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 , 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 (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 (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 [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 , (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 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 abdomen. After the imaging exam, all patients with a high probability of acute To answer our primary research question of mesenteric ischemia were prepared for increasing the diagnosis rate of bowel infarction, emergency surgical intervention, including the various clinical features were analyzed with the placement of a central venous catheter and help of machine learning algorithms. We ensuring for the availability of cross-matched employed five different Machine Learning blood in expectance for intraoperative Algorithms: Random Forest, Logistic hemorrhage. Regression, Gradient Boosted Trees, Naive After selection for surgical intervention, all Bayes, Multiple Layer Perceptron (Neural Net- patients underwent exploratory laparotomy by work). The training was performed on 80% of performing a midline incision from the xiphoid our data, while the testing was performed on the process to the pubic symphysis. Exploring the remaining 20% of data by cross-validation abdominal cavity would settle for an ischemic or method (10-fold). The best model was chosen necrotic appearance of the intestine. Diffuse based on the area under the ROC curve. bowel ischemia and/or necrosis would determine Sensitivity, Specificity, Negative predictive the action of no resection, and where possible, value (NPV), and precision were computed in the vascular surgery with embolectomy/ specific analysis. Machine Learning models were thrombectomy was performed to avoid intestinal built using KNIME Analytics Platform 4.3.2 resection. The resected samples were sent for (KNIME AG, Zurich, Switzerland). histologic examination. Based on the diagnosis, we split our patients into two groups. There were 67 (44.4%) patients Results with the acute abdomen of other etiology and 80 (65.5%) patients correctly diagnosed and Population analysis confirmed with bowel infarction. After patient selection and surgical intervention, a total of 67 (44.4%) patients with the acute abdomen of other etiology were

44 ATTEMPTING TO INCREASE THE CLINICAL DIAGNOSTIC RATE OF ACUTE INTESTINAL ISCHEMIA USING ML ALGORITHMS identified, and 80 (65.5%) patients were distribution was close to a 1:1 ratio. In the AMI correctly diagnosed and confirmed with AMI. group there was a statistically significant higher The analysis of patients’ general characteristics prevalence of atrial fibrillation (42.5% vs. (Table 1) identified a statistically significant 26.9%, p-value 0.048) and history of a older age of patients with AMI vs. Non-AMI neurological event (40% vs. 23.9%, p-value = acute abdomen (65 years vs. 73 years, p-value < 0.038). Significantly fewer patients with AMI 0.001). Other significant findings showed that were obese (16.5% vs. 34.3%, p-value = 0.032), the male gender was more frequent in the non- with neoplasia (10% vs. 28.4%, p-value = 0.004), AMI acute abdomen group (68.6%), as compared and came from urban environment (48.8% vs. to the AMI group (51.2%), where the gender 68.7%, p-value = 0.015).

Acute abdomen Mesenteric infarction Characteristic P-value (n=67) (n=80)

Age (years) (a) 65.19 ± 12.18 73.35 ± 10.56 <0.001** Male gender (b) 46 (68.6%) 41 (51.2%) 0.032* Urban environment (b) 46 (68.7%) 39 (48.8%) 0.015* Obesity (b) 23 (34.3%) 13 (16.5%) 0.032* Atrial fibrillation (b) 18 (26.9%) 34 (42.5%) 0.048* Coronary disease (b) 30 (44.8%) 24 (30%) 0.064 Cardiac valvulopathy (b) 15 (22.4%) 26 (32.5%) 0.173 Neurological event (b) 16 (23.9%) 32 (40%) 0.038* PAD (b) 12 (17.9%) 21 (26.3%) 0.227 Neoplasia (b) 19 (28.4%) 8 (10%) 0.004**

Legend: (a) mean ± SD; Student t-test; (b) observed frequency counts (percentage); Chi-square test; *,** statistically significant differences (p < 0.05; p < 0.01); PAD – peripheral artery disease; Table 1 -General characteristics of study population

