Clinical Scoring Systems to Diagnose Complicated Acute Appendicitis in a Rural Hospital: Are They Good Enough?

Introduction Acute appendicitis (AA) is the most common surgical emergency in developed countries, whose incidence peaks in the second and third decades. The risk of mortality in uncomplicated AA is very low. There are many scoring systems to predict AA. Prediction scores are used less frequently to predict complicated AA. Rural hospitals are often constrained by a lack of round-the-clock imaging or special laboratory services, which may enable accurate diagnosis. Materials and methods This study aimed to determine whether prediction scores without imaging or C-reactive protein (CRP) levels could predict complicated AA in a rural setting. All cases of AA for the previous 13 months were recruited for the study. Demographic data, clinical signs and symptoms, complete blood counts, intraoperative findings, and the corresponding histopathological results were collated. The scoring systems (Alvarado, RIPASA, Tzanakis, and Ohmann) were calculated from the clinical and laboratory data. Demographic variables, clinical features, and histopathological findings are described as frequencies/proportions. Chi-squared and Student's t-tests were used to analyze differences between patients with complicated and uncomplicated AA. A receiver operating curve (ROC) analysis was performed to calculate the area under the curve (AUC) and determine whether appendicitis scores could predict complicated AA. Results There were 76 patients with a mean age of 29.1±13.0 years. Serositis was observed in 65% of the patients; mucosal ulceration was the most common microscopic finding, with a pathological diagnosis of AA in 58 (76.3%) patients. Rovsing's sign and the presence of phlegmon and granuloma were significantly different between those with and without complicated AA. The clinical prediction scores were not significantly different between the two groups. The Tzanakis and Ohmann scores were significant (cutoff: 6.5 and 7.25, p=0.001 and 0.01, respectively) in diagnosing AA (sensitivity/specificity of 98.3/66.7 and 98.3/94.4, respectively). With a cutoff of 5.75, the RIPASA score, with an AUC of 0.663 (p=0.09), showed the highest sensitivity (90.7) and specificity (76.6) for diagnosing complicated AA. Conclusion Diagnosing AA based solely on clinical presentation remains a challenge. This study showed that clinical scores such as those of Alvarado, RIPASA, Tzanakis, and Ohmann could not accurately predict complicated AA. Scoring systems without imaging and intraoperative diagnoses are not infallible; therefore, histopathological examination of the resected appendix is mandatory.

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

Perioperative risk stratification scores in infective endocarditis and its usefulness.

Infective endocarditis continues to represent a serious disease worldwide with high morbidity and mortality rates despite advances in diagnosis and treatment. Risk assessment plays a pivotal role in determining the appropriate course of treatment for patients diagnosed with infective endocarditis. In this context, specific endocarditis risk scores have been developed trying to help in the risk assessment process. During the last 15 years, 19 specific endocarditis scores have been published. These newly created scores are very heterogenous in their characteristics, factors included, and validation strategies. The purpose of this review is to analyze the published specific infective endocarditis risk scores and discuss their advantages, limitations, and usefulness. Supplementary Information: The online version contains supplementary material available at 10.1007/s12055-023-01644-y.

Nonconvulsive status epilepticus in neurocritical care: A critical reappraisal of outcome prediction scores.

OBJECTIVE: Nonconvulsive status epilepticus (NCSE) is a frequent condition in the neurocritical care unit (NCCU) patient population, with high morbidity and mortality. We aimed to assess the validity of available outcome prediction scores for prognostication in an NCCU patient population in relation to their admission reason (NCSE vs. non-NCSE related). METHODS: All 196 consecutive patients diagnosed with NCSE during the NCCU stay between January 2010 and December 2020 were included. Demographics, Simplified Acute Physiology Score II (SAPS II), NCSE characteristics, and in-hospital and 3-month outcome were extracted from the electronic charts. Status Epilepticus Severity Score (STESS), Epidemiology-Based Mortality Score in Status Epilepticus (EMSE), and encephalitis, NCSE, diazepam resistance, imaging features, and tracheal intubation score (END-IT) were evaluated as previously described. Univariable and multivariable analysis and comparison of sensitivity/specificity/positive and negative predictive values/accuracy were performed. RESULTS: A total of 30.1% died during the hospital stay, and 63.5% of survivors did not achieve favorable outcome at 3 months after onset of NCSE. Patients admitted primarily due to NCSE had longer NCSE duration and were more likely to be intubated at diagnosis. The receiver operating characteristic (ROC) for SAPS II, EMSE, and STESS when predicting mortality was between .683 and .762. The ROC for SAPS II, EMSE, STESS, and END-IT when predicting 3-month outcome was between .649 and .710. The accuracy in predicting mortality/outcome was low, when considering both proposed cutoffs and optimized cutoffs (estimated using the Youden Index) as well as when adjusting for admission reason. SIGNIFICANCE: The scores EMSE, STESS, and END-IT perform poorly when predicting outcome of patients with NCSE in an NCCU environment. They should be interpreted cautiously and only in conjunction with other clinical data in this particular patient group.

