scholarly journals Predictive Value of Prognostic Nutritional Index on COVID-19 Severity

2021 ◽  
Vol 7 ◽  
Author(s):  
Zhong-hua Wang ◽  
Ying-Wen Lin ◽  
Xue-biao Wei ◽  
Fei Li ◽  
Xiao-Long Liao ◽  
...  
Keyword(s):  
Critically Ill ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Prognostic Nutritional Index ◽  
Multivariate Logistic Regression ◽  
Good Discrimination ◽  
Nutritional Index ◽  
Predictive Role

Background: The prognostic nutritional index (PNI) has been described as a simple risk-stratified tool for several diseases. We explored the predictive role of the PNI on coronavirus disease 2019 (COVID-19) severity.Methods: A total of 101 patients with COVID-19 were included in this retrospective study from January 2020 to March 2020. They were divided into two groups according to COVID-19 severity: non-critical (n = 56) and critical (n = 45). The PNI was calculated upon hospital admission: 10 × serum albumin (g/dL) + 0.005 × total lymphocyte count (/mm3). Critical COVID-19 was defined as having one of the following features: respiratory failure necessitating mechanical ventilation; shock; organ dysfunction necessitating admission to the intensive care unit (ICU). The correlation between the PNI with COVID-19 severity was analyzed.Results: The PNI was significantly lower in critically ill than that in non-critically ill patients (P < 0.001). The receiver operating characteristic curve indicated that the PNI was a good discrimination factor for identifying COVID-19 severity (P < 0.001). Multivariate logistic regression analysis showed the PNI to be an independent risk factor for critical illness due to COVID-19 (P = 0.002).Conclusions: The PNI is a valuable biomarker that could be used to discriminate COVID-19 severity.

Prognostic Nutritional Index Predicts Clinical Outcome in Patients with Cerebral Venous Sinus Thrombosis

2021 ◽  
Author(s):  
Jiawei Zhao ◽  
Kai Liu ◽  
Shen Li ◽  
Yuan Gao ◽  
Lu Zhao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Poor Prognosis ◽  
Sinus Thrombosis ◽  
Multivariate Logistic Regression Analysis ◽  
Prognostic Nutritional Index ◽  
Cerebral Venous Sinus Thrombosis ◽  
Multivariate Logistic Regression ◽  
Venous Sinus Thrombosis ◽  
Nutritional Index

Abstract Background: Lower prognostic nutritional index (PNI) is related to poor prognosis of cardiovascular disease. However, little is known about PNI and its relationship with prognosis in cerebral venous sinus thrombosis (CVST).Methods: From January 2013 to June 2019, we retrospectively identified consecutive CVST patients. We selected patients in acute / subacute phase as subjects. Poor prognosis was defined as modified Rankin Scale (mRS) of 3-6. Multivariate logistic regression analysis was used to confirm if lower PNI was associated with poor prognosis. Results: A total of 297 subjects with 12-month follow-up data were enrolled. Thirty-three (11.1%) had poor outcome. Multivariate logistic regression analysis suggested that PNI was an important predictive factor of poor outcome in acute/subacute CVST (odds ratio, 0.903; 95% CI, 0.833-0.978; P = 0.012). The optimal cut-off value for predicting a poor prognosis of PNI was 44.2. Kaplan-Meier analysis and log-rank test suggested that the lower the PNI value, the higher the mortality rate (P<0.001). In addition, the nomogram we set up showed that lower PNI was an index of poor prognosis. The c-indexes for the cute/subacute patients with CVST was 0.872.Conclusions: Lower PNI is correlated with a higher risk of adverse clinical outcome in patients with acute/subacute CVST.

scholarly journals The association between neutrophil-to-lymphocyte ratio and anaphylaxis refractory to epinephrine treatment

2021 ◽  
Keyword(s):  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Allergic Diseases ◽  
Neutrophil To Lymphocyte Ratio ◽  
Youden Index ◽  
Time Of Arrival ◽  
Analysis Model ◽  
Multivariate Logistic Regression ◽  
Lymphocyte Ratio

