A risk-prediction score about colorectal lesions based on the Chinese population of high-risk participants aged 50-65 years.

OBJECTIVES: The present study aims to develop an effective risk-prediction score (RPS) to improve screening efficiency and contribute to secondary prevention of colorectal cancer (CRC). STUDY DESIGN: Screening for colorectal lesions. METHODS: 14,398 high-risk individuals aged 50-65 years were included. The baseline characteristics of participants with and without colorectal lesions (CL) were compared using a Chi-squared test. The overall population was randomly split into a training set and a test set in the ratio of 80% and 20%. One-factor and multifactor logistic regression analyses were performed in the training set to construct the RPS (scores of 0-9.62). Area under curve (AUC) was calculated as an estimate of predictive performance using the receiver-operating characteristic (ROC) curve in the test set. RESULTS: In the study population, being male, advanced age, current or previous smoking, weekly alcohol consumption, high body mass index (BMI ≥24 kg/m2), and previously detected colonic polyp were associated with higher risk of CL. Compared to the low-risk group (0-2.31 points), the ORs and 95% confidence intervals (CIs) for the moderate-risk group (2.31-3.85 points) and high-risk group (3.85-8.42 points) were 1.58 (1.44, 1.73) and 2.52 (2.30, 2.76), respectively. For every 1-point increase in score, participants had a 27% increased risk of CL (OR:1.27, 95% CI: 1.24, 1.30). For participants with CL predicted by RPS, the area under the working characteristic curve was 0.61 (P < 0.001). CONCLUSION: Our RPS can quickly and efficiently identify multiple lesions of the colorectum. Combining RPS with existing screening strategies facilitates the identification of very high-risk individuals and may help to prevent CRC.

Development and validation of risk prediction model for recurrent cardiovascular events among Chinese: the Personalized CARdiovascular DIsease risk Assessment for Chinese model.

Aims: Cardiovascular disease (CVD) is a leading cause of mortality, especially in developing countries. This study aimed to develop and validate a CVD risk prediction model, Personalized CARdiovascular DIsease risk Assessment for Chinese (P-CARDIAC), for recurrent cardiovascular events using machine learning technique. Methods and results: Three cohorts of Chinese patients with established CVD were included if they had used any of the public healthcare services provided by the Hong Kong Hospital Authority (HA) since 2004 and categorized by their geographical locations. The 10-year CVD outcome was a composite of diagnostic or procedure codes with specific International Classification of Diseases, Ninth Revision, Clinical Modification. Multivariate imputation with chained equations and XGBoost were applied for the model development. The comparison with Thrombolysis in Myocardial Infarction Risk Score for Secondary Prevention (TRS-2°P) and Secondary Manifestations of ARTerial disease (SMART2) used the validation cohorts with 1000 bootstrap replicates. A total of 48 799, 119 672 and 140 533 patients were included in the derivation and validation cohorts, respectively. A list of 125 risk variables were used to make predictions on CVD risk, of which 8 classes of CVD-related drugs were considered interactive covariates. Model performance in the derivation cohort showed satisfying discrimination and calibration with a C statistic of 0.69. Internal validation showed good discrimination and calibration performance with C statistic over 0.6. The P-CARDIAC also showed better performance than TRS-2°P and SMART2. Conclusion: Compared with other risk scores, the P-CARDIAC enables to identify unique patterns of Chinese patients with established CVD. We anticipate that the P-CARDIAC can be applied in various settings to prevent recurrent CVD events, thus reducing the related healthcare burden.

Platelet and large platelet ratios are useful in predicting severity of COVID-19.

