Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset

Certain diseases have strong comorbidity and co-occurrence with others. Understanding disease-disease associations can potentially increase awareness among healthcare providers of co-occurring conditions and facilitate earlier diagnosis, prevention and treatment of patients. In this study, we utilized the valuable and large The Guideline Advantage (TGA) longitudinal electronic health record dataset from 70 outpatient clinics across the United States to investigate potential disease-disease associations. Specifically, the most prevalent 50 disease diagnoses were manually identified from 165,732 unique patients. To investigate the co-occurrence or dependency associations among the 50 diseases, the categorical disease terms were first mapped into numerical vectors based on disease co-occurrence frequency in individual patients using the Word2Vec approach. Then the novel and interesting disease association clusters were identified using correlation and clustering analyses in the numerical space. Moreover, the distribution of time delay (Δt) between pair-wise strongly associated diseases (correlation coefficients ≥ 0.5) were calculated to show the dependency among the diseases. The results can indicate the risk of disease comorbidity and complications, and facilitate disease prevention and optimal treatment decision-making

Pulmonary capillary hemangiomatosis: a lesson learned

Pulmonary capillary hemangiomatosis (PCH) is a rare and controversial entity that is known to be a cause of pulmonary hypertension and is microscopically characterized by proliferation of dilated capillary-sized channels along and in the alveolar walls. Clinically, it is mostly seen in adults. Clinical features are characterized by nonspecific findings such as shortness of breath, cough, chest pain, and fatigue. It can be clinically indistinguishable from pre-capillary pulmonary arterial hypertension disorders such as primary pulmonary arterial hypertension (PAH) or chronic thromboembolic pulmonary hypertension. However, the diagnostic distinction, which usually requires a multidisciplinary approach, is crucial in order to avoid inappropriate treatment with vasodilator medications usually used for PAH treatment. Prognosis of PCH remains poor with lung transplant being the only definitive treatment. We report an autopsy case of pulmonary capillary hemangiomatosis unmasked at autopsy that was treated with a prostacyclin analog, usually contraindicated in such patients. We emphasize that this entity should always be on the differential diagnosis in a patient with pulmonary hypertension and requires great vigilance on the part of the clinician, radiologist and pathologist to make the diagnosis and guide appropriate management

Women and ethnoracial minorities with poor cardiovascular health measures associated with a higher risk of developing mood disorder

BACKGROUND: Mood disorders (MDS) are a type of mental health illness that effects millions of people in the United States. Early prediction of MDS can give providers greater opportunity to treat these disorders. We hypothesized that longitudinal cardiovascular health (CVH) measurements would be informative for MDS prediction. METHODS: To test this hypothesis, the American Heart Association\u27s Guideline Advantage (TGA) dataset was used, which contained longitudinal EHR from 70 outpatient clinics. The statistical analysis and machine learning models were employed to identify the associations of the MDS and the longitudinal CVH metrics and other confounding factors. RESULTS: Patients diagnosed with MDS consistently had a higher proportion of poor CVH compared to patients without MDS, with the largest difference between groups for Body mass index (BMI) and Smoking. Race and gender were associated with status of CVH metrics. Approximate 46% female patients with MDS had a poor hemoglobin A1C compared to 44% of those without MDS; 62% of those with MDS had poor BMI compared to 47% of those without MDS; 59% of those with MDS had poor blood pressure (BP) compared to 43% of those without MDS; and 43% of those with MDS were current smokers compared to 17% of those without MDS. CONCLUSIONS: Women and ethnoracial minorities with poor cardiovascular health measures were associated with a higher risk of development of MDS, which indicated the high utility for using routine medical records data collected in care to improve detection and treatment for MDS among patients with poor CVH

Time-series cardiovascular risk factors and receipt of screening for breast, cervical, and colon cancer: The Guideline Advantage

BACKGROUND: Cancer is the second leading cause of death in the United States. Cancer screenings can detect precancerous cells and allow for earlier diagnosis and treatment. Our purpose was to better understand risk factors for cancer screenings and assess the effect of cancer screenings on changes of Cardiovascular health (CVH) measures before and after cancer screenings among patients. METHODS: We used The Guideline Advantage (TGA)-American Heart Association ambulatory quality clinical data registry of electronic health record data (n = 362,533 patients) to investigate associations between time-series CVH measures and receipt of breast, cervical, and colon cancer screenings. Long short-term memory (LSTM) neural networks was employed to predict receipt of cancer screenings. We also compared the distributions of CVH factors between patients who received cancer screenings and those who did not. Finally, we examined and quantified changes in CVH measures among the screened and non-screened groups. RESULTS: Model performance was evaluated by the area under the receiver operator curve (AUROC): the average AUROC of 10 curves was 0.63 for breast, 0.70 for cervical, and 0.61 for colon cancer screening. Distribution comparison found that screened patients had a higher prevalence of poor CVH categories. CVH submetrics were improved for patients after cancer screenings. CONCLUSION: Deep learning algorithm could be used to investigate the associations between time-series CVH measures and cancer screenings in an ambulatory population. Patients with more adverse CVH profiles tend to be screened for cancers, and cancer screening may also prompt favorable changes in CVH. Cancer screenings may increase patient CVH health, thus potentially decreasing burden of disease and costs for the health system (e.g., cardiovascular diseases and cancers)

Developing a risk stratification model for surgical site infection after abdominal hysterectomy

OBJECTIVE: The incidence of surgical site infection (SSI) ranges widely from 2-21% after hysterectomy. There is insufficient understanding of risk factors to build a specific risk stratification index. METHODS: Retrospective case-control study of 545 abdominal and 275 vaginal hysterectomies from 7/1/03 - 6/30/05 at four institutions. SSIs were defined using CDC/NNIS criteria. Independent risk factors for abdominal hysterectomy were identified by logistic regression. RESULTS: There were 13 deep incisional, 53 superficial incisional, and 18 organ-space SSI after abdominal and 14 organ-space SSI after vaginal hysterectomy. Because risk factors for organ-space SSI were different in univariate analysis, further analyses focused on incisional SSI after abdominal hysterectomy. The maximum serum glucose within 5 days after operation was highest in patients with deep incisional SSI, lower in patients with superficial incisional SSI and lowest in uninfected patients (median 189, 156, and 141mg/dL, p = .005). Independent risk factors for incisional SSI included blood transfusion (odds ratio (OR) 2.4) and morbid obesity (body mass index (BMI) > 35, OR 5.7). Duration of operation > 75th percentile (OR 1.7), obesity (BMI 30-35, OR 3.0), and lack of private health insurance (OR 1.7) were marginally associated with increased odds of SSI. CONCLUSIONS: Incisional SSI after abdominal hysterectomy was associated with increased BMI and blood transfusion. Longer operative time and lack of private health insurance were marginally associated with SSI. A specific risk stratification index could help to more accurately predict the risk of incisional SSI following abdominal hysterectomy

Enhanced surgical site infection surveillance following hysterectomy, vascular, and colorectal surgery

Objective.To evaluate the use of inpatient pharmacy and administrative data to detect surgical site infections (SSIs) following hysterectomy and colorectal and vascular surgery.Design.Retrospective cohort study.Setting.Five hospitals affiliated with academic medical centers.Patients.Adults who underwent abdominal or vaginal hysterectomy, colorectal surgery, or vascular surgery procedures between July 1, 2003, and June 30, 2005.Methods.We reviewed the medical records of weighted, random samples drawn from 3,079 abdominal and vaginal hysterectomy, 4,748 colorectal surgery, and 3,332 vascular surgery procedures. We compared routine surveillance with screening of inpatient pharmacy data and diagnosis codes and then performed medical record review to confirm SSI status.Results.Medical records from 823 hysterectomy, 736 colorectal surgery, and 680 vascular surgery procedures were reviewed. SSI rates determined by antimicrobial- and/or diagnosis code-based screening followed by medical record review (enhanced surveillance) were substantially higher than rates determined by routine surveillance (4.3% [95% confidence interval, 3.6%—5.1%] vs 2.7% for hysterectomies, 7.1% [95% confidence interval, 6.7%–8.2%] vs 2.0% for colorectal procedures, and 2.3% [95% confidence interval, 1.9%–2.9%] vs 1.4% for vascular procedures). Enhanced surveillance had substantially higher sensitivity than did routine surveillance to detect SSI (92% vs 59% for hysterectomies, 88% vs 22% for colorectal procedures, and 72% vs 43% for vascular procedures). A review of medical records confirmed SSI for 31% of hysterectomies, 20% of colorectal procedures, and 31% of vascular procedures that met the enhanced screening criteria.Conclusion.Antimicrobial- and diagnosis code-based screening may be a useful method for enhancing and streamlining SSI surveillance for a variety of surgical procedures, including those procedures targeted by the Centers for Medicare and Medicaid Services.</jats:sec

Beyond 30 days: Does limiting the duration of surgical site infection follow-up limit detection?

Concern over consistency and completeness of surgical site infection (SSI) surveillance has increased due to public reporting of hospital SSI rates and imminent non-payment rules for hospitals that do not meet national benchmarks. Already, hospitals no longer receive additional payment from the Centers for Medicare &amp; Medicaid Services (CMS) for certain infections following coronary artery bypass graft (CABG) surgery, orthopedic procedures, and bariatric surgery. One major concern is incomplete and differential post-discharge surveillance. At present, substantial variation exists in how and whether hospitals identify SSI events after the hospitalization in which the surgery occurred. Parameters used for SSI surveillance such as the duration of the window of time that surveillance takes place following the surgical procedure can impact the completeness of surveillance data. Determination of the optimal surveillance time period involves balancing the potential increased case ascertainment associated with a longer follow-up period with the increased resources that would be required. Currently, the time window for identifying potentially preventable SSIs related to events at the time of surgery is not fully standardized. The Centers for Disease Control and Prevention (CDC) National Healthcare Surveillance Network (NHSN) requires a 365-day postoperative surveillance period for procedures involving implants and a 30-day period for non-implant procedures. In contrast, the National Surgical Quality Improvement Program (NSQIP) and the Society of Thoracic Surgeons (STS) systems employ 30-day post-operative surveillance regardless of implant. As consensus builds towards national quality measures for hospital-specific SSI rates, it will be important to assess the frequency of events beyond the 30-day post-surgical window that may quantify the value of various durations of surveillance, and ultimately inform the choice of specific outcome measures

A multicenter study of Clostridium difficile infection-related colectomy, 2000-2006

BACKGROUND: The incidence of Clostridium difficile infection (CDI) has been increasing. Previous studies report that the number of colectomies for CDI is also rising. Outside of a few notable outbreaks, there are few published data documenting increasing severity of CDI. The specific aims of this multiyear, multicenter study were to assess CDI-related colectomy rates and compare CDI-related colectomy rates by CDI surveillance definition. METHODS: Cases of CDI and patients who underwent colectomy were identified electronically from 5 US tertiary-care centers from July 2000 through June 2006. Chart review was performed to determine if a colectomy was for CDI. Monthly CDI-related colectomy rates were calculated as the number of CDI-related colectomies per 1,000 CDI cases. Data between observational groups were compared using χ(2) and Mann-Whitney U tests. Logistic regression was performed to evaluate risk factors for CDI-related colectomy. RESULTS: 8569 cases of CDI were identified and 75 patients had CDI-related colectomy. The overall colectomy rate was 8.7/1,000 CDI cases. The CDI-related colectomy rate ranged from 0 to 23 per 1,000 CDI episodes across hospitals. The colectomy rates for healthcare facility (HCF)-onset CDI was 4.3/1000 CDI cases and 16.5 /1000 CDI cases for community-onset CDI (p <.05). There were significantly more CDI-related colectomies at hospitals B and C (p<.05). CONCLUSIONS: The overall CDI-related colectomy rate was low, and there was no significant change in the CDI-related colectomy rate over time. Onset of disease outside of the study hospital was an independent risk factor for colectomy

Deriving measures of intensive care unit antimicrobial use from computerized pharmacy data: Methods, validation, and overcoming barriers

OBJECTIVE: To outline methods for deriving and validating intensive care unit (ICU) antimicrobial utilization (AU) measures from computerized data and to describe programming problems that emerged. DESIGN: Retrospective evaluation of computerized pharmacy and administrative data. SETTING: ICUs from four academic medical centers over 36 months. INTERVENTIONS: Investigators separately developed and validated programming code to report AU measures in selected ICUs. Antibacterial and antifungal drugs for systemic administration were categorized and expressed as antimicrobial days (each day that each antimicrobial drug was given to each patient) and patient-days on antimicrobials (each day that any antimicrobial drug was given to each patient). Monthly rates were compiled and analyzed centrally with ICU patient-days as the denominator. Results were validated against data collected from manual medical record review. Frequent discussion among investigators aided identification and correction of programming problems. RESULTS: AU data were successfully programmed though a reiterative process of computer code revision. After identifying and resolving major programming errors, comparison of computerized patient-level data with data collected by manual medical record review revealed discrepancies in antimicrobial days and patient-days on antimicrobials ranging from <1% to 17.7%. The hospital for which numerator data were derived from electronic medication administration records had the least discrepant results. CONCLUSIONS: Computerized AU measures can be derived feasibly, but threats to validity must be sought and corrected. The magnitude of discrepancies between computerized AU data and a gold standard based on manual chart review varies, with electronic medication administration records providing maximal accuracy

Multicenter evaluation of computer automated versus traditional surveillance of hospital-acquired bloodstream infections

Objective.Central line–associated bloodstream infection (BSI) rates are a key quality metric for comparing hospital quality and safety. Traditional BSI surveillance may be limited by interrater variability. We assessed whether a computer-automated method of central line–associated BSI detection can improve the validity of surveillance.Design.Retrospective cohort study.Setting.Eight medical and surgical intensive care units (ICUs) in 4 academic medical centers.Methods.Traditional surveillance (by hospital staff) and computer algorithm surveillance were each compared against a retrospective audit review using a random sample of blood culture episodes during the period 2004–2007 from which an organism was recovered. Episode-level agreement with audit review was measured with κ statistics, and differences were assessed using the test of equal κ coefficients. Linear regression was used to assess the relationship between surveillance performance (κ) and surveillance-reported BSI rates (BSIs per 1,000 central line–days).Results.We evaluated 664 blood culture episodes. Agreement with audit review was significantly lower for traditional surveillance (κ [95% confidence interval (CI)] = 0.44 [0.37–0.51]) than computer algorithm surveillance (κ [95% CI] [0.52–0.64]; P = .001). Agreement between traditional surveillance and audit review was heterogeneous across ICUs (P = .001); furthermore, traditional surveillance performed worse among ICUs reporting lower (better) BSI rates (P = .001). In contrast, computer algorithm performance was consistent across ICUs and across the range of computer-reported central line–associated BSI rates.Conclusions.Compared with traditional surveillance of bloodstream infections, computer automated surveillance improves accuracy and reliability, making interfacility performance comparisons more valid.Infect Control Hosp Epidemiol 2014;35(12):1483–1490</jats:sec