Coronary Care Unit Patients Research Articles

Incidence Pattern and Outcome of Diseases in Patients Admitted in Coronary Care Unit in a Tertiary Care Hospital in Dhaka - A 3 Year Retrospective Study

Background: Cardiovascular disease (CVD) is the leading causes of death globally. Like other countries, CVD prevalence is also rising among the adults in Bangladesh. Most of the cardiac emergencies and acute ailments are preferably managed in a specially designed place of a hospital called Coronary Care Unit, which has better facilities of monitoring, care and support. In every day-to-day practice a proportion of diabetic patients need to admit in CCU for different life threatening crisis. For better understanding of such CVD prevalence scenario, we conducted this retrospective observational study. Aim of the Study: Our objective was to assess the pattern and incidence of CVD admitted in CCU with their outcomes among the population using the data from the record book. Methodology: This is a retrospective observational study carried out in the Coronary Care Unit (CCU) of BIRDEM Hospital Dhaka Bangladesh from 2017-2019. Total 1069 patients (861 were Diabetic and rest were non diabetic) who were admitted to the CCU of this Institute from 2017-2019 were studied and evaluated to see the pattern of cardiac emergencies in diabetic population. Results: Among 1069 patients, 57.2% were male and 42.8% were female. Majority (80.54%) of them were diabetic. Most of the patients belonged to age 50 years and above. Seven hundred and one (65.57%) patients were admitted due to Ischemic Heart Disease (IHD) in different forms. Among the Acute Coronary Syndrome, most prevalent one was NSTEMI (354). Among NSTEMI group, where 50.37% were male and 50.67% were female, 102 patients had Unstable Angina and 73 had STEMI. Eighty nine patients were admitted after intervention (PCI or CABG) and 61 patients had Ischemic Dilated Cardiomyopathy (IDCM) due to some complications. Four hundred and twenty patients admitted due to Heart Failure and 137 patients were admitted due to different forms of Shock. Cardiac function was assessed (by Teichholz method and Biplane Simpson's method) among the admitted patients and 57.31% of patients had moderate LV dysfunction, 29.26% had severe systolic dysfunction and remainder 13.43% found to have mild LV systolic dysfunction. Mean hospital stay was assessed and it was found to be gradually declining from 13.33 in 2017 to 9.23 in 2019 (p=0.041*). Outcome of the study population shows that ~ 66% were transfer to general ward or cabin of the department of cardiology after initial stabilization, 17.3% were discharged directly from CCU, and 5.42% patients were transferred to other department after recovery. About 5.5% were become critically ill for which they need to transfer under care of ICU, 2.5% were transfer for intervention (to other institutes according to patient’s economical ability and preference for CABG, Pace maker implantation, PTCI etc.) and 3.3% patients were expired. Conclusion: In this study, incidence of IHD found higher than other cardiac diseases. NSTEMI is about three times more than Unstable Angina and about five times more prevalent than STEMI for the reason of admission in CCU. Diabetes Mellitus was found in majority of the admitted patients in CCU and the number of diabetic population is increasing which may also responsible for leading more coronary heart disease in our country in future. 88.7% of study population was shifted to general ward after initial stabilization after acute crisis, 2.5% required further cardiac intervention and 5.5% become critical and required ICU support. Only 3.3% of total population expired after all possible support. Bangladesh Crit Care J September 2024; 12 (2): 125-131

Serum glucose-potassium ratio predicts inhospital mortality in patients admitted to coronary care unit.

The aim of our study was to determine the role of serum glucose-potassium ratio in predicting inhospital mortality in coronary care unit patients. This study used data from the MORtality in CORonary Care Units in Turkey study, a national, observational, multicenter study that included all patients admitted to coronary care units between September 1, 2022, and September 30, 2022. Statistical analyses assessed the independent predictors of mortality. Two models were created. Model 1 included age, history of heart failure, chronic kidney disease, hypertension, diabetes mellitus, and coronary artery disease. Model 2 included glucose-potassium ratio in addition to these variables. Multivariate regression and receiver operating characteristic analysis were performed to compare Model 1 and Model 2 to identify if the glucose-potassium ratio is an independent predictor of inhospital mortality. In a study of 3,157 patients, the mortality rate was 4.3% (n=137). Age (p=0.002), female gender (p=0.004), mean blood pressure (p<0.001), serum creatinine (p<0.001), C-reactive protein (p=0.002), white blood cell (p=0.002), and glucose-potassium ratio (p<0.001) were identified as independent predictors of mortality through multivariate regression analysis. The receiver operating characteristic analysis indicated that Model 2 had a statistically higher area under the curve than Model 1 (area under the curve 0.842 vs area under the curve 0.835; p<0.001). A statistically significant correlation was found between the inhospital mortality and glucose-potassium ratio (OR 1.015, 95%CI 1.006-1.024, p<0.001). Our study showed that the glucose-potassium ratio may be a significant predictor of inhospital mortality in coronary care unit patients.

A Predictive Nomogram of In-Hospital Mortality After 48 h for Atrial Fibrillation Patients in the Coronary Care Unit.

