Development and Internal Validation of a Nomogram to Predict Mortality During the ICU Stay of Thoracic Fracture Patients Without Neurological Compromise: An Analysis of the MIMIC-III Clinical Database

Background: This study aimed to develop and validate a nomogram for predicting mortality in patients with thoracic fractures without neurological compromise and hospitalized in the intensive care unit.Methods: A total of 298 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included in the study, and 35 clinical indicators were collected within 24 h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was established, and a nomogram was constructed. Internal validation was performed by the 1,000 bootstrap samples; a receiver operating curve (ROC) was plotted, and the area under the curve (AUC), sensitivity, and specificity were calculated. In addition, the calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow goodness-of-fit test (HL test). A decision curve analysis (DCA) was performed, and the nomogram was compared with scoring systems commonly used during clinical practice to assess the net clinical benefit.Results: Indicators included in the nomogram were age, OASIS score, SAPS II score, respiratory rate, partial thromboplastin time (PTT), cardiac arrhythmias, and fluid-electrolyte disorders. The results showed that our model yielded satisfied diagnostic performance with an AUC value of 0.902 and 0.883 using the training set and on internal validation. The calibration curve and the Hosmer-Lemeshow goodness-of-fit (HL). The HL tests exhibited satisfactory concordance between predicted and actual outcomes (P = 0.648). The DCA showed a superior net clinical benefit of our model over previously reported scoring systems.Conclusion: In summary, we explored the incidence of mortality during the ICU stay of thoracic fracture patients without neurological compromise and developed a prediction model that facilitates clinical decision making. However, external validation will be needed in the future.

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Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach

BMJ Open Gastroenterology ◽

10.1136/bmjgast-2021-000761 ◽

2021 ◽

Vol 8 (1) ◽

pp. e000761

Author(s):

Hao Du ◽

Kewin Tien Ho Siah ◽

Valencia Zhang Ru-Yan ◽

Readon Teh ◽

Christopher Yu En Tan ◽

...

Keyword(s):

Machine Learning ◽

Critical Care ◽

Intensive Care ◽

Hospital Mortality ◽

Receiver Operating Curve ◽

Study Cohort ◽

Risk Of Death ◽

Severity Scores ◽

Mimic Iii ◽

Care Database

Research objectivesClostriodiodes difficile infection (CDI) is a major cause of healthcare-associated diarrhoea with high mortality. There is a lack of validated predictors for severe outcomes in CDI. The aim of this study is to derive and validate a clinical prediction tool for CDI in-hospital mortality using a large critical care database.MethodologyThe demographics, clinical parameters, laboratory results and mortality of CDI were extracted from the Medical Information Mart for Intensive Care-III (MIMIC-III) database. We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. The individual performances of the models were compared against current severity scores (Clostridiodes difficile Associated Risk of Death Score (CARDS) and ATLAS (Age, Treatment with systemic antibiotics, leukocyte count, Albumin and Serum creatinine as a measure of renal function) by calculating area under receiver operating curve (AUROC). We identified factors associated with higher mortality risk in each model.Summary of resultsFrom 61 532 intensive care unit stays in the MIMIC-III database, there were 1315 CDI cases. The mortality rate for CDI in the study cohort was 18.33%. AUROC was 0.69 (95% CI, 0.60 to 0.76) for LR, 0.71 (95% CI, 0.62 to 0.77) for RF and 0.72 (95% CI, 0.64 to 0.78) for GBM, while previously AUROC was 0.57 (95% CI, 0.51 to 0.65) for CARDS and 0.63 (95% CI, 0.54 to 0.70) for ATLAS. Albumin, lactate and bicarbonate were significant mortality factors for all the models. Free calcium, potassium, white blood cell, urea, platelet and mean blood pressure were present in at least two of the three models.ConclusionOur machine learning derived CDI in-hospital mortality prediction model identified pertinent factors that can assist critical care clinicians in identifying patients at high risk of dying from CDI.

