ICU scoring systems.
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ABSTRACT:
SUBMITTER: Quintairos A
PROVIDER: S-EPMC9619008 | biostudies-literature | 2022 Oct
REPOSITORIES: biostudies-literature
Publications
ICU scoring systems.
Quintairos Amanda A Pilcher David D Salluh Jorge I F JIF
Intensive care medicine 20221031 2
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Project description:BackgroundThe clinical deterioration of patientsin general hospital wards is an important safety issue. Aggregate-weighted early warning systems (EWSs) may not detect risk until patients present with acute decline.PurposeWe aimed to compare the prognostic test accuracy and clinical workloads generated by EWSs using statistical modeling (multivariable regression or machine learning) versus aggregate-weighted tools.Data sourcesWe searched PubMed and CINAHL using terms that described clinical deterioration and use of an advanced EWS.Study selectionThe outcome was clinical deterioration (intensive care unit transfer or death) of adult patients on general hospital wards. We included studies published from January 1, 2012 to September 15, 2018.Data extractionFollowing 2015 PRIMSA systematic review protocol guidelines; 2015 TRIPOD criteria for predictive model evaluation; and the Cochrane Collaboration guidelines, we reported model performance, adjusted positive predictive value (PPV), and conducted simulations of workup-to-detection ratios.Data synthesisOf 285 articles, six studies reported the model performance of advanced EWSs, and five were of high quality. All EWSs using statistical modeling identified at-risk patients with greater precision than aggregate-weighted EWSs (mean AUC 0.80 vs 0.73). EWSs using statistical modeling generated 4.9 alerts to find one true positive case versus 7.1 alerts in aggregate-weighted EWSs; a nearly 50% relative workload increase for aggregate-weighted EWSs.ConclusionsCompared with aggregate-weighted tools, EWSs using statistical modeling consistently demonstrated superior prognostic performance and generated less workload to identify and treat one true positive case. A standardized approach to reporting EWS model performance is needed, including outcome definitions, pretest probability, observed and adjusted PPV, and workup-to-detection ratio.
| S-EPMC6628701 | biostudies-literature
Project description:IntroductionA rapid surge in cases during the COVID-19 pandemic can overwhelm any healthcare system. It is imperative to triage patients who would require oxygen and ICU care, and predict mortality. Specific parameters at admission may help in identifying them.MethodologyA prospective observational study was undertaken in a COVID-19 ward of a tertiary care center. All baseline clinical and laboratory data were captured. Patients were followed till death or discharge. Univariable and multivariable logistic regression was used to find predictors of the need for oxygen, need for ICU care, and mortality. Objective scoring systems were developed for the same using the predictors.ResultsThe study included 209 patients. Disease severity was mild, moderate, and severe in 98 (46.9%), 74 (35.4%), and 37 (17.7%) patients, respectively. The neutrophil-to-lymphocyte ratio (NLR) >4 was a common independent predictor of the need for oxygen (p<0.001), need for ICU transfer (p=0.04), and mortality (p=0.06). Clinical risk scores were developed (10*c-reactive protein (CRP) + 14.8*NLR + 12*urea), (10*aspartate transaminase (AST) + 15.7*NLR + 14.28*CRP), (10*NLR + 10.1*creatinine) which, if ≥14.8, ≥25.7, ≥10.1 predicted need for oxygenation, need for ICU transfer and mortality with a sensitivity and specificity (81.6%, 70%), (73.3%, 75.7%), (61.1%, 75%), respectively. Conclusion: The NLR, CRP, urea, creatinine, and AST are independent predictors in identifying patients with poor outcomes. An objective scoring system can be used at the bedside for appropriate triaging of patients and utilization of resources.
