Current status of artificial intelligence methods for skin cancer survival analysis: a scoping review.

Skin cancer mortality rates continue to rise, and survival analysis is increasingly needed to understand who is at risk and what interventions improve outcomes. However, current statistical methods are limited by inability to synthesize multiple data types, such as patient genetics, clinical history, demographics, and pathology and reveal significant multimodal relationships through predictive algorithms. Advances in computing power and data science enabled the rise of artificial intelligence (AI), which synthesizes vast amounts of data and applies algorithms that enable personalized diagnostic approaches. Here, we analyze AI methods used in skin cancer survival analysis, focusing on supervised learning, unsupervised learning, deep learning, and natural language processing. We illustrate strengths and weaknesses of these approaches with examples. Our PubMed search yielded 14 publications meeting inclusion criteria for this scoping review. Most publications focused on melanoma, particularly histopathologic interpretation with deep learning. Such concentration on a single type of skin cancer amid increasing focus on deep learning highlight growing areas for innovation; however, it also demonstrates opportunity for additional analysis that addresses other types of cutaneous malignancies and expands the scope of prognostication to combine both genetic, histopathologic, and clinical data. Moreover, researchers may leverage multiple AI methods for enhanced benefit in analyses. Expanding AI to this arena may enable improved survival analysis, targeted treatments, and outcomes.

Using Machine Learning (ML) Models to Predict Risk of Venous Thromboembolism (VTE) Following Spine Surgery.

STUDY DESIGN: A retrospective cohort study. OBJECTIVES: Venous thromboembolism (VTE) is a potentially high-risk complication for patients undergoing spine surgery. Although guidelines for assessing VTE risk in this population have been established, development of new techniques that target different aspects of the medical history may prove to be of further utility. The goal of this study was to develop a predictive machine learning (ML) model to identify nontraditional risk factors for predicting VTE in spine surgery patients. SUMMARY OF BACKGROUND DATA: A cohort of 63 patients was identified who had undergone spine surgery at a single center from 2015 to 2021. Thirty-one patients had a confirmed VTE, while 32 had no VTE. A total of 113 attributes were defined and collected via chart review. Attribute categories included demographics, medications, labs, past medical history, operative history, and VTE diagnosis. METHODS: The Waikato Environment for Knowledge Analysis (WEKA) software was used in creating and evaluating the ML models. Six classifier models were tested with 10-fold cross-validation and statistically evaluated using t tests. RESULTS: Comparing the predictive ML models to the control model (ZeroR), all predictive models were significantly better than the control model at predicting VTE risk, based on the 113 attributes ( P <0.001). The Random Forest model had the highest accuracy of 88.89% with a positive predictive value of 93.75%. The Simple Logistic algorithm had an accuracy of 84.13% and defined risk attributes to include calcium and phosphate laboratory values, history of cardiac comorbidity, history of previous VTE, anesthesia time, selective serotonin reuptake inhibitor use, antibiotic use, and antihistamine use. The J48 model had an accuracy of 80.95% and it defined hemoglobin laboratory values, anesthesia time, beta-blocker use, dopamine agonist use, history of cancer, and Medicare use as potential VTE risk factors. CONCLUSION: Further development of these tools may provide high diagnostic value and may guide chemoprophylaxis treatment in this setting of high-risk patients.

Predicting Bone Health Using Machine Learning in Patients undergoing Spinal Reconstruction Surgery.

