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Abstract BACKGROUND As spine surgery becomes increasingly common in the elderly, frailty has been used to risk stratify these patients. The Hospital Frailty Risk Score (HFRS) is a novel method of assessing frailty using International Classification of Diseases, Tenth Revision (ICD-10) codes. However, HFRS utility has not been evaluated in spinal surgery. OBJECTIVE To assess the accuracy of HFRS in predicting adverse outcomes of surgical spine patients. METHODS Patients undergoing elective spine surgery at a single institution from 2008 to 2016 were reviewed, and those undergoing surgery for tumors, traumas, and infections were excluded. The HFRS was calculated for each patient, and rates of adverse events were calculated for low, medium, and high frailty cohorts. Predictive ability of the HFRS in a model containing other relevant variables for various outcomes was also calculated. RESULTS Intensive care unit (ICU) stays were more prevalent in high HFRS patients (66%) than medium (31%) or low (7%) HFRS patients. Similar results were found for nonhome discharges and 30-d readmission rates. Logistic regressions showed HFRS improved the accuracy of predicting ICU stays (area under the curve [AUC] = 0.87), nonhome discharges (AUC = 0.84), and total complications (AUC = 0.84). HFRS was less effective at improving predictions of 30-d readmission rates (AUC = 0.65) and emergency department visits (AUC = 0.60). CONCLUSION HFRS is a better predictor of length of stay (LOS), ICU stays, and nonhome discharges than readmission and may improve on modified frailty index in predicting LOS. Since ICU stays and nonhome discharges are the main drivers of cost variability in spine surgery, HFRS may be a valuable tool for cost prediction in this specialty.

Background The Clinical Frailty Scale (CFS) is the most commonly used frailty measure in intensive care unit (ICU) patients. The hospital frailty risk score (HFRS) was recently proposed for the quantification of frailty. We aimed to compare the HFRS with the CFS in critically ill patients in predicting long-term survival up to one year following ICU admission. Methods In this retrospective multicentre cohort study from 16 public ICUs in the state of Victoria, Australia between 1st January 2017 and 30th June 2018, ICU admission episodes listed in the Australian and New Zealand Intensive Care Society Adult Patient Database registry with a documented CFS, which had been linked with the Victorian Admitted Episode Dataset and the Victorian Death Index were examined. The HFRS was calculated for each patient using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes that represented pre-existing conditions at the time of index hospital admission. Descriptive methods, Cox proportional hazards and area under the receiver operating characteristic (AUROC) were used to investigate the association between each frailty score and long-term survival up to 1 year, after adjusting for confounders including sex and baseline severity of illness on admission to ICU (Australia New Zealand risk-of-death, ANZROD). Results 7001 ICU patients with both frailty measures were analysed. The overall median (IQR) age was 63.7 (49.1–74.0) years; 59.5% ( n  = 4166) were male; the median (IQR) APACHE II score 14 (10–20). Almost half (46.7%, n  = 3266) were mechanically ventilated. The hospital mortality was 9.5% ( n  = 642) and 1-year mortality was 14.4% ( n  = 1005). HFRS correlated weakly with CFS (Spearman’s rho 0.13 (95% CI 0.10–0.15) and had a poor agreement (kappa = 0.12, 95% CI 0.10–0.15). Both frailty measures predicted 1-year survival after adjusting for confounders, CFS (HR 1.26, 95% CI 1.21–1.31) and HFRS (HR 1.08, 95% CI 1.02–1.15). The CFS had better discrimination of 1-year mortality than HFRS (AUROC 0.66 vs 0.63 p  < 0.0001). Conclusion Both HFRS and CFS independently predicted up to 1-year survival following an ICU admission with moderate discrimination. The CFS was a better predictor of 1-year survival than the HFRS.

