Comparison of Intensive Care Unit Scoring Systems in Predicting Overall Mortality of Sepsis Patients

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Methods Among 740 patients admitted to the tertiary intensive care unit within 2 years, 165 patients diagnosed with sepsis and septic shock were included in the study. Demographic data, comorbidities, SOFA, SAPS-2, OASIS and APACHE II scores, invasive or noninvasive mechanical ventilation requirement and duration, ICU admission, hospital stay and 28-day mortality were retrospectively evaluated. Results All scoring systems were positively correlated with mortality and CCI score. OASIS correlated with ICU admission time and duration of mechanical ventilation.When the role of mortality scoring systems was evaluated, APACHE was found to be the lowest, while SOFA, OASIS and SAPS were found to be the highest. Conclusion SAPS II and OASIS have a higher correlation with mortality compared to others. APACHE II Intensive Care Scoring Systems Mortality OASIS SAPS-II SOFA Figures Figure 1 Introduction Predicting a condition’s prognosis is an important topic, especially in cases of those with high mortality, such as sepsis. In intensive care units (ICU), predictive scoring systems have been implemented to observe patients’ treatment responses and guide medical practitioners on treatment modalities ( 1 ).Different scoring systems are used to evaluate the disease severity of intensive care patients. Determining intensive care severity scores is very useful in clinical practice ( 2 ). Vincent et al. defined Sequential Organ Failure Assessment (SOFA) in 1996 to evaluate patients in critical status due to sepsis, which includes vital results, clinical observation, and laboratory parameters ( 3 ). The scoring system assigns a score between 0 to 4 to six categories, including cardiovascular, respiratory, and central nervous systems, with additional parameters for coagulation status, liver and renal function test results. An increase of 2 or more points in SOFA is attributed to the presence of sepsis in a patient. In 1993, Le Gall, Lemeshow and Saulnier proposed a scoring system to evaluate overall mortality and morbidity in a patient ( 4 ). This scoring system, named Simplified Acute Physiology Score (SAPS II), consists of an overall evaluation of phsyilogical changes in a patient to assess the status, including patients’ demographic results, hospitalization etiology, comorbidities, and cardiac or respiratory support requirements. A former scoring system, APACHE II, was defined in 1985 by Knaus et al. with a similar goal in mind, albeit with a different approach ( 5 ). The score, proposed as an admission evaluation of a patient to predict overall risk in critical care, is designed to be calculated within the first 24 hours of ICU admission. With a range between 0 and 71, the more severe the patient is deemed, the higher the overall score is. Currently, this scoring system is among the most commonly used in ICU units. However, APACHE II is more complex than other scoring systems due to the abundant parameters it requires for assessment. Johnson et al. had proposed another scoring system, one that did not require laboratory parameters and with fewer parameters, relying on clinician’s observation for ease of use. Named Oxford Acute Severity of Illness (OASIS), this score relied on machine algorithms in its creation and included ten components, seven of which were physiological parameters, and the remaining three were age, elective surgery, and former hospitalization duration ( 6 ). This study aims to compare the discussed scores in their role of predicting overall mortality in patients admitted to ICU with a diagnosis of sepsis or septic shock. Materials and Method Patients admitted to our center's tertiary intensive care unit between April 1, 2018, and April 30, 2020, were included in the study. A total of 740 patients were admitted during the described period, and their computer and file records were investigated. Each category was assigned a point at admission: a systolic blood pressure below 100 mmHg, Glasgow Coma Score (GSS) below 15, and respiratory rate at or above 22. A total of 112 patients with a score of 2 or above were accepted under the sepsis category and included in the study. Additionally, patients requiring vasopressor support to keep a mean arterial pressure (MAP) at or above 65 mmHg despite adequate fluid resuscitation or those with an arterial blood gas sampling lactate result of 2 mmol/L or above were defined as under septic shock. Including 53 patients with this definition, the study included a total of 165 patients. Exclusion criteria were defined and included patients without sepsis at the time of admission, less than 24 hours in ICU, inadequate presence of parameters for proper evaluation, and those younger than 18 years. Demographic data, comorbidities, results of SOFA, SAPS-2, OASIS, and APACHE II scores; invasive or noninvasive mechanical ventilation requirement and duration if required; ICU admission duration, total hospitalization duration, and overall mortality within 28 days of the included patients were retrospectively evaluated and recorded. Statistical Analysis Statistical analysis was performed on IBM SPSS Statistics Version 25th Microsoft Edition after initial data collection was done on Microsoft Excel. All parameters were investigated with descriptive analysis, in which mean and standard deviation (SD) were given for parametric values, while median, 25th, and 75th percentage values were used for non-parametric values. To assess whether a result is distributed parametrically or not, histogram charts were primarily used along with Kolmograv-Smirknov analysis for confirmation when required. For values deemed parametric, comparisons between two groups were made by independent samples T-test after evaluating with Shapiro-Wilk’s test for linearity and confirmed by Levene’s test for equality of variance. For correlation analysis, Pearson correlation was utilized. Receiver Operating Characteristic (ROC) Curve analyses were given with charts and p scores, with p results compared to a 50% area under curve (AUC) assumption. Results A total of 740 patients were evaluated for the study. Those diagnosed with sepsis and septic shock (n = 165, 22%) were accepted as the study group. The majority of the patients (n = 110, 66.7%) were male, and the average age was 70.3 (± 15.8) years old. The patients had an average Charlson Comorbidity Index (CCI) score of 6.88 (± 2.66). A median of 12 (6–22) days for hospitalization and 3 ( 1 – 6 ) days of ICU admission was reported, with less than half (n = 71, 43%) patients requiring inotropic support and a median of 1 day (0–5) on invasive mechanical ventilation history. White blood cell levels were elevated, with a mean of 13.0 109/L (7.9–19.7), and neutrophils were observed at 83.8% (± 16.2). Correlated with sepsis diagnosis, procalcitonin and C-reactive protein levels were increased at 4.9 ng/ml (2.9–13.9) and 15.6 mg/dl (8.2–23.0), respectively. All mortality scoring systems were found to be increased, with APACHE