Stroke is a disease characterized by sudden cerebral ischemia and is the second leading cause of death worldwide. We aimed to develop and validate a nomogram model to predict mortality in intensive care unit patients with stroke.All data involved in this study were extracted from the Medical Information Mart for Intensive Care III database (MIMIC-III). The data were analyzed using multivariate Cox regression, and the performance of the novel nomogram, which assessed the patient's overall survival at 30, 180, and 360 days after stroke, was evaluated using Harrell's concordance index (C-index) and the area under the receiver operating characteristic curve. A calibration curve and decision curve were introduced to test the clinical value and effectiveness of our prediction model.A total of 767 patients with stroke were randomly divided into derivation (n = 536) and validation (n = 231) cohorts at a 7:3 ratio. Multivariate Cox regression showed that 12 independent predictors, including age, weight, ventilation, cardiac arrhythmia, metastatic cancer, explicit sepsis, Oxford Acute Severity of Illness Score or OASIS score, diastolic blood pressure, bicarbonate, chloride, red blood cell and white blood cell counts, played a significant role in the survival of individuals with stroke. The nomogram model was validated based on the C-indices, calibration plots, and decision curve analysis results.The plotted nomogram accurately predicted stroke outcomes and, thus may contribute to clinical decision-making and treatment as well as consultation services for patients. 

https://bmcmedinformdecismak.biomedcentral.com/track/pdf/10.1186/s12911-022-01836-3

A novel nomogram to predict mortality in patients with stroke: a survival analysis based on the MIMIC-III clinical database

To compare the performance of the Oxford Acute Severity of Illness Score (OASIS), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Simplified Acute Physiology Score II (SAPS II), and the Sequential Organ Failure Assessment (SOFA) score in predicting 28-day mortality in acute kidney injury (AKI) patients.Data were extracted from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 2954 patients with complete clinical data were included in this study. Receiver operating characteristic (ROC) curves were used to analyze and evaluate the predictive effects of the four scoring systems on the 28-day mortality risk of AKI patients and each subgroup. The best cutoff value was identified by the highest combined sensitivity and specificity using Youden's index.Among the four scoring systems, the area under the curve (AUC) of OASIS was the highest. The comparison of AUC values of different scoring systems showed that there were no significant differences among OASIS, APACHE II, and SAPS II, which were better than SOFA. Moreover, logistic analysis revealed that OASIS was an independent risk factor for 28-day mortality in AKI patients. OASIS also had good predictive ability for the 28-day mortality of each subgroup of AKI patients.OASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of AKI patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute.This study was registered at www.chictr.org.cn (registration number Chi CTR-ONC-11001875). Registered on 14 December 2011. 

https://www.tandfonline.com/doi/pdf/10.1080/0886022X.2022.2027247?needAccess=true

The predictive value of the Oxford Acute Severity of Illness Score for clinical outcomes in patients with acute kidney injury

