在疑似肺栓塞患者中，诊断算法的实施效果不佳，以及过度使用计算机断层扫描-肺血管造影术。

IF 0.7 3区计算机科学 Q3 COMPUTER SCIENCE, THEORY & METHODS

Computational Complexity Pub Date : 2016-10-01 DOI:10.4103/1817-1737.191875

Sulaiman Alhassan, Alaa Abu Sayf, Camelia Arsene, Hicham Krayem

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引用次数: 0

摘要

背景：我们的大多数计算机断层扫描-肺血管造影（CTPA）扫描结果均为阴性。我们推测，对诊断算法的次优依赖导致了该检查的明显过度使用：方法：对一家大型医院系统的 2031 个 CTPA 病例进行了回顾性分析。研究人员回顾性地计算了检查前概率（PTP）。如果未检查 D-二聚体 (DD)，或在 DD 阴性后为 PTP 较低的患者开具 CTPA 检查单，则认为 CTPA 的使用是不恰当的：在 2031 例患者中，7.4%（151 例）发现肺栓塞（PE）。根据威尔斯评分，约有 1784 例患者（88%）被认为 "不太可能发生 PE"。在这些患者中，有 1084 例（61%）在 CTPA 之前没有进行 DD 检测。此外，78 名 DD 阴性的患者接受了不必要的 CTPA，但他们都没有发生 PE：结论：PTP 评估工具的不理想实施会导致 CTPA 的过度使用，造成医院资源的无效利用、成本增加以及对患者的潜在伤害。

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Suboptimal implementation of diagnostic algorithms and overuse of computed tomography-pulmonary angiography in patients with suspected pulmonary embolism.

Background: Majority of our computed tomography-pulmonary angiography (CTPA) scans report negative findings. We hypothesized that suboptimal reliance on diagnostic algorithms contributes to apparent overuse of this test.

Methods: A retrospective review was performed on 2031 CTPA cases in a large hospital system. Investigators retrospectively calculated pretest probability (PTP). Use of CTPA was considered as inappropriate when it was ordered for patients with low PTP without checking D-dimer (DD) or following negative DD.

Results: Among the 2031 cases, pulmonary embolism (PE) was found in 7.4% (151 cases). About 1784 patients (88%) were considered "PE unlikely" based on Wells score. Out of those patients, 1084 cases (61%) did not have DD test prior to CTPA. In addition, 78 patients with negative DD underwent unnecessary CTPA; none of them had PE.

Conclusions: The suboptimal implementation of PTP assessment tools can result in the overuse of CTPA, contributing to ineffective utilization of hospital resources, increased cost, and potential harm to patients.

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来源期刊

Computational Complexity 数学-计算机：理论方法

CiteScore

1.50

自引率

0.00%

发文量

审稿时长

>12 weeks

期刊介绍： computational complexity presents outstanding research in computational complexity. Its subject is at the interface between mathematics and theoretical computer science, with a clear mathematical profile and strictly mathematical format. The central topics are: Models of computation, complexity bounds (with particular emphasis on lower bounds), complexity classes, trade-off results for sequential and parallel computation for "general" (Boolean) and "structured" computation (e.g. decision trees, arithmetic circuits) for deterministic, probabilistic, and nondeterministic computation worst case and average case Specific areas of concentration include: Structure of complexity classes (reductions, relativization questions, degrees, derandomization) Algebraic complexity (bilinear complexity, computations for polynomials, groups, algebras, and representations) Interactive proofs, pseudorandom generation, and randomness extraction Complexity issues in: crytography learning theory number theory logic (complexity of logical theories, cost of decision procedures) combinatorial optimization and approximate Solutions distributed computing property testing.