Time filter

Source Type

Castro V.M.,Massachusetts General Hospital | Castro V.M.,Partners Research Information Systems and Computing | Roberson A.M.,Massachusetts General Hospital | McCoy T.H.,Massachusetts General Hospital | And 7 more authors.
Neuropsychopharmacology | Year: 2016

Although lithium preparations remain first-line treatment for bipolar disorder, risk for development of renal insufficiency may discourage their use. Estimating such risk could allow more informed decisions and facilitate development of prevention strategies. We utilized electronic health records from a large New England health-care system between 2006 and 2013 to identify patients aged 18 years or older with a lithium prescription. Renal insufficiency was identified using the presence of renal failure by ICD9 code or laboratory-confirmed glomerular filtration rate below 60 ml/min. Logistic regression was used to build a predictive model in a random two-Thirds of the cohort, which was tested in the remaining one-Third. Risks associated with aspects of pharmacotherapy were also examined in the full cohort. We identified 1445 adult lithium-Treated patients with renal insufficiency, matched by risk set sampling 1: 3 with 4306 lithium-exposed patients without renal insufficiency. In regression models, features associated with risk included older age, female sex, history of smoking, history of hypertension, overall burden of medical comorbidity, and diagnosis of schizophrenia or schizoaffective disorder (p<0.01 for all contrasts). The model yielded an area under the ROC curve exceeding 0.81 in an independent testing set, with 74% of renal insufficiency cases among the top two risk quintiles. Use of lithium more than once daily, lithium levels greater than 0.6 mEq/l, and use of first-generation antipsychotics were independently associated with risk. These results suggest the possibility of stratifying risk for renal failure among lithium-Treated patients. Once-daily lithium dosing and maintaining lower lithium levels where possible may represent strategies for reducing risk. © 2016 American College of Neuropsychopharmacology.


Castro V.M.,Partners Research Information Systems and Computing | Apperson W.K.,Partners Research Information Systems and Computing | Gainer V.S.,Partners Research Information Systems and Computing | Ananthakrishnan A.N.,Massachusetts General Hospital | And 5 more authors.
Journal of Biomedical Informatics | Year: 2014

The success of many population studies is determined by proper matching of cases to controls. Some of the confounding and bias that afflict electronic health record (EHR)-based observational studies may be reduced by creating effective methods for finding adequate controls. We implemented a method to match case and control populations to compensate for sparse and unequal data collection practices common in EHR data. We did this by matching the healthcare utilization of patients after observing that more complete data was collected on high healthcare utilization patients vs. low healthcare utilization patients. In our results, we show that many of the anomalous differences in population comparisons are mitigated using this matching method compared to other traditional age and gender-based matching. As an example, the comparison of the disease associations of ulcerative colitis and Crohn's disease show differences that are not present when the controls are chosen in a random or even a matched age/gender/race algorithm. In conclusion, the use of healthcare utilization-based matching algorithms to find adequate controls greatly enhanced the accuracy of results in EHR studies. Full source code and documentation of the control matching methods is available at https://community.i2b2.org/wiki/display/conmat/. © 2014 Elsevier Inc.


PubMed | Massachusetts General Hospital and Partners Research Information Systems and Computing
Type: | Journal: Journal of biomedical informatics | Year: 2014

The success of many population studies is determined by proper matching of cases to controls. Some of the confounding and bias that afflict electronic health record (EHR)-based observational studies may be reduced by creating effective methods for finding adequate controls. We implemented a method to match case and control populations to compensate for sparse and unequal data collection practices common in EHR data. We did this by matching the healthcare utilization of patients after observing that more complete data was collected on high healthcare utilization patients vs. low healthcare utilization patients. In our results, we show that many of the anomalous differences in population comparisons are mitigated using this matching method compared to other traditional age and gender-based matching. As an example, the comparison of the disease associations of ulcerative colitis and Crohns disease show differences that are not present when the controls are chosen in a random or even a matched age/gender/race algorithm. In conclusion, the use of healthcare utilization-based matching algorithms to find adequate controls greatly enhanced the accuracy of results in EHR studies. Full source code and documentation of the control matching methods is available at https://community.i2b2.org/wiki/display/conmat/.

Loading Partners Research Information Systems and Computing collaborators
Loading Partners Research Information Systems and Computing collaborators