Atenolol before and after non-cardiac surgery reduced mortality and cardiovascular morbidity in patients at risk of acute coronary events

1997 ◽  
Vol 1 (2) ◽  
pp. 55-56
Author(s):  
Valerie A. Palda ◽  
Allan S. Detsky
Keyword(s):  
At Risk ◽  
Cardiac Surgery ◽  
Cardiovascular Morbidity ◽  
Before And After ◽  
Patients At Risk ◽  
Coronary Events

Trastuzumab-Induced Cardiotoxicity: Clinical and Prognostic Implications of Troponin I Evaluation

2010 ◽  
Vol 28 (25) ◽  
pp. 3910-3916 ◽  
Author(s):  
Daniela Cardinale ◽  
Alessandro Colombo ◽  
Rosalba Torrisi ◽  
Maria T. Sandri ◽  
Maurizio Civelli ◽  
...  
Keyword(s):  
At Risk ◽  
Cardiac Dysfunction ◽  
Troponin I ◽  
Left Ventricular ◽  
Left Ventricular Ejection ◽  
Ventricular Ejection Fraction ◽  
Trastuzumab Therapy ◽  
Before And After ◽  
Patients At Risk ◽  
Prognostic Implications

Purpose Treatment of breast cancer with trastuzumab is complicated by cardiotoxicity in up to 34% of the patients. In most patients, trastuzumab-induced cardiotoxicity (TIC) is reversible: left ventricular ejection fraction (LVEF) improves after trastuzumab withdrawal and with, or sometimes without, initiation of heart failure (HF) therapy. The reversibility of TIC, however, is not foreseeable, and identification of patients at risk and of those who will not recover from cardiac dysfunction is crucial. The usefulness of troponin I (TNI) in the identification of patients at risk for TIC and in the prediction of LVEF recovery has never been investigated. Patients and Methods In total, 251 women were enrolled. TNI was measured before and after each trastuzumab cycle. LVEF was evaluated at baseline, every 3 months during trastuzumab therapy, and every 6 months afterward. In case of TIC, trastuzumab was discontinued, and HF treatment with enalapril and carvedilol was initiated. TIC was defined as LVEF decrease of > 10 units and below 50%. Recovery from TIC was defined as LVEF increase above 50%. Results TIC occurred in 42 patients (17%) and was more frequent in patients with TNI elevation (TNI+; 62% v 5%; P < .001). Twenty-five patients (60%) recovered from TIC. LVEF recovery occurred less frequently in TNI+ patients (35% v 100%; P < .001). At multivariate analysis, TNI+ was the only independent predictor of TIC (hazard ratio [HR], 22.9; 95% CI, 11.6 to 45.5; P < .001) and of lack of LVEF recovery (HR, 2.88; 95% CI,1.78 to 4.65; P < .001). Conclusion TNI+ identifies trastuzumab-treated patients who are at risk for cardiotoxicity and are unlikely to recover from cardiac dysfunction despite HF therapy.

scholarly journals The Papworth Bleeding Risk Score: a stratification scheme for identifying cardiac surgery patients at risk of excessive early postoperative bleeding

2011 ◽  
Vol 39 (6) ◽  
pp. 924-930 ◽  
Author(s):  
Alain Vuylsteke ◽  
Christina Pagel ◽  
Caroline Gerrard ◽  
Brian Reddy ◽  
Samer Nashef ◽  
...  
Keyword(s):  
At Risk ◽  
Cardiac Surgery ◽  
Risk Score ◽  
Postoperative Bleeding ◽  
Bleeding Risk ◽  
Patients At Risk

scholarly journals Stratifying risk of acute kidney injury in pre and post cardiac surgery patients using a novel biomarker-based algorithm and clinical risk score

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
William T. McBride ◽  
Mary Jo Kurth ◽  
Gavin McLean ◽  
Anna Domanska ◽  
John V. Lamont ◽  
...  
Keyword(s):  
At Risk ◽  
Acute Kidney Injury ◽  
Cardiac Surgery ◽  
Risk Score ◽  
Kidney Injury ◽  
Predictive Ability ◽  
Clinical Risk Score ◽  
Clinical Risk ◽  
Patients At Risk ◽  
Post Cardiac Surgery

