Thirty-day hospital readmission prediction model based on common data model with weather and air quality data.
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ABSTRACT:
SUBMITTER: Ryu B
PROVIDER: S-EPMC8639801 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveThirty-day hospital readmission rate is receiving increasing attention as a quality-of-care indicator. The objective of this study was to determine readmission rates and to identify factors associated with readmission among persons living with HIV.DesignProspective multicenter observational cohort.SettingNine US HIV clinics affiliated through the HIV Research Network.ParticipantsPatients engaged in HIV care during 2005-2010.Main outcome measure(s)Readmission rate was defined as the proportion of hospitalizations followed by a readmission within 30 days. Factors in multivariate analyses included diagnostic categories, patient demographic and clinical characteristics, and having an outpatient follow-up visit.ResultsAmong 11,651 total index hospitalizations, the 30-day readmission rate was 19.3%. AIDS-defining illnesses (ADIs, 9.6% of index hospitalizations) and non-AIDS-defining infections (26.4% of index hospitalizations) had readmission rates of 26.2 and 16.6%, respectively. Factors independently associated with readmission included lower CD4 cell count [adjusted odds ratio 1.80 (1.53-2.11) for CD4 cell count <50 vs. ≥351 cells/μl], longer length of stay [1.77 (1.53-2.04) for ≥9 days vs. 1-3 days], and several diagnostic categories including ADI. Having an outpatient follow-up clinic visit was not associated with lower readmission risk [adjusted hazard ratio 0.98 (0.88-1.08)].ConclusionThe 19.3% readmission rate exceeds the 13.3% rate reported for the general population of 18-64-year-olds. HIV providers may use the 19.3% rate as a basis of comparison. Policymakers may consider the impact of HIV when estimating expected readmissions for a hospital or region. Preventing or recovering from severe immune dysfunction may be the most important factor to reducing readmissions.
| S-EPMC3796165 | biostudies-literature
Project description:BackgroundExisting readmission reduction solutions tend to focus on complementing inpatient care with enhanced care transition and postdischarge interventions. These solutions are initiated near or after discharge, when clinicians' impact on inpatient care is ending. Preventive intervention during hospitalization is an underexplored area that holds potential for reducing readmission risk. However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available.ObjectiveThe objective of this study was to build an early prediction model of unplanned 30-day hospital readmission using a large and diverse sample. We were also interested in identifying novel readmission risk factors and protective factors.MethodsWe extracted the medical records of 96,550 patients in 205 participating Cerner client hospitals across four US census regions in 2016 from the Health Facts database. The model was built with index admission data that can become available within 24 hours and data from previous encounters up to 1 year before the index admission. The candidate models were evaluated for performance, timeliness, and generalizability. Multivariate logistic regression analysis was used to identify readmission risk factors and protective factors.ResultsWe developed six candidate readmission models with different machine learning algorithms. The best performing model of extreme gradient boosting (XGBoost) achieved an area under the receiver operating characteristic curve of 0.753 on the development data set and 0.742 on the validation data set. By multivariate logistic regression analysis, we identified 14 risk factors and 2 protective factors of readmission that have never been reported.ConclusionsThe performance of our model is better than that of the most widely used models in US health care settings. This model can help clinicians identify readmission risk at the early stage of hospitalization so that they can pay extra attention during the care process of high-risk patients. The 14 novel risk factors and 2 novel protective factors can aid understanding of the factors associated with readmission.
| S-EPMC8077543 | biostudies-literature
Project description:BackgroundReadmission rates are an important metric for evaluating healthcare quality. Stroke is a major complication following cardiac surgery. Our study aimed to evaluate the frequency and predictors of 30-day unplanned hospital readmission after cardiac surgeries and to evaluate the impact of perioperative stroke on readmission.MethodsSurgical discharge records spanning the years of 2008 through 2011 were analyzed utilizing California State Inpatient Database. International Classification of Diseases, ninth revision-Clinical Modification (ICD-9-CM) codes and Clinical Classification Software (CCS) codes were used to identify surgeries and variables of interest. Surgical records were then followed up for 30 days through linking admission records. Perioperative stroke was defined as brain infarction of ischemic or hemorrhagic etiology that occurred during or within 30 days after surgery.ResultsBaseline characteristics associated with increased readmission rates were female gender, age above 65, non-white race, lower income, and increased number of comorbidities. Among 199,617 hospitalizations for cardiac surgeries, 1,817 (0.91%) patients developed perioperative stroke. The rate of readmission in perioperative stroke patients was 21.89%. They had a longer length of hospital stay and their discharge was vastly non-routine (84%). Our univariate analysis yielded significant association between stroke and readmission rates (odds ratio: 1.82, 95% confidence interval: 1.63 - 2.04). This association failed to remain significant upon controlling for other variables in our multivariate analysis.ConclusionBaseline patient characteristics and perioperative complications are significant predictors of readmission. More than one in five patients who develop a stroke after cardiac surgery are readmitted to the hospital within 30 days of discharge.
