Blood cholesterol-to-lymphocyte ratio as a novel prognostic marker to predict postoperative overall survival in patients with colorectal cancer

Background Lipid disequilibrium and systemic inflammation are reported to correlate with tumorigenesis and development of colorectal cancer (CRC). We construct the novel biomarker cholesterol-to-lymphocyte ratio (CLR) to reflect the synergistic effect of cholesterol metabolism and inflammation on CRC outcomes. This study aims to investigate the clinical significance of CLR and establish a prognostic model for CRC. Methods Our study retrospectively enrolled 223 CRC patients who underwent curative surgical resection. The Kaplan-Meier method was employed to estimate the overall survival (OS) rates, and the association between serological biomarkers and survival was assessed with a log-rank test. Cox proportional hazard regression was applied in the univariate and multivariate analyses to identify independent prognostic factors, which were then used to develop a predictive nomogram model for OS in CRC. The nomogram was evaluated by the C-index, receiver operator characteristic curve (ROC) analysis, and calibration plot. All cases were grouped into three stratifications according to the total risk points calculated from the nomogram, and the difference in OS between them was assessed with the Kaplan-Meier method. Results At the end of the study, death occurred in 47 (21%) cases. Patients with low CLR (< 3.23) had significantly prolonged survival (P < 0.001). Multivariate analyses revealed that N stage (P < 0.001), harvested lymph nodes (P = 0.021), and CLR (P = 0.005) were independent prognostic factors for OS and a prognostic nomogram was established based on these variables. The nomogram showed good calibration and predictive performance with a superior C-index than TNM stage (0.755 (0.719–0.791) vs. 0.663 (0.629–0.697), P = 0.001). Patients of different risk stratifications based on the total score of nomogram showed distinct survival (P < 0.001). Conclusions The nomogram based on CLR and other clinical features can be used as a potentially convenient and reliable tool in predicting survival in patients with CRC.

Determination of levetiracetam by GC-MS and effects of storage conditions and gastric digestive systems on drug samples

Background: Epilepsy is a neurologic condition that is occurs globally and is associated with various degrees of seizures. Levetiracetam is an approved drug that is commonly used to treat seizures in juvenile epileptic patients. Accurate quantification of the drug’s active compound and determining its stability in the stomach after oral administration are important tasks that must be performed. Results & methodology: Levetiracetam was extracted from drug samples and quantified by gas chromatography mass spectrometry using calibration standards. Stability of levetiracetam was studied under various storage conditions and in simulated gastric conditions. The calibration plot determined for levetiracetam showed good linearity with a coefficient of determination value of 0.9991. The limits of detection and quantification were found to be 0.004 and 0.014 μg·ml-1, respectively. The structural integrity of levetiracetam did not change within a 4-h period under the simulated gastric conditions, and no significant degradation was observed for the different storage temperatures tested. Discussion & conclusion: An accurate and sensitive quantitative method was developed for the determination of levetiracetam in drug samples. The stability of the drug active compound was monitored under various storage and gastric conditions. The levetiracetam content determined in the drug samples were within ±10% of the value stated on the drug labels.

Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection

Background Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection. Methods Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis. Results The risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713–0.773) and 0.746 (95% C.I.: 0.699–0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets. Conclusion Our new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU patients.

A Nomogram for Predicting Intraoperative Hemodynamic Instability in Patients With Pheochromocytoma

