Sepsis is a critical medical condition characterized by the body's widespread immune reaction to an infection, which may result in severe organ failure and mortality. Although there have been notable progressions in comprehending the pathophysiology of this clinical disease, improvements in instruments for monitoring hemodynamics, and strategies for resuscitation, sepsis continues to be a leading cause of illness and death in critically sick patients.¹

Severe sepsis and septic shock are primary factors for admissions to the Intensive Care Unit (ICU) and the primary causes of death in ICUs that are not related to cardiac issues. Estimating the worldwide prevalence of sepsis is challenging. Approximately 30 million individuals are impacted by sepsis on a global scale each year, leading to a possible 6 million fatalities yearly.^2,3

The global sepsis-related death rate in 2017 was 19.7%, with a greater prevalence among men, particularly in regions with low, low-middle, or medium sociodemographic index. Swiftly identifying sepsis leads to prompt intervention, hence reducing the fatality rate. Presently, the Surviving Sepsis Campaign offers directives for prompt identification and management.^4,5

Sepsis may rapidly advance to severe sepsis and septic shock, with septic shock defined as the occurrence of low blood pressure in the context of severe sepsis that cannot be improved via fluid resuscitation.⁶ There is a dearth of a universally accepted approach for measuring the severity of sickness in people with sepsis. Without a mechanism in place, it is difficult to analyze research on the outcomes of sepsis. Mortality prediction systems have been developed and used as valuable instruments for evaluating the effectiveness of Intensive Care Units (ICUs).⁷

The APACHE scoring system has shown efficacy in predicting mortality and calculating the duration of stay following ICU admission. There are four variants, namely APACHE I to IV. The estimated risk of in-hospital mortality is determined using a logistic regression equation, which utilizes special beta coefficients designed for this purpose based on the APACHE II score. ^8,9

The SAPS II score records the most unfavorable value of certain variables during the first 24 hours after admission. The SAPS II score ranges from 0 to 163 points, with 0 to 116 points allocated for physiological characteristics, 0 to 17 points for age, and 0 to 30 points for prior diagnosis. The probability of mortality is then determined by the use of logistic regression. Nevertheless, the discrimination and calibration of the SAPS II model are not suitable when applied to a different population.

APACHE II and SAPS II scoring system are commonly used in prediction of mortality in intensive care units. These two models have been compared in several research, which have produced conflicting findings.^10,11,12

This Prospective observational study was conducted among patients more than 18 years who has been admitted to Intensive care unit in Akash Institute of Medical Science & Research center Devanahalli, Bengaluru rural. Duration of study was August 2022 to November 2023 (16 Months).

Sampling method: Stratified random sampling.

Inclusion Criteria:

• All the patients >18 years.
• Those who are willing to give consent.
• Meeting the criteria for sepsis by sepsis-3 Guidelines 2021.

Exclusion Criteria:

• Patients <18 years.
• Those who are refusing to give consent.
• Pregnant and lactating women are excluded.
• Post operative sepsis patients are excluded.

Method of Data Collection

After obtaining approval and clearance from the institutional ethics committee, the patients fulfilling the inclusion criteria will be enrolled for the study after obtaining informed consent.

Patients who were admitted to Intensive Care Unit of Akash Hospital either from emergency department or transferred from hospital ward diagnosed with sepsis/septic shock.

Patients who are meeting the SEPSIS-3 (2021) criteria for Sepsis\ Septic shock, were selected for this observational study according to inclusion and exclusion criteria.

For each patients following data were obtained demographic data, admission diagnosis and comorbidities, they were subjected to routine blood tests like complete hemogram, renal function test, arterial blood gas, serum electrolytes and liver function test. APACHE II & SAPS II score were calculated to each patient. They were followed up for outcome in terms of recovery or mortality.

SAMPLE SIZE ESTIMATION

n= Z21- α/2 pq / d2

p = 17.5%

q = 2100- p= 100- 25= 75

d= 8%

n= (1.96)² (25) (75) / (8)²

n= 114

Assessment tools: AP CHE II SAPS II

Investigations

CBC, RFT, LFT, ABG, Serum Electrolyte, urine culture and blood culture.

