Readmission Risk Prediction Research Articles

Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study.

Patients with heart failure frequently face the possibility of rehospitalization following an initial hospital stay, placing a significant burden on both patients and health care systems. Accurate predictive tools are crucial for guiding clinical decision-making and optimizing patient care. However, the effectiveness of existing models tailored specifically to the Chinese population is still limited. This study aimed to formulate a predictive model for assessing the likelihood of readmission among patients diagnosed with heart failure. In this study, we analyzed data from 1948 patients with heart failure in a hospital in Sichuan Province between 2016 and 2019. By applying 3 variable selection strategies, 29 relevant variables were identified. Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. The graph convolutional network model showed the highest prediction accuracy with an area under the receiver operating characteristic curve of 0.831, accuracy of 75%, sensitivity of 52.12%, and specificity of 90.25%. The model crafted in this study proves its effectiveness in forecasting the likelihood of readmission among patients with heart failure, thus serving as a crucial reference for clinical decision-making.

Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease.

To investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method. We selected 575 elderly patients with CHD admitted to the Affiliated Lu'an Hospital of Anhui Medical University from January 2020 to January 2023. Based on whether patients were readmitted within 3 years, they were divided into two groups: those readmitted within 3 years (215 patients) and those not readmitted within 3 years (360 patients). Lasso regression and multivariate logistic regression were used to compare the predictive value of these models. XGBoost, LR, RF, KNN and DT algorithms were used to build prediction models for readmission risk. ROC curves and calibration plots were used to evaluate the prediction performance of the model. For external validation, 143 patients who were admitted between February and June 2023 from a different associated hospital in Lu'an City were also used. The XGBoost model demonstrated the most accurate prediction performance out of the five machine learning techniques. Diabetes, Red blood cell distribution width (RDW), and Triglyceride glucose-body mass index (TyG-BMI), as determined by Lasso regression and multivariate logistic regression. Calibration plot analysis demonstrated that the XGBoost model maintained strong calibration performance across both training and testing datasets, with calibration curves closely aligning with the ideal curve. This alignment signifies a high level of concordance between predicted probabilities and observed event rates. Additionally, decision curve analysis highlighted that both decision trees and XGBoost models achieved higher net benefits within the majority of threshold ranges, emphasizing their significant potential in clinical decision-making processes. The XGBoost model's area under the ROC curve (AUC) reached 0.903, while the external validation dataset yielded an AUC of 0.891, further validating the model's predictive accuracy and its ability to generalize across different datasets. TyG-BMI, RDW, and diabetes mellitus at the time of admission are the factors affecting readmission of elderly patients with coronary artery disease, and the model constructed based on the XGBoost algorithm for readmission risk prediction has good predictive efficacy, which can provide guidance for identifying high-risk patients and timely intervention strategies.

Development and validation of a machine learning model to predict the risk of readmission within one year in HFpEF patients: Short title: Prediction of HFpEF readmission

Heart failure with preserved ejection fraction (HFpEF) is associated with elevated rates of readmission and mortality. Accurate prediction of readmission risk is essential for optimizing healthcare resources and enhancing patient outcomes. We conducted a retrospective cohort study utilizing HFpEF patient data from two institutions: the First Affiliated Hospital Zhejiang University School of Medicine for model development and internal validation, and the Affiliated Hospital of Xuzhou Medical University for external validation. A machine learning (ML) model was developed and validated using 53 variables to predict the risk of readmission within one year. The model's performance was assessed using several metrics, including the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, F1 score, model training time, model prediction time and brier score. SHAP (SHapley Additive exPlanations) analysis was employed to enhance model interpretability, and a dynamic nomogram was constructed to visualize the predictive model. Among the 766 HFpEF patients included in the study, 203 (26.5%) were readmitted within one year. The LightGBM model exhibited the highest predictive performance, with an AUC of 0.88 (95% confidence interval (CI):0.84-0.91), an accuracy of 0.79, a sensitivity of 0.81, and a specificity of 0.78. Key predictors included the E/e' ratio, NYHA classification, LVEF, age, BNP levels, MLR, history of atrial fibrillation (AF), use of ACEI/ARB/ARNI, and history of myocardial infarction (MI). External validation also demonstrated strong predictive performance, with an AUC of 0.87 (95%CI:0.83-0.91). The LightGBM model exhibited robust performance in predicting one-year readmission risk among HFpEF patients, providing a valuable tool for clinicians to identify high-risk individuals and implement timely interventions.

