Dataset Information

Combining Phenylalanine and Leucine Levels Predicts 30-Day Mortality in Critically Ill Patients Better than Traditional Risk Factors with Multicenter Validation.

ABSTRACT: In critically ill patients, risk scores are used; however, they do not provide information for nutritional intervention. This study combined the levels of phenylalanine and leucine amino acids (PLA) to improve 30-day mortality prediction in intensive care unit (ICU) patients and to see whether PLA could help interpret the nutritional phases of critical illness. We recruited 676 patients with APACHE II scores ≥ 15 or intubated due to respiratory failure in ICUs, including 537 and 139 patients in the initiation and validation (multicenter) cohorts, respectively. In the initiation cohort, phenylalanine ≥ 88.5 μM (indicating metabolic disturbance) and leucine < 68.9 μM (indicating malnutrition) were associated with higher mortality rate. Based on different levels of phenylalanine and leucine, we developed PLA scores. In different models of multivariable analyses, PLA scores predicted 30-day mortality independent of traditional risk scores (p < 0.001). PLA scores were then classified into low, intermediate, high, and very-high risk categories with observed mortality rates of 9.0%, 23.8%, 45.6%, and 81.8%, respectively. These findings were validated in the multicenter cohort. PLA scores predicted 30-day mortality better than APACHE II and NUTRIC scores and provide a basis for future studies to determine whether PLA-guided nutritional intervention improves the outcomes of patients in ICUs.

SUBMITTER: Tsou YL

PROVIDER: S-EPMC9921772 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combining Phenylalanine and Leucine Levels Predicts 30-Day Mortality in Critically Ill Patients Better than Traditional Risk Factors with Multicenter Validation.

Tsou Yi-Liang YL Wang Chao-Hung CH Chen Wei-Siang WS Wu Huang-Ping HP Liu Min-Hui MH Lin Hsuan-Ching HC Chang Jung-Jung JJ Tsai Meng-Shu MS Chen Tien-Yu TY Cheng Cheng-I CI Yeh Jih-Kai JK Hsieh I-Chang IC

Nutrients 20230127 3

In critically ill patients, risk scores are used; however, they do not provide information for nutritional intervention. This study combined the levels of phenylalanine and leucine amino acids (PLA) to improve 30-day mortality prediction in intensive care unit (ICU) patients and to see whether PLA could help interpret the nutritional phases of critical illness. We recruited 676 patients with APACHE II scores ≥ 15 or intubated due to respiratory failure in ICUs, including 537 and 139 patients in ...[more]

PMID: 36771356

Similar Datasets

Project description:OBJECTIVES:To identify characteristics that predict 30-day mortality among patients critically ill with coronavirus disease 2019 in England, Wales, and Northern Ireland. DESIGN:Observational cohort study. SETTING:A total of 258 adult critical care units. PATIENTS:A total of 10,362 patients with confirmed coronavirus disease 2019 with a start of critical care between March 1, 2020, and June 22, 2020, of whom 9,990 were eligible (excluding patients with a duration of critical care less than 24 hr or missing core variables). MEASUREMENTS AND MAIN RESULTS:The main outcome measure was time to death within 30 days of the start of critical care. Of 9,990 eligible patients (median age 60 yr, 70% male), 6,027 died within 30 days of the start of critical care. As of July 22, 2020, 189 patients were still receiving critical care and a further 446 were still in acute hospital. Data were missing for between 0.1% and 7.2% of patients across prognostic factors. We imputed missing data ten-fold, using fully conditional specification and continuous variables were modeled using restricted cubic splines. Associations between the candidate prognostic factors and time to death within 30 days of the start of critical care were determined after adjustment for multiple variables with Cox proportional hazards modeling. Significant associations were identified for age, ethnicity, deprivation, body mass index, prior dependency, immunocompromise, lowest systolic blood pressure, highest heart rate, highest respiratory rate, PaO2/FIO2 ratio (and interaction with mechanical ventilation), highest blood lactate concentration, highest serum urea, and lowest platelet count over the first 24 hours of critical care. Nonsignificant associations were found for sex, sedation, highest temperature, and lowest hemoglobin concentration. CONCLUSIONS:We identified patient characteristics that predict an increased likelihood of death within 30 days of the start of critical care for patients with coronavirus disease 2019. These findings may support development of a prediction model for benchmarking critical care providers.

