Unplanned 30-day Hospital Readmission Research Articles

Artificial intelligence informing clinical decision making on the risk of hospital readmissions in multi-morbid patients: a systematic review

Abstract Introduction Multi-morbid patients have complex care needs and are more likely to utilise healthcare services, which represents a financial burden on healthcare organisations. 30-day readmission of patients is a method of measuring the quality of the services provided within healthcare.[1] Artificial intelligence (AI) models can flag multi-morbidity patients at increased risk of readmission.[2] However, it is unclear which common predictors have been used to develop these models. Aim To systematically review the medical literature to identify predictors that have been used to develop AI models that predicted unplanned 30-day hospital readmissions of multi-morbid patients, and whether these predictors were modifiable or non-modifiable. Methods Four large databases: Medline, Embase, Web of Science and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched on 15th November 2022. Only publications that developed a machine learning algorithm to predict unplanned 30-day hospital readmission for patients with multi-morbidity were included. Articles published in English language were eligible for inclusion. A narrative synthesis of all eligible studies was undertaken. Key findings were identified through inductive strategy, which includes simplifying raw data in the included articles into a comprehensive format. This systematic review was registered with the PROSPRO database (CRD42022373937) and followed PRISMA guidelines. Results There were 1,906 articles extracted from all databases, 18 of which met our inclusion criteria. A total of 669 predictors of hospital readmission were found, and were divided into 103 modifiable (i.e., action could be taken to change the outcome) and 566 non-modifiable (i.e. age, gender, number of previous emergency admissions. The average number of modifiable predictors per prediction model was six, with the most common being length of hospital stay, multi-morbidity, obesity, hypertension, diabetes, depression, and anaemia. The most commonly used AI algorithm was the gradient boosting algorithm. This algorithm generally showed a better performance than other models reported. The performance of all models in the included studies was compared; 13 studies showed an average sensitivity of 72%. The average specificity was calculated at 73% from 11 studies. The highest AUC reported was above 0.9, which demonstrates an excellent performing model, while five models did not report a value for the AUC. Conclusion We identified modifiable and non-modifiable predictors used in previously developed AI algorithms that predict 30-day hospital readmissions of multi-morbid patients. Modifiable predictors in particular can help guide clinical decision-making by allowing early actions to be taken in primary care to potentially reduce the risk of readmission. However, one limitation of this review is that some included studies used balanced (i.e. synthetic) data which could have introduced a level of bias.

Multi-Trajectories of Symptoms and their Associations with Unplanned 30-day Hospital Readmission among Patients with Heart Failure: A longitudinal study.

This study aimed to uncover hidden patterns and predictors of symptom multi-trajectories within 30 days after discharge in patients with heart failure and assess the risk of unplanned 30-day hospital readmission in different patterns. The study was conducted from September 2022 to September 2023 in four third-class hospitals in Tianjin, China. A total of 301 patients with heart failure were enrolled in the cohort, and 248 patients completed a 30-day follow-up after discharge. Three multi-trajectory groups were identified: mild symptom status (24.19%), moderate symptom status (57.26%), and severe symptom status (18.55%). With the mild symptom status group as a reference, physical frailty, psychological frailty, and comorbid renal dysfunction were predictors of the moderate symptom status group. Physical frailty, psychological frailty, resilience, taking diuretics, and comorbid renal dysfunction were predictors of the severe symptom status group. Compared with the mild symptom status group, the severe symptom status group was significantly associated with high unplanned 30-day hospital readmission risks. This study identified three distinct multi-trajectory groups among patients with heart failure within 30 days after discharge. The severe symptom status group was associated with a significantly increased risk of unplanned 30-day hospital readmission. Common and different factors predicted different symptom multi-trajectories. Healthcare providers should assess the physical and psychological frailty and renal dysfunction of patients with heart failure before discharge. Inpatient care aimed at alleviating physical and psychological frailty and enhancing resilience may be important to improve patients' symptom development post-discharge.

Predicting 30-day unplanned hospital readmission after revision total knee arthroplasty: machine learning model analysis of a national patient cohort.