| S-EPMC8827220 | 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:ImportanceThe Centers for Medicare & Medicaid Services recently identified 30-day readmission after discharge from inpatient rehabilitation facilities as a national quality indicator. Research is needed to determine the rates and factors related to readmission in this patient population.ObjectiveTo determine 30-day readmission rates and factors related to readmission for patients receiving postacute inpatient rehabilitation.Design, setting, and patientsRetrospective cohort study of records for 736,536 Medicare fee-for-service beneficiaries (mean age, 78.0 [SD, 7.3] years) discharged from 1365 inpatient rehabilitation facilities to the community in 2006 through 2011. Sixty-three percent of patients were women, and 85.1% were non-Hispanic white.Main outcomes and measuresThirty-day readmission rates for the 6 largest diagnostic impairment categories receiving inpatient rehabilitation. These included stroke, lower extremity fracture, lower extremity joint replacement, debility, neurologic disorders, and brain dysfunction.ResultsMean rehabilitation length of stay was 12.4 (SD, 5.3) days. The overall 30-day readmission rate was 11.8% (95% CI, 11.7%-11.8%). Rates ranged from 5.8% (95% CI, 5.8%-5.9%) for patients with lower extremity joint replacement to 18.8% (95% CI, 18.8%-18.9%). for patients with debility. Rates were highest in men (13.0% [ 95% CI, 12.8%-13.1%], vs 11.0% [95% CI, 11.0%-11.1%] in women), non-Hispanic blacks (13.8% [95% CI, 13.5%-14.1%], vs 11.5% [95% CI, 11.5%-11.6%] in whites, 12.5% [95% CI, 12.1%-12.8%] in Hispanics, and 11.9% [95% CI, 11.4%-12.4%] in other races/ethnicities), beneficiaries with dual eligibility (15.1% [95% CI, 14.9%-15.4%], vs 11.1% [95% CI, 11.0%-11.2%] for no dual eligibility), and in patients with tier 1 comorbidities (25.6% [95% CI, 24.9%-26.3%], vs 18.9% [95% CI, 18.5%-19.3%] for tier 2, 15.1% [95% CI, 14.9%-15.3%] for tier 3, and 9.9% [95% CI, 9.9%-10.0%] for no tier comorbidities). Higher motor and cognitive functional status were associated with lower hospital readmission rates across the 6 impairment categories. Adjusted readmission rates by state ranged from 9.2% to 13.6%. Approximately 50% of patients rehospitalized within the 30-day period were readmitted within 11 days of discharge. Medicare Severity Diagnosis-Related Group codes for heart failure, urinary tract infection, pneumonia, septicemia, nutritional and metabolic disorders, esophagitis, gastroenteritis, and digestive disorders were common reasons for readmission.Conclusions and relevanceAmong postacute rehabilitation facilities providing services to Medicare fee-for-service beneficiaries, 30-day readmission rates ranged from 5.8% to 18.8% for selected impairment groups. Further research is needed to understand the causes of readmission.
| S-EPMC4085109 | biostudies-literature
Project description:Background The contemporary incidence of and reasons for early readmission after infective endocarditis ( IE ) are not well known. Therefore, we analyzed 30-day readmission demographics after IE from the US Nationwide Readmission Database. Methods and Results We examined the 2010 to 2014 Nationwide Readmission Database to identify index admissions for a primary diagnosis of IE with survival at discharge. Incidence, reasons, and independent predictors of 30-day unplanned readmissions were analyzed. In total, 11 217 patients (24.8%) were nonelectively readmitted within 30 days among the 45 214 index admissions discharged after IE . The most common causes of readmission were IE (20.5%), sepsis (8.7%), complications of device/graft (8.1%), and congestive heart failure (7.6%). In-hospital mortality and the valvular surgery rates during the readmissions were 8.1% and 9.1%, respectively. Discharge to home or self-care, undergoing valvular surgery, aged ≥60 years, and having private insurance were independently associated with lower rates of 30-day readmission. Length of stay of ≥10 days, congestive heart failure, diabetes mellitus, renal failure, chronic pulmonary disease, peripheral artery disease, and depression were associated with higher risk. The total hospital costs of readmission were $48.7 million per year (median, $11 267; interquartile range, $6021-$25 073), which accounted for 38.6% of the total episodes of care (index+readmission). Conclusions Almost 1 in 4 patients was readmitted within 30 days of admission for IE . The most common reasons were IE , other infectious causes, and cardiac causes. A multidisciplinary approach to determine the surgical indications and close monitoring are necessary to improve outcomes and reduce complications in in-hospital and postdischarge settings.