Project description:Background: Liver injury commonly occurs in patients with COVID-19. There is limited data describing the course of liver injury occurrence in patients with different disease severity, and the causes and risk factors are unknown. We aim to investigate the incidence, characteristics, risk factors, and clinical outcomes of liver injury in patients with COVID-19. Methods: This retrospective observational study was conducted in three hospitals (Zhejiang, China). From January 19, 2020 to February 20, 2020, patients confirmed with COVID-19 (≥18 years) and without liver injury were enrolled and divided into non-critically ill and critically ill groups. The incidence and characteristics of liver injury were compared between the two groups. Demographics, clinical characteristics, treatments, and treatment outcomes between patients with or without liver injury were compared within each group. The multivariable logistic regression model was used to explore the risk factors for liver injury. Results: The mean age of 131 enrolled patients was 51.2 years (standard deviation [SD]: 16.1 years), and 70 (53.4%) patients were male. A total of 76 patients developed liver injury (mild, 40.5%; moderate, 15.3%; severe, 2.3%) with a median occurrence time of 10.0 days. Critically ill patients had higher and earlier occurrence (81.5 vs. 51.9%, 12.0 vs. 5.0 days; p < 0.001), greater injury severity (p < 0.001), and slower recovery (50.0 vs. 61.1%) of liver function than non-critically ill patients. Multivariable regression showed that the number of concomitant medications (odds ratio [OR]: 1.12, 95% confidence interval [CI]: 1.05-1.21) and the combination treatment of lopinavir/ritonavir and arbidol (OR: 3.58, 95% CI: 1.44-9.52) were risk factors for liver injury in non-critically ill patients. The metabolism of arbidol can be significantly inhibited by lopinavir/ritonavir in vitro (p < 0.005), which may be the underlying cause of drug-related liver injury. Liver injury was related to increased length of hospital stay (mean difference [MD]: 3.2, 95% CI: 1.3-5.2) and viral shedding duration (MD: 3.0, 95% CI: 1.0-4.9). Conclusions: Critically ill patients with COVID-19 suffered earlier occurrence, greater injury severity, and slower recovery from liver injury than non-critically ill patients. Drug factors were related to liver injury in non-critically ill patients. Liver injury was related to prolonged hospital stay and viral shedding duration in patients with COVID-19. Clinical Trial Registration: World Health Organization International Clinical Trials Registry Platform, ChiCTR2000030593. Registered March 8, 2020.

Project description:BackgroundPrevious studies linked a high intensity of ventilation, measured as mechanical power, to mortality in patients suffering from "classic" ARDS. By contrast, mechanically ventilated patients with a diagnosis of COVID-19 may present with intact pulmonary mechanics while undergoing mechanical ventilation for longer periods of time. We investigated whether an association between higher mechanical power and mortality is modified by a diagnosis of COVID-19.MethodsThis retrospective study included critically ill, adult patients who were mechanically ventilated for at least 24 h between March 2020 and December 2021 at a tertiary healthcare facility in Boston, Massachusetts. The primary exposure was median mechanical power during the first 24 h of mechanical ventilation, calculated using a previously validated formula. The primary outcome was 30-day mortality. As co-primary analysis, we investigated whether a diagnosis of COVID-19 modified the primary association. We further investigated the association between mechanical power and days being alive and ventilator free and effect modification of this by a diagnosis of COVID-19. Multivariable logistic regression, effect modification and negative binomial regression analyses adjusted for baseline patient characteristics, severity of disease and in-hospital factors, were applied.Results1,737 mechanically ventilated patients were included, 411 (23.7%) suffered from COVID-19. 509 (29.3%) died within 30 days. The median mechanical power during the first 24 h of ventilation was 19.3 [14.6-24.0] J/min in patients with and 13.2 [10.2-18.0] J/min in patients without COVID-19. A higher mechanical power was associated with 30-day mortality (ORadj 1.26 per 1-SD, 7.1J/min increase; 95% CI 1.09-1.46; p = 0.002). Effect modification and interaction analysis did not support that this association was modified by a diagnosis of COVID-19 (95% CI, 0.81-1.38; p-for-interaction = 0.68). A higher mechanical power was associated with a lower number of days alive and ventilator free until day 28 (IRRadj 0.83 per 7.1 J/min increase; 95% CI 0.75-0.91; p < 0.001, adjusted risk difference - 2.7 days per 7.1J/min increase; 95% CI - 4.1 to - 1.3).ConclusionA higher mechanical power is associated with elevated 30-day mortality. While patients with COVID-19 received mechanical ventilation with higher mechanical power, this association was independent of a concomitant diagnosis of COVID-19.

Dataset Information

Combining Phenylalanine and Leucine Levels Predicts 30-Day Mortality in Critically Ill Patients Better than Traditional Risk Factors with Multicenter Validation.

Publications

Combining Phenylalanine and Leucine Levels Predicts 30-Day Mortality in Critically Ill Patients Better than Traditional Risk Factors with Multicenter Validation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets