Development and evaluation of an ensemble resource linking medications to their indications

Objective

To create a computable MEDication Indication resource (MEDI) to support primary and secondary use of electronic medical records (EMRs).

Materials and methods

We processed four public medication resources, RxNorm, Side Effect Resource (SIDER) 2, MedlinePlus, and Wikipedia, to create MEDI. We applied natural language processing and ontology relationships to extract indications for prescribable, single-ingredient medication concepts and all ingredient concepts as defined by RxNorm. Indications were coded as Unified Medical Language System (UMLS) concepts and International Classification of Diseases, 9th edition (ICD9) codes. A total of 689 extracted indications were randomly selected for manual review for accuracy using dual-physician review. We identified a subset of medication–indication pairs that optimizes recall while maintaining high precision.

Results

MEDI contains 3112 medications and 63 343 medication–indication pairs. Wikipedia was the largest resource, with 2608 medications and 34 911 pairs. For each resource, estimated precision and recall, respectively, were 94% and 20% for RxNorm, 75% and 33% for MedlinePlus, 67% and 31% for SIDER 2, and 56% and 51% for Wikipedia. The MEDI high-precision subset (MEDI-HPS) includes indications found within either RxNorm or at least two of the three other resources. MEDI-HPS contains 13 304 unique indication pairs regarding 2136 medications. The mean±SD number of indications for each medication in MEDI-HPS is 6.22±6.09. The estimated precision of MEDI-HPS is 92%.

Conclusions

MEDI is a publicly available, computable resource that links medications with their indications as represented by concepts and billing codes. MEDI may benefit clinical EMR applications and reuse of EMR data for research.     

Influenza surveillance using electronic health records in the American Indian and Alaska Native population

Objective

Increasing use of electronic health records (EHRs) provides new opportunities for public health surveillance. During the 2009 influenza A (H1N1) virus pandemic, we developed a new EHR-based influenza-like illness (ILI) surveillance system designed to be resource sparing, rapidly scalable, and flexible. 4 weeks after the first pandemic case, ILI data from Indian Health Service (IHS) facilities were being analyzed.

Materials and methods

The system defines ILI as a patient visit containing either an influenza-specific International Classification of Disease, V.9 (ICD-9) code or one or more of 24 ILI-related ICD-9 codes plus a documented temperature ≥100°F. EHR-based data are uploaded nightly. To validate results, ILI visits identified by the new system were compared to ILI visits found by medical record review, and the new system''s results were compared with those of the traditional US ILI Surveillance Network.

Results

The system monitored ILI activity at an average of 60% of the 269 IHS electronic health databases. EHR-based surveillance detected ILI visits with a sensitivity of 96.4% and a specificity of 97.8% based on chart review (N=2375) of visits at two facilities in September 2009. At the peak of the pandemic (week 41, October 17, 2009), the median time from an ILI visit to data transmission was 6 days, with a mode of 1 day.

Discussion

EHR-based ILI surveillance was accurate, timely, occurred at the majority of IHS facilities nationwide, and provided useful information for decision makers. EHRs thus offer the opportunity to transform public health surveillance.     

Leveraging informatics for genetic studies: use of the electronic medical record to enable a genome-wide association study of peripheral arterial disease

Background

There is significant interest in leveraging the electronic medical record (EMR) to conduct genome-wide association studies (GWAS).

Methods

A biorepository of DNA and plasma was created by recruiting patients referred for non-invasive lower extremity arterial evaluation or stress ECG. Peripheral arterial disease (PAD) was defined as a resting/post-exercise ankle-brachial index (ABI) less than or equal to 0.9, a history of lower extremity revascularization, or having poorly compressible leg arteries. Controls were patients without evidence of PAD. Demographic data and laboratory values were extracted from the EMR. Medication use and smoking status were established by natural language processing of clinical notes. Other risk factors and comorbidities were ascertained based on ICD-9-CM codes, medication use and laboratory data.

Results

Of 1802 patients with an abnormal ABI, 115 had non-atherosclerotic vascular disease such as vasculitis, Buerger''s disease, trauma and embolism (phenocopies) based on ICD-9-CM diagnosis codes and were excluded. The PAD cases (66±11 years, 64% men) were older than controls (61±8 years, 60% men) but had similar geographical distribution and ethnic composition. Among PAD cases, 1444 (85.6%) had an abnormal ABI, 233 (13.8%) had poorly compressible arteries and 10 (0.6%) had a history of lower extremity revascularization. In a random sample of 95 cases and 100 controls, risk factors and comorbidities ascertained from EMR-based algorithms had good concordance compared with manual record review; the precision ranged from 67% to 100% and recall from 84% to 100%.

