Unplanned Readmission Of Patients Research Articles

Impact of mild cognitive impairment on unplanned readmission in patients with coronary artery disease.

To investigate the effect of mild cognitive impairment (MCI) on unplanned readmission in patients with coronary artery disease (CAD). From 2132 CAD patients, MCI was estimated with the Japanese version of the Montreal Cognitive Assessment (MoCA-J) in 243 non-dementia patients who met the study criteria. The primary outcome was unplanned hospital readmission after discharge. The incidence of MCI in this cohort was 33.3%, and 51 patients (21.0%) had unplanned readmission during a mean follow-up period of 418.6 ± 203.5 days. After adjusting for the covariates, MCI (hazard ratio, 2.28; 95% confidence interval: 1.09-4.76; P = 0.03) was independently associated with unplanned readmission in the multivariable Cox proportional hazard regression analysis. In the Kaplan-Meier analysis, the cumulative incidence of unplanned readmission for the MCI group was significantly higher than that for the non-MCI group (log-rank test, P < 0.001). Even after exclusion of the patients readmitted within 30 days of discharge, the main results did not change (log-rank test, P < 0.001). Mild cognitive impairment was independently associated with unplanned readmission after adjustment for many independent variables in CAD patients. In addition to its short-term effects, the adverse effects of MCI had a persistent, long-term impact on CAD patients. Assessment of cognitive function should be conducted by health professionals prior to hospital discharge and during follow-up. To prevent readmission of CAD patients, it will be necessary to support solutions to the problems that inhibit secondary prevention behaviours based on the assessment of the patients' cognitive function.

Predictors for Recurrent 30-Day Unplanned Readmissions in Patients with Hematologic Malignancies

BackgroundNewly diagnosed hospitalized patients (pts) with hematologic malignancies (HM) frequently have high symptom burden, often need prolonged courses of highly myelosuppressive chemotherapy and consequent protracted transfusion support- factors that predispose these pts to unplanned readmissions (UR). Given the paucity of data on risk factors associated with repeated UR in HM pts, we first analyzed the baseline characteristics of pts with one or more 30-day UR and then evaluated factors that predicted for additional 30-day UR following the first 30-day UR event.MethodsAdult (≥ 18 years) HM pts treated at Cleveland Clinic between January 2011-February 2016 were included in the study. Thirty-day UR was defined as hospitalization within 30 days of index admission for any reason other than planned chemotherapy. A readmission event served as an index admission for the subsequent readmission. Bone marrow transplant pts were excluded. HM included acute leukemias, myelodysplastic syndromes (MDS) and aggressive lymphomas (Diffuse Large B-Cell Lymphoma, Burkitt's). Demographics (age, race, sex, highest education attained, median income, payer status), clinical characteristics (HM type, symptoms at index admission/readmission, disease status, body mass index at discharge, absolute neutrophil count (ANC) at discharge/readmission, and discharge on intravenous antibiotics or narcotics), hospitalization (reason for readmission, length of stay (LOS) at index admission/readmission), and discharge characteristics (discharged to and readmitted from) were collected. Data were analyzed using Fisher's exact test chi-square tests, and t-tests for univariable analyses; and logistic regression for multivariable analyses. Recursive partitioning was used to find cut points for continuous factors.ResultsA total of 259 thirty-day UR were observed in 157 pts - 107 had a single 30-day UR, while 50 (32%) had > 2 UR. The majority of pts were males (59%), with a median age of 66 years (yrs, interquartile range (IQR) 48-75). Acute myeloid leukemia was the most common HM diagnosis (44%) and 50% had relapsed or refractory disease. Pt median income was $51,700 and 86% (IQR, 80-90%) had greater than a high school education, using census tract based data (Table 1). By payer source, 49% of pts were covered by Medicare, 12% by Medicaid, and 36% by private insurance. Discharges following UR were to: home (50%), home with home health (32%), nursing facility (15%) and hospice (3%). Of the discharges after UR, 10% were discharged on intravenous antibiotics, 44% on opioids and 48% on psychotropic drugs. Neutropenic fever with or without a confirmed source was the primary diagnosis (61%) for 30-day UR and 59% had symptoms at presentation. Median ANC at discharge from an UR was 870/mL (IQR 190-3570) and was 940/mL (IQR, 100-3700) at the time of readmission. The median LOS during 30-day UR was 5 days (IQR 3-10) and the median interval from prior discharge to readmission was 11 days (IQR 5-19). The majority of 30-day UR originated from outpatient clinic, emergency department, or pt's home (46%) followed by a non-health care facility (27%) and outside hospital transfers (22%). Table 1 shows univariable analysis of factors significantly associated with 30-day UR. In multivariate analysis, factors that independently predicted for subsequent 30-day UR included pts with the following characteristics - ANC <2000/mL at last discharge (OR 7.5, P=.0002), constitutional symptoms (fevers/chills/sweats/severe fatigue at index admission presentation) (OR 5.84, P=.002), gastrointestinal symptoms (OR 5.11, P=.009), if pt was transferred from an outside facility (OR 3.26, P=.005), febrile neutropenia as reason for index admission (OR 3.18, P=.0001), relapsed/refractory disease (OR 2.94, P=.009), and higher education (OR 2.08, P=.01).ConclusionOur findings suggest directing UR reduction strategies to optimize symptom control (constitutional and gastrointestinal) prior to discharge and design specific measures for the cohort with relapsed/refractory disease who might further benefit with individualized care from a multidisciplinary care team, and closer ambulatory follow up. Increased risk of additional 30-day UR with education may reflect better understanding of care complexities resulting in a lower threshold for seeking care with a change in medical condition, or possibly a surrogate for better health care access. [Display omitted] DisclosuresGerds:Incyte: Consultancy; CTI BioPharma: Consultancy. Advani:Takeda/ Millenium: Research Funding; Pfizer: Consultancy. Sekeres:Celgene: Membership on an entity's Board of Directors or advisory committees.

