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Handgrip strength, a component of the physical frailty phenotype, may be a simple tool to help predict readmission.

Weak Handgrip at Index Admission for Acute Exacerbation of COPD Predicts All-Cause 30-Day Readmission

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The cardiac biomarker N-terminal pro-brain natriuretic peptide (NT-proBNP) can help predict readmission in adult populations, but the estimated utility in predicting risk of readmission or mortality after pediatric congenital heart surgery has not previously been studied.

The Association Between Cardiac Biomarker NT-proBNP and 30-Day Readmission or Mortality After Pediatric Congenital Heart Surgery

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predict readmission risk

Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients.

The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers

predict readmission risk

However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available.

Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

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predict readmission within

Objective The study aimed to (1) quantify readmission rates and common causes of readmission following endoscopic transsphenoidal pituitary surgery (ETPS); (2) identify risk factors that may predict readmission within 30 days; (3) assess postoperative care coordination with endocrinology follow-up; and (4) identify patients for whom targeted interventions may reduce 30-day readmissions.

30-Day Readmissions and Coordination of Care Following Endoscopic Transsphenoidal Pituitary Surgery: Experience with 409 Patients

predict readmission within

While studies have been undertaken to predict readmission within 30-days for orthopedic procedures, such as total shoulder arthroplasty and adult spine fusion surgery, no research has been conducted on the utility of machine learning algorithms to predict postoperative complications for pediatric patients undergoing spinal procedures.

P19. Predicting readmission following fusion for scoliosis in pediatric patients: A machine-learning approach

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10.1166/jmihi.2021.3304

This paper presents the use of early clinical notes in building a machine learning model to predict readmission at 48 h immediately after a patient's admission.

Early Detection of Readmission Risk for Decision Support Based on Clinical Notes

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10.1093/eurjcn/zvab017

In mixed patient populations, readiness for hospital discharge has shown to predict readmission and mortality in the short term.

Unreadiness for hospital discharge predicts readmission among cardiac patients: results from the national DenHeart survey.

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10.1186/s12911-021-01423-y

Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients.

The 30-days hospital readmission risk in diabetic patients: predictive modeling with machine learning classifiers

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10.2196/16306

However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available.

Early Prediction of Unplanned 30-Day Hospital Readmission: Model Development and Retrospective Data Analysis

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10.24251/HICSS.2021.417

In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days.

Predicting Unplanned Hospital Readmissions using Patient Level Data

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10.3390/SU13020531

This study aims to examine economic and social factors that predict readmission likelihood for mental health and drug abuse patients in the state of New York.

Economic and Social Factors That Predict Readmission for Mental Health and Drug Abuse Patients

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10.12688/F1000RESEARCH.52374.1

Conclusions:  Multiple factors including HT comorbidity, anaemia, atrial fibrillation, chronic kidney disease, and smoking predict readmission among Thais with post myocardial infarction.

Factors Predicting Hospital Readmission among Thais with Post Myocardial Infarction

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10.1097/JHQ.0000000000000318

We compared the ability of manually extracted variables to predict readmission compared with EHR-based prediction using multivariate logistic regression on 1 year of admission data from an academic medical center.

Using Routinely Collected Electronic Health Record Data to Predict Readmission and Target Care Coordination.

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10.1016/j.wneu.2021.02.114

In this investigation, a neural network, a supervised machine learning technique, is evaluated to determine whether it can predict readmission after three lumbar fusion procedures.

Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach.

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10.1891/JNM-D-20-00066

The Arabic PT-RHDS did not predict readmissions.

Cross-Cultural Adaptation and Psychometrics Evaluation of the Arabic Version of the Patient-Readiness for Hospital Discharge Scale.

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10.1111/jce.15247

In multivariate model, gender and CHA2 DS2 -VASc failed to predict readmission.

Outcomes and Predictors of Readmission after Implantation of a Percutaneous Left Atrial Appendage Occlusion device in the United States: A Propensity Score-matched Analysis from the National Readmission Database.

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10.1136/bmjopen-2020-044964

Methods We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models.

Published models that predict hospital readmission: a critical appraisal

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10.14569/ijacsa.2021.0120840

This paper proposes a group model to predict readmission by choosing between Machine Learning and Deep Learning algorithms based on performance improvement.

IDD-HPO: A Proposed Model for Improving Diabetic Detection using Hyperparameter Optimization and Cloud Mapping Storage

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10.1016/j.ijrobp.2021.07.265

The Hospital Frailty Risk Score (HFRS) represents a validated risk stratification tool used to predict readmission and death among non-cancer patients, however this tool has not been evaluated among cancer patients, which represent a unique cohort of patients.

