Michael E. Matheny

Hypoglycemia and clinical outcomes in patients with diabetes hospitalized in the general ward.

OBJECTIVE Hypoglycemia is associated with adverse outcomes in mixed populations of patients in intensive care units. It is not known whether the same risks exist for diabetic patients who are less severely ill. In this study, we aimed to determine whether hypoglycemic episodes are associated with higher mortality in diabetic patients hospitalized in the general ward. RESEARCH DESIGN AND METHODS This retrospective cohort study analyzed 4,368 admissions of 2,582 patients with diabetes hospitalized in the general ward of a teaching hospital between January 2003 and August 2004. The associations between the number and severity of hypoglycemic (≤50 mg/dl) episodes and inpatient mortality, length of stay (LOS), and mortality within 1 year after discharge were evaluated. RESULTS Hypoglycemia was observed in 7.7% of admissions. In multivariable analysis, each additional day with hypoglycemia was associated with an increase of 85.3% in the odds of inpatient death (P = 0.009) and 65.8% (P = 0.0003) in the odds of death within 1 year from discharge. The odds of inpatient death also rose threefold for every 10 mg/dl decrease in the lowest blood glucose during hospitalization (P = 0.0058). LOS increased by 2.5 days for each day with hypoglycemia (P < 0.0001). CONCLUSIONS Hypoglycemia is common in diabetic patients hospitalized in the general ward. Patients with hypoglycemia have increased LOS and higher mortality both during and after admission. Measures should be undertaken to decrease the frequency of hypoglycemia in this high-risk patient population.

Automated identification of postoperative complications within an electronic medical record using natural language processing.

CONTEXT Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach. OBJECTIVE To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record. DESIGN, SETTING, AND PATIENTS Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006. MAIN OUTCOME MEASURES Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information. RESULTS The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses. CONCLUSION Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.

Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury

Studies of acute kidney injury usually lack data on pre-admission kidney function and often substitute an inpatient or imputed serum creatinine as an estimate for baseline renal function. In this study, we compared the potential error introduced by using surrogates such as (1) an estimated glomerular filtration rate of 75 ml/min per 1.73 m(2) (suggested by the Acute Dialysis Quality Initiative), (2) a minimum inpatient serum creatinine value, and (3) the first admission serum creatinine value, with values computed using pre-admission renal function. The study covered a 12-month period and included a cohort of 4863 adults admitted to the Vanderbilt University Hospital. Use of both imputed and minimum baseline serum creatinine values significantly inflated the incidence of acute kidney injury by about half, producing low specificities of 77-80%. In contrast, use of the admission serum creatinine value as baseline significantly underestimated the incidence by about a third, yielding a low sensitivity of 39%. Application of any surrogate marker led to frequent misclassification of patient deaths after acute kidney injury and differences in both in-hospital and 60-day mortality rates. Our study found that commonly used surrogates for baseline serum creatinine result in bi-directional misclassification of the incidence and prognosis of acute kidney injury in a hospital setting.

Contemporary Incidence, Predictors, and Outcomes of Acute Kidney Injury in Patients Undergoing Percutaneous Coronary Interventions: Insights From the NCDR Cath-PCI Registry

