The association between polypharmacy and health-related quality of life among non-dialysis chronic kidney disease patients.

BACKGROUND AND OBJECTIVE: The United States government spends over $85 billion annually on treating non-dialysis chronic kidney disease (CKD). Patients with CKD are prescribed a multitude of medications to manage numerous comorbidities associated with CKD. Thus, this study aims to investigate the association between polypharmacy and health-related quality of life (HRQoL) in non-dialysis CKD patients. METHODS: This cross-sectional study utilized data from the Medical Expenditure Panel Survey (MEPS) from 2010 through 2019. We classified polypharmacy into three groups based on the number of medication classes: ≤ 4 (minor polypharmacy), 5 through 9 (major polypharmacy), and ≥ 10 (hyperpolypharmacy). To measure HRQoL, a Physical Component Summary (PCS) and a Mental Component Summary (MCS) were obtained from the 12-item Short-Form Health Survey version 2 and Veteran's Rand 12 item. We applied multivariable ordinary least squares regression to assess the association between polypharmacy and HRQoL in non-dialysis CKD patients. RESULTS: A total of 649 CKD patients (weighted n = 667,989) were included. Patients with minor polypharmacy, major polypharmacy, and hyperpolypharmacy were 22.27%, 48.24%, and 29.48%, respectively. Major polypharmacy and hyperpolypharmacy were significantly and negatively associated with lower PCS scores when compared with minor polypharmacy [Beta = -3.12 (95% CI: -3.62, -2.62), p-value<0.001; Beta = -4.13 (95CI: -4.74, -3.52), p-value<0.001]. Similarly, major polypharmacy and hyperpolypharmacy were significantly and negatively associated with lower MCS scores when compared to minor polypharmacy [Beta = -0.38 (95% CI: -0.55, -0.20), p-value<0.001; Beta = -1.70 (95% CI: -2.01, -1.40), p-value<0.001]. The top 5 classes of medications used by CKD patients were antihyperlipidemic (56.31%), beta-adrenergic blockers (49.71%), antidiabetics (42.14%), analgesics (42.17%), and diuretics (39.65%). CONCLUSION: Our study found that both major polypharmacy and hyperpolypharmacy were associated with lower HRQoL among non-dialysis CKD patients. This study highlights the need for further evaluation of the combination of medications taken by non-dialysis CKD patients to minimize unnecessary and inappropriate medication use.

Thiamine utilization and the lack of prescribing standardization: A critical examination.

