Deep learning model to predict lupus nephritis renal flare based on dynamic multivariable time-series data.

OBJECTIVES: To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data. DESIGN: A single-centre, retrospective cohort study in China. SETTING: A Chinese central tertiary hospital. PARTICIPANTS: The cohort study consisted of 1694 LN patients who had been registered in the Nanjing Glomerulonephritis Registry at the National Clinical Research Center of Kidney Diseases, Jinling Hospital from January 1985 to December 2010. METHODS: We developed a deep learning algorithm to predict LN relapse that consists of 59 features, including demographic, clinical, immunological, pathological and therapeutic characteristics that were collected for baseline analysis. A total of 32 227 data points were collected by the sliding window method and randomly divided into training (80%), validation (10%) and testing sets (10%). We developed a deep learning algorithm-based interpretable multivariable long short-term memory model for LN relapse risk prediction considering censored time-series data based on a cohort of 1694 LN patients. A mixture attention mechanism was deployed to capture variable interactions at different time points for estimating the temporal importance of the variables. Model performance was assessed according to C-index (concordance index). RESULTS: The median follow-up time since remission was 4.1 (IQR, 1.7-6.7) years. The interpretable deep learning model based on dynamic multivariable time-series data achieved the best performance, with a C-index of 0.897, among models using only variables at the point of remission or time-variant variables. The importance of urinary protein, serum albumin and serum C3 showed time dependency in the model, that is, their contributions to the risk prediction increased over time. CONCLUSIONS: Deep learning algorithms can effectively learn through time-series data to develop a predictive model for LN relapse. The model provides accurate predictions of LN relapse for different renal disease stages, which could be used in clinical practice to guide physicians on the management of LN patients.

A New Risk Model based on the Machine Learning Approach for Prediction of Mortality in the Respiratory Intensive Care Unit.

BACKGROUND: Intensive care unit (ICU) resources are inadequate for the large population in China, so it is essential for physicians to evaluate the condition of patients at admission. In this study, our objective was to construct a machine-learning risk prediction model for mortality in respiratory intensive care units (RICUs). METHODS: This study involved 817 patients who made 1,063 visits and who were admitted to the RICU from 2012 to 2017. Potential predictors such as demographic information, laboratory results, vital signs and clinical characteristics were considered. We constructed eXtreme Gradient Boosting (XGBoost) models and compared performances with random forest models, logistic regression models and clinical scores such as Acute Physiology and Chronic Health Evaluation II (APACHE II) and the sequential organ failure assessment (SOFA) system. The model was externally validated using data from Medical Information Mart for Intensive Care (MIMIC-III) database. A web-based calculator was developed for practical use. RESULTS: Among the 1,063 visits, the RICU mortality rate was 13.5%. The XGBoost model achieved the best performance with the area under the receiver operating characteristics curve (AUROC) of 0.860 (95% confidence interval (CI): 0.808 - 0.909) in the test set, which was significantly greater than APACHE II (0.749, 95% CI: 0.674 - 0.820; P = 0.015) and SOFA (0.751, 95% CI: 0.669 - 0.818; P = 0.018). The Hosmer-Lemeshow test indicated a good calibration of our predictive model in the test set with a P-value of 0.176. In the external validation dataset, the AUROC of XGBoost model was 0.779 (95% CI: 0.714 - 0.813). The final model contained variables that were previously known to be associated with mortality, but it also included some features absent from the clinical scores. The mean N-terminal pro-B-type natriuretic peptide (NTproBNP) of survivors was significantly lower than that of the non-survival group (2066.43 pg/mL vs. 8232.81 pg/mL; P < 0.001). CONCLUSIONS: Our results showed that the XGBoost model could be a suitable model for predicting RICU mortality with easy-to-collect variables at admission and help intensivists improve clinical decision-making for RICU patients. We found that higher NT-proBNP can be a good indicator of poor prognosis.

An integrated framework for modelling quantitative effects of entry restrictions and travel quarantine on importation risk of COVID-19.