Effect of Frailty on Unplanned 30-Day Readmissions and Mortality in Cancer Patients.

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10.1055/S-0041-1729980

Objective The study aimed to (1) quantify readmission rates and common causes of readmission following endoscopic transsphenoidal pituitary surgery (ETPS); (2) identify risk factors that may predict readmission within 30 days; (3) assess postoperative care coordination with endocrinology follow-up; and (4) identify patients for whom targeted interventions may reduce 30-day readmissions.

30-Day Readmissions and Coordination of Care Following Endoscopic Transsphenoidal Pituitary Surgery: Experience with 409 Patients

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10.1513/AnnalsATS.202102-156OC

We used mixed-effects logistic regression to predict readmission odds.

Effects of Obstructive Sleep Apnea and Obesity on Thirty-Day Readmissions in Patients with Chronic Obstructive Pulmonary Disease: A Cross-Sectional Mediation Analysis.

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10.1016/J.SPINEE.2021.05.227

While studies have been undertaken to predict readmission within 30-days for orthopedic procedures, such as total shoulder arthroplasty and adult spine fusion surgery, no research has been conducted on the utility of machine learning algorithms to predict postoperative complications for pediatric patients undergoing spinal procedures.

P19. Predicting readmission following fusion for scoliosis in pediatric patients: A machine-learning approach

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10.1016/j.ajogmf.2020.100252

CONCLUSION The strategy of using the maximum systolic or diastolic blood pressure exceeding the blood pressure threshold on 2 occasions at least 4 hours apart at lower blood pressure thresholds can predict readmissions with higher sensitivity or specificity.

Evaluation of different blood pressure assessment strategies and cutoff values to predict postpartum hypertension-related readmissions: a retrospective cohort study.

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10.1245/s10434-020-09547-7

The aim of this study was to develop and validate a simple score to predict readmissions following CRS/HIPEC.

Predischarge Prediction of Readmission After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: Derivation and Validation of a Risk Prediction Score

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10.1097/CND.0000000000000319

Age did not predict readmission.

Readmissions After Guillain-Barre Syndrome: Nationally Representative Data.

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10.3389/fneur.2021.649521

Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke.

Prediction of 30-Day Readmission After Stroke Using Machine Learning and Natural Language Processing

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10.3389/fmed.2021.611989

Handgrip strength, a component of the physical frailty phenotype, may be a simple tool to help predict readmission.

Weak Handgrip at Index Admission for Acute Exacerbation of COPD Predicts All-Cause 30-Day Readmission

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10.1093/EHJACC/ZUAB020.046

On the other hand, KKC at admission did not predict readmission (either 1mRA or 1yRA, respectively p = 0.

GRACE score vs GWTG-HF score: risk stratification in acute heart failure

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10.1101/2021.03.15.21253416

We describe the use of a data-driven approach that relies on machine learning algorithms to predict readmission at the time of discharge.

Using Random Forests with Asymmetric Costs to Predict Hospital Readmissions

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10.1016/j.jbi.2021.103826

RESULTS Allocating readmission-mitigating interventions based on a GBDT model trained to predict readmissions achieved an estimated utility gain of $104 per patient, and an AUC of 0.

Improving hospital readmission prediction using individualized utility analysis

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10.1007/s11892-021-01402-7

The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization.

Predicting and Preventing Acute Care Re-Utilization by Patients with Diabetes

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10.1016/j.amjsurg.2019.11.016

This study aims to evaluate whether mFI could be utilized to predict readmissions after colorectal resection for patients with cancer by using nationwide cohort.

Modified frailty index predicts high-risk patients for readmission after colorectal surgery for cancer.

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10.1183/13993003.congress-2019.pa5231

Introduction and Objective: Different scores have been developed to predict readmission or death in patients with COPD like BODE, ADO and CODEX score.

Pearl score prediction for mortality in severe acute COPD re-exacerbation

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10.1007/s11060-019-03317-6

A predictive model was built to assess the capability of these results to predict readmission and validated using leave-one-out cross-validation.

Analysis of intra-operative variables as predictors of 30-day readmission in patients undergoing glioma surgery at a single center

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10.1109/BIBM47256.2019.8983095

To address this, we propose a deep learning approach to predict readmission from clinical notes.

