Background: The Controlling Nutritional Status (CONUT) score and the prognostic nutritional index (PNI) have emerged as important nutritional indices because they provide an objective assessment based on data. We aimed to investigate how these nutritional indices relate to outcomes in patients with sepsis.Methods: Data were collected retrospectively at five hospitals for patients aged ≥18 years receiving treatment for sepsis between January 1, 2017, and December 31, 2021. Serum albumin and total cholesterol concentrations, and peripheral lymphocytes were used to calculate the CONUT score and PNI. To identify predictors correlated with 30-day mortality, analyses were conducted using univariate and multivariate Cox proportional hazards models.Results: The 30-day mortality rate among 9,763 patients was 15.8% (n=1,546). The median CONUT score was 5 (interquartile range [IQR], 3–7) and the median PNI score was 39.6 (IQR, 33.8–46.4). Higher 30-day mortality rates were associated with individuals with moderate (CONUT score: 5–8; PNI: 35–38) or severe (CONUT: 9–12; PNI: 38). With CONUT scores, the hazard ratio (HR) associated with moderate malnutrition was 1.52 (95% confidence interval [CI], 1.24–1.87, P<0.001); for severe, HR=2.42 (95% CI, 1.95–3.02, P<0.001). With PNI scores, the HR for moderate malnutrition was 1.29 (95% CI, 1.09–1.53, P=0.003); for severe, HR=1.88 (95% CI, 1.67–2.12, P<0.001).Conclusions: The nutritional indices CONUT score and PNI showed significant associations with mortality of sepsis patients within 30 days.

Inhaled corticosteroids (ICS) could increase both the risk of coronavirus disease 2019 (COVID-19) and experiencing poor outcomes. To compare the clinical outcomes between ICS users and nonusers, COVID-19-related claims in the Korean Health Insurance Review and Assessment database were evaluated. To evaluate susceptibility to COVID-19 among patients with COPD or asthma, a nested case-control study was performed using the same database. In total, 7341 patients were confirmed to have COVID-19, including 114 ICS users and 7227 nonusers. Among 5910 patients who were hospitalized, death was observed for 9% of ICS users and 4% of nonusers. However, this association was not significant when adjusted for age, sex, region, comorbidities, and hospital type (aOR, 0.94; 95% CI, 0.43–2.07). The case-control analysis of COPD compared 640 cases with COVID-19 to 2560 matched controls without COVID-19, and the analysis of asthma compared 90 cases with COVID-19 to 360 matched controls without COVID-19. Use of ICS was not significantly associated with COVID-19 among patients with COPD (aOR, 1.02; 95% CI, 0.46–2.25) or asthma (aOR, 0.38; 95% CI, 0.13–1.17). Prior ICS use was not significantly associated with COVID-19 in patients with COPD or asthma, nor with clinical outcomes among patients with COVID-19.

Background: The ratio of oxygen saturation (ROX) index, defined as the ratio of oxygen saturation (SpO₂)/fraction of inspired oxygen (FiO₂) to respiratory rate, can help identify patients with hypoxemic respiratory failure at high risk for intubation following high-flow nasal cannula (HFNC) initiation; however, whether it is effective for predicting intubation in coronavirus disease 2019 (COVID-19) patients receiving HFNC remains unknown. Moreover, the SpO₂/FiO₂ ratio has been assessed as a prognostic marker for acute hypoxemic respiratory failure. This study aimed to determine the utility of the ROX index and the SpO₂/FiO₂ ratio as predictors of failure in COVID-19 patients who received HFNC. Methods: This multicenter study was conducted in seven university-affiliated hospitals in Korea. Data of consecutive hospitalized patients diagnosed with COVID-19 between February 10, 2020 and February 28, 2021 were retrospectively reviewed. We calculated the ROX index and the SpO₂/FiO₂ ratio at 1 h, 4 h, and 12 h after HFNC initiation. The primary outcome was HFNC failure defined as the need for subsequent intubation despite HFNC application. The receiver operating characteristic curve analysis was used to evaluate discrimination of prediction models for HFNC failure. Results: Of 1,565 hospitalized COVID-19 patients, 133 who received HFNC were analyzed. Among them, 63 patients (47.4%) were successfully weaned from HFNC, and 70 (52.6%) were intubated. Among patients with HFNC failure, 32 (45.7%) died. The SpO₂/FiO₂ ratio at 1 h after HFNC initiation was an important predictor of HFNC failure (AUC 0.762 [0.679–0.846]). The AUCs of SpO₂/FiO₂ ratio at 4 h and ROX indices at 1 h and 4 h were 0.733 (0.640–0.826), 0.697 (0.597–0.798), and 0.682 (0.583–0.781), respectively. Multivariable analysis showed that the patients aged ≥70 years are 3.4 times more likely to experience HFNC failure than those aged <70 years (HR 3.367 [1.358–8.349], p = 0.009). The SpO₂/FiO₂ ratio (HR 0.983 [0.972–0.994], p = 0.003) at 1 h was significantly associated with HFNC failure. Conclusions: The SpO₂/FiO₂ ratio following HFNC initiation was an acceptable predictor of HFNC failure. The SpO₂/FiO₂ ratio may be a good prognostic marker for predicting intubation in COVID-9 patients receiving HFNC.

