scholarly journals Neutrophil-to-lymphocyte ratio as a predictor of mortality in intensive care unit patients: a retrospective analysis of the Medical Information Mart for Intensive Care III Database

BMJ Open ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. e053548
Author(s):  
Xie Wu ◽  
Qipeng Luo ◽  
Zhanhao Su ◽  
Yinan Li ◽  
Hongbai Wang ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Inflammatory Markers ◽  
Predictive Power ◽  
Medical Information ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Mortality Rates ◽  
Simplified Acute Physiology Score ◽  
Icu Patients

ObjectivesIdentifying high-risk patients in the intensive care unit (ICU) is important given the high mortality rate. However, existing scoring systems lack easily accessible, low-cost and effective inflammatory markers. We aimed to identify inflammatory markers in routine blood tests to predict mortality in ICU patients and evaluate their predictive power.DesignRetrospective case–control study.SettingSingle secondary care centre.ParticipantsWe analysed data from the Medical Information Mart for Intensive Care III database. A total of 21 822 ICU patients were enrolled and divided into survival and death groups based on in-hospital mortality.Primary and secondary outcome measuresThe predictive values of potential inflammatory markers were evaluated and compared using receiver operating characteristic curve analysis. After identifying the neutrophil-to-lymphocyte ratio (NLR) as having the best predictive ability, patients were redivided into low (≤1), medium (1–6) and high (>6) NLR groups. Univariate and multivariate logistic regression analyses were performed to evaluate the association between the NLR and mortality. The area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were used to assess whether incorporating the NLR could improve the predictive power of existing scoring systems.ResultsThe NLR had the best predictive ability (AUC: 0.609; p<0.001). In-hospital mortality rates were significantly higher in the low (OR (OR): 2.09; 95% CI 1.64 to 2.66) and high (OR 1.64; 95% CI 1.50 to 1.80) NLR groups than in the medium NLR group. Adding the NLR to the Simplified Acute Physiology Score II improved the AUC from 0.789 to 0.798, with an NRI and IDI of 16.64% and 0.27%, respectively.ConclusionsThe NLR predicted mortality in ICU patients well. Both low and high NLRs were associated with elevated mortality rates, including the NLR may improve the predictive power of the Simplified Acute Physiology Score II.

scholarly journals The effect of ventilator-associated pneumonia on the prognosis of intensive care unit patients within 90 days and 180 days

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wenjuan Luo ◽  
Rui Xing ◽  
Canmin Wang
Keyword(s):  
Intensive Care Unit ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Medical Information ◽  
Length Of Hospital Stay ◽  
Simplified Acute Physiology Score ◽  
Serum Lactate ◽  
Binary Logistic Regression Analysis ◽  
Ventilator Associated Pneumonia ◽  
Icu Patients

Abstract Background Mechanical ventilation (MV) is often applied in critically ill patients in intensive care unit (ICU) to protect the airway from aspiration, and supplement more oxygen. MV may result in ventilator-associated pneumonia (VAP) in ICU patients. This study was to estimate the 90-day and 180-day mortalities of ICU patients with VAP, and to explore the influence of VAP on the outcomes of ICU patients. Methods Totally, 8182 patients who aged ≥18 years and received mechanical ventilation (MV) in ICU from Medical Information Mart for Intensive Care III (MIMIC III) database were involved in this study. All subjects were divided into the VAP group (n = 537) and the non-VAP group (n = 7626) based on the occurrence of VAP. Clinical data of all participants were collected. The effect of VAP on the prognosis of ICU patients was explored by binary logistic regression analysis. Results The results delineated that the 90-day mortality of VAP patients in ICU was 33.33% and 180-day mortality was 37.62%. The 90-day and 180-day mortality rates were higher in the VAP group than in the non-VAP group. After adjusting the confounders including age, ethnicity, heart failure, septicemia, simplified acute physiology score II (SAPSII) score, sequential organ failure assessment (SOFA) score, serum lactate, white blood cell (WBC), length of ICU stay, length of hospital stay, length of ventilation, antibiotic treatment, Pseudomonas aeruginosa (P.aeruginosa), methicillin-resistant Staphylococcus aureus (MRSA), other pathogens, the risk of 90-day and 180-day mortalities in VAP patients were 1.465 times (OR = 1.465, 95%CI: 1.188–1.807, P < 0.001) and 1.635 times (OR = 1.635, 95%CI: 1.333–2.005, P < 0.001) higher than those in non-VAP patients, respectively. Conclusions Our study revealed that ICU patients with VAP had poorer prognosis than those without VAP. The results of this study might offer a deeper insight into preventing the occurrence of VAP.