| S-EPMC6512130 | biostudies-literature
Project description:BackgroundExamining health care system characteristics possibly associated with 30-day readmission may reveal opportunities to improve healthcare quality as well as reduce costs.Aims of the studyExamine the relationship between 30-day mental health readmission for persons with schizophrenia and county-level community treatment characteristics.MethodsObservational study of 18 state Medicaid programs (N=274 counties, representing 103,967 enrollees with schizophrenia 28,083 of whom received more than 1 mental health hospitalization) using Medicaid administrative and United States Area Health Resource File data from 2005. Medicaid is a federal-state program and major health insurance provider for low income and disabled individuals, and the predominant provider of insurance for individuals with schizophrenia. The Area Health Resource File provides county-level estimates of providers. We first fit a regression model examining the relationship between 30-day mental health readmission and enrollee characteristics (e.g., demographics, substance use disorder [SUD], and general medical comorbidity) from which we created a county-level demographic and comorbidity case-mix adjuster. The case-mix adjuster was included in a second regression model examining the relationship between 30-day readmission and county-level factors: (i) quality (antipsychotic/visit continuity, post-hospital follow-up); (ii) mental health hospitalization (length of stay, admission rates); and (iii) treatment capacity (e.g., population-based estimates of outpatient providers/clinics). We calculated predicted probabilities of readmission for significant patient and county-level variables.ResultsHigher county rates of mental health visits within 7-days post-hospitalization were associated with lower readmission probabilities (e.g., county rates of 7-day follow up of 55% versus 85%, readmission predicted probability (PP) [95%CI]=16.1% [15.8%-16.4%] versus 13.3% [12.9%-13.6%]). In contrast, higher county rates of mental health hospitalization were associated with higher readmission probabilities (e.g., country admission rates 10% versus 30%, readmission predicted probability=11.3% [11.0%-11.6%] versus 16.7% [16.4%-17.0%]). Although not our primary focus, enrollee comorbidity was associated with higher predicted probability of 30-day mental health readmission: PP [95%CI] for enrollees with SUD=23.9% [21.5%-26.3%] versus 14.7% [13.9%-15.4%] for those without; PP [95% CI] for those with=three chronic medical conditions=25.1% [22.1%-28.2%] versus none=17.7% [16.3%-19.1].DiscussionCounty rates of hospitalization and 7-day follow-up post hospital discharge were associated with readmission, along with patient SUD and general medical comorbidity. This observational design limits causal inference and utilization patterns may have changed since 2005. However, overall funding for U.S. Medicaid programs remained constant since 2005, reducing the likelihood significant changes. Last, our inability to identify community capacity variables associated with readmission may reflect imprecision of some variables as measured in the Area Health Resource File. IMPLICATIONS FOR HEALTH CARE PROVISION AND USE AND FOR HEALTH POLICIES: Healthcare policy and programming to reduce 30-day mental health readmissions should focus on county-level factors that contribute to hospitalization in general and improving transitions to community care, as well as patient comorbidity.Implications for further researchGiven the likely importance of local care systems, to reduce readmission future research is needed to refine community-level capacity variables that are associated with reduced readmissions; and to evaluate models of care coordination in this population.