STUDY DESIGN: Retrospective study of data collected prospectively. OBJECTIVE: The goal of this study is to create a predictive model of preoperative bone health status in adult patients undergoing adult spinal reconstructive (ASR) surgery using machine learning (ML). SUMMARY OF BACKGROUND DATA: Despite understanding that bone health impacts spine surgery outcomes, spine surgeons lack the tools to risk stratify patients preoperatively to determine who should undergo bone health screening. An ML approach mines patterns in data to determine the risk for poor bone health in ASR patients. MATERIALS AND METHODS: Two hundred and eleven subjects over the age of 30 with dual energy X-ray absorptiometry scans, who underwent spinal reconstructive surgery were reviewed. Data was collected by manual and automated collection from the electronic health records. The Weka software was used to develop predictive models for multiclass classification of healthy, osteopenia, and osteoporosis (OPO) bone status. Bone status was labeled according to the World Health Organization (WHO) criteria using dual energy X-ray absorptiometry T scores. The accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC) were calculated. The model was evaluated on a test set of unseen data for generalizability. RESULTS: The prevalence of OPO was 23.22% and osteopenia was 52.61%. The random forest model achieved optimal performance with an average sensitivity of 0.81, specificity of 0.95, and AUC of 0.96 on the training set. The model yielded an averaged sensitivity of 0.64, specificity of 0.78, and AUC of 0.69 on the test set. The model was best at predicting OPO in patients. Numerous patient features exhibited predictive value, such as body mass index, insurance type, serum sodium level, serum creatinine level, history of bariatric surgery, and the use of medications such as selective serotonin reuptake inhibitors. CONCLUSION: Predicting bone health status in ASR patients is possible with an ML approach. Additionally, data mining using ML can find unrecognized risk factors for bone health in ASR surgery patients.

Prevalence of potentially harmful multidrug interactions on medication lists of elderly ambulatory patients.

BACKGROUND: It has been hypothesized that polypharmacy may increase the frequency of multidrug interactions (MDIs) where one drug interacts with two or more other drugs, amplifying the risk of associated adverse drug events (ADEs). The main objective of this study was to determine the prevalence of MDIs in medication lists of elderly ambulatory patients and to identify the medications most commonly involved in MDIs that amplify the risk of ADEs. METHODS: Medication lists stored in the electronic health record (EHR) of 6,545 outpatients ≥60 years old were extracted from the enterprise data warehouse. Network analysis identified patients with three or more interacting medications from their medication lists. Potentially harmful interactions were identified from the enterprise drug-drug interaction alerting system. MDIs were considered to amplify the risk if interactions could increase the probability of ADEs. RESULTS: MDIs were identified in 1.3 % of the medication lists, the majority of which involved three interacting drugs (75.6 %) while the remainder involved four (15.6 %) or five or more (8.9 %) interacting drugs. The average number of medications on the lists was 3.1 ± 2.3 in patients with no drug interactions and 8.6 ± 3.4 in patients with MDIs. The prevalence of MDIs on medication lists was greater than 10 % in patients prescribed bupropion, tramadol, trazodone, cyclobenzaprine, fluoxetine, ondansetron, or quetiapine and greater than 20 % in patients prescribed amiodarone or methotrexate. All MDIs were potentially risk-amplifying due to pharmacodynamic interactions, where three or more medications were associated with the same ADE, or pharmacokinetic, where two or more drugs reduced the metabolism of a third drug. The most common drugs involved in MDIs were psychotropic, comprising 35.1 % of all drugs involved. The most common serious potential ADEs associated with the interactions were serotonin syndrome, seizures, prolonged QT interval and bleeding. CONCLUSIONS: An identifiable number of medications, the majority of which are psychotropic, may be involved in MDIs in elderly ambulatory patients which may amplify the risk of serious ADEs. To mitigate the risk, providers will need to pay special attention to the overlapping drug-drug interactions which result in MDIs.

Clinical Features and Outcomes of Patients with Dementia Compared to an Aging Cohort Hospitalized During the Initial New York City COVID-19 Wave.

BACKGROUND: Patients with dementia are vulnerable during the coronavirus disease 2019 (COVID-19) pandemic, yet few studies describe their hospital course and outcomes. OBJECTIVE: To describe and compare the hospital course for COVID-19 patients with dementia to an aging cohort without dementia in a large New York City academic medical center. METHODS: This was a single-center retrospective cohort study describing all consecutive patients age 65 or older with confirmed COVID-19 who presented to the emergency department or were hospitalized at New York-Presbyterian/Columbia University Irving Medical Center between March 6 and April 7, 2020. RESULTS: A total of 531 patients were evaluated, including 116 (21.8%) with previously diagnosed dementia, and 415 without dementia. Patients with dementia had higher mortality (50.0%versus 35.4%, p = 0.006); despite similar comorbidities and complications, multivariate analysis indicated the association was dependent on age, sex, comorbidities, and code status. Patients with dementia more often presented with delirium (36.2%versus 11.6%, p < 0.001) but less often presented with multiple other COVID-19 symptoms, and these findings remained after adjusting for age and sex. CONCLUSION: Hospitalized COVID-19 patients with dementia had higher mortality, but dementia was not an independent risk factor for death. These patients were approximately 3 times more likely to present with delirium but less often manifested or communicated other common COVID-19 symptoms. For this high-risk population in a worsening pandemic, understanding the unique manifestations and course in dementia and aging populations may help guide earlier diagnosis and optimize medical management.