OBJECTIVES This study sought to examine the contemporary incidence, predictors and outcomes of acute kidney injury in patients undergoing percutaneous coronary interventions. BACKGROUND Acute kidney injury (AKI) is a serious and potentially preventable complication of percutaneous coronary interventions (PCIs) that is associated with adverse outcomes. The contemporary incidence, predictors, and outcomes of AKI are not well defined, and clarifying these can help identify high-risk patients for proactive prevention. METHODS A total of 985,737 consecutive patients underwent PCIs at 1,253 sites participating in the National Cardiovascular Data Registry Cath-PCI registry from June 2009 through June 2011. AKI was defined on the basis of changes in serum creatinine level in the hospital according to the Acute Kidney Injury Network (AKIN) criteria. Using multivariable regression analyses with generalized estimating equations, we identified patient characteristics associated with AKI. RESULTS Overall, 69,658 (7.1%) patients experienced AKI, with 3,005 (0.3%) requiring new dialysis. On multivariable analyses, the factors most strongly associated with development of AKI included ST-segment elevation myocardial infarction (STEMI) presentation (odds ratio [OR]: 2.60; 95% confidence interval [CI]: 2.53 to 2.67), severe chronic kidney disease (OR: 3.59; 95% CI: 3.47 to 3.71), and cardiogenic shock (OR: 2.92; 95% CI: 2.80 to 3.04). The in-hospital mortality rate was 9.7% for patients with AKI and 34% for those requiring dialysis compared with 0.5% for patients without AKI (p < 0.001). After multivariable adjustment, AKI (OR: 7.8; 95% CI: 7.4 to 8.1, p < 0.001) and dialysis (OR: 21.7; 95% CI: 19.6 to 24.1; p < 0.001) remained independent predictors of in-hospital mortality. CONCLUSIONS Approximately 7% of patients undergoing a PCI experience AKI, which is strongly associated with in-hospital mortality. Defining strategies to minimize the risk of AKI in patients undergoing PCI are needed to improve the safety and outcomes of the procedure.

Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs

Objective Adverse drug reaction (ADR) is one of the major causes of failure in drug development. Severe ADRs that go undetected until the post-marketing phase of a drug often lead to patient morbidity. Accurate prediction of potential ADRs is required in the entire life cycle of a drug, including early stages of drug design, different phases of clinical trials, and post-marketing surveillance. Methods Many studies have utilized either chemical structures or molecular pathways of the drugs to predict ADRs. Here, the authors propose a machine-learning-based approach for ADR prediction by integrating the phenotypic characteristics of a drug, including indications and other known ADRs, with the drugs chemical structures and biological properties, including protein targets and pathway information. A large-scale study was conducted to predict 1385 known ADRs of 832 approved drugs, and five machine-learning algorithms for this task were compared. Results This evaluation, based on a fivefold cross-validation, showed that the support vector machine algorithm outperformed the others. Of the three types of information, phenotypic data were the most informative for ADR prediction. When biological and phenotypic features were added to the baseline chemical information, the ADR prediction model achieved significant improvements in area under the curve (from 0.9054 to 0.9524), precision (from 43.37% to 66.17%), and recall (from 49.25% to 63.06%). Most importantly, the proposed model successfully predicted the ADRs associated with withdrawal of rofecoxib and cerivastatin. Conclusion The results suggest that phenotypic information on drugs is valuable for ADR prediction. Moreover, they demonstrate that different models that combine chemical, biological, or phenotypic information can be built from approved drugs, and they have the potential to detect clinically important ADRs in both preclinical and post-marketing phases.

Outpatient Nephrology Referral Rates after Acute Kidney Injury

AKI associates with an increased risk for the development and progression of CKD and mortality. Processes of care after an episode of AKI are not well described. Here, we examined the likelihood of nephrology referral among survivors of AKI at risk for subsequent decline in kidney function in a US Department of Veterans Affairs database. We identified 3929 survivors of AKI hospitalized between January 2003 and December 2008 who had an estimated GFR (eGFR) <60 ml/min per 1.73 m(2) 30 days after peak injury. We analyzed time to referral considering improvement in kidney function (eGFR ≥60 ml/min per 1.73 m(2)), dialysis initiation, and death as competing risks over a 12-month surveillance period. Median age was 73 years (interquartile range, 62-79 years) and the prevalence of preadmission kidney dysfunction (baseline eGFR <60 ml/min per 1.73 m(2)) was 60%. Overall mortality during the surveillance period was 22%. The cumulative incidence of nephrology referral before dying, initiating dialysis, or experiencing an improvement in kidney function was 8.5% (95% confidence interval, 7.6-9.4). Severity of AKI did not affect referral rates. These data demonstrate that a minority of at-risk survivors are referred for nephrology care after an episode of AKI. Determining how to best identify survivors of AKI who are at highest risk for complications and progression of CKD could facilitate early nephrology-based interventions.

iDASH: integrating data for analysis, anonymization, and sharing

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.