II, SOFA, SAPS II, and OASIS scores being reported at 26.17 (± 7.96), 8.0 (± 3.0), 56.73 (± 14.53) and 34.38 (± 10.0), respectively. The first-month mortality rate was at 63.6% (105) (Table 1 ). Table 1 Demographic Characteristics and Laboratory Parameters of the Patients Parameters No of Patients (n = 165, 25th -75th ) Gender Male (%) 110 (66.7) Female (%) 55 (33.3) Age (years, SD) 70.38 (± 15.85) Charlson Comorbidity Index (SD) 6.88(± 2.66) Hospital Admission Days 12 (6–22) ICU Admission Days 3 ( 1 – 6 ) Days on Mechanical Ventilation 1 (0–5) Inotropic Support Requirement (%) 71 (43) White Blood Cell (10 9 /L) 13.0 (7.9–19.7) Neutrophile (10 9 /L) 10.5 (5.6–15.8) Neutrophile (%, SD) 83.8 (± 16.25) Procalcitonin (ng/ml) 4.9 (2.9–13.9) C-Reactive Protein (mg/L) 15.6 (8.2–23.9) Mortality Scoring and Overall Mortality No of Patients (n = 165, SD) APACHE II 26.17 (± 7.96) SOFA 8.0 (± 3.0) SAPS II 56.73 (± 14.53) OASIS 34.38 (± 10.0) First Month Mortality (%) 105 (63.6) SD : Standard Deviation, ICU : Intensive Care Unit, APACHE II : Acute Physiology and Chronic Health Evaluation II, SOFA : Sequential Organ Failure Assessment, SAPS II : Simplified Acute Physiology Score II, OASIS : Oxford Acute Severity of Illness Score. 25th -75th refers to the 25th and 75th percentile; the values given here are median values. Comparing parameters between the exitus group and the survival one showed that there was a significant difference in gender, age, CCI score, hospital admission days, ICU admission days, days on mechanical ventilation and inotropic support, with female gender and longer hospital admission duration being observed more commonly in the survival group and the rest of the parameters being more common in the exitus group. There was also significance in all mortality scoring parameters, with all parameters being higher in the exit group. (Table 2 ). Table 2 Comparison Between Parameters Regarding First Month Mortality Independent Samples T-Test t dF p 95% CI of the Difference Lower Higher Gender 2.015 112 0.046 0.003 0.312 Age (years) -4.590 86 0.001 -17.675 -6.992 Charlson Comorbidity Index -9.327 155 0.001 -3.716 -2.417 Hospital Admission Days 3.950 94 0.001 4.436 13.402 ICU Admission Days -2.270 160 0.025 -4.488 -0.312 Days on Mechanical Ventilation -3.495 161 0.001 -6.104 -1.696 Inotropic Support Requirement -8.428 161 0.001 -0.641 -0.397 White Blood Cell (10 9 /L) -0.337 156 0.737 -4.338 3.075 Neutrophile (10 9 /L) -1.221 148 0.224 -5.276 1.246 Neutrophile (%) -1.762 163 0.080 -9.768 0.556 Procalcitonin -1.732 163 0.085 -12.387 0.809 C-Reactive Protein 0.475 163 0.636 -4.026 6.576 APACHE II -8.777 162 0.001 -10.401 -6.580 SOFA -10.512 155 0.001 -4.231 -2.893 SAPS II 1 -10.637 163 0.001 -22.854 -15.698 OASIS 1 -10.553 163 0.001 -15.780 -10.806 CI : Confidence interval, dF : Degrees of Freedom ICU : Intensive Care Unit, APACHE II : Acute Physiology and Chronic Health Evaluation II, SOFA : Sequential Organ Failure Assessment, SAPS II : Simplified Acute Physiology Score II, OASIS : Oxford Acute Severity of Illness Score. All parameters having a statistical difference regarding mortality were then evaluated by correlation analysis. All four scoring systems, APACHE II, SOFA, SAPS, and OASIS, positively correlated with mortality and CCI score. Excluding OASIS, a negative correlation was observed between hospitalization duration and the scoring system. Only OASIS correlated with ICU admission duration and mechanical ventilation duration, which was found to be positive in both cases. Gender only correlated with SOFA scoring, while age positively correlated with SAPS II and OASIS scores (Table 3 ). Table 3 Correlation Between Scoring Systems, Mortality and Prognostic Factors Pearson Correlation and P-value APACHE II SOFA SAPS II OASIS Gender Correlation -0.106 − .178 * -0.149 -0.018 p-value 0.176 0.022 0.057 0.823 Age Correlation 0.102 0.145 .430 ** .360 ** p-value 0.194 0.063 0.001 0.001 Charlson Comorbidity Index Correlation .340 ** .370 ** .613 ** .470 ** p-value 0.001 0.001 0.001 0.001 Hospital Admission Days Correlation − .169 * − .245 ** − .226 ** -0.138 p-value 0.030 0.002 0.003 0.077 ICU Admission Days Correlation -0.071 -0.078 0.085 .271 ** p-value 0.365 0.320 0.280 0.001 Days on Mechanical Ventilation Correlation 0.020 0.059 0.139 .339 ** p-value 0.796 0.454 0.076 0.001 First Month Mortality Correlation .515 ** .564 ** .640 ** .637 ** p-value 0.001 0.001 0.001 0.001 ICU : Intensive Care Unit, APACHE II : Acute Physiology and Chronic Health Evaluation II, SOFA : Sequential Organ Failure Assessment, SAPS II : Simplified Acute Physiology Score II, OASIS : Oxford Acute Severity of Illness Score. Receiver operating characteristic (ROC) curves were created to evaluate the role of mortality scoring systems, in which all scoring system models had statistical relevance (p score of 0.001 for all analyses). APACHE II had the lowest AUC at 0.803, followed by SOFA with 0.873, OASIS at 0.879, and the highest being SAPS at 0.903. All models had relatively small standard errors, ranging between 0.027 to 0.033 (Table 4 ) (Fig. 1 ). Table 4 Receiver Operating Characteristic Curve Tables of Scoring Systems and First Month Mortality Parameters Area under Curve Standard Error P 1 %95 Confidence Interval Lower Bound Upper Bound APACHE II 0.803 0.033 0.001 0.738 0.868 SOFA 0.873 0.027 0.001 0.821 0.925 SAPS II 0.902 0.027 0.001 0.849 0.955 OASIS 0.879 0.028 0.001 0.823 0.935 AUC : Area under curve, APACHE II : Acute Physiology and Chronic Health Evaluation II, SOFA : Sequential Organ Failure Assessment, SAPS II : Simplified Acute Physiology Score II, OASIS : Oxford Acute Severity of Illness Score. 1 P value is reported to a test of AUC over 0.5. Discussion The study investigated and compared the score systems of SOFA, SAPS II, APACHE II, and OASIS in their respective role in predicting mortality and prognosis in patients admitted to ICU with sepsis or septic shock diagnosis. Sepsis is defined as an irregulated response of the host to an infection resulting in organ dysfunction. Septic shock, as stated in the inclusion criteria, could be defined as a vasopressor requirement despite adequate fluid resuscitation to ensure a MAP at or above 65 mmHg and/or a serum lactate level at or above 2 mmol/L (> 18 mg/dL) ( 7 ). SOFA, SAPS II, and APACHE II are solid and reliable mortality scoring systems. In our study, an APACHE II score of 26 (estimated mortality rate 55%), SOFA score of 8 (estimated mortality rate 33.3%), and SAPS II score of 56.7 (estimated mortality rate 61.9%) were observed, with the actual mortality being reported at 63.6%. The most significant difference between the actual and estimated mortality was seen in SOFA scoring. This difference was confirmed in the analysis performed between survivors, as all four systems had statistically significant differences between them. All four scoring systems had lower mortality correlated with a lower overall score. The presence of higher mortality in patients with inotropic support requirements and/or mechanical ventilation requirements also confirmed the four scores' validity and