AI-Powered Post-Discharge Monitoring to Prevent Patients Readmissions and Reduce Workforce Burden

Abstract Background Patient readmission poses a significant burden on healthcare systems, straining resources, negatively impacting patient treatment, and it is often a Public Health problem. Traditional post-discharge follow-up methods, such as phone calls, often lack efficiency and personalization. Artificial intelligence (e.g., Machine Learning (ML)) offers a transformative approach that can potentially improve post-discharge care. Methods This scoping review aimed to map existing research on AI-powered solutions for post-discharge monitoring, focusing on their application in risk stratification and their potential for reducing hospital readmissions within a public health framework. Following PRISMA-ScR guidelines, a search was conducted across four databases using keywords related to AI, post-discharge, and risk stratification. Results Studies published between 2018 and January 2024 were included if they described the development and application of an ML model in a post-discharge setting for risk stratification. Sixteen studies met the inclusion criteria. Tree-based algorithms, particularly XGBoost, were the predominant ML approach, with promising performance metrics for readmission risk prediction. Conclusions However, limitations were identified, including restricted generalizability due to data source limitations and a lack of real-time implementation in many studies. This review highlights the potential of AI in post-discharge monitoring, particularly the use of ML for risk stratification, as a promising tool for public health contact tracing. While challenges remain regarding generalizability and real-world implementation, the findings suggest AI holds immense promise for improving post-discharge care, potentially reducing readmissions, and optimizing resource allocation. Future research with broader datasets and real-time applications can further solidify the role of AI in revolutionizing post-discharge care within public health systems. Key messages • This study shows the potential of AI and risk stratification in post-discharge monitoring, improving public health outcomes by reducing hospital readmissions and optimizing the healthcare workforce. • This review highlights the value of AI in public health, particularly for improving post-discharge care through effective prediction of high-risk readmissions.

Associations between readmission and patient-reported measures in acute psychiatric inpatients: a multicenter prospective longitudinal study.

This study examined whether patient-reported measures (PRMs) addressing quality of life, personal agency, functional impairment, and treatment satisfaction at hospital discharge were associated with future readmission during a 12-month follow-up period. The study also examined whether readmission influenced changes in the same measures. A multicenter prospective cohort study was conducted at 21 psychiatric hospitals in Japan. Participants completed the EuroQol-five-dimensions-five-level (EQ-5D), the Five-item Subjective Personal Agency Scale, and the Sheehan Disability Scale (SDS) at the time of index admission (T1), discharge from index admission (T2), and 6months (T3) and 12months (T4) after discharge. Inpatient treatment satisfaction was assessed at T2. Readmission and variables potentially associated with hospitalization and PRMs were evaluated using mixed-effects logistic regression models and mixed models for repeated measures. A total of 491 participants were followed for 12months (attrition rate: 19.4%), and 480 were included in the EQ-5D analysis. The most common diagnoses were schizophrenia (59%), depression (14%), and bipolar disorder (13%). No patient-reported measures were significantly associated with readmission over the follow-up period. Interaction of readmission and time did not significantly affect changes in EQ-5D. Readmission did significantly influence SDS score changes between T2 and T3 (B = 1.78, 95% CI = 0.30-3.25, p = 0.018) and between T3 and T4 (B = 1.43, 95% CI = 0.14-2.72, p = 0.029).The same influence of readmission on SDS score changes was not observed in the model which adjusted for all potential covariates. Readmission was potentially associated with changes in self-reported functional impairment. Findings highlight the potential role of intensive post-discharge services in preventing readmission, rather than relying on time-of-discharge PRMs in order to predict readmission risk. This study was registered in UMIN Clinical Trials Registry (UMIN000034220).

SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation

Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission risk prediction models for elderly patients with HF, few have integrated ML-selected features with those chosen by human experts to assess HF patients readmission. A retrospective analysis of 8396 elderly HF patients hospitalized at the Affiliated Hospital of North Sichuan Medical College from January 1, 2018 to December 31, 2021 was conducted. Variables selected by XGBoost, LASSO regression, and random forest constituted the machine group, while the human expert group comprised variables chosen by two experienced cardiovascular professors. The variables selected by both groups were combined to form a human–machine collaboration group. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanations (SHAP) method was used to elucidate the importance of each predictive feature, explain the impact of individual features on the model, and provide visual representation. A total of 73 features were included for model development. The human–machine collaboration model, utilizing CatBoost, achieved an AUC of 0.83617, an F1-score of 0.73521, and a Brier score of 0.16536 on the validation set. This model demonstrated superior predictive performance compared to those created solely by human experts or machine. The SHAP plot was then used to visually display the feature analysis of the human–machine collaboration model, revealing HGB, NT-proBNP, smoking history, NYHA classification, and LVEF as the 5 most important features. This study indicate that the human–machine collaboration model outperforms those relying solely on human expert selection or machine algorithm at predicting all-cause readmission in elderly HF patients. The application of the SHAP method enhanced the interpretability of the model outcomes, aiding clinicians in accurately pinpointing risk factors associated with HF readmission. This advancement enables the formulation of tailored treatment strategies, offering a more personalized approach to patient care.

Machine Learning-Based Prediction of Readmission Risk in Cardiovascular and Cerebrovascular Conditions Using Patient EMR Data.

The primary objective of this study was to develop a risk-based readmission prediction model using the EMR data available at discharge. This model was then validated with the LACE plus score. The study cohort consisted of about 310,000 hospital admissions of patients with cardiovascular and cerebrovascular conditions. The EMR data of the patients consisted of lab results, vitals, medications, comorbidities, and admit/discharge settings. These data served as the input to an XGBoost model v1.7.6, which was then used to predict the number of days until the next readmission. Our model achieved remarkable results, with a precision score of 0.74 (±0.03), a recall score of 0.75 (±0.02), and an overall accuracy of approximately 82% (±5%). Notably, the model demonstrated a high accuracy rate of 78.39% in identifying the patients readmitted within 30 days and 80.81% accuracy for those with readmissions exceeding six months. The model was able to outperform the LACE plus score; of the people who were readmitted within 30 days, only 47.70 percent had a LACE plus score greater than 70, and, for people with greater than 6 months, only 10.09 percent had a LACE plus score less than 30. Furthermore, our analysis revealed that the patients with a higher comorbidity burden and lower-than-normal hemoglobin levels were associated with increased readmission rates. This study opens new doors to the world of differential patient care, helping both clinical decision makers and healthcare providers make more informed and effective decisions. This model is comparatively more robust and can potentially substitute the LACE plus score in cardiovascular and cerebrovascular settings for predicting the readmission risk.

Development and validation of a machine learning-based readmission risk prediction model for non-ST elevation myocardial infarction patients after percutaneous coronary intervention

To investigate the factors that influence readmissions in patients with acute non-ST elevation myocardial infarction (NSTEMI) after percutaneous coronary intervention (PCI) by using multiple machine learning (ML) methods to establish a predictive model. In this study, 1576 NSTEMI patients who were hospitalized at the Affiliated Hospital of North Sichuan Medical College were selected as the research subjects. They were divided into two groups: the readmitted group and the non-readmitted group. The division was based on whether the patients experienced complications or another incident of myocardial infarction within one year after undergoing PCI. Common variables selected by univariate and multivariate logistic regression, LASSO regression, and random forest were used as independent influencing factors for NSTEMI patients’ readmissions after PCI. Six different ML models were constructed using these common variables. The area under the ROC curve, accuracy, sensitivity, and specificity were used to evaluate the performance of the six ML models. Finally, the optimal model was selected, and a nomogram was created to visually represent its clinical effectiveness. Three different methods were used to select seven representative common variables. These variables were then utilized to construct six different ML models, which were subsequently compared. The findings indicated that the LR model exhibited the most optimal performance in terms of AUC, accuracy, sensitivity, and specificity. The outcome, admission mode (walking and non-walking), communication ability, CRP, TC, HDL, and LDL were identified as independent predicators of readmissions in NSTEMI patients after PCI. The prediction model constructed by the LR algorithm was the best. The established column graph model established proved to be effective in identifying high-risk groups with high accuracy and differentiation. It holds a specific predictive value for the occurrence of readmissions after direct PCI in NSTEMI patients.