Acute abdomen Mesenteric infarction Characteristic (n=67) (n=80) P-value

Symptom onset (hours) (a) 49.34 ± 32.01 50.75 ± 35.31 0.802 Melena / Rectal (b) 3 (4.5%) 12 (15%) 0.036* Intestinal transit (absent) (b) 44 (65.2%) 60 (75%) 0.194 Oliguria (b) 37 (55.2%) 58 (72.5%) 0.029* (b) 30 (44.8%) 54 (67.5%) 0.006** Shock (b) 27 (40.3%) 38 (47.5%) 0.381 Legend: (a) mean ± SD; Student t-test; (b) observed frequency counts (percentage); Chi-square test; *,** statistically significant differences (p < 0.05; p < 0.01); Table 2 - Symptoms presented in the ER

Regarding patients’ symptoms at presentation p-value = 0.036). Oliguria and hypotension were in the ER (Table 2.), there was a signifi-cantly also more common in patients di-agnosed with higher proportion of patients with melena or AMI (72.5% vs. 55.2%, p-value = 0.029), rectal bleeding in the AMI group (15% vs. 4.5%, respectively (67.5% vs. 44.8%, p-value = 0.006).

45 Journal of Surgical Sciences Vol.8, No.2, April – June 2021 space fluid loss were more common in pa-tients correctly diagnosed with AMI, with median A regular blood test in the ER identified hemoglobin 14 g/dL vs. 12.5 g/dL in the non- statistically significant higher median values of AMI patients (CI[11.9-15.3] vs.CI[9.9-14.5], p- leukocytosis in the group of patients with AMI value = 0.014), respectively median hema-tocrit (14.9 x 103/mcL – CI[9-22.8] vs. 12.3 x 103/mcL 40.2% vs. 37.2% (CI[34.4-45.2] vs. CI[29.7- – CI[6.63-11.69), p-value = 0.023). The 42.2], p-value = 0.048). Creatinine levels were neutrophils to leukocytes ratio (NLR) was also found to be increased in patients affected by significantly higher in the AMI group (90.5% - AMI (1.49mg/dL– CI[1.1-2.3] vs. 0.99mg/dL – CI[86.8-62.5] vs. 87.95% - CI[77-92], p-value = CI[0.8-1.9], p-value = 0.011). 0.049). Signs of hemoconcentration due to third

Mesenteric Characteristic Acute abdomen infarction P-value (n=67) (n=80) Leukocytes (a) (103/mcL) 12.3 [6.63-11.69] 14.9 [9-22.8] 0.023* Neutrophils (103/mcL) 9.9 [4.9-13.6] 19.6 [14.3-25.9] 0.002** Neutrophils/Leukocytes ratio (%) 87.95 [77-92] 90.5 [86.8-62.5] 0.049* Lymphocytes (103/mcL) 1.3 [0.8-1.97] 1.9 [1.4-2.5] 0.076 Lymphocytes/Leukocytes ratio (%) 10.8 [6.7-20.3] 8.5 [6.5-11.6] 0.059 Hb (g/dL) (a) 12.5 [9.9-14.5] 14 [11.9-15.3] 0.014* Ht (%) (a) 37.2 [29.7-42.2] 40.2 [34.4-45.2] 0.048* (a) 221 [164-313] 221 [157-286] 0.480 ALAT (U/L) (c) 32 [17-49] 29 [21-45] 0.968 ASAT (U/L) (c) 34 [18-59] 36 [23-76] 0.214 Creatinine (mg/dL) (c) 0.99 [0.8-1.9] 1.49 [1.1-2.3] 0.011* CK (c) 111 [42-286] 173 [78-567] 0.059 CK-MB (c) 20 [12-44] 29 [16-77] 0.097 LDH (c) 359 [210-677] 555 [292-772] 0.096 Amilase (c) 39 [14-111] 59 [20-108] 0.761 Cholinesterase (c) 4130 [2291-5928] 4295 [1960-5748] 0.863 Total bilirubin (c) 0.81 [0.6-1.3] 1 [0.67-1.37] 0.309 Legend: (a) median (IQR); Mann-Whitney U test; *;** statistically significant differences (p < 0.05; p < 0.01); ALAT – alanine aminotransferase; CK – creatine kinase; CK-MB – creatine kinase MB; LDH – lactate dehydrogenase Table 3 - Laboratory results in the ER

