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

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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Thirty-day readmission rate as a surrogate marker for quality of care in neurosurgical patients: a single-center Canadian experience

Journal of Neurosurgery ◽

10.3171/2018.2.jns172962 ◽

2019 ◽

Vol 130 (5) ◽

pp. 1692-1698 ◽

Cited By ~ 1

Author(s):

Mitchell P. Wilson ◽

Andrew S. Jack ◽

Andrew Nataraj ◽

Michael Chow

Keyword(s):

Multivariate Analysis ◽

Quality Of Care ◽

Readmission Rate ◽

Univariate Analysis ◽

Surrogate Marker ◽

Readmission Rates ◽

Factors Associated ◽

Neurosurgical Patients ◽

Avoidable Readmissions

OBJECTIVEReadmission to the hospital within 30 days of discharge is used as a surrogate marker for quality and value of care in the United States (US) healthcare system. Concern exists regarding the value of 30-day readmission as a quality of care metric in neurosurgical patients. Few studies have assessed 30-day readmission rates in neurosurgical patients outside the US. The authors performed a retrospective review of all adult neurosurgical patients admitted to a single Canadian neurosurgical academic center and who were discharged to home to assess for the all-cause 30-day readmission rate, unplanned 30-day readmission rate, and avoidable 30-day readmission rate.METHODSA retrospective review was performed assessing 30-day readmission rates after discharge to home in all neurosurgical patients admitted to a single academic neurosurgical center from January 1, 2011, to December 31, 2011. The primary outcomes included rates of all-cause, unplanned, and avoidable readmissions within 30 days of discharge. Secondary outcomes included factors associated with unplanned and avoidable 30-day readmissions.RESULTSA total of 184 of 950 patients (19.4%) were readmitted to the hospital within 30 days of discharge. One-hundred three patients (10.8%) were readmitted for an unplanned reason and 81 (8.5%) were readmitted for a planned or rescheduled operation. Only 19 readmissions (10%) were for a potentially avoidable reason. Univariate analysis identified factors associated with readmission for a complication or persistent/worsening symptom, including age (p = 0.009), length of stay (p = 0.007), general neurosurgery diagnosis (p < 0.001), cranial pathology (p < 0.001), intensive care unit (ICU) admission (p < 0.001), number of initial admission operations (p = 0.01), and shunt procedures (p < 0.001). Multivariate analysis identified predictive factors of readmission, including diagnosis (p = 0.002, OR 2.4, 95% CI 1.4–5.3), cranial pathology (p = 0.002, OR 2.7, 95% CI 1.4–5.3), ICU admission (p = 0.004, OR 2.4, 95% CI 1.3–4.2), and number of first admission operations (p = 0.01, OR 0.51, 95% CI 0.3–0.87). Univariate analysis performed to identify factors associated with potentially avoidable readmissions included length of stay (p = 0.03), diagnosis (p < 0.001), cranial pathology (p = 0.02), and shunt procedures (p < 0.001). Multivariate analysis identified only shunt procedures as a predictive factor for avoidable readmission (p = 0.02, OR 5.6, 95% CI 1.4–22.8).CONCLUSIONSAlmost one-fifth of neurosurgical patients were readmitted within 30 days of discharge. However, only about half of these patients were admitted for an unplanned reason, and only 10% of all readmissions were potentially avoidable. This study demonstrates unique challenges encountered in a publicly funded healthcare setting and supports the growing literature suggesting 30-day readmission rates may serve as an inappropriate quality of care metric in neurosurgical patients. Potentially avoidable readmissions can be predicted, and further research assessing predictors of avoidable readmissions is warranted.

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Identification of important factors in an inpatient fall risk prediction model to improve the quality of care using EHR and electronic administrative data: A machine-learning approach

International Journal of Medical Informatics ◽

10.1016/j.ijmedinf.2020.104272 ◽

2020 ◽

Vol 143 ◽

pp. 104272

Author(s):

David S. Lindberg ◽

Mattia Prosperi ◽

Ragnhildur I. Bjarnadottir ◽

Jaime Thomas ◽

Marsha Crane ◽

...

Keyword(s):

Machine Learning ◽

Quality Of Care ◽

Prediction Model ◽

Risk Prediction ◽

Administrative Data ◽

Fall Risk ◽

Risk Prediction Model ◽

Learning Approach ◽

Machine Learning Approach

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Readmission rate as an indicator of hospital performance: The case of Spain

International Journal of Technology Assessment in Health Care ◽

10.1017/s0266462304001230 ◽

2004 ◽

Vol 20 (3) ◽

pp. 385-391 ◽

Cited By ~ 23

Author(s):

Alberto Jiménez-Puente ◽

Javier García-Alegría ◽

Jorge Gómez-Aracena ◽

Luis Hidalgo-Rojas ◽

Luisa Lorenzo-Nogueiras ◽

...

Keyword(s):

Quality Of Care ◽

Sensitivity And Specificity ◽

Readmission Rate ◽

Acute Care Hospital ◽

Hospital Performance ◽

Care Hospital ◽

Cross Sectional ◽

Clinical Records ◽

Avoidable Readmissions

Objectives:Hospital readmission rate is currently used as a quality of care indicator, although its validity has not been established. Our aims were to identify the frequency and characteristics of potential avoidable readmissions and to compare the assessment of quality of care derived from readmission rate with other measure of quality (judgment of experts).Methods:Design: cross-sectional observational study; Setting: acute care hospital located in Marbella, South of Spain; Study participants: random sample of patients readmitted at the hospital within six months from discharge (n=363); Interventions: review of clinical records by a pair of observers to assess the causes of readmissions and their potential avoidability; Main measures: logistic regression analysis to identify the variables from the databases of hospital discharges which are related to avoidability of readmissions. Determination of sensitivity and specificity of different definitions of readmission rate to detect avoidable situations.Results:Nineteen percent of readmissions were considered potentially avoidable. Variables related to readmission avoidability were (i) time elapsed between index admission and readmission and (ii) difference in diagnoses of both episodes. None of the definitions of readmission rate used in this study provided adequate values of sensitivity and specificity in the identification of potentially avoidable readmissions.Conclusions:Most readmissions in our hospital were unavoidable. Thus, readmission rate might not be considered a valid indicator of quality of care.

