External validation of the EFFECT mortality prediction scale in patients admitted for acute heart failure in Araba, Spain

AIM

To validate the EFFECT (Enhanced Feedback for Effective Cardiac Treatment) scales, which predict mortality at 1 month and 1 year after admission, in a defined cohort of patients admitted to the Araba University Hospital (HUA) with a diagnosis of acutely decompensated heart failure.

METHOD

External validation study of a predictive model, in a retrospective cohort of patients admitted between October 1, 2020 and September 30, 2021.

RESULTS

A total of 550 patients were included. The two scales demonstrated good overall discriminatory ability in our series, with an area under ROC (0.755 y 0.756) and values in Brier score (0.094 y 0.194) similar to the original series. Calibration was assessed using the Hosmer-Lemeshow test and calibration plots and was also adequate. All this despite the fact that significant differences were observed in many clinical characteristics between our series and the original one.

CONCLUSIONS

The EFFECT scales showed good predictive ability and transportability. The one-month prediction scale was also useful for predicting mortality at one year. For both time periods, mortality was similar in the groups established in the original as low and very low risk.

Predicting the response to methylprednisolone pulses in patients with SARS-COV-2 infection

INTRODUCTION

Treating systemic inflammation caused by SARS-COV 2 (COVID-19) has become a challenge for the clinician. Corticosteroids have been the turning point in the treatment of this disease. Preliminary data from Recovery clinical trial raises hope by showing that treatment with dexamethasone at doses of 6mg/day shows a reduction on morbidity in patients requiring added oxygen therapy. However, both the start day or what kind of corticosteroid, are still questions to be clarified. Since the pandemic beginning, we have observed large differences in the type of corticosteroid, dose and initiation of treatment. Our objective is to assess the predictive capacity of the characteristics of patients treated with methylprednisolone pulses to predict hospital discharge.

MATERIALS AND METHODS

We presented a one-center observational study of a retrospective cohort. We included all patients admitted between 03/06/2020 and 05/15/2020 because of COVID-19. We have a total number of 1469 patients, of whom 322 received pulses of methylprednisolone. Previous analytical, radiographic, previous disease data were analyzed on these patients. The univariant analysis was performed using Chi-squared and the T test of Student according to the qualitative or quantitative nature of the variables respectively. For multivariate analysis, we have used binary logistic regression and ROC curves.

RESULTS

The analysis resulted statistically significant in dyspnea, high blood pressure, dyslipidemia, stroke, ischemic heart disease, cognitive impairment, solid tumor, C-reactive protein (CRP), lymphopenia and d-dimer within 5 days of admission. Radiological progression and FIO₂ input are factors that are associated with a worst prognosis in COVID-19 that receive pulses of methylprednisolone. Multivariate analysis shows that age, dyspnea and C-reactive protein are markers of hospital discharge with an area below the curve of 0.816.

CONCLUSIONS

In patients with methylprednisolone pulses, the capacity of the predictive model for hospital discharge including variables collected at 5 days was (area under the curve) 0.816.

Usefulness of the 5MPB-Toledo model to predict bacteremia in patients with urinary tract infections in the emergency department

OBJECTIVE

To analyze the usefulness of a new predictive model of bacteremia (5MPB-Toledo) in patients treated for urinary tract infection (UTI) in the emergency department (ED).

METHODS

Prospective and multicenter observational cohort study of the blood cultures (BC) ordered for patients with UTIs in 65 Spanish ED from November 1, 2019, to March 31, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The diagnostic performance was calculated with the chosen cut-off point for sensitivity, specificity, positive predictive value, and negative predictive value.

RESULTS

A total of 1,499 blood cultures were evaluated. True cases of bacteremia were confirmed in 277 (18.5%). The remaining 1,222 cultures (81.5%) were negative. Ninety-four (6.3%) were considered contaminated. The model's area under the ROC curve was 0.937 (95% CI, 0.926-0.949). The prognostic performance with a model's cut-off value of ≥5 points achieved 97.47% (95% CI, 94.64-98.89) sensitivity, 76.68% (95% CI, 74.18-79.00) specificity, 48.65% (95% CI, 44.42-52.89) positive predictive value and 99.26% (95% CI, 98.41-99.67) negative predictive value.