Predicting Heart Failure Readmission from Clinical Notes Using Deep Learning

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10.1016/j.spinee.2019.12.004

PURPOSE To determine the frequency and type of unplanned readmissions after complex spine surgery, and to investigate if prospectively registered AEs can predict readmissions.

Readmission following complex spine surgery in a prospective cohort of 679 patients - 2-years follow up using the Spine AdVerse Event Severity (SAVES) system.

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10.18690/978-961-286-280-0.6

The out-of-sample results show that all of the models performed similarly and adequately to predict readmission risk.

Real-time Prediction of the Risk of Hospital Readmissions

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10.1136/bmjoq-2018-000544

Knowledge is sparse on how different social factors can contribute to predict readmission.

Development of a prediction model for 30-day acute readmissions among older medical patients: the influence of social factors along with other patient-specific and organisational factors

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10.1016/j.urolonc.2019.06.001

Multivariable logistic regression modeling was used to determine factors that predict readmission and major complications.

Surgical approach as a determinant factor of clinical outcome following radical cystectomy: Does Enhanced Recovery After Surgery (ERAS) level the playing field?

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10.1542/hpeds.2018-0174

METHODS After extracting sociodemographic and clinical characteristics from electronic health records for children with CCCs admitted to an academic medical center, we constructed a multivariable logistic regression model to predict readmission from characteristics obtainable at admission and then a second model adding hospitalization and discharge variables to the first model.

Developing Prediction Models for 30-Day Unplanned Readmission Among Children With Medical Complexity.

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10.3928/01477447-20191001-02

Univariate and multivariate techniques were used to predict readmission and complications.

Risk Factors for Readmission After Surgical Treatment of Slipped Capital Femoral Epiphysis.

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10.1007/s40138-019-00197-y

This review describes the current literature on the use of data science to predict readmissions of patients with heart failure.

Using Data Science to Predict Readmissions in Heart Failure

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10.1177/2150135119842864

The cardiac biomarker N-terminal pro-brain natriuretic peptide (NT-proBNP) can help predict readmission in adult populations, but the estimated utility in predicting risk of readmission or mortality after pediatric congenital heart surgery has not previously been studied.

The Association Between Cardiac Biomarker NT-proBNP and 30-Day Readmission or Mortality After Pediatric Congenital Heart Surgery

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10.1101/2019.12.26.19015909

We used the OMOP-CDM version of MIMIC-III (MIMIC-OMOP) to train and evaluate a lightweight tree boosting method to predict readmission in ICU at different time points after discharge (3, 7, 15 and 30 days), outperforming existing solutions with an AUROC of 0.

Accurate and reproducible prediction of ICU readmissions

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10.1016/j.cjca.2019.10.023

BACKGROUND The ability to predict readmission accurately after hospitalization for acute myocardial infarction (AMI) is limited in current statistical models.

Evaluation of Machine Learning Algorithms for Predicting Readmission After Acute Myocardial Infarction Using Routinely Collected Clinical Data.

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10.1016/J.CARDFAIL.2019.07.408

Variability in assessment of HF symptoms at discharge may affect capacity to predict readmission risk.

Persistent Heart Failure Symptoms at Hospital Discharge Predicts 30-Day Clinical Events

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10.1145/3314395

We study the feasibility of detecting rhythms in data that is passively collected from Fitbit devices and using the obtained model parameters to predict readmission risk after pancreatic surgery.

Modeling Biobehavioral Rhythms with Passive Sensing in the Wild

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10.1097/JNN.0000000000000419

However, there are no statistical models to predict readmissions among neurology patients.

Evaluation of Hospital-wide Readmission Risk Calculator to Predict 30-Day Readmission in Neurocritical Care Patients

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10.1016/j.carrev.2019.05.032

BACKGROUND Predictors of hospital readmissions and tools to predict readmissions after TAVR are scarce.

Predictors and risk calculator of early unplanned hospital readmission following contemporary self-expanding transcatheter aortic valve replacement from the STS/ACC TVT-registry.

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10.1007/s11605-019-04463-y

Tumor factors failed to predict readmission, whereas preoperative functional status and depression along with individual cytoreductive procedures predicted readmission.

Readmissions After Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy: a US HIPEC Collaborative Study

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10.1016/J.CARDFAIL.2019.07.422

Conclusions: Lymphopenia may be one of the most potent hematological markers to predict readmission within 6 months.

The Prognostic Value of Complete Blood Count among Patients with Acute Decompensated Heart Failure

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10.1177/1358863X18816816

Revascularization modality did not independently predict readmission.

