scholarly journals ACEI/ARB Medication During ICU Stay Decrease All-Cause In-hospital Mortality in Critically Ill Patients With Hypertension: A Retrospective Cohort Study Based on Machine Learning

2022 ◽  
Vol 8 ◽  
Author(s):  
Boshen Yang ◽  
Sixuan Xu ◽  
Di Wang ◽  
Yu Chen ◽  
Zhenfa Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Retrospective Cohort ◽  
Learning Models ◽  
Hyperparameter Optimization ◽  
Icu Stay ◽  
Machine Learning Models

Background: Hypertension is a rather common comorbidity among critically ill patients and hospital mortality might be higher among critically ill patients with hypertension (SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg). This study aimed to explore the association between ACEI/ARB medication during ICU stay and all-cause in-hospital mortality in these patients.Methods: A retrospective cohort study was conducted based on data from Medical Information Mart for Intensive Care IV (MIMIC-IV) database, which consisted of more than 40,000 patients in ICU between 2008 and 2019 at Beth Israel Deaconess Medical Center. Adults diagnosed with hypertension on admission and those had high blood pressure (SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg) during ICU stay were included. The primary outcome was all-cause in-hospital mortality. Patients were divided into ACEI/ARB treated and non-treated group during ICU stay. Propensity score matching (PSM) was used to adjust potential confounders. Nine machine learning models were developed and validated based on 37 clinical and laboratory features of all patients. The model with the best performance was selected based on area under the receiver operating characteristic curve (AUC) followed by 5-fold cross-validation. After hyperparameter optimization using Grid and random hyperparameter search, a final LightGBM model was developed, and Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature. The features closely associated with hospital mortality were presented as significant features.Results: A total of 15,352 patients were enrolled in this study, among whom 5,193 (33.8%) patients were treated with ACEI/ARB. A significantly lower all-cause in-hospital mortality was observed among patients treated with ACEI/ARB (3.9 vs. 12.7%) as well as a lower 28-day mortality (3.6 vs. 12.2%). The outcome remained consistent after propensity score matching. Among nine machine learning models, the LightGBM model had the highest AUC = 0.9935. The SHAP plot was employed to make the model interpretable based on LightGBM model after hyperparameter optimization, showing that ACEI/ARB use was among the top five significant features, which were associated with hospital mortality.Conclusions: The use of ACEI/ARB in critically ill patients with hypertension during ICU stay is related to lower all-cause in-hospital mortality, which was independently associated with increased survival in a large and heterogeneous cohort of critically ill hypertensive patients with or without kidney dysfunction.

scholarly journals Association Between Dexamethasone and Delirium in Critically ill Patients: A Retrospective Cohort Study of a Large Clinical Database

2020 ◽  
Author(s):  
Zehao Wu ◽  
Huili Li ◽  
Kaihua Liao ◽  
Yun Wang
Keyword(s):  
Cohort Study ◽  
Length Of Stay ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Retrospective Cohort Study ◽  
Critically Ill Patients ◽  
Retrospective Cohort ◽  
Hospital Death ◽  
Icu Stay ◽  
The Relationship