Revision total knee arthroplasty (TKA) is associated with a higher risk of readmission than primary TKA. Identifying individual patients predisposed to readmission can facilitate proactive optimization and increase care efficiency. This study developed machine learning (ML) models to predict unplanned readmission following revision TKA using a national-scale patient dataset. A total of 17,443 revision TKA cases (2013-2020) were acquired from the ACS NSQIP database. Four ML models (artificial neural networks, random forest, histogram-based gradient boosting, and k-nearest neighbor) were developed on relevant patient variables to predict readmission following revision TKA. The length of stay, operation time, body mass index (BMI), and laboratory test results were the strongest predictors of readmission. Histogram-based gradient boosting was the best performer in distinguishing readmission (AUC: 0.95) and estimating the readmission probability for individual patients (calibration slope: 1.13; calibration intercept: -0.00; Brier score: 0.064). All models produced higher net benefit than the default strategies of treating all or no patients, supporting the clinical utility of the models. ML demonstrated excellent performance for the prediction of readmission following revision TKA. Optimization of important predictors highlighted by our model may decrease preventable hospital readmission following surgery, thereby leading to reduced financial burden and improved patient satisfaction.

A novel risk score predicting 30-day hospital re-admission of patients with acute stroke by machine learning model.

The 30-day hospital re-admission rate is a quality measure of hospital care to monitor the efficiency of the healthcare system. The hospital re-admission of acute stroke (AS) patients is often associated with higher mortality rates, greater levels of disability and increased healthcare costs. The aim of our study was to identify predictors of unplanned 30-day hospital re-admissions after discharge of AS patients and define an early re-admission risk score (RRS). This observational, retrospective study was performed on AS patients who were discharged between 2014 and 2019. Early re-admission predictors were identified by machine learning models. The performances of these models were assessed by receiver operating characteristic curve analysis. Of 7599 patients with AS, 3699 patients met the inclusion criteria, and 304 patients (8.22%) were re-admitted within 30 days from discharge. After identifying the predictors of early re-admission by logistic regression analysis, RRS was obtained and consisted of seven variables: hemoglobin level, atrial fibrillation, brain hemorrhage, discharge home, chronic obstructive pulmonary disease, one and more than one hospitalization in the previous year. The cohort of patients was then stratified into three risk categories: low (RRS = 0-1), medium (RRS = 2-3) and high (RRS >3) with re-admission rates of 5%, 8% and 14%, respectively. The identification of risk factors for early re-admission after AS and the elaboration of a score to stratify at discharge time the risk of re-admission can provide a tool for clinicians to plan a personalized follow-up and contain healthcare costs.

Heart failure drug classes and 30-day unplanned hospital readmission among patients with heart failure in Ethiopia

BackgroundDrug therapy is a crucial aspect of heart failure management and has been shown to reduce morbidity and mortality in heart failure patients. However, the comparative effects of these drug classes on readmission rates have not been well studied. Therefore, the aim of this study was to examine the association between different classes of heart failure drugs and 30-day readmission rates in patients with heart failure.MethodA multicenter, hospital-based retrospective cohort design was employed and 572 randomly selected patients with heart failure were included. Data were entered in Epi-data version 4.6 and analyzed with STATA version 17. Kaplan-Meier and log-rank tests were used to estimate and compare survival time. A Cox proportional hazard model was utilized, employing both bi-variable and multi-variable analyses, to examine the effect of predictors on the timing of unplanned hospital readmissions. The strength of the association was assessed using an adjusted hazard ratio (aHR), and statistical significance was declared for p-values < 0.05 and a 95% confidence interval (CI).ResultsIn this study, a total of 151 (26.40%) heart failure patients were readmitted within 30 days of discharge. In the multivariate cox proportional hazards analysis being an age (> 65 year) (AHR: 2.34, 95%CI: 1.63, 3.37), rural in residency (AHR: 1.85, 95%CI: 1.07, 3.20), hospital stays > 7 Days (AHR: 3.68, 95%CI: 2.51,5.39), discharge with Diuretics (AHR: 2.37, 95%CI: 1.45, 3.86), and discharge with Beta-Blocker (AHR: 0.48, 95%CI: 0 0.34, 0.69) were identified as independent predictors of unplanned hospital readmission.ConclusionElderly patients, being in rural areas, longer hospital stays, and discharges of patients on diuretics and not on beta-blockers were independent predictors of unplanned hospital readmission. Therefore, working on these factors will help to reduce the hazard of unplanned hospital readmissions, improve patient outcomes, and increase the efficiency of heart failure management.

