Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis

Koola, Jejo D.; Ho, Sam; Cao, Aize; Chen, Guanhua; Perkins, Amy M.; Davis, Sharon E.; Matheny, Michael E.

doi:10.1007/s10620-019-05826-w

Cited by 20 publications

(19 citation statements)

References 73 publications

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“…Readmission among patients with AD cirrhosis in the current study was common, with incidence rates of 24.6, 43.0, and 51.8% at 30, 60, and 90 days, respectively. This finding was similar to that obtained from earlier studies based in India and North America (10,16,18). The etiology of cirrhosis in India was mainly hepatitis B virus (50.4%), while in North America and Europe, the main causes of cirrhosis were alcohol consumption (29.4%) and HCV (39.3%) (10,11).…”

Section: Discussionsupporting

confidence: 90%

“…An estimated 27.1% of readmissions may be avoidable (5). To date, several studies have focused on assessing the predictors of readmission in decompensated cirrhosis (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18). However, hospital readmission rates of patients with cirrhosis remain high, ranging from 10 to 50%, with a pooled estimate of 26% at 30 days and 21-71% at 90 days (19).…”

Section: Introductionmentioning

confidence: 99%

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Risk Stratification Score to Predict Readmission of Patients With Acute Decompensated Cirrhosis Within 90 Days

Tan

Wang

et al. 2021

Front. Med.

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Background and Aims: Patients with acute decompensated (AD) cirrhosis are frequently readmitted to the hospital. An accurate predictive model for identifying high-risk patients may facilitate the development of effective interventions to reduce readmission rates.Methods: This cohort study of patients with AD cirrhosis was conducted at six tertiary hospitals in China between September 2012 and December 2016 (with 705 patients in the derivation cohort) and between January 2017 and April 2020 (with 251 patients in the temporal validation cohort). Least absolute shrinkage and selection operator Cox regression was used to identify the prognostic factors and construct a nomogram. The discriminative ability, calibration, and clinical net benefit were evaluated based on the C-index, area under the curve, calibration curve, and decision curve analysis. Kaplan–Meier curves were constructed for stratified risk groups, and log-rank tests were used to determine significant differences between the curves.Results: Among 956 patients, readmission rates were 24.58, 42.99, and 51.78%, at 30, 60, and 90 days, respectively. Bacterial infection was the main reason for index hospitalization and readmission. Independent factors in the nomogram included gastrointestinal bleeding [hazard rate (HR): 2.787; 95% confidence interval (CI): 2.221–3.499], serum sodium (HR: 0.955; 95% CI: 0.933–0.978), total bilirubin (HR: 1.004; 95% CI: 1.003–1.005), and international normalized ratio (HR: 1.398; 95% CI: 1.126–1.734). For the convenience of clinicians, we provided a web-based calculator tool (https://cqykdx1111.shinyapps.io/dynnomapp/). The nomogram exhibited good discrimination ability, both in the derivation and validation cohorts. The predicted and observed readmission probabilities were calibrated with reliable agreement. The nomogram demonstrated superior net benefits over other score models. The high-risk group (nomogram score >56.8) was significantly likely to have higher rates of readmission than the low-risk group (nomogram score ≤ 56.8; p < 0.0001).Conclusions: The nomogram is useful for assessing the probability of short-term readmission in patients with AD cirrhosis and to guide clinicians to develop individualized treatments based on risk stratification.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Risk Stratification Score to Predict Readmission of Patients With Acute Decompensated Cirrhosis Within 90 Days

Tan

Wang

et al. 2021

Front. Med.

