scholarly journals Risk Stratification Model for Predicting Coronary Care Unit Readmission

2021 ◽  
Author(s):  
Tien-Yu Chen ◽  
Chien-Hao Tseng ◽  
Po-Jui Wu ◽  
Wen-Jung Chung ◽  
Chien-Ho Lee ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Model Building ◽  
Coronary Care Unit ◽  
Care Quality ◽  
Receiver Operating Curve ◽  
Surgical Intensive Care ◽  
Number Of Patients ◽  
Coronary Care ◽  
Readmission Risk

Abstract Background: Use of statistical models for assessing the clinical risk of readmission to medical and surgical intensive care units is well established. However, models for predicting risk of coronary care unit (CCU) readmission are rarely reported. Therefore, this study investigated the characteristics and outcomes of patients readmitted to CCU to identify risk factors for CCU readmission and to establish a scoring system for identifying patients at high risk for CCU readmission. Methods: Medical data were collected for 40,187 patients with a history of readmission to the CCU of a single multi‐center healthcare provider in Taiwan during 2010-2019. Characteristics and outcomes were compared between a readmission group and a non-readmission group. Data were segmented at a 9:1 ratio for model building and validation.Results: The number of patients with a CCU readmission history after transfer to a standard care ward was 2397 (5.9%). The twelve factors that had the strongest associations with CCU readmission were used to develop and validate a CCU readmission risk scoring and prediction model. When the model was used to predict CCU readmission, the receiver-operating curve characteristic was 0.7217 for risk score model group and 0.7316 for the validation group. A CCU readmission risk score was assigned to each patient. The patients were then stratified by risk score into low risk (-20-5), moderate risk (6-26) and high risk (27-33) cohorts check scores, which showed that CCU readmission risk significantly differed among the three groups.Conclusions: This study developed a model for estimating CCU readmission risk. By using the proposed model, clinicians can improve CCU patient outcomes and medical care quality.

Calculation of individual cardiovascular risk in a primary prevention setting shows a high number of patients with a high risk score

2004 ◽  
Vol 93 (5) ◽  
pp. 413-415 ◽  
Author(s):  
S. Kelle ◽  
P. Stawowy ◽  
E. Fleck ◽  
M. Neuss ◽  
M. Roser ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Primary Prevention ◽  
High Risk ◽  
Risk Score ◽  
Number Of Patients

scholarly journals Validation of the Finnish diabetes risk score (FINDRISC) for the Caucasian population of Siberia

2016 ◽  
Vol 19 (2) ◽  
pp. 113-118 ◽  
Author(s):  
Svetlana V. Mustafina ◽  
Oksana D. Rymar ◽  
Olga V. Sazonova ◽  
Liliya V. Shcherbakova ◽  
Michail I. Voevoda
Keyword(s):  
Risk Factors ◽  
High Risk ◽  
Risk Score ◽  
Adult Population ◽  
Risk Groups ◽  
Diabetes Risk ◽  
Receiver Operating Curve ◽  
Siberian Population ◽  
Cross Sectional ◽  
Diabetes Risk Score

Aim. A validation of the Finnish diabetes risk score (FINDRISC) was conducted among the Siberian population. FINDRISC was used to study the prevalence of risk factors for type 2 diabetes mellitus (T2DM) and to estimate the incidence of T2DM in high-risk groups during a 10-year observation period. Materials and methods. A total of 9,360 subjects aged between 45 and 69 years were enrolled in this cross-sectional, population-based study. FINDRISC was used to group 8,050 people without diabetes according to their risk for T2DM. Statistical analysis was performed using SPSS. Results. When a cutoff point of 11 was used to identify those with diabetes, sensitivity was 76. 0% and specificity was 60. 2%. The area under the receiver operating curve for diabetes was 0. 73 (0. 73 for men and 0. 70 for women). More than one-third (31. 7%) of the adult population of Novosibirsk was estimated to have medium, high or very high risk of developing T2DM in the next 10 years. Cases of T2DM estimated to occur during the 10 years of follow-up had significantly higher incidence of risk factors such as BMI ≥30 kg/m2, waist circumference 102 cm in men and 88 cm in women and a family history of T2DM and were more likely to take drugs to lower blood pressure. Conclusion. FINDRISC provided good results in our sample, and we recommend its use in the Siberian population. 