PurposeSurgical removal of pheochromocytoma (PCC), including open, laparoscopic, and robot-assisted adrenalectomy, is the cornerstone of therapy, which is associated with high risk of intraoperative and postoperative life-threatening complications due to intraoperative hemodynamic instability (IHD). This study aims to develop and validate a nomogram based on clinical characteristics as well as computed tomography (CT) features for the prediction of IHD in pheochromocytoma surgery.MethodsThe data from 112 patients with pheochromocytoma were collected at a single center between January 1, 2010, and December 31, 2019. Clinical and radiological features were selected with the least absolute shrinkage and selection operator regression analysis to predict IHD then constitute a nomogram. The performance of the nomogram was assessed in terms of discrimination, calibration, and clinical utility.ResultsAge, tumor shape, Mayo Adhesive Probability score, laterality, necrosis, body mass index, and surgical technique were identified as risk predictors of the presence of IHD. The nomogram was then developed using these seven variables. The model showed good discrimination with a C-index of 0.773 (95% CI, 0.683–0.862) and an area under the receiver operating characteristic curve (AUC) of 0.739 (95% CI, 0.642–0.837). The calibration plot suggested good agreement between predicted and actual probabilities. Besides, calibration was tested with the Hosmer–Lemeshow test (P = 0.961). The decision curve showed the clinical effectiveness of the nomogram.ConclusionsOur nomogram based on clinical and CT parameters could facilitate the treatment strategy according to assessment of the risk of IHD in patients with pheochromocytoma.

Prognostic nomogram for patients with unresectable pancreatic cancer treated with gemcitabine plus nab–paclitaxel or FOLFIRINOX: A post–hoc analysis of a multicenter retrospective study in Japan (NAPOLEON study)

Background No reliable nomogram has been developed until date for predicting the survival in patients with unresectable pancreatic cancer undergoing treatment with gemcitabine plus nab–paclitaxel (GnP) or FOLFIRINOX. Methods This analysis was conducted using clinical data of Japanese patients with unresectable pancreatic cancer undergoing GnP or FOLFIRINOX treatment obtained from a multicenter study (NAPOLEON study). A Cox proportional hazards model was used to identify the independent prognostic factors. A nomogram to predict 6–, 12–, and 18–month survival probabilities was generated, validated by using the concordance index (C–index), and calibrated by the bootstrapping method. And then, we attempted risk stratification for survival by classifying the patients according to the sum of the scores on the nomogram (total nomogram points). Results A total of 318 patients were enrolled. A prognostic nomogram was generated using data on the Eastern Cooperative Oncology Group performance status, liver metastasis, serum LDH, serum CRP, and serum CA19–9. The C–indexes of the nomogram were 0.77, 0.72 and 0.70 for 6–, 12–, and 18–month survival, respectively. The calibration plot showed optimal agreement at all points. Risk stratification based on tertiles of the total nomogram points yielded clear separations of the survival curves. The median survival times in the low–, moderate–, and high–risk groups were 15.8, 12.8 and 7.8 months (P<0.05), respectively. Conclusions Our nomogram might be a convenient and inexpensive tool to accurately predict survival in Japanese patients with unresectable pancreatic cancer undergoing treatment with GnP or FOLFIRINOX, and will help clinicians in selecting appropriate therapeutic strategies for individualized management.

Survey of Opioid-Induced Respiratory Depression in Critically Ill Patients Without Mechanical Ventilation and Construction of a Risk Nomogram

Objectives To investigate the current status of opioid-induced respiratory depression (OIRD) and potential risk factors in critically ill patients without mechanical ventilation in the intensive care unit (ICU) and to construct a risk nomogram to predict OIRD. Methods A total of 103 patients without (or who were weaned from) mechanical ventilation who had stayed for more than 24 h in the ICU between June 1, 2021 and September 31, 2021, were included. Patient data, including respiratory depression events, were recorded. The least absolute shrinkage and selection operator regression model were used to select features that were then used to construct a prediction model by multivariate logistic regression analysis. A nomogram was established for the risk of respiratory depression events in patients without mechanical ventilation. The discriminatory performance and calibration of the nomogram were assessed with Harrell’s concordance index and a calibration plot, respectively, and a bootstrap procedure was used for internal validation. Results Respiratory depression was diagnosed in 49/103 (47.6%) patients. Factors included in the nomogram were cardiopulmonary disease (odds ratio [OR]=5.569, 95% confidence interval [CI]=0.751–118.083), respiratory disease (OR=32.833, 95% CI=4.189–725.164), sepsis (OR=6.898, 95% CI=1.756–33.000), duration of mechanical ventilation (OR=3.019, 95% CI=0.862–11.322), lack of mechanical ventilation (OR=20.757, 95% CI=2.409–502.222), and oxygenation index (OR=7.350, 95% CI=2.483–24.286). The nomogram showed good performance for predicting respiratory depression events in critically ill patients without mechanical ventilation. Conclusion The nomogram can be used to identify ICU patients without mechanical ventilation who are at risk of opioid-induced respiratory depression and may therefore benefit from early intervention.