High-Dimensional Fixed Effects Profiling Models and Applications in End-Stage Kidney Disease Patients: Current State and Future Directions

Profiling analysis aims to evaluate health care providers, including hospitals, nursing homes, or dialysis facilities among others with respect to a patient outcome, such as 30-day unplanned hospital readmission or mortality. Fixed effects (FE) profiling models have been developed over the last decade, motivated by the overall need to (a) improve accurate identification or “flagging” of under-performing providers, (b) relax assumptions inherent in random effects (RE) profiling models, and (c) take into consideration the unique disease characteristics and care/treatment processes of end-stage kidney disease (ESKD) patients on dialysis. In this paper, we review the current state of FE methodologies and their rationale in the ESKD population and illustrate applications in four key areas: profiling dialysis facilities for (1) patient hospitalizations over time (longitudinally) using standardized dynamic readmission ratio (SDRR), (2) identification of dialysis facility characteristics (e.g., staffing level) that contribute to hospital readmission, and (3) adverse recurrent events using standardized event ratio (SER). Also, we examine the operating characteristics with a focus on FE profiling models. Throughout these areas of applications to the ESKD population, we identify challenges for future research in both methodology and clinical studies.

Remote monitoring after hospital discharge.

561 Background: Strategies to improve transitions from the hospital to home for patients with cancer are considered an important component of quality, patient-centered care in oncology. CMS evaluates cancer hospital performance based on the 30-day unplanned hospital readmission rate, and this measure has been endorsed by the National Quality Forum. Nationally, the 30-day readmission rate for oncology patients ranges from 19%-27%. These readmissions come at high psychosocial, physical, and financial costs for patients and caregivers. A remote monitoring intervention that includes frequent contacts with the patient is likely to be effective in improving this transition. Methods: We evaluated the feasibility, acceptability, and perceived value of a mobile health intervention to monitor and manage symptoms of adult medical oncology patients discharged from an NCI-designated cancer center to home. Patients were monitored for 10 days, which is the median time to readmission for an oncology patient. The technology supporting the program included: 1) a patient portal enabling daily electronic patient-reported outcomes assessments; 2) alerts for concerning symptoms; 3) an application to allow staff to review and trend symptom data; 4) a secure platform to support communications and televisits between staff and patients; 5) an advanced feedback report to provide just-in-time patient symptom education. Feasibility and acceptability were evaluated through engagement (goal: &gt;50% response rate) and symptom alerts and perceived value was measured through a patient engagement survey that included a net promoter score (how likely the patient is to recommend the program to similar patients; goal &gt;0.70). Results: Between January 1, 2021 to December 31, 2022, the program enrolled 2,258 medical oncology patients (median age: 63 years, 55% female). Patients with gastrointestinal (25%) and thoracic (19%) malignancies were the most prevalent diagnoses. Of those enrolled, 66% of patients participated in home remote monitoring by self-reporting symptom data and 49% of patients generated a severe symptom alert. The most prevalent severe symptom alerts were pain (24%), constipation (7%), and fatigue (7%). Patients expressed a great deal of satisfaction with the intervention with a net promoter score of 0.86. In preliminary analysis, we found that 30-day readmission decreased by 3% (from 33% to 30%) representing savings of $2.3 - $5.8 MM annually. Conclusions: A remote monitoring program after discharge was feasible, acceptable, and perceived to be of value by oncology patients discharged from a cancer center. Future work will evaluate the value of a remote symptom monitoring platform in decreasing readmissions.

Risk factors and consequences of wound complications following sartorius flap reconstruction

ObjectiveGroin wound complications are common following vascular surgery and can lead to significant patient morbidity. Sartorius muscle flap coverage may help to prevent vascular graft infection in the setting of wound dehiscence or infection. However, risk factors and consequences of wound complications following sartorius flap reconstruction remain incompletely investigated. MethodsWe retrospectively queried all patients who underwent sartorius flap reconstruction at a tertiary academic medical center. Data collected included patient demographics, medical comorbidities, surgical indication, index vascular procedure, and postoperative outcomes. The primary outcome was wound complication following sartorius flap procedure, which was defined as groin wound infection, dehiscence, or lymphocutaneous fistula. ResultsFrom 2012 to 2022, a total of 113 patients underwent sartorius flap reconstruction. Of these, 66 (58.4%) were performed after the development of a prior groin complication, and 47 (41.6%) were prophylactic. A total of 88 patients (77.9%) had a prosthetic bypass graft adjacent to the flap. Twenty-nine patients (25.7%) suffered a wound complication following sartorius flap reconstruction, including 14 (12.4%) with wound dehiscence, 13 (11.5%) with wound infection, and two (1.8%) with lymphocutaneous fistula. Patients with wound complications had a higher body mass index (28.8 vs 26.4 kg/m2; P =.03) and more frequently active smokers (86.2% vs 66.7%; P = .04). Additionally, patients with wound complications had a higher unplanned 30-day hospital readmission rates (72.4% vs 15.5%; P < .001), reintervention rates (75.9% vs 8.3%; P < .001), and re-do flap reconstruction rates (13.8% vs 2.4%; P = .02). On multivariable analysis, higher body mass index was independently associated with post-flap wound complications (adjusted odds ratio [aOR], 1.01; 95% confidence interval [CI], 1.001-1.03; P = .037). Consequently, wound complications were associated with both surgical reintervention (aOR, 35.4; 95% CI, 9.9-126.3; P < .001) and unplanned hospital readmission (aOR, 17.8; 95% CI, 5.9-54.1; P < .001). ConclusionsSartorius flap reconstruction is an effective adjunct in facilitating wound healing of groin wounds. However, wound complications are common following sartorius flap reconstruction and may be associated with reintervention and unplanned hospital readmission. These data support the judicious and thoughtful utilization of sartorius flap procedures among high-risk patients.

