Predictive models for all-cause and cardiovascular mortality in type 2 diabetic inpatients. A cohort study

Epidemiology and Economic Burden of Cardiovascular Disease in Patients with Type 2 Diabetes Mellitus in Spain: A Systematic Review

INTRODUCTION

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in people with type 2 diabetes mellitus (T2DM). The objectives of this systematic literature review were to identify and synthesize published data describing the epidemiology and mortality of CVD in the T2DM population and the associated economic burden.

METHODS

We conducted a systematic review searching the PubMed and MEDES databases from 2009 to 2019 using predefined selection criteria. Peer-reviewed observational studies reporting primary or secondary data on CVD prevalence, incidence, mortality, resource use and costs in patients with T2DM in Spain, written in English and Spanish, were included. Data were tabulated and summarized descriptively.

RESULTS

Of 706 articles identified, 52 were included in the review. Most studies were based on data from hospital discharge databases and registries. The reported prevalence of CVD among patients with T2DM ranged from 6.9 to 40.8%. The prevalence of coronary heart disease ranged from 4.7 to 37%, stroke from 3.5 to 19.6%, peripheral artery disease from 2.5 to 13.0%, and heart failure from 4.3 to 20.1%. In-hospital CVD mortality rates ranged from 5.6 to 10.8%. Direct costs due to CVD in hospitalized patients with T2DM were increased (> 50%) compared with patients without CVD. No studies analysed indirect costs of CVD in patients with T2DM.

CONCLUSIONS

The burden of CVD among patients with T2DM, combined with the elevated costs of care, highlights the importance of early prevention as part of integrated management of the disease to improve clinical and economic outcomes.

A one-year risk score to predict all-cause mortality in hypertensive inpatients

The aim of this study was to construct and internally validate a scoring system to estimate the probability of death in hypertensive inpatients. Existing predictive models do not meet all the indications for clinical application because they were constructed in patients enrolled in clinical trials and did not use the recommended statistical methodology. This cohort study comprised 302 hypertensive patients hospitalized between 2015 and 2017 in Spain. The main variable was time-to-death (all-cause mortality). Secondary variables (potential predictors of the model) were: age, gender, smoking, blood pressure, Charlson Comorbidity Index (CCI), physical activity, diet and quality of life. A Cox model was constructed and adapted to a points system to predict mortality one year from admission. The model was internally validated by bootstrapping, assessing both discrimination and calibration. The system was integrated into a mobile application for Android. During the study, 63 patients died (20.9%). The points system prognostic variables were: gender, CCI, personal care and daily activities. Internal validation showed good discrimination (mean C statistic of 0.76) and calibration (observed probabilities adjusted to predicted probabilities). In conclusion, a points system was developed to determine the one-year mortality risk for hypertensive inpatients. This system is very simple to use and has been internally validated. Clinically, we could monitor more closely those patients with a higher risk of mortality to improve their prognosis and quality of life. However, the system must be externally validated to be applied in other geographic areas.

Development and validation of prediction models for the risks of diabetes-related hospitalization and in-hospital mortality in patients with type 2 diabetes

OBJECTIVES

Diabetes is a major cause of hospitalization and in-hospital mortality. However, a scoring system that can be used to identify diabetic patients at risk of diabetes-related hospitalization and in-hospital mortality is lacking.

METHODS

We included 32,653 patients in this retrospective cohort study. All recruited patients had type 2 diabetes, were 30-84 years of age, and were enrolled in the National Diabetes Care Management Program over the period of 2001-2003. We used the Cox proportional hazard regression model to derive risk scores. The predictive accuracy of the models was evaluated using receiver operating characteristic curves. We conducted the Hosmer-Lemeshow test to assess the agreement between predicted and observed risks.

RESULTS

Over a follow-up period of eight years, 6243 patients were hospitalized for diabetes-related events, and 2048 deaths were registered in hospital records. For the one-, three-, five-, and eight-year periods, the areas under the curve (AUC) for diabetes-related hospitalization in the validation set were 0.80, 077, 0.76, and 0.74, respectively. The corresponding values for in-hospital mortality in the validation set were 0.87, 080, 0.77, and 0.76. The goodness-of-fit test showed that the predicted and observed probabilities in the one-, three-, five-, and eight-year periods were similar for diabetes-related hospitalization and in-hospital mortality in the validation set (all p values > 0.05).

CONCLUSION

We developed models for the estimation of the risks of diabetes-related hospitalization and in-hospital mortality in patients with type 2 diabetes. The models may be used to identify diabetic patients who are at high risk for hospital admission and in-hospital mortality.

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.

