Delirium in the Intensive Care Unit (ICU) is a severe form of acute brain dysfunction characterized by fluctuating mental status, inattention, and altered levels of consciousness. For years, delirium was often dismissed as "ICU psychosis"—a temporary and expected byproduct of critical illness. However, we now understand that it is a serious medical complication linked to prolonged hospital stays, long-term cognitive decline, and increased mortality.

Despite its clinical importance, ICU delirium remains underdiagnosed in many busy tertiary care centers in India due to heavy patient loads and a lack of routine screening protocols. At MKCG Medical College and Hospital (MKCG MCH) in Berhampur, the ICU caters to a vast, largely rural population from southern Odisha. Understanding how delirium affects this specific demographic is crucial for improving local critical care practices.

The primary objective of this study was to determine the prevalence of ICU delirium among our patients over a one-year period (2024–2025). Secondly, we wanted to identify simple, easily recognizable risk factors—such as age, mechanical ventilation, and sepsis—that can help clinicians predict which patients are most at risk, without relying on overly complicated scoring systems.

2.1 Study Design and Setting This was a prospective observational study conducted in the Medical and Surgical Intensive Care Units of MKCG Medical College and Hospital, Berhampur, Odisha. The study period ran from June 2025 to May 2026. 2.2 Study Population We enrolled 112 adult patients (aged ≥ 18 years) admitted to the ICU for more than 24 hours. Patients were excluded if they had a prior history of dementia, severe psychiatric illnesses, structural brain injury, or were in a deep coma (Richmond Agitation-Sedation Scale score of -4 or -5), as these conditions prevent accurate delirium assessment. 2.3 Data Collection Patient demographics (age, gender) and basic clinical parameters (reason for admission, use of mechanical ventilation, presence of sepsis) were recorded upon admission. To keep our methodology straightforward and practical for a busy ICU setting, we used the Confusion Assessment Method for the ICU (CAM-ICU) to screen for delirium. Assessments were performed twice daily (morning and evening) by trained resident doctors. Delirium was categorized into three subtypes based on motor activity: hypoactive (lethargic/drowsy), hyperactive (agitated/restless), and mixed. 2.4 Statistical Analysis Data were entered into an Excel spreadsheet and analyzed. Categorical variables were expressed as frequencies and percentages. To determine statistical significance between groups (delirious vs. non-delirious), we used the Chi-square (χ^2) test. A p-value of less than 0.05 was considered statistically significant.

A total of 112 patients met the inclusion criteria and were monitored throughout their ICU stay. The findings are summarized in the tables below.

3.1 Patient Demographics

Figure 1 outlines the baseline characteristics of our study population. The cohort was predominantly male, with a fairly even distribution between those above and below 60 years of age.

Figure 1: Baseline Demographic Characteristics (N = 112)

3.2 Prevalence and Subtypes of Delirium

Out of 112 patients, 42 developed delirium during their ICU stay, yielding an overall prevalence of 37.5%. As detailed in Table 1, the hypoactive subtype—often the hardest to detect without active screening—was the most common.

Table 1: Prevalence and Subtypes of ICU Delirium

3.3 Demographic Risk Factors

We looked at simple demographic factors to see who was most vulnerable. Table 2 shows that older age is a significant risk factor. Patients over 60 years old were significantly more likely to develop delirium (). Gender, however, did not play a significant role.

Table 2: Association of Age and Gender with ICU Delirium

( * indicates statistical significance, . NS = Not Significant)

3.4 Clinical Risk Factors

We evaluated two major, easily identifiable clinical parameters: mechanical ventilation and the presence of clinical sepsis. Table 3 demonstrates that both are strong, statistically significant predictors of acute brain dysfunction.

Table 3: Clinical Risk Factors and ICU Delirium

( * indicates statistical significance, )

3.5 Clinical Outcomes

Finally, we assessed how delirium impacted patient recovery. Table 4 clearly shows that patients who experienced delirium stayed in the ICU significantly longer and faced a higher risk of mortality compared to those whose minds remained clear.

Table 4: Clinical Outcomes of ICU Delirium

( * indicates statistical significance, )

Our study, conducted over the 2024–2025 period at MKCG MCH, reveals a delirium prevalence of 37.5% among ICU patients. This figure aligns well with broader global literature, which generally places ICU delirium rates anywhere between 30% and 50% depending on the patient mix. What stands out in our day-to-day observations is the high rate of hypoactive delirium (52.4% of delirious patients). These patients do not pull at their lines or shout; they simply lie quietly, staring blankly or sleeping excessively. Because they aren't disruptive, they are easily overlooked by nursing staff and physicians. This underscores exactly why we need active, daily screening tools like the CAM-ICU rather than relying on casual bedside observation. When looking at risk factors, our findings clearly show that the elderly (over 60 years) are highly susceptible. The aging brain is simply less resilient to the massive physiological stress of critical illness. Furthermore, the clinical interventions we use to save lives—specifically mechanical ventilation—act as major triggers. We found a highly significant correlation (p<0.001) between ventilator use and delirium. This is likely a combination of the underlying respiratory failure, the sedatives required to tolerate the breathing tube, and the unnatural, restrictive environment of being tied to a machine. Sepsis also proved to be a strong trigger (p=0.003), highlighting how systemic inflammation directly impacts brain function. The human cost of this complication is evident in our outcome data. Patients who developed delirium were stuck in the ICU much longer (over 7 days) and had a statistically higher mortality rate (21.4% vs 7.1%, p=0.031). Delirium is not just a transient fog; it is an independent predictor of a poor outcome. Limitations: The sample size of 112 patients from a single center means our results might reflect the specific demographics of southern Odisha. Additionally, we kept our parameters intentionally simple to ensure practical applicability, which means we did not run complex multivariate regression models to adjust for every possible confounding variable (like specific sedative drug dosages).

ICU delirium is a frequent, serious, and deadly complication among critically ill patients at MKCG Medical College and Hospital. Advanced age, mechanical ventilation, and sepsis are easily identifiable red flags. Because the quiet, hypoactive form of delirium is the most common, visual observation alone is not enough. Implementing routine, daily CAM-ICU scoring in our wards is a simple, cost-free step that can dramatically improve our ability to detect acute brain dysfunction early and improve patient survival.

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