The Intensive Care Unit (ICU) serves as a vital component of tertiary healthcare delivery, dedicated to the management of patients with life-threatening conditions who require comprehensive and continuous monitoring, complex medical interventions, and specialized clinical expertise. ICU care is distinctly characterized by access to advanced diagnostic modalities, organ support systems, and rapid-response interventions that are typically beyond the scope of general medical or surgical wards. As medical science advances and access to healthcare improves, the demand for ICU services has grown substantially, particularly in developing nations such as India. Among the trends contributing to this surge is the increasing survival of individuals with chronic illnesses from childhood into adulthood, coupled with lifestyle modifications that have led to a rise in non-communicable disease burdens in younger populations.1 This trend is best understood in the context of the broader concept of epidemiological transition, which describes the progressive shift in the pattern of disease and mortality from predominantly communicable diseases to non-communicable diseases (NCDs). This transition is intrinsically linked to societal developments, including economic growth, urbanization, improved sanitation, vaccination programs, and access to healthcare. India, currently undergoing this transition, presents a unique public health scenario where communicable diseases, non-communicable diseases, and injuries all significantly contribute to mortality and morbidity. This overlap creates a “triple burden” of disease, posing complex challenges to healthcare providers, especially in resource-intensive settings like the ICU.2 One of the most visible outcomes of healthcare advancement is the significant improvement in life expectancy. Historical data reveal that in 1901, the average life expectancy in India was merely 23.63 years for males and 23.96 years for females. Over the past century, this figure has increased dramatically, reaching 68.6 years for males and 71.4 years for females by 2020. These gains are a testament to progress in public health, disease prevention, maternal and child health services, and the availability of modern therapeutic interventions.3 However, longevity alone does not ensure quality of life, especially when an aging population faces an increased risk of complex, chronic, and acute illnesses requiring critical care admission. Despite these advances, developing countries like India continue to carry a disproportionate burden of communicable diseases, which co-exist with a rising prevalence of non-communicable conditions. It is estimated that approximately 40% of all deaths in India are due to infectious and parasitic diseases, respiratory infections, and other communicable illnesses—a stark contrast to developed countries, where such causes account for fewer than 10% of total deaths.4 These epidemiological patterns not only impact the profile of patients admitted to the ICU but also influence mortality rates, resource allocation, and outcomes. The coexistence of malnutrition, poor sanitation, and limited access to preventive care further complicates the clinical landscape in resource-limited settings. Understanding mortality trends in ICUs is crucial, as mortality remains one of the most definitive indicators of the performance of any healthcare delivery system. Hospital-based mortality data provide detailed insight into the disease burden, severity of illness at the time of admission, care-seeking behavior, and adequacy of emergency and intensive care services. Such data are instrumental in assessing healthcare system efficiency, identifying gaps in service delivery, and guiding public health interventions. Furthermore, these data help classify patients by demographic variables such as age and sex and delineate the underlying causes of death, thereby aiding in effective emergency preparedness and healthcare planning in tertiary care settings.5 Mortality surveillance also enables policymakers and health administrators to prioritize key areas of intervention. By analyzing population-level death patterns, healthcare systems can detect emerging health threats, monitor the success of intervention programs, and allocate resources to areas with the greatest need. Mortality metrics form the backbone of national health information systems and are pivotal for tracking progress in achieving public health goals. Without robust mortality data, healthcare planning becomes reactive rather than proactive, often leading to suboptimal outcomes and inefficient use of limited resources.6 In the setting of Medical Intensive Care Units (MICUs), the causes of death are multifactorial. Early ICU mortality is frequently linked to the severity of the presenting illness, complications such as sepsis, septic shock, and multi-organ dysfunction syndrome (MODS), or irreversible organ failure. In contrast, late-phase ICU deaths are often attributed to hospital-acquired infections, ventilator-associated complications, adverse drug reactions, or iatrogenic injuries stemming from invasive procedures. Moreover, the presence of comorbidities such as diabetes, chronic kidney disease, chronic liver disease, and malignancies substantially increases the risk of adverse outcomes. The interplay between acute physiological insult and chronic health conditions poses a major challenge in critical care decision-making.7 Despite the clinical significance of MICUs and their central role in managing the most critically ill patients, there is a paucity of comprehensive, prospective data from Indian tertiary care centers regarding the trends in ICU admissions and mortality among adult patients. Most available studies are either retrospective in nature or limited in sample size, geographic scope, or methodological rigor. There exists a clear need to generate robust local data to better understand the epidemiology of ICU admissions, identify the leading causes of mortality, and evaluate the influence of comorbidities, demographic factors, and clinical interventions on patient outcomes. This prospective study was thus conceived to assess the trends of admissions and mortality among adult patients admitted to the Medical Intensive Care Unit (MICU) of a tertiary care hospital in central India. The objectives of the study were to identify the most common causes of ICU admission and death, evaluate the role of pre-existing comorbidities, examine clinical variables such as length of ICU stay and intervention requirements, and assess gender-based differences in outcomes. The findings of this study aim to provide actionable insights to clinicians, hospital administrators, and policymakers, and to contribute to the growing body of evidence guiding the optimization of critical care delivery in resource-constrained healthcare systems.