Management of afebrile neonates with pustules and vesicles in a pediatric emergency department.

OBJECTIVES: To assess the management and outcomes of vesicles and pustules in afebrile neonates presenting to the pediatric emergency department. METHODS: Using International Classification of Diseases, Ninth Revision, codes, we identified patients 0-60 days old presenting to our pediatric emergency department from 2008 to 2015 with a possible diagnosis of pustules or vesicles. We then used natural language processing followed by manual chart review to identify afebrile neonates with pustules or vesicles. We collected clinical data from the electronic medical record. We also assessed current practice patterns for neonatal pustules or vesicles using a survey administered to attending physicians. RESULTS: Of the 971 possible cases identified using International Classification of Diseases, Ninth Revision, codes for fluid-filled lesions, only 64 patients had vesicles (n = 9) and pustules (n = 55). One-third (22/64) of afebrile neonates with pustules and vesicles were admitted to the hospital and received empiric parenteral therapy. Admission, parenteral antibiotics, and antiviral therapy were more common in neonates presenting with vesicles than in those with pustules alone. Apart from 2 presumed blood culture contaminants, there were no positive blood or cerebrospinal fluid cultures. Two patients had positive urine cultures. Institutional survey data showed practice patterns consistent with these retrospective results. CONCLUSION: Although one-third of neonates with pustules and vesicles were admitted to the hospital and received parenteral therapy, there were no cerebrospinal fluid or blood infections or any confirmed evidence of herpes simplex virus disease. These findings suggest that afebrile, well-appearing neonates presenting with pustules alone may not need a full serious bacterial infection examination. Larger studies are needed to confirm these findings and assess outcomes, especially in afebrile neonates with vesicles.

Monitoring prescribing patterns using regression and electronic health records.

BACKGROUND: It is beneficial for health care institutions to monitor physician prescribing patterns to ensure that high-quality and cost-effective care is being provided to patients. However, detecting treatment patterns within an institution is challenging, given that medications and conditions are often not explicitly linked in the health record. Here we demonstrate the use of statistical methods together with data from the electronic health care record (EHR) to analyze prescribing patterns at an institution. METHODS: As a demonstration of our method, which is based on regression, we collect EHR data from outpatient notes and use a case/control study design to determine the medications that are associated with hypertension. We also use regression to determine which conditions are associated with a preferential use of one or more classes of hypertension agents. Finally, we compare our method to methods based on tabulation. RESULTS: Our results show that regression methods provide more reasonable and useful results than tabulation, and successfully distinguish between medications that treat hypertension and medications that do not. These methods also provide insight into in which circumstances certain drugs are preferred over others. CONCLUSIONS: Our method can be used by health care institutions to monitor physician prescribing patterns and ensure the appropriateness of treatment.

Early recognition of multiple sclerosis using natural language processing of the electronic health record.

BACKGROUND: Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs and symptoms of diseases such as multiple sclerosis (MS). The focus this study was to determine if patients with MS could be identified from their clinical notes prior to the initial recognition by their healthcare providers. METHODS: An MS-enriched cohort of patients with well-established MS (n = 165) and controls (n = 545), was generated from the adult outpatient clinic. A random sample cohort was generated from randomly selected patients (n = 2289) from the same adult outpatient clinic, some of whom had MS (n = 16). Patients' notes were extracted from the data warehouse and signs and symptoms mapped to UMLS terms using MedLEE. Approximately 1000 MS-related terms occurred significantly more frequently in MS patients' notes than controls'. Synonymous terms were manually clustered into 50 buckets and used as classification features. Patients were classified as MS or not using Naïve Bayes classification. RESULTS: Classification of patients known to have MS using notes of the MS-enriched cohort entered after the initial ICD9[MS] code yielded an ROC AUC, sensitivity, and specificity of 0.90 [0.87-0.93], 0.75[0.66-0.82], and 0.91 [0.87-0.93], respectively. Similar classification accuracy was achieved using the notes from the random sample cohort. Classification of patients not yet known to have MS using notes of the MS-enriched cohort entered before the initial ICD9[MS] documentation identified 40% [23-59%] as having MS. Manual review of the EHR of 45 patients of the random sample cohort classified as having MS but lacking an ICD9[MS] code identified four who might have unrecognized MS. CONCLUSIONS: Diagnostic accuracy might be improved by mining patients' clinical notes for signs and symptoms of specific diseases using NLP. Using this approach, we identified patients with MS early in the course of their disease which could potentially shorten the time to diagnosis. This approach could also be applied to other diseases often missed by primary care providers such as cancer. Whether implementing computerized diagnostic support ultimately shortens the time from earliest symptoms to formal recognition of the disease remains to be seen.