led to the assumption that the parameters included in the study were robust. In the study of Chen et al., OASIS was observed to be correlated with clinical outcomes in patients with sepsis; however, SAPS II was reported to be superior in predicting mortality ( 8 ). Jia et al., in a similar study, showed that age was the most prominent factor in terms of all causes mortality ( 9 ). Age was a part of scoring systems, excluding SOFA, in our study and was lower in the survival group. In many studies, SOFA was considered a valuable factor in estimating the prognosis of patients with sepsis or ICU care ( 10 – 12 ). However, in Granholm et al.’s study, SAPS II was stated to be superior to SOFA in-hospital mortality and 90 days all-cause mortality ( 13 ). SOFA was also reported to be inferior to OASIS, SAPS II, and APACHE II in terms of mortality estimation in the multi-center study of Wang et al. ( 14 ). The same study suggested the usage of OASIS due to ease of calculation ( 14 ). Another study highlighted SAPS II's ability to estimate mortality without the requirement of the initial admission diagnosis, providing another difference between scoring systems ( 15 ). Our study revealed results favoring those discussed, as SAPS II followed by OASIS was more prominent in mortality prediction, with SOFA having the highest difference between the four groups in estimated and real mortality results. OASIS was created with a reduction of parameters required for calculation while keeping the prediction reliability in mind, by utilizing APACHE IV as the basis ( 16 ). Chen et al. stated that OASIS was simpler to calculate, with fewer laboratory parameters being required in estimation ( 8 ). Our study confirmed this assumption, as OASIS was practical and accessible enough. However, a note of interest and perhaps limitation was that OASIS was the sole scoring system that included ICU admission duration and mechanical ventilation requirement history. These in-built requirements could have led to the assumption that OASIS is only valid for evaluating sepsis patients only in the ICU setting. In our study, ROC analysis of all four scoring systems was statically significant and similar in shape, with SAPS II having the highest AUC, followed by OASIS. This supported the correlation analysis, as while all scoring systems had higher mortality as the points given to them were increased, the highest correlation between mortality and scores was present in SAPS II, followed by OASIS.In another study we conducted, it was found that high CCI, APACHE II, SOFA scores significantly increase mortality, and disease severity, age and infection in intensive care are important factors affecting mortality ( 17 ). Furthermore, the study conducted by Kao et al. also supports this ( 18 ). Similarly, CCI scores and scoring systems had a positive correlation, which was an expected observation, as all scoring systems either had comorbidity evaluation or comorbidity-related laboratory results. Excluding OASIS, all scoring systems negatively correlated with overall hospitalization duration. This could be attributed to the fact that, as the scores increased, the overall mortality of the patients increased, thus reducing the days spent in the hospital or an urgent admission to the ICU due to the severity of the patients. As stated above, OASIS was also the only system not related to the former hospitalization duration and limited to the ICU, and thus, this could have contributed to the lack of negative correlation observed between OASIS and total hospitalization duration. The study's limitations could be summarized as the patient selection and count. Considering only patients with confirmed sepsis were recruited into the study, an elevated mortality was observed, which was attributed to the sepsis requirement. Overall, this assumption could have created a selection bias, as some scoring systems could only be performed under a sepsis diagnosis, such as SOFA, while APACHE II could be performed on a patient without sepsis. This recruitment approach could have skewed the results, especially regarding mortality. However, this bias was assumed to be partially remedied, as the primary comparison scoring system used in the study, OASIS, did not require sepsis diagnosis and was compared with scoring systems with and without sepsis requirement. Another limitation of the study was the retrospective nature of it. However, similar to the OASIS bias, this was compensated by the routine scoring methods used in intensive care, including SOFA and APACHE II. These two scoring systems were routinely performed on every patient, while OASIS and SAPS II were calculated from the patients’ files and computer records. As such, the retrospective aspect of the study was in design rather than in data investigation, with nearly all data presented already available in patient follow-up. While statistically adequate in terms of parametric count, a larger population of patients could have led to a possibility of subgroup analysis according to parameters not available in scoring systems, such as rare comorbidities, which is another limitation of our study. Conclusion OASIS, in summary, appears to be robust in the evaluation of mortality estimation in ICU patients diagnosed with sepsis. Excluding SAPS II, OASIS had a higher correlation with mortality compared to other scoring systems and had components that allowed it to be correlated with ICU admission and mechanical ventilation, which was not observed in other scoring systems. Declarations Conflicts of Interest: The authors have declared that no competing interests exist. Funding sources : This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data Access Statement: All relevant data are within the paper and its Supporting Information files. Acknowledgments: None. Author Contributions: GED and MOC wrote the the main text, KE and MD analysed, prepared the tables and figure. All of the authors declare that they have all participated in the concept, design, literature search, data collection and that they have approved the final version. Compliance with Ethical Standards: All the authors mentioned in the manuscript have agreed for authorship, read and approved the manuscript, and given consent for submission and subsequent publication of the manuscript. The manuscript in part or in full has not been submitted or published anywhere. Ethics Committee Approval: This study was conducted following the ethical principles stated in the "Declaration of Helsinki" and approved by the Ankara Ataturk Sanatorium Training and Research Hospital, Clinical Research Ethics Committee (Date of Approval: 11.05.2023, Protocol no: E-53610172-799-213187445). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5341064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377080392,"identity":"cf4df458-03f2-4cb9-98bf-0999b357d2ee","order_by":0,"name":"Mustafa Ozgur Cirik","email":"","orcid":"","institution":"Ataturk Sanatorium Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"Ozgur","lastName":"Cirik","suffix":""},{"id":377080393,"identity":"655a89c5-f3a3-4ec5-a24e-192247e598be","order_by":1,"name":"Guler Eraslan 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Maside","middleName":"","lastName":"Ari","suffix":""},{"id":377080398,"identity":"a1efe964-6b96-4370-9e44-ed00ddc48794","order_by":6,"name":"Abdullah Kahraman","email":"","orcid":"","institution":"Ministry of Health Ankara Etlik City Hospital, Anesthesiology and Reanimation Intensive Care Unit","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Kahraman","suffix":""},{"id":377080399,"identity":"2a95645b-8ff3-4390-b20d-1271f3ab3f52","order_by":7,"name":"Seray Hazer","email":"","orcid":"","institution":"Ataturk Sanatorium Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Seray","middleName":"","lastName":"Hazer","suffix":""},{"id":377080400,"identity":"9c0dda25-44d8-4300-bed9-d87c4dfd13d2","order_by":8,"name":"Mehtap Tunc","email":"","orcid":"","institution":"Ataturk Sanatorium Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mehtap","middleName":"","lastName":"Tunc","suffix":""},{"id":377080401,"identity":"3bb2f2b3-27e1-490c-8932-057c914ee8fe","order_by":9,"name":"Kerem Ensarioglu","email":"","orcid":"","institution":"Ataturk Sanatorium Training and Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kerem","middleName":"","lastName":"Ensarioglu","suffix":""}],"badges":[],"createdAt":"2024-10-27 11:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5341064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5341064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70038293,"identity":"7885f055-214d-40c8-a4d5-c1f95e9efce1","added_by":"auto","created_at":"2024-11-27 17:35:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54750,"visible":true,"origin":"","legend":"\u003cp\u003eROC Analysis of Scoring Systems\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5341064/v1/43b9db71560268d6d7aa164f.png"},{"id":70039731,"identity":"12b31f18-3028-4087-8540-4b81e696b5b8","added_by":"auto","created_at":"2024-11-27 17:51:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":736477,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5341064/v1/a858a6fa-5626-4a5e-9f2c-929458e5680d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparison of Intensive Care Unit Scoring Systems in Predicting Overall Mortality of Sepsis Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePredicting a condition\u0026rsquo;s prognosis is an important topic, especially in cases of those with high mortality, such as sepsis. In intensive care units (ICU), predictive scoring systems have been implemented to observe patients\u0026rsquo; treatment responses and guide medical practitioners on treatment modalities (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).Different scoring systems are used to evaluate the disease severity of intensive care patients. Determining intensive care severity scores is very useful in clinical practice (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVincent et al. defined Sequential Organ Failure Assessment (SOFA) in 1996 to evaluate patients in critical status due to sepsis, which includes vital results, clinical observation, and laboratory parameters (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The scoring system assigns a score between 0 to 4 to six categories, including cardiovascular, respiratory, and central nervous systems, with additional parameters for coagulation status, liver and renal function test results. An increase of 2 or more points in SOFA is attributed to the presence of sepsis in a patient.\u003c/p\u003e \u003cp\u003eIn 1993, Le Gall, Lemeshow and Saulnier proposed a scoring system to evaluate overall mortality and morbidity in a patient (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This scoring system, named Simplified Acute Physiology Score (SAPS II), consists of an overall evaluation of phsyilogical changes in a patient to assess the status, including patients\u0026rsquo; demographic results, hospitalization etiology, comorbidities, and cardiac or respiratory support requirements.\u003c/p\u003e \u003cp\u003eA former scoring system, APACHE II, was defined in 1985 by Knaus et al. with a similar goal in mind, albeit with a different approach (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The score, proposed as an admission evaluation of a patient to predict overall risk in critical care, is designed to be calculated within the first 24 hours of ICU admission. With a range between 0 and 71, the more severe the patient is deemed, the higher the overall score is. Currently, this scoring system is among the most commonly used in ICU units. However, APACHE II is more complex than other scoring systems due to the abundant parameters it requires for assessment.\u003c/p\u003e \u003cp\u003eJohnson et al. had proposed another scoring system, one that did not require laboratory parameters and with fewer parameters, relying on clinician\u0026rsquo;s observation for ease of use. Named Oxford Acute Severity of Illness (OASIS), this score relied on machine algorithms in its creation and included ten components, seven of which were physiological parameters, and the remaining three were age, elective surgery, and former hospitalization duration (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study aims to compare the discussed scores in their role of predicting overall mortality in patients admitted to ICU with a diagnosis of sepsis or septic shock.\u003c/p\u003e"},{"header":"Materials and Method","content":"\u003cp\u003e Patients admitted to our center's tertiary intensive care unit between April 1, 2018, and April 30, 2020, were included in the study. A total of 740 patients were admitted during the described period, and their computer and file records were investigated. Each category was assigned a point at admission: a systolic blood pressure below 100 mmHg, Glasgow Coma Score (GSS) below 15, and respiratory rate at or above 22. A total of 112 patients with a score of 2 or above were accepted under the sepsis category and included in the study. Additionally, patients requiring vasopressor support to keep a mean arterial pressure (MAP) at or above 65 mmHg despite adequate fluid resuscitation or those with an arterial blood gas sampling lactate result of 2 mmol/L or above were defined as under septic shock. Including 53 patients with this definition, the study included a total of 165 patients. Exclusion criteria were defined and included patients without sepsis at the time of admission, less than 24 hours in ICU, inadequate presence of parameters for proper evaluation, and those younger than 18 years.\u003c/p\u003e \u003cp\u003eDemographic data, comorbidities, results of SOFA, SAPS-2, OASIS, and APACHE II scores; invasive or noninvasive mechanical ventilation requirement and duration if required; ICU admission duration, total hospitalization duration, and overall mortality within 28 days of the included patients were retrospectively evaluated and recorded.