Objective:To investigate the clinical features, management, and prognosis of amphotericin B (AmB)-associated nephrogenic diabetes insipidus (NDI).Methods:The diagnosis and treatment process of a patient with AmB-related NDI who was admitted to the General Hospital of Tianjin Medical University was reported, and the main clinical data of the patient and related cases such as gender, age, primary disease, medication, NDI occurrence, treatment and outcome collected from CNKI, Wanfang Medical, and VIP databases, PubMed, Web of Science, and ScienceDirect (up to August 22, 2023) were analyzed descriptively.Results:A total of 11 patients were included in the analysis, including 7 males and 4 females, with an average age of 41 years, ranging from 12 to 66 years. The correlation between AmB and NDI was probable in 7 cases and possible in 4 cases. The time from the beginning of medication to the occurrence of NDI was 3 days to 3 weeks, and the urine volume was 3.8-10.8 L/d. Among the 11 patients, 6 patients had hypokalemia and 5 patients had elevated serum creatinine; 9 patients did not stop using AmB due to their conditions, the intervention plans were described in details in 4 patients and AmB treatments were completed after symptomatic treatments, 7 patients were cured or improved, and 2 patients died. Among the 2 patients who stopped taking the medication, 1 patient showed significant improvement in symptoms and resumed using AmB without recurrence; the other case was improved after stopping the medication, but relapsed after resuming use without stopping the medication, and the patient died from the primary disease.Conclusions:During the use of AmB, close attention should be paid to the occurrence of NDI. After occurrence of NDI, most patients can continue taking the AmB and have a good prognosis after symptomatic treatments.

Objective:To investigate the clinical features, management, and prognosis of amphotericin B (AmB)-associated nephrogenic diabetes insipidus (NDI).Methods:The diagnosis and treatment process of a patient with AmB-related NDI who was admitted to the General Hospital of Tianjin Medical University was reported, and the main clinical data of the patient and related cases such as gender, age, primary disease, medication, NDI occurrence, treatment and outcome collected from CNKI, Wanfang Medical, and VIP databases, PubMed, Web of Science, and ScienceDirect (up to August 22, 2023) were analyzed descriptively.Results:A total of 11 patients were included in the analysis, including 7 males and 4 females, with an average age of 41 years, ranging from 12 to 66 years. The correlation between AmB and NDI was probable in 7 cases and possible in 4 cases. The time from the beginning of medication to the occurrence of NDI was 3 days to 3 weeks, and the urine volume was 3.8-10.8 L/d. Among the 11 patients, 6 patients had hypokalemia and 5 patients had elevated serum creatinine; 9 patients did not stop using AmB due to their conditions, the intervention plans were described in details in 4 patients and AmB treatments were completed after symptomatic treatments, 7 patients were cured or improved, and 2 patients died. Among the 2 patients who stopped taking the medication, 1 patient showed significant improvement in symptoms and resumed using AmB without recurrence; the other case was improved after stopping the medication, but relapsed after resuming use without stopping the medication, and the patient died from the primary disease.Conclusions:During the use of AmB, close attention should be paid to the occurrence of NDI. After occurrence of NDI, most patients can continue taking the AmB and have a good prognosis after symptomatic treatments.