The Role of Ferroptosis-Related Gene in the Immune Activity and Prognosis of Sepsis

IntroductionSepsis is a leading cause of mortality in intensive care units worldwide. Ferroptosis, a form of regulated cell-death–related iron, has been proven to be altered during sepsis, including increased iron transport and uptake into cells and decreased iron export. A better understanding of the role of ferroptosis in sepsis should expedite the identification of biomarkers for prognostic evaluation and therapeutic interventions.Material and methodsWe used the mRNA expression profiles of sepsis patients from Gene Expression Omnibus (GEO) to analyze the expression level of ferroptosis-related genes and construct molecular subtypes.ResultsTwo distinct ferroptosis patterns were determined, and the overall survival of the two clusters was highly significantly different. Gene comparison analysis was performed on these two groups, and there were a total of 106 differentially expressed genes(DEGs). Pathway enrichment analysis of these genes showed that ferroptosis and immune-related pathways were enriched, suggesting that immune pathways might be critically involved in sepsis. To systematically predict the prognosis of sepsis, we constructed a nomogram model, the calibration plot nomogram showed excellent concordance for the 7-, 14-, and 28-days predicted and actual overall survival probabilities. Finally, the results of bioinformatics analysis were validated in animal and cell modelsConclusionsIn this study, we construct a ferroptosis-related nomogram that can be used for prognostic prediction in sepsis. In addition, we revealed the ferroptosis played a non-negligible role in immune cell infiltration and guiding more effective immunotherapy strategies.

Hospital admission risk stratification of patients with gout presenting to the emergency department

To characterise gout patients at high risk of hospitalisation and to develop a web-based prognostic model to predict the likelihood of gout-related hospital admissions. This was a retrospective single-centre study of 1417 patients presenting to the emergency department (ED) with a gout flare between 2015 and 2017 with a 1-year look-back period. The dataset was randomly divided, with 80% forming the derivation and the remaining forming the validation cohort. A multivariable logistic regression model was used to determine the likelihood of hospitalisation from a gout flare in the derivation cohort. The coefficients for the variables with statistically significant adjusted odds ratios were used for the development of a web-based hospitalisation risk estimator. The performance of this risk estimator model was assessed via the area under the receiver operating characteristic curve (AUROC), calibration plot, and brier score. Patients who were hospitalised with gout tended to be older, less likely male, more likely to have had a previous hospital stay with an inpatient primary diagnosis of gout, or a previous ED visit for gout, less likely to have been prescribed standby acute gout therapy, and had a significant burden of comorbidities. In the multivariable-adjusted analyses, previous hospitalisation for gout was associated with the highest odds of gout-related admission. Early identification of patients with a high likelihood of gout-related hospitalisation using our web-based validated risk estimator model may assist to target resources to the highest risk individuals, reducing the frequency of gout-related admissions and improving the overall health-related quality of life in the long term. Key points • We reported the characteristics of gout patients visiting a tertiary hospital in Singapore. • We developed a web-based prognostic model with non-invasive variables to predict the likelihood of gout-relatedhospital admissions.