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“…This work was part of a multiyear, multisite study to improve cirrhosis care at the Department of VA. We have previously published our efforts on predicting hospital readmission, and we refer readers to that study for further details on this cohort 23. We analysed a retrospective cohort of patients with cirrhosis hospitalised for any cause from among 123 medical centres in the Department of VA between 1 January 2006 and 31 December 2013, with historical data from 1 January 2005 to allow for variable ascertainment.…”

Section: Methodsmentioning

confidence: 99%

Development of a national Department of Veterans Affairs mortality risk prediction model among patients with cirrhosis

Koola

Chen

et al. 2019

BMJ Open Gastroenterol

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ObjectiveCirrhotic patients are at high hospitalisation risk with subsequent high mortality. Current risk prediction models have varied performances with methodological room for improvement. We used current analytical techniques using automatically extractable variables from the electronic health record (EHR) to develop and validate a posthospitalisation mortality risk score for cirrhotic patients and compared performance with the model for end-stage liver disease (MELD), model for end-stage liver disease with sodium (MELD-Na), and the CLIF Consortium Acute Decompensation (CLIF-C AD) models.DesignWe analysed a retrospective cohort of 73 976 patients comprising 247 650 hospitalisations between 2006 and 2013 at any of 123 Department of Veterans Affairs hospitals. Using 45 predictor variables, we built a time-dependent Cox proportional hazards model with all-cause mortality as the outcome. We compared performance to the three extant models and reported discrimination and calibration using bootstrapping. Furthermore, we analysed differential utility using the net reclassification index (NRI).ResultsThe C-statistic for the final model was 0.863, representing a significant improvement over the MELD, MELD-Na, and the CLIF-C AD, which had C-statistics of 0.655, 0.675, and 0.679, respectively. Multiple risk factors were significant in our model, including variables reflecting disease severity and haemodynamic compromise. The NRI showed a 24% improvement in predicting survival of low-risk patients and a 30% improvement in predicting death of high-risk patients.ConclusionWe developed a more accurate mortality risk prediction score using variables automatically extractable from an EHR that may be used to risk stratify patients with cirrhosis for targeted postdischarge management.

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“…The C-V model is a kind of region-based segmentation method [53]. For images with a large difference between the target region and the background, the C-V model can get a better segmentation effect.…”

Section: A Aggregation Model Based On Guayes Hypothesismentioning

confidence: 99%

Application of Deep Learning Algorithm in Feature Mining and Rapid Identification of Colorectal Image

Tao

Jian

et al. 2020

IEEE Access

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Based on deep learning technology, this paper proposes a two-stage colorectal image feature mining and fast recognition model to achieve fully automatic medical image pathology discrimination. Drawing on the ideas of multi-factor Meta-regression analysis widely used in the medical field and the model aggregation framework based on Bayesian prior probability theory, a prognostic model of colorectal tumors suitable for various situations and scenarios is constructed. And using a combination of public data sets and real data sets, design two sets of experiments to verify these models from different angles. The algorithm was used to select one, four, and five related features from three sequences to construct three sets of prediction models. The application of the six algorithms failed to obtain a better predictive model (AUC value range 0.439 ~ 0.640). The algorithm (AUC value 0.750±0.137) and the algorithm (AUC value 0.764±0.128) can be used to obtain models with better predictive performance, and the four models are less effective (AUC value<0.7). In the joint model, the algorithm (AUC value 0.742± 0.101) and the algorithm (AUC value 0.718±0.069) can also be used to obtain a model with better prediction performance. Imagebased imaging histology tags can be used as a non-invasive auxiliary tool for preoperative evaluation of histological grading of CRAC, and are expected to be applied in clinical practice to assist in the development of individualized treatment plans. INDEX TERMS deep learning; colorectal imaging; feature mining; rapid identification.

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Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis

Cited by 20 publications

References 73 publications

Risk Stratification Score to Predict Readmission of Patients With Acute Decompensated Cirrhosis Within 90 Days

Risk Stratification Score to Predict Readmission of Patients With Acute Decompensated Cirrhosis Within 90 Days

Development of a national Department of Veterans Affairs mortality risk prediction model among patients with cirrhosis

Application of Deep Learning Algorithm in Feature Mining and Rapid Identification of Colorectal Image

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