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    "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0095">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">The concept of sepsis began to be defined in 1992&#44; when the first consensus on sepsis&#44; Sepsis-1&#44; was published&#46; The concept gave rise to the Systemic Inflammatory Response &#40;SIRS&#41;&#44; defining sepsis as a &#8220;systemic inflammatory response associated with a disease&#46;&#8221; In this consensus&#44; levels of severity were also added&#58; severe sepsis and septic shock&#46; In 2001&#44; modifications were made&#58; a group of experts met and called this meeting Sepsis-2&#46; Then&#44; some values &#8203;used for diagnosis were adjusted&#59; however&#44; there were no significant changes in the definition of sepsis&#46; A working group met again in 2016 and published an update on sepsis called Sepsis-3&#44; which determined the most current definition&#46; It was published by The Sepsis Definitions Working Group and defined sepsis as &#8220;a life-threatening organ dysfunction caused by a dysregulated host response to infection&#46;&#8221; In addition to the definition&#44; it included tools to help diagnose sepsis&#46;<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1&#44;2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Concerning the loss of human life&#44; sepsis entails a high economic cost for healthcare systems&#46; In the United States&#44; one-third of patients diagnosed with sepsis die at a cost of about &#36;20&#44;3 million a year&#46; In Spain&#44; the incidence is 100 cases per 100&#44;000 people&#47;year&#44; and mortality is also between 20&#37; and 43&#37;&#46; The estimated average cost is about &#36;20&#44;000 for each episode of severe sepsis&#46;<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3&#8211;5</span></a> A considerable drawback in managing sepsis is the great difficulty in reaching a diagnosis&#46;There is no specific test to establish the diagnosis&#44; and the symptoms present very heterogeneously&#44; making it more challenging to determine the onset of the disease and&#44; consequently&#44; start treatment as soon as possible&#46; There is evidence that early diagnosis of sepsis and&#44; therefore&#44; Early initiation of treatment significantly reduces morbidity and mortality&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Initially&#44; during the onset of sepsis&#44; it is difficult to find symptoms or parameters that help us diagnose it&#46; When we find easily recognizable signs&#44; the disease is usually in an advanced stage&#44; which entails more complex treatment and a worse prognosis&#46; The efforts of many researchers are focused on developing tools that allow early detection of sepsis and its optimal management&#46; Most hospitals have tools that attempt to detect and predict the onset of sepsis and its complications&#46; The most used currently are the Modified Early Warning Score &#40;MEWS&#41; and its different versions&#44; Systemic Inflammatory Response Syndrome &#40;SIRS&#41;&#44; the Sequential Organ Failure Assessment &#40;SOFA&#41;&#44; and its faster variant qSOFA&#46;<a class="elsevierStyleCrossRefs" href="#bib0025"><span class="elsevierStyleSup">5&#8211;7</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">The introduction of electronic medical records in most hospitals makes it easier to access and use patient data &#40;since&#41; it is collected in a structured way and can be accessed quickly&#46; It favors the development and implementation of prediction and decision-making systems&#46; These tools facilitate processing the large amount of data we face when making decisions when treating patients&#46; These tools aim to improve patient outcomes by facilitating early diagnosis and treatment decision-making&#46;<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3&#44;7</span></a></p><p id="par0025" class="elsevierStylePara elsevierViewall">Traditional methods &#40;MEWS&#44; SOFA&#44; SIRS&#44; among others&#41; are being widely questioned&#59; Numerous studies defend Artificial Intelligence &#40;AI&#41; programs&#46; Implement algorithms that predict sepsis&#44; obtaining results with greater sensitivity and specificity than traditional methods&#46;</p><p id="par0030" class="elsevierStylePara elsevierViewall">Machine learning and Big Data &#40;BD&#41; are techniques expected to displace traditional methods and assist in research that cannot be performed with patients due to ethical limitations or other reasons&#46;<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8&#44;9</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">Some algorithms&#39; lack of transparency and the staff&#39;s poor knowledge of their operation also generate resistance to their application&#44; creating distrust and rejection of the system&#46;<a class="elsevierStyleCrossRefs" href="#bib0050"><span class="elsevierStyleSup">10&#44;11</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">Our objective was to develop a machine learning model of ICU mortality due to sepsis in a local and in an international population as MIMIC III open database and to evaluate the performance of each one</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0100">Methods</span><p id="par0045" class="elsevierStylePara elsevierViewall">Design&#58; Cross-sectional descriptive study&#46;</p><p id="par0050" class="elsevierStylePara elsevierViewall">Setting&#58; It was carried out in the