Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Objective To compare different strategies predicting hyperkalemia (serum potassium level ≥5.5 mEq/l) in hospitalized patients for whom medications triggering potassium-increasing drug-drug interactions (DDIs) re ordered.#N##N#Materials and Methods  investigated 5 strategies that combined prediction triggered at onset of DDI versus continuous monitoring and taking into account an increasing number of patient parameters. The considered patient parameters re identified using generalized additive models, and the thresholds of the prediction strategies re calculated by applying Youden’s J statistic to receiver operation characteristic curves. Half of the data served as the calibration set, half as the validation set.#N##N#Results  identified 132 incidences of hyperkalemia induced by 8413 potentially severe potassium-increasing DDIs among 76 467 patients. The positive predictive value (PPV) of those strategies predicting hyperkalemia at the onset of DDI ranged from 1.79% (undifferentiated anticipation of hyperkalemia due to the DDI) to 3.02% (additionally considering the baseline serum potassium) and 3.10% (including further patient parameters). Continuous monitoring significantly increased the PPV to 8.25% (considering the current serum potassium) and 9.34% (additional patient parameters).#N##N#Conclusion Continuous monitoring of the risk for hyperkalemia based on current potassium level shows a better predictive por than predictions triggered at the onset of DDI. This contrasts with efforts to improve DDI alerts by taking into account more patient parameters at the time of ordering.
['Emmanuel Eschmann', 'Patrick E. Beeler', 'Markus Schneemann', 'Jürg Blaser']
I am writing a research paper on a idea Developing strategies for predicting hyperkalemia in potassium-increasing drug-drug interactions
[{"from": "human", "value": "I am writing a research paper on a idea Developing strategies for predicting hyperkalemia in potassium-increasing drug-drug interactions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Objective To compare different strategies predicting hyperkalemia (serum potassium level \u22655.5\u2009mEq/l) in hospitalized patients for whom medications triggering potassium-increasing drug-drug interactions (DDIs) re ordered.#N##N#Materials and Methods  investigated 5 strategies that combined prediction triggered at onset of DDI versus continuous monitoring and taking into account an increasing number of patient parameters. The considered patient parameters re identified using generalized additive models, and the thresholds of the prediction strategies re calculated by applying Youden\u2019s J statistic to receiver operation characteristic curves. Half of the data served as the calibration set, half as the validation set.#N##N#Results  identified 132 incidences of hyperkalemia induced by 8413 potentially severe potassium-increasing DDIs among 76\u2009467 patients. The positive predictive value (PPV) of those strategies predicting hyperkalemia at the onset of DDI ranged from 1.79% (undifferentiated anticipation of hyperkalemia due to the DDI) to 3.02% (additionally considering the baseline serum potassium) and 3.10% (including further patient parameters). Continuous monitoring significantly increased the PPV to 8.25% (considering the current serum potassium) and 9.34% (additional patient parameters).#N##N#Conclusion Continuous monitoring of the risk for hyperkalemia based on current potassium level shows a better predictive por than predictions triggered at the onset of DDI. This contrasts with efforts to improve DDI alerts by taking into account more patient parameters at the time of ordering."}]