Abstract BackgroundDelirium is a common complication in ICU patients, and it can significantly increase the length of hospital stay and cost. Dexamethasone is widely used in various inflammatory diseases and is a glucocorticoid commonly used in critically ill patients. There are no studies on the effect of dexamethasone on the development of delirium in critically ill patients, therefore, this study aimed to confirm the effect of dexamethasone use and the dose on the incidence of delirium and patient prognosis in critically ill patients through a large cohort study.MethodsA retrospective cohort study was conducted using data extracted from the MIMIC III database, and the primary outcome was the development of delirium, using multivariate logistic regression analysis to reveal the relationship between dexamethasone and delirium. Secondary endpoints were in-hospital mortality, total length of stay and length of ICU stay, and the relationship between dexamethasone and prognosis was assessed with Cox proportional hazards models. The Lowess smoothing technique was used to investigate the dose correlation between dexamethasone and outcomes, subgroup analysis was used to account for heterogeneity, and different correction models and propensity matching analysis were used to eliminate potential confounders.ResultsFinally, 38,509 patients were included, and 2,204 (5.7%) used dexamethasone. A significantly higher incidence of delirium (5.0% vs. 3.4%, P < 0.001), increased in-hospital mortality (15.0% vs. 11.3%, P < 0.001), and longer length of stay and ICU stay were observed in patients taking dexamethasone compared with those not taking dexamethasone. Multivariate logistic and Cox regression analyses confirmed that dexamethasone was significantly associated with delirium (adjusted OR = 1.45, 95% CI = 1.08-1.95, P = 0.014) and in-hospital mortality (adjusted HR = 1.19, 95% CI = 1.02-1.40, P = 0.032). The risk of delirium and in-hospital death was lower with dexamethasone less than 10 mg, and subjects with 10-14 mg had the shortest length of hospital stay.ConclusionsThis study demonstrated that the use of dexamethasone in critically ill patients exacerbated the occurrence of delirium, while increasing the risk of in-hospital death and length of stay, and the use of low-dose dexamethasone had a lower risk of delirium and death, which appeared to be safer.

Association of delayed time in the emergency department with the clinical outcomes for critically ill patients

QJM ◽  
2020 ◽  
Author(s):  
S Lin ◽  
S Ge ◽  
W He ◽  
M Zeng
Keyword(s):  
Emergency Department ◽  
Cohort Study ◽  
Hospital Mortality ◽  
Clinical Outcomes ◽  
Critically Ill ◽  
Waiting Time ◽  
Critically Ill Patients ◽  
Retrospective Cohort ◽  
Stratified Analysis ◽  
Delayed Time

Summary Background Previous studies have shown the association of waiting time in the emergency department with the prognosis of critically ill patients, but these studies linking the waiting time to clinical outcomes have been inconsistent and limited by small sample size. Aim To determine the relationship between the waiting time in the emergency department and the clinical outcomes for critically ill patients in a large sample population. Design A retrospective cohort study of 13 634 patients. Methods We used the Medical Information Mart for Intensive Care III database. Multivariable logistic regression was used to determine the independent relationships of the in-hospital mortality rate with the delayed time and different groups. Interaction and stratified analysis were conducted to test whether the effect of delayed time differed across various subgroups. Results After adjustments, the in-hospital mortality in the ≥6 h group increased by 38.1% (OR 1.381, 95% CI 1.221–1.562). Moreover, each delayed hour was associated independently with a 1.0% increase in the risk of in-hospital mortality (OR 1.010, 95% CI 1.008–1.010). In the stratified analysis, intensive care unit (ICU) types, length of hospital stay, length of ICU stay, simplified acute physiology score II and diagnostic category were found to have interactions with ≥6 h group in in-hospital mortality. Conclusions In this large retrospective cohort study, every delayed hour was associated with an increase in mortality. Furthermore, clinicians should be cautious of patients diagnosed with sepsis, liver/renal/metabolic diseases, internal hemorrhage and cardiovascular disease, and if conditions permit, they should give priority to transferring to the corresponding ICUs.

scholarly journals A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis

2021 ◽  
Vol 7 ◽  
Author(s):  
Qin-Yu Zhao ◽  
Le-Ping Liu ◽  
Jing-Chao Luo ◽  
Yan-Wei Luo ◽  
Huan Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Compact Model ◽  
Bayesian Optimization ◽  
Research Database ◽  
Learning Models ◽  
Dynamic Prediction ◽  
Machine Learning Models

Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients.Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis.Methods: Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction.Results: Of 11,362 patients in MIMIC-IV included in the final cohort, a total of 6,744 (59%) patients developed SIC during sepsis. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869; 95% CI: 0.850–0.886). Coagulation profile and renal function indicators were the most important features for predicting SIC. A compact model was developed with an AUC of 0.854 (95% CI: 0.832–0.872), while the AUCs of Logistic Regression and SIC scores were 0.746 (95% CI: 0.735–0.755) and 0.709 (95% CI: 0.687–0.733), respectively. A cohort of 35,252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837–0.846) and 0.803 (95% CI: 0.798–0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653–0.667) and SIC scores (0.752; 95% CI: 0.747–0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable.Conclusions: We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.