Intensive Care Units &#40;ICUs&#41; of the Virgen de la Arrixaca University Hospital&#44; Santa Luc&#237;a Hospital&#44; and Los Arcos Hospital&#46; The MIMIC III database&#46;</p><p id="par0055" class="elsevierStylePara elsevierViewall">Study population&#58; 180 patients diagnosed with sepsis&#44; 56 of whom died in the ICU&#46; All patients who met the hospital&#39;s inclusion and exclusion criteria during 2022&#8722;23 were selected&#46; Informed consent was obtained from the patients and&#47;or family members for the anonymous use of the data&#46; This study was carried out by the principles of the Declaration of Helsinki and was registered with the hospital&#39;s research committee&#46; IRB Declaration Code 646 Health Area1Arrixaca&#46; Murcian Health Service&#46; Murcia Spain&#46; Authorization date 07&#47;02&#47;2022&#46;<ul class="elsevierStyleList" id="lis0005"><li class="elsevierStyleListItem" id="lsti0005"><span class="elsevierStyleLabel">1</span><p id="par0060" class="elsevierStylePara elsevierViewall">Database obtained from local hospital</p></li></ul></p><p id="par0065" class="elsevierStylePara elsevierViewall">Inclusion criteria&#58;<ul class="elsevierStyleList" id="lis0010"><li class="elsevierStyleListItem" id="lsti0010"><span class="elsevierStyleLabel">-</span><p id="par0070" class="elsevierStylePara elsevierViewall">Over 18 years&#46;</p></li><li class="elsevierStyleListItem" id="lsti0015"><span class="elsevierStyleLabel">-</span><p id="par0075" class="elsevierStylePara elsevierViewall">ICD-10 &#40;International Classification of Diseases&#41;&#58; sepsis&#44; severe sepsis&#44; or septic shock&#46;</p></li><li class="elsevierStyleListItem" id="lsti0020"><span class="elsevierStyleLabel">-</span><p id="par0080" class="elsevierStylePara elsevierViewall">Computerized clinical history&#46;</p></li></ul></p><p id="par0085" class="elsevierStylePara elsevierViewall">Exclusion criteria&#58;<ul class="elsevierStyleList" id="lis0015"><li class="elsevierStyleListItem" id="lsti0025"><span class="elsevierStyleLabel">-</span><p id="par0090" class="elsevierStylePara elsevierViewall">Under 18 years</p></li><li class="elsevierStyleListItem" id="lsti0030"><span class="elsevierStyleLabel">-</span><p id="par0095" class="elsevierStylePara elsevierViewall">Patients admitted for reasons other than the CDI above&#46;</p></li><li class="elsevierStyleListItem" id="lsti0035"><span class="elsevierStyleLabel">-</span><p id="par0100" class="elsevierStylePara elsevierViewall">Patients were readmitted to the ICU within &#60;&#160;24&#160;h&#46;</p></li></ul></p><p id="par0105" class="elsevierStylePara elsevierViewall">Variables from the hospital database obtained during the first 24&#160;h&#58;</p><p id="par0110" class="elsevierStylePara elsevierViewall">Qualitative&#58;</p><p id="par0115" class="elsevierStylePara elsevierViewall">Exitus&#58; Death of the patient diagnosed with sepsis in the Intensive Care Unit&#46; Dichotomous qualitative variable Yes &#40;1&#41;&#47;No &#40;0&#41;</p><p id="par0120" class="elsevierStylePara elsevierViewall">Sex&#58; The patient&#39;s sex is reflected in the medical history&#46; The qualitative&#44; nominal&#44; discrete&#44; independent variable is male &#40;0&#41; or female &#40;1&#41;&#46;<ul class="elsevierStyleList" id="lis0020"><li class="elsevierStyleListItem" id="lsti0040"><span class="elsevierStyleLabel">-</span><p id="par0125" class="elsevierStylePara elsevierViewall">Hypertension&#58; Reflected in the medical history in the medical evaluation upon admission&#46; Independent dichotomous qualitative variable&#58; Yes &#40;0&#41;&#44; No &#40;1&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0045"><span class="elsevierStyleLabel">-</span><p id="par0130" class="elsevierStylePara elsevierViewall">Diabetes&#58; Reflected in the medical history in the medical evaluation upon admission&#46; Independent dichotomous qualitative variable&#58; Yes &#40;0&#41;&#44; No &#40;1&#41;&#46;</p></li><li class="elsevierStyleListItem" id="lsti0050"><span class="elsevierStyleLabel">-</span><p id="par0135" class="elsevierStylePara elsevierViewall">Consumption of vasoactive drugs&#58; Existence of a record of vasoactive drug consumption in the electronic medical record&#46; Variable&#44; qualitative&#44; dichotomous&#44; dependent&#58; Yes &#40;0&#41;&#44; No &#40;1&#41;&#46;</p></li></ul></p><p id="par0140" class="elsevierStylePara elsevierViewall">Quantitative&#58;</p><p id="par0145" class="elsevierStylePara elsevierViewall">Heart rate &#40;heart rate&#41;&#58; The number of beats per minute is automatically uploaded to the electronic medical record every hour&#46; It is quantitative&#44; discrete&#44; and dependent&#46;</p><p id="par0150" class="elsevierStylePara elsevierViewall">Respiratory rate &#40;Resprate&#41;&#58; The number of breaths per minute is automatically uploaded to the electronic medical record every hour&#46; It is quantitative&#44; discrete&#44; and dependent&#46;</p><p id="par0155" class="elsevierStylePara elsevierViewall">Systolic blood pressure &#40;SBP&#41;&#58; Expressed in mmHG&#44; it is captured by the blood pressure monitor automatically or manually with a cuff or arterial line and automatically uploaded to the electronic medical record every hour&#46; It is quantitative&#44; discrete&#44; and dependent&#46;<ul class="elsevierStyleList" id="lis0025"><li class="elsevierStyleListItem" id="lsti0055"><span class="elsevierStyleLabel">-</span><p id="par0160" class="elsevierStylePara