| S-EPMC9424646 | biostudies-literature
Project description:Hidradenitis suppurativa (HS) is an inflammatory disorder characterized by chronic deep-seated nodules, abscesses, fistulae, sinus tracts, and scars in apocrine gland-bearing regions. Assessing its severity is challenging because of its clinical heterogeneity, lack of a standardized tool, and increasing severity scores. This article provides a chronological overview of HS grading scales to aid in the understanding and comparison of different scoring systems. A literature review of articles published in English on PubMed was conducted searched from 1989 to 2023. The review includes 15 scores that are the most relevant and widely used and acknowledges the existence of over 30 scoring systems for HS. The expanding landscape of HS scoring systems presents challenges when patients evaluated using different systems are compared. A universally accepted scoring system is required for consistent application across diverse populations. A comprehensive assessment should balance subjective and objective items, considering observer-reported signs and patient-reported symptoms to make meaningful treatment decisions.
| S-EPMC10861310 | biostudies-literature
Project description:BackgroundMultiple scoring systems have been developed for both the intensive care unit (ICU) and the emergency department (ED) to risk stratify patients and predict mortality. However, it remains unclear whether the additional data needed to compute ICU scores improves mortality prediction for critically ill patients compared to the simpler ED scores.MethodsWe studied a prospective observational cohort of 227 critically ill patients admitted to the ICU directly from the ED at an academic, tertiary care medical center. We compared Acute Physiology and Chronic Health Evaluation (APACHE) II, APACHE III, Simplified Acute Physiology Score (SAPS) II, Modified Early Warning Score (MEWS), Rapid Emergency Medicine Score (REMS), Prince of Wales Emergency Department Score (PEDS), and a pre-hospital critical illness prediction score developed by Seymour et al. (JAMA 2010, 304(7):747-754). The primary endpoint was 60-day mortality. We compared the receiver operating characteristic (ROC) curves of the different scores and their calibration using the Hosmer-Lemeshow goodness-of-fit test and visual assessment.ResultsThe ICU scores outperformed the ED scores with higher area under the curve (AUC) values (p = 0.01). There were no differences in discrimination among the ED-based scoring systems (AUC 0.698 to 0.742; p = 0.45) or among the ICU-based scoring systems (AUC 0.779 to 0.799; p = 0.60). With the exception of the Seymour score, the ED-based scoring systems did not discriminate as well as the best-performing ICU-based scoring system, APACHE III (p = 0.005 to 0.01 for comparison of ED scores to APACHE III). The Seymour score had a superior AUC to other ED scores and, despite a lower AUC than all the ICU scores, was not significantly different than APACHE III (p = 0.09). When data from the first 24 h in the ICU was used to calculate the ED scores, the AUC for the ED scores improved numerically, but this improvement was not statistically significant. All scores had acceptable calibration.ConclusionsIn contrast to prior studies of patients based in the emergency department, ICU scores outperformed ED scores in critically ill patients admitted from the emergency department. This difference in performance seemed to be primarily due to the complexity of the scores rather than the time window from which the data was derived.
| S-EPMC4424730 | biostudies-literature
Project description:Assessment in the Education system plays a significant role in judging student performance. The present evaluation system is through human assessment. As the number of teachers' student ratio is gradually increasing, the manual evaluation process becomes complicated. The drawback of manual evaluation is that it is time-consuming, lacks reliability, and many more. This connection online examination system evolved as an alternative tool for pen and paper-based methods. Present Computer-based evaluation system works only for multiple-choice questions, but there is no proper evaluation system for grading essays and short answers. Many researchers are working on automated essay grading and short answer scoring for the last few decades, but assessing an essay by considering all parameters like the relevance of the content to the prompt, development of ideas, Cohesion, and Coherence is a big challenge till now. Few researchers focused on Content-based evaluation, while many of them addressed style-based assessment. This paper provides a systematic literature review on automated essay scoring systems. We studied the Artificial Intelligence and Machine Learning techniques used to evaluate automatic essay scoring and analyzed the limitations of the current studies and research trends. We observed that the essay evaluation is not done based on the relevance of the content and coherence.Supplementary informationThe online version contains supplementary material available at 10.1007/s10462-021-10068-2.
