Readmission to medical intensive care units: risk factors and prediction.
Ontology highlight
ABSTRACT:
SUBMITTER: Jo YS
PROVIDER: S-EPMC4329370 | biostudies-literature | 2015 Mar
REPOSITORIES: biostudies-literature
Publications
Readmission to medical intensive care units: risk factors and prediction.
Jo Yong Suk YS Lee Yeon Joo YJ Park Jong Sun JS Yoon Ho Il HI Lee Jae Ho JH Lee Choon-Taek CT Cho Young-Jae YJ
Yonsei medical journal 20150301 2
<h4>Purpose</h4>The objectives of this study were to find factors related to medical intensive care unit (ICU) readmission and to develop a prediction index for determining patients who are likely to be readmitted to medical ICUs.<h4>Materials and methods</h4>We performed a retrospective cohort study of 343 consecutive patients who were admitted to the medical ICU of a single medical center from January 1, 2008 to December 31, 2012. We analyzed a broad range of patients' characteristics on the d ...[more]
Similar Datasets
Project description:BackgroundDelirium is a brain dysfunction syndrome, which children have a higher incidence. At present, there have been more and more studies and reports on delirium in paediatric intensive care unit, but there are some differences in the risk factor results among different studies. To better manage delirium, this study was performed.ObjectiveTo integrate and clarify the risk factors for delirium in paediatric intensive care unit.MethodsCNKI, CBMdisc, Wanfang Data Knowledge Service Platform, VIP, PubMed, Embase, Cochrane Library, JBI and PsycInfo were searched for relevant literature. The study subjects were patients in PICU and literature was included according to the PICOS principle. Literature screening and risk of bias assessment were mainly completed by two researchers, and RevMan 5.3 software and Stata software were used for data analysis. The GRADE systerm was used to assess the quality of evidence.ResultsA total of 10 studies were included, all in English, involving 4343 children. Within the GRADE system, 4 indicators were scored A, 1 indicators were scored B, and 3 indicators were scored C regarding evidence levels. Three studies analysed the influence of developmental delay on the occurrence of delirium in PICU, total sample size of which was 1823, and the results showed that the combined effect was statistically significant [OR = 3.34, 95%CI(2.46-4.53), Z = 7.75, P<0.001]; Five studies analysed the effects of mechanical ventilation on the occurrence of delirium in PICU, sample size of which was 1562, and the results showed that the combined effect was statistically significant [OR = 4.11, 95%CI(3.13-5.40), Z = 10.16, P<0.001]; Two studies analysed the effects of benzodiazepines on children developing delirium, sample size of which was 1635, and the results showed that the combined effect was statistically significant [OR = 5.05, 95%CI(3.65-6.97), Z = 9.83, P<0.001]; Two studies analysed the effects of anticholinergic drug use on children developing delirium in PICU, sample size of which was 1703, and the results suggested the combined effect was statistically significant [OR = 5.04, 95%CI (3.62-7.00), Z = 9.63, P<0.001]; Two studies compared the same age period, sample size of which was 1724 and the results showed that children 2-5 years old has a 48% incidence rate of delirium relative to children younger than 2 years old, and the combined effect was statistically significant [OR = 0.48, 95%CI(0.25-0.92), Z = 2.22, P = 0.030], children 5-13 years old has a 39% incidence rate of delirium relative to children younger than 2 years old, and the combined effect was statistically significant [OR = 0.39, 95%CI(0.26-0.59), Z = 4.43, P<0.001]. Two studies analysed the effects of PICU LOS on children developing delirium and the combined effect of PICU LOS on the occurrence of delirium in children in PICU was statistically significant [OR = 1.10, 95%CI(1.05-1.15), Z = 4.07, P<0.001].ConclusionDevelopmental delay, mechanical ventilation, benzodiazepine use, anticholinergic use, age and PICU length of stay are independent risk factors for delirium in children in PICU. However, only a few articles were included in this study, which may lead to a certain bias and affect the analysing results. More large-sample, multicentre studies should be conducted to further explore and clarify the independent influencing factors of delirium in children in PICU and to provide guidance for clinical practice.
