To evaluate the effect of continuing of angiotensin-converting enzyme inhibitor (ACEI) or angiotensin II receptor blocker (ARB) prescriptions 24 h before surgery on postoperative myocardial injury ...and blood pressure in patients undergoing non-cardiac surgery.
A single-center, retrospective study.
Operating room and perioperative care area.
42,432 patients who had been taking chronic ACEI/ARB underwent non-cardiac surgery from January 2012 to June 2022.
Patients who discontinued ACEI/ARB 24 h before surgery (withheld group, n=31,055) and those who continued ACEI/ARB 24 h before surgery (continued group, n=11,377).
Primary outcome was myocardial injury after non-cardiac surgery (MINS) within 7 days postoperatively. MINS was defined as an elevated postoperative cardiac troponin measurement above the 99th percentile of the upper reference limit with a rise/fall pattern. Perioperative blood pressure and clinical outcomes were secondary outcomes.
Among 42,432 patients, MINS occurred in 2848 patients (6.7%) and was the all-cause of death within 30 days in 122 patients (0.3%). Incidence of MINS was significantly higher in the continued group than the withheld group (847/11,377 7.4% vs. 2001/31,055 6.4%; OR 95% CI 1.17 1.07–1.27; P<0.001). After 1:1 propensity score matching, 11,373 patients were included in each group. There was still a significant difference for the occurrence of MINS between two groups in matched cohort (7.4% vs. 6.6%, OR 95% CI 1.14 1.03–1.26; P=0.015). Time-average weight of mean arterial pressure <65 mmHg during surgery was significantly higher in the continued group (mean 0.11 vs. 0.09 95% CI of mean difference 0.01–0.03; P<0.001). However, there was no significant difference in other clinical outcomes and mortality.
Withholding ACEI/ARB before surgery was associated with a reduced risk of intraoperative hypotension and postoperative myocardial injury, but it did not affect overall clinical outcomes in patients undergoing non-cardiac surgery.
•Discontinuation of ACEI/ARB on the day of surgery is recommended by guidelines•Taking ACEI/ARB 24 h before surgery was associated with intraoperative hypotension•Postoperative myocardial injury was more common in patients continuing ACEI/ARB•Myocardial infarction, cerebral vascular event and 30 days mortality were not different
Research Objective
To predict occurrence of preventable patient safety events using only data elements that are almost universally supported in EHR systems.
Study Design
We drew a random sample was ...drawn from the EHR records of adult patients not restricted to any particular age range or diagnosis and therefore representative of a real‐world health system population. This sample was subdivided into a developmental and a test subset. We used the Rockwood Deficit Accumulation method to construct a frailty index from ICD10 codes, lab value‐flags, and vital signs on a per‐visit basis with a rolling two‐year time‐window (EFI, electronic frailty index). The Cox proportional hazard model was used to model days elapsed from a randomly selected visit to the first occurrence of the selected outcomes (which are also based on routinely available EHR data elements). In contrast to previous studies, we treated EFI as a time‐varying predictor with multiple follow‐ups per patient, which is more realistic than relying on one static time‐point. We used a representative sample of the adult patient population rather than limiting it to older individuals and found EFI to be a useful metric even at relatively young ages.
Population Studied
We accessed EHR data for 14,844 patients randomly sampled from our academic health center's data warehouse which supports our ACT/SHRINE node and is regularly updated from our institution's EHR system.
Principal Findings
We found that this electronic frailty index was robustly predictive of ED utilization, hospitalization, discharge from hospital to SNF/ICF, hospital readmission, all‐cause mortality, and complications including in‐hospital trauma and infections.
Conclusions
Though currently frailty indexes are most used in geriatrics and gerontology, we find evidence that age‐related declines follow a lifelong trajectory that is observable in patient charts even in younger patients (18–45).
Implications for Policy or Practice
Frailty predicts poor patient outcomes and healthcare services utilization. Accurate assessment of frailty can inform clinical management decisions and assist with anticipating healthcare resource utilization. Deficit accumulation indexes have a much simpler algorithm than most other frailty scores (e.g. HCC‐RAF). Because they capture a large number of conditions, they are robust against missing data and variations between disparate data sources. To further reduce barriers to implementation and accelerate the evolution of this tool, we are making the source code freely available under and open source license. Our next goal will be a side‐by‐side comparison of HCC‐RAF and EFI for predicting the outcomes presented here as well as cost of care. If EFI can be shown to out‐perform CMS risk scores, the implications for value‐based care will be significant.
Primary Funding Source
National Institutes of Health.
