrimination, and clinical application value, respectively. For external validation, the test set data was evaluated.Results: The predictive nomogram had 8 predictor variables for prolonged PACU LOS, including age, ASA grade, active smoker, gastrointestinal disease, liver disease, and cardiovascular disease. This model displayed efficient calibration and moderate discrimination with a C-index of 0.662 (95% confidence interval, 0.603 to 0.721) for the training set, and 0.609 (95% confidence interval, 0.549 to 0.669) for the test set. Decision curve analysis demonstrated that the prolonged PACU LOS nomogram was reliable for clinical application when an intervention was decided at the possible threshold of 7%.Conclusions: We developed and validated a predictive nomogram with efficient calibration and moderate discrimination, and can be applied to identify patients most likely to be subjected to prolonged PACU LOS. This novel tool may shun overcrowding in PACU and optimize medical resource utilization.

Medical resource utilization among multiple myeloma (MM) patients who were triple-exposed to a proteasome inhibitor, an immunomodulatory agent, and daratumumab.

e20539 Background: MM patients undergo several lines of treatment (LOT), mainly involving ≥1 proteasome inhibitor (PI), immunomodulatory (IMiD), and novel agents such as daratumumab (dara). Research is underway to address the need of patients previously exposed to a PI, an IMiD, and dara (triple-exposed). This retrospective cohort analysis assessed costs and healthcare resource (HR) utilization in triple-exposed patients. Methods: A pooled cohort of patients from Optum’s Humedica Electronic Health Records and Clinformatics claims data with an index MM diagnosis between 2008-2018 and who 1) had ≥12 months of activity/insurance coverage prior to index MM diagnosis, 2) did not have any other cancer during this period, 3) were aged ≥18 years at index diagnosis, 4) were triple exposed and 5) had ≥1 LOT subsequent to triple-exposure were studied. Costs per LOT after triple-exposure, broken down into cost categories, were analyzed in patients from Optum claims data (N = 94), while HR utilization (hospitalization, ER/outpatient visits, lab tests and MM drugs) was analyzed in the pooled cohort (N = 517). Descriptive statistics are reported. Results: 53% of patients were male; mean follow-up was 8.9 months (272 days). At LOT1 following triple-exposure, mean age was 68 yrs, mean time from index MM diagnosis was 37 months, and median prior LOTs were 4. 50% were penta-exposed (2 PIs + 2 IMiDs + dara) following triple exposure. 42% received a pomalidomide-containing regimen in LOT1 after triple-exposure. 56% of patients had ≥2 LOTs after triple-exposure; 28% had ≥3. Mean duration of LOT1, LOT2, and LOT3 in claims data patients was 132, 102 and 101 days, respectively, with associated mean cost per LOT of $165,453, $131,759, and $116,915. Over the follow-up, patients had a mean of 1.75 hospitalizations, 0.92 ER visits, 32.1 outpatient/office visits and 28.8 lab tests; post triple-exposure mean cost per patient per month was $38,214 (Table). Conclusions: Substantial MM drug costs, inpatient stays and frequent outpatient visits contribute to high cost per month of post triple-exposure treatment, underscoring the need for effective MM treatments with durable response. [Table: see text]

Partial Unlock for COVID-19-Like Epidemics Can Save 1-3 Million Lives Worldwide

This paper accounts in lives-saved partial unlock strategies that may be used to facilitate reopening economies that have been shut down due to an epidemic or pandemic. For this purpose it introduces a new approach to simulation using an internal SIR engine with seasonality, and external behavior forcing calibrated with case data to account for initial human behavior under social distancing. The overall method relies on public goal setting and both professional and public feedback behavior. In this way it avoids much of the chaotic sensitivity to parameters and divergence of predictions and behavior which undermine the public image of epidemiology models and create rebounds. We study reducing the total cases by controlling threshold overshoot as economies reopen, controlling medical resource utilization, and reducing economic shutdown duration, all of these across significant scenario variation. We provide a quantitative analysis of overshoot and demonstrate a two-step manual method as well as the feedback method of avoiding it. We show goal-managed partial unlock to manage critical resources has the consequential effects of reducing economic downtime and bringing the cumulative cases down about 9%-27%, thereby saving lives with some degree of certainty. The optimization of overshoot does leave some risk of creating a residual small infection existing on birth rate and migration, and we provide some guidelines for minimizing the risk. Effectiveness is demonstrated using COVID-19 actual data and parameters for other diseases with replication factors up to 15.