Association between the HTR2C rs1414334 C/G gene polymorphism and the development of the metabolic syndrome in patients treated with atypical antipsychotics

Few studies have assessed the association between the rs1414334 C/G polymorphism in the HTR2C gene and the development of the metabolic syndrome in patients treated with atypical antipsychotics. To provide further evidence, a cross-sectional study was conducted in Spain between 2012 and 2013 in 166 patients with these characteristics. In these patients, the association between the polymorphism and the presence of the metabolic syndrome was determined by implementing binary logistic regression models adjusted for variables associated with the metabolic syndrome. We did not confirm previous claims that the C allele of the polymorphism was linked to the metabolic syndrome: the association was in the opposite direction and non-significant. This conclusion held after taking gender and lifestyle variables into account.

A valid model for predicting responsible nerve roots in lumbar degenerative disease with diagnostic doubt

Objective

To construct and validate a model to predict responsible nerve roots in lumbar degenerative disease with diagnostic doubt (DD).

Methods

From January 2009-January 2013, 163 patients with DD were assigned to the construction (n = 106) or validation sample (n = 57) according to different admission times to hospital. Outcome was assessed according to the Japanese Orthopedic Association (JOA) recovery rate as excellent, good, fair, and poor. The first two results were considered as effective clinical outcome (ECO). Baseline patient and clinical characteristics were considered as secondary variables. A multivariate logistic regression model was used to construct a model with the ECO as a dependent variable and other factors as explanatory variables. The odds ratios (ORs) of each risk factor were adjusted and transformed into a scoring system. Area under the curve (AUC) was calculated and validated in both internal and external samples. Moreover, calibration plot and predictive ability of this scoring system were also tested for further validation.

Results

Patients with DD with ECOs in both construction and validation models were around 76 % (76.4 and 75.5 % respectively). Risk factors: more preoperative visual analog pain scale (VAS) score (OR = 1.56, p < 0.01), stenosis levels of L4/5 or L5/S1 (OR = 1.44, p = 0.04), stenosis locations with neuroforamen (OR = 1.95, p = 0.01), neurological deficit (OR = 1.62, p = 0.01), and more VAS improvement of selective nerve route block (SNRB) (OR = 3.42, p = 0.02). Validation: the internal area under the curve (AUC) was 0.85, and the external AUC was 0.72, with a good calibration plot of prediction accuracy. Besides, the predictive ability of ECOs was not different from the actual results (p = 0.532).

Conclusions

We have constructed and validated a predictive model for confirming responsible nerve roots in patients with DD. The associated risk factors were preoperative VAS score, stenosis levels of L4/5 or L5/S1, stenosis locations with neuroforamen, neurological deficit, and VAS improvement of SNRB. A tool such as this is beneficial in the preoperative counseling of patients, shared surgical decision making, and ultimately improving safety in spine surgery.

Construction and Validation of a 14-Year Cardiovascular Risk Score for Use in the General Population: The Puras-GEVA Chart

The current cardiovascular risk tables are based on a 10-year period and therefore, do not allow for predictions in the short or medium term. Thus, we are unable to take more aggressive therapeutic decisions when this risk is very high.To develop and validate a predictive model of cardiovascular disease (CVD), to enable calculation of risk in the short, medium and long term in the general population.Cohort study with 14 years of follow-up (1992-2006) was obtained through random sampling of 342,667 inhabitants in a Spanish region.

MAIN OUTCOME

time-to-CVD. The sample was randomly divided into 2 parts [823 (80%), construction; 227 (20%), validation]. A stepwise Cox model was constructed to determine which variables at baseline (age, sex, blood pressure, etc) were associated with CVD. The model was adapted to a points system and risk groups based on epidemiological criteria (sensitivity and specificity) were established. The risk associated with each score was calculated every 2 years up to a maximum of 14. The estimated model was validated by calculating the C-statistic and comparison between observed and expected events.In the construction sample, 76 patients experienced a CVD during the follow-up (82 cases per 10,000 person-years). Factors in the model included sex, diabetes, left ventricular hypertrophy, occupational physical activity, age, systolic blood pressure × heart rate, number of cigarettes, and total cholesterol. Validation yielded a C-statistic of 0.886 and the comparison between expected and observed events was not significant (P: 0.49-0.75).We constructed and validated a scoring system able to determine, with a very high discriminating power, which patients will develop a CVD in the short, medium, and long term (maximum 14 years). Validation studies are needed for the model constructed.

Six simple questions to detect malnutrition or malnutrition risk in elderly women

Of the numerous instruments available to detect nutritional risk, the most widely used is the Mini Nutritional Assessment (MNA), but it takes 15-20 min to complete and its systematic administration in primary care units is not feasible in practice. We developed a tool to evaluate malnutrition risk that can be completed more rapidly using just clinical variables. Between 2008 and 2013, we conducted a cross-sectional study of 418 women aged ≥60 years from Mexico. Our outcome was positive MNA and our secondary variables included were: physical activity, diabetes mellitus, hypertension, educational level, dentition, psychological problems, living arrangements, history of falls, age and the number of tablets taken daily. The sample was divided randomly into two groups: construction and validation. Construction: a risk table was constructed to estimate the likelihood of the outcome, and risk groups were formed.

