ICU scoring systems

Evaluation of scoring systems and hematological parameters in the severity stratification of early-phase acute pancreatitis

BACKGROUND

Acute pancreatitis (AP) is an emergency gastrointestinal disease that requires immediate diagnosis and urgent clinical treatment. An accurate assessment and precise staging of severity are essential in initial intensive therapy.

AIM

To explore the prognostic value of inflammatory markers and several scoring systems [Acute Physiology and Chronic Health Evaluation II, the bedside index of severity in AP (BISAP), Ranson’s score, the computed tomography severity index (CTSI) and sequential organ failure assessment] in severity stratification of early-phase AP.

METHODS

A total of 463 patients with AP admitted to our hospital between 1 January 2021 and 30 June 2024 were retrospectively enrolled in this study. Inflammation marker and scoring system levels were calculated and compared between different severity groups. Relationships between severity and several predictors were evaluated using univariate and multivariate logistic regression models. Predictive ability was estimated using receiver operating characteristic curves.

RESULTS

Of the 463 patients, 50 (10.80%) were classified as having severe AP (SAP). The results revealed that the white cell count significantly increased, whereas the prognostic nutritional index measured within 48 hours (PNI₄₈) and calcium (Ca²⁺) were decreased as the severity of AP increased (P < 0.001). According to multivariate logistic regression, C-reactive protein measured within 48 hours (CRP₄₈), Ca²⁺ levels, and PNI₄₈ were independent risk factors for predicting SAP. The area under the curve (AUC) values for the CRP₄₈, Ca²⁺, PNI₄₈, Acute Physiology and Chronic Health Evaluation II, sequential organ failure assessment, BISAP, CTSI, and Ranson scores for the prediction of SAP were 0.802, 0.736, 0.871, 0.799, 0.783, 0.895, 0.931 and 0.914, respectively. The AUC for the combined CRP₄₈ + Ca²⁺ + PNI₄₈ model was 0.892. The combination of PNI₄₈ and Ranson achieved an AUC of 0.936.

CONCLUSION

Independent risk factors for developing SAP include CRP₄₈, Ca²⁺, and PNI₄₈. CTSI, BISAP, and the combination of PNI₄₈ and the Ranson score can act as reliable predictors of SAP.

The definition of predictor and outcome variables in mortality prediction models: a scoping review and quality of reporting study

BACKGROUND AND OBJECTIVES

Mortality prediction models are promising tools for guiding clinical decision-making and resource allocation in intensive care units (ICUs). Clearly specified predictor and outcome variables are necessary to enable external validation and safe clinical application of prediction models. The objective of this study was to identify the predictor and outcome variables used in different mortality prediction models in the ICU and investigate their reporting.

METHODS

For this scoping review, MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials were searched. Studies developed within a general ICU population reporting on prediction models with mortality as a primary or secondary outcome were eligible. The selection criteria were adopted from a review by Keuning et al. Predictor and outcome variables, variable characteristics (defined as units, definitions, moments of measurement, and methods of measurement), and publication details (defined as first author, year of publication and title) were extracted from the included studies. Predictor and outcome variable categories were demographics, chronic disease, care logistics, acute diagnosis, clinical examination and physiological derangement, laboratory assessment, additional diagnostics, support and therapy, risk scores, and (mortality) outcomes.

RESULTS

A total of 56 mortality prediction models, containing 204 unique predictor and outcome variables, were included. The predictor variables most frequently included in the models were age (40 times), admission type (27 times), and mechanical ventilation (21 times). We observed that single variables were measured with different units, according to different definitions, at a different moment, and with a different method of measurement in different studies. The reporting of the unit was mostly complete (98% overall, 95% in the laboratory assessment category), whereas the definition of the variable (74% overall, 63% in the chronic disease category) and method of measurement (70% overall, 34% in the demographics category) were most often lacking.

CONCLUSION

Accurate and transparent reporting of predictor and outcome variables is paramount to enhance reproducibility, model performance in different contexts, and validity. Since unclarity about the required input data may introduce bias and thereby affect model performance, this study advocates that prognostic ICU models can be improved by transparent and clear reporting of predictor and outcome variables and their characteristics.