The role of platelets in coronavirus disease (COVID-19) severity requires further exploration. To determine whether the platelet index is useful in predicting COVID-19 severity, we compared the platelet index in patients with higher and lower oxygen requirements (≥ 4 L/min vs. < 4 L/min) and patients without COVID-19. We also analyzed the time course of the platelet index in each group. A total of 285 patients with COVID-19 and 36 without COVID-19 who were hospitalized at Fussa Hospital were analyzed. After matching for oxygen requirement at admission, multivariate analysis was performed. Platelets (≤ 16.6 × 104/µL) and platelet-large cell ratio (P-LCR) (≥ 27.8%) were significant factors influencing severity. Based on these factors, we created the Fussa platelet score, and the group with a Fussa platelet score ≥ 2 was significantly more likely to reach the 4 L/min oxygen requirement (event-free survival: Fussa platelet score ≥ 2 versus < 2, P < 0.00000001). Analysis of platelet index by time period showed a significant increase from 6-10 days after onset. The Fussa platelet score can be measured quickly, easily, and inexpensively in a clinic and may be useful in determining need for transfer to a critical care hospital.

Risk factor analysis of refracture in the same cemented vertebra after percutaneous kyphoplasty for Kümmell's disease.

OBJECTIVE: The objective of this study was to evaluate the factors that affect refracture in the same cemented vertebra after percutaneous kyphoplasty (PKP) for Kümmell's disease (KD) and establish a risk prediction score. METHODS: A total of 2932 patients who were treated with PKP for KD between January 2019 and December 2021 were retrospectively reviewed. After inclusion and exclusion criteria were applied, 191 patients were included in the study. According to the criteria for refracture, there were 50 patients in the refracture group and 141 patients in the no-refracture group. Twenty-five factors were analyzed. Patient demographics, medical history, imaging data, surgical data, and postoperative management were reviewed. Multivariate logistic regression modeling was used to identify the independent risk factors for refracture. Receiver operating characteristic (ROC) curve analysis was used to assess and establish a risk score system and further predict the risk of refracture. RESULTS: In this study, 50 (26.2%) patients developed a refracture. Through univariate analysis, bone mineral density (BMD) (p < 0.001), compression rate (p = 0.007), classification (i.e., the stages determined by the compression ratios) (p < 0.001), bone cement volume (p < 0.001), volume fraction (p < 0.001), distribution pattern (p = 0.007), non-PMMA endplate contact (p < 0.001), and anti-osteoporosis therapy (p < 0.001) were found to be significant factors for post-cement vertebral refracture after PKP in patients with KD. Three independent risk factors were found to be significant for refracture: small volume fraction, low BMD, and no anti-osteoporosis therapy. One point was assigned for each factor. The incidence rates of refracture in patients with scores of 0, 1, 2, and 3 were 3.7%, 4.4%, 42.0%, and 100%, respectively. The area under the ROC curve for this risk prediction score was 0.888 (p < 0.001), indicating moderate accuracy. CONCLUSIONS: Volume fraction, BMD, and osteoporosis therapy are the main factors influencing the refracture of the same cemented vertebra in KD. On the basis of these factors, the risk prediction score developed in this paper can be used to forecast the incidence of refracture.

A transfusion risk stratification score to facilitate quality management in cardiopulmonary bypass.

BACKGROUND: Our previous showed that a blood management program in the cardiopulmonary bypass (CPB) department, reduced red blood cell (RBC) transfusion and complications, but assessing transfusion practice solely based on transfusion rates was insufficient. This study aimed to design a risk stratification score to predict perioperative RBC transfusion to guide targeted measures for on-pump cardiac surgery patients. STUDY DESIGN AND METHODS: We analyzed data from 42,435 adult cardiac patients. Eight predictors were entered into the final model including age, sex, anemia, New York Heart Association classification, body surface area, cardiac surgery history, emergency surgery, and surgery type. We then simplified the score to an integer-based system. The area under the receiver operating characteristic curve (AUC), Hosmer-Lemeshow goodness-of-fit test, and a calibration curve were used for its performance test. The score was compared to existing scores. RESULTS: The final score included eight predictors. The AUC for the model was 0.77 (95% CI, 0.76-0.77) and 0.77 (95% CI, 0.76-0.78) in the training and test set, respectively. The calibration curves showed a good fit. The risk score was finally grouped into low-risk (score of 0-13 points), medium-risk (14-19 points), and high-risk (more than 19 points). The score had better predictive power compared to the other two existing risk scores. DISCUSSION: We developed an effective risk stratification score with eight variables to predict perioperative RBC transfusion for on-pump cardiac surgery. It assists perfusionists in proactively preparing blood conservation measures for high-risk patients before surgery.

