In recent years, the hemoglobin, albumin, lymphocyte, and platelet (HALP) score has emerged as a potential marker of immunological and nutritional status. This study aimed to evaluate the association between the HALP score and prognosis in patients with sepsis. This retrospective cohort study analyzed sepsis patients using clinical data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Patients were classified into Low-score and High-score groups. Confounding factors were controlled through propensity score matching (PSM) analysis. The primary outcome was 28-day mortality in individuals with sepsis. Survival probabilities between groups were compared using Kaplan-Meier curves. Multivariable Cox regression analysis and a smoothing spline fitting curve were employed to investigate the relationship between the HALP score and 28-day mortality. ROC curve analysis and subgroup analysis were performed to evaluate the predictive ability of the HALP score and its components. A total of 2,968 sepsis patients were included, with 809 (27.26%) deaths within 28 days. After PSM analysis, the High-score group had a 24% lower risk of 28-day mortality compared to the Low-score group (HR, 0.76; 95% CI, 0.64–0.91). In the unmatched cohort, the multivariable Cox regression model also indicated that the High-score group had a lower 28-day mortality risk (HR, 0.78; 95% CI, 0.67–0.91). The smoothing spline fitting curve showed a nonlinear relationship between the HALP score and 28-day mortality, with an inflection point at 24.69. When the HALP score was below 24.69, an increase of one point in the HALP score was associated with a 2% reduction in 28-day mortality (HR, 0.98; 95% CI, 0.97–0.99). The HALP score provided incremental predictive value for 28-day mortality when combined with the SOFA score. Albumin was identified as the most influential component of the HALP score. Among patients with sepsis, the HALP score exhibited a nonlinear relationship with 28-day mortality. An elevated HALP score is associated with reduced 28-day, 90-day, 360-day, and in-hospital mortality among sepsis patients.

Background: The platelet-lymphocyte ratio (PLR), a novel inflammatory marker, is generally associated with increased in-hospital mortality risk. We aimed to investigate the association between PLR and postoperative in-hospital mortality risk in patients with type A acute aortic dissection (AAAD).Methods: Patients (n = 270) who underwent emergency surgery for AAAD at Xiangya Hospital of Central South University between January 2014 and May 2019 were divided into three PLR-based tertiles. We used multiple regression analyses to evaluate the independent effect of PLR on in-hospital mortality, and smooth curve fitting and a segmented regression model with adjustment of confounding factors to analyze the threshold effect between PLR and in-hospital mortality risk.Results: The overall postoperative in-hospital mortality was 13.33%. After adjusting for confounders, in-hospital mortality risk in the medium PLR tertile was the lowest (Odds ratio [OR] = 0.20, 95% confidence interval [CI] = 0.06–0.66). We observed a U-shaped relationship between PLR and in-hospital mortality risk after smoothing spline fitting was applied. When PLR < 108, the in-hospital mortality risk increased by 10% per unit decrease in PLR (OR = 0.90,P = 0.001). When the PLR was between 108 and 188, the mortality risk was the lowest (OR = 1.02,P = 0.288). When PLR > 188, the in-hospital mortality risk increased by 6% per unit increase in PLR (OR = 1.06,P = 0.045).Conclusions: There was a U-shaped relationship between PLR and in-hospital mortality in patients with AAAD, with an optimal PLR range for the lowest in-hospital mortality risk of 108–188. PLR may be a useful preoperative prognostic tool for predicting in-hospital mortality risk in patients with AAAD and can ensure risk stratification and early treatment initiation.

We aimed to explore the association between FFP transfusion and outcomes of DC patients with significant coagulopathy. A total of 693 DC patients with significant coagulopathy were analyzed with 233 patients per group after propensity score matching (PSM). Patients who received FFP transfusion were matched with those receiving conventional therapy via PSM. Regression analysis showed FFP transfusion had no benefit in 30-day (HR: 1.08, 95% CI 0.83–1.4), 90-day (HR: 1.03, 95% CI 0.80–1.31) and in-hospital(HR: 1.30, 95% CI 0.90–1.89) mortality, associated with increased risk of liver failure (OR: 3.00, 95% CI 1.78–5.07), kidney failure (OR: 1.90, 95% CI 1.13–3.18), coagulation failure (OR: 2.55, 95% CI 1.52–4.27), respiratory failure (OR: 1.76, 95% CI 1.15–2.69), and circulatory failure (OR: 2.15, 95% CI 1.27–3.64), and even associated with prolonged the LOS ICU (β: 2.61, 95% CI 1.59–3.62) and LOS hospital (β: 6.59, 95% CI 2.62–10.57). In sensitivity analysis, multivariate analysis (HR: 1.09, 95%CI 0.86, 1.38), IPTW (HR: 1.11, 95%CI 0.95–1.29) and CAPS (HR: 1.09, 95% CI 0.86–1.38) showed FFP transfusion had no beneficial effect on the 30-day mortality. Smooth curve fitting demonstrated the risk of liver failure, kidney failure and circulatory failure increased by 3%, 2% and 2% respectively, for each 1 ml/kg increase in FFP transfusion. We found there was no significant difference of CLIF-SOFA and MELD score between the two group on day 0, 3, 7, 14. Compared with the conventional group, INR, APTT, and TBIL in the FFP transfusion group significantly increased, while PaO2/FiO2 significantly decreased within 14 days. In conclusion, FFP transfusion had no beneficial effect on the 30-day, 90-day, in-hospital mortality, was associated with prolonged the LOS ICU and LOS hospital, and the increased risk of liver failure, kidney failure, coagulation failure, respiratory failure and circulatory failure events. However, large, multi-center, randomized controlled trials, prospective cohort studies and external validation are still needed to verify the efficacy of FFP transfusion in the future.