Abstract Objective To compare the performance of the Oxford Acute Severity of Illness Score (OASIS), the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, the Simplified Acute Physiology Score II (SAPS II), and the Sequential Organ Failure Assessment (SOFA) score in predicting 28-day mortality in acute kidney injury (AKI) patients. Methods Data were extracted from the Beijing Acute Kidney Injury Trial (BAKIT). A total of 2954 patients with complete clinical data were included in this study. Receiver operating characteristic (ROC) curves were used to analyze and evaluate the predictive effects of the four scoring systems on the 28-day mortality risk of AKI patients and each subgroup. The best cutoff value was identified by the highest combined sensitivity and specificity using Youden’s index. Results Among the four scoring systems, the area under the curve (AUC) of OASIS was the highest. The comparison of AUC values of different scoring systems showed that there were no significant differences among OASIS, APACHE II, and SAPS II, which were better than SOFA. Moreover, logistic analysis revealed that OASIS was an independent risk factor for 28-day mortality in AKI patients. OASIS also had good predictive ability for the 28-day mortality of each subgroup of AKI patients. Conclusion OASIS, APACHE II, and SAPS II all presented good discrimination and calibration in predicting the 28-day mortality risk of AKI patients. OASIS, APACHE II, and SAPS II had better predictive accuracy than SOFA, but due to the complexity of APACHE II and SAPS II calculations, OASIS is a good substitute. Trial Registration This study was registered at www.chictr.org.cn (registration number Chi CTR-ONC-11001875). Registered on 14 December 2011.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is associated with a robust immune response. The development of systemic inflammation leads to a hyperinflammatory state due to cytokine release syndrome during severe COVID-19. The emergence of many new SARS-CoV-2 variants across the world deteriorates the protective antiviral immunity induced after infection or vaccination. The innate immune response to SARS-CoV-2 is crucial for determining the fate of COVID-19 symptomatology. T cell-mediated immunity is the main factor of the antiviral immune response; moreover, SARS-CoV-2 infection initiates a rapid B-cell response. In this paper, we present the current state of knowledge on immunity after COVID-19 infection and vaccination. We discuss the mechanisms of immune response to various types of vaccines (nucleoside-modified, adenovirus-vectored, inactivated virus vaccines and recombinant protein adjuvanted formulations). This includes specific aspects of vaccination in selected patient populations with altered immune activity (the elderly, children, pregnant women, solid organ transplant recipients, patients with systemic rheumatic diseases or malignancies). We also present diagnostic and research tools available to study the anti-SARS-CoV-2 cellular and humoral immune responses.

/pdf/immunity-after-covid-19-recovery-and-vaccination-2wxgg28a.pdf

Immunity after COVID-19 Recovery and Vaccination: Similarities and Differences

A simple prognostic model is needed for ICU patients. This study aimed to construct a modified prognostic model using easy-to-use indexes for prediction of the 28-day mortality of critically ill patients. Clinical information of ICU patients included in the Medical Information Mart for Intensive Care III (MIMIC-III) database were collected. After identifying independent risk factors for 28-day mortality, an improved mortality prediction model (mionl-MEWS) was constructed with multivariate logistic regression. We evaluated the predictive performance of mionl-MEWS using area under the receiver operating characteristic curve (AUROC), internal validation and fivefold cross validation. A nomogram was used for rapid calculation of predicted risks. A total of 51,121 patients were included with 34,081 patients in the development cohort and 17,040 patients in the validation cohort (17,040 patients). Six predictors, including Modified Early Warning Score, neutrophil-to-lymphocyte ratio, lactate, international normalized ratio, osmolarity level and metastatic cancer were integrated to construct the mionl-MEWS model with AUROC of 0.717 and 0.908 for the development and validation cohorts respectively. The mionl-MEWS model showed good validation capacities with clinical utility. The developed mionl-MEWS model yielded good predictive value for prediction of 28-day mortality in critically ill patients for assisting decision-making in ICU patients. 

/pdf/an-improved-prognostic-model-for-predicting-the-mortality-of-q7i41eff.pdf

An improved prognostic model for predicting the mortality of critically ill patients: a retrospective cohort study

