Associations Between First Parity Wean-to-Service Interval and Sow Lifetime Productivity Traits

This study aimed to 1) investigate associations between first parity wean-to-service interval (WSI) and sows’ lifetime reproductive traits and 2) identify cut-off values for WSI associated with lifetime traits. Data collected in 3,900-sows of farrow-to-finish commercial farm in Yucatan, Mexico. Lifetime productivity records including parity number at culling (NPC), lifetime number piglets born alive (LNBA) from parity two until culling, lifetime non-productive days (LNPD) and length of productive life (LPL) for sows were used. Association between WSI and sow productive traits were evaluated using general linear models, including year and season as categorical fixed effects and WSI as a continuous linear and quadratic predictors. Cut-off values for WSI were estimated using regression tree analysis. WSI was associated (P < 0.05) with LNBA (linear = -0.62 ± 0.025; quadratic 0.02 ± 0.008) and NCP (linear = -0.04 ± 0.018). Similarly, an association (P < 0.05) was observed between WSI and LNPD (linear = 2.81 ± 0.687; quadratic -0.05 ± 0.023). Cut-off values for WSI varied according to each of the predicted variables: WSI > 5 days would translate into longer 13 more days of LPL, WSI < 7 days would increase LNBA by two extra pigs, WSI ≥ 9 days increases NCP by 0.2 parities, and WSI < 10 days would mean 24 fewer LNPD. Shorter WSI during the first parity was associated with improved lifetime productivity traits. The estimated cut-off values for WSI could be used by producers, to decide when to implement strategies to improve management.

Simple demographic characteristics and laboratory findings on admission may predict in-hospital mortality in patients with SARS-CoV-2 infection: development and validation of the covid-19 score

Background Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) constitutes a major health burden worldwide due to high mortality rates and hospital bed shortages. SARS-CoV-2 infection is associated with several laboratory abnormalities. We aimed to develop and validate a risk score based on simple demographic and laboratory data that could be used on admission in patients with SARS-CoV-2 infection to predict in-hospital mortality. Methods Three cohorts of patients from different hospitals were studied consecutively (developing, validation, and prospective cohorts). The following demographic and laboratory data were obtained from medical records: sex, age, hemoglobin, mean corpuscular volume (MCV), platelets, leukocytes, sodium, potassium, creatinine, and C-reactive protein (CRP). For each variable, classification and regression tree analysis were used to establish the cut-off point(s) associated with in-hospital mortality outcome based on data from developing cohort and before they were used for analysis in the validation and prospective cohort. The covid-19 score was calculated as a sum of cut-off points associated with mortality outcome. Results The developing, validation, and prospective cohorts included 129, 239, and 497 patients, respectively (median age, 71, 67, and 70 years, respectively). The following cut of points associated with in-hospital mortality: age > 56 years, male sex, hemoglobin < 10.55 g/dL, MCV > 92.9 fL, leukocyte count > 9.635 or < 2.64 103/µL, platelet count, < 81.49 or > 315.5 103/µL, CRP > 51.14 mg/dL, creatinine > 1.115 mg/dL, sodium < 134.7 or > 145.4 mEq/L, and potassium < 3.65 or > 6.255 mEq/L. The AUC of the covid-19 score for predicting in-hospital mortality was 0.89 (0.84–0.95), 0.850 (0.75–0.88), and 0.773 (0.731–0.816) in the developing, validation, and prospective cohorts, respectively (P < 0.001The mortality of the prospective cohort stratified on the basis of the covid-19 score was as follows: 0–2 points,4.2%; 3 points, 15%; 4 points, 29%; 5 points, 38.2%; 6 and more points, 60%. Conclusion The covid-19 score based on simple demographic and laboratory parameters may become an easy-to-use, widely accessible, and objective tool for predicting mortality in hospitalized patients with SARS-CoV-2 infection.

Determining the Factors Affecting 305-Day Milk Yield of Dairy Cows with Regression Tree

The purpose of this study was to determine the factors affecting the 305-day milk yield of dairy cattle by using Regression Tree Analysis (RTA). The data set of this study consisted of 8 different cattle breeds grown in Turkey. Breed (B), Province (P), Lactation Length (LL), Service Period (SP), Dry Period (DP), Parity (PR), Calving Year (CY), Calving Age (CA) and Calving Month (CM) were used to predict the 305-day milk yield. Results of RTM showed that the usage of this method might be appropriate for determining the important factors that would be able to affect the 305-day milk yield (R2=71.3%). It was seen that the most important factors affecting the 305-day milk yield were the Breed, Lactation Length, Province, and Parity. Therefore, those selected factors were more efficient than the others in predicting the 305-day milk yield. RTA results also indicated that the lowest milk yield was estimated for Jersey, Jersey Crossbred, and Yerli Kara. Among the highest 305-day milk yield cows, the milk yield estimates of the cows in the second, third, fourth, fifth, and the sixth parities were found significantly higher than that of the cows in the first and seventh parities.

Patient Selection for Routine Troponin Monitoring After Noncardiac Surgery

Background Myocardial infarction is an important complication after noncardiac surgery. Therefore, perioperative troponin surveillance is recommended for patients at risk. The aim of this study was to identify patients at high risk of perioperative myocardial infarction (POMI), in order to aid appropriate selection and to omit redundant laboratory measurements in patients at low risk. Methods and Results This observational cohort study included patients ≥60 years of age who underwent intermediate to high risk noncardiac surgery. Routine postoperative troponin I monitoring was performed. The primary outcome was POMI. Classification and regression tree analysis was used to identify patient groups with varying risks of POMI. In each subgroup, the number needed to screen to identify 1 patient with POMI was calculated. POMI occurred in 216 (4%) patients and other myocardial injury in 842 (15%) of the 5590 included patients. Classification and regression tree analysis divided patients into 14 subgroups in which the risk of POMI ranged from 1.7% to 42%. Using a risk of POMI ≥2% to select patients for routine troponin I monitoring, this monitoring would be advocated in patients ≥60 years of age undergoing emergency surgery, or those undergoing elective surgery with a Revised Cardiac Risk Index class >2 (ie >1 risk factor). The number needed to screen to detect a patient with POMI would be 14 (95% CI 14–14) and 26% of patients with POMI would be missed. Conclusions To improve selection of high‐risk patients ≥60 years of age, routine postoperative troponin I monitoring could be considered in patients undergoing emergency surgery, or in patients undergoing elective surgery classified as having a revised cardiac risk index class >2.