Objectives: Anaphylaxis refractory to epinephrine treatment is a potentially fatal condition requiring additional medications. Neutrophil-to lymphocyte ratio (NLR) is commonly used to predict severity in allergic diseases. The aim of this study was to determine the association between NLR and refractory anaphylaxis. Methods: This was a retrospective, observational study of 126 adult anaphylaxis patients arriving at the emergency department between January 2015 and December 2019. Patients were placed into refractory anaphylaxis, if they required more than two 0.3 mg injections of intramuscular epinephrine for symptom resolution, and non-refractory anaphylaxis groups. NLRs were determined at the time of arrival at the hospital and were compared between groups. Results: Thirty-two (25.4%) patients were categorized as refractory anaphylaxis cases. NLR was significantly lower in the refractory anaphylaxis than in the non-refractory anaphylaxis group (P < 0.001). In the multivariate logistic regression analysis model, NLR was inversely associated with the occurrence of refractory anaphylaxis (adjusted odds ratio 0.33, 95% confidence interval 0.13-0.81, P = 0.016). The area under the receiver operating characteristic curve of NLR for prediction of refractory anaphylaxis was 0.717 (P < 0.001). The optimal cut-off value of NLR was < 0.68 using the Youden index, with 50.0% sensitivity and 80.9% specificity. Conclusions: NLR was independently and inversely associated with the occurrence of refractory anaphylaxis among anaphylactic patients. Therefore, NLR has the potential to be used as an easy and inexpensive test to predict refractory anaphylaxis in patients.

scholarly journals Prognostic nutritional index predicts clinical outcomes in patients with cerebral venous sinus thrombosis

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiawei Zhao ◽  
Kai Liu ◽  
Shen Li ◽  
Yuan Gao ◽  
Lu Zhao ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Poor Prognosis ◽  
Sinus Thrombosis ◽  
Multivariate Logistic Regression Analysis ◽  
Prognostic Nutritional Index ◽  
Cerebral Venous Sinus Thrombosis ◽  
Multivariate Logistic Regression ◽  
Venous Sinus Thrombosis ◽  
The Poor ◽  
Nutritional Index

Abstract Background Lower prognostic nutritional index (PNI) is related to the poor prognosis of cardiovascular diseases. However, little is known about PNI and its relationship with the prognosis of cerebral venous sinus thrombosis (CVST). Methods CVST patients were retrospectively identified from January 2013 till June 2019. Patients in the acute / subacute phase were selected as subjects. Poor prognosis was defined as a modified Rankin Scale (mRS) of 3–6. Multivariate logistic regression analysis was used to confirm if lower PNI was associated with a poor prognosis. Results A total of 297 subjects with follow-up data were enrolled. Thirty-three (11.1%) had a poor outcome. Multivariate logistic regression analysis suggested that PNI was an important predictive factor of poor outcome in acute/subacute CVST (odds ratio, 0.903; 95% CI, 0.833–0.978; P = 0.012). The optimal cut-off value for predicting the poor prognosis of PNI was 44.2. Kaplan-Meier analysis and log-rank test suggested that the lower the PNI value, the higher the mortality rate (P < 0.001). In addition, the nomogram that was set up showed that lower PNI was an index of poor prognosis. The c-index for acute/subacute patients with CVST was 0.872. Conclusion Lower PNI is correlated with a higher risk of adverse clinical outcomes in patients with acute/subacute CVST.

Prognostic Nutritional Index and Major Adverse Cardiac Events After Burn Surgery: A Propensity Score Matching Analysis

2021 ◽  
Author(s):  
Hee Yeong Kim ◽  
Jihion Yu ◽  
Yu-Gyeong Kong ◽  
Jun-Young Park ◽  
Donghyeok Shin ◽  
...  
Keyword(s):  
Propensity Score ◽  
Propensity Score Matching ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Prognostic Nutritional Index ◽  
Cardiac Events ◽  
Nutritional Index ◽  
Adverse Cardiac Events ◽  
Burn Surgery ◽  
Operating Characteristic Curve

Abstract Burn injuries can cause significant malnutrition, leading to cardiovascular impairments. The prognostic nutritional index (PNI) predicts postoperative complications. We evaluated the impact of preoperative PNI on major adverse cardiac events (MACE) after burn surgery. PNI was calculated using the equation, 10×(serum albumin level)+0.005×(total lymphocyte count). Multivariable logistic regression analysis was conducted to evaluate the predictors for MACE at 6 months after burn surgery. Receiver operating characteristic curve and propensity score matching analyses were conducted. Additionally, Kaplan–Meier analysis was conducted to compare postoperative 1-year mortality between MACE and non-MACE groups. MACE after burn surgery occurred in 184 (17.5%) of 1049 patients. PNI, age, American Society of Anesthesiologists physical status, and TBSA burned were significantly related to MACE. The area under the receiver operating characteristic curve of PNI was 0.729 (optimal cutoff value = 35). After propensity score matching, the incidence of MACE in the PNI &lt;35 group was higher than that in the PNI ≥35 group (20.1% vs 9.6%, P &lt; .001). PNI &lt;35 was related to an increased incidence of MACE (odds ratio = 2.373, 95% confidence interval = 1.499–3.757, P &lt; .001). The postoperative 1-year mortality was higher in the MACE group than in the non-MACE group (54.9% vs 9.1%, P &lt; .001). Preoperative PNI was a predictor for MACE after burn surgery. PNI &lt;35 was significantly related to an increased incidence of MACE. Moreover, MACE was related to higher postoperative 1-year mortality.