OBJECTIVE: As the potential spread of COVID-19 sparked by imported cases from overseas will pose continuous challenges, it is essential to estimate the effects of control measures on reducing the importation risk of COVID-19. Our objective is to provide a framework of methodology for quantifying the combined effects of entry restrictions and travel quarantine on managing the importation risk of COVID-19 and other pandemics by leveraging different sets of parameters. METHODS: Three major categories of control measures on controlling importation risk were parameterized and modelled by the framework: 1) entry restrictions, 2) travel quarantine, and 3) domestic containment measures. Integrating the parameterized intensity of control measures, a modified SEIR model was developed to simulate the case importation and local epidemic under different scenarios of global epidemic dynamics. A web-based tool was also provided to enable interactive visualization of epidemic simulation. RESULTS: The simulated number of case importation and local spread modelled by the proposed framework of methods fitted well to the historical epidemic curve of China and Singapore. Based on the simulation results, the total numbers of infected cases when reducing 30% of visitor arrivals would be 88·4 (IQR 87·5-89·6) and 58·8 (IQR 58·3-59·5) times more than those when reducing 99% of visitor arrivals in mainland China and Singapore respectively, assuming actual time-varying Rt and travel quarantine policy. If the number of global daily new infections reached 100,000, 85%-91% of inbound travels should be reduced to keep the daily new infected number below 100 for a country with a similar travel volume as Singapore (daily 52,000 tourist arrivals in 2019). Whereas if the number was lower than 10,000, the daily new infected case would be less than 100 even with no entry restrictions. DISCUSSIONS: We proposed a framework that first estimated the intensity of travel restrictions and local containment measures for countries since the first overseas imported case. Our approach then quantified the combined effects of entry restrictions and travel quarantine using a modified SEIR model to simulate the potential epidemic spread under hypothetical intensities of these control measures. We also developed a web-based system that enables interactive simulation, which could serve as a valuable tool for health system administrators to assess policy effects on managing the importation risk. By leveraging different sets of parameters, it could adapt to any specific country and specific type of epidemic. CONCLUSIONS: This framework has provided a valuable tool to parameterize the intensity of control measures, simulate both the case importation and local epidemic, and quantify the combined effects of entry restrictions and travel quarantine on managing the importation risk.

Machine Learning for Prediction and Risk Stratification of Lupus Nephritis Renal Flare.

BACKGROUND: Renal flare of lupus nephritis (LN) is strongly associated with poor kidney outcomes, and predicting renal flare and stratifying its risk are important for clinical decision-making and individualized management to reduce LN flare. METHODS: We randomly divided 1,694 patients with biopsy-proven LN, who had achieved remission after treatment, into a derivation cohort (n = 1,186) and an internal validation cohort (n = 508), at a ratio of 7:3. The risk of renal flare 5 years after remission was predicted using an eXtreme Gradient Boosting (XGBoost) method model, developed from 59 variables, including demographic, clinical, immunological, pathological, and therapeutic characteristics. A simplified risk score prediction model (SRSPM) was developed from important variables selected by XGBoost model using stepwise Cox regression for practical convenience. RESULTS: The 5-year relapse rates were 39.5% and 38.2% in the derivation and internal validation cohorts, respectively. Both the XGBoost model and the SRSPM had good predictive performance, with a C-index of 0.819 (95% confidence interval [CI]: 0.774-0.857) and 0.746 (95% CI: 0.697-0.795), respectively, in the validation cohort. The SRSPM comprised 6 variables, including partial remission and endocapillary hypercellularity at baseline, age, serum Alb, anti-dsDNA, and serum complement C3 at the point of remission. Using Kaplan-Meier analysis, the SRSPM identified significant risk stratification for renal flares (p < 0.001). CONCLUSIONS: Renal flare of LN can be readily predicted using the XGBoost model and the SRSPM, and the SRSPM can also stratify flare risk. Both models are useful for clinical decision-making and individualized management in LN.

Regional Practice Patterns and Racial/Ethnic Differences in Intensity of End-of-Life Care.

OBJECTIVE: To examine whether regional practice patterns impact racial/ethnic differences in intensity of end-of-life care for cancer decedents. DATA SOURCES: The linked Surveillance, Epidemiology, and End Results (SEER)-Medicare database. STUDY DESIGN: We classified hospital referral regions (HRRs) based on mean 6-month end-of-life care expenditures, which represented regional practice patterns. Using hierarchical generalized linear models, we examined racial/ethnic differences in the intensity of end-of-life care across levels of HRR expenditures. PRINCIPAL FINDINGS: There was greater variation in intensity of end-of-life care among Hispanics, Asians, and whites in high-expenditure HRRs than in low-expenditure HRRs. CONCLUSIONS: Local practice patterns may influence racial/ethnic differences in end-of-life care.

Longer Periods Of Hospice Service Associated With Lower End-Of-Life Spending In Regions With High Expenditures.

Hospice use is expected to decrease end-of-life expenditures, yet evidence for its financial impact remains inconclusive. One potential explanation is that the use of hospice may produce differential cost-savings effects by region because of geographic variation in end-of-life spending patterns. We examined 103,745 elderly Medicare fee-for-service beneficiaries in the Surveillance, Epidemiology, and End Results Program Medicare database who died from cancer in 2004-11. We created quintiles by the adjusted mean end-of-life expenditures per hospital referral region (HRR), and we examined HRR-level variation in the association between length of hospice service and expenditures across quintiles. Longer periods of hospice service were associated with decreased end-of-life expenditures for patients residing in regions with high average expenditures but not for those in regions with low average expenditures. Hospice use accounted for 8 percent of the expenditure variation between the highest and the lowest spending quintiles, which demonstrates the powers and limitations of hospice use for saving on costs.