scholarly journals Predicting mortality in critically ill patients with diabetes using machine learning and clinical notes

2020 ◽  
Vol 20 (S11) ◽  
Author(s):  
Jiancheng Ye ◽  
Liang Yao ◽  
Jiahong Shen ◽  
Rethavathi Janarthanam ◽  
Yuan Luo
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Critically Ill ◽  
Health Care Providers ◽  
Critically Ill Patients ◽  
Care Providers ◽  
Learning Models ◽  
Clinical Notes ◽  
Risk Of Mortality ◽  
Machine Learning Models

Abstract Background Diabetes mellitus is a prevalent metabolic disease characterized by chronic hyperglycemia. The avalanche of healthcare data is accelerating precision and personalized medicine. Artificial intelligence and algorithm-based approaches are becoming more and more vital to support clinical decision-making. These methods are able to augment health care providers by taking away some of their routine work and enabling them to focus on critical issues. However, few studies have used predictive modeling to uncover associations between comorbidities in ICU patients and diabetes. This study aimed to use Unified Medical Language System (UMLS) resources, involving machine learning and natural language processing (NLP) approaches to predict the risk of mortality. Methods We conducted a secondary analysis of Medical Information Mart for Intensive Care III (MIMIC-III) data. Different machine learning modeling and NLP approaches were applied. Domain knowledge in health care is built on the dictionaries created by experts who defined the clinical terminologies such as medications or clinical symptoms. This knowledge is valuable to identify information from text notes that assert a certain disease. Knowledge-guided models can automatically extract knowledge from clinical notes or biomedical literature that contains conceptual entities and relationships among these various concepts. Mortality classification was based on the combination of knowledge-guided features and rules. UMLS entity embedding and convolutional neural network (CNN) with word embeddings were applied. Concept Unique Identifiers (CUIs) with entity embeddings were utilized to build clinical text representations. Results The best configuration of the employed machine learning models yielded a competitive AUC of 0.97. Machine learning models along with NLP of clinical notes are promising to assist health care providers to predict the risk of mortality of critically ill patients. Conclusion UMLS resources and clinical notes are powerful and important tools to predict mortality in diabetic patients in the critical care setting. The knowledge-guided CNN model is effective (AUC = 0.97) for learning hidden features.

scholarly journals Statins’ safety and impact on the clinical outcomes in COVID-19 critically ill patients: A Multicenter, Cohort Study

2021 ◽  
Author(s):  
Khalid Al Sulaiman ◽  
Ohoud Aljuhani ◽  
Ghazwa B. Korayem ◽  
Ali F. Altebainawi ◽  
Shmeylan Al Harbi ◽  
...  
Keyword(s):  
Cohort Study ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Hazard Ratio ◽  
Icu Stay ◽  
Markers Of Inflammation ◽  
The Impact ◽  
Statin Use

Abstract Purpose The complications of Severe Corona Virus Disease 2019 (COVID-19) are attributed to the overproduction of early response proinflammatory cytokines, causing a systemic hyperinflammatory state. Statins are potentially a potent adjuvant therapy in COVID-19 infection due to their pleiotropic and anti-inflammatory effects, which are independent of their cholesterol-lowering activity. This study investigates the impact of statin use on the outcome of critically ill patients with COVID-19. Methods A multicenter, retrospective cohort study of all adult critically ill patients with confirmed COVID-19 admitted to Intensive Care Units (ICUs) between March 1, 2020, and March 31, 2021. Eligible patients were classified into two groups based on statin use during ICU stay and were matched with a propensity score which was based on patient’s age and admission APACHE II and SOFA scores. The primary endpoint was in-hospital mortality. Other outcomes were considered secondary... Results A total of 1049 patients were eligible; 502 patients were included after propensity score matching (1:1 ratio). The 30-day (hazard ratio 0.75 (95% CI 0.58, 0.98), P = 0.03) and in-hospital mortality (hazard ratio 0.69 (95% CI 0.54, 0.89), P = 0.004) were significantly lower in patients who received statin therapy on multivariable cox proportional hazards regression analysis. Moreover, patients who received statin have a lower risk of hospital-acquired pneumonia (OR 0.48(95% CI 0.32, 0.69), P = < 0.001), lower levels of markers of inflammation on follow up and no increased risk of liver injury. Conclusion The use of statin during ICU stay in COVID-19 critically ill patients may have a beneficial role and survival benefits with a good safety profile.

