Significant Predictors Of Readmission Research Articles

Rates, Diagnoses, and Predictors of Unplanned 30-Day Readmissions of Critical Care Survivors Hospitalized for Lung Involvement in Systemic Lupus Erythematosus: An Analysis of National Representative US Readmissions Data.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that frequently involves the lungs, contributing to significant morbidity in hospitalized patients. Critical care survivors with lung involvement in SLE are at particularly high risk for unplanned hospital readmissions, which can reflect the complexity of their disease, which often affects multiple organs and requires immunosuppressive therapy that increases infection risk. Severe pulmonary complications, critical illness sequelae, and challenges in medication adherence or follow-up care further contribute to their vulnerability. These factors result in frequent complications and flare-ups, making unplanned readmissions common in this population. This study assessed rates, most commonreasons, and predictors of all-cause and SLE-related 30-day readmission among critically ill patients hospitalized for lung involvement in SLE. We analyzed the 2021 National Readmissions Database. Critically ill non-elective adult hospitalizations for lung involvement in SLE were identified for analysis using a combination of the ICD-10 diagnostic code for SLE with lung involvement (M32.13) and presence of any procedure codes for mechanical ventilation, tracheostomy, extracorporeal membrane oxygenation, or bronchoscopy. Non-lung-related SLE admissions, non-SLE-related lung disorders, patients with concomitant COPD, history of COVID-19 or severe asthma, patients transferred in from other hospitals or admitted for <24 hours, and patients with a DNR order were excluded. We used χ2 tests to compare baseline characteristics between readmissions and index hospitalizations. Stata ranking commands were used to identify the most recurrent diagnoses associated with 30-day readmissions. We used multivariate Cox regression analysis to identify independent predictors of readmissions. Out of 3,472 index hospitalizations analyzed, 2,641 were discharged alive. Five hundred ninety-three (593; 22.5%) readmissions occurred within 30 days. Lung involvement in SLE was the most common reason for readmission (137; 23.1% of readmissions). Approximately 31.9% (189) of readmissions were due to other SLE-related complications. Readmissions were associated with higher inpatient mortality (18 (3.1%) versus 43 (1.6%); P=0.022), longer hospital stay (8 versus 5.2 days; P<0.001), younger mean age (26 versus 31 years; P=0.010), higher mean hospital costs (US $84,830 versus $64,628; P<0.001), and higher prevalence of heart failure (146 (24.6%) versus 526 (19.6%); P=0.024),CKD (435 (73.3%) versus 1,573 (58.6%); P<0.001), and anemia (138 (23.2%) versus 432 (16.1%); P=0.003) compared with index hospitalizations.Age ≥60 years (adjusted hazard ratio (AHR): 1.22; P=0.028), multiple (≥3) procedures during the initial admission (AHR: 2.57; P=0.003), discharge AMA (AHR: 1.68; P=0.047), lack of insurance/self-pay (AHR: 1.23; P=0.034), another coexisting autoimmune disorder (AHR: 1.19; P=0.041), index hospitalizations in the highest income quartile (AHR: 2.05; P=0.006), hyperlipidemia (AHR: 1.89; P=0.026), coexisting kidney disease (AHR: 1.56; P=0.017), and heart failure (AHR: 1.11; P=0.031) were significantly correlated with 30-day readmissions. SLE lung readmissions were associated with worse outcomesthan index hospitalization. Age ≥60 years, multiple procedures, discharge AMA, lack of insurance, kidney disease, and heart failure are significant predictors of readmission.

Forecasting Patient Early Readmission from Irish Hospital Discharge Records Using Conventional Machine Learning Models.