Abstract 89: Validating The Identification Of Patients At High Risk For Readmission By Examining Hospitalization History

2012 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
Natalia Egorova ◽  
Prashant Vaishnava ◽  
Maria Basso Lipani ◽  
Doran Ricks ◽  
Claudia Colgan ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
High Risk Patient ◽  
Mount Sinai Hospital ◽  
Patient Identification ◽  
Time High Risk ◽  
Post Discharge ◽  
Predictive Tool ◽  
Readmission Risk

OBJECTIVES: To identify patients at high risk of readmission by validating a simple predictive tool based solely on hospitalization history. BACKGROUND: There is a federal mandate to reduce preventable readmissions. Predicting hospital readmission risk is of great interest to identify which patients would benefit most from transition interventions. Current models perform poorly. Mount Sinai Hospital (MSH) has implemented the Preventable Admissions Care Team (PACT), which has achieved significant results for patients not targeted by other transitional programs. PACT, a social worker-led transitional program, decreased 30-day readmission rate from 30% to 12%, ED visits by 63%, and achieved a 90% primary care show rate at 7-10-days post-discharge. Patients are identified for PACT solely by readmission history: one readmission in 30 days or 2 in 6 months, prior to the index hospitalization. Thus, our objective here was to determine the concordance of predictions based on hospitalization history with a more formal risk model based on factors that characterize patients through demographics and comorbidities. METHODS: Using logistic regression, we developed a risk prediction model for readmission within 30-days. The model, which used patient demographics and co-morbidities (alcohol abuse, AMI, afib, breast cancer, CKD, COPD, CVA, depression, hip fracture, or osteoporosis), was developed in a cohort of Medicare FFS beneficiaries with a high proportion of cardiovascular disease, hospitalized at MSH. The higher the risk score, the higher risk of readmission. Scores of 0-2 had a 7% risk of readmission; scores of 3 or 4 and above 5 had 30-day readmission rates of 19%, and 29%, respectively. We then applied this risk scoring model to patients enrolled in PACT to determine how many of them would have been identified as high risk for readmission based on the regression model. RESULTS: A total of 393 patients were enrolled in PACT in a year and completed a 5 week intervention. Eighty seven percent had 1 cardiac comorbid illness (76% CAD, 66% CHF, and 17% Afib). Readmission data was available through 2010 thus, the analysis was completed for 111 patients. Ninety-five percent of PACT enrollees had a risk score greater than 3: 19 patients (17.1%) had a risk score of 3-4, and 87 patients (78.4%) had a risk score of 5 or greater. CONCLUSIONS: Hospitalization history alone is a reasonable proxy to more formal multivariable regression models in predicting 30-day readmission risk. If substantiated through further study, this could have national implications for real time high risk patient identification for transitional services.

The Family as a Source of Support for the Critically Ill Adult

1991 ◽  
Vol 2 (2) ◽  
pp. 229-235 ◽  
Author(s):  
Terri Simpson
Keyword(s):  
Coronary Care Unit ◽  
Previous Analysis ◽  
Surgical Intensive Care Unit ◽  
Transcript Analysis ◽  
Surgical Intensive Care ◽  
Illness Episode ◽  
The Family ◽  
Supportive Family ◽  
Coronary Care ◽  
Different Order

A conceptual framework of supportive family functions derived from a previous analysis of taped visits to coronary care unit (CCU) patients was used to analyze CCU and surgical intensive care unit (SICU) patients’ recollections about visits. The framework of family functions was supported by the patients’ recollections. However, CCU patients recall behaviors in a different order of frequency than previously described with visit transcript analysis. Both CCU and SICU patients frequently recall caring actions of the family and evaluated visits overall as helpful. Principles are proposed for nurses in assisting families and patients to cope with the critical illness episode and thus provide a supportive visiting environment for patients

scholarly journals Assessment of cardiovascular risk tools as predictors of cardiovascular disease events in systemic lupus erythematosus

2021 ◽  
Vol 8 (1) ◽  
pp. e000448
Author(s):  
Jagan Sivakumaran ◽  
Paula Harvey ◽  
Ahmed Omar ◽  
Oshrat Tayer-Shifman ◽  
Murray B Urowitz ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Risk ◽  
High Risk ◽  
Negative Predictive Value ◽  
Risk Score ◽  
Predictive Value ◽  
Framingham Risk Score ◽  
Cvd Risk ◽  
Framingham Risk ◽  
Number Of Patients

