Admission Determinants of Prolonged Intensive Care Unit Stay: A Five Years Multidisciplinary Cohort Study Comparing Neurological and Non-Neurological Critical Illness

Imagine the Intensive Care Unit (ICU) as the most high-stakes pit crew in a hospital. When a car (a patient) breaks down catastrophically, it gets towed here for a deep, complex repair. The mechanics need to know two things immediately: how bad the damage is, and how long the repair will take. If they guess wrong, they might run out of tools (beds), run out of fuel (money), or keep a car in the shop too long when it could have been fixed faster. Scientists have been trying to build a "crystal ball" to predict exactly how long a patient will stay in the ICU. They know that some patients bounce back quickly, while others get stuck in a long, complicated recovery. The big question is: Is the length of the stay determined by what broke the car (the specific disease), or by how badly the engine is sputtering when it first arrives at the shop?

This paper dives into that mystery by looking at a massive group of patients over five years. The researchers wanted to see if they could predict a "prolonged stay" (defined as 14 days or more) just by looking at the patient the moment they walked through the ICU door. They compared two big groups: those with brain and nervous system issues (neurological) and those with everything else (non-neurological). They used a few key tools to measure the "sputtering engine": the Glasgow Coma Scale (GCS), which is like a quick test to see how awake and responsive a person is; the SOFA score, which checks how well different organs are working; and a bunch of blood tests. The goal was to find out if the specific diagnosis (like a stroke vs. pneumonia) was the main reason for a long stay, or if it was just the overall severity of the illness that mattered most.

The Study: A Five-Year Detective Hunt

The researchers, working out of a large hospital in Turkey, acted like detectives reviewing case files from the last five years (October 2019 to October 2024). They gathered data on 609 adults who had stayed in one of the hospital's five different ICUs for at least five days. They deliberately skipped anyone who left in less than five days because those cases were usually either "super-fast recoveries" or "very quick, sad endings," which don't help explain what causes a long, sustained struggle in the ICU.

The team split the patients into two teams: the "Neurological Squad" (180 patients with brain or nerve issues) and the "Non-Neurological Squad" (429 patients with heart, lung, or other body system issues). They looked at everything available at the moment of admission: age, gender, medical history, and those critical scores (GCS, SOFA, APACHE II). They also checked blood work for things like inflammation markers. Crucially, they refused to use information that happened during the stay, like whether a patient needed a breathing machine or a tracheostomy later on. They knew that including those would be like trying to predict a car repair time by looking at how many hours the mechanic spent working on it after they started; that's a result, not a cause.

The Big Surprise: It's Not About the Diagnosis

The most exciting discovery was that the specific type of illness didn't actually matter as much as everyone thought. You might expect that patients with brain injuries would stay longer because their brains take time to heal. But the data told a different story. The "Neurological Squad" actually had shorter stays on average (15 days) compared to the "Non-Neurological Squad" (20 days). Even more surprisingly, only about 58% of the brain patients had a "prolonged stay" (over 14 days), while nearly 70% of the other patients did.

So, what did predict a long stay? The paper suggests that the answer lies in the patient's starting condition, not their diagnosis. Two main factors stood out as independent predictors:

1. The "Wakefulness" Score (GCS): The lower a patient's Glasgow Coma Scale score when they arrived, the more likely they were to stay longer. Think of GCS as a battery level indicator. If the battery is already low (a low score), the repair takes longer. For every single point the score dropped, the odds of a long stay increased.
2. The "Diabetes" Twist: Here is where it gets a little weird. The study found that patients without diabetes were actually more likely to have a prolonged stay than those with diabetes. The authors are careful to say this doesn't mean diabetes is "good" or "protective." Instead, they suggest it's likely a mix-up in the group of people. Maybe the patients with diabetes were different in other ways (like age or other health issues) that made them easier to manage in the short term, or perhaps the non-diabetic group had more severe, unexpected complications. It's a clue, not a cure.

Other factors like high inflammation in the blood or a high SOFA score (indicating organ failure) showed a "borderline" connection to long stays, meaning they were close to being significant but didn't quite cross the finish line in this specific analysis.

The Verdict: Severity Wins Over Diagnosis

The study concludes that when trying to guess how long a patient will need the ICU, doctors should focus less on the label of the disease (brain vs. heart vs. lung) and more on the "physiological severity"—how broken the body's systems are right at the start. Whether you have a neurological problem or a non-neurological one, the rules seem to be the same: if you arrive with a low consciousness score and no diabetes (in this specific group), you are at higher risk for a long stay.

The researchers also looked at who died in the hospital. They found that older age, higher organ failure scores (SOFA), and low levels of a protein called albumin were the real drivers of death. Interestingly, the length of the stay itself wasn't a cause of death; rather, staying a long time was just a sign that the patient was already very sick and struggling to recover.

Why This Matters

This paper suggests that we don't need to wait for a patient to get worse or develop complications to know they will need a long bed. By looking at simple, available numbers the moment they arrive—like how awake they are and their blood sugar history—hospitals might be able to spot the "long-haul" patients earlier. This could help them plan better, allocate resources more wisely, and perhaps even start discharge planning sooner for those who are likely to stay. However, the authors are careful to note that this is just one hospital's data over five years. They suggest that before we change the whole world's ICU rules, we need to test these ideas in many different hospitals to make sure the pattern holds up everywhere. For now, it's a strong hint that the "sputtering engine" matters more than the "make and model" of the car.