Prediction and detection models for acute kidney injury in hospitalized older adults.

BACKGROUND: Acute Kidney Injury (AKI) occurs in at least 5 % of hospitalized patients and can result in 40-70 % morbidity and mortality. Even following recovery, many subjects may experience progressive deterioration of renal function. The heterogeneous etiology and pathophysiology of AKI complicates its diagnosis and medical management and can add to poor patient outcomes and incur substantial hospital costs. AKI is predictable and may be avoidable if early risk factors are identified and utilized in the clinical setting. Timely detection of undiagnosed AKI in hospitalized patients can also lead to better disease management. METHODS: Data from 25,521 hospital stays in one calendar year of patients 60 years and older was collected from a large health care system. Four machine learning models (logistic regression, support vector machines, decision trees and naïve Bayes) along with their ensemble were tested for AKI prediction and detection tasks. Patient demographics, laboratory tests, medications and comorbid conditions were used as the predictor variables. The models were compared using the area under ROC curve (AUC) evaluation metric. RESULTS: Logistic regression performed the best for AKI detection (AUC 0.743) and was a close second to the ensemble for AKI prediction (AUC ensemble: 0.664, AUC logistic regression: 0.660). History of prior AKI, use of combination drugs such as ACE inhibitors, NSAIDS and diuretics, and presence of comorbid conditions such as respiratory failure were found significant for both AKI detection and risk prediction. CONCLUSIONS: The machine learning models performed fairly well on both predicting AKI and detecting undiagnosed AKI. To the best of our knowledge, this is the first study examining the difference between prediction and detection of AKI. The distinction has clinical relevance, and can help providers either identify at risk subjects and implement preventative strategies or manage their treatment depending on whether AKI is predicted or detected.

Risk factors of trastuzumab-induced cardiotoxicity in breast cancer: A meta-analysis.

BACKGROUND: Trastuzumab targets the human epidermal growth factor receptor 2 oncogene and in combination with first-line therapy results in significantly improved survival outcomes and has thus become standard of care in both adjuvant and metastatic settings. While it is estimated that 1% to 4% of patients treated with trastuzumab will develop heart failure and ∼10% will experience a reduction in left ventricular ejection fraction (LVEF), the patient risk factors associated with trastuzumab-induced cardiotoxicity (TIC) are unclear. This meta-analysis aims to consolidate previously published data to identify the risk factors most likely leading to TIC. METHODS: A search of the MEDLINE literature database using the keywords trastuzumab/Herceptin, risk factors, outcomes, cardiac, cardiotoxicity, cardiomyopathy, LVEF, and chemotherapy was performed. Only prospective/retrospective human studies were included, with additional studies excluded if they reported baseline LVEF > 68%, a cohort of <50 patients, or results that were not stratified based on cardiotoxic events. Pooled odds ratio (OR) and 95% confidence interval (CI) for each potential risk factor were calculated, with heterogeneity of data and samples explored using random-effects modeling. RESULTS: Data were collected from 17 articles, capturing 6527 patients. Hypertension (OR 1.61, 95% CI 1.14-2.26; P < 0.01), diabetes (OR 1.62; 95% CI 1.10-2.38; P < 0.02), previous anthracycline use (OR 2.14; 95% CI 1.17-3.92; P < 0.02), and older age (P = 0.013) were all shown to be associated with TIC. CONCLUSION: Cardiac performance should be closely monitored in women treated with trastuzumab. Recognizing potential risk factors along with careful attention to symptoms/LVEF measurements could minimize the occurrence of TIC in this population.