What Causes Ammonia Levels To Be High – Ammonia is a nitrogenous waste that is normally excreted in the urine. An elevated blood ammonia level is an excessive accumulation of ammonia in the blood. High blood ammonia levels occur when the kidneys or liver aren’t working properly, allowing waste to build up in the blood. Ammonia, like any other waste product in the body, can be toxic to cells, and high levels of ammonia in the blood can affect the entire body.

Determination of ammonia levels in the blood caused by severe liver disease, kidney failure, or certain rare genetic disorders; to help investigate the cause of behavioral and cognitive changes; to help diagnose hepatic encephalopathy or Reye’s syndrome

What Causes Ammonia Levels To Be High

If someone with liver disease or kidney failure experiences mental changes or falls into a coma; when the newborn experiences frequent vomiting and diarrhea, or when the child experiences persistent vomiting and abnormal sleep after recovering from a viral illness such as the flu or chicken pox;

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High blood ammonia symptoms may occur frequently, even daily or occasionally. Sometimes any of these symptoms can be severe:

The site covers a wide range of laboratory tests, including blood tests, urine tests, stool tests, and imaging tests such as X-rays and CT scans. It also provides information about various health conditions and diseases, as well as tips for maintaining good health.

Although lab tests can provide valuable information about their interpretation, it is important to note that if you have concerns or questions about your lab results, it is best to consult with a healthcare professional. We can provide personalized guidance and advice based on your individual health conditions and medical history. Background and Objectives: Hyperammonemia usually develops due to liver disease, but it can occur in patients with non-hepatic hyperammonemia (NHH). However, studies on the prognosis and potential risk factors of this disorder are lacking. The aim of this study was to find possible predictors and risk factors for NHH in critically ill patients.

Methods: Data were retrieved from the MIMIC III database. Survival was analyzed using the Kaplan-Meier method. Univariate and multivariate analyzes were performed to identify prognostic factors.

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Results: Valproic acid, carbamazepine, corticosteroids, recent orthopedic surgery, epilepsy, urea cycle disorders and obesity were found to be risk factors for NHH. Patients in the hyperammonemia group had a higher 30-day mortality than the non-hyperammonemia group. After post hoc regression analysis, ammonia was an independent predictor of mortality.

Conclusions: Ammonia was an independent predictor of 30-day mortality in critical care patients without liver disease.

Ammonia is a major metabolite of amino acids. Elevated blood ammonia levels can cause encephalopathy (1). Hyperammonemia is more common in patients with acute liver failure or chronic liver disease, but may occur in patients without liver problems (2–4). Elevated blood ammonia levels without a history of liver disease are defined as non-hepatic hyperammonemia (NHH). NHH is more common in critically ill patients (up to 73% in a recent study) (5). NHH can occur in patients with a variety of serious conditions, including intracranial hypertension (6) or congestive heart failure, malnutrition, infectious enterocolitis, or lung transplantation (7–11). Patients with severe heart disease and low serum ammonia levels have significantly lower mortality than patients with high ammonia levels (12). In addition, the inflammatory response and multiorgan dysfunction in patients with sepsis are exacerbated by elevated blood ammonia levels (13, 14). NHH prolongs intensive care unit (ICU) stay and is associated with higher mortality (6).

Previous studies have focused primarily on hyperammonemia secondary to liver disease. Clinically, hyperammonemia due to non-liver disease may be overlooked or misdiagnosed. NHH (15) has small sample sizes and examples. NHH is associated with organ failure, prolonged fasting, and urinary tract infection (16), but there are very few studies investigating predictors or risk factors on this topic. The aim of this retrospective cohort study was to determine which risk factors are associated with the development of NHH after hospital admission in critical patients.

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The MIMIC (Medical Information March for Resuscitation) critical care database was used to conduct this study (17). Patients admitted to the Beth Israel Deaconess Medical Center ICU from 2001 to 2012 were enrolled (17). The project was approved by the institutional review boards of Massachusetts Institute of Technology and Beth Israel Deaconess Medical Center; there was no requirement for individual patient consent, as the project did not affect clinical care and all protected health information was identified. Raw data were obtained using structured query language (SQL) with Navicat and processed with R software. A blood ammonia level >35 mol/L was defined as hyperammonemia in the MIMIC-III database. The MIMIC III database (version 1.4) is publicly available at https://mimic.physionet.org/.

