Discordance of databases designed for claims payment versus clinical information systems. Implications for outcomes research

Care Fragmentation, Social Determinants of Health, and Postoperative Mortality in Older Veterans

INTRODUCTION

Veterans Affairs Surgical Quality Improvement Program (VASQIP) benchmarking algorithms helped the Veterans Health Administration (VHA) reduce postoperative mortality. Despite calls to consider social risk factors, these algorithms do not adjust for social determinants of health (SDoH) or account for services fragmented between the VHA and the private sector. This investigation examines how the addition of SDoH change model performance and quantifies associations between SDoH and 30-d postoperative mortality.

METHODS

VASQIP (2013-2019) cohort study in patients ≥65 y old with 2-30-d inpatient stays. VASQIP was linked to other VHA and Medicare/Medicaid data. 30-d postoperative mortality was examined using multivariable logistic regression models, adjusting first for clinical variables, then adding SDoH.

RESULTS

In adjusted analyses of 93,644 inpatient cases (97.7% male, 79.7% non-Hispanic White), higher proportions of non-veterans affairs care (adjusted odds ratio [aOR] = 1.02, 95% CI = 1.01-1.04) and living in highly deprived areas (aOR = 1.15, 95% CI = 1.02-1.29) were associated with increased postoperative mortality. Black race (aOR = 0.77, CI = 0.68-0.88) and rurality (aOR = 0.87, CI = 0.79-0.96) were associated with lower postoperative mortality. Adding SDoH to models with only clinical variables did not improve discrimination (c = 0.836 versus c = 0.835).

CONCLUSIONS

Postoperative mortality is worse among Veterans receiving more health care outside the VA and living in highly deprived neighborhoods. However, adjusting for SDoH is unlikely to improve existing mortality-benchmarking models. Reduction efforts for postoperative mortality could focus on alleviating care fragmentation and designing care pathways that consider area deprivation. The adjusted survival advantage for rural and Black Veterans may be of interest to private sector hospitals as they attempt to alleviate enduring health-care disparities.

Atopy improves survival and decreases risk of brain metastasis in cutaneous melanoma

Importance

Development of new therapies in melanoma has increased survival, and as a result more patients are living to develop brain metastasis (BrM). Identifying patients at increased risk of BrM is therefore of significant public health importance.

Objective

To determine whether history of atopy is associated with improved survival or reduced incidence of BrM in cutaneous melanoma.

Design

A retrospective cohort study conducted from June 2022 to March 2024.

Setting

Population-based in states with Surveillance, Epidemiology and End Results (SEER) supported cancer registries.

Participants

Individuals (≥65 years) diagnosed with cutaneous melanoma between January 1, 2008 and December 31, 2017 that are participants in traditional Medicare.

Exposures

Individuals were compared that had history of atopy (allergic rhinitis, atopic dermatitis, asthma, and/or allergic/atopic conjunctivitis) diagnosed prior to melanoma diagnosis, ascertained using ICD-9 or ICD-10 codes in Medicare claims.

Main Outcomes and Measures

Primary endpoints were diagnosis with a BrM or death during the follow-up period. Associations between atopy and endpoints were assessed using cox proportional hazards models to estimate hazard ratios (HR) and p-values.

Results

A total of 29,956 cutaneous melanoma cases were identified (median age 76, 60% male and 97% non-Hispanic White). Overall, 7.1% developed BrM during follow up. Among the 35% that had history of atopy, the most common condition was atopic dermatitis (19%). After adjustment for demographic and prognostic factors, atopy was associated with a 16% decrease in death (HR=0.84 [95%CI:0.80-0.87], pFDR<0.001). Among those with non-metastatic disease at time of diagnosis, atopy conferred a 15% decrease in cumulative incidence BrM (HR=0.85 [95%CI: 0.76-0.94], pFDR=0.006), with a 25% decrease associated with atopic dermatitis (HR=0.75 [95%CI:0.65-0.86], pFDR<0.001). Among those with metastatic disease at diagnosis (any metastatic site), only those who received immune checkpoint inhibitors had a survival benefit associated with atopy (HR=0.31, [95%CI:0.15-0.64], p=0.001 vs HR=1.41, [95%CI:0.87-2.27], p=0.165).

Conclusions and Relevance

Atopy, particularly atopic dermatitis, was significantly associated with improved survival and decreased incidence of BrM. The improved survival associated with these conditions in the context of immunotherapy suggests that these conditions in the elderly may identify those with more robust immune function that may be more responsive to treatment.

Presentation Acuity and Surgical Outcomes for Patients With Health Insurance Living in Highly Deprived Neighborhoods

Importance

Insurance coverage expansion has been proposed as a solution to improving health disparities, but insurance expansion alone may be insufficient to alleviate care access barriers.

Objective

To assess the association of Area Deprivation Index (ADI) with postsurgical textbook outcomes (TO) and presentation acuity for individuals with private insurance or Medicare.

Design, Setting, and Participants

This cohort study used data from the National Surgical Quality Improvement Program (2013-2019) merged with electronic health record data from 3 academic health care systems. Data were analyzed from June 2022 to August 2023.

Exposure

Living in a neighborhood with an ADI greater than 85.

Main Outcomes and Measures

TO, defined as absence of unplanned reoperations, Clavien-Dindo grade 4 complications, mortality, emergency department visits/observation stays, and readmissions, and presentation acuity, defined as having preoperative acute serious conditions (PASC) and urgent or emergent cases.

Results

Among a cohort of 29 924 patients, the mean (SD) age was 60.6 (15.6) years; 16 424 (54.9%) were female, and 13 500 (45.1) were male. A total of 14 306 patients had private insurance and 15 618 had Medicare. Patients in highly deprived neighborhoods (5536 patients [18.5%]), with an ADI greater than 85, had lower/worse odds of TO in both the private insurance group (adjusted odds ratio [aOR], 0.87; 95% CI, 0.76-0.99; P = .04) and Medicare group (aOR, 0.90; 95% CI, 0.82-1.00; P = .04) and higher odds of PASC and urgent or emergent cases. The association of ADIs greater than 85 with TO lost significance after adjusting for PASC and urgent/emergent cases. Differences in the probability of TO between the lowest-risk (ADI ≤85, no PASC, and elective surgery) and highest-risk (ADI >85, PASC, and urgent/emergent surgery) scenarios stratified by frailty were highest for very frail patients (Risk Analysis Index ≥40) with differences of 40.2% and 43.1% for those with private insurance and Medicare, respectively.

Conclusions and Relevance

This study found that patients living in highly deprived neighborhoods had lower/worse odds of TO and higher presentation acuity despite having private insurance or Medicare. These findings suggest that insurance coverage expansion alone is insufficient to overcome health care disparities, possibly due to persistent barriers to preventive care and other complex causes of health inequities.

Accuracy of heart failure ascertainment using routinely collected healthcare data: a systematic review and meta-analysis

BACKGROUND

Ascertainment of heart failure (HF) hospitalizations in cardiovascular trials is costly and complex, involving processes that could be streamlined by using routinely collected healthcare data (RCD). The utility of coded RCD for HF outcome ascertainment in randomized trials requires assessment. We systematically reviewed studies assessing RCD-based HF outcome ascertainment against "gold standard" (GS) methods to study the feasibility of using such methods in clinical trials.

METHODS

Studies assessing International Classification of Disease (ICD) coded RCD-based HF outcome ascertainment against GS methods and reporting at least one agreement statistic were identified by searching MEDLINE and Embase from inception to May 2021. Data on study characteristics, details of RCD and GS data sources and definitions, and test statistics were reviewed. Summary sensitivities and specificities for studies ascertaining acute and prevalent HF were estimated using a bivariate random effects meta-analysis. Heterogeneity was evaluated using I2 statistics and hierarchical summary receiver operating characteristic (HSROC) curves.

RESULTS

A total of 58 studies of 48,643 GS-adjudicated HF events were included in this review. Strategies used to improve case identification included the use of broader coding definitions, combining multiple data sources, and using machine learning algorithms to search free text data, but these methods were not always successful and at times reduced specificity in individual studies. Meta-analysis of 17 acute HF studies showed that RCD algorithms have high specificity (96.2%, 95% confidence interval [CI] 91.5-98.3), but lacked sensitivity (63.5%, 95% CI 51.3-74.1) with similar results for 21 prevalent HF studies. There was considerable heterogeneity between studies.

CONCLUSIONS

RCD can correctly identify HF outcomes but may miss approximately one-third of events. Methods used to improve case identification should also focus on minimizing false positives.

Use of Electronic Health Records to Characterize Patients with Uncontrolled Hypertension in Two Large Health System Networks

Background

Improving hypertension control is a public health priority. However, consistent identification of uncontrolled hypertension using computable definitions in electronic health records (EHR) across health systems remains uncertain.

Methods

In this retrospective cohort study, we applied two computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥ 18 years) with hypertension receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single blood pressure (BP) measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit).

Results

Overall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p < 0.001; 49.7% versus 41.2% in OneFlorida; p < 0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p = 0.04; 42.2% versus 34.8% in OneFlorida; p < 0.001). Patients with controlled and uncontrolled hypertension had similar rates of short-term (at 3 and 6 months) and long-term (at 12 and 24 months) clinical outcomes. The two computable definitions generated consistent results.

Conclusions

Our findings illustrate the potential of leveraging EHR data, employing computable definitions, to conduct effective digital population surveillance in the realm of hypertension management.

