“…Risk-adjusted mortality was represented by the Observed:Expected mortality ratio (OEMR), calculated as the quotient of observed mortality and mortality predicted by the severity of illness (APACHE IV prediction of mortality). RESULTS: Of 5567 patients 294 (5.3%) were undiagDM. …”
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“…The mean admission SOFA score was 9.3 ± 3.63, the mean APACHE 2 score was 18.15 ± 7.7, and the mean lactic acid level was 3.8 ± 4.0 mmol/L. …”
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“…CONCLUSION: There are many risk factors for lower respiratory tract infection of S. maltophilia, which can occur in patients with severe illness, high APACHE-II score, invasive procedures, and the need for broad-spectrum antibiotics. …”
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“…Among all factors, the APACHE II Score (OR 2.607, 95% CI 1.896–3.584, P < 0.001), the Radiomics_Score of the middle lung (OR 2.230, 95% CI 1.387–3.583, P = 0.01), the Radiomics_Score of the lower lung (OR 1.633, 95% CI 1.143–2.333, P = 0.01) were associated with the 28-day mortality. …”
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“…After adjusting for age, sex, Acute Physiology and Chronic Health Evaluation II (APACHE) score, coronary artery disease, immunosuppression, ICU care duration, mechanical ventilation requirement, vasopressor requirement and study time period, the cumulative daily intake of potassium, chloride, sodium, phosphate, calcium, glucose, lipids and water was associated with one-year mortality in separate multivariable cox proportional hazards models. …”
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“…The risk factors recognized were higher age, increased acute physiology and chronic health evaluation (APACHE-II) score, hyperuricemia, raised creatinine, hypoalbuminemia, hyperbilirubinemia, alcoholism, and smoking. …”
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“…Positive correlations between levels of these peritoneal cytokines with APACHE II scores were found: IL-6, in particular, had the highest correlation coefficient of 0.833. …”
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“…Acute physiology, age and chronic health evaluation (APACHE II) score and the sequential organ failure assessment (SOFA) score were 14 (8–24.5) and 6 (4–8), respectively. …”
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“…The following indexes related to organ dysfunction influenced LacTW: acute physiology and chronic health evaluation II (APACHE II) score (P < 0.001), activated partial thromboplastin time (APTT) (P = 0.002), total bilirubin (P = 0.012), creatinine (P = 0.037), with hypotension (P < 0.001), chronic kidney disease (P = 0.013), and required continuous renal replacement therapy (CRRT) (P < 0.001). …”
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“…Subcutaneous edema exhibited gravity-dependent distribution patterns, particularly in the abdominal wall. The APACHE II, NT-proBNP, serum creatinine, and sepsis were independent risk factors for subcutaneous edema development. …”
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“…The average CD4+ cell count of HIV-positive patients treated with quinine was 200 cells/µL compared with 217.17 cells/µL for those treated with artesunate (p=0.875). The mean APACHE II score at admission was 20.85 and 19.62 in the quinine group and artesunate group, respectively (p=0.380). …”
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“…METHODS: Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). …”
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“…The patients that underwent mechanical ventilation in a prone position present less caloric and protein intake, similar to those with APACHE II > 18. CONCLUSIONS: Critically ill patients with COVID-19 associated respiratory failure requiring mechanical ventilation who died in ICU had less caloric and protein intake than those who survived. …”
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“…The Acute Physiology and Chronic Health Evaluation II (APACHE II) scores of successfully weaned patients (15.8 ± 5.0) were lower than those who were not (20.4 ± 8.4, P < 0.05). …”
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“…Non-survivors had a marginally higher mean Charlson Comorbidity Index (5.9 ± 3.6 vs. 4.5 ± 4.1 points, P = 0.063), and significantly higher mean Clinical Frailty Score (4.8 ± 1.5 vs. 3.9 ± 1.4 points, P = 0.004), admission APACHE II score (22.9 ± 7.9 vs. 19.1 ± 7.8 points, P = 0.017) and SAPS II score (62.1 ± 18.1 vs. 54.0 ± 16.7 points, P = 0.023). …”
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“…Clinical results were as follows: N (HR 0.91, 95% CI 0.86–0.97, P=0.003); TBIL (HR 1.98, 95% CI 1.02–1.99, P=0.048); APACHE II (HR 1.94, 95% CI 1.39, 2.48, P=0.045); WPRN (HR 6.22, 95% CI 2.86–13.53, P<0.001); WTOST (HR 0.56, 95% CI 0.21–1.46, P<0.001); and DMV [HR1.87, 95% CI 1.12–2.33]. …”
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“…According to univariable analyses, a lot of parameters (e.g., admission APACHE II and SOFA scores, length of ICU stay, some laboratory parameters at the ICU admission, the reason for ICU admission, comorbidities, type of HM, type of HSCT, infections on ICU admission and during ICU stay, etc.) were significantly different between survivors and nonsurvivors. …”
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“…The OR group had higher hemodynamic instability (48.0% vs 19.0%, p = 0.040), lower hemoglobin (12.9 vs 10.1, p = 0.001) and platelet counts (235.2 vs 155.1, p = 0.003), and worse Acute Physiology and Chronic Health Evaluation (APACHE) score (4.1 vs 10.2, p < 0.0001) at the time of initial presentation. …”
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“…The effect of various patient variables on the development of thromboembolism was evaluated, including cardiovascular disease (p<0.001), age (p=0.003), use of acetylsalicylic acid (ASA) (p<0.001), antiplatelet therapy (p<0. 001), acute physiology and chronic health evaluation (APACHE) II score (p=0.003), D-dimer (p=0.015), fibrinogen (p=0.032), ferritin (p=0.015), prothrombin time (PT) (p=0.015), international normalized ratio (INR) (p=0.012), troponin (p=0.012) values at the ICU admission were found statistically significant. …”
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“…RESULTS: We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra–based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. …”
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