Predicting patient readmission is an important task for healthcare risk management, as it can help prevent adverse events, reduce costs, and improve patient outcomes. In this paper, we compare various conventional machine learning models and deep learning models on a multimodal dataset of electronic discharge records from an Irish acute hospital. We evaluate the effectiveness of several widely used machine learning models that leverage patient demographics, historical hospitalization records, and clinical diagnosis codes to forecast future clinical risks. Our work focuses on addressing two key challenges in the medical fields, data imbalance and the variety of data types, in order to boost the performance of machine learning algorithms. Furthermore, we also employ SHapley Additive Explanations (SHAP) value visualization to interpret the model predictions and identify both the key data features and disease codes associated with readmission risks, identifying a specific set of diagnosis codes that are significant predictors of readmission within 30 days. Through extensive benchmarking and the application of a variety of feature engineering techniques, we successfully improved the area under the curve (AUROC) score from 0.628 to 0.7 across our models on the test dataset. We also revealed that specific diagnoses, including cancer, COPD, and certain social factors, are significant predictors of 30-day readmission risk. Conversely, bacterial carrier status appeared to have minimal impact due to lower case frequencies. Our study demonstrates how we effectively utilize routinely collected hospital data to forecast patient readmission through the use of conventional machine learning while applying explainable AI techniques to explore the correlation between data features and patient readmission rate.

Tackling the challenge of 90-day readmissions in hip fracture management: a retrospective study

Background: Hip fracture readmissions within 90 days pose a significant challenge in orthopaedics, burdening healthcare systems and affecting patient outcomes. This study, set against the backdrop of an aging population and rising hip fracture incidences, seeks to analyse the factors contributing to these readmissions and propose viable management strategies. Methods: Conducted as a single centre study at a hospital which is part of the NHS UK, this retrospective study reviewed 1264 patients treated for neck of femur (NOF) fractures between April 2020 and December 2022. Data on patient demographics, comorbidities, surgical interventions, and readmission reasons were collected and analysed using descriptive statistics and multivariable logistic regression. Results: The study found a 21.8% readmission rate, with the majority being for non-orthopaedic reasons such as infections and cardiovascular events. Age and comorbidities were significant predictors of readmission, while surgical factors had a less pronounced impact. A year-wise trend showed a peak in readmissions during the COVID-19 pandemic. Conclusions: The findings emphasize the complexity of managing NOF fractures and the importance of a comprehensive, multidisciplinary approach to care. Targeted interventions, including orthogeriatric co-management and falls assessment, are recommended to mitigate readmission risks and enhance patient care, though further research is needed for more definitive strategies.

Understanding Hospital Readmissions: Insights, Patterns, and Interventions for Improvement in Chronic Kidney Disease.

Hospital readmissions among chronic kidney disease (CKD) patients pose substantial challenges in healthcare, impacting both patients and healthcare systems. Understanding the patterns and determinants of CKD-related readmissions is crucial for devising effective interventions. This research aimed to investigate the factors contributing to hospital readmissions among CKD patients, identify the primary reasons for readmissions, and explore potential interventions to mitigate readmission risks. A retrospective analysis was conducted among a cohort of 300 CKD patients over an 18-month period at a tertiary care unit specializing in nephrology services. Data on demographics, CKD stages, comorbidities, reasons for readmissions, and lengths of hospital stays were analyzed. Logistic regression models were employed to identify predictors of readmissions. Advancing CKD stages were associated with increased readmission rates, with higher rates observed in older patients. Cardiovascular complications and acute kidney injury were prominent reasons for readmissions. Age, comorbid conditions, and previous hospitalizations emerged as significant predictors of readmissions. Lengths of hospital stays during readmissions were also correlated with CKD stages. The research underscores the complex interplay of demographic and clinical factors contributing to hospital readmissions among CKD patients. Tailored interventions addressing disease severity, comorbidities, and patient-specific characteristics are pivotal in reducing readmission risks and enhancing care outcomes for this population.

Correlates of Frailty in Hospitalized Older Adults with Hypertension and Its Influence on Clinical Prognosis.