COMPARISON OF 3 SEVERITY OF ILLNESS SCORING SYSTEMS FOR INTENSIVE CARE UNIT (ICU) PATIENTS

1998 ◽  
Vol 26 (Supplement) ◽  
pp. 125A ◽  
Author(s):  
Janete Livianu ◽  
Jose Maria Orlando ◽  
Flavio M. Maciel ◽  
Jose O. Proenca
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Severity Of Illness ◽  
Scoring Systems ◽  
Icu Patients

scholarly journals Comparison of three severity of illness scoring systems for intensive care unit (ICU) patients

Critical Care ◽  
10.1186/cc289 ◽  
1998 ◽  
Vol 2 (Suppl 1) ◽  
pp. P160
Author(s):  
J Livianu ◽  
JMC Orlando ◽  
FMB Maciel ◽  
JO Proença
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Severity Of Illness ◽  
Scoring Systems ◽  
Icu Patients

scholarly journals Sex differences in crude mortality rates and predictive value of intensive care unit-based scores when applied to the cardiac intensive care unit

2019 ◽  
Vol 9 (8) ◽  
pp. 966-974 ◽  
Author(s):  
Romana Herscovici ◽  
James Mirocha ◽  
Jed Salomon ◽  
Noel B Merz ◽  
Bojan Cercek ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Sex Differences ◽  
Intensive Care ◽  
Predictive Value ◽  
Scoring Systems ◽  
Mortality Rates ◽  
P Value ◽  
Cardiac Intensive Care Unit ◽  
C Statistic ◽  
Cardiac Intensive Care

Background: Limited data exists regarding sex differences in outcome and predictive accuracy of intensive care unit-based scoring systems when applied to cardiac intensive care unit patients. Methods: We reviewed medical records of patients admitted to cardiac intensive care unit from 1 January 2011–31 December 2016. Sex differences in mortality rates and the performance of intensive care unit-based scoring systems in predicting in-hospital mortality were analyzed. Calibration was assessed by the Hosmer-Lemeshow test and locally weighted scatterplot smoothing curves. Discrimination was assessed using the c statistic and receiver-operating characteristic curve. Results: Among 6963 patients, 2713 (39%) were women. Overall in-hospital and cardiac intensive care unit mortality rates were similar in women and men (9.1% vs 9.4%, p=0.67 and 5.9% vs 6%, p=0.88, respectively) and in age and major diagnosis subgroups. Of the scoring systems, Acute Physiology and Chronic Health Evaluation III and Sequential Organ Failure Assessment had poor calibration (Hosmer-Lemeshow p value <0.001), while Simplified Acute Physiology Score II performed better (Hosmer-Lemeshow p value 0.09), in both women and men. All scores had good discrimination (C statistics >0.8). In the subgroups of acute myocardial infarction and heart failure patients, all scores had good calibration (Hosmer-Lemeshow p>0.001) and discrimination (C statistic >0.8) while in diagnosis subgroups with highest mortality, the calibration varied among scores and by sex, and discrimination was poor. Conclusions: No sex differences in mortality were seen in cardiac intensive care unit patients. The mortality predictive value of intensive care unit-based scores is limited in both sexes and variable among different subgroups of diagnoses.

scholarly journals Comparison of illness severity scoring systems for mortality prediction in Neurointensive Care Unit in India