The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis

BACKGROUND

Smoking is a risk factor for many chronic diseases. Multiple smoking status ascertainment algorithms have been developed for population-based electronic health databases such as administrative databases and electronic medical records (EMRs). Evidence syntheses of algorithm validation studies have often focused on chronic diseases rather than risk factors. We conducted a systematic review and meta-analysis of smoking status ascertainment algorithms to describe the characteristics and validity of these algorithms.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. We searched articles published from 1990 to 2022 in EMBASE, MEDLINE, Scopus, and Web of Science with key terms such as validity, administrative data, electronic health records, smoking, and tobacco use. The extracted information, including article characteristics, algorithm characteristics, and validity measures, was descriptively analyzed. Sources of heterogeneity in validity measures were estimated using a meta-regression model. Risk of bias (ROB) in the reviewed articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

The initial search yielded 2086 articles; 57 were selected for review and 116 algorithms were identified. Almost three-quarters (71.6%) of algorithms were based on EMR data. The algorithms were primarily constructed using diagnosis codes for smoking-related conditions, although prescription medication codes for smoking treatments were also adopted. About half of the algorithms were developed using machine-learning models. The pooled estimates of positive predictive value, sensitivity, and specificity were 0.843, 0.672, and 0.918 respectively. Algorithm sensitivity and specificity were highly variable and ranged from 3 to 100% and 36 to 100%, respectively. Model-based algorithms had significantly greater sensitivity (p = 0.006) than rule-based algorithms. Algorithms for EMR data had higher sensitivity than algorithms for administrative data (p = 0.001). The ROB was low in most of the articles (76.3%) that underwent the assessment.

CONCLUSIONS

Multiple algorithms using different data sources and methods have been proposed to ascertain smoking status in electronic health data. Many algorithms had low sensitivity and positive predictive value, but the data source influenced their validity. Algorithms based on machine-learning models for multiple linked data sources have improved validity.

A Surgical Desirability of Outcome Ranking (DOOR) Reveals Complex Relationships Between Race/Ethnicity, Insurance Type, and Neighborhood Deprivation

OBJECTIVE

Develop an ordinal Desirability of Outcome Ranking (DOOR) for surgical outcomes to examine complex associations of Social Determinants of Health.

BACKGROUND

Studies focused on single or binary composite outcomes may not detect health disparities.

METHODS

Three health care system cohort study using NSQIP (2013-2019) linked with EHR and risk-adjusted for frailty, preoperative acute serious conditions (PASC), case status and operative stress assessing associations of multilevel Social Determinants of Health of race/ethnicity, insurance type (Private 13,957; Medicare 15,198; Medicaid 2835; Uninsured 2963) and Area Deprivation Index (ADI) on DOOR and the binary Textbook Outcomes (TO).

RESULTS

Patients living in highly deprived neighborhoods (ADI>85) had higher odds of PASC [adjusted odds ratio (aOR)=1.13, CI=1.02-1.25, P <0.001] and urgent/emergent cases (aOR=1.23, CI=1.16-1.31, P <0.001). Increased odds of higher/less desirable DOOR scores were associated with patients identifying as Black versus White and on Medicare, Medicaid or Uninsured versus Private insurance. Patients with ADI>85 had lower odds of TO (aOR=0.91, CI=0.85-0.97, P =0.006) until adjusting for insurance. In contrast, patients with ADI>85 had increased odds of higher DOOR (aOR=1.07, CI=1.01-1.14, P <0.021) after adjusting for insurance but similar odds after adjusting for PASC and urgent/emergent cases.

CONCLUSIONS

DOOR revealed complex interactions between race/ethnicity, insurance type and neighborhood deprivation. ADI>85 was associated with higher odds of worse DOOR outcomes while TO failed to capture the effect of ADI. Our results suggest that presentation acuity is a critical determinant of worse outcomes in patients in highly deprived neighborhoods and without insurance. Including risk adjustment for living in deprived neighborhoods and urgent/emergent surgeries could improve the accuracy of quality metrics.

The Norton scale is an important predictor of in-hospital mortality in internal medicine patients

BACKGROUND

The Norton scale, a marker of patient frailty used to predict the risk of pressure ulcers, but the predictive value of the Norton scale for in-hospital mortality after adjustment for a wide range of demographic, and abnormal admission laboratory test results shown in themselves to have a high predictive value for in-hospital mortality is unclear.

AIM

The study aims to determine the value of the Norton scale and the presence of a urinary catheter in predicting in hospital mortality.

METHODS

The study population included all acutely admitted adult patients in 2020 through October 2021 to one of three internal medicine departments at the Laniado Hospital, a regional hospital with 400 beds in Israel. The main objective was to (a) identify the variables associated with the Norton Scale and (b) determine whether it predicts in-hospital mortality after adjustment for these variables.

RESULTS

The Norton scale was associated with an older age, female gender, presence of a urinary catheter, and abnormal laboratory tests. The odds of in-hospital mortality in those with intermediate, high, and very high Norton scale risk groups were 3.10 (2.23-3.56), 6.48 (4.02-10.46), and 12.27 (7.37-20.44), respectively, after adjustment for the remaining predictors. Adding the Norton scale and the presence of a urinary catheter to the prediction logistic regression model that included age, gender, and abnormal laboratory test results increased the c-statistic from 0.870 (0.864-0.876) to 0.908 (0.902-0.913).

CONCLUSIONS

The Norton scale and presence of a urinary catheter are important predictors of in-hospital mortality in acutely hospitalized adults in internal medicine departments.

Use of Electronic Health Records to Characterize Patients with Uncontrolled Hypertension in Two Large Health System Networks

Background

Improving hypertension control is a public health priority. However, uncertainty remains regarding the optimal way to identify patients with uncontrolled hypertension using electronic health records (EHR) data.

Methods

In this retrospective cohort study, we applied computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥18 years) with hypertension receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single blood pressure (BP) measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit).

Results

Overall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p<0.001; 49.7% versus 41.2% in OneFlorida; p<0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p=0.04; 42.2% versus 34.8% in OneFlorida; p<0.001). Patients with controlled and uncontrolled hypertension had similar rates of short-term (at 3 and 6 months) and long-term (at 12 and 24 months) clinical outcomes. The two computable definitions generated consistent results.

Conclusions

Computable definitions can be successfully applied to health system EHR data to conduct population surveillance for hypertension and identify patients with uncontrolled hypertension who may benefit from additional treatment.

Association of Insurance Type with Inpatient Surgical 30-day Readmissions, Emergency Department Visits/Observation Stays, and Costs

OBJECTIVE

To assess the association of Private, Medicare, and Medicaid/Uninsured insurance type with 30-day Emergency Department visits/Observation Stays (EDOS), readmissions, and costs in a safety-net hospital (SNH) serving diverse socioeconomic status patients.

SUMMARY BACKGROUND DATA

Medicare's Hospital Readmission Reduction Program (HRRP) disproportionately penalizes SNHs.

METHODS

This retrospective cohort study used inpatient National Surgical Quality Improvement Program (2013-2019) data merged with cost data. Frailty, expanded Operative Stress Score, case status, and insurance type were used to predict odds of EDOS and readmissions, as well as index hospitalization costs.

RESULTS

The cohort had 1,477 Private; 1,164 Medicare; and 3,488 Medicaid/Uninsured cases with a patient mean age 52.1 years [SD=14.7] and 46.8% of the cases were performed on male patients. Medicaid/Uninsured (aOR=2.69, CI=2.38-3.05, P<.001) and Medicare (aOR=1.32, CI=1.11-1.56, P=.001) had increased odds of urgent/emergent surgeries and complications versus Private patients. Despite having similar frailty distributions, Medicaid/Uninsured compared to Private patients had higher odds of EDOS (aOR=1.71, CI=1.39-2.11, P<.001), and readmissions (aOR=1.35, CI=1.11-1.65, P=.004), after adjusting for frailty, OSS, and case status, while Medicare patients had similar odds of EDOS and readmissions versus Private. Hospitalization variable cost %change was increased for Medicare (12.5%) and Medicaid/Uninsured (5.9%), but Medicaid/Uninsured was similar to Private after adjusting for urgent/emergent cases.

CONCLUSIONS

Increased rates and odds of urgent/emergent cases in Medicaid/Uninsured patients drive increased odds of complications and index hospitalization costs versus Private. SNHs care for higher cost populations while receiving lower reimbursements and are further penalized by the unintended consequences of HRRP. Increasing access to care, especially for Medicaid/Uninsured patients, could reduce urgent/emergent surgeries resulting in fewer complications, EDOS/readmissions, and costs.

Independent Associations of Neighborhood Deprivation and Patient-level Social Determinants of Health with Textbook Outcomes after Inpatient Surgery

Objective

Assess associations of Social Determinants of Health (SDoH) using Area Deprivation Index (ADI), race/ethnicity and insurance type with Textbook Outcomes (TO).

Summary Background Data

Individual- and contextual-level SDoH affect health outcomes, but only one SDoH level is usually included.

Methods

Three healthcare system cohort study using National Surgical Quality Improvement Program (2013-2019) linked with ADI risk-adjusted for frailty, case status and operative stress examining TO/TO components (unplanned reoperations, complications, mortality, Emergency Department/Observation Stays and readmissions).

Results

Cohort (34,251 cases) mean age 58.3 [SD=16.0], 54.8% females, 14.1% Hispanics, 11.6% Non-Hispanic Blacks, 21.6% with ADI>85, and 81.8% TO. Racial and ethnic minorities, non-Private insurance, and ADI>85 patients had increased odds of urgent/emergent surgeries (aORs range: 1.17-2.83, all P<.001). Non-Hispanic Black patients, ADI>85 and non-Private insurances had lower TO odds (aORs range: 0.55-0.93, all P<.04), but ADI>85 lost significance after including case status. Urgent/emergent versus elective had lower TO odds (aOR=0.51, P<.001). ADI>85 patients had higher complication and mortality odds. Estimated reduction in TO probability was 9.9% (CI=7.2%-12.6%) for urgent/emergent cases, 7.0% (CI=4.6%-9.3%) for Medicaid, and 1.6% (CI=0.2%-3.0%) for non-Hispanic Black patients. TO probability difference for lowest-risk (White-Private-ADI≤85-elective) to highest-risk (Black-Medicaid-ADI>85-urgent/emergent) was 29.8% for very frail patients.

Conclusion

Multi-level SDoH had independent effects on TO, predominately affecting outcomes through increased rates/odds of urgent/emergent surgeries driving complications and worse outcomes. Lowest-risk versus highest-risk scenarios demonstrated the magnitude of intersecting SDoH variables. Combination of insurance type and ADI should be used to identify high-risk patients to redesign care pathways to improve outcomes. Risk adjustment including contextual neighborhood deprivation and patient-level SDoH could reduce unintended consequences of value-based programs.

