Predictors of 30-day readmission, mortality, and length of stay for hospitalized U.S. patients with Alzheimer's and related dementias from 2010 to 2015.

OBJECTIVES: Examine predictors of clinical and resource utilization outcomes associated with Alzheimer's disease and related dementias (ADRD), stratified by patient severity profiles. METHODS: Cross-sectional study of adults (30+ year old) with ADRD discharged from US hospitals to home health care (HHC) and identified from the 2010-2015 Nationwide Readmissions Database (NRD) using ICD 9th-10th codes. Outcomes of interest included 30-day hospital readmissions, in-hospital mortality, and hospital length of stay (LOS). Covariates consisted of sociodemographic and clinical variables. Multiple logistic regressions (for readmissions and mortality) and generalized linear regressions (for LOS) were used to examine associations between outcomes and study covariates, stratified by patient severity profiles. RESULTS: Of 164,598 ADRD patients, 3,848 were mild, 68803 were moderate, 72428 were severe, and 19,519 were extreme. The 30-day readmission rate was 3.2%, death rate was 14.5%, and LOS was 3.0 days, (95%, CI: 15.0, 17.0) to 5.0 days, (95%, CI: 18.0, 19.0), all with a p-value<0.0001. Across outcomes and severity levels, the top predictors included number of diagnoses, gender, hospital bed size, primary and secondary diagnoses, and income size. CONCLUSIONS: Severe and extreme stages of HHC discharge may lead to increased readmissions, death, LOS, and costs. Specialized care is needed to reduce these negative outcomes in the ADRD patient population.

Predictors of 30-day readmission and hospitalization costs of patients with hepatic encephalopathy in the US from 2010 to 2014.

BACKGROUND: Hepatic encephalopathy (HE) is a complex and reversible neuropsychiatric syndrome that is associated with growing, substantial healthcare resource utilization. We aim to examine the predictors of 30-day readmission and hospitalization cost associated with HE. RESEARCH DESIGN AND METHODS: We conducted a cross-sectional study using the Nationwide Readmissions Database from 2010 to 2014. We assessed the readmission rates using multivariate logistic regression and established temporal trends of readmission rates and hospitalization cost. Weighted hierarchical logistic regression and generalized linear mixed models were used to identify predictors for nationally representative readmissions and hospitalization costs, respectively. RESULTS: The number of index hospitalizations with HE increased with a significant trend from 34,967 in 2010 to 44,791 in 2014. 16.8% of patients were readmitted within 30 days. Predictors increasing readmission risk included female sex, Elixhauser readmission score < 25, elective admission, patient's state residential status, privately insured, number of diagnoses >13, and length of stay >4 days. CONCLUSIONS: Our results indicate there is a need to implement better management strategies to improve outcomes in patients hospitalized with HE to curb the increase in the economic burden associated with the disease.

The Potential Effects of Implementing an Antibiotic Stewardship Program by Integrating It with Medication Therapy Service in a Low-Income Serving Clinic - A Single-Center Experience.

Background: Bacterial antimicrobial resistance (AMR) is a leading cause of mortality worldwide. Although AMR is common in low-income communities, there is limited evidence of the effect of antibiotic stewardship programs in low-income communities in the United States. Objectives: Our goal is to assess the effects of implementing pharmacist-led ASP by integrating it with medication therapy management service (MTM) in a low-income serving clinic. We evaluated the following 1) antibiotic prescriptions per 1000 patients, 2) the frequency of clinic (office) visits 30-day post-index clinic visits for recurring infections. Methods: To achieve our goal, we conducted a pre-post, quasi-experimental intervention study using an interrupted time-series analysis to assess the following: 1) antibiotic prescriptions per 1000 patients and the 2) frequency of office visits (including telehealth) within 30-day post-index clinic visits associated with recurrent infection. Results: Our findings revealed that the long-term effect of our antibiotic stewardship program intervention was associated with 63.69% reduction in antibiotic prescriptions per 1000 patients (change in slope = -0.173, [95% CI: (-0.30, -0.05)], P < 0.007) and a reduction in the frequency of office visits within 30-day post-index clinic visits by 67.27% (change in slope = -2.043, [95% CI: (-3.84, -0.24)], P < 0.028). Conclusion: Implementing antibiotic stewardship programs is feasible for clinics serving low-income populations. It was associated with a reduction in antibiotic prescriptions and preventable clinic (office) visits within 30 days due to infection recurrence.

Identification of risk factors of 30-day readmission and 180-day in-hospital mortality, and its corresponding relative importance in patients with Ischemic heart disease: a machine learning approach.

Background: The primary objective of this study is to identify non-laboratory predictors for 30-day hospital readmission and 180-day in-hospital mortality rates among patients hospitalized with ischemic heart disease (IHD).Research design and methods: This is a retrospective cohort study of hospitalized patients (≥ 40 years) with a primary diagnosis of IHD. Data were extracted from the Florida Agency for Health Care Administration dataset from 2006 to 2016. A machine learning approach was used to identify predictors of 30-day hospital readmission and 180-day in-hospital mortality.Results: 346,390 patient records for incident IHD cases were identified. The top two predictors of 30-day readmission were the length of stay and the Elixhauser comorbidity index for readmission [ECI] (Area Under the Curve [AUC]=88%) using decision tree algorithms. For in-hospital mortality, the top two predictors were LOS and ECI (AUC=92%) using gradient boosting regressors. The cumulative 30-day readmission and the 180-day probability of mortality rates were 9.82% and 4.6% respectively.Conclusions: Risk factors of 30-day readmission and 180-day mortality in hospitalized IHD patients identified by machine learning and their relative importance (value) will help pharmacists and other health care providers to prioritize their disease management strategies as they improve the care provided to IHD patients.

