High haemoglobin levels and mortality in males with intracerebral haemorrhage: a retrospective cohort study

ObjectivesTo examine the association between high haemoglobin levels and outcomes in intracerebral haemorrhage (ICH) in a multicentre cohort study.DesignProspective multicentre cohort study.Settings21 tertiary hospitals across mainland China.ParticipantsA total of 5318 consecutive in-hospital spontaneous ICH patients were recruited between January 2012 and June 2016.Primary and secondary outcome measuresHaemoglobin levels were measured on admission. Binary or ordinary logistic regression was used to evaluate the independent relationship of haemoglobin level with clinical outcomes at 3 months, measured as death or disability. Restricted cubic spline regression was fitted to examine the potential non-linear shape of the dose–response curve between the whole haemoglobin levels and 3-month poor outcomes.ResultsA total of 5031 patients with ICH were analysed (64.3% male; mean age (SD), 57.8 (15.2) years). We found that the highest haemoglobin quintile was associated with poor outcomes 3 months in males (adjusted OR (aOR) 1.65, 95% CI 1.21 to 2.25) but not in females, which was also observed in the pooled analysis of three subcohorts in male patients (average aOR 1.70, 95% CI 1.23 to 2.33). The spline regression suggested a non-linear association between haemoglobin levels and outcomes and a linear relationship was observed between an elevated haemoglobin level and 3-month disability/death in males (haemoglobin level per 10 g/L: aOR 1.24, 95% CI 1.10 to 1.40, p<0.001), which was mediated by larger haematoma volume (effect size: 0.115, 95% CI 0.012 to 0.231).ConclusionsThis study found a sex-specific association between an elevated haemoglobin level and poor 3-month outcomes, which might be mediated by larger haematoma volume.

Growth Rate Estimation of Rabi Pulse Production of Odisha by Using Spline Regression Technique

Pulses are considered to be important crop for ensuring nutritional security in Odisha. Proper estimation of growth rate in production of pulse crops allows for more effective cropping system planning and formulation of the agricultural policy of the state. To capture any abrupt changes and the variation in data in different phases of a long time period, spline regression technique is used as it can fit different models in different segments of the time period as necessary without losing the continuity of the model. The present study deals with the estimation of growth rate of area, yield and production of all rabi pulses in Odisha by using best fit spline regression model. To fit the spline regression model, the entire period of study is divided into different segments based on the scatter plot diagram which is further confirmed by testing the significance of change in coefficient of variation between the consecutive segments by chi square test. The regression model found to be suitable from the study of scatter plot of data are linear, compound, logarithmic, power, quadratic and cubic model. The best fit model is selected on the basis of error assumption test and model fit statistics such as R2, adjusted R2 and Mean Absolute Percentage error (MAPE). The respective selected best fit model is used for the estimation of growth rates of area, yield and production of rabi pulses in Odisha for each segment and the whole period of study. Among the spline regression models, the respective linear spline regression model is found to be best fit for area, yield and production of rabi pulses and are used for growth rate estimation of these variables. It is found that though the growth rate in area and yield of rabi pulses are not significant, the growth rate of production is found to be significant for the whole period of study which shows that the interaction effect of area and yield on production seems to dominate.

Alcohol consumption and the risk of gastric intestinal metaplasia in a U.S. Veterans population

Background Chronic alcohol use is a risk factor for non-cardia gastric adenocarcinoma. However, it is less well understood whether alcohol use is a risk factor for premalignant mucosal changes, namely gastric intestinal metaplasia. We examined the association between various parameters of alcohol use and risk of gastric intestinal metaplasia. Methods We used data from 2084 participants (including 403 with gastric intestinal metaplasia) recruited between February 2008-August 2013 into a cross-sectional study at the Michael E. DeBakey Veterans Affairs Medical Center in Houston, Texas. All participants underwent a study upper endoscopy with systematic gastric mapping biopsies. Cases had intestinal metaplasia on any non-cardia gastric biopsy. Participants self-reported lifetime history of alcohol consumption, along with other lifestyle risk factors, through a study survey. We calculated odds ratios (OR) and 95% confidence intervals (95% CI) for categories of average alcohol consumption using multivariable logistic regression, and restricted cubic spline regression to explore the potential shape of a dose-response relationship. Results Compared to lifelong non-drinkers, individuals who consumed on average ≥28 drinks per week had no elevated risk for gastric intestinal metaplasia (adjusted OR, 1.27; 95% CI, 0.74–2.19). Based on a spline regression curve and its 95% CI, there was also no demonstrable association between cumulative lifetime alcohol consumption and risk of gastric intestinal metaplasia. Similarly, we found no association between beverage type (beer, wine, liquor/spirits) and risk for gastric intestinal metaplasia. Conclusions Neither amount of alcohol consumed nor specific beverage type was associated with risk of gastric intestinal metaplasia.