Database establishment was approved by the institutional review boards of the Massachusetts Institute of Technology (Cambridge, MA) and Beth Israel Deaconess Medical Center (Boston, MA). This work was performed in accordance with the World Medical Association Code of Ethics (Declaration of Helsinki).

Inclusion criteria were as follows: patients were (1) ≥18 and ≤89 years of age, (2) admission time >24 hours in the ICU, and (3) blood ammonia level on record.

Exclusion criteria: (1) patients with acute or chronic liver disease, including: hepatitis, cirrhosis, hepatic encephalopathy, hepatorenal syndrome, liver injury, or other chronic liver disease, International Classification of Diseases, Ninth Revision (ICD-9) patient (2) patients with no significant symptoms or ICD 9 diagnosis code(s) were also excluded.

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R statistical software (R Statistical Computing Foundation, Vienna, Austria) was used to extract patient data from the MIMIC III database. The following basic data were collected from each patient: age, gender, heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), respiratory rate (RR), and temperature (T). The following biochemical test results were also collected from each patient: alanine aminotransferase (ALT), aspartate aminotransferase (AST), partial thromboplastin time (PTT), international normalized ratio (INR), prothrombin time (PT), white blood cell count (WBC). ), hemoglobin, platelets, blood urea nitrogen (BUN), creatinine (Cr), and glucose. Simplified Acute Patient Physiology Score (SAPSII), Quick Sequential Organ Dysfunction Assessment (qSOFA) score, Sequential Organ Dysfunction Assessment (SOFA), and Glasgow Coma Scale (GCS) were also recorded. Patients’ primary care unit (i.e., type of ICU treated) was recorded based on data obtained during each patient’s first 24 hours of ICU stay: intensive care unit (ICU). Peak and trough values ​​of sodium and potassium were obtained during the first 24 hours of each patient’s ICU stay. The peak value of ammonia obtained during each patient’s ICU period, the worst scores and laboratory parameters, as well as the mean values ​​of vital signs during the first 24 hours of ICU admission were used.

The distribution of data was tested using the Shapiro-Wilk test. Patient characteristics were expressed as median (P25, P75) (interquartile range [IQR]) or frequency and percentage when appropriate. A nonparametric test (Mann-Whitney U test or Kruskal-Wallis test) was used for data with non-normal distribution or heterogeneity. Categorical data were compared using the Pearson Chi-square test, and Kaplan-Meier curves were analyzed using rank-sum tests. Cox regression model was used to analyze the independent effect of different parameters on 30-day mortality. Statistical significance was defined as p < 0.05. All statistical analyzes were performed with R software (version 3.4.3).

A total of 1,051 patients were enrolled in this study. Patients were randomized to either a hyperammonemia group (n = 443) or a nonhyperammonemia group (n = 608). Variables with missing data are more common in the MIMIC III database. The percentage of missing values ​​was significant for lactate (33.2%), albumin (45.6%), bilirubin (71.3%), pH (25.7%), and SpO2 (27.2%) excluded from this study. The percentage of missing values ​​for PTT (11.23%), INR (10.6%), PT (10.6%), ALT (12.3), AST (12.3) was <13%, and other variables were <2%. We replaced missing values ​​of included variables by multiple imputation. The detailed process of data acquisition is shown in Figure 1.

Baseline characteristics, vital signs, laboratory parameters, diagnoses, microbiology results, drugs used, operations performed, and patient outcomes are summarized in Table 1. Differences between age, gender, systolic blood pressure, INR, PT, and ammonia in the hyperammonemic and non-hyperammonemic groups group was statistically significant. Obesity (8.8 vs 4.8%) and orthopedic surgery (4.5 vs 2.5%), corticosteroids (61.6 vs 44.4%), carbamazepine (8.1 vs 3.1%), valproic acid (9, 7 vs 5.9%), epilepsy (19.0 vs 10.9%) and urea cycle disorders (0.9 vs 0.0%) were significantly higher in patients with hyperammonemia than in non-hyperammonemic patients. In our cohort, ammonia levels did not show any association with sepsis, gastrointestinal bleeding, intestinal infections, urinary tract infections, bleeding, heart failure, renal failure, microbiological outcomes, or surgery elsewhere in the body.

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Table 2 shows the results of hyperammonemia and hyperammonemia groups. There were no significant differences

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