Patients with atrial fibrillation (AF) suffer a higher risk of death, and it is necessary to develop prediction tools for mortality risk in critically ill patients with AF. This study aimed to develop a novel predictive nomogram of in-hospital mortality after 48 h in the coronary care unit (CCU) for patients with AF. We collected information on CCU patients with AF from the "Medical Information Mart for Intensive Care-III" database and developed a nomogram model for predicting the all-cause mortality risk after 48 h in the hospital. Key variables were selected by univariate logistic and least absolute shrinkage and selection operator regression. The independent predictors with p < 0.05 were screened out by multivariate logistic regression. A predictive nomogram was constructed using these independent predictors, and the model calibration and discrimination were evaluated. This study finally enrolled 1248 CCU patients with AF, and the in-hospital mortality was 17% (209/1248). The predictive nomogram was constructed by 13 selected independent predictors, including age, smoking status, acute kidney injury, chronic obstructive pulmonary disease, ventricular arrhythmia, shock, urea, red cell distribution width, leucocytosis, continuous renal replacement therapy, continuous positive airway pressure, anticoagulation, and heart rate. The area under the curve of the nomogram was 0.803 (95% confidence interval 0.771-0.835). The nomogram was verified to have good accuracy and calibration. This study developed a novel nomogram containing age, acute kidney injury, and heart rate that can be a good predictor of potential in-hospital mortality after 48 h in CCU patients with AF.

The Effect of Foot Reflexology on Fatigue, Sleep Quality, Physiological Indices, and Electrocardiogram Changes in Patients with Acute Myocardial Infarction: A Randomized Clinical Trial.

Limited evidence is available regarding the effect of reflexology on Acute Myocardial Infarction (AMI). The present study evaluated the effect of foot reflexology on fatigue, sleep quality, physiological indices, and electrocardiogram changes in AMI. This clinical trial was conducted on 80 subjects with AMI. They were divided into an intervention (received reflexology for 3 consecutive days) and a control (received the routine care) group. The Multidimensional Fatigue Inventory, the Pittsburgh Sleep Quality Index, a pain numeric analog scale, a daily physiological indices form, and daily electrocardiogram were used to collect data. The collected data were analyzed in SPSS software. The study was conducted based on CONSORT criteria. After controlling the covariates, a significant difference was found between the intervention and control groups with regard to the mean scores of fatigue (F5,80 = 16.33; p < 0.001), sleep quality (F5,80 = 16.56; p < 0.001), and chest pain intensity (F5,80 = 6.86; p = 0.010); means of systolic blood pressure (F5,80 = 22.20; p < 0.001), heart rate (F5,80 = 5.86; p = 0.010), respiration (F5,80 = 9.37; p = 0.003), and temperature (F5,80= -4.23; p < 0.001); and incidence of ST-segment (χ2 1,80 = 5.00; p = 0.020) and T-wave changes (χ2 1,80 = 6.05, p = 0.010) on the fourth day of the intervention. Given the effectiveness of foot reflexology in different aspects of AMI patients, the implementation of this intervention is recommended for these patients in coronary care units.

Predictive Ability of Inflammatory Markers on In-Hospital Outcomes in Patients Admitted to Coronary Care Unit (MORCOR-TURK INFLAME).

We investigated the prognostic implications of the systemic immune-inflammatory index (SII), atherogenic index of plasma (AIP), C-reactive protein/albumin ratio (CAR), neutrophil-lymphocyte ratio (NLR), prognostic nutritional index (PNI), and triglyceride/glucose index (TGI) in the MORtality predictors in the CORonary Care Units in TURKey (MORCOR-TURK) population. This is the largest registry of coronary care unit (CCU) patients in Turkey (3157 patients admitted to CCU in 50 different centers). The study population was divided into two according to in-hospital survival status; 137 patients (4.3%) died in-hospital follow-up. A significant correlation was found between death and SII, CAR, NLR, and PNI but not for AIP and TGI in logistic regression. In Model 1 (combining parameters proven to be risk predictors), the -2 log-likelihood ratio was 888.439, Nagelkerke R2 was 0.235, and AUC (area under curve) was 0.814 (95% CI: 0.771-0.858). All other models were constructed by adding each inflammatory marker separately to Model 1. Only Model 3 (CAR + Model 1) had a significantly greater AUC than Model 1 (DeLong P = .01). Our study showed that CAR, but not other inflammatory index, is a significant predictor of in-hospital mortality in CCU patients when added to proven risk predictors.