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Do changes in health reveal the possibility of undiagnosed pancreatic cancer? Development of a risk-prediction model based on healthcare claims data

PLoS ONE ◽

10.1371/journal.pone.0218580 ◽

2019 ◽

Vol 14 (6) ◽

pp. e0218580 ◽

Cited By ~ 4

Author(s):

Aileen Baecker ◽

Sungjin Kim ◽

Harvey A. Risch ◽

Teryl K. Nuckols ◽

Bechien U. Wu ◽

...

Keyword(s):

Pancreatic Cancer ◽

Prediction Model ◽

Risk Prediction ◽

Claims Data ◽

Risk Prediction Model ◽

Cancer Development ◽

Model Based

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Hospital readmission risk prediction based on claims data available at admission: a pilot study in Switzerland

BMJ Open ◽

10.1136/bmjopen-2018-028409 ◽

2019 ◽

Vol 9 (6) ◽

pp. e028409 ◽

Cited By ~ 2

Author(s):

Beat Brüngger ◽

Eva Blozik

Keyword(s):

Health Insurance ◽

Prediction Model ◽

Readmission Rate ◽

Claims Data ◽

Hospital Admissions ◽

Care Hospital ◽

Insurance Claims ◽

Insurance Claims Data ◽

Health Insurance Claims Data ◽

Health Insurance Claims

ObjectivesEvaluating whether future studies to develop prediction models for early readmissions based on health insurance claims data available at the time of a hospitalisation are worthwhile.DesignRetrospective cohort study of hospital admissions with discharge dates between 1 January 2014 and 31 December 2016.SettingAll-cause acute care hospital admissions in the general population of Switzerland, enrolled in the Helsana Group, a large provider of Swiss mandatory health insurance.ParticipantsThe mean age of 138 222 hospitalised adults included in the study was 60.5 years. Patients were included only with their first index hospitalisation. Patients who deceased during the follow-up period were excluded, as well as patients admitted from and/or discharged to nursing homes or rehabilitation clinics.MeasuresThe primary outcome was 30-day readmission rate. Area under the receiver operating characteristic curve (AUC) was used to measure the discrimination of the developed logistic regression prediction model. Candidate variables were theory based and derived from a systematic literature search.ResultsWe observed a 30-day readmission rate of 7.5%. Fifty-five candidate variables were identified. The final model included pharmacy-based cost group (PCG) cancer, PCG cardiac disease, PCG pain, emergency index admission, number of emergency visits, costs specialists, costs hospital outpatient, costs laboratory, costs therapeutic devices, costs physiotherapy, number of outpatient visits, sex, age group and geographical region as predictors. The prediction model achieved an AUC of 0.60 (95% CI 0.60 to 0.61).ConclusionsBased on the results of our study, it is not promising to invest resources in large-scale studies for the development of prediction tools for hospital readmissions based on health insurance claims data available at admission. The data proved appropriate to investigate the occurrence of hospitalisations and subsequent readmissions, but we did not find evidence for the potential of a clinically helpful prediction tool based on patient-sided variables alone.

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Reporting and Performance of Hepatocellular Carcinoma Risk Prediction Models: Based on TRIPOD Statement and Meta-Analysis

Canadian Journal of Gastroenterology and Hepatology ◽

10.1155/2021/9996358 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Liuqing Yang ◽

Qiang Wang ◽

Tingting Cui ◽

Jinxin Huang ◽

Hui Jin

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Model ◽

Risk Prediction ◽

Missing Values ◽

Prediction Models ◽

Meta Analysis ◽

External Validation ◽

Discrimination Performance ◽

Risk Prediction Models

Background. The performance of risk prediction models for hepatocellular carcinoma (HCC) in patients with chronic hepatitis B (CHB) was uncertain. The aim of the study was to critically evaluate the reports of transparent and external validation performances of these prediction models based on system review and meta-analysis. Methods. A systematic search of the Web of Science and PubMed was performed for studies published until October 17, 2020. The transparent reporting of a multivariable prediction model for the individual prognosis or diagnosis (TRIPOD) tool was used to critically evaluate the quality of external validation reports for six models (CU-HCC, GAG-HCC, PAGE-B, mPAGE-B, REACH-B, and mREACH-B). The area under the receiver operator characteristic curve (AUC) values was to estimate the pooled external validating performance based on meta-analysis. Subgroup analysis and metaregression were also performed to explore heterogeneity. Results. Our meta-analysis included 22 studies published between 2011 and 2020. The compliance of the included studies to TRIPOD ranged from 59% to 90% (median, 74%; interquartile range (IQR), 70%, 79%). The AUC values of the six models ranged from 0.715 to 0.778. In the antiviral therapy subgroups, the AUC values of mREACH-B, GAG-HCC, and mPAGE-B were 0.785, 0.760, and 0.778, respectively. In the cirrhosis subgroup, all models had poor discrimination performance (AUC < 0.7). Conclusions. A full report of calibration and handling of missing values would contribute to a greater improvement in the quality of external validation reports for CHB-related HCC risk prediction. It was necessary to develop a specific HCC risk prediction model for patients with cirrhosis.

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