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Comparison of Sensitivity, Specificity and Accuracy of APACHE II, SAPS II and SOFA Scoring Systems as Predictors

10.53350/pjmhs2115123364 ◽

2021 ◽

Vol 15 (12) ◽

pp. 3364-3366

Author(s):

Aamir Furqan ◽

Mehwish Naseer ◽

Rafia Tabassum

Keyword(s):

Intensive Care ◽

Scoring System ◽

Scoring Systems ◽

Mortality Prediction ◽

Apache Ii ◽

Icu Patients ◽

Saps Ii ◽

Icu Stay ◽

Study Results ◽

Sensitivity Specificity

Aim: To compare the APACHE II, SAPS II and SOFA scoring systems as predictors of mortality in ICU patients in terms of sensitivity, specificity and accuracy. Methodology: A prospective observational study. Intensive care unit from May 13, 2018 to September 15, 2021. For 1368 patients included in study, results for APACHE II, SAPS II and SOFA were calculated with the worst values recorded. At the end of ICU stay, patient outcome was labelled as survivors and non-survivors. The cut off value for APACHE II, SAPS II and SOFA was taken as 50% of the highest possible score, with <50% expected to survive and with ≥50% expected to die during their ICU stay. Cross tables were made against real outcome of the patients, and sensitivity, specificity and accuracy for APACHE II, SAPS II and SOFA were calculated. Results: Sensitivity, specificity and accuracy were 77.53%, 94.28% and 85.45% for APACHE II scoring system; 47.29%, 87.32%, and 66.23% for SAPS II scoring system; and 73.37%, 60.28%, and 67.18% for SOFA scoring system, respectively. Conclusion: Apache Ii scoring system has highest sensitivity, specificity and accuracy in mortality prediction in ICU patients as compared to SAPS II and SOFA scoring systems, with SAPS II being least sensitive and accurate. Keywords: Sensitivity, specificity, accuracy, Acute Physiology and Chronic Health Evaluation (APACHE II), Simplified Acute Physiology Score (SAPS II), Sequential Organ Failure Assessment (SOFA), Intensive care units (ICU), Mortality.

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Predictive value of Score for Neonatal Acute Physiology and Perinatal Extension II for neonatal mortality in Sanglah Hospital, Denpasar, Indonesia

Paediatrica Indonesiana ◽

10.14238/pi56.5.2016.257-61 ◽

2017 ◽

Vol 56 (5) ◽

pp. 257

Author(s):

I Gede Ketut Aryana ◽

I Made Kardana ◽

I Nyoman Adipura

Keyword(s):

Intensive Care ◽

Neonatal Mortality ◽

Goodness Of Fit ◽

Assessment Tool ◽

Area Under The Curve ◽

Severe Infection ◽

Scoring Systems ◽

World Health ◽

Severe Illness ◽

Goodness Of Fit Test

Background Neonatal mortality, which is largely caused by severe illness, is the biggest contributor to overall infant mortality. The World Health Organization (WHO) estimated that 4 million neonates die yearly worldwide, often due to severe infection and organ system immaturity. Neonates with severe illness require treatment in the neonatal intensive care unit (NICU), in which a reliable assessment tool for illness severity is needed to guide intensive care requirements and prognosis. Neonatal disease severity scoring systems have been developed, including Score for Neonatal Acute Physiology and Perinatal Extension II (SNAPPE II), but it has never been validated in our setting.ObjectiveTo study the prognostic value of SNAPPE II as a predictor of neonatal mortality in Sanglah Hospital, Denpasar, Indonesia.Methods This prospective cohort study was conducted in the NICU of Sanglah Hospital, Denpasar from November 2014 to February 2015. All neonates, except those with congenital anomaly, were observed during the first 12 hours of admission and their outcomes upon discharge from the NICU was recorded. We assessed the SNAPPE II cut-off point to predict neonatal mortality. The calibration of SNAPPE II was done using the Hosmer-Lemeshow goodness-of-fit test, and discrimination of SNAPPE II was determined from the receiver-operator characteristic (ROC) curve and area under the curve (AUC) value calculation.ResultsDuring the period of study, 63 children were eligible, but 5 were excluded because of major congenital abnormalities. The SNAPPE II optimum cut-off point of 37 gave a high probability of mortality and the ROC showed an AUC of 0.92 (95%CI 0.85 to 0.99). The Hosmer-Lemeshow goodness-of-fit test showed a good calibration with P = 1.0Conclusion The SNAPPE II has a good predictive ability for neonatal mortality in Sanglah Hospital, Denpasar, Indonesia.