Statistical analysis: The data will be analyzed statistically using descriptive statistics namely mean, standard deviation, percentage whenever applicable. The data will be entered into Microsoft excel and SPSS version 25 will be used for statistical analysis. Categorical data will be expressed in the form of frequencies and chi square will be used for test of significance. Continuous data will be expressed in the form of mean and standard deviation. Student t-test will be used for statistically significance between the two groups (variables) p- value <=0.05 will be considered statistically significant

Table 1: Distribution of age among the study participants (N=114)

Among the 114 participants:

• Most are aged 40-50 years (23.7%), followed by those aged 50-60 years (21.1%) and 60-70 years (17.5%).
• The 30-40 years and 70-80 years age groups each make up 12.3% of the total.
• A smaller proportion is aged 20-30 years (6.1%), and 80 years and above (7.0%).

Table 2: Distribution of age among Survivors and Non-Survivors (N=114)

The mean age of survivors is 52.97 years with a standard deviation of 14.75 years, whereas the mean age of non-survivors is slightly higher 54.88 years with a standard deviation of 19.365 years. This difference in age distribution between the two groups is statistically not significant, with a P value 0.555.

Among the survivors, 29 participants are female, while 22 participants of the non-survivors are female. Among males 43 are survivors and 20 are non survivors. This gender distribution difference is statistically not significant, with a chi-square value of 1.572 and a p-value of 0.210.

Table 3: Mortality rate among study the participants (N=114)

Among the 114 individuals, the majority survived, with 63.2% (72 individuals), while 36.8% (42 individuals) did not survive.

Table 4: Distribution of vitals among the study participants (N=114)

Systolic Blood Pressure (SBP): The mean SBP for survivors is 118.25 mmHg with a standard deviation of 27.5 mmHg, while for non-survivors, the mean SBP is lower at 114.19 mmHg with a standard deviation of 30.24 mmHg. This difference is statistically not significant, with a p-value of 0.465.

Heart Rate: The mean heart rate for survivors is 103.79 beats per minute (bpm) with a standard deviation of 19.9 bpm, compared to 109.05 bpm with a standard deviation of 24.23 bpm for non-survivors. The p-value is 0.213, indicating no significant difference.

Respiratory Rate: The mean respiratory rate for survivors is 21.72 cycles per minute (cpm) with a standard deviation of 6.66 cpm, while for non-survivors, it is 29.12 cpm with a standard deviation of 7.91 cpm. This difference is statistically significant, with p-value of less than 0.001

Mean Arterial Pressure (MAP): The mean MAP for survivors is 90.06 mm Hg with a standard deviation of 24.82 mm Hg, and for non-survivors, it is 85.86 mm Hg with a standard deviation of 26.97mm Hg. The p-value is 0.401, indicating no significant difference.

Temperature: The mean temperature for survivors is 38.05°C with a standard deviation of 0.91°C, compared to 37.72°C with a standard deviation of 1.15°C for non-survivors. The p-value is 0.09, indicating no significant difference.

Table 5: Distribution of ABG among the study participants (N=114)

PaO2: The mean PaO2 for survivors is 71.39 mmHg with a standard deviation of 9.96 mmHg, while for non-survivors, the mean PaO2 is 67.74 mmHg with a standard deviation of 10.87 mmHg. The p-value is 0.071, indicating marginally significant.

Arterial pH: The mean arterial pH for survivors is 7.41 with a standard deviation of 0.102, compared to 7.36 with a standard deviation of 0.126 for non-survivors. The p-value is 0.029, indicating statistically significant.

Bicarbonate (HCO3) (mEq/L): The mean bicarbonate (HCO3) level for survivors is 18.54 mEq/L with a standard deviation of 4.55 mEq/L, while for non-survivors, it is 17.45 mEq/L with a standard deviation of 3.96 mEq/L. The p-value is 0.19, indicating no significant difference.

Pao2\Fi02 ratio: The mean Pao2\Fio2 for survivors is 316.85 with a standard deviation of 38.92, compared to 214.69 with a standard deviation of 88 for non-survivors. The p-value is less than 0.001, indicating statistically significant.

Serum bilirubin: The mean serum bilirubin level for survivors is 1.629 mg/dL with a standard deviation of 1.2 mg/dL, while for non-survivors, it is higher at

3.43 mg/dL with a standard deviation of 3.0 mg/dL. The p-value of less than 0.001.

Creatinine: The mean creatinine level for survivors is 1.57 mg/dL with a standard deviation of 1.01 mg/dL, while for non-survivors, the mean creatinine level is 2.29 mg/dL with a standard deviation of 0.247 mg/dL. This difference is statistically significant with the p-value is less than 0.001.