Conclusion

This study demonstrates use of the EMR to ascertain phenocopies, phenotype heterogeneity and relevant covariates to enable a GWAS of PAD. Biorepositories linked to EMR may provide a relatively efficient means of conducting GWAS.     

Evaluation of safety profile of black shilajit after 91 days repeated administration in rats

Objective

To evaluate the safety of shilajit by 91 days repeated administration in different dose levels in rats.

Methods

In this study the albino rats were divided into four groups. Group I received vehicle and group II, III and IV received 500, 2 500 and 5 000 mg/kg of shilajit, respectively. Finally animals were sacrificed and subjected to histopathology and iron was estimated by flame atomic absorption spectroscopy and graphite furnace.

Results

The result showed that there were no significant changes in iron level of treated groups when compared with control except liver (5 000 mg/kg) and histological slides of all organs revealed normal except negligible changes in liver and intestine with the highest dose of shilajit. The weight of all organs was normal when compared with control.

Conclusions

The result suggests that black shilajit, an Ayurvedic formulation, is safe for long term use as a dietary supplement for a number of disorders like iron deficiency anaemia.     

Phospholipon 90H (P90H)-based PEGylated microscopic lipospheres delivery system for gentamicin: an antibiotic evaluation

Objective

To formulate gentamicin liposphere by solvent-melting method using lipids and polyethylene glycol 4 000 (PEG-4 000) for oral administration.

Methods

Gentamicin lipospheres were prepared by melt-emulsification using 30% w/w Phospholipon® 90H in Beeswax as the lipid matrix containing PEG-4 000. These lipospheres were characterized by evaluating on encapsulation efficiency, loading capacity, change in pH and the release profile. Antimicrobial activities were evaluated against Escherichia coli, Pseudomonas aeruginosa, Salmonella paratyphii and Staphylococcus aureus using the agar diffusion method.

Results

Photomicrographs revealed spherical particles within a micrometer range with minimal growth after 1 month. The release of gentamicin in vitro varied widely with the PEG-4 000 contents. Moreover, significant (P>0.05) amount of gentamicin was released in vivo from the formulation. The encapsulation and loading capacity were all high, indicating the ability of the lipids to take up the drug. The antimicrobial activities were very high especially against Pseudomonas compare to other test organisms. This strongly suggested that the formulation retain its bioactive characteristics.

Conclusions

This study strongly suggest that the issue of gentamicin stability and poor absorption in oral formulation could be adequately addressed by tactical engineering of lipid drug delivery systems such as lipospheres.     

Does user-centred design affect the efficiency,usability and safety of CPOE order sets?

Background

Application of user-centred design principles to Computerized provider order entry (CPOE) systems may improve task efficiency, usability or safety, but there is limited evaluative research of its impact on CPOE systems.

Objective

We evaluated the task efficiency, usability, and safety of three order set formats: our hospital''s planned CPOE order sets (CPOE Test), computer order sets based on user-centred design principles (User Centred Design), and existing pre-printed paper order sets (Paper).

Participants

27staff physicians, residents and medical students.

Setting

Sunnybrook Health Sciences Centre, an academic hospital in Toronto, Canada.

Methods

Participants completed four simulated order set tasks with three order set formats (two CPOE Test tasks, one User Centred Design, and one Paper). Order of presentation of order set formats and tasks was randomized. Users received individual training for the CPOE Test format only.

Main Measures

Completion time (efficiency), requests for assistance (usability), and errors in the submitted orders (safety).

Results

27 study participants completed 108 order sets. Mean task times were: User Centred Design format 273 s, Paper format 293 s (p=0.73 compared to UCD format), and CPOE Test format 637 s (p<0.0001 compared to UCD format). Users requested assistance in 31% of the CPOE Test format tasks, whereas no assistance was needed for the other formats (p<0.01). There were no significant differences in number of errors between formats.

Conclusions

The User Centred Design format was more efficient and usable than the CPOE Test format even though training was provided for the latter. We conclude that application of user-centred design principles can enhance task efficiency and usability, increasing the likelihood of successful implementation.     

Is the quality of data in an electronic medical record sufficient for assessing the quality of primary care?

Objective

Quality indicators for the treatment of type 2 diabetes are often retrieved from a chronic disease registry (CDR). This study investigates the quality of recording in a general practitioner''s (GP) electronic medical record (EMR) compared to a simple, web-based CDR.

