Performance of the Australian hospital-acquired complication algorithm for detecting hospital-onset bloodstream infections.

Evaluation of Hospital-onset Bloodstream Infections in Comparison to Central Line-associated Bloodstream Infections at an Acute, Tertiary Care Hospital

One challenge in transgender research is reliably identifying patients through electronic medical records data, as there is no universal transgender International Classification of Diseases (ICD) code, but rather multiple ICD codes that can be used. To explore the sensitivity and specificity of 5 commonly used ICD codes to identify transgender patients overall and transgender women specifically (assigned male sex at birth) by using data from the Veterans Affairs (VA), the largest integrated health system in the United States. Patients aged ≥18 years were identified via ICD-9 codes 302.5 and 302.6 (Ninth Revision) and ICD-10 codes F64.0, F64.8, and F64.9 (Tenth Revision) using VA health records from 2000 to 2021 and stratified by bilateral orchiectomy status. Detailed chart review was performed on 32 randomly selected patients for each code (half with and half without orchiectomy) to confirm transgender status and to perform descriptive analyses. For each ICD code, rates of confirmed transgender status ranged from 88% to 100% for those with and without an orchiectomy, with the majority being transgender women (consistent with most veterans being assigned male sex at birth). Most transgender women (66%-100%) were undergoing estrogen gender-affirming therapy. The majority of provider-driven entries of transgender status took place from 2011 to 2020, with 75% of entries made from 2011 to 2020, consistent with increased recognition and societal acceptance of this population. False negatives were detected at a rate of 15%. Based upon these 5 ICD codes alone, we estimate that the VA has records for 9,449 to 10,738 transgender individuals. All 5 codes are very sensitive in identifying transgender patients, and the combination of these codes with orchiectomy is extremely sensitive in identifying transgender women, specifically. Major strengths of the study are the use of universal ICD codes and a large patient sample size that spans health records nationally and across multiple decades, potentially making our data more generalizable. The main limitation of this study is that subanalyses were performed on a limited number of patients, which prevented us from capturing all false positives and thus from calculating specificity for each code. Similarly, our true negatives were derived from a small, random subset of the population; as such, our calculation for specificity is an estimate. This study highlights a novel method to identify transgender women and paves the way for further research.

To determine whether the discharge diagnosis codes used within tertiary hospitals accurately identified patients with Giant Cell Arteritis (GCA), as determined by expert opinion at 6 months. The study was performed across three major hospitals within Perth, Western Australia. Patients with an International Classification of Diseases (ICD) code for GCA (M31.5 and M31.6) at discharge on first inpatient presentation were identified. Review of case notes, discharge summaries, letters, pathology, and imaging results were undertaken. The percentage of patients with an ICD code for GCA at initial discharge with confirmed GCA by expert opinion at 6 months (as the anchor diagnosis) was calculated. As validation of the anchor diagnosis, the percentage who fulfilled the 2022 ACR/EULAR criteria was also calculated, the number of hospital discharges per patient with an ICD code for GCA was calculated, to determine if multiple discharges with an ICD code for GCA increased the specificity of the ICD coding. 93 out of 157 admissions with an ICD for GCA were identified as a first inpatient presentation. At 6 months follow up, 65.6%, 95 CI [55.0%, 75.1%] had confirmed GCA by expert opinion. 88.2%, 95 CI [79.8%, 93.9%] met the 2022 ACR/EULAR criteria for GCA. The specificity of the ICD coding increased with increasing number of discharges– 67.4% with single episode, 80% with two episodes, and 100% with three or more episodes (p = 0.1373). Only 43.8%, 95 CI [26.4%, 62.3%] of patients who did not have GCA had an alternative diagnosis provided. 31.3%, 95 CI [16.1%, 50.0%] of patients who did not have GCA still have the GCA diagnosis listed in their subsequent clinical records and discharge summaries. Only 2/3rds of those patients with an initial discharge ICD code for GCA were found to have confirmed GCA at follow-up. This poor correlation between the ICD coding and confirmed diagnosis of GCA will impact the quality of data extracted from administrative health datasets for epidemiological and longitudinal studies. Selecting patients with two or more GCA coded episodes could improve the homogeneity of the cohort for recruitment into GCA studies, but a larger sample size study is required.

Confusion surrounding cardiovascular disease trends arises when measurement and reporting of classification systems such as the International Classification of Diseases (ICD) code are revised. The current study examined the impact of ICD code revision on mortality trends for heart disease, cerebrovascular disease, and diabetes in 16 Southeast states using data published in the 1994-2005 Centers for Disease Control National Vital Statistics Reports. Data were averaged by year and analyzed separately before (1994-1998) and after (1999-2005) the ICD code change, pooled across codes using comparability ratios (1994-2005), and further compared at the year of ICD code change using standard error of 1998 data to determine whether corresponding 1999 rates fell within 95% confidence intervals. The change in classification did not alter Southeast US trends regarding a decrease in heart disease and cerebrovascular disease rates and an increase in diabetes mortality in years 1994-2005. On the other hand, the change in ICD code classification systems did impact mortality rates for heart disease, cerebrovascular disease, and diabetes, suggesting that change in code to ICD-10 in 1999 underestimates heart disease and cerebrovascular disease and overestimates diabetes mortality rates in the Southeast United States. Health and disease burden profiles, which use mortality data to measure health status, need to carefully evaluate and report the influence of ICD code revisions as they draw conclusions. Primary care health providers should question the impact and comparability of ICD revision before accepting mortality trend conclusions.

