Usefulness of mean platelet volume to platelet count ratio for predicting the risk of mortality in community-acquired pneumonia

Hospital Mortality in Acute Decompensation of Alcoholic Liver Cirrhosis: Can Novel Survival Markers Outperform Traditional Ones?

Background: There is a strong correlation between systemic inflammation intensity and clinical presentation, disease progression, and survival during liver cirrhosis decompensation. This study aimed to evaluate the prognostic performance of blood-based biomarkers as meta-inflammation markers, including NLR, PLR, LMR, INPR, MPR, ALBI, FIB4, and APRI, in predicting hospital mortality in patients with acute decompensation of alcohol-related liver cirrhosis. Methods: Data from 411 patients with their first onset of acute decompensation were analyzed, forming two groups: deceased and survived during hospitalization. Generalized partial least squares regression analysis was applied to explore the effects of surrogate indicators on mortality rates, using mortality rate as the dependent variable. Root Mean Square Error, Akaike's, and Bayesian information criteria determined that four components accounted for most of the variance. Results: Variables with significant negative contributions to the outcome prediction (ranked by standardized regression coefficients) were encephalopathy grade, total bilirubin, Child-Turcotte-Pugh score, MELD, NLR, MPV, FIB4, INR, PLR, and ALT. Coefficient sizes ranged from -0.63 to -0.09, with p-values from 0 to 0.018. Conclusions: NLR, PLR, and FIB4 significantly contribute to hospital mortality prediction in patients with acute decompensation of alcohol-related liver cirrhosis. Conversely, some variables used to predict liver disease severity, including INPR, APRI, LMR, and ALBI score, did not significantly contribute to hospital mortality prediction in this patient population.

Is the prognosis of traumatic critically ill pediatric patients predictable? : A multicenter retrospective analysis

BACKGROUND

In this retrospective study the effects of the neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), mean platelet volume to platelet count ratio (MPV/PC) values as well as C‑reactive protein (CRP) and procalcitonin (PCT) levels on the severity and mortality in critically ill child trauma cases were evaluated.

METHODS

A total of 80 trauma cases aged 31 days to 16 years that were followed-up in the pediatric intensive care unit (PICU) were included in the study. The data of the patients on the first day of hospitalization (T1), the median day of intensive care admission (T2), and before discharge or exitus (T3) were analyzed. The cases were divided into three groups according to the injury severity score (ISS) as minor, moderate, and severe.

RESULTS

Of the 80 cases 59 (73.75%) were male and 21 (26.25%) were female. The mean age of all the cases was 54.5 ± 47.8 months, and the mean PICU stay was 7.35 ± 6.64 days. Of the cases 19 (23.75%) due to motor vehicle accidents and 61 (76.25%) due to falling from heights were followed-up. The mortality rate was found to be 13.75% (11 cases). The T1, T2 and T3 NLR, MLR, MPV/PC and PCT values did not differ between the groups. The T1 and T2 CRP levels were higher in the moderate trauma group than in the severe trauma group. Also, ISS and pediatric risk of mortality 3 (PRISM-3) scores were higher while the revised injury severity classification version II (RISC II), RISC II survival and Glasgow coma scale (GCS) scores were lower in the nonsurvivors. While the T3 MLR value was lower in nonsurvival cases, the T3 MPV/PC value was found to be higher.

CONCLUSION

The NLR, MLR, and MPV/PC values do not predict the severity of the trauma in children. In children with severe trauma, low MLR and high MPV/PC values can be used to predict mortality.

Early platelet level reduction as a prognostic factor in intensive care unit patients with severe aspiration pneumonia

Introduction: This study investigates risk factors underlying the prognosis of severe aspiration pneumonia (SAP) in intensive care unit (ICU) patients and attempts to provide early prognosis reference for clinical tasks. Methods: Patients diagnosed with SAP and admitted to the ICU of Jinshan Hospital, Fudan University, Shanghai, China, between January 2021 and December 2021 were recruited in this retrospective cohort study. Clinical data on a patient's general condition, underlying diseases, laboratory indicators, and 90-day outcomes (survival or death) were recorded. Results: Multivariate logistic regression analysis showed that a low platelet count was an independent risk factor affecting the prognosis of death (OR = 6.68, 95% CI:1.10-40.78, β = 1.90, P = 0.040). Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of variables; cut-off values were calculated and the area under the curve was 0.7782 [(95% CI:0.686-0.871), p < 0.001] for the prediction of death at 90 days in all patients. The Kaplan-Meier curve used for survival analysis showed that, compared with the normal platelet group, the overall survival rate of patients with low platelet levels was significantly lower, and the difference was statistically significant [HR = 2.11, (95% CI:1.47-3.03), p = 0.0001, z = 4.05, X ² = 14.89]. Cox regression analysis, used to further verify the influence of prognostic risk factors, showed that a concurrent low platelet count was the most important independent risk factor affecting the prognosis of SAP (HR = 2.12 [95% CI:1.12-3.99], X² = 50.95, p = 0.021). Conclusion: These findings demonstrate an association between SAP mortality and platelet levels on admission. Thus, platelet level at admission may be used as a readily available marker for assessing the prognosis of patients with SAP.

A risk scoring system integrating postoperative factors for predicting early mortality after major non-cardiac surgery

We aimed to develop a risk scoring system for 1-week and 1-month mortality after major non-cardiac surgery, and assess the impact of postoperative factors on 1-week and 1-month mortality using machine learning algorithms. We retrospectively reviewed the medical records of 21,510 patients who were transfused with red blood cells during non-cardiac surgery and collected pre-, intra-, and postoperative features. We derived two patient cohorts to predict 1-week and 1-month mortality and randomly split each of them into training and test cohorts at a ratio of 8:2. All the modeling steps were carried out solely based on the training cohorts, whereas the test cohorts were reserved for the evaluation of predictive performance. Incorporation of postoperative information demonstrated no significant benefit in predicting 1-week mortality but led to substantial improvement in predicting 1-month mortality. Risk scores predicting 1-week and 1-month mortality were associated with area under receiver operating characteristic curves of 84.58% and 90.66%, respectively. Brain surgery, amount of intraoperative red blood cell transfusion, preoperative platelet count, preoperative serum albumin, and American Society of Anesthesiologists physical status were included in the risk score predicting 1-week mortality. Postoperative day (POD) 5 (neutrophil count × mean platelet volume) to (lymphocyte count × platelet count) ratio, preoperative and POD 5 serum albumin, and occurrence of acute kidney injury were included in the risk score predicting 1-month mortality. Our scoring system advocates the importance of postoperative complete blood count differential and serum albumin to better predict mortality beyond the first week post-surgery.