OBJECTIVES: Thiamine is often prescribed for thiamine deficiency during hospitalization despite the lack of US-based clinical guidelines. This study aims to evaluate thiamine prescribing patterns and key characteristics associated with the deficiency to address gaps in care. METHODS: Data were obtained from electronic health records of hospitalized patients between September 1, 2021, and March 30, 2022. Alcohol use disorder (AUD) was defined by a positive Clinical Institute Withdrawal Assessment score or a positive serum alcohol level upon admission. Geriatric patients were defined as age ≥65. Cohort 1 was defined as: AUD, albumin <4 g/L, INR >1.5, and total bilirubin >3 mg/dL. Cohort 2 was defined as: age >65, albumin <4 g/L, hemoglobin <15 g/dL, and folate <4 ng/mL. A multivariable LASSO regression model was used to identify characteristics associated with higher thiamine dosing (>100 mg/day). RESULTS: Among 780 patients, 520 (66.7%) were identified as AUD, of which 265 (50.1%) were between the ages of 45-64 years. The AUD cohort was significantly different (p < 0.05) in the mean serum albumin 4.16 g/L (IQR: 3.8-4.5), AST 73.55 U/L (23.75-82.00), ALT 52.57 U/L (17.00-57.00), total bilirubin 0.98 (0.3-1.0), and INR 1.1 (0.99-1.12), compared to non-AUD patients with a mean serum albumin 3.75 g/L (3.3-4.2), AST 35.07 U/L (11.00-42.00), ALT 32.77 U/L (5.00-34.00), total bilirubin 0.89 (0.2-0.9), and INR 1.21 (1.0-1.22). In the geriatric cohort, 136 patients (17%) had a mean serum albumin 3.77 g/L (3.4-4.2), AST 38.66 U/L (14.0-41.0), ALT 29.36 U/L (9.0-37.0), total bilirubin 0.62 mg/dL (0.30-0.90), and direct bilirubin 0.12 mg/dL (0.00-0.20), compared to the non-geriatric cohort with a mean serum albumin 4.10 g/L (3.8-4.40), AST 66.44 U/L (21.0-75.0), ALT 50.03 U/L (16.00-53.75), total bilirubin 1.02 mg/dL (0.30-1.00), and direct bilirubin 0.31 mg/dL (0.00-0.20). In cohort 1, 40.6% patients were between 51 and 64 years old, (66.5%) male, and had a BMI <25 (36.4%). In cohort 2, 52.6% were between 65 and 70 years old, (57.9%) male, and had a BMI <25 (57.9%). Cohort 1 were prescribed a dose of 100 mg (47.7 %), oral (63.5%), intramuscular (18.2%), daily (58.9%), one-day duration (49.4%) most frequently. Cohort 2 were prescribed a dose of 100 mg (56.0%), oral (77.2%), daily (77.2%), one-day duration (29.8%) most frequently. The AUD was significantly associated with having a higher dosage (e.g., >100 mg) of thiamine prescribed per day OR 1.62 (1.11-2.37) (p < 0.01). CONCLUSIONS: This study confirms that thiamine prescribing patterns vary during hospitalization and suggest specific laboratory findings may aid in identifying cohorts associated with the deficiency.

Medication Regimen Complexity Index Score at Admission as a Predictor of Inpatient Outcomes: A Machine Learning Approach.

BACKGROUND: In the intensive care unit, traditional scoring systems use illness severity and/or organ failure to determine prognosis, and this usually rests on the patient's condition at admission. In spite of the importance of medication reconciliation, the usefulness of home medication histories as predictors of clinical outcomes remains unexplored. METHODS: A retrospective cohort study was conducted using the medical records of 322 intensive care unit (ICU) patients. The predictors of interest included the medication regimen complexity index (MRCI) at admission, the Acute Physiology and Chronic Health Evaluation (APACHE) II, the Sequential Organ Failure Assessment (SOFA) score, or a combination thereof. Outcomes included mortality, length of stay, and the need for mechanical ventilation. Machine learning algorithms were used for outcome classification after correcting for class imbalances in the general population and across the racial continuum. RESULTS: The home medication model could predict all clinical outcomes accurately 70% of the time. Among Whites, it improved to 80%, whereas among non-Whites it remained at 70%. The addition of SOFA and APACHE II yielded the best models among non-Whites and Whites, respectively. SHapley Additive exPlanations (SHAP) values showed that low MRCI scores were associated with reduced mortality and LOS, yet an increased need for mechanical ventilation. CONCLUSION: Home medication histories represent a viable addition to traditional predictors of health outcomes.

Comparative Safety Analysis of Opioid Agonist Treatment in Pregnant Women with Opioid Use Disorder: A Population-Based Study.