Prediction of sperm extraction in non-obstructive azoospermia patients: a machine-learning perspective

2020 ◽  
Vol 35 (7) ◽  
pp. 1505-1514 ◽  
Author(s):  
A Zeadna ◽  
N Khateeb ◽  
L Rokach ◽  
Y Lior ◽  
I Har-Vardi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Cohort Study ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Obstructive Azoospermia ◽  
Learning Models ◽  
Testicular Sperm ◽  
Sperm Retrieval ◽  
Machine Learning Models

Abstract STUDY QUESTION Can a machine-learning-based model trained in clinical and biological variables support the prediction of the presence or absence of sperm in testicular biopsy in non-obstructive azoospermia (NOA) patients? SUMMARY ANSWER Our machine-learning model was able to accurately predict (AUC of 0.8) the presence or absence of spermatozoa in patients with NOA. WHAT IS KNOWN ALREADY Patients with NOA can conceive with their own biological gametes using ICSI in combination with successful testicular sperm extraction (TESE). Testicular sperm retrieval is successful in up to 50% of men with NOA. However, to the best of our knowledge, there is no existing model that can accurately predict the success of sperm retrieval in TESE. Moreover, machine-learning has never been used for this purpose. STUDY DESIGN, SIZE, DURATION A retrospective cohort study of 119 patients who underwent TESE in a single IVF unit between 1995 and 2017 was conducted. All patients with NOA who underwent TESE during their fertility treatments were included. The development of gradient-boosted trees (GBTs) aimed to predict the presence or absence of spermatozoa in patients with NOA. The accuracy of these GBTs was then compared to a similar multivariate logistic regression model (MvLRM). PARTICIPANTS/MATERIALS, SETTING, METHODS We employed univariate and multivariate binary logistic regression models to predict the probability of successful TESE using a dataset from a retrospective cohort. In addition, we examined various ensemble machine-learning models (GBT and random forest) and evaluated their predictive performance using the leave-one-out cross-validation procedure. A cutoff value for successful/unsuccessful TESE was calculated with receiver operating characteristic (ROC) curve analysis. MAIN RESULTS AND THE ROLE OF CHANCE ROC analysis resulted in an AUC of 0.807 ± 0.032 (95% CI 0.743–0.871) for the proposed GBTs and 0.75 ± 0.052 (95% CI 0.65–0.85) for the MvLRM for the prediction of presence or absence of spermatozoa in patients with NOA. The GBT approach and the MvLRM yielded a sensitivity of 91% vs. 97%, respectively, but the GBT approach has a specificity of 51% compared with 25% for the MvLRM. A total of 78 (65.3%) men with NOA experienced successful TESE. FSH, LH, testosterone, semen volume, age, BMI, ethnicity and testicular size on clinical evaluation were included in these models. LIMITATIONS, REASONS FOR CAUTION This study is a retrospective cohort study, with all the associated inherent biases of such studies. This model was used only for TESE, since micro-TESE is not performed at our center. WIDER IMPLICATIONS OF THE FINDINGS Machine-learning models may lay the foundation for a decision support system for clinicians together with their NOA patients concerning TESE. The findings of this study should be confirmed with further larger and prospective studies. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the Division of Obstetrics and Gynecology, Soroka University Medical Center, there are no potential conflicts of interest for all authors.

scholarly journals Statins’ safety and impact on the clinical outcomes in COVID-19 critically ill patients: A Multicenter, Cohort Study

2021 ◽  
Author(s):  
◽  
Ohoud Aljuhani ◽  
Ghazwa B. Korayem ◽  
Ali F. Altebainawi ◽  
Shmeylan Al Harbi ◽  
...  
Keyword(s):  
Cohort Study ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Hazard Ratio ◽  
Icu Stay ◽  
Markers Of Inflammation ◽  
The Impact ◽  
Statin Use