AbstractAcute kidney injury (AKI) following cardiac surgery significantly increases morbidity and mortality risks. Improving existing clinical methods of identifying patients at risk of perioperative AKI may advance management and treatment options. This study investigated whether a combination of biomarkers and clinical factors pre and post cardiac surgery could stratify patients at risk of developing AKI. Patients (n = 401) consecutively scheduled for elective cardiac surgery were prospectively studied. Clinical data was recorded and blood samples were tested for 31 biomarkers. Areas under receiver operating characteristic (AUROCs) were generated for biomarkers pre and postoperatively to stratify patients at risk of AKI. Preoperatively sTNFR1 had the highest predictive ability to identify risk of developing AKI postoperatively (AUROC 0.748). Postoperatively a combination of H-FABP, midkine and sTNFR2 had the highest predictive ability to identify AKI risk (AUROC 0.836). Preoperative clinical risk factors included patient age, body mass index and diabetes. Perioperative factors included cardio pulmonary bypass, cross-clamp and operation times, intra-aortic balloon pump, blood products and resternotomy. Combining biomarker risk score (BRS) with clinical risk score (CRS) enabled pre and postoperative assignment of patients to AKI risk categories. Combining BRS with CRS will allow better management of cardiac patients at risk of developing AKI.

A Pharmacist-Driven Glycemic Control Protocol to Reduce the Rate of Severe Hypoglycemia in High-Risk Patients

2020 ◽  
pp. 001857872097389
Author(s):  
Colleen A. Cook ◽  
Victor Vakayil ◽  
Kyle Pribyl ◽  
Derek Yerxa ◽  
John Kriz ◽  
...  
Keyword(s):  
At Risk ◽  
High Risk ◽  
Glycemic Control ◽  
Risk Reduction ◽  
Severe Hypoglycemia ◽  
Diabetic Patients ◽  
Control Protocol ◽  
Before And After ◽  
Patients At Risk ◽  
Acceptance Rates

Purpose: Hospital pharmacists contribute to patient safety and quality initiatives by overseeing the prescribing of antidiabetic medications. A pharmacist-driven glycemic control protocol was developed to reduce the rate of severe hypoglycemia events (SHE) in high-risk hospitalized patients. Methods: We retrospectively analyzed the rates of SHE (defined as blood glucose ≤40 mg/dL), before and after instituting a pharmacist-driven glycemic control protocol over a 4-year period. A hospital glucose management team that included a lead Certified Diabetes Educator Pharmacist (CDEP), 5 pharmacists trained in diabetes, a lead hospitalist, critical care and hospital providers established a process to first identify patients at risk for severe hypoglycemia and then implement our protocol. Criteria from the American Diabetes Association and the American Association of Clinical Endocrinologists was utilized to identify and treat patients at risk for SHE. We analyzed and compared the rate of SHE and physician acceptance rates before and after protocol initiation. Results: From January 2015 to March 2019, 18 297 patients met criteria for this study; 139 patients experienced a SHE and approximately 80% were considered high risk diabetes patients. Physician acceptance rates for the new protocol ranged from 77% to 81% from the year of initiation (2016) through 2018. The absolute risk reduction of SHE was 9 events per 1000 hospitalized diabetic patients and the relative risk reduction was 74% SHE from the start to the end of the protocol implementation. Linear regression analysis demonstrated that SHE decreased by 1.5 events per 1000 hospitalized diabetic patients (95% confidence interval, −1.54 to −1.48, P < .001) during the 2 years following the introduction of the protocol. This represents a 15% relative reduction of SHE per year. Conclusion: The pharmacist-driven glycemic control protocol was well accepted by our hospitalists and led to a significant reduction in SHE in high-risk diabetes patient groups at our hospital. It was cost effective and strengthened our physician-pharmacist relationship while improving diabetes care.

scholarly journals Preoperative identification of cardiac surgery patients at risk of receiving a platelet transfusion: The Australian Cardiac Surgery Platelet Transfusion ( ACSePT ) risk prediction tool

Transfusion ◽  
2020 ◽  
Vol 60 (10) ◽  
pp. 2272-2283
Author(s):  
Andrew W. J. Flint ◽  
Michael Bailey ◽  
Christopher M. Reid ◽  
Julian A. Smith ◽  
Lavinia Tran ◽  
...  
Keyword(s):  
At Risk ◽  
Cardiac Surgery ◽  
Risk Prediction ◽  
Platelet Transfusion ◽  
Prediction Tool ◽  
Patients At Risk

Improving the Identification of Patients at Risk of Postoperative Renal Failure after Cardiac Surgery

