Comparison of four nutritional screening tools in perioperative elderly patients: Taking orthopedic and neurosurgical patients as examples.

Background and aims: Malnutrition is widely present in elderly surgical patients and is highly correlated with prognosis after surgery. However, studies comparing the effectiveness of comprehensive nutritional screening tools in geriatric surgical patients have not yet been published. The nutritional risk among elderly orthopedic and neurosurgical patients and their associated clinical indicators and outcomes was assessed using four screening tools. The aim of this study was to explore suitable tools for screening the nutritional status and identify their potential to act as prognostic indicators. Methods: The Nutritional Risk Score 2002 (NRS2002), Mini Nutritional Assessment - Short Form (MNA-SF), Geriatric Nutritional Risk Index (GNRI), and Prognostic Nutritional Index (PNI) were all performed within two days of admission and before surgery. The relationships between nutritional risk classifications and conventional nutritional markers, complications and length of hospital stay (LOS) were evaluated. Results: In this study, a total of 167 orthopedic patients and 103 neurosurgical patients were evaluated. In neurosurgical patients, the rates of malnutrition or patients at risk of malnutrition according to the MNA-SF, GNRI, NRS2002 and PNI were 26.4, 24.6, 8.4, and 12.6%, respectively. According to the NRS2002 and PNI, the rates of old neurosurgical patients who were malnourished or at risk of malnutrition were 14.6 and 3.9%, respectively, which were lower than the results assessed by the MNA-SF (24.3%) and GNRI (15.5%). Multiple regression analysis revealed a significant relationship between the PNI (malnourished vs.well-nourished, OR = 5.39, 95% CI:1.11-26.18, P = 0.037), GNRI (at risk vs.no risk, OR = 3.96, 95% CI: 1.01-15.45, P = 0.048) and the complications in orthopedic patients. Only GNRI was significantly related to LOS > 7 days (at risk vs.no risk, OR = 4.01, 95% CI: 1.64-9.80, P = 0.002). For neurosurgical patients, an association between GNRI and LOS > 8 days was discovered (at risk vs.no risk, OR = 3.35, 95% CI: 1.03-10.86, P = 0.002). Conclusion: Among the four nutritional risk screening tools, the GNRI exhibited better predictive value for short-term outcomes in elderly perioperative orthopedic and neurosurgical patients, thereby suggesting that it might be a more suitable tool for nutritional risk screening. Additional studies are required to determine the applicability of GNRI in other surgical fields.

Pharmacist-managed titration of urate-lowering therapy to streamline gout management.

The treat-to-target approach for serum uric acid is the recommended model in gout management according to the 2012 American College of Rheumatology (ACR) guidelines. Adherence to urate-lowering therapy (ULT) can be difficult for patients due to barriers, which include medication burden, financial hardship, and lack of medical literacy. Our aim was to create a pharmacist-managed referral for the titration of ULT to target serum uric acid (sUA) levels in a complex patient population. We utilized a clinical database to query patients seen at a rheumatology clinic over a 12-month period with an ICD-10 diagnosis for gout. The referral criteria were indications for ULT per the 2012 ACR guidelines. Rheumatology providers, consisting of attendings, fellows, and a physician assistant, were asked to refer the identified patients to the pharmacist-managed titration program. The intervention group consisted of 19 referred patients and the control group consisted of 28 non-referred patients. The baseline sUA (median (IQR)) at the time of referral was 8.8 (2) mg/dL for the intervention group and 7.6 (2.8) mg/dL for the control group (p = 0.2). At the end of the study period, the sUA was 6.1 (1.4) mg/dL for the intervention group and 6.8 (3.2) mg/dL for the control group (p = 0.08). At the end of the study period, 6 of 19 (32%) intervention group and 7 of 28 (25%) control group were at goal (p = 0.3). A newly instituted pharmacist-managed titration program was able to achieve lower average sUA levels in referred patients compared to demographically similar individuals who received standard gout management.

Valproic Acid as a potentiator of metabolic syndrome in institutionalized residents on concomitant antipsychotics: fat chance, or slim to none?

BACKGROUND: Valproic acid (VPA) is one of the most commonly used antiepileptic medications worldwide; it is also a popular mood stabilizer for use in bipolar disorder and dementia. This study assessed whether VPA may potentiate metabolic side effects in patients with psychiatric disorders taking concomitant antipsychotics (APs). VPA alone has been associated with weight gain, dyslipidemia, hypertension, and diabetes. Patients with psychiatric disorders, especially those on second-generation (atypical) APs, appear to be at increased risk of these metabolic effects. A secondary purpose was to determine if a linear dose-response relationship exists between the VPA dose and adverse metabolic effects. METHODS: This cross-sectional study was conducted using data collected on all patients in the four state-operated psychiatric hospitals in Michigan using a comprehensive assessment instrument, the interRAI Mental Health. All patients taking both VPA and APs (n = 200) were compared to a control group of patients taking APs without VPA (n = 426). Patients were assessed for the presence of the following surrogate indicators of metabolic syndrome: weight gain; high body mass index (BMI greater than 30 kg/m(2)); very high BMI (BMI greater than 40 kg/m(2)); a diagnosis of diabetes mellitus; use of a prescribed statin medication; diagnosis of hyperlipidemia or dyslipidemia; hypertension; or the combination of any three of these factors: high BMI, hyperlipidemia or dyslipidemia, diabetes, and hypertension. Analysis also included assessment of the effect of VPA dosage on metabolic side effects. RESULTS: Patients in the VPA plus APs group were 3.2 kg heavier than those in the APs group (P = 0.05) at baseline. Compared with the APs group, the VPA plus APs group had a higher prevalence of high and very high BMI, diabetes, hypertension, and the combination of any three factors of high BMI, hyperlipidemia/dyslipidemia, diabetes, and hypertension. However, these differences were not statistically significant. Conversely, there was a slight but non-significant reduction in the prevalence of weight gain, prescribed statins, and hyperlipidemia/dyslipidemia in the VPA plus APs group than the APs group. Finally, higher doses of VPA were not found to be associated with a higher incidence of these metabolic side effects. CONCLUSION: Although the patients on VPA were slightly more than 3 kg heavier, VPA did not appear to be associated with significant metabolic effects in patients with psychiatric conditions taking typical and atypical APs. These metabolic effects also do not appear to be related to the dose of VPA.