elsevierViewall">Diastolic blood pressure &#40;DBP&#41;&#58; Expressed in mmHG&#44; captured by the blood pressure monitor automatically or manually with a cuff or arterial line&#44; automatically uploaded to the electronic medical record every hour&#46; Quantitative&#44; discrete&#44; dependent&#46;</p></li></ul></p><p id="par0165" class="elsevierStylePara elsevierViewall">Oxygen saturation &#40;SpO2&#41;&#58; This is expressed as a percentage of the saturation captured by the monitor with hourly finger pulse oximetry&#46; It is quantitative&#44; discrete&#44; and dependent&#46;</p><p id="par0170" class="elsevierStylePara elsevierViewall">Temperature&#58; Taken manually by a clinical assistant with a tympanic meter at least every hour&#44; expressed with two whole numbers and one decimal&#46; It is quantitative&#44; continuous&#44; and dependent&#46;</p><p id="par0175" class="elsevierStylePara elsevierViewall">Arterial or venous lactate level&#58; The lactate figure obtained after arterial or venous blood gases with variable frequency are analyzed in the units available in the ICU and expressed in millimoles per liter &#40;mmo&#47;l&#41;&#46; It is quantitative&#44; continuous&#44; and dependent&#46;</p><p id="par0180" class="elsevierStylePara elsevierViewall">Potassium levels &#40;K&#41;&#58; Potassium levels obtained after performing arterial or venous blood gases with variable frequency&#44; analyzed in the units available in the ICU&#44; and expressed in mEq&#47;l&#46; They are quantitative&#44; continuous&#44; and dependent&#46;<ul class="elsevierStyleList" id="lis0030"><li class="elsevierStyleListItem" id="lsti0060"><span class="elsevierStyleLabel">-</span><p id="par0185" class="elsevierStylePara elsevierViewall">arterial or venous pH&#58; The pH level figure obtained after performing arterial or venous blood gases with variable frequency&#44; analyzed in the units available in the ICU&#46; It is quantitative&#44; continuous&#44; and dependent&#46;</p></li></ul></p><p id="par0190" class="elsevierStylePara elsevierViewall">Body weight&#58; Obtained from the electronic medical record &#40;EHR&#41;&#44; expressed in kilograms &#40;kg&#41;&#46; It is quantitative&#44; continuous&#44; and independent&#46;<ul class="elsevierStyleList" id="lis0035"><li class="elsevierStyleListItem" id="lsti0065"><span class="elsevierStyleLabel">-</span><p id="par0195" class="elsevierStylePara elsevierViewall">Age&#58; Figure obtained from the EHR&#44; expressed in years completed&#46; Quantitative&#44; discreet&#44; independent&#46;</p></li></ul></p><p id="par0200" class="elsevierStylePara elsevierViewall">The hospital database was automatically extracted by dumping by the research staff of the centers into a cell format of the IntelliSpace Critical Care &#38; Anesthesia &#40;ICCA&#41; healthcare software&#46;<ul class="elsevierStyleList" id="lis0040"><li class="elsevierStyleListItem" id="lsti0070"><span class="elsevierStyleLabel">2</span><p id="par0205" class="elsevierStylePara elsevierViewall">MIMIC III database</p></li></ul></p><p id="par0210" class="elsevierStylePara elsevierViewall">The MIMIC III database was extracted from Physionet&#58; <a href="https://physionet.org/content/mimic3wdb/1.0/.1">https&#58;&#47;&#47;physionet&#46;org&#47;content&#47;mimic3wdb&#47;1&#46;0&#47;&#46;1</a>&#46;</p><p id="par0215" class="elsevierStylePara elsevierViewall">The total database initially consisted of 80 variables and 4559 patients&#46; A correlation study was performed&#44; and strongly correlated variables were eliminated&#46; The reduced database contained 31 variables and 4559 patients&#46; The objective was to find a classification model based on exitus &#40;hospital&#95;expire&#95;flag&#41; A total of 31 variables were collected from the MIMIC III database&#58; categorical sex&#44; ethnicity&#44; metastatic&#95;cancer&#44; diabetes&#44; quantitative&#59; age&#44; hospital&#95;elixhauser&#44; vent&#44; couch&#44; sirs&#44; qsofa&#44; aniongap&#95;medium&#44; bocarbonate&#95;medium&#44; creatinine&#95;medium&#44; glucose&#95;medium&#44; hemoglobin&#95;medium&#44; lactate&#95;medium&#44; platelet&#95;means&#44; potassium&#95;means&#44; inr&#95;means&#44; sodium&#95;means&#44; wbc&#95;means&#44; heartrate&#95;means&#44; sysbp&#95;means&#44; diasbp&#95;means&#44; resprate&#95;means&#44; tempc&#95;means&#44; spo2&#95;media s&#44; urine output&#44; sepsis&#44; hospital&#95;expire&#95;flag&#46;</p><p id="par0220" class="elsevierStylePara elsevierViewall">Statistical analysis in both databases was performed using SPSS software for Windows &#40;version 25&#46;0&#44; SPSS&#44; Chicago&#44; Illinois&#44; USA&#41; and R &#40;version 3&#46;5&#46;1&#41;&#46; A bivariate analysis was performed&#44; and the normality of the variables was checked using the Kolmogorov-Smirnoff test&#46; The non-parametric Wilcoxon rank sum test &#40;Mann-Whitney test&#41; was used for two samples&#46; Random forest-type classification models were implemented&#46; The ROC curve calculated the AUC &#40;Area Under the Curve&#41;&#44; and the confusion matrix &#40;actual vs predicted group&#41; estimated the classification models&#39; accuracy&#44; precision&#44; sensitivity&#44; and specificity&#46;</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0105">Results</span><p id="par0225" class="elsevierStylePara elsevierViewall">In the database collected at the hospital&#44; the study population was 180 patients&#44; of which 42&#46;85&#37; of the 110 male patients and 46&#46;80&#37; of