A Readmission Risk Model for Hospitalized Patients Receiving Dialysis: Evaluation of Predictive Performance.
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ABSTRACT:
SUBMITTER: Gallagher DM
PROVIDER: S-EPMC9437601 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
A Readmission Risk Model for Hospitalized Patients Receiving Dialysis: Evaluation of Predictive Performance.
Gallagher David M DM Zhao Congwen C Goldstein Benjamin A BA
Kidney medicine 20220624 8
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Project description:BackgroundPatients receiving maintenance hemodialysis (HD) and peritoneal dialysis (PD) are frequently hospitalized. Reducing unplanned 30-day hospital readmissions is a key priority for improving the quality of health care. The purpose of this study was to assess the association between the Charlson Comorbidity Index (CCI), which has been used to evaluate multi-comorbidities status, and 30-day readmission in patients on HD and PD therapy.MethodsThe Hospital Quality Monitoring System (HQMS), a national administrative database for hospitalized patients in China was used to extract dialysis patients admitted from January 2013 to December 2015. The outcome was the unplanned readmission following the hospital discharge within 30 days. For patients with multiple hospitalizations, a single hospitalization was randomly selected as the index hospitalization. A cause-specific Cox proportional hazard model was utilized to assess the association of CCI with readmission within 30 days.ResultsOf the 124,721 patients included in the study, 19,893 patients (16.0%) were identified as experiencing unplanned readmissions within 30 days. Compared with patients without comorbidity (CCI = 2, scored for dialysis), the risk of 30-day readmission increased with elevated CCI score. The hazards ratio (HR) for those with CCI 3-4, 5-6 and > 6 was 1.01 (95% confidence interval [CI] 0.98-1.05), 1.09 (95% CI 1.05-1.14), and 1.14 (95% CI 1.09-1.20), respectively.ConclusionsOur study indicated that CCI was independently associated with the risk of 30-day readmission for patients receiving dialysis including HD and PD, and could be used for risk-stratification.
| S-EPMC6781396 | biostudies-literature
Project description:BackgroundPrevious studies have shown cardiovascular disease (CVD) to be a risk factor in the prediction of 30-day hospital readmission among patients receiving dialysis. However, studies of Asian populations are limited. In the present study, we examined the association between CVD and 30-day hospital readmission in Chinese patients receiving maintenance dialysis.MethodsPatients receiving maintenance dialysis were identified by searching a national claims database, the China Health Insurance Research Association (CHIRA) database, using the International Classification of Diseases revision 10 (ICD-10) and items of medical service claims. Patients aged ≥18 years who were discharged after index hospitalization between January 2015 and December 2015 were included in our retrospective analysis. CVD-related diagnoses were divided into three categories: coronary heart disease (CHD), heart failure (HF), and stroke. Thirty-day hospital readmission was defined as any hospital readmission within the 30 days following discharge. Logistic regression models adjusted for logit of propensity scores (PS) were used to assess the association of CVD with 30-day hospital readmission.ResultsOf 4,700 patients receiving dialysis, the 30-day hospital readmission rate was 10.4%. Compared with patients without CVD, there was an increased risk of 30-day hospital readmission among maintenance dialysis patients with total CVD [odds ratio (OR): 1.33, 95% confidence interval (CI): 1.06-1.66]. Patients with HF (OR: 1.77, CI: 1.27-2.47) and stroke (OR: 2.14, 95% CI: 1.53-2.98) had a greater risk of 30-day hospital readmission. The fully adjusted OR of CHD for the risk of 30-day hospital readmission was 1.22 (95% CI: 0.97-1.55).ConclusionsCVDs, especially stroke and HF, are independent predictors of 30-day hospital readmission in Chinese patients receiving dialysis, and could help to guide interventions to improve the quality of care for these patients.