BackgroundSLE is an independent risk factor for cardiovascular disease (CVD). This study aimed to determine which among QRISK2, QRISK3, Framingham Risk Score (FRS), modified Framingham Risk Score (mFRS) and SLE Cardiovascular Risk Equation (SLECRE) best predicts CVD.MethodsThis is a single-centre analysis on 1887 patients with SLE followed prospectively according to a standard protocol. Tools’ scores were evaluated against CVD development at/within 10 years for patients with CVD and without CVD. For patients with CVD, the index date for risk score calculation was chosen as close to 10 years prior to CVD event. For patients without CVD, risk scores were calculated as close to 10 years prior to the most recent clinic appointment. Proportions of low-risk (<10%), intermediate-risk (10%–20%) and high-risk (>20%) patients for developing CVD according to each tool were determined, allowing sensitivity, specificity, positive/negative predictive value and concordance (c) statistics analysis.ResultsAmong 1887 patients, 232 CVD events occurred. QRISK2 and FRS, and QRISK3 and mFRS, performed similarly. SLECRE classified the highest number of patients as intermediate and high risk. Sensitivities and specificities were 19% and 93% for QRISK2, 22% and 93% for FRS, 46% and 83% for mFRS, 47% and 78% for QRISK3, and 61% and 64% for SLECRE. Tools were similar in negative predictive value, ranging from 89% (QRISK2) to 92% (SLECRE). FRS and mFRS had the greatest c-statistics (0.73), while QRISK3 and SLECRE had the lowest (0. 67).ConclusionmFRS was superior to FRS and was not outperformed by the QRISK tools. SLECRE had the highest sensitivity but the lowest specificity. mFRS is an SLE-adjusted practical tool with a simple, intuitive scoring system reasonably appropriate for ambulatory settings, with more research needed to develop more accurate CVD risk prediction tools in this population.

Risk Score at Diagnosis and the Dynamics of Response to TKI Therapy In Chronic Myeloid Leukemia.

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1236-1236
Author(s):  
Tom Lenearts ◽  
Fausto Castagnetti ◽  
Arne Traulsen ◽  
Jorge M Pacheco ◽  
Gianantonio Rosti ◽  
...  
Keyword(s):  
High Risk ◽  
Computational Model ◽  
Risk Score ◽  
Response To Therapy ◽  
The Self ◽  
Self Renewal ◽  
Number Of Patients ◽  
High Risk Disease ◽  
Hasford Score ◽  
The Impact

Abstract Abstract 1236 Background: Although most patients with early chronic phase CML (ECP-CML) respond to TKI therapy, the depth and speed of response can be different. While it is known that both the Sokal and Hasford scores have an impact on the speed and depth of response, no mechanisms explaining these differences have been identified. Objective: To provide explanations for any potential differences in CML response as a function of the Sokal and Hasford score using a computational model. Methods: We utilize a computational model of hematopoiesis and CML, together with serial quantitative data of disease burden under nilotinib therapy to determine the fraction of CML cells responding to therapy (z) and the impact of TKI on the self-renewal probability of CML cells (e) under therapy. Patients were stratified at diagnosis on both the Sokal and Hasford scoring system. A non-linear least squares method was used to separately fit the model to serial Q-RT-PCR data for BCR-ABL in response to therapy in each cohort. Results: A total of 73 patients were studied. The number of patients with low, intermediate and high risk disease based on the Sokal score was 34, 29 and 10 respectively while the respective distribution of patients on the Hasford score was 29, 43 and 1. Although the impact of nilotinib on the self-renewal probability of CML progenitor cells was similar across all risk groups (there were substantial differences in the fraction of cells responding to therapy: For the Sokal groups, the fraction of cells (z) responding to therapy decreased from 0.09 to 0.086 and 0.069 respectively for low, intermediate and high risk disease. In the case of the Hasford score, the difference in z between low and intermediate categories becomes more pronounced, i.e. z is 0.093 for low and 0.08 for intermediate risk disease. Conclusions: The risk score at diagnosis of CML has a direct impact on the dynamics of response to TKI therapy. Patients with a lower risk score respond faster to the same therapy when compared to high risk patients. The impact of TKI on the self renewal of CML cells appears to be the same regardless of the risk score but the fraction of cells responding decreases as the risk score increases. This suggests that subclones that may be less sensitive to TKI therapy may be emerging. Strategies that increase the fraction of cells responding to therapy in patients with higher risk disease may be indicated. Disclosures: Rosti: Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Bristol Myers Squibb: Honoraria, Speakers Bureau; Roche: Speakers Bureau.

