Many previous studies have discussed an association between alcohol use disorder (AUD) and seizure incidents. There are also case reports of seizures during opioid withdrawals. Therefore, it is possible that AUD patients may have a higher risk of seizures if they also have opioid use disorder (OUD). However, it remains unproven whether AUD patients with a dual diagnosis of OUD have higher seizure incidents, to our knowledge. This study explored seizure incidents among the patients with a dual diagnosis of AUD and OUD as well as seizures among AUD only or OUD only patients. This study utilized de-identified data from 30 777 928 hospital inpatient encounters at 948 healthcare systems over 4 years (9/1/2018-8/31/2022) from the Vizient® Clinical Database for this study. Applying the International Classification of Diseases 10th Revision (ICD-10) diagnostic codes, AUD (1 953 575), OUD (768 982), and seizure (1 209 471) encounters were retrieved from the database to examine the effects of OUD on seizure incidence among AUD patients. This study also stratified patient encounters for demographic factors such as gender, age, and race, as well as the Vizient-categorized primary payer. Greatest gender differences were identified among AUD followed by OUD, and seizure patient groups. The mean age for seizure incidents was 57.6 years, while that of AUD was 54.7 years, and OUD 48.9 years. The greatest proportion of patients in all 3 groups were White, followed by Black, with Medicare being the most common primary payer in all 3 categories. Seizure incidents were statistically more common ( P < .001, chi-square) in patients with a dual diagnosis of AUD and OUD (8.07%) compared to those with AUD only (7.55%). The patients with the dual diagnosis had a higher odd ratio than those with AUD only or OUD only. These findings across more than 900 health systems provide a greater understanding of seizure risks. Consequently, this information may help in triaging AUD and OUD patients in certain higher-risk demographic groups.
Introduction/Objective Benchmarking establishes baseline laboratory utilization patterns across health systems. Nicholas et al. (2021) recently evaluated the use of test volume ratios to identify utilization intervention targets at VA hospitals. We aim to investigate potential differences of reported test volume ratios with those of non-VA hospitals. Methods/Case Report Using the Vizient® Clinical Data Base with permission from Vizient, Inc. (All rights reserved.), we obtained aggregated annual test and patient volumes from 2019-2021. To assess the accuracy of high-volume tests as a surrogate for patient volumes, we compared mean annual sodium and MCV test volumes with mean annual number of patient encounters at 712 hospitals (7,685,149 mean annual patient volume). We then calculated test volume ratios for eight less common analytes (homocysteine, prolactin, vitamin D (25-OH), HCV viral load, HIV viral load, Lyme disease serology, CD4, and HSV serology) at hospitals with patient encounters greater than 10,000 (279 hospitals, 6,046,670 mean annual patient volume). We calculated ratios using mean annual number of 1) sodium tests and 2) patient encounters as the denominator for comparison to reported ratios. All analytes are included in Choosing Wisely™ guidelines. Results (if a Case Study enter NA) There was strong positive correlation between mean annual sodium and MCV test volumes and mean annual patient volume (R = 0.92 and R=0.96, respectively). Our analyte-to-sodium volume ratios were smaller than expected. However, the analyte-to-patient volume median ratios were similar to those reported. A comparison of the calculated ratios showed a strong positive correlation (R=0.90), but the sodium ratios were nearly 4x smaller than the patient volume ratios. On average, the two methods agreed nearly 70% of the time for institutions with ratios greater than the 90th percentile- the intervention threshold. In comparison to previously reported median ratios, five analytes (homocysteine, prolactin, vitamin D (25-OH), Lyme disease serology and HSV serology) showed a significant difference from previously published ratios. Conclusion In comparison to VA hospitals, significant differences in median ratios were found. For calculating test volume ratios, high-volume tests are an adequate surrogate for patient volumes but may pose a challenge for inter- institution comparisons. Standardization for quantifying high-volume tests would improve the use of test volume ratios for benchmarking.
Introduction/Objective Evaluation of obstetric clinical laboratory test utilization differences between academic and non-academic health systems would provide important benchmarking information for healthcare leaders and policy makers. Methods/Case Report Over a 3-year period (2019-2021), using the Vizient® Clinical Data Base with permission from Vizient, Inc. (All rights reserved.), we compared clinical laboratory utilization for adult obstetrics inpatients at mid-size (250-450 hospital beds) academic (N=9; 27,036 hospitalizations) and non-academic (N=17; 50,462 hospitalizations) hospitals across the US. We used Medicare Severity Diagnosis Related Groups (MSDRG), employed by the US Centers for Medicare and Medicaid Services (CMS), to identify Cesarean section (MSDRG triplet 786-788) and vaginal delivery (MSDGR triplet 805-807) patient groups. For both groups, we stratified by comorbid conditions and complications into high, moderate, and low severity subgroups. We compared the mean number of laboratory tests (CPT codes 80000-89999) per hospitalization and per hospital day. We measured aggregate Clinical Resource Intensity Weight (RIW) per hospitalization to quantify laboratory resource consumption for each encounter. The RIW is based on the CMS Ambulatory Payment Classification (APC) weights. Results (if a Case Study enter NA) Academic hospitals had a significantly (p<.01) higher mean number of lab tests per Cesarean section hospitalization (20.3 vs. 10.3 tests, 97.1% higher) and hospital day (4.7 vs. 3.3 tests, 42.4% higher). Laboratory RIW per case was also significantly (p<.01) higher at academic hospitals (4.8 vs. 3.6 RIW, 33.3% higher); however, RIW per day did not differ between academic and non-academic hospitals. Significant differences (p<.01) persisted after adjusting for severity level. Although differences were smaller, academic hospitals had greater lab tests per case (12.1 vs. 8.1 tests, 49.4% higher) and day (4.3 vs. 3.6 tests, 19.4% higher) for vaginal delivery hospitalizations. Laboratory RIW per case was also significantly (p<.01) higher at academic hospitals (3.4 vs. 2.8 RIW, 21.4% higher). Laboratory RIW per day did not differ significantly. Significant differences remained for the high and moderate severity vaginal delivery groups. Conclusion Academic hospitals have greater laboratory test utilization for obstetrics patients. This difference appears to be largely due to a longer mean length of stay as well as use of tests with higher RIW.
