The Contribution of Pharmacogenetic Drug Interactions to 90-Day Hospital Readmissions: Preliminary Results from a Real-World Healthcare System.
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SUBMITTER: David SP
PROVIDER: S-EPMC8705172 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The present study builds on our prior work that demonstrated an association between pharmacogenetic interactions and 90-day readmission. In a substantially larger, more diverse study population of 19,999 adults tracked from 2010 through 2020 who underwent testing with a 13-gene pharmacogenetic panel, we included additional covariates to evaluate aggregate contribution of social determinants and medical comorbidity with the presence of identified gene-x-drug interactions to moderate 90-day hospital readmission (primary outcome). Univariate logistic regression analyses demonstrated that strongest associations with 90 day hospital readmissions were the number of medications prescribed within 30 days of a first hospital admission that had Clinical Pharmacogenomics Implementation Consortium (CPIC) guidance (CPIC medications) (5+ CPIC medications, odds ratio (OR) = 7.66, 95% confidence interval 5.45-10.77) (p < 0.0001), major comorbidities (5+ comorbidities, OR 3.36, 2.61-4.32) (p < 0.0001), age (65 + years, OR = 2.35, 1.77-3.12) (p < 0.0001), unemployment (OR = 2.19, 1.88-2.64) (p < 0.0001), Black/African-American race (OR 2.12, 1.47-3.07) (p < 0.0001), median household income (OR = 1.63, 1.03-2.58) (p = 0.035), male gender (OR = 1.47, 1.21-1.80) (p = 0.0001), and one or more gene-x-drug interaction (defined as a prescribed CPIC medication for a patient with a corresponding actionable pharmacogenetic variant) (OR = 1.41, 1.18-1.70). Health insurance was not associated with risk of 90-day readmission. Race, income, employment status, and gene-x-drug interactions were robust in a multivariable logistic regression model. The odds of 90-day readmission for patients with one or more identified gene-x-drug interactions after adjustment for these covariates was attenuated by 10% (OR = 1.31, 1.08-1.59) (p = 0.006). Although the interaction between race and gene-x-drug interactions was not statistically significant, White patients were more likely to have a gene-x-drug interaction (35.2%) than Black/African-American patients (25.9%) who were not readmitted (p < 0.0001). These results highlight the major contribution of social determinants and medical complexity to risk for hospital readmission, and that these determinants may modify the effect of gene-x-drug interactions on rehospitalization risk.
| S-EPMC9319564 | biostudies-literature
Project description:Hospital readmission rates are increasingly used to assess quality. Little is known, however, about potential preventability of readmissions among children. Our objective was to evaluate potential preventability of 30-day readmissions using medical record review and interviews.A cross-sectional study in 305 children (<18 years old) readmitted within 30 days to a freestanding children's hospital between December 2012 and February 2013. Interviews (N = 1459) were conducted with parents/guardians, patients (if ≥13 years old), inpatient clinicians, and primary care providers. Reviewers evaluated medical records, interview summaries, and transcripts, and then rated potential preventability. Multivariate regression analysis was used to identify factors associated with potentially preventable readmission. Adjusted event curves were generated to model days to readmission.Of readmissions, 29.5% were potentially preventable. Potentially preventable readmissions occurred sooner after discharge than non-potentially preventable readmissions (5 vs 9 median days; P < .001). The odds of a readmission being potentially preventable were greatest when the index admission and readmission were causally related (adjusted odds ratio [AOR]: 2.6; 95% confidence interval [CI]: 1.0-6.8) and when hospital (AOR: 16.3; 95% CI: 5.9-44.8) or patient (AOR: 7.1; 95% CI: 2.5-20.5) factors were identified. Interviews provided new information about the readmission in 31.2% of cases.Nearly 30% of 30-day readmissions to a children's hospital may be potentially preventable. Hospital and patient factors are associated with potential preventability and may provide targets for quality improvement efforts. Interviews contribute important information and should be considered when evaluating readmissions.