| S-EPMC9269883 | biostudies-literature
Project description:BackgroundDelirium is a primary adverse event in ventilated patients who receive long-term monosedative treatment. Sequential sedation may reduce these adverse effects. This study evaluated risk factors for delirium in sequential sedation patients.MethodsA total of 141 patients who underwent sequential sedation were enrolled. Delirium was diagnosed using Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) scale. Univariate and multivariate Cox proportional hazards regressions were used to predict risk factors.ResultsOlder age (≥51) (RR = 2.432, 95% CL 1.316-4.494, p = 0.005), higher SOFA score (≥14) (RR = 2.022, 95% CL 1.076-3.798, p = 0.029), regular smoking (RR = 2.366, 95% CL 1.277-4.382, p = 0.006), and higher maintenance dose of midazolam (RR = 1.052, 95% CL 1.000-1.107, p = 0.049) and fentanyl (RR = 1.045, 95% CL 1.019-1.072, p = 0.001) when patients met sequential criteria, were independent risk factors of delirium. Sequential sedation with dexmedetomidine (RR = 0.448, 95% CL 0.209-0.963, p = 0.040) was associated with a lower risk of delirium.ConclusionsOlder age, higher SOFA score, regular smoking, and higher maintenance dose of midazolam and fentanyl when patients met sequential criteria were independent risk factors of delirium in sequential sedation patients. Sequential sedation with dexmedetomidine reduced risk of delirium.
| S-EPMC5684530 | biostudies-literature
Project description:Background and purposeTo date, sepsis remains the main cause of mortality in intensive care units (ICU). This study aimed analyze the risk factors of the prognosis in sepsis patients.MethodsIn this case-control study, patients with sepsis admitted to the intensive care unit of a Chinese hospital between January and November 2020 were analyzed. Ultrasound and clinical data were analyzed and compared between non-survivors and survivors. The ROC curve analysis was also performed to determine the best indicator for predicting mortality.ResultsA total of 72 patients with sepsis in ICU were included for analysis. The basic characteristics between the survivals and non-survivals were similar, except for acute physiology and chronic health evaluation (APACHE) Ⅱ score, sepsis-related organ failure assessment (SOFA) score, lactate level, ultrasound parameters from superior mesenteric artery (SMA) such as peak systolic velocity (PSV), end-diastolic velocity (EDV) and resistive index (RI). Univariate analysis revealed that the APACHE Ⅱ score, SOFA score, lactate, low PSV, EDV, and RI were potential risk factors for mortality in sepsis, while multivariate analysis suggested that low PSV was an independent risk factor for mortality, and the adjusted odds ratio was 0.295 (95% CI: 0.094-0.925). The ROC analysis showed that the PSV (AUC = 0.99; sensitivity and specificity were 0.99 and 0.96, respectively) had good predictive value for mortality in sepsis.ConclusionLow PSV as found to be an independent risk factor and good predictor for mortality in patients with sepsis. This study shows the promise of ultrasound in predicting mortality in patients with sepsis; however, further studies are needed to validate these results.
| S-EPMC9447880 | biostudies-literature
Project description:BackgroundDespite the high mortality in patients with pneumonia admitted to an ICU, data on risk factors for death remain limited.MethodsIn this secondary analysis of PROTECT (Prophylaxis for Thromboembolism in Critical Care Trial), we focused on the patients admitted to ICU with a primary diagnosis of pneumonia. The primary outcome for this study was 90-day hospital mortality and the secondary outcome was 90-day ICU mortality. Cox regression model was conducted to examine the relationship between baseline and time-dependent variables and hospital and ICU mortality.ResultsSix hundred sixty seven patients admitted with pneumonia (43.8 % females) were included in our analysis, with a mean age of 60.7 years and mean APACHE II score of 21.3. During follow-up, 111 patients (16.6 %) died in ICU and in total, 149 (22.3 %) died in hospital. Multivariable analysis demonstrated significant independent risk factors for hospital mortality including male sex (hazard ratio (HR) = 1.5, 95 % confidence interval (CI): 1.1 - 2.2, p-value = 0.021), higher APACHE II score (HR = 1.2, 95 % CI: 1.1 - 1.4, p-value < 0.001 for per-5 point increase), chronic heart failure (HR = 2.9, 95 % CI: 1.6 - 5.4, p-value = 0.001), and dialysis (time-dependent effect: HR = 2.7, 95 % CI: 1.3 - 5.7, p-value = 0.008). Higher APACHE II score (HR = 1.2, 95 % CI: 1.1 - 1.4, p-value = 0.002 for per-5 point increase) and chronic heart failure (HR = 2.6, 95 % CI: 1.3 - 5.0, p-value = 0.004) were significantly related to risk of death in the ICU.ConclusionIn this study using data from a multicenter thromboprophylaxis trial, we found that male sex, higher APACHE II score on admission, chronic heart failure, and dialysis were independently associated with risk of hospital mortality in patients admitted to ICU with pneumonia. While high illness severity score, presence of a serious comorbidity (heart failure) and need for an advanced life support (dialysis) are not unexpected risk factors of mortality, male sex might necessitate further exploration. More studies are warranted to clarify the effect of these risk factors on survival in critically ill patients admitted to ICU with pneumonia.Trial registrationClinicalTrials.gov Identifier: NCT00182143 .