Developing an Actionable Taxonomy of Persistent Hypertension Using Electronic Health Records

BACKGROUND

The digital transformation of medical data presents opportunities for novel approaches to manage patients with persistent hypertension. We sought to develop an actionable taxonomy of patients with persistent hypertension (defined as 5 or more consecutive measurements of blood pressure ≥160/100 mmHg over time) based on data from the electronic health records.

METHODS

This qualitative study was a content analysis of clinician notes in the electronic health records of patients in the Yale New Haven Health System. Eligible patients were 18 to 85 years and had blood pressure ≥160/100 mmHg at 5 or more consecutive outpatient visits between January 1, 2013 and October 31, 2018. A total of 1664 patients met criteria, of which 200 records were randomly selected for chart review. Through a systematic, inductive approach, we developed a rubric to abstract data from the electronic health records and then analyzed the abstracted data qualitatively using conventional content analysis until saturation was reached.

RESULTS

We reached saturation with 115 patients, who had a mean age of 66.0 (SD, 11.6) years; 54.8% were female; 52.2%, 30.4%, and 13.9% were White, Black, and Hispanic patients. We identified 3 content domains related to persistence of hypertension: (1) non-intensification of pharmacological treatment, defined as absence of antihypertensive treatment intensification in response to persistent severely elevated blood pressure; (2) non-implementation of prescribed treatment, defined as a documentation of provider recommending a specified treatment plan to address hypertension but treatment plan not being implemented; and (3) non-response to prescribed treatment, defined as clinician-acknowledged persistent hypertension despite documented effort to escalate existing pharmacologic agents and addition of additional pharmacologic agents with presumption of adherence.

CONCLUSIONS

This study presents a novel actionable taxonomy for classifying patients with persistent hypertension by their contributing causes based on electronic health record data. These categories can be automated and linked to specific types of actions to address them.

Cost of Failure to Achieve Textbook Outcomes: Association of Insurance Type with Outcomes and Cumulative Cost for Inpatient Surgery

BACKGROUND

Surgical outcome/cost analyses typically focus on single outcomes and do not include encounters beyond the index hospitalization.

STUDY DESIGN

This cohort study used NSQIP (2013-2019) data with electronic health record and cost data risk-adjusted for frailty, preoperative acute serious conditions (PASC), case status, and operative stress assessing cumulative costs of failure to achieve textbook outcomes defined as absence of 30-day Clavien-Dindo level III and IV complications, emergency department visits/observation stays (EDOS), and readmissions across insurance types (private, Medicare, Medicaid, uninsured). Return costs were defined as costs of all 30-day emergency department visits/observation stays and readmissions.

RESULTS

Cases were performed on patients (private 1,506; Medicare 1,218; Medicaid 1,420; uninsured 2,178) with a mean age 52.3 years (SD 14.7) and 47.5% male. Medicaid and uninsured patients had higher odds of presenting with preoperative acute serious conditions (adjusted odds ratios 1.89 and 1.81, respectively) and undergoing urgent/emergent surgeries (adjusted odds ratios 2.23 and 3.02, respectively) vs private. Medicaid and uninsured patients had lower odds of textbook outcomes (adjusted odds ratios 0.53 and 0.78, respectively) and higher odds of emergency department visits/observation stays and readmissions vs private. Not achieving textbook outcomes was associated with a greater than 95.1% increase in cumulative costs. Medicaid patients had a relative increase of 23.1% in cumulative costs vs private, which was 18.2% after adjusting for urgent/emergent cases. Return costs were 37.5% and 65.8% higher for Medicaid and uninsured patients, respectively, vs private.

CONCUSIONS

Higher costs for Medicaid patients were partially driven by increased presentation acuity (increased rates/odds of preoperative acute serious conditions and urgent/emergent surgeries) and higher rates of multiple emergency department visits/observation stays and readmission occurrences. Decreasing surgical costs/improving outcomes should focus on reducing urgent/emergent surgeries and improving postoperative care coordination, especially for Medicaid and uninsured populations.

The presence of a urinary catheter is an important predictor of in-hospital mortality in internal medicine patients

RATIONALE AND OBJECTIVE

Mortality rates are used to assess the quality of hospital care after appropriate adjustment for case-mix. Urinary catheters are frequent in hospitalized adults and might be a marker of patient frailty and illness severity. However, we know of no attempts to estimate the predictive value of indwelling catheters for specific patient outcomes. The objective of the present study was to (a) identify the variables associated with the presence of a urinary catheter and (b) determine whether it predicts in-hospital mortality after adjustment for these variables.

METHODS

The study population included all acutely admitted adult patients in 2020 (exploratory cohort) and January-October 2021 (validation cohort) to internal medicine, cardiology and intensive care departments at the Laniado Hospital, a regional hospital with 400 beds in Israel. There were no exclusion criteria. The predictor variables were the presence of a urinary catheter on admission, age, gender, comorbidities and admission laboratory test results. We used bivariate and multivariate logistic regression to test the associations between the presence of a urinary catheter and mortality after adjustment for the remaining independent variables on admission.

RESULTS

The presence of a urinary catheter was associated with other independent variables. In 2020, the odds of in-hospital mortality in patients with a urinary catheter before and after adjustment for the remaining predictors were 14.3 (11.6-17.7) and 6.05 (4.78-7.65), respectively. Adding the presence of a urinary catheter to the prediction logistic regression model increased its c-statistic from 0.887 (0.880-0.894) to 0.907 (0.901-0.913). The results of the validation cohort reduplicated those of the exploratory cohort.

CONCLUSIONS

The presence of a urinary catheter on admission is an important and independent predictor of in-hospital mortality in acutely hospitalized adults in internal medicine departments.

[Impact of Home Return Assistance Service in Heart Failure (PRADO-IC) on the one year re-hospitalisation and mortality in a heart failure hospitalized population of patients]

INTRODUCTION

Congestive heart failure (CHF) is associated with prolonged and recurrent hospitalizations; the prognosis remains poor. Since 2013, the Caisse Primaire d'Assurance Maladie (CPAM) has set up a support program PRADO-IC (support program for returning home after hospitalisation for heart failure). The aim of this study was to evaluate the impact of PRADO-IC on the heart failure readmission rate and death rate at one year.

METHODS

From September 2016 to September 2018, all patients hospitalized for heart failure at Saint-Joseph Hospital were included in an observational study. The inclusion in PRADO-IC program was at physician's discretion. Two groups were compared according to the inclusion in PRADO-IC or not (T). The primary endpoints were the comparison of one-year mortality and heart failure readmission rate between the two groups.

RESULTS

Six hundred and thirty-three patients were included, 262 in the PRADO-IC group and 371 in the non-PRADO group. Patients in the PRADO-IC cohort more frequently present severity criteria (age, weight, BNP level, arrhythmia, anemia, renal failure). Mortality at one year (19.5% vs 16.2%, p = 0.28) are equivalent in both groups. There were no significant differences in one-year rehospitalization rate for heart failure (HF) (35.1% in PRADO cohort vs 28% in T group, p = 0.06), the time to first hospitalization (74.5 days in PRADO vs 54.5 days in T, p = 0.55) and the length of hospitalization (6.0 days in PRADO vs 7.0 days in T, p = 0.29) between the two groups. Age, hyponatremia, anemia, cancer, HF re-hospitalization were variables linked to a risk of mortality, in a multivariable analysis.

CONCLUSION

Our study shows that the PRADO-IC program concerned to the most severe patients. Despite this, the one-year mortality and the HF readmission rate are similar between the two groups.

Low mean HbA1c does not increase all-cause and cardiovascular mortality in patients with diabetes: Effect-modifications by anemia and chronic kidney disease stages

Background

Previous studies reported that low levels of glycated hemoglobin A1c (HbA1c) were associated with increased mortality. We investigated rates and risks of all-cause and cardiovascular mortality in association with mean HbA1c levels with stratification of anemia and chronic kidney disease (CKD) stages, major causes of low HbA1c.

Methods

47,145 patients with prescription of antidiabetic agents >6 months in the outpatient visits (2003–2018) were linked to Taiwan’s National Death Registry to identify all-cause and cardiovascular mortality. Poisson assumption was used to estimate the mortality rates, and the Cox proportional hazard regression model was used to evaluate the relative hazards of respective mortality in relation to mean HbA1c in different statuses of anemia and CKD stages.

Results

All-cause and cardiovascular mortality rates were the lowest in non-anemic stages 1–2 CKD patients, and the highest in anemic stages 3–5 CKD patients. In stages 1–2 CKD, excessive HRs observed in those with mean HbA1c <6.0% (Hazard Ratio [HR]) 1.58; 95% Confidence Interval [CI] 1.18–2.12) became inconsequential after adjustment of medications and laboratory results (HR: 1.26; 95% CI 0.89–1.79). The similar patterns were observed in anemic stages 1–2 CKD, anemic or non-anemic stages 3–5 CKD. Low HbA1c was not related to cardiovascular mortality in any anemia status or CKD staging.

Conclusions

Higher risks associated with low mean HbA1c and all-cause mortality were attenuated by adjustment of medications and comorbidities. It is imperative for the diabetologists to consider confounding effects of underlying illness before concluding low HbA1c associated higher mortality.

Data Quality of Automated Comorbidity Lists in Patients With Mental Health and Substance Use Disorders

EHRs provide an opportunity to conduct research on underrepresented oncology populations with mental health and substance use disorders. However, a lack of data quality may introduce unintended bias into EHR data. The objective of this article is describe our analysis of data quality within automated comorbidity lists commonly found in EHRs. Investigators conducted a retrospective chart review of 395 oncology patients from a safety-net integrated healthcare system. Statistical analysis included κ coefficients and a condition logistic regression. Subjects were racially and ethnically diverse and predominantly used Medicaid insurance. Weak κ coefficients ( κ = 0.2-0.39, P < .01) were noted for drug and alcohol use disorders indicating deficiencies in comorbidity documentation within the automated comorbidity list. Further, conditional logistic regression analyses revealed deficiencies in comorbidity documentation in patients with drug use disorders (odds ratio, 11.03; 95% confidence interval, 2.71-44.9; P = .01) and psychoses (odds ratio, 0.04; confidence interval, 0.02-0.10; P < .01). Findings suggest deficiencies in automatic comorbidity lists as compared with a review of provider narrative notes when identifying comorbidities. As healthcare systems increasingly use EHR data in clinical studies and decision making, the quality of healthcare delivery and clinical research may be affected by discrepancies in the documentation of comorbidities.

