Search

Shi-Tong Diao, Run Dong, Jin-Min Peng, Yan Chen, Shan Li, Shu-Hua He, Yi-Fan Wang, Bin Du, Li Weng On behalf of the China Critical Care Clinical Trials Group (CCCCTG)Medical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, People’s Republic of ChinaCorrespondence: Li Weng, Medical Intensive Care Unit, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, 1 Shuai Fu Yuan, Beijing, 100730, People’s Republic of China, Email wengli@gmail.comBackground: Sepsis surveillance was important for resources allocation, prevention, and development of health policy.Objective: The aim of the study was to validate a modified International Classification of Diseases (ICD)-10 based algorithm for identifying hospitalized patients with sepsis.Methods: We retrospectively analyzed a prospective, single-center cohort of adult patients who were consecutively admitted to one medical ICU ward and ten non-ICU wards with suspected or confirmed infections during a 6-month period. A modified ICD-10 based algorithm was validated against a reference standard of Sequential Organ Failure Assessment (SOFA) score based on Sepsis-3. Sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and areas under the receiver operating characteristic curves (AUROCs) were calculated for modified ICD-10 criteria, eSOFA criteria, Martin’s criteria, and Angus’s criteria.Results: Of the 547 patients in the cohort, 332 (61%) patients met Sepsis-3 criteria and 274 (50%) met modified ICD-10 criteria. In the ICU setting, modified ICD-10 criteria had SE (84.47%), SP (88.57%), PPV (95.60), and NPV (65.96). In non-ICU settings, modified ICD-10 had SE (64.19%), SP (80.00%), PPV (80.33), and NPV (63.72). In the whole cohort, the AUROCs of modified ICD-10 criteria, eSOFA, Angus’s criteria, and Martin’s criteria were 0.76, 0.75, 0.62, and 0.62, respectively.Conclusion: This study demonstrated that modified ICD-10 criteria had higher validity compared with Angus’s criteria and Martin’s criteria. Validity of the modified ICD-10 criteria was similar to eSOFA criteria. Modified ICD-10 algorithm can be used to provide an accurate estimate of population-based sepsis burden of China.Keywords: sepsis, validation, International Classification of Diseases, administrative health data, identification

Afsaneh Roshanghalb,1 Cristina Mazzali,2 Emanuele Lettieri1 1Department of Management, Economics & Industrial Engineering, Politecnico di Milano, Milan, Italy; 2Quality and Clinical Risk Unit, Niguarda Hospital, Milan, ItalyCorrespondence: Afsaneh Roshanghalb Email afsaneh.roshanghalb@polimi.itBackground: Controlling the quality of care through readmissions and mortality for patients with heart failure (HF) is a national priority for healthcare regulators in developed countries. In this longitudinal cohort study, using administrative data such as hospital discharge forms (HDFs), emergency departments (EDs) accesses, and vital statistics, we test new covariates for predicting mortality and readmissions of patients hospitalized for HF and discuss the use of combined outcome as an alternative.Methods: Logistic models, with a stepwise selection method, were estimated on 70% of the sample and validated on the remaining 30% to evaluate 30-day mortality, 30-day readmissions, and the combined outcome. We followed an extraction method for any-cause mortality and unplanned readmission within 30 days after incident HF hospitalization. Data on patient admission and previous history were extracted by HDFs and ED dataset.Results: Our principal findings demonstrate that the model’s discriminant ability is consistent with literature both for mortality (AUC=0.738, CI (0.729– 0.748)) and readmissions (AUC=0.578, CI (0.562– 0.594)). Additionally, the discriminant ability of the composite outcome model is satisfactory (AUC=0.675, CI (0.666– 0.684)).Conclusion: Hospitalization characteristics and patient history introduced in the logistic models do not improve their discriminant ability. The composite outcome prediction is led more by mortality than readmission, without improvements for the comprehension of the readmission phenomenon.Keywords: administrative health data, heart failure, mortality, patient readmission

