A four-year cardiovascular risk score for type 2 diabetic inpatients

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Risk Models and Scores of Cardiovascular Disease in Patients with Diabetes Mellitus

Diabetes mellitus (DM) is an established risk factor for atherosclerotic cardiovascular disease (CVD), and patients with DM are at a two to four-fold higher cardiovascular risk, including myocardial infraction, unstable angina, stroke, and heart failure. All of the above have arisen interest in CVD preventive strategies by the use of non-invasive methods, such as risk scores. The most common approach is to consider DM as a CVD equivalent and, therefore, to treat patients with DM in a similar way to those who required secondary CVD prevention. However, this approach has been disputed as all patients with DM do not have the same risk for CVD, and since other potentially important factors within the context of DM, such as DM duration, presence of albuminuria, and comorbidities, should be taken into consideration. Thus, the second and third approach is the application of risk models that were either developed initially for the general population or designed specifically for patients with DM, respectively. This review summarizes the evidence and implications for clinical practice regarding these scores. Up to date, several models that can be applied to the diabetic population have been proposed. However, only a few meet the minimum requirement of adequate external validation. In addition, moderate discrimination and poor calibration, which might lead to inaccurate risk estimations in populations with different characteristics, have been reported. Therefore, future research is needed before recommending a specific risk model for universal clinical practice in the management of diabetes.

The timing of last hemodialysis influences the prognostic value of serum lactate levels in predicting mortality of end-stage renal disease patients with sepsis in the emergency department

Sepsis is a life-threatening condition, and serum lactate levels have been used to predict patient prognosis. Studies on serum lactate levels in patients undergoing regular hemodialysis who have sepsis are limited. This study aimed to determine the predictive value of serum lactate levels for sepsis-related mortality among patients who underwent last hemodialysis at three different times before admission to the emergency department (ED).This retrospective cohort study was conducted from January 2007 to December 2013 in southern Taiwan. All hemodialysis patients with sepsis, receiving antibiotics within 24 hours of sepsis confirmation, admitted for at least 3 days, and whose serum lactate levels were known were examined to determine the difference in the serum lactate levels of patients who underwent last hemodialysis within 4 hours (Groups A), in 4-12 hours (Group B), and beyond 12 hours (Group C) before visited to the ED. All the continuous variables, categorical variables and mortality were compared by using Kruskal-Wallis test or Mann-Whitney test, the χ2 or Fisher exact tests, and multiple logistic regression model, respectively.A total of 490 patients were enrolled in the study, and 8.0% (39), 21.5% (84), and 74.9% (367) of the patients were in Group A, Group B and Group C, respectively; the serum lactate levels (2.91 vs 2.13 vs 2.79 mmol/L, respectively; P = .175) and 28-day in-hospital mortality (17.9% vs 14.6% vs 22.9%) showed no statistically significant difference between 3 groups. The association between serum lactate levels and 28-day in-hospital mortality was reliable in Group B (P = .002) and Group C (P < .001), but it was unreliable in Group A (P = .629).Serum lactate level has acceptable sensitivity in predicting 28-day in-hospital mortality among patients with sepsis who undergo last hemodialysis after 4 hours, but is not reliable when the last hemodialysis takes place within 4 hours.

A Prognostic Model for the Outcome of Nobel Biocare Dental Implants with Peri-Implant Disease after One Year

Background: This investigation, based on a 1-year retrospective cohort study, aimed to estimate and validate a prognostic model for ailing and failing implants due to peri-implant disease. Methods: A total of 240 patients (male: 97; female: 143; average age of 57.3 years) with at least one ailing or failing implant were included: 120 patients for model derivation and 120 patients for model validation. The primary outcome measure was the implant status: success, defined as the arrest of the disease, or failure defined as implant extraction, prevalence or re-incidence of peri-implant disease). Potential prognostic risk indicators were collected at the baseline evaluation. The relative risk (RR) was estimated for the predictors through logistic regression and the c-statistic (95% confidence interval) was calculated for both derivation and validation sets. The significance level was set at 5%. Results: The risk model retrieved the prognostic factors age (RR = 1.04), history of Periodontitis (RR = 3.13), severe peri-implant disease status (RR = 3.26), implant length (RR = 3.52), early disease development (RR = 3.99), with good discrimination in both the derivation set (0.763 [0.679; 0.847]) and validation set (0.709 [0.616; 0.803]). Conclusions: A prognostic risk model for estimating the outcome of implants with peri-implant disease is available, with a good performance considering the c-statistic evaluation.

Prognostic tools for cardiovascular disease in patients with type 2 diabetes: A systematic review and meta-analysis of C-statistics

BACKGROUND

Diabetes is associated with an increased risk for cardiovascular diseases (CVD). Risk prediction models are tools widely used to identify individuals at particularly high-risk of adverse events. Many CVD risk prediction models have been developed but their accuracy and consistency vary.

OBJECTIVE

This study reviews the literature on available CVD risk prediction models specifically developed or validated in patients with diabetes and performs a meta-analysis of C-statistics to assess and compare their predictive performance.

METHODS

The online databases and manual reference checks of all identified relevant publications were searched.

RESULTS

Fifteen CVD prediction models developed for patients with diabetes and 11 models developed in a general population but later validated in diabetes patients were identified. Meta-analysis of C-statistics showed an overall pooled C-statistic of 0.67 and 0.64 for validated models developed in diabetes patients and in general populations respectively. This small difference in the C-statistic suggests that CVD risk prediction for diabetes patients depends little on the population the model was developed in (p = 0.068).

