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BACKGROUND: Acute kidney injury patients on continuous renal replacement therapy are subjected to alterations in metabolism, which in turn are associated with worse clinical outcome and mortality. The aim of this study is to determine which metabolism indicators can be used as independent predictors of 30 days intensive care unit (ICU) mortality. METHODS: This was a prospective observational study on critical care patients on renal replacement therapy. Integrated approach of metabolism evaluation was used, combining the energy expenditure measured by indirect calorimetry, bioelectrical impedance provided fat free mass index (FFMI), amino acid and glucose concentrations. ICU mortality was defined as all cause 30 days mortality. Regression analysis was conducted to determine the conventional and metabolism associated predictors of mortality. RESULTS: The study was conducted between the 2021 March and 2022 October. 60 high mortality risk patients (APACHE II of 22.98 ± 7.87, 97% on vasopressors, 100% on mechanical ventilation) were included during the period of the study. The rate of 30 days ICU mortality was 50% (n = 30). Differences across survivors and non-survivors in metabolic predictors were noted in energy expenditure (kcal/kg/day) (19.79 ± 5.55 vs 10.04 ± 3.97 p = 0.013), amino acid concentrations (mmol/L) (2.40 ± 1.06 vs 1.87 ± 0.90 p = 0.040) and glucose concentrations (mmol/L) (7.89 ± 1.90 vs 10.04 ± 3.97 p = 0.010). No differences were noted in FFMI (23.38 ± 4.25 vs 21.95 ± 3.08 p = 0.158). In the final linear regression analysis model, lower energy expenditure (exp(B) = 0.852 CI95%: 0.741-0.979 p = 0.024) and higher glucose (exp(B) = 1.360 CI95%: 1.013-1.824 p = 0.041) remained as independent predictors of the higher mortality. CONCLUSION: The results of the study imply strong association between the metabolic alterations and ICU outcome. Our findings suggest that lower systemic amino acid concentration, lower energy expenditure and higher systemic glucose concentration are predictive of 30 days ICU mortality.
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PURPOSE: The aim of this study was to examine the impact of detoxification from prescription opioids on the quality of life (QoL) and pain levels among patients reliant on these medications for chronic pain management. OBJECTIVE: Long-term use of opioids for pain management may lead to a range of adverse effects, including tolerance, dependence, significant societal costs, and a decline in overall quality of life (QoL). Despite these challenges, there is a limited amount of research focusing on the effects of detoxification and its impact on the QoL for patients with chronic pain tolerant to prescription opioids. METHODS: This prospective study included 45 patients who underwent elective detoxification from prescription opioids. Prescription opioids were discontinued during the detoxification treatment in 44 of the 45 cases. QoL was monitored using SF-36v2™ questionnaires administered before detoxification, on the day of discharge, and at least six months after detoxification. Pain levels were assessed using Visual Analogue Scale (VAS) scores before and after the detoxification process. RESULTS: The study was fully completed by 30 patients. At the third SF-36v2™ evaluation, 25 out of 30 patients (83.3%) reported the detoxification treatment as beneficial to their overall health status compared to that before the treatment, and SF-36v2™ questionnaires after detoxification were significantly higher than before the treatment (p < 0.001). A decreased pain level right after the detoxification was reported by 44 of the 45 patients (97.7%), with a significant average reduction of 4.51 points observed (p < 0.001). CONCLUSIONS: The observed enhancement in QoL, significant reduction in pain, and cessation of opioid use in most patients with chronic pain tolerant to prescription opioids following opioid detoxification indicate that this method of treatment can be safely and effectively administered and must be considered for chronic pain patients.
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Background and objectives: Amino acid (AA) loss is a prevalent unwanted effect of continuous renal replacement therapy (CRRT) in critical care patients, determined both by the machine set-up and individual characteristics. The aim of this study was to evaluate the bioelectrical impedance analysis-derived fat-free mass index (FFMI) effect on amino acid loss. Materials and methods: This was a prospective, observational, single sample study of critical care patients upon initiation of CRRT. AA loss during a 24 h period was estimated. Conventional determinants of AA loss (type and dose of CRRT, concentration of AA) and FFMI were entered into the multivariate regression analysis to determine the individual predictive value. Results: Fifty-two patients were included in the study. The average age was 66.06 ± 13.60 years; most patients had a high mortality risk with APAHCE II values of 22.92 ± 8.15 and SOFA values of 12.11 ± 3.60. Mean AA loss in 24 h was 14.73 ± 9.83 g. There was a significant correlation between the lost AA and FFMI (R = 0.445, B = 0.445 CI95%: 0.541-1.793 p = 0.02). Multivariate regression analysis revealed the independent predictors of lost AA to be the systemic concentration of AA (B = 6.99 95% CI:4.96-9.04 p = 0.001), dose of CRRT (B = 0.48 95% CI:0.27-0.70 p < 0.001) and FFMI (B = 0.91 95% CI:0.42-1.41 p < 0.001). The type of CRRT was eliminated in the final model due to co-linearity with the dose of CRRT. Conclusions: A substantial amount of AA is lost during CRRT. The amount lost is increased by the conventional factors as well as by higher FFMI. Insights from our study highlight the FFMI as a novel research object during CRRT, both when prescribing the dosage and evaluating the nutritional support needed.
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Lesión Renal Aguda , Terapia de Reemplazo Renal Continuo , Humanos , Persona de Mediana Edad , Anciano , Estudios Prospectivos , Enfermedad Crítica , Aminoácidos , Cuidados Críticos , Lesión Renal Aguda/terapia , Estudios RetrospectivosRESUMEN
BACKGROUND: As the COVID-19 pandemic continues, the number of patients admitted to the intensive care unit (ICU) is still increasing. The aim of our article is to estimate which of the conventional ICU mortality risk scores is the most accurate at predicting mortality in COVID-19 patients and to determine how these scores can be used in combination with the 4C Mortality Score. METHODS: This was a retrospective study of critically ill COVID-19 patients treated in tertiary reference COVID-19 hospitals during the year 2020. The 4C Mortality Score was calculated upon admission to the hospital. The Simplified Acute Physiology Score (SAPS) II, Acute Physiology and Chronic Health Evaluation (APACHE) II, and Sequential Organ Failure Assessment (SOFA) scores were calculated upon admission to the ICU. Patients were divided into two groups: ICU survivors and ICU non-survivors. RESULTS: A total of 249 patients were included in the study, of which 63.1% were male. The average age of all patients was 61.32 ± 13.3 years. The all-cause ICU mortality ratio was 41.4% (n = 103). To determine the accuracy of the ICU mortality risk scores a ROC-AUC analysis was performed. The most accurate scale was the APACHE II, with an AUC value of 0.772 (95% CI 0.714-0.830; p < 0.001). All of the ICU risk scores and 4C Mortality Score were significant mortality predictors in the univariate regression analysis. The multivariate regression analysis was completed to elucidate which of the scores can be used in combination with the independent predictive value. In the final model, the APACHE II and 4C Mortality Score prevailed. For each point increase in the APACHE II, mortality risk increased by 1.155 (OR 1.155, 95% CI 1.085-1.229; p < 0.001), and for each point increase in the 4C Mortality Score, mortality risk increased by 1.191 (OR 1.191, 95% CI 1.086-1.306; p < 0.001), demonstrating the best overall calibration of the model. CONCLUSIONS: The study demonstrated that the APACHE II had the best discrimination of mortality in ICU patients. Both the APACHE II and 4C Mortality Score independently predict mortality risk and can be used concomitantly.