Multimodal Machine Learning for Prediction of 30-Day Readmission Risk in Elderly Population

Readmission within 30 days is a prevalent issue among elderly patients, linked to unfavorable health outcomes. Our objective was to develop and validate multimodal machine learning models for predicting 30-day readmission risk in elderly patients discharged from internal medicine departments. This was a retrospective cohort study which included elderly patients aged 75 or older, who were hospitalized at the Hadassah Medical Center internal medicine departments between 2014 and 2020. Three machine learning algorithms were developed and employed to predict 30-day readmission risk. The primary measures were predictive model performance scores, specifically area under the receiver operator curve (AUROC), and average precision. This study included 19,569 admissions. Of them, 3258 (16.65%) resulted in 30-day readmission. Our 3 proposed models demonstrated high accuracy and precision on an unseen test set, with AUROC values of 0.87, 0.89, and 0.93, respectively, and average precision values of 0.76, 0.78, and 0.81. Feature importance analysis revealed that the number of admissions in the past year, history of 30-day readmission, Charlson score, and admission length were the most influential variables. Notably, the natural language processing score, representing the probability of readmission according to a textual-based model trained on social workers' assessment letters during hospitalization, ranked among the top 10 contributing factors. Leveraging multimodal machine learning offers a promising strategy for identifying elderly patients who are at high risk for 30-day readmission. By identifying these patients, machine learning models may facilitate the effective execution of preventive actions to reduce avoidable readmission incidents.

Generalizability of machine learning models predicting 30-day unplanned readmission after primary total knee arthroplasty using a nationally representative database.

Unplanned readmission after primary total knee arthroplasty (TKA) costs an average of US $39,000 per episode and negatively impacts patient outcomes. Although predictive machine learning (ML) models show promise for risk stratification in specific populations, existing studies do not address model generalizability. This study aimed to establish the generalizability of previous institutionally developed ML models to predict 30-day readmission following primary TKA using a national database. Data from 424,354 patients from the ACS-NSQIP database was used to develop and validate four ML models to predict 30-day readmission risk after primary TKA. Individual model performance was assessed and compared based on discrimination, accuracy, calibration, and clinical utility. Length of stay (> 2.5days), body mass index (BMI) (> 33.21kg/m2), and operation time (> 93min) were important determinants of 30-day readmission. All ML models demonstrated equally good accuracy, calibration, and discriminatory ability (Brier score, ANN = RF = HGB = NEPLR = 0.03; ANN, slope = 0.90, intercept = - 0.11; RF, slope = 0.93, intercept = - 0.12; HGB, slope = 0.90, intercept = - 0.12; NEPLR, slope = 0.77, intercept = 0.01; AUCANN = AUCRF = AUCHGB = AUCNEPLR = 0.78). This study validates the generalizability of four previously developed ML algorithms in predicting readmission risk in patients undergoing TKA and offers surgeons an opportunity to reduce readmissions by optimizing discharge planning, BMI, and surgical efficiency.

Readmission following methotrexate treatment for tubal pregnancy.

To identify risk factors for readmission following methotrexate treatment for tubal pregnancy. A retrospective study undertaken in two tertiary medical centers, including all individuals with medically treated tubal pregnancy (N = 511), between December 2009 and June 2021. Individuals with and without readmission following methotrexate treatment were compared. The primary outcome was the readmission rate. Secondary outcomes included the rate of post-discharge gynecological emergency department visits, tubal rupture rate, and the eventual need for surgical treatment. Readmission following methotrexate treatment occurred in 224/511 patients (43.8%). Most readmissions were due to abdominal pain or suspicion of treatment failure. Readmitted individuals were more likely to have a history of pelvic inflammatory disease and pretreatment serum human chorionic gonadotropin (hCG) >2000 mIU/mL. Both factors remained significantly associated with higher readmission rates in a logistic regression analysis (adjusted odds ratio [OR] 6.28, 95% confidence interval [CI] 1.30-30.45, and adjusted OR 2.73, 95% CI 1.83-4.07, respectively) after adjustment for age, endometriosis, tubal pathology, abdominal pain, and presence of yolk sac or embryo at diagnosis. A dose-dependent association was observed between pretreatment serum hCG levels and readmission rate (P < 0.001). Pretreatment hCG levels were also associated with tubal rupture and the eventual need for surgical treatment (P < 0.001 for both). A prediction model using hCG was not sufficiently accurate to predict readmission risk. Readmission following methotrexate treatment for tubal pregnancy was independently associated with previous pelvic inflammatory disease and pretreatment serum hCG levels. The latter was also associated with surgical intervention rate.

Machine learning-based readmission risk prediction for diabetic patients

Machine learning-based readmission risk prediction for diabetic patients

Augmenting the Hospital Score with social risk factors to improve prediction for 30-day readmission following acute myocardial infarction.

Hospital Score is a well-known and validated tool for predicting readmission risk among diverse patient populations. Integrating social risk factors using natural language processing with the Hospital Score may improve its ability to predict 30-day readmissions following an acute myocardial infarction. A retrospective cohort included patients hospitalized at Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary index diagnosis of acute myocardial infarction, who were discharged alive. To supplement ascertainment of 30-day readmissions, data were linked to Center for Medicare & Medicaid Services (CMS) administrative data. Clinical notes from the cohort were extracted, and a natural language processing model was deployed, counting mentions of eight social risk factors. A logistic regression prediction model was run using the Hospital Score composite, its component variables, and the natural language processing-derived social risk factors. ROC comparison analysis was performed. The cohort included 6,165 unique patients, where 4,137 (67.1%) were male, 1,020 (16.5%) were Black or other people of color, the average age was 67 years (SD: 13), and the 30-day hospital readmission rate was 15.1% (N=934). The final test-set AUROCs were between 0.635 and 0.669. The model containing the Hospital Score component variables and the natural language processing-derived social risk factors obtained the highest AUROC. Social risk factors extracted using natural language processing improved model performance when added to the Hospital Score composite. Clinicians and health systems should consider incorporating social risk factors when using the Hospital Score composite to evaluate risk for readmission among patients hospitalized for acute myocardial infarction.

A Causal Network-Based Markov Decision Process Model for Intervention Planning

Patients suffering from chronic diseases often receive interventions after they are discharged from hospitals for their health management. The intervention decisions can be made sequentially during the course of disease progression and treatment process, and timely and proper interventions can lead to better health condition and quality of life and reduce the rate of re-hospitalization. Moreover, to determine effective intervention plans, personalized care should be considered. However, in many cases, intervention plans are not tailored to patients’ health conditions or dynamically adjusted based on their status changes due to the lack of adequate analytical methods. To address this issue, we introduce a causal Bayesian network-based Markov decision process (CNMDP) model to dynamically evaluate the health status and effectiveness of interventions for each patient and update the corresponding intervention strategy. In such a model, a causal Bayesian network is integrated in order to predict the effect of interventions in each patient’s health status. The resulting risk at each decision point is considered in a Markov decision process model by examining diverse factors of a patient to make a personalized optimal decision on intervention planning to minimize the risk of health deterioration or readmission. To illustrate the applicability of the model and develop a specific formulation, an intervention decision model for chronic obstructive pulmonary disease (COPD) patients is presented using the proposed method. Such a model can be applied to other diseases as well to design personalized, prompt, and effective intervention plans. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Substantial efforts have been devoted to treatment and care management of chronic diseases. Particularly, there has been an initiative to design appropriate follow-up plans and interventions for postdischarge care to reduce hospital readmissions. However, many intervention plans are not patient specific and do not change with patient conditions. To mitigate such an limitation, based on identified risk factors and causal relationships, a causal Bayesian network model is introduced to predict readmission risk and evaluate intervention effectiveness. The results are then embedded into a Markov decision process to derive optimal intervention plan to minimize readmission risk. Such a process is repeated dynamically to evaluate health status and update intervention strategies. Using chronic obstructive pulmonary disease (COPD) as an example of chronic disease, a case study at a community hospital is presented to illustrate the applicability of the model.

A validation study of the high acuity readmission risk pediatric screen (HARRPS) tool©: Predicting readmission risk within the pediatric population

The initial research study of the High Acuity Readmission Risk Pediatric Screen (HARRPS) Tool © focused on using retrospective data to apply weighted values to the questions within the tool, identify overall risk score, and attribute risk categories (low, moderate, high risk) to the overall risk score. This study focused on validating the data from the initial study, as well as cross examining the need to include admission diagnosis within the tool. Study was a single-centered, retrospective chart review study using a different subset of patients from the initial study. Pediatric patients with thirty-day readmissions were compared to pediatric patients without a thirty-day readmission over a twelve-month period. Utilized same statistical software and methodology from initial study to identify if readmission risk probability could be replicated with a different population. The initial study performed in 2018 demonstrated a c-statistic score/ area under the curve (AUC) of 0.68 [95% CI: 0.67, 0.69]. In addition, the initial study demonstrated as risk score increases, the probability of readmission gradually increased until a patient had a risk score of seven or greater, at which point readmission risk plateaued. This resulted in low, moderate, and high readmission risk categories. The current study performed using data from 2019 demonstrated an improved c-statistic score / AUC of 0.83 [95% CI: 0.80, 0.87] with admission diagnosis included, and a c-statistic score / AUC of 0.80 [95% CI: 0.76, 0.83] without the admission diagnosis included. The analysis of overall risk score demonstrated a substantial difference in how to interpret final readmission risk scores. Both the initial study and validation study were consistent in demonstrating a risk score of three or less was associated with low readmission risk. However, in the validation study, there was no substantial difference between moderate or high risk, leading to updating the tool from 3 risk categories into 2 risk categories of low risk and at risk of readmission. Based on the finding from the validation study, the admission diagnosis was removed from the HARRPS Tool© as the difference in c-statistic score was nominal, and the risk categories were changed from three categories (low, moderate, high risk) to two categories of low risk (score 0-2) and at risk of readmission for a score of 3+. The ability of the HARRPS Tool© to predict readmission risk preforms best with a c-statistic=0.80, outperforming the following tools: LACE (0.65), LACE -SDH (0.67), LACE + (0.61), Epic's readmission risk model (0.69), and SQLAPE ® (0.71) (Ryan, et al., 2021; Hwang, et al., 2021).

Association between Peripartum Mean Arterial Pressure and Postpartum Readmission for Preeclampsia with Severe Features.

This study aimed to evaluate the relationship between peripartum mean arterial pressure (MAP) and postpartum readmission for preeclampsia with severe features. This is a retrospective case-control study comparing adult parturients readmitted for preeclampsia with severe features to matched nonreadmitted controls. Our primary objective was to evaluate the association between MAP at three time points during the index hospitalization (admission, 24-hour postpartum, and discharge) and readmission risk. We also evaluated readmission risk by age, race, body mass index, and comorbidities. Our secondary aim was to establish MAP thresholds to identify the population at highest risk of readmission. Multivariate logistic regression and chi-squared tests were used to determine the adjusted odds of readmission based on MAP. Receiver operating characteristic analyses were performed to evaluate risk of readmission relative to MAP; optimal MAP thresholds were established to identify those at highest risk of readmission. Pairwise comparisons were made between subgroups after stratifying for history of hypertension, with a focus on readmitted patients with new-onset postpartum preeclampsia. A total of 348 subjects met inclusion criteria, including 174 controls and 174 cases. We found that elevated MAP at both admission (adjusted odds ratio [OR]: 1.37 per 10 mm Hg, p < 0.0001) and 24-hour postpartum (adjusted OR: 1.61 per 10 mm Hg, p = 0.0018) were associated with increased risk of readmission. African American race and hypertensive disorder of pregnancy were independently associated with increased risk of readmission. Subjects with MAP > 99.5 mm Hg at admission or >91.5 mm Hg at 24-hour postpartum had a risk of at least 46% of requiring postpartum readmission for preeclampsia with severe features. Admission and 24-hour postpartum MAP correlate with risk of postpartum readmission for preeclampsia with severe features. Evaluating MAP at these time points may be useful for identifying women at higher risk for postpartum readmission. These women may otherwise be missed based on standard clinical approaches and may benefit from heightened surveillance. · Existing literature focuses on management of antenatal hypertensive disorders of pregnancy.. · Elevated peripartum MAP is associated with increased odds of readmission for preeclampsia.. · Peripartum MAP may predict readmission risk for de novo postpartum preeclampsia..