This study aims to explore the correlates of frailty in hospitalized elderly hypertensive patients and its impact on clinical prognosis, and to construct a predictive model for the occurrence of frailty in this population. A cross-sectional and prospective observational cohort study was conducted, involving 312 elderly hypertensive patients diagnosed at the institution from January to June 2022. Frailty was diagnosed using the Fried Frailty Phenotype (FP), while the Charlson Comorbidities Index (CCI) assessed the presence of chronic conditions. Data analysis was performed using SPSS 22.0. Binary logistic regression analysis was conducted with frailty as the dependent variable to identify risk factors. Patients were followed for one year to monitor readmission rates and all-cause mortality. Multivariate logistic regression identified CCI grade (P=0.030), gender (OR=21.618, 95% CI: 4.062-115.061, P < 0.001), age (OR=1.147, 95% CI: 1.086-1.211, P < 0.001), bedridden state (OR=11.620, 95% CI: 3.282-41.140, P < 0.001), arrhythmia (OR=14.414, 95% CI: 4.558-45.585, P < 0.001), heart failure (OR=5.439, 95% CI: 1.029-28.740, P < 0.05), along with several biochemical markers, as independent predictors of frailty. A predictive model was developed, demonstrating a robust discriminative ability with an area under the receiver operating characteristic curve (AUC) of 0.915. Statistically significant differences in readmission rates and all-cause mortality were observed among the frail, pre-frail, and non-frail groups (P<0.001), with the frail group exhibiting the highest incidence of these adverse outcomes. Notably, frailty emerged as a significant predictor of readmission (P<0.05) but not of all-cause mortality in this cohort. This study establishes a robust frailty prediction model for elderly hypertensive patients, highlighting the influence of CCI grade, gender, age, and other clinical and biochemical factors on frailty. The model offers a valuable tool for healthcare providers to identify at-risk elderly individuals, facilitating targeted intervention strategies for cardiovascular disease management.

Predicting ICU readmission risks in intracerebral hemorrhage patients: Insights from machine learning models using MIMIC databases

BackgroundIntracerebral hemorrhage (ICH) is a stroke subtype characterized by high mortality and complex post-event complications. Research has extensively covered the acute phase of ICH; however, ICU readmission determinants remain less explored. Utilizing the MIMIC-III and MIMIC-IV databases, this investigation develops machine learning (ML) models to anticipate ICU readmissions in ICH patients. MethodsRetrospective data from 2242 ICH patients were evaluated using ICD-9 codes. Recursive feature elimination with cross-validation (RFECV) discerned significant predictors of ICU readmissions. Four ML models—AdaBoost, RandomForest, LightGBM, and XGBoost—underwent development and rigorous validation. SHapley Additive exPlanations (SHAP) elucidated the effect of distinct features on model outcomes. ResultsICU readmission rates were 9.6% for MIMIC-III and 10.6% for MIMIC-IV. The LightGBM model, with an AUC of 0.736 (95% CI: 0.668–0.801), surpassed other models in validation datasets. SHAP analysis revealed hydrocephalus, sex, neutrophils, Glasgow Coma Scale (GCS), specific oxygen saturation (SpO2) levels, and creatinine as significant predictors of readmission. ConclusionThe LightGBM model demonstrates considerable potential in predicting ICU readmissions for ICH patients, highlighting the importance of certain clinical predictors. This research contributes to optimizing patient care and ICU resource management. Further prospective studies are warranted to corroborate and enhance these predictive insights for clinical utilization.

Total laryngectomy and readmission: causes, rates and predictors

BackgroundTotal laryngectomy (TL) is a complex procedure, and patients undergoing TL are at high risk for readmission, which exposes them to hospital-acquired complications. Readmission rate is a metric for quality of care. We aimed to identify the rate, causes, and predictors of hospital readmission within 60 days after discharge following TL.MethodsThis is a 12-year retrospective study where we included all patients undergoing TL in a single tertiary care center between 2008 and 2022. Patient charts were reviewed for demographics, comorbidities, and causes for readmission.ResultsOf 83 patients who underwent TL, 12 (14.50%) were readmitted within 60 days. Common causes were surgical site infection (33.33%) and mucocutaneous fistula (25%). Significant predictors for readmission were tobacco use (P = 0.003), African ethnicity (P = 0.004), being unmarried (P < 0.001), lower preoperative serum albumin (P < 0.001), higher preoperative TSH (P = 0.03), higher preoperative neutrophil count (P = 0.035), higher American Society of Anesthesiology (ASA) score (P = 0.028), and higher Cumulative Illness Rating Scale (CIRS) score (P = 0.029).ConclusionOne in every seven patients were readmitted following TL. Frequent causes include wound infection and fistulas. Predictors include preoperative hypoalbuminemia, hypothyroidism, African ethnicity, being unmarried, tobacco use, and a higher baseline burden of comorbidities. Such factors can be targeted to reduce hospital readmission rates.

