Biomarker Testing, Treatment, and Outcomes in Patients With Advanced/Metastatic Non-Small Cell Lung Cancer Using a Real-World Database.

BACKGROUND: Little is known about the impact of up-front biomarker testing on long-term outcomes in patients with advanced or metastatic non-small cell lung cancer (a/mNSCLC). This study compared overall survival (OS) by biomarker testing status and by receipt of guideline-concordant therapy in a large real-world cohort of patients with a/mNSCLC in the United States. PATIENTS AND METHODS: This retrospective study used an a/mNSCLC database derived from real-world electronic healthcare records. Patients diagnosed with nonsquamous a/mNSCLC who initiated first-line therapy on or after January 1, 2015, were included. We describe the testing of patients for actionable biomarkers and whether they subsequently received guideline-recommended first-line treatment. OS was defined as the number of months from the initiation of first-line therapy to the date of death or end of follow-up, and was described using Kaplan-Meier analysis. Multivariable Cox proportional hazard modeling was conducted to compare OS between groups adjusting for baseline covariates; adjusted hazard ratios (HRs) were reported. RESULTS: A total of 21,572 patients with a median age of 69 years (IQR, 61-76 years) and follow-up of 9.5 months (IQR, 3.5-21.5 months) were included. Among patients in the database, 88% had a record of receiving testing for at least 1 biomarker at any time, and 69% of these patients received testing before or at the start of first-line treatment. The adjusted hazard of death was 30% higher in patients who never (vs ever) received biomarker testing in the database (HR, 1.30; 95% CI, 1.24-1.37), and 12% higher in patients who did not receive (vs did receive) biomarker testing before or at the start of first-line treatment (HR, 1.12; 95% CI, 1.08-1.16). The adjusted hazard of death was 25% higher in patients who did not receive guideline-concordant first-line treatment (vs those who did) after having a biomarker-positive disease (HR, 1.25; 95% CI, 1.13-1.40). CONCLUSIONS: Findings suggest that receipt of first-line treatment that is concordant with biomarker testing results and treatment guidelines is associated with improved survival outcomes in patients with a/mNSCLC in the United States.

Use of Japanese big data from electronic medical records to investigate risk factors and identify their high-risk combinations for linezolid-induced thrombocytopenia.

PURPOSE: Thrombocytopenia is a major event associated with linezolid (LZD) therapy. Factors affecting LZD-induced thrombocytopenia (LIT) have been reported in previous studies. However, several issues pertaining to LIT have not yet been clarified. In the present study, we used Japanese big data to investigate associated factors and their high-risk combinations that influence LIT. METHODS: Patients administered LZD between May 2006 and October 2020 were included in this study. LIT was defined as either a 30% or more reduction from the baseline platelets or platelet values of < 100,000/µL. We evaluated factors affecting LIT and combinations of factors that alter LIT risk according to a decision tree (DT) analysis, a typical machine learning method. RESULTS: We successfully enrolled 1399 patients and LIT occurred in 44.7% of the patients (n = 626). We classified the laboratory data on renal function, LZD duration, age, and body weight (BW) into smaller categories. The results of multivariate analysis showed that prolonged LZD therapy, BW < 45 kg, estimated glomerular filtration rate (eGFR) < 30 mL/min/1.73 m2, and dialysis were risk factors for LIT. The DT analysis revealed that the highest risk was a combination of LZD duration ≥ 14 days and eGFR < 30 mL/min/1.73 m2. CONCLUSIONS: The present study extracted four risk factors and identified high-risk combinations for LIT. Patients with these risk factors should be closely monitored.

Factors affecting creatine phosphokinase elevation during daptomycin therapy using a combination of machine learning and conventional methods.

AIMS: Musculoskeletal toxicity is a typical side effect of daptomycin (DAP). However, the risk factors have not been well established. Here, we aimed to identify independent factors affecting DAP-induced musculoskeletal toxicity using a combination of machine learning and conventional statistical methods. METHODS: A population-based, retrospective, observational cohort study was conducted using the Japanese electronic medical record database. Patients who received DAP between October 2011 and December 2020 were enrolled. Two definitions of musculoskeletal toxicity were employed: (1) elevation of creatine phosphokinase (CPK) value more than twice from baseline and >200 IU/L, and (2) >1000 IU/L. First, multiple logistic regression analyses (a conventional statistical method) were performed to identify independent factors affecting CPK elevation. Then, decision tree analyses, a machine learning method, were performed to detect combinations of factors that change CPK elevation risk. RESULTS: Of the 2970 patients who received DAP, 706 were included. Elevation of CPK values >200 IU/L and >1000 IU/L occurred in 83 (11.8%) and 17 (2.41%) patients, respectively. In multiple logistic regression analysis, baseline CPK value and concomitant use of hydrophobic statins were commonly extracted as independent factors affecting each CPK elevation, but concomitant use of hydrophilic statins was not. In decision tree analysis, patients who received hydrophobic statins and had high baseline CPK values were classified into the high-risk group. CONCLUSION: Our novel approach revealed new risk factors for CPK elevation. Our findings suggest that high-risk patients require frequent CPK monitoring.