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Dynamic APACHE II Score to Predict Outcome Among Intensive Care Unit Patients

10.21203/rs.3.rs-642050/v1 ◽

2021 ◽

Author(s):

Yao Tian ◽

Yang YAO ◽

Jing Zhou ◽

Xin Diao ◽

Hui Chen ◽

...

Keyword(s):

Intensive Care ◽

Hospital Mortality ◽

Goodness Of Fit ◽

Apache Ii ◽

Secondary Outcome ◽

Apache Ii Score ◽

Optimal Timing ◽

Goodness Of Fit Test ◽

Icu Patients ◽

Mimic Iii

Abstract Purpose: The Acute Physiology and Chronic Health Evaluation II (APACHE II) score is used to determine disease severity and predict outcomes in critically ill patients. However, there is no dynamic APACHE II score for predicting outcomes among ICU patients.The aim of this study is to explore the optimal timing to predict the outcomes of ICU patients by dynamically evaluating APACHE II score.Methods: Study data of demographics and comorbidities from the first 24 h after ICU admission were retrospectively extracted from MIMIC-III, a multiparameter intensive care database. The primary outcome was hospital mortality. 90-day mortality was a secondary outcome. APACHE II scores on days 1, 2, 3, 5, 7, 14 and 28 were compared using area under the receiver operating characteristic (AUROC) analysis. Hospital survival was visualised using Kaplan-Meier Curves.Results:A total of 6374 eligible subjects were extracted from the MIMIC-III. Mean APACHE II score on day 1 were 18.4±6.3, hospital and 90-day mortality was 19.1% and 25.8%, respectively.The optimal timing where predicted hospital mortality was on day 3 with an area under the cure of 0.666 (0.607-0.726)(P＜0.0001). The best tradeoff for preciction was found at 17 score, more than 17 score predicted mortality of non-survivors with a sensitivity of 92.8% and PPV of 23.1%. Hosmer-lemeshow goodness of fit test showed that APACHE II 3 has a good predictive calibration ability (X2 =6.198, P=0.625) and consistency of predicted death and actual death was 79.4%. The calibration of APACHE II 1 was poor (X2=294.898, P<0.001).Conclusions: APACHE II on 3 dayis the optimal prognostic marker and 17 score provided the best dignostic accuracy to predict outcomes for ICU patients. These finding will help medical make clinical judgment.

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Assessment of Nurses’ Workload in Intensive Care Unit by Use of Scoring Systems

Croatian Nursing Journal ◽

10.24141/2/4/1/5 ◽

2020 ◽

Vol 4 (1) ◽

pp. 59-71

Author(s):