Patient- and Process-Related Contributors to the Underuse of Aortic Valve Replacement and Subsequent Mortality in Ambulatory Patients With Severe Aortic Stenosis

Background

Many patients with severe aortic stenosis (AS) and an indication for aortic valve replacement (AVR) do not undergo treatment. The reasons for this have not been well studied in the transcatheter AVR era. We sought to determine how patient‐ and process‐specific factors affected AVR use in patients with severe AS.

Methods and Results

We identified ambulatory patients from 2016 to 2018 demonstrating severe AS, defined by aortic valve area ≤1.0 cm². Propensity scoring analysis with inverse probability of treatment weighting was used to evaluate associations between predictors and the odds of undergoing AVR at 365 days and subsequent mortality at 730 days. Of 324 patients with an indication for AVR (79.3±9.7 years, 57.4% men), 140 patients (43.2%) did not undergo AVR. The odds of AVR were reduced in patients aged >90 years (odds ratio [OR], 0.24 [95% CI, 0.08–0.69]; P=0.01), greater comorbid conditions (OR, 0.88 per 1‐point increase in Combined Comorbidity Index [95% CI, 0.79–0.97]; P=0.01), low‐flow, low‐gradient AS with preserved left ventricular ejection fraction (OR, 0.11 [95% CI, 0.06–0.21]), and low‐gradient AS with reduced left ventricular ejection fraction (OR, 0.18 [95% CI, 0.08–0.40]) and were increased if the transthoracic echocardiogram ordering provider was a cardiologist (OR, 2.46 [95% CI, 1.38–4.38]). Patients who underwent AVR gained an average of 85.8 days of life (95% CI, 40.9–130.6) at 730 days.

Conclusions

The proportion of ambulatory patients with severe AS and an indication for AVR who do not receive AVR remains significant. Efforts are needed to maximize the recognition of severe AS, especially low‐gradient subtypes, and to encourage patient referral to multidisciplinary heart valve teams.

Establishing a National Cardiovascular Disease Surveillance System in the United States Using Electronic Health Record Data: Key Strengths and Limitations

Cardiovascular disease surveillance involves quantifying the evolving population-level burden of cardiovascular outcomes and risk factors as a data-driven initial step followed by the implementation of interventional strategies designed to alleviate this burden in the target population. Despite widespread acknowledgement of its potential value, a national surveillance system dedicated specifically to cardiovascular disease does not currently exist in the United States. Routinely collected health care data such as from electronic health records (EHRs) are a possible means of achieving national surveillance. Accordingly, this article elaborates on some key strengths and limitations of using EHR data for establishing a national cardiovascular disease surveillance system. Key strengths discussed include the: (1) ubiquity of EHRs and consequent ability to create a more "national" surveillance system, (2) existence of a common data infrastructure underlying the health care enterprise with respect to data domains and the nomenclature by which these data are expressed, (3) longitudinal length and detail that define EHR data when individuals repeatedly patronize a health care organization, and (4) breadth of outcomes capable of being surveilled with EHRs. Key limitations discussed include the: (1) incomplete ascertainment of health information related to health care-seeking behavior and the disconnect of health care data generated at separate health care organizations, (2) suspect data quality resulting from the default information-gathering processes within the clinical enterprise, (3) questionable ability to surveil patients through EHRs in the absence of documented interactions, and (4) the challenge in interpreting temporal trends in health metrics, which can be obscured by changing clinical and administrative processes.

A multicenter evaluation of computable phenotyping approaches for SARS-CoV-2 infection and COVID-19 hospitalizations

Diagnosis codes are used to study SARS-CoV2 infections and COVID-19 hospitalizations in administrative and electronic health record (EHR) data. Using EHR data (April 2020–March 2021) at the Yale-New Haven Health System and the three hospital systems of the Mayo Clinic, computable phenotype definitions based on ICD-10 diagnosis of COVID-19 (U07.1) were evaluated against positive SARS-CoV-2 PCR or antigen tests. We included 69,423 patients at Yale and 75,748 at Mayo Clinic with either a diagnosis code or a positive SARS-CoV-2 test. The precision and recall of a COVID-19 diagnosis for a positive test were 68.8% and 83.3%, respectively, at Yale, with higher precision (95%) and lower recall (63.5%) at Mayo Clinic, varying between 59.2% in Rochester to 97.3% in Arizona. For hospitalizations with a principal COVID-19 diagnosis, 94.8% at Yale and 80.5% at Mayo Clinic had an associated positive laboratory test, with secondary diagnosis of COVID-19 identifying additional patients. These patients had a twofold higher inhospital mortality than based on principal diagnosis. Standardization of coding practices is needed before the use of diagnosis codes in clinical research and epidemiological surveillance of COVID-19.

[Not Available]

INTRODUCTION

Congestiveheart failure (CHF) is associated with prolonged and recurrent hospitalizations, the prognosis remains poor. The aim of this study was to collect epidemiologic data at admission and at six month follow-up in a cohort of patients with CHF admitted to a single center between 2017 and 2019 (Saint Joseph Hospital, HSJ) and to compare these data with regional data (Ile-de-France, IdF).

METHODS

Local and regional data were provided by National Health Service, Regional Department of Ile de France(DRSM) using national data base. CHF in-hospital stay was defined by appropriate CIM 10 code reported on the final medical form.

RESULTS

From 2017 to 2019, 1967 CHF in-hospital stays were collected, mean age of the population was 81.4 (=mean) ± 11.7 yearsIC_95% [80.8; 81.9], mean length of stay was 8.6 ± 6.8 days IC_95% [8.3; 8.9], in-hospital mortality was 5.3 %, 9.6% at 2^nd month and 15.9% at 6^th month. Readmission rate was 23.7%, time to readmission was 59.5 ± 47.5 days IC_95% [57.4; 61.6]. IdF data collected 60973 CHF in-hospital stays at the same period. Compared to the IdF population, our population was older (81.4 ± 11.6 versus 80.4 ± 12.6 years, p = 0.001). Length of stay was shorter (8.6 ± 6.8 versus 11.3 ± 10.1 days p<0.001), in-hospital mortality was lower (5.3% versus 7.8% p < 0.001), 2nd month and 6^th month mortality was lower (respectively 9.6% versus 14.2% and 15.9% versus 21.3%, p <0.001), home discharge rate was higher (66.9% versus 60.8%, p < 0.001) in the HSJ population. The proportion of patients included in PRADO-IC program (Programme d'aide au retour à domicile-Insuffisance Cardiaque, Returning home support program) was higher in SJ population (22.6% versus 8.8% p < 0.001).

CONCLUSION

CHF admission involved elderly patients, the in-hospital and 6^th month mortality is high, with early and frequent readmissions. Differences between HSJ and IdF populations may be explained by the heterogeneity of health care facilities, management facilities and organization of transition of care.

Systematic Undercoding of Diagnostic Procedures in National Inpatient Sample (NIS): A Threat to Validity Due to Surveillance Bias

BACKGROUND AND OBJECTIVES

Health services research often relies on readily available data, originally collected for administrative purposes and used for public reporting and pay-for-performance initiatives. We examined the prevalence of underreporting of diagnostic procedures for acute myocardial infarction (AMI), deep venous thrombosis (DVT), and pulmonary embolism (PE), used for public reporting and pay-for-performance initiatives.

METHOD

We retrospectively identified procedures for AMI, DVT, and PE in the National Inpatient Sample (NIS) database between 2012 and 2016. From January 1, 2012, through September 30, 2015, the NIS used the International Classification of Diseases, Ninth Revision (ICD-9) coding scheme. From October 1, 2015, through December 31, 2016, the NIS used the International Classification of Diseases, Tenth Revision (ICD-10) coding scheme. We grouped the data by ICD code definitions (ICD-9 or ICD-10) to reflect these code changes and to prevent any confounding or misclassification. In addition, we used survey weighting to examine the utilization of venous duplex ultrasound scan for DVT, electrocardiogram (ECG) for AMI, and chest computed tomography (CT) scan, pulmonary angiography, echocardiography, and nuclear medicine ventilation/perfusion () scan for PE.

RESULTS

In the ICD-9 period, by primary diagnosis, only 0.26% (n = 5930) of patients with reported AMI had an ECG. Just 2.13% (n = 7455) of patients with reported DVT had a peripheral vascular ultrasound scan. For patients with PE diagnosis, 1.92% (n = 12 885) had pulmonary angiography, 3.92% (n = 26 325) had CT scan, 5.31% (n = 35 645) had cardiac ultrasound scan, and 0.45% (n = 3025) had scan. In the ICD-10 period, by primary diagnosis, 0.04% (n = 345) of reported AMI events had an ECG and 0.91% (n = 920) of DVT events had a peripheral vascular ultrasound scan. For patients with PE diagnosis, 2.08% (n = 4805) had pulmonary angiography, 0.63% (n = 1460) had CT scan, 1.68% (n = 3890) had cardiac ultrasound scan, and 0.06% (n = 140) had scan. Small proportions of diagnostic procedures were observed for any diagnoses of AMI, DVT, or PE.

CONCLUSIONS

Our findings question the validity of using NIS and other administrative databases for health services and outcomes research that rely on certain diagnostic procedures. Unfortunately, the NIS does not provide granular data that can control for differences in diagnostic procedure use, which can lead to surveillance bias. Researchers and policy makers must understand and acknowledge the limitations inherent in these databases, when used for pay-for-performance initiatives and hospital benchmarking.

Natural Language Processing to Identify Cancer Treatments With Electronic Medical Records

PURPOSE

Knowing the treatments administered to patients with cancer is important for treatment planning and correlating treatment patterns with outcomes for personalized medicine study. However, existing methods to identify treatments are often lacking. We develop a natural language processing approach with structured electronic medical records and unstructured clinical notes to identify the initial treatment administered to patients with cancer.