the 70 female patients died from sepsis&#46; There were no statistically significant differences between death and ICU admission days&#46; And the total days of stay in the ICU &#40;until death or discharge to the ward&#41;&#46; A summary of the descriptive statistics of our database is shown in <a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>&#46;</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0230" class="elsevierStylePara elsevierViewall">Regarding the qualitative variables from the local database&#44; no statistically significant relationship was found between sex and exitus&#44; hypertension and exitus &#40;p-value&#160;&#61;&#160;0&#44;098&#41;&#44; and diabetes and exitus &#40;p-value&#160;&#61;&#160;0&#44;138&#41;&#46;</p><p id="par0235" class="elsevierStylePara elsevierViewall">In the case of the qualitative variables collected in the MIMIC database the following variables were statistically significant&#58; mechanic ventilation-exitus &#40;p-value &#60;&#160;-2&#44;2e-16&#41;&#44; renal Replacement therapy-exitus &#40;p-value&#160;&#61;&#160;0&#44;00075&#41;&#44; metastatic cancer &#40;p-value&#160;&#61;&#160;3&#46;02e-10&#41; and blood culture positive &#40;p-value&#160;&#61;&#160;7&#46;528e-06&#41;&#46;Diabetes-exitus variable was non-statistically significant &#40;p-value&#160;&#61;&#160;0&#44;7735&#41;</p><p id="par0240" class="elsevierStylePara elsevierViewall">The model was validated by dividing the local database into a training database containing 80&#37; of the data on which a random forest was computed and a test database containing the remaining 20&#37; of the data on which this model was applied&#46; <a class="elsevierStyleCrossRef" href="#fig0005">Fig&#46; 1</a> shows the random forest with the variables that have been important in determining death in patients with sepsis&#46;</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="par0245" class="elsevierStylePara elsevierViewall">The most critical variables in our database have been the average lactate level&#44; diuresis&#40;urine output&#41;&#44; pH&#44; and systolic pressure&#46;</p><p id="par0250" class="elsevierStylePara elsevierViewall">The model has an accuracy of 98&#37; &#40;0&#46;89&#8722;0&#46;99&#41;&#44; a sensitivity of 97&#37; and a specificity of 100&#37;&#46; The ROC curve and the AUC &#40;0&#44;97&#40;0&#44;91&#8211;1&#41;&#41; are shown in the <a class="elsevierStyleCrossRef" href="#fig0010">Fig&#46; 2</a>&#46;</p><elsevierMultimedia ident="fig0010"></elsevierMultimedia><p id="par0255" class="elsevierStylePara elsevierViewall">The study&#39;s second objective was calculating the random forest obtained from the public database MIMIC III&#46; The study population was 4559 patients&#44; of which 741 patients died from sepsis&#46; There were no significant differences between the sexes&#46; <a class="elsevierStyleCrossRef" href="#tbl0010">Table 2</a> shows the descriptive statistics from the MIMIC III database&#46; The variables &#40;mean&#41;&#58; Age&#44; anion gap&#44; bicarbonate&#44; creatinine&#44; glucose&#44; lactate&#44; potassium&#44;inr&#44; sodium&#44; bun&#44;wbc&#44; heart rate&#44; sysbp&#44; diasbp&#44; bp&#44; respiration rate&#44;tempc&#44; spo2&#44; and urine output were statistically significant differences A Random Forest based on exitus &#40;hospital&#95;expire&#95;flag&#41; is carried out in the SEPSIS&#95;GROUP database that shows the variables of importance &#40;<a class="elsevierStyleCrossRef" href="#fig0015">Fig&#46; 3</a>&#41;&#46; The sensitivity of the classification model was 97&#46;55&#37;&#44; the specificity was 100&#37;&#44; and the accuracy was 98&#46;28&#37; with an AUC of 97&#46;3&#37; &#40;0&#46;968&#8722;0&#46;981&#41; &#40;<a class="elsevierStyleCrossRef" href="#fig0020">Fig&#46; 4</a>&#41;&#46;</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia><elsevierMultimedia ident="fig0015"></elsevierMultimedia><elsevierMultimedia ident="fig0020"></elsevierMultimedia><p id="par0260" class="elsevierStylePara elsevierViewall">The most significant variables are the average urinary output&#44; lactate mean&#44; anion gap&#44; spO2&#44; age&#44; potassium mean&#44; respiration rate&#44; and systolic blood pressure mean&#46;</p><p id="par0265" class="elsevierStylePara elsevierViewall">The standard variables in both databases that have shown the most significant importance have been the average lactate and the average urine production&#44; adding the average oxygen saturation&#44; the average temperature&#44; and&#44; in our case&#44; the pH measurement that could correspond&#46; In the case of the MIMIC III&#44; with the bicarbonate mean and Annion gap&#46; Other fundamental constants in the MIMIC III&#44; such as the average respiratory rate and potassium mean that the hospital&#39;s database appears&#44; but not with relevance&#46; Systolic pressure is quite important in both databases&#46;</p><p id="par0270" class="elsevierStylePara elsevierViewall">Finally&#44; we proposed to evaluate the model calculated from the local database as an external validation of the MIMIC III database&#46; For this purpose&#44; we use the common variables between the local and MIMIC III databases&#46; We included the common 12 variables&#58; Age&#44; exitus&#44; uci&#95;days&#44; Heartrate&#95;mean&#44; Resprate&#95;mean&#44; Sysbp&#95;mean&#44; Diasbp&#95;mean&#44; Spo2&#95;mean&#44; Tempc&#95;mean&#44; Urine&#95;mean&#44; Lactate&#95;mean and Potassium&#95;mean&#46; The accuracy was 0&#44;78 &#40;0&#44;49&#8211;0&#44;95&#41; from the local database and an AUC of 0&#44;70 &#40;0&#44;40&#8211;1&#41;&#46; The explanation for this low accuracy was that the variable ph mean was essential in the model built with our local database&#46; From the MIMIC database&#44; the accuracy was only 0&#44;56 &#40;0&#44;55&#8211;0&#44;58&#41; and an AUC of 0&#44;58 &#40;0&#44;56&#8211;0&#44;60&#41;&#46; The accuracy in this case could have been lower due to the reduction of the number of essential variables from 31 to 12 variables&#46;</p><p id="par0275" class="elsevierStylePara elsevierViewall">We concluded that the external validation of our model was not possible due to the different number of variables and the absence in the MIMIC III database of a critical variable such as ph mean</p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0110">Discussion</span><p id="par0280" class="elsevierStylePara elsevierViewall">Since the 2000s&#44; the publication of articles related to machine learning has increased significantly&#44; becoming one of the study trends at the health research level&#46; Open access to extensive patient databases allows machine learning techniques to be applied to diagnosing and prognosis different diseases&#46; In this work&#44; we have used the MIMIC III public access database of ICU patients with sepsis to contrast the model calculated with machine learning techniques with the one generated with our database&#46;</p><p id="par0285" class="elsevierStylePara elsevierViewall">Many published articles use the MIMIC III database to evaluate risk variables in patients with sepsis&#46; Vital signs&#44; age&#44; demographic data&#44; and the MIMIC III database helped develop the Insight algorithm&#44; which performed well compared to the various methods used until then&#46;<a class="elsevierStyleCrossRef" href="#bib0060"><span class="elsevierStyleSup">12</span></a></p><p id="par0290" class="elsevierStylePara elsevierViewall">Calvert et al&#46; carried out the study with the MIMIC II database&#44; using both vital signs and laboratory values &#8203;&#8203;&#40;Procalcitonin and lactic acid&#41;&#59; using Insight&#44; they obtained better results than other models&#46;<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a> Other authors&#44; such as Kam and Kim&#44; extracted vital signs from MIMIC II to perform a deep-learning study&#46; They compared deep learning networks with LSTM &#40;long short-term memory&#41; architectures&#46; They obtained better LSTM results than other sepsis prediction methods&#46;<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a> Moor et al&#46; performed a sepsis prediction study mixing machine learning techniques with DTW &#40;Dynamic et al&#46;&#41;&#46; They used MIMIC III as a database to extract vital signs and laboratory values&#46; They obtained good results compared to other works&#46;<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0295" class="elsevierStylePara elsevierViewall">Nemati et al&#46; carried out a test with two different databases&#59; they used both Emory Healthcare and MIMIC III&#44; the former to develop the algorithm and the latter as a control&#46; They took as values &#8203;&#8203;the vital signs&#44; laboratory results&#44; demographic data&#44; surgical history&#44; and statistical data&#46;<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a></p><p id="par0300" class="elsevierStylePara elsevierViewall">Mao et al&#46; relied on the Insight algorithm and used vital signs from several hospitals to try to predict sepsis&#44; severe sepsis&#44; and septic shock&#46; They used a Gradient tree-boosting algorithm&#46; The database used to train the algorithm was MIMIC III&#44; using transfer learning&#44; with which they obtained better results than other databases&#46;<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a></p><p id="par0305" class="elsevierStylePara elsevierViewall">Scherph et al&#46; calculated a neural network architecture using the MIMIC III database&#44; from which they obtained vital signs and white blood cell counts&#46; They compared the results with the Insight algorithm&#46; They obtained better results in terms of AUC&#46;<a class="elsevierStyleCrossRef" href="#bib0010"><span class="elsevierStyleSup">2</span></a></p><p id="par0310" class="elsevierStylePara elsevierViewall">Van Steenkiste et al&#46; developed a blood culture results prediction study with an LSTM structure&#46; Blood cultures are essential in choosing sepsis treatment&#46; They took laboratory and vital sign data in the hospital emergency environment to carry it out&#46; They concluded that the prediction period of the previous 72&#160;h is ideal and that they obtained better results with deep learning than with other methods&#46;<a class="elsevierStyleCrossRef" href="#bib0085"><span class="elsevierStyleSup">17</span></a> Schaomoni et al&#46; experimented with linear and non-linear models&#44; their significant contribution being a new and verified database&#46; They used several parameters&#44; such as respiratory rate and C-reactive protein &#40;CRP&#41;&#46; Furthermore&#44; they investigated the individual evolution of each prediction&#46;<a class="elsevierStyleCrossRef" href="#bib0090"><span class="elsevierStyleSup">18</span></a></p><p id="par0315" class="elsevierStylePara elsevierViewall">Ribas Ripoll et al&#46; analyzed the mortality prediction in ICU patients with sepsis by developing a new method called the Quotient Basis Kernel &#40;QBK&#41;&#46; They