Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome

Development and Validation Nomogram for Predicting the Survival of Patients with Thrombocytopenia in Intensive Care Units

Background

The number of patients with thrombocytopenia (TCP) is relatively high in intensive care units (ICUs). It is therefore necessary to evaluate the prognostic risk of such patients.

Aim

This study investigated the risk factors affecting the survival of patients with TCP in the ICU. Using the findings of this investigation, we developed and validated a risk prediction model.

Methods

We evaluated patients admitted to the ICU who presented with TCP. We used LASSO regression to identify important clinical indicators. Based on these indicators, we developed a prediction model complete with a nomogram for the development cohort set. We then evaluated the mode's accuracy using a receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA) in a validation cohort.

Results

A total of 141 cases of ICU TCP were included in the sample, of which 47 involved death of the patient. Clinical results were as follows: N (HR 0.91, 95% CI 0.86-0.97, P=0.003); TBIL (HR 1.98, 95% CI 1.02-1.99, P=0.048); APACHE II (HR 1.94, 95% CI 1.39, 2.48, P=0.045); WPRN (HR 6.22, 95% CI 2.86-13.53, P<0.001); WTOST (HR 0.56, 95% CI 0.21-1.46, P<0.001); and DMV [HR1.87, 95% CI 1.12-2.33]. The prediction model yielded an area under the curve (AUC) of 0.918 (95% CI 0.863-0.974) in the development cohort and 0.926 (95% CI 0.849-0.994) in the validation cohort. Application of the nomogram in the validation cohort gave good discrimination (C-index 0.853, 95% CI 0.810-0.922) and good calibration. DCA indicated that the nomogram was clinically useful.

Conclusion

The individualized nomogram developed through our analysis demonstrated effective prognostic prediction for patients with TCP in ICUs. Use of this prediction metric may reduce TCP-related morbidity and mortality in ICUs.

Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center

The medical specialty of critical care, or intensive care, provides emergency medical care to patients suffering from life-threatening complications and injuries. The medical specialty is featured by the generation of a huge amount of high-granularity data in routine practice. Currently, these data are well archived in the hospital information system for the primary purpose of routine clinical practice. However, data scientists have noticed that in-depth mining of such big data may provide insights into the pathophysiology of underlying diseases and healthcare practices. There have been several openly accessible critical care databases being established, which have generated hundreds of scientific outputs published in scientific journals. However, such work is still in its infancy in China. China is a large country with a huge patient population, contributing to the generation of large healthcare databases in hospitals. In this data descriptor article, we report the establishment of an openly accessible critical care database generated from the hospital information system.

External validation of five predictive models for postoperative cardiopulmonary morbidity in a Chinese population receiving lung resection

BACKGROUND

No postoperative cardiopulmonary morbidity models have been developed or validated in Chinese patients with lung resection. This study aims to externally validate five predictive models, including Eurolung models, the Brunelli model and the Age-adjusted Charlson Comorbidity Index, in a Chinese population.

METHODS

Patients with lung cancer who underwent anatomic lung resection between 2018/09/01 and 2019/08/31 in our center were involved. Model discrimination was assessed by the area under the receiver operating characteristic curve. Model calibration was evaluated by the Hosmer-Lemeshow test. Calibration curves were plotted. Specificity, sensitivity, negative predictive value, positive predictive value and accuracy were calculated. Model updating was achieved by re-estimating the intercept and/or the slope of the linear predictor and re-estimating all coefficients.

RESULTS

Among 1085 patients, 91 patients had postoperative cardiopulmonary complications defined by the European Society of Thoracic Surgeons. For original models, only parsimonious Eurolung1 had acceptable discrimination (area under the receiver operating characteristic curve = 0.688, 95% confidence interval 0.630-0.745) and calibration (p = 0.23 > 0.05) abilities simultaneously. Its sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 0.700, 0.649, 0.153, 0.960 and 0.653, respectively. In the secondary analysis, increased pleural effusion (n = 94), which was nonchylous and nonpurulent, was labeled as a kind of postoperative complication. The area under the receiver operating characteristic curve of the models increased slightly, but all models were miscalibrated. The original Eurolung1 model had the highest discrimination ability but poor calibration, and thus it was updated by three methods. After model updating, new models showed good calibration and small improvements in discrimination. The discrimination ability was still merely acceptable.

