Evaluation of the Yale New Haven Readmission Risk Score for Pneumonia in a General Hospital Population

Schaefer, Gabrielle; El‐Kareh, Robert; Quartarolo, Jennifer; Seymann, Gregory

doi:10.1016/j.amjmed.2017.04.039

Cited by 6 publications

(5 citation statements)

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“…Causes and factors that contribute to avoidable readmission can be classified into four categories, including social context, patient health status, care organisation and patient behaviour. 11 Socioeconomic features include lower education level, lower income, the lack of occupational activity [12][13][14][15] and health insurance status. 16 Markers of the patient's health status include age greater than 65 years, [17][18][19] multiple hospitalisations within the previous year, 20 frailty, sensory deficiencies and the presence of comorbidities with higher Charlson Index.…”

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confidence: 99%

“…34 More recently published models included various risk factors for readmission including comorbidities, pneumonia severity, clinical instability on discharge, number of previous hospitalisations, index length of stay, and various clinical and biological data. 15 20 24 32 35 The broad objective of this study is to develop an administrative claims-based risk prediction model for identifying readmissions that are potentially avoidable within 30 days of index hospitalisation for patients with CAP. The specific aims of this project are: ► To assess the positive predictive value of International Classification of Diseases, 10th revision (ICD-10) discharge diagnosis codes for CAP using a retrospective structured chart review as the reference method.…”

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Development of a risk prediction model of potentially avoidable readmission for patients hospitalised with community-acquired pneumonia: study protocol and population

et al. 2020

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Introduction30-day readmission rate is considered an adverse outcome reflecting suboptimal quality of care during index hospitalisation for community-acquired pneumonia (CAP). However, potentially avoidable readmission would be a more relevant metric than all-cause readmission for tracking quality of hospital care for CAP. The objectives of this study are (1) to estimate potentially avoidable 30-day readmission rate and (2) to develop a risk prediction model intended to identify potentially avoidable readmissions for CAP.Methods and analysisThe study population consists of consecutive patients admitted in two hospitals from the community or nursing home setting with pneumonia. To qualify for inclusion, patients must have a primary or secondary discharge diagnosis code of pneumonia. Data sources include routinely collected administrative claims data as part of diagnosis-related group prospective payment system and structured chart reviews. The main outcome measure is potentially avoidable readmission within 30 days of discharge from index hospitalisation. The likelihood that a readmission is potentially avoidable will be quantified using latent class analysis based on independent structured reviews performed by four panellists. We will use a two-stage approach to develop a claims data-based model intended to identify potentially avoidable readmissions. The first stage implies deriving a clinical model based on data collected through retrospective chart review only. In the second stage, the predictors comprising the medical record model will be translated into International Classification of Diseases, 10th revision discharge diagnosis codes in order to obtain a claim data-based risk model.The study sample consists of 1150 hospital stays with a diagnosis of CAP. 30-day index hospital readmission rate is 17.5%.Ethics and disseminationThe protocol was reviewed by the Comité de Protection des Personnes Sud Est V (IRB#6705). Efforts will be made to release the primary study results within 6 months of data collection completion.Trial registration numberClinicalTrials.gov Registry (NCT02833259).

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Development of a risk prediction model of potentially avoidable readmission for patients hospitalised with community-acquired pneumonia: study protocol and population

et al. 2020

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“…The study cohort was defined using both demographics and clinical-related criteria from prior work involving readmission analysis in a pneumonia setting [6,25,26]. The study cohort included all adult patients (aged ≥ 18 years) who had an index (first) hospitalization from January 1, 2016, through November 31, 2016, with a principal inpatient diagnosis of pneumonia using the International Classification of Diseases, Tenth Revision, codes (J10.0, J10.1, J10.8, J11.0, J11.1, J11.8, J12.0, J12.1, J12.2, J12.3, J12.8, J12.9, J13, J14, J15.x, J16.0, J16.8, J17.0, J17.1, J17.2, J17.3, J17.8, J18.0, J18.1, J18.2, J18.8, J18.9, J69.0, B01.2, B20.6, B25.0, B59) [27,28] between January 1, 2016, and November 30, 2016. The following hospitalizations were excluded: (1) patients who died during the hospitalization, (2) patients whose index hospitalization was in December because 30-day of follow-up time for readmission analysis is not available, (3) patients with missing data for the length of stay (LOS) or patient linkage number.…”