VALIDATION

the area under the ROC curve (AUC) was calculated and we compared the expected and the observed outcomes. The following risk factors were identified: physical activity, hypertension, diabetes, dentition, psychological problems and living with the family. The AUC was 0.77 (95% CI [0.68-0.86], p < 0.001). No differences were found between the expected and the observed outcomes (p = 0.902). This study presents a new malnutrition screening test for use in elderly women. The test is based on six very simple, quick and easy-to-evaluate questions, enabling the MNA to be reserved for confirmation. However, it should be used with caution until validation studies have been performed in other geographical areas.

Validity of Optical Coherence Tomography as a Diagnostic Method for Diabetic Retinopathy and Diabetic Macular Edema

To validate optical coherence tomography (OCT) for the diagnosis of referable retinopathy (severe, very severe or proliferative retinopathy, and macular edema) in diabetic patients. We performed a cross-sectional observational study. A random sample was analyzed comprising 136 eyes of diabetic patients referred to the hospital in Elche (Spain) with suspected referable retinopathy between October 2012 and June 2013. Primary variable: Referable retinopathy measured by ophthalmological examination of the retina. OCT data included: central foveal thickness, presence of intraretinal fluid, and fundus photographs. The receiver operating characteristic (ROC) curve was calculated to determine the minimum thickness value with a positive likelihood ratio >10. To determine the validity of OCT, the following diagnostic test was defined: Positive: if the patient had at least 1 of these criteria: foveal thickness greater than the point obtained on the previously defined ROC curve, intraretinal fluid, abnormal fundus photographs; Negative: none of the above criteria. Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and Kappa statistic were determined. Of the 136 eyes, 48 had referable retinopathy (35.3%, 95% confidence interval [CI]: 27.3-43.3). The minimum thickness value with a positive likelihood ratio >10 was 275 μm. The diagnostic test constructed showed: sensitivity, 91.67% (95% CI: 79.13-97.30); specificity, 93.18% (95% CI: 85.19-97.20); positive predictive value, 88.00% (95% CI: 75.00-95.03); negative predictive value, 95.35% (95% CI: 87.87-98.50); positive likelihood ratio, 13.44 (95% CI: 6.18-29.24); negative likelihood ratio, 0.09 (95% CI: 0.03-0.23). The Kappa value was 0.84 (95% CI: 0.75-0.94, P < 0.001. This study constructed a diagnostic test for referable diabetic retinopathy with type A evidence. Nevertheless, studies are needed to determine the validity of this test in the general diabetic population.

Scoring System for Mortality in Patients Diagnosed with and Treated Surgically for Differentiated Thyroid Carcinoma with a 20-Year Follow-Up

Background

Differentiated thyroid carcinoma (DTC) is associated with an increased mortality. Few studies have constructed predictive models of all-cause mortality with a high discriminating power for patients with this disease that would enable us to determine which patients are more likely to die.

Objective

To construct a predictive model of all-cause mortality at 5, 10, 15 and 20 years for patients diagnosed with and treated surgically for DTC for use as a mobile application.

Design

We undertook a retrospective cohort study using data from 1984 to 2013.

Setting

All patients diagnosed with and treated surgically for DTC at a general university hospital covering a population of around 200,000 inhabitants in Spain.

Participants

The study involved 201 patients diagnosed with and treated surgically for DTC (174, papillary; 27, follicular).

Exposures

Age, gender, town, family history, type of surgery, type of cancer, histological subtype, microcarcinoma, multicentricity, TNM staging system, diagnostic stage, permanent post-operative complications, local and regional tumor persistence, distant metastasis, and radioiodine therapy.

Main outcome measure

All-cause mortality.

Methods

A Cox multivariate regression model was constructed to determine which variables at diagnosis were associated with mortality. Using the model a risk table was constructed based on the sum of all points to estimate the likelihood of death. This was then incorporated into a mobile application.

Results

The mean follow-up was 8.8±6.7 years. All-cause mortality was 12.9% (95% confidence interval [CI]: 8.3–17.6%). Predictive variables: older age, local tumor persistence and distant metastasis. The area under the ROC curve was 0.81 (95% CI: 0.72–0.91, p<0.001).

Conclusion

This study provides a practical clinical tool giving a simple and rapid indication (via a mobile application) of which patients with DTC are at risk of dying in 5, 10, 15 or 20 years. Nonetheless, caution should be exercised until validation studies have corroborated our results.