Early unplanned readmissions following same-admission cholecystectomy for acute biliary pancreatitis.

Same-admission cholecystectomy (CCY) is recommended for mild acute biliary pancreatitis (biliary-AP). However, there is a paucity of research investigating reasons for early (30-day) unplanned readmissions in patients who undergo CCY for biliary-AP. Hence, we sought to investigate this gap using a large population database. Using the Nationwide Readmission Database (2010-2014), we identified all adults (age ≥ 18years) with a principal diagnosis of biliary-AP who had undergone CCY during the index hospitalization. Multivariable logistic regression models were obtained to assess independent predictors for 30-day readmission. Principal diagnosis for all readmissions was collected to ascertain the indications for early readmission. During the study period, 118,224 patients underwent same-admission CCY for biliary-AP. Three-fourths of all patients underwent invasive cholangiography during the hospitalization (intraoperative cholangiogram (IOC) = 57,038, ERCP = 31,500). The rate of early (30-day) readmission was 7.25% (n = 8574). Exacerbation of prior medical conditions (42.2%), sequelae of biliary-AP (resolving and recurrent pancreatitis, pseudocysts) (27.6%), surgical site and other postoperative complications (16%), choledocholithiasis and/or bile leak (9.6%), and preventable hospital-acquired conditions (4.6%) accounted for early readmissions. On multivariable analysis, predictors for readmission included male sex (odds ratio [OR] 1.18, 95% confidence interval [CI] 1.08-1.28), insurance type (Medicare insurance [OR 1.26, 95% CI 1.13-1.40]; Medicaid [OR 1.22, 95% CI 1.09-1.38]), outside-facility discharge (OR 1.35, 95% CI 1.16-1.57), severe AP (OR 1.35, 95% CI 1.21-1.50), and ≥ 3 Elixhauser comorbidities (OR 1.55, 95% CI 1.41-1.69). Performance of IOC (OR 0.90, 95% CI 0.82-0.97) and ERCP (OR 0.81, 95% CI 0.73-0.89) were associated with decreased risk of early readmission. In this study, using a national population database evaluating patients who underwent same-admission CCY after biliary-AP, we identified potentially modifiable risk factors and causes for early readmission as well as opportunities to improve clinical care.