Analyzing how the components of the SOFA score change over time in their contribution to mortality

OBJECTIVE

Determine how each organ component of the SOFA score differs in its contribution to mortality risk and how that contribution may change over time.

METHODS

We performed multivariate logistic regression analysis to assess the contribution of each organ component to mortality risk on Days 1 and 7 of an intensive care unit stay. We used data from two publicly available datasets, eICU Collaborative Research Database (eICU-CRD) (208 hospitals) and Medical Information Mart for Intensive Care IV (MIMIC-IV) (1 hospital). The odds ratio of each SOFA component that contributed to mortality was calculated. Mortality was defined as death either in the intensive care unit or within 72 hours of discharge from the intensive care unit.

RESULTS

A total of 7,871 intensive care unit stays from eICU-CRD and 4,926 intensive care unit stays from MIMIC-IV were included. Liver dysfunction was most predictive of mortality on Day 1 in both cohorts (OR 1.3; 95%CI 1.2 - 1.4; OR 1.3; 95%CI 1.2 - 1.4, respectively). In the eICU-CRD cohort, central nervous system dysfunction was most predictive of mortality on Day 7 (OR 1.4; 95%CI 1.4 - 1.5). In the MIMIC-IV cohort, respiratory dysfunction (OR 1.4; 95%CI 1.3 - 1.5) and cardiovascular dysfunction (OR 1.4; 95%CI 1.3 - 1.5) were most predictive of mortality on Day 7.

CONCLUSION

The SOFA score may be an oversimplification of how dysfunction of different organ systems contributes to mortality over time. Further research at a more granular timescale is needed to explore how the SOFA score can evolve and be ameliorated.

Association between D-dimer-to-albumin ratio and 28-days all-cause mortality in patients with sepsis

We aimed to investigate the correlation between the serum D-dimer (D-D) to albumin (ALB) ratio (DAR) and 28-day all-cause mortality in patients with sepsis. Data from sepsis patients admitted to the intensive care unit (ICU) of Wuhan Fourth Hospital from October 2021 to January 2024 were collected. Univariate cox analysis was performed for mortality factors in sepsis patients, and multiple cox regression models were used to analyze independent mortality risk factors. The receiver operating characteristics (ROC) curve was used to analyze the value of DAR in predicting sepsis mortality, and the Kaplan-Meier method was used to plot the survival curve. A total of 833 patients with sepsis in the ICU of our hospital were selected and divided into alive group (n = 574) and death group (n = 171) according to their 28-day survival. In the death group, D-D and DAR levels were higher, while ALB levels was lower than in the alive group. Spearman analysis found that DAR level were positively correlated with APACHE II and SOFA scores. Multivariate cox regression analysis showed that DAR was an independent predictor of all-cause mortality within 28 days of admission for sepsis patients (HR = 17.956, 95% CI 3.435-93.851, p < 0.001). The ROC curve results showed that the cut-off value of DAR was 0.068, with a sensitivity of 78.4% and a Youden index of 0.375, predicting mortality in sepsis patients, with an area under curve (AUC) of 0.767 (95% CI 0.744-0.790, P < 0.001). Further analysis divided patients into low DAR (DAR < 0.068) and high DAR (DA ≥ 0.068) groups based on the optimal cut-off value. Kaplan-Meier analysis found higher mortality in the high DAR group. DAR is an independent predictor of all-cause mortality within 28 days of admission in sepsis patients. Combining these two indicators can improve clinical treatment guidance and reduce the risk of death.

Predictive Mortality of the Prognostic Nutritional Index Combined with APACHE II Score for Critically Ill Tuberculosis Patients