2017 ◽  
Vol 04 (01) ◽  
pp. 042-048
Author(s):  
Sonia Bansal ◽  
Rohini Surve ◽  
Radhakrishnan Muthuchellappan ◽  
Ganne Umamaheswara Rao ◽  
Mariamma Philip
Keyword(s):  
Predictive Power ◽  
Severity Of Illness ◽  
Predictive Ability ◽  
Scoring Systems ◽  
Simplified Acute Physiology Score ◽  
Illness Severity ◽  
Mortality Prediction ◽  
Discriminative Ability ◽  
Severity Scoring ◽  
Mortality Prediction Model

Abstract Background: Illness severity scoring systems (SSs) are increasingly being used to provide information about patients’ severity of illness and outcome in terms of mortality or length of Intensive care Unit (ICU) and hospital stay. In this retrospective study, we compared the predictive power of Acute Physiology and Chronic Health Evaluation (APACHE) II and IV, Simplified Acute Physiology Score (SAPS), Mortality Prediction Model at 24 h and Glasgow Coma Scale (GCS) with actual in-hospital 28 day mortality in patients admitted to neuro-ICU over a period of 6 months. Methods: The data required for calculation of above scores was retrieved from medical records. The 28-day post-admission outcome including in-hospital mortality was measured by Glasgow Outcome Scale (GOS). Logistic regression was used to determine the mortality prediction power of each SS. Results: A total of 197 adult patients with varied neurological diagnosis were included in this study. The in-hospital 28-day mortality rate was 19.8%, and the scores of all the SSs correlated significantly with GOS (P < 0.001). All the scores were significantly different between survivors and non-survivors. The accuracy of all the SSs to predict survival and non-survival was more than 80%. The highest accuracy rate was seen for GCS and SAPS (84.3% and 83.8%, respectively). Conclusions: The SSs used in this study had good predictive power, and they had good discriminative ability between survivors and non-survivors. GCS and SAPS have the highest predictive ability, GCS having added advantage of being simple and practical.

Mid-regional pro-adrenomedullin predicts poor outcome in non-selected patients admitted to an intensive care unit

2019 ◽  
Vol 57 (4) ◽  
pp. 549-555 ◽  
Author(s):  
Chiara Bellia ◽  
Luisa Agnello ◽  
Bruna Lo Sasso ◽  
Giulia Bivona ◽  
Maurizio Santi Raineri ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Area Under The Curve ◽  
Scoring Systems ◽  
Simplified Acute Physiology Score ◽  
Icu Admission ◽  
Open Issue ◽  
The One

Abstract Background Mortality risk and outcome in critically ill patients can be predicted by scoring systems, such as APACHE and SAPS. The identification of prognostic biomarkers, simple to measure upon admission to an intensive care unit (ICU) is an open issue. The aim of this observational study was to assess the prognostic value of plasma mid-regional pro-adrenomedullin (MR-proADM) at ICU admission in non-selected patients in comparison to Acute Physiology and Chronic Health Evaluation II (APACHEII) and Simplified Acute Physiology Score II (SAPSII) scores. Methods APACHEII and SAPSII scores were calculated after 24 h from ICU admission. Plasma MR-proADM levels were measured by TRACE-Kryptor on admission (T0) and after 24 h (T24). The primary endpoint was intra-hospital mortality; secondary endpoint was length of stay (LOS). Results One hundred and twenty-six consecutive non-selected patients admitted to an ICU were enrolled. Plasma MR-proADM levels were correlated with LOS (r=0.28; p=0.0014 at T0; r=0.26; p=0.005 at T24). Multivariate analysis showed that T0 MR-proADM was a significant predictor of mortality (odds ratio [OR]: 1.27; 95% confidence interval [95%CI]: 1.03–1.55; p=0.022). Receiver operating characteristic curves analysis revealed that MR-proADM on ICU admission identified non-survivors with high accuracy, not inferior to the one of APACHEII and SAPSII scores (area under the curve [AUC]: 0.71; 95%CI: 0.62–0.78; p=0.0002 for MR-proADM; AUC: 0.71; 95%CI: 0.62–0.79; p<0.0001 for APACHEII; AUC: 0.8; 95%CI: 0.71–0.87; p<0.0001 for SAPSII). Conclusions Our findings point out a role of MR-proADM as a prognostic tool in non-selected patients in ICUs being a reliable predictor of mortality and LOS and support its use on admission to an ICU to help the management of critically ill patients.