METHODS

We used a total number of 4,412 patients with 483,782 clinical notes from the Stanford Cancer Institute Research Database containing patients with nonmetastatic prostate, oropharynx, and esophagus cancer. We trained treatment identification models for each cancer type separately and compared performance of using only structured, only unstructured (bag-of-words, doc2vec, fasttext), and combinations of both (structured + bow, structured + doc2vec, structured + fasttext). We optimized the identification model among five machine learning methods (logistic regression, multilayer perceptrons, random forest, support vector machines, and stochastic gradient boosting). The treatment information recorded in the cancer registry is the gold standard and compares our methods to an identification baseline with billing codes.

RESULTS

For prostate cancer, we achieved an f1-score of 0.99 (95% CI, 0.97 to 1.00) for radiation and 1.00 (95% CI, 0.99 to 1.00) for surgery using structured + doc2vec. For oropharynx cancer, we achieved an f1-score of 0.78 (95% CI, 0.58 to 0.93) for chemoradiation and 0.83 (95% CI, 0.69 to 0.95) for surgery using doc2vec. For esophagus cancer, we achieved an f1-score of 1.0 (95% CI, 1.0 to 1.0) for both chemoradiation and surgery using all combinations of structured and unstructured data. We found that employing the free-text clinical notes outperforms using the billing codes or only structured data for all three cancer types.

CONCLUSION

Our results show that treatment identification using free-text clinical notes greatly improves upon the performance using billing codes and simple structured data. The approach can be used for treatment cohort identification and adapted for longitudinal cancer treatment identification.

Evidence-Based Decision Making 6: Administrative Databases as Secondary Data Source for Epidemiologic and Health Service Research

Health-care systems require reliable information on which to base health-care planning and make decisions, as well as to evaluate their policy impact. Administrative data, predominantly captured for non-research purposes, provide important information about health services use, expenditures, and clinical outcomes and may be used to assess quality of care. With increased digitalization and accessibility of administrative databases, this data is more readily available for health service research purposes, aiding evidence-based decision making. This chapter discusses the utility of administrative data for population-based studies of health and health care.

Accuracy of Computable Phenotyping Approaches for SARS-CoV-2 Infection and COVID-19 Hospitalizations from the Electronic Health Record

Objective:

Real-world data have been critical for rapid-knowledge generation throughout the COVID-19 pandemic. To ensure high-quality results are delivered to guide clinical decision making and the public health response, as well as characterize the response to interventions, it is essential to establish the accuracy of COVID-19 case definitions derived from administrative data to identify infections and hospitalizations.

Methods:

Electronic Health Record (EHR) data were obtained from the clinical data warehouse of the Yale New Haven Health System (Yale, primary site) and 3 hospital systems of the Mayo Clinic (validation site). Detailed characteristics on demographics, diagnoses, and laboratory results were obtained for all patients with either a positive SARS-CoV-2 PCR or antigen test or ICD-10 diagnosis of COVID-19 (U07.1) between April 1, 2020 and March 1, 2021. Various computable phenotype definitions were evaluated for their accuracy to identify SARS-CoV-2 infection and COVID-19 hospitalizations.

Results:

Of the 69,423 individuals with either a diagnosis code or a laboratory diagnosis of a SARS-CoV-2 infection at Yale, 61,023 had a principal or a secondary diagnosis code for COVID-19 and 50,355 had a positive SARS-CoV-2 test. Among those with a positive laboratory test, 38,506 (76.5%) and 3449 (6.8%) had a principal and secondary diagnosis code of COVID-19, respectively, while 8400 (16.7%) had no COVID-19 diagnosis. Moreover, of the 61,023 patients with a COVID-19 diagnosis code, 19,068 (31.2%) did not have a positive laboratory test for SARS-CoV-2 in the EHR. Of the 20 cases randomly sampled from this latter group for manual review, all had a COVID-19 diagnosis code related to asymptomatic testing with negative subsequent test results. The positive predictive value (precision) and sensitivity (recall) of a COVID-19 diagnosis in the medical record for a documented positive SARS-CoV-2 test were 68.8% and 83.3%, respectively. Among 5,109 patients who were hospitalized with a principal diagnosis of COVID-19, 4843 (94.8%) had a positive SARS-CoV-2 test within the 2 weeks preceding hospital admission or during hospitalization. In addition, 789 hospitalizations had a secondary diagnosis of COVID-19, of which 446 (56.5%) had a principal diagnosis consistent with severe clinical manifestation of COVID-19 (e.g., sepsis or respiratory failure). Compared with the cohort that had a principal diagnosis of COVID-19, those with a secondary diagnosis had a more than 2-fold higher in-hospital mortality rate (13.2% vs 28.0%, P<0.001). In the validation sample at Mayo Clinic, diagnosis codes more consistently identified SARS-CoV-2 infection (precision of 95%) but had lower recall (63.5%) with substantial variation across the 3 Mayo Clinic sites. Similar to Yale, diagnosis codes consistently identified COVID-19 hospitalizations at Mayo, with hospitalizations defined by secondary diagnosis code with 2-fold higher in-hospital mortality compared to those with a primary diagnosis of COVID-19.

Conclusions:

COVID-19 diagnosis codes misclassified the SARS-CoV-2 infection status of many people, with implications for clinical research and epidemiological surveillance. Moreover, the codes had different performance across two academic health systems and identified groups with different risks of mortality. Real-world data from the EHR can be used to in conjunction with diagnosis codes to improve the identification of people infected with SARS-CoV-2.

A simple index predicting mortality in acutely hospitalized patients

BACKGROUND

Mortality rates used to evaluate and improve the quality of hospital care are adjusted for comorbidity and disease severity. Comorbidity, measured by International Classification of Diseases codes, do not reflect the severity of the medical condition, that requires clinical assessments not available in electronic databases, and/or laboratory data with clinically relevant ranges to permit extrapolation from one setting to the next.

AIM

To propose a simple index predicting mortality in acutely hospitalized patients.

DESIGN

Retrospective cohort study with internal and external validation.

METHODS

The study populations were all acutely admitted patients in 2015-16, and in January 2019-November 2019 to internal medicine, cardiology and intensive care departments at the Laniado Hospital in Israel, and in 2002-19, at St. James Hospital, Ireland. Predictor variables were age and admission laboratory tests. The outcome variable was in-hospital mortality. Using logistic regression of the data in the 2015-16 Israeli cohort, we derived an index that included age groups and significant laboratory data.

RESULTS

In the Israeli 2015-16 cohort, the index predicted mortality rates from 0.2% to 32.0% with a c-statistic (area under the receiver operator characteristic curve) of 0.86. In the Israeli 2019 validation cohort, the index predicted mortality rates from 0.3% to 38.9% with a c-statistic of 0.87. An abbreviated index performed similarly in the Irish 2002-19 cohort.

CONCLUSIONS

Hospital mortality can be predicted by age and selected admission laboratory data without acquiring information from the patient's medical records. This permits an inexpensive comparison of performance of hospital departments.

Cost of Arthroscopic Rotator Cuff Repairs Is Primarily Driven by Procedure-Level Factors: A Single-Institution Analysis of an Ambulatory Surgery Center

PURPOSE

To identify intraoperative drivers of cost associated with arthroscopic rotator cuff repairs (RCRs) through analysis of an institutional database.

METHODS

This was a single-institution retrospective review of arthroscopic RCRs performed at an ambulatory surgical center between November 2016 and July 2019. Patient-level factors analyzed included age, sex, insurance type (private, Medicare, Medicaid, self-pay, and other government), American Society of Anesthesiologists grade (I, II, III, and missing), and Charlson comorbidity index (0, 1, 2, and ≥3). Procedure-level factors included use of biologics (decellularized dermal allograft or bioinductive healing implant), anesthesia type (regional block, monitored anesthesia care, or general), number of anchors and sutures, additional procedures (biceps tenodesis, distal clavicle resection, subacromial decompression), and operative time. Multivariate linear regression analysis was used to identify factors significantly associated with higher or lower charges.

RESULTS

A total of 712 arthroscopic RCRs were included. The risk-adjusted operative charges were $19,728 (95% confidence interval $16,543 to $22,913). The above factors predicted nearly 65% of the variability in operative charges. The only patient-level factor significantly associated with lower charges was female sex (- $1,339; P = .002). Procedure-level factors significantly associated with higher charges were use of biologics (+ $17,791; P < .001), concurrent open biceps tenodesis (+ $4,027; P < .001), distal clavicle resection (+ $2,266; P = .039), use of regional block (+ $1,256; P = .004), number of anchors (+ $2,245/anchor; P < .001), and increasing operative time ($26/min). Other factors had no significant association.

CONCLUSIONS

Procedural factors are the most significant drivers of operative cost in arthroscopic RCRs, such as quantity and type of implants; additional procedures such as biceps tenodesis and distal clavicle resection; and perioperative conditions such as type of anesthesia and total operating room time. Overall, patient-level factors were not shown to correlate well with operative costs, other than lower charges with female sex.

LEVEL OF EVIDENCE

IV, economic study.

Prevalence and Trends in Smoking Among Surgical Patients in Michigan, 2012-2019

IMPORTANCE

Surgery is a teachable moment, and smoking cessation interventions that coincide with an episode of surgical care are especially effective. Implementing these interventions at a large scale requires understanding the prevalence and characteristics of smoking among surgical patients.

OBJECTIVES

To describe the prevalence of smoking in a population of patients undergoing common surgical procedures and to identify any clinical or demographic characteristics associated with smoking.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study included all adult patients (aged ≥18 years) in a statewide registry who underwent general and vascular surgical procedures from 2012 to 2019 at 70 hospitals in Michigan. Data analysis was conducted from August to October 2020.

EXPOSURES

Undergoing a surgical procedure in any of the following categories: appendectomy, cholecystectomy, colon procedures, gastric or esophageal procedures, hepatopancreatobiliary procedures, hernia repair, small-bowel procedures, hysterectomy, vascular procedures, thyroidectomy, and other unspecific abdominal procedures.

MAIN OUTCOMES AND MEASURES

The prevalence of smoking prior to surgery, defined as cigarette use in the year prior to surgery, obtained from medical record review. Multivariable logistic regression was performed to analyze smoking prevalence based on insurance type and year of surgery while adjusting for demographic and clinical factors, including age, sex, race/ethnicity (determined from the medical record), insurance type, geographic region, comorbidities (ie, hypertension, diabetes, congestive heart failure, chronic obstructive pulmonary disease, chronic steroid use, and obstructive sleep apnea), American Society of Anesthesiologists classification, admission status, surgical priority, procedure type, and year of surgery.