CONCLUSIONS

Overall, none of the models performed well on postoperative cardiopulmonary morbidity prediction in this Chinese population. The original parsimonious Eurolung1 and the updated Eurolung1 were the best-performing models on morbidity prediction, but their discrimination ability only achieved an acceptable level. A multicenter study with more relevant variables and sophisticated statistical methods is warranted to develop new models among Chinese patients in the future.

Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach

Background

Timely decision-making regarding intensive care unit (ICU) admission for children with pneumonia is crucial for a better prognosis. Despite attempts to establish a guideline or triage system for evaluating ICU care needs, no clinically applicable paradigm is available.

Objective

The aim of this study was to develop machine learning (ML) algorithms to predict ICU care needs for pediatric pneumonia patients within 24 hours of admission, evaluate their performance, and identify clinical indices for making decisions for pediatric pneumonia patients.

Methods

Pneumonia patients admitted to National Taiwan University Hospital from January 2010 to December 2019 aged under 18 years were enrolled. Their underlying diseases, clinical manifestations, and laboratory data at admission were collected. The outcome of interest was ICU transfer within 24 hours of hospitalization. We compared clinically relevant features between early ICU transfer patients and patients without ICU care. ML algorithms were developed to predict ICU admission. The performance of the algorithms was evaluated using sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and average precision. The relative feature importance of the best-performing algorithm was compared with physician-rated feature importance for explainability.

Results

A total of 8464 pediatric hospitalizations due to pneumonia were recorded, and 1166 (1166/8464, 13.8%) hospitalized patients were transferred to the ICU within 24 hours. Early ICU transfer patients were younger (P<.001), had higher rates of underlying diseases (eg, cardiovascular, neuropsychological, and congenital anomaly/genetic disorders; P<.001), had abnormal laboratory data, had higher pulse rates (P<.001), had higher breath rates (P<.001), had lower oxygen saturation (P<.001), and had lower peak body temperature (P<.001) at admission than patients without ICU transfer. The random forest (RF) algorithm achieved the best performance (sensitivity 0.94, 95% CI 0.92-0.95; specificity 0.94, 95% CI 0.92-0.95; AUC 0.99, 95% CI 0.98-0.99; and average precision 0.93, 95% CI 0.90-0.96). The lowest systolic blood pressure and presence of cardiovascular and neuropsychological diseases ranked in the top 10 in both RF relative feature importance and clinician judgment.

Conclusions

The ML approach could provide a clinically applicable triage algorithm and identify important clinical indices, such as age, underlying diseases, abnormal vital signs, and laboratory data for evaluating the need for intensive care in children with pneumonia.

Advances in the Pharmacological Management of Pediatric Acute Respiratory Distress Syndrome

INTRODUCTION

Noninvasive mechanical ventilation is the main supportive measure used in patients with pediatric ARDS (PARDS), but adjunctive pharmacological therapies (corticosteroids, inhaled nitric oxide [iNO], surfactant replacement therapy and neuromuscular blocking drugs) are also used, although limited data exists to inform of this practice.

AREAS COVERED

The authors review the current challenges in the pharmacological management of PARDS and highlight the few certainties currently available.

EXPERT OPINION

Children with PARDS must not be treated as young adults with ARDS, essentially because children's lungs differ substantially from those of adults and PARDS occurs in children differently than ARDS in adults. Pharmacological treatments available for PARDS are relatively few and, since there is great uncertainty about their effectiveness also because of the extreme heterogeneity of this syndrome, it is necessary to conduct large clinical trials using currently available definitions and considering recent pathobiological knowledge. The aim is to identify homogeneous subgroups or phenotypes of children with PARDS that may benefit from the specific pharmaceutical approach examined. It will be then necessary to link endotypes and outcomes to appropriately target therapies in future trials, but this will be possible only after it will be possible to identify the different PARDS endotypes.

Secondary bloodstream infection in critically ill patients with COVID-19

Objective

Secondary infection, especially bloodstream infection, is an important cause of death in critically ill patients with COVID-19. We aimed to describe secondary bloodstream infection (SBI) in critically ill adults with COVID-19 in the intensive care unit (ICU) and to explore risk factors related to SBI.

Methods

We reviewed all SBI cases among critically ill patients with COVID-19 from 12 February 2020 to 24 March 2020 in the COVID-19 ICU of Jingmen First People's Hospital. We compared risk factors associated with bloodstream infection in this study. All SBIs were confirmed by blood culture.