Using Japanese big data to investigate novel factors and their high-risk combinations that affect vancomycin-induced nephrotoxicity.

AIMS: Several factors related to vancomycin-induced nephrotoxicity (VIN) have not yet been clarified. In the present study, we used Japanese big data to investigate novel factors and their high-risk combinations that influence VIN. METHODS: We employed a large Japanese electronic medical record database and included patients who had been administered intravenous vancomycin between June 2000 and December 2020. VIN was defined as an increase in serum creatinine ≥0.5 mg/dL or 1.5-fold higher than the baseline. The outcomes were: (1) factors affecting VIN that were identified using multiple logistic regression analysis, and (2) combinations of factors that affect the risk of VIN according to a decision tree analysis, which is a typical machine learning method. RESULTS: Of the 7306 patients that were enrolled, VIN occurred in 14.2% of them (1035). A multivariate analysis extracted 22 variables as independent factors. Concomitant ramelteon use (odds ratio 0.701, 95% confidence interval 0.512-0.959), ward pharmacy service (0.741, 0.638-0.861), duration of VCM < 7 days (0.748, 0.623-0.899) and trough concentrations 10-15 mg/L (0.668, 0.556-0.802) reduce the risk of VIN. Meanwhile, concomitant piperacillin-tazobactam use (2.056, 1.754-2.409) and piperacillin use (2.868, 1.298-6.338) increase the risk. The decision tree analysis showed that a combination of vancomycin trough concentrations ≥20 mg/L and concomitant piperacillin-tazobactam use was associated with the highest risk. CONCLUSIONS: We revealed that the concomitant ramelteon use and ward pharmacy service may decrease the risk of VIN, while the concomitant use of not only piperacillin-tazobactam but also piperacillin may increase the risk.

Factors Associated With Attainment of Glycemic Targets Among Adults With Type 1 and Type 2 Diabetes in Canada: A Cross-sectional Study Using Primary and Specialty Care Electronic Medical Record Data.

OBJECTIVE: Using a new database combining primary and specialty care electronic medical record (EMR) data in Canada, we determined attainment of glycemic targets and associated predictors among adults with diabetes. METHODS: We conducted a cross-sectional observational study combining primary and specialty care EMR data in Canada. Adults with diabetes whose primary care provider contributed to the National Diabetes Repository or who were assessed at a diabetes specialty clinic (LMC Diabetes and Endocrinology) between July 3, 2015, and June 30, 2019, were included. Diabetes type was categorized as type 2 diabetes (T2D) not prescribed insulin, T2D prescribed insulin, and type 1 diabetes (T1D). Covariates were age, sex, income quintile, province, rural/urban location, estimated glomerular filtration rate, medications, and insulin pump use. Associations between predictors and the outcome (glycated hemoglobin [A1C] of ≤7.0%) were assessed by multivariable logistic regressions. RESULTS: Among 122,106 adults, consisting of 91,366 with T2D not prescribed insulin, 25,131 with T2D prescribed insulin, and 5,609 with T1D, attainment of an A1C of ≤7.0% was 60%, 25%, and 23%, respectively. Proportions with an A1C of ≤7.5% and ≤8.0% were 75% and 84% for those with T2D not prescribed insulin, 41% and 57% for those with T2D prescribed insulin, and 37% and 53% for those with T1D. Highest vs lowest income quintile was associated with greater odds of meeting the A1C target (adjusted odds ratio [95% confidence interval] for each diabetes category: 1.15 [1.10 to 1.21], 1.21 [1.10 to 1.33], and 1.29 [1.04 to 1.60], respectively). Individuals in Alberta and Manitoba had less antihyperglycemic medication use and attainment of A1C target than other provinces. CONCLUSIONS: Attainment of glycemic targets among adults with diabetes was poor and differed by income and geographic location, which must be addressed in national diabetes strategies.