Dataset Information

Measuring adverse events following hip arthroplasty surgery using administrative data without relying on ICD-codes.

ABSTRACT: INTRODUCTION:Measure and monitor adverse events (AEs) following hip arthroplasty is challenging. The aim of this study was to create a model for measuring AEs after hip arthroplasty using administrative data, such as length of stay and readmissions, with equal or better precision than an ICD-code based model. MATERIALS AND METHODS:This study included 1 998 patients operated with an acute or elective hip arthroplasty in a national multi-centre study. We collected AEs within 90 days following surgery with retrospective record review. Additional data came from the Swedish Hip Arthroplasty Register, the Swedish National Patient Register and the Swedish National Board of Health and Welfare. We made a 2:1 split of the data into a training and a holdout set. We used the training set to train different machine learning models to predict if a patient had sustained an AE or not. After training and cross-validation we tested the best performing model on the holdout-set. We compared the results with an established ICD-code based measure for AEs. RESULTS:The best performing model was a logistic regression model with four natural age splines. The variables included in the model were as follows: length of stay at the orthopaedic department, discharge to acute care, age, number of readmissions and ED visits. The sensitivity and specificity for the new model was 23 and 90% for AE within 30 days, compared with 5 and 94% for the ICD-code based model. For AEs within 90 days the sensitivity and specificity were 31% and 89% compared with 16% and 92% for the ICD-code based model. CONCLUSION:We conclude that a prediction model for AEs following hip arthroplasty surgery, relying on administrative data without ICD-codes is more accurate than a model based on ICD-codes.

SUBMITTER: Magneli M

PROVIDER: S-EPMC7644076 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Measuring adverse events following hip arthroplasty surgery using administrative data without relying on ICD-codes.

Magnéli Martin M Unbeck Maria M Rogmark Cecilia C Sköldenberg Olof O Gordon Max M

PloS one 20201105 11

<h4>Introduction</h4>Measure and monitor adverse events (AEs) following hip arthroplasty is challenging. The aim of this study was to create a model for measuring AEs after hip arthroplasty using administrative data, such as length of stay and readmissions, with equal or better precision than an ICD-code based model.<h4>Materials and methods</h4>This study included 1 998 patients operated with an acute or elective hip arthroplasty in a national multi-centre study. We collected AEs within 90 days ...[more]

PMID: 33152055

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Project description:BACKGROUND:Validation of administrative databases for cerebrovascular diseases is crucial for epidemiological, outcome, and health services research. The aim of this study was to validate ICD-9 codes for hemorrhagic or ischemic stroke in administrative databases, to use them for a comprehensive assessment of the burden of disease in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. METHODS:We considered the hospital discharge abstract database of the Umbria Region (890,000 residents). Source population was represented by patients aged >18 discharged from hospital with a diagnosis of hemorrhagic or ischemic stroke between 2012 and 2014 using ICD-9-CM codes in primary position. We randomly selected and reviewed medical charts of cases and non-cases from hospitals. For case ascertainment we considered symptoms and instrumental tests reported in the medical charts. Diagnostic accuracy measures were computed using 2x2 tables. RESULTS:We reviewed 767 medical charts for cases and 78 charts for non-cases. Diagnostic accuracy measures were: subarachnoid hemorrhage: sensitivity (SE) 100% (95% CI: 97%-100%), specificity (SP) 96% (90-99), positive predictive value (PPV) 98% (93-100), negative predictive value (NPV) 100% (95-100); intracerebral hemorrhage: SE 100% (97-100), SP 98% (91-100), PPV 98% (94-100), NPV 100% (95-100); other and unspecified intracranial hemorrhage: SE 100% (97-100), SP 96% (90-99), PPV 98% (93-100), NPV 100% (95-100); ischemic stroke due to occlusion and stenosis of precerebral arteries: SE 99% (94-100), SP 66 (57-75), PPV 70% (61-77), NPV 99% (93-100); occlusion of cerebral arteries: SE 100% (97-100), SP 87% (78-93), PPV 91% (84-95), NPV 100% (95-100); acute, but ill-defined, cerebrovascular disease: SE 100% (97-100), SP 78% (69-86), PPV % 83 (75-89), NPV 100% (95-100). CONCLUSIONS:Case ascertainment for both ischemic and hemorrhagic stroke showed good or high levels of accuracy within the regional healthcare databases in Umbria. This database can confidently be employed for epidemiological, outcome, and health services research related to any type of stroke.

Project description:BackgroundAdministrative healthcare databases are useful and inexpensive tools that can provide a comprehensive assessment of the burden of diseases in terms of major outcomes, such as mortality, hospital readmissions, and use of healthcare resources. However, a crucial issue is the reliability of information gathered. The aim of this study was to validate ICD-9 codes for several major cardiovascular conditions, i.e., acute myocardial infarction (AMI), atrial fibrillation/flutter (AF), and heart failure (HF), in order to use them for epidemiological, outcome, and health services research.MethodsData from the centralised administrative database of the Umbria Region (890,000 residents, located in Central Italy) were considered. Patients with a first hospital discharge for AMI, AF/flutter, and HF, between 2012 and 2014, were identified using ICD-9-CM codes in primary position. A sample of cases and non-cases was randomly selected, and the corresponding medical charts reviewed by specifically trained investigators. For each disease, case ascertainment was based on all clinical, laboratory, and instrumental examinations available in medical charts. Sensitivity, specificity, and predictive values with 95% confidence intervals (CIs), were calculated.ResultsWe reviewed 458 medical charts, 128 for AMI, 127 for AF/flutter, 127 for HF, and 76 of non-cases for each condition. Diagnostic accuracy measures of the original discharge diagnosis were as follows. AMI: sensitivity 98% (95% CI, 94-100%), specificity 91% (95% CI, 83-97%), positive predictive value (PPV) 95% (95% CI, 89-98%), negative predictive value (NPV) 97% (95% CI, 91-100%). AF/flutter: sensitivity 95% (95% CI, 90-98%), specificity 95% (95% CI, 87-99%), PPV 97% (95% CI, 92-99%), NPV 92% (95% CI, 84-97%). HF: sensitivity 96% (95% CI, 91-99%), specificity 90% (95% CI, 81-96%), PPV 94% (95% CI, 88-97%), NPV 93% (95% CI, 85-98%).ConclusionThe case ascertainment for AMI, AF and flutter, and HF, showed a high level of accuracy (≥ 90%). The healthcare administrative database of the Umbria Region can be confidently used for epidemiological, outcome, and health services research.

Dataset Information

Measuring adverse events following hip arthroplasty surgery using administrative data without relying on ICD-codes.

Publications

Measuring adverse events following hip arthroplasty surgery using administrative data without relying on ICD-codes.

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