Abstract Purpose The complications of Severe Corona Virus Disease 2019 (COVID-19) are attributed to the overproduction of early response proinflammatory cytokines, causing a systemic hyperinflammatory state. Statins are potentially a potent adjuvant therapy in COVID-19 infection due to their pleiotropic and anti-inflammatory effects, which are independent of their cholesterol-lowering activity. This study investigates the impact of statin use on the outcome of critically ill patients with COVID-19. Methods A multicenter, retrospective cohort study of all adult critically ill patients with confirmed COVID-19 admitted to Intensive Care Units (ICUs) between March 1, 2020, and March 31, 2021. Eligible patients were classified into two groups based on statin use during ICU stay and were matched with a propensity score which was based on patient’s age and admission APACHE II and SOFA scores. The primary endpoint was in-hospital mortality. Other outcomes were considered secondary... Results A total of 1049 patients were eligible; 502 patients were included after propensity score matching (1:1 ratio). The 30-day (hazard ratio 0.75 (95% CI 0.58, 0.98), P = 0.03) and in-hospital mortality (hazard ratio 0.69 (95% CI 0.54, 0.89), P = 0.004) were significantly lower in patients who received statin therapy on multivariable cox proportional hazards regression analysis. Moreover, patients who received statin have a lower risk of hospital-acquired pneumonia (OR 0.48(95% CI 0.32, 0.69), P = < 0.001), lower levels of markers of inflammation on follow up and no increased risk of liver injury. Conclusion The use of statin during ICU stay in COVID-19 critically ill patients may have a beneficial role and survival benefits with a good safety profile.

scholarly journals Statins’ safety and impact on the clinical outcomes in COVID-19 critically ill patients: A Multicenter, Cohort Study

2021 ◽  
Author(s):  
Khalid Al Sulaiman ◽  
Ohoud Aljuhani ◽  
Ghazwa B. Korayem ◽  
Ali F. Altebainawi ◽  
Shmeylan Al Harbi ◽  
...  
Keyword(s):  
Cohort Study ◽  
Propensity Score ◽  
Hospital Mortality ◽  
Statin Therapy ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Hazard Ratio ◽  
Icu Stay ◽  
Markers Of Inflammation ◽  
Statin Use

Abstract Background The cardiovascular complications of Severe Coronavirus Disease 2019 (COVID-19) may be attributed to the hyperinflammatory state leading to increased mortality in patients with COVID-19. Statins are known to have pleiotropic and anti-inflammatory effects and may influence viral transmission along with their cholesterol-lowering activity. Thus, statin therapy is potentially a potent adjuvant therapy in COVID-19 infection. This study investigated the association of statin use on the outcome of critically ill patients with COVID-19. MethodsA multicenter, retrospective cohort study of all adult critically ill patients with confirmed COVID-19 admitted to Intensive Care Units (ICUs) between March 1, 2020, and March 31, 2021. Eligible patients were classified into two groups based on statin use during ICU stay and were matched with a propensity score based on patient’s age and admission APACHE II and SOFA scores. The primary endpoint was in-hospital mortality, while 30 days Ventilator-free days (VFDs) and ICU complications were secondary endpoints. ResultsA total of 1049 patients were eligible; 502 patients were included after propensity score matching (1:1 ratio). The 30-day (hazard ratio 0.75 (95% CI 0.58, 0.98), P=0.03) and in-hospital mortality (hazard ratio 0.69 (95% CI 0.54, 0.89), P=0.004) were significantly lower in patients who received statin therapy on multivariable cox proportional hazards regression analysis. Moreover, patients who received statin had a lower risk of hospital-acquired pneumonia (OR 0.48(95% CI 0.32, 0.69), P=<0.001), lower levels of markers of inflammation on follow up and no increased risk of liver injury. ConclusionThe use of statin during ICU stay in COVID-19 critically ill patients may have a beneficial role and survival benefits with a good safety profile.

Sign in / Sign up

Export Citation Format

Share Document