Dataset Information

The Value of Claims-Based Nontraditional Risk Factors in Predicting Long-term Mortality After MitraClip Procedure.

ABSTRACT: BACKGROUND:We sought to identify nontraditional risk factors coded in administrative claims data and evaluate their ability to improve prediction of long-term mortality in patients undergoing percutaneous mitral valve repair. METHODS:Patients undergoing transcatheter mitral valve repair using MitraClip implantation between September 28, 2010, and September 30, 2015 were identified among Medicare fee-for-service beneficiaries. We used nested Cox regression models to identify claims codes predictive of long-term mortality. Four groups of variables were introduced sequentially: cardiac and noncardiac risk factors, presentation characteristics, and nontraditional risk factors. RESULTS:A total of 3782 patients from 280 clinical sites received treatment with MitraClip over the study period. During the follow-up period, 1114 (29.5%) patients died with a median follow-up time period of 13.6 (9.6 to 17.3) months. The discrimination of a model to predict long-term mortality including only cardiac risk factors was 0.58 (0.55 to 0.60). Model discrimination improved with the addition of noncardiac risk factors (c = 0.63, 0.61 to 0.65; integrated discrimination improvement [IDI] = 0.038, P < 0.001), and with the subsequent addition of presentation characteristics (c = 0.67, 0.65 to 0.69; IDI = 0.033, P < 0.001 compared with the second model). Finally, the addition of nontraditional risk factors significantly improved model discrimination (c = 0.70, 0.68 to 0.72; IDI = 0.019, P < 0.001, compared with the third model). CONCLUSIONS:Risk-prediction models, which include nontraditional risk factors as identified in claims data, can be used to predict long-term mortality risk more accurately in patients who have undergone MitraClip procedures.

SUBMITTER: Kundi H

PROVIDER: S-EPMC6424362 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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The Value of Claims-Based Nontraditional Risk Factors in Predicting Long-term Mortality After MitraClip Procedure.

Kundi Harun H Popma Jeffrey J JJ Valsdottir Linda R LR Shen Changyu C Faridi Kamil F KF Pinto Duane S DS Yeh Robert W RW

The Canadian journal of cardiology 20181013 12

<h4>Background</h4>We sought to identify nontraditional risk factors coded in administrative claims data and evaluate their ability to improve prediction of long-term mortality in patients undergoing percutaneous mitral valve repair.<h4>Methods</h4>Patients undergoing transcatheter mitral valve repair using MitraClip implantation between September 28, 2010, and September 30, 2015 were identified among Medicare fee-for-service beneficiaries. We used nested Cox regression models to identify claims ...[more]

PMID: 30527154

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Project description:ObjectiveThis study aims to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients post-cardiac surgery.MethodsThe Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Four-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA).ResultsOf 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings.ConclusionsThe Ada model performs best in predicting 4-year mortality after cardiac surgery among the eight ML models, which might have significant application in the development of early warning systems for patients following operations.

Project description:BackgroundLong-term invasive mechanical ventilation (IMV) is a major burden for those affected and causes high costs for the health care system. Early risk assessment is a prerequisite for the best possible support of high-risk patients during the weaning process. We aimed to identify risk factors for long-term IMV within 96 h (h) after the onset of IMV.MethodsThe analysis was based on data from one of Germany's largest statutory health insurance funds; patients who received IMV ≥ 96 h and were admitted in January 2015 at the earliest and discharged in December 2017 at the latest were analysed. OPS and ICD codes of IMV patients were considered, including the 365 days before intubation and 30 days after discharge. Long-term IMV was defined as evidence of invasive home mechanical ventilation (HMV), IMV ≥ 500 h, or readmission with (re)prolonged ventilation.ResultsIn the analysis of 7758 hospitalisations, criteria for long-term IMV were met in 38.3% of cases, of which 13.9% had evidence of HMV, 73.1% received IMV ≥ 500 h and/or 40.3% were re-hospitalised with IMV. Several independent risk factors were identified (p < 0.005 each), including pre-diagnoses such as pneumothorax (OR 2.10), acute pancreatitis (OR 2.64), eating disorders (OR 1.99) or rheumatic mitral valve disease (OR 1.89). Among ICU admissions, previous dependence on an aspirator or respirator (OR 5.13), and previous tracheostomy (OR 2.17) were particularly important, while neurosurgery (OR 2.61), early tracheostomy (OR 3.97) and treatment for severe respiratory failure such as positioning treatment (OR 2.31) and extracorporeal lung support (OR 1.80) were relevant procedures in the first 96 h after intubation.ConclusionThis comprehensive analysis of health claims has identified several risk factors for the risk of long-term ventilation. In addition to the known clinical risks, the information obtained may help to identify patients at risk at an early stage. Trial registration The PRiVENT study was retrospectively registered at ClinicalTrials.gov (NCT05260853). Registered at March 2, 2022.

Dataset Information

The Value of Claims-Based Nontraditional Risk Factors in Predicting Long-term Mortality After MitraClip Procedure.

Publications

The Value of Claims-Based Nontraditional Risk Factors in Predicting Long-term Mortality After MitraClip Procedure.

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