Dynamic predictive scores for cardiac surgery-associated agitated delirium: a single-center retrospective observational study.

BACKGROUND: Prevention, screening, and early treatment are the aims of postoperative delirium management. The scoring system is an objective and effective tool to stratify potential delirium risk for patients undergoing cardiac surgery. METHODS: Patients who underwent cardiac surgery between January 1, 2012, and January 1, 2019, were enrolled in our retrospective study. The patients were divided into a derivation cohort (n = 45,744) and a validation cohort (n = 11,436). The AD predictive systems were formulated using multivariate logistic regression analysis at three time points: preoperation, ICU admittance, and 24 h after ICU admittance. RESULTS: The prevalence of AD after cardiac surgery in the whole cohort was 3.6% (2,085/57,180). The dynamic scoring system included preoperative LVEF ≤ 45%, serum creatinine > 100 µmol/L, emergency surgery, coronary artery disease, hemorrhage volume > 600 mL, intraoperative platelet or plasma use, and postoperative LVEF ≤ 45%. The area under the receiver operating characteristic curve (AUC) values for AD prediction were 0.68 (preoperative), 0.74 (on the day of ICU admission), and 0.75 (postoperative). The Hosmer‒Lemeshow test indicated that the calibration of the preoperative prediction model was poor (P = 0.01), whereas that of the pre- and intraoperative prediction model (P = 0.49) and the pre, intra- and postoperative prediction model (P = 0.35) was good. CONCLUSIONS: Using perioperative data, we developed a dynamic scoring system for predicting the risk of AD following cardiac surgery. The dynamic scoring system may improve the early recognition of and the interventions for AD.

Development of a risk prediction score for acute kidney injury in critically-ill septic Filipino patients admitted in perpetual succour hospital

Introduction@#Acute kidney injury (AKI) is a lethal complication of critical illness characterized by the rapid loss of the kidney's excretory function encountered in 50% of intensive care unit (ICU) admissions. Its impact on the outcome of critically ill patients makes AKI a significant cause of morbidity and mortality.@*Objectives@#To develop and validate an acute kidney injury risk prediction score based on routinely available variables and common laboratories of admitted critically-ill septic Filipino patients.@*Methods@#This is a prospective cohort study conducted in a tertiary hospital in Cebu from February to September 2020. The data of 2545 patients were identified by chart review but only 607 patients with a quick Sepsis Organ Failure Assessment Score (qSOFA) score of >2 were included in the pre-screening. After stratified sampling, a total of 198 septic ICU patients were enrolled. Demographic profile, laboratory results and outcome data were collated. Variables were screened then stepwise forward elimination was done to identify the significant predictors. An AKI risk score model was developed with binomial regression analysis by identifying independent prognostic factors. The diagnostic ability of the model was determined by the Area under the Receiver Operating Characteristics (AuROC).@*Results@#AKI developed in 155 (78%) patients. The significant predictors for Acute Kidney Injury were age, hypertension, atherosclerotic cardiovascular disease, weight, white blood count, creatinine, and BUN. An AKI prediction model with a cut off score of 161.9 was made with a fair diagnostic ability for predicting AKI at 0.79 based on AuROC.@*Conclusion@#The developed risk prediction tool using routinely available variables is found to be fairly accurate to predict the development of AKI among critically ill septic patients.