OBJECTIVE To evaluate the rationality of clinical application of polymyxin B in the inpatients of a third grade class A hospital，so as to provide evidence for the optimization of clinical scheme of the drug. METHODS A retrospective method was conducted on the electronic medical records of inpatients treated with Polymyxin B sulfate for injection from January 2020 to March 2022 to collect the basic information of patients， inpatient departments and time， infection diagnosis， results of pathogenic bacteria test， laboratory test indicators， usage and dosage， and combined medication，etc. Based on the drug instructions， according to relevant guidelines and consensus， the rationality， efficacy and safety of polymyxin B in inpatient were evaluated. RESULTS & CONCLUSIONS A total of 101 inpatients were included， respiratory system infection was the main cause （62.4%）. All patients had received the etiological examination， and the pathogen with the highest detection rate was carbapenem‑resistant Acinetobacter baumannii （40.6%）. One hundred patients were treated by intravenous drip， and 4 patients were treated by combination of aerosol inhalation or intrathecal injection； 99 patients were given the dose of 500 thousand units by continuous intravenous infusion， q12 h. Totally 51.5% of patients were treated for 7-14 days； and 77 patients were treated with other anti-Gram-negative drugs. There were unreasonable phenomena including too short time of medication （29.7%）， no combination of medication （23.8%）， and no indication of medication （17.8%）. The clinical effective rate of 101 patients treated with polymyxin B was 49.5%， and 16 patients （15.8%） had acute kidney injury during the treatment. Clinical pharmacists should actively participate in the clinical treatment of polymyxin B， formulate individualized treatment plans according to the guidelines/consensus and in combination with the patient’s condition and infection status to improve the rationality of clinical medication.

Objective:To provide a basis for the selection of antiemetic regimen by establishing an artificial intelligence model for predicting chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving platinum-based chemotherapy with high emetic risk.Methods:The clinical information on cancer patients who received cisplatin or carboplatin with area under the blood concentration-time curve (AUC) ≥4 and registered in the Department of Oncology, Tianjin Medical University General Hospital from January 2018 to December 2022 was collected, including gender, age, history of alcohol consumption, history of vomiting in pregnancy, chemotherapy cycle, patient expects to have CINV, chemotherapeutic agents, antiemetic regimen, out-of-hospital antiemetic treatment, sleep of less than 7 hours on the night before chemotherapy, occurrence of CINV in the previous cycle, and creatinine clearance (Ccr). After pre-processing, the data were randomly divided into the training set and the test set. The training set was used to construct the prediction model, and the test set was used to evaluate the prediction efficiency of the model. Three algorithms, gradient boosting decision tree (GBDT), random forest (RF), and logistic regression (LR), were used to build a prediction model and evaluate the model performance, respectively. The evaluation metrics included accuracy, sensitivity, recall, F1 value (the reconciled mean of sensitivity and recall), and area under the receiver operating characteristic curve (AUROC). Finally, Shapley Additive exPlanation (SHAP) was applied to analyze the interpretability of the clinical features with predictive significance.Results:A total of 698 patients, 439 males (62.9%) with a median age of 64 (21, 84) years, were included in this study and received a total of 1 654 cycles of chemotherapy. The chemotherapy regimen contained cisplatin in 364 cases with 864 cycles of chemotherapy, and carboplatin with AUC ≥4 in 361 cases with 790 cycles of chemotherapy. The number of treatment cycles in which neurokinin-1 receptor antagonist (NK-1 RA), 5-hydroxytryptamine-3 receptor antagonist (5-HT3 RA), and dexamethasone were selected as the antiemetic regimen was 1 347, and in those with the selection of 5-HT3 RA and dexamethasone was 307. The Spearman′s correlation analysis showed no strong correlation between the feature variables in the patients, and all of them could be used for model building. GBDT optimal hyperparameters n_estimators=500, max_depth=9; RF optimal hyperparameters max_depth=5; LR optimal hyperparameters penalty=L2. Three prediction models, GBDT, RF and LR, were established based on the optimal hyperparameter training data, respectively. The accuracy of GBDT model was 0.903, sensitivity was 0.882, recall was 0.903, F1 value was 0.883, and AUROC was 0.778±0.036 (95% CI: 0.739-0.814); the accuracy of RF model was 0.885, sensitivity was 0.861, recall was 0.885, F1 value was 0.870, and AUROC was 0.679±0.041 (95% CI: 0.636-0.720); the LR model had an accuracy of 0.817, a sensitivity of 0.851, a recall of 0.817, an F1 value of 0.832, and an AUROC of 0.682±0.042 (95% CI: 0.639-0.723). Ccr, age, chemotherapy cycle, history of alcohol consumption, and patient expects to have CINV were the main features predicted by the model. The risk of CINV was negatively associated with Ccr, age, and chemotherapy cycle. And the risk of CINV was lower in patients with no history of drinking alcohol and patient expects to have CINV. Conclusion:The GBDT, RF, and LR models could all predict the risk of CINV in patients receiving platinum-based chemotherapy with high emetic risk, with the GBDT model having the best predictive effect.