Modified-frailty index does not independently predict complications, hospital length of stay or 30-day readmission rates following posterior lumbar decompression and fusion for spondylolisthesis

BACKGROUND CONTEXTFrailty has been associated with inferior surgical outcomes in various fields of spinal surgery. With increasing healthcare costs, hospital length of stay (LOS) and unplanned readmissions have emerged as clinical proxies reflecting overall value of care. However, there is a paucity of data assessing the impact that baseline frailty has on quality of care in patients with spondylolisthesis. PURPOSEThe aim of this study was to investigate the impact that frailty has on LOS, complication rate, and unplanned readmission after posterior lumbar spinal fusion for spondylolisthesis. STUDY DESIGNA retrospective cohort study was performed using the National Surgical Quality Improvement Program (NSQIP) database from 2010 through 2016. PATIENT SAMPLEAll adult (≥18 years old) patients who underwent lumbar spinal decompression and fusion for spondylolisthesis were identified using ICD-9-CM diagnosis and procedural coding systems. We calculated the modified frailty index (mFI) for each patient using 5 dichotomous comorbidities - diabetes mellitus, congestive heart failure, hypertension requiring medication, chronic obstructive pulmonary disease, and dependent functional status. Each comorbidity is assigned 1 point and the points are summed to give a score between 0 and 5. As in previous literature, we defined a score of 0 as “not frail”, 1 as “mild” frailty, and 2 or greater as “moderate to severe” frailty. OUTCOME MEASURESPatient demographics, comorbidities, complications, LOS, readmission, and reoperation were assessed. METHODSA multivariate logistic regression analysis was used to identify independent predictors of adverse events (AEs), extended LOS, complications, and unplanned readmission. RESULTSThere were a total of 5,296 patients identified, of which 2,030 (38.3%) were mFI=0, 2,319 (43.8%) patients mFI=1, and 947 (17.9%) were mFI ≥2. The mFI≥2 cohort was older (p≤.001) and had a greater average BMI (p≤.001). The mFI≥2 cohort had a slightly longer hospital stay (3.7 ± 2.3 days vs. mFI=1: 3.5 ± 2.8 days and mFI=0: 3.2 ± 2.1 days,p≤.001). Both surgical AEs and medical AEs were significantly greater in the mFI≥2 cohort than the other cohorts, (2.6% vs. mFI=1: 1.8% and mFI=0: 1.2%,p=.022) and (6.3% vs. mFI=1: 4.8% and mFI=0: 2.6%,p≤.001), respectively. While there was no significant difference in reoperation rates, the mFI≥2 cohort had greater unplanned 30-day readmission rates (8;4% vs. mFI=5.6: 4.8% and mFI=0: 3.4%,p≤.001). However, on multivariate regression analysis, mFI≥2 was not a significant independent predictor of LOS (p=.285), complications (p=.667), or 30-day unplanned readmission (p=.378). CONCLUSIONSOur study indicates that frailty, as measured by the mFI, does not significantly predict LOS, 30-day adverse events, or 30-day unplanned readmission in patients undergoing lumbar spinal decompression and fusion for spondylolisthesis. Further work is needed to better define variable inputs that make up frailty to optimize surgical outcome prediction tools that impact the value of care.

Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN).

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

Pattern and Predictors of Unplanned Readmission in Patients After Index Hospitalisation for AF in Western Australia, 2001–2015

Hospitalisations constitute the majority of the cost burden associated with atrial fibrillation (AF). Therefore, understanding the pattern and predictors of unplanned readmissions after AF hospitalisation is important.

622. Risk Factors for 30-Day Unplanned Readmissions in Patients Discharged with Outpatient Parenteral Antimicrobial Therapy