| S-EPMC4940754 | biostudies-literature
Project description:BACKGROUND:This retrospective, multicenter study aimed to reveal risk predictors for mortality in the intensive care unit (ICU) as well as survival after ICU discharge in patients with acute myeloid leukemia (AML) requiring treatment in the ICU. METHODS AND RESULTS:Multivariate analysis of data for 187 adults with AML treated in the ICU in one institution revealed the following as independent prognostic factors for death in the ICU: arterial oxygen partial pressure below 72 mmHg, active AML and systemic inflammatory response syndrome upon ICU admission, and need for hemodialysis and mechanical ventilation in the ICU. Based on these variables, we developed an ICU mortality score and validated the score in an independent cohort of 264 patients treated in the ICU in three additional tertiary hospitals. Compared with the Simplified Acute Physiology Score (SAPS) II, the Logistic Organ Dysfunction (LOD) score, and the Sequential Organ Failure Assessment (SOFA) score, our score yielded a better prediction of ICU mortality in the receiver operator characteristics (ROC) analysis (AUC = 0.913 vs. AUC = 0.710 [SAPS II], AUC = 0.708 [LOD], and 0.770 [SOFA] in the training cohort; AUC = 0.841 for the developed score vs. AUC = 0.730 [SAPSII], AUC = 0.773 [LOD], and 0.783 [SOFA] in the validation cohort). Factors predicting decreased survival after ICU discharge were as follows: relapse or refractory disease, previous allogeneic stem cell transplantation, time between hospital admission and ICU admission, time spent in ICU, impaired diuresis, Glasgow Coma Scale <8 and hematocrit of ?25% at ICU admission. Based on these factors, an ICU survival score was created and used for risk stratification into three risk groups. This stratification discriminated distinct survival rates after ICU discharge. CONCLUSIONS:Our data emphasize that although individual risks differ widely depending on the patient and disease status, a substantial portion of critically ill patients with AML benefit from intensive care.
| S-EPMC5004890 | biostudies-literature
Project description:Purpose: Young adults receive severe sepsis treatment across pediatric and adult care settings. However, little is known about young adult sepsis outcome differences in pediatric versus adult hospital settings. Material and Methods: Using Truven MarketScan database from 2010-2015, we compared in-hospital mortality and hospital length of stay in young adults ages 18-26 treated for severe sepsis in Pediatric Intensive Care Units (PICUs) versus Medical ICUs (MICUs)/Surgical ICUs (SICUs) using logistic regression models and accelerated time failure models, respectively. Comorbidities were identified using Complex Chronic Conditions (CCC) and Charlson Comorbidity Index (CCI). Results: Of the 18 900 young adults hospitalized with severe sepsis, 163 (0.9%) were treated in the PICU and 952 (5.0%) in the MICU/SICU. PICU patients were more likely to have a comorbid condition compared to MICU/SICU patients. Compared to PICU patients, MICU/SICU patients had a lower odds of in-hospital mortality after adjusting for age, sex, Medicaid status, and comorbidities (adjusting for CCC, odds ratio [OR]: 0.50, 95% CI 0.29-0.89; adjusting for CCI, OR: 0.51, 95% CI 0.29-0.94). There was no difference in adjusted length of stay for young adults with severe sepsis (adjusting for CCC, Event Time Ratio [ETR]: 1.14, 95% CI 0.94-1.38; adjusting for CCI, ETR: 1.09, 95% CI 0.90-1.33). Conclusions: Young adults with severe sepsis experience higher adjusted odds of mortality when treated in PICUs versus MICU/SICUs. However, there was no difference in length of stay. Variation in mortality is likely due to significant differences in the patient populations, including comorbidity status.