| S-EPMC8106029 | biostudies-literature
Project description:BackgroundThirty-day readmissions (30DRA) are a highly scrutinized measure of healthcare quality and relatively frequent among kidney transplants (KTX). Development of predictive risk models is critical to reducing 30DRA and improving outcomes. Current approaches rely on fixed variables derived from administrative data. These models may not capture clinical evolution that is critical to predicting outcomes.MethodsWe directed a retrospective analysis toward: (1) developing parsimonious risk models for 30DRA and (2) comparing efficiency of models based on the use of immutable versus dynamic data. Baseline and in-hospital clinical and outcomes data were collected from adult KTX recipients between 2005 and 2012. Risk models were developed using backward logistic regression and compared for predictive efficacy using receiver operating characteristic curves.ResultsOf 1147 KTX patients, 123 had 30DRA. Risk factors for 30DRA included recipient comorbidities, transplant factors, and index hospitalization patient level clinical data. The initial fixed variable model included 9 risk factors and was modestly predictive (area under the curve, 0.64; 95% confidence interval [95% CI], 0.58-0.69). The model was parsimoniously reduced to 6 risks, which remained modestly predictive (area under the curve, 0.63; 95% CI, 0.58-0.69). The initial predictive model using 13 fixed and dynamic variables was significantly predictive (AUC, 0.73; 95% CI, 0.67-0.80), with parsimonious reduction to 9 variables maintaining predictive efficacy (AUC, 0.73; 95% CI, 0.67-0.79). The final model using dynamically evolving clinical data outperformed the model using static variables (P=0.009). Internal validation demonstrated that the final model was stable with minimal bias.ConclusionsWe demonstrate that modeling dynamic clinical data outperformed models using immutable data in predicting 30DRA.
| S-EPMC4369585 | biostudies-literature
Project description:BackgroundEarly readmissions among older adults hospitalized for acute myocardial infarction (AMI) are costly and difficult to predict. Aging-related functional impairments may inform risk prediction but are unavailable in most studies. Our objective was to, therefore, develop and validate an AMI readmission risk model for older patients who considered functional impairments and was suitable for use before hospital discharge.Methods and resultsSILVER-AMI (Comprehensive Evaluation of Risk in Older Adults with AMI) is a prospective cohort study of 3006 patients of age ≥75 years hospitalized with AMI at 94 US hospitals. Participants underwent in-hospital assessment of functional impairments including cognition, vision, hearing, and mobility. Other variables plausibly associated with readmissions were also collected. The outcome was all-cause readmission at 30 days. We used backward selection and Bayesian model averaging to derive (N=2004) a risk model that was subsequently validated (N=1002). Mean age was 81.5 years, 44.4% were women, and 10.5% were nonwhite. Within 30 days, 547 participants (18.2%) were readmitted. Readmitted participants were older, had more comorbidities, and had a higher prevalence of functional impairments, including activities of daily living disability (17.0% versus 13.0%; P=0.013) and impaired functional mobility (72.5% versus 53.6%; P<0.001). The final risk model included 8 variables: functional mobility, ejection fraction, chronic obstructive pulmonary disease, arrhythmia, acute kidney injury, first diastolic blood pressure, P2Y12 inhibitor use, and general health status. Functional mobility was the only functional impairment variable retained but was the strongest predictor. The model was well calibrated (Hosmer-Lemeshow P value >0.05) with moderate discrimination (C statistics: 0.65 derivation cohort and 0.63 validation cohort). Functional mobility significantly improved performance of the risk model (net reclassification improvement index =20%; P<0.001).ConclusionsIn our final risk model, functional mobility, previously not included in readmission risk models, was the strongest predictor of 30-day readmission among older adults after AMI. The modest discrimination indicates that much of the variability in readmission risk among this population remains unexplained by patient-level factors.Clinical trial registrationURL: https://www.clinicaltrials.gov. Unique identifier: NCT01755052.
| S-EPMC6481309 | biostudies-literature
Project description: Not available
| S-EPMC9459027 | biostudies-literature
Project description:To analyze a single-center experience with locally advanced pancreatic cancer (LAPC) patients treated with chemoradiation (CRT) and to evaluate predictive variables of outcome.LAPC patients at our institution between 1997 and 2009 were identified (n = 109). Progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier analysis. Cox proportional hazard models were used to evaluate predictive factors for survival. Patterns of failure were characterized, and associations between local progression and distant metastasis were explored.Median OS was 12.1 months (2.5-34.7 months) and median PFS was 6.7 months (1.1-34.7 months). Poor prognostic factors for OS include Karnofsky performance status ?80 (P = .0062), treatment interruption (P = .0474), and locally progressive disease at time of first post-therapy imaging (P = .0078). Karnofsky performance status ?80 (P = .0128), pretreatment CA19-9 >1000 U/mL (P = .0224), and treatment interruption (P = .0009) were poor prognostic factors for PFS. Both local progression (36%) and distant failure (62%) were common. Local progression was associated with a higher incidence of metastasis (P < .0001) and decreased time to metastasis (P < .0001).LAPC patients who suffer local progression following definitive CRT may experience inferior OS and increased risk of metastasis, warranting efforts to improve control of local disease. However, patients with poor pretreatment performance status, elevated CA19-9 levels, and treatment interruptions may experience poor outcomes despite aggressive management with CRT, and may optimally be treated with induction chemotherapy or supportive care. Novel therapies aimed at controlling both local and systemic progression are needed for patients with LAPC.