| S-EPMC5557411 | biostudies-other
Project description:BackgroundPredictive models based on electronic health records (EHRs) are used to identify patients at high risk for 30-day hospital readmission. However, these models' ability to accurately detect who could benefit from inclusion in prevention interventions, also termed "perceived impactibility", has yet to be realized.ObjectiveWe aimed to explore healthcare providers' perspectives of patient characteristics associated with decisions about which patients should be referred to readmission prevention programs (RPPs) beyond the EHR preadmission readmission detection model (PREADM).DesignThis cross-sectional study employed a multi-source mixed-method design, combining EHR data with nurses' and physicians' self-reported surveys from 15 internal medicine units in three general hospitals in Israel between May 2016 and June 2017, using a mini-Delphi approach.ParticipantsNurses and physicians were asked to provide information about patients 65 years or older who were hospitalized at least one night.Main measuresWe performed a decision-tree analysis to identify characteristics for consideration when deciding whether a patient should be included in an RPP.Key resultsWe collected 817 questionnaires on 435 patients. PREADM score and RPP inclusion were congruent in 65% of patients, whereas 19% had a high PREADM score but were not referred to an RPP, and 16% had a low-medium PREADM score but were referred to an RPP. The decision-tree analysis identified five patient characteristics that were statistically associated with RPP referral: high PREADM score, eligibility for a nursing home, having a condition not under control, need for social-services support, and need for special equipment at home.ConclusionsOur study provides empirical evidence for the partial congruence between classifications of a high PREADM score and perceived impactibility. Findings emphasize the need for additional research to understand the extent to which combining EHR data with provider insights leads to better selection of patients for RPP inclusion.
| S-EPMC7210355 | biostudies-literature
Project description:New tools to accurately identify potentially preventable 30-day readmissions are needed. The HOSPITAL score has been internationally validated for medical inpatients, but its performance in select conditions targeted by the Hospital Readmission Reduction Program (HRRP) is unknown.Retrospective cohort study.Six geographically diverse medical centers.All consecutive adult medical patients discharged alive in 2011 with 1 of the 4 medical conditions targeted by the HRRP (acute myocardial infarction, chronic obstructive pulmonary disease, pneumonia, and heart failure) were included. Potentially preventable 30-day readmissions were identified using the SQLape algorithm. The HOSPITAL score was calculated for all patients.A multivariable logistic regression model accounting for hospital effects was used to evaluate the accuracy (Brier score), discrimination (c-statistic), and calibration (Pearson goodness-of-fit) of the HOSPITAL score for each 4 medical conditions.Among the 9181 patients included, the overall 30-day potentially preventable readmission rate was 13.6%. Across all 4 diagnoses, the HOSPITAL score had very good accuracy (Brier score of 0.11), good discrimination (c-statistic of 0.68), and excellent calibration (Hosmer-Lemeshow goodness-of-fit test, P=0.77). Within each diagnosis, performance was similar. In sensitivity analyses, performance was similar for all readmissions (not just potentially preventable) and when restricted to patients age 65 and above.The HOSPITAL score identifies a high-risk cohort for potentially preventable readmissions in a variety of practice settings, including conditions targeted by the HRRP. It may be a valuable tool when included in interventions to reduce readmissions within or across these conditions.
| S-EPMC5309170 | biostudies-other
Project description:The primary objective of this study was to determine if nonadherence to continuous positive airway pressure (CPAP) is associated with increased 30-day all-cause, cardiovascular-cause, and pulmonary-cause hospital readmissions.Retrospective cohort study at a Veterans Affairs hospital of patients with obstructive sleep apnea (OSA) who were hospitalized from January 1, 2007 to December 31, 2015. Odds ratio of 30-day readmission was calculated for all-cause, cardiovascular-cause, and pulmonary-cause readmissions. Logistic regression model was used to evaluate odds of nonadherent versus adherent group while adjusting for age, sex, body mass index, living situation, race, comorbidities, and medication adherence.Out of 2,077 records reviewed, 345 patients (183 adherent and 162 nonadherent) met our inclusion criteria. The adherent group had a total of 215 initial admissions, and the nonadherent group had a total of 268 index admissions. Thirty-day all-cause readmission rate was significantly higher in the nonadherent group, with an adjusted odds ratio (OR) of 3.52 (95% confidence interval [CI], 2.04-6.08, P < .001). Thirty-day cardiovascular-cause readmission rate was significantly higher in the nonadherent group, with an adjusted OR of 2.31 (95% CI, 1.11-4.78, P = .024). Difference in 30-day pulmonary-cause readmissions was not statistically significant, with an adjusted OR of 3.66 (95% CI, 0.41-32.76, P = .25).Nonadherence to CPAP is associated with increased 30-day all-cause and cardiovascular-cause readmission in patients with OSA. Ensuring CPAP adherence is crucial in addressing general and cardiovascular-related healthcare utilization and morbidity in patients with OSA.A commentary on this article appears in this issue on page 161.