Blood urea nitrogen (BUN): The mean BUN level for survivors is 63.53 mg/dL with a standard deviation of 31.93 mg/dL, compared to 70.52 mg/dL with a standard deviation of 29.09 mg/dL for non-survivors. The p-value is 0.247, indicating no significant difference.

Urine output (ml\dL): The mean urine output in survivors is 1565.28 ml/dL with a standard deviation of 521.95 ml/dL, compared to 733.33 ml/dL with a standard deviation of 430.35 ml/dL for non-survivors. This difference is statistically significant with p-value less than 0.001.

Potassium (mmol/L): The mean potassium level for survivors is 3.87 mmol/L with a standard deviation of 0.91 mmol/L, while for non-survivors, the mean potassium level is very similar at 3.9 mmol/L with a standard deviation of 9.08 mmol/L. The p-value is 0.892, indicating no significant difference.

Sodium (mmol/L): The mean sodium level for survivors is 141.43 mmol/L with a standard deviation of 9.59 mmol/L, compared to 145.6 mmol/L with a standard deviation of 9.08 mmol/L for non-survivors. The p-value is 0.025, indicating statistically significant.

Hematocrit (%): The mean hematocrit for survivors is 37.36% with a standard deviation of 3.15%, compared to 37.49% with a standard deviation of 3.21% for non-survivors. The p-value is 0.84, indicating no significant difference.

TLC (Total leucocyte count): The mean white blood cell count for survivors is 12,826.6 cells/μL with a standard deviation of 6,139.6 cells/μL, while for non-survivors, the mean WBC count is 12,289.76 cells/μL with a standard deviation of 5,567,69 cells/μL. The p-value is 0.642, indicating no significant difference.

Table 6: Distribution GCS among the study participants (N=114)

Glasgow Coma Scale (GCS): The mean GCS in survivors is 12.36 with a standard deviation of 2.59 compared to 9.6 with a standard deviation of 2.92 for non-survivors. This difference is statistically significant with p-value less than 0.001.

In our study, the mean age of survivors was 46.43 years, with a standard deviation of 18.18 years, whereas the mean age of non-survivors was significantly higher at 61.67 years with a standard deviation of 16.53 years. This difference in age distribution between the two groups was statistically significant, with a P value of less than 0.001. Comparatively, in the study by Aminiahidashti et al13., the  mean age of survivors was 45.90 ± 20.78 years, while that of non-survivors was 61.38 ± 16.84 years, also showing a significant difference (P = 0.0006). Palavras et al14. reported a mean age of 69.5 ± 12.8 years among their cohort, with a significant difference between survivors (64.5 ± 13.8 years) and non-survivors (75.2 ± 8.8 years, P < 0.01). Godinjak et al15. found no significant age difference between survivors and non-survivors (P = 0.654), with a mean age of 61.7 ± 16.3 years. Faruq et al16. reported a non-significant difference in age between survivors and non-survivors (P = 0.121).

Gender distribution in our study revealed that 63.9% of survivors were female, compared to only 35.7% of non-survivors. Conversely, 64.3% of non-survivors were male, compared to 36.1% of survivors. This gender distribution difference was statistically significant (P = 0.008). In contrast, Aminiahidashti et al13. found no significant gender difference (P=0.53), with 69.05% of survivorsbeing male and 62.50% of non-survivors being male. Godinjak et al15. also reported no significant gender difference between survivors and non-survivors (P=0.232), with a male predominance in both groups.

The mean APACHE II score for survivors in our study was 14.79 ± 5.25, significantly lower than the mean score of 25.79 ± 7.43 for non-survivors (P=<0.001). Similarly, the mean SAPS II score for survivors was 39.01 ± 8.95 significantly lower than the mean score of 55.71 ± 14.95 for non-survivors (P < 0.001).

Godinjak et al a15. lso found significantly higher mean scores in non-survivors: APACHE II (31.5 ± 10.2 vs. 16.9 ± 6.4, P < 0.0001) and SAPS II (63.9 ± 11.2 vs. 41.2 ± 14.1, P < 0.0001).

Faruq et al re16. ported mean APACHE II and SAPS II scores of 23.91 ± 7.45 vs. 16.88 ± 7.07 (P = 0.000) and 55.14 ± 18.70 vs. 40.58 ± 14.82 (P = 0.000), respectively.

Apart from these scores, some of their component variables namely the respiratory rate, PaO2\FiO2, serum bilirubin, urine output and Glasgow coma scale were also noted to have a significant (p< 0.001) correlation with mortality in sepsis.