Clinical Characteristics and Risk Prediction Score in Patients With Mild-to-Moderate Coronavirus Disease 2019 in Japan.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has rapidly spread worldwide, causing widespread mortality. Many patients with COVID-19 have been treated in homes, hotels, and medium-sized hospitals where doctors were responsible for assessing the need for critical care hospitalization. This study aimed to establish a severity prediction score for critical care triage. METHOD: We analyzed the data of 368 patients with mild-to-moderate COVID-19 who had been admitted to Fussa Hospital, Japan, from April 2020 to February 2022. We defined a high-oxygen group as requiring ≥4 l/min of oxygen. Multivariable logistic regression was used to construct a risk prediction score, and the best model was selected using a stepwise selection method. RESULTS: Multivariable analysis showed that older age (≥70 years), elevated creatine kinase (≥127 U/L), C-reactive protein (≥2.19 mg/dL), and ferritin (≥632.7 ng/mL) levels were independent risk factors associated with the high-oxygen group. Each risk factor was assigned a score ranging from 0 to 4, and we referred to the final overall score as the Fussa score. Patients were classified into two groups, namely, high-risk (total risk factors, ≥2) and low-risk (total risk score, <2) groups. The high-risk group had a significantly worse prognosis (low-risk group, undefined vs. high-risk group, undefined; P< 0.0001). CONCLUSIONS: The Fussa score might help to identify patients with COVID-19 who require critical care hospitalization.

Optimization of the preoperative requirements of blood units for the surgical treatment of extra-abdominal soft tissue sarcoma: the TRANSAR score.

BACKGROUND AND OBJECTIVES: Excessive preoperative blood orders frequently occur during the preoperative planning of resections of sarcomas. We aimed to develop a prediction score model that would be able to identify a patient cohort in which the cross-matching could be safely evaded. PATIENTS AND METHODS: We retrospectively analyzed data of 309 consecutive patients with extra-abdominal soft tissue sarcomas treated between September 2012 and December 2014. Scorecard scores for variables were calculated and summarized to a total score that can be used for risk stratification. The score was used in a logistic regression model. Results of the optimized model were described as a receiver operating characteristic curve. RESULTS: Preoperative units of red blood cells were requested for 206 (66.7%) patients, of which only 31 (10%) received them. Five parameters were identified with high predictive power. In the visualized barplot, there was an increased risk of blood transfusion with a higher score of TRANSAR. CONCLUSION: A TRANSAR score is a new tool that can predict the probability of transfusion for patients with sarcoma. This may reduce the number of preoperative cross-matching and blood product ordering and associated costs without compromising patient care.

Development and Validation of a Prediction Rule for Major Adverse Cardiac and Cerebrovascular Events in High-Risk Myocardial Infarction Patients After Primary Percutaneous Coronary Intervention.

Background and Aims: We aimed to develop a clinical prediction tool to improve the prognosis of major adverse cardiac and cerebrovascular events (MACCE) among high-risk myocardial infarction (MI) patients undergoing primary percutaneous coronary intervention (PCI). Methods: The present study was a prospective and observational study. A total of 4151 consecutive MI patients who underwent primary PCI at Fuwai Hospital in Beijing, China (January 2010 and June 2017) were enrolled. Forty-eight patients without follow-up data were excluded from the study. The pre-specified criteria (Supplementary Information 1) were chosen to enroll MI patients at high risk for MACCE complications after PCI. Results: The full model included seven variables, with a risk score of 160 points. Derivation and validation cohort models predicting MACCE had C-statistics of 0.695 and 0.673. The area under the curve (AUC) of the survival receiver operating characteristic curve (ROC) for predicting MACCE was 0.991 and 0.883 in the derivation and validation cohorts, respectively. Conclusion: The predicted model was internally validated and calibrated in large cohorts of patients with high-risk MI receiving primary PCI to predict MACCE and showed modest accuracy in the derivation and validation cohorts.

Risk Prediction Score for Pediatric Patients with Suspected Ebola Virus Disease.