The Relationship Between Mortality and Leuko-Glycemic Index in Coronary Care Unit Patients (MORCOR-TURK LGI)

Introduction&amp;Objective: Identifying high-risk patients with a poor prognosis in coronary care unit (CCU) patients can assist physicians in providing optimal care and implementing preventive strategies. Leuko-glycaemic index (LGI), synthesized by multiplying the blood glucose level by the leukocyte count, has gained popularity in risk stratification of myocardial infarction patients. In this context, this study was carried out to investigate the relationship between LGI assessed at admission and in-hospital mortality in CCU patients. Methods: This is a multi-center, cross-sectional and observational study. (MORCOR-TURK LGI: Mortality Predictors in Coronary Care in Turkey, ClinicalTrials.gov number NCT05296694). The population of this study consisted of 2917 consecutive patients admitted to the CCU. Blood samples were collected into serum separator tubes in the immediate admission to the CCU. LGI was calculated by multiplying both values and dividing them by a thousand. LGI units were expressed in mg/dl. mm³. The sample was divided into two groups based on the LGI cut-off value of 1.23. Logistic regression analysis was used to find the significant predictors of mortality. Receiver operating characteristics (ROC) curve was to find out the cut-off value of LGI. A p value less than 0.05 was considered to be statistically significant in all analyses. Results: Univariable logistic regression analysis revealed that age, heart failure (HF), LGI, coronary artery disease, hypertension, diabetes mellitus and atrial fibrillation are clinically and statistically significant predictors. Further analysis of these variables using the multivariable logistic regression analysis indicated that age (Odds Ratio [OR]: 1.040, 95% confidence interval [CI]: 1.017-1.063; p=0.001), HF (OR: 2.426, 95% CI: 1.419-4.149; p:0.001) and LGI (OR: 1.349, 95% CI: 1.176-1.549; p3.72 predicted in-CCU mortality with 95.56% sensitivity and 49.19% specificity ([AUC]: 0.659 [95% CI: 0.641–0.676, p

Serum Vitamin D and Vaspin Levels Among Patients with Acute Myocardial Infarction and Their Association with Risk Factors.

The current study investigated and compared serum levels of vitamin D (VD) and vaspin in AMI patients and healthy subjects and correlated these biomarkers with other biochemical risk factors for AMI. The research was carried out at King Abdulaziz University Hospital (KAUH) in Jeddah. Blood samples and additional information were gathered from 110 admitted AMI patients in the Intensive Coronary Care Unit (ICCU) (ages 40-65 years) and 50 adult, healthy volunteers whose BMI and age were similar to those of the patients. AMI patients had significantly lower vaspin (p < 0.001) and VD levels (p < 0.001) than the control group. Fasting plasma glucose (FPG), hemoglobin A1C (HbA1c), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoproteincholesterol (LDL-C) levels were shown to be significantly different between AMI patients and controls. Among the AMI patients, 15 (13.6%) had deficient serum VD levels (≤20 ng/mL), 60 (54.5%) had insufficient levels (>20 - <30 ng/mL), and 35 (31.8%) had sufficient levels (≥30 ng/mL). In healthy subjects, VD levels were deficient in 4(8%), insufficient in 13 (26%), and sufficient in 33 (66%). VD insufficiency was more prevalent in AMI patients compared to the healthy group (54.5% vs 26%; p < 0.001). In AMI patients, serum vaspin was found to be related to age and HbA1c in the control group. VD did not show a significant correlation with any variable in AMI patients and healthy subjects. Serum vaspin (p = 0.89) and VD levels (p = 0.29) did not differ significantly between female and male control groups. Compared to the healthy group, AMI patients showed significantly lower vaspin and VD levels. Additionally, AMI patients had a higher prevalence of VD deficiency and insufficiency, suggesting its possible role in the occurrence of AMI.

Clinical characteristics and prognosis of patients with infective endocarditis admitted to a coronary unit in a hospital with cardiac surgery

Abstract Funding Acknowledgements None. Introduction Cardiac critical care units are essential for the management of complicated infective endocarditis (IE). Objective We set out to analyze the baseline characteristics, clinical complications and prognosis of patients with IE admitted to a Coronary Care Unit (CCU), as well as to compare them with patients admitted to hospital wards. Material and Methods Prospective, single-centre, observational study including patients who were diagnosed of IE from 2016-2022 and admitted in a hospital with cardiac surgery. They were classified into two groups according to whether or not they were admitted to the CCU. We collected data on microbiological isolation, cardiac and systemic complications as well as in-hospital mortality. Patients were followed long-term for cardiovascular (CV) and all-cause mortality, readmission and IE recurrence. All variables (qualitative) were compared by Chi-square test and overall mortality was compared in both groups by the LogRank test. We established a significance level of p&amp;lt;0.05. Results From a total of 162 patients with a median age of 68 years (IQR 68), 28.4% (46) were admitted to the CCU. These patients were similar in age to those not admitted (mean 65 vs 66 years, NS), but had a higher percentage of diabetics (47.8 vs 27.6%, p=0.01). The most frequent previous heart disease in both groups was valvular heart disease. The most frequently isolated microorganisms in CCU patients were Staphylococcus aureus (46.4%) and Streptococcus epidermidis (37.9%) which, in our series, produced the highest percentage of shock (35.7% and 23.3% respectively) in a statistically significant way (p&amp;lt;0.01), with the need for noradrenaline (60.9%) and dobutamine (28.3%); no ventricular assist devices were used. Critically ill patients had a higher incidence of atrioventricular block requiring a permanent pacemaker (32.6% vs 4.3%, p&amp;lt;0.01), valve dysfunction (76.1% vs 55.2%, p=0.014) and were more frequently operated than those admitted to the ward (56.%% vs 38%, p=0.04). Among all IE patients, 24.6% died during admission, most of them (15.7% of the total) were admitted to the coronary unit (p&amp;lt;0.01). In our 36-month mean follow-up, we found significantly higher readmission rates in the CCU group (36.6% vs. 15%, p&amp;lt;0.01), unrelated to IE recurrences. The critically ill group exhibited a higher mortality rate due to cardiovascular disease and all-cause causes (43.5% vs 15.9%, p0.01; 63% vs 23.9%, p0.01). In the CCU group there is significantly lower survival (mean 65 vs 37 months, LogRank p&amp;lt;0.01) with respect to the hospital ward group. Conclusion Our CCU is responsible for treating patients with IE that is highly complex and that progresses poorly, with a higher frequency of shock and in-hospital mortality, which leads to a decreased prognosis during follow-up. Anticipating complications and collaborating with cardiac surgery is essential to improve clinical outcomes.

