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Rationale: In renal impairment, the pharmacokinetic properties of the drug are altered, and the systemic clearance is reduced. The current study aimed to assess the optimization of antibiotics dosing in renally impaired patients in a healthcare setting. Methodology: A prospective study was conducted on in-patients in the nephrology department, prescribed with antibiotics in a tertiary care hospital. The creatinine clearance was calculated by Cockroft-Gault and Jelliffe method. The dose appropriateness was cross-checked using standard databases and literature from the manufacturer data. Results: Of 139 participants 112 (80.6%) had CKD and 27(19.4%) had AKI. Urinary tract infection was most common. Monotherapy (62) was the most preferred choice, followed by dual in (43) and triple in (7%). A positive clinical outcome of 79.1% was achieved. Cefoperazone-sulbactam was most widely used antibiotic. The mean difference in creatinine clearance was 4.55ml/min in AKI patients. Conclusion: Dose appropriateness is a significant factor in achieving favorable clinical outcomes.

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The length of stay (LOS) and healthcare expenses for patients are drastically impacted by antimicrobial resistance (AMR). In addition to building a prediction model for AMR infection outcomes, the study will examine how AMR influences the attributable cost and length of stay in hospitalized patients. WEKA-ML version 3.8.6 was used to build the models. The discretization of LOS and cost into distinct bins is normalized. Utilizing a number of feature selection techniques, the best characteristics associated with the outcome were selected. The optimal feature selection strategy was selected, and several methods were used to the training (66 percent / 80 percent) and test (34 percent /20 percent) data sets to prevent underfitting and overfitting. Using ROC curves, prediction error, and accuracy metrics, the best-predicted model is selected. In terms of forecasting LOS, RF performed better (accuracy=69.6, ROC=0.852) than bagging (accuracy=69.6, ROC=0.862) while using the cfs subset attribute evaluation+greedy stepwise approach and the Infogain+ranker method. The majority of patients fell between the ranges of 7 and 14 days. With 34% of test data sets, RF outperformed marginally better using the infogain attribute selection+ranker technique (Accuracy=80.8 ROC=0.967) in predicting cost. Most fell into the >$1720 range, then came the $814 range. Effective LOS and treatment cost prediction for resistant infections gives crucial data that helps hospital administration, and the medical staff make crucial decisions. While avoiding a significant loss of resources, hospital administration can provide the appropriate and essential resources and the best medical team for treating the patient.