| S-EPMC5786836 | biostudies-other
Project description:Objective:To compare patients' and providers' views on contributors to 30-day hospital readmissions. Design:Analysis of a qualitative interview survey between 18 May-30 June 2015. Setting:Interviews were conducted during the 30-day readmission hospitalisation at a single tertiary care academic hospital. Participants:We conducted 178 interviews of readmitted patients. Measures:We queried opinions of what factors patients believed contributed to their rehospitalisation and compared this with the perspective of the index admission provider. The primary outcome was the view that the readmission was preventable. A review by a RN (nurse) case manager also provided an assessment based on patient report, provider report and chart review. Results:Patients were more likely to view a readmission as preventable compared with physicians (p<0.0001). Patients identified system issues (defined as factors controlled by the hospital discharge process) as contributors to their readmission in 58% (103/178) of cases while providers identified system issues as the contributor to a patients' readmission in 2% (2/101) of cases. Patients with poor functional status were more likely to feel the cause of their readmission was due to system issues than patients with better functional status (p=0.03). A RN case manager review determined that in 48% (86/178) of cases the system had some amount of contribution to a patient's readmission. There was no significant difference in belief that the readmission was preventable between the RN case manager and the patient (p=0.47). Conclusions:Readmitted patients often feel that the hospital system contributed to their readmission. Providers did not recognise patient and RN case manager identified issues as contributors to hospital readmissions.
| S-EPMC6327873 | biostudies-literature
Project description:OBJECTIVE:To use patient-level clinical variables to develop and validate a parsimonious model to predict hospital readmissions from home healthcare (HHC) in Medicare fee-for-service beneficiaries. DESIGN:Retrospective analysis using multivariable logistic regression and gradient boosting machine (GBM) learning to develop and validate a predictive model. SETTING/PARTICIPANTS/MEAUREMENTS:A 5% national sample of patients, aged 65?years or older, with Medicare fee-for-service who received skilled HHC services within 5 days of hospital discharge in 2012 (n = 43 407). Multiple data sets were merged, including Medicare Outcome and Assessment Information Set, Home Health Claims, Medicare Provider Analysis and Review, and Master Beneficiary Summary Files, to extract patient-level variables from the first HHC visit after discharge and measure 30-day readmission outcomes. RESULTS:Among 43 407 patients with inpatient hospitalizations followed by HHC, 14.7% were readmitted within 30?days. Of the 53 candidate variables, seven remained in the final model as individually predictive of outcome: Elixhauser comorbidity index, index hospital length of stay, urinary catheter presence, patient status (ie, fragile health with high risk of complications or serious progressive condition), two or more hospitalizations in prior year, pressure injury risk or presence, and surgical wound presence. Of interest, surgical wounds, either from a total hip or total knee arthroplasty procedure or another surgical procedure, were associated with fewer readmissions. The optimism-corrected c-statistics for the full model and parsimonious model were 0.67 and 0.66, respectively, indicating fair discrimination. The Brier score for both models was 0.120, indicating good calibration. The GBM model identified similar predictive variables. CONCLUSION:Variables available to HHC clinicians at the first postdischarge HHC visit can predict readmission risk and inform care plans in HHC. Future analyses incorporating measures of social determinants of health, such as housing instability or social support, have the potential to enhance prediction of this outcome. J Am Geriatr Soc 67:2505-2510, 2019.