scholarly journals Comparison of Septic Shock Due to Multidrug-ResistantAcinetobacter baumanniiorKlebsiella pneumoniaeCarbapenemase-ProducingK. pneumoniaein Intensive Care Unit Patients

2018 ◽  
Vol 62 (6) ◽  
Author(s):  
Alessandro Russo ◽  
Simone Giuliano ◽  
Giancarlo Ceccarelli ◽  
Francesco Alessandri ◽  
Alessandra Giordano ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Septic Shock ◽  
Intensive Care ◽  
Clinical Features ◽  
Mortality Rates ◽  
Multidrug Resistant ◽  
Simplified Acute Physiology Score ◽  
Content Type ◽  
Definitive Therapy

ABSTRACTA significant cause of mortality in the intensive care unit (ICU) is multidrug-resistant (MDR) Gram-negative bacteria, such as MDRAcinetobacter baumannii(MDR-AB) andKlebsiella pneumoniaecarbapenemase-producingK. pneumoniae(KPC-Kp). The aim of the present study was to compare the clinical features, therapy, and outcome of patients who developed septic shock due to either MDR-AB or KPC-Kp. We retrospectively analyzed patients admitted to the ICU of a teaching hospital from November 2010 to December 2015 who developed septic shock due to MDR-AB or KPC-Kp infection. Data from 220 patients were analyzed: 128 patients (58.2%) were diagnosed with septic shock due to KPC-Kp, and 92 patients (41.8%) were diagnosed with septic shock due to MDR-AB. The 30-day mortality rate was significantly higher for the MDR-AB group than the KPC-Kp group (84.8% versus 44.5%, respectively;P< 0.001). Steroid exposure and pneumonia were associated with MDR-AB infection, whereas hospitalization in the previous 90 days, primary bacteremia, and KPC-Kp colonization were associated with KPC-Kp infection. For patients with KPC-Kp infections, the use of ≥2in vitro-active antibiotics as empirical or definitive therapy was associated with higher 30-day survival, while isolation of colistin-resistant strains was linked to mortality. Patients with MDR-AB infections, age >60 years, and a simplified acute physiology score II (SAPS II) of >45 points were associated with increased mortality rates. We concluded that septic shock due to MDR-AB infection is associated with very high mortality rates compared to those with septic shock due to KPC-Kp. Analysis of the clinical features of these critically ill patients might help physicians in choosing appropriate empirical antimicrobial therapy.

scholarly journals A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jing Xia ◽  
Su Pan ◽  
Min Zhu ◽  
Guolong Cai ◽  
Molei Yan ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Medical Information ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Scoring Systems ◽  
Superior Performance ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In intensive care unit (ICU), it is essential to predict the mortality of patients and mathematical models aid in improving the prognosis accuracy. Recently, recurrent neural network (RNN), especially long short-term memory (LSTM) network, showed advantages in sequential modeling and was promising for clinical prediction. However, ICU data are highly complex due to the diverse patterns of diseases; therefore, instead of single LSTM model, an ensemble algorithm of LSTM (eLSTM) is proposed, utilizing the superiority of the ensemble framework to handle the diversity of clinical data. The eLSTM algorithm was evaluated by the acknowledged database of ICU admissions Medical Information Mart for Intensive Care III (MIMIC-III). The investigation in total of 18415 cases shows that compared with clinical scoring systems SAPS II, SOFA, and APACHE II, random forests classification algorithm, and the single LSTM classifier, the eLSTM model achieved the superior performance with the largest value of area under the receiver operating characteristic curve (AUROC) of 0.8451 and the largest area under the precision-recall curve (AUPRC) of 0.4862. Furthermore, it offered an early prognosis of ICU patients. The results demonstrate that the eLSTM is capable of dynamically predicting the mortality of patients in complex clinical situations.

Sign in / Sign up

Export Citation Format

Share Document