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Management of Post-Surgical Complications in A Journal of Surgical Sciences Vol.8, No.2, April – June 2021 ORIGINAL ARTICLE ATTEMPTING TO INCREASE THE CLINICAL DIAGNOSTIC RATE OF ACUTE INTESTINAL ISCHEMIA USING MACHINE LEARNING ALGORITHMS Pîrvu Cătălin Alexandru1,2, Cristian Nica1,2, Mărgăritescu Dragoș3,4, Pătrașcu Ștefan3,4, Valeriu Șurlin3,4, Konstantions Sapalidis5,6, Eugen Georgescu3,4, Ion Georgescu3,4, Stelian Pantea1,2 1Pius Brânzeu County Emergency Clinical Hospital Timisoara, Romania 2Victor Babeș Medicine and Pharmacy University Timisoara, Romania 3Clinical County Emegrency Hospital of Craiova, Romania 4University of Medicine and Pharmacy Craiova, Romania 53rd Department of Surgery, “AHEPA” University Hospital, Thessaloniki, Greece; 6Aristotle University of Thessaloniki, Medical School, Thessaloniki, Greece Corresponding author: Cristian Nica E-mail: [email protected] Abstract Acute intestinal ischemia (AMI) is a life-threatening surgical emergency where more than half of the affected patients do not survive. In spite of the medical advance, mortal-ity rates remain high due to late diagnosis, when proper surgical management and reperfusion techniques do not conclude to a successful outcome. The current study aims to find a proper diagnosis method with a high-reliability rate using machine learning (ML) algorithms. Methods: In this prospective cross-sectional study, we have collected and evaluated over the course of two years a total of 147 patients with a clini-cal presentation resembling acute mesenteric ischemia. Five ML algorithms, including Random Forest, Logistic Regression, Gradient Boosted Trees, Naive Bayes, and Multi-ple Layer Perceptron, were compared for their reliability in diagnosing acute intestinal ischemia by using regular blood tests performed in the emergency room (ER), on top of the main clinical characteristics of the researched condition. An algorithm score using Gradient Boosted Trees and Logistic Regression proved good diagnostic performance with an AUROC 0.784, p<0.001, with a sensitivity of 83.8%, specificity of 58.2%, 70.5% positive predictive value, and 75% negative predictive value. The ML algorithm is use- ful in detecting AMI using only anamnesis data and regular laboratory blood tests available in the ER, although it was not internally validated. Keywords: bowel infarction, acute abdomen, intestinal ischemia, machine learning Introduction vasculature to the midgut, including the small intestine and proximal mid-colon up to the Intestinal ischemia or acute mesenteric splenic flexure, while the superior mesenteric ischemia (AMI) represents the totality of vein (SMV) drains the blood flow from the small pathophysiological mechanisms leading to the intestine and cecum [1]. Although it represents a sudden loss of blood flow, respectively intestinal small percentage of the total number of surgical necrosis of the small or large intestine. Here, the emergencies, accounting for 0.09%-0.2% of all superior mesenteric artery (SMA) provides cases [2], mortality from AMI re-mains high (50- 42 ATTEMPTING TO INCREASE THE CLINICAL DIAGNOSTIC RATE OF ACUTE INTESTINAL ISCHEMIA USING ML ALGORITHMS 90%) over the last 50 years [3], despite the healthcare by harnessing massive amounts of technical diagnosis and therapeutic patient data to give exact and personalized advancement. The pathophysiological substrate diagnoses [10]. Despite major research efforts of the disease is represented by the radical and increasing commercial interest, diagnostic alteration of the gas exchanges at the level of algorithms have struggled to match clinicians' ac- tissues and enteral cells, where oxygen and curacy in differential diagnosis, the process nutrients deprivation lead to loss of function of through which a patient's symptoms might have the enteral cells, intestinal necrosis, gangrene, numerous probable causes. sepsis, and toxic shock with quick progression to The present study aims to identify the patient’s death [4]. The major difficulty in the elements of anamnesis, clinical and paraclinical diagnosis and implicitly the treatment of this examination that raise a justified suspicion of condition results from the heterogeneous and AMI, which can be subsequently confirmed by nonspecific manifestations in the initial stages of imaging and evaluated intraoperatively. These ischemia, where reperfusion could potentially variables should be included in machine learning save the cell function and the patient's life. It models to detect a reliable diagnostic tool with becomes clinically and biologically evi-dent, the purpose of non-inferiority to the surgeon’s facilitating the diagnosis only in the final stage diagnostic accuracy with the purpose of when it is manifested by the complications of providing clinical support. ischemia