Section: Study Populationmentioning

confidence: 99%

Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database

Huang

Talwar

Lin

et al. 2022

BMC Med Inform Decis Mak

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Background Hospital readmissions for pneumonia are a growing concern in the US, with significant consequences for costs and quality of care. This study developed the rule-based model and other machine learning (ML) models to predict 30-day readmission risk in patients with pneumonia and compared model performance. Methods This population-based study involved patients aged ≥ 18 years hospitalized with pneumonia from January 1, 2016, through November 30, 2016, using the Healthcare Cost and Utilization Project-National Readmission Database (HCUP-NRD). Rule-based algorithms and other ML algorithms, specifically decision trees, random forest, extreme gradient descent boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to model all-cause readmissions 30 days post-discharge from index pneumonia hospitalization. A total of 61 clinically relevant variables were included for ML model development. Models were trained on randomly partitioned 50% of the data and evaluated using the remaining dataset. Model hyperparameters were tuned using the ten-fold cross-validation on the resampled training dataset. The area under the receiver operating curves (AUROC) and area under precision-recall curves (AUPRC) were calculated for the testing set to evaluate the model performance. Results Of the 372,293 patients with an index hospital hospitalization for pneumonia, 48,280 (12.97%) were readmitted within 30 days. Judged by AUROC in the testing data, rule-based model (0.6591) significantly outperformed decision tree (0.5783, p value < 0.001), random forest (0.6509, p value < 0.01) and LASSO (0.6087, p value < 0.001), but was less superior than XGBoost (0.6606, p value = 0.015). The AUPRC of the rule-based model in the testing data (0.2146) was higher than the decision tree (0.1560), random forest (0.2052), and LASSO (0.2042), but was similar to XGBoost (0.2147). The top risk-predictive rules captured by the rule-based algorithm were comorbidities, illness severity, disposition locations, payer type, age, and length of stay. These predictive risk factors were also identified by other ML models with high variable importance. Conclusion The performance of machine learning models for predicting readmission in pneumonia patients varied. The XGboost was better than the rule-based model based on the AUROC. However, important risk factors for predicting readmission remained consistent across ML models.

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“…Among all those methods, majority research uses regression based methods (logistic regression), neural networks, and ensemble methods including bagging, boosting, random forest and gradient boosting. Due to complications of human diseases, some models are developed based on specific disease types, like heart failure [32], [94], [101], pneumonia [39], and organ transplantation [113].…”

Section: Introductionmentioning

confidence: 99%

Predictive Modeling of Hospital Readmission: Challenges and Solutions

Wang

Zhu

2021

Preprint

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Hospital readmission prediction is a study to learn models from historical medical data to predict probability of a patient returning to hospital in a certain period, e.g. 30 or 90 days, after the discharge. The motivation is to help health providers deliver better treatment and post-discharge strategies, lower the hospital readmission rate, and eventually reduce the medical costs. Due to inherent complexity of diseases and healthcare ecosystems, modeling hospital readmission is facing many challenges. By now, a variety of methods have been developed, but existing literature fails to deliver a complete picture to answer some fundamental questions, such as what are the main challenges and solutions in modeling hospital readmission; what are typical features/models used for readmission prediction; how to achieve meaningful and transparent predictions for decision making; and what are possible conflicts when deploying predictive approaches for real-world usages. In this paper, we systematically review computational models for hospital readmission prediction, and propose a taxonomy of challenges featuring four main categories: (1) data variety and complexity; (2) data imbalance, locality and privacy; (3) model interpretability; and (4) model implementation. The review summarizes methods in each category, and highlights technical solutions proposed to address the challenges. In addition, a review of datasets and resources available for hospital readmission modeling also provides firsthand materials to support researchers and practitioners to design new approaches for effective and efficient hospital readmission prediction.

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Evaluation of the Yale New Haven Readmission Risk Score for Pneumonia in a General Hospital Population

Abstract: The YNHRRS can be applied to an unselected population as a tool to predict patients with pneumonia at risk for readmission.

Cited by 6 publications

References 13 publications

Development of a risk prediction model of potentially avoidable readmission for patients hospitalised with community-acquired pneumonia: study protocol and population

Development of a risk prediction model of potentially avoidable readmission for patients hospitalised with community-acquired pneumonia: study protocol and population

Machine learning methods to predict 30-day hospital readmission outcome among US adults with pneumonia: analysis of the national readmission database

Predictive Modeling of Hospital Readmission: Challenges and Solutions

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