High mortality rates are commonly found in critically ill patients with tuberculosis (TB), which is due partially to limitations in the existing prognostic evaluation methods. Therefore, we aimed to find more effective prognostic evaluation tools to reduce the mortality rate. Data from critically ill patients with TB admitted to the intensive care unit of The Second Hospital of Nanjing, Nanjing, China, between January 2020 and December 2022 were analyzed retrospectively. A total of 115 patients were enrolled and divided into a survival group (n = 62) and a death group (n = 53) according to 30-day survival. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to investigate the risk factors for 30-day death in critically ill patients with TB. A prediction model for risk of 30-day mortality was developed for critically ill patients with TB in the intensive care unit. The LASSO regression model showed that the prognostic nutritional index (PNI) and Acute Physiology and Chronic Health Status (APACHE II) scores on the third day after admission to the intensive care unit were independent risk factors for 30-day mortality in critically ill patients with TB (P <0.05). The area under the curve value and that PA3 represents the combination of the PNI and APACHE II score on the third day, which was 0.952 (95% CI: 0.913-0.991, P <0.001), was significantly higher than that of the PNI or the APACHE II score on the third day. The new model is as follows: PA3 = APACHE II score (on the third day) × 0.421 - PNI × 0.204. The PNI combined with the APACHE II score on the third day could well predict the 30-day mortality risk of critically ill patients with TB.

Single-center nomogram model for sepsis complicated by acute lung injury

OBJECTIVE

To construct and validate a nomogram model for predicting sepsis complicated by acute lung injury (ALI).

METHODS

The healthcare records of 193 sepsis patients hospitalized at The Affiliated Tai'an City Central Hospital of Qingdao University from January 2022 to December 2023 were retrospectively reviewed. Among these patients, 69 were in the ALI group and 124 in the non-ALI group. A nomogram prediction model was constructed using logistic regression analysis. Its predictive performance was evaluated through various measures, including the area under the curve (AUC), calibration curve, decision curve, sensitivity, specificity, accuracy, recall rate, and precision rate.

RESULTS

The predictive factors included the neutrophil/lymphocyte ratio (NLR), oxygenation index (PaO2/FiO2), tumor necrosis factor-α (TNF-α), and acute physiology and chronic health evaluation II (APACHE II). The nomogram training set achieved an AUC of 0.959 (95% CI: 0.924-0.995), an accuracy of 92.59%, a recall of 96.70%, and a precision of 92.63%. In the validation set, the AUC was 0.938 (95% CI: 0.880-0.996), with an accuracy of 89.66%, a recall of 93.94%, and a precision of 88.57%. The calibration curve demonstrated that the prediction results were consistent with the actual findings. The decision curve indicated that the model has clinical applicability.

CONCLUSION

NLR, PaO2/FiO2, TNF-α, and APACHE II are closely associated with ALI in sepsis patients. A nomogram model based on these four variables shows strong predictive performance and may be used as a clinical decision-support tool to help physicians better identify high-risk groups.

Digital cholangioscope assisted radiation-free bedside one-stage endoscopic lithotomy and biliary drainage for severe acute cholangitis caused by choledocholithiasis

Radiation-free one-stage bedside endoscopic stone removal and biliary drainage for severe acute cholangitis (SAC) caused by choledocholithiasis in intensive care unit (ICU) has not been reported. Herein, we introduce our preliminary experience of such intervention. Radiation-free bedside digital cholangioscope-assisted one-stage endoscopic stone removal and biliary drainage was performed in an urgent manner. Data on clinical outcomes and follow-up from thirty patients were retrospectively analyzed. Time interval was 7.6 ± 4.7 (2-18) h between ICU admission and endoscopic intervention, and was 35.5 ± 14.5 (5-48) h between the seizure and endoscopic intervention. A 100% technical success was achieved. Except for one mild pancreatitis, no other complication occurred. Patients showed good responses to endoscopic interventions, which were reflected by ameliorated disease severities and laboratory findings. Time lengths of ICU stay and total in-hospital stay were 8.7 ± 4.9 (2-23) days and 14.5 ± 7.4 (5-39) days, respectively. In-hospital mortality occurred in three patients. According to a 6-month follow-up, two patients died of pneumonia and acute myocardial infarction. No SAC and/or biliary stone residual occurred. The current intervention demonstrated favorable results compared to traditional endoscopic retrograde cholangiopancreatography. Our study provides a novel bedside endoscopic intervention method for SAC caused by choledocholithiasis.

A nomogram based on quantitative EEG to predict the prognosis of nontraumatic coma patients in the neuro-intensive care unit

OBJECTIVE

We aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work.

METHODS

This retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model.

RESULTS

After excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244-1074.9000)], sleep cycle [OR: 0.006 (0.0002-0.1808)], and RAV [0.068 (0.0049-0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899-0.979).