Introduction/Objective Comparisons of surgical inpatient clinical laboratory test and radiology imaging utilization between multiple academic and non-academic health systems are very uncommon. Methods/Case Report Using the Vizient Clinical Data Base®, with permission from Vizient, Inc. (All rights reserved.), we compared clinical laboratory and imaging utilization over 3 years (2019-2021) for adult patients hospitalized for major GI surgical procedures at mid-size (250-450 hospital beds) academic (N=12; 8,706 hospitalizations) and non- academic (N=18; 7,930 hospitalizations) hospitals across the US. We used Medicare Severity Diagnosis Related Groups (MSDRG), employed by the US Centers for Medicare and Medicaid Services (CMS), to identify hospitalizations for major small and large bowel procedures (MSDRG triplet 329-331). Using single MSDRGs, we stratified patients by comorbid conditions and complications into high, moderate, and low severity groups. We compared the mean number of laboratory tests (CPT codes 80000-89999), and imaging studies (CPT codes 70000- 79999) per hospitalization and per hospital day. We measured aggregate Clinical Resource Intensity Weight (RIW) per hospitalization to quantify diagnostic resource consumption for each encounter. The RIW is based on the CMS Ambulatory Payment Classification weights. Results (if a Case Study enter NA) Academic hospitals had significantly (p<.01) higher mean laboratory tests per encounter (65.6 vs. 43.0 tests, 52.6% higher) and per hospital day (7.6 vs. 5.1 tests, 49.0% higher). Laboratory RIW was also higher at academic hospitals per hospitalization (12.8 vs. 10.3, 24.3% higher) and per hospital day (1.7 vs. 1.5, 13.3% higher). Mean imaging studies were also higher at academic centers per case (3.5 vs. 3.1 studies, 12.9% higher) and per day (0.37 vs. 0.33, 12.1% higher), but lacked statistical significance. Imaging RIW was not significantly different between academic and non-academic hospitals either by encounter (6.5 vs.6.2, 4.8% higher) or per day (0.697 vs. 0.700, 0.4% lower). Analysis of utilization after stratification by severity revealed similar patterns with larger differences at higher severity levels. Conclusion Mean diagnostic tests per hospitalization are greater at academic hospitals. These differences persist after adjustment for hospital length of stay and severity level. Laboratory test RIW is higher at academic hospitals. However, differences are not significant for imaging RIW.
Introduction/Objective Comparisons of diagnostic test utilization for outpatient encounters across multiple health systems are uncommon. Such comparisons are helpful to establish baseline diagnostic testing patterns across health systems. Methods/Case Report Using the Vizient Clinical Database, we compared the number of clinical laboratory tests (CPT codes 80000-89999) and imaging studies (CPT codes 70000-79999) per outpatient encounter for patients with urinary tract infection (UTI, N=97,714) or dysuria (N=125,388). We employed primary ICD-10 codes for UTI (N39.0) and dysuria (R30.0, R30.9, R35.0, R39.15) to define each patient group. We measured the aggregate Clinical Resource Intensity Weight (RIW) per outpatient visit to quantify diagnostic resource consumption for each encounter. The RIW is based on the US Centers for Medicare and Medicaid Services (CMS) Ambulatory Payment Classification (APC) weights. CMS assigns an APC weight to each diagnostic test based on the geometric mean cost of each particular test. We compared diagnostic testing at 20 academically affiliated health systems across the United States (2017- 2019) with >500 annual outpatient visits for UTI and dysuria. We also examined two relevant tests: urinalysis (CPT codes 8100, 81001, 81002, 81003, 81005, 81007, and 81015) and basic urine culture (CPT code 87086) testing patterns. Results (if a Case Study enter NA) The mean number of laboratory tests was 4.30 tests (range 3.04-5.89) and 3.01 (range 2.41-3.86) for UTI and dysuria patients, respectively. Average laboratory RIW and quantity were moderately correlated with a mean RIW of 0.50 and 0.38 for UTI and dysuria patients, respectively. The mean number of imaging studies for each group was 0.26 (range 0.08-0.46) and 0.05 (range 0.01-0.14), respectively. Average imaging RIW and quantity were more highly correlated with a mean RIW of 0.52 and 0.11 for UTI and dysuria patients, respectively. UTI encounters averaged 0.78 (range 0.38-1.28) urinalysis tests and 0.65 (range 0.39-0.86) urine cultures. Dysuria patient visits averaged 0.77 (range 0.45-1.17) urinalysis tests and 0.58 (range 0.13-0.76) urine cultures. Conclusion There are significant differences in diagnostic tests and resource utilization between large health systems for patients with UTI and dysuria. These differences persist with specific test comparisons for urinalysis and urine culture.
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