Results

We identified five cases of SBI among the 32 patients: three with Enterococcus faecium, one mixed septicemia (E. faecium and Candida albicans), and one C. parapsilosis. There were no significant differences between the SBI group and non-SBI group. Significant risk factors for SBI were extracorporeal membrane oxygenation, central venous catheter, indwelling urethral catheter, and nasogastric tube.

Conclusions

Our findings confirmed that the incidence of secondary infection, particularly SBI, and mortality are high among critically ill patients with COVID-19. We showed that long-term hospitalization and invasive procedures such as tracheotomy, central venous catheter, indwelling urethral catheter, and nasogastric tube are risk factors for SBI and other complications.

Predicting fracture healing with blood biomarkers: the potential to assess patient risk of fracture nonunion

Fracture non-union is a significant orthopaedic problem affecting a substantial number of patients yearly. Treatment of nonunions is devastating to patients and costly to the healthcare system. Unfortunately, the diagnosis of non-union is typically made in a reactionary fashion by an orthopaedic surgeon based on clinical assessment and radiographic features several months into treatment. For this reason, investigators have been trying to develop prediction algorithms; however, these have relied on population-based approaches and lack the predictive capability necessary to make individual treatment decisions. There is also a growing body of literature focussed on identifying blood biomarkers that are associated with non-union. This review describes the research that has been done in this area. Further studies of patient-centered, precision medicine approaches will likely improve fracture non-union diagnostic/prognostic capabilities.

ARDS Patients Exhibiting a "Hyperinflammatory Anasarca" Phenotype Could Benefit From a Conservative Fluid Management Strategy

Object: The fluid management strategy in ARDS is not very clear. A secondary analysis of RCT data was conducted to identify patients with ARDS benefitting from a conservative strategy of fluid management.

Methods: The data of this study were downloaded from the ARDS network series of randomized controlled trials (Conservative Strategy vs. Liberal Strategy in 2006). Based on the clinical feature of patients, within the first 24 h after admission, clustering was performed using the k-means clustering algorithm to identify the phenotypes of ARDS. Survival was analyzed using the Kaplan-Meier survival analysis to assess the effect of the two fluid management strategies on the 90-day cumulative mortality. Categorical/dichotomic variables were analyzed by the chi-square test. Continuous variables were expressed as the mean and standard deviation and evaluated through a one-way ANOVA. A P-value < 0.05 was defined as the statistically significant cut-off value.

Results: A total of 1,000 ARDS patients were enrolled in this unsupervised clustering research study, of which 503 patients were treated with a conservative fluid-management strategy, and 497 patients were treated with a liberal fluid-management strategy. The first 7-day cumulative fluid balance in patients with the conservative strategy and liberal strategy were −136 ± 491 ml and 6,992 ± 502 ml, respectively (P < 0.001). Four phenotypes were found, and the conservative fluid-management strategy significantly improved the 90-day cumulative mortality compared with the liberal fluid-management strategy (HR = 0.532, P = 0.024) in patients classified as “hyperinflammatory anasarca” phenotype (phenotype II). The characteristics of this phenotype exhibited a higher WBC count (20487.51 ± 7223.86/mm³) with a higher incidence of anasarca (8.3%) and incidence of shock (26.6%) at baseline. The furthermore analysis found that the conservative fluid management strategy was superior to the liberal fluid management strategy in avoiding superinfection (10.10 vs. 14.40%, P = 0.037) and returned to assisted breathing (4.60 vs. 16.20%, P = 0.030) in patients classified as “hyperinflammatory anasarca” phenotype. In addition, patients with other phenotypes given the different fluid management strategies did not show significant differences in clinical outcomes.

Conclusion: Patients exhibiting a “hyperinflammatory anasarca” phenotype could benefit from a conservative fluid management strategy.