Prediction of 30-day unplanned hospital readmission through survival analysis

Background and ObjectiveUnplanned hospital readmissions are a severe and recurrent problem that affects all health systems. Estimating the risk of being readmitted the following days after discharge is difficult since many heterogeneous factors can influence this. The extensive work concerning this problem proposes solutions mostly based on classification machine-learning models. Survival analysis methods could make a better match with the assessment of readmission risk and are yet to become well-established in this field. MethodsWe compare different statistical and machine learning survival analysis models trained with right-censored all-cause hospital admission data with covariates available at the moment of discharge. The main focus is on tree-ensemble regression methods based on the assumption of proportional hazards. These models are more thoroughly evaluated at a 30-day time period after discharge, although the actual prediction could be set to any time up to 90 days. ResultsThe mean performance obtained by each of the proposed survival models ranges from 0.707 to 0.716 C-Index and 0.709 to 0.72 ROC-AUC at a 30-day time period after discharge. The model with the lower performance on both metrics was Cox Proportional Hazards, while the model marking the upper end on both ranges is an XGBoost Regression model with a Cox objective function. ConclusionsOur findings indicate that survival models perform well addressing the hospital readmission problem, machine-learning models getting the edge over statistical methods. There seems to be an improvement over classification models when attempting to predict at a 30-day period since discharge, perhaps due to a better handling of cases nearing the 30-day boundary. Some preprocessing steps, such as limiting the observation period to 90 days after discharge, are also highlighted since they resulted in a performance boost.

Racial Disparities in 30-day Readmissions after Surgery for Head and Neck Cancer.

Native Hawaiians and other Pacific Islanders (NHPI) patients with head and neck cancer are often aggregated with Asian individuals despite evidence of heterogeneous health outcomes and mortality. The aim of this study was to determine the association of race with unplanned 30-day hospital readmission rate after head and neck surgery across the five federally recognized racial categories. This retrospective cohort study used a national hospital-based database and included patients ≥18 years old with diagnostically confirmed, nonmetastatic head and neck cancer of any subsite treated surgically between 2004 and 2017. The primary endpoint was unplanned readmission within 30 days of discharge after primary surgery. A total of 365,834 patients were included who were predominantly White (87%), treated at academic cancer centers (47%), lower income (63%), with early-stage disease (60%), and with thyroid (47%) or oral cavity (23%) cancers. Median follow-up duration was 47 months. Of the 10,717 (3%) readmissions, 5,845 (1.6%) were unplanned. Adjusted for confounders and compared with White patients, NHPI patients had the highest likelihood of unplanned (aOR 2.07, 95%CI 1.16-3.40, p = 0.008) readmissions. Within the NHPI group, patients with lower income (aOR 4.27, 95%CI 1.28-20.4, p = 0.035) and those residing in an urban or rural area (aOR 7.42, 95%CI 1.14-49.5, p = 0.034) were more likely to be readmitted. NHPI patients with head and neck cancers experience significantly higher 30-day readmissions following definitive surgical treatment. These results highlight the importance of racial disaggregation in clinical studies. 4 Laryngoscope, 134:1282-1287, 2024.

High-performing primary care clinics across high-need, high-cost Medicare populations

BackgroundTo reduce spending and improve quality, some primary care clinics in the USA have focused on high-need, high-cost (HNHC) Medicare beneficiaries, which include clinically distinct subpopulations: older adults with frailty,...

Stroke nurse navigator utilization reduces unplanned 30-day readmission in stroke patients treated with thrombolysis.