Machine Learning models formed the best, having a 61.5% respectively 62.5% precision, an NPV of 64.7% respectively In order to assess the independent risk factors 59.1%, and the AUROC at 0.580, respectively for Mesenteric Infarction, we employed various 0.616, this being the only satisfactory model in Machine Learning (ML) algorithms. In Table 4, terms of overall diagnostic accuracy. we present the performance of these ML algorithms. We can observe that Gradient Boosted Trees and Logistic Regression per-

46 ATTEMPTING TO INCREASE THE CLINICAL DIAGNOSTIC RATE OF ACUTE INTESTINAL ISCHEMIA USING ML ALGORITHMS

Developing the score highest impact on the diagnostic were: Age>65 years, Female gender, Hemo-globin > 12 g/dl, We selected the independent risk factors with Leucocytes >15.000/mm3, Melena/rectal the highest impact to construct the score from the bleeding, Oliguria, and Hypoten-sion. This Gradient Boosted Trees and Logistic Regression. model showed good performance: AUROC We employed ROC curves on the numerical 0.784, 95% CI [0.711-0.857], p<0.001. With a variables in order to find the best diagnostic cut- SN 83.8%, SP 58.2%, PPV 70.5%, NPV 75% off value. The independent risk factors with the and a total accuracy of 70.07%.

Model AUROC SN (%) SP (%) NPV (%) Precision (%)

Gradient Boosted Trees 0.580 57.1 68.7 64.7 61.5

Naive Bayes 0.459 50 31.2 41.6 38.8 Random Forest 0.582 28.5 68.7 52.4 44.4 Neural Network 0.504 14.3 81.2 52 40 Logistic Regression 0.616 35.7 81.2 59.1 62.5 Legend: AUROC – area under the receiver operand curve; SN – sensitivity; SP – Specificity; NPV – negative predictive value Table 4 - Machine Learning models performance

Discussions its low incidence among the acute abdomen cases. With the immense development in artificial Similar to our findings, in other wider studies intelligence (AI), including machine learning [12], the logistic regression ML model was found (ML) processes, the focus of researchers and to have non-inferiority reliability. Here, using a engineers moved towards the applicability of dataset of 960 patients, the re-searchers trained these performant algorithms into the medical models using logistic regression and six ML world, with the purpose of achieving algorithms to predict whether a patient diagnosed economically feasible high-accuracy diagnostic with diabetes has type 1 or type 2 diabetes. The tools and clinical decision systems. Several discrimination efficiency adjustment and studies [11] have tested such a model, where the decision curve analysis of each methodology findings provide the first demonstration of were compared in the external validation dataset. counterfactual reasoning's advantages over The results indicated that the average associative reasoning in a challenging real-world performance attained during internal validation problem. Here While the associative algorithm was comparable across all models, indicating an fared similarly to the average doctor, while the unusually high degree of dependability with an counter-factual algorithm ranked among the top AUROC > 94%. Only external validation 25% of clinicians in the study group, reaching revealed a minor loss in dis-crimination, but the expert clinical accuracy. The improvement logistic regression model had the quantitatively proved most obvious for uncommon and maximum value in ex-ternal validation, with an extremely un-common diseases, where AUROC larger than 95%. Additionally, the diagnostic mistakes are often more frequent and logistic regression model performed well with severe. The counterfactual algorithm rated the regard to calibration and decision curve analysis. genuine disease higher than the associative Regarding the diagnosis of the same condition algorithm in 29.2 percent and 32.9 percent of that was researched in our study, a team of these cases respectively. The model proved researchers in Norway attempted to use ML for useful for rare condi-tions such as the AMI that the intraoperative prediction of the small was researched in the current study, considering intestine viability using bioimpedance data, as