scholarly journals The severity and risk factors for mortality in immunocompromised adult patients hospitalized with influenza-related pneumonia

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Liang Chen ◽  
Xiudi Han ◽  
YanLi Li ◽  
Chunxiao Zhang ◽  
Xiqian Xing
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Neuraminidase Inhibitor ◽  
Teaching Hospitals ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression ◽  
Optimal Cutoff

Abstract Objective To explore disease severity and risk factors for 30-day mortality of adult immunocompromised (IC) patients hospitalized with influenza-related pneumonia (Flu-p). Method A total of 122 IC and 1191 immunocompetent patients hospitalized with Flu-p from January 2012 to December 2018 were recruited retrospectively from five teaching hospitals in China. Results After controlling for confounders, multivariate logistic regression analysis showed that immunosuppression was associated with increased risks for invasive ventilation [odds ratio: (OR) 2.475, 95% confidence interval (CI): 1.511–4.053, p < 0.001], admittance to the intensive care unit (OR: 3.247, 95% CI 2.064–5.106, p < 0.001), and 30-day mortality (OR: 3.206, 95% CI 1.926–5.335, p < 0.001) in patients with Flu-p. Another multivariate logistic regression model revealed that baseline lymphocyte counts (OR: 0.993, 95% CI 0.990–0.996, p < 0.001), coinfection (OR: 5.450, 95% CI 1.638–18.167, p = 0.006), early neuraminidase inhibitor therapy (OR 0.401, 95% CI 0.127–0.878, p = 0.001), and systemic corticosteroid use at admission (OR: 6.414, 95% CI 1.348–30.512, p = 0.020) were independently related to 30-day mortality in IC patients with Flu-p. Based on analysis of the receiver operating characteristic curve (ROC), the optimal cutoff for lymphocyte counts was 0.6 × 109/L [area under the ROC (AUROC) = 0.824, 95% CI 0.744—0.887], sensitivity: 97.8%, specificity: 73.7%]. Conclusions IC conditions are associated with more severe outcomes in patients with Flu-p. The predictors for mortality that we identified may be valuable for the management of Flu-p among IC patients.

scholarly journals The severity and risk factors for mortality in immunocompromised adult patients hospitalized with influenza-related pneumonia

2021 ◽  
Author(s):  
Liang Chen ◽  
Xiudi Han ◽  
YanLi Li ◽  
Chunxiao Zhang ◽  
Xiqian Xing
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Roc Analysis ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Neuraminidase Inhibitor ◽  
Teaching Hospitals ◽  
Multivariate Logistic Regression Model ◽  
Multivariate Logistic Regression

Abstract Objective To explore disease severity and risk factors for 30-day mortality of adult immunocompromised (IC) patients hospitalized with influenza-related pneumonia (Flu-p).Method A total of 122 IC and 1,191 immunocompetent patients hospitalized with Flu-p from January 2012 to December 2018 were recruited retrospectively from five teaching hospitals in China. Results After controlling for confounders, multivariate logistic regression analysis showed that immunosuppression was associated with increased risks for invasive ventilation [odds ratio: (OR) 2.475, 95% confidence interval (CI): 1.511-4.053, p < 0.001], admittance to the intensive care unit (OR: 3.247, 95% CI: 2.064-5.106, p < 0.001), and 30-day mortality (OR: 3.206, 95% CI: 1.926-5.335, p < 0.001) in patients with Flu-p. Another multivariate logistic regression model revealed that baseline lymphocyte counts (OR: 0.993, 95% CI: 0.990-0.996, p < 0.001), coinfection (OR: 5.450, 95% C:I 1.638-18.167, p = 0.006), early neuraminidase inhibitor therapy (OR 0.401, 95% CI 0.127-0.878, p = 0.001), and systemic corticosteroid use at admission (OR: 6.414, 95% C:I 1.348-30.512, p = 0.020) were independently related to 30-day mortality in IC patients with Flu-p. Based on receiver operating characteristic curve (ROC) analysis, the optimal cutoff for lymphocyte counts was 0.6×109/L [area under the ROC (AUROC) = 0.824, 95% CI: 0.744 - 0.887], sensitivity: 97.8%, specificity: 73.7%].Conclusions IC conditions are associated with more severe outcomes in patients with Flu-p. The predictors for mortality that we identified may be valuable for the management of Flu-p among IC patients.

