ABSTRACT
As an important indicator of phytoplankton biomass in lakes, the chlorophyll-a (Chl-a) concentration reflects the abundance and variation of phytoplankton in the water. Based on the monthly monitoring data of Chl-a and environmental factors in Lake Taihu from December 1999 to August 2019, key environmental factors related to Chl-a and their relationships were found using the principal component analysis (PCA) method. A multiple linear stepwise regression model and an auto-regressive integrated moving average (ARIMA) model were developed to predict the monthly Chl-a concentrations. The results showed that the Chl-a concentrations in Lake Taihu exhibited clear seasonal change characteristics and an overall trend of a gradual increase. The changes in total phosphorus (TP), the permanganate index, monthly average temperature (MAT), and monthly rainfall (MR) matched the Chl-a concentrations relatively well, whereas the changes in total nitrogen (TN) and ammonium nitrogen (NH4+-N) lagged significantly. The PCA results showed that the increased phytoplankton biomass and consequent algae outbreaks in Lake Taihu were not limited to the effect of a single factor such as TN or TP, but were comprehensively affected by multiple factors such as TN, NH4+-N, TP, the permanganate index, MR, and MAT. Through further validation, the ARIMA model of Chl-a concentrations was proved to be significantly better than the multiple linear stepwise regression model, especially when considering the key environmental factors as independent variables and optimizing their values. The established ARIMA (0,1,1) (0,1,1) model would be helpful for forecasting algae blooms in Lake Taihu and provide useful suggestions for water environmental management, such as water resources dispatch and regulation.
ABSTRACT
BACKGROUND AND PURPOSE: Mechanical thrombectomy (MT) is a standard care for most acute ischemic stroke (AIS) patients. For AIS patients underwent MT, predicting the patients at high risk of unfavorable outcome and adjusting therapeutic strategies accordingly can greatly improve patient outcomes. We aimed to develop and validate a nomogram for individualized prediction of Chinese AIS patients underwent MT. METHODS: We conducted a multicenter prospective study including 238 AIS patients who underwent MT from January 2014 to December 2018. The main outcome measure was three-month unfavorable outcome (modified Rankin Scale 3-6). A nomogram was generated based on multivariate logistic model. We assessed the discriminative performance by using the area under the receiver-operating characteristic curve and calibration of risk prediction model by using the Hosmer-Lemeshow test. RESULTS: In NAC nomogram, NIHSS (National Institutes of Health Stroke Scale) score on admission (OR: 1.193, p < 0.0001), Age (OR: 1.025, p = 0.037) and Creatinine (OR: 1.028, p < 0.0001) remained independent predictors of 3-month unfavorable outcome in Chinese AIS patients treated with MT. The NAC nomogram exhibited an area under the curve of 0.816 for predicting functional impairment. Calibration was good (p = 0.560 for the Hosmer-Lemeshow test). CONCLUSIONS: The NAC nomogram is the first nomogram developed and validated in Chinese AIS patients treated with MT and it may be used to predict 3 months unfavorable outcome for these patients.