Ljiljana Vuković

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Scoring System ◽

Scoring Systems ◽

University Hospital ◽

Study Patient ◽

Nursing Workload ◽

Icu Stay ◽

Study Results ◽

Surgical Icu

Aim. To assess the level of correlation between two scoring systems: patient categorization according to the Croatian Nursing Council consensus and Nine Equivalents of Nursing Manpower Use Score (NEMS) and their ability to determine if the number of nurses working in the intensive care unit (ICU) is optimal to provide adequate nursing care, and to assess the level of correlation between the severity of patients’ illness and the level of nurses’ satisfaction with provided care. Methods. Research was performed in surgical ICU of the Clinical Department of Anesthesiology, Resuscitation and Intensive Care Medicine, University Hospital Dubrava, in the period between January 8th and April 14th, 2014. 256 patients aged 18-92 years were included in the study. Patient categorization and NEMS were calculated each day during the first 7 days of the ICU stay. NEMS was calculated using a pre-made table of variables and categorization was calculated using an electronic form included in nursing electronic patient files. Satisfaction of provided care was expressed using the Likert scale (1-5). Results. Study results have shown a moderate but significant level of correlation between the categorization and NEMS scores. Mean NEMS score during the first 7 days in the ICU was 26.93 ± 4.64 and the highest measured values were at day 4 (30.34±8.1) after which they started decreasing. Mean cumulative NEMS throughout the whole ICU stay was 269.3. According to the fact that according to NEMS scoring system one nurse can provide maximum of 45 points for 24 hours, the results have shown that a 10 bed ICU needs at least 5.98 (6) nurses to provide adequate level of care. Average categorization score was 57.83±4.29 and the highest recorded score was at day 7 (59.7±4.44). According to the categorization scoring system time needed to provide care for one 4th category patient throughout 24 hours is 10 or more hours. Since the description of the 4th category doesn’t specify what is the upper limit of time needed to provide care for each patient, 14 hours was used to determine a minimum number of nurses, and according to the categorization score 5.83 (6) nurses are needed in the ICU. Nurses’ satisfaction with provided care has shown a significant negative correlation with NEMS score and categorization score. Conclusion. Both scoring systems can be used to assess nursing workload in a surgical ICU. However, NEMS is simpler and quicker to use, more applicable, useful and should be routinely used in place of categorization to assess nursing workload in surgical ICUs.

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Effectiveness of LODS, OASIS, and SAPS II to predict in-hospital mortality for intensive care patients with ST elevation myocardial infarction

Scientific Reports ◽

10.1038/s41598-021-03397-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Liang Wang ◽

Zhengwei Zhang ◽

Tianyang Hu

Keyword(s):

Myocardial Infarction ◽

Intensive Care ◽

Hospital Mortality ◽

Clinical Benefit ◽

Predictive Value ◽

Scoring Systems ◽

Study Cohort ◽

Intensive Care Patients ◽

Saps Ii ◽

Saps Ii Score

AbstractThe relationship between three scoring systems (LODS, OASIS, and SAPS II) and in-hospital mortality of intensive care patients with ST segment elevation myocardial infarction (STEMI) is currently inconclusive. The baseline data, LODS score, OASIS score, SAPS II score, and in-hospital prognosis of intensive care patients with STEMI were retrieved from the Medical Information Mart for Intensive Care IV database. Propensity score matching analysis was performed to reduce bias. Receiver operating characteristic curves (ROC) were drawn for the three scoring systems, and comparisons between the areas under the ROC curves (AUC) were conducted. Decision curve analysis (DCA) was performed to determine the net benefits of the three scoring systems. LODS and SAPS II were independent risk factors for in-hospital mortality. For the study cohort, the AUCs of LODS, OASIS, SAPS II were 0.867, 0.827, and 0.894; after PSM, the AUCs of LODS, OASIS, SAPS II were 0.877, 0.821, and 0.881. A stratified analysis of the patients who underwent percutaneous coronary intervention/coronary artery bypass grafting (PCI/CABG) or not was conducted. In the PCI/CABG group, the AUCs of LODS, OASIS, SAPS II were 0.853, 0.825, and 0.867, while in the non-PCI/CABG group, the AUCs of LODS, OASIS, SAPS II were 0.857, 0.804, and 0.897. The results of the Z test suggest that the predictive value of LODS and SAPS II was not statistically different, but both were higher than OASIS. According to the DCA, the net clinical benefit of LODS was the greatest. LODS and SAPS II have excellent predictive value, and in most cases, both were higher than OASIS. With a more concise composition and greater clinical benefit, LODS may be a better predictor of in-hospital mortality for intensive care patients with STEMI.

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Elevated Lactate Dehydrogenase Levels Display a Poor Prognostic Factor for Non-Hodgkin’s Lymphoma in Intensive Care Unit: An Analysis of the MIMIC-III Database Combined With External Validation

Frontiers in Oncology ◽

10.3389/fonc.2021.753712 ◽

2021 ◽

Vol 11 ◽

Author(s):

Jiaqian Qi ◽

Chengyuan Gu ◽

Weijuan Wang ◽

Mengqi Xiang ◽

Xiaochen Chen ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Prognostic Factors ◽