Unplanned 30-day hospital readmissions following a stroke is a serious quality and safety issue in the United States. The transition period between the hospital discharge and ambulatory follow-up is viewed as a vulnerable period in which medication errors and loss of follow-up plans can potentially occur. We sought to determine whether unplanned 30-day readmission in stroke patients treated with thrombolysis can be reduced with the utilization of a stroke nurse navigator team during the transition period. We included 447 consecutive stroke patients treated with thrombolysis from an institutional stroke registry between January 2018 and December 2021. The control group consisted of 287 patients before the stroke nurse navigator team implementation between January 2018 and August 2020. The intervention group consisted of 160 patients after the implementation between September 2020 and December 2021. The stroke nurse navigator interventions included medication reviews, hospitalization course review, stroke education, and review of outpatient follow-ups within 3 days following the hospital discharge. Overall, baseline patient characteristics (age, gender, index admission NIHSS, and pre-admission mRS), stroke risk factors, medication usage, and length of hospital stay were similar in control vs. intervention groups (P > 0.05). Differences included higher mechanical thrombectomy utilization (35.6 vs. 24.7%, P = 0.016), lower pre-admission oral anticoagulant use (1.3 vs. 5.6%, P = 0.025), and less frequent history of stroke/TIA (14.4 vs. 27.5%, P = 0.001) in the implementation group. Based on an unadjusted Kaplan-Meier analysis, 30-day unplanned readmission rates were lower during the implementation period (log-rank P = 0.029). After adjustment for pertinent confounders including age, gender, pre-admission mRS, oral anticoagulant use, and COVID-19 diagnosis, the nurse navigator implementation remained independently associated with lower hazards of unplanned 30-day readmission (adjusted HR 0.48, 95% CI 0.23-0.99, P = 0.046). The utilization of a stroke nurse navigator team reduced unplanned 30-day readmissions in stroke patients treated with thrombolysis. Further studies are warranted to determine the extent of the results of stroke patients not treated with thrombolysis and to better understand the relationship between resource utilization during the transition period from discharge and quality outcomes in stroke.

Machine Learning on Medicare Claims Poorly Predicts the Individual Risk of 30-Day Unplanned Readmission After Total Joint Arthroplasty, Yet Uncovers Interesting Population-level Associations With Annual Procedure Volumes.

Unplanned hospital readmissions after total joint arthroplasty (TJA) represent potentially serious adverse events and remain a critical measure of hospital quality. Predicting the risk of readmission after TJA may provide patients and clinicians with valuable information for preoperative decision-making. (1) Can nonlinear machine-learning models integrating preoperatively available patient, surgeon, hospital, and county-level information predict 30-day unplanned hospital readmissions in a large cohort of nationwide Medicare beneficiaries undergoing TJA? (2) Which predictors are the most important in predicting 30-day unplanned hospital readmissions? (3) What specific information regarding population-level associations can we obtain from interpreting partial dependency plots (plots describing, given our modeling choice, the potentially nonlinear shape of associations between predictors and readmissions) of the most important predictors of 30-day readmission? National Medicare claims data (chosen because this database represents a large proportion of patients undergoing TJA annually) were analyzed for patients undergoing inpatient TJA between October 2016 and September 2018. A total of 679,041 TJAs (239,391 THAs [61.3% women, 91.9% White, 52.6% between 70 and 79 years old] and 439,650 TKAs [63.3% women, 90% White, 55.2% between 70 and 79 years old]) were included. Model features included demographics, county-level social determinants of health, prior-year (365-day) hospital and surgeon TJA procedure volumes, and clinical classification software-refined diagnosis and procedure categories summarizing each patient's Medicare claims 365 days before TJA. Machine-learning models, namely generalized additive models with pairwise interactions (prediction models consisting of both univariate predictions and pairwise interaction terms that allow for nonlinear effects), were trained and evaluated for predictive performance using area under the receiver operating characteristic (AUROC; 1.0 = perfect discrimination, 0.5 = no better than random chance) and precision-recall curves (AUPRC; equivalent to the average positive predictive value, which does not give credit for guessing "no readmission" when this is true most of the time, interpretable relative to the base rate of readmissions) on two holdout samples. All admissions (except the last 2 months' worth) were collected and split randomly 80%/20%. The training cohort was formed with the random 80% sample, which was downsampled (so it included all readmissions and a random, equal number of nonreadmissions). The random 20% sample served as the first test cohort ("random holdout"). The last 2 months of admissions (originally held aside) served as the second test cohort ("2-month holdout"). Finally, feature importances (the degree to which each variable contributed to the predictions) and partial dependency plots were investigated to answer the second and third research questions. For the random holdout sample, model performance values in terms of AUROC and AUPRC were 0.65 and 0.087, respectively, for THA and 0.66 and 0.077, respectively, for TKA. For the 2-month holdout sample, these numbers were 0.66 and 0.087 and 0.65 and 0.075. Thus, our nonlinear models incorporating a wide variety of preoperative features from Medicare claims data could not well-predict the individual likelihood of readmissions (that is, the models performed poorly and are not appropriate for clinical use). The most predictive features (in terms of mean absolute scores) and their partial dependency graphs still confer information about population-level associations with increased risk of readmission, namely with older patient age, low prior 365-day surgeon and hospital TJA procedure volumes, being a man, patient history of cardiac diagnoses and lack of oncologic diagnoses, and higher county-level rates of hospitalizations for ambulatory-care sensitive conditions. Further inspection of partial dependency plots revealed nonlinear population-level associations specifically for surgeon and hospital procedure volumes. The readmission risk for THA and TKA decreased as surgeons performed more procedures in the prior 365 days, up to approximately 75 TJAs (odds ratio [OR] = 1.2 for TKA and 1.3 for THA), but no further risk reduction was observed for higher annual surgeon procedure volumes. For THA, the readmission risk decreased as hospitals performed more procedures, up to approximately 600 TJAs (OR = 1.2), but no further risk reduction was observed for higher annual hospital procedure volumes. A large dataset of Medicare claims and machine learning were inadequate to provide a clinically useful individual prediction model for 30-day unplanned readmissions after TKA or THA, suggesting that other factors that are not routinely collected in claims databases are needed for predicting readmissions. Nonlinear population-level associations between low surgeon and hospital procedure volumes and increased readmission risk were identified, including specific volume thresholds above which the readmission risk no longer decreases, which may still be indirectly clinically useful in guiding policy as well as patient decision-making when selecting a hospital or surgeon for treatment. Level III, therapeutic study.