Abstract 3264: Remarkable Differences in calculating the Cardiovascular Risk according the Framingham-, Procam- and ESC Risk-Scores. First Results of the DETECT Study

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Sigmund Silber ◽  
Barbara M Richartz ◽  
Frauke Jarre ◽  
David Pittrow ◽  
Jens Klotsche ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
High Risk ◽  
Risk Score ◽  
High Risk Group ◽  
Risk Scores ◽  
Individual Risk ◽  
High Risk Patients ◽  
Number Of Patients ◽  
Risk Patients ◽  
The Individual

The identification of high-risk patients is of utmost importance for an intensive and effective primary prevention program. Currently, three different scores are used to identify high-risk patients: In the USA, the Framingham risk score, in Germany the Procam risk score and in Europe the European Society of Cardiology ESC) recommended ESC risk score. There is, however, little knowledge how these three risk scores compare to each other in the same population. Therefore we calculated the individual risk of 7519 pats with no known cardiovascular disease according to these three scoring systems. In the DETECT study, 55 518 patients in 3188 primary care offices were enrolled. A representative subgroup of 7,519 randomly chosen patients participated in a cohort sub-study. According to the Framingham-Procam- and ESC-Score, the individual 10-year-risk was determined and patients were _ategorized into groups of high, medium or low risk. The mean 10-year cardiovascular risk is estimated by the PROCAM score at 4.4%, with the ESC score at 8.8% and with the Framingham-Score at 11.5%. The number of patients assigned to a group differs most for the high risk group (please see table ). Unexpectedly, major discrepancies were observed in the same pats, if the Framingham, Procam- or ESC score was used, especially in the identification of high-risk pats. Follow-up will show, which of these risk scores will best predict the actual occurrence of cardiovascular events. Results:

scholarly journals Weaning of invasive mechanical ventilation in a critical coronary care unit

2021 ◽  
Vol 10 (Supplement_1) ◽  
Author(s):  
DF Arroyo Monino ◽  
M Rivadeneira Ruiz ◽  
MP Ruiz Garcia ◽  
T Seoane Garcia ◽  
JC Garcia Rubira
Keyword(s):  
Cardiac Arrest ◽  
Coronary Care Unit ◽  
Invasive Mechanical Ventilation ◽  
Ventilatory Support ◽  
Older Age ◽  
P Value ◽  
Coronary Syndrome ◽  
Number Of Patients ◽  
Coronary Care ◽  
Frequent Reason

Abstract Funding Acknowledgements Type of funding sources: None. Introduction In the recent years, we have assisted to a change of the prototype of the patient admitted to a Critical Coronary Care Unit (CCCU), with an increasing number of patients admitted due to acute heart failure (AHF) and the reduction of the patients diagnosed of acute coronary syndrome (ACS). It is common in these patients the requirement of ventilatory support, both invasive (IMV) and non-invasive. As a consequence, our knowledge about this technique must be improved. A critical moment when using IMV is the weaning of the IMV. Objective Our aim is to describe which factors may have an influence on the success or the failure of the weaning of IMV. Methods Observational, retrospective study, using a cohort of patients admitted to a CCCU between January 2.018 and November 2.020 who needed IMV. Data related with the personal history, basal situation and events in the follow-up during the hospitalization were collected. Results A total number of 94 patients were included, being 68 (72,3%) male and with a mean age of 68 years old. The most frequent reason of intubation was cardiac arrest (48 patients – 51,1%). Failure on weaning occurred in 19 patients (20,2%), being the most frequent reason of this failure need of re-intubation due to respiratory failure or a new event of cardiac arrest (14 patients – 14,9%). When assessing which factors could have an impact in this failure, we found that older age (66,6 years old vs. 73,9 years old, p value = 0,035), the previous diagnosis of chronic obstructive pulmonary disease (COPD) (17,1% vs. 28,5%, p value = 0,01), and the develop of sepsis during the hospitalization (45,7% vs. 57%, p value =0,04), determined a significative higher probability of failing in the weaning. As expected, failure in the weaning conditioned a significative longer stay in the CCCU (9 days vs. 22 days; p value &lt;0,001). However, failure in the weaning was not related with a higher intra-hospital mortality in our study (p value 0,6). Conclusion In our population, the older age, the presence of COPD and the development of sepsis during the stay in the CCCU were related with a significative higher probability of failure in the weaning of IMV. This conditioned a longer stay in the CCCU but not a higher intra-hospitalary mortality.

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