Methods

The GPs entered data directly in the CDR and in their own EMR during the study period (2011). We extracted data from 58 general practices (8235 patients) with type 2 diabetes and compared the occurrence and value of seven process indicators and 12 outcome indicators in both systems. The CDR, specifically designed for monitoring type 2 diabetes and reporting to health insurers, was used as the reference standard. For process indicators we examined the presence or absence of recordings on the patient level in both systems, for outcome indicators we examined the number of compliant or non-compliant values of recordings present in both systems. The diagnostic OR (DOR) was calculated for all indicators.

Results

We found less concordance for process indicators than for outcome indicators. HbA1c testing was the process indicator with the highest DOR. Blood pressure measurement, urine albumin test, BMI recorded and eye assessment showed low DOR. For outcome indicators, the highest DOR was creatinine clearance <30 mL/min or mL/min/1.73 m² and the lowest DOR was systolic blood pressure <140 mm Hg.

Conclusions

Clinical items are not always adequately recorded in an EMR for retrieving indicators, but there is good concordance for the values of these items. If the quality of recording improves, indicators can be reported from the EMR, which will reduce the workload of GPs and enable GPs to maintain a good patient overview.     

The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data

Objective

Predicting patient outcomes from genome-wide measurements holds significant promise for improving clinical care. The large number of measurements (eg, single nucleotide polymorphisms (SNPs)), however, makes this task computationally challenging. This paper evaluates the performance of an algorithm that predicts patient outcomes from genome-wide data by efficiently model averaging over an exponential number of naive Bayes (NB) models.

Design

This model-averaged naive Bayes (MANB) method was applied to predict late onset Alzheimer''s disease in 1411 individuals who each had 312 318 SNP measurements available as genome-wide predictive features. Its performance was compared to that of a naive Bayes algorithm without feature selection (NB) and with feature selection (FSNB).

Measurement

Performance of each algorithm was measured in terms of area under the ROC curve (AUC), calibration, and run time.

Results

The training time of MANB (16.1 s) was fast like NB (15.6 s), while FSNB (1684.2 s) was considerably slower. Each of the three algorithms required less than 0.1 s to predict the outcome of a test case. MANB had an AUC of 0.72, which is significantly better than the AUC of 0.59 by NB (p<0.00001), but not significantly different from the AUC of 0.71 by FSNB. MANB was better calibrated than NB, and FSNB was even better in calibration. A limitation was that only one dataset and two comparison algorithms were included in this study.

Conclusion

MANB performed comparatively well in predicting a clinical outcome from a high-dimensional genome-wide dataset. These results provide support for including MANB in the methods used to predict outcomes from large, genome-wide datasets.     

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record

Background

Accurate knowledge of a patient''s medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete.

Objective

To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems.

Study design and methods

We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100 000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100 000 records to assess its accuracy.

Results

Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100 000 randomly selected patients showed high sensitivity (range: 62.8–100.0%) and positive predictive value (range: 79.8–99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone.

Conclusion

We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.     

ICU nurses' acceptance of electronic health records

Objective

To assess intensive care unit (ICU) nurses'' acceptance of electronic health records (EHR) technology and examine the relationship between EHR design, implementation factors, and nurse acceptance.

Design

The authors analyzed data from two cross-sectional survey questionnaires distributed to nurses working in four ICUs at a northeastern US regional medical center, 3 months and 12 months after EHR implementation.

Measurements

Survey items were drawn from established instruments used to measure EHR acceptance and usability, and the usefulness of three EHR functionalities, specifically computerized provider order entry (CPOE), the electronic medication administration record (eMAR), and a nursing documentation flowsheet.

Results

On average, ICU nurses were more accepting of the EHR at 12 months as compared to 3 months. They also perceived the EHR as being more usable and both CPOE and eMAR as being more useful. Multivariate hierarchical modeling indicated that EHR usability and CPOE usefulness predicted EHR acceptance at both 3 and 12 months. At 3 months postimplementation, eMAR usefulness predicted EHR acceptance, but its effect disappeared at 12 months. Nursing flowsheet usefulness predicted EHR acceptance but only at 12 months.

Conclusion

As the push toward implementation of EHR technology continues, more hospitals will face issues related to acceptance of EHR technology by staff caring for critically ill patients. This research suggests that factors related to technology design have strong effects on acceptance, even 1 year following the EHR implementation.     

Studies on the seasonal variations in the proximate composition of ascidians from the Palk Bay,Southeast coast of India

Objective

To investigate the seasonal fluctuations of the proximate composition of the ascidians muscle.