RESULTS

From 2012 to 2019, 328 578 patients underwent surgery and were included in analysis. Mean (SD) age was 54.0 (17.0) years, and 197 501 patients (60.1%) were women. The overall prevalence of smoking was 24.1% (79 152 patients). Prevalence varied regionally from 21.5% (95% CI, 21.0%-21.9%; 6686 of 31 172 patients) in southeast Michigan to 28.0% (95% CI, 27.1%-28.9%; 2696 of 9614 patients) in northeast Michigan. When adjusting for clinical and demographic factors, there were greater odds of smoking among patients with Medicaid (odds ratio [OR], 2.75; 95% CI, 2.69-2.82) and patients without insurance (OR, 2.21; 95% CI, 2.10-2.33) compared with patients with private insurance. Among procedure categories, patients undergoing vascular surgery had greater odds of smoking (OR, 3.24; 95% CI, 3.11-3.38) than those undergoing cholecystectomy. Compared with 2012, the adjusted odds of smoking decreased significantly each year (eg, 2019: OR, 0.78; 95% CI, 0.74-0.81). In 2019, the adjusted prevalence of smoking was 22.3% (95% CI, 22.0%-22.7%) among all patients, 43.0% (95% CI, 42.4%-43.6%) among patients with Medicaid, and 36.3% (95% CI, 35.2%-37.4%) among patients without insurance.

CONCLUSIONS AND RELEVANCE

In a statewide population of surgical patients, nearly one-quarter of patients smoked cigarettes, which is higher than the national average. The prevalence of smoking was especially high among patients without insurance and among those receiving Medicaid. Given the established association between undergoing a major surgical procedure and health behavior change, targeted smoking cessation interventions at the time of surgery may be an effective strategy to improve population health, especially among at-risk patient groups.

Temporal relationship of computed and structured diagnoses in electronic health record data

Background

The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM).

Methods

We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. We used EHR data for encounters from January 1, 2012 through February 10, 2019 from an academic health system. Diagnoses for HTN, HLD, and DM were computed for patients with at least two observations above threshold separated by at least 30 days, where the thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 6.5%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list.

Results

We found that 39.8% of those with HTN, 21.6% with HLD, and 5.2% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 166 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR.

Conclusions

We found a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.

Development and validation of a procedure-based organ failure assessment model for patients in the intensive care unit: an administrative database study

Aim

To develop a procedure‐based organ failure assessment model for intensive care unit (ICU) patients and to examine the ability of this model to predict in‐hospital mortality, with reference to the Sequential Organ Failure Assessment (SOFA) score.

Methods

Using the Japanese nationwide Diagnosis Procedure Combination database, we identified patients aged ≥15 years who were admitted to the ICUs April 2018–March 2019. Since April 2018, Japanese health care providers have been required to input ICU patients' SOFA scores into this database. We extracted data on the following procedures on ICU admission: oxygen supplementation, invasive mechanical ventilation, blood transfusions, catecholamines, chest compression, extracorporeal membrane oxygenation, and renal replacement therapy. A procedure‐based organ failure assessment model (Model 1) for in‐hospital mortality was developed using therapeutic procedures for organ failure on the day of ICU admission in the derivation cohort. We also constructed a model using the SOFA score (Model 2). Discriminatory ability was assessed using area under the receiver operating characteristic curve (AUROC) in the validation cohort, and the discriminatory abilities of the models were compared.

Results

In total, 69,019 patients were included. Overall in‐hospital mortality was 7.2%. The AUROCs for Model 1 (0.810) and Model 2 (0.817) in the validation cohort did not show a statistically significant difference (P = 0.20).

Conclusion

The models established using procedure‐based organ failure assessment showed no statistically significant differences from those using the SOFA score, suggesting that procedure records in administrative databases can be used for risk adjustment in clinical studies on ICU mortality.

Incidence of idiopathic cardiomyopathy in patients with type 2 diabetes in Taiwan: age, sex, and urbanization status-stratified analysis

BACKGROUND

The epidemiology of diabetes and idiopathic cardiomyopathy have limited data. We investigated the overall and the age-, sex-, and urbanization-specific incidence and relative hazard of idiopathic cardiomyopathy in association with type 2 diabetes and various anti-diabetic medications used in Taiwan.

METHODS

A total of 474,268 patients with type 2 diabetes were identified from ambulatory care and inpatient claims in 2007-2009 from Taiwan's National Health Insurance (NHI) database. We randomly selected 474,266 age-, sex-, and diagnosis date-matched controls from the registry of NHI beneficiaries. All study subjects were linked to ambulatory care and inpatient claims (up to the end of 2016) to identify the possible diagnosis of idiopathic cardiomyopathy. The person-year approach with Poisson assumption was used to estimate the incidence, and Cox proportional hazard regression model with Fine and Gray's method was used to estimate the relative hazards of idiopathic cardiomyopathy in relation to type 2 diabetes.

RESULTS

The overall incidence of idiopathic cardiomyopathy for men and women patients, respectively, was 3.83 and 2.94 per 10,000 person-years, which were higher than the corresponding men and women controls (2.00 and 1.34 per 10,000 person-years). Compared with the control group, patients with type 2 diabetes were significantly associated with an increased hazard of idiopathic cardiomyopathy (adjusted hazard ratio [aHR]: 1.60, 95% confidence interval [CI]: 1.45-1.77] in all age and sex stratifications except in those men aged > 64 years. Patients with type 2 diabetes aged < 45 years confronted the greatest increase in the hazard of idiopathic cardiomyopathy, with an aHR of 3.35 (95% CI 2.21-5.06) and 3.48 (95% CI 1.60-7.56) for men and women, respectively. The usage of some anti-diabetic medications revealed lower risks of idiopathic cardiomyopathy.

CONCLUSIONS

In Taiwan, diabetes increased the risk of idiopathic cardiomyopathy in both sexes and in all age groups, except in men aged > 64 years. Younger patients were vulnerable to have higher HRs of idiopathic cardiomyopathy. Some anti-diabetic medications may reduce the risks of cardiomyopathy.

Performance of ICD-10-CM Diagnosis Codes for Identifying Acute Ischemic Stroke in a National Health Insurance Claims Database

Purpose

The validity of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding for the identification of acute ischemic stroke (AIS) in Taiwan’s National Health Insurance claims database has not been investigated. This study aimed to construct and validate the case definition algorithms for AIS based on ICD-10-CM diagnostic codes.

Patients and Methods

This study identified all hospitalizations with ICD-10-CM code of I63* in any position of the discharge diagnoses from the inpatient claims database and all patients with a final diagnosis of AIS from the stroke registry between Jan 2018 and Dec 2019. Hospitalizations in the claims data that could be successfully linked to those in the registry data were regarded as true episodes of AIS. Otherwise, their electronic medical records and images were manually reviewed to ascertain whether they were true episodes of AIS. Using the true episodes of AIS as the reference standard, the positive predictive value (PPV) and sensitivity of various case definition algorithms for AIS were calculated.

Results

A total of 1227 hospitalizations were successfully linked. Among the 155 hospitalizations that could not be linked, 54 were determined to be true episodes of AIS. Using ICD-10-CM code of I63* in any position of the discharge diagnoses to identify AIS yielded a PPV and sensitivity of 92.7% and 99.4%, respectively. The PPV increased to 99.8% with >12% decrease in the sensitivity when AIS was restricted to those with I63* as the primary diagnosis. When AIS was defined to be I63* as the primary, first secondary, or second secondary diagnosis, both PPV and sensitivity were greater than 97%.

Conclusion

This study demonstrated the validity of various case definition algorithms for AIS based on ICD-10-CM coding and can provide a reference for future claims-based stroke research.

Risk Factors for Heart Failure with Preserved or Reduced Ejection Fraction Among Medicare Beneficiaries: Application of Competing Risks Analysis and Gradient Boosted Model

BACKGROUND

The differential impact of various demographic characteristics and comorbid conditions on development of heart failure (HF) with preserved (pEF) and reduced ejection fraction (rEF) is not well studied among the elderly.

METHODS

Using Medicare claims data linked to electronic health records, we conducted an observational cohort study of individuals ≥65 years of age without HF. A Cox proportional hazards model accounting for competing risk of HFrEF and HFpEF incidence was constructed. A gradient-boosted model (GBM) assessed the relative influence (RI) of each predictor in the development of HFrEF and HFpEF.

RESULTS

Among 138,388 included individuals, 9701 developed HF (incidence rate = 20.9 per 1000 person-years). Males were more likely to develop HFrEF than HFpEF (HR = 2.07, 95% CI: 1.81-2.37 vs. 1.11, 95% CI: 1.02-1.20, P for heterogeneity <0.01). Atrial fibrillation and pulmonary hypertension had stronger associations with the risk of HFpEF (HR = 2.02, 95% CI: 1.80-2.26 and 1.66, 95% CI: 1.23-2.22) while cardiomyopathy and myocardial infarction were more strongly associated with HFrEF (HR = 4.37, 95% CI: 3.21-5.97 and 1.94, 95% CI: 1.23-3.07). Age was the strongest predictor across all HF subtypes with RI from GBM >35%. Atrial fibrillation was the most influential comorbidity for the development of HFpEF (RI = 8.4%) while cardiomyopathy was the most influential comorbidity for the development of HFrEF (RI = 20.7%).

CONCLUSION

These findings of heterogeneous relationships between several important risk factors and heart failure types underline the potential differences in the etiology of HFpEF and HFrEF.