Background Hemorrhagic fever with renal syndrome (HFRS) is still attracting public attention because of its outbreak in various cities in China. Predicting future outbreaks or epidemics disease based on past incidence data can help health departments take targeted measures to prevent diseases in advance. In this study, we propose a multistep prediction strategy based on extreme gradient boosting (XGBoost) for HFRS as an extension of the one-step prediction model. Moreover, the fitting and prediction accuracy of the XGBoost model will be compared with the autoregressive integrated moving average (ARIMA) model by different evaluation indicators. Methods We collected HFRS incidence data from 2004 to 2018 of mainland China. The data from 2004 to 2017 were divided into training sets to establish the seasonal ARIMA model and XGBoost model, while the 2018 data were used to test the prediction performance. In the multistep XGBoost forecasting model, one-hot encoding was used to handle seasonal features. Furthermore, a series of evaluation indices were performed to evaluate the accuracy of the multistep forecast XGBoost model. Results There were 200,237 HFRS cases in China from 2004 to 2018. A long-term downward trend and bimodal seasonality were identified in the original time series. According to the minimum corrected akaike information criterion (CAIC) value, the optimal ARIMA (3, 1, 0) × (1, 1, 0) 12 model is selected. The index ME, RMSE, MAE, MPE, MAPE, and MASE indices of the XGBoost model were higher than those of the ARIMA model in the fitting part, whereas the RMSE of the XGBoost model was lower. The prediction performance evaluation indicators (MAE, MPE, MAPE, RMSE and MASE) of the one-step prediction and multistep prediction XGBoost model were all notably lower than those of the ARIMA model. Conclusions The multistep XGBoost prediction model showed a much better prediction accuracy and model stability than the multistep ARIMA prediction model. The XGBoost model performed better in predicting complicated and nonlinear data like HFRS. Additionally, Multistep prediction models are more practical than one-step prediction models in forecasting infectious diseases.

PurposeThe Hospital Frailty Risk Score (HFRS) is derived from routinely collected data and validated as a geriatric risk stratification tool. This study aimed to evaluate the utility of the HFRS as a predictor for postoperative adverse events in spine surgery.MethodsIn this retrospective analysis of 2042 patients undergoing spine surgery at a university spine center between 2011 and 2019, HFRS was calculated for each patient. Multivariable logistic regression models were used to assess the relationship between the HFRS and postoperative adverse events. Adverse events were compared between patients with high or low frailty risk.ResultsPatients with intermediate or high frailty risk showed a higher rate of reoperation (19.7% vs. 12.2%, p < 0.01), surgical site infection (3.4% vs. 0.4%, p < 0.001), internal complications (4.1% vs. 1.1%, p < 0.01), Clavien–Dindo IV complications (8.8% vs. 3.4%, p < 0.001) and transfusion (10.9% vs. 1.5%, p < 0.001). Multivariable logistic regression analyses revealed a high HFRS as independent risk factor for reoperation [odds ratio (OR) = 1.1; 95% confidence interval (CI) 1.0–1.2], transfusion (OR = 1.3; 95% CI 1.2–1.4), internal complications (OR = 1.2; 95% CI 1.1–1.3), surgical site infections (OR = 1.3; 95% CI 1.2–1.5) and other complications (OR = 1.3; 95% CI 1.2–1.4).ConclusionThe HFRS can predict adverse events and is an easy instrument, fed from routine hospital data. By identifying risk patients at an early stage, the individual patient risk could be minimized, which leads to less complications and lower costs.Level of evidenceLevel III – retrospective cohort studyTrial registrationThe study was approved by the local ethics committee (20-1821-104) of the University of Regensburg in February 2020.

