Prediction Model for Severe Thrombocytopenia Induced by Gemcitabine Plus Cisplatin Combination Therapy in Patients with Urothelial Cancer.

BACKGROUND: Chemotherapy-induced thrombocytopenia is often a use-limiting adverse reaction to gemcitabine and cisplatin (GC) combination chemotherapy, reducing therapeutic intensity, and, in some cases, requiring platelet transfusion. OBJECTIVE: A retrospective cohort study was conducted on patients with urothelial cancer at the initiation of GC combination therapy and the objective was to develop a prediction model for the incidence of severe thrombocytopenia using machine learning. METHODS: We performed receiver operating characteristic analysis to determine the cut-off values of the associated factors. Multivariate analyses were conducted to identify risk factors associated with the occurrence of severe thrombocytopenia. The prediction model was constructed from an ensemble model and gradient-boosted decision trees to estimate the risk of an outcome using the risk factors associated with the occurrence of severe thrombocytopenia. RESULTS: Of 186 patients included in this study, 46 (25%) experienced severe thrombocytopenia induced by GC therapy. Multivariate analyses revealed that platelet count ≤ 21.4 (×104/µL) [odds ratio 7.19, p < 0.01], hemoglobin ≤ 12.1 (g/dL) [odds ratio 2.41, p = 0.03], lymphocyte count ≤ 1.458 (×103/µL) [odds ratio 2.47, p = 0.02], and dose of gemcitabine ≥ 775.245 (mg/m2) [odds ratio 4.00, p < 0.01] were risk factors of severe thrombocytopenia. The performance of the prediction model using these associated factors was high (area under the curve 0.76, accuracy 0.82, precision 0.68, recall 0.50, and F-measure 0.58). CONCLUSIONS: Platelet count, hemoglobin level, lymphocyte count, and gemcitabine dose contributed to the development of a novel prediction model to identify the incidence of GC-induced severe thrombocytopenia.

Investigation of geriatric syndromes associated with medication in Japan using insurance claims data.

AIM: Multiple risk factors are involved in geriatric syndrome (GS) occurring in older adults. Although drug therapy often contributes to GS, the specific causes among older adults in Japan remain unclear. In this study, we examined the possible prescribing cascade rate among older outpatients eligible for Late-stage Elderly Health Insurance and elucidated the differences between GS and GS associated with medication (GSAM) trends. METHODS: This retrospective study enrolled patients from health insurance claims data in Japan between October 2018 and March 2019; hospitalized patients were excluded. Two groups were identified among the participants with GS: GS (no use of GS-causing medications) and possible-GSAM (p-GSAM; use of GS-causing medications). The collected data were analyzed using the Bell Curve for Excel, and statistical significance was set at P < 0.05. RESULTS: In total, 137 781 outpatients were enrolled. Of the 32 259 outpatients who did not use GS-causing medications, 7342 were classified into the GS group. Among 105 522 outpatients who used GS-causing medications, 8347 were classified as having p-GSAM. The mean number of prescriptions was significantly higher in the p-GSAM group than in the GS group (P < 0.01). Furthermore, all GS symptoms showed significant differences, with impaired appetite being the most prevalent in the p-GSAM group than in the GS group (P < 0.01). A possible prescribing cascade was suspected in 2826 (33.9%) of 8347 outpatients in the p-GSAM group. CONCLUSION: Impaired appetite in patients taking GS-causing medications might lead to prescribing cascades. Further studies are needed to prevent such prescribing cascades. Geriatr Gerontol Int 2024; 24: 61-67.

Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients.

Objective: To compare the performance of the diagnostic model for fall risk based on the short physical performance battery (SPPB) developed using commercial machine learning software (MLS) and binomial logistic regression analysis (BLRA). Methods: We enrolled 797 out of 850 outpatients who visited the clinic between March 2016 and November 2021. Patients were categorized into the development (n = 642) and validation (n = 155) datasets. Age, sex, number of comorbidities, number of medications, body mass index (BMI), calf circumference (left-right average), handgrip strength (left-right average), total SPPB score, and history of falls were determined. We defined fall risk by an SPPB score of ≤6 in men and ≤9 in women. The main metrics used for evaluating the machine learning model and BLRA were the area under the curve (AUC), accuracy, precision, recall (sensitivity), specificity, and F-measure. The commercial MLS automatically calculates the parameter range of the highest contribution. Results: The participants included 797 outpatients (mean age, 76.3 years; interquartile range, 73.0-81.0; 288 men). The metrics of the current diagnostic model in the commercial MLS were as follows: AUC = 0.78, accuracy = 0.74, precision = 0.46, recall (sensitivity) = 0.81, specificity = 0.71, F-measure = 0.59. The metrics of the current diagnostic model in the BLRA were as follows: AUC = 0.77, accuracy = 0.75, precision = 0.47, recall (sensitivity) = 0.67, specificity = 0.77, F-measure = 0.55. The risk factors for falls in older adult outpatients were handgrip strength, female sex, experience of falls, BMI, and calf circumference in the commercial MLS. Conclusions: The diagnostic model for fall risk based on SPPB scores constructed using commercial MLS is noninferior to BLRA.

Creation of synthetic contrast-enhanced computed tomography images using deep neural networks to screen for renal cell carcinoma.