Emerging opportunities to harness real world data: An introduction to data sources, concepts, and applications

While randomized controlled trials (RCTs) are the gold standard for comparative effectiveness research, they are unable to provide the answers to all pertinent clinical and research questions. Real world evidence (RWE), that is, clinical evidence obtained outside RCTs and often through routine clinical practice, offers the potential to conduct observational studies that accelerate advances in care, improve outcomes for patients, and provide important insights that can answer important questions. Once appropriate information technology is available, real world data can be cost-effective to generate. RWE serves a vital role in the evaluation of treatment strategies for which there are no RCTs and for describing patterns of care. RWE also serves as an important adjunct to RCTs and can be used to determine if benefits seen in RCTs extend to clinical practice, provide insight into the findings of RCTs, generate hypotheses for future RCTs, and inform the design of future RCTs. These potential benefits must be balanced against some of the important limitations of RWE, including variable data quality, lack of granularity for important clinical variables, and the potential for bias and confounding. By using appropriate analytic techniques and study design, these limitations can be minimized but not eliminated. Going forward, RWE studies may be enhanced by using rigorous data quality standards, incorporating randomization, developing more prospective registries, and better leveraging data from electronic health records.

Feasibility analysis of conducting observational studies with the electronic health record

Background

The secondary use of electronic health records (EHRs) promises to facilitate medical research. We reviewed general data requirements in observational studies and analyzed the feasibility of conducting observational studies with structured EHR data, in particular diagnosis and procedure codes.

Methods

After reviewing published observational studies from the University Hospital of Erlangen for general data requirements, we identified three different study populations for the feasibility analysis with eligibility criteria from three exemplary observational studies. For each study population, we evaluated the availability of relevant patient characteristics in our EHR, including outcome and exposure variables. To assess data quality, we computed distributions of relevant patient characteristics from the available structured EHR data and compared them to those of the original studies. We implemented computed phenotypes for patient characteristics where necessary. In random samples, we evaluated how well structured patient characteristics agreed with a gold standard from manually interpreted free texts. We categorized our findings using the four data quality dimensions “completeness”, “correctness”, “currency” and “granularity”.

Results

Reviewing general data requirements, we found that some investigators supplement routine data with questionnaires, interviews and follow-up examinations. We included 847 subjects in the feasibility analysis (Study 1 n = 411, Study 2 n = 423, Study 3 n = 13). All eligibility criteria from two studies were available in structured data, while one study required computed phenotypes in eligibility criteria. In one study, we found that all necessary patient characteristics were documented at least once in either structured or unstructured data. In another study, all exposure and outcome variables were available in structured data, while in the other one unstructured data had to be consulted. The comparison of patient characteristics distributions, as computed from structured data, with those from the original study yielded similar distributions as well as indications of underreporting. We observed violations in all four data quality dimensions.

Conclusions

While we found relevant patient characteristics available in structured EHR data, data quality problems may entail that it remains a case-by-case decision whether diagnosis and procedure codes are sufficient to underpin observational studies. Free-text data or subsequently supplementary study data may be important to complement a comprehensive patient history.

Automated Survival Prediction in Metastatic Cancer Patients Using High-Dimensional Electronic Medical Record Data

BACKGROUND

Oncologists use patients' life expectancy to guide decisions and may benefit from a tool that accurately predicts prognosis. Existing prognostic models generally use only a few predictor variables. We used an electronic medical record dataset to train a prognostic model for patients with metastatic cancer.

METHODS

The model was trained and tested using 12 588 patients treated for metastatic cancer in the Stanford Health Care system from 2008 to 2017. Data sources included provider note text, labs, vital signs, procedures, medication orders, and diagnosis codes. Patients were divided randomly into a training set used to fit the model coefficients and a test set used to evaluate model performance (80%/20% split). A regularized Cox model with 4126 predictor variables was used. A landmarking approach was used due to the multiple observations per patient, with t0 set to the time of metastatic cancer diagnosis. Performance was also evaluated using 399 palliative radiation courses in test set patients.

RESULTS

The C-index for overall survival was 0.786 in the test set (averaged across landmark times). For palliative radiation courses, the C-index was 0.745 (95% confidence interval [CI] = 0.715 to 0.775) compared with 0.635 (95% CI = 0.601 to 0.669) for a published model using performance status, primary tumor site, and treated site (two-sided P < .001). Our model's predictions were well-calibrated.

CONCLUSIONS

The model showed high predictive performance, which will need to be validated using external data. Because it is fully automated, the model can be used to examine providers' practice patterns and could be deployed in a decision support tool to help improve quality of care.

The opportunities and shortcomings of using big data and national databases for sarcoma research

The rarity and heterogeneity of sarcomas make performing appropriately powered studies challenging and magnify the significance of large databases in sarcoma research. Established large tumor registries and population-based databases have become increasingly relevant for answering clinical questions regarding sarcoma incidence, treatment patterns, and outcomes. However, the validity of large databases has been questioned and scrutinized because of the inaccuracy and wide variability of coding practices and the absence of clinically relevant variables. In addition, the utilization of large databases for the study of rare cancers such as sarcoma may be particularly challenging because of the known limitations of administrative data and poor overall data quality. Currently, there are several large national cancer databases, including the Surveillance, Epidemiology, and End Results database, the National Cancer Data Base of the American College of Surgeons and the American Cancer Society, and the National Program of Cancer Registries of the Centers for Disease Control and Prevention. These databases are often used for sarcoma research, but they are limited by their dependence on administrative or billing data, the lack of agreement between chart abstractors on diagnosis codes, and the use of preexisting documented hospital diagnosis codes for tumor registries, which lead to a significant underestimation of sarcomas in large data sets. Current and future initiatives to improve databases and big data applications for sarcoma research include increasing the utilization of sarcoma-specific registries and encouraging national initiatives to expand on real-world, evidence-based data sets.

Frequency, predictors, and prognosis of ejection fraction improvement in heart failure: an echocardiogram-based registry study

Aims

To identify variables predicting ejection fraction (EF) recovery and characterize prognosis of heart failure (HF) patients with EF recovery (HFrecEF).

Methods and results

Retrospective study of adults referred for ≥2 echocardiograms separated by ≥6 months between 2008 and 2016 at the two largest echocardiography centres in Alberta who also had physician-assigned diagnosis of HF. Of 10 641 patients, 3124 had heart failure reduced ejection fraction (HFrEF) (EF ≤ 40%) at baseline: while mean EF declined from 30.2% on initial echocardiogram to 28.6% on the second echocardiogram in those patients with persistent HFrEF (defined by <10% improvement in EF), it improved from 26.1% to 46.4% in the 1174 patients (37.6%) with HFrecEF (defined by EF absolute improvement ≥10%). On multivariate analysis, female sex [adjusted odds ratio (aOR) 1.66, 95% confidence interval (CI) 1.40–1.96], younger age (aOR per decade 1.16, 95% CI 1.09–1.23), atrial fibrillation (aOR 2.00, 95% CI 1.68–2.38), cancer (aOR 1.52, 95% CI 1.03–2.26), hypertension (aOR 1.38, 95% CI 1.18–1.62), lower baseline ejection fraction (aOR per 1% decrease 1.07 (1.06–1.08), and using hydralazine (aOR 1.69, 95% CI 1.19–2.40) were associated with EF improvements ≥10%. HFrecEF patients demonstrated lower rates per 1000 patient years of mortality (106 vs. 164, adjusted hazard ratio, aHR 0.70 [0.62–0.79]), all-cause hospitalizations (300 vs. 428, aHR 0.87 [0.79–0.95]), all-cause emergency room (ER) visits (569 vs. 799, aHR 0.88 [0.81–0.95]), and cardiac transplantation or left ventricular assist device implantation (2 vs. 10, aHR 0.21 [0.10–0.45]) compared to patients with persistent HFrEF. Females with HFrEF exhibited lower mortality risk (aHR 0.94 [0.88–0.99]) than males after adjusting for age, time between echocardiograms, clinical comorbidities, medications, and whether their EF improved or not during follow-up.

Conclusion

HFrecEF patients tended to be younger, female, and were more likely to have hypertension, atrial fibrillation, or cancer. HFrecEF patients have a substantially better prognosis compared to those with persistent HFrEF, even after multivariable adjustment, and female patients exhibit lower mortality risk than men within each subgroup (HFrecEF and persistent HFrEF) even after multivariable adjustment.

Validation of text-mining and content analysis techniques using data collected from veterinary practice management software systems in the UK

Electronic patient records from practice management software systems have been used extensively in medicine for the investigation of clinical problems leading to the creation of decision support frameworks. To date, technologies that have been utilised for this purpose such as text mining and content analysis have not been employed significantly in veterinary medicine. The aim of this research was to pilot the use of content analysis and text-mining software for the synthesis and analysis of information extracted from veterinary electronic patient records. The purpose of the work was to be able to validate this approach for future employment across a number of practices for the purposes of practice based research. The approach utilised content analysis (Prosuite) and text mining (WordStat) software to aggregate the extracted text. Text mining tools such as Keyword in Context (KWIC) and Keyword Retrieval (KR) were employed to identify specific occurrences of data across the records. Two different datasets were interrogated, a bespoke test dataset that had been set up specifically for the purpose of the research, and a functioning veterinary clinic dataset that had been extracted from one veterinary practice. Across both datasets, the KWIC analysis was found to have a high level of accuracy with the search resulting in a sensitivity of between 85.3-100%, a specificity of between 99.1-99.7%, a positive predictive value between 93.5-95.8% and a negative predictive value between 97.7-100%. The KR search, based on machine learning, was utilised for the clinic-based dataset and was found to perform slightly better than the KWIC analysis. This study is the first to demonstrate the application of content analysis and text mining software for validation purposes across a number of different datasets for the purpose of search and recall of specific information across electronic patient records. This has not been demonstrated previously for small animal veterinary epidemiological research for the purposes of large scale analysis for practice-based research. Extension of this work to investigate more complex diseases across larger populations is required to fully explore the use of this approach in veterinary practice.

Risk of heart failure in a population with type 2 diabetes versus a population without diabetes with and without coronary heart disease

AIMS

To conduct a population-based study comparing age- and sex-specific risk estimates of heart failure (HF) between people with type 2 diabetes and people without diabetes, and to investigate the risks of HF in association with type 2 diabetes in people with various coronary heart diseases (CHDs).