CONCLUSION

A lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge.

CLINICAL IMPLICATION

This study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice.

Development and validation of an interpretable model for predicting sepsis mortality across care settings

There are numerous prognostic predictive models for evaluating mortality risk, but current scoring models might not fully cater to sepsis patients' needs. This study developed and validated a new model for sepsis patients that is suitable for any care setting and accurately forecasts 28-day mortality. The derivation dataset, gathered from 20 hospitals between September 2019 and December 2021, contrasted with the validation dataset, collected from 15 hospitals from January 2022 to December 2022. In this study, 7436 patients were classified as members of the derivation dataset, and 2284 patients were classified as members of the validation dataset. The point system model emerged as the optimal model among the tested predictive models for foreseeing sepsis mortality. For community-acquired sepsis, the model's performance was satisfactory (derivation dataset AUC: 0.779, 95% CI 0.765-0.792; validation dataset AUC: 0.787, 95% CI 0.765-0.810). Similarly, for hospital-acquired sepsis, it performed well (derivation dataset AUC: 0.768, 95% CI 0.748-0.788; validation dataset AUC: 0.729, 95% CI 0.687-0.770). The calculator, accessible at https://avonlea76.shinyapps.io/shiny_app_up/ , is user-friendly and compatible. The new predictive model of sepsis mortality is user-friendly and satisfactorily forecasts 28-day mortality. Its versatility lies in its applicability to all patients, encompassing both community-acquired and hospital-acquired sepsis.

The Association Between Prepandemic ICU Performance and Mortality Variation in COVID-19: A Multicenter Cohort Study of 35,619 Critically Ill Patients

BACKGROUND

During the COVID-19 pandemic, ICUs remained under stress and observed elevated mortality rates and high variations of outcomes. A knowledge gap exists regarding whether an ICU performing best during nonpandemic times would still perform better when under high pressure compared with the least performing ICUs.

RESEARCH QUESTION

Does prepandemic ICU performance explain the risk-adjusted mortality variability for critically ill patients with COVID-19?

STUDY DESIGN AND METHODS

This study examined a cohort of adults with real-time polymerase chain reaction-confirmed COVID-19 admitted to 156 ICUs in 35 hospitals from February 16, 2020, through December 31, 2021, in Brazil. We evaluated crude and adjusted in-hospital mortality variability of patients with COVID-19 in the ICU during the pandemic. Association of baseline (prepandemic) ICU performance and in-hospital mortality was examined using a variable life-adjusted display (VLAD) during the pandemic and a multivariable mixed regression model adjusted by clinical characteristics, interaction of performance with the year of admission, and mechanical ventilation at admission.

RESULTS

Thirty-five thousand six hundred nineteen patients with confirmed COVID-19 were evaluated. The median age was 52 years, median Simplified Acute Physiology Score 3 was 42, and 18% underwent invasive mechanical ventilation. In-hospital mortality was 13% and 54% for those receiving invasive mechanical ventilation. Adjusted in-hospital mortality ranged from 3.6% to 63.2%. VLAD in the most efficient ICUs was higher than the overall median in 18% of weeks, whereas VLAD was 62% and 84% in the underachieving and least efficient groups, respectively. The least efficient baseline ICU performance group was associated independently with increased mortality (OR, 2.30; 95% CI, 1.45-3.62) after adjusting for patient characteristics, disease severity, and pandemic surge.

INTERPRETATION

ICUs caring for patients with COVID-19 presented substantial variation in risk-adjusted mortality. ICUs with better baseline (prepandemic) performance showed reduced mortality and less variability. Our findings suggest that achieving ICU efficiency by targeting improvement in organizational aspects of ICUs may impact outcomes, and therefore should be a part of the preparedness for future pandemics.

Immune Dysfunction-Associated Elevated RDW, APACHE-II, and SOFA Scores Were a Possible Cause of 28-Day Mortality in Sepsis Patients

Objective

To explore the early predictors and their predicting value of 28-day mortality in sepsis patients and to investigate the possible causes of death.

