Prevalence and Factors Associated with Sepsis and Septic Shock Among ICU Patients at Adama Hospital Medical College: a Retrospective Chart Review Evaluating Outcome Predictive Performance of the Addition of Platelet Count on Modified SOFA Score

Prevalence and Factors Associated with Sepsis and Septic Shock Among ICU Patients at Adama Hospital Medical College: a Retrospective Chart Review Evaluating Outcome Predictive Performance of the Addition of Platelet Count on Modified SOFA Score | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence and Factors Associated with Sepsis and Septic Shock Among ICU Patients at Adama Hospital Medical College: a Retrospective Chart Review Evaluating Outcome Predictive Performance of the Addition of Platelet Count on Modified SOFA Score Behaylu Tesfamaryam, Kubee Matewos, Mohammed Hussein, Kbreab Amare, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6745438/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sepsis and, its subset, septic shock are major health problems disproportionately affecting low- and middle-income countries. Despite the potentially high burden in low- and middle-income settings, data on prevalence and factors contributing for the development of sepsis and septic shock remains scarce. Methods A Retroactive Chart Review (RCR) was conducted on 298 patients admitted to the ICU, with the aim of identifying the prevalence and factors related to sepsis and septic shock. Coded data were transferred to and validated using Microsoft Excel, and analyzed using SPSS version 25. A binary logistic regression model was employed to identify factors contributing to sepsis and septic shock. Results Among the 298 patients, 142 (47.7%) had sepsis, and 55 (38.7% of septic patients, 18.5% of total) developed septic shock. The median age of septic patients was 40 years, and females had a higher burden of sepsis (52.8%) and septic shock (63.64%). Pneumonia was the most common site of infection (30.99% of sepsis cases). Multivariate analysis revealed that being on mechanical ventilation (AOR 5.59, 95% CI: 2.28–13.70), high white blood cell count (AOR 1.45, 95% CI: 1.00-2.12), and low platelet count (AOR 0.99, 95% CI: 0.99-1.00) were significantly associated with sepsis. Male gender (AOR 0.10, 95% CI: 0.03–0.29), high mSOFA score (AOR 1.92, 95% CI: 1.55–2.38), and low lymphocyte count (AOR 0.52, 95% CI: 0.31–0.86) were associated with septic shock. Septic shock also showed an association with low platelet count (AOR: 0.98, 95% CI: 0.98–0.99). The inclusion of platelet count in the mSOFA score resulted in a slightly higher but statistically insignificant increase in the area under the ROC curve for mortality prediction (AUC 0.832 vs. 0.826, p = 0.071). Conclusion This study found a higher prevalence of sepsis and septic shock compared to high-income countries, but comparable to most Low- and middle-income countries, and the affected population was relatively younger. Notably, despite a marginal improvement in discriminative performance, platelet count did not significantly enhance the prognostic accuracy of the mSOFA score for mortality among infection-specific ICU cohorts. Sepsis Septic Shock Prevalence Associated Factors ICU modified SOFA score Low- and middle-income countries Adama Ethiopia Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Sepsis, derived from the Greek word "SEPO" meaning decomposition, has been recognized for millennia, with its initial documentation appearing in Homer's poems around 2700 years ago. Hippocrates later characterized it by rotting flesh and festering wounds. Over time, various definitions and theories emerged [1,2]. The first consensus definition (Sepsis 1) in 1991 defined sepsis as the presence of Systemic Inflammatory Response Syndrome (SIRS) in a patient with confirmed or suspected infection [3]. This was revised in 2001 (Sepsis 2) to include organ dysfunction in the definition of severe sepsis, which could progress to septic shock, defined as sepsis-induced hypotension despite adequate fluid resuscitation. The current definition, Sepsis 3 (2016), defines sepsis as a dysregulated host response to infection leading to organ dysfunction, and septic shock as a subset of sepsis with underlying circulatory, cellular, and metabolic abnormalities significantly increasing mortality risk [2,4]. A significant limitation of the Sepsis 3 definition is that four out of the six criteria for organ dysfunction rely on laboratory results, posing challenges in resource-limited settings. A prospective observational study demonstrated that a modified Sequential Organ Dysfunction Assessment (mSOFA) score predicts mortality as effectively as the SOFA score and is more feasible in settings with limited resources [5]. Sepsis manifests when the body's reaction to an infection becomes unchecked, leading to effects on tissues that are far removed from the original infection site. The reasons behind the transition of typically localized immune responses to a widespread reaction, resulting in sepsis, remain unclear. This phenomenon is likely attributable to multiple factors, which may encompass the direct impact of pathogenic microorganisms or their toxic byproducts, the release of substantial amounts of proinflammatory mediators, and the activation of the complement system. Furthermore, certain individuals might possess a genetic predisposition that increases their likelihood of developing sepsis [6]. The trajectory from infection to sepsis, as well as the outcomes of treatment, are significantly influenced by patient-related variables, including comorbidities such as AIDS, liver disease, and cancer, as well as by non-patient-related factors such as the type and origin of the infection, whether nosocomial or community-acquired [7–11]. As of Global Sepsis Alliance, sepsis is a major health crisis affecting 47 to 50 million individuals annually, resulting in at least 11 million deaths, equating to one death every 2.8 seconds. Mortality rates vary significantly by country, ranging from 15% to over 50%. Many survivors also experience long-term consequences [12]. A 2020 meta-analysis endorsed by the WHO highlighted significant regional differences in sepsis incidence and mortality in ICU patients, with minimal data from LMICs and none from Africa, indicating an urgent need for improved surveillance in these regions [13]. The WHO estimates a higher prevalence and mortality of sepsis in LMICs compared to high-income countries, despite the scarcity of evidence [14]. Furthermore, the limited available data from LMICs often utilize the Sepsis 2 definition, which has inferior predictive validity for septic shock and mortality compared to Sepsis 3, although it identifies a similar sepsis population with a 92% overlap [15]. Early and appropriate resuscitation of shock with early goal-directed therapy (EGDT) during the critical "golden hours" of sepsis can potentially prevent the progression to multiple organ dysfunction. This includes identifying high-risk patients, obtaining appropriate cultures, source control, early antibiotic administration, and hemodynamic optimization [2,16]. Given the high burden of infectious diseases in LMICs, it is plausible that sepsis is common, and delayed identification and treatment of infections may further increase the risk of developing sepsis. However, despite the anticipated higher prevalence and poorer prognosis of sepsis in LMICs, there is a paucity of research on this topic, particularly in sub-Saharan Africa. Moreover, much of the existing data predates the 2016 revision of the sepsis definition, potentially underestimating the mortality associated with sepsis and septic shock. This study aims to address this significant knowledge gap by identifying the prevalence and factors related to the transitions from infection to sepsis and then to septic shock in a resource-limited setting, which can inform future policies and strategies for early risk stratification and improved patient outcomes. Methodology This study employed a retrospective chart review (RCR) design to determine the prevalence and associated factors of sepsis and septic shock among patients admitted to the central intensive care unit (ICU) of Adama Hospital Medical College (AHMC) in Adama, Ethiopia. AHMC is a 232-bed capacity medical college serving a catchment population exceeding 6 million. The ICU functions as a combined adult and pediatric unit, equipped with 12 beds and 10 mechanical ventilators. The initial target sample size for this study, based on the assumption (Prevalence [17] = 26%, 95% CI, and a 5% margin of error), was 338. Charts were selected using systematic random sampling from a cohort of 1320 eligible admissions. However, during the chart retrieval process, 32 charts were found to be missing. Furthermore, among the retrieved charts, 8 had labeling errors: 4 with mislabeled age and 4 with incorrect Medical Record Numbers (MRN), rendering them unusable for the study. Consequently, the final analysis included 298 patient charts that met the inclusion criteria and had complete and accurate documentation. The primary dependent variable was the presence of sepsis, defined according to the Sepsis-3 consensus criteria as life-threatening organ dysfunction resulting from a dysregulated host response to infection. Operationally, organ dysfunction was defined as an increase in the modified Sequential Organ Failure Assessment (mSOFA) score of 2 or more points from baseline (or zero if previously unknown). Septic shock was defined clinically by the requirement for vasopressors to maintain a mean arterial pressure ≥ 65 mmHg and/or a serum lactate level > 2 mmol/L following adequate fluid resuscitation. The independent variables examined included sociodemographic factors (age, sex), pre-existing comorbidities (diabetes mellitus, HIV infection, malignancy of any type), microbiological findings (culture and sensitivity results), the primary site of infection (chest, central nervous system, genitourinary, gastrointestinal, bloodstream), the acquisition of infection (community-acquired versus nosocomial, defined based on the assessment of treating physician), biochemical/laboratory parameters (leukopenia, anemia, thrombocytopenia), and treatment-related factors (need for and duration of mechanical ventilation, length of ICU stay). The collected data from the 298 usable charts were initially checked for completeness using Microsoft Excel and subsequently transferred to and analyzed using the Statistical Package for Social Sciences (SPSS) version 25. Descriptive statistics were computed to characterize the study participants and determine proportions. Chi-square tests and independent sample t-tests were employed to identify significant categorical and continuous independent variables, respectively. Variables found to be significant in these initial analyses were then entered into a binary logistic regression model to determine their independent effects while controlling for other variables. The strength of associations was quantified using odds ratios (OR) with a 95% confidence interval (CI), and a p-value of 0.05 or less was considered indicative of statistically significant associations. We justified excluding the intercept (constant) from our binary logistic regression models based on both theoretical and statistical grounds. Biologically, sepsis and septic shock cannot occur at a SOFA score of zero, making a baseline risk estimate at this point nonsensical. Statistically, removing the intercept stabilized coefficient estimates, resulting in narrower and more clinically meaningful confidence intervals for the site of infection and SOFA