Estimating Baseline Kidney Function in Hospitalized Patients with Impaired Kidney Function

BACKGROUND AND OBJECTIVES Inaccurate determination of baseline kidney function can misclassify acute kidney injury (AKI) and affect the study of AKI-related outcomes. No consensus exists on how to optimally determine baseline kidney function when multiple preadmission creatinine measurements are available. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS The accuracy of commonly used methods for estimating baseline serum creatinine was compared with that of a reference standard adjudicated by a panel of board-certified nephrologists in 379 patients with AKI or CKD admitted to a tertiary referral center. RESULTS Agreement between estimating methods and the reference standard was highest when using creatinine values measured 7-365 days before admission. During this interval, the intraclass correlation coefficient (ICC) for the mean outpatient serum creatinine level (0.91 [95% confidence interval (CI), 0.88-0.92]) was higher than the most recent outpatient (ICC, 0.84 [95% CI, 0.80-0.88]; P<0.001) and the nadir outpatient (ICC, 0.83 [95% CI, 0.76-0.87; P<0.001) serum creatinine. Using the final creatinine value from a prior inpatient admission increased the ICC of the most recent outpatient creatinine method (0.88 [95% CI, 0.85-0.91]). Performance of all methods declined or was unchanged when the time interval was broadened to 2 years or included serum creatinine measured within a week of admission. CONCLUSIONS The mean outpatient serum creatinine measured within a year of hospitalization most closely approximates nephrologist-adjudicated serum creatinine values.

Occurrence of adverse, often preventable, events in community hospitals involving nephrotoxic drugs or those excreted by the kidney

Medication errors in patients with reduced creatinine clearance are harmful and costly; however, most studies have been conducted in large academic hospitals. As there are few studies regarding this issue in smaller community hospitals, we conducted a multicenter, retrospective cohort study in six community hospitals (100 to 300 beds) to assess the incidence and severity of adverse drug events (ADEs) in patients with reduced creatinine clearance. A chart review was performed on adult patients hospitalized during a 20-month study period with serum creatinine over 1.5 mg/dl who were exposed to drugs that are nephrotoxic or cleared by the kidney. Among 109,641 patients, 17,614 had reduced creatinine clearance, and in a random sample of 900 of these patients, there were 498 potential ADEs and 90 ADEs. Among these ADEs, 91% were preventable, 51% were serious, 44% were significant, and 4.5% were life threatening. Of the potential ADEs, 54% were serious, 44% were significant, 1.6% were life threatening, and 96.6% were not intercepted. All 82 preventable events could have been intercepted by renal dose checking. Our study shows that ADEs were common in patients with impaired kidney function in community hospitals, and many appear potentially preventable with renal dose checking.

Risk Predictors of Retroperitoneal Hemorrhage Following Percutaneous Coronary Intervention

Retroperitoneal hemorrhage (RPH) is a potentially catastrophic complication after percutaneous coronary intervention (PCI). Previous studies identified female gender, body surface area, and high arterial puncture location as independent risk factors for RPH. There have been conflicting reports regarding the association with vascular closure devices (VCDs). Chronic renal insufficiency (CRI) and diabetes mellitus have been associated with both peripheral vascular disease and vascular access-site complications. The putative association of VCDs, CRI, and diabetes mellitus with RPH in the contemporary PCI era was investigated. A total of 3,062 consecutive patients undergoing 3,482 PCIs at Brigham and Womens Hospital from January 2005 to April 2007 were evaluated for the study. All 3,311 patients with femoral angiography underwent hand-caliper-based quantitative vascular analysis and were included in this analysis. Multivariate analysis was performed using a backwards selection algorithm, and a propensity adjustment was developed to control for possible confounding variables regarding VCD use. The incidence of RPH was 0.49% (17 of 3,482 patients). After multivariate and propensity analyses, covariates that significantly influenced the risk of RPH were CRI, glycoprotein IIb/IIIa inhibitors, and high arterial puncture (p < or =0.007). VCD use was not independently associated with the development of RPH (p = 0.74). In conclusion, this large prospective cohort study identified CRI, but not VCD use, as an independent predictor for RPH and peripheral vascular disease.