Methods

The moisture content was estimated by drying 1 g of fresh tissue at a constant temperature at 105 °C for 24 h.The loss of weight was taken as moisture content. The total protein was estimated using the Biuret method. The total carbohydrate in dried sample was estimated spectrophotometrically following the phenol- sulphuric acid method. The lipid in the dried sample tissue was gravimetrically estimated following the chloroform-methanol mixture method. Ash content was determined gravimetrically by incinerating 1 g dried sample in muffle furnace at about 550 °C for 6 h and results are expressed in percentage.

Results

It was found very difficult to compare the monthly variations, as all the ten species, exhibited wide fluctuations in their proximate compositions. For the sake of convenience, average seasonal values were calculated by summing the monthly values.

Conclusions

The proximate composition of the 10 commonly available ascidians showed high nutritive value and hence these groups especially solitary ascidians can be recommended for human consumption in terms of pickles, soup, curry and others after ensuring the safety of consumers.     

The prevalence of syndrome Z (the interaction of obstructive sleep apnoea with the metabolic syndrome) in a teaching hospital in Singapore

Background

Syndrome Z describes the interaction of obstructive sleep apnoea (OSA) with the metabolic syndrome.

Purpose of study

A pilot study to determine the prevalence of syndrome Z in a teaching hospital in Singapore.

Methods

Patients (age ⩾18 years) recruited for this prospective study had to satisfy three of the following five inclusion criteria: fasting glucose >6.1 mmol/l, blood pressure ⩾130/85 mm Hg, HDL cholesterol <1.04 mmol/l in men and <1.2 mmol/l in women, triglycerides ⩾1.7 mmol/l, and a waist circumference >102 cm in men and >88 cm in women. All subjects underwent standard overnight polysomnography. Overnight fasting glucose and lipid levels were measured and baseline anthropometric data recorded. All sleep studies were scored and reported by a sleep physician. OSA was deemed to be present if the respiratory disturbance index (RDI) was ⩾5, with mild, moderate and severe categories classified according to the Chicago criteria.

Results

There were 24 patients (19 males and five females) of whom 10 were Chinese, eight Malay and five of Indian origin, with one other. Mean age was 48±13.5 years, mean body mass index was 34.9±6.1 kg/m² and mean waist circumference was 111.3±15.7 cm. 23 (95.8%) of the patients had OSA with a mean RDI of 39.6±22.4 events/h with 15 patients (62.5%) in the severe category. The five patients who fulfilled all five criteria for diagnosis of the metabolic syndrome had severe OSA.

Conclusion

The prevalence of OSA in our studied population exhibiting the metabolic syndrome is very high. Therefore, a polysomnogram should always be considered for this subset of patients.     

Research and applications: Impact of an electronic medication management system on hospital doctors’ and nurses’ work: a controlled pre–post,time and motion study

Objective

To quantify and compare the time doctors and nurses spent on direct patient care, medication-related tasks, and interactions before and after electronic medication management system (eMMS) introduction.

Methods

Controlled pre–post, time and motion study of 129 doctors and nurses for 633.2 h on four wards in a 400-bed hospital in Sydney, Australia. We measured changes in proportions of time on tasks and interactions by period, intervention/control group, and profession.

Results

eMMS was associated with no significant change in proportions of time spent on direct care or medication-related tasks relative to control wards. In the post-period control ward, doctors spent 19.7% (2 h/10 h shift) of their time on direct care and 7.4% (44.4 min/10 h shift) on medication tasks, compared to intervention ward doctors (25.7% (2.6 h/shift; p=0.08) and 8.5% (51 min/shift; p=0.40), respectively). Control ward nurses in the post-period spent 22.1% (1.9 h/8.5 h shift) of their time on direct care and 23.7% on medication tasks compared to intervention ward nurses (26.1% (2.2 h/shift; p=0.23) and 22.6% (1.9 h/shift; p=0.28), respectively). We found intervention ward doctors spent less time alone (p=0.0003) and more time with other doctors (p=0.003) and patients (p=0.009). Nurses on the intervention wards spent less time with doctors following eMMS introduction (p=0.0001).

Conclusions

eMMS introduction did not result in redistribution of time away from direct care or towards medication tasks. Work patterns observed on these intervention wards were associated with previously reported significant reductions in prescribing error rates relative to the control wards.     

Research and applications: Predicting complications of percutaneous coronary intervention using a novel support vector method

Objective

To explore the feasibility of a novel approach using an augmented one-class learning algorithm to model in-laboratory complications of percutaneous coronary intervention (PCI).