2006 ◽  
Vol 104 (1) ◽  
pp. 65-72 ◽  
Author(s):  
Duminda N. Wijeysundera ◽  
Keyvan Karkouti ◽  
W Scott Beattie ◽  
Vivek Rao ◽  
Joan Ivanov
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Renal Function ◽  
Cardiac Surgery ◽  
Renal Insufficiency ◽  
Care Center ◽  
Tertiary Care ◽  
Receiver Operating Curve ◽  
Alternative Measure ◽  
Patients At Risk

Background Preoperative renal insufficiency is an important predictor of the need for postoperative renal replacement therapy (RRT). Serum creatinine (sCr) has a limited ability to identify patients with preoperative renal insufficiency because it varies with age, sex, and muscle mass. Calculated creatinine clearance (CrCl) is an alternative measure of renal function that may allow better estimation of renal reserve. Methods Data were prospectively collected for consecutive patients who underwent cardiac surgery requiring cardiopulmonary bypass at a tertiary care center. The relation between CrCl (Cockcroft-Gault equation) and RRT was initially described using descriptive statistics, logistic regression, and receiver operating curve analysis. Based on these analyses, preoperative renal insufficiency was defined as CrCl of 60 ml/min or less. Preoperative renal function was classified as moderate insufficiency (sCr &gt; 133 microM), mild insufficiency (100 microM &lt; sCr &lt; or = 133 microM), occult insufficiency (sCr &lt; or = 100 microM and CrCl &lt; or = 60 ml/min), or normal function (sCr &lt; or = 100 microM and CrCl &gt; 60 ml/min). The independent association of preoperative renal function with RRT was subsequently determined using multiple logistic regression. Results Of the 10,751 patients in the sample, 137 (1.2%) required postoperative RRT. Approximately 13% of patients with normal sCr had occult renal insufficiency. Occult renal insufficiency was independently associated with RRT (odds ratio, 2.80; 95% confidence interval, 1.39-5.33). The magnitude of this risk was similar to patients with mild renal insufficiency (P = 0.73). Conclusions The inclusion of a simple CrCl-based criterion in preoperative assessments may improve identification of patients at risk of needing postoperative RRT.

scholarly journals Antibody Responses in HIV-Infected Patients With Advanced Immunosuppression and Asymptomatic Cryptococcal Antigenemia

2018 ◽  
Vol 6 (1) ◽  
Author(s):  
Admire Hlupeni ◽  
Antonio Nakouzi ◽  
Tao Wang ◽  
Kathryn F Boyd ◽  
Tariro A Makadzange ◽  
...  
Keyword(s):  
At Risk ◽  
T Cell ◽  
Characteristic Curve ◽  
Information Criteria ◽  
Immunoglobulin M ◽  
Cd4 T Cell ◽  
Enzyme Linked Immunosorbent Assay ◽  
Before And After ◽  
Plasma Antibody ◽  
Patients At Risk

Abstract Background There are no host biomarkers of risk for HIV-associated cryptococcal meningitis (CM) except CD4+ T-cell deficiency. At present, serum cryptococcal antigen (CrAg) screening of those with CD4 &lt;100 cells/µL is used to identify persons at risk for HIV-associated CM. We determined if plasma antibody profiles could discriminate CrAg+ from CrAg- patients. Methods We performed serological analyses of 237 HIV-infected asymptomatic Zimbabwean patients with CD4 &lt;100 cells/µL; 125 CrAg- and CrAg+ but cerebrospinal fluid CrAg- by CrAg lateral flow assay. We measured plasma immunoglobulin M (IgM), immunoglobulin G (IgG) 1, and IgG2 concentrations by Luminex, and titers of Cryptococcus neoformans (Cn) glucuronoxylomannan (GXM) polysaccharide and naturally occurring Laminarin (natural Lam, a β-(1–3)-glucan linked polysaccharide)-binding IgM and IgG by enzyme-linked immunosorbent assay. Results GXM-IgG, -IgM, and -IgG2 levels were significantly higher in CrAg+ patients, whereas natural Lam-IgM and Lam-IgG were higher in CrAg- patients before and after adjustment for age, sex, and CD4 T-cell count, despite overlap of values. To address this variability and better discriminate the groups, we used Akaike Information Criteria to select variables that independently predicted CrAg+ status and included them in a receiver operating characteristic curve to predict CrAg status. By inclusion of CD4, GXM-IgG, GXM-IgM, and Lam-IgG, -IgG2, and -IgM, this model had an 80.4% probability (95% confidence interval, 0.75–0.86) of predicting CrAg+ status. Conclusions Statistical models that include multiple serological variables may improve the identification of patients at risk for CM and inform new directions in research on the complex role that antibodies may play in resistance and susceptibility to CM.

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