| S-EPMC4764135 | biostudies-literature
Project description:BackgroundUnplanned hospital readmission after discharge reflects low satisfaction and reliability in care and the possibility of potential medical accidents, and is thus indicative of the quality of patient care and the appropriateness of discharge plans.ObjectivesThe purpose of this study was to develop and validate prediction models for all-cause unplanned hospital readmissions within 30 days of discharge, based on a common data model (CDM), which can be applied to multiple institutions for efficient readmission management.MethodsRetrospective patient-level prediction models were developed based on clinical data of two tertiary general university hospitals converted into a CDM developed by Observational Medical Outcomes Partnership. Machine learning classification models based on the LASSO logistic regression model, decision tree, AdaBoost, random forest, and gradient boosting machine (GBM) were developed and tested by manipulating a set of CDM variables. An internal 10-fold cross-validation was performed on the target data of the model. To examine its transportability, the model was externally validated. Verification indicators helped evaluate the model performance based on the values of area under the curve (AUC).ResultsBased on the time interval for outcome prediction, it was confirmed that the prediction model targeting the variables obtained within 30 days of discharge was the most efficient (AUC of 82.75). The external validation showed that the model is transferable, with the combination of various clinical covariates. Above all, the prediction model based on the GBM showed the highest AUC performance of 84.14 ± 0.015 for the Seoul National University Hospital cohort, yielding in 78.33 in external validation.ConclusionsThis study showed that readmission prediction models developed using machine-learning techniques and CDM can be a useful tool to compare two hospitals in terms of patient-data features.
| S-EPMC8714301 | biostudies-literature
Project description:Accurate population-based data are needed on the rate, economic impact, and the long-term outcomes of readmission among patients with cirrhosis. To examine the rates, costs, and 1-year outcomes of patients readmitted within 30 days following their index hospitalization for complications of cirrhosis, we conducted a nationwide, population-based cohort study involving all patients with cirrhosis in Thailand from 2009 through 2013, using data from the National Health Security Office databases, which included those from nationwide hospitalizations. Readmission was captured from hospitals at all health care levels across the country within the Universal Coverage Scheme. For the 134,038 patients hospitalized with cirrhosis, the overall 30-day readmission rate was 17%. Common causes of readmission consisted of complications of portal hypertension (47%) and infections (17%). After adjusting for multiple covariates, predictors of 30-day readmission included hepatocellular carcinoma (odds ratio [OR] 1.95, 95% confidence interval [CI] 1.84-2.06), human immunodeficiency virus-related admission (OR 1.81, 95% CI 1.51-2.17) and cholangiocarcinoma (OR 1.64, 95% CI 1.3-2.05). In all, 2,936 deaths (13%) occurred during readmission, and an additional 14,425 deaths up to 1 year (63.5% total mortality among readmitted patients). Causes of death were mostly from liver-related mortality. Average cost at index admission for those with a 30-day readmission were significantly higher than those readmitted beyond 30 days or not readmitted. Conclusions: Patients hospitalized with cirrhosis complications had high rates of unscheduled 30-day readmission. Average hospitalization costs were high, and only 36.5% of patients readmitted within 30 days survived at 1 year.
| S-EPMC7049670 | biostudies-literature
Project description:BackgroundResearch on age-related complications secondary to shunts in normal pressure hydrocephalus (NPH) is primarily limited to single-center studies and small cohorts.ObjectiveTo determine the rates of hospital readmission and surgical complications, and factors that predict them, following shunt surgery for NPH in a large healthcare network.MethodsSurgical procedures, complications, and readmissions for adults undergoing ventricular shunting for NPH were determined using de-identified claims from a privately insured United States healthcare network in years 2007-2014. Univariate and multivariate statistics were used to determine factors that predict poor surgical outcomes. The primary outcome variable was surgical complications or readmissions (composite variable for any major perioperative complication or 30-d readmission).ResultsThe 30-d readmission rate for 974 patients with NPH who underwent ventricular shunting was 7.29%; the most common reasons for readmission were shunt-related complications, infection, hemorrhage, altered mental status, and cardiopulmonary and musculoskeletal problems. The perioperative complication rate was 21.15%, including intraparenchymal hemorrhage (5.85%) and extra-axial (subdural or epidural) hematoma (5.54%). The overall rate of having a surgical complication or 30-d readmission was 25.15%. Age did not predict surgical complication or 30-d readmission. Preoperative comorbidities independently associated with poor outcome were myocardial infarction within 1 yr (OR = 3.984, 95% CI = 1.105-14.368); existing cerebrovascular disease (odds ratio [OR] = 2.206, 95% CI = 1.544-3.152); and moderate/severe renal disease (OR = 2.000, 95% CI = 1.155-3.464).ConclusionThe rate of complications or readmission within 30 d of ventricular shunting for NPH is 25.15%. Preoperative comorbidities of myocardial infarction within 1 yr, cerebrovascular disease, and moderate/severe renal disease are independent risk factors for poor outcome.
| S-EPMC7528659 | biostudies-literature