Lactate Dehydrogenase ◽

Critically Ill ◽

Calibration Curve ◽

External Validation ◽

B Cell Lymphoma ◽

New Model ◽

Mimic Iii

BackgroundAmong the growing number of patients with hematologic neoplasms hospitalized in the intensive care unit (ICU), the largest proportion of these patients are diagnosed with lymphoma. However, less attention has been paid in the past to identifying critically ill patients and assessing the prognosis of patients in ICU. Traditional critical care-related scores have shown limitations and inaccuracy in predicting mortality risk.MethodsPatients diagnosed with diffuse large B-cell lymphoma (DLBCL) were searched for in the Marketplace for Information in Intensive Care Medicine III (MIMIC-III) database. We searched mortality within 28 days as the primary endpoint. Logistics regression was used to screen risk factors. A calibration curve was used for internal validation, and the ROC curve and AUC were used to compare the new model with traditional scores.Results405 patients with DLBCL are enrolled in the project. Multivariate analysis shows the patients with the level of lactate dehydrogenase (LDH) > 327 U/L had an increased risk of 28-day mortality in ICU than others (OR = 13.04, p<0.01). Notably, length of ICU stay, LDH, creatinine, white blood cell counts, and APS III score are independent prognostic factors for patients with DLBCL in the ICU. Then, all these independent prognostic factors are selected into our prediction model. The new model has good accuracy (C-index=0.863) and a calibration curve, which improves clinical status concerning established ratings such as IPI, NCCN-IPI score, SOFA, APS III, and LODS. The results of a multicenter external validation including 124 DLBCL patients also showed that the new model was more accurate than all other models.ConclusionsThe elevated level of LDH indicates a poor prognosis of patients with DLBCL in the ICU. Our risk score with crossed validation based on the level of LDH shows a significant prognostic value and may be a valuable tool for assessing the critically ill as well.

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Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning

Clinical Kidney Journal ◽

10.1093/ckj/sfaa145 ◽

2020 ◽

Author(s):

Khaled Shawwa ◽

Erina Ghosh ◽

Stephanie Lanius ◽

Emma Schwager ◽

Larry Eshelman ◽

...

Keyword(s):

Machine Learning ◽

Acute Kidney Injury ◽

Intensive Care ◽

Mayo Clinic ◽

Medical Information ◽

Kidney Injury ◽

Feature Model ◽

Receiver Operating Curve ◽

Gradient Boosting ◽

Mimic Iii

Abstract Background Acute kidney injury (AKI) carries a poor prognosis. Its incidence is increasing in the intensive care unit (ICU). Our purpose in this study is to develop and externally validate a model for predicting AKI in the ICU using patient data present prior to ICU admission. Methods We used data of 98 472 adult ICU admissions at Mayo Clinic between 1 January 2005 and 31 December 2017 and 51 801 encounters from Medical Information Mart for Intensive Care III (MIMIC-III) cohort. A gradient-boosting model was trained on 80% of the Mayo Clinic cohort using a set of features to predict AKI acquired in the ICU. Results AKI was identified in 39 307 (39.9%) encounters in the Mayo Clinic cohort. Patients who developed AKI in the ICU were older and had higher ICU and in-hospital mortality compared to patients without AKI. A 30-feature model yielded an area under the receiver operating curve of 0.690 [95% confidence interval (CI) 0.682–0.697] in the Mayo Clinic cohort set and 0.656 (95% CI 0.648–0.664) in the MIMIC-III cohort. Conclusions Using machine learning, AKI among ICU patients can be predicted using information available prior to admission. This model is independent of ICU information, making it valuable for stratifying patients at admission.

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Massive Blood Transfusion for Trauma Score to Predict Massive Blood Transfusion in Trauma

Critical Care Research and Practice ◽

10.1155/2021/3165390 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Osaree Akaraborworn ◽

Boonying Siribumrungwong ◽

Burapat Sangthong ◽

Komet Thongkhao

Keyword(s):