Clinical outcomes of lower gastrointestinal bleeding in patients managed with lower endoscopy: A tertiary center results.

Lower gastrointestinal bleeding (LGIB) is an urgent presentation with increasing prevalence and remains a common cause of hospitalization. The clinical outcome can vary based on several factors, including the cause of bleeding, its severity, and the effectiveness of management strategies. The aim of this study is to provide a comprehensive report on the clinical outcomes observed in patients with LGIB who underwent lower endoscopy. All patients who underwent emergency lower endoscopy for fresh bleeding per rectum, from May 2015 to December 2021, were included. The primary outcome was to identify the rate of rebleeding after initial control of bleeding. The second was to measure the clinical outcomes and the potential predictors leading to intervention and readmission. A total of 84 patients were included. Active bleeding was found in 20% at the time of endoscopy. Rebleeding within 90 days occurred in 6% of the total patients; two of which (2.38%) were within the same admission. Ninety-day readmission was reported in 19% of the cases. Upper endoscopy was performed in 32.5% of the total cases and was found to be a significant predictor for intervention (OR 4.1, P = 0.013). Personal history of inflammatory bowel disease (IBD) and initial use of sigmoidoscopy were found to be significant predictors of readmission [(OR 5.09, P = 0.008) and (OR 5.08, P = 0.019)]. LGIB is an emergency that must be identified and managed using an agreed protocol between all associated services to determine who needs upper GI endoscopy, ICU admission, or emergency endoscopy within 12 hours.

Predictors of Readmission, for Patients with Chronic Obstructive Pulmonary Disease (COPD) - A Systematic Review.

Chronic obstructive pulmonary disease (COPD) is the third-leading cause of death globally and is responsible for over 3 million deaths annually. One of the factors contributing to the significant healthcare burden for these patients is readmission. The aim of this review is to describe significant predictors and prediction scores for all-cause and COPD-related readmission among patients with COPD. A search was conducted in Ovid MEDLINE, Ovid Embase, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials, from database inception to June 7, 2022. Studies were included if they reported on patients at least 40 years old with COPD, readmission data within 1 year, and predictors of readmission. Study quality was assessed. Significant predictors of readmission and the degree of significance, as noted by the p-value, were extracted for each study. This review was registered on PROSPERO (CRD42022337035). In total, 242 articles reporting on 16,471,096 patients were included. There was a low risk of bias across the literature. Of these, 153 studies were observational, reporting on predictors; 57 studies were observational studies reporting on interventions; and 32 were randomized controlled trials of interventions. Sixty-four significant predictors for all-cause readmission and 23 for COPD-related readmission were reported across the literature. Significant predictors included 1) pre-admission patient characteristics, such as male sex, prior hospitalization, poor performance status, number and type of comorbidities, and use of long-term oxygen; 2) hospitalization details, such as length of stay, use of corticosteroids, and use of ventilatory support; 3) results of investigations, including anemia, lower FEV1, and higher eosinophil count; and 4) discharge characteristics, including use of home oxygen and discharge to long-term care or a skilled nursing facility. The findings from this review may enable better predictive modeling and can be used by clinicians to better inform their clinical gestalt of readmission risk.

Prognostic Factors Predicting Bleeding After Discharge Requiring Readmission After Thulium Laser Enucleation of the Prostate (ThuLEP)

ObjectivesPostoperative haematuria is a feared complication following prostate enucleation, and it may occur even weeks after discharge. We evaluated the risk of bleeding after discharge and its predictive factors in patients who underwent Thulium laser enucleation of the prostate (ThuLEP).MethodsBetween January 2015 and December 2018, patients with surgical indication for benign prostatic hyperplasia underwent ThuLEP in 4 urology departments. All procedures were performed by experienced urologists. Postoperative haematuria was defined as macroscopic haematuria occurring 7 days after discharge. Readmission rate due to macroscopic haematuria was assessed. Logistic regression models tested for predictors of postoperative bleeding requiring readmission.ResultsOverall, 748 patients were included, and 52 (6.9%) of them were readmitted after discharge because of macroscopic haematuria. No cases of deep venous thrombosis or pulmonary embolism were diagnosed. Mean length of stay for surgical treatment was 2.1 days, and mean hospitalization for readmission was 5.6 days. None of the readmitted patients required reintervention, while 46.1% of them required blood transfusions. Diabetes mellitus (P &lt; 0.05), a long bridge of oral anticoagulant drugs with low molecular weight heparin (LMWH) (P &lt; 0.05) and history of constipation (P &lt; 0.05) were significant predictors of readmission after discharge due to macroscopic haematuria.ConclusionsPatients with a clinical history of diabetes mellitus or constipation and patients who undergo a long bridge of oral anticoagulant therapy with LMWH are at higher risk of bleeding requiring readmission after ThuLEP.