Retrospective Analysis of Hospital Acquired Infection and Antibiotic Resistance in Coronary Care Unit (CCU), Adult, and Pediatric Cardiology Wards.

Antibiotics resistance is an paramount threat affecting the whole world but nowhere situation is as gloomy as in India. No study till date regarding epidemiology of hospital acquired infections in coronary care units(CCU) and cardiology wards from India. From Indian perspective it is the first observational study to analyse microbiological profile and antibiotic resistance in CCU. The purpose of this observational study is to explore the epidemiology and importance of infections in CCU patients. After ethics committee approval, the records of all patients who were admitted in coronary care units, adult and pediatric cardiology wards surgery between January 2020 and December 2021 were reviewed retrospectively. The type of organism,source of infection ,age wise distribution and seasonal variability among patients who developed hospital acquired infection (HAI) were determined. 271 patients developed microbiologically documented HAI during from January 2020 to December 2021. Maximum number of organisms(78/271 28.78%) are isolated from urinary samples ,followed by blood stream(60/271 22.14%) and Endotracheal tube (54/271 19.92%). Acinetobacter baumanii (53/271, 19.5%) being the most common isolate among all the samples taken . Acinetobacter was the most frequent pathogens isolated in patients with LRTI and blood stream infection while E. coli was from urinary tract infection . In the adult population, infection with E. coli(24.6%) is the most common followed by Klebsiella pneumoniae (12.8%) and Acinetobacter baumanii (10.1%). In the pediatric population Acinetobacter baumanii (38.6%%) is the most common followed by Klebsiella pneumoniae (20.5%) and Methicillin Resistant Staphylococcus aureus, MRSA (6.8%). Commonly used antibiotics eg ciprofloxacin,ceftazidime and amikacin were found to be resistant against the top three isolates. Urinary tract was the most common site of infection and Gram-negative bacilli, the most common pathogens in adult as well as pediatric population. Antibiotic resistance was maximum with commonly isolated microorganisms.

Short-Term Prognosis of Elderly Patients Admitted to the Coronary Care Unit: A Subgroup Analysis of the MORCOR-TURK (Mortality and Morbidity in Coronary Care Units in Türkiye) Trial.

This subgroup analysis of the MORCOR-TURK (Mortality and Morbidity in Coronary Care Units in Türkiye) trial aimed to determine the short-term prognosis, mortality rates, and predictors for elderly patients followed in coronary care units (CCUs) in Türkiye. The MORCOR-TURK trial is a national, non-interventional, multicenter observational study conducted in Türkiye (ClinicalTrials.gov number NCT05296694). The study population includes CCU patients from 50 centers selected from all regions of Türkiye (between September 1 and 30, 2022 prospectively). In the subgroup analysis, patients were divided into two groups: Group 1 (ages 65 to < 75 years, n = 923 patients) and Group 2 (ages ≥ 75 years, n = 713 patients). At the end of the analysis, short-term prognosis, mortality rates, and predictors were documented. The mean age of Group 1 was 69 (67-72) years, and Group 2 was 80 (77-84) years. Chest pain was the most common reason for admission (968 patients [59.16%]), and acute coronary syndrome was the most common reason for hospitalization in the CCU (1,053 patients [64%]). Atrial fibrillation (AF) was the most common arrhythmia (356 patients [21.76%]). The mortality rate was 6.11% in elderly patients (4.23% in Group 1 and 8.56% in Group 2). The multivariate regression analysis showed that age (P = 0.046, P = 0.003), chronic kidney disease (P = 0.011, P = 0.045), and ventricular tachycardia/ventricular fibrillation (VT/VF) during hospitalization (P < 0.001) were the main factors that increased mortality in both groups. Other independent mortality risk factors were smoking for Group 1 and aortic stenosis for Group 2. This study represents the most comprehensive assessment of the short-term prognosis for elderly patients admitted to CCUs in Türkiye. It showed that coronary artery disease was the most common reason for admission and age over 75 and chronic kidney disease were the main determinants of mortality.