We aimed to establish a predictive model assessing perioperative blood transfusion risk using a nomogram. Clinical data for 97,443 surgery patients were abstracted from the DATADRYAD website; approximately 75% of these patients were enrolled in the derivation cohort, while approximately 25% were enrolled in the validation cohort. Multivariate logical regression was used to identify predictive factors for transfusion. Receiver operating characteristic (ROC) curves, calibration plots, and decision curves were used to assess the model performance. In total, 5888 patients received > 1 unit of red blood cells; the total transfusion rate was 6.04%. Eight variables including age, race, American Society of Anesthesiologists' Physical Status Classification (ASA-PS), grade of kidney disease, type of anaesthesia, priority of surgery, surgery risk, and an 18-level variable were included. The nomogram achieved good concordance indices of 0.870 and 0.865 in the derivation and validation cohorts, respectively. The Youden index identified an optimal cut-off predicted probability of 0.163 with a sensitivity of 0.821 and a specificity of 0.744. Decision curve (DCA) showed patients had a standardized net benefit in the range of a 5–60% likelihood of transfusion risk. In conclusion, a nomogram model was established to be used for risk stratification of patients undergoing surgery at risk for blood transfusion. The URLs of web calculators for our model are as follows: http://www.empowerstats.net/pmodel/?m=11633_transfusionpreiction.

Background: Platelet distribution width (PDW), as a widely applied and reliable marker of platelet activation, was associated with adverse outcomes in cardiovascular diseases. However, there is little literature on the relationship between PDW and postoperative pneumonia in patients with type A acute aortic dissection (AAAD).Methods: In this retrospective cohort study, we collected consecutive patients who underwent emergency surgery for AAAD at Xiangya Hospital of Central South University from January 1, 2014 and June 30, 2020. Patients were divided into three tertiles on the basis of the PDW. The independent effect of the PDW on postoperative pneumonia was evaluated using multivariate logistic regression analysis, and smooth curve fitting was performed to visualize the linear relationship between PDW and the risk of postoperative pneumonia in patients with AAAD.Results: A total of 210 patients with AAAD were enrolled and the overall incidence of postoperative pneumonia was 25.24% (n = 53). Multivariate logistic regression revealed that PDW was positively associated with the risk of postoperative pneumonia (OR: 1.07, 95% CI: 1.02–1.13, P < 0.05) after adjusting the confounders. Compared with the lowest PDW tertile, the risk of postoperative pneumonia increased by 1.21-fold in the medium PDW tertile (OR: 2.21, 95% CI: 0.73–6.72) and by 3.16-fold in the highest PDW tertile (OR: 4.16, 95% CI: 1.40–12.33). A straight-line relationship was observed between PDW and postoperative pneumonia risk in smoothing spline fitting.Conclusion: Our findings indicate that high PDW is an independent risk factor of postoperative pneumonia in patients with AAAD. Preoperative PDW may serve as an available indicator of pneumonia, which helps identify AAAD patients with a high risk of postoperative pneumonia.

Given the high incidence and mortality rate of sepsis, early identification of high-risk patients and timely intervention are crucial. However, existing mortality risk prediction models still have shortcomings in terms of operation, applicability, and evaluation on long-term prognosis. This study aims to investigate the risk factors for death in patients with sepsis, and to construct the prediction model of short-term and long-term mortality risk. Patients meeting sepsis 3.0 diagnostic criteria were selected from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database and randomly divided into a modeling group and a validation group at a ratio of 7꞉3. Baseline data of patients were analyzed. Univariate Cox regression analysis and full subset regression were used to determine the risk factors of death in patients with sepsis and to screen out the variables to construct the prediction model. The time-dependent area under the curve (AUC), calibration curve, and decision curve were used to evaluate the differentiation, calibration, and clinical practicability of the model. A total of 14 240 patients with sepsis were included in our study. The 28-day and 1-year mortality were 21.45% (3 054 cases) and 36.50% (5 198 cases), respectively. Advanced age, female, high sepsis-related organ failure assessment (SOFA) score, high simplified acute physiology score II (SAPS II), rapid heart rate, rapid respiratory rate, septic shock, congestive heart failure, chronic obstructive pulmonary disease, liver disease, kidney disease, diabetes, malignant tumor, high white blood cell count (WBC), long prothrombin time (PT), and high serum creatinine (SCr) levels were all risk factors for sepsis death (all P<0.05). Eight variables, including PT, respiratory rate, body temperature, malignant tumor, liver disease, septic shock, SAPS II, and age were used to construct the model. The AUCs for 28-day and 1-year survival were 0.717 (95% CI 0.710 to 0.724) and 0.716 (95% CI 0.707 to 0.725), respectively. The calibration curve and decision curve showed that the model had good calibration degree and clinical application value. The short-term and long-term mortality risk prediction models of patients with sepsis based on the MIMIC-IV database have good recognition ability and certain clinical reference significance for prognostic risk assessment and intervention treatment of patients.