Objectives
To compare 90‐day postoperative complication rates between Veterans receiving cataract surgery in VA vs Community Care (CC) during the first year of implementation of the Veterans Choice ...Act.
Data Sources
Fiscal Year (FY) 2015 VA and CC outpatient data from VA’s Corporate Data Warehouse (CDW) 10/01/14‐9/30/15). FY14 data were used to obtain baseline clinical information prior to surgery.
Study Design
Retrospective one‐year study using secondary data to compare 90‐day complication rates following cataract surgery (measured using National Quality Forum (NQF) criteria) in VA vs CC. NQF defines major complications from a specified list of Current Procedural Terminology (CPT) codes. We ran a series of logistic regression models to predict 90‐day complication rates, adjusting for Veterans’ sociodemographic characteristics, comorbidities, preoperative ocular conditions, eye risk group, and type of cataract surgery (classified as routine vs complex).
Data Collection
We linked VA and CC users through patient identifiers obtained from the CDW files. Our sample included all enrolled Veterans who received outpatient cataract surgery either in the VA or through CC during FY15. Cataract surgeries were identified through CPT codes 66 984 (routine) and 66 982 (complex).
Principal Findings
Of the 83,879 cataract surgeries performed in FY15, 31 percent occurred through CC. Undergoing complex surgery and having a high‐risk eye (based on preoperative ocular conditions) were the strongest clinical predictors of 90‐day postoperative complications. Overall, we found low complication rates, ranging from 1.1 percent in low‐risk eyes to 3.6 percent in high‐risk eyes. After adjustment for important confounders (eg, race, rurality, and preoperative ocular conditions), there were no statistically significant differences in 90‐day complication rates between Veterans receiving cataract surgery in VA vs CC.
Conclusions
As more Veterans seek care through CC, future studies should continue to monitor quality of care across the two care settings to help inform VA’s “make vs buy decisions.”
With the rising usage of technology, a tremendous volume of data is being produced in the current scenario. This data contains a lot of personal data and may be given to third parties throughout the ...data mining process. Individual privacy is extremely difficult for the data owner to protect. Privacy-Preservation in Data Mining (PPDM) offers a solution to this problem. Encryption or anonymization have been recommended to preserve privacy in existing research. But encryption has high computing costs, and anonymization may drastically decrease the utility of data. This paper proposed a privacy-preserving strategy based on dimensionality reduction and feature selection. The proposed strategy is based on dimensionality reduction and feature selection that is difficult to reverse. The objective of this paper is to propose a perturbation-based privacy-preserving technique. Here, random projection and principal component analysis are utilized to alter the data. The main reason for this is that the dimension reduction combined with feature selection would cause the records to be perturbed more efficiently. The hybrid approach picks relevant features, decreases data dimensionality, and reduces training time, resulting in improved classification performance as measured by accuracy, kappa statistics, mean absolute error and other metrics. The proposed technique outperforms all other approaches in terms of classification accuracy increasing from 63.13 percent to 68.34 percent, proving its effectiveness in detecting cardiovascular illness. Even in its reduced form, the approach proposed here ensures that the dataset's classification accuracy is improved.
Background
Premature mortality observed among the mentally ill is largely attributable to chronic illnesses. Veterans seen within Veterans Affairs (VA) have a higher prevalence of mental illness than ...the general population but there is limited investigation into the common causes of death of Veterans with mental illnesses.
Objective
To characterize the life expectancy of mentally ill Veterans seen in VA primary care, and to determine the most death rates of combinations of mental illnesses.
Design
Retrospective cohort study of decedents.
Setting/Participants
Veterans seen in VA primary care clinics between 2000 and 2011 were included. Records from the VA Corporate Data Warehouse (CDW) were merged with death information from the National Death Index.
Main Measures
Mental illnesses were determined using ICD9 codes. Direct standardization methods were used to calculate age-adjusted gender and cause-specific death rates per 1000 deaths for patients with and without depression, anxiety, post-traumatic stress disorder (PTSD), substance use disorder (SUD), serious mental illness (SMI), and combinations of those diagnoses.
Key Results
Of the 1,763,982 death records for Veterans with 1 + primary care visit, 556,489 had at least one mental illness. Heart disease and cancer were the two leading causes of death among Veterans with or without a mental illness, accounting for approximately 1 in 4 deaths. Those with SUD (
n
= 204,950) had the lowest mean age at time of death (64 ± 12 years). Among men, the death rates were as follows: SUD (55.9/1000); anxiety (49.1/1000); depression (45.1/1000); SMI (40.3/1000); and PTSD (26.2/1000). Among women, death rates were as follows: SUD (55.8/1000); anxiety (36.7/1000); depression (45.1/1000); SMI (32.6/1000); and PTSD (23.1/1000 deaths). Compared to men (10.8/1000) and women (8.7/1000) without a mental illness, these rates were multiple-fold higher in men and in women with a mental illness. A greater number of mental illness diagnoses was associated with higher death rates among men and women (
p
< 0.0001).