Rapid diagnostic tools for children with Ebola virus disease (EVD) are needed to expedite isolation and treatment. To evaluate a predictive diagnostic tool, we examined retrospective data (2014-2015) from the International Medical Corps Ebola Treatment Centers in West Africa. We incorporated statistically derived candidate predictors into a 7-point Pediatric Ebola Risk Score. Evidence of bleeding or having known or no known Ebola contacts was positively associated with an EVD diagnosis, whereas abdominal pain was negatively associated. Model discrimination using area under the curve (AUC) was 0.87, which outperforms the World Health Organization criteria (AUC 0.56). External validation, performed by using data from International Medical Corps Ebola Treatment Centers in the Democratic Republic of the Congo during 2018-2019, showed an AUC of 0.70. External validation showed that discrimination achieved by using World Health Organization criteria was similar; however, the Pediatric Ebola Risk Score is simpler to use.

A risk-prediction score for colorectal lesions on 12,628 participants at high risk of colorectal cancer.

BACKGROUND: The uptake of colonoscopy is low in individuals at risk of colorectal cancer (CRC). We constructed a risk-prediction score (RPS) in a large community-based sample at high risk of CRC to enable more accurate risk stratification and to motivate and increase the uptake rate of colonoscopy. METHODS: A total of 12,628 participants classified as high-risk according to positivity of immunochemical fecal occult blood tests or High-Risk Factor Questionnaire underwent colonoscopy. Logistic regression was used to derive a RPS and analysed the associations of the RPS with colorectal lesions, giving odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Of the participants, men (OR = 1.73, 95% CI = 1.58-1.90), older age (≥65 years; 1.41, 1.31-1.53), higher body mass index (≥28 kg/m2; 1.22, 1.07-1.39), ever smoking (1.47, 1.31-1.65), and weekly alcohol use (1.28, 1.09-1.52) were associated with a higher risk of colorectal lesions. We assigned 1 point to each of the above five risk factors and derived a RPS ranging from 0 to 5, with a higher score indicating a higher risk. Compared with a RPS of 0, a RPS of 1, 2, 3, and 4-5 showed a higher risk of colorectal lesions, with the OR (95% CI) being 1.50 (1.37-1.63), 2.34 (2.12-2.59), 3.58 (3.13-4.10), and 3.91 (3.00-5.10), respectively. The area under the receiver-operating characteristic curve of RPS in predicting colorectal lesions was 0.62. CONCLUSIONS: Participants with an increase in the RPS of ≥1 point had a significantly higher risk of colorectal lesions, suggesting the urgency for measuring colonoscopy in this very high-risk group. High-risk strategies incorporating RPS may be employed to achieve a higher colonoscopy-uptake rate.

Predicting fever in neutropenia with safety-relevant events in children undergoing chemotherapy for cancer: The prospective multicenter SPOG 2015 FN Definition Study.

BACKGROUND: Fever in neutropenia (FN) remains a frequent complication in pediatric patients undergoing chemotherapy for cancer. Preventive strategies, like primary antibiotic prophylaxis, need to be evidence-based. PROCEDURE: Data on pediatric patients with any malignancy from the prospective multicenter SPOG 2015 FN Definition Study (NCT02324231) were analyzed. A score predicting the risk to develop FN with safety-relevant events (SRE; bacteremia, severe sepsis, intensive care unit admission, death) was developed using multivariate mixed Poisson regression. Its predictive performance was assessed by internal cross-validation and compared with the performance of published rules. RESULTS: In 238 patients, 318 FN episodes were recorded, including 53 (17%) with bacteremia and 68 (21%) with SRE. The risk-prediction score used three variables: chemotherapy intensity, defined according to the expected duration of severe neutropenia, time since diagnosis, and type of malignancy. Its cross-validated performance, assessed by the time needed to cover (TNC) one event, exceeded the performance of published rules. A clinically useful score threshold of ≥11 resulted in 2.3% time at risk and 4.1 months TNC. Using external information on efficacy and timing of intermittent antibiotic prophylaxis, 4.3 months of prophylaxis were needed to prevent one FN with bacteremia, and 5.2 months to prevent one FN with SRE, using a threshold of ≥11. CONCLUSIONS: This score, based on three routinely accessible characteristics, accurately identifies pediatric patients at risk to develop FN with SRE during chemotherapy. The score can help to design clinical decision rules on targeted primary antibiotic prophylaxis and corresponding efficacy studies.