simplified the Fisher kernel and used variables such as days of stay or vasoactive agents from the MIMIC II database&#46;<a class="elsevierStyleCrossRef" href="#bib0095"><span class="elsevierStyleSup">19</span></a></p><p id="par0320" class="elsevierStylePara elsevierViewall">In our study carried out with the databases of several hospitals and the public database MIMIC III&#44; the essential variables standard to the local databases were mean lactate&#44; mean pH&#44; urine output&#44; and partial O2 saturation in addition to age&#44; and the minimum SBP&#44; achieving an accuracy of 95&#37;&#8211;98&#37; of the predictions of death due to sepsis&#46; The variables respiratory rate&#44; amount of urine&#44; and minimum potassium were also added to the classification model calculated on the MIMIC III database&#46; The importance of potassium in the MIMIC III database is relevant&#44; while in our hospital&#44; it is identified in the random forest but with less importance&#46;</p><p id="par0325" class="elsevierStylePara elsevierViewall">Of all the variables in both databases&#44; lactate has particular relevance&#46; According to recent publications&#44; high circulating levels of lactate are associated with the severity and mortality of sepsis&#46; Lactate could promote the release of HMGB1 during sepsis&#46; Some studies have determined that lactate participates in the lactylation and acetylation of HMGB1 in macrophages during polymicrobial sepsis&#46; Macrophages can uptake extracellular lactate via monocarboxylate transporters &#40;MCTs&#41; to promote HMGB1 lactylation through a p300&#47;CBP-dependent mechanism&#46; Lactate has been shown to stimulate HMGB1 acetylation through suppression of the Hippo&#47;YAP-mediated SIRT1 deacetylase and &#946;-arrestin2-mediated recruitment of p300&#47;CBP acetylases to the nucleus via G protein-coupled receptor 81 &#40;GPR81&#41;&#46; Lactylated&#47;acetylated HMGB1 is released from macrophages through exosome secretion&#44; increasing endothelial permeability&#46; In vivo&#44; reducing lactate production and&#47;or inhibition of GPR81-mediated signaling decreases circulating levels of exosomal HMGB1 and improves survival outcomes in polymicrobial sepsis&#46;<a class="elsevierStyleCrossRef" href="#bib0100"><span class="elsevierStyleSup">20</span></a></p><p id="par0330" class="elsevierStylePara elsevierViewall">The main limitation of this work was that the variables of the local database&#44; obtained from local hospital records&#44; and the MIMIC III database have a very different number of variables &#40;31 MIMIC III and 15 local databases&#41;&#44; and some variables are not common to both databases&#46; In our study&#44; the variable pH mean was the most critical variable in the random forest and is not included in the MIMIC III database&#46; We could equipare pH with the bicarbonate and anion gap values of the MIMC II database&#46; These variables are important in the random forest calculated with this database&#46; As these variables are not comparable to pH&#44; it is impossible to validate our model with this database&#46; In any case&#44; the study&#39;s main conclusion is the importance of the variables urine output&#44; satO2&#44; Sisbp mean&#44; lactate levels&#44; and variables related to acid-base equilibrium&#44; such as pH and anion gap&#46; Another conclusion is that despite the difference in the number of variables&#44; the machine learning models calculated with the local database and the MIMIC III database have similar accuracies&#44; sensitivities&#44; and specificities&#46;</p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0115">Conclusion</span><p id="par0335" class="elsevierStylePara elsevierViewall">Many authors have developed predictive models for death in sepsis patients admitted to intensive care units&#46; Artificial intelligence techniques and access to standardized and public databases are essential to developing these models&#46; In both databases&#44; lactate levels&#44; average urine production&#44; and variables related to acid-base equilibrium are critical variables in the prognosis of sepsis&#46;</p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0120">Authors&#8217; contributions</span><p id="par0340" class="elsevierStylePara elsevierViewall">All authors contributed to the study conception and design&#46; Material preparation&#44; data collection and analysis were performed by Javier Carrillo P&#233;rez-Tom&#233;&#44; Gracia Castro-Luna&#44; Ana Bel&#233;n Casta&#241;o-Fernandez&#44; Bruno Jos&#233; Nievas-Soriano and Tesif&#243;n Parr&#243;n-Carre&#241;o&#46;</p><p id="par0345" class="elsevierStylePara elsevierViewall">The first draft of the manuscript was written by Gracia Castro-Luna and all authors commented on previous versions of the manuscript&#46; All authors read and approved the final manuscript&#46;</p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0125">Statements and declarations</span><p id="par0350" class="elsevierStylePara elsevierViewall"><ul class="elsevierStyleList" id="lis0045"><li class="elsevierStyleListItem" id="lsti0075"><span class="elsevierStyleLabel">&#9642;</span><p id="par0355" class="elsevierStylePara elsevierViewall">The authors have no relevant financial or non-financial interests to disclose&#46;</p></li><li class="elsevierStyleListItem" id="lsti0080"><span class="elsevierStyleLabel">&#9642;</span><p id="par0360" class="elsevierStylePara elsevierViewall">The authors have no conflicts of interest to declare relevant to this article&#39;s content&#46;</p></li><li class="elsevierStyleListItem" id="lsti0085"><span class="elsevierStyleLabel">&#9642;</span><p id="par0365" class="elsevierStylePara elsevierViewall">All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript&#46;</p></li><li class="elsevierStyleListItem" id="lsti0090"><span class="elsevierStyleLabel">&#9642;</span><p id="par0370" class="elsevierStylePara elsevierViewall">The authors have no financial or proprietary interests in any material discussed in this article&#46;</p></li></ul></p></span></span>"
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        "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0010">Objective</span><p id="spar0040" class="elsevierStyleSimplePara elsevierViewall">To develop a sepsis death classification model based on machine learning techniques for patients admitted to the Intensive Care Unit &#40;ICU&#41;&#46;</p></span> <span id="abst0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0015">Design</span><p id="spar0045" class="elsevierStyleSimplePara elsevierViewall">Cross-sectional descriptive study&#46;</p></span> <span id="abst0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0020">Setting</span><p id="spar0050" class="elsevierStyleSimplePara elsevierViewall">The Intensive Care Units &#40;ICUs&#41; of three Hospitals from Murcia &#40;Spain&#41; and patients from the MIMIC III open-access database&#46;</p></span> <span id="abst0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0025">Patients</span><p id="spar0055" class="elsevierStyleSimplePara elsevierViewall">180 patients diagnosed with sepsis in the ICUs of three hospitals and a total of 4559 patients from the MIMIC III database&#46;</p></span> <span id="abst0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Main variables of interest</span><p id="spar0060" class="elsevierStyleSimplePara elsevierViewall">Age&#44; weight&#44; heart rate&#44; respiratory rate&#44; temperature&#44; lactate levels&#44; partial oxygen saturation&#44; systolic and diastolic blood pressure&#44; pH&#44; urine&#44; and potassium levels&#46;</p></span> <span id="abst0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">Results</span><p id="spar0065" class="elsevierStyleSimplePara elsevierViewall">A random forest classification model was calculated using the local and MIMIC III databases&#46; The sensitivity of the model of our database&#44; considering all the variables classified as important by the random forest&#44; was 95&#46;45&#37;&#44; the specificity was 100&#37;&#44; the accuracy was 96&#46;77&#37;&#44; and an AUC of 95&#37;&#46; &#46; In the case of the model based on the MIMIC III database&#44; the sensitivity was 97&#46;55&#37;&#44; the specificity was 100&#37;&#44; and the precision was 98&#46;28&#37;&#44; with an AUC of 97&#46;3&#37;&#46;</p></span> <span id="abst0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Conclusions</span><p id="spar0070" class="elsevierStyleSimplePara elsevierViewall">According to random forest classification in both databases&#44; lactate levels&#44; urine output and variables related to acid&#46;base equilibrium were the most important variable in mortality due to sepsis in the ICU&#46; The potassium levels were more critical in the MIMIC III database than the local database&#46;</p></span>"
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        "resumen" => "<span id="abst0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">Objetivo</span><p id="spar0075" class="elsevierStyleSimplePara elsevierViewall">Desarrollar un modelo de clasificaci&#243;n basado en t&#233;cnicas de machine-learning de muerte por sepsis para pacientes ingresados en la Unidad de Cuidados Intensivos &#40;UCI&#41;&#46;</p></span> <span id="abst0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Dise&#241;o</span><p id="spar0080" class="elsevierStyleSimplePara elsevierViewall">Estudio descriptivo transversal&#46;</p></span> <span id="abst0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">&#196;mbito</span><p id="spar0085" class="elsevierStyleSimplePara elsevierViewall">Unidades de Cuidados Intensivos &#40;UCI&#41; de tres hospitales de Murcia &#40;Espa&#241;a&#41; y pacientes con sepsis-3 de la base de datos de acceso abierto MIMIC III&#46;</p></span> <span id="abst0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Pacientes</span><p id="spar0090" class="elsevierStyleSimplePara elsevierViewall">180 pacientes diagnosticados de sepsis en las UCI de tres hospitales y un total de 4559 pacientes con la base de datos MIMIC III&#46;</p></span> <span id="abst0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Variables de inter&#233;s principales</span><p id="spar0095" class="elsevierStyleSimplePara elsevierViewall">Se evaluaron la edad&#44; el peso&#44; la frecuencia cardiaca&#44; la frecuencia respiratoria&#44; la temperatura&#44; los niveles de lactato&#44; la saturaci&#243;n parcial de ox&#237;geno&#44; la presi&#243;n arterial sist&#243;lica y diast&#243;lica&#44; el pH&#44; los niveles de orina y los niveles de potasio&#46;</p></span> <span id="abst0065" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">Resultados</span><p id="spar0100" class="elsevierStyleSimplePara elsevierViewall">Se calcularon un modelo de clasificaci&#243;n de bosque aleatorio con la base de datos local y la base de datos MIMIC III&#46; La sensibilidad del modelo de nuestra base de datos teniendo en cuenta todas las variables catalogadas como importantes por el random