INTRODUCTION AND OBJECTIVE: Receipt of opioid agonist treatment during early and late pregnancy for opioid use disorder may relate to varying perinatal risks. We aimed to assess the effect of time-varying prenatal exposure to opioid agonist treatment using buprenorphine or methadone on adverse neonatal and pregnancy outcomes. METHODS: We conducted a retrospective cohort study of pregnant women with opioid use disorder using Rhode Island Medicaid claims data and vital statistics during 2008-16. Time-varying exposure was evaluated in early (0-20 weeks) and late (≥ 21 weeks) pregnancy. Marginal structural models with inverse probability of treatment weighting were applied. RESULTS: Of 400 eligible pregnancies, 85 and 137 individuals received buprenorphine and methadone, respectively, during early pregnancy. Compared with 152 untreated pregnancies with opioid use disorders, methadone exposure in both periods was associated with an increased risk of preterm birth (adjusted odds ratio [aOR]: 2.52; 95% confidence interval [CI] 1.07-5.95), low birth weight (aOR: 2.99; 95% CI 1.34-6.66), neonatal intensive care unit admission (aOR, 5.04; 95% CI 2.49-10.21), neonatal abstinence syndrome (aOR: 11.36; 95% CI 5.65-22.82), respiratory symptoms (aOR, 2.71; 95% CI 1.17-6.24), and maternal hospital stay > 7 days (aOR, 14.51; 95% CI 7.23-29.12). Similar patterns emerged for buprenorphine regarding neonatal abstinence syndrome (aOR: 10.27; 95% CI 4.91-21.47) and extended maternal hospital stay (aOR: 3.84; 95% CI 1.83-8.07). However, differences were found favoring the use of buprenorphine for preterm birth versus untreated pregnancies (aOR: 0.17; 95% CI 0.04-0.77), and for several outcomes versus methadone. CONCLUSIONS: Methadone and buprenorphine prescribed for the treatment of opioid use disorder during pregnancy are associated with varying perinatal risks. However, buprenorphine may be preferred in the setting of pregnancy opioid agonist treatment. Further research is necessary to confirm our findings and minimize residual confounding.

Evaluating the Medication Regimen Complexity Score as a Predictor of Clinical Outcomes in the Critically Ill.

Background: Medication Regimen Complexity (MRC) refers to the combination of medication classes, dosages, and frequencies. The objective of this study was to examine the relationship between the scores of different MRC tools and the clinical outcomes. Methods: We conducted a retrospective cohort study at Roger William Medical Center, Providence, Rhode Island, which included 317 adult patients admitted to the intensive care unit (ICU) between 1 February 2020 and 30 August 2020. MRC was assessed using the MRC Index (MRCI) and MRC for the Intensive Care Unit (MRC-ICU). A multivariable logistic regression model was used to identify associations among MRC scores, clinical outcomes, and a logistic classifier to predict clinical outcomes. Results: Higher MRC scores were associated with increased mortality, a longer ICU length of stay (LOS), and the need for mechanical ventilation (MV). MRC-ICU scores at 24 h were significantly (p < 0.001) associated with increased ICU mortality, LOS, and MV, with ORs of 1.12 (95% CI: 1.06−1.19), 1.17 (1.1−1.24), and 1.21 (1.14−1.29), respectively. Mortality prediction was similar using both scoring tools (AUC: 0.88 [0.75−0.97] vs. 0.88 [0.76−0.97]. The model with 15 medication classes outperformed others in predicting the ICU LOS and the need for MV with AUCs of 0.82 (0.71−0.93) and 0.87 (0.77−0.96), respectively. Conclusion: Our results demonstrated that both MRC scores were associated with poorer clinical outcomes. The incorporation of MRC scores in real-time therapeutic decision making can aid clinicians to prescribe safer alternatives.

Survival and recovery modeling of acute kidney injury in critically ill adults.