Nocturnal Dipping and Left Ventricular Mass Index in the Chronic Kidney Disease in Children Cohort

Background and objectives: The physiological nocturnal blood pressure decline is often blunted in patients with chronic kidney disease (CKD); however, the consequences of blood pressure non-dipping in children are largely unknown. Our objective was to determine risk factors for non-dipping and to investigate if non-dipping is associated with higher left ventricular mass index (LVMI) in children with CKD. Design, setting, participants, and measurements: We conducted a cross-sectional analysis of ambulatory blood pressure monitoring and echocardiographic data in participants of the Chronic Kidney Disease in Children study. Multivariable linear and spline regression analyses were used to evaluate the relationship of risk factors with dipping, and of dipping with LVMI. Results: Within 552 participants, mean age was 11 (± 4) years, mean eGFR was 53 (± 20) ml/min/1.73m2, and 41% were classified as non-dippers. In subjects with non-glomerular CKD, female sex and higher sodium intake were significantly associated with less systolic and diastolic dipping (p≤ 0.05). In those with glomerular CKD, African American race and greater proteinuria were significantly associated with less systolic and diastolic dipping (p≤ 0.05). Systolic and diastolic dipping were not significantly associated with LVMI; however, in spline regression plots, diastolic dipping appeared to have a non-linear relationship with LVMI. As compared to diastolic dipping of 20-25%, dipping of < 20% was associated with 1.41 g/m2.7 higher LVMI (95% CI -0.47, 3.29), and dipping of > 25% was associated with 1.98 g/m2.7 higher LVMI (95% CI -0.77, 4.73), though these relationships did not achieve statistical significance. Conclusion: African American race, female sex, and greater proteinuria and sodium intake were significantly associated with blunted dipping in children with CKD. We did not find a statistically significant association between dipping and LVMI.

The association between first and second wave COVID-19 mortality in Italy

Background The relation between the magnitude of successive waves of the COVID-19 outbreak within the same communities could be useful in predicting the scope of new outbreaks. Methods We investigated the extent to which COVID-19 mortality in Italy during the second wave was related to first wave mortality within the same provinces. We compared data on province-specific COVID-19 2020 mortality in two time periods, corresponding to the first wave (February 24–June 30, 2020) and to the second wave (September 1–December 31, 2020), using cubic spline regression. Results For provinces with the lowest crude mortality rate in the first wave (February–June), i.e. < 22 cases/100,000/month, mortality in the second wave (September–December) was positively associated with mortality during the first wave. In provinces with mortality greater than 22/100,000/month during the first wave, higher mortality in the first wave was associated with a lower second wave mortality. Results were similar when the analysis was censored at October 2020, before the implementation of region-specific measures against the outbreak. Neither vaccination nor variant spread had any role during the study period. Conclusions These findings indicate that provinces with the most severe initial COVID-19 outbreaks, as assessed through mortality data, faced milder second waves.

Forecasting of Kharif Cereal Production in Odisha by Using Spline Regression Technique

Cereals are prime determinant of agricultural status of the state mainly during kharif season. Forecasting of the production of kharif cereals is of utmost importance to formulate the agricultural policy and strategy of the state. The ARIMA model can be reliably used to forecast for short future periods because uncertainty in prediction increases when done for longer future periods. The predictions obtained from the ordinary regression model are valid only when the relationship between the independent variables and the dependent variable does not change significantly in the future period which can be rarely assumed. It is expected that the spline regression will overcome the respective discrepancies in both ARIMA and ordinary regression techniques of forecasting with the assumption that the future period which needs forecasting follows the same pattern as the last partitioned period. The entire period of data is split into different periods based on the scatter plot of the data The suitable regression models, such as, linear, compound, logarithmic and power model are fitted to the data on area and yield of kharif cereals by using the training set data. Selection of best fit model is done on the basis of overall significance of the model, model diagnostic test for error assumptions and model fit statistics. The selected best fit model is then cross validated with the testing set data. After successful cross validation of the selected best fit models, they are used for forecasting of the future values for their respective variables. The models found to be best fit and thus selected for cross validation purpose are compound spline model for both area and yield of kharif cereals respectively. Forecasting of area, yield and hence production of kharif cereals for six years ahead i.e., for the year 2020-21 to 2025-26 by using the selected best fit model after successful cross validation. The forecast values for production of kharif cereals are found to decrease despite increase in forecast values of yield which is due to decrease in forecast value of area.