Risk Factors for Maternal and Fetal Mortality in Acute Fatty Liver of Pregnancy and New Predictive Models

Acute fatty liver of pregnancy (AFLP) is a rare but potentially life-threatening hepatic disorder that leads to considerable maternal and fetal mortality. To explore the risk factors for maternal and fetal mortality in AFLP and develop new predictive models, through this retrospective study, we analyzed the demographic characteristics, clinical symptoms, and laboratory findings of 106 patients with AFLP who were admitted to Shandong Provincial Hospital. Risk factors for maternal and fetal mortality were analyzed by univariate and multivariate logistic regression analysis. The new models based on the multivariate logistic regression analysis and the model for end-stage liver disease (MELD) were tested in AFLP. The receiver operating characteristic curve (ROC) was applied to compare the predictive efficiency, sensitivity, and specificity of the two models. Prenatal nausea (p = 0.037), prolonged prothrombin time (p = 0.003), and elevated serum creatinine (p = 0.003) were independent risk factors for maternal mortality. The ROC curve showed that the area under the curve (AUC) of the MELD was 0.948, with a sensitivity of 100% and a specificity of 83.3%. The AUC of the new model for maternal mortality was 0.926, with a sensitivity of 90% and a specificity of 94.8%. Hepatic encephalopathy (p = 0.016) and thrombocytopenia (p = 0.001) were independent risk factors for fetal mortality. Using the ROC curve, the AUC of the MELD was 0.694, yielding a sensitivity of 68.8% and a specificity of 64.4%. The AUC of the new model for fetal mortality was 0.893, yielding a sensitivity of 100% and a specificity of 73.3%. Both the new predictive model for maternal mortality and the MELD showed good predictive efficacy for maternal mortality in patients with AFLP (AUC = 0.926 and 0.948, respectively), and the new predictive model for fetal mortality was superior to the MELD in predicting fetal mortality (AUC = 0.893 and 0.694, respectively). The two new predictive models were more readily available, less expensive, and easier to implement clinically, especially in low-income countries.

Individualized resuscitation strategy for septic shock formalized by finite mixture modeling and dynamic treatment regimen

BACKGROUND

Septic shock comprises a heterogeneous population, and individualized resuscitation strategy is of vital importance. The study aimed to identify subclasses of septic shock with non-supervised learning algorithms, so as to tailor resuscitation strategy for each class.

METHODS

Patients with septic shock in 25 tertiary care teaching hospitals in China from January 2016 to December 2017 were enrolled in the study. Clinical and laboratory variables were collected on days 0, 1, 2, 3 and 7 after ICU admission. Subclasses of septic shock were identified by both finite mixture modeling and K-means clustering. Individualized fluid volume and norepinephrine dose were estimated using dynamic treatment regime (DTR) model to optimize the final mortality outcome. DTR models were validated in the eICU Collaborative Research Database (eICU-CRD) dataset.

RESULTS

A total of 1437 patients with a mortality rate of 29% were included for analysis. The finite mixture modeling and K-means clustering robustly identified five classes of septic shock. Class 1 (baseline class) accounted for the majority of patients over all days; class 2 (critical class) had the highest severity of illness; class 3 (renal dysfunction) was characterized by renal dysfunction; class 4 (respiratory failure class) was characterized by respiratory failure; and class 5 (mild class) was characterized by the lowest mortality rate (21%). The optimal fluid infusion followed the resuscitation/de-resuscitation phases with initial large volume infusion and late restricted volume infusion. While class 1 transitioned to de-resuscitation phase on day 3, class 3 transitioned on day 1. Classes 1 and 3 might benefit from early use of norepinephrine, and class 2 can benefit from delayed use of norepinephrine while waiting for adequate fluid infusion.

CONCLUSIONS

Septic shock comprises a heterogeneous population that can be robustly classified into five phenotypes. These classes can be easily identified with routine clinical variables and can help to tailor resuscitation strategy in the context of precise medicine.

Disentangling the Association of Hydroxychloroquine Treatment with Mortality in Covid-19 Hospitalized Patients through Hierarchical Clustering

The efficacy of hydroxychloroquine (HCQ) in treating SARS-CoV-2 infection is harshly debated, with observational and experimental studies reporting contrasting results. To clarify the role of HCQ in Covid-19 patients, we carried out a retrospective observational study of 4,396 unselected patients hospitalized for Covid-19 in Italy (February–May 2020). Patients' characteristics were collected at entry, including age, sex, obesity, smoking status, blood parameters, history of diabetes, cancer, cardiovascular and chronic pulmonary diseases, and medications in use. These were used to identify subtypes of patients with similar characteristics through hierarchical clustering based on Gower distance. Using multivariable Cox regressions, these clusters were then tested for association with mortality and modification of effect by treatment with HCQ. We identified two clusters, one of 3,913 younger patients with lower circulating inflammation levels and better renal function, and one of 483 generally older and more comorbid subjects, more prevalently men and smokers. The latter group was at increased death risk adjusted by HCQ (HR[CI95%] = 3.80[3.08-4.67]), while HCQ showed an independent inverse association (0.51[0.43-0.61]), as well as a significant influence of cluster∗HCQ interaction (p < 0.001). This was driven by a differential association of HCQ with mortality between the high (0.89[0.65-1.22]) and the low risk cluster (0.46[0.39-0.54]). These effects survived adjustments for additional medications in use and were concordant with associations with disease severity and outcome. These findings suggest a particularly beneficial effect of HCQ within low risk Covid-19 patients and may contribute to clarifying the current controversy on HCQ efficacy in Covid-19 treatment.