Objective To evaluate the value of Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score Ⅱ (SAPS-Ⅱ), Oxford Acute Severity of Illness Score (OASIS) and Logistic Organ Dysfunction System (LODS) scoring systems for predicting ICU mortality in patients with sepsis. Methods We collected the data of a total of 2470 cases of sepsis recorded in the MIMIC-III database from 2001 to 2012 and retrieved the scores of SOFA, SAPS-Ⅱ, OASIS and LODS of the patients within the first day of ICU admission. We compared with the score between the survivors and the non-survivors and analyzed the differences in the area under the ROC curve (AUC) of the 4 scoring systems. Binomial logistic regression was performed to compare the predictive value of the 4 scoring systems for ICU mortality of the patients. Results In the 2470 patients with sepsis, 1966 (79.6%) survived and 504 (20.4%) died in the ICU. Compared with the survivors, the non-survivors had a significantly older mean age, higher proportion of patients receiving mechanical ventilation, and higher initial lactate level, creatinine, urea nitrogen, SOFA score, SAPS-Ⅱ score, OASIS score and LODS score (P l 0.05) but with significantly lower body weight and platelet counts (P l 0.05). The AUCs of the SOFA score, SAPS-Ⅱ score, OASIS score, and LODS score were 0.729 (P l 0.001), 0.768 (P l 0.001), 0.757 (P l 0.001), and 0.739 (P l 0.001), respectively. The AUC of SAPS-Ⅱ score was significantly higher than those of SOFA score (Z=3.679, P l 0.001) and LODS score (Z=3.698, P l 0.001) but was comparable with that of OASIS score (Z=1.102, P=0.271); the AUC of OASIS score was significantly higher than that of LODS score (Z=2.172, P=0.030) and comparable with that of SOFA score (Z=1.709, P=0.088). For predicting ICU mortality in patients without septic shock, the AUC of SAPS-Ⅱ score was 0.769 (0.743-0.793), the highest among the 4 scoring systems; in patients with septic shock, the AUCs SAPS-Ⅱ score and OASIS score, 0.768 (0.745-0.791) and 0.762 (0.738-0.785), respectively, were significantly higher than those of the other two scoring systems. Binomial logistic regression showed the corrected SOFA, SAPS-Ⅱ, and OASIS scores, but not LODS scores, were significantly correlated with ICU mortality in patients with sepsis, and their ORs were 1.08 (95% CI: 1.03-1.14, P=0.001), 1.04 (95% CI: 1.02-1.05, P l 0.001), 1.04 (95% CI: 1.01-1.06, P=0.001), 0.96 (95% CI: 0.89-1.04, P=0.350), respectively. Conclusions The scores of SOFA, SAPS-Ⅱ, OASIS, and LODS can predict ICU mortality in patients with sepsis, but SAPS-Ⅱ and OASIS scores have better predictive value than SOFA and LODS scores.

Comparison of four scoring systems for predicting ICU mortality in patients with sepsis

Background There is a need to assess the long-term outcomes of survivors from critically ill COVID-19. Methods Ninety-two critically ill survivors of COVID-19 from four hospitals in Hubei province, China participated in this prospective cohort study. Multiple characteristics including lung function (lung volumes, diffusing capacity of lung for carbon monoxide, chest CT scores and walking capacity), immune status (SARS-COV-2 neutralizing antibody and all sub-types of IgG against SARS-CoV-2, immune cells in response to ex-vivo antigen peptide stimuli, lymphocyte count and its sub-type), liver, coagulation and kidney function, quality of life, cognitive function and mental status were assessed after 3, 6 and 12-months of follow-up. Results Among the 92 enrolled survivors, 72 (78%) patients required mechanical ventilation. At 12 months, the predicted percentage diffusing capacity of lung for carbon monoxide was 82% (IQR, 76%-97%) with a residual volume of 77% (64%-88%). Other lung function parameters and the 6-minute walk test improved gradually over time and were almost back to normal by 12 months. The titers of IgG and neutralizing antibody to COVID-19 remained high at 12 months compared to non-COVID-19 infected controls, although IgG titers decreased significantly from 33.96 AU/ml (IQR, 23.79-74.30) to 14.98 AU/ml (5.75-24.30) (P Conclusions Critically ill COVID-19 survivors show some persistent long-term impairments in lung function, however the majority of these tests are normal by 12 months. These patients still have detectable levels of neutralising antibodies against SARS-COV-2 and all types of IgG at 12 months, but the levels had declined over this time period.

Xiao Yang

Papers

Comparison of four scoring systems for predicting ICU mortality in patients with sepsis

Prognosis and antibody profiles in survivors of critical illness from COVID-19: a prospective, multi-centre, cohort study