IntroductionThe prevalence of frailty among patients with chronic pancreatitis (CP) and its impact on clinical outcomes is unclear. We report the impact of frailty on mortality, readmission rates, and healthcare utilization among patients with chronic pancreatitis in the United States.MethodsWe extracted data on patients hospitalized with a primary or secondary diagnosis of CP from the Nationwide Readmissions Database 2019. We applied a previously validated hospital frailty risk scoring system to classify CP patients into frail and non-frail on index hospitalization and compared the characteristics of frail and non-frail patients. We studied the impact of frailty on mortality, readmission, and healthcare utilization.ResultsOf 56,072 patients with CP, 40.78% of patients were classified as frail. Frail patients experienced a higher rate of unplanned and preventable hospitalizations. Almost two-thirds of frail patients were younger than 65, and one-third had no or only single comorbidity. On multivariate analysis, frailty was independently associated with two times higher mortality risk (adjusted hazard ratio [aHR], 2.05; 95% CI 1.7–2.5). Frailty was also associated with a higher risk of all-cause readmission with an aHR of 1.07; (95% CI 1.03–1.1). Frail patients experienced a longer length of stay, higher hospitalization costs, and hospitalization charges. Infectious causes were the most common cause of readmission among frail patients compared to acute pancreatitis among non-frail patients.ConclusionsFrailty is independently associated with higher mortality, readmission rates, and healthcare utilization among patients with chronic pancreatitis in the US.

Introduction: Hemorrhagic fever with renal syndrome (HFRS) is a globally prevalent infectious disease caused by the hantavirus in rodents. Case study: This report describes a case of a 36-year-old male presenting with elevated ferritin, vitamin B12, and folic acid deficiency during the early onset phase of HFRS. Despite normal renal function at admission, the patient exhibited persistent fever and thrombocytopenia, leading to a potential misdiagnosis of an atypical HFRS presentation. Abnormal serum levels of ferritin, vitamin B12, and folic acid served as early indicators of renal dysfunction in patients with HRFS. The patient's condition improved rapidly with a combination of vitamin B6, methyl cobalamin, and folic acid, as evidenced by a subsequent decrease in the ferritin levels, from 3000 to 600 ng/mL, on days 4 and 24, respectively, and an increase in the vitamin B12 and folic acid levels to 200 pg/mL and 36.7 ng/mL, separately. Conclusions: These findings suggest that ferritin, vitamin B12, and folic acid have the potential to serve as biomarkers for HFRS and play a predictive role in the diagnosis and treatment of the disease.

Introduction The Hospital Frailty Risk Score (HFRS) is a validated risk stratification model referring to the cumulative deficits model of frailty. The purpose of this study was to evaluate the HFRS as a predictor of 90-day readmission and complications after revision total hip (rTHA) and knee (rTKA) arthroplasty. Methods In a retrospective analysis of 565 patients who had undergone rTHA or rTKA between 2011 and 2019, the HFRS was calculated for each patient. Rates of adverse events were compared between patients with low and intermediate or high frailty risk. Multivariable logistic regression models were used to assess the relationship between the HFRS and post-operative adverse events. Results Patients with intermediate or high frailty risk showed higher rates of readmission (30days: 23.8% vs. 9.9%, p = 0.006; 90days: 26.2% vs. 13.0%, p < 0.018), surgical complications (28.6% vs. 7.8%, p < 0.001), medical complications (11.9% vs. 1.0%, p < 0.001), other complications (28.6% vs. 2.3%, p < 0.001), Clavien-Dindo grade IV complications (14.3% vs. 4.8%, p = 0.009), and transfusion (33.3% vs. 6.1%, p < 0.001). Multivariable logistic regression analyses revealed a high HFRS as independent risk factor for surgical complications (OR = 3.45, 95% CI 1.45-8.18, p = 0.005), medical complications (OR = 7.29, 95% CI 1.72-30.97, p = 0.007), and other complications (OR = 14.15, 95% CI 5.16-38.77, p < 0.001). Conclusion The HFRS predicts adverse events after rTHA and rTKA. As it derives from routinely collected data, the HFRS could be implemented automated in hospital information systems to facilitate identification of at-risk patients.