This prospective observational study was conducted over a 12-month period, from February 18, 2024, to February 17, 2025, in the medical intensive care unit (MICU) of a tertiary care hospital in central India. The study received approval from the institutional ethics committee. Participation was voluntary, and confidentiality was maintained throughout The study enrolled 2,400 consecutive adult patients admitted to the MICU who met the inclusion criteria. The unit functions as an open ICU under the department of general medicine, with independent operations from the surgical, cardiac, and respiratory ICUs. The MICU is equipped with advanced critical care facilities, including continuous multiparameter monitoring, invasive and non-invasive mechanical ventilation, central venous pressure monitoring, and intermittent hemodialysis. Therapeutic modalities such as plasma exchange and red cell exchange are available. Diagnostic support includes bedside ultrasonography, transthoracic echocardiography, portable radiography, and arterial blood gas analysis. Additional imaging (CT and MRI), laboratory diagnostics (pathology, microbiology, and biochemistry), and transfusion services are accessible 24/7. Multidisciplinary specialist input is available on-call. Patients aged 18–60 years admitted to the MICU during the study period were eligible for inclusion. Those outside this age range or who declined consent were excluded. Data were collected prospectively using a standardized, pre-validated case record form following informed written consent from the patient or their legally authorized representative. Variables collected included demographic characteristics (age, sex), clinical history (presenting complaints, comorbidities, medication use, substance abuse), and findings from general and systemic examinations. Routine laboratory and imaging investigations were recorded as per the treating team’s protocol; no additional diagnostic testing was performed for study purposes. Admission trends were analyzed based on age and sex distribution. Mortality outcomes were assessed with respect to demographic variables, comorbidities, primary diagnosis at admission, duration of ICU stay, and interventions received. Severity of illness was assessed using the APACHE II score (based on the worst parameters within the first 24 hours of admission) and the SOFA score (based on the most deranged values during the ICU stay). Data were entered in Microsoft Excel and analyzed using SPSS version 25. Continuous variables were presented as mean ± standard deviation or median with interquartile range, depending on distribution. Categorical variables were expressed as counts and percentages. Associations between categorical variables were assessed using the chi-square test or Fisher’s exact test. For continuous variables, the Student’s t-test or Mann–Whitney U test was applied as appropriate. A p-value <0.05 was considered statistically significant.