Objective:To provide a basis for the selection of antiemetic regimen by establishing an artificial intelligence model for predicting chemotherapy-induced nausea and vomiting (CINV) in cancer patients receiving platinum-based chemotherapy with high emetic risk.Methods:The clinical information on cancer patients who received cisplatin or carboplatin with area under the blood concentration-time curve (AUC) ≥4 and registered in the Department of Oncology, Tianjin Medical University General Hospital from January 2018 to December 2022 was collected, including gender, age, history of alcohol consumption, history of vomiting in pregnancy, chemotherapy cycle, patient expects to have CINV, chemotherapeutic agents, antiemetic regimen, out-of-hospital antiemetic treatment, sleep of less than 7 hours on the night before chemotherapy, occurrence of CINV in the previous cycle, and creatinine clearance (Ccr). After pre-processing, the data were randomly divided into the training set and the test set. The training set was used to construct the prediction model, and the test set was used to evaluate the prediction efficiency of the model. Three algorithms, gradient boosting decision tree (GBDT), random forest (RF), and logistic regression (LR), were used to build a prediction model and evaluate the model performance, respectively. The evaluation metrics included accuracy, sensitivity, recall, F1 value (the reconciled mean of sensitivity and recall), and area under the receiver operating characteristic curve (AUROC). Finally, Shapley Additive exPlanation (SHAP) was applied to analyze the interpretability of the clinical features with predictive significance.Results:A total of 698 patients, 439 males (62.9%) with a median age of 64 (21, 84) years, were included in this study and received a total of 1 654 cycles of chemotherapy. The chemotherapy regimen contained cisplatin in 364 cases with 864 cycles of chemotherapy, and carboplatin with AUC ≥4 in 361 cases with 790 cycles of chemotherapy. The number of treatment cycles in which neurokinin-1 receptor antagonist (NK-1 RA), 5-hydroxytryptamine-3 receptor antagonist (5-HT3 RA), and dexamethasone were selected as the antiemetic regimen was 1 347, and in those with the selection of 5-HT3 RA and dexamethasone was 307. The Spearman′s correlation analysis showed no strong correlation between the feature variables in the patients, and all of them could be used for model building. GBDT optimal hyperparameters n_estimators=500, max_depth=9; RF optimal hyperparameters max_depth=5; LR optimal hyperparameters penalty=L2. Three prediction models, GBDT, RF and LR, were established based on the optimal hyperparameter training data, respectively. The accuracy of GBDT model was 0.903, sensitivity was 0.882, recall was 0.903, F1 value was 0.883, and AUROC was 0.778±0.036 (95% CI: 0.739-0.814); the accuracy of RF model was 0.885, sensitivity was 0.861, recall was 0.885, F1 value was 0.870, and AUROC was 0.679±0.041 (95% CI: 0.636-0.720); the LR model had an accuracy of 0.817, a sensitivity of 0.851, a recall of 0.817, an F1 value of 0.832, and an AUROC of 0.682±0.042 (95% CI: 0.639-0.723). Ccr, age, chemotherapy cycle, history of alcohol consumption, and patient expects to have CINV were the main features predicted by the model. The risk of CINV was negatively associated with Ccr, age, and chemotherapy cycle. And the risk of CINV was lower in patients with no history of drinking alcohol and patient expects to have CINV. Conclusion:The GBDT, RF, and LR models could all predict the risk of CINV in patients receiving platinum-based chemotherapy with high emetic risk, with the GBDT model having the best predictive effect.