A-BBL: A Risk Prediction Model for Patient Readmission based on Electronic Medical Records

With the spread of medical digitization, electronic health record data has been accumulated in large quantities, laying the foundation for intelligent medical changes. ICU data is mined and analyzed to identify the risk of patient readmission in a timely manner, prevent and control the deterioration of patients' conditions, and reduce the burden of patient costs. However, due to the poor quality of medical data, potential information cannot be effectively mined. In view of the above problems, a patient readmission risk prediction model A-BBL is proposed. By extracting and analyzing the patient 's discharge summary information, the readmission risk of discharged patients within 30 days is predicted. The A-BBL model consists of three parts: firstly, BioBert is used to pre-train the medical text data, extract the semantic information of the medical text, and then generate the corresponding word vector. Then, the sequence model BiLSTM is used to capture the context information and model the input sequence. Finally, the self-attention mechanism is used to extract the key information in the input sequence, enhance the vector representation ability of the sequence, thereby improving the performance and accuracy of the model, so as to predict the readmission rate of patients. Based on the MIMIC-III real medical data set, the A-BBL model for patient readmission prediction proposed in this paper is verified. Compared with the baseline model, the accuracy is improved by 7.2 %. This study can help medical staff better understand and pay attention to the progression of critically ill patients, im-prove the survival rate of patients, and reduce the readmission rate of patients.

Comparison of short and long forms of the Flinders program of chronic disease SELF-management for participants starting SGLT-2 inhibitors for congestive heart failure (SELFMAN-HF): protocol for a prospective, observational study.

Congestive heart failure (CHF) causes significant morbidity and mortality. It is an epidemic, and costs are escalating. CHF is a chronic disease whose trajectory includes stable phases, periods of decompensation, and finally palliation. Health services and medical therapies must match the various patient needs. Chronic disease self-management (CDSM) programmes that are patient-focused, identify problems and set actionable goals that appear as a logical, cost-friendly method to navigate patient journeys. There have been challenges in standardising and implementing CHF programmes. SELFMAN-HF is a prospective, observational study to evaluate the feasibility and validity of the SCRinHF tool, a one-page self-management and readmission risk prediction tool for CHF, with an established, comprehensive CDSM tool. Eligible patients will have CHF with left ventricular ejection fraction <40% and commenced sodium glucose co-transporter-2 inhibitors (SGLT2-i) within 6 months of recruitment. The primary endpoint is the 80% concordance in readmission risk predicted by the SCRinHF tool. The study will recruit >40 patients and is expected to last 18 months. This study has been approved by the St Vincent's ethics committee (approval no. LRR 177/21). All participants will complete a written informed consent prior to enrolment in the study. The study results will be disseminated widely via local and international health conferences and peer-reviewed publications.

Predicting colorectal surgery readmission risk: a surgery-specific predictive model

Most current predictive models for risk of readmission were primarily designed from non-surgical patients and often utilize administrative data alone. Models built upon comprehensive data sources specific to colorectal surgery may be key to implementing interventions aimed at reducing readmissions. This study aimed to develop a predictive model for risk of 30-day readmission specific to colorectal surgery patients including administrative, clinical, laboratory, and socioeconomic status (SES) data. Patients admitted to the colorectal surgery service who underwent surgery and were discharged from an academic tertiary hospital between 2017 and 2019 were included. A total of 1549 patients met eligibility criteria for this retrospective split-sample cohort study. The 30-day readmission rate of the cohort was 19.62%. A multivariable logistic regression was developed (C = 0.70, 95% CI 0.61–0.73), which outperformed two internationally used readmission risk prediction indices (C = 0.58, 95% CI 0.52–0.65) and (C = 0.60, 95% CI 0.53–0.66). Tailored surgery-specific readmission models with comprehensive data sources outperform the most used readmission indices in predicting 30-day readmission in colorectal surgery patients. Model performance is improved by using more comprehensive datasets that include administrative and socioeconomic details about a patient, as well as clinical information used for decision-making around the time of discharge.

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

The objective of this study was to leverage a national database of TAVR procedures to create a risk model for 30-day readmissions. The 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. A 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 30days. 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. The 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.