Blood Transfusion ◽

Goodness Of Fit ◽

Prehospital Care ◽

Multivariable Analysis ◽

Positive Likelihood Ratio ◽

Scoring Systems ◽

Trauma Team ◽

Trauma Patients ◽

Internal Validation ◽

Massive Blood Transfusion

Background. Massive blood loss is the most common cause of immediate death in trauma. A massive blood transfusion (MBT) score is a prediction tool to activate blood banks to prepare blood products. The previously published scoring systems were mostly developed from settings that had mature prehospital systems which may lead to a failure to validate in settings with immature prehospital systems. This research aimed to develop a massive blood transfusion for trauma (MBTT) score that is able to predict MBT in settings that have immature prehospital care. Methods. This study was a retrospective cohort that collected data from trauma patients who met the trauma team activation criteria. The predicting parameters included in the analysis were retrieved from the history, physical examination, and initial laboratory results. The significant parameters from a multivariable analysis were used to develop a clinical scoring system. The discrimination was evaluated by the area under a receiver operating characteristic (AuROC) curve. The calibration was demonstrated with Hosmer–Lemeshow goodness of fit, and an internal validation was done. Results. Among 867 patients, 102 (11.8%) patients received MBT. Four factors were associated with MBT: a score of 3 for age ≥60 years; 2.5 for base excess ≤–10 mEq/L; 2 for lactate >4 mmol/L; and 1 for heart rate ≥105 /min. The AuROC was 0.85 (95% CI: 0.78–0.91). At the cut point of ≥4, the positive likelihood ratio of the score was 6.72 (95% CI: 4.7–9.6, p < 0.001), the sensitivity was 63.6%, and the specificity was 90.5%. Internal validation with bootstrap replications had an AuROC of 0.83 (95% CI: 0.75–0.91). Conclusions. The MBTT score has good discrimination to predict MBT with simple and rapidly obtainable parameters.

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Presepsin - New Marker of Sepsis Romanian Neonatal Intensive Care Unit Experience

Revista de Chimie ◽

10.37358/rc.19.8.7475 ◽

2019 ◽

Vol 70 (8) ◽

pp. 3008-3013

Author(s):

Silvia Maria Stoicescu ◽

Ramona Mohora ◽

Monica Luminos ◽

Madalina Maria Merisescu ◽

Gheorghita Jugulete ◽

...

Keyword(s):

Intensive Care Unit ◽

Intensive Care ◽

Neonatal Intensive Care Unit ◽

Neonatal Sepsis ◽

Predictive Value ◽

Neonatal Intensive Care ◽

Complete Blood Count ◽

Clinical Manifestations ◽

Receiver Operating Curve ◽

Predictive Values

Difficulties in establishing the onset of neonatal sepsis has directed the medical research in recent years to the possibility of identifying early biological markers of diagnosis. Overdiagnosing neonatal sepsis leads to a higher rate and duration in the usage of antibiotics in the Neonatal Intensive Care Unit (NICU), which in term leads to a rise in bacterial resistance, antibiotherapy complications, duration of hospitalization and costs.Concomitant analysis of CRP (C Reactive Protein), procalcitonin, complete blood count, presepsin in newborn babies with suspicion of early or late neonatal sepsis. Presepsin sensibility and specificity in diagnosing neonatal sepsis. The study group consists of newborns admitted to Polizu Neonatology Clinic between 15th February- 15th July 2017, with suspected neonatal sepsis. We analyzed: clinical manifestations and biochemical markers values used for diagnosis of sepsis, namely the value of CRP, presepsin and procalcitonin on the onset day of the disease and later, according to evolution. CRP values may be influenced by clinical pathology. Procalcitonin values were mainly influenced by the presence of jaundice. Presepsin is the biochemical marker with the fastest predictive values of positive infection. Presepsin can be a useful tool for early diagnosis of neonatal sepsis and can guide the antibiotic treatment. Presepsin value is significantly higher in neonatal sepsis compared to healthy newborns (939 vs 368 ng/mL, p [ 0.0001); area under receiver operating curve (AUC) for presepsine was 0.931 (95% confidence interval 0.86-1.0). PSP has a greater sensibility and specificity compared to classical sepsis markers, CRP and PCT respectively (AUC 0.931 vs 0.857 vs 0.819, p [ 0.001). The cut off value for presepsin was established at 538 ng/mLwith a sensibility of 79.5% and a specificity of 87.2 %. The positive predictive value (PPV) is 83.8 % and negative predictive value (NPV) is 83.3%.

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