A total of 2400 patients were included in the study after applying the inclusion and exclusion criteria. The cohort consisted of 1519 males (63.3%) and 881 females (36.7%), resulting in a male-to-female ratio of approximately 1.7:1. The mean age was 39.4 ± 14.1 years, with a predominance of admissions in the younger age group of 18–30 years (34.3%). However, mortality was significantly associated with increasing age, particularly among patients aged 51–60 years, where 34% of deaths occurred (p = 0.001). The leading primary system-based causes for MICU admission were gastrointestinal disorders (21.0%), followed by neurological conditions (18.7%) and infections (14.6%). Notably, gastrointestinal diseases (e.g., gastroenteritis, hepatic encephalopathy) were responsible for 116 of the 403 deaths, yielding a case fatality ratio (CFR) of 0.22. Similarly, neurological illnesses such as stroke and seizures resulted in a CFR of 0.21, while infections, particularly sepsis, showed a CFR of 0.21. Poisoning cases, though relatively frequent (10.1%), had a lower CFR of 0.13, suggesting better reversibility or earlier intervention. Comorbid conditions were common in this patient population. Hypertension (28.3%) and diabetes mellitus (14.3%) were the most prevalent. However, the highest CFRs were associated with coronary artery disease (0.25), HIV (0.58), chronic alcoholism (0.30), and smoking (0.35), all statistically associated with mortality. These findings underscore the impact of both non-communicable diseases and behavioral risk factors on MICU outcomes. Analysis of vital signs at admission revealed that 70.3% of patients presented with tachycardia (>100 bpm), and over half had elevated respiratory rates (>20 breaths/min). Hypoxia (SpO₂ < 90%) was observed in 15.5% of cases. A significant proportion (22.8%) had mean arterial pressures <65 mm Hg on admission, suggestive of shock physiology. Febrile presentations were seen in 18.6% of the cohort. The severity of illness, as measured by scoring systems, was a strong predictor of outcome. Patients who died had significantly higher SOFA scores (mean: 9.4 ± 3.87) compared to survivors (2.9 ± 2.83), p < 0.01. Similarly, mean APACHE II scores were markedly elevated in non-survivors (24.9 ± 8.6 vs. 8.1 ± 6.9; p < 0.01). These findings affirm the utility of these indices in risk stratification upon MICU admission. Regarding interventions, a large subset required respiratory and hemodynamic support: 27.4% underwent non-invasive ventilation (NIV) while 24.3% required invasive mechanical ventilation. Notably, invasive ventilation was associated with a case fatality ratio of 0.47 (p < 0.01), and patients requiring three or more inotropes had the highest CFR of 0.46. Use of central venous catheters (CFR: 0.39), surgical tracheostomy (0.53), and echocardiography (0.52) were also associated with significantly increased mortality. In contrast, use of one or two antibiotics or whole blood transfusion showed more favorable outcomes. Non-invasive ventilation (NIV) had a significantly lower CFR of 0.08 (p < 0.01), emphasizing its value when applicable. Complications during MICU stay were diverse and significantly impacted outcomes. Sepsis (15.2%) was the most common, followed by multiple organ dysfunction syndrome (MODS, 9.2%), hypovolemic shock (8.4%), and acute kidney injury (5.5%). Other complications such as metabolic acidosis (5.1%), septic shock (4.8%), and Type 1 respiratory failure (4.4%) were also observed. Many of these were significantly associated with poor outcomes. In multivariate logistic regression analysis, several variables remained independently associated with increased mortality. The most robust predictors were higher SOFA (adjusted odds ratio [aOR]: 1.26, 95% CI: 1.15–1.39) and APACHE II scores (aOR: 1.16, 95% CI: 1.12–1.20), both with p < 0.0001. Smoking was a strong independent predictor (aOR: 5.85, 95% CI: 3.57–9.60), as was the presence of sepsis (aOR: 2.03, 95% CI: 1.41–2.91). Interestingly, anemia (aOR: 0.46), MODS (aOR: 0.47), and ARDS (aOR: 0.22) were associated with lower adjusted odds of mortality, potentially reflecting confounding due to early diagnosis and aggressive management in those cases. Age, hypertension, and coronary artery disease did not retain independent significance after adjustment for other variables. Overall, the study highlights both systemic and patient-level factors associated with ICU mortality and underscores the importance of early recognition, aggressive intervention, and stratification based on validated severity scores. Table 1: Univariate Comparison between Survivors and Non-Survivors in MICU (n = 2400) Variable Survivors (n=1997) Non-Survivors (n=403) p-value Age (mean ± SD) 38.6 ± 14.0 43.5 ± 13.8 <0.001 Male sex (%) 62.2 68.5 0.02 Smokers (%) 7.3 18.6 <0.001 Alcohol use (%) 15.7 27.6 <0.001 Sepsis (%) 13.3 25.3 <0.001 MODS (%) 8.3 13.4 0.002 Anemia (%) 13.3 7.7 0.002 ARDS (%) 0.5 1.5 0.04 SOFA score (mean ± SD) 2.93 ± 2.83 9.40 ± 3.87 <0.001 APACHE II score (mean ± SD) 8.13 ± 6.85 24.91 ± 8.56 <0.001 Legend: This table summarizes the univariate comparison of demographic, clinical, and severity parameters between survivors and non-survivors. Statistical significance was assessed using the chi-square test or independent t-test as appropriate. A p-value < 0.05 was considered statistically significant. Table 2: Multivariate Logistic Regression Identifying Independent Predictors of Mortality in MICU Variable Adjusted Odds Ratio (aOR) 95% Confidence Interval (CI) p-value SOFA score 1.26 1.15 – 1.39 <0.001 APACHE II score 1.16 1.12 – 1.20 <0.001 Smoking 5.85 3.57 – 9.60 <0.001 Sepsis 2.03 1.41 – 2.91 <0.001 Anemia 0.46 0.30 – 0.69 <0.001 MODS 0.47 0.26 – 0.83 0.009 ARDS 0.22 0.05 – 0.92 0.04 Legend: This table presents the results of multivariate logistic regression to identify independent predictors of ICU mortality. Variables included were those significant in univariate analysis. Odds ratios greater than 1 indicate increased odds of death; values less than 1 suggest a protective association. All variables with p< 0.05 were retained as independent predictors.