Methods

127 sepsis patients were included, including 79 cases in the survival group and 48 cases in the death group. The results of all patients on admission were recorded. After screening the risk factors of 28-day mortality, the receiver operating characteristic curve (ROC) was used to determine their predictive value for the 28-day mortality rate on admission, and the Kaplan-Meier curve was drawn to compare the 28-day mortality rate between groups. Finally, patients with cytokine and lymphocyte subsets results were included for investigating the possible causes of death through correlation analysis.

Results

APACHE II (acute physiology and chronic health evaluation II), SOFA (Sequential Organ Failure Assessment) and red blood cell distribution width (RDW) were the risk factors for 28-day mortality in sepsis patients (OR: 1.130 vs.1.160 vs.1.530, P < 0.05). The area under the curve (AUC), sensitivity and specificity of APACHE II, SOFA and RDW in predicting the mortality rate at 28 days after admission in sepsis patients were 0.763 vs 0.806 vs 0.723, 79.2% vs 68.8% vs 75.0%, 65.8% vs 89.9% vs 68.4%. The combined predicted AUC was 0.873, the sensitivity was 89.6%, and the specificity was 82.3%. The Kaplan-Meier survival curve showed that the 28-day mortality rates of sepsis patients with APACHE II≥18.5, SOFA≥11.5 and RDW≥13.8 were 58.5%, 80.5% and 59.0%, respectively. In the death group, APACHE II was positively correlated with SOFA, IL-2, and IL-10, and RDW was positively correlated with PLT, TNF-α, CD3+ lymphocyte count, and CD8+ lymphocyte count.

Conclusion

Sepsis patients with high APACHE II, SOFA and RDW levels at admission have an increased 28-day mortality rate. The elevation of these indicators in dead patients are related to immune dysfunction.

Digital twins in medicine

Medical digital twins, which are potentially vital for personalized medicine, have become a recent focus in medical research. Here we present an overview of the state of the art in medical digital twin development, especially in oncology and cardiology, where it is most advanced. We discuss major challenges, such as data integration and privacy, and provide an outlook on future advancements. Emphasizing the importance of this technology in healthcare, we highlight the potential for substantial improvements in patient-specific treatments and diagnostics.

Earthquake victims in focus: a cross-sectional examination of trauma and management in intensive care unit

BACKGROUND

After the Kahramanmaras earthquake of February 6, 2023, the disaster of the century, a significant number of victims were admitted to intensive care units (ICUs). In this study, we aimed to share the characteristics and management of critical earthquake victims and shed light on our experiences as intensivists in future earthquakes.

METHODS

The study included 62 earthquake victims in two tertiary ICUs. Demographic characteristics, laboratory findings, clinical characteristics, trauma and disease severity scores, treatments administered to patients, and the clinical course of the patients were recorded retrospectively. The patients were divided into two groups, survivors and nonsurvivors, according to 7-day mortality and into two groups according to the duration of their stay under the rubble: those who remained under the rubble for 72 hours or less and those who remained under the rubble for more than 72 hours. A receiver operating characteristic (ROC) curve analysis was used to determine the best cutoff value for the 'Circulation, Respiration, Abdomen, Motor, and Speech' (CRAMS) score.

RESULTS

The median age of the 62 patients included in the study was 35.5 (23-53) years. The median length of stay under the rubble for the patients was 30.5 (12-64.5) hours. The patient was transferred to the ward with a maximum duration of 222 hours under the rubble. The limb (75.8%) was the most common location of trauma in patients admitted to the ICU. Crush syndrome developed in 96.8% of the patients. There was a positive correlation between the development of acute kidney injury (AKI) and myoglobin, serum lactate, and uric acid levels (r = 0.372, p = 0.003; r = 0.307, p = 0.016; r = 0.428, p = 0.001, respectively). The best cutoff of the CRAMS score to predict in-7-day mortality was < 4.5 with 0.94 area under the curve (AUC); application of this threshold resulted in 75% sensitivity and 96.3% specificity.

CONCLUSION

Search and rescue operations should continue for at least ten days after an earthquake. The CRAMS score can be used to assess trauma severity and predict mortality in critically ill earthquake victims.