Short-Term Impact of Hyperuricemia in Acute Coronary Syndrome among Coronary Care Unit Patients in Suez Canal Authority Hospital and Ismailia Medical Complex

Short-Term Impact of Hyperuricemia in Acute Coronary Syndrome among Coronary Care Unit Patients in Suez Canal Authority Hospital and Ismailia Medical Complex

Serum Lipoprotein Ratios as Markers for Insulin Resistance Among Non-Diabetic Acute Coronary Syndrome Patients with Impaired Fasting Glucose

Background: Insulin resistance is a major risk factor in the development of cardiovascular diseases and type 2 diabetes mellitus. Some studies concluded that serum lipoproteins levels and hence lipoprotein ratios were altered in patients with insulin resistance. Objective: To identify the possibility of using lipoprotein ratios as markers for insulin resistance. Methods: A cross sectional study conducted in Baghdad (Medical City) and in Maysan (Al Shaheed Al Sadir Teaching Hospital) from February to December 2020. Eighty-three male and 51 female in Coronary Care Unit and Internal Medicine Wards patients were selected in the study group. Results: Lipoprotein ratios were significantly higher in individuals with homeostatic model assessment for insulin resistance (HOMA-IR) ≥2.5 as compared to subjects with HOMA-IR &lt;2.5. There was a statistically significant association between lipoprotein ratios and insulin resistance when HOMA-IR ≥2.5 (P less than 0.05). Fasting insulin correlated significantly with lipoprotein ratios. Conclusion: Serum lipoprotein ratios and the best one is triglyceride/high density lipoprotein cholesterol could be used as markers for insulin resistance. Keywords: Lipoprotein ratios, insulin resistance, diabetes Citation: Issa EF, Rasheed MK. Serum lipoprotein ratios as markers for insulin resistance among non-diabetic acute coronary syndrome patients with impaired fasting glucose. Iraqi JMS. 2022; 20(2): 239-244. doi: 10.22578/IJMS.20.2.11

Simplified acute physiology score III is excellent for predicting in-hospital mortality in coronary care unit patients with acute myocardial infarction: A retrospective study.

Coronary care unit (CCU) patients with acute myocardial infarction (AMI) lack effective predictors of in-hospital mortality. This study aimed to investigate the performance of four scoring systems in predicting in-hospital mortality in CCU patients with AMI. The baseline data, the logistic organ dysfunction system (LODS), the Oxford acute severity of illness score (OASIS), the simplified acute physiology score II (SAPS II), and the simplified acute physiology score III (SAPS III) scores of the patients were extracted from the fourth edition of the Medical Information Mart for Critical Care (MIMIC-IV) database. Independent risk factors for in-hospital mortality were identified by regression analysis. We performed receiver operating characteristic (ROC) curves and compared the area under the curve (AUC) to clarify the predictive value of the four scoring systems. Meanwhile, Kaplan-Meier curves and decision curve analysis (DCA) were performed to determine the optimal scoring system for predicting in-hospital mortality. A total of 1,098 patients were included. The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI before and after the propensity score matching (PSM) analysis. The discrimination of in-hospital mortality by SAPS III was superior to that of LODS, OASIS, and SAPS II. The AUC of the SAPS III scoring system was the highest among the four scoring systems, at 0.901 (before PSM) and 0.736 (after PSM). Survival analysis showed that significantly more in-hospital mortality occurred in the high-score SAPS III group compared to the low-score SAPS III group before PSM (HR 7.636, P < 0.001) and after PSM (HR 2.077, P = 0.005). The DCA curve of SAPS III had the greatest benefit score across the largest threshold range compared to the other three scoring systems. The SAPS III was an independent risk factor for predicting in-hospital mortality in CCU patients with AMI. The predictive value for in-hospital mortality with SAPS III is superior to that of LODS, OASIS, and SAPS II. The results of the DCA analysis suggest that SAPS III may provide a better clinical benefit for patients. We demonstrated that SAPS III is an excellent scoring system for predicting in-hospital mortality for CCU patients with AMI.

Impact of admitting department on the management of acute coronary syndrome after an out of hospital cardiac arrest.

This study aimed to analyze the influence of the hospital admitting department on adherence to the Guidelines of European Society of Cardiology for management of acute coronary syndromes in patients after out-of-hospital cardiac arrest (OHCA) of coronary etiology. We studied retrospective-prospective register of 102 consecutive patients with OHCA as a manifestation of acute coronary syndrome (ACS). Patients were admitted to the coronary care unit (CCU) 52, general intensive care unit (GICU) 21, or GICU after initial Cath lab treatment (CAG-GICU) 29. This study compared the differences in the management of ACS in patients with OHCA of coronary etiology based on the admitting department in a tertiary care institution. Twelve of the 21 (57.1%) patients admitted to the GICU were evaluated as having ACS on-site where they experienced OHCA. In the CCU group, 50 out of 52 (96.2%) and 28 of 29 (100%) patients in the CAG-GICU group (P<0.001). Coronary angiography was performed in 10 of 21 patients (48%) admitted to the GICU. It was performed in 49 out of 52 (94%) CCU patients and, in the CAG-GICU group, 28 out of 29 patients. The mean time to CAG differed significantly across groups (that is, GICU 200.7 min., CCU 71.2 min., and CAG-GICU 7.5 min. (P<0.001)). Aspirin was used in 48% of GICU, 96% of CCU, and 79% of CAG-GICU patients (P<0.001), while in the pre-hospital phase, aspirin was used in 9.5% of GICU, 71.2% of CCU, and 50% of CAG-GICU patients (P<0.001). P2Y12 inhibitor prescriptions were lower in patients admitted to the GICU (33% vs. 89% CCU and 57% CAG-GICU, P<0.001). The department's choice significantly affected the time to initiation of antithrombotics, which was the longest in the GICU. The choice of admission department for patients with OHCA caused by ACS was found to affect the extent to which the recommended treatments were used. An examination of OHCA patients by a cardiologist upon admission to the hospital increased the likelihood of an early diagnosis of ACS as the cause of OHCA.