The Medical Intensive Care Unit (MICU) remains a cornerstone of critical care delivery, catering to adult patients with life-threatening conditions. As the spectrum of communicable and non-communicable diseases continues to broaden, particularly in developing nations, tertiary care ICUs have witnessed a substantial rise in patient volume and complexity. The shift in disease epidemiology, fueled by changes in lifestyle, environmental factors, and healthcare infrastructure, underscores the importance of continuous assessment of ICU admission patterns and patient outcomes. Despite advancements in diagnostics, monitoring, and therapeutic interventions, mortality within MICUs remains significant, necessitating a deeper understanding of associated risk factors to enhance clinical outcomes and optimize the use of healthcare resources. In this prospective study, we observed that 63.3% of the patients were male and 36.7% were female. Mortality was significantly higher among male patients (68.5%) compared to females (31.5%), with a p-value of 0.02. The association between advanced age and increased mortality was also significant (p = 0.001), with the highest mortality seen in the 51–60 years age group. These findings align with those reported by Divatia et al., where the mean age of non-survivors was significantly higher than that of survivors (55.6 ± 17.7 vs. 53.7 ± 17.7 years; p = 0.01) and the gender distribution did not show a significant association with outcomes.8,9 Similar demographic trends were noted by Demass et al.10, Oh DK et al.11, and Abuhasira et al.12, who reported male predominance and older age as major characteristics among ICU admissions. Ismail et al. further highlighted that advanced age remained the strongest independent predictor of mortality with an adjusted hazard ratio of 4.777 (95% CI: 1.128–20.231; p = 0.03).13 In terms of admission etiology, gastrointestinal (21%) and neurological (18.7%) conditions were the most common reasons for MICU admission in this study’s cohort. This finding partially contrasts with studies by Divatia et al., where respiratory and cardiovascular causes predominated.8,9 The observed variation might reflect regional differences in disease burden and referral practices. Comorbid conditions also played a pivotal role in determining patient outcomes. Hypertension (28.3%) and diabetes (14.3%) were the most prevalent comorbidities. Chronic liver disease, chronic kidney disease, and sickle cell anemia were also common. Notably, smoking and chronic alcohol use were linked to higher case fatality rates (CFRs of 0.35 and 0.30, respectively). These trends mirror findings from studies by Divatia et al., where conditions such as COPD, heart failure, cirrhosis, and malignancy were associated with poor outcomes.8,9 Similarly, Ismail et al. reported a statistically significant association between mortality and comorbidities such as diabetes and hypertension.13 Vital parameters at admission also offered prognostic value. A majority of patients presented with tachycardia (pulse >100 bpm) and tachypnea (RR >20/min). While most patients were normotensive and afebrile, reduced oxygen saturation was a common finding. Ismail et al. found significantly lower PaO2/FiO2 ratios and mean arterial pressures among non-survivors, supporting the prognostic relevance of deranged vitals.13 A variety of therapeutic interventions were administered in the MICU. Non-invasive ventilation (27.4%) and invasive mechanical ventilation (24.3%) were commonly required. The need for multiple inotropes, antibiotics, hemodialysis, and advanced procedures like red cell exchange were strongly associated with increased mortality. Case fatality rates were highest among patients receiving three or more inotropes (CFR 0.46), invasive ventilation (0.47), or central venous catheterization (0.39). These observations are corroborated by findings from Divatia et al., where the use of mechanical ventilation and multiple inotropes was significantly associated with mortality.8,9 Complications such as sepsis (364 cases), MODS (221), hypovolemic shock (202), and acute kidney injury (132) were frequently encountered and had a profound impact on patient outcomes. Septic shock (33.7%) and MODS (16.9%) were the leading causes of death. The distribution and nature of complications in our study are similar to those described by Demass et al., where infectious and sepsis-related complications dominated ICU mortality profiles.10 The overall mortality rate in this MICU cohort was 16.8%, with 82.3% of patients eventually transferred to the ward. This outcome is comparable to that observed in large Indian ICU studies by Divatia et al., who reported mortality rates ranging from 18.05% to 23.4% 8,9, and to the 29.6% mortality seen in the study by Demass et al. 10. The role of severity scores in predicting ICU mortality was evident in our analysis. The mean SOFA score among non-survivors was 9.40 ± 3.87, compared to 2.93 ± 2.83 among survivors (p = 0.01). Similarly, APACHE II scores were significantly higher in non-survivors (24.91 ± 8.56 vs. 8.13 ± 6.85; p = 0.01). These findings are in line with studies by Divatia et al. and Oh DK et al., who demonstrated the robust predictive power of these scores in ICU mortality stratification 8,11. This study highlights multiple demographic, clinical, and therapeutic factors influencing outcomes in MICU settings. High disease severity, older age, male gender, comorbidities like smoking and hypertension, and the need for intensive interventions were independently associated with increased mortality.