Using Rich Data on Comorbidities in Case-Control Study Design with Electronic Health Record Data Improves Control of Confounding in the Detection of Adverse Drug Reactions.

Recent research has suggested that the case-control study design, unlike the self-controlled study design, performs poorly in controlling confounding in the detection of adverse drug reactions (ADRs) from administrative claims and electronic health record (EHR) data, resulting in biased estimates of the causal effects of drugs on health outcomes of interest (HOI) and inaccurate confidence intervals. Here we show that using rich data on comorbidities and automatic variable selection strategies for selecting confounders can better control confounding within a case-control study design and provide a more solid basis for inference regarding the causal effects of drugs on HOIs. Four HOIs are examined: acute kidney injury, acute liver injury, acute myocardial infarction and gastrointestinal ulcer hospitalization. For each of these HOIs we use a previously published reference set of positive and negative control drugs to evaluate the performance of our methods. Our methods have AUCs that are often substantially higher than the AUCs of a baseline method that only uses demographic characteristics for confounding control. Our methods also give confidence intervals for causal effect parameters that cover the expected no effect value substantially more often than this baseline method. The case-control study design, unlike the self-controlled study design, can be used in the fairly typical setting of EHR databases without longitudinal information on patients. With our variable selection method, these databases can be more effectively used for the detection of ADRs.

Medication-indication knowledge bases: a systematic review and critical appraisal.

OBJECTIVE: Medication-indication information is a key part of the information needed for providing decision support for and promoting appropriate use of medications. However, this information is not readily available to end users, and a lot of the resources only contain this information in unstructured form (free text). A number of public knowledge bases (KBs) containing structured medication-indication information have been developed over the years, but a direct comparison of these resources has not yet been conducted. MATERIAL AND METHODS: We conducted a systematic review of the literature to identify all medication-indication KBs and critically appraised these resources in terms of their scope as well as their support for complex indication information. RESULTS: We identified 7 KBs containing medication-indication data. They notably differed from each other in terms of their scope, coverage for on- or off-label indications, source of information, and choice of terminologies for representing the knowledge. The majority of KBs had issues with granularity of the indications as well as with representing duration of therapy, primary choice of treatment, and comedications or comorbidities. DISCUSSION AND CONCLUSION: This is the first study directly comparing public KBs of medication indications. We identified several gaps in the existing resources, which can motivate future research.

Presence of early CKD-related metabolic complications predict progression of stage 3 CKD: a case-controlled study.

BACKGROUND: Only a subset of patients who enter stage 3 chronic kidney disease (CKD) progress to stage 4. Identifying which patients entering stage 3 are most likely to progress could improve outcomes, by allowing more appropriate referrals for specialist care, and spare those unlikely to progress the adverse effects and costliness of an unnecessarily aggressive approach. We hypothesized that compared to non-progressors, patients who enter stage 3 CKD and ultimately progress have experienced greater loss of renal function, manifested by impairment of metabolic function (anemia, worsening acidosis and mineral abnormalities), than is reflected in the eGFR at entry to stage 3. The purpose of this case-controlled study was to design a prediction model for CKD progression using laboratory values reflecting metabolic status. METHODS: Using data extracted from the electronic health record (EHR), two cohorts of patients in stage 3 were identified: progressors (eGFR declined >3 ml/min/1.73 m2/year; n=117) and non-progressors (eGFR declined <1 ml/min/1.713 m2; n=364). Initial laboratory values recorded a year before to a year after the time of entry to stage 3, reflecting metabolic complications (hemoglobin, bicarbonate, calcium, phosphorous, and albumin) were obtained. Average values in progressors and non-progressors were compared. Classification algorithms (Naïve Bayes and Logistic Regression) were used to develop prediction models of progression based on the initial lab data. RESULTS: At the entry to stage 3 CKD, hemoglobin, bicarbonate, calcium, and albumin values were significantly lower and phosphate values significantly higher in progressors compared to non-progressors even though initial eGFR values were similar. The differences were sufficiently large that a prediction model of progression could be developed based on these values. Post-test probability of progression in patients classified as progressors or non-progressors were 81% (73% - 86%) and 17% (13% - 23%), respectively. CONCLUSIONS: Our studies demonstrate that patients who enter stage 3 and ultimately progress to stage 4 manifest a greater degree of metabolic complications than those who remain stable at the onset of stage 3 when eGFR values are equivalent. Lab values (hemoglobin, bicarbonate, phosphorous, calcium and albumin) are sufficiently different between the two cohorts that a reasonably accurate predictive model can be developed.