| S-EPMC3622285 | biostudies-other
Project description:This study investigated the prevalence of falls in maintenance hemodialysis (MHD) patients, and established a nomogram model for evaluating the fall risk of MHD patients. This study enrolled 303 MHD patients from the dialysis department of a tertiary hospital in July 2021. The general data of the participants, as well as the scores on the FRAIL scale, Sarcopenia Screening Questionnaire (SARC-F), Short Physical Performance Battery (SPPB) Scale, and of anxiety and depression, and the occurrence of falls were recorded. Using R language, data were assigned to the training set (n = 212) and test set (n = 91), and a logistic regression model was established. The regression model was verified by the receiver operating characteristic (ROC) curve, area under the curve (AUC), and the calibration curve. As a result, the prevalence of falls in MHD patients was 20.46%. Risk factors for falls in the optimal multivariate logistic regression model included hearing impairment, the depression score, and the SPPB score, of which a higher depression score (odds ratio (OR): 1.28, 95% confidence interval (CI): 1.09-1.49, p = 0.002) and SPPB ≤ 6 (ORvsSPPB9-12: 3.69, 95% CI: 1.04-13.14, p = 0.043) conferred independent risk for falls. AUC of the nomogram in the training was 0.773, which in the test group was 0.663. The calibration and standard curves were fitted closely, indicated that the evaluation ability of the model was good. Thus, a higher depression score and SPPB ≤ 6 are independent risk factors for falls in MHD patients, and the nomogram with good accuracy and discrimination that was established in this study has clinical application value.
| S-EPMC9980417 | biostudies-literature
Project description:Trial enrichment using gut microbiota derived biomarkers by high-risk individuals can improve the feasibility of randomized controlled trials for prevention of Clostridioides difficile infection (CDI). Here, we report in a prospective observational cohort study the incidence of CDI and assess potential clinical characteristics and biomarkers to predict CDI in 1,007 patients ≥ 50 years receiving newly initiated antibiotic treatment with penicillins plus a beta-lactamase inhibitor, 3rd/4th generation cephalosporins, carbapenems, fluoroquinolones or clindamycin from 34 European hospitals. The estimated 90-day cumulative incidences of a first CDI episode is 1.9% (95% CI 1.1-3.0). Carbapenem treatment (Hazard Ratio (95% CI): 5.3 (1.7-16.6)), toxigenic C. difficile rectal carriage (10.3 (3.2-33.1)), high intestinal abundance of Enterococcus spp. relative to Ruminococcus spp. (5.4 (2.1-18.7)), and low Shannon alpha diversity index as determined by 16 S rRNA gene profiling (9.7 (3.2-29.7)), but not normalized urinary 3-indoxyl sulfate levels, predicts an increased CDI risk.
| S-EPMC8046770 | biostudies-literature
Project description:BackgroundReduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients.MethodsThe California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results.ResultsArea under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation.DiscussionThe results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.
| S-EPMC5407280 | biostudies-literature
Project description:BackgroundThirty-day hospital readmissions are common among maintenance dialysis patients. Prior studies have evaluated easily measurable readmission risk factors such as comorbid conditions, laboratory results, and hospital discharge day. We undertook this prospective study to investigate the associations between hospital-assessed depression, health literacy, social support, and self-rated health (separately) and 30-day hospital readmission among dialysis patients.MethodsParticipants were recruited from the University of North Carolina Hospitals, 2014-2016. Validated depression, health literacy, social support, and self-rated health screening instruments were administered during index hospitalizations. Multivariable logistic regression models with 30-day readmission as the dependent outcome were used to examine readmission risk factors.ResultsOf the 154 participants, 58 (37.7%) had a 30-day hospital readmission. In unadjusted analyses, individuals with positive screening for depression, lower health literacy, and poorer social support were more likely to have a 30-day readmission (vs. negative screening). Positive depression screening and poorer social support remained significantly associated with 30-day readmission in models adjusted for race, heart failure, admitting service, weekend discharge day, and serum albumin: adjusted OR (95% CI) 2.33 (1.02-5.15) for positive depressive symptoms and 2.57 (1.10-5.91) for poorer social support. The area under the receiver operating characteristic curve (AUC) of the multivariable model adjusted for social support status was significantly greater than the AUC of the multivariable model without social support status (test for equality; p value = 0.04).ConclusionPoor social support and depressive symptoms identified during hospitalizations may represent targetable readmission risk factors among dialysis patients. Our findings suggest that hospital-based assessments of select psychosocial factors may improve readmission risk prediction.
| S-EPMC5483850 | biostudies-literature