A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data

Objective: To develop and validate an algorithm for predicting non-attendance to outpatient appointments. Results: We developed two decision tree models for dermatology and pneumology services (trained with 33,329 and 21,050 appointments, respectively). The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and a balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) - 65.53% for pneumology, respectively. When using the algorithm for identifying patients at high risk of non-attendance in the context of a phone-call reminder program, the non-attendance rate decreased 50.61% (P<.001) and 39.33% (P=.048) in the dermatology and pneumology services, respectively. Conclusions: A machine learning model can effectively identify patients at high risk of non-attendance based on information stored in electronic medical records. The use of this model to prioritize phone call reminders to patients at high risk of non-attendance significantly reduced the non-attendance rate.

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Preventing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

10.36227/techrxiv.14885478.v1 ◽

2021 ◽

Author(s):

Damià Valero-Bover ◽

Pedro González ◽

Gerard Carot-Sans ◽

Isaac Cano ◽

Pilar Saura ◽

...

Keyword(s):

Machine Learning ◽

High Risk ◽

Electronic Medical Records ◽

Medical Records ◽

Model Development ◽

Phone Call ◽

Attendance Rate ◽

Machine Learning Model ◽

Tree Models ◽

Outpatient Appointments

Objective: To develop and validate an algorithm for predicting non-attendance to outpatient appointments. Results: We developed two decision tree models for dermatology and pneumology services (trained with 33,329 and 21,050 appointments, respectively). The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and a balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) - 65.53% for pneumology, respectively. When using the algorithm for identifying patients at high risk of non-attendance in the context of a phone-call reminder program, the non-attendance rate decreased 50.61% (P<.001) and 39.33% (P=.048) in the dermatology and pneumology services, respectively. Conclusions: A machine learning model can effectively identify patients at high risk of non-attendance based on information stored in electronic medical records. The use of this model to prioritize phone call reminders to patients at high risk of non-attendance significantly reduced the non-attendance rate.

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A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy

Nature Communications ◽

10.1038/s41467-021-22876-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Ahsan Huda ◽

Adam Castaño ◽

Anindita Niyogi ◽

Jennifer Schumacher ◽

Michelle Stewart ◽

...

Keyword(s):

Machine Learning ◽

Heart Failure ◽

At Risk ◽

Cardiac Amyloidosis ◽

Learning Model ◽

Wild Type ◽

Derivation Cohort ◽

Machine Learning Model ◽

Patients At Risk ◽

Amyloid Cardiomyopathy

AbstractTransthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure.

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Prediction for cardiac prognosis in patients with congestive heart failure by machine learning on dual-isotope myocardial semiconductor SPECT

European Heart Journal ◽

10.1093/ehjci/ehaa946.0275 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

M Shimizu ◽

S Cho ◽

K Hara ◽

M Ohmori ◽

R Tateishi ◽

...

Keyword(s):

Machine Learning ◽

Heart Failure ◽

Random Forest ◽

Roc Curve ◽

Cardiac Death ◽

Learning Model ◽

Curve Analysis ◽

Roc Curve Analysis ◽

Dual Isotope ◽

Machine Learning Model

Abstract Background Dual-isotope (low doze 201TlCl and 123I-β-methyl-P-iodophenyl-pentadecanoic acid (BMIPP)) single photon emission computed tomography (SPECT) is utilized to estimate myocardial damage in patients with congestive heart failure (CHF). However, predictive model construction on the SPECT for cardiac death by machine learning was not studied. Purpose To elucidate predictive value of machine learning model on dual-isotope SPECT for CHF. Methods We enrolled consecutive 310 patients who admitted with CHF (77.1±3.1 years, 164 males). After initial treatment, they underwent electrocardiography gated SPECT and observed in median 507 days [IQR: 165, 1032]. Multivariate Cox regression analysis for cardiac death was performed, and predictive model was constructed by ROC curve analysis and machine learning (Random Forest and Deep Learning). The accuracies (= [True positive + True negative] / Total) of the prediction models were compared with ROC curve model. Results Thirty-six patients fell into cardiac death. Cox analysis showed Age, left ventricular ejection fraction (LVEF), summed rest score (SRS) of BMIPP, and mismatch score were significant predictors (Hazard ratio: 1.068, 0.970, 1.032, 1.092, P value: <0.001, 0.014, 0.002, <0.001, respectively). ROC curve analysis of them revealed the accuracy of the cut-off value was 0.479–0.773. Conversely, machine learning model demonstrated higher accuracy for cardiac death (Random Forest: 0.895, Deep Learning: 0.935). The top 4 feature importance of the random forest were LVEF (0.299), SRS BMIPP (0.263), Age (0.262), and mismatch score (0.160). Conclusion Machine learning model on SPECT was superior to conventional statistic model for predicting cardiac death in patients with CHF. Funding Acknowledgement Type of funding source: None

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