Beeinflussbare chirurgische und anästhesiologische Risikofaktoren für die Entwicklung kardialer und pulmonaler Komplikationen nach laparoskopischer Kolonchirurgie

Hermeneit, S.; Müller, Markus K.; Terzic, A.; Rodehorst, A.; Schamberger, Marcus S.; Böttger, Th.

doi:10.1055/s-2008-1004739

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…7 While some studies used the Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity score scheme to indicate high-risk patients, this was not seen in distinct research. 8…”

Section: Introductionmentioning

confidence: 99%

“…7 While some studies used the Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity score scheme to indicate highrisk patients, this was not seen in distinct research. 8 Pre-operative risk classification of patients undergoing anesthesia is an essential interest and has been the focus of many research and categorizations. Especially lowand high-risk studies were performed out and some classifications were developed.…”

Section: Introductionmentioning

confidence: 99%

“…In literature there are increasing numbers of studies on laparoscopic surgery, but there are few studies on the case of high-risk patients. 3,8,9 In some studies, it was tried to establish a relationship between pre-operative and postoperative patient information, and an approach was proposed for the selection of surgery type. 10 Initially, ASA score was also used to identify high-risk patients before surgery.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Logic Preanesthetic Risk Evaluation of Laparoscopic Cholecystectomy Operations

Sandal

Hacioğlu

Salihoğlu

et al. 2021

The American Surgeon™

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Background and Objective Pre-operative risk classification of patients undergoing anesthesia is an essential interest and has been the focus of many research and categorizations. On the other hand, the ideal categorization system, based on medical doctors’ clinical experience and cooperation with other disciplines, has not been developed yet. Methods In this study, 218 consecutive patient undergoing laparoscopic cholecystectomy operations were included. A novel fuzzy logic evaluation model consisting of 270 rules was constructed. Five major (pulmonary, cardiac, diabetes mellitus and renal or liver disease) and three minor criteria (patientsʼ age, cigarette smoking and body mass index) were chosen to be used during high-risk groups determination. Results The verification of the success of risk value decision with the proposed novel fuzzy logic algorithm is the main goal of this study. On the other hand, though not essential aim, a statistical consistency check was also included to have a deeper understanding and evaluation of the graphical results. During the statistical analysis the 0-30%, 30-60% and 60-90% risk ranges were found to be in a very strong positive relationship with complication occurrence. In this study, 172, 31, 15 patients were in 0-30, 30-60 and 60-90% risk ranges, respectively. Complication rates were 7/172 (4.07%) in 0-30% range, 3/31 (9.68%) in 30-60% range; and 2/15 (13.33%) in 60-90% range. Conclusions Fuzzy based risk classification model was successfully used to predict medical results for patients undergoing laparoscopic cholecystectomy operations and reliable deductions were reached.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy Logic Preanesthetic Risk Evaluation of Laparoscopic Cholecystectomy Operations

Sandal

Hacioğlu

Salihoğlu

et al. 2021

The American Surgeon™

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Analysis of Laparoscopic Colorectal Surgery in High-risk Patients

Salihoğlu

Baca²,

Köksal

et al. 2009

Surgical Laparoscopy, Endoscopy & Percutaneous Techniques

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The aim of this retrospective study is to examine the feasibility and safety of laparoscopic colorectal resection for colorectal malignancies to determine "high-risk" patients. In our classification, 3 minor criteria including patients over 70 years of age, body mass index over 30 m/kg, and cigarette smoking and 5 major criteria including cardiac, pulmonary, renal, liver disease, and diabetes mellitus were selected to determine a high-risk group. Patients carrying 1 minor and 1 major criteria were classified as the high-risk group. Concerning patients and operations, hemodynamic values (mean arterial systolic and diastolic pressures and heart rates), oxygen saturations, end-expiratory carbon dioxide levels, respiratory mechanics (dynamic compliance, peak inspiratory pressure, airway resistance) were analyzed. Cardiovascular system (myocardial infarction, arrhythmia, hypertension), pulmonary system (respiratory insufficiency), digestive system (anastomotic leak, fistula, and paralytic ileus), fever, thrombophlebitis, urinary infections, wound infections, and central nervous system (delirium and cerebrovascular accident) were also investigated. A total number of 85 high-risk patients were included in the study. Gastrointestinal leaks in 2.3%, fistula in 1.1%, ileus in 3.5%, postoperative bleeding in 2.3%, postoperative fever in 5.8%, wound infection in 5.8%, and cerebrovascular accidents in 1.1% of patients were detected. The lowest values of hemodynamic and respiratory mechanics were observed at the induction of pneumoperitoneum and in this period the compliance and mean arterial pressure were determined to be 36+/-14 mm Hg and 84+/-14 mm Hg, respectively. No mortalities occurred. In experienced hands, laparoscopic colorectal resection can be performed safely for "high-risk" surgical patients.

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How to Improve the Performance of Intraoperative Risk Models

Hyder¹,

Kor²,

Cima

et al. 2013

Anesthesia &Amp; Analgesia

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BACKGROUND Computerized reviews of patient data promise to improve patient care through early and accurate identification of at-risk and well patients. The significance of sampling strategy for patient vital signs data is not known. In the instance of the surgical Apgar score (SAS), we hypothesized that larger sampling intervals would improve the specificity and overall predictive ability of this tool. METHODS We used electronic intraoperative data from general and vascular surgical patients in a single-institution registry of the American College of Surgeons National Surgical Quality Improvement Program. The SAS, consisting of lowest heart rate, lowest mean arterial blood pressure, and estimated blood loss between incision and skin closure, was calculated using 5 methods: instantaneously and using intervals of of 5 and 10 minutes with and without interval overlap. Major complications including death were assessed at 30 days postoperatively. RESULTS Among 3000 patients, 272 (9.1%) experienced major complications or death. As the sampling interval increased from instantaneous (shortest) to 10 minutes without overlap (largest), the sensitivity, positive predictive value, and negative predictive value did not change significantly, but significant improvements were noted for specificity (79.5% to 82.9% across methods, P for trend <0.001) and accuracy (76.0% to 79.3% across methods, P for trend <0.01). In multivariate modeling, the predictive utility of the SAS as measured by the c-statistic nearly increased from Δc = +0.012 (P = 0.038) to Δc = +0.021 (P < 0.002) between the shortest and largest sampling intervals, respectively. Compared with a preoperative risk model, the net reclassification improvement and integrated discrimination improvement for the shortest versus largest sampling intervals of the SAS were net reclassification improvement 0.01 (P = 0.8) vs 0.06 (P = 0.02), and for integrated discrimination improvement, they were 0.008 (P < 0.01) vs 0.015 (P < 0.001). CONCLUSIONS When vital signs data are recorded in compliance with American Society of Anesthesiologists’ standards, the sampling strategy for vital signs significantly influences performance of the SAS. Computerized reviews of patient data are subject to the choice of sampling methods for vital signs and may have the potential to be optimized for safe, efficient patient care.

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Beeinflussbare chirurgische und anästhesiologische Risikofaktoren für die Entwicklung kardialer und pulmonaler Komplikationen nach laparoskopischer Kolonchirurgie

Cited by 3 publications

References 7 publications

Fuzzy Logic Preanesthetic Risk Evaluation of Laparoscopic Cholecystectomy Operations

Fuzzy Logic Preanesthetic Risk Evaluation of Laparoscopic Cholecystectomy Operations

Analysis of Laparoscopic Colorectal Surgery in High-risk Patients

How to Improve the Performance of Intraoperative Risk Models

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