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Christopher H Lee

Publications and source records attributed to Christopher H Lee.

2 recordsLinked to original sources

Predictors of complications after a prospective evaluation of diagnostic and therapeutic endovascular procedures.

OBJECTIVE: To prospectively evaluate complications after diagnostic and therapeutic endovascular procedures (DTEPs) and determine what factors are predictive. METHODS: From December 2002 to December 2003, all patients undergoing DTEPs performed by university vascular surgeons in a catheterization laboratory were prospectively evaluated. Medical demographics, procedure-related details, and type and severity of complications were recorded at the time of the procedure, during the first 24 hours, and at 2 to 4 weeks. Complications were classified as local vascular (LV), local nonvascular (LNV), systemic remote (SR), and major, minor, and nonsignificant. RESULTS: Three hundred-three DTEPs were performed (54.5% DEPs, 45.5% TEPs). At the time of DTEP, 28 complications occurred in 23 patients: 10 LV (3.3%), 15 LNV (5.0%), and 3 SR (1.0%). At 24 hours, 26 complications occurred in 25 patients: 5 LV (1.7%), 7 LNV (2.3%), and 14 SR (4.7%). At 2 to 4 weeks, 26 complications occurred 25 patients: 5 LV (1.7%), 7 LNV (2.3%), and 14 SR (4.7%). The combined major (7.3%) and minor (4.3%) complication rate attributed to DTEPs was 11.6%. Significant predictors (P < .05) by multivariate analysis included thrombolysis, prior stroke, an additional procedure during the study period, and diabetes mellitus (odds ratios: 9.1, 3.2, 2.7, and 2.4, respectively). CONCLUSION: According to newly applied reporting standards, the prospective evaluation of DTEPs reveals that complications are uniformly distributed by type and follow-up period. Just over 1 in 10 patients will suffer either a major or minor complication. Potential predictors have been identified that may assist in patient selection and treatment plans to lower complications resulting from DTEPs.

Adolescent↗

A phase space spline smoother for fitting trajectories.

This paper presents a phase space spline smoother, which is especially useful for finding a best-fit trajectory from multiple examples of a given physical motion. Unlike conventional spline smoothers, the phase space spline smoother can simultaneously fit position and velocity information. The use of velocity information is important for modeling the dynamic motion of physical systems because the state space of these systems typically includes both position and velocity variables. A detailed description of the computational procedure is presented, along with a discussion of computational expense and practical guidelines for variance estimation, preprocessing of the target dataset, smoothing of multidimensional datasets, and cross-validation for selection of smoothing weights. The smoother is demonstrated on a dataset of handwriting motions.

Journal Article↗