A Nomogram Prediction of Length of Hospital Stay in Patients with COVID-19 Pneumonia: A Retrospective Cohort Study

Assessing the length of hospital stay (LOS) in patients with coronavirus disease 2019 (COVID-19) pneumonia is helpful in optimizing the use efficiency of hospital beds and medical resources and relieving medical resource shortages. This retrospective cohort study of 97 patients was conducted at Beijing You'An Hospital between January 21, 2020, and March 21, 2020. A multivariate Cox proportional hazards regression based on the smallest Akaike information criterion value was used to select demographic and clinical variables to construct a nomogram. Discrimination, area under the receiver operating characteristic curve (AUC), calibration, and Kaplan–Meier curves with the log-rank test were used to assess the nomogram model. The median LOS was 13 days (interquartile range [IQR]: 10–18). Age, alanine aminotransferase, pneumonia, platelet count, and PF ratio (PaO₂/FiO₂) were included in the final model. The C-index of the nomogram was 0.76 (95%confidence interval [CI] = 0.69–0.83), and the AUC was 0.88 (95%CI = 0.82–0.95). The adjusted C-index was 0.75 (95%CI = 0.67–0.82) and adjusted AUC 0.86 (95%CI = 0.73–0.95), both after 1000 bootstrap cross internal validations. A Brier score of 0.11 (95%CI = 0.07–0.15) and adjusted Brier score of 0.130 (95%CI = 0.07–0.20) for the calibration curve showed good agreement. The AUC values for the nomogram at LOS of 10, 20, and 30 days were 0.79 (95%CI = 0.69–0.89), 0.89 (95%CI = 0.83–0.96), and 0.96 (95%CI = 0.92–1.00), respectively, and the high fit score of the nomogram model indicated a high probability of hospital stay. These results confirmed that the nomogram model accurately predicted the LOS of patients with COVID-19. We developed and validated a nomogram that incorporated five independent predictors of LOS. If validated in a future large cohort study, the model may help to optimize discharge strategies and, thus, shorten LOS in patients with COVID-19.

Plasma Endogenous Sulfur Dioxide: A Novel Biomarker to Predict Acute Kidney Injury in Critically Ill Patients

Purpose

Sulfur dioxide (SO₂) is a novel gaseous signaling molecule that plays an important role in inflammation, which contributes the pathogenesis of acute kidney injury (AKI). The aim of this study was to explore the predictive value of plasma SO₂ for AKI in high-risk patients.

Patients and Methods

A prospective cohort of 167 patients who underwent major noncardiac surgery was enrolled in the study. Plasma SO₂, urine neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinase-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP7) levels were detected immediately after the operation. The primary endpoint was new-onset AKI within 72 h after admission. The ability of biomarkers including SO₂ and a clinical risk model to predict AKI was compared by receiver operator characteristic (ROC) curve analysis and decision curve analysis (DCA), additional contributions were evaluated by integrated discrimination improvement (IDI) and net reclassification improvement (NRI) analyses.

Results

A total of 61 (36.5%) patients developed AKI within 72 h of surgery. Compared to NGAL and [TIMP-2]·[IGFBP7], SO₂ showed better predictive ability for new-onset AKI with an area under the ROC curve of 0.771 (95% confidence interval: 0.700–0.832, p<0.001). The improvement in predictive value by including SO₂ in the clinical risk model was supported by NRI (0.28; P=0.04) and IDI (0.15; P<0.001) analyses. The net benefit of the combination of SO₂ and clinical variables was the max in DCA.

Conclusion

Plasma SO₂ shows a useful value for predicting new-onset AKI, and improved AKI prediction based on clinical variables, which can guide the implementation of preventive measures for high-risk patients.