Hemorrhagic fever with renal syndrome (HFRS) is a rodent-borne disease that has threatened Chinese residents for nearly a century. Although comprehensive prevent and control measures were taken, the HFRS epidemic in China presents a rebounding trend in some areas. Urbanization is considered as an important influencing factor for the HFRS epidemic in recent years; however, the relevant research has not been systematically summarized. This review aims to summarize urbanization-related environmental factors and the HFRS epidemic in China and provide an overview of research perspectives. The literature review was conducted following the PRISMA protocol. Journal articles on the HFRS epidemic in both English and Chinese published before 30 June 2022 were identified from PubMed, Web of Science, and Chinese National Knowledge Infrastructure (CNKI). Inclusion criteria were defined as studies providing information on urbanization-related environmental factors and the HFRS epidemic. A total of 38 studies were included in the review. Changes brought by urbanization on population, economic development, land use, and vaccination program were found to be significantly correlated with the HFRS epidemic. By changing the ecological niche of humans—affecting the rodent population, its virus-carrying rate, and the contact opportunity and susceptibility of populations—urbanization poses a biphasic effect on the HFRS epidemic. Future studies require systematic research framework, comprehensive data sources, and effective methods and models.

Background Hebei Province is one of China’s high-incidence regions for hemorrhagic fever with renal syndrome (HFRS), posing significant public health challenges. This study aims to evaluate the efficacy of key Hemorrhagic Fever with Renal Syndrome (HFRS) control measures. Methods This study analyzed annual incidence data (1981–2024) and monthly data (2004–2024) using interrupted time series (ITS) analysis and the X-12 seasonal adjustment method. The X-12 procedure decomposes the series into trend, seasonal, and irregular components and removes seasonal variation to produce seasonally adjusted data. Specifically, we assessed the impact of two interventions: the 2002 Hebei Epidemic Hemorrhagic Fever Prevention and Control Plan and the 2008 Vaccination Work Plan for Key HFRS-endemic Counties in Hebei Province. Results The results demonstrated that before 2002, the HFRS incidence rate increased significantly at 28.8% annually ( β₁ = 0.25, p  < 0.01). After policy implementation, the trend reversed, with an annual decline of 32.0% ( β₁ + β₃ = −0.117, p  < 0.05), indicating effective control. Notably, epidemic peaks temporarily elevated incidence levels ( β₄ = 0.83, p  < 0.01). Following the 2008 vaccination program, short-term incidence rate reductions of 1.229 cases per 100,000 ( β₂ = -1.229, p  = 0.044) and 0.351 cases per 100,000 ( β₂ = -0.351, p  = 0.10) were observed in Qinhuangdao and Tangshan, respectively, though the latter was not statistically significant. Long-term trends revealed slower monthly declines of 0.003 cases per 100,000 in Qinhuangdao ( β₁ + β₃ = −0.003, p  = 0.049) and 0.002 cases per 100,000 in Tangshan ( β₁ + β₃ = −0.002, p  = 0.019). Conclusions These findings highlight the differential effects of policy interventions on HFRS incidence, with the 2002 plan achieving sustained reduction, while the 2008 vaccination program showed limited long-term impact. Clinical trial number Not applicable.

Since the discovery of ABO blood types, there has been mounting evidence of the association between blood types and infectious diseases. However, so far, there is rarely available research about the potential role of ABO blood types in haemorrhagic fever with renal syndrome (HFRS) infection. Our aim was to investigate the relationship between ABO blood types and the development of HFRS infection in Qingdao, China. We carried out a retrospective study enrolling 116 HFRS patients as the case group and 373 healthy subjects as the control group. ABO blood type distribution was analysed using the Chi-square test and logistic regression analysis. Results showed that the distribution of ABO blood types between the two groups was significantly different (X2 = 18.151, P < 0.05). Blood type B was less frequently observed [odds ratio (OR), 0.404; confidence interval (CI), 0.238–0.684; P < 0.01], while blood type AB was more frequently observed in the case group (OR, 2.548; CI, 1.427–4.549; P < 0.01). Since significantly more males were affected than females, we further analysed the data by gender as well as blood types and obtained consistent results for males. Our findings indicated that populations with blood type AB might be more prone to HFRS infection, whereas those with blood type B might be less susceptible to HFRS infection, which will help to make risk stratification in infection control.