MATERIALS AND METHODS

We used a nationally representative sample (one million people) selected from Taiwan's National Health Insurance (NHI) system. A total of 34 291 patients with type 2 diabetes were identified from ambulatory care claims in 2000, and the same number of age- and sex-matched controls were randomly selected from the registry of NHI beneficiaries in the same year. All study subjects were linked to inpatient claims (2000-2013) to identify the possible admissions for HF. Using a Cox proportional hazard regression model, we compared the relative hazards of HF in relation to type 2 diabetes according to various age and sex stratifications. We also compared the relative hazard of HF between type 2 diabetes and controls, with and without histories of various CHDs and coronary revascularization procedures.

RESULTS

Compared with absence of diabetes (control group), type 2 diabetes was significantly associated with an increased hazard of HF (adjusted hazard ratio [aHR] 1.47, 95% confidence interval [CI] 1.40-1.54]. In both sexes, those with type 2 diabetes aged <45 years had the highest increased hazard of HF, with an aHR of 2.54 (95% CI 1.62-3.98) and 4.12 (95% CI 2.35-7.23) for men and women, respectively. Compared with the control subjects without any CHD, people with type 2 diabetes without prior CHD had increased hazards of HF (aHR 1.54, 95% CI 1.41-1.68, in men and aHR 1.56, 95% CI 1.43-1.71, in women), which were similar to the aHRs for people without diabetes who had histories of heart diseases (aHR 1.60 and 1.55 for men and women, respectively).

CONCLUSIONS

Diabetes mellitus may increase the risk of HF in both men and women, as well as in all age groups, especially in young people. People with type 2 diabetes without CHD had a similarly increased risk of HF to that of control subjects with CHD. Certain coronary revascularization procedures and CHDs, including percutaneous transluminal coronary angiography, coronary artery bypass surgery and acute myocardial infarction, were found to greatly increase risk of HF in people with type 2 diabetes.

Short-term association between outdoor air pollution and osteoporotic hip fracture

This study examines the association of the levels of different airborne pollutants on the incidence of osteoporotic hip fracture in a southern European region. Association was detected between SO₂ and NO₂ and hospital admissions due to hip fracture.

INTRODUCTION

To examine the short-term effects of outdoor air pollution on the incidence of osteoporotic hip fracture in a southern European region.

METHODS

This is an ecological retrospective cohort study based on data obtained from three databases. In a time-series analysis, we examined the association between hip fracture incidence and different outdoor air pollutants (sulfur dioxide (SO₂), monoxide (NO), nitrogen dioxide (NO₂), ozone (O₃), and particulate matter in suspension < 2.5 (PM_2.5) and < 10-μm (PM₁₀) conditions by using general additive models (Poisson distribution). The incidence rate ratio (IRR), crude and adjusted by season and different weather conditions, was estimated for all parameters. Hip incidence was later analyzed by sex and age (under or over age 75) subgroups. The main outcome measure was daily hospital admissions due to fracture.

RESULTS

Hip fracture incidence showed association with SO₂ (IRR 1.11 (95% CI 1.04-1.18)), NO (IRR 1.01 (95% CI 1.01-1.02)), and NO₂ (IRR 1.02 (95% CI 1.01-1.04)). For O₃ levels, this association was negative (IRR 0.97 (95% CI 0.95-0.99)). The association persisted for SO₂ and NO₂ when the models were adjusted by season. After adjusting by season and weather conditions, the association persisted for NO₂. When participants were stratified by age and sex, associations persisted only in women older than 75 years.

CONCLUSIONS

A short-term association was observed with several indicators of air pollution on hip fracture incidence. This is the first study that shows these associations.

Developing and Testing a Chart Abstraction Tool for ICU Quality Measurement

Quality measures are increasingly used to measure the performance of providers, hospitals, and health care systems. Intensive care units (ICUs) are an important clinical area in hospitals, given that they generate high costs and present high risks to patients. Yet, currently, few valid and clinically significant ICU-specific outcome measures are reported nationally. This study reports on the creation and evaluation of new abstraction tools that evaluate ICU patients for the following clinically important outcomes: central line-associated bloodstream infection, methicillin-resistant Staphylococcus aureus, gastrointestinal bleed, and pressure ulcer. To allow ICUs and institutions to compare their outcomes, the tools include risk-adjustment variables that can be abstracted from the chart.

Clinical and Administrative Data on the Research of Acute Coronary Syndrome in Spain. Minimum Basic Data Set Validity

INTRODUCTION AND OBJECTIVES

Health outcomes research is done from clinical registries or administrative databases. The aim of this work was to evaluate the concordance of the Minimum Basic Data Set (MBDS) with the DIOCLES (Descripción de la Cardiopatía Isquémica en el Territorio Español) registry and to analyze the implications of use of the MBDS in the study of acute coronary syndrome in Spain.

METHODS

Through indirect identifiers, DIOCLES was linked with MBDS and unique matches were selected. Some of most relevant variables for risk adjustment of in-hospital mortality due to acute myocardial infarction were considered. Kappa coefficient was used to evaluate the concordance; sensitivity, specificity and positive and negative predictive values to measure the validity of the MBDS, and the area under ROC (receiver operating characteristic) curve to calculate its discrimination. The results were compared among hospitals quintiles according to their contribution to DIOCLES. The influence of unmatched episodes on results was assessed by a sensitivity analysis, using looser linking criteria.

RESULTS

Overall, 1539 (60.85%) unique matches were achieved. The prevalence was higher in DIOCLES (acute myocardial infarction: 71.09%; Killip 3-4: 9.17%; cerebrovascular accident: 0.97%; thrombolysis: 8.64%; angioplasty: 61.92% and coronary bypass: 1.75%) than in the MBDS (P < .001). The agreement level observed was almost perfect (κ = 0.863). The MBDS showed a sensitivity of 85.10% and a specificity of 98.31%. Most results were confirmed by using sensitivity analysis (79.95% episodes matched).

CONCLUSIONS

The MBDS can be a useful tool for outcomes research of acute coronary syndrome in Spain. The contrast of DIOCLES and MBDS with medical records could verify their validity.

Emergency pediatric surgery: Comparing the economic burden in specialized versus nonspecialized children's centers

BACKGROUND

The American College of Surgeons has developed a verification program for children's surgery centers. Highly specialized hospitals may be verified as Level I, while those with fewer dedicated resources as Level II or Level III, respectively. We hypothesized that more specialized children's centers would utilize more resources.

STUDY DESIGN

We performed a retrospective study of the Maryland Health Services Cost Review Commission (HSCRC) database from 2009 to 2013. We assessed total charge, length of stay (LOS), and charge per day for all inpatients with an emergency pediatric surgery diagnosis, controlling for severity of illness (SOI). Using published resources, we assigned theoretical level designations to each hospital.

RESULTS

Two hospitals would qualify as Level 1 hospitals, with 4593 total emergency pediatric surgery admissions (38.5%) over the five-year study period. Charges were significantly higher for children treated at Level I hospitals (all P<0.0001). Across all SOI, children at Level I hospitals had significantly longer LOS (all P<0.0001).

CONCLUSION

Hospitals defined as Level II and Level III provided the majority of care and were able to do so with shorter hospitalizations and lower charges, regardless of SOI. As care shifts towards specialized centers, this charge differential may have significant impact on future health care costs.

LEVEL OF EVIDENCE

Level III Cost Effectiveness Study.

Repeated blood transfusions: Identification of a novel culprit of early graft failure in children

BACKGROUND

The attrition of right ventricle to pulmonary artery (RV-PA) grafts has been attributed in part to the body's immunologic response. We hypothesized that antibodies developed through blood transfusion, directed against the grafts, may result in accelerated degeneration and the need for re-intervention.

METHODS

This is a population-based study of the province of Quebec. We included children born between January 1, 1987 to December 31, 2006 who were diagnosed with a cono-truncal anomaly and had an RV-PA graft. The patients were followed for transfusion exposure and RV-PA graft re-intervention. Time to re-intervention in those exposed versus non-exposed was analyzed using Cox regression. Analysis was done in two time periods, before and after the calendar year 2000, given the change in blood preparation in the province of Quebec.

RESULTS

There were 413 patients who met the inclusion criteria of a cono-truncal disorder. Of the whole study population, 69% received a blood transfusion. Cox regression analysis showed that among patients who had the initial graft performed (n=181) before year 2000, having 2 or more blood transfusion was associated with an nearly tripled risk of a re-intervention comparing to no blood transfusion (hazard ratio of 2.88; 95% confidence interval 1.05-7.91). In patients who had the initial graft performed after year 2000 (n=232), the associated risk increase was 7-fold (hazard ratio of 7.01; 95% confidence interval 3.06-16.02). Kaplan-Meier analyses confirmed the significant difference in the re-intervention free survival probabilities between those who received 2 or more blood product transfusion and those who did not as well: prior to year 2000 (67.9% vs. 88.0% at 5years, p=0.0201) as well as after year 2000 (39.7% vs. 82.8% at 5years, p<0.0001).

CONCLUSION

In this population-based analysis, repeated blood product transfusion was associated with a significant increased risk of a need for RV-PA graft re-intervention. This data strongly suggest that repeated blood transfusion may adversely impact graft longevity.

Incorporating Longitudinal Comorbidity and Acute Physiology Data in Template Matching for Assessing Hospital Quality: An Exploratory Study in an Integrated Health Care Delivery System

OBJECTIVE

We sought to build on the template-matching methodology by incorporating longitudinal comorbidities and acute physiology to audit hospital quality.

STUDY SETTING

Patients admitted for sepsis and pneumonia, congestive heart failure, hip fracture, and cancer between January 2010 and November 2011 at 18 Kaiser Permanente Northern California hospitals.

STUDY DESIGN

We generated a representative template of 250 patients in 4 diagnosis groups. We then matched between 1 and 5 patients at each hospital to this template using varying levels of patient information.

DATA COLLECTION

Data were collected retrospectively from inpatient and outpatient electronic records.

PRINCIPAL FINDINGS

Matching on both present-on-admission comorbidity history and physiological data significantly reduced the variation across hospitals in patient severity of illness levels compared with matching on administrative data only. After adjustment for longitudinal comorbidity and acute physiology, hospital rankings on 30-day mortality and estimates of length of stay were statistically different from rankings based on administrative data.

CONCLUSIONS

Template matching-based approaches to hospital quality assessment can be enhanced using more granular electronic medical record data.