In this study, we elucidate if synthetic contrast enhanced computed tomography images created from plain computed tomography images using deep neural networks could be used for screening, clinical diagnosis, and postoperative follow-up of small-diameter renal tumors. This retrospective, multicenter study included 155 patients (artificial intelligence training cohort [n = 99], validation cohort [n = 56]) who underwent surgery for small-diameter (≤40 mm) renal tumors, with the pathological diagnosis of renal cell carcinoma, during 2010-2020. We created a learned deep neural networks using pix2pix. We examined the quality of the synthetic enhanced computed tomography images created using this deep neural networks and compared them with real enhanced computed tomography images using the zero-mean normalized cross-correlation parameter. We assessed concordance rates between real and synthetic images and diagnoses according to 10 urologists by creating a receiver operating characteristic curve and calculating the area under the curve. The synthetic computed tomography images were highly concordant with the real computed tomography images, regardless of the existence or morphology of the renal tumor. Regarding the concordance rate, a greater area under the curve was obtained with synthetic computed tomography (area under the curve = 0.892) than with only computed tomography (area under the curve = 0.720; p < 0.001). In conclusions, this study is the first to use deep neural networks to create a high-quality synthetic computed tomography image that was highly concordant with a real computed tomography image. Our synthetic computed tomography images could be used for urological diagnoses and clinical screening.

Characteristics of uroflowmetry patterns in men with detrusor underactivity revealed by artificial intelligence.

OBJECTIVES: To elucidate the characteristics of uroflowmetry (UFM) observed in men with detrusor underactivity (DU) using our developed artificial intelligence (AI) diagnostic algorithm to distinguish between DU and bladder outlet obstruction (BOO). METHODS: Subjective and objective parameters, including four UFM parameters (first peak flow rate, time to first peak, gradient to first peak, and the ratio of first peak flow rate to maximum flow rate [Qmax ]) selected by analyzing the judgment basis of the AI diagnostic system, were compared in 266 treatment-naive men with lower urinary tract symptoms (LUTS). Patients were divided into the DU (70; 26.32%) and non-DU (196; 73.68%) groups, and the UFM parameters for predicting the presence of DU were determined by multivariate analysis and receiver operating characteristic (ROC) curve analysis. Detrusor underactivity was defined as a bladder contractility index <100 and a BOO index <40. RESULTS: Most parameters on the first peak flow of UFM were significantly lower in the DU group. On multivariate analysis, lower first peak flow rate and lower ratio of first peak flow rate to Qmax were significant parameters to predict DU. In the ROC analysis, the ratio of the first peak flow rate to Qmax showed the highest area under the curve (0.848) and yielded sensitivities of 76% and specificities of 83% for DU diagnosis, with cutoff values of 0.8. CONCLUSIONS: Parameters on the first peak flow of UFM, especially the ratio of the first peak flow rate to Qmax , can diagnose DU with high accuracy in men with LUTS.

Prediction model of acute kidney injury induced by cisplatin in older adults using a machine learning algorithm.

BACKGROUND: Early detection and prediction of cisplatin-induced acute kidney injury (Cis-AKI) are essential for the management of patients on chemotherapy with cisplatin. This study aimed to evaluate the performance of a prediction model for Cis-AKI. METHODS: Japanese patients, who received cisplatin as the first-line chemotherapy at Fujita Health University Hospital, were enrolled in the study. The main metrics for evaluating the machine learning model were the area under the curve (AUC), accuracy, precision, recall, and F-measure. In addition, the rank of contribution as a predictive factor of Cis-AKI was determined by machine learning. RESULTS: A total of 1,014 and 226 patients were assigned to the development and validation data groups, respectively. The current prediction model showed the highest performance in patients 65 years old and above (AUC: 0.78, accuracy: 0.77, precision: 0.38, recall: 0.70, F-measure: 0.49). The maximum daily cisplatin dose and serum albumin levels contributed the most to the prediction of Cis-AKI. CONCLUSION: Our prediction model for Cis-AKI performed effectively in older patients.

Development of an artificial intelligence diagnostic system for lower urinary tract dysfunction in men.

OBJECTIVES: To establish an artificial intelligence diagnostic system for lower urinary tract function in men with lower urinary tract symptoms using only uroflowmetry data and to evaluate its usefulness. METHODS: Uroflowmetry data of 256 treatment-naive men with detrusor underactivity, bladder outlet obstruction, or detrusor underactivity + bladder outlet obstruction were used for artificial intelligence learning and validation using neural networks. An optimal artificial intelligence diagnostic model was established using 10-fold stratified cross-validation and data augmentation. Correlations of bladder contractility index and bladder outlet obstruction index values for the artificial intelligence system and pressure flow study values were examined using Spearman's correlation coefficients. Additionally, diagnostic accuracy was compared between the established artificial intelligence system and trained urologists with uroflowmetry data of 25 additional patients by χ2 -tests. Detrusor underactivity was defined as bladder contractility index ≤100 and bladder outlet obstruction index ≤40, bladder outlet obstruction was defined as bladder contractility index >100 and bladder outlet obstruction index >40, and detrusor underactivity + bladder outlet obstruction was defined as bladder contractility index ≤100 and bladder outlet obstruction index >40. RESULTS: The artificial intelligence system's estimated bladder contractility index and bladder outlet obstruction index values showed significant positive correlations with pressure flow study values (bladder contractility index: r = 0.60, P < 0.001; bladder outlet obstruction index: r = 0.46, P < 0.001). The artificial intelligence system's detrusor underactivity diagnosis had a sensitivity and specificity of 79.7% and 88.7%, respectively, and those for bladder outlet obstruction diagnosis were 76.8% and 84.7%, respectively. The artificial intelligence system's average diagnostic accuracy was 84%, which was significantly higher than that of urologists (56%). CONCLUSIONS: Our artificial intelligence diagnostic system developed using the uroflowmetry waveform distinguished between detrusor underactivity and bladder outlet obstruction with high sensitivity and specificity in men with lower urinary tract symptoms.