It remains unclear whether high titers of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies aggravate clinical manifestations in patients or whether severe clinical manifestations result in high antibody titers. Thus, we investigated the cause–effect relationship between SARS-CoV-2 antibody titers and disease severity. We prospectively enrolled patients admitted with the diagnosis of coronavirus disease-19 (COVID-19) from February 2020 to August 2020. We measured SARS-CoV-2 antibody titers, namely anti-receptor-binding domain (RBD) antibody and neutralizing antibody (NAb), from blood samples and calculated the chest radiograph (CXR) scores of the patients to evaluate the severity of COVID-19. Overall, 40 patients with COVID-19 were enrolled. Pneumonia was observed in more than half of the patients (25/40, 60%). SARS-CoV-2 antibody titers were higher in patients who were aged >60 years (anti-RBD antibodies, P = 0.003 and NAb, P = 0.009), presented with pneumonia (P = 0.006 and 0.007, respectively), and required oxygen therapy (P = 0.003 and 0.004, respectively) than in those who were not. CXR scores peaked (at 15–21 days after the onset of symptoms) statistically significantly earlier than SARS-CoV-2 antibody titers (at 22–30 days for NAb and at 31–70 days for anti-RBD antibody). There was a close correlation between the maximum CXR score and the maximum SAR-CoV-2 antibody titer. Based on the comparison of the peak time of SARS-CoV-2 antibody titers with the CXR score after symptom onset, we suggest that severe clinical manifestations result in high titers of SARS-CoV-2 antibodies.

Previous scoring models, such as the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, do not adequately predict the mortality of patients receiving mechanical ventilation in the intensive care unit. Therefore, this study aimed to apply machine learning algorithms to improve the prediction accuracy for 30-day mortality of mechanically ventilated patients. The data of 16,940 mechanically ventilated patients were divided into the training-validation (83%, n = 13,988) and test (17%, n = 2952) sets. Machine learning algorithms including balanced random forest, light gradient boosting machine, extreme gradient boost, multilayer perceptron, and logistic regression were used. We compared the area under the receiver operating characteristic curves (AUCs) of machine learning algorithms with those of the APACHE II and ProVent score results. The extreme gradient boost model showed the highest AUC (0.79 (0.77–0.80)) for the 30-day mortality prediction, followed by the balanced random forest model (0.78 (0.76–0.80)). The AUCs of these machine learning models as achieved by APACHE II and ProVent scores were higher than 0.67 (0.65–0.69), and 0.69 (0.67–0.71)), respectively. The most important variables in developing each machine learning model were APACHE II score, Charlson comorbidity index, and norepinephrine. The machine learning models have a higher AUC than conventional scoring systems, and can thus better predict the 30-day mortality of mechanically ventilated patients.

The quick sepsis-related organ failure assessment (qSOFA) score has been introduced to predict the likelihood of organ dysfunction in patients with suspected infection. We hypothesized that machine-learning models using qSOFA variables for predicting three-day mortality would provide better accuracy than the qSOFA score in the emergency department (ED). Between January 2016 and December 2018, the medical records of patients aged over 18 years with suspected infection were retrospectively obtained from four EDs in Korea. Data from three hospitals (n = 19,353) were used as training-validation datasets and data from one (n = 4234) as the test dataset. Machine-learning algorithms including extreme gradient boosting, light gradient boosting machine, and random forest were used. We assessed the prediction ability of machine-learning models using the area under the receiver operating characteristic (AUROC) curve, and DeLong’s test was used to compare AUROCs between the qSOFA scores and qSOFA-based machine-learning models. A total of 447,926 patients visited EDs during the study period. We analyzed 23,587 patients with suspected infection who were admitted to the EDs. The median age of the patients was 63 years (interquartile range: 43–78 years) and in-hospital mortality was 4.0% (n = 941). For predicting three-day mortality among patients with suspected infection in the ED, the AUROC of the qSOFA-based machine-learning model (0.86 [95% CI 0.85–0.87]) for three -day mortality was higher than that of the qSOFA scores (0.78 [95% CI 0.77–0.79], p < 0.001). For predicting three-day mortality in patients with suspected infection in the ED, the qSOFA-based machine-learning model was found to be superior to the conventional qSOFA scores.