scholarly journals Presepsin values and prognostic nutritional index predict mortality in intensive care unit patients with sepsis: a pilot study

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Yuichiro Shimoyama ◽  
Osamu Umegaki ◽  
Noriko Kadono ◽  
Toshiaki Minami
Keyword(s):  
Multivariate Analysis ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Prognostic Nutritional Index ◽  
Early Mortality ◽  
Curve Analysis ◽  
Rank Test ◽  
Roc Curve Analysis ◽  
Log Rank Test ◽  
Nutritional Index

Abstract Objective Sepsis is a major cause of mortality for critically ill patients. This study aimed to determine whether presepsin values can predict mortality in patients with sepsis. Results Receiver operating characteristic (ROC) curve analysis, Log-rank test, and multivariate analysis identified presepsin values and Prognostic Nutritional Index as predictors of mortality in sepsis patients. Presepsin value on Day 1 was a predictor of early mortality, i.e., death within 7 days of ICU admission; ROC curve analysis revealed an AUC of 0.84, sensitivity of 89%, and specificity of 77%; and multivariate analysis showed an OR of 1.0007, with a 95%CI of 1.0001–1.0013 (p = 0.0320).

Predictors of thermal increase in magnetic resonance–guided focused ultrasound treatment for essential tremor: histogram analysis of skull density ratio values for 1024 elements

2021 ◽  
pp. 1-6
Author(s):  
Ken Iijima ◽  
Hajime Yokota ◽  
Toshio Yamaguchi ◽  
Masayuki Nakano ◽  
Takahiro Ouchi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Operating Characteristic ◽  
Focused Ultrasound ◽  
Characteristic Curve ◽  
Density Ratio ◽  
Predictive Ability ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Operating Characteristic Curve

OBJECTIVE Sufficient thermal increase capable of generating thermocoagulation is indispensable for an effective clinical outcome in patients undergoing magnetic resonance–guided focused ultrasound (MRgFUS). The skull density ratio (SDR) is one of the most dominant predictors of thermal increase prior to treatment. However, users currently rely only on the average SDR value (SDRmean) as a screening criterion, although some patients with low SDRmean values can achieve sufficient thermal increase. The present study aimed to examine the numerical distribution of SDR values across 1024 elements to identify more precise predictors of thermal increase during MRgFUS. METHODS The authors retrospectively analyzed the correlations between the skull parameters and the maximum temperature achieved during unilateral ventral intermediate nucleus thalamotomy with MRgFUS in a cohort of 55 patients. In addition, the numerical distribution of SDR values was quantified across 1024 elements by using the skewness, kurtosis, entropy, and uniformity of the SDR histogram. Next, the authors evaluated the correlation between the aforementioned indices and a peak temperature > 55°C by using univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis was performed to compare the predictive ability of the indices. The diagnostic performance of significant factors was also assessed. RESULTS The SDR skewness (SDRskewness) was identified as a significant predictor of thermal increase in the univariate and multivariate logistic regression analyses (p < 0.001, p = 0.013). Moreover, the receiver operating characteristic curve analysis indicated that the SDRskewness exhibited a better predictive ability than the SDRmean, with area under the curve values of 0.847 and 0.784, respectively. CONCLUSIONS The SDRskewness is a more accurate predictor of thermal increase than the conventional SDRmean. The authors suggest setting the SDRskewness cutoff value to 0.68. SDRskewness may allow for the inclusion of treatable patients with essential tremor who would have been screened out based on the SDRmean exclusion criterion.

scholarly journals Prognostic Threshold for Circulating Tumor Cells in Patients With Pancreatic and Midgut Neuroendocrine Tumors