CONCLUSION

The 5MPB-Toledo score is useful for predicting bacteremia in patients with UTIs who visit the ED.

Validation of a predictive model for bacteraemia (MPB5-Toledo) in the patients seen in emergency departments due to infections

OBJECTIVE

To validate a simple risk score to predict bacteremia (MPB5-Toledo) in patients seen in the emergency departments (ED) due to infections.

METHODS

Prospective and multicenter observational cohort study of the blood cultures (BC) ordered in 74 Spanish ED for adults (aged 18 or older) seen from October 1, 2019, to February 29, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the cut-off values chosen for getting the sensitivity, specificity, positive predictive value and negative predictive value.

RESULTS

A total of 3.843 blood samples wered cultured. True cases of bacteremia were confirmed in 839 (21.83%). The remaining 3.004 cultures (78.17%) were negative. Among the negative, 172 (4.47%) were judged to be contaminated. Low risk for bacteremia was indicated by a score of 0-2 points, intermediate risk by 3-5 points, and high risk by 6-8 points. Bacteremia in these 3 risk groups was predicted for 1.5%, 16.8%, and 81.6%, respectively. The model's area under the receiver operating characteristic curve was 0.930 (95% CI, 0.916-0.948). The prognostic performance with a model's cut-off value of ≥5 points achieved 94.76% (95% CI: 92.97-96.12) sensitivity, 81.56% (95% CI: 80.11-82.92) specificity, and negative predictive value of 98.24% (95% CI: 97.62-98.70).

CONCLUSION

The 5MPB-Toledo score is useful for predicting bacteremia in patients attended in hospital emergency departments for infection.

Risk models for predicting in-hospital mortality from COVID-19 pneumonia in the elderly

OBJECTIVES

To compare the prognostic value of 3 severity scales: the Pneumonia Severity Index (PSI), the CURB-65 pneumonia severity score, and the Severity Community-Acquired Pneumonia (SCAP) score. To build a new predictive model for in-hospital mortality in patients over the age of 75 years admitted with pneumonia due to the coronavirus disease 2019 (COVID-19).

MATERIAL AND METHODS

Retrospective study of patients older than 75 years admitted from the emergency department for COVID-19 pneumonia between March 12 and April 27, 2020. We recorded demographic (age, sex, living in a care facility or not), clinical (symptoms, comorbidities, Charlson Comorbidity Index [CCI]), and analytical (serum biochemistry, blood gases, blood count, and coagulation factors) variables. A risk model was constructed, and the ability of the 3 scales to predict all-cause in-hospital mortality was compared.

RESULTS

We included 186 patients with a median age of 85 years (interquartile range, 80-89 years); 44.1% were men. Mortality was 47.3%. The areas under the receiver operating characteristic curves (AUCs) were as follows for each tool: PSI, 0.74 (95% CI, 0.64-0.82); CURB-65 score, 0.71 (95% CI, 0.62-0.79); and SCAP score, 0.72 (95% CI, 0.63-0.81). Risk factors included in the model were the presence or absence of symptoms (cough, dyspnea), the CCI, and analytical findings (aspartate aminotransferase, potassium, urea, and lactate dehydrogenase. The AUC for the model was 0.81 (95% CI, 0.73-0.88).

CONCLUSION

This study shows that the predictive power of the PSI for mortality is moderate and perceptibly higher than the CURB-65 and SCAP scores. We propose a new predictive model for mortality that offers significantly better performance than any of the 3 scales compared. However, our model must undergo external validation.

Evaluation of Preoperative Clinical and Serological Determinations in Complicated Acute Appendicitis: A Score for Predicting Complicated Appendicitis

BACKGROUND

To analyze whether clinical and analytical parameters differ according to histopathology in cases of acute appendicitis (AA).