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

As cardiovascular risk tables currently in use were constructed using data from the general population, the cardiovascular risk of patients admitted via the hospital emergency department may be underestimated. Accordingly, we constructed a predictive model for the appearance of cardiovascular diseases in patients with type 2 diabetes admitted via the emergency department. We undertook a four-year follow-up of a cohort of 112 adult patients with type 2 diabetes admitted via the emergency department for any cause except patients admitted with acute myocardial infarction, stroke, cancer, or a palliative status. The sample was selected randomly between 2010 and 2012. The primary outcome was time to cardiovascular disease. Other variables (at baseline) were gender, age, heart failure, renal failure, depression, asthma/chronic obstructive pulmonary disease, hypertension, dyslipidaemia, insulin, smoking, admission for cardiovascular causes, pills per day, walking habit, fasting blood glucose and creatinine. A cardiovascular risk table was constructed based on the score to estimate the likelihood of cardiovascular disease. Risk groups were established and the c-statistic was calculated. Over a mean follow-up of 2.31 years, 39 patients had cardiovascular disease (34.8%, 95% CI [26.0-43.6%]). Predictive factors were gender, age, hypertension, renal failure, insulin, admission due to cardiovascular reasons and walking habit. The c-statistic was 0.734 (standard error: 0.049). After validation, this study will provide a tool for the primary health care services to enable the short-term prediction of cardiovascular disease after hospital discharge in patients with type 2 diabetes admitted via the emergency department.

Construction and validation of a model to predict nonadherence to guidelines for prescribing antiplatelet therapy to hypertensive patients

OBJECTIVE

To construct and validate a model to predict nonadherence to guidelines for prescribing antiplatelet therapy (NGAT) to hypertensive patients.

METHODS

This 3 month prospective study was undertaken in 2007-2009 to determine whether 712 hypertensive patients were or were not being prescribed antiplatelet therapy.

OUTCOME

NGAT according to clinical guidelines (just for patients in secondary prevention or with Systematic COronary Risk Evaluation (SCORE) ≥10%). Secondary variables: Duration of hypertension (years), blood pressure (BP), age, gender, smoking, diabetes, dyslipidemia, cardiovascular disease, lipid parameters, SCORE. Of the whole sample 80% was used to construct the model and 20% to validate it. To construct the model, we performed a multivariate logistic regression model which was adapted to be a scoring system with risk groups. The adjusted odds ratios (ORs) were obtained through the model. To validate the model we calculated the area under the ROC curve (AUC) and then compared the expected and the observed NGAT. The final model was adapted for use as a mobile application.

RESULTS

NGAT: 18.5%, construction; 17.9%, validation.

FACTORS

higher duration of hypertension diagnosis, higher systolic BP, older age, male gender, smoking, diabetes, dyslipidemia and cardiovascular disease.

VALIDATION

AUC = 0.82 (95% CI: 0.74-0.90, p < 0.001), with no differences between the observed and the expected NGAT (p = 0.334).

CONCLUSION

A tool was constructed and validated to predict NGAT. The associated factors were related with a greater cardiovascular risk. The scoring system has to be validated in other areas.

Nonadherence to guidelines for prescribing antibiotic therapy to patients with tonsillitis or pharyngotonsillitis: a cross-sectional study

OBJECTIVES

Although previous studies have assessed nonadherence to guidelines for prescribing antibiotic therapy in patients with pharyngotonsillitis (prescription of another antibiotic rather than one of first choice: amoxicillin and/or penicillin V, except in the case of penicillin allergy), none analyzed associated factors. Consequently, we conducted a study to assess the possible factors associated with the problem of nonadherence.

METHODS

This cross-sectional study analyzed a sample of 417 patients with tonsillitis or pharyngotonsillitis treated with antibiotics during February 2014 in a Spanish region. The data were obtained through electronic records.

INDEPENDENT VARIABLES

gender, disease (tonsillitis or pharyngotonsillitis), clinical specialty (primary care or pediatrics), age and physician's gender. To identify factors associated with nonadherence, the adjusted odds ratios (ORs) were calculated through a binary logistic regression model with all the independent variables. The confidence interval (CI) was calculated for all relevant parameters.

RESULTS

Of 417 patients, 303 were categorized to the nonadherence group (72.7%, 95% CI: 68.4-76.9%). Two factors associated with the physician were relevant and close to statistical significance: primary healthcare as a clinical specialty (OR = 2.3, p = 0.055) and male gender (OR = 1.5, p = 0.088).

CONCLUSIONS

Nonadherence in prescribing first-choice antibiotics to patients with tonsillitis or pharyngotonsillitis is highly prevalent. We emphasize the need to implement measures to help minimize this problem and, thus, antibiotic resistance, such as undertaking qualitative studies to assess the reasons for inappropriate prescription or the incorporation of alert systems in the electronic records.

LIMITATIONS

As we analyzed data from all the patients in winter, it would be interesting to replicate this study at another time of the year.