Thirty-day readmission after endovascular or surgical revascularization for chronic mesenteric ischemia: Insights from the Nationwide Readmissions Database

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10.1007/s00586-019-05937-y

ConclusionThe RAPSF can accurately predict readmission rates in PSF patients and may be used to guide an evidence-based approach to preoperative optimization and risk adjustment within alternative payment models for elective spine surgery.

A validated preoperative score for predicting 30-day readmission after 1–2 level elective posterior lumbar fusion

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10.1177/1179572719827610

Participation in CR, age, gender prior history of cardiac event, and diagnosis were used to predict readmission, mortality, and survival.

Predicting Long-Term Mortality, Morbidity, and Survival Outcomes Following a Cardiac Event: A Cardiac Rehabilitation Study

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What is/are Predict Readmission?

Predict Readmission - This paper presents the use of early clinical notes in building a machine learning model to predict readmission at 48 h immediately after a patient's admission. [1] In mixed patient populations, readiness for hospital discharge has shown to predict readmission and mortality in the short term. [2] Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. [3] However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. [4] In this study, we develop two novel models: PURE (Predicting Unplanned Readmissions using Embeddings) and Hybrid DeepR, which uses the historical medical events of patients to predict readmissions within 30 days. [5] This study aims to examine economic and social factors that predict readmission likelihood for mental health and drug abuse patients in the state of New York. [6] Conclusions:  Multiple factors including HT comorbidity, anaemia, atrial fibrillation, chronic kidney disease, and smoking predict readmission among Thais with post myocardial infarction. [7] We compared the ability of manually extracted variables to predict readmission compared with EHR-based prediction using multivariate logistic regression on 1 year of admission data from an academic medical center. [8] In this investigation, a neural network, a supervised machine learning technique, is evaluated to determine whether it can predict readmission after three lumbar fusion procedures. [9] The Arabic PT-RHDS did not predict readmissions. [10] In multivariate model, gender and CHA2 DS2 -VASc failed to predict readmission. [11] Methods We used the modified Delphi process to create Critical Appraisal of Models that Predict Readmission (CAMPR), which lists expert recommendations focused on development and validation of readmission models. [12] This paper proposes a group model to predict readmission by choosing between Machine Learning and Deep Learning algorithms based on performance improvement. [13] The Hospital Frailty Risk Score (HFRS) represents a validated risk stratification tool used to predict readmission and death among non-cancer patients, however this tool has not been evaluated among cancer patients, which represent a unique cohort of patients. [14] Objective The study aimed to (1) quantify readmission rates and common causes of readmission following endoscopic transsphenoidal pituitary surgery (ETPS); (2) identify risk factors that may predict readmission within 30 days; (3) assess postoperative care coordination with endocrinology follow-up; and (4) identify patients for whom targeted interventions may reduce 30-day readmissions. [15] We used mixed-effects logistic regression to predict readmission odds. [16] While studies have been undertaken to predict readmission within 30-days for orthopedic procedures, such as total shoulder arthroplasty and adult spine fusion surgery, no research has been conducted on the utility of machine learning algorithms to predict postoperative complications for pediatric patients undergoing spinal procedures. [17] CONCLUSION The strategy of using the maximum systolic or diastolic blood pressure exceeding the blood pressure threshold on 2 occasions at least 4 hours apart at lower blood pressure thresholds can predict readmissions with higher sensitivity or specificity. [18] The aim of this study was to develop and validate a simple score to predict readmissions following CRS/HIPEC. [19] Age did not predict readmission. [20] Conclusion: NLP-enhanced machine learning models potentially advance our ability to predict readmission after stroke. [21] Handgrip strength, a component of the physical frailty phenotype, may be a simple tool to help predict readmission. [22] On the other hand, KKC at admission did not predict readmission (either 1mRA or 1yRA, respectively p = 0. [23] We describe the use of a data-driven approach that relies on machine learning algorithms to predict readmission at the time of discharge. [24] RESULTS Allocating readmission-mitigating interventions based on a GBDT model trained to predict readmissions achieved an estimated utility gain of $104 per patient, and an AUC of 0. [25] The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization. [26] This study aims to evaluate whether mFI could be utilized to predict readmissions after colorectal resection for patients with cancer by using nationwide cohort. [27] Introduction and Objective: Different scores have been developed to predict readmission or death in patients with COPD like BODE, ADO and CODEX score. [28] A predictive model was built to assess the capability of these results to predict readmission and validated using leave-one-out cross-validation. [29] To address this, we propose a deep learning approach to predict readmission from clinical notes. [30] PURPOSE To determine the frequency and type of unplanned readmissions after complex spine surgery, and to investigate if prospectively registered AEs can predict readmissions. [31] The out-of-sample results show that all of the models performed similarly and adequately to predict readmission risk. [32] Knowledge is sparse on how different social factors can contribute to predict readmission. [33] Multivariable logistic regression modeling was used to determine factors that predict readmission and major complications. [34] METHODS After extracting sociodemographic and clinical characteristics from electronic health records for children with CCCs admitted to an academic medical center, we constructed a multivariable logistic regression model to predict readmission from characteristics obtainable at admission and then a second model adding hospitalization and discharge variables to the first model. [35] Univariate and multivariate techniques were used to predict readmission and complications. [36] This review describes the current literature on the use of data science to predict readmissions of patients with heart failure. [37] The cardiac biomarker N-terminal pro-brain natriuretic peptide (NT-proBNP) can help predict readmission in adult populations, but the estimated utility in predicting risk of readmission or mortality after pediatric congenital heart surgery has not previously been studied. [38] We used the OMOP-CDM version of MIMIC-III (MIMIC-OMOP) to train and evaluate a lightweight tree boosting method to predict readmission in ICU at different time points after discharge (3, 7, 15 and 30 days), outperforming existing solutions with an AUROC of 0. [39] BACKGROUND The ability to predict readmission accurately after hospitalization for acute myocardial infarction (AMI) is limited in current statistical models. [40] Variability in assessment of HF symptoms at discharge may affect capacity to predict readmission risk. [41] We study the feasibility of detecting rhythms in data that is passively collected from Fitbit devices and using the obtained model parameters to predict readmission risk after pancreatic surgery. [42] However, there are no statistical models to predict readmissions among neurology patients. [43] BACKGROUND Predictors of hospital readmissions and tools to predict readmissions after TAVR are scarce. [44] Tumor factors failed to predict readmission, whereas preoperative functional status and depression along with individual cytoreductive procedures predicted readmission. [45] Conclusions: Lymphopenia may be one of the most potent hematological markers to predict readmission within 6 months. [46] Revascularization modality did not independently predict readmission. [47] ConclusionThe RAPSF can accurately predict readmission rates in PSF patients and may be used to guide an evidence-based approach to preoperative optimization and risk adjustment within alternative payment models for elective spine surgery. [48] Participation in CR, age, gender prior history of cardiac event, and diagnosis were used to predict readmission, mortality, and survival. [49]