Prevalence and clinical significance of potential drug-drug interactions among lung transplant patients

Background: Drug-drug interactions (DDIs) are a major but preventable cause of adverse drug reactions. There is insufficient information regarding DDIs in lung transplant recipients. Objective: This study aimed to determine the prevalence of potential DDIs (pDDIs) in intensive care unit (ICU) lung transplant recipients, identify the real DDIs and the most frequently implicated medications in this vulnerable population, and determine the risk factors associated with pDDIs. Methods: This retrospective cross-sectional study included lung transplant recipients from January 2018 to December 2021. Pertinent information was retrieved from medical records. All prescribed medications were screened for pDDIs using the Lexicomp® drug interaction software. According to this interaction software, pDDIs were classified as C, D, or X (C = monitor therapy, D = consider therapy modification, X = avoid combination). The Drug Interaction Probability Scale was used to determine the causation of DDIs. All statistical analysis was performed in SPSS version 26.0. Results: 114 patients were qualified for pDDI analysis, and total pDDIs were 4051. The most common type of pDDIs was category C (3323; 82.0%), followed by D (653; 16.1%) and X (75; 1.9%). Voriconazole and posaconazole were the antifungal medicine with the most genuine DDIs. Mean tacrolimus concentration/dose (Tac C/D) before or after co-therapy was considerably lower than the Tac C/D during voriconazole or posaconazole co-therapy (p < 0.001, p = 0.027). Real DDIs caused adverse drug events (ADEs) in 20 patients. Multivariable logistic regression analyses found the number of drugs per patient (OR, 1.095; 95% CI, 1.048-1.145; p < 0.001) and the Acute Physiology and Chronic Health Evaluation II (APACHE Ⅱ) score (OR, 1.097; 95% CI, 1.021-1.179; p = 0.012) as independent risk factors predicting category X pDDIs. Conclusion: This study revealed a high incidence of both potential and real DDIs in ICU lung transplant recipients. Immunosuppressive drugs administered with azole had a high risk of causing clinically significant interactions. The number of co-administered drugs and APACHE Ⅱ score were associated with an increased risk of category × drug interactions. Close monitoring of clinical and laboratory parameters is essential for ensuring successful lung transplantation and preventing adverse drug events associated with DDIs.

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence

BACKGROUND

In intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2.

METHODS

We conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm's performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability.

MEASUREMENTS AND MAIN RESULTS

The study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2.

CONCLUSIONS

The AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.

National ICU Registries as Enablers of Clinical Research and Quality Improvement

OBJECTIVES

Clinical quality registries (CQRs) have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. This narrative review describes the challenges, proposed solutions, and evidence generated by National ICU registries as facilitators for research and quality improvement.

DATA SOURCES

English language articles were identified in PubMed using phrases related to ICU registries, CQRs, outcomes, and case-mix.

STUDY SELECTION

Original research, review articles, letters, and commentaries, were considered.

DATA EXTRACTION

Data from relevant literature were identified, reviewed, and integrated into a concise narrative review.

DATA SYNTHESIS

CQRs have been implemented worldwide by several medical specialties aiming to generate a better characterization of epidemiology, treatments, and outcomes of patients. National ICU registries were created almost 3 decades ago to improve the understanding of case-mix, resource use, and outcomes of critically ill patients. The initial experience in European countries and in Oceania ensured that through locally generated data, ICUs could assess their performances by using risk-adjusted measures and compare their results through fair and validated benchmarking metrics with other ICUs contributing to the CQR. The accomplishment of these initiatives, coupled with the increasing adoption of information technology, resulted in a broad geographic expansion of CQRs as well as their use in quality improvement studies, clinical trials as well as international comparisons, and benchmarking for ICUs.

CONCLUSIONS

ICU registries have provided increased knowledge of case-mix and outcomes of ICU patients based on real-world data and contributed to improve care delivery through quality improvement initiatives and trials. Recent increases in adoption of new technologies (i.e., cloud-based structures, artificial intelligence, machine learning) will ensure a broader and better use of data for epidemiology, healthcare policies, quality improvement, and clinical trials.