This prospective observational study provides important insights into the demographic patterns, clinical profiles, and outcomes of critically ill patients admitted to a medical intensive care unit. Older age, male gender, higher illness severity scores, and the need for advanced interventions were significantly associated with mortality. The findings underscore the importance of early risk stratification and timely intervention in improving ICU outcomes.

1. Marshall JC, Bosco L, Adhikari NK, Connolly B, Diaz JV, Dorman T, et al. What is an intensive care unit? A report of the task force of the World Federation of Societies of Intensive and Critical Care Medicine. J Crit Care. 2017 Feb;37:270–6. 2. Park K. Park’s textbook of preventive and social medicine. 27th ed. Jabalpur: Bhanot Publishers; 2023. p. 35. 3. Office of the Registrar General & Census Commissioner, India. SRS based abridged life tables 2016–20. [cited 2024 Oct 10]. Available from: https://censusindia.gov.in/nada/index.php/catalog/44377 4. Park K. Park’s textbook of preventive and social medicine. 21st ed. Jabalpur: Bhanot Publishers; 2011. p. 42, 52. 5. Godale L, Mulaje S. Mortality trend and pattern in tertiary care hospital of Solapur in Maharashtra. Indian J Community Med. 2013 Jan;38(1):49–52. doi:10.4103/0970-0218.106628. 6. Celine T. A study on childhood death at a tertiary care level in Ernakulam district. Ann Med Health Sci Res. 2013 Jul;3(3):407–11. doi:10.4103/2141-9248.117945. 7. Brilli RJ, Spevetz A, Branson RD, Campbell GM, Cohen H, Dasta JF, et al. Critical care delivery in the intensive care unit: defining clinical roles and the best practice model. Crit Care Med. 2001Oct;29(10):2007–19. 8. Divatia JV, Amin PR, Ramakrishnan N, Kapadia FN, Todi S, Sahu S, et al. Intensive care in India: the Indian intensive care case mix and practice patterns study. Indian J Crit Care Med. 2016 Apr;20(4):216. 9. Divatia JV, Mehta Y, Govil D, Zirpe K, Amin PR, Ramakrishnan N, et al. Intensive care in India in 2018–2019: the second Indian intensive care case mix and practice patterns study. Indian J Crit Care Med. 2021 Oct;25(10):1093 10. Demass TB, Guadie AG, Mengistu TB, Belay ZA, Melese AA, Berneh AA, et al. The magnitude of mortality and its predictors among adult patients admitted to the intensive care unit in Amhara Regional State, Northwest Ethiopia. Sci Rep. 2023 Jul;13(1):12010. 11. Oh DK, Na W, Park YR, Hong SB, Lim CM, Koh Y, et al. Medical resource utilization patterns and mortality rates according to age among critically ill patients admitted to a medical intensive care unit. Medicine (Baltimore). 2019 May 1;98(22):e15835. 12. Abuhasira R, Anstey M, Novack V, Bose S, Talmor D, Fuchs L. Intensive care unit capacity and mortality in older adults: a three nations retrospective observational cohort study. Ann Intensive Care. 2022 Mar 4;12(1):20. 13. Ismail AJ, Hassan WM, Nor MB, Shukeri WF. The impact of age on mortality in the intensive care unit: a retrospective cohort study in Malaysia. Acute Crit Care. 2024 Aug 12;39(3):390.