Development and validation of an automated algorithm for identifying patients at higher risk for drug-induced acute kidney injury

PURPOSE

Using information from institutional electronic health records, we aimed to develop dynamic predictive models to identify patients at high risk of acute kidney injury (AKI) among those who received a nephrotoxic medication during their hospital stay.

METHODS

Candidate predictors were measured for each of the first 5 hospital days where a patient received a nephrotoxic medication (risk model days) to predict an AKI, using logistic regression with reduced backward variables elimination in 100 bootstrap samples. An AKI event was defined as an increase of serum creatinine ≥ 200% of a baseline SCr within 5 days after a risk model day. Final models were internally validated by replication in 100 bootstrap samples and a risk score for each patient was calculated from the validated model. As performance measures, the area under the receiver operation characteristic curves (AUC) and the number of AKI events among patients who had high risk scores were estimated.

RESULTS

The study population included 62,561 admissions followed by 1,212 AKI events (1.9 events/100 admissions). We constructed 5 risk models corresponding to the first 5 hospital days where patients were exposed to at least one nephrotoxic medication. Validated AUCs of the 5 models ranged from 0.78 to 0.81. Depending on risk model day, admissions ranked in the 90th percentile of the risk score captured between 43% to 49% of all AKI events.

CONCLUSION

A dynamic prediction model was built successfully for inpatient AKI with excellent discriminative validity and good calibration, allowing clinicians to focus on a select high-risk population that captures the majority of AKI events.

Common inpatient hypoglycemia phenotypes identified from an automated electronic health record-based prediction model

PURPOSE

Common inpatient hypoglycemia risk factor patterns (phenotypes) from an electronic health record (EHR)-based prediction model and preventive strategies were identified.

METHODS

Patients admitted to 2 large academic medical centers who were in the top fifth percentile of a previously developed hypoglycemia risk score and developed hypoglycemia (blood glucose [BG] of <50mg/dL) were included in the study. Frequencies of all combinations of ≥4 risk factors contributing to the risk score among these patients were determined to identify common risk patterns. Clinical pharmacists developed clinical vignettes for each common pattern and formulated medication therapy management recommendations for hypoglycemia prevention.

RESULTS

A total of 401 admissions with a hypoglycemic event were identified among 1,875 admissions whose hypoglycemic risk was in the top fifth percentile among all admissions that received antihyperglycemic drugs and evaluated. Five distinct phenotypes emerged: (1) frail patients with history of hypoglycemia receiving insulin on hospital day 1, (2) a rapid downward trend in BG values in patients receiving an insulin infusion or with a history of hypoglycemia, (3) administration of insulin in the presence of an active nothing by mouth order in frail patients, (4) repeated low BG level in frail patients, and (5) inadequate night-time BG monitoring for patients on long-acting insulin. The 5 themes jointly described 53.0% of high-risk patients who experienced hypoglycemia.

CONCLUSION

Five distinct phenotypes that are prevalent in patients at greatest risk for inpatient hypoglycemia were identified.

Measurement of selected preventable adverse drug events in electronic health records: Toward developing a complexity score

PURPOSE

The defining of a select number of high-priority preventable adverse drug events (pADEs) for measurement in the electronic health record (EHR) and the estimation of pADE incidences in two tertiary care facilities are described.

METHODS

This study was part of a larger effort aimed at developing an automated electronic health record (EHR)-based complexity-score (C-score) that ranks hospitalized patients according to their risk for pADEs for clinical intervention. We developed measures for 16 high-priority pADEs often deemed preventable using discrete clinical and administrative EHR data. For each pADE we specified inclusion and exclusion criteria that were used to define risk populations for each specific pADE. The incidence of each type of pADE was then measured during a designated follow-up period considering all adult admissions to 2 large academic tertiary care hospitals, who were eligible for the pADE-specific risk populations during any of their first 5 hospital days.

RESULTS

Utilizing the data from 83,787 admissions who were at risk for at least one pADE during at least one of their first five hospital days, we found that 27,193 admissions (32.5%) developed at least one pADE. Uncontrolled postsurgical pain, uncontrolled pneumonia, and drug-associated hypotension had the highest incidences with the following number of days with pADE per number of patients at risk: 13,484 of 19,640; 527 of 1,530; and 13,394 of 43,630, while drug-associated falls (446 of 75,036), drug-associated acute mental status changes (262 of 66,875) and venous thromboembolism (214 of 74,283) had the lowest incidence rates.

CONCLUSION

EHR-based definitions of clinically important pADEs were developed, and the incidence of the pADEs was estimated. These definitions will be advanced for the creation of prediction models to develop a C-score for identifying patients at risk for pADEs to prioritize pharmacist intervention.

Nitrite adulteration of workplace urine drug-testing specimens. I. Sources and associated concentrations of nitrite in urine and distinction between natural sources and adulteration

The active ingredient in the commercial workplace urine drug-testing adulterant, Klear, was previously determined to be nitrite ion. Nitrite adulteration compromises the confirmation of some drugs, notably the marijuana metabolite. A previously reported bisulfite step overcomes some nitrite adulteration, but it cannot do so in every case, which leaves the laboratory to report the specimen as not suitable for testing. Unlike many other adulterants, nitrite is found in normal urine at low concentrations. In order to defend a report of nitrite adulteration, it is necessary to provide evidence that the amount of nitrite in a workplace urine specimen could not arise by normal means. The objectives of this study were to identify all sources of nitrite in urine and the range of concentrations associated with these sources and to determine if nitrite adulteration can be supported based upon a quantitative result. The scientific literature was reviewed for internal and external sources of nitrite and their concentration ranges and are reported. The following specimens were obtained and nitrite concentrations measured by a spectrophotometric method: clinical specimens nitrite positive by test strip (< 15 micrograms/mL); specimens culture positive for nitrate-reducing microorganisms (< 36 micrograms/mL); specimens from patients on medications that may metabolize to nitrite (< 6 micrograms/mL); and drug-test specimens, both negative (< 130 micrograms/mL) and others that appeared to be adulterated with nitrite (range 1910-12,200 micrograms/mL, mean 5910). The literature and the nitrite measurements of this study indicate a substantial difference between concentrations from natural sources compared with adulteration. A quantitative measurement of nitrite by a well-structured assay can provide scientifically valid and forensically defensible proof of adulteration with a nitrite-containing substance.