BackgroundOutpatient parenteral antimicrobial therapy (OPAT) programs have shown to reduce hospital readmissions; however, 20-25% of OPAT patients are readmitted. As 30-day readmissions is a healthcare quality measure, it is important to recognize predictors for readmissions in OPAT patients in an effort to minimize risk factors and optimize patient outcomes. The aim of this study was to identify modifiable and non-modifiable risk factors for 30-day unplanned readmission in patients discharged with OPAT.MethodsThis was a retrospective cohort study of patients admitted to University of Virginia (UVA) Health System between March 2019 and December 2019 who were discharged home with intravenous antimicrobials followed by the UVA OPAT program. Data collected included patient demographics, comorbidities, infection diagnosis, source control, and antimicrobial class. Variables were compared between patients with a 30-day unplanned readmission and those without a readmission. Mann-Whitney U, Pearson chi-squared, and Fisher’s exact tests were utilized, as appropriate. A multiple logistic regression analysis was performed to determine predictors of 30-day unplanned readmission.ResultsThere were 334 OPAT patients who met inclusion criteria. Median age was 58 years, 58% were male, and the most common infection diagnoses were bone/joint (49%), bloodstream (22%), and endovascular (13%). There were 64 (19%) patients who had an unplanned 30-day readmission. The most common reasons for readmission included non-infection related (45%), worsening infection (28%), and antimicrobial-related complication (17%). Readmitted patients were more likely to have a higher Charlson Comorbidity Index (CCI); prior admissions; bloodstream, endovascular, or pulmonary infection; no source control; and an infection caused by a multi-drug resistant organism. CCI was found to be an independent predictor of readmission (OR 1.096, 95% CI 1.001-1.200).ConclusionUnplanned readmissions were common in patients discharged with OPAT. There should be an emphasis on interventions to prevent readmissions in OPAT patients, particularly those with high-risk clinical characteristics.Disclosures All Authors: No reported disclosures

Salvage surgery, chylothorax and pneumonia are the main drivers of unplanned readmissions after oesophagectomy for cancer.

Unplanned readmissions after surgery can be cumbersome to patients and costly on healthcare resources. The aim of this single-centre study was to identify the independent risk factors for unplanned readmissions in patients who had undergone oesophagectomy for cancer. We retrospectively reviewed the clinical records of 526 consecutive patients with oesophageal cancer who received transthoracic oesophagectomy and were discharged home between 2006 and 2017. Risk factors for unplanned readmission within the first 30 days from discharge were identified by multivariable competing risk analysis. The mean age of the study patients was 55.14 years and 93.7% were men. Squamous cell carcinoma was identified in 94.1% of the participants, and 68.0% received chemoradiotherapy. There were 299 (56.8%) patients who experienced at least 1 postoperative complication. Fifty-five patients (10.5%) experienced an unplanned readmission. The postoperative 90-day mortality rate among patients who experienced an unplanned readmission was significantly higher than that of cases who did not (9.1% vs 0.2%, respectively, P < 0.001). Multivariable analysis identified chylothorax [hazard ratio (HR): 3.86, 95% confidence interval (CI): 1.89-7.91, P < 0.001], pneumonia (HR: 1.98, 95% CI 1.03-3.82, P = 0.042) and salvage surgery (HR: 2.27, 95% CI: 1.10-4.69, P = 0.027) as independent risk factors for unplanned readmissions. Salvage surgery, postoperative chylothorax and pneumonia are the main drivers of 30-day unplanned readmissions in patients who had undergone oesophagectomy for cancer. Patients who required unplanned readmissions showed increased early mortality rates.

Prolonged Length of Stay and Risk of Unplanned 30-Day Readmission After Elective Spine Surgery: Propensity Score-Matched Analysis of 33,840 Patients.

Retrospective database study. To assess the association between prolonged length of hospital stay (pLOS) (≥4 d) and unplanned readmission in patients undergoing elective spine surgery by controlling the clinical and statistical confounders. pLOS has previously been cited as a risk factor for unplanned hospital readmission. This potentially modifiable risk factor has not been distinguished as an independent risk factor in a large-scale, multi-institutional, risk-adjusted study. Data were collected from the American College of Surgeons National Surgical Quality Improvement Program database. A retrospective propensity score-matched analysis was used to reduce baseline differences between the cohorts. Univariate and multivariate analyses were performed to assess the degree of association between pLOS and unplanned readmission. From the 99,575 patients that fit the inclusion criteria, propensity score matching yielded 16,920 well-matched pairs (mean standard propensity score difference = 0.017). The overall 30-day unplanned readmission rate of these 33,840 patients was 5.5%. The mean length of stay was 2.0 ± 0.9 days and 6.0 ± 4.5 days (P ≤ 0.001) for the control and pLOS groups, respectively. In our univariate analysis, pLOS was associated with postoperative complications, especially medical complications (22.7% vs. 8.3%, P < 0.001). Multivariate analysis of the propensity score-matched population, which adjusted identified confounders (P < 0.02 and ≥10 occurrences), showed pLOS was associated with an increased risk of 30-day unplanned readmission (odds ratio [OR] 1.423, 95% confidence interval [CI] 1.290-1.570, P < 0.001). Patients who undergo elective spine procedures who have any-cause pLOS (≥4 d) are at greater risk of having unplanned 30-day readmission compared with patients with shorter hospital stays. 4.