| S-EPMC10561306 | biostudies-literature
Project description:BackgroundResearch on pediatric intensive care unit (PICU) readmission is lacking in China. This study was conducted to describe the risk factors associated with PICU readmission within 1 year after PICU discharge.MethodsThis retrospective case-control study included patients aged from 1 month to 16 years who were discharged between January 2018 and May 2020. The case group included readmitted patients with two or more PICU admissions within 1 year during the study period. The control group included survivors with only one PICU admission during the same study period, and the controls were matched on age and sex. Demographic and clinical variables were collected from the electronic administrative database. Risk factors were analyzed by univariate and multivariate analyses.ResultsFrom January 2018 to May 2020, 2,529 patients were discharged from the PICU, and 103 (4.07%) were readmitted within 1 year. In the univariate analysis, PICU readmission within 1 year was associated with lower weight, the presence of chronic conditions, a higher StrongKids score on admission, length of PICU stay of more than 2 weeks, the presence of dysfunction at discharge, sedation medications use, vasopressor use, and invasive mechanical ventilation in the first PICU stay. Patients had a higher StrongKids score as a surrogate for increased risk of malnutrition. In the multivariate analysis, the factors most significantly associated with PICU readmission within 1 year were the presence of chronic conditions, a higher StrongKids score on admission, and length of PICU stay of more than 2 weeks in the first PICU stay. In the subgroup analysis, compared with the control group, the factors most significantly associated with readmission within 48 h of discharge were the presence of chronic conditions, a higher StrongKids score on admission, and vasopressor use during the first PICU stay. The mortality rate was 8.74% (9/103) in patients with PICU readmission. The overall PICU mortality rate was 7.39% (201/2,721) during the study period.ConclusionsPatients with chronic conditions, a higher StrongKids score on admission, and length of PICU stay of more than 2 weeks were at much higher risk for PICU readmission within 1 year. Patients with vasopressor use during the first PICU hospitalization were more likely to be readmitted within 48 h of discharge.
| S-EPMC9133623 | biostudies-literature
Project description:BACKGROUND:The Space GlucoseControl system (SGC) is a nurse-driven, computer-assisted device for glycemic control combining infusion pumps with the enhanced Model Predictive Control algorithm (B. Braun, Melsungen, Germany). We aimed to investigate the performance of the SGC in medical critically ill patients. METHODS:Two open clinical investigations in tertiary centers in Graz, Austria and Zurich, Switzerland were performed. Efficacy was assessed by percentage of time within the target range (4.4-8.3 mmol/L; primary end point), mean blood glucose, and sampling interval. Safety was assessed by the number of hypoglycemic episodes (?2.2 mmol/L) and the percentage of time spent below this cutoff level. Usability was analyzed with a standardized questionnaire given to involved nursing staff after the trial. RESULTS:Forty medical critically ill patients (age, 62?±?15 years; body mass index, 30.0?±?8.9 kg/m2; APACHE II score, 24.8?±?5.4; 27 males; 8 with diabetes) were included for a period of 6.5?±?3.7 days (n?=?20 in each center). The primary endpoint (time in target range 4.4 to 8.3 mmol/l) was reached in 88.3%?±?9.3 of the time and mean arterial blood glucose was 6.7?±?0.4 mmol/l. The sampling interval was 2.2?±?0.4 hours. The mean daily insulin dose was 87.2?±?64.6 IU. The adherence to the given insulin dose advice was high (98.2%). While the percentage of time spent in a moderately hypoglycemic range (2.2 to 3.3 mmol/L) was low (0.07?±?0.26% of the time), one severe hypoglycemic episode (<2.2 mmol/L) occurred (2.5% of patients or 0.03% of glucose readings). CONCLUSIONS:SGC is a safe and efficient method to control blood glucose in critically ill patients as assessed in two European medical intensive care units.