AimsHeart failure (HF) is a clinical syndrome with no definitive diagnostic tests. HF registries are often based on manual reviews of medical records of hospitalized HF patients identified using International Classification of Diseases (ICD) codes. However, most HF patients are not hospitalized, and manual review of big electronic health record (EHR) data is not practical. The US Department of Veterans Affairs (VA) has the largest integrated healthcare system in the nation, and an estimated 1.5 million patients have ICD codes for HF (HF ICD‐code universe) in their VA EHR. The objective of our study was to develop artificial intelligence (AI) models to phenotype HF in these patients.Methods and resultsThe model development cohort (n = 20 000: training, 16 000; validation 2000; testing, 2000) included 10 000 patients with HF and 10 000 without HF who were matched by age, sex, race, inpatient/outpatient status, hospital, and encounter date (within 60 days). HF status was ascertained by manual chart reviews in VA's External Peer Review Program for HF (EPRP‐HF) and non‐HF status was ascertained by the absence of ICD codes for HF in VA EHR. Two clinicians annotated 1000 random snippets with HF‐related keywords and labelled 436 as HF, which was then used to train and test a natural language processing (NLP) model to classify HF (positive predictive value or PPV, 0.81; sensitivity, 0.77). A machine learning (ML) model using linear support vector machine architecture was trained and tested to classify HF using EPRP‐HF as cases (PPV, 0.86; sensitivity, 0.86). From the ‘HF ICD‐code universe’, we randomly selected 200 patients (gold standard cohort) and two clinicians manually adjudicated HF (gold standard HF) in 145 of those patients by chart reviews. We calculated NLP, ML, and NLP + ML scores and used weighted F scores to derive their optimal threshold values for HF classification, which resulted in PPVs of 0.83, 0.77, and 0.85 and sensitivities of 0.86, 0.88, and 0.83, respectively. HF patients classified by the NLP + ML model were characteristically and prognostically similar to those with gold standard HF. All three models performed better than ICD code approaches: one principal hospital discharge diagnosis code for HF (PPV, 0.97; sensitivity, 0.21) or two primary outpatient encounter diagnosis codes for HF (PPV, 0.88; sensitivity, 0.54).ConclusionsThese findings suggest that NLP and ML models are efficient AI tools to phenotype HF in big EHR data to create contemporary HF registries for clinical studies of effectiveness, quality improvement, and hypothesis generation.

The national hospital-acquired complication programme captures complications arising from patient-related and hospital-related factors, but the proportion of the two is unclear. Health services are encouraged to evaluate data from the national hospital-acquired complications (HAC) programme and identify strategies to mitigate them. A retrospective chart review compared HAC extracted from administrative data. The setting was a 430-bed university-affiliated metropolitan hospital. Records from 260 participants with, and 462 without, reported HAC from 2619 multi-day stay adults were reviewed. The main outcome measures were prevalence and positive predictive value (PPV) of HAC methodology. No errors of HAC coding or classification were identified. Four hundred and twenty-three HAC events were reported in 260 records; most commonly delirium (n = 57; 13.4%), pneumonia (n = 46; 10.9%), blood stream infection (n = 39; 9.2%), hypoglycaemia (n = 33; 7.8%) and cardiac arrhythmias (n = 33; 7.8%). One hundred and eight (25.5%) 'HAC' events in 69 separations (95% confidence interval (CI) = 2.05-3.33 per 100 separations) were false positive, and 43 of 462 (95% CI=6.72-12.22 per 100 separations) were false negative. Prevalence of total (reported plus missing) HAC was 16.06 (95% CI=14.02-19.52), reported HAC was 9.93 (95% CI=8.76-11.21), potentially preventable HAC was 1.68 (95% CI=1.22-2.26) and healthcare errors was 0.31 (95% CI=0.13-1.30) per 100 separations. PPV of HAC for true clinical events was 0.74 (0.68-0.79), preventable events 0.18 (0.13-0.23) and healthcare error 0.03 (0.01-0.06). Prevalence of HAC events was higher than expected, but PPV for healthcare errors was low, suggesting provision of care is a less common cause of HAC events than patient factors. HAC may be an indicator of hospital admission complexity rather than HAC.

Defining suspected child maltreatment using International Classification of Diseases codes: A scoping literature review

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related Adverse Events (irAEs). Estimating prevalence and patient impact of these irAEs in the Real-World Data...