Predicting 30-day hospital readmissions using artificial neural networks with medical code embedding.

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

Body Mass Index and 1-Year Unplanned Readmission in Chinese Patients with Acute Myocardial Infarction: A Retrospective Cohort Study.

Background Evidence regarding the relationship between body mass index (BMI) and 1-year unplanned readmission was limited. Therefore, the objective of this research is to investigate whether BMI was independently related to 1-year unplanned readmission in Chinese patients with acute myocardial infarction (AMI) after percutaneous transluminal coronary intervention (PCI) after adjusting for other covariates. Methods The present study was a cohort study. A total of 214 participants with AMI after PCI were involved in a hospital in China from 1st January 2017 to 1st January 2018. The target independent variable and the dependent variable were BMI measured at baseline and 1-year unplanned readmission, respectively. Covariates involved in this study included age, gender, TC, triglyceride, HDL-C, LDL-C, PT, APTT, INR, creatinine, HGB, LVEF, discharge medication, marital status, educational level, COPD, diabetes mellitus, heart failure, history of ischemic stroke, history of hemorrhagic stroke, arrhythmia, and hypertension. Results The average age of 172 selected participants was 60.2 ± 10.8 years old, and about 68.6% of them was male. The rate of readmission in patients with AMI was 26.14%. The result of fully adjusted binary logistic regression showed BMI was negatively associated with risk of readmission after adjusting confounders (hazard ratio (HR) = 1.1, 95% CI 0.93–1.29). Nonlinear relationship was detected between BMI and 1-year unplanned readmission, whose point was 29.3. The effect sizes and the confidence intervals of the left and right sides of inflection point were 0.9 (0.7–1.2, P for nonlinearity = 0.530) and 2.8 (1.3–5.8, P for nonlinearity = 0.530) and 2.8 (1.3–5.8, Conclusion BMI has a nonlinear relationship with 1-year unplanned readmission in patients with myocardial infarction. The 1-year unplanned readmission rate of overweight patients (BMI > 29.3 kg/m2) has increased significantly. Obesity paradox does not exist in terms of readmission of Chinese patients with myocardial infarction after PCI.

Complications and Readmissions After Total Hip Replacement in Octogenarians and Nonagenarians.

Introduction:As the US population ages, the need for total hip arthroplasty (THA) is predicted to increase by 174% by 2030. The purpose of our study was to examine the rate and risks of 30-day complications and unplanned readmission in patients over the age of 80 years old.Materials and Methods:The National Surgical Quality Improvement Project database for the years 2008 to 2014 was queried for patients over the age of 80 undergoing THA. The risks of major complications, minor complications, bleeding occurrences, unplanned readmissions, and deaths over a 30-day period were examined using univariate and multivariate analyses.Results:A total of 7730 patients were included for analysis; 324 (4.2%) patients experienced a major complication, 1944 (25.1%) patients experienced a minor complication, 1776 (22.9%) patients had a bleeding occurrence requiring transfusion, and 376 (4.9%) patients experienced an unplanned readmission to the hospital within 30 days. A total of 33 (0.4%) patient deaths were recorded within 30 days postoperatively. Patients with congestive heart failure were at increased risk for developing a major complication, odds ratio (OR) 3.618 (1.052-12.437), and postoperative death, OR 11.920 (1.362-104.322). Patients with an American Society of Anesthesiologists >2, OR: 1.351 (1.131-1.613), and an operative time greater than 120 minutes, OR: 1.346 (1.084-1.670), had increased risks of minor complications. Patients with a body mass index greater than 30 had reduced rates of minor complications, OR: 0.609 (0.486-0.763). Increased risk of unplanned readmission was seen in patients with chronic obstructive pulmonary disease, OR: 2.403 (1.324-4.359).Discussion/Conclusion:Elderly patients undergoing THA have high complication and readmission rates. Surgeons should convey these increased risk factors and rates of complications in elective THAs to their elderly patients and work with primary care physicians to mitigate these risks.

