ABSTRACT
Technical errors have an impact on the results of surgical lower limb revascularization. Use of ultrasound scanning or angiography on the operating table is inconvenient and, in case of angiography, carries a certain risk of radiation and contrast exposure. A simpler method of screening for errors is required. This study assessed the accuracy of a new simple hydrostatic bypass flow technique during surgical limb revascularization. In all, 885 patients were included in the retrospective study. All were treated for Chronic Limb-Threatening Ischemia (CLTI) with a femoropopliteal bypass. Preoperatively, the radiological Vascular Surgery/International Society of Cardiovascular Surgery (SVS/ISCVS) score was used to assess the complexity of the anatomical changes. The surgeon made a subjective runoff assessment for every surgery. In 267 cases, the hydrostatic bypass flow (HBF) technique was used, and, in 66 cases, a digital subtraction angiography (DSA) was used. In each case, a postoperative Doppler ultrasound (DUS) examination was performed following the HBF. Good early results were achieved in 89.46%, and 154 errors (17.4%) were detected (85 were detected on the operating table, including 57 technical errors). Independent efficacy in error detection was proven with a postoperative Doppler examination (Aera Under Curve (AUC) = 0.89; criterion mid-graft peak systolic velocity (PSV) <24 cm/s, p = 0.00001) and hydrostatic bypass flow (AUC = 0.71, criterion HBF < 53 mL/min, p = 0.00001) during surgery. The hydrostatic bypass flow technique is an effective intraoperative screening method in bypass surgery. Algorithmic use of HBF, DSA if needed, and DUS postoperatively improves the outcome. HBF sufficiently reduced the need for on-table angiography.
ABSTRACT
BACKGROUND: Preoperative risk assessment is a key issue in the process of patient preparation for surgery and the control of quality improvement in health care and certification programs. Hence, there is a need for a prognostic tool, whose usefulness can be assessed only after validation in the center other than the home one. The aim of the study was to validate the Surgical Mortality Probability Model (S-MPM) for detecting deaths and complications in patients undergoing non-cardiac surgery and to assess its suitability for various surgical disciplines. METHODS: This retrospective study involved 38,555 adult patients undergoing non-cardiac surgery in a single center in 2012-2015. The observation period concerned in-hospital mortality. RESULTS: In-hospital mortality for the total population was 0.89%. Mortality in the S-MPM I class amounted to 0.26%, S-MPM II 2.51%, and in the S-MPM III class 22.14%. This result was in line with those obtained by the authors. The discriminatory power for in-hospital mortality was good (area under curve (AUC) = 0.852, 95% CI: 0.834-0.869, p = 0.0000). The scale was the most accurate in general surgery (AUC = 0.89, 95% CI: 0.858-0.922) and trauma (AUC = 0.89; 95% CI: 0.87-0.915). In the logistic regression analysis, the scale showed a perfect fit/goodness of fit in the cross-validation method (v-fold cross-validation): Hosmer-Lemeshow (HL) = 7.945; p = 0.159. This result was confirmed by the traditional derivation and validation data set method (1:3; 9712 vs 22.748 cases): HL test = 3.073 (p = 0.546) in the teaching derivation data set and 10.77 (p = 0.029) in the test sample (validation data set). CONCLUSION: The S-MPM scale by Glance et al has proven to be a useful tool to assess the risk of in-hospital death and can be taken into account when considering treatment indications, patient information, planning post-operative care, and quality control.