score. Diagnostic tests confirmed the absence of multicollinearity. The resulting models exhibited excellent discrimination (AUC = 0.922 for sepsis and 0.956 for septic shock), good calibration (Hosmer-Lemeshow p > 0.05), and substantial explained variance (Nagelkerke R² = 0.659 and 0.89, respectively). Residual analysis further supported the models' robustness and lack of bias. By aligning our statistical model with the physiological reality that sepsis and infection are absent at a SOFA score or infection site of zero, we improved interpretability and predictive accuracy for sepsis and septic shock risk assessment. Results Prevalence of Infection, Sepsis, and Septic Shock Among 298 ICU patients, 193 (64.8%) had suspected or confirmed infections. Of these, 142 (73.6%) met Sepsis-3 criteria, yielding an overall sepsis prevalence of 47.7%. Septic shock developed in 55 patients (38.7% of septic cases; 18.5% of total admissions). Pneumonia was the most common infection site (53.9% of sepsis cases), followed by intra-abdominal (21.8%) and skin/soft tissue infections (15.5%) (Fig. 1). Hospital-acquired infections (HAIs) accounted for 36.8% of infections. Cultures were performed in only 31 patients (10.7%), with 48.4% positivity; Proteus species were predominant (46.7%). Sociodemographic and Clinical Characteristics Among 298 ICU patients, 193 (64.8%) had suspected or confirmed infections. Of these, 142 (73.6%) met Sepsis-3 criteria, yielding an overall sepsis prevalence of 47.7%. Septic shock developed in 55 patients (38.7% of septic cases; 18.5% of total admissions). Pneumonia was the most common infection site (53.9% of sepsis cases), followed by intra-abdominal (21.8%) and skin/soft tissue infections (15.5%) (Fig. 1). Hospital-acquired infections (HAIs) accounted for 36.8% of infections. Cultures were performed in only 31 patients (10.7%), with 48.4% positivity; Proteus species were predominant (46.7%). Table 1 Demographic and Clinical Characteristics of ICU Patients with Sepsis, Non-Septic, and Septic Shock Cohorts at Adama Hospital Medical College (October 2021–July 2023). Variables Septic (n = 142) Non-septic (n = 156) Septic shock (n = 55) Age (year), median (IQR) 40 (25–59) 35 (26–53) 40 (25–60) Gender, n (%) Male 67 (47.18) 84 (53.85) 20 (36.36) Female 75 (52.82) 72 (46.15) 35 (63.64) Site of infection, n (%) Pneumonia 44 (30.99) 11 (7.05) 18 (25.45) Intra-abdominal 19 (13.38) 9 (5.77) 13 (23.64) Skin/soft tissue 17 (11.97) 11 (7.03) 10 (18.18) HAI 58 (40.85) 13 (8.33) 18 (32.73) Other infection sites* 4 (2.82) 7 (4.49) - Department, n (%) Gyn/ Obs 10 (7,0) 10 (7.0) 4 (7.27) Medical 94 (66.20) 70 (44.87) 29 (52.73) Surgery 38 (26.76) 76 (48.72) 22 (40.00) Laboratory, median (IQR) WBC 12.4 (7.1–17.7) 10.4 (7.7–14) 10.6 (7.1–15) Lymphocyte 1 (0.5-2) 1.3 (0.7-2.0) 0.7 (0.3–1.7) Neutrophil 9 (5.5–13.9) 5.4 (5.5–12.1) 8.9 (5,2-13.4) Platelet 151 (88–242) 241 (200–347) 145 (75–196) Potassium 4 (3.5–4.5) 3.8 (3.3–4.1) 4.0 (3.2–5.1) Hemoglobin 11.2 (8.9–13.8) 12.8 (10.2–14.2) 10.4 (8.8–13.9) Sodium 140 (135-146.7) 137 (130–141) 140 (133–144) Chloride 106 (99–132) 102 (97–107) 99 (107–117) Comorbidity, n (%) Asthma 2(1.3) 2(1.3) 2(3.6) Congestive heart failure 7 (4.93) 23 (14.74) 2 (3.64) Chronic kidney disease 5(3.5) 4(2.6) 0 Chronic liver disease 5(3.5) 4(2.6) 2(3.6) Diabetes mellitus 22(15.5) 16(10.3) 13(23.6) Hypertension 10 (7.0) 32 (20.5) 2 (3.6) Human immunodeficiency virus 14 (9.9) 4 (2.56) 8 (14.5) Malignancy 2(1.4) 2(1.4) 2(3.6) Other comorbidities a 9(6.3) 10(6.4) 5(9.1) More than one 9(6.3) 18(11.5) 4(7.3) On mechanical ventilator, n (%) 109(76.2) 47(30.1) 42(76.4) Length of ICU stay (days), median (IQR) 4.5(3–8) 4(2–5) 4(2–6) Legend : Data presented as median (IQR) for continuous variables and frequency (%) for categorical variables. Comparisons stratified by clinical subgroups (septic vs. non-septic vs. septic shock). Abbreviations: SD, standard deviation; IQR, interquartile range; HAI, hospital-acquired infection; CKD, chronic kidney disease; CLD, chronic liver disease; DM, diabetes mellitus; CHF, congestive heart failure; HIV, human immunodeficiency virus; WBC, white blood cell count. a (Thyroid disorders, Peripheral arterial disease, Interstitial lung diseases, inflammatory bowel disease), *(Meningitis, urinary tract infection, bloodstream, no identified primary site). Laboratory and Comorbidity Profiles Low platelet count (median (IQR) 151 (88-242) in septic vs. 241 (200-347) in non-septic patients; p < 0.05), leukocytosis (median (IQR) 12.4 (7.1-17.7) vs. 10.4 (7.7-14); p < 0.05), high serum sodium (11.2 (8.9-13.8) vs. 137 (130-141); p < 0.05), and high serum chloride levels (106 (99-132 vs. 102 (97-107); p < 0.05) distinguished septic from non-septic patients (Table 1). Diabetes mellitus (15.5% in sepsis, 23.6% in septic shock) and HIV (14.5% in septic shock; p < 0.05) were key comorbidities. The addition of platelet criteria to mSOFA identified 5 additional sepsis cases with median mSOFA and mSOFA + platelet scores of 1 and 2, respectively. Among these, 2 died (40% mortality), and 3 were transferred to general wards without developing septic shock. Mortality rates for the original mSOFA diagnosed cohort (69%) and the modified mSOFA + platelet cohort (68%) were comparable. Treatment Mechanical ventilation was required in 59.4% of patients, with 76% of ventilated patients developing sepsis. Antibiotics were prescribed to 80.2% of admissions, predominantly ceftriaxone (35%) and metronidazole (22%) (Fig. 3). The overall median length of ICU stay was 4 days (IQR: 4–6), and 4 days (IQR: 3–8) for patients with infection, 4.5 days (IQR: 3–8) and 4 days (IQR: 2–6) for patients with sepsis and septic shock, respectively. Multivariate Analysis Binary logistic regression, as shown in Table 2 , identified mechanical ventilation (AOR: 5.59, 95% CI: 2.28–13.70), leukocytosis (AOR: 1.45, 95% CI: 1.00–2.12), and low platelet count (AOR: 0.99, 95% CI: 0.99-1.00) as independent factors related to sepsis. For septic shock, Table 3 shows that male gender (AOR: 0.10, 95% CI: 0.03–0.29), elevated mSOFA score (AOR: 1.92, 95% CI: 1.55–2.38), and lymphopenia (AOR: 0.52, 95% CI: 0.31–0.86) were significant. Thrombocytopenia correlated with both sepsis (AOR: 0.99) and septic shock (AOR: 0.98; p < 0.05). Table 2 Factors Associated with Sepsis: Unadjusted (Chi-Squared and Logistic Regression) and Adjusted Logistic Regression Analysis COR (95%CI) AOR (95% CI) Variables COR Lower Upper AOR Lower Upper P-value Ventilation Mechanically ventilated 4.12 2.49 6.81 5.59 2.28 13.70 0.00 Infection pneumonia 10.06 5.65 17.92 10.10 3.66 27.88 0.00 Intra-abdominal 3.19 1.56 6.51 11.52 3.30 40.20 0.00 Nosocomial 7.59 3.92 14.68 3.81 1.27 11.47 0.02 CBC Hemoglobin 0.86 0.80 0.94 0.88 0.76 1.03 0.11 WBC 1.35 1.07 1.70 1.45 1.00 2.12 0.05 Platelet 0.99 0.99 0.99 0.99 0.99 1.00 0.00 Electrolytes Chloride 1.06 1.03 1.09 1.00 0.98 1.02 0.98 Comorbidities Hypertensive 0.29 0.13 0.62 0.35 0.09 1.33 0.12 CHF 0.30 0.12 0.72 0.58 0.15 2.31 0.44 Legend Univariate (COR) and multivariate (AOR) logistic regression analyses identifying risk factors associated with sepsis. Adjusted models included comorbidities (hypertension, CHF, and diabetes), and laboratory markers (platelet count, hemoglobin and electrolytes). Odds ratios reported with 95% confidence intervals (CI); bolded values denote statistical significance (p < 0.05). Table 3 Factors Associated with Septic Shock: Unadjusted (Chi-Squared and Logistic Regression) and Adjusted Logistic Regression Analysis COR (95% CI) AOR (95% CI) Variables COR Lower Upper AOR Lower Upper P-value Gender Male 0.49 0.27 0.89 0.10 0.03 0.29 0.00 Infection Pneumonia 1.54 0.85 2.79 0.10 0.02 0.41 0.00 Intra-abdominal 0.26 0.13 0.52 0.68 0.19 2.49 0.56 Skin/Soft tissue 0.24 0.11 0.54 1.56 0.38 6.48 0.54 HAI 1.74 0.92 3.31 1.09 0.38 3.14 0.87 Lymphocyte 0.59 0.42 0.83 0.52 0.31 0.86 0.01 CBC Platelet 0.99 0.99 0.99 0.98 0.98 0.99 0.00 Comorbidities HIV/AIDS 3.97 1.49 10.58 3.31 0.76 14.29 0.11 DM 2.70 1.28 5.70 2.73 0.70 10.67 0.15 Organ failure mSOFA score 26.69 10.64 66.96 1.92 1.55 2.38 0.00 Legend Logistic regression analyses of factors associated with progression to septic shock among septic patients. Multivariate models (AOR) adjusted for sepsis severity (mSOFA score), platelet trends, comorbidities, and gender. Odds ratios reported with 95% confidence intervals (CI); bolded values denote statistical significance (p < 0.05). Discussion In this retrospective chart review, it was shown that the prevalence of infection, sepsis and septic shock to be 64.8%, 47.7% and 18.5% respectively. When compared to the data from intensive care over nations (ICON) audit it was higher than the global prevalence which was 29.5% (varies between 13.6 to 39.6 in different regions) [ 18 ] and found to be comparable with that of Australia, Asia and Middle east [ 19 ]. Again, the prevalence of septic shock 18.5% were found to be higher than that of Europe and North America which was estimated to be 10.4% [ 20 ] and it was also higher than some published local studies including Tikur Anbesa Specialized Hospital (TASH) which had prevalence of 14% [ 21 ], 8.9% from a study conducted in multiple ICUs in Addis Abeba [ 22 ] and 7.7% another study from southern Ethiopia done in three teaching hospitals [ 23 ]. Sepsis is generally considered a disease of the elderly, particularly in high-income countries where studies report a mean age at diagnosis ranging from 63 to 67 years, with older populations disproportionately affected (13.1 times more frequently than younger individuals) [ 24 , 25 ]. However, based on this study the median age at diagnosis of both sepsis and septic shock was 40 years, contradicting this established pattern. Interestingly, such a younger age profile aligns with trends observed in sub-Saharan Africa, as evidenced by a systematic review and meta-analysis of fifteen studies from the region [ 26 ]. Sepsis and septic shock are typically reported to occur more frequently in male patients compared to female patients; however, this observation is largely rooted in clinical reports that provide limited empirical evidence. Suggested reasons for this disparity encompass both social dynamics, such as an elevated risk of infection or exposure to violence, and biological differences, including variations in hormonal levels or immune responses [ 27 ]. Nevertheless, our research yields findings that challenge this established pattern, revealing no statistically significant association based on sex in cases of sepsis and indicating a negative correlation among males with septic shock. While this assertion diverges from prevailing expectations, a limited number of studies corroborate our findings, thereby indicating a potential variability in sex-related risk profiles [ 28 ]. The relationship between the primary infection site and the development of septic shock is complex and not consistently observed across studies. While pneumonia is frequently identified as the most common source of infection leading to sepsis [ 17 , 22 , 29 , 30 ], its association with septic shock varies. For example, a subgroup analysis within the Japanese FORCAST study (2016–2017) found intra-abdominal infection to be significantly linked to septic shock and the most prevalent infection site in those patients (72%). In contrast, the same study reported pneumonia as the most common infection in patients with sepsis [ 31 ]. This contrasts with findings from a large retrospective cohort study spanning ICUs in Canada, the USA, and Saudi Arabia, which indicated that pneumonia was the most common infection, amongst patients with septic shock [ 32 ]. However, the progression from sepsis to septic shock is not solely determined by the anatomical location of the infection. The specific microorganisms responsible for the infection appear to play a crucial role, with microbial factors potentially exerting a stronger influence on patient outcomes than the infection site alone. This emphasis on microbial etiology is supported by the work of Florian et al., demonstrating that the type of organism significantly impacts mortality. Their findings showed that gram-negative pathogens such as Pseudomonas and Acinetobacter are linked to higher odds of death (OR 1.4 and 1.5, respectively) [ 33 ]. Interestingly, our study revealed that while intra-abdominal infections showed no statistically significant correlation with septic shock and pneumonia even exhibited a negative correlation, both infection sites were strongly and positively associated with the initial development of sepsis (AOR = 10.0 for pneumonia; AOR = 11.5 for intra-abdominal infections). Based on the observed discrepancy we propose that the specific characteristics of the infecting microorganisms , including their virulence, resistance patterns, and interactions with the host's immune system, may be key modulators in the progression from sepsis to septic shock, and the observed variation in anatomic site of infection among different studies is attributed to the difference in tissues tropism of predominant virulent microorganisms in the study area. For instance [ 33 ], pneumonia caused by organisms like Streptococcus pneumoniae (associated with lower mortality) or viral pathogens might explain the negative correlation with shock despite a high risk of sepsis. Conversely, intra-abdominal infections, often involving a mix of bacteria or less aggressive pathogens, may primarily drive sepsis without necessarily leading to shock. According to several studies sepsis is the most expensive medical condition and among the reasons for the higher cost, longer length of stay and multiple treatment provided including antibiotics were of significant value medication alone accounting for 15–25% of the total cost [ 34 – 36 ]. in this study the duration of ICU stays and number and type of antibiotics used was computed as means to compare the relative cost of sepsis and the findings was septic patients had longer median length of stay than non-septic patients (4.5 Vs 4 days), in addition septic patients were the reason for 75% of prescribed antibiotics, suggesting the higher cost of sepsis. This RCR showed that being on mechanical ventilator, low platelet, and higher white cell count were statistically significant factors associated with sepsis, and these findings are heavily supported by findings from other studies. The association between being on a mechanical ventilator and an increased risk of sepsis was one of the key findings in the systematic review by Fathi et al., in 2019Gc [ 37 ]. The authors reported that patients who required mechanical ventilation had a significantly higher likelihood of developing sepsis compared to those who did not require this intervention. This result emphasizes the importance of monitoring ventilated patients closely, as they may be more susceptible to sepsis. Thrombocytopenia was included in the sequential organ failure assessment criteria which is used to define organ dysfunction in septic patients according to sepsis 3 definition of sepsis which was formulated by a group of experts based on previous data and consensus [ 4 ]. An observational study demonstrated that a modified, using jaundice rather than bilirubin measurement and removal of platelet count from criterion for defining organ dysfunction, mSOFA score predicts mortality as effectively as the SOFA score and is more feasible in settings with limited resources [ 5 ]. Receiver operating characteristic (ROC) analysis demonstrated that the inclusion of platelet count on mSOFA achieved an AUC of 0.832 (95% CI: 0.776–0.888), while the standard mSOFA alone yielded an AUC of 0.826 (95% CI: 0.768–0.883). Despite marginally higher discriminative performance with platelet incorporation, the overlapping 95% confidence intervals between the two models indicated no statistically significant improvement in mortality prediction. This was further supported by the likelihood ratio test (Chi-square = 3.249, df = 1, P = 0.071), which demonstrated that platelet count does not meaningfully enhance the prognostic accuracy of the mSOFA score in this infection-specific cohort for distinguishing between patients who died in the ICU and those who survived to transfer. The findings underscore the limited incremental value of platelet parameters in refining mortality risk stratification within the mSOFA framework, even in populations with infections where thrombocytopenia may reflect disease severity. Larger studies are needed to validate these observations and explore context-dependent utility. The other hematological finding was higher white cell counts, which was shown to be associated with sepsis in several studies [ 38 , 39 ]. In addition, leukocytosis was among the SIRS criteria which again, was diagnostic tool of sepsis before the introduction of sepsis 3 definition of sepsis. in our model WBC count showed AUC of 0.56 (95% CI: 0.49–0.63) and at the cut off point for leukocytosis based on our institution’s laboratory i.e. 10 thousand, this model showed 65% sensitivity and 45% specificity for sepsis. Modified SOFA score also showed significant odds for occurrence of septic shock. The second aspect explored in the study is the association between the mSOFA score and septic shock occurrence. Since the definition of septic shock was part of the score which partly explain this finding the non-hemodynamic components of the score was computed in binary logistic regression excluding the total score and the Glasgow coma score (GCS) was found to have significant (p < 0.05) association with septic shock with odds ratio of 1.7 (95% CI: 1.1–1.6). An evidence going along with our finding was a systematic review published in the Egyptian journal of hospital medicine [ 40 ] showed significant association between GCS and septic shock. Limitations This study has several limitations: Retrospective Chart Review (RCR) and Sample Size: The use of RCR relies on the accuracy and completeness of medical records. The relatively small sample size may limit the generalizability of the findings to other populations. Limited Investigation Details: The study was unable to comprehensively address details of investigations like cultures and sensitivities due to missing information in the medical records. Conclusion This study found a higher prevalence of sepsis and septic shock compared to high-income countries, but comparable to most Low- and middle-income countries. Notably, the affected population was relatively younger. Additionally, the findings indicate that incorporating platelet count into the modified SOFA score does not enhance its predictive accuracy in differentiating clinical outcomes among ICU patients. Abbreviations AOR Adjusted Odds Ratio AUC Area Under the Curve CBC Complete Blood Count CHF Congestive Heart Failure CI Confidence Interval CKD Chronic Kidney Disease CLD Chronic Liver Disease COR Crude Odds Ratio DM Diabetes Mellitus EGDT Early Goal-Directed Therapy GCS Glasgow Coma Scale HAI Hospital-Acquired Infection HIV Human Immunodeficiency Virus ICU Intensive Care Unit IQR Interquartile Range IRB Institutional Review Board LMICs Low- and Middle-Income Countries MRN Medical Record Number mSOFA Modified Sequential Organ Failure Assessment ROC Receiver Operating Characteristic RCR Retrospective Chart Review SIRS Systemic Inflammatory Response Syndrome SOFA Sequential Organ Failure Assessment SPSS Statistical Package for the Social Sciences TASH Tikur Anbesa Specialized Hospital (Ethiopia) WBC White Blood Cell Declarations 1. Ethics approval and consent to participate This research received ethical approval from the Institutional Review Board (IRB) of Adama Hospital Medical College, as documented in letter (0925/K-373). Considering the study's design, the requirement for individual participant consent was waived by the board, a decision consistent with the institution's ethical protocols, and the study was conducted and reported in accordance with the principles outlined in the Declaration of Helsinki. 2. Consent for publication: Not applicable. 3. Availability of data and materials : The datasets generated and analyzed during this study are available in the Zenodo repository (https://zenodo.org/records/15476903) [41]. 4. Competing interests : All the authors declare that they have no competing interests. 5. Funding : Partial support for this research was provided by Adama Hospital Medical College in the context of a thesis project conducted by the corresponding author. 6. Authors' contributions B.T. conceptualization, data curation, analysis, and drafting of the manuscript. K.M. and M.H. critical review, editing, and supervision. K.A. and Z.T. critical review, editing, and validation. T.G. data curation, drafting of the manuscript, validation. 7. Acknowledgements: The authors gratefully acknowledge Adama Hospital Medical College for their support of this research and for their partial financial contribution towards the study costs, which was provided in the context of this thesis work. We also extend our sincere thanks to Professor Sileshi Garoma for his review and guidance during the proposal development phase of this study. References Gul F, Arslantas MK, Cinel I, Kumar A. Changing Definitions of Sepsis. Turkish J Anesth Reanim [Internet]. 2017 Jul 10;45(3):129–38. https://turkjanaesthesiolreanim.org/articles/doi/TJAR.2017.93753 Gyawali B, Ramakrishna K, Dhamoon AS. Sepsis: The evolution in definition, pathophysiology, and management [Internet]. 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Elsevier BV; 2017 Oct 1 [cited 2022 Dec 29];119(4):626–36. http://dx.doi.org/10.1093/bja/aex234 RIVERS E, NGUYEN BR, HAVSTAD S, RESSLER J, MUZZIN A, KNOBLICH B, PETERSON E, TOMLANOVICH M. EARLY GOAL-DIRECTED THERAPY IN THE TREATMENT OF SEVERE SEPSIS AND SEPTIC SHOCK. N Engl J Med [Internet]. 2001;345(19):1368–77. www.nejm.org Abera H, Bayisa T, Worku Y, Lazarus JJ, Woldeyes E, Mulatu HA, Bayisa T, Worku Y, Lazarus JJ, Woldeyes E, et al. Prevalence and outcome of sepsis and septic shock in intensive care units in Addis Ababa, Ethiopia: A prospective observational study. African J Emerg Med [Internet]. 2020;11(1):188–95. https://doi.org/10.1016/j.afjem.2020.10.001 Sakr Y, Jaschinski U, Wittebole X, Szakmany T, Lipman J, Ñamendys-Silva SA, Martin-Loeches I, Leone M, Lupu MN, Vincent JL. Sepsis in Intensive Care Unit Patients: Worldwide Data From the Intensive Care over Nations Audit. Open forum Infect Dis. 2012;5(12):1–8. Vincent JL, Sakr Y, Singer M, Martin-Loeches I, Machado FR, Marshall JC, Finfer S, Pelosi P, Brazzi L, Aditianingsih D, et al. Prevalence and Outcomes of Infection Among Patients in Intensive Care Units in 2017. JAMA - J Am Med Assoc. 2017;323(15):323(15):1478–1487. Vincent J louis, Jones G, David S, Olariu E, Cadwell KK. Frequency and mortality of septic shock in Europe and North America : a systematic review and meta-analysis. Critical Care; 2019;1–11. Teklemichael H. Assessment of Incidence and Treatment Outcome of Septic Shock Among Patients Admitted To Adult Intensive Care Unit of Tikur Anbessa Specialized Hospital , Addis Ababa , Ethiopia. Addis Abeba University; 2017. Abera H, Bayisa T, Worku Y, Lazarus JJ, Woldeyes E. Prevalence and outcome of sepsis and septic shock in intensive care units in Addis Ababa , Ethiopia : A prospective observational study. African J Emerg Med [Internet]. Elsevier B.V.; 2020;(September):0–1. https://doi.org/10.1016/j.afjem.2020.10.001 Abate SM, Assen S, Yinges M, Basu B. Survival and predictors of mortality among patients admitted to the intensive care units in southern Ethiopia: A multi-center cohort study. Ann Med Surg [Internet]. Elsevier; 2021;65. https://doi.org/10.1016/j.amsu.2021.102318 DC A, WT LZ, Lidicker J CG, J C, MR P. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med [Internet]. United States; 2001 Dec;29(7):1303–10. http://dx.doi.org/10.1097/00003246-200107000-00002 Chen CM, Cheng KC, Chan KS, Yu WL. Age May Not Influence the Outcome of Patients with Severe Sepsis in Intensive Care Units. Int J Gerontol [Internet]. Elsevier Taiwan LLC.; 2014;8(1):22–6. http://dx.doi.org/10.1016/j.ijge.2013.08.004 Lewis JM, Feasey NA, Rylance J. Aetiology and outcomes of sepsis in adults in sub-Saharan Africa: a systematic review and meta-analysis. Crit Care [Internet]. England; 2019 Jun 11;23(212):1–11. https://pubmed.ncbi.nlm.nih.gov/31186062 Lakbar I, Einav S, Lalevée N, Martin-Loeches I, Bruno P, Marc L. Interactions between Gender and Sepsis—Implications for the Future. Microorganisms. 2023;11(746):1–16. Liu N, Ren J, Yu L, Xie J. Mechanical ventilation associated with worse survival in septic patients: a retrospective analysis of MIMIC-III. J Emerg Crit Care Med [Internet]. 2020;4(0):14. http://dx.doi.org/10.21037/jeccm.2020.01.01 V K, AM H, J M, K F, HC S, H K. Site of infection and mortality in patients with severe sepsis or septic shock. A cohort study of patients admitted to a Danish general intensive care unit. Infect Dis [Internet]. 2016;48(10):726–31. http://dx.doi.org/10.3109/23744235.2016.1168938 Zhou J, Qian C, Zhao M, Yu X, Kang Y, Ma X, Ai Y, Xu Y, Liu D, An Y, et al. Epidemiology and outcome of severe sepsis and septic shock in intensive care units in Mainland China. PLoS One [Internet]. 2014;9(9):1–8. https://pubmed.ncbi.nlm.nih.gov/25226033 Abe T, Ogura H, Kushimoto S, Shiraishi A, Sugiyama T, Deshpande GA, Uchida M, Nagata I, Saitoh D, Fujishima S, et al. Variations in infection sites and mortality rates among patients in intensive care units with severe sepsis and septic shock in Japan. J Intensive Care [Internet]. 2019 Dec 3;7(1):28. https://pubmed.ncbi.nlm.nih.gov/31073407 Leligdowicz A, Dodek PM, Norena M, Wong H, Kumar A, Kumar A. Association between Source of Infection and Hospital Mortality in Patients Who Have Septic Shock. Am J Respir Crit Care Med [Internet]. 2014 May 15;189(10):1204–13. https://www.atsjournals.org/doi/10.1164/rccm.201310-1875OC Mayr FB, Yende S, Angus DC. Epidemiology of severe sepsis. Virulence [Internet]. 2014 Jan 11;5(1):4–11. http://www.tandfonline.com/doi/abs/10.4161/viru.27372 Paoli CJ, Reynolds MA, Sinha M, Gitlin M, Crouser E. Epidemiology and costs of sepsis in the United States-an analysis based on timing of diagnosis and severity level. Crit Care Med [Internet]. 2018 Dec;46(12):1889–97. https://journals.lww.com/00003246-201812000-00001 Tiru B, Dinino EK, Orenstein A, Mailloux PT, Pesaturo A, Gupta A, Mcgee WT. The Economic and Humanistic Burden of Severe Sepsis. Pharmacoeconomics. Springer International Publishing; 2015; Chalupka AN, Talmor D. The Economics of Sepsis. Crit Care Clin [Internet]. Elsevier BV; 2012 Jan;28(1):57–76. http://dx.doi.org/10.1016/j.ccc.2011.09.003 Fathi M, Markazi-Moghaddam N, Ramezankhani A. A systematic review on risk factors associated with sepsis in patients admitted to intensive care units. Aust Crit Care [Internet]. Elsevier Ltd; 2019;32(2):155–64. https://doi.org/10.1016/j.aucc.2018.02.005 Anggraini D, Hasni D, Amelia R. Pathogenesis of Sepsis. Sci J [Internet]. 2022 Jul 30;1(4):332–9. http://dx.doi.org/10.56260/sciena.v1i4.63 Mammen EF. The haematological manifestations of sepsis. J Antimicrob Chemother [Internet]. Oxford University Press (OUP); 1998 Jan 1;41(suppl 1):17–24. http://dx.doi.org/10.1093/jac/41.suppl_1.17 Alalawi MSM, Aljabran HAM, Alkhamri AM, Alwahbi AM, Alqarrash ZI, Iraqi HAM, Alonazi MSM, Raja A, Alotaibi N, Ali M, et al. Glasgow Coma Scale in Anticipation of Sepsis and Septic Shock : Review Article. 2017;69(October):2663–6. Tesfamaryam B. Prevalence and Determinants of Sepsis and Septic Shock Among Intensive Care Unit Patients at Adama Hospital Medical College [Internet]. Zenodo; 2025. https://zenodo.org/records/15476903 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6745438","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466327171,"identity":"9d761f08-630c-4936-93f4-4efb3ef38659","order_by":0,"name":"Behaylu Tesfamaryam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAwYeIFnAIMdwA8RlI1qLAYMxQgshbTAtiQ1EazFn7z34mcfgcHrf7R4Dhg9lhxn45Rvwa7HsOZcsDdSSO/POGQPGGecOM0i2EXLYjRwDsJYNQAYzb9thBoNjhLTcf2P8G+QwkF7mv0At9gS13OAxA9mSANbCCLKFkPcte3LMLOcYpBvOvHOs4GDPuXQeiWMJ+LWYs58xvvGmwlqe73bzxgc/yqzl+JsPELAGCJh4oAyQWh48ChGA8QdRykbBKBgFo2DEAgApwUM28jkbsgAAAABJRU5ErkJggg==","orcid":"","institution":"Debre Berhan University","correspondingAuthor":true,"prefix":"","firstName":"Behaylu","middleName":"","lastName":"Tesfamaryam","suffix":""},{"id":466327172,"identity":"23d8fbc0-4b2c-494b-a83e-9d12f49b5f5d","order_by":1,"name":"Kubee Matewos","email":"","orcid":"","institution":"Adama Hospital Medical College","correspondingAuthor":false,"prefix":"","firstName":"Kubee","middleName":"","lastName":"Matewos","suffix":""},{"id":466327173,"identity":"e2a2b9e5-e767-4928-9ce4-20f622e7a339","order_by":2,"name":"Mohammed Hussein","email":"","orcid":"","institution":"Adama Hospital Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Hussein","suffix":""},{"id":466327174,"identity":"5a91703e-3423-48b7-b812-8ae90b72b18b","order_by":3,"name":"Kbreab Amare","email":"","orcid":"","institution":"Ayder Health Science College, Mekelle University","correspondingAuthor":false,"prefix":"","firstName":"Kbreab","middleName":"","lastName":"Amare","suffix":""},{"id":466327175,"identity":"81ea14aa-b6ab-4d6f-9f41-2a68429e2d84","order_by":4,"name":"Zelalem Tadesse","email":"","orcid":"","institution":"Debre Berhan University","correspondingAuthor":false,"prefix":"","firstName":"Zelalem","middleName":"","lastName":"Tadesse","suffix":""},{"id":466327176,"identity":"5ef7ca85-b107-4e81-afd1-9ed05b2c99a2","order_by":5,"name":"Tesfaye Getachew","email":"","orcid":"","institution":"Adama Hospital Medical College","correspondingAuthor":false,"prefix":"","firstName":"Tesfaye","middleName":"","lastName":"Getachew","suffix":""}],"badges":[],"createdAt":"2025-05-25 19:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6745438/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6745438/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84547538,"identity":"1aa9ae57-6753-4d75-b775-565d29982b49","added_by":"auto","created_at":"2025-06-13 09:27:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124762,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Primary Infection Sites Among ICU Patients with Sepsis at eterAdama Hospital Medical College (October 2021–July 2023)\u003c/p\u003e\n\u003cp\u003eBar chart illustrating the frequency of anatomic infection sites (e.g., pneumonia, intra-abdominal) and mode of infection acquisition (community acquired vs. hospital-acquired infection) in patients admitted to the ICU. HAI: hospital-acquired infection.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6745438/v1/c7af0ef14ebeb08ac786ec11.png"},{"id":84547542,"identity":"1d8c2329-44a3-428f-a373-800ce42317ab","added_by":"auto","created_at":"2025-06-13 09:27:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91500,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in mean platelet counts across clinical subgroups.\u003c/p\u003e\n\u003cp\u003eLine graph comparing mean platelet counts (×10⁹/L) between (A) septic vs. non-septic patients and (B) septic shock vs. non-shock cases at Adama Hospital Medical College (October 2021–July 2023).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6745438/v1/c2f7e344941d167ce134176f.png"},{"id":84548578,"identity":"b9d2e662-fa0b-4aca-a1c9-9140a6a61df7","added_by":"auto","created_at":"2025-06-13 09:43:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74870,"visible":true,"origin":"","legend":"\u003cp\u003eAntibiotic Prescription Patterns Among ICU Patients with and without Sepsis\u003c/p\u003e\n\u003cp\u003eBar chart comparing the frequency of prescribed antibiotics (e.g., ceftriaxone, metronidazole) in septic vs. non-septic cohorts at Adama Hospital Medical College (October 2021–July 2023).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6745438/v1/eca16030f8af38894f8f78a2.png"},{"id":84548895,"identity":"a532bb40-9395-48f6-8f30-c8a37a6f3b8f","added_by":"auto","created_at":"2025-06-13 09:51:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":146338,"visible":true,"origin":"","legend":"\u003cp\u003eDiscriminative performance of mSOFA with/without platelet counts for ICU mortality prediction\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curves comparing the area under the curve (AUC) for ICU mortality prediction using modified Sequential Organ Failure Assessment (mSOFA) scores alone (AUC = 0.826, 95% CI 0.768–0.883) versus mSOFA combined with platelet counts (AUC = 0.832, 95% CI 0.776–0.888). Platelet counts marginally improved discriminative performance but did not reach statistical significance (p\u0026lt;0.05)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6745438/v1/861edeb698901bd83f93d50d.png"},{"id":96803220,"identity":"b9db3dc7-cdd7-4849-b8f4-7ac333bf085d","added_by":"auto","created_at":"2025-11-26 08:55:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1397817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6745438/v1/04f7cd35-b564-42fa-b82e-52ae43302d9f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003ePrevalence and Factors Associated with Sepsis and Septic Shock Among ICU Patients at Adama Hospital Medical College: a Retrospective Chart Review Evaluating Outcome Predictive Performance of the Addition of Platelet Count on Modified SOFA Score\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSepsis, derived from the Greek word \u0026quot;SEPO\u0026quot; meaning decomposition, has been recognized for millennia, with its initial documentation appearing in Homer\u0026apos;s poems around 2700 years ago. Hippocrates later characterized it by rotting flesh and festering wounds. Over time, various definitions and theories emerged\u0026nbsp;[1,2].\u003c/p\u003e\n\u003cp\u003eThe first consensus definition (Sepsis 1) in 1991 defined sepsis as the presence of Systemic Inflammatory Response Syndrome (SIRS) in a patient with confirmed or suspected infection [3]. This was revised in 2001 (Sepsis 2) to include organ dysfunction in the definition of severe sepsis, which could progress to septic shock, defined as sepsis-induced hypotension despite adequate fluid resuscitation. The current definition, Sepsis 3 (2016), defines sepsis as a dysregulated host response to infection leading to organ dysfunction, and septic shock as a subset of sepsis with underlying circulatory, cellular, and metabolic abnormalities significantly increasing mortality risk [2,4].\u003c/p\u003e\n\u003cp\u003eA significant limitation of the Sepsis 3 definition is that four out of the six criteria for organ dysfunction rely on laboratory results, posing challenges in resource-limited settings. A prospective observational study demonstrated that a modified Sequential Organ Dysfunction Assessment (mSOFA) score predicts mortality as effectively as the SOFA score and is more feasible in settings with limited resources [5].