Objectives: Acute kidney injury is common among the critically ill. However, the incidence, medication use, and outcomes of acute kidney injury have been variably described. We conducted a single-center, retrospective cohort study to examine the risk factors and correlates associated with acute kidney injury in critically ill adults with a particular focus on medication class usage. Methods: We reviewed the electronic medical records of all adult patients admitted to an intensive care unit between 1 February and 30 August 2020. Acute kidney injury was defined by the 2012 Kidney Disease: Improving Global Outcomes guidelines. Data included were demographics, comorbidities, symptoms, laboratory parameters, interventions, and outcomes. The primary outcome was acute kidney injury incidence. A Least Absolute Shrinkage and Selection Operator regression model was used to determine risk factors associated with acute kidney injury. Secondary outcomes including acute kidney injury recovery and intensive care unit mortality were analyzed using a Cox regression model. Results: Among 226 admitted patients, 108 (47.8%) experienced acute kidney injury. 37 (34.3%), 39 (36.1%), and 32 patients (29.6%) were classified as acute kidney injury stages I-III, respectively. Among the recovery and mortality cohorts, analgesics/sedatives, anti-infectives, and intravenous fluids were significant (p-value < 0.05). The medication classes IV-fluid electrolytes nutrition (96.7%), gastrointestinal (90.2%), and anti-infectives (81.5%) were associated with an increased odds of developing acute kidney injury, odd ratios: 1.27, 1.71, and 1.70, respectively. Cox regression analyses revealed a significantly increased time-varying mortality risk for acute kidney injury-stage III, hazard ratio: 4.72 (95% confidence interval: 1-22.33). In the recovery cohort, time to acute kidney injury recovery was significantly faster in stage I, hazard ratio: 9.14 (95% confidence interval: 2.14-39.06) cohort when compared to the stage III cohort. Conclusion: Evaluation of vital signs, laboratory, and medication use data may be useful to determine acute kidney injury risk stratification. The influence of particular medication classes further impacts the risk of developing acute kidney injury, necessitating the importance of examining pharmacotherapeutic regimens for early recognition of renal impairment and prevention.

Drug-Induced QTc Prolongation: What We Know and Where We Are Going.

Drug-induced QTc prolongation is a concerning electrocardiogram (ECG) abnormality. This cardiac disturbance carries a 10% risk of sudden cardiac death due to the malignant arrhythmia, Torsades de Pointes. The Arizona Center for Education and Research on Therapeutics (AzCERT) has classified QTc prolonging therapeutic classes, such as antiarrhythmics, antipsychotics, anti-infectives, and others. AzCERT criteria categorize medications into three risk categories: "known," "possible," and "conditional risk" of QTc prolongation and Torsades de Pointes. The list of QTc prolonging medications continues to expand as new drug classes are approved and studied. Risk factors for QTc prolongation can be delineated into modifiable or non-modifiable. A validated risk scoring tool may be utilized to predict the likelihood of prolongation in patients receiving AzCERT classified medication. The resultant risk score may be applied to a clinical decision support system, which offers mitigation strategies. Mitigation strategies including discontinuation of possible offending agents with a selection of an alternative agent, assessment of potential drug interactions or dose adjustments through pharmacokinetic and pharmacodynamic monitoring, and initiation of both ECG and electrolyte monitoring are essential to prevent a drug-induced arrhythmia. The challenges presented by the COVID-19 pandemic have led to the development of innovative continuous monitoring technology, increasing protection for both patients and healthcare workers. Early intervention strategies may reduce adverse events and improve clinical outcomes in patients identified to be at risk of QTc prolongation.

An analysis of the management and incidence of postoperative nausea and vomiting.

BACKGROUND: Postoperative nausea and vomiting significantly increases recovery time, reduces patient satisfaction, and increases time to discharge. Consensus guidelines for the management of postoperative nausea and vomiting highlight effective methods for prophylaxis and treatment. Implications of adherence to these guidelines include both improved patient outcomes and reduced healthcare costs. OBJECTIVE: This study aimed to assess the incidence, contributing factors, and current prescribing practices for prophylaxis and treatment of postoperative nausea and vomiting. METHODS: Electronic medical records were assessed for adult patients who had an elective gastrointestinal or gynaecologic surgical procedure over a one-year period. Patient demographics and perioperative data were collected to assess risk factors and the incidence of postoperative nausea and vomiting. The appropriateness of prophylaxis and treatment was assessed according to current guidelines. RESULTS: The incidence of postoperative nausea and vomiting was consistent with previously noted findings. The average time spent under anaesthesia was significantly higher in patients who experienced postoperative nausea and vomiting. Appropriate evidence-based rescue therapy was administered in a minority of the cohort experiencing postoperative nausea and vomiting. CONCLUSION: There is substantial opportunity for provider education and adherence to best prescribing practices. Enhanced adherence to evidence-based rescue therapy prescribing may improve patient outcomes and satisfaction.