External validation of a model to predict women most at risk of postpartum venous thromboembolism: Maternity clot risk.

INTRODUCTION: Venous thromboembolism (VTE) is the leading cause of direct maternal mortality in high-income countries. We previously developed a risk prediction score for postpartum venous thromboembolism (VTE) in women without a previous VTE. In this paper, we provide further external validation and assess its performance across various groups of postpartum women from England. MATERIALS AND METHODS: Cohort study using primary and secondary care data covering England. We used data from QResearch comprising women with pregnancies ending in live birth or stillbirth recoded in Hospital Episodes Statistics between 2004 and 2015. Outcome was VTE in the 6 weeks postpartum. Our predictor variables included sociodemographic and lifestyle characteristics, pre-existing comorbidities, and pregnancy and delivery characteristics. RESULTS: Among 535,583 women with 700,185 deliveries, 549 VTE events were recorded (absolute risk of 7.8 VTE events per 10,000 deliveries). When we compared predicted probabilities of VTE for each woman from the original model with actual VTE events, we obtained a C-statistic of 0.67 (95% CI 0.65 to 0.70). However, our model slightly over-predicted VTE risk for the higher risk women (calibration slope = 0.84; 95% CI 0.74 to 0.94). Performance was similar across groups defined by calendar time, socioeconomic status, age group and geographical area. The score performed comparably with the existing algorithm used by the UK Royal College of Obstetrician and Gynaecologists. CONCLUSIONS: Our model enables flexibility in setting new treatment thresholds. Adopting it in clinical practice may help optimise use of low-molecular-weight heparin postpartum to maximise health gain by better targeting of high-risk groups.

Prediction Scores for Any-Stage and Stage-3 Acute Kidney Injury After Adult Cardiac Surgery in a Chinese Population.

OBJECTIVES: This study was performed to internally derive and then validate risk score systems using preoperative and intraoperative variables to predict the occurrence of any-stage (stage 1, 2, 3) and stage-3 acute kidney injury (AKI) within seven days of cardiac surgery. DESIGN: Single-center, retrospective, observational study. SETTING: Single, large, tertiary care center. PARTICIPANTS: Adult patients undergoing open cardiac surgery between January 1, 2012, and January 1, 2019. MEASUREMENTS AND MAIN RESULTS: The clinical data were divided into the following two groups: a derivation cohort (n = 43,799) and a validation cohort (n = 14,600). AKI was defined using the Kidney Disease: Improving Global Outcomes criteria. Multivariate logistic regression analysis was used to develop the prediction models. The overall prevalence of any-stage AKI and stage-3 AKI after cardiac surgery were 34.3% and 1.7%, respectively. The discriminatory ability of the any-stage AKI prediction model measured with the area under the curve (AUC) was acceptable (AUC = 0.69, 95% confidence interval 0.68-0.69), and the calibration measured with the Hosmer-Lemeshow test was good (p = 0.95). The AUC for the stage-3 AKI prediction model was 0.84 (95% confidence interval 0.83-0.85), and the Hosmer-Lemeshow test also indicated a good calibration (p = 0.73). CONCLUSIONS: This research study, which used preoperative and intraoperative variables, derived and internally validated two predictive scoring systems for any-stage AKI and stage-3 AKI as defined by modified Kidney Disease: Improving Global Outcomes criteria using a very large cohort of Chinese cardiac surgical patients.

Validation of risk prediction scores for hepatocellular carcinoma in patients with chronic hepatitis B treated with entecavir or tenofovir.