Materials and methods

Data from the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) multicenter registry for the years 2007 and 2008 (n=41 016) were used to train models to predict 13 different in-laboratory PCI complications using a novel one-plus-class support vector machine (OP-SVM) algorithm. The performance of these models in terms of discrimination and calibration was compared to the performance of models trained using the following classification algorithms on BMC2 data from 2009 (n=20 289): logistic regression (LR), one-class support vector machine classification (OC-SVM), and two-class support vector machine classification (TC-SVM). For the OP-SVM and TC-SVM approaches, variants of the algorithms with cost-sensitive weighting were also considered.

Results

The OP-SVM algorithm and its cost-sensitive variant achieved the highest area under the receiver operating characteristic curve for the majority of the PCI complications studied (eight cases). Similar improvements were observed for the Hosmer–Lemeshow χ² value (seven cases) and the mean cross-entropy error (eight cases).

Conclusions

The OP-SVM algorithm based on an augmented one-class learning problem improved discrimination and calibration across different PCI complications relative to LR and traditional support vector machine classification. Such an approach may have value in a broader range of clinical domains.     

Toward creation of a cancer drug toxicity knowledge base: automatically extracting cancer drug–side effect relationships from the literature

Objective

A comprehensive and machine-understandable cancer drug–side effect (drug–SE) relationship knowledge base is important for in silico cancer drug target discovery, drug repurposing, and toxicity predication, and for personalized risk–benefit decisions by cancer patients. While US Food and Drug Administration (FDA) drug labels capture well-known cancer drug SE information, much cancer drug SE knowledge remains buried the published biomedical literature. We present a relationship extraction approach to extract cancer drug–SE pairs from the literature.

Data and methods

We used 21 354 075 MEDLINE records as the text corpus. We extracted drug–SE co-occurrence pairs using a cancer drug lexicon and a clean SE lexicon that we created. We then developed two filtering approaches to remove drug–disease treatment pairs and subsequently a ranking scheme to further prioritize filtered pairs. Finally, we analyzed relationships among SEs, gene targets, and indications.

Results

We extracted 56 602 cancer drug–SE pairs. The filtering algorithms improved the precision of extracted pairs from 0.252 at baseline to 0.426, representing a 69% improvement in precision with no decrease in recall. The ranking algorithm further prioritized filtered pairs and achieved a precision of 0.778 for top-ranked pairs. We showed that cancer drugs that share SEs tend to have overlapping gene targets and overlapping indications.

Conclusions

The relationship extraction approach is effective in extracting many cancer drug–SE pairs from the literature. This unique knowledge base, when combined with existing cancer drug SE knowledge, can facilitate drug target discovery, drug repurposing, and toxicity prediction.     

Toward personalizing treatment for depression: predicting diagnosis and severity

Objective

Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment.

Materials and methods

We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute.

Results

Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70–0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy.

Conclusions

It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.     

Electronic medical record systems are associated with appropriate placement of HIV patients on antiretroviral therapy in rural health facilities in Kenya: a retrospective pre-post study

Background and objective

There is little evidence that electronic medical record (EMR) use is associated with better compliance with clinical guidelines on initiation of antiretroviral therapy (ART) among ART-eligible HIV patients. We assessed the effect of transitioning from paper-based to an EMR-based system on appropriate placement on ART among eligible patients.

Methods

We conducted a retrospective, pre-post EMR study among patients enrolled in HIV care and eligible for ART at 17 rural Kenyan clinics and compared the: (1) proportion of patients eligible for ART based on CD4 count or WHO staging who initiate therapy; (2) time from eligibility for ART to ART initiation; (3) time from ART initiation to first CD4 test.

Results

7298 patients were eligible for ART; 54.8% (n=3998) were enrolled in HIV care using a paper-based system while 45.2% (n=3300) were enrolled after the implementation of the EMR. EMR was independently associated with a 22% increase in the odds of initiating ART among eligible patients (adjusted OR (aOR) 1.22, 95% CI 1.12 to 1.33). The proportion of ART-eligible patients not receiving ART was 20.3% and 15.1% for paper and EMR, respectively (χ²=33.5, p<0.01). Median time from ART eligibility to ART initiation was 29.1 days (IQR: 14.1–62.1) for paper compared to 27 days (IQR: 12.9–50.1) for EMR.

Conclusions

EMRs can improve quality of HIV care through appropriate placement of ART-eligible patients on treatment in resource limited settings. However, other non-EMR factors influence timely initiation of ART.