METHODS

This is a retrospective, observational study including patients (>14 years of age) admitted for suspicion of AA from 1 April 2014 to 31 July 2016. Histopathology was divided into complicated (including perforated and gangrenous AA) and uncomplicated appendicitis (phlegmonous). Sex, age, temperature of patients on admission to the Emergency Department, symptom duration, preoperative white blood cell (WBC) count, neutrophil percentage, mean platelet volume (MPV), platelet distribution width (PDW), C-reactive protein (CRP) and hospital stay were compared in the two groups.

RESULTS

Three hundred and thirty-five patients were analyzed, and 284 were included. Appendicitis was uncomplicated in 194 (68.3%) and complicated in 90 (31.7%). Age, symptom duration, neutrophil percentage, CRP and hospital stay were higher in the complicated AA group (P < .05). The mean differences between uncomplicated and complicated AA were: age 13.2 years (95% CI: 8.2-18.2), symptom duration 14.1hours (95% CI: 6.3-21.9), neutrophil percentage 5.0% (95% CI: 3.2-6.8), CRP 73.6mg/l (95% CI: 50.0-97.2) and hospital stay 2.2 days (95% CI: 1.4-3.0), with p<0.05 for all these variables. A model based on the preoperative parameters (age, symptom duration, neutrophil percentage and CRP) was calculated to predict the likelihood of complicated AA. The receiver operating characteristic (ROC) of the model had an area under the curve of 0.80 (95% CI 0.75-0.85).

CONCLUSION

This model is able to diagnose complicated AA without the need for imaging techniques, although it must be validated with prospective analysis.

Validation of a predictive model for bacteraemia (MPB5-Toledo) in the patients seen in emergency departments due to infections

OBJECTIVE

To validate a simple risk score to predict bacteremia (MPB5-Toledo) in patients seen in the emergency departments (ED) due to infections.

METHODS

Prospective and multicenter observational cohort study of the blood cultures (BC) ordered in 74 Spanish ED for adults (aged 18 or older) seen from from October 1, 2019, to February 29, 2020. The predictive ability of the model was analyzed with the area under the Receiver Operating Characteristic curve (AUC-ROC). The prognostic performance for true bacteremia was calculated with the cut-off values chosen for getting the sensitivity, specificity, positive predictive value and negative predictive value.

RESULTS

A total of 3.843 blood samples wered cultured. True cases of bacteremia were confirmed in 839 (21.83%). The remaining 3.004 cultures (78.17%) were negative. Among the negative, 172 (4.47%) were judged to be contaminated. Low risk for bacteremia was indicated by a score of 0 to 2 points, intermediate risk by 3 to 5 points, and high risk by 6 to 8 points. Bacteremia in these 3 risk groups was predicted for 1.5%, 16.8%, and 81.6%, respectively. The model's area under the receiver operating characteristic curve was 0.930 (95% CI, 0.916-0.948). The prognostic performance with a model's cut-off value of ≥ 5 points achieved 94.76% (95% CI: 92.97-96.12) sensitivity, 81.56% (95% CI: 80.11-82.92) specificity, and negative predictive value of 98.24% (95% CI: 97.62-98.70).

CONCLUSION

The 5MPB-Toledo score is useful for predicting bacteremia in patients attended in hospital emergency departments for infection.

Predictive model of in-hospital mortality in left-sided infective endocarditis

INTRODUCTION AND OBJECTIVES

Infective endocarditis (IE) is a complex disease with high in-hospital mortality. Prognostic assessment is essential to select the most appropriate therapeutic approach; however, international IE guidelines do not provide objective assessment of the individual risk in each patient. We aimed to design a predictive model of in-hospital mortality in left-sided IE combining the prognostic variables proposed by the European guidelines.

METHODS

Two prospective cohorts of consecutive patients with left-sided IE were used. Cohort 1 (n=1002) was randomized in a 2:1 ratio to obtain 2 samples: an adjustment sample to derive the model (n=688), and a validation sample for internal validation (n=314). Cohort 2 (n=133) was used for external validation.