Several prediction scores for the early detection of hepatocellular carcinoma (HCC) are available. We validated the predictive accuracy of age, albumin, sex, liver cirrhosis (AASL), RESCUE-B, PAGE-B and modified PAGE-B (mPAGE-B) scores in chronic hepatitis B (CHB) patients treated with entecavir (ETV) or tenofovir disoproxil fumarate (TDF). Between 2007 and 2014, 3171 patients were recruited (1645, ETV; 1517, TDF). The predictive accuracy of each prediction score was assessed. The mean age of the study population (1977 men; 1194 women) was 48.8 years. Liver cirrhosis was present in 1040 (32.8%) patients. During follow-up (median, 58.2 months), 280 (8.8%) patients developed HCC; these patients were significantly older; more likely to be male; had significantly higher proportions of liver cirrhosis, hypertension and diabetes; and had significantly higher values for the four risk scores than those who did not develop HCC (all P < .05). Older age (hazard ratio [HR] = 1.048), male sex (HR = 2.142), liver cirrhosis (HR = 3.144) and prolonged prothrombin time (HR = 2.589) were independently associated with an increased risk of HCC (all P < .05), whereas a higher platelet count (HR = 0.996) was independently associated with a decreased risk of HCC (P < .05). The predictive accuracy of AASL score was the highest for 3- and 5-year HCC predictions (areas under the curve [AUCs] = 0.818 and 0.816, respectively), followed by RESCUE-B, PAGE-B and mPAGE-B scores (AUC = 0.780-0.815 and 0.769-0.814, respectively). In conclusion, four HCC prediction scores were assessed in Korean CHB patients treated with ETV or TDF. The AASL score showed the highest predictive accuracy.

Risk prediction of in-hospital mortality among patients with type 2 diabetes mellitus and concomitant community-acquired pneumonia.

BACKGROUND: The aim of the present study was to investigate the risk factors for in-hospital mortality among patients with type 2 diabetes mellitus (T2DM) and concomitant community-acquired pneumonia (CAP) and establish a risk prediction score. METHODS: Data from 1,360 adult patients with T2DM and concomitant CAP hospitalized in two grade 3A hospitals between 2009 and 2019 were collected through electronic medical records. Data obtained included the status of diabetes mellitus, comorbidities, laboratory and imaging findings, and treatment outcomes. Statistical analysis was conducted to investigate the risk factors affecting prognosis, and a clinical risk prediction score was designed. RESULTS: Based on the patients' treatment outcomes (deceased, improved and cured), the cohort was divided into two groups: deceased and improved; 16 parameters were significant after segmentation. However, the following nine parameters were independent predictors of mortality: neutrophil-lymphocyte ratio (NLR) ≥4, pulse rate ≥125 bpm, change in state of consciousness, arterial blood pH ≤7.35, age ≥65 years, serum sodium ≤130 mmol/L, initial fasting blood glucose ≥9 mmol/L, multilobar involvement, and diabetic nephropathy. Based on these findings, a risk prediction score was established, and bootstrap validation was performed. The risk prediction score was significantly superior to CURB-65 [confusion, urea >7 mmol/L, respiratory rate >30/min, low blood pressure (systolic <90 mmHg or diastolic <60 mmHg), age >65 years] and slightly superior than the pneumonia severity index (PSI). CONCLUSIONS: The influencing factors for in-hospital mortality among patients with T2DM and concomitant CAP included advanced age, change in state of consciousness, increased pulse rate, acidosis, high NLR, high platelet-lymphocyte ratio, hyponatremia, hyperglycemia, and diabetic nephropathy. These parameters should be recognized in clinical practice, with active interventions to improve the treatment success rate. The risk prediction score effectively differentiated the mortality risk of inpatients, thereby providing guidance on clinical decision-making.

Acute kidney injury risk prediction score for critically-ill surgical patients.