\u003c/p\u003e\n\u003cp\u003eSepsis manifests when the body\u0026apos;s reaction to an infection becomes unchecked, leading to effects on tissues that are far removed from the original infection site. The reasons behind the transition of typically localized immune responses to a widespread reaction, resulting in sepsis, remain unclear. This phenomenon is likely attributable to multiple factors, which may encompass the direct impact of pathogenic microorganisms or their toxic byproducts, the release of substantial amounts of proinflammatory mediators, and the activation of the complement system. Furthermore, certain individuals might possess a genetic predisposition that increases their likelihood of developing sepsis [6]. The trajectory from infection to sepsis, as well as the outcomes of treatment, are significantly influenced by patient-related variables, including comorbidities such as AIDS, liver disease, and cancer, as well as by non-patient-related factors such as the type and origin of the infection, whether nosocomial or community-acquired [7\u0026ndash;11].\u003c/p\u003e\n\u003cp\u003eAs of Global Sepsis Alliance, sepsis is a major health crisis affecting 47 to 50 million individuals annually, resulting in at least 11 million deaths, equating to one death every 2.8 seconds. Mortality rates vary significantly by country, ranging from 15% to over 50%. Many survivors also experience long-term consequences [12]. A 2020 meta-analysis endorsed by the WHO highlighted significant regional differences in sepsis incidence and mortality in ICU patients, with minimal data from LMICs and none from Africa, indicating an urgent need for improved surveillance in these regions [13]. The WHO estimates a higher prevalence and mortality of sepsis in LMICs compared to high-income countries, despite the scarcity of evidence [14]. Furthermore, the limited available data from LMICs often utilize the Sepsis 2 definition, which has inferior predictive validity for septic shock and mortality compared to Sepsis 3, although it identifies a similar sepsis population with a 92% overlap [15].\u003c/p\u003e\n\u003cp\u003eEarly and appropriate resuscitation of shock with early goal-directed therapy (EGDT) during the critical \u0026quot;golden hours\u0026quot; of sepsis can potentially prevent the progression to multiple organ dysfunction. This includes identifying high-risk patients, obtaining appropriate cultures, source control, early antibiotic administration, and hemodynamic optimization [2,16].\u003c/p\u003e\n\u003cp\u003eGiven the high burden of infectious diseases in LMICs, it is plausible that sepsis is common, and delayed identification and treatment of infections may further increase the risk of developing sepsis. However, despite the anticipated higher prevalence and poorer prognosis of sepsis in LMICs, there is a paucity of research on this topic, particularly in sub-Saharan Africa. Moreover, much of the existing data predates the 2016 revision of the sepsis definition, potentially underestimating the mortality associated with sepsis and septic shock. This study aims to address this significant knowledge gap by identifying the prevalence and factors related to the transitions from infection to sepsis and then to septic shock in a resource-limited setting, which can inform future policies and strategies for early risk stratification and improved patient outcomes.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employed a retrospective chart review (RCR) design to determine the prevalence and associated factors of sepsis and septic shock among patients admitted to the central intensive care unit (ICU) of Adama Hospital Medical College (AHMC) in Adama, Ethiopia. AHMC is a 232-bed capacity medical college serving a catchment population exceeding 6 million. The ICU functions as a combined adult and pediatric unit, equipped with 12 beds and 10 mechanical ventilators.\u003c/p\u003e\n\u003cp\u003eThe initial target sample size for this study, based on the assumption (Prevalence [17] = 26%, 95% CI, and a 5% margin of error), was 338. Charts were selected using systematic random sampling from a cohort of 1320 eligible admissions. However, during the chart retrieval process, 32 charts were found to be missing. Furthermore, among the retrieved charts, 8 had labeling errors: 4 with mislabeled age and 4 with incorrect Medical Record Numbers (MRN), rendering them unusable for the study. Consequently, the final analysis included 298 patient charts that met the inclusion criteria and had complete and accurate documentation.\u003c/p\u003e\n\u003cp\u003eThe primary dependent variable was the presence of sepsis, defined according to the Sepsis-3 consensus criteria as life-threatening organ dysfunction resulting from a dysregulated host response to infection. Operationally, organ dysfunction was defined as an increase in the modified Sequential Organ Failure Assessment (mSOFA) score of 2 or more points from baseline (or zero if previously unknown). Septic shock was defined clinically by the requirement for vasopressors to maintain a mean arterial pressure \u0026ge; 65 mmHg and/or a serum lactate level \u0026gt; 2 mmol/L following adequate fluid resuscitation.\u003c/p\u003e\n\u003cp\u003eThe independent variables examined included sociodemographic factors (age, sex), pre-existing comorbidities (diabetes mellitus, HIV infection, malignancy of any type), microbiological findings (culture and sensitivity results), the primary site of infection (chest, central nervous system, genitourinary, gastrointestinal, bloodstream), the acquisition of infection (community-acquired versus nosocomial, defined based on the assessment of treating physician), biochemical/laboratory parameters (leukopenia, anemia, thrombocytopenia), and treatment-related factors (need for and duration of mechanical ventilation, length of ICU stay).\u003c/p\u003e\n\u003cp\u003eThe collected data from the 298 usable charts were initially checked for completeness using Microsoft Excel and subsequently transferred to and analyzed using the Statistical Package for Social Sciences (SPSS) version 25. Descriptive statistics were computed to characterize the study participants and determine proportions. Chi-square tests and independent sample t-tests were employed to identify significant categorical and continuous independent variables, respectively. Variables found to be significant in these initial analyses were then entered into a binary logistic regression model to determine their independent effects while controlling for other variables. The strength of associations was quantified using odds ratios (OR) with a 95% confidence interval (CI), and a p-value of 0.05 or less was considered indicative of statistically significant associations.\u003c/p\u003e\n\u003cp\u003eWe justified excluding the intercept (constant) from our binary logistic regression models based on both theoretical and statistical grounds. Biologically, sepsis and septic shock cannot occur at a SOFA score of zero, making a baseline risk estimate at this point nonsensical. Statistically, removing the intercept stabilized coefficient estimates, resulting in narrower and more clinically meaningful confidence intervals for the site of infection and SOFA score. Diagnostic tests confirmed the absence of multicollinearity. The resulting models exhibited excellent discrimination (AUC = 0.922 for sepsis and 0.956 for septic shock), good calibration (Hosmer-Lemeshow p \u0026gt; 0.05), and substantial explained variance (Nagelkerke R\u0026sup2; = 0.659 and 0.89, respectively). Residual analysis further supported the models\u0026apos; robustness and lack of bias. By aligning our statistical model with the physiological reality that sepsis and infection are absent at a SOFA score or infection site of zero, we improved interpretability and predictive accuracy for sepsis and septic shock risk assessment.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec2\"\u003e\n \u003ch2\u003ePrevalence of Infection, Sepsis, and Septic Shock\u003c/h2\u003e\n \u003cp\u003eAmong 298 ICU patients, 193 (64.8%) had suspected or confirmed infections. Of these, 142 (73.6%) met Sepsis-3 criteria, yielding an overall sepsis prevalence of 47.7%. Septic shock developed in 55 patients (38.7% of septic cases; 18.5% of total admissions). Pneumonia was the most common infection site (53.9% of sepsis cases), followed by intra-abdominal (21.8%) and skin/soft tissue infections (15.5%) (Fig. 1). Hospital-acquired infections (HAIs) accounted for 36.8% of infections. Cultures were performed in only 31 patients (10.7%), with 48.4% positivity; Proteus species were predominant (46.7%).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003eSociodemographic and Clinical Characteristics\u003c/h2\u003e\n \u003cp\u003eAmong 298 ICU patients, 193 (64.8%) had suspected or confirmed infections. Of these, 142 (73.6%) met Sepsis-3 criteria, yielding an overall sepsis prevalence of 47.7%. Septic shock developed in 55 patients (38.7% of septic cases; 18.5% of total admissions). Pneumonia was the most common infection site (53.9% of sepsis cases), followed by intra-abdominal (21.8%) and skin/soft tissue infections (15.5%) (Fig. 1). Hospital-acquired infections (HAIs) accounted for 36.8% of infections. Cultures were performed in only 31 patients (10.7%), with 48.4% positivity; Proteus species were predominant (46.7%). \u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDemographic and Clinical Characteristics of ICU Patients with Sepsis, Non-Septic, and Septic Shock Cohorts at Adama Hospital Medical College (October 2021\u0026ndash;July 2023).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeptic\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-septic\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeptic shock\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;55)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (year), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (25\u0026ndash;59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (26\u0026ndash;53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (25\u0026ndash;60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (47.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (53.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (36.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (52.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (63.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSite of infection, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (30.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (7.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (25.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntra-abdominal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (13.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (5.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (23.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin/soft tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (11.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (7.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHAI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (40.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (32.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther infection sites*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (4.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDepartment, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGyn/ Obs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (7,0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (7.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedical\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (66.