| S-EPMC8460059 | biostudies-literature
Project description:Outcome prediction of critically ill patients is an integral part of care in an Intensive Care Unit (ICU). Acute Physiology and Chronic Health Evaluation (APACHE) scoring systems provide an objective means of mortality prediction in ICU. The aim of this study was to compare the performance of APACHE II and IV scoring system in our ICU.All patients admitted to the ICU between January and June 2014 and who met the inclusion criteria were evaluated. APACHE II and IV score were calculated during the first 24 h of ICU stay based on the worst values. All patients were followed up till discharge from the hospital or death. Statistical analysis was performed using SPSS version 19.0. Discrimination of the model for mortality was assessed using receiver operating characteristic curve and calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test.Of a total 1268, 1003 patients were included in this study. The mean (±standard deviation) admission APACHE II score was 19.4 ± 8.9, and APACHE IV score was 59.1 ± 27.2. The APACHE scores were significantly higher among nonsurvivors than survivors (P < 0.001). The overall crude hospital mortality rate was 17.6%. APACHE IV had better discriminative power area under the ROC curve ([AUC] -0.82) than APACHE II (AUC-0.75). Both APACHE II and APACHE IV had poor calibration.APACHE IV showed better discrimination compared to APACHE II in our ICU population. Both APACHE II and APACHE IV had poor calibration. However, APACHE II calibrated better compared to APACHE IV.
| S-EPMC5520601 | biostudies-other
Project description:BackgroundWith the widespread spread of carbapenem-resistant gram-negative bacteria (CR-GNB) in medical facilities, the carriage of CR-GNB among critically ill patients has become a significant concern in intensive care units (ICU). This study aimed to develop a scoring system to identify CR-GNB carriers upon ICU admission.MethodsConsecutive critically ill patients admitted to the ICU of Shanghai Ruijin Hospital between January 2017 and December 2020 were included. The patients were then divided into training and testing datasets at a 7:3 ratio. Parameters associated with CR-GNB carriage were identified using least absolute shrinkage and selection operator regression analysis. Each parameter was assigned a numerical score ranging from 0 to 100 using logistic regression analysis. Subsequently, a four-tier risk-level system was developed based on the cumulative scores, and assessed using the area under the receiver operating characteristic curve (AUC).ResultsOf the 1736 patients included in this study, the prevalence of CR-GNB carriage was 10.60%. The clinical scoring system including seven variables (neurological disease, high-risk department history, length of stay ≥ 14 days, ICU history, invasive mechanical ventilation, gastrointestinal tube placement, and carbapenem usage) exhibited promising predictive capabilities. Patients were then stratified using the scoring system, resulting in CR-GNB carriage rates of 2.4%, 12.0%, 36.1%, and 57.9% at the respective risk levels (P < 0.001). Furthermore, the AUC of the developed model in the training set was calculated to be 0.82 (95% CI, 0.78-0.86), while internal validation yielded an AUC of 0.83 (95% CI, 0.77-0.89).ConclusionsThe ICU-CARB Score serves as a straightforward and precise tool that enables prompt evaluation of the risk of CR-GNB carriage at the time of ICU admission, thereby facilitating the timely implementation of targeted pre-emptive isolation.