RESULTS

The model included age, prosthetic valve IE, comorbidities, heart failure, renal failure, septic shock, Staphylococcus aureus, fungi, periannular complications, ventricular dysfunction, and vegetations as independent predictors of in-hospital mortality. The model showed good discrimination (area under the ROC curve=0.855; 95%CI, 0.825-0.885) and calibration (P value in Hosmer-Lemeshow test=0.409), which were ratified in the internal (area under the ROC curve=0.823; 95%CI, 0.774-0.873) and external validations (area under the ROC curve=0.753; 95%CI, 0.659-0.847). For the internal validation sample (observed mortality: 29.9%) the model predicted an in-hospital mortality of 30.7% (95%CI, 27.7-33.7), and for the external validation cohort (observed mortality: 27.1%) the value was 26.4% (95%CI, 22.2-30.5).

CONCLUSIONS

A predictive model of in-hospital mortality in left-sided IE based on the prognostic variables proposed by the European Society of Cardiology IE guidelines has high discriminatory ability.

Development and validation of a tool for predicting type 2 diabetes in Mexican women of reproductive age

INTRODUCTION

Diabetes is a worldwide problem with a greater impact in developing countries, where many people are unaware of their risk. In Mexico, women show the greatest risk for T2D. Current risk scores have been developed and validated in predominantly older European cohorts. They are not the best option in Mexican women. The development of a risk model/score in this population would be useful.

OBJECTIVE

To develop and validate a risk model and score that incorporates the most relevant risk factors for T2D in Mexican women of reproductive age.

METHODS

The study was carried out in two phases, with the first phase being the development of the predictive model and the second phase the validation of the model in a separate independent population. A cohort of Mexican patients of reproductive age ("Derivation Cohort") was used to create the predictive model. It included data on 3161 women. Risk factors for identification were assessed using Cox proportional hazards regression. Finally a score with a range of 0 to 19 points was developed to identify the 2.4 year probability of developing DM2 in Mexican women of reproductive age.

RESULTS

147 new cases of T2D (4.6%) were identified in the Derivation Cohort model, 97 of 925 participants (10.48%) in the validation cohort. The risk factor predictors of T2D were: history of gestational diabetes (HR 2.69, 95% CI 1.10-6.58), BMI (HR 1.03, 95% CI 1.01-1.06), hypertriglyceridemia (HR 1.54, 95% CI 1.11-2.14) and fasting blood glucose (HR 1.06, 95% CI 1.05-1.08), with an AUC of 0.75. The AUC in the validation cohort was 0.91 (95% CI 0.87-0.94). The score had a sensitivity of 73% and specificity of 67% at a cutoff of ≥15.

CONCLUSIONS

A predictive model and risk score was developed to detect cases at risk for incident T2D. It was generated using the characteristics of Mexican women of reproductive age. This risk score is a step forward in attempting to address the generational legacy that diabetes in pregnancy could have on women and their children.

Mortality prediction model for patients with bladder urothelial tumor after radical cystectomy

OBJECTIVE

Based on preoperative clinical and postoperative pathological variables, we aim to build a prediction model of cancer specific mortality (CSM) at 1, 3, and 5 years for patients with bladder transitional cell carcinoma treated with RC.

MATERIAL AND METHODS

Retrospective analysis of 517 patients with diagnosis of cell carcinoma treated by RC (1986-2009). Demographic, clinical, surgical and pathological variables were collected, as well as complications and evolution after RC. Comparative analysis included Chi square test and ANOVA technique. Survival analysis was performed using Kaplan-Meier method and log-rank test. Univariate and multivariate analyses were performed using logistic regression to identify the independent predictors of CSM. The individual probability of CSM was calculated at 1, 3 and 5 years according to the general equation (logistic function). Calibration was obtained by the Hosmer-Lemeshow method and discrimination with the elaboration of a ROC curve (area under the curve).

RESULTS

BC was the cause of death in 225 patients (45%). One, three and five-year CSM were 17%, 39.2% and 46.3%, respectively. The pT and pN stages were identified as independent prognostic variables of CSM at 1, 3 and 5 years. Three prediction models were built. The predictive capacity was 70.8% (CI 95% 65-77%, p=.000) for the 1st year, 73.9% (CI95% 69.2-78.6%, p=.000) for the third and 73.2% (CI% 68.5-77.9%, p=.000) for the 5th.