47 Journal of Surgical Sciences Vol.8, No.2, April – June 2021 opposed to our study that has only used clinical Conclusions parameters and paraclinical laboratory tests [13]. Although our research investigated human The logistic regression and gradient-boosted patients, the Norwegian researchers used in-vitro ML algorithms proved useful in creating a score pig jejunum to test the accuracy of their ML that is efficient in detecting AMI using only algorithm, proving that accuracy levels anamnesis data and regular laboratory blood tests comparable to those reported clinically can be available in the ER, as well as proving to be an attained when feedforward neural networks are economically-feasible method since it does not applied to a single bioimpedance meas-urement, involve expensive and unpractical medical tests and better accuracy levels can be attained when or procedures. However, the reli-ability score is long short-term memory units are applied to a fairly limited, thus making the diagnostic method series of data history. a practical tool only when accompanied by the Other major studies have implemented the use surgeon’s experience and decision. Larger of logistic regression models, as at-tempted by studies in the future should externally validate our research, to develop prediction models for this model's accuracy. intestinal necrosis in AMI [14]. Af-ter demonstrating that the white blood cell (WBC) count, blood urea nitrogen (BUN) level, Funding: This research received no external neutrophil ratio, prothrombin time (PT), and funding LnD-dimer are all independent predictors of in- Institutional Review Board Statement: The study testinal necrosis, they used them in a logistic was conducted according to the guidelines of the regression algorithm and discovered that the Declaration of Helsinki and approved by the model performs exceptionally well at predicting Hospital's Ethics Committee of the "Pius the onset of intestinal necrosis, with an AU-ROC Brinzeu" County Emergency Clinical Hos-pital of 89 percent, specificity of 78 percent, and Timisoara sensitivity of 78 percent. Informed Consent Statement: Informed consent was obtained from all subjects involved in the Limitations study. Conflicts of Interest: The authors declare no It is difficult to translate AI research in a safe conflict of interest. and timely manner into clinically tested and suitably regulated systems that benefit everyone. It is critical to conduct robust clinical evaluations References that utilize criteria that are obvious to physicians and, ideally, go beyond technical correctness to [1] Mastoraki A., Mesenteric ischemia: Pathogenesis include measures of quality of treatment and and challenging di-agnostic and therapeutic patient outcomes [15]. Additional work is modalities. World Journal of Gastrointestinal necessary to uncover and mitigate algorithmic Pathophysiolo-gy, 7(1), 125, 2016 bias and unfairness, minimize brittleness and [2]Bala M., Kashuk, Jeffry, Moore, Ernest E.; Kluger, increase generalizability, and create approaches Yoram; Biffl, Walter; Gomes, Carlos Augusto; Ben- for improving the interpretability of machine Ishay, Offir; Rubinstein, Chen; Balogh, Zsolt J.; learning predictions. If these objectives are met, Civil, Ian; Cocco-lini, Federico; Leppaniemi, Ari; Peitzman, Andrew; Ansaloni, Luca; Sugrue, Michael; the resulting benefits to patients are likely to be Sartelli, Massimo; Di Saverio, Salomone; Fraga, transformative. Gustavo P.; Catena, Fausto (2017). Acute mesenteric The current research did not test for internal ischemia: guidelines of the World Society of or external validation, thus a reduction in the Emergency Surgery. World Journal of Emergency model's discrimination cannot be ruled out, as Surgery, 12(1), 38. well as the diagnostic tools remain to be [3] Florim, S.; Almeida, A.; Rocha, D.; Portugal, P. validated in other studies to allow for its (2018). Acute mesenteric ischaemia: a pictorial implementation in clinical practice [16]. review. Insights into Imaging. [4]Patel, Amit; Kaleya, Ronald N.; Sammartano, Robert J. (1992). Pathophysiology of Mesenteric

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