Association between dynamic fluctuations in triiodothyronine levels and prognosis among critically ill patients within comprehensive intensive care units

Objective

Decrease in free thyroid hormone T3 (FT3) can be used as an independent prognostic indicator for the risk of death in ICUs. However, FT3 as a predictive marker is hindered by its accuracy. The study introduces the concept of dynamic FT3 data as a means to bolster the value of FT3 as a prognostic tool. Therefore, the aim of this study is to investigate the prognostic value of dynamic FT3 evolution in a comprehensive ICU setting, analyze the consistency between dynamic FT3 changes and variations in disease severity, and explore the feasibility of FT3 as an objective indicator for real-time clinical treatment feedback.

Methods

Employing a single-center prospective observational study, FT3 measurements were taken on multiple days following enrollment, corresponding clinical data were collected. To investigated the pattern of dynamic changes of FT3,its prognostic significance in forecasting the risk of 28-day mortality, the alignment between dynamic FT3 changes and variations in the Sequential Organ Failure Assessment (SOFA) score.

Results

The survival group exhibited higher last FT3 levels compared to the lowest point (p<0.05), while the death group did not show statistically significant differences (p>0.05). The study also identifies the optimal correlation between FT3 and SOFA score at day 5 (optimal correlation coefficient -0.546).The ROC curve for FT3 at day 5 yielded an optimal AUC of 0.88, outperforming the SOFA score. The study categorizes FT3 curve patterns,Kaplan-Meier survival analysis of these patterns highlighted that the descending-type curve was significantly associated with increased risk of death (P<0.001). Additionally, the research explores the consistency between changes in FT3 and SOFA scores. While overall consistency rates were modest, subgroup analyses unveiled that greater disease severity led to higher consistency rates.

Conclusions

This study introduces the concept of dynamic FT3 changes to augment its prognostic utility in comprehensive ICU settings. The research identifies day 5 as the optimal time point for predictive efficacy, the descending FT3 curve as indicative of poor prognosis. While overall consistency with SOFA scores is modest, the correlation strengthens with greater disease severity.

Basophils absence predicts poor prognosis and indicates immunosuppression of patients in intensive care units

Immune cells and immunity are associated with the prognosis of patients with critical illness. Here, medical records retrospectively extracted from the Medical Information Mart for Intensive Care IV were used for screening an immune-related biomarker in intensive care units (ICU) patients and applied for validating the identified indicator in septic patients. In this work, the count of innate immune cells, basophils, harbored a superior role in predicting ICU patients' prognosis compared with those of other blood immune cells (OR 0.013, 95% CI 0.001, 0.118, P < 0.001). Importantly, basophils absence during ICU stay was positively correlated with the 28-day mortality of ICU patients and served as an independent predictor of ICU patients' prognosis (OR 3.425, 95% CI 3.717-3.165, P < 0.001). Moreover, the association between critical illness progression, poor outcome, and basophils absence was verified in septic patients. Subsequent investigations revealed the positive relationship between basophils absence and immunosuppression, and suggested the potential of basophils-mediated immunity in predicting the 28-day mortality of ICU patients. Collectively, we identify basophils absence during ICU stay as a novel and unfavorable indicator for evaluating the prognosis of ICU patients and recognizing a branch of ICU patients potentially suitable for intensified treatment and immunoenhancement therapy.

Prognostic Performance of Sequential Organ Failure Assessment, Acute Physiology and Chronic Health Evaluation III, and Simplified Acute Physiology Score II Scores in Patients with Suspected Infection According to Intensive Care Unit Type

We investigated the prognostic performance of scoring systems by the intensive care unit (ICU) type. This was a retrospective observational study using data from the Marketplace for Medical Information in the Intensive Care IV database. The primary outcome was in-hospital mortality. We obtained Sequential Organ Failure Assessment (SOFA), Acute Physiology and Chronic Health Evaluation (APACHE) III, and Simplified Acute Physiology Score (SAPS) II scores in each ICU type. Prognostic performance was evaluated with the area under the receiver operating characteristic curve (AUROC) and was compared among ICU types. A total of 29,618 patients were analyzed, and the in-hospital mortality was 12.4%. The overall prognostic performance of APACHE III was significantly higher than those of SOFA and SAPS II (0.807, [95% confidence interval, 0.799-0.814], 0.785 [0.773-0.797], and 0.795 [0.787-0.811], respectively). The prognostic performance of SOFA, APACHE III, and SAPS II scores was significantly different between ICU types. The AUROC ranges of SOFA, APACHE III, and SAPS II were 0.723-0.826, 0.728-0.860, and 0.759-0.819, respectively. The neurosurgical and surgical ICUs had lower prognostic performance than other ICU types. The prognostic performance of scoring systems in patients with suspected infection is significantly different according to ICU type. APACHE III systems have the highest prediction performance. ICU type may be a significant factor in the prognostication.