Conclusions
Veterans with mental illnesses, particularly those with SUD, and those with multiple diagnoses, had shorter life expectancy than those without a mental illness. Future studies should examine both patient and systemic sources of disparities in providing chronic illness care to Veterans with a mental illness.
Extensive evidence demonstrates that medical record modernization and a vast amount of available data have not overcome the gap between recommended and delivered care. This study aimed to evaluate ...the use of clinical decision support (CDS) in conjunction with feedback (post-hoc reporting) to improve PONV medication administration compliance and postoperative nausea and vomiting (PONV) outcomes.
Single center, prospective observational study between January 1, 2015, and June 30, 2017.
Perioperative care at a university-affiliated tertiary care center.
57,401 adult patients who received general anesthesia in a non-emergency setting.
A multi-phased intervention that consisted of post-hoc reporting for individual providers by email about PONV occurrences in their patients, followed by directive CDS through preoperative daily case emails that provided therapeutic PONV prophylaxis recommendations based on patients' PONV risk scores.
Compliance with PONV medication recommendations, as well as hospital rates of PONV were measured.
Over the study period, there was a 5.5% (95% CI, 4.2% to 6.4%; p < 0.001) improvement in the compliance of PONV medication administration along with an 8.7% (95% CI, 7.1% to 10.2%, p < 0.001) reduction in PONV rescue medication administration in the PACU. However, there was no statistically or clinically significant reduction in the prevalence of PONV in the PACU. The prevalence of PONV rescue medication administration decreased during the Intervention Rollout Period (odds ratio 0.95 per month; 95% CI, 0.91 to 0.99; p = 0.017), and during the Feedback with CDS Recommendation Period (odds ratio, 0.96 per month; 95% CI, 0.94 to 0.99; p = 0.013).
PONV medication administration compliance modestly improves with CDS in conjunction with post-hoc reporting; however, no improvement in PACU rates of PONV occurred.
•Audit and feedback, combined with a clinical decision support tool, modestly improves compliance with PONV recommendations.•Audit and feedback, combined with a clinical decision support tool, did not reduce the prevalence of PONV in the PACU.•Audit and feedback, alone, do not improve compliance with PONV prophylaxis recommendations.•Compliance with PONV recommendations improved by 5.5%; there was an 8.7% reduction in rescue medication use in the PACU.
Nonsteroidal anti-inflammatory drugs (NSAIDs) and gastro-protective agents should be co-prescribed following a standard clinical practice guideline; however, adherence to this guideline in routine ...practice is unknown. This study applied an association rule model (ARM) to estimate rational NSAIDs and gastro-protective agents use in an outpatient prescriptions dataset.
A database of hospital outpatients from October 1st, 2013 to September 30th, 2015 was searched for any of following drugs: oral antacids (A02A), peptic ulcer and gastro-oesophageal reflux disease drugs (GORD, A02B), and anti-inflammatory and anti-rheumatic products, non-steroids or NSAIDs (M01A). Data including patient demographics, diagnoses, and drug utilization were also retrieved. An association rule model was used to analyze co-prescription of the same drug class (i.e., prescriptions within A02A-A02B, M01A) and between drug classes (A02A-A02B & M01A) using the Apriori algorithm in R. The lift value, was calculated by a ratio of confidence to expected confidence, which gave information about the association between drugs in the prescription.
We identified a total of 404,273 patients with 2,575,331 outpatient visits in 2 fiscal years. Mean age was 48 years and 34% were male. Among A02A, A02B and M01A drug classes, 12 rules of associations were discovered with support and confidence thresholds of 1% and 50%. The highest lift was between Omeprazole and Ranitidine (340 visits); about one-third of these visits (118) were prescriptions to non-GORD patients, contrary to guidelines. Another finding was the concomitant use of COX-2 inhibitors (Etoricoxib or Celecoxib) and PPIs. 35.6% of these were for patients aged less than 60 years with no GI complication and no Aspirin, inconsistent with guidelines.