Simplified Acute Physiology Score 3 Performance in Austrian COVID-19 Patients with and Without Diabetes

Background: TheSimplified Acute Physiology Score 3 (SAPS 3) is routinely used in intensive care units (ICUs) to predict in-hospital mortality. However, its predictive performance has not been widely evaluated in Coronavirus disease 19 (COVID-19) patients.This studyevaluated and comparedthe performance of SAPS 3for predicting in-hospital mortalityinCOVID-19patients with and without diabetesin Austria.Methods: This study analyzed the Austrian national public health institute (GÖG) data ofCOVID-19patients admitted to ICUs (N=5,850)fromMarch 2020 to March 2021.The SAPS 3 score was calculated and the predicted in-hospital mortality was estimatedusingthreelogit regression equations: standard equation, Central European equation, and Austrian equation recalibrated for COVID-19 patients. Concordance between observed and predicted mortalities was assessed using the standardized mortality ratio (SMR). Discrimination was assessed using the C-statistic. The DeLong test was applied to compare discrimination between diabetes and non-diabetes patients. Accuracy was assessed using the Brier score andcalibration using the calibration plot and Hosmer-Lemeshow test. Results: Theobservedin-hospital mortality was 38.9% in all patients, 42.9% in diabetes, and 37.3% innon-diabetes patients. Themean ±SD SAPS 3 score was 57.4 ±13.2 in all patients,58.8 ±12.9 in diabetes, and 56.8 ±13.2 in non-diabetes patients.The SMR was significantly greater than 1 for standard and Central European equations, while it was close to 1 for the Austrian equation in all, diabetes, and non-diabetes patients. TheC-statistics was 0.69 with aninsignificant (P=0.193) difference between diabetes (0.70)and non-diabetes (0.68)patients. The Brier score was >0.20 for all SAPS 3 equations. Calibration was unsatisfactory for both standard and Central European equations in all cohorts, whereas it was satisfactory for the Austrian equation in diabetes patients.Conclusions:The SAPS 3 score demonstratedlow discrimination and accuracy in COVID-19 patients in Austria with aninsignificant difference between diabetes and non-diabetes patients. All three equations of SAPS 3 were miscalibrated particularly in non-diabetes patients, while the Austrian equation demonstrated satisfactory calibration in diabetes patients. These findingssuggest that both uncalibrated and calibrated versions ofSAPS 3 should be used with caution in COVID-19 patients.

Development and Validation of a Prediction Model for Survival in Diabetic Patients With Acute Kidney Injury

ObjectiveWe aimed to analyze the risk factors affecting all-cause mortality in diabetic patients with acute kidney injury (AKI) and to develop and validate a nomogram for predicting the 90-day survival rate of patients.MethodsClinical data of diabetic patients with AKI who were diagnosed at The First Affiliated Hospital of Guangxi Medical University from April 30, 2011, to April 30, 2021, were collected. A total of 1,042 patients were randomly divided into a development cohort and a validation cohort at a ratio of 7:3. The primary study endpoint was all-cause death within 90 days of AKI diagnosis. Clinical parameters and demographic characteristics were analyzed using Cox regression to develop a prediction model for survival in diabetic patients with AKI, and a nomogram was then constructed. The concordance index (C-index), receiver operating characteristic curve, and calibration plot were used to evaluate the prediction model.ResultsThe development cohort enrolled 730 patients with a median follow-up time of 87 (40–98) days, and 86 patients (11.8%) died during follow-up. The 90-day survival rate was 88.2% (644/730), and the recovery rate for renal function in survivors was 32.9% (212/644). Multivariate analysis showed that advanced age (HR = 1.064, 95% CI = 1.043–1.085), lower pulse pressure (HR = 0.964, 95% CI = 0.951–0.977), stage 3 AKI (HR = 4.803, 95% CI = 1.678–13.750), lower 25-hydroxyvitamin D3 (HR = 0.944, 95% CI = 0.930–0.960), and multiple organ dysfunction syndrome (HR = 2.056, 95% CI = 1.287–3.286) were independent risk factors affecting the all-cause death of diabetic patients with AKI (all p &lt; 0.01). The C-indices of the prediction cohort and the validation cohort were 0.880 (95% CI = 0.839–0.921) and 0.798 (95% CI = 0.720–0.876), respectively. The calibration plot of the model showed excellent consistency between the prediction probability and the actual probability.ConclusionWe developed a new prediction model that has been internally verified to have good discrimination, calibration, and clinical value for predicting the 90-day survival rate of diabetic patients with AKI.