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed on IBM SPSS Statistics Version 25th Microsoft Edition after initial data collection was done on Microsoft Excel. All parameters were investigated with descriptive analysis, in which mean and standard deviation (SD) were given for parametric values, while median, 25th, and 75th percentage values were used for non-parametric values. To assess whether a result is distributed parametrically or not, histogram charts were primarily used along with Kolmograv-Smirknov analysis for confirmation when required. For values deemed parametric, comparisons between two groups were made by independent samples T-test after evaluating with Shapiro-Wilk\u0026rsquo;s test for linearity and confirmed by Levene\u0026rsquo;s test for equality of variance. For correlation analysis, Pearson correlation was utilized. Receiver Operating Characteristic (ROC) Curve analyses were given with charts and p scores, with p results compared to a 50% area under curve (AUC) assumption.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 740 patients were evaluated for the study. Those diagnosed with sepsis and septic shock (n\u0026thinsp;=\u0026thinsp;165, 22%) were accepted as the study group. The majority of the patients (n\u0026thinsp;=\u0026thinsp;110, 66.7%) were male, and the average age was 70.3 (\u0026plusmn;\u0026thinsp;15.8) years old. The patients had an average Charlson Comorbidity Index (CCI) score of 6.88 (\u0026plusmn;\u0026thinsp;2.66). A median of 12 (6\u0026ndash;22) days for hospitalization and 3 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) days of ICU admission was reported, with less than half (n\u0026thinsp;=\u0026thinsp;71, 43%) patients requiring inotropic support and a median of 1 day (0\u0026ndash;5) on invasive mechanical ventilation history. White blood cell levels were elevated, with a mean of 13.0 109/L (7.9\u0026ndash;19.7), and neutrophils were observed at 83.8% (\u0026plusmn;\u0026thinsp;16.2). Correlated with sepsis diagnosis, procalcitonin and C-reactive protein levels were increased at 4.9 ng/ml (2.9\u0026ndash;13.9) and 15.6 mg/dl (8.2\u0026ndash;23.0), respectively. All mortality scoring systems were found to be increased, with APACHE II, SOFA, SAPS II, and OASIS scores being reported at 26.17 (\u0026plusmn;\u0026thinsp;7.96), 8.0 (\u0026plusmn;\u0026thinsp;3.0), 56.73 (\u0026plusmn;\u0026thinsp;14.53) and 34.38 (\u0026plusmn;\u0026thinsp;10.0), respectively. The first-month mortality rate was at 63.6% (105) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics and Laboratory Parameters of the Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of Patients (n\u0026thinsp;=\u0026thinsp;165, 25th -75th )\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge \u003cem\u003e(years, SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.38 (\u0026plusmn;\u0026thinsp;15.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index \u003cem\u003e(SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.88(\u0026plusmn;\u0026thinsp;2.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHospital Admission Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (6\u0026ndash;22)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eICU Admission Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eDays on Mechanical Ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInotropic Support Requirement \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWhite Blood Cell \u003cem\u003e(10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.0 (7.9\u0026ndash;19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNeutrophile \u003cem\u003e(10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5 (5.6\u0026ndash;15.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNeutrophile \u003cem\u003e(%, SD)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.8 (\u0026plusmn;\u0026thinsp;16.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eProcalcitonin \u003cem\u003e(ng/ml)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.9 (2.9\u0026ndash;13.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eC-Reactive Protein \u003cem\u003e(mg/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.6 (8.2\u0026ndash;23.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMortality Scoring and Overall Mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNo of Patients (n\u0026thinsp;=\u0026thinsp;165, SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.17 (\u0026plusmn;\u0026thinsp;7.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.0 (\u0026plusmn;\u0026thinsp;3.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSAPS II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.73 (\u0026plusmn;\u0026thinsp;14.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eOASIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.38 (\u0026plusmn;\u0026thinsp;10.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFirst Month Mortality \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (63.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSD\u003c/b\u003e: Standard Deviation, \u003cb\u003eICU\u003c/b\u003e: Intensive Care Unit, \u003cb\u003eAPACHE II\u003c/b\u003e: Acute Physiology and Chronic Health Evaluation II, \u003cb\u003eSOFA\u003c/b\u003e: Sequential Organ Failure Assessment, \u003cb\u003eSAPS II\u003c/b\u003e: Simplified Acute Physiology Score II, \u003cb\u003eOASIS\u003c/b\u003e: Oxford Acute Severity of Illness Score.\u003c/p\u003e \u003cp\u003e25th -75th refers to the 25th and 75th percentile; the values given here are median values.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComparing parameters between the exitus group and the survival one showed that there was a significant difference in gender, age, CCI score, hospital admission days, ICU admission days, days on mechanical ventilation and inotropic support, with female gender and longer hospital admission duration being observed more commonly in the survival group and the rest of the parameters being more common in the exitus group. There was also significance in all mortality scoring parameters, with all parameters being higher in the exit group. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison Between Parameters Regarding First Month Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndependent Samples T-Test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% CI of the Difference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.046\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-17.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital Admission Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU Admission Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays on Mechanical Ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInotropic Support Requirement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.397\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Blood Cell \u003cem\u003e(10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.337\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile \u003cem\u003e(10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophile \u003cem\u003e(%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Reactive Protein\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-6.580\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAPS II\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-22.