Birthweight and gestational age as modifiers of rehospitalization after NICU admission.

To assess interaction effects between gestational age and birth weight on 30-day unplanned hospital readmission following discharge from the neonatal intensive care unit (NICU). To assess the relationship between gestational age and day of life (DOL) on neonatal intensive care unit (NICU) admission and the risk for 30-day unplanned hospital readmission (UHR). Retrospective study using the study site's Children's Hospitals Neonatal Database (CHND) and electronic health records were used. Population included patients discharged from a NICU between January 2017 and March 2020. Variables encompassing demographics, gestational age, birth weight, medications, maternal data, and surgical procedures were controlled for. A statistical interaction between gestational age and DOL birth weight was tested for statistical significance. 2,307 neonates were included, with 7.2% readmitted within 30 days of discharge. Statistical interaction between birth weight and gestational age was statistically significant, indicating that the odds of readmission amongst low-birth-weight premature patients increases with increasing gestational age, while it decreases with increasing gestational age amongst their normal or high birth weight peers. The effect of gestational age on odds of hospital readmission is dependent on birth weight.

Gait speed, muscle strength, and functional status predict 30-day unplanned hospital readmission of critical care survivors.

Despite intense efforts, predicting hospital readmission risks remains an imprecise task. Growing evidence suggests that unmeasured patient-related factors, such as functional impairment, seem to be strongly associated with acute readmission and have yet to be extensively explored. We hypothesized that gait speed, hand grip strength, and the Functional independence measure (FIM) might be associated with acute rehospitalization rates after an ICU stay. In our study, we assessed gait speed using a 10-m walk test. Muscle strength was determined by a hydraulic handgrip dynamometer and functional status through the FIM. Our primary outcome was the cumulative incidence of the first unplanned early rehospitalization (occurring within 30days of hospital discharge) for the entire cohort, and a Receiver Operator Characteristic (ROC) analysis was used to determine the accuracy of gait speed, handgrip strength, and FIM domains in predicting hospital readmission. ROC analysis indicated that the gait speed (AUC 0.96 95% CI 0.93 to 0.99), FIM score (AUC 0.96 95% CI 0.94 to 0.99) and handgrip strength (0.85 95% CI 0.76 to 0.94) were considered accurate predictors of unplanned readmission in the population studied. Additionally, we found that each 0.1m/s lower gait speed was associated with a 10% higher odd of unplanned readmissions. Hence, our results suggest gait speed, handgrip strength and functional status demonstrated high potential to contribute to the determination of 30-day unplanned hospital readmission prediction of critical care survivors.