899 The Potential Effect of Cardiac Rehabilitation on Outcome and Improved Adherence to Evidence-Based Medication After Percutaneous Coronary Intervention

Evidence suggest that adherence to a cardiac rehabilitation program increase the potential of continuing with evidence-based medication. Data was prospectively collected on 5638 patients for adherence to a cardiac rehabilitation program. Attendance was assessed for effect on outcome as well as ongoing adherence to evidence-based medication at 1-year. The data was obtained from the Genesis Cardiovascular Outcome Registry (GCOR). Only 46.0% of patients attended the cardiac rehabilitation program. The rate of attendance was similar for men and women. Attendance was associated with a significant reduction in death at 1-year (HR 0.41; 95% CI 0.23-0.74, p=0.003), with no difference observed between women and men. Adherence to all four classes of evidence-based medication (statin, ACEI/ARB, betablocker, and aspirin/P2Y12) was significantly improved in patients attending cardiac rehabilitation. Unplanned readmission was increased in patients attending cardiac rehabilitation (HR 1.25; 95% CI 1.05-1.49, p=0.01), with the increase rate of unplanned readmission more significantly in men (HR 1.27; 95% CI 1.04-1.56, p=0.02). Adherence to a cardiac rehabilitation program after percutaneous coronary intervention should be encouraged in view of the positive benefits. Further investigation is needed to assess the increase rate of unplanned readmission in patients attending cardiac rehabilitation.

Risk factors for hospital readmission of patients with heart failure: A cohort study.

ABSTRACTAim:The aim of this study was to develop a risk factor model for hospital readmission in patients with heart failure.Background:Identification of risk factors and predictors of readmission to hospital in patients with heart failure is very crucial for improved clinical outcomes.Objective:The objective of the current study was to investigate and delineate the risk factors that may be implicated in putting a patient at greater risk of readmission due to uncontrolled heart failure.Materials and Methods:This is a prospective follow-up cohort study of 170 patients with heart failure at a tertiary hospital in Al Ain city in the United Arab Emirates. We have developed a risk factor model based on the recommendations of validated published data. We have used univariate and multivariate logistic regression analyses on structured steps based on the published data. The main outcome was the risk factors for readmission to hospital due to heart failure.Results:A final predictive model (10 variables) was produced for unplanned readmission of patients with heart failure. The risk factors identified in the final model with their odds ratios (ORs) and confidence intervals (CIs) were as follows: four or more prescribed medicines (OR = 4.13; CI = 3.5–4.1; P = 0.003), more than twice daily dosing regimen (OR = 2.34; CI = 1.0–5.0; P = 0.023), poor knowledge of prescribed medications (OR = 4.24; CI = 1.213–14.781; P = 0.006), diabetes mellitus (OR = 3.78; CI = 1.6–8.7; P = 0.006), edema (OR = 2.64; CI = 1.2–5.6; P = 0.011), being house bound (OR = 2.77; CI = 1.2–6.2; P = 0.014), and being prescribed diuretics (OR = 3.69; CI = 1.4–9.2; P = 0.042).Conclusion:The specificity of the developed risk prediction model was 82.2%, the sensitivity was 74.3%, and the overall accuracy was 72.9%. The model can be emulated in population with similar characteristics to prevent early readmission of patient with heart failure.

Unplanned readmission of patients with heart transplantation in 1 year: A retrospective study.

To investigate readmission rate and its association with patient characteristics to identify key risk factors associated with unplanned readmission in 1year after heart transplantation. Unplanned readmission after heart transplantation due to complications affects patients' quality of life and long-term survival rate. A retrospective cohort study. Individual, disease-related and therapeutic characteristics were included from electronic medical records. Participants were 484 adult patients who underwent heart transplantation between 1992 - 2016 at a tertiary hospital in Korea. A total of 204 patients underwent unplanned readmission. The most frequent causes were infection or rejection. Multivariate analysis revealed hypertension and pre-operative estimated glomerular filtration rate to be risk factors for unplanned readmission. Early screening educational programs and interventions based on recommended risk factors are needed to reduce the unplanned readmission rate of heart transplantation patients. Nurses who care for heart-transplanted patients should initially assess risk factors during the postoperative and recovery periods to reduce risk of readmission following surgery. These findings may contribute to early assessment criteria and development of discharge nursing education materials.