Renal insufficiency is associated with an increased risk of papillary renal cell carcinoma histology.

PURPOSE: End-stage renal disease (ESRD) and acquired renal cystic disease associated with dialysis are known risk factors of papillary renal cell carcinoma (pRCC); however, it is not known whether renal insufficiency alone is a risk factor for pRCC. Our aim was to test whether renal insufficiency is associated with an increased preponderance of pRCC. METHODS: Retrospective review of institutional database to identify all patients who underwent extirpative renal surgery for renal cell carcinoma (RCC) with complete records from 1992 to 2012. We excluded those patients with preoperative ESRD as defined by GFR < 15 mL/min/1.73 m(2). The dependent variable was histologic RCC subtype. Independent variables included demographic data, comorbidities, and renal functional data. Multivariate analysis by binary logistic regression was used to determine factors that independently were associated with pRCC development. RESULTS: A total of 1,226 patients met inclusion criteria, of which 15 % were pRCC. There was a positive association between likelihood of pRCC histology of RCC and increasing preoperative chronic kidney disease (CKD) stage (p = 0.021). Multivariate regression analysis indicated that male gender, race, and declining renal function categorized both by GFR and CKD stage were independently associated with a higher likelihood of pRCC histology as compared to other RCC histology. CONCLUSIONS: Within a large cohort of patients with a diagnosis of RCC, declining renal function was independently associated with an increased likelihood of pRCC histology. This finding and the available molecular evidence indicating protein expression similarity between pRCC and resident stem cells, which appear to be upregulated with kidney damage, suggest a possible causal relationship between renal injury and pRCC.

Vitamin D resistance in chronic kidney disease (CKD).

BACKGROUND: Previous studies have shown that treatment with ergocalciferol in patients with CKD stage 3 + 4 is not effective with less than 33% of patients achieving a 25-OH vitamin D target of >30 ng/ml. The aim of this study was to test the response to cholecalciferol in CKD. We attempted to replete 25-OH vitamin D to a target level of 40-60 ng/ml using the response to treatment and PTH suppression as an outcome measure. METHODS: This retrospective cohort study identified patients (Stages 2-5 and Transplant) from 2001-2010 who registered at the Chronic Kidney Disease Clinic. Patients received cholecalciferol 10,000 IU capsules weekly as initial therapy. When levels above 40 ng/ml were not achieved, doses were titrated up to a maximum of 50,000 IU weekly. Active vitamin D analogs were also used in some Stage 4-5 CKD patients per practice guidelines. Patients reaching at least one level of 40 ng/mL were designated RESPONDER, and if no level above 40 ng/mL they were designated NON-RESPONDER. Patients were followed for at least 6 months and up to 5 years. RESULTS: 352 patients were included with a mean follow up of 2.4 years. Of the CKD patients, the initial 25-OH vitamin D in the NON-RESPONDER group was lower than the RESPONDER group (18 vs. 23 ng/ml) (p = 0.03). Among all patients, the initial eGFR in the RESPONDER group was significantly higher than the NON-RESPONDER group (36 vs. 30 ml/min/1.73 m2) (p < 0.001). Over time, the eGFR of the RESPONDER group stabilized or increased (p < 0.001). Over time, the eGFR in the NON-RESPONDER group decreased toward a trajectory of ESRD. Proteinuria did not impact the response to 25-OH vitamin D replacement therapy. There were no identifiable variables associated with the response or lack of response to cholecalciferol treatment. CONCLUSIONS: CKD patients treated with cholecalciferol experience treatment resistance in raising vitamin D levels to a pre-selected target level. The mechanism of vitamin D resistance remains unknown and is associated with progressive loss of eGFR. Proteinuria modifies but does not account for the vitamin D resistance.