Romina Pace,1 Elham Rahme,1,2 Deborah Da Costa,1,2 Kaberi Dasgupta1,2 1Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, QC, Canada; 2Department of Medicine, McGill University Health Center, Montreal, QC, Canada Purpose: The aim of this study was to examine the association between gestational diabetes mellitus (GDM) and depression incidence in mothers and fathers during prenatal and postnatal periods.Patients and methods: Matched pairs (GDM vs no GDM) of randomly selected mothers with singleton live births (matched by age group, delivery year, and health region) and their partners (Quebec, Canada; cohort inception 1990–2007) were assessed for a composite outcome of depression/self-harm/suicide using a health administrative database. We examined the association of GDM and the composite outcome in the following three nonoverlapping periods: 1) 24 weeks gestation up to delivery; 2) delivery up to 1 year postpartum; and 3) 1 year postpartum to study end (March 31, 2012). We used stratified Cox proportional regression hazards models, with three models in mothers and three models in fathers, corresponding to each of the time periods of interest.Results: In the 58,400 mothers, women with GDM had a nearly twofold greater risk (adjusted HR: 1.82, 95% CI 1.28, 2.59) of being diagnosed with depression compared to those without GDM during the prenatal period. In the first year postpartum, there was no conclusive difference observed between the two groups of mothers (adjusted HR: 1.05, 95% CI 0.84, 1.30). Beyond the first year postpartum, there was an 8% increased risk (adjusted HR: 1.08, 95% CI 1.03, 1.14) of depression in women with a history GDM compared to those without. A total of 63,384 fathers were included in our analyses, and no association between GDM in one’s partner and depression was found during any of the three time periods evaluated.Conclusion: GDM is associated with an increased risk of depression in women particularly during pregnancy highlighting the need to screen for depression and provide supportive interventions during this period. Keywords: depression, self-harm, suicide, administrative health data, pregnancy, postpartum

Tasnim F Imran,1–3,* Daniel Posner,1,4,* Jacqueline Honerlaw,1 Jason L Vassy,1,2 Rebecca J Song,1 Yuk-Lam Ho,1 Steven J Kittner,5 Katherine P Liao,1,2 Tianxi Cai,1,6 Christopher J O’Donnell,1,2 Luc Djousse,1,2 David R Gagnon,1,4 J Michael Gaziano,1,2 Peter WF Wilson,7,8 Kelly Cho1,2 On behalf of the VA Million Veteran Program 1Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA, USA; 2Department of Medicine, Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; 3Department of Medicine, Cardiology Section, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA; 4Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA; 5Department of Neurology, Baltimore VA Medical Center and University of Maryland School of Medicine, Baltimore, MD, USA; 6Harvard T. H. Chan School of Public Health, Boston, MA, USA; 7Atlanta VA Medical Center, Decatur, GA, USA; 8Department of Medicine, Division of Cardiovascular Disease, Emory University School of Medicine, Atlanta, GA, USA *These authors contributed equally to this work Background: Large databases provide an efficient way to analyze patient data. A challenge with these databases is the inconsistency of ICD codes and a potential for inaccurate ascertainment of cases. The purpose of this study was to develop and validate a reliable protocol to identify cases of acute ischemic stroke (AIS) from a large national database.Methods: Using the national Veterans Affairs electronic health-record system, Center for Medicare and Medicaid Services, and National Death Index data, we developed an algorithm to identify cases of AIS. Using a combination of inpatient and outpatient ICD9 codes, we selected cases of AIS and controls from 1992 to 2014. Diagnoses determined after medical-chart review were considered the gold standard. We used a machine-learning algorithm and a neural network approach to identify AIS from ICD9 codes and electronic health-record information and compared it with a previous rule-based stroke-classification algorithm.Results: We reviewed administrative hospital data, ICD9 codes, and medical records of 268 patients in detail. Compared with the gold standard, this AIS algorithm had a sensitivity of 91%, specificity of 95%, and positive predictive value of 88%. A total of 80,508 highly likely cases of AIS were identified using the algorithm in the Veterans Affairs national cardiovascular disease-risk cohort (n=2,114,458).Conclusion: Our algorithm had high specificity for identifying AIS in a nationwide electronic health-record system. This approach may be utilized in other electronic health databases to accurately identify patients with AIS. Keywords: acute ischemic stroke, algorithm, large databases, big data, administrative health data, cerebrovascular accident