Modified frailty index independently predicts morbidity in patients undergoing 3-column osteotomy.

Adult Spinal Deformity (ASD) includes a spectrum of spinal conditions that can be associated with significant pain and loss of function. While 3-column osteotomies have been the procedures of choice for ASD patients, there is also a substantial risk for complications. The prognostic value of the modified 5-item frailty index (mFI-5) for these procedures has not yet been studied. The goal of this study is to evaluate the association of mFI-5 with 30-day morbidity, readmission, and reoperation following a 3-column osteotomy. The National Surgical Quality Improvement Program (NSQIP) database was queried to identify patients undergoing 3-Column Osteotomy procedures from 2011-2019. Multivariate modeling was utilized to assess mFI-5 and other demographic, comorbidity, laboratory, and perioperative variables as independent predictors of morbidity, readmission, and reoperation. N = 971. Multivariate analysis revealed that mFI-5 = 1 (OR = 1.62, p = 0.015) and mFI-5 ≥ 2 (OR = 2.17, p = 0.004) were significant independent predictors of morbidity. mFI-5 ≥ 2 was a significant independent predictor of readmission (OR = 2.16, p = 0.022) while mFI-5 = 1 was not a significant predictor of readmission (p = 0.053). Frailty did not predict reoperation. Frailty as defined by mFI-5 strongly and independently predicted increased odds of postoperative morbidity for patients undergoing 3-column osteotomy as surgical intervention for ASD. Only mFI-5 ≥ 2 was a significant independent predictor of readmission, while frailty did not predict reoperation. Other variables independently predicted increased and decreased odds of postoperative morbidity, readmission, and reoperation. III.

Effect of the population health inpatient Medicare Advantage pharmacist intervention on hospital readmissions: A quasi-experimental controlled study

BACKGROUND: The population health inpatient Medicare Advantage pharmacist (PHIMAP) intervention is a pharmacist-led, transitions-of-care intervention that aims to reduce hospital readmissions among Medicare Advantage beneficiaries. PHIMAP includes inpatient pharmacist participation in interdisciplinary rounds, admission and discharge medication reconciliation, pharmacy staff delivery of discharge medications to the bedside, personalized discharge medication lists and counseling, and communication with outpatient pharmacists through an electronic health record. OBJECTIVE: To evaluate the effect of the PHIMAP intervention on unplanned 30-day same-hospital readmissions among Medicare Advantage patients. METHODS: Those included were patients admitted to a large urban academic medical center between May 2018 and March 2020 who had a Medicare Advantage plan and were aged at least 18 years. A 2-group, quasi-experimental design was utilized. Control patients received the usual care, which included a best possible medication history and a postdischarge phone call. A multivariable logistic regression model was estimated to predict unplanned 30-day same-hospital readmissions. This study was a Hypothesis Evaluating Treatment Effectiveness study. RESULTS: In total, 884 patients were included. The majority were White (59.0%), non-Hispanic (87.7%), English speaking (90.5%), and older adults (median age, 75 years; interquartile range, 70-83 years). We detected no statistically significant association between the PHIMAP intervention and unplanned 30-day same-hospital readmissions (odds ratio [OR] = 0.91, 95% CI = 0.56-1.52). After adjusting for patient demographics and clinical covariates, significant predictors of 30-day readmissions included the number of emergency department/inpatient visits within 180 days prior to index admission (OR = 1.40, 95% CI = 1.11-1.77); discharge to a post-acute care facility, such as an inpatient rehabilitation facility, long-term acute care facility, or skilled nursing facility (OR = 1.69, 95% CI = 1.06-2.66); hospital length of stay in days (OR = 1.04, 95% CI=1.01-1.07); and the Agency for Healthcare Research and Quality Elixhauser Comorbidity Index score (OR = 1.01, 95% CI = 1.01-1.02). CONCLUSIONS: Significant predictors of readmissions among Medicare Advantage beneficiaries were consistent with greater illness severity, including a recent history of prior hospital utilization, a discharge to post-acute care facility (vs home), a longer length of hospital stay, and a higher comorbidity burden. Although we detected no statistically significant association between PHIMAP and unplanned 30-day same-hospital readmissions, differences in study group assignment based on the day of hospital discharge (weekend vs weekday) was a noted limitation of this study. Future studies of inpatient pharmacist-led interventions should plan to minimize the risk of selection bias due to differences in the time of patient discharge. DISCLOSURES: This study was supported in part by the National Institute on Aging under award number R01AG058911 (to Pevnick) and the UCLA Clinical Translational Science Institute (UL1 TR001881). The sponsor had no role in the design and conduct of the study, nor the writing of this report.