Predictive outcomes of APACHE II and expanded SAPS II mortality scoring systems in coronary care unit

ObjectiveWe investigated the predictive values of the expanded Simplified Acute Physiology Score (SAPS) II and Acute Physiologic Score and Chronic Health Evaluation (APACHE) II score in predicting in-hospital mortality in coronary care unit (CCU) patients. MethodsIn this study, expanded SAPS II and APACHE II scores were calculated in the CCU of a single-center tertiary hospital. Patients admitted to CCU with any cardivascular indication were included in the study. Both scores were calculated according to previously determined criteria. Calibration and discrimination abilities of the scores in predicting in-hospital mortality were tested with Hosmer-Lemeshow goodness-of-fit C chi-square and receiver operating characteristics (ROC) curve analyses. ResultsA total of 871 patients were included in the analysis. The goodness-of-fit C chi-square test showed that both scores have a good performance in predicting survivors and nonsurvivors in CCU. Expanded SAPS II score has a sensitivity of 80% and a specificity of 91.8% with the cut-off value of 5.55, while APACHE II has a sensitivity of 75.9% and a specificity of 87.4% with the cut-off value of 16.5 in predicting mortality. ConclusionExpanded SAPS II and APACHE II scores have good ability to predict in-hospital mortality in CCU patients. Therefore, they can be used as a tool to predict short-term mortality in cardiovascular emergencies.

Neutrophil Percentage to Albumin Ratio was Associated with Clinical Outcomes in Coronary Care Unit Patients.

Neutrophil percentage to albumin ratio (NPAR) has been shown to be correlated with the prognosis of various diseases. This study aimed to explore the effect of NPAR on the prognosis of patients in coronary care units (CCU). All data in this study were extracted from the Medical Information Mart for Intensive Care III (MIMIC-III, version1.4) database. All patients were divided into four groups according to their NPAR quartiles. The primary outcome was in-hospital mortality. Secondary outcomes were 30-day mortality, 365-day mortality, length of CCU stay, length of hospital stay, acute kidney injury (AKI), and continuous renal replacement therapy (CRRT). A multivariate binary logistic regression analysis was performed to confirm the independent effects of NPAR. Cox regression analysis was performed to analyze the association between NPAR and 365-day mortality. The curve in line with overall trend was drawn by local weighted regression (Lowess). Subgroup analysis was used to determine the effect of NPAR on in-hospital mortality in different subgroups. Receiver operating characteristic (ROC) curves were used to evaluate the ability of NPAR to predict in-hospital mortality. Kaplan-Meier curves were constructed to compare the cumulative survival rates among different groups. A total of 2364 patients in CCU were enrolled in this study. The in-hospital mortality rate increased significantly as the NPAR quartiles increased (p 0.001). In multivariate logistic regression analysis, NPAR was independently associated with in-hospital mortality (quartile 4 versus quartile 1: odds ratio [OR], 95% confidence interval [CI]: 1.83, 1.20-2.79, p = 0.005, p for trend 0.001). In Cox regression analysis, NPAR was independently associated with 365-day mortality (quartile 4 versus quartile 1: OR, 95% CI: 1.62, 1.16-2.28, p = 0.005, p for trend 0.001). The Lowess curves showed a positive relationship between NPAR and in-hospital mortality. The moderate ability of NPAR to predict in-hospital mortality was demonstrated through ROC curves. The area under the curves (AUC) of NPAR was 0.653 (p 0.001), which is better than that of the platelet to lymphocyte ratio (PLR) (p 0.001) and neutrophil count (p 0.001) but lower than the Sequential Organ Failure Assessment (p = 0.046) and Simplified Acute Physiology Score II (p 0.001). Subgroup analysis did not reveal any obvious interactions in most subgroups. However, Kaplan-Meier curves showed that as NPAR quartiles increased, the 30-day (log-rank, p 0.001) and 365-day (log-rank, p 0.001) cumulative survival rates decreased significantly. NPAR was also independently associated with AKI (quartile 4 versus quartile 1: OR, 95% CI: 1.57, 1.19-2.07, p = 0.002, p for trend = 0.001). The CCU and hospital stay length was significantly prolonged in the higher NPAR quartiles. NPAR is an independent risk factor for in-hospital mortality in patients in CCU and has a moderate ability to predict in-hospital mortality.

