Predictive Factors of Long-Term Stay in the ICU after Cardiac Surgery: Logistic CASUS Score, Serum Bilirubin Dosage and Extracorporeal Circulation Time

Pimentel, Marcio Fernandes; Soares, Marcelo José Ferreira; Murad, Jamil Alli; Oliveira, Marcos Aurélio Barboza de; Faria, Fernanda Luiza de; Faveri, Vinícius Zani de; Iano, Yuzo; Guido, Rodrigo Capobianco

doi:10.21470/1678-9741-2016-0072

Cited by 4 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A study using patients with sepsis [ 35 ] reported that increased mortality was associated with higher serum total bilirubin level compared with lower serum total bilirubin level (≤1 mg/dL). Another study [ 36 ] reported that a higher mean value of total bilirubin level on the second day postoperation was associated with longer LOS in the ICU among patients who had undergone cardiac surgery with extracorporeal circulation. In our study, together with APACHE II score, serum total bilirubin level was significantly associated with ICU LOS.…”

Section: Discussionmentioning

confidence: 99%

Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

et al. 2021

View full text Add to dashboard Cite

Predicting the clinical progression of intensive care unit (ICU) patients is crucial for survival and prognosis. Therefore, this retrospective study aimed to develop the risk scoring system of mortality and the prediction model of ICU length of stay (LOS) among patients admitted to the ICU. Data from ICU patients aged at least 18 years who received parenteral nutrition support for ≥50% of the daily calorie requirement from February 2014 to January 2018 were collected. In-hospital mortality and log-transformed LOS were analyzed by logistic regression and linear regression, respectively. For calculating risk scores, each coefficient was obtained based on regression model. Of 445 patients, 97 patients died in the ICU; the observed mortality rate was 21.8%. Using logistic regression analysis, APACHE II score (15–29: 1 point, 30 or higher: 2 points), qSOFA score ≥ 2 (2 points), serum albumin level < 3.4 g/dL (1 point), and infectious or respiratory disease (1 point) were incorporated into risk scoring system for mortality; patients with 0, 1, 2–4, and 5–6 points had approximately 10%, 20%, 40%, and 65% risk of death. For LOS, linear regression analysis showed the following prediction equation: log(LOS) = 0.01 × (APACHE II) + 0.04 × (total bilirubin) − 0.09 × (admission diagnosis of gastrointestinal disease or injury, poisoning, or other external cause) + 0.970. Our study provides the mortality risk score and LOS prediction equation. It could help clinicians to identify those at risk and optimize ICU management.

show abstract

Section: Discussionmentioning

confidence: 99%

Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Despite the beneficial effects of CABG in controlling the signs and symptoms of coronary artery disease [ 2 ] , this surgery has many short and long-term complications. Because of these complications, all patients after CABG must be treated in the intensive care unit (ICU) [ 4 , 5 ] . The length of stay (LOS) of a patient after CABG in the ICU depends on the patient's individual characteristics, pre- and intraoperative care, complications, and events during surgery, as well as hospital policies and regulations.…”

Section: Introductionmentioning

confidence: 99%

Predictors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Grafting: Development a Risk Scoring System

Zarrizi

Paryad

Khanghah

et al. 2021

Braz J Cardiovasc Surg

View full text Add to dashboard Cite

Introduction To determine predictors of length of stay (LOS) in the intensive care unit (ICU) after coronary artery bypass grafting (CABG) and to develop a risk scoring system were the objectives of this study. Methods In this retrospective study, 1202 patients' medical records after CABG were evaluated by a research-made checklist. Tarone-Ware test was used to determine the predictors of patients' LOS in the ICU. Cox regression model was used to determine the risk factors and risk ratios associated with ICU LOS. Results The mean ICU LOS after CABG was 55.27±17.33 hours. Cox regression model showed that having more than two chest tubes (95% confidence interval [CI] 1.005-1.287, Relative Risk [RR]=1.138), occurrence of atelectasis (95% CI 1.000-3.007, RR=1.734), and occurrence of atrial fibrillation after CABG (95% CI 1.428-2.424, RR=1.861) were risk factors associated with longer ICU LOS. The discrimination power of this set of predictors was demonstrated with an area under the receiver operating characteristic curve and it was 0.69. A simple risk scoring system was developed based on three identified predictors that can raise ICU LOS. Conclusion The simple risk scoring system developed based on three identified predictors can help to plan more accurately a patient's LOS in hospital for CABG and can be useful in managing human and financial resources.

show abstract

“…In ICUs, physicians must control these parameters, care for subjects' needs, and prevent complications in order to achieve the optimal conditions of these patients. Therefore, professionals must make elaborate decisions in some situations and make modifications in treatment [15][16][17]. These situations cause high pressure and intense burden that can cause medical errors and may negatively influence patients' outcomes [18,19].…”

Section: Introductionmentioning

confidence: 99%

Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

et al. 2021

View full text Add to dashboard Cite

The advances achieved in recent decades regarding cardiac surgery have led to a new risk that goes beyond surgeons' dexterity; postoperative hours are crucial for cardiac surgery patients and are usually spent in intensive care units (ICUs), where the patients need to be continuously monitored to adjust their treatment. Clinical decision support systems (CDSSs) have been developed to take this real-time information and provide clinical suggestions to physicians in order to reduce medical errors and to improve patient recovery. In this review, an initial total of 499 papers were considered after identification using PubMed, Web of Science, and CINAHL. Twenty-two studies were included after filtering, which included the deletion of duplications and the exclusion of titles or abstracts that were not of real interest. A review of these papers concluded the applicability and advances that CDSSs offer for both doctors and patients. Better prognosis and recovery rates are achieved by using this technology, which has also received high acceptance among most physicians. However, despite the evidence that well-designed CDSSs are effective, they still need to be refined to offer the best assistance possible, which may still take time, despite the promising models that have already been applied in real ICUs.

show abstract

Predictive Factors of Long-Term Stay in the ICU after Cardiac Surgery: Logistic CASUS Score, Serum Bilirubin Dosage and Extracorporeal Circulation Time

Cited by 4 publications

References 10 publications

Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

Risk Scoring System of Mortality and Prediction Model of Hospital Stay for Critically Ill Patients Receiving Parenteral Nutrition

Predictors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Grafting: Development a Risk Scoring System

Applicability of Clinical Decision Support in Management among Patients Undergoing Cardiac Surgery in Intensive Care Unit: A Systematic Review

Contact Info

Product

Resources

About