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (44.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (52.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (26.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76 (48.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.4 (7.1\u0026ndash;17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4 (7.7\u0026ndash;14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.6 (7.1\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.5-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.3 (0.7-2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7 (0.3\u0026ndash;1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (5.5\u0026ndash;13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.4 (5.5\u0026ndash;12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.9 (5,2-13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlatelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151 (88\u0026ndash;242)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241 (200\u0026ndash;347)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145 (75\u0026ndash;196)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (3.5\u0026ndash;4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8 (3.3\u0026ndash;4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0 (3.2\u0026ndash;5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.2 (8.9\u0026ndash;13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.8 (10.2\u0026ndash;14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4 (8.8\u0026ndash;13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (135-146.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (130\u0026ndash;141)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140 (133\u0026ndash;144)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106 (99\u0026ndash;132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102 (97\u0026ndash;107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (107\u0026ndash;117)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCongestive heart failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (4.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (14.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (3.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic kidney disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic liver disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(10.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (7.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuman immunodeficiency virus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther comorbidities\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMore than one\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOn mechanical ventilator, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109(76.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(30.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(76.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of ICU stay (days), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5(3\u0026ndash;8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(2\u0026ndash;6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eLegend\u003c/strong\u003e: Data presented as median (IQR) for continuous variables and frequency (%) for categorical variables. Comparisons stratified by clinical subgroups (septic vs. non-septic vs. septic shock). Abbreviations: SD, standard deviation; IQR, interquartile range; HAI, hospital-acquired infection; CKD, chronic kidney disease; CLD, chronic liver disease; DM, diabetes mellitus; CHF, congestive heart failure; HIV, human immunodeficiency virus; WBC, white blood cell count. \u003csup\u003ea\u003c/sup\u003e(Thyroid disorders, Peripheral arterial disease, Interstitial lung diseases, inflammatory bowel disease), *(Meningitis, urinary tract infection, bloodstream, no identified primary site).\u003c/p\u003e\n\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eLaboratory and Comorbidity Profiles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLow platelet count (median (IQR) 151 (88-242) in septic vs. 241 (200-347) in non-septic patients; p \u0026lt; 0.05), leukocytosis (median (IQR) 12.4 (7.1-17.7) vs. 10.4 (7.7-14); p \u0026lt; 0.05), high serum sodium (11.2 (8.9-13.8) vs. 137 (130-141); p \u0026lt; 0.05), and high serum chloride levels (106 (99-132 vs. 102 (97-107); p \u0026lt; 0.05) distinguished septic from non-septic patients (Table 1). Diabetes mellitus (15.5% in sepsis, 23.6% in septic shock) and HIV (14.5% in septic shock; p \u0026lt; 0.05) were key comorbidities.\u003c/p\u003e\n\u003cp\u003eThe addition of platelet criteria to mSOFA identified 5 additional sepsis cases with median mSOFA and mSOFA + platelet scores of 1 and 2, respectively. Among these, 2 died (40% mortality), and 3 were transferred to general wards without developing septic shock. Mortality rates for the original mSOFA diagnosed cohort (69%) and the modified mSOFA + platelet cohort (68%) were comparable.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTreatment\u003c/h3\u003e\n\u003cp\u003eMechanical ventilation was required in 59.4% of patients, with 76% of ventilated patients developing sepsis. Antibiotics were prescribed to 80.2% of admissions, predominantly ceftriaxone (35%) and metronidazole (22%) (Fig.\u0026nbsp;3). The overall median length of ICU stay was 4 days (IQR: 4\u0026ndash;6), and 4 days (IQR: 3\u0026ndash;8) for patients with infection, 4.5 days (IQR: 3\u0026ndash;8) and 4 days (IQR: 2\u0026ndash;6) for patients with sepsis and septic shock, respectively.\u003c/p\u003e \n\u003ch3\u003eMultivariate Analysis\u003c/h3\u003e\n\u003cp\u003eBinary logistic regression, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, identified mechanical ventilation (AOR: 5.59, 95% CI: 2.28\u0026ndash;13.70), leukocytosis (AOR: 1.45, 95% CI: 1.00\u0026ndash;2.12), and low platelet count (AOR: 0.99, 95% CI: 0.99-1.00) as independent factors related to sepsis. For septic shock, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that male gender (AOR: 0.10, 95% CI: 0.03\u0026ndash;0.29), elevated mSOFA score (AOR: 1.92, 95% CI: 1.55\u0026ndash;2.38), and lymphopenia (AOR: 0.52, 95% CI: 0.31\u0026ndash;0.86) were significant. Thrombocytopenia correlated with both sepsis (AOR: 0.99) and septic shock (AOR: 0.98; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eFactors Associated with Sepsis: Unadjusted (Chi-Squared and Logistic Regression) and Adjusted Logistic Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanically ventilated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e27.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntra-abdominal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNosocomial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectrolytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChloride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.44\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 \u003cstrong\u003eLegend\u003c/strong\u003e \u003cp\u003eUnivariate (COR) and multivariate (AOR) logistic regression analyses identifying risk factors associated with sepsis. Adjusted models included comorbidities (hypertension, CHF, and diabetes), and laboratory markers (platelet count, hemoglobin and electrolytes). Odds ratios reported with 95% confidence intervals (CI); bolded values denote statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\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\u003eFactors Associated with Septic Shock: Unadjusted (Chi-Squared and Logistic Regression) and Adjusted Logistic Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eCOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAOR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntra-abdominal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkin/Soft tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCBC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatelet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHIV/AIDS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrgan failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emSOFA score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.00\u003c/b\u003e\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 \u003cstrong\u003eLegend\u003c/strong\u003e \u003cp\u003eLogistic regression analyses of factors associated with progression to septic shock among septic patients. Multivariate models (AOR) adjusted for sepsis severity (mSOFA score), platelet trends, comorbidities, and gender. Odds ratios reported with 95% confidence intervals (CI); bolded values denote statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective chart review, it was shown that the prevalence of infection, sepsis and septic shock to be 64.8%, 47.7% and 18.5% respectively. When compared to the data from intensive care over nations (ICON) audit it was higher than the global prevalence which was 29.5% (varies between 13.6 to 39.6 in different regions) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and found to be comparable with that of Australia, Asia and Middle east [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAgain, the prevalence of septic shock 18.5% were found to be higher than that of Europe and North America which was estimated to be 10.4% [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and it was also higher than some published local studies including Tikur Anbesa Specialized Hospital (TASH) which had prevalence of 14% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], 8.9% from a study conducted in multiple ICUs in Addis Abeba [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and 7.7% another study from southern Ethiopia done in three teaching hospitals [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSepsis is generally considered a disease of the elderly, particularly in high-income countries where studies report a mean age at diagnosis ranging from 63 to 67 years, with older populations disproportionately affected (13.1 times more frequently than younger individuals) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, based on this study the median age at diagnosis of both sepsis and septic shock was 40 years, contradicting this established pattern. Interestingly, such a younger age profile aligns with trends observed in sub-Saharan Africa, as evidenced by a systematic review and meta-analysis of fifteen studies from the region [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSepsis and septic shock are typically reported to occur more frequently in male patients compared to female patients; however, this observation is largely rooted in clinical reports that provide limited empirical evidence. Suggested reasons for this disparity encompass both social dynamics, such as an elevated risk of infection or exposure to violence, and biological differences, including variations in hormonal levels or immune responses [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nevertheless, our research yields findings that challenge this established pattern, revealing no statistically significant association based on sex in cases of sepsis and indicating a negative correlation among males with septic shock. While this assertion diverges from prevailing expectations, a limited number of studies corroborate our findings, thereby indicating a potential variability in sex-related risk profiles [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between the primary infection site and the development of septic shock is complex and not consistently observed across studies. While pneumonia is frequently identified as the most common source of infection leading to sepsis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], its association with septic shock varies. For example, a subgroup analysis within the Japanese FORCAST study (2016\u0026ndash;2017) found intra-abdominal infection to be significantly linked to septic shock and the most prevalent infection site in those patients (72%). In contrast, the same study reported pneumonia as the most common infection in patients with sepsis [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis contrasts with findings from a large retrospective cohort study spanning ICUs in Canada, the USA, and Saudi Arabia, which indicated that pneumonia was the most common infection, amongst patients with septic shock [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, the progression from sepsis to septic shock is not solely determined by the anatomical location of the infection. The specific microorganisms responsible for the infection appear to play a crucial role, with microbial factors potentially exerting a stronger influence on patient outcomes than the infection site alone.