CONCLUSIONS

The discriminative ability of diabetes-specific CVD prediction models were modest. Improvements in the predictive ability of these models are required to understand both short and long-term risk before implementation into clinical practice.

Difference between elderly and non-elderly patients in using serum lactate level to predict mortality caused by sepsis in the emergency department

Elderly people are more susceptible to sepsis and experience more comorbidities and complications than young adults. Serum lactate is a useful biomarker to predict mortality in patients with sepsis. Lactate production is affected by the severity of sepsis, organ dysfunction, and adrenergic stimulation. Whether the predictive ability of serum lactate will be different between non-elderly and elderly patients is unknown.A retrospective cohort study was conducted to compare the prognostic value of hyperlactatemia in predicting the mortality between elderly (≥65 years) and non-elderly (<65 years) patients with sepsis.This is a single-center retrospective observational cohort study conducted from January 2007 to December 2013 in southern Taiwan. All patients with sepsis, who used antibiotics, with blood culture collected, and with available serum lactate levels in the emergency department, were included in the analysis. We evaluated the difference in serum lactate level between the elderly and non-elderly septic patients by using multiple regression models.A total of 7087 patients were enrolled in the study. Elderly and non-elderly patients accounted for 62.3% (4414) and 40.2% (2673) of all patients, respectively. Statistically significant difference of serum lactate levels was not observed between elderly and non-elderly survivors (2.9 vs 3.0 mmol/L; P = .57); however, elderly patients had lower lactate levels than those within the 28-day in-hospital mortality (5.5 vs 6.6 mmol/L, P < .01). Multiple logistic regression revealed higher adjusted mortality risk in elderly and non-elderly patients with lactate levels of ≥4.0 mmol/L (odds ratio [OR], 4.98 and 5.82; P < .01, respectively), and lactate level between 2 and 4 mmol/L (OR, 1.57 and 1.99; P < .01, respectively) compared to that in the reference group with lactate levels of <2.0 mmol/L in each group. In receiver operating characteristic curve analysis, sensitivity rates for predicting mortality were 0.80 and 0.77 for non-elderly and elderly patients, respectively, by using serum lactate levels higher than 2.0 mmol/L.Septic elderly non-survivors had 1 mmol/L lower serum lactate level than those of the non-elderly non-survivors. Lactate >2 mmol/L still could provide enough sensitivity in predicting sepsis mortality in elder patients.

Construction, internal validation and implementation in a mobile application of a scoring system to predict nonadherence to proton pump inhibitors

BACKGROUND

Other studies have assessed nonadherence to proton pump inhibitors (PPIs), but none has developed a screening test for its detection.

OBJECTIVES

To construct and internally validate a predictive model for nonadherence to PPIs.

METHODS

This prospective observational study with a one-month follow-up was carried out in 2013 in Spain, and included 302 patients with a prescription for PPIs. The primary variable was nonadherence to PPIs (pill count). Secondary variables were gender, age, antidepressants, type of PPI, non-guideline-recommended prescription (NGRP) of PPIs, and total number of drugs. With the secondary variables, a binary logistic regression model to predict nonadherence was constructed and adapted to a points system. The ROC curve, with its area (AUC), was calculated and the optimal cut-off point was established. The points system was internally validated through 1,000 bootstrap samples and implemented in a mobile application (Android).

RESULTS

The points system had three prognostic variables: total number of drugs, NGRP of PPIs, and antidepressants. The AUC was 0.87 (95% CI [0.83-0.91], p < 0.001). The test yielded a sensitivity of 0.80 (95% CI [0.70-0.87]) and a specificity of 0.82 (95% CI [0.76-0.87]). The three parameters were very similar in the bootstrap validation.

CONCLUSIONS

A points system to predict nonadherence to PPIs has been constructed, internally validated and implemented in a mobile application. Provided similar results are obtained in external validation studies, we will have a screening tool to detect nonadherence to PPIs.

Construction and internal validation of a new mortality risk score for patients admitted to the intensive care unit

BACKGROUND

The existing models to predict mortality in intensive care units (ICU) present difficulties in clinical practice.

OBJECTIVES

The aim of this study was to develop and internally validate a points system to predict mortality in the ICU, which can be applied instantly and with high discriminating power.

METHODS

This cohort study comprised all patients admitted to the ICU in a Spanish region between January 2013 and April 2014, followed from admission to death or discharge (N=1113). Primary variable: ICU mortality. Secondary variables at admission: gender, Fried criteria for frailty, function scale, medical admission, cardiac arrest, cardiology admission, sepsis, mechanical ventilation, inotropic support, age, frailty index and clinical frailty scale. The sample was divided randomly into two groups (80% and 20%): construction (n=844) and internal validation (n=269). Construction: A logistic regression model was implemented and adapted to the points system.

VALIDATION

the area under the ROC curve (AUC) of the model was calculated and the risk quintiles were created to determine whether differences existed between observed and expected deaths.

RESULTS

The points system included: function scale, medical admission, cardiology admission, sepsis, mechanical ventilation and inotropic support. The validation showed: (i) AUC=0.95 (95% CI: 0.91-0.99, p<.001); (ii) No differences between observed and expected deaths (p=.799).

CONCLUSIONS

A predictive model of mortality in the ICU has been constructed and internally validated. This model improves on the previous models through its simplicity, its discriminating power and free use. External validation studies are needed in other geographical areas.