Prediction of Multiple Organ Failure Complicated by Moderately Severe or Severe Acute Pancreatitis Based on Machine Learning: A Multicenter Cohort Study

Background

Multiple organ failure (MOF) may lead to an increased mortality rate of moderately severe (MSAP) or severe acute pancreatitis (SAP). This study is aimed to use machine learning to predict the risk of MOF in the course of disease.

Methods

Clinical and laboratory features with significant differences between patients with and without MOF were screened out by univariate analysis. Prediction models were developed for selected features through six machine learning methods. The models were internally validated with a five-fold cross-validation, and a series of optimal feature subsets were generated in corresponding models. A test set was used to evaluate the predictive performance of the six models.

Results

305 (68%) of 455 patients with MSAP or SAP developed MOF. Eighteen features with significant differences between the group with MOF and without it in the training and validation set were used for modeling. Interleukin-6 levels, creatinine levels, and the kinetic time were the three most important features in the optimal feature subsets selected by K-fold cross-validation. The adaptive boosting algorithm (AdaBoost) showed the best predictive performance with the highest AUC value (0.826; 95% confidence interval: 0.740 to 0.888). The sensitivity of AdaBoost (80.49%) and specificity of logistic regression analysis (93.33%) were the best scores among the six models in the test set.

Conclusions

A predictive model of MOF complicated by MSAP or SAP was successfully developed based on machine learning. The predictive performance was evaluated by a test set, for which AdaBoost showed a satisfactory predictive performance. The study is registered with the China Clinical Trial Registry (Identifier: ChiCTR1800016079).

Cost-Effective Machine Learning Based Clinical Pre-Test Probability Strategy for DVT Diagnosis in Neurological Intensive Care Unit

In order to overcome the shortage of the current costly DVT diagnosis and reduce the waste of valuable healthcare resources, we proposed a new diagnostic approach based on machine learning pre-test prediction models using EHRs. We examined the sociodemographic and clinical factors in the prediction of DVT with 518 NICU admitted patients, including 189 patients who eventually developed DVT. We used cross-validation on the training data to determine the optimal parameters, and finally, the applied ROC analysis is adopted to evaluate the predictive strength of each model. Two models (GLM and SVM) with the strongest ROC were selected for DVT prediction, based on which, we optimized the current intervention and diagnostic process of DVT and examined the performance of the proposed approach through simulations. The use of machine learning based pre-test prediction models can simplify and improve the intervention and diagnostic process of patients in NICU with suspected DVT, and reduce the valuable healthcare resource occupation/usage and medical costs.

Clinical Factors and Quantitative CT Parameters Associated With ICU Admission in Patients of COVID-19 Pneumonia: A Multicenter Study

The clinical spectrum of COVID-19 pneumonia is varied. Thus, it is important to identify risk factors at an early stage for predicting deterioration that require transferring the patients to ICU. A retrospective multicenter study was conducted on COVID-19 patients admitted to designated hospitals in China from Jan 17, 2020, to Feb 17, 2020. Clinical presentation, laboratory data, and quantitative CT parameters were also collected. The result showed that increasing risks of ICU admission were associated with age > 60 years (odds ratio [OR], 12.72; 95% confidence interval [CI], 2.42-24.61; P = 0.032), coexisting conditions (OR, 5.55; 95% CI, 1.59-19.38; P = 0.007) and CT derived total opacity percentage (TOP) (OR, 8.0; 95% CI, 1.45-39.29; P = 0.016). In conclusion, older age, coexisting conditions, larger TOP at the time of hospital admission are associated with ICU admission in patients with COVID-19 pneumonia. Early monitoring the progression of the disease and implementing appropriate therapies are warranted.

Precision Phenomapping of Acute Coronary Syndromes to Improve Patient Outcomes

Acute coronary syndromes (ACS) are a global leading cause of death. These syndromes show heterogeneity in presentation, mechanisms, outcomes and responses to treatment. Precision medicine aims to identify and synthesize unique features in individuals, translating the acquired data into improved personalised interventions. Current precision treatments of ACS include immediate coronary revascularisation driven by ECG ST-segment elevation, early coronary angiography based on elevated blood cardiac troponins in patients without ST-segment elevation, and duration of intensified antithrombotic therapy according to bleeding risk scores. Phenotypically stratified analyses of multi-omic datasets are urgently needed to further refine and couple the diagnosis and treatment of these potentially life-threatening conditions. We provide definitions, examples and possible ways to advance precision treatments of ACS.

