Prediction of Length of Stay Following Elective Percutaneous Coronary Intervention

Abstract

There have been published risk stratification approaches to predict complications following percutaneous coronary interventions (PCI). However, a formal assessment of such approaches with respect to predicting length of stay (LOS) is lacking. Therefore, we sought to assess the performance of, an easy-to-use, tree-structured prognostic classification model in predicting LOS among patients with elective PCI. The study is based on the New York State PCI database. The model was developed on data for 1999-2000, consisting of 67,766 procedures. Validation was carried out, with respect to LOS, using data for 2001-2002, consisting of 79,545 procedures. The risk groups identified by the model exhibited a strong progressively increasing relative risk pattern of longer LOS. The predicted average LOS ranged from 3 to 9 days. The performance of this model was comparable to other published risk scores. In conclusion, the tree-structured prognostic classification is a model which can be easily applied to aid practitioners early on in their decision process regarding the need for extra resources required for the management of more complicated patients following PCI, or to justify to payors the extra costs required for the management of patients who have required extended observation and care after PCI.

Comparison of the treatment practice and hospitalization cost of percutaneous coronary intervention between a teaching hospital and a general hospital in Malaysia: A cross sectional study

“…Patients with urgent/emergent indication for PCI, or with shock and hemodynamic instability were excluded as they usually have more severe disease presentation and complications, thus incurring higher cost. This definition is compatible with the definition of elective PCI adopted by other studies in the literature [ 11 , 12 ]. This also enabled us to eliminate any likely operator-dependent variations as elective PCI is a fundamental, entry-level procedure for all cardiologists.…”

Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients

“…Most models in the cardiac disease area have predicted in-hospital mortality [16], and statistical methods, especially descriptive analyses, have been applied in that research. Hence, raising our awareness of factors that have an impact on cardiac patients' LOS is essential in order to determine and develop a useful and efficient model to predict LOS.…”

Impact of Body Mass Index on In-Hospital Length of Stay after Percutaneous Coronary Interventions

“…LOS is likely to be influenced by both patient presenting features and procedural complications; therefore, it is logical to assess the performance of postprocedural risk classification models with respect to LOS. 12 Underweight patients with cardiovascular disease have been relatively understudied, with attention generally focused on the obese people. 13,14 In the current study, the mean length of hospital stay (LOS) after PCI was significantly (p < 0.01) longer in lower BMI groups.…”