Help Predict Readmission

Handgrip strength, a component of the physical frailty phenotype, may be a simple tool to help predict readmission. [1] The cardiac biomarker N-terminal pro-brain natriuretic peptide (NT-proBNP) can help predict readmission in adult populations, but the estimated utility in predicting risk of readmission or mortality after pediatric congenital heart surgery has not previously been studied. [2]

predict readmission risk

Here we established and compared machine learning (ML)-based readmission prediction methods to predict readmission risks of diabetic patients. [1] However, it is challenging to predict readmission risk at the early stage of hospitalization because few data are available. [2] The goals of this paper are to (1) review the epidemiology of readmissions among patients with diabetes, (2) describe models that predict readmission risk, and (3) address various strategies for reducing the risk of acute care re-utilization. [3] The out-of-sample results show that all of the models performed similarly and adequately to predict readmission risk. [4] Variability in assessment of HF symptoms at discharge may affect capacity to predict readmission risk. [5] We study the feasibility of detecting rhythms in data that is passively collected from Fitbit devices and using the obtained model parameters to predict readmission risk after pancreatic surgery. [6]

predict readmission within

Objective The study aimed to (1) quantify readmission rates and common causes of readmission following endoscopic transsphenoidal pituitary surgery (ETPS); (2) identify risk factors that may predict readmission within 30 days; (3) assess postoperative care coordination with endocrinology follow-up; and (4) identify patients for whom targeted interventions may reduce 30-day readmissions. [1] While studies have been undertaken to predict readmission within 30-days for orthopedic procedures, such as total shoulder arthroplasty and adult spine fusion surgery, no research has been conducted on the utility of machine learning algorithms to predict postoperative complications for pediatric patients undergoing spinal procedures. [2] Conclusions: Lymphopenia may be one of the most potent hematological markers to predict readmission within 6 months. [3]