\u003c/p\u003e \u003cp\u003eThis emphasis on microbial etiology is supported by the work of Florian et al., demonstrating that the type of organism significantly impacts mortality. Their findings showed that gram-negative pathogens such as Pseudomonas and Acinetobacter are linked to higher odds of death (OR 1.4 and 1.5, respectively) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Interestingly, our study revealed that while intra-abdominal infections showed no statistically significant correlation with septic shock and pneumonia even exhibited a negative correlation, both infection sites were strongly and positively associated with the initial development of sepsis (AOR\u0026thinsp;=\u0026thinsp;10.0 for pneumonia; AOR\u0026thinsp;=\u0026thinsp;11.5 for intra-abdominal infections).\u003c/p\u003e \u003cp\u003eBased on the observed discrepancy we propose that the specific characteristics of the \u003cb\u003einfecting microorganisms\u003c/b\u003e, including their virulence, resistance patterns, and interactions with the host's immune system, may be key modulators in the progression from sepsis to septic shock, and the observed variation in anatomic site of infection among different studies is attributed to the difference in tissues tropism of predominant virulent microorganisms in the study area. For instance [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], pneumonia caused by organisms like Streptococcus pneumoniae (associated with lower mortality) or viral pathogens might explain the negative correlation with shock despite a high risk of sepsis. Conversely, intra-abdominal infections, often involving a mix of bacteria or less aggressive pathogens, may primarily drive sepsis without necessarily leading to shock.\u003c/p\u003e \u003cp\u003eAccording to several studies sepsis is the most expensive medical condition and among the reasons for the higher cost, longer length of stay and multiple treatment provided including antibiotics were of significant value medication alone accounting for 15\u0026ndash;25% of the total cost [\u003cspan additionalcitationids=\"CR35\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. in this study the duration of ICU stays and number and type of antibiotics used was computed as means to compare the relative cost of sepsis and the findings was septic patients had longer median length of stay than non-septic patients (4.5 Vs 4 days), in addition septic patients were the reason for 75% of prescribed antibiotics, suggesting the higher cost of sepsis.\u003c/p\u003e \u003cp\u003eThis RCR showed that being on mechanical ventilator, low platelet, and higher white cell count were statistically significant factors associated with sepsis, and these findings are heavily supported by findings from other studies. The association between being on a mechanical ventilator and an increased risk of sepsis was one of the key findings in the systematic review by Fathi et al., in 2019Gc [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The authors reported that patients who required mechanical ventilation had a significantly higher likelihood of developing sepsis compared to those who did not require this intervention. This result emphasizes the importance of monitoring ventilated patients closely, as they may be more susceptible to sepsis.\u003c/p\u003e \u003cp\u003eThrombocytopenia was included in the sequential organ failure assessment criteria which is used to define organ dysfunction in septic patients according to sepsis 3 definition of sepsis which was formulated by a group of experts based on previous data and consensus [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. An observational study demonstrated that a modified, using jaundice rather than bilirubin measurement and removal of platelet count from criterion for defining organ dysfunction, mSOFA score predicts mortality as effectively as the SOFA score and is more feasible in settings with limited resources [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) analysis demonstrated that the inclusion of platelet count on mSOFA achieved an AUC of 0.832 (95% CI: 0.776\u0026ndash;0.888), while the standard mSOFA alone yielded an AUC of 0.826 (95% CI: 0.768\u0026ndash;0.883). Despite marginally higher discriminative performance with platelet incorporation, the overlapping 95% confidence intervals between the two models indicated no statistically significant improvement in mortality prediction. This was further supported by the likelihood ratio test (Chi-square\u0026thinsp;=\u0026thinsp;3.249, df\u0026thinsp;=\u0026thinsp;1, P\u0026thinsp;=\u0026thinsp;0.071), which demonstrated that platelet count does not meaningfully enhance the prognostic accuracy of the mSOFA score in \u003cb\u003ethis infection-specific cohort\u003c/b\u003e for distinguishing between patients who died in the ICU and those who survived to transfer.\u003c/p\u003e \u003cp\u003eThe findings underscore the limited incremental value of platelet parameters in refining mortality risk stratification within the mSOFA framework, even in populations with infections where thrombocytopenia may reflect disease severity. Larger studies are needed to validate these observations and explore context-dependent utility.\u003c/p\u003e \u003cp\u003eThe other hematological finding was higher white cell counts, which was shown to be associated with sepsis in several studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In addition, leukocytosis was among the SIRS criteria which again, was diagnostic tool of sepsis before the introduction of sepsis 3 definition of sepsis. in our model WBC count showed AUC of 0.56 (95% CI: 0.49\u0026ndash;0.63) and at the cut off point for leukocytosis based on our institution\u0026rsquo;s laboratory i.e. 10 thousand, this model showed 65% sensitivity and 45% specificity for sepsis.\u003c/p\u003e \u003cp\u003eModified SOFA score also showed significant odds for occurrence of septic shock. The second aspect explored in the study is the association between the mSOFA score and septic shock occurrence. Since the definition of septic shock was part of the score which partly explain this finding the non-hemodynamic components of the score was computed in binary logistic regression excluding the total score and the Glasgow coma score (GCS) was found to have significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) association with septic shock with odds ratio of 1.7 (95% CI: 1.1\u0026ndash;1.6). An evidence going along with our finding was a systematic review published in the Egyptian journal of hospital medicine [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] showed significant association between GCS and septic shock.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eRetrospective Chart Review (RCR) and Sample Size: The use of RCR relies on the accuracy and completeness of medical records. The relatively small sample size may limit the generalizability of the findings to other populations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLimited Investigation Details: The study was unable to comprehensively address details of investigations like cultures and sensitivities due to missing information in the medical records.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found a higher prevalence of sepsis and septic shock compared to high-income countries, but comparable to most Low- and middle-income countries. Notably, the affected population was relatively younger. Additionally, the findings indicate that incorporating platelet count into the modified SOFA score does not enhance its predictive accuracy in differentiating clinical outcomes among ICU patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAOR Adjusted Odds Ratio\u003c/p\u003e\n\u003cp\u003eAUC Area Under the Curve\u003c/p\u003e\n\u003cp\u003eCBC Complete Blood Count\u003c/p\u003e\n\u003cp\u003eCHF Congestive Heart Failure\u003c/p\u003e\n\u003cp\u003eCI Confidence Interval\u003c/p\u003e\n\u003cp\u003eCKD Chronic Kidney Disease\u003c/p\u003e\n\u003cp\u003eCLD Chronic Liver Disease\u003c/p\u003e\n\u003cp\u003eCOR Crude Odds Ratio\u003c/p\u003e\n\u003cp\u003eDM Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eEGDT Early Goal-Directed Therapy\u003c/p\u003e\n\u003cp\u003eGCS Glasgow Coma Scale\u003c/p\u003e\n\u003cp\u003eHAI Hospital-Acquired Infection\u003c/p\u003e\n\u003cp\u003eHIV Human Immunodeficiency Virus\u003c/p\u003e\n\u003cp\u003eICU Intensive Care Unit\u003c/p\u003e\n\u003cp\u003eIQR Interquartile Range\u003c/p\u003e\n\u003cp\u003eIRB Institutional Review Board\u003c/p\u003e\n\u003cp\u003eLMICs Low- and Middle-Income Countries\u003c/p\u003e\n\u003cp\u003eMRN Medical Record Number\u003c/p\u003e\n\u003cp\u003emSOFA Modified Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eROC Receiver Operating Characteristic\u003c/p\u003e\n\u003cp\u003eRCR Retrospective Chart Review\u003c/p\u003e\n\u003cp\u003eSIRS Systemic Inflammatory Response Syndrome\u003c/p\u003e\n\u003cp\u003eSOFA Sequential Organ Failure Assessment\u003c/p\u003e\n\u003cp\u003eSPSS Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eTASH Tikur Anbesa Specialized Hospital (Ethiopia)\u003c/p\u003e\n\u003cp\u003eWBC White Blood Cell\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e1. Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received ethical approval from the Institutional Review Board (IRB) of Adama Hospital Medical College, as documented in letter (0925/K-373). Considering the study\u0026apos;s design, the requirement for individual participant consent was waived by the board, a decision consistent with the institution\u0026apos;s ethical protocols, and the study was conducted and reported in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e2. \u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e3. \u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets generated and analyzed during this study are available in the Zenodo repository (https://zenodo.org/records/15476903) \u0026nbsp;[41].\u003c/p\u003e\n\u003cp\u003e4. \u003cstrong\u003eCompeting interests\u003c/strong\u003e: All the authors declare that they have no competing interests. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e5. \u003cstrong\u003eFunding\u003c/strong\u003e: Partial support for this research was provided by Adama Hospital Medical College in the context of a thesis project conducted by the corresponding author.\u003c/p\u003e\n\u003cp\u003e6. \u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eB.T. conceptualization, data curation, analysis, and drafting of the manuscript. \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eK.M. and M.H. critical review, editing, and supervision.