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-15.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOASIS\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-15.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-10.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCI\u003c/b\u003e: Confidence interval, \u003cb\u003edF\u003c/b\u003e: Degrees of Freedom \u003cb\u003eICU\u003c/b\u003e: Intensive Care Unit, \u003cb\u003eAPACHE II\u003c/b\u003e: Acute Physiology and Chronic Health Evaluation II, \u003cb\u003eSOFA\u003c/b\u003e: Sequential Organ Failure Assessment, \u003cb\u003eSAPS II\u003c/b\u003e: Simplified Acute Physiology Score II, \u003cb\u003eOASIS\u003c/b\u003e: Oxford Acute Severity of Illness Score.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAll parameters having a statistical difference regarding mortality were then evaluated by correlation analysis. All four scoring systems, APACHE II, SOFA, SAPS, and OASIS, positively correlated with mortality and CCI score. Excluding OASIS, a negative correlation was observed between hospitalization duration and the scoring system. Only OASIS correlated with ICU admission duration and mechanical ventilation duration, which was found to be positive in both cases. Gender only correlated with SOFA scoring, while age positively correlated with SAPS II and OASIS scores (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Between Scoring Systems, Mortality and Prognostic Factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePearson Correlation and P-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAPACHE II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSOFA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSAPS II\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOASIS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorrelation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.178\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorrelation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.430\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.360\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCharlson Comorbidity Index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorrelation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e.340\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.370\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.613\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.470\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHospital Admission Days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorrelation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.169\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.245\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026minus;\u0026thinsp;.226\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eICU Admission Days\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorrelation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.271\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDays on Mechanical Ventilation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorrelation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.339\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFirst Month Mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorrelation\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e.515\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e.564\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e.640\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e.637\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eICU\u003c/b\u003e: Intensive Care Unit, \u003cb\u003eAPACHE II\u003c/b\u003e: Acute Physiology and Chronic Health Evaluation II, \u003cb\u003eSOFA\u003c/b\u003e: Sequential Organ Failure Assessment, \u003cb\u003eSAPS II\u003c/b\u003e: Simplified Acute Physiology Score II, \u003cb\u003eOASIS\u003c/b\u003e: Oxford Acute Severity of Illness Score.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) curves were created to evaluate the role of mortality scoring systems, in which all scoring system models had statistical relevance (p score of 0.001 for all analyses). APACHE II had the lowest AUC at 0.803, followed by SOFA with 0.873, OASIS at 0.879, and the highest being SAPS at 0.903. All models had relatively small standard errors, ranging between 0.027 to 0.033 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReceiver Operating Characteristic Curve Tables of Scoring Systems and First Month Mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea under Curve\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandard Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e%95 Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAPACHE II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSAPS II\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOASIS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAUC\u003c/b\u003e: Area under curve, \u003cb\u003eAPACHE II\u003c/b\u003e: Acute Physiology and Chronic Health Evaluation II, \u003cb\u003eSOFA\u003c/b\u003e: Sequential Organ Failure Assessment, \u003cb\u003eSAPS II\u003c/b\u003e: Simplified Acute Physiology Score II, \u003cb\u003eOASIS\u003c/b\u003e: Oxford Acute Severity of Illness Score.\u003c/p\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eP value is reported to a test of AUC over 0.5.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study investigated and compared the score systems of SOFA, SAPS II, APACHE II, and OASIS in their respective role in predicting mortality and prognosis in patients admitted to ICU with sepsis or septic shock diagnosis. Sepsis is defined as an irregulated response of the host to an infection resulting in organ dysfunction. Septic shock, as stated in the inclusion criteria, could be defined as a vasopressor requirement despite adequate fluid resuscitation to ensure a MAP at or above 65 mmHg and/or a serum lactate level at or above 2 mmol/L (\u0026gt;\u0026thinsp;18 mg/dL) (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSOFA, SAPS II, and APACHE II are solid and reliable mortality scoring systems. In our study, an APACHE II score of 26 (estimated mortality rate 55%), SOFA score of 8 (estimated mortality rate 33.3%), and SAPS II score of 56.7 (estimated mortality rate 61.9%) were observed, with the actual mortality being reported at 63.6%. The most significant difference between the actual and estimated mortality was seen in SOFA scoring. This difference was confirmed in the analysis performed between survivors, as all four systems had statistically significant differences between them. All four scoring systems had lower mortality correlated with a lower overall score. The presence of higher mortality in patients with inotropic support requirements and/or mechanical ventilation requirements also confirmed the four scores' validity and led to the assumption that the parameters included in the study were robust.