Development of a Nomogram to Predict 28-Day Mortality of Patients With Sepsis-Induced Coagulopathy: An Analysis of the MIMIC-III Database

Background: Sepsis-induced coagulopathy (SIC) is a common cause for inducing poor prognosis of critically ill patients in intensive care unit (ICU). However, currently there are no tools specifically designed for assessing short-term mortality in SIC patients. This study aimed to develop a practical nomogram to predict the risk of 28-day mortality in SIC patients.

Methods: In this retrospective cohort study, we extracted patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Sepsis was defined based on Sepsis 3.0 criteria and SIC based on Toshiaki Iba's criteria. Kaplan–Meier curves were plotted to compare the short survival time between SIC and non-SIC patients. Afterward, only SIC cohort was randomly divided into training or validation set. We employed univariate logistic regression and stepwise multivariate analysis to select predictive features. The proposed nomogram was developed based on multivariate logistic regression model, and the discrimination and calibration were verified by internal validation. We then compared model discrimination with other traditional severity scores and machine learning models.

Results: 9432 sepsis patients in MIMIC III were enrolled, in which 3280 (34.8%) patients were diagnosed as SIC during the first ICU admission. SIC was independently associated with the 7- and 28-day mortality of ICU patients. K–M curve indicated a significant difference in 7-day (Log-Rank: P < 0.001 and P = 0.017) and 28-day survival (Log-Rank: P < 0.001 and P < 0.001) between SIC and non-SIC groups whether the propensity score match (PSM) was balanced or not. For nomogram development, a total of thirteen variables of 3,280 SIC patients were enrolled. When predicted the risk of 28-day mortality, the nomogram performed a good discrimination in training and validation sets (AUROC: 0.78 and 0.81). The AUROC values were 0.80, 0.81, 0.71, 0.70, 0.74, and 0.60 for random forest, support vector machine, sequential organ failure assessment (SOFA) score, logistic organ dysfunction score (LODS), simplified acute physiology II score (SAPS II) and SIC score, respectively, in validation set. And the nomogram calibration slope was 0.91, the Brier value was 0.15. As presented by the decision curve analyses, the nomogram always obtained more net benefit when compared with other severity scores.

Conclusions: SIC is independently related to the short-term mortality of ICU patients. The nomogram achieved an optimal prediction of 28-day mortality in SIC patient, which can lead to a better prognostics assessment. However, the discriminative ability of the nomogram requires validation in external cohorts to further improve generalizability.

Musculoskeletal Corticosteroid Injection during COVID-19 Pandemic in Sabah: Is It Safe?

Musculoskeletal corticosteroid injection is commonly used as an adjunct to help patients in pain management. In this current COVID-19 pandemic, many clinicians would differ from this treatment as steroid is considered an immunosuppressive drug and could risk the patient of developing severe adverse effects if contracting COVID-19. This is a retrospective study based in Sabah, Malaysia, examining the prevalence of COVID-19 infection following musculoskeletal corticosteroid injection from 1 December 2019 until 30 June 2020 in the sports medicine clinic and the orthopedic clinic. Patients who received musculoskeletal corticosteroid injection were called by telephone and asked about visits to the emergency department or government health clinic for influenza-like illness symptoms or severe acute respiratory infection that would require screening of COVID-19. Thirty-five patients who responded to the call were included, with mean ages of 47.9 years ± 15.1. 52% were male respondents, while 48% were female. 25% of them were diabetics, and 2.9% of them had a history of lymphoproliferative disorders. The mean pain score before injection was 6.74 ± 1.03 and after injection pain was 2.27 ± 1.63. In this study, there were 11.4% (n = 4) with minor complications of steroid injection, that is, skin discoloration. Nonetheless, there were no severe complications due to corticosteroids reported. There were no reported cases of COVID-19 among the respondents following corticosteroid injection. Musculoskeletal pain would affect a person's well-being and activities; thus, its management requires that careful consideration with risk-benefit analysis be made before administering musculoskeletal corticosteroid injection during COVID-19 pandemic.

Registered Trials on Artificial Intelligence Conducted in Emergency Department and Intensive Care Unit: A Cross-Sectional Study on ClinicalTrials.gov

Objective: Clinical trials contribute to the development of clinical practice. However, little is known about the current status of trials on artificial intelligence (AI) conducted in emergency department and intensive care unit. The objective of the study was to provide a comprehensive analysis of registered trials in such field based on ClinicalTrials.gov.