A method for controlling complex confounding effects in the detection of adverse drug reactions using electronic health records.

OBJECTIVE: Electronic health records (EHRs) contain information to detect adverse drug reactions (ADRs), as they contain comprehensive clinical information. A major challenge of using comprehensive information involves confounding. We propose a novel data-driven method to identify ADR signals accurately by adjusting for confounders. MATERIALS AND METHODS: We focused on two serious ADRs, rhabdomyolysis and pancreatitis, and used information in 264,155 unique patient records. We identified an ADR using established criteria, selected potential confounders, and then used penalized logistic regressions to estimate confounder-adjusted ADR associations. A reference standard was created to evaluate and compare the precision of the proposed method and four others. RESULTS: Precision was 83.3% for rhabdomyolysis and 60.8% for pancreatitis when using the proposed method, and we identified several drug safety signals that are interesting for further clinical review. DISCUSSION: The proposed method effectively estimated ADR associations after adjusting for confounders. A main cause of error was probably due to the nature of the dataset in that a substantial number of patients had a single visit only and, therefore, it was not possible to determine correctly the appropriate sequence of events for them. It is likely that performance will be improved with use of EHR data that contain more longitudinal records. CONCLUSIONS: This data-driven method is effective in controlling for confounding, resulting in either a higher or similar precision when compared with four comparators, has the unique ability to provide insight into confounders for each specific medication-ADR pair, and can be easily adapted to other EHR systems.

Development and validation of an electronic phenotyping algorithm for chronic kidney disease.

Twenty-six million Americans are estimated to have chronic kidney disease (CKD) with increased risk for cardiovascular disease and end stage renal disease. CKD is frequently undiagnosed and patients are unaware, hampering intervention. A tool for accurate and timely identification of CKD from electronic medical records (EMR) could improve healthcare quality and identify patients for research. As members of eMERGE (electronic medical records and genomics) Network, we developed an automated phenotyping algorithm that can be deployed to identify rapidly diabetic and/or hypertensive CKD cases and controls in health systems with EMRs It uses diagnostic codes, laboratory results, medication and blood pressure records, and textual information culled from notes. Validation statistics demonstrated positive predictive values of 96% and negative predictive values of 93.3. Similar results were obtained on implementation by two independent eMERGE member institutions. The algorithm dramatically outperformed identification by ICD-9-CM codes with 63% positive and 54% negative predictive values, respectively.

Survival Analysis with Electronic Health Record Data: Experiments with Chronic Kidney Disease.

This paper presents a detailed survival analysis for chronic kidney disease (CKD). The analysis is based on the EHR data comprising almost two decades of clinical observations collected at New York-Presbyterian, a large hospital in New York City with one of the oldest electronic health records in the United States. Our survival analysis approach centers around Bayesian multiresolution hazard modeling, with an objective to capture the changing hazard of CKD over time, adjusted for patient clinical covariates and kidney-related laboratory tests. Special attention is paid to statistical issues common to all EHR data, such as cohort definition, missing data and censoring, variable selection, and potential for joint survival and longitudinal modeling, all of which are discussed alone and within the EHR CKD context.

Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions.