A Readmission Risk Score for Transcatheter Aortic Valve Replacement: An Analysis of 200,000 Patients

ObjectiveThe objective of this study was to leverage a national database of TAVR procedures to create a risk model for 30-day readmissions. MethodsThe National Readmissions Database was reviewed for all TAVR procedures from 2011 to 2018. Previous ICD coding paradigms created comorbidity and complication variables from the index admission. Univariate analysis included any variables with a P-value of ≤0.2. A bootstrapped mixed-effects logistic regression was run using the hospital ID as a random effect variable. By bootstrapping, a more robust estimate of the variables' effect can be generated, reducing the risk of model overfitting. The odds ratio of variables with a P-value <0.1 was turned into a risk score following the Johnson scoring method. A mixed-effect logistic regression was run using the total risk score, and a calibration plot of the observed to expected readmission was generated. ResultsA total of 237,507 TAVRs were identified, with an in-hospital mortality of 2.2 %. A total of 17.4 % % of TAVR patients were readmitted within 30 days. The median age was 82 with 46 % of the population being women. The risk score values ranged from −3 to 37 corresponding to a predicted readmission risk between 4.6 % and 80.4 %, respectively. Discharge to a short-term facility and being a resident of the hospital state were the most significant predictors of readmission. The calibration plot shows good agreement between the observed and expected readmission rates with an underestimation at higher probabilities. ConclusionThe readmission risk model agrees with the observed readmissions throughout the study period. The most significant risk factors were being a resident of the hospital state and discharge to a short-term facility. This suggests that using this risk score in conjunction with enhanced post-operative care in these patients could reduce readmissions and associated hospital costs, improving outcomes.

Predicting Short-term Readmission in Hospitalized Patients with Acute Heart Failure: The Use of 6-minute Walk Test at Discharge.

Hospitalization is an important landmark in the history of heart failure. Patients admitted for acute heart failure have a high chance of readmission. We require predictors which can foresee readmission. Functional capacity assessment by the 6-minute walk test is one such predictor. To compare the mean 6-minute walk distance among acute heart failure patients readmitted within 30 days, those readmitted within 60 days, those readmitted within 90 days, and those not readmitted at 90 days following discharge after the first admission. This is a follow-up study conducted in Madras Medical College from March 2021 to August 2021. The study included patients who were admitted for the first time in their life for acute heart failure. The clinical details were noted, and the patients were managed as per standard protocols. Before discharge, a 6-minute walk test was performed. The patients were followed up at the end of 30, 60, and 90 days. 25% of the patients had one readmission at the end of 90 days. Majority of readmissions occurred at the end of 30 days. A low 6-minute walk distance at discharge was a significant predictor of readmission at the end of 30 days. A distance of 200 m was a reasonable cutoff in our population. A lower time walked was also a significant predictor of readmission at the end of 30 days. A slower walking speed was also a significant predictor of readmission at the end of 30 days. Some other parameters such as a longer duration of stay, a longer duration of intravenous (IV) diuretic requirement, discharge respiratory rate, and lower serum albumin at admission were also significant predictors of readmission at the end of 30 days.