Predictive value of six critical illness scores for 28-day death risk in comprehensive and specialized intensive care unit patients based on MIMIC-IV database

To explore the basic characteristics of various types of intensive care unit (ICU) patients and the predictive value of six common disease severity scores in critically ill patients on the first day on the 28-day death risk. The general information, disease severity scores [acute physiology score III (APS III), Oxford acute disease severity (OASIS) score, Logistic organ dysfunction score (LODS), simplified acute physiology score II (SAPS II), systemic inflammatory response syndrome (SIRS) score and sequential organ failure assessment (SOFA) score], prognosis and other indicators of critically ill patients admitted from 2008 to 2019 were extracted from Medical Information Mart for Intensive Care-IV 2.0 (MIMIC-IV 2.0). The receiver operator characteristic curve (ROC curve) of six critical illness scores for 28-day death risk of patients in various ICU, and the area under the ROC curve (AUC) was calculated, the optimal Youden index was used to determine the cut-off value, and the AUC of various ICU was verified by Delong method. A total of 53 150 critically ill patients were enrolled, with medical ICU (MICU) accounted for the most (19.25%, n = 10 233), followed by cardiac vascular ICU (CVICU) with 17.78%(n = 9 450), and neurological ICU (NICU) accounted for the least (6.25%, n = 3 320). The patients in coronary care unit (CCU) were the oldest [years old: 71.79 (60.27, 82.33)]. The length of ICU stay in NICU was the longest [days: 2.84 (1.51, 5.49)] and accounted for the highest proportion of total length of hospital stay [63.51% (34.61%, 97.07%)]. The patients in comprehensive ICU had the shortest length of ICU stay [days: 1.75 (0.99, 3.05)]. The patients in CVICU had the lowest proportion of length of ICU stay to total length of hospital stay [27.69% (18.68%, 45.18%)]. The six scores within the first day of ICU admission in NICU patients were lower than those in the other ICU, while APS III, LODS, OASIS, and SOFA scores in MICU patients were higher than those in the other ICU. SAP II and SIRS scores were both the highest in CVICU, respectively. In terms of prognosis, MICU patients had the highest 28-day mortality (14.14%, 1 447/10 233), while CVICU patients had the lowest (2.88%, 272/9 450). ROC curve analysis of the predictive value of each score on the 28-day death risk of various ICU patients showed that, the predictive value of APS III, LODS, and SAPS II in comprehensive ICU were higher [AUC and 95% confidence interval (95%CI) were 0.84 (0.83-0.85), 0.82 (0.81-0.84), and 0.83 (0.82-0.84), respectively]. The predictive value of OASIS, LODS, and SAPS II in surgical ICU (SICU) were higher [AUC and 95%CI were 0.80 (0.79-0.82), 0.79 (0.78-0.81), and 0.79 (0.77-0.80), respectively]. The predictive value of APS III and SAPS II in MICU were higher [AUC and 95%CI were 0.84 (0.82-0.85) and 0.82 (0.81-0.83), respectively]. The predictive value of APS III and SAPS II in CCU were higher [AUC and 95%CI were 0.86 (0.85-0.88) and 0.85 (0.83-0.86), respectively]. The predictive value of LODS and SAPS II in trauma ICU (TICU) were higher [AUC and 95%CI were 0.83 (0.82-0.83) and 0.83 (0.82-0.84), respectively]. The predictive value of OASIS and SAPS II in NICU were higher [AUC and 95%CI were 0.83 (0.80-0.85) and 0.81 (0.78-0.83), respectively]. The predictive value of APS III, LODS, and SAPS II in CVICU were higher [AUC and 95%CI were 0.84 (0.83-0.85), 0.81 (0.80-0.82), and 0.78 (0.77-0.78), respectively]. For the patients in comprehensive ICU, MICU, CCU, and CVICU, APS III or SAPS II can be applied for predicting 28-day death risk. For the patients in SICU and NICU, OASIS or SAPS II can be applied to predict 28-day death risk. For the patients in TICU, SAPS II or LODS can be applied for predicting 28-day death risk. For CVICU patients, APS III or LODS can be applied to predict 28-day death risk.

The effect of a family-based participatory care program on anxiety in patients with acute coronary syndrome in coronary care units: A randomised controlled clinical trial

BackgroundAdmission to the coronary care units (CCUs) and the patient's reduced interaction with family are regarded as important sources of anxiety. Family participation in care programs is pivotal to patient outcomes. ObjectivesThe present study was conducted to determine the effect of a care program based on family participation on anxiety in patients with acute coronary syndrome. MethodsThis randomised controlled trial was conducted on 90 patients in CCUs and their families. The patients were randomly assigned to one of the following three groups: routine care, control, and intervention. Routine care measures were provided to the routine care group, increased participation of the family was ensured in the control group, and a family-based participatory care program was implemented in the intervention group with the interaction of the nurse, patient, and family based on five principles, including presence, determination of needs, communication, participation in decision-making, and cooperation in care. The patients’ anxiety was measured in the three groups on day 1 and 3 days after the admission to the CCU using the Spielberger State-Trait Anxiety Inventory. ResultsThe patients’ mean state anxiety score before the intervention was 44.4 ± 12.7, 46.6 ± 12.4, and 45.5 ± 12.1 in the routine care, control, and intervention groups, respectively, with no significant differences between them (P = 0.81). After adjusting for anxiety before the intervention and study hospital, the mean (before–after) changes in anxiety score in the three groups showed that anxiety was significantly lower in the intervention group than in the other groups (P < 0.05). ConclusionIncreased family presence alone has no effect on the patients’ anxiety, but the family’s participation and interaction with the care team can affect anxiety levels in cardiac patients in CCUs and improve the care processes.Trial registration: Iranian Registry of Clinical Trials, Trial No. IRCT201105146481N1.