| S-EPMC7323864 | biostudies-literature
Project description:Identification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit.To externally validate the HOSPITAL score in an international multicenter study to assess its generalizability.International retrospective cohort study of 117?065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded.The HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay.30-day potentially avoidable readmission to the index hospital using the SQLape algorithm.Overall, 117?065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16?992 of 117?065 (14.5%) were followed by a 30-day readmission, and 11?307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n?=?73?031 [62.4%]), intermediate (n?=?27?612 [23.6%]), and high risk (n?=?16?422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson ?2 test P?=?.89).The HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.
| S-EPMC5070968 | biostudies-literature
Project description:Risk-adjusted 30-day hospital readmission rate is a commonly used benchmark for hospital quality of care and for Medicare reimbursement. Persons living with HIV (PLWH) may have high readmission rates. This study compared 30-day readmission rates by HIV status in a multi-state sample with planned subgroup comparisons by insurance and diagnostic categories.Data for all acute care, nonmilitary hospitalizations in nine states in 2011 were obtained from the Healthcare Costs and Utilization Project. The primary outcome was readmission for any cause within 30 days of hospital discharge. Factors associated with readmission were evaluated using multivariate logistic regression.A total of 5?484?245 persons, including 33?556 (0.6%) PLWH, had a total of 6?441?695 index hospitalizations, including 45?382 (0.7%) among PLWH. Unadjusted readmission rates for hospitalizations of HIV-uninfected persons and PLWH were 11.2% [95% confidence interval (CI) 11.2, 11.2%] and 19.7% (95% CI 19.3, 20.0%), respectively. After adjustment for age, gender, race, insurance, and diagnostic category, HIV infection was associated with 1.50 (95% CI 1.46, 1.54) times higher odds of readmission. Predicted, adjusted readmission rates were higher for PLWH within every insurance category, including Medicaid [12.9% (95% CI 12.8, 13.0%) and 19.1% (95% CI 18.4, 19.7%) for HIV-uninfected persons and PLWH, respectively] and Medicare [13.2% (95% CI 13.1, 13.3%) and 18.0% (95% CI 17.4, 18.7%), respectively], and within every diagnostic category.HIV infection is associated with significantly increased readmission risk independent of demographics, insurance, and diagnostic category. The 19.7% 30-day readmission rate may serve as a preliminary benchmark for assessing quality of care of PLWH. Policy-makers may consider adjusting for HIV infection when calculating a hospital's expected readmission rate.
| S-EPMC4713370 | biostudies-literature
Project description:A focus of health care reform has been on reducing 30-day hospital readmissions. Patients with ESRD are at high risk for hospital readmission. It is unknown whether more monitoring by outpatient providers can reduce hospital readmissions in patients receiving hemodialysis. In nationally representative cohorts of patients in the United States receiving in-center hemodialysis between 2004 and 2009, we used a quasi-experimental (instrumental variable) approach to assess the relationship between frequency of visits to patients receiving hemodialysis following hospital discharge and the probability of rehospitalization. We then used a multivariable regression model and published hospitalization data to estimate the cost savings and number of hospitalizations that could be prevented annually with additional provider visits to patients in the month following hospitalization. In the main cohort (n=26,613), one additional provider visit in the month following hospital discharge was estimated to reduce the absolute probability of 30-day hospital readmission by 3.5% (95% confidence interval, 1.6% to 5.3%). The reduction in 30-day hospital readmission ranged from 0.5% to 4.9% in an additional four cohorts tested, depending on population density around facilities, facility profit status, and patient Medicaid eligibility. At current Medicare reimbursement rates, the effort to visit patients one additional time in the month following hospital discharge could lead to 31,370 fewer hospitalizations per year, and $240 million per year saved. In conclusion, more frequent physician visits following hospital discharge are estimated to reduce rehospitalizations in patients undergoing hemodialysis. Incentives for closer outpatient monitoring following hospital discharge could lead to substantial cost savings.
| S-EPMC4147977 | biostudies-literature