Abstract WMP18: Stroke Nurse Navigator Team Utilization Towards Reduction Of Unplanned 30-day Readmissions Following Stroke

Background: Unplanned 30-day hospital readmissions following a stroke is recognized as a serious quality and safety issue in the United States. The transition period from the hospital discharge is recognized as a vulnerable period in which medication errors and loss of ambulatory follow-ups can potentially occur. There, we sought to determine if 30-day stroke readmission can be prevented with utilization of a stroke nurse navigator team. Our secondary outcome of interest was 90-day outcome events. Methods: We first retrospectively analyzed 1657 consecutive patients presenting with ischemic or hemorrhagic strokes, included in an institutional stroke registry between January 2018 and August 2020, the time before the utilization of stroke nurse navigator teams. The stroke nurse navigator interventions included medication reviews, hospitalization course review, stroke education, and establishment of outpatient follow-ups within 3 days following the discharge. Following the stroke nurse navigator team phone checks ins in September 2020, we then retrospectively analyzed 851 consecutive patients presenting with stroke, included in the registry between September 2020 and December 2021. Results: Baseline 30-day stroke readmission rate was 11.7% (194/1657) before nurse navigator team. The Readmission rate reduced to 8.2% (71/870), which was statistically significant (p&lt;0.05) following the period that utilized stroke nurse navigator team. Baseline 90-day death and cardiovascular events before the stroke nurse intervention was 5.4% (89/1657) and 8.1% (134/1657), respectively. Following the intervention, there was also no statistical difference in the 90-day outcome events of death and cardiovascular event 6.2% (27/433) and 7.3% (32/433), p&gt;0.05. Conclusion: Utilization of the Stroke nurse navigator team can reduce unplanned 30-day stroke readmissions, but it did not have an impact on 90-day clinical outcome. Further studies are warranted to study the relationship between resource utilization during transition period from discharge and quality outcome in stroke.

Characterising nationwide reasons for unplanned hospital readmission after colorectal cancer surgery.

Readmissions after colorectal cancer surgery are common, despite advancements in surgical care, and have a significant impact on both individual patients and overall healthcare costs. The aim of this study was to determine the 30-and 90 days readmission rate after colorectal cancer surgery, and to investigate the risk factors and clinical reasons for unplanned readmissions. A multicenter, population-based study including all patients discharged after index colorectal cancer resection from 2010 to 2020 in Aotearoa New Zealand (AoNZ) was completed. The Ministry of Health National Minimum Dataset was used. Rates of readmission at 30 days and 90 days were calculated. Mixed-effect logistic regression models were built to investigate factors associated with unplanned readmission. Reasons for readmission were described. Data were obtained on 16,885 patients. Unplanned 30-day and 90-day hospital readmission rates were 15.1% and 23.7% respectively. The main readmission risk factors were comorbidities, advanced disease, and postoperative complications. Hospital level variation was not present. Despite risk adjustment, R2 value of models was low (30 days: 4.3%, 90 days: 5.2%). The most common reasons for readmission were gastrointestinal causes (32.1%) and wound complications (14.4%). Rates of readmission did not improve over the 11 years study period (p= 0.876). Readmissions following colorectal resections in AoNZ are higher than other comparable healthcare systems and rates have remained constant over time. While patient comorbidities and postoperative complications are associated with readmission, the explanatory value of these variables is poor. To reduce unplanned readmissions, efforts should be focused on prevention and early detection of post-discharge complications.

Predictors of 30-day Unplanned Hospital Readmissions among Maintenance Dialysis Patients

Background and Objectives. Patients on dialysis are twice as likely to have early readmissions. This study aimed to identify risk factors for 30-day unplanned readmission among patients on maintenance dialysis in a tertiary hospital.Methods. We conducted a retrospective, unmatched, case-control study. Data were taken from patients onmaintenance hemodialysis admitted in the University of the Philippines–Philippine General Hospital (UP-PGH)between January 2018 and December 2020. Patients with 30-day readmission were included as cases and patients with &gt;30-day readmissions were taken as controls. Multivariable regression with 30-day readmission as the outcome was used to identify significant predictors of early readmission.Results. The prevalence of 30-day unplanned readmission among patients on dialysis is 36.96%, 95%CI [31.67,42.48]. In total, 119 cases and 203 controls were analyzed. Two factors were significantly associated with earlyreadmission: the presence of chronic glomerulonephritis [OR 2.35, 95% CI 1.36 to 4.07, p-value=0.002] and number of comorbidities [OR 1.34, 95% CI 1.12 to 1.61, p-value=0.002]. The most common reasons for early readmission are infection, anemia, and uremia/underdialysis.Conclusion. Patients with chronic glomerulonephritis and multiple comorbidities have significantly increased odds of early readmission. Careful discharge planning and close follow up of these patients may reduce early readmissions. &nbsp