| S-EPMC10613373 | biostudies-literature
Project description:PurposeTo compare the diagnostic performance and inter-observer agreement of five different CT chest severity scoring systems for COVID-19 to find the most precise one with the least interpretation time.Methods and materialsThis retrospective study included 85 patients (54 male and 31 female) with PCR-confirmed COVID-19. They underwent CT to assess the severity of pulmonary involvement. Three readers were asked to assess the pulmonary abnormalities and score the severity using five different systems, including chest CT severity score (CT-SS), chest CT score, total severity score (TSS), modified total severity score (m-TSS), and 3-level chest CT severity score. Time consumption on reporting of each system was calculated.ResultsTwo hundred fifty-five observations were reported for each system. There was a statistically significant inter-observer agreement in assessing qualitative lung involvement using the m-TSS and the other four quantitative systems. The ROC curves revealed excellent and very good diagnostic accuracy for all systems when cutoff values for detection severe cases were > 22, > 17, > 12, and > 26 for CT-SS, chest CT score, TSS, and 3-level CT severity score. The AUC was very good (0.86), excellent (0.90), very good (0.89), and very good (0.86), respectively. Chest CT score showed the highest specificity (95.2%) in discrimination of severe cases. Time consumption on reporting was significantly different (< 0.001): CT-SS > 3L-CT-SS > chest CT score > TSS.ConclusionAll chest CT severity scoring systems in this study demonstrated excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. CT-SS and TSS had the highest specificity and least time for interpretation.Key points• All chest CT severity scoring systems discussed in this study revealed excellent inter-observer agreement and reasonable performance to assess COVID-19 in relation to the clinical severity. • Chest CT scoring system and TSS had the highest specificity. • Both TSS and m-TSS consumed the least time compared to the other three scoring systems.
| S-EPMC8760133 | biostudies-literature
Project description:Few studies have focused on assessing the usefulness of scoring systems such as the Rockall score (RS), Glasgow-Blatchford score (GBS), and AIMS65 score for risk stratification and prognosis prediction in peptic ulcer bleeding patients. This study aimed to assess scoring systems in predicting clinical outcomes of patients with peptic ulcer bleeding. A total of 682 peptic ulcer bleeding patients who underwent esophagogastroduodenoscopy between January 2013 and December 2017 were found eligible for this study. The area under the receiver-operating characteristic curve (AUROC) of each score was calculated for predicting rebleeding, hospitalization, blood transfusion, and mortality. The median age of patients was 64 (interquartile range, 56-75) years. Of the patients, 74.9% were men, and 373 underwent endoscopic intervention. The median RS, GBS, and AIMS65 scores were significantly higher in patients who underwent endoscopic intervention than in those who did not. The AUROC of RS for predicting rebleeding was significantly higher than that of GBS (P = .022) or AIMS65 (P < .001). GBS best predicted the need for blood transfusion than either pre-RS (P = .013) or AIMS65 (P = .001). AIMS65 score showed the highest AUROC for mortality (0.652 vs. 0.622 vs. 0.691). RS was significantly associated with rebleeding (odds ratio, 1.430; P < .001) and overall survival (hazard ratio, 1.217; P < .001). The RS, GBS, and AIMS65 scoring systems are acceptable tools for predicting clinical outcomes in peptic ulcer bleeding. RS is an independent prognostic factor of rebleeding and overall survival.
| S-EPMC10980471 | biostudies-literature
Project description:BackgroundScoring systems that weigh the degree of abnormality of bedside observations might be able to identify patients at risk of catastrophic deterioration.ObjectivesTo establish a frequency distribution for typical physiological scoring systems and to establish the potential benefit of adding these to an existing triage system in accident and emergency departments.MethodsPhysiological data were collected from 53 unselected emergency department admissions, from 50 patients admitted from the emergency department to intensive care, and from 50 patients admitted from emergency department to general wards and then to intensive care. Three different physiological scores were calculated from the data. Identification of sick patients by the scores was compared with triage information from the Manchester Triage System (MTS).ResultsMost patients admitted to the emergency department would not be identified as critically ill with the aid of physiological scoring systems. This was true even for patients who were admitted to intensive care. Only in 0-8% of unselected patients did the scores indicate increased risk. In 100 patients admitted to the intensive care, adding of medical emergency team call-out criteria, Modified Early Warning Score or Assessment Score for Sick patient Identification and Step-up in Treatment would identify none, seven or one patient in addition to those triaged as orange and red by the MTS.ConclusionsIntroduction of a physiological scoring system would have identified only a small number of additional patients as critically ill and added little to the triage system currently in use.
| S-EPMC2464409 | biostudies-literature