Prognostic models of in-hospital mortality of intensive care patients using neural representation of unstructured text: A systematic review and critical appraisal

OBJECTIVE

To review and critically appraise published and preprint reports of prognostic models of in-hospital mortality of patients in the intensive-care unit (ICU) based on neural representations (embeddings) of clinical notes.

METHODS

PubMed and arXiv were searched up to August 1, 2022. At least two reviewers independently selected the studies that developed a prognostic model of in-hospital mortality of intensive-care patients using free-text represented as embeddings and extracted data using the CHARMS checklist. Risk of bias was assessed using PROBAST. Reporting on the model was assessed with the TRIPOD guideline. To assess the machine learning components that were used in the models, we present a new descriptive framework based on different techniques to represent text and provide predictions from text. The study protocol was registered in the PROSPERO database (CRD42022354602).

RESULTS

Eighteen studies out of 2,825 were included. All studies used the publicly-available MIMIC dataset. Context-independent word embeddings are widely used. Model discrimination was provided by all studies (AUROC 0.75-0.96), but measures of calibration were scarce. Seven studies used both structural clinical variables and notes. Model discrimination improved when adding clinical notes to variables. None of the models was externally validated and often a simple train/test split was used for internal validation. Our critical appraisal demonstrated a high risk of bias in all studies and concerns regarding their applicability in clinical practice.

CONCLUSION

All studies used a neural architecture for prediction and were based on one publicly available dataset. Clinical notes were reported to improve predictive performance when used in addition to only clinical variables. Most studies had methodological, reporting, and applicability issues. We recommend reporting both model discrimination and calibration, using additional data sources, and using more robust evaluation strategies, including prospective and external validation. Finally, sharing data and code is encouraged to improve study reproducibility.

A Simple Nomogram for Predicting Hospital Mortality of Patients Over 80 Years in ICU: An International Multicenter Retrospective Study

OBJECTIVES

This study aimed to develop and validate an easy-to-use intensive care unit (ICU) illness scoring system to evaluate the in-hospital mortality for very old patients (VOPs, over 80 years old).

METHODS

We performed a multicenter retrospective study based on the electronic ICU (eICU) Collaborative Research Database (eICU-CRD), Medical Information Mart for Intensive Care Database (MIMIC-III CareVue and MIMIC-IV), and the Amsterdam University Medical Centers Database (AmsterdamUMCdb). Least Absolute Shrinkage and Selection Operator regression was applied to variables selection. The logistic regression algorithm was used to develop the risk score and a nomogram was further generated to explain the score.

RESULTS

We analyzed 23 704 VOPs, including 3 726 deaths (10 183 [13.5% mortality] from eICU-CRD [development set], 12 703 [17.2%] from the MIMIC, and 818 [20.8%] from the AmsterdamUMC [external validation sets]). Thirty-four variables were extracted on the first day of ICU admission, and 10 variables were finally chosen including Glasgow Coma Scale, shock index, respiratory rate, partial pressure of carbon dioxide, lactate, mechanical ventilation (yes vs no), oxygen saturation, Charlson Comorbidity Index, blood urea nitrogen, and urine output. The nomogram was developed based on the 10 variables (area under the receiver operating characteristic curve: training of 0.792, testing of 0.788, MIMIC of 0.764, and AmsterdamUMC of 0.808 [external validating]), which consistently outperformed the Sequential Organ Failure Assessment, acute physiology score III, and simplified acute physiology score II.

CONCLUSIONS

We developed and externally validated a nomogram for predicting mortality in VOPs based on 10 commonly measured variables on the first day of ICU admission. It could be a useful tool for clinicians to identify potentially high risks of VOPs.