1025. Utility of a Risk Assessment Model in Predicting 30-day Unplanned Hospital Readmission in Adult Patients Receiving Outpatient Parenteral Antimicrobial Therapy

Abstract Background Outpatient parenteral antimicrobial therapy (OPAT) is used for patients that require prolonged durations of intravenous (IV) antimicrobials and who are healthy enough to receive the medications in the outpatient setting. While OPAT is both efficacious and cost-effective, hospital readmission rates are high. Durojaiye and colleagues in the UK developed a 30-day unplanned readmission risk prediction model for OPAT patients. Given differences in patient mix and methods of OPAT delivery, we validated the established risk assessment model for Duke University Health System (DUHS) patients receiving OPAT. Methods A retrospective review of 606 OPAT episodes of adult patients who were enrolled in the DUHS OPAT program between July 1, 2019 and February 1, 2020 was conducted. The review captured the 6 risk predictors of the established model: age, Charlson Comorbidity Score, number of admissions in the preceding 12 months, concurrent receipt of more than one IV antimicrobial agent, type of infection, and mode of OPAT delivery. Additional risk predictors were captured: aminoglycoside use, vancomycin use, OPAT delivery in a skilled nursing facility, and history of IV drug abuse. The discriminative ability of the model to predict 30-day unplanned readmission as well as 30-day OPAT-related unplanned readmission was validated with the collected data using scaled Brier score, Hosmer-Lemeshow goodness-of-fit, and area under the receiver operating curve. A logistic regression model fitted with the additional risk factors was conducted to determine their impact on the model. Results When comparing DUHS OPAT patients with those of the UK model, DUHS patients were sicker (mean Charlson Comorbidity Score 3 vs 1), were treated for deeper seated infections, and received OPAT through different modes. Overall the 30-day unplanned readmission rate was 20.0% (94/470), with 59.5% of those being OPAT-related. The UK model was unable to discriminate between patients with readmission and those without, both overall and OPAT-related. The additional risk factors were also non-significant between the groups and the updated model could not predict 30-day readmission risk. Conclusion The UK 30-day unplanned hospital readmission model did not predict patient risk of readmission for the Duke OPAT population. Disclosures Richard H. Drew, PharmD MS, American College of Clinical Pharmacists: Publication royalties|Takeda: Advisor/Consultant|UpToDate: publication royalties.

Association between the LACE+ index and unplanned 30-day hospital readmissions in hospitalized patients with stroke.

BackgroundThe LACE+ index is used to predict unplanned 30-day hospital readmissions, but its utility to predict 30-day readmission in hospitalized patients with stroke is unknown.MethodsWe retrospectively analyzed 1,657 consecutive patients presenting with ischemic or hemorrhagic strokes, included in an institutional stroke registry between January 2018 and August 2020. The primary outcome of interest was unplanned 30-day readmission for any reason after index hospitalization for stroke. The 30-day readmission risk was categorized by LACE+ index to high risk (≥78), medium-to-high risk (59–77), medium risk (29–58), and low risk (≤ 28). Kaplan-Meier analysis, Log rank test, and multivariable Cox regression analysis (with backward elimination) were used to determine whether the LACE+ score was an independent predictor for 30-day unplanned readmission.ResultsThe overall 30-day unplanned readmission rate was 11.7% (194/1,657). The median LACE+ score was higher in the 30-day readmission group compared to subjects that had no unplanned 30-day readmission [74 (IQR 67–79) vs. 70 (IQR 62–75); p < 0.001]. On Kaplan-Meier analysis, the high-risk group had the shortest 30-day readmission free survival time as compared to medium and medium-to-high risk groups (p < 0.01, each; statistically significant). On fully adjusted multivariable Cox-regression, the highest LACE+ risk category was independently associated with the unplanned 30-day readmission risk (per point: HR 1.67 95%CI 1.23–2.26, p = 0.001).ConclusionSubjects in the high LACE+ index category had a significantly greater unplanned 30-day readmission risk after stroke as compared to lower LACE+ risk groups. This supports the validity of the LACE+ scoring system for predicting unplanned readmission in subjects with stroke. Future studies are warranted to determine whether LACE+ score-based risk stratification can be used to devise early interventions to mitigate the risk for unplanned readmission.