Modeling of poverty in Kalimantan with nonparametric spline regression approach

Poverty is a problem that is always present in any country, including Indonesia. Kalimantan is one of the islands in Indonesia that has not been free from poverty. Therefore, more effective efforts to further reduce poverty are needed. The purpose of the is study was to develop a model that can explain significant factors to poverty in Kalimantan. To achive the objective, first, factors that may have significant influence to the changes in the percentage of poor people should be identified, using regression analysis. In this study, nonparametric regression analysis was used with a spline approach since the relationship between poverty and the explanatory factors did not have a particular pattern. The results of this study showed that, the best was using three knot points, where Open Unemployment Rate (OUR), Human Development Index (HDI), and Economic Growth have a significant effect on poverty in Kalimantan.

Statistical Inference on Nonparametric Spline Models and its Applications

One of the components of the Human Development Index which is still a problem and concern in the world today is the Life Expectancy Rate (LER). United Nations Development Program (UNDP). United Nations Development Program (UNDP) uses the LER to measure community health status as well as a benchmark for development success. LER in Indonesia continues to increase almost throughout the year. That is, the hope of a newborn baby to be able to live longer is getting higher. LER data modelling with parametric regression is not necessarily suitable to be applied because the LER relationship pattern has a pattern that varies at certain age intervals. Spline regression is a regression method that can handle data whose pattern changes at certain intervals. Spline is one of the models in nonparametric regression that has a very special and very good visual statistical interpretation. In addition, splines are also able to handle data characters or functions that are smooth (smooth). This study aims to derive the form of the estimator and the shortest confidence interval for the quadratic spline model and model the LER data in Indonesia.

Application of Nonparametric Geographically Weighted Spline Regression Model for Spatial Mapping of Open Unemployment Rate in Kalimantan

This study was conducted specifically to GIS mapping based on Nonparametric-Geographically Weighted Spline Regression (NGWSR) Estimation Model for the factors that affect the open unemployment rate (OUR) in Kalimantan. Observational data in this study were categorized into 56 regions based on the Regency/City scale in Kalimantan. The variables used in this study consisted of the open unemployment rate, the labor force participation rate, population density, human development index, expected years of schooling, and regional minimum wage. This study utilized the spatial analysis of the NGWSR model with the geographic weighting of the Gaussian and Bisquare kernel functions. The NGWSR model was considered capable of providing a solution to the geographically weighted spatial regression for the unknown regression curve. Regarding to the result of this study, NGWSR with geographic weighting of the Bisquare kernel function was considered as the best model. The model criteria were based on the coefficient of determination and RMSE. The results of the significance test of model parameters for 56 Regencies/Cities data in Kalimantan had succeeded in mapping the area into 14 categories based on the significant variables of each region.

Prolonged Operative Time Associated with Increased Healthcare Utilization after Open Reduction and Internal Fixation of Intra-Articular and Extra-Articular Distal Radial Fractures: An Analysis of 17,482 Cases

Background The current literature does not contain a quantitative description of the associations between operative time and adverse outcomes after open reduction and internal fixation (ORIF) of distal radial fractures (DRF). Questions/Purpose We aimed to quantify associations between DRF ORIF operative time and 1) 30-day postoperative health care utilization and 2) the incidence of local wound complications. Methods The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for DRF ORIF cases (January 2012–December 2018). A total of 17,482 cases were identified. Primary outcomes included health care utilization (length of stay [LOS], discharge dispositions, 30-day readmissions, and reoperations) per operative-time category. Secondary outcome was incidence of wound complications per operative-time category. Multivariate regression was conducted to determine operative-time categories associated with increased risk while adjusting for demographics, comorbidities, and fracture type. Spline regression models were constructed to visualize associations. Results The 121 to 140-minute category was associated with significantly higher risk of a LOS > 2 days (odds ration [OR]: 1.64; 95% confidence interval [CI]:1.1–2.45; p = 0.014) and nonhome discharge (OR: 1.72; 95% CI:1.09–2.72; p = 0.02) versus 41 to 60-minute category. The ≥ 180-minute category exhibited highest odds of LOS > 2 days (OR: 2.08; 95%CI: 1.33–3.26; p = 0.001), nonhome discharge disposition (OR: 1.87; 95% CI: 1.05–3.33; p = 0.035), and 30-day reoperation occurrence (OR: 3.52; 95% CI: 1.59–7.79; p = 0.002). There was no association between operative time and 30-day readmission (p > 0.05 each). Higher odds of any-wound complication was first detected at 81 to 100-minute category (OR: 3.02; 95% CI: 1.08–8.4; p = 0.035) and peaked ≥ 181 minutes (OR: 9.62; 95% CI: 2.57–36.0; p = 0.001). Spline regression demonstrated no increase in risk of adverse outcomes if operative times were 50 minutes or less. Conclusion Our findings demonstrate that prolonged operative time is correlated with increased odds of health care utilization and wound complications after DRF ORIF. Operative times greater than 60 minutes seem to carry higher odds of postoperative complications.