Methods: Registered trials on AI conducted in emergency department and intensive care unit were searched on ClinicalTrials.gov up to 12th January 2021. The characteristics were analyzed using SPSS21.0 software.

Results: A total of 146 registered trials were identified, including 61 in emergency department and 85 in intensive care unit. They were registered from 2004 to 2021. Regarding locations, 58 were conducted in Europe, 58 in America, 9 in Asia, 4 in Australia, and 17 did not report locations. The enrollment of participants was from 0 to 18,000,000, with a median of 233. Universities were the primary sponsors, which accounted for 43.15%, followed by hospitals (35.62%), and industries/companies (9.59%). Regarding study designs, 85 trials were interventional trials, while 61 were observational trials. Of the 85 interventional trials, 15.29% were for diagnosis and 38.82% for treatment; of the 84 observational trials, 42 were prospective, 14 were retrospective, 2 were cross-sectional, 2 did not report clear information and 1 was unknown. Regarding the trials' results, 69 trials had been completed, while only 10 had available results on ClinicalTrials.gov.

Conclusions: Our study suggest that more AI trials are needed in emergency department and intensive care unit and sponsors are encouraged to report the results.

Diabetes Mellitus as a Risk Factor for Progression from Acute Kidney Injury to Acute Kidney Disease: A Specific Prediction Model

PURPOSE

Acute kidney injury is very common in hospitalized patients and carries a significant risk of mortality. Although timely intervention may improve patient prognosis, studies on the development of acute kidney disease in patients with acute kidney injury remain scarce. Thus, we constructed a prediction model to identify patients likely to develop acute kidney disease.

PATIENTS AND METHODS

Among 474 patients screened for eligibility, 261 were enrolled and randomly divided into training (185 patients) and independent validation cohorts (76 patients). Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were used to select features and build a nomogram incorporating the selected predictors: diabetes, anemia, oliguria, and peak creatinine. Calibration, discrimination, and the clinical usefulness of the model were assessed using calibration plots, the C-index, receiver operating characteristic curves, and decision curve analysis.

RESULTS

Diabetes was significantly associated with the presence of AKD. Peak creatinine, oliguria, and anemia also contributed to the progression of acute kidney injury. The model displayed good predictive power with a C-index of 0.834 and an AUC of 0.834 (95% confidence interval (CI): 0.773-0.895) in the training cohort and a C-index of 0.851 and an AUC of 0.851 (95% CI: 0.753-0.949) in the validation cohort. The calibration curves also showed that the model had a medium ability to predict acute kidney disease risk. Decision curve analysis showed that the nomogram was clinically useful when interventions were decided at the possibility threshold of 22%.

CONCLUSION

This novel prediction nomogram may allow for convenient prediction of acute kidney disease in patients with acute kidney injury, which may help to improve outcomes.

Latent transition analysis of cardiac arrest patients treated in the intensive care unit

BACKGROUND AND OBJECTIVE

Post-cardiac arrest (CA) syndrome is heterogenous in their clinical presentations and outcomes. This study aimed to explore the transition and stability of subphenotypes (profiles) of CA treated in the intensive care unit (ICU).

PATIENTS AND METHODS

Clinical features of CA patients on day 1 and 3 after ICU admission were modeled by latent transition analysis (LTA) to explore the transition between subphenotypes over time. The association between different transition patterns and mortality outcome was explored using multivariable logistic regression.

RESULTS

We identified 848 eligible patients from the database. The LPA identified three distinct subphenotypes: Profile 1 accounted for the largest proportion (73%) and was considered as the baseline subphenotype. Profile 2 (13%) was characterized by brain injury and profile 3 (14%) was characterized by multiple organ dysfunctions. The same three subphenotypes were identified on day 3. The LTA showed consistent subphenotypes. A majority of patients in profile 2 (72%) and 3 (82%) on day 1 switched to profile 1 on day 3. In the logistic regression model, patients in profile 1 on day 1 transitioned to profile 3 had worse survival outcome than those continue to remain in profile 1 (OR: 20.64; 95% CI: 6.01 to 70.94; p < 0.001) and transitioned to profile 2 (OR: 8.42; 95% CI: 2.22 to 31.97; p = 0.002) on day 3.

CONCLUSION

The study identified three subphenotypes of CA, which was consistent on day 1 and 3 after ICU admission. Patients who transitioned to profile 3 on day 3 had significantly worse survival outcome than those remained in profile 1 or 2.