Disparity on Unplanned Readmission in Melanoma Patients: A National Cancer Database Analysis.

This study aimed to analyze associated factors of 30-day hospital readmission after surgery for melanoma. We conducted a retrospective analysis of postoperative 30-day unplanned readmission in patients with melanoma in the National Cancer Database (NCDB). Higher odds of unplanned readmission were found in non-white patients compared to white, uninsured patients compared to those with private insurance, tumors with invasive behavior compared to in situ, presence of ulceration, American Joint Committee on Cancer stages greater than II, and location in the extremities. Lower odds of unplanned readmission were found in women living in areas where the percentage of adults who did not graduate from high school was below 13.0% with an annual income of $38,000 or more, who were treated in Academic/Research Programs or Integrated Network Cancer Programs. Non-white patients and low-income zip-codes were associated with unplanned readmission.

American College of Surgeons National Surgical Quality Improvement Program assessment of risk factors for 30-day unplanned readmission in patients undergoing head and neck surgery requiring free tissue reconstruction.

Unplanned readmissions have become a metric for measuring quality of care. We analyzed the factors associated with 30-day unplanned readmission (30dUR) following head and neck cancer resections that included free tissue reconstruction (FTR). The 2012-2014 ACS-National Surgical Quality Improvement Program (NSQIP) data set was queried. Univariate and multivariate logistic regression analyses were performed. Out of 1114 cases, 121 had a 30dUR. The most common reasons were wound complications, including incisional infections, hematoma, and hemorrhage. A significant independent risk factor for 30dUR included a clean/contaminated wound class (odds ratio [OR], 2.27; 95% confidence interval [CI], 1.20-4.76). Patients receiving an osseous FTR had lower readmission rates (OR, 0.51; CI, 0.27-0.91). Discharge destination had no statistical significance. Based on the NSQIP data set, 10.9% of patients receiving an FTR for head and neck malignancy had a 30dUR. Although large, population-based data sets have limitations, these results elucidate that these patients are at an increased risk for unplanned readmissions, which can guide patient expectations and discharge planning.

Association between the Charlson Comorbidity Index and the risk of 30-day unplanned readmission in patients receiving maintenance dialysis

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.

Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry.

Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion. The authors queried the 2012-2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets. A total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85-0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data. In an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.

Respiratory complications and 30-day unplanned hospital readmissions in patients with epilepsy.

To help mitigate the burden of health care on US economy, public policymakers and health care legislation have been focusing on reducing hospital readmissions. Respiratory complications have been identified among the commonest of adverse events in neurologic patients. The goal of our study was to better understand respiratory complications and their contribution to rehospitalizations in patients with seizures. We used the 2013 Nationwide Readmission Database to analyze unplanned 30-day readmission rate (30RR). The study population comprised of patients with index hospital discharge diagnosis of generalized convulsive epilepsy and status epilepticus. Patients under 18 years of age, who died during hospitalization or who had missing demographic data, were excluded. Patients hospitalized in December were also excluded due to lack of 30-day follow-up. The primary outcome of interest was 30-day readmission. The causes of readmission were determined by corresponding International Classification of Diseases, Ninth Revision, Clinical Modification codes. The 30RR was highest in patients with index hospitalization discharge diagnosis of status epilepticus, followed by generalized convulsive epilepsy (intractable), followed by generalized convulsive epilepsy (nonintractable). While seizure was the most common reason for readmission, contribution of respiratory complications to readmissions was 7.85%, 12.39%, and 6.93%, respectively. Pneumonia/aspiration pneumonitis and respiratory insufficiency accounted for the majority of the readmissions in all subgroups. Respiratory complications are the leading nonseizure cause of 30-day unplanned readmissions in patients with generalized convulsive epilepsy and status epilepticus. Further research on identifying appropriate interventions to reduce readmissions from respiratory causes may improve outcomes for patients in these epilepsy subgroups.