OBJECTIVE: Data-mining algorithms that can produce accurate signals of potentially novel adverse drug reactions (ADRs) are a central component of pharmacovigilance. We propose a signal-detection strategy that combines the adverse event reporting system (AERS) of the Food and Drug Administration and electronic health records (EHRs) by requiring signaling in both sources. We claim that this approach leads to improved accuracy of signal detection when the goal is to produce a highly selective ranked set of candidate ADRs. MATERIALS AND METHODS: Our investigation was based on over 4 million AERS reports and information extracted from 1.2 million EHR narratives. Well-established methodologies were used to generate signals from each source. The study focused on ADRs related to three high-profile serious adverse reactions. A reference standard of over 600 established and plausible ADRs was created and used to evaluate the proposed approach against a comparator. RESULTS: The combined signaling system achieved a statistically significant large improvement over AERS (baseline) in the precision of top ranked signals. The average improvement ranged from 31% to almost threefold for different evaluation categories. Using this system, we identified a new association between the agent, rasburicase, and the adverse event, acute pancreatitis, which was supported by clinical review. CONCLUSIONS: The results provide promising initial evidence that combining AERS with EHRs via the framework of replicated signaling can improve the accuracy of signal detection for certain operating scenarios. The use of additional EHR data is required to further evaluate the capacity and limits of this system and to extend the generalizability of these results.

FGF-23 levels in patients with AKI and risk of adverse outcomes.

BACKGROUND AND OBJECTIVES: Fibroblast growth factor 23 plays an important role in regulating phosphate and vitamin D homeostasis. Elevated levels of fibroblast growth factor 23 are independently associated with mortality in patients with CKD and ESRD. Whether fibroblast growth factor 23 levels are elevated and associated with adverse outcomes in patients with AKI has not been studied. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: This study had 30 participants with AKI, which was defined as an increase in serum creatinine ≥ 0.3 mg/dl or ≥ 50% from baseline, and 30 controls from the general hospital wards and intensive care units. Plasma levels of C-terminal fibroblast growth factor 23 and vitamin D metabolites were measured within 24 hours of AKI onset and 5 days later. The composite endpoint was death or need for renal replacement therapy. RESULTS: Enrollment fibroblast growth factor 23 levels were significantly higher among participants with AKI than controls (median [interquartile range]=1471 [224-2534] versus 263 [96-574] RU/ml, P=0.003). Enrollment fibroblast growth factor 23 correlated negatively with 25-hydroxyvitamin D (r=-0.43, P<0.001) and 1,25-dihydroxyvitamin D (r=-0.39, P=0.003) and positively with phosphate (r=0.32, P=0.02) and parathyroid hormone (r=0.37, P=0.005). Among participants with AKI, enrollment fibroblast growth factor 23 (but not other serum parameters) was significantly associated with the composite endpoint, even after adjusting for age and enrollment serum creatinine (11 events; adjusted odds ratio per 1 SD higher ln[fibroblast growth factor 23]=13.73, 95% confidence interval=1.75-107.50). CONCLUSIONS: Among patients with AKI, fibroblast growth factor 23 levels are elevated and associated with greater risk of death or need for renal replacement therapy.

Developing an online and in-person HIT workforce training program using a team-based learning approach.

Workforce training in health information technology (HIT) is in demand as electronic health record adoption becomes a nationwide priority. Columbia University and Weill Cornell Medical College worked together to develop a 6-month ONC-supported certificate course. To identify relevant skills and knowledge, we conducted a needs assessment that included: interviews and focus groups with potential employers and current HIT employees; an analysis of both published literature on competencies and actual job listings; and the development of a diverse operations-oriented curriculum advisory committee, which help to synthesize the findings into 6 core curriculum modules. We selected a team-based learning approach, allowing us to train a diverse student body and providing opportunities to build collaboration skills. Our novel hybrid adaptation of team-based learning combines online and in-person activities. Lessons learned from the development of this program are likely to have widespread applicability as training programs in the field become more prevalent.

Determining the reasons for medication prescriptions in the EHR using knowledge and natural language processing.

Knowledge of medication indications is significant for automatic applications aimed at improving patient safety, such as computerized physician order entry and clinical decision support systems. The Electronic Health Record (EHR) contains pertinent information related to patient safety such as information related to appropriate prescribing. However, the reasons for medication prescriptions are usually not explicitly documented in the patient record. This paper describes a method that determines the reasons for medication uses based on information occurring in outpatient notes. The method utilizes drug-indication knowledge that we acquired, and natural language processing. Evaluation showed the method obtained a sensitivity of 62.8%, specificity of 93.9%, precision of 90% and F-measure of 73.9%. This pilot study demonstrated that linking external drug indication knowledge to the EHR for determining the reasons for medication use was promising, but also revealed some challenges. Future work will focus on increasing the accuracy and coverage of the indication knowledge and evaluating its performance using a much larger set of drugs frequently used in the outpatient population.