Personal digital health in Parkinson's disease: Case histories and commentary.

The use of self-tracking of bio-behavioral states along with prescription dosing information is increasingly popular in the care and study of many human diseases. Parkinson's Disease is particularly amenable to such tracking, as patients live with the progressive disease for many years, increasing motivation to pursue quality of life changes through careful monitoring of symptoms and self-guided management of their medications and lifestyle choices. Through the use of digital self-tracking technologies, patients independently or in conjunction with professional medical advice are modulating their medications and behavioral regimens based on self-tracking data. Self-trackers engage in self-experimentation with their health, and more broadly, in personal digital health. This paper briefly depicts notable, recent patient accounts of self-tracking and the uses of digital health in Parkinson's disease: those of Sara Riggare and Kevin Krejci. It also highlights important facets of a previously unreported case: Velva Walden's care as managed jointly by her caregiver son. Key aspects of self-tracking inherent to these cases are examined and potential opportunities to advance personalized medicine through the use of digital health and self-experimentation are outlined.

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients.

INTRODUCTION: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship between medication regimen complexity and patient outcomes. METHODS: This study was a single-center, retrospective observational evaluation of 130 adults admitted to the medical ICU. The MRC-ICU score was utilized to improve the inpatient model's prediction accuracy. Three models were proposed: model I, demographic data without medication data; model II, demographic data and medication regimen complexity variables; and model III: demographic data and the MRC-ICU score. A total of 6 ML classifiers was developed: k-nearest neighbor (KNN), naïve Bayes (NB), random forest, support vector machine, neural network, and logistic classifier (LC). They were developed and tested using electronic health record data to predict inpatient mortality. RESULTS: The results demonstrated that adding medication regimen complexity variables (model II) and the MRC-ICU score (model III) improved inpatient mortality prediction.. The LC outperformed the other classifiers (KNN and NB), with an overall accuracy of 83%, sensitivity (Se) of 87%, specificity of 67%, positive predictive value of 93%, and negative predictive value of 46%. The APACHE III score and the MRC-ICU score at the 24-hour interval were the 2 most important variables. CONCLUSION AND RELEVANCE: Inclusion of the MRC-ICU score improved the prediction of patient outcomes on the previously established APACHE III score. This novel, proof-of-concept methodology shows promise for future application of the MRC-ICU scoring tool for patient outcome predictions.

Rate and Risk Factors Associated With Prolonged Opioid Use After Surgery: A Systematic Review and Meta-analysis.

Importance: Prolonged opioid use after surgery may be associated with opioid dependency and increased health care use. However, published studies have reported varying estimates of the magnitude of prolonged opioid use and risk factors associated with the transition of patients to long-term opioid use. Objectives: To evaluate the rate and characteristics of patient-level risk factors associated with increased risk of prolonged use of opioids after surgery. Data Sources: For this systematic review and meta-analysis, a search of MEDLINE, Embase, and Google Scholar from inception to August 30, 2017, was performed, with an updated search performed on June 30, 2019. Key words may include opioid analgesics, general surgery, surgical procedures, persistent opioid use, and postoperative pain. Study Selection: Of 7534 articles reviewed, 33 studies were included. Studies were included if they involved participants 18 years or older, evaluated opioid use 3 or more months after surgery, and reported the rate and adjusted risk factors associated with prolonged opioid use after surgery. Data Extraction and Synthesis: The Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines were followed. Two reviewers independently assessed and extracted the relevant data. Main Outcomes and Measures: The weighted pooled rate and odds ratios (ORs) of risk factors were calculated using the random-effects model. Results: The 33 studies included 1 922 743 individuals, with 1 854 006 (96.4%) from the US. In studies with available sex and age information, participants were mostly female (1 031 399; 82.7%) and had a mean (SD) age of 59.3 (12.8) years. The pooled rate of prolonged opioid use after surgery was 6.7% (95% CI, 4.5%-9.8%) but decreased to 1.2% (95% CI, 0.4%-3.9%) in restricted analyses involving only opioid-naive participants at baseline. The risk factors with the strongest associations with prolonged opioid use included preoperative use of opioids (OR, 5.32; 95% CI, 2.94-9.64) or illicit cocaine (OR, 4.34; 95% CI, 1.50-12.58) and a preoperative diagnosis of back pain (OR, 2.05; 95% CI, 1.63-2.58). No significant differences were observed with various study-level factors, including a comparison of major vs minor surgical procedures (pooled rate: 7.0%; 95% CI, 4.9%-9.9% vs 11.1%; 95% CI, 6.0%-19.4%; P = .20). Across all of our analyses, there was substantial variability because of heterogeneity instead of sampling error. Conclusions and Relevance: The findings suggest that prolonged opioid use after surgery may be a substantial burden to public health. It appears that strategies, such as proactively screening for at-risk individuals, should be prioritized.