2020 ◽  
Author(s):  
Dalvinder Mandair ◽  
Mohid S Khan ◽  
Andre Lopes ◽  
Luke Furtado O’Mahony ◽  
Leah Ensell ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Neuroendocrine Tumors ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Multivariate Logistic Regression Analysis ◽  
Progression Free Survival ◽  
Free Survival ◽  
Optimum Threshold

Abstract Background Circulating tumor cells (CTCs) are detectable in patients with neuroendocrine tumors (NETs) and are accurate prognostic markers although the optimum threshold has not been defined. Objective This work aims to define optimal prognostic CTC thresholds in PanNET and midgut NETs. Patients and Methods CellSearch was used to enumerate CTCs in 199 patients with metastatic pancreatic (PanNET) (90) or midgut NETs (109). Patients were followed for progression-free survival (PFS) and overall survival (OS) for a minimum of 3 years or until death. Results The area under the receiver operating characteristic curve (AUROC) for progression at 12 months in PanNETs and midgut NETs identified the optimal CTC threshold as 1 or greater and 2 or greater, respectively. In multivariate logistic regression analysis, these thresholds were predictive for 12-month progression with an odds ratio (OR) of 6.69 (P &lt; .01) for PanNETs and 5.88 (P &lt; .003) for midgut NETs. The same thresholds were found to be optimal for predicting death at 36 months, with an OR of 2.87 (P &lt; .03) and 5.09 (P &lt; .005) for PanNETs and midgut NETs, respectively. In multivariate Cox hazard regression analysis for PFS in PanNETs, 1 or greater CTC had a hazard ratio (HR) of 2.6 (P &lt; .01), whereas 2 or greater CTCs had an HR of 2.25 (P &lt; .01) in midgut NETs. In multivariate analysis OS in PanNETs, 1 or greater CTCs had an HR of 3.16 (P &lt; .01) and in midgut NETs, 2 or greater CTCs had an HR of 1.73 (P &lt; .06). Conclusions The optimal CTC threshold to predict PFS and OS in metastatic PanNETs and midgut NETs is 1 and 2, respectively. These thresholds can be used to stratify patients in clinical practice and clinical trials.

scholarly journals A novel procalcitonin-based score for detecting sepsis among critically ill patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245748
Author(s):  
Tung-Lin Tsui ◽  
Ya-Ting Huang ◽  
Wei-Chih Kan ◽  
Mao-Sheng Huang ◽  
Min-Yu Lai ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Validation Cohort ◽  
Multivariate Logistic Regression Analysis ◽  
Sepsis Group ◽  
C Reactive Protein ◽  
Multivariate Logistic Regression ◽  
Reactive Protein

Background Procalcitonin (PCT) has been widely investigated as an infection biomarker. The study aimed to prove that serum PCT, combining with other relevant variables, has an even better sepsis-detecting ability in critically ill patients. Methods We conducted a retrospective cohort study in a regional teaching hospital enrolling eligible patients admitted to intensive care units (ICU) between July 1, 2016, and December 31, 2016, and followed them until March 31, 2017. The primary outcome measurement was the occurrence of sepsis. We used multivariate logistic regression analysis to determine the independent factors for sepsis and constructed a novel PCT-based score containing these factors. The area under the receiver operating characteristics curve (AUROC) was applied to evaluate sepsis-detecting abilities. Finally, we validated the score using a validation cohort. Results A total of 258 critically ill patients (70.9±16.3 years; 55.4% man) were enrolled in the derivation cohort and further subgrouped into the sepsis group (n = 115) and the non-sepsis group (n = 143). By using the multivariate logistic regression analysis, we disclosed five independent factors for detecting sepsis, namely, “serum PCT level,” “albumin level” and “neutrophil-lymphocyte ratio” at ICU admission, along with “diabetes mellitus,” and “with vasopressor.” We subsequently constructed a PCT-based score containing the five weighted factors. The PCT-based score performed well in detecting sepsis with the cut-points of 8 points (AUROC 0.80; 95% confidence interval (CI) 0.74–0.85; sensitivity 0.70; specificity 0.76), which was better than PCT alone, C-reactive protein and infection probability score. The findings were confirmed using an independent validation cohort (n = 72, 69.2±16.7 years, 62.5% men) (cut-point: 8 points; AUROC, 0.79; 95% CI 0.69–0.90; sensitivity 0.64; specificity 0.87). Conclusions We proposed a novel PCT-based score that performs better in detecting sepsis than serum PCT levels alone, C-reactive protein, and infection probability score.

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