Predicting the Prognosis of Patients in the Coronary Care Unit: A Novel Multi-Category Machine Learning Model Using XGBoost.

BackgroundEarly prediction and classification of prognosis is essential for patients in the coronary care unit (CCU). We applied a machine learning (ML) model using the eXtreme Gradient Boosting (XGBoost) algorithm to prognosticate CCU patients and compared XGBoost with traditional classification models.MethodsCCU patients' data were extracted from the MIMIC-III v1.4 clinical database, and divided into four groups based on the time to death: <30 days, 30 days−1 year, 1–5 years, and ≥5 years. Four classification models, including XGBoost, naïve Bayes (NB), logistic regression (LR), and support vector machine (SVM) were constructed using the Python software. These four models were tested and compared for accuracy, F1 score, Matthews correlation coefficient (MCC), and area under the curve (AUC) of the receiver operating characteristic curves. Subsequently, Local Interpretable Model-Agnostic Explanations method was performed to improve XGBoost model interpretability. We also constructed sub-models of each model based on the different categories of death time and compared the differences by decision curve analysis. The optimal model was further analyzed using a clinical impact curve. At last, feature ablation curves of the XGBoost model were conducted to obtain the simplified model.ResultsOverall, 5360 CCU patients were included. Compared to NB, LR, and SVM, the XGBoost model showed better accuracy (0.663, 0.605, 0.632, and 0.622), micro-AUCs (0.873, 0.811, 0.841, and 0.818), and MCC (0.337, 0.317, 0.250, and 0.182). In subgroup analysis, the XGBoost model had a better predictive performance in acute myocardial infarction subgroup. The decision curve and clinical impact curve analyses verified the clinical utility of the XGBoost model for different categories of patients. Finally, we obtained a simplified model with thirty features.ConclusionsFor CCU physicians, the ML technique by XGBoost is a potential predictive tool in patients with different conditions, and it may contribute to improvements in prognosis.

MO331: Acute Kidney Failure as a Risk Factor for Longer Hospital Stay, Renal Replacement Therapy and Mortality: A Public Health System Analysis

Abstract BACKGROUND AND AIMS Acute kidney failure (AKI) is a well-known risk factor in coronary care unit (CCU) and postoperative patients. We aim to analyse their role in a multicentre database (DB) from a public regional health system (6 million inhabitants). METHOD Observational, retrospective study, including all hospital admissions between January 2013 and December 2014 in all referral hospitals in Madrid. We grouped into three categories, admissions due to AKI (ON-AKI), AKI during stay (HOSP-AKI) secondary to other disease and admissions without AKI (no-AKI); ICD-9 code was 584. Admission from patients aged under 16, women to give birth or previous renal replacement therapy (RRT) were excluded. Study was approved by the ethics committee. RESULTS Of the 419 851 admissions registered in 2 years, 6.7% had an associated AKI (0.6% on arrival AKI and 6.1% during admission AKI). Patients admitted for AKI are older (ON-AKI: 74.8 years versus HOSP-AKI: 77.9 versus no-AKI: 63.6), with greater comorbidity (Charlson index 2.9 versus 3.1 versus 1.7); patients had more previous CV events (28.5% versus 46.8 versus 21.0), diabetes mellitus (34.1% versus 30.4 versus 17.1) and a higher prevalence of previous chronic kidney disease-CKD (41.3 versus 31.5 versus 4.9%). AKI kidney failure lengthens hospital stay by 3.2 days 95% confidence interval (95% CI) (2.8–3.5) after adjust by age, gender, Charlson index, surgery and major diagnostic categories. Admissions with AKI are usually unscheduled and have a longer hospital stay (9.6 ON-AKI versus 12.6 HOSP-AKI versus 7.1 days in no-AKI admission). More patient died during hospital stay in AKI group (14.4% ON-AKI; 22.9 HOSP-AKI versus 3.5% No-AKI) and although 4.3% of admission needed dialysis, only 0.5% started a chronic RRT during admission. Principal risk factor for developing secondary AKI (R2 = 16%) is previous CKD [OR 3.6; (3.48–3.74)], after corrected by age, male, the Charlson index and non-surgical admission. Mortality risk for patients with an admission for AKI (R2 = 17%), corrected for age, sex, comorbidity, previous CKD, and type of admission is OR: 1.8 95% CI (1.57–2.08); secondary AKI is OR 3.73 (3.59–3.88) than no-AKI admission. CONCLUSION AKI is a huge burden for health system and patients, and associates significant longer stay, cost and a higher mortality. Main factor to develop secondary AKI is age and previous CKD.