BACKGROUND: There has been a global increase in the incidence of acute kidney injury (AKI), including among critically-ill surgical patients. AKI prediction score provides an opportunity for early detection of patients who are at risk of AKI; however, most of the AKI prediction scores were derived from cardiothoracic surgery. Therefore, we aimed to develop an AKI prediction score for major non-cardiothoracic surgery patients who were admitted to the intensive care unit (ICU). METHODS: The data of critically-ill patients from non-cardiothoracic operations in the Thai Surgical Intensive Care Unit (THAI-SICU) study were used to develop an AKI prediction score. Independent prognostic factors from regression analysis were included as predictors in the model. The outcome of interest was AKI within 7 days after the ICU admission. The AKI diagnosis was made according to the Kidney Disease Improving Global Outcomes (KDIGO)-2012 serum creatinine criteria. Diagnostic function of the model was determined by area under the Receiver Operating Curve (AuROC). Risk scores were categorized into four risk probability levels: low (0-2.5), moderate (3.0-8.5), high (9.0-11.5), and very high (12.0-16.5) risk. Risk of AKI was presented as likelihood ratios of positive (LH+). RESULTS: A total of 3474 critically-ill surgical patients were included in the model; 333 (9.6%) developed AKI. Using multivariable logistic regression analysis, older age, high Sequential Organ Failure Assessment (SOFA) non-renal score, emergency surgery, large volume of perioperative blood loss, less urine output, and sepsis were identified as independent predictors for AKI. Then AKI prediction score was created from these predictors. The summation of the score was 16.5 and had a discriminative ability for predicting AKI at AuROC = 0.839 (95% CI 0.825-0.852). LH+ for AKI were: low risk = 0.117 (0.063-0.200); moderate risk = 0.927 (0.745-1.148); high risk = 5.190 (3.881-6.910); and very high risk = 9.892 (6.230-15.695), respectively. CONCLUSIONS: The function of AKI prediction score to predict AKI among critically ill patients who underwent non-cardiothoracic surgery was good. It can aid in early recognition of critically-ill surgical patients who are at risk from ICU admission. The scores could guide decision making for aggressive strategies to prevent AKI during the perioperative period or at ICU admission. TRIAL REGISTRATION: TCTR20190408004, registered on April 4, 2019.

Accuracy of early warning scores for predicting serious adverse events in pre-hospital traumatic injury.

BACKGROUND: Traumatically injured patients are at higher risk of serious adverse events. Numerous physiological scoring systems are employed as diagnostic and/or prognostic tools. The objective of this study was to evaluate the scales most commonly used by emergency medical services for the early detection of prehospital serious adverse events. METHODS: Design. Preliminary longitudinal prospective observational study without intervention study in adults with prehospital traumatic injury. SETTING: The study was carried out in the public health system of Castile and León (Spain), from April 1, 2018 to October 31, 2019, involving seven advanced life support units and five hospitals. PARTICIPANTS: Traumatically injured patients over 18 years of age who were stabilized and transferred in advanced life support units to their referral hospital. MAIN OUTCOME MEASURES: Appearance of serious adverse events at the prehospital level at the scene or during the transfer to the emergency department. RESULTS: A total of 346 patients were included in the study. The median age was 50 years (IQR: 38-65). 32 cases (7.8%) presented serious adverse events at the prehospital level. Areas under the curve for the detection of serious adverse events were obtained with the Prehospital Index (0.979; 95% CI: 0.94-1.00) and National Early Warning Score 2 (0.956; 95% CI: 0.90-1.00); p <0.001 for all scores. The Prehospital Index had a positive probability coefficient of 78.4 (95% CI: 62.8-68.6) and the National Early Warning Score 2 obtained 52.9 (95% CI: 39.7-65.6). A comparison of the curves was not significant for any of the scores studied (p> 0.05). CONCLUSIONS: All scoring systems were able to detect prehospital serious adverse events early in traumatic injury; therefore, any of the scoring systems could be useful and represent an ideal tool for routine use by emergency medical services in cases of traumatic injury.