However age, gender, SBP, heart rate, MAP, temperature, PaO2, arterial pH, bicarbonate, creatinine, BUN, serum potassium, serum sodium, hematocrit and total leucocyte count did not show any correlation with mortality.

A The findings also reveal a significant difference in parameters like PaO2\FiO2 ratio, GCS, urine output, creatinine, pH and serum sodium, respiratory rate and bilirubin. However, no significant differences were observed in age gender and clinical parameters, including vitals, CBC, serum potassium, blood urea nitrogen and PaO2 . These results align with previous studies, reinforcing the utility of APACHE II and SAPS II scores as valuable tools for predicting patient outcomes in intensive care units also in our study APACHE II has stronger relationship with survivors compared to SAPSII. Future research should focus on larger, multicenter studies to validate these findings and explore the impact of additional factors such as comorbidities and treatment interventions on patient outcomes

1. Kaukonen K, Bailey M, Suzuki S, et al. Mortality related to severe sepsis and septic shock among critically Ill patients in Australia and New Zealand, 2000–2012. JAMA 2014; 311(13): 1308–1316.
2. Levy MM, Artigas A, Phillips GS, et al. Outcomes of the surviving sepsis campaign in intensive care units in the USA and Europe: a prospective cohort study. Lancet Infect Dis 2012; 12(12): 919–924.
3. Torio CM, Andrews RM. National inpatient hospital costs: the most expensive conditions by payer, 2011. Statistical Brief #160. August, 2013. Rockville, MD: Agency for Healthcare Research and Quality.
4. Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395:200–11.
5. Levy MM, Evans LE, Rhodes A. The Surviving Sepsis Campaign Bundle: 2018 update. Crit Care Med. 2018;46:997–1000
6. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome and associated costs of care. Crit Care Med. 2001;29:1303-10.
7. Angus DC, Wax RS. Epidemiology of sepsis: an update. Crit Care Med.

2001;29 (Suppl 7):S109-16.

8. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of  disease  classification  Critical  care medicine. 1985;13(10):818–29.
9. Rapsang AG, Shyam DCJIjoccmp-r, official publication of Indian Society of Critical Care Medicine. Scoring systems in the intensive care unit: a compendium. 2014; 18(4): 220
10. Alizadeh AM, Hassanian-Moghaddam H, Shadnia S, Zamani N, Mehrpour O. Simplified Acute Physiology Score II/Acute Physiology and Chronic Health Evaluation II and Prediction of the Mortality and Later Development of Complications in Poisoned Patients Admitted to Intensive Care Unit. Basic & Clinical Pharmacology & Toxicology. 2014;115(3):297–300.
11. Brinkman S, Bakhshi-Raiez F, Abu-Hanna A, de Jonge E, Bosman RJ, Peelen L, et al. External validation of Acute Physiology and Chronic Health Evaluation IV in Dutch intensive care units and comparison with Acute Physiology and Chronic Health Evaluation II and Simplified Acute Physiology Score II. Journal of Critical Care. 2011;26(1):105. e11-e18.
12. Seak C-J, Ng C-J, Yen DH-T, Wong Y-C, Hsu K-H, Seak JC-Y, et al.
13. Performance assessment of the Simplified Acute Physiology Score II, the Acute Physiology and Chronic Health Evaluation II score, and the Sequential Organ Failure Assessment score in predicting the outcomes of
14. adult patients with hepatic portal venous gas in the ED. The American Journal of Emergency Medicine. 2014;32(12):1481–4
15. Aminiahidashti H, Bozorgi F, Montazer SH, Baboli M, Firouzian A. Comparison of APACHE II and SAPS II scoring systems in prediction of critically ill patients’ outcome. Emergency. 2017;5(1).
16. Palavras MJ, Faria C, Cabral M, Santos F, Gonçalves J, Araújo A, Pereira
17. Validez de APACHE II y SAPS II como predictores de mortalidad precoz ya los 3 meses en pacientes con sepsis. Galicia Clínica. 2021;82(3):139-41.
18. Godinjak A, Iglica A, Rama A, et al. Predictive value of SAPS II and APACHE II scoring systems for patient outcome in a medical intensive care unit. Acta Med Acad. 2016;45(2):97-103.
19. Furqan A, Naseer M, Tabassum R. Comparison of Sensitivity, Specificity and Accuracy of APACHE II, SAPS II and SOFA Scoring Systems as Predictors of Mortality in ICU Patients. P J M H S . 2021. 15(12): 3364-66.