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eK.A. and Z.T. critical review, editing, and validation.\u003c/li\u003e\n \u003cli\u003eT.G. data curation, drafting of the manuscript, validation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e7.\u003cstrong\u003e\u0026nbsp;Acknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors gratefully acknowledge Adama Hospital Medical College for their support of this research and for their partial financial contribution towards the study costs, which was provided in the context of this thesis work. We also extend our sincere thanks to Professor Sileshi Garoma for his review and guidance during the proposal development phase of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGul F, Arslantas MK, Cinel I, Kumar A. Changing Definitions of Sepsis. Turkish J Anesth Reanim [Internet]. 2017 Jul 10;45(3):129\u0026ndash;38. https://turkjanaesthesiolreanim.org/articles/doi/TJAR.2017.93753\u003c/li\u003e\n\u003cli\u003eGyawali B, Ramakrishna K, Dhamoon AS. Sepsis: The evolution in definition, pathophysiology, and management [Internet]. Vol. 7, SAGE Open Medicine. 2019. p. 1\u0026ndash;13. https://journals.sagepub.com/doi/10.1177/2050312119835043\u003c/li\u003e\n\u003cli\u003eJune VINI, Supreme S, Justice C, Stewart P. editorials. Chest [Internet]. 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African J Emerg Med [Internet]. 2020;11(1):188\u0026ndash;95. https://doi.org/10.1016/j.afjem.2020.10.001\u003c/li\u003e\n\u003cli\u003eSakr Y, Jaschinski U, Wittebole X, Szakmany T, Lipman J, \u0026Ntilde;amendys-Silva SA, Martin-Loeches I, Leone M, Lupu MN, Vincent JL. Sepsis in Intensive Care Unit Patients: Worldwide Data From the Intensive Care over Nations Audit. Open forum Infect Dis. 2012;5(12):1\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eVincent JL, Sakr Y, Singer M, Martin-Loeches I, Machado FR, Marshall JC, Finfer S, Pelosi P, Brazzi L, Aditianingsih D, et al. Prevalence and Outcomes of Infection Among Patients in Intensive Care Units in 2017. JAMA - J Am Med Assoc. 2017;323(15):323(15):1478\u0026ndash;1487. \u003c/li\u003e\n\u003cli\u003eVincent J louis, Jones G, David S, Olariu E, Cadwell KK. Frequency and mortality of septic shock in Europe and North America : a systematic review and meta-analysis. Critical Care; 2019;1\u0026ndash;11. \u003c/li\u003e\n\u003cli\u003eTeklemichael H. Assessment of Incidence and Treatment Outcome of Septic Shock Among Patients Admitted To Adult Intensive Care Unit of Tikur Anbessa Specialized Hospital , Addis Ababa , Ethiopia. Addis Abeba University; 2017. \u003c/li\u003e\n\u003cli\u003eAbera H, Bayisa T, Worku Y, Lazarus JJ, Woldeyes E. Prevalence and outcome of sepsis and septic shock in intensive care units in Addis Ababa , Ethiopia : A prospective observational study. African J Emerg Med [Internet]. Elsevier B.V.; 2020;(September):0\u0026ndash;1. https://doi.org/10.1016/j.afjem.2020.10.001\u003c/li\u003e\n\u003cli\u003eAbate SM, Assen S, Yinges M, Basu B. Survival and predictors of mortality among patients admitted to the intensive care units in southern Ethiopia: A multi-center cohort study. Ann Med Surg [Internet]. Elsevier; 2021;65. https://doi.org/10.1016/j.amsu.2021.102318\u003c/li\u003e\n\u003cli\u003eDC A, WT LZ, Lidicker J CG, J C, MR P. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med [Internet]. United States; 2001 Dec;29(7):1303\u0026ndash;10. http://dx.doi.org/10.1097/00003246-200107000-00002\u003c/li\u003e\n\u003cli\u003eChen CM, Cheng KC, Chan KS, Yu WL. Age May Not Influence the Outcome of Patients with Severe Sepsis in Intensive Care Units. Int J Gerontol [Internet]. Elsevier Taiwan LLC.; 2014;8(1):22\u0026ndash;6. http://dx.doi.org/10.1016/j.ijge.2013.08.004\u003c/li\u003e\n\u003cli\u003eLewis JM, Feasey NA, Rylance J. Aetiology and outcomes of sepsis in adults in sub-Saharan Africa: a systematic review and meta-analysis. Crit Care [Internet]. England; 2019 Jun 11;23(212):1\u0026ndash;11. https://pubmed.ncbi.nlm.nih.gov/31186062\u003c/li\u003e\n\u003cli\u003eLakbar I, Einav S, Lalev\u0026eacute;e N, Martin-Loeches I, Bruno P, Marc L. Interactions between Gender and Sepsis\u0026mdash;Implications for the Future. Microorganisms. 2023;11(746):1\u0026ndash;16. \u003c/li\u003e\n\u003cli\u003eLiu N, Ren J, Yu L, Xie J. Mechanical ventilation associated with worse survival in septic patients: a retrospective analysis of MIMIC-III. J Emerg Crit Care Med [Internet]. 2020;4(0):14. http://dx.doi.org/10.21037/jeccm.2020.01.01\u003c/li\u003e\n\u003cli\u003eV K, AM H, J M, K F, HC S, H K. Site of infection and mortality in patients with severe sepsis or septic shock. A cohort study of patients admitted to a Danish general intensive care unit. Infect Dis [Internet]. 2016;48(10):726\u0026ndash;31. http://dx.doi.org/10.3109/23744235.2016.1168938\u003c/li\u003e\n\u003cli\u003eZhou J, Qian C, Zhao M, Yu X, Kang Y, Ma X, Ai Y, Xu Y, Liu D, An Y, et al. Epidemiology and outcome of severe sepsis and septic shock in intensive care units in Mainland China. PLoS One [Internet]. 2014;9(9):1\u0026ndash;8. https://pubmed.ncbi.nlm.nih.gov/25226033\u003c/li\u003e\n\u003cli\u003eAbe T, Ogura H, Kushimoto S, Shiraishi A, Sugiyama T, Deshpande GA, Uchida M, Nagata I, Saitoh D, Fujishima S, et al. Variations in infection sites and mortality rates among patients in intensive care units with severe sepsis and septic shock in Japan. J Intensive Care [Internet]. 2019 Dec 3;7(1):28. https://pubmed.ncbi.nlm.nih.gov/31073407\u003c/li\u003e\n\u003cli\u003eLeligdowicz A, Dodek PM, Norena M, Wong H, Kumar A, Kumar A. Association between Source of Infection and Hospital Mortality in Patients Who Have Septic Shock. Am J Respir Crit Care Med [Internet]. 2014 May 15;189(10):1204\u0026ndash;13. https://www.atsjournals.org/doi/10.1164/rccm.201310-1875OC\u003c/li\u003e\n\u003cli\u003eMayr FB, Yende S, Angus DC. Epidemiology of severe sepsis. Virulence [Internet]. 2014 Jan 11;5(1):4\u0026ndash;11. http://www.tandfonline.com/doi/abs/10.4161/viru.27372\u003c/li\u003e\n\u003cli\u003ePaoli CJ, Reynolds MA, Sinha M, Gitlin M, Crouser E. Epidemiology and costs of sepsis in the United States-an analysis based on timing of diagnosis and severity level. Crit Care Med [Internet]. 2018 Dec;46(12):1889\u0026ndash;97. https://journals.lww.com/00003246-201812000-00001\u003c/li\u003e\n\u003cli\u003eTiru B, Dinino EK, Orenstein A, Mailloux PT, Pesaturo A, Gupta A, Mcgee WT. The Economic and Humanistic Burden of Severe Sepsis. Pharmacoeconomics. Springer International Publishing; 2015; \u003c/li\u003e\n\u003cli\u003eChalupka AN, Talmor D. The Economics of Sepsis. Crit Care Clin [Internet]. Elsevier BV; 2012 Jan;28(1):57\u0026ndash;76. http://dx.doi.org/10.1016/j.ccc.2011.09.003\u003c/li\u003e\n\u003cli\u003eFathi M, Markazi-Moghaddam N, Ramezankhani A. A systematic review on risk factors associated with sepsis in patients admitted to intensive care units. Aust Crit Care [Internet]. Elsevier Ltd; 2019;32(2):155\u0026ndash;64. https://doi.org/10.1016/j.aucc.2018.02.005\u003c/li\u003e\n\u003cli\u003eAnggraini D, Hasni D, Amelia R. Pathogenesis of Sepsis. Sci J [Internet]. 2022 Jul 30;1(4):332\u0026ndash;9. http://dx.doi.org/10.56260/sciena.v1i4.63\u003c/li\u003e\n\u003cli\u003eMammen EF. The haematological manifestations of sepsis. J Antimicrob Chemother [Internet]. Oxford University Press (OUP); 1998 Jan 1;41(suppl 1):17\u0026ndash;24. http://dx.doi.org/10.1093/jac/41.suppl_1.17\u003c/li\u003e\n\u003cli\u003eAlalawi MSM, Aljabran HAM, Alkhamri AM, Alwahbi AM, Alqarrash ZI, Iraqi HAM, Alonazi MSM, Raja A, Alotaibi N, Ali M, et al. Glasgow Coma Scale in Anticipation of Sepsis and Septic Shock : Review Article. 2017;69(October):2663\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eTesfamaryam B. Prevalence and Determinants of Sepsis and Septic Shock Among Intensive Care Unit Patients at Adama Hospital Medical College [Internet]. Zenodo; 2025. https://zenodo.org/records/15476903 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Sepsis, Septic Shock, Prevalence, Associated Factors, ICU, modified SOFA score, Low- and middle-income countries, Adama, Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-6745438/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6745438/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSepsis and, its subset, septic shock are major health problems disproportionately affecting low- and middle-income countries. Despite the potentially high burden in low- and middle-income settings, data on prevalence and factors contributing for the development of sepsis and septic shock remains scarce.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA Retroactive Chart Review (RCR) was conducted on 298 patients admitted to the ICU, with the aim of identifying the prevalence and factors related to sepsis and septic shock. Coded data were transferred to and validated using Microsoft Excel, and analyzed using SPSS version 25. A binary logistic regression model was employed to identify factors contributing to sepsis and septic shock.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 298 patients, 142 (47.7%) had sepsis, and 55 (38.7% of septic patients, 18.5% of total) developed septic shock. The median age of septic patients was 40 years, and females had a higher burden of sepsis (52.8%) and septic shock (63.64%). Pneumonia was the most common site of infection (30.99% of sepsis cases). Multivariate analysis revealed that being on mechanical ventilation (AOR 5.59, 95% CI: 2.28\u0026ndash;13.70), high white blood cell count (AOR 1.45, 95% CI: 1.00-2.12), and low platelet count (AOR 0.99, 95% CI: 0.99-1.00) were significantly associated with sepsis. Male gender (AOR 0.10, 95% CI: 0.03\u0026ndash;0.29), high mSOFA score (AOR 1.92, 95% CI: 1.55\u0026ndash;2.38), and low lymphocyte count (AOR 0.52, 95% CI: 0.31\u0026ndash;0.86) were associated with septic shock. Septic shock also showed an association with low platelet count (AOR: 0.98, 95% CI: 0.98\u0026ndash;0.99). The inclusion of platelet count in the mSOFA score resulted in a slightly higher but statistically insignificant increase in the area under the ROC curve for mortality prediction (AUC 0.832 vs. 0.826, p\u0026thinsp;=\u0026thinsp;0.071).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study found a higher prevalence of sepsis and septic shock compared to high-income countries, but comparable to most Low- and middle-income countries, and the affected population was relatively younger. Notably, despite a marginal improvement in discriminative performance, platelet count did not significantly enhance the prognostic accuracy of the mSOFA score for mortality among infection-specific ICU cohorts.\u003c/p\u003e","manuscriptTitle":"Prevalence and Factors Associated with Sepsis and Septic Shock Among ICU Patients at Adama Hospital Medical College: a Retrospective Chart Review Evaluating Outcome Predictive Performance of the Addition of Platelet Count on Modified SOFA Score","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-13 09:27:38","doi":"10.21203/rs.3.rs-6745438/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":"65b5a1c9-db84-4697-a05d-7258af0803f6","owner":[],"postedDate":"June 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-26T08:54:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-13 09:27:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6745438","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6745438","identity":"rs-6745438","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}