Adverse Drug Reactions in Palliative Care.

Adverse drug reactions (ADRs) have an impact on patient morbidity and mortality. Palliative care patients constitute a vulnerable population due to the complexity of their care and treatments. This study sought to identify ADRs in palliative care, assess their severity and preventability, and identify specific medications most commonly involved. This retrospective cohort study included patients who received a consult by the hospital's palliative care service over a 1-year period. Records were reviewed to identify ADR occurrences, causative and resulting events, and variables used to determine preventability and severity. Of the 430 patients who met inclusion criteria, 247 patients experienced an ADR (57.4%). In total, 440 ADRs were documented, with 45.7% of patients experiencing more than one. Patients with repeated hospitalizations, increased medication usage, documented drug allergies, and cancer diagnoses were more likely to experience an ADR. No associations were found between occurrences of ADR with gender or Charlson comorbidity scores. The majority of ADRs were of moderate severity (64.6%) and considered potentially preventable (81.5%). Organ systems most commonly affected by ADRs were gastrointestinal (32.7%) and neurological (15.9%). Antimicrobials, opioids, and anticoagulants were the most common causative agents. ADRs are commonly experienced in palliative care patients and are often preventable. Identification of risk factors for ADRs may prevent occurrences in the complex palliative care patient.

The effect of a rapid response team on major clinical outcome measures in a community hospital.

OBJECTIVE: To determine the effect of a rapid response system composed primarily of a rapid response team led by physician assistants on the rates of in-hospital cardiac arrests, total and unplanned intensive care unit admissions, and hospital mortality. DESIGN: Prospective, controlled, before and after trial. SETTING: A 350-bed nonteaching community hospital. PATIENTS: All adult patients admitted to the hospital from May 1, 2005, to October 1, 2006. INTERVENTIONS: We introduced a hospital-wide rapid response system that included a rapid response team (RRT) led by physician assistants with specialized critical care training. MEASUREMENTS AND MAIN RESULTS: We measured the incidence of cardiac arrests that occurred outside of the intensive care unit, total intensive care unit admissions, unplanned intensive care unit admissions, intensive care unit length of stay, and the total hospital mortality rate occurring over the study period. There were 344 RRT calls during the study period. In the 5 months before the rapid response system began, there were an average of 7.6 cardiac arrests per 1,000 discharges per month. In the subsequent 13 months, that figure decreased to 3.0 cardiac arrests per 1,000 discharges per month. Overall hospital mortality the year before the rapid response system was 2.82% and decreased to 2.35% by the end of the RRT year. The percentage of intensive care unit admissions that were unplanned decreased from 45% to 29%. Linear regression analysis of key outcome variables showed strong associations with the implementation of the rapid response system, as did analysis of variables over time. Physician assistants successfully managed emergency airway situations without assistance in the majority of cases. CONCLUSIONS: The deployment of an RRT led by physician assistants with specialized skills was associated with significant decreases in rates of in-hospital cardiac arrest and unplanned intensive care unit admissions.