represented by gangrene and perforation with peritoneal signs, severe toxic and septic shock in a patient with abdominal Materials and Methods suffering [5]. Unfortunately, the therapeutic possibilities are extremely low at the stage when Participants diagnosis becomes clear. Thus, the sooner the therapeutic moment is established, the greater the Our study was conducted between September chance of providing effective treatment to the 2015 and September 2020 in the Department of patient. There are four different mechanisms of Emergency Medicine and Department of General AMI whose common pathway is enter-al hypoxia Surgery at “Pius Brinzeu” Emergency Clinical evolving into necrosis: (1) atheroma plaque Hospital from Timisoara, affiliated to the “Victor thrombosis, (2) SMA thromboembolism [6], (3) Babes” University of Medi-cine and Pharmacy superior mesenteric vein thrombosis [7], and (4) from Timisoara and 1st Surgical Department in non-occlusive arterial AMI [8]. Improving Emergency County Clinical Hospital Craiova, mortality by early initiation of therapeutic affilitated to the University of Medicine and measures can be achieved only by a diagnosis in Pharmacy Craiova. This study followed a the initial phases of ischemia onset, for example, prospective, cross-sectional design and targeted in one of the four aforementioned situations, patients with acute abdo-men. before the common path of necrosis and sepsis. Study subjects were recruited from patients Global healthcare systems have a basic issue attending the hospital emergency room de- in providing an accurate and accessible partment for abdominal pain with signs of acute diagnosis. Each year, around 5% of outpatients in abdomen. A total of 1042 patients with acute the United States obtain the incorrect diagnosis. abdomen but without a clear etiology were These mistakes are particularly prevalent when enrolled after they signed a written in-formed diagnosing patients with significant medical consent form to participate in the current study. illnesses. An estimated 20% of these patients Those having a clear etiology for the acute receive an incorrect diagnosis at the primary care abdomen presentation, such as stabbed wounds, level and one in three of these errors, resulting in acute appendicitis, intestinal obstruction, etc., substantial patient damage [9]. Artificial were excluded from the study. Other exclusion intelligence and machine learning have criteria included the cases that resulted from developed as strong methods for resolving secondary mechanisms of mesenteric ischemia, difficult issues across a range of fields in recent such as post-traumatic thrombosis of the superior years. Machine learning aided diagnosis, in mesenteric artery, respectively the dissection of particular, has the potential to revolutionize the abdominal aorta involving the ostium of the 43 Journal of Surgical Sciences Vol.8, No.2, April – June 2021 SMA. From the initial cohort, 147 patients had a Statistical Analysis clinical presentation resembling mesenteric ischemia, including severe abdominal pain, Continuous variables were presented as mean diarrhea or absence of intestinal peristalsis, and standard deviation (SD) or as me-dian and melena, emesis, dehydration, tachycardia, Interquartile Range (IQR), based on their tachypnea, and circulatory failure, all correlated distribution, which was determined by with laboratory abnormalities such as metabolic employing the Shapiro-Wilk test. Categorical acidosis, high anion gap, and lactate levels, data were described as frequency counts and their leukocytosis, hemoconcentration, high aspartate respective percentages calculated from the total. aminotransferase (ASAT) levels, high serum Descriptive and inferential statistical analysis amylase and creatine phosphokinase. was conducted to summarize the characteristics of the study population. Chi-square was Methods employed to evaluate the significance of the differences in the proportions of clinical findings The patient selection process was tightly between groups. Student's t-test or Mann- dependent on their presentation to the ER, where Whitney U-test were used for comparing suspicion of the acute abdomen would require a continuous variables based on their distribution. specialty check performed by the on-duty A p-value of less than 0.05 was considered to surgeon. A complete clinical exam with history- indicate the statistical significance. Data analysis taking and laboratory tests would be necessary to was performed using SPSS v.26 (Statistical settle the acute abdomen diagnosis and a Package for the Social Sciences, Chicago, IL, presumptive diagnosis of acute mesenteric USA). ischemia, further continuing with a computed tomography scan in order to rule out other causes Machine Learning Methods of acute
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