The objective was to assess the predictive value of weight-for-age to identify overweight children and adolescents in the unusual research or public health situations where height is not available to calculate BMI. Data from the National Health and Nutrition Examination Survey 1999 to 2004 were used to calculate the sensitivity, specificity, and positive and negative predictive values of selected weight-for-age cut-off points to identify overweight children and adolescents (as defined by BMI >or=95th percentile). Positive and negative predictive values are dependent on prevalence and are reported here for this study population only. The 50th and 75th weight-for-age percentiles had good sensitivity (100% and 99.6%, respectively), but poor positive predictive value (23.7% and 37.0%, respectively), while the 95th and 97th percentiles had reasonable positive predictive value (80.3% and 91.5%, respectively), but limited sensitivity (82.0% and 66.7%, respectively) to identify overweight subjects. The properties of weight-for-age percentiles to identify overweight subjects differed between sex, age, and race/ethnicity but remain within a relatively narrow range. No single weight-for-age cut-off point was found to identify overweight children and adolescents with acceptable values for all properties and, therefore, cannot be used in the clinical setting. Furthermore, the positive predictive values reported here may be lower in populations with a lower prevalence of obesity. However, in unusual research or public health situations where height is not available, such as existing databases, weight-for-age percentiles may be useful to target limited resources to groups more likely to include overweight children and adolescents than the general population.

IntroductionObservational studies constitute an important evidence base for hypoglycemia in diabetes management. This requires consistent and reliable ascertainment and reporting methodology, particularly in studies of type 2 diabetes where hypoglycemia...

Recognition of congenital heart disease in subsequent hospital visits among children with a prenatal diagnosis

23. Natural language processing for automated identification of intraoperative vascular injury in anterior lumbar spine surgery

To evaluate the phenotyping performance of three major electronic health record (EHR) components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications. We conducted the evaluation using de-identified Vanderbilt EHR data. We preselected ten diseases: atrial fibrillation, Alzheimer's disease, breast cancer, gout, human immunodeficiency virus infection, multiple sclerosis, Parkinson's disease, rheumatoid arthritis, and types 1 and 2 diabetes mellitus. For each disease, patients were classified into seven categories based on the presence of evidence in diagnosis codes, primary notes, and specific medications. Twenty-five patients per disease category (a total number of 175 patients for each disease, 1750 patients for all ten diseases) were randomly selected for manual chart review. Review results were used to estimate the positive predictive value (PPV), sensitivity, andF-score for each EHR component alone and in combination. The PPVs of single components were inconsistent and inadequate for accurately phenotyping (0.06-0.71). Using two or more ICD codes improved the average PPV to 0.84. We observed a more stable and higher accuracy when using at least two components (mean ± standard deviation: 0.91 ± 0.08). Primary notes offered the best sensitivity (0.77). The sensitivity of ICD codes was 0.67. Again, two or more components provided a reasonably high and stable sensitivity (0.59 ± 0.16). Overall, the best performance (Fscore: 0.70 ± 0.12) was achieved by using two or more components. Although the overall performance of using ICD codes (0.67 ± 0.14) was only slightly lower than using two or more components, its PPV (0.71 ± 0.13) is substantially worse (0.91 ± 0.08). Multiple EHR components provide a more consistent and higher performance than a single one for the selected phenotypes. We suggest considering multiple EHR components for future phenotyping design in order to obtain an ideal result.

Objectives: To evaluate the clinical characteristics, hospital courses, outcomes after hospitalization, and factors associated with outcomes in patients with nontraumatic spinal cord injuries (NTSCI). Design: Retrospective analysis. Setting: A large for-profit United States health care system. Participants: 2807 inpatients with NTSCI between 2014 and 2020 were identified using International Classification of Disease codes. Main Outcome Measure: Demographic, clinical characteristics, hospital course, and disposition data collected from electronic health record. Results: The mean age was 57.91 ± 16.41 years with 69.83% being male. Incomplete cervical level injury was the most common injury type, spinal stenosis was the most common diagnostic etiology and central cord syndrome was the most common clinical syndrome. The average length of stay was 9.52 ± 15.8 days, with the subgroup of 1308 (46.6%) patients who were discharged home demonstrating a shorter length of stay (6.42 ± 10.24 days). Falls were the most common hospital-acquired complication (n = 424, 15.11%) and 83 patients deceased. There were increased odds of non-home discharge among patients with the following characteristics: older age, Medicare insurance, non-black racial minority, increased Charlson Comorbidity Index (CCI), intensive care unit (ICU) stay, use of steroid or anticoagulant medications, and hospital-acquired pulmonary complications. Increased in-hospital mortality was observed in those with Medicaid insurance, ICU stay, increased CCI, diagnosis of degenerative spine disease, other unspecified level of injury, and hospital-acquired pulmonary complications. Conclusions: NTSCI in this sample were predominantly incomplete cervical central SCIs. Increased CCI, ICU stay, and hospital-acquired pulmonary complications were associated with poorer outcomes after acute care hospitalization among patients with NTSCI.

DRCNNTLe: A deep recurrent convolutional neural network with transfer learning through pre-trained embeddings for automated ICD coding.