\u003c/p\u003e \u003cp\u003eIn the study of Chen et al., OASIS was observed to be correlated with clinical outcomes in patients with sepsis; however, SAPS II was reported to be superior in predicting mortality (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Jia et al., in a similar study, showed that age was the most prominent factor in terms of all causes mortality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Age was a part of scoring systems, excluding SOFA, in our study and was lower in the survival group.\u003c/p\u003e \u003cp\u003eIn many studies, SOFA was considered a valuable factor in estimating the prognosis of patients with sepsis or ICU care (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, in Granholm et al.\u0026rsquo;s study, SAPS II was stated to be superior to SOFA in-hospital mortality and 90 days all-cause mortality (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). SOFA was also reported to be inferior to OASIS, SAPS II, and APACHE II in terms of mortality estimation in the multi-center study of Wang et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The same study suggested the usage of OASIS due to ease of calculation (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Another study highlighted SAPS II's ability to estimate mortality without the requirement of the initial admission diagnosis, providing another difference between scoring systems (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study revealed results favoring those discussed, as SAPS II followed by OASIS was more prominent in mortality prediction, with SOFA having the highest difference between the four groups in estimated and real mortality results.\u003c/p\u003e \u003cp\u003eOASIS was created with a reduction of parameters required for calculation while keeping the prediction reliability in mind, by utilizing APACHE IV as the basis (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Chen et al. stated that OASIS was simpler to calculate, with fewer laboratory parameters being required in estimation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Our study confirmed this assumption, as OASIS was practical and accessible enough. However, a note of interest and perhaps limitation was that OASIS was the sole scoring system that included ICU admission duration and mechanical ventilation requirement history. These in-built requirements could have led to the assumption that OASIS is only valid for evaluating sepsis patients only in the ICU setting. In our study, ROC analysis of all four scoring systems was statically significant and similar in shape, with SAPS II having the highest AUC, followed by OASIS.\u003c/p\u003e \u003cp\u003eThis supported the correlation analysis, as while all scoring systems had higher mortality as the points given to them were increased, the highest correlation between mortality and scores was present in SAPS II, followed by OASIS.In another study we conducted, it was found that high CCI, APACHE II, SOFA scores significantly increase mortality, and disease severity, age and infection in intensive care are important factors affecting mortality (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Furthermore, the study conducted by Kao et al. also supports this (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Similarly, CCI scores and scoring systems had a positive correlation, which was an expected observation, as all scoring systems either had comorbidity evaluation or comorbidity-related laboratory results.\u003c/p\u003e \u003cp\u003eExcluding OASIS, all scoring systems negatively correlated with overall hospitalization duration. This could be attributed to the fact that, as the scores increased, the overall mortality of the patients increased, thus reducing the days spent in the hospital or an urgent admission to the ICU due to the severity of the patients. As stated above, OASIS was also the only system not related to the former hospitalization duration and limited to the ICU, and thus, this could have contributed to the lack of negative correlation observed between OASIS and total hospitalization duration.\u003c/p\u003e \u003cp\u003eThe study's limitations could be summarized as the patient selection and count. Considering only patients with confirmed sepsis were recruited into the study, an elevated mortality was observed, which was attributed to the sepsis requirement. Overall, this assumption could have created a selection bias, as some scoring systems could only be performed under a sepsis diagnosis, such as SOFA, while APACHE II could be performed on a patient without sepsis. This recruitment approach could have skewed the results, especially regarding mortality. However, this bias was assumed to be partially remedied, as the primary comparison scoring system used in the study, OASIS, did not require sepsis diagnosis and was compared with scoring systems with and without sepsis requirement.\u003c/p\u003e \u003cp\u003eAnother limitation of the study was the retrospective nature of it. However, similar to the OASIS bias, this was compensated by the routine scoring methods used in intensive care, including SOFA and APACHE II. These two scoring systems were routinely performed on every patient, while OASIS and SAPS II were calculated from the patients\u0026rsquo; files and computer records. As such, the retrospective aspect of the study was in design rather than in data investigation, with nearly all data presented already available in patient follow-up. While statistically adequate in terms of parametric count, a larger population of patients could have led to a possibility of subgroup analysis according to parameters not available in scoring systems, such as rare comorbidities, which is another limitation of our study.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOASIS, in summary, appears to be robust in the evaluation of mortality estimation in ICU patients diagnosed with sepsis. Excluding SAPS II, OASIS had a higher correlation with mortality compared to other scoring systems and had components that allowed it to be correlated with ICU admission and mechanical ventilation, which was not observed in other scoring systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors have declared that no competing interests exist.