A quantitative analysis of the effect of continuity of care on 30-day readmission and in-hospital mortality among patients with acute ischemic stroke.

BACKGROUND: Continuity of care is a core element of high-quality patient care in a primary care setting and one of a national priority. OBJECTIVE: To assess and quantify the impact of continuity of care on 30-day readmissions, 30-day inpatient mortality, and hospital length of stay (LOS), among hospitalized patients with acute ischemic stroke disease. DESIGN AND SUBJECTS: Observational retrospective cohort (n = 356,134) using a 2.75% random sample (n=1,036,753) from the State of Florida Agency for Health Care Administration (AHCA) database from 2006 to 2016. MEASURES: We assessed continuity of care using an integrated continuity of care CoC score, calculated by merging three standard indices of continuity of care - Bice-Boxerman Continuity of Care Index (COCI), Herfindahl Index (HI), and Usual Provider of Care (UPC) Index via a Principal Component Analysis (PCA). We measured 30-day hospital readmissions, 30-day inpatient mortality, and LOS. RESULTS: Our analysis revealed that hospital LOS was significantly affected by CoC. The statistically significant average treatment effect (ATEs), expressed in risk difference (RD), ranged between 0.27 [95%CI: (0.07, 0.48)] and 1.0 day [95%CI: (0.57, 1.43)]. A similar trend was observed for 30-day readmission (ATEs ranging from 0.0067 [95%CI: (0.0002, 0.0132) to 0.0071 [95%CI: (0.0005, 0.0136)]), and inpatient mortality (ATEs ranging from 0.0006 [95% confidence interval (CI): (0.0001, 0.0012)] to 0.0007 [95%CI: (0.0001, 0.0012)]). CONCLUSIONS: Our findings suggest a strong association between continuity of care and clinical outcomes. Continuity of care leads to a reduction in mortality, rehospitalization, and hospital length of stay.

ELICIT: An alternative imprecise weight elicitation technique for use in multi-criteria decision analysis for healthcare.

OBJECTIVE: In this paper, the readers are introduced to ELICIT, an imprecise weight elicitation technique for multicriteria decision analysis for healthcare. METHODS: The application of ELICIT consists of two steps: the rank ordering of evaluation criteria based on decision-makers' (DMs) preferences using the principal component analysis; and the estimation of criteria weights and their descriptive statistics using the variable interdependent analysis and the Monte Carlo method. The application of ELICIT is illustrated with a hypothetical case study involving the elicitation of weights for five criteria used to select the best device for eye surgery. RESULTS: The criteria were ranked from 1-5, based on a strict preference relationship established by the DMs. For each criterion, the deterministic weight was estimated as well as the standard deviation and 95% credibility interval. CONCLUSIONS: ELICIT is appropriate in situations where only ordinal DMs' preferences are available to elicit decision criteria weights.

A review of systematic reviews of the cost-effectiveness of hormone therapy, chemotherapy, and targeted therapy for breast cancer.

Breast cancer is a global health concern. In fact, breast cancer is the primary cause of death among women worldwide and constitutes the most expensive malignancy to treat. As health care resources are finite, decisions regarding the adoption and coverage of breast cancer treatments are increasingly being based on "value for money," i.e., cost-effectiveness. As the evidence about the cost-effectiveness of breast cancer treatments is abundant, therefore difficult to navigate, systematic reviews of published systematic reviews offer the advantage of bringing together the results of separate systematic reviews in a single report. As a consequence, this paper presents an overview of systematic reviews of the cost-effectiveness of hormone therapy, chemotherapy, and targeted therapy for breast cancer to inform policy and reimbursement decision-making. A systematic review was conducted of published systematic reviews documenting cost-effectiveness analyses of breast cancer treatments from 2000 to 2014. Systematic reviews identified through a literature search of health and economic databases were independently assessed against inclusion and exclusion criteria. Systematic reviews of original evaluations were included only if they targeted breast cancer patients and specific breast cancer treatments (hormone therapy, chemotherapy, and targeted therapy only), documented incremental cost-effectiveness ratios, and were reported in the English language. The search strategy used a combination of these key words: "breast cancer," "systematic review/meta-analysis," and "cost-effectiveness/economics." Data were extracted using predefined extraction forms and qualitatively appraised using the assessment of multiple systematic reviews (AMSTAR) tool. The literature search resulted in 511 bibliographic records, of which ten met our inclusion criteria. Five reviews were conducted in the early-stage breast cancer setting and five reviews in the metastatic setting. In early-stage breast cancer, evidence about trastuzumab value differed by age. Trastuzumab was cost-effective only in women with HER2-positive breast cancer younger than 65 years and over a life-time horizon. The cost-effectiveness of trastuzumab in HER2-positive metastatic breast cancer yielded conflicting results. The same conclusions were reached in comparisons between vinorelbine and taxanes. In both early stage and advanced/metastatic breast cancer, newer aromatase inhibitors (AIs) have proved cost-effective compared to older treatments. This overview of systematic reviews shows that there is heterogeneity in the evidence concerning the cost-effectiveness of hormone therapy, chemotherapy, and targeted therapy for breast cancer. The cost-effectiveness of these treatments depends not only on the comparators but the context, i.e., adjuvant or metastatic setting, subtype of patient population, and perspective adopted. Decisions involving the cost-effectiveness of breast cancer treatments could be made easier and more transparent by better harmonizing the reporting of economic evaluations assessing the value of these treatments.