Around one-third of occasions where these medications were co-prescribed were inconsistent with guidelines. With the rapid growth of health datasets, data mining methods may help assess quality of care and concordance with guidelines and best evidence.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Background
The Veterans Health Administration (VA) recently has been scrutinized for prolonged wait times for routine medical care, including elective outpatient procedures such as colonoscopy. Wait ...times for colonoscopy following positive fecal occult blood test (FOBT) are associated with worse clinical outcomes only if greater than 6 months.
Objective
We aimed to investigate time trends in wait time for outpatient colonoscopy in VA and factors influencing wait time.
Design
Retrospective cohort study using mixed-effects regression of VA administrative data from the Corporate Data Warehouse.
Participants
Veterans who underwent outpatient colonoscopy for positive FOBT in 2008–2015 at 124 VA endoscopy facilities.
Main Measures
The main outcome measure was wait time (in days) between positive FOBT and colonoscopy completion, stratified by year and adjusted for sedation type, year, and potentially influential patient- and facility-level factors.
Key Results
In total, 125,866 outpatient colonoscopy encounters for positive FOBT occurred during the study period. The number of colonoscopies for this indication declined slightly over time (17,586 in 2008 vs. 13,245 in 2015; range 13,425–19,814). In 2008, median wait time across sites was 50 days (interquartile range IQR = 33, 75). There was no secular trend in wait times (2015 median = 52 days, IQR = 34, 77). Examining the adjusted effect of patient- and facility-level factors on wait time, no clinically meaningful difference was found.
Conclusions
Wait times for colonoscopy for positive FOBT have been stable over time. Despite the perception of prolonged VA wait times, wait times for outpatient colonoscopy for positive FOBT are well below the threshold at which clinically meaningful differences in patient outcomes have been observed.
BACKGROUNDThe Centers for Disease Control and Prevention (CDC) recommends specific regimens for chlamydia and dual therapy for gonorrhea to mitigate antimicrobial resistant gonorrhea in the CDC 2015 ...STD Treatment Guidelines. Only limited studies examining adherence to these recommendations have been conducted at private practices in the United States.
METHODSWe used the OptumLabs® Data Warehouse (OLDW), a comprehensive, longitudinal data asset with de-identified persons with linked commercial insurance claims and clinical information, to identify persons aged 15-60 years who had valid nucleic acid amplification testing results demonstrating urogenital or extragenital gonorrhea or chlamydia in 2016-2018. We defined valid lab results as positive or negative. We then assessed the time of their first positive test and the type of treatment within 30 days to determine if there was evidence in the claims record that the CDC-recommended treatment was provided. We defined presumed treatment if the date of treatment was before the date of the positive test within 30 days.
RESULTSAmong 6,476 patients with positive gonorrhea tests and 26,847 patients with positive chlamydia tests only, 34.8% and 64.2% had evidence of receiving the CDC-recommended therapy, respectively. About 11.6% of patients with positive gonorrhea tests with recommended dual treatment and 7.1% of patients with positive chlamydia tests only with recommended chlamydia treatment were presumptively treated.
CONCLUSIONAnalysis of treatment claims and medical records from private settings indicated low rates of recommended gonorrhea and chlamydia treatment. Validation of treatment claims is needed to support further quality of care interventions based on these data.
Business intelligence (BI) systems and tools are deemed to be a transformative source with the potential to contribute to reshaping the way different healthcare organizations' (HCOs) services are ...offered and managed. However, this emerging field of research still appears underdeveloped and fragmented. Hence, this paper aims to reconciling, analyzing and synthesizing different strands of managerial-oriented literature on BI in HCOs and to enhance both theoretical and applied future contributions.
A literature-based framework was developed to establish and guide a three-stage state-of-the-art systematic literature review (SLR). The SLR was undertaken adopting a hybrid methodology that combines a bibliometric and a content analysis.
In total, 34 peer-review articles were included. Results revealed significant heterogeneity in theoretical basis and methodological strategies. Nonetheless, the knowledge structure of this research's stream seems to be primarily composed of five clusters of interconnected topics: (1) decision-making, relevant capabilities and value creation; (2) user satisfaction and quality; (3) process management, organizational change and financial effectiveness; (4) decision-support information, dashboard and key performance indicators; and (5) performance management and organizational effectiveness.
To the authors' knowledge, this is the first SLR providing a business and management-related state-of-the-art on the topic. Besides, the paper offers an original framework disentangling future research directions from each emerged cluster into issues pertaining to BI implementation, utilization and impact in HCOs. The paper also discusses the need of future contributions to explore possible integrations of BI with emerging data-driven technologies (e.g. artificial intelligence) in HCOs, as the role of BI in addressing sustainability challenges.
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