Impact of anticoagulants on the clinical outcomes of colonic diverticular bleeding comparing warfarin and direct oral anticoagulants

Recently, direct oral anticoagulants (DOACs) have been widely used as antithrombotic agents to replace warfarin, but their clinical impact in patients with gastrointestinal bleeding is unclear. We compared the effects of warfarin and DOACs on the outcomes of patients with colonic diverticular bleeding. The patients were divided into warfarin and DOAC groups. We compared the clinical outcomes and the effect of the DOAC dosing and examined any readmissions due to colonic diverticular bleeding within 1 year. A total of 95 events (warfarin group: n = 43 and DOAC group: n = 52) were included. Compared with the warfarin group, the DOAC group was significantly older, had a lower rate of concomitant antiplatelet agents, and a shorter hospital stay, but no significant differences were found in the other clinical outcomes. Thirty-seven patients (71.2%) in the DOAC group had appropriate dosing, whereas 15 patients (28.9%) had an inappropriate dose. The patients with overdose or contraindications had significantly lower minimum hemoglobin levels. In the univariate analysis, prior hospitalization for colonic diverticular bleeding was a significant predictor of readmission. Compared with warfarin, patients with colonic diverticular bleeding treated with DOACs were older and had shorter hospital stays, and the inappropriate use of DOACs may worsen outcomes.

Ground Level Falls in Patients Over 60, Do Race and Socioeconomic Status Affect Outcome?

Falls are a significant source of morbidity, mortality, and health care spending in the elderly. The objective was to identify whether race, insurance status, and median income by zip code were associated with discharge disposition, readmission within 90days, or mortality within 1 year of ground-level falls in patients aged 60-90. We conducted a retrospective chart review of 926 patients aged 60-90 treated for ground-level falls. We created a binomial linear regression model to identify predictors of discharge disposition, 90-day readmission, and mortality within 1 year of discharge. Length of stay (P < .01), having orthopedic surgery (P < .01), score on Charlson Comorbidity Index (CCI) (P < .01), increasing age (P = .014), female sex (P = .05), and admission to the ICU (P = .05) were associated with discharge to a secondary facility. Readmission within 90days was only associated with higher scores on the CCI (P < .01). Charlson Comorbidity Index (P < .0001), hospital length of stay (P < .001), and admission to the ICU (P = .015) were associated with increased mortality at 1 year. Predictors of discharge to another facility included hospital length of stay, having orthopedic surgery, CCI scores, increasing age, female sex, and admission to the ICU. Charlson Comorbidity Index score was the only significant predictor of readmission. Predictors of mortality at 1-year post-fall included CCI score, hospital length of stay, and admission to the ICU. Race, median income by zip code, and insurance provider were not statistically significant.

Readmission, reoperation, and nonhome discharge rates in patients receiving surgical treatment for proximal humerus fractures.

BackgroundProximal humerus fractures (PHFs) are generally surgically treated with open reduction internal fixation (ORIF), hemiarthroplasty (HA), or total shoulder arthroplasty (TSA). Diverse fracture patterns and a high prevalence in the elderly population make it difficult to establish objective guidelines for the decision to undergo surgical treatment. The purpose of this study was to investigate risk factors associated with readmission, reoperation, and nonhome discharge following ORIF, HA, and TSA for PHFs.MethodsData on all patients who underwent ORIF, TSA, or HA for treatment of closed PHF between 2015 and 2017 were obtained by querying the American College of Surgeons National Surgical Quality Improvement database. Rates of postoperative readmission, nonhome discharge, and reoperation within 30 days were collected. Multivariate logistic regression was employed to identify predictors of readmission, nonhome discharge, and reoperation.ResultsA total of 2825 patients were included in this study: 1829 underwent ORIF, 707 underwent TSA, and 289 underwent HA. The significant predictors for readmission were having an American Society of Anesthesiologists class ≥ 3 (odds ratio [OR] 1.95, P = .003) and being of dependent functional status (OR 3.15, P < .001). The significant predictors for reoperation were male sex (OR 2.41, P < .001) and dependent functional status (OR 2.92, P = .006). The significant predictors for nonhome discharge were age 66-80 years (OR 7.00, P < .001), age ≥ 81 years (OR 16.31, P < .001), American Society of Anesthesiologists ≥3 (OR 2.34, P < .001), dependent functional status (OR 2.48, P < .001), and inpatient status (OR 3.32, P < .001). TSA showed slightly higher rates of nonhome discharge than HA and ORIF.ConclusionSignificant risk factors for readmission, reoperation, and nonhome discharge within 30 days following surgical treatment for PHF were identified. Additionally, TSA was significantly associated with nonhome discharge compared with HA and ORIF.