CONCLUSIONS

The prediction model allows the estimation of CSM risk at 1, 3 and 5 years, with a reliability of 70.8, 73.9 and 73.2%, respectively.

When to perform renal biopsy in patients with type2 diabetes mellitus? Predictive model of non-diabetic renal disease

INTRODUCTION

Diabetic nephropathy (DN) is one of the most frequent complications in patients with diabetes mellitus (DM) and its diagnosis is usually established on clinical grounds. However, kidney involvement in some diabetic patients can be due to other causes, and renal biopsy might be needed to exclude them. The aim of our study was to establish the clinical and analytical data that predict DN and no-diabetic renal disease (NDRD), and to develop a predictive model (score) to confirm or dismiss DN.

MATERIAL AND METHODS

We conducted a transversal, observational and retrospective study, including renal biopsies performed in type2 DM patients, between 2000 and 2018.

RESULTS

Two hundred seven DM patients were included in our study. The mean age was 64.5±10.6 years and 74% were male. DN was found in 126 (61%) of the biopsies and NDRD in 81 (39%). Diabetic retinopathy was presented in 58% of DN patients, but only in 6% of NDRD patients (P<.001). Patients with NDRD were diagnosed of primary glomerulopathies (52%), nephroangiosclerosis (16%), inmunoallergic interstitial nephritis (15%) and vasculitis (8.5%). In the multivariate analysis, retinopathy (OR26.7; 95%CI: 6.8-104.5), chronic ischaemia of lower limbs (OR4,37; 95%CI: 1.33-14.3), insulin therapy (OR3.05; 95%CI: 1.13-8.25), time course of DM ≥10years (OR2.71; 95%CI: 1.1-6.62) and nephrotic range proteinuria (OR2.91; 95%CI: 1.2-7.1) were independent predictors for DN. Microhaematuria defined as ≥10 red blood cells per high-power field (OR0.032; 95%CI: 0.01-0.11) and overweight (OR0.21; 95%CI: 0.08-0.5) were independent predictors of NDRD. According to the predictive model based on the multivariate analysis, all patients with a score >3 had DN and 94% of cases with a score ≤1 had NDRD (score ranked from -6 to 8points).

CONCLUSIONS

NDRD is common in DM patients (39%), being primary glomerulonephritis the most frequent ethology. The absence of retinopathy and the presence of microhematuria are highly suggestive of NDRD. The use of our predictive model could facilitate the indication of performing a renal biopsy in DM patients.

Development and External Validation of an Early Prognostic Model for Survivors of Out-of-hospital Cardiac Arrest

INTRODUCTION AND OBJECTIVES

Despite therapeutic hypothermia, unconscious survivors of out-of-hospital cardiac arrest have a high risk of death or poor neurologic function. Our objective was to assess the usefulness of the variables obtained in the early moments after resuscitation in the prediction of 6-month prognosis.

METHODS

A multicenter study was performed in 3 intensive cardiac care units. The analysis was done in 153 consecutive survivors of out-of-hospital cardiac arrest who underwent targeted temperature management between January 2007 and July 2015. Significant neurological sequelae at 6 months were considered to be present in patients with Cerebral Performance Categories Scale > 2. An external validation was performed with data from 91 patients admitted to a third hospital in the same time interval.

RESULTS

Among the 244 analyzed patients (median age, 60 years; 77.1% male; 50.0% in the context of acute myocardial ischemia), 107 patients (43.8%) survived with good neurological status at 6 months. The prediction model included 5 variables (Shockable rhythm, Age, Lactate levels, Time Elapsed to return of spontaneous circulation, and Diabetes - SALTED) and provided an area under the curve of 0.90 (95%CI, 0.85-0.95). When external validation was performed, the predictive model showed a sensitivity of 73.5%, specificity of 78.6%, and area under the curve of 0.82 (95%CI, 0.73-0.91).

CONCLUSIONS

A predictive model that includes 5 clinical and easily accessible variables at admission can help to predict the probability of survival without major neurological damage following out-of-hospital cardiac arrest.