forest fue del 95&#44;45&#37;&#37;&#44; la especificidad del 100&#37; y la exactitud del 96&#44;77&#37; y un AUC del 95&#37;&#46; En el caso del modelo sobre la base de datos MIMIC III la sensibilidad fue del 97&#44;55&#37;&#44; la especificidad del 100&#37; y la exactitud del 98&#44;28&#37; con un AUC del 97&#44;3&#37;&#46;</p></span> <span id="abst0070" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Conclusiones</span><p id="spar0105" class="elsevierStyleSimplePara elsevierViewall">Seg&#250;n la clasificaci&#243;n de bosque aleatorio en ambas bases de datos&#44; los niveles de lactato&#44; la diuresis y las variables relacionadas con el equilibrio &#225;cido-base fueron las variables m&#225;s importantes para determinar las muertes por sepsis en la UCI&#46; Los niveles medios de potasio fueron m&#225;s cr&#237;ticos en la base de datos MIMIC III que en las locales&#46;</p></span>"
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                  \t\t\t\t\ttop\n
                  \t\t\t\t">Age&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Live&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">64&#44;40&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">15&#44;94&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">56&#44;00&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#44;00&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">76&#44;00&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#44;65&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Dead&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">65&#44;35&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">11&#44;28&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">60&#44;00&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">67&#44;00&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">72&#44;50&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Breathrate&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Live&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#44;64&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">5&#44;13&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">18&#44;26&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">19&#44;84&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">25&#44;03&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">0&#44;62&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Dead&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">21&#44;24&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">4&#44;16&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">17&#44;91&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">20&#44;85&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">24&#44;48&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">DBP&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Live&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58&#44;49&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">13&#44;54&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">51&#44;67&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">58&#44;83&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">65&#44;38&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Dead&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">56&#44;14&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">63&#44;88&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Heartrate&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Live&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">92&#44;86&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
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                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">81&#44;16&nbsp;\t\t\t\t\t\t\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">93&#44;82&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">104&#44;79&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">0&#44;01<a class="elsevierStyleCrossRef" href="#tblfn0005">&#42;</a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Dead&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">100&#44;96&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">18&#44;18&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">89&#44;19&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">97&#44;12&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">115&#44;46&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Lactate&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Live&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">2&#44;33&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&#44;02&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
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                  \t\t\t\t">1&#44;23&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t">1&#44;90&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
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                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">2&#44;83&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&#60;&#160;0&#44;001<a class="elsevierStyleCrossRef" href="#tblfn0005">&#42;</a>&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">&nbsp;\t\t\t\t\t\t\n
                  \t\t\t\t</td><td class="td" title="\n
                  \t\t\t\t\ttable-entry\n
                  \t\t\t\t  " align="left" valign="\n
                  \t\t\t\t\ttop\n
                  \t\t\t\t">Dead&nbsp;\t\t\t\t\t\t\n
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