Data analytics and artificial intelligence in predicting length of stay, readmission, and mortality: a population-based study of surgical management of colorectal cancer

Data analytics and artificial intelligence (AI) have been used to predict patient outcomes after colorectal cancer surgery. A prospectively maintained colorectal cancer database was used, covering 4336 patients who underwent colorectal cancer surgery between 2003 and 2019. The 47 patient parameters included demographics, peri- and post-operative outcomes, surgical approaches, complications, and mortality. Data analytics were used to compare the importance of each variable and AI prediction models were built for length of stay (LOS), readmission, and mortality. Accuracies of at least 80% have been achieved. The significant predictors of LOS were age, ASA grade, operative time, presence or absence of a stoma, robotic or laparoscopic approach to surgery, and complications. The model with support vector regression (SVR) algorithms predicted the LOS with an accuracy of 83% and mean absolute error (MAE) of 9.69 days. The significant predictors of readmission were age, laparoscopic procedure, stoma performed, preoperative nodal (N) stage, operation time, operation mode, previous surgery type, LOS, and the specific procedure. A BI-LSTM model predicted readmission with 87.5% accuracy, 84% sensitivity, and 90% specificity. The significant predictors of mortality were age, ASA grade, BMI, the formation of a stoma, preoperative TNM staging, neoadjuvant chemotherapy, curative resection, and LOS. Classification predictive modelling predicted three different colorectal cancer mortality measures (overall mortality, and 31- and 91-days mortality) with 80–96% accuracy, 84–93% sensitivity, and 75–100% specificity. A model using all variables performed only slightly better than one that used just the most significant ones.

Outcomes of patients discharged home with a chest tube after lung resection: a multicentre cohort study.

Background:Prolonged air leaks are increasingly treated in the outpatient setting, with patients discharged with chest tubes in place. We evaluated the incidence and risk factors associated with readmission, empyema development and further interventions in this patient population.Methods:We undertook a retrospective cohort analysis of all patients from 4 tertiary academic centres (January 2014 to December 2017) who were discharged home with a chest tube after lung resection for a postoperative air leak lasting more than 5 days. We analyzed demographics, patient factors, surgical details, hospital readmission, reintervention, antibiotics at discharge, empyema and death.Results:Overall, 253 of 2794 patients were analyzed (9.0% of all resections), including 30 of 759 from centre 1 (4.0%), 67 of 857 from centre 2 (7.8%), 9 of 247 from centre 3 (3.6%) and 147 of 931 from centre 4 (15.8%) (p < 0.001). Our cohort consisted of 56.5% men, and had a median age of 69 (range 19–88) years. Despite similar initial lengths of stay (p = 0.588), 49 patients (19.4%) were readmitted (21%, 0%, 23% and 11% from centres 1 to 4, respectively, p = 0.029), with 18 (36.7%) developing empyema, 11 (22.4%) requiring surgery and 3 (6.1%) dying. Only chest tube duration was a significant predictor of readmission (p < 0.001) and empyema development (p = 0.003), with a nearly threefold increased odds of developing empyema when the chest tube remained in situ for more than 20 days.Conclusion:Discharge with chest tube after lung resection is associated with serious adverse events. Given the high risk of empyema development, removal of chest tubes should be considered, when appropriate, within 20 days of surgery. Our data suggest a potential need for proactive postdischarge outpatient management programs to diminish risk of morbidity and death.

Perioperative Stroke and Thirty-Day Hospital Readmission After Cardiac Surgeries: State Inpatient Database Study.

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.