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Access Statement:\u003c/strong\u003e All relevant data are within the paper and its Supporting Information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e GED and MOC wrote the the main text, KE and MD analysed, prepared the tables and figure. All of the authors declare that they have all participated in the concept, design, literature search, data collection and that they have approved the final version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards:\u003c/strong\u003e All the authors mentioned in the manuscript have agreed for authorship, read and approved the manuscript, and given consent for submission and subsequent publication of the manuscript. The manuscript in part or in full has not been submitted or published anywhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Committee Approval:\u003c/strong\u003e This study was conducted following the ethical principles stated in the \u0026quot;Declaration of Helsinki\u0026quot; and \u0026nbsp;approved by the Ankara Ataturk Sanatorium Training and Research Hospital, Clinical Research Ethics Committee (Date of Approval: 11.05.2023, Protocol no: E-53610172-799-213187445). The informed consent was waived by the Ankara Ataturk Sanatorium Training and Research Hospital, Clinical Research Ethics Committee considering the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRapsang AG, Shyam DC. Scoring systems in the intensive care unit: a compendium. Indian J Crit Care Med. 2014;18(4):220\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaldemir R, Doğanay GE, Cirik M\u0026Ouml;, \u0026Uuml;lger G, Yurtseven G, Zengin M. The relationship between acute physiology and chronic health evaluation-II, sequential organ failure assessment, Charlson comorbidity index and nutritional scores and length of intensive care unit stay of patients hospitalized in the intensive care unit due to chronic obstructive pulmonary disease. J Health Sci Med. 2022;5(5):1399\u0026ndash;404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVincent JL, Moreno R, Takala J, et al. The SOFA (Sepsis-Related Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive Care Med. 1996;22(7):707\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLe Gall JR, Lemeshow S, Saulnier F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA: J Am Med Association. 1993;270(24):2957\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson AE, Kramer AA, Clifford GD. A new severity of illness scale using a subset of Acute Physiology And Chronic Health Evaluation data elements shows comparable predictive accuracy. Crit Care Med. 2013;41(7):1711\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSinger M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Angus DC. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016;315(8):801\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Q, Zhang L, Ge S, He W, Zeng M. (2019). Prognosis predictive value of the Oxford Acute Severity of Illness Score for sepsis: a retrospective cohort study. PeerJ, 7, e7083.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia L, Hao L, Li X, Jia R, Zhang HL. Comparing the predictive values of five scales for 4-year all-cause mortality in critically ill elderly patients with sepsis. Ann Palliat Med. 2021;10(3):2387\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones AE, Trzeciak S, Kline JA. The Sequential Organ Failure Assessment score for predicting outcome in patients with severe sepsis and evidence of hypoperfusion at the time of emergency department presentation. Crit Care Med. 2009;37(5):1649.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLie KC, Lau CY, Van Vinh Chau N, West TE, Limmathurotsakul D. Utility of SOFA score, management and outcomes of sepsis in Southeast Asia: a multinational multicenter prospective observational study. J intensive care. 2018;6:1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerreira FL, Bota DP, Bross A, M\u0026eacute;lot C, Vincent JL. Serial evaluation of the SOFA score to predict outcome in critically ill patients. JAMA. 2001;286(14):1754\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGranholm A, Moller MH, Krag M, Perner A, Hjortrup PB. Predictive performance of the simplified acute physiology score (SAPS) II and the initial sequential organ failure assessment (SOFA) score in acutely Ill intensive care patients: post-hoc analyses of the SUP-ICU inception cohort study. PLoS ONE. 2016;11(12):e168948.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang N, Wang M, Jiang L, Du B, Zhu B, Xi X. The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury. Ren Fail. 2022;44(1):320\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno-Torres V, Royuela A, M\u0026uacute;\u0026ntilde;ez E, Ortega A, Gutierrez \u0026Aacute;, Mills P, Ramos-Mart\u0026iacute;nez A. Better prognostic ability of NEWS2, SOFA and SAPS-II in septic patients. Med Cl\u0026iacute;nica (English Edition). 2022;159(5):224\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang WC, Xie HJ, Fan HT, Yan MH, Hong YC. (2021). Comparison of prognosis predictive value of 4 disease severity scoring systems in patients with acute respiratory failure in intensive care unit: a STROBE report. Medicine, 100(39).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoganay GE, Cirik MO. (2021). Determinants of prognosis in geriatric patients followed in respiratory ICU; either infection or malnutrition. Medicine, 100(36).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKao KC, Hsieh MJ, Lin SW, Chuang LP, Chang CH, Hu HC, et al. Survival predictors in elderly patients with acute respiratory distress syndrome: a prospective observational cohort study. Sci Rep. 2018;8(1):13459.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"APACHE II, Intensive Care Scoring Systems, Mortality, OASIS, SAPS-II, SOFA","lastPublishedDoi":"10.21203/rs.3.rs-5341064/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5341064/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction\u003c/h2\u003e \u003cp\u003ePredictive scoring systems are applied in intensive care units(ICU) to monitor patients' response to treatment and guide treatment modalities.These scoring systems are also used as predictors in sepsis where mortality is high.This study aims to compare the discussed scores (APACHE II, SOFA, SAPS II, OASIS) in their role of predicting overall mortality in patients admitted to ICU with a diagnosis of sepsis or septic shock.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAmong 740 patients admitted to the tertiary intensive care unit within 2 years, 165 patients diagnosed with sepsis and septic shock were included in the study. Demographic data, comorbidities, SOFA, SAPS-2, OASIS and APACHE II scores, invasive or noninvasive mechanical ventilation requirement and duration, ICU admission, hospital stay and 28-day mortality were retrospectively evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAll scoring systems were positively correlated with mortality and CCI score. OASIS correlated with ICU admission time and duration of mechanical ventilation.When the role of mortality scoring systems was evaluated, APACHE was found to be the lowest, while SOFA, OASIS and SAPS were found to be the highest.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSAPS II and OASIS have a higher correlation with mortality compared to others.\u003c/p\u003e","manuscriptTitle":"Comparison of Intensive Care Unit Scoring Systems in Predicting Overall Mortality of Sepsis Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 17:35:16","doi":"10.21203/rs.3.rs-5341064/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d510fd69-5993-4d5d-85eb-91ae3f477bfc","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-27T17:35:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-27 17:35:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5341064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5341064","identity":"rs-5341064","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}