| S-EPMC4118658 | biostudies-literature
Project description:BackgroundRisk prediction models are used in critical care for risk stratification, summarising and communicating risk, supporting clinical decision-making and benchmarking performance. However, they require validation before they can be used with confidence, ideally using independently collected data from a different source to that used to develop the model. The aim of this study was to validate the Intensive Care National Audit & Research Centre (ICNARC) model using independently collected data from critical care units in Scotland.MethodsData were extracted from the Scottish Intensive Care Society Audit Group (SICSAG) database for the years 2007 to 2009. Recoding and mapping of variables was performed, as required, to apply the ICNARC model (2009 recalibration) to the SICSAG data using standard computer algorithms. The performance of the ICNARC model was assessed for discrimination, calibration and overall fit and compared with that of the Acute Physiology And Chronic Health Evaluation (APACHE) II model.ResultsThere were 29,626 admissions to 24 adult, general critical care units in Scotland between 1 January 2007 and 31 December 2009. After exclusions, 23,269 admissions were included in the analysis. The ICNARC model outperformed APACHE II on measures of discrimination (c index 0.848 versus 0.806), calibration (Hosmer-Lemeshow chi-squared statistic 18.8 versus 214) and overall fit (Brier's score 0.140 versus 0.157; Shapiro's R 0.652 versus 0.621). Model performance was consistent across the three years studied.ConclusionsThe ICNARC model performed well when validated in an external population to that in which it was developed, using independently collected data.
| S-EPMC4277842 | biostudies-literature
Project description:Hypothermia is a life-threatening condition where the temperature of the body drops below 35°C and is a key source of concern in Intensive Care Units (ICUs). Early identification can help to nudge clinical management to initiate early interventions. Despite its importance, very few studies have focused on the early prediction of hypothermia. In this study, we aim to monitor and predict Hypothermia (30 min-4 h) ahead of its onset using machine learning (ML) models developed on physiological vitals and to prospectively validate the best performing model in the pediatric ICU. We developed and evaluated ML algorithms for the early prediction of hypothermia in a pediatric ICU. Sepsis advanced forecasting engine ICU Database (SafeICU) data resource is an in-house ICU source of data built in the Pediatric ICU at the All-India Institute of Medical Science (AIIMS), New Delhi. Each time-stamp at 1-min resolution was labeled for the presence of hypothermia to construct a retrospective cohort of pediatric patients in the SafeICU data resource. The training set consisted of windows of the length of 4.2 h with a lead time of 30 min-4 h from the onset of hypothermia. A set of 3,835 hand-engineered time-series features were calculated to capture physiological features from the time series. Features selection using the Boruta algorithm was performed to select the most important predictors of hypothermia. A battery of models such as gradient boosting machine, random forest, AdaBoost, and support vector machine (SVM) was evaluated utilizing five-fold test sets. The best-performing model was prospectively validated. A total of 148 patients with 193 ICU stays were eligible for the model development cohort. Of 3,939 features, 726 were statistically significant in the Boruta analysis for the prediction of Hypothermia. The gradient boosting model performed best with an Area Under the Receiver Operating Characteristic curve (AUROC) of 85% (SD = 1.6) and a precision of 59.2% (SD = 8.8) for a 30-min lead time before the onset of Hypothermia onset. As expected, the model showed a decline in model performance at higher lead times, such as AUROC of 77.2% (SD = 2.3) and precision of 41.34% (SD = 4.8) for 4 h ahead of Hypothermia onset. Our GBM(gradient boosting machine) model produced equal and superior results for the prospective validation, where an AUROC of 79.8% and a precision of 53% for a 30-min lead time before the onset of Hypothermia whereas an AUROC of 69.6% and a precision of 38.52% for a (30 min-4 h) lead time prospective validation of Hypothermia. Therefore, this work establishes a pipeline termed ThermoGnose for predicting hypothermia, a major complication in pediatric ICUs.
| S-EPMC9511412 | biostudies-literature