External validation and comparison of clinical scores for predicting late seizures after intracerebral hemorrhage in Chinese patients.

PURPOSE: Clinical scores have been established to predict the probability of late seizures following intracerebral hemorrhage (ICH) for individual patients, including the CAVE, CAVS and LANE scores. The purpose of this study was to compare these prediction scores in the Chinese population and undertake an independent external validation on them. METHODS: At one tertiary hospital in China, we retrospectively recruited consecutive inpatients who had been diagnosed with ICH. Medical records and tele interviews with a modified standardized questionnaire were used to identify late seizures. All the predictors of the prediction scores were collected from patient charts and databases by a standardized data collection protocol. The external validation of the prediction scores was quantified by the area under the curve (AUC), sensitivity, specificity, Youden index (YI), positive predictive value (PPV), and negative predictive value (NPV). RESULTS: 69 (5.4%) of 1276 patients experienced late seizures after ICH. There was no significant difference in the CAVE, CAVS, and LANE scores, which had AUCs of 0.75 (95% CI = 0.70-0.81), 0.74 (95% CI = 0.68-0.80), and 0.76 (95% CI = 0.70-0.82), respectively. At the optimal cutoff score, the LANE score had a lower sensitivity but a higher specificity than the CAVE and CAVS scores. Among the three prediction scores, the LANE score had a higher PPV than the others (0.145 vs. 0.088, 0.083), while the NPV was similar among the three prediction scores (0.989, 0.989, and 0.972). CONCLUSION: Our study showed that the CAVE, CAVS and LANE scores had similar AUCs for the occurrence of late seizures, but the LANE score had a relatively high PPV at the optimal cutoff score. Due to low evidence for using prophylactic antiseizure medications (ASM) in patients with ICH and poor availability of specialist stroke care in China, the LANE score with a cutoff score of 3 could be an applicable prediction tool in Chinese patients with ICH.

The value of multiparametric prediction scores in heart failure varies with the type of follow-up after discharge: a comparative analysis.

AIMS: Multiple prediction score models have been validated to predict major adverse events in patients with heart failure. However, these scores do not include variables related to the type of follow-up. This study aimed to evaluate the impact of a protocol-based follow-up programme of patients with heart failure regarding scores accuracy for predicting hospitalizations and mortality occurring during the first year after hospital discharge. METHODS AND RESULTS: Data from two heart failure populations were collected: one composed of patients included in a protocol-based follow-up programme after an index hospitalization for acute heart failure and a second one-the control group-composed of patients not included in a multidisciplinary HF management programme after discharge. For each patient, the risk of hospitalization and/or mortality within a period of 12 months after discharge was calculated using four different scores: BCN Bio-HF Calculator, COACH Risk Engine, MAGGIC Risk Calculator, and Seattle Heart Failure Model. The accuracy of each score was established using the area under the receiver operating characteristic curve (AUC), calibration graphs, and discordance calculation. AUC comparison was established by the DeLong method. The protocol-based follow-up programme group included 56 patients, and the control group, 106 patients, with no significant differences between groups (median age: 67 years vs. 68.4 years; male sex: 58% vs. 55%; median ejection fraction: 28.2% vs. 30.5%; functional class II: 60.7% vs. 56.2%, I: 30.4% vs. 31.9%; P = not significant). Hospitalization and mortality rates were significantly lower in the protocol-based follow-up programme group (21.4% vs. 54.7%; P < 0.001 and 5.4% vs. 17.9%; P < 0.001, respectively). When applied to the control group, COACH Risk Engine and BCN Bio-HF Calculator had, respectively, good (AUC: 0.835) and reasonable (AUC: 0.712) accuracy to predict hospitalization. There was a significant reduction of COACH Risk Engine accuracy (AUC: 0.572; P = 0.011) and a non-significant accuracy reduction of BCN Bio-HF Calculator (AUC: 0.536; P = 0.1) when applied to the protocol-based follow-up programme group. All scores showed good accuracy to predict 1 year mortality (AUC: 0.863, 0.87, 0.818, and 0.82, respectively) when applied to the control group. However, when applied to the protocol-based follow-up programme group, a significant predictive accuracy reduction of COACH Risk Engine, BCN Bio-HF Calculator, and MAGGIC Risk Calculator (AUC: 0.366, 0.642, and 0.277, P < 0.001, 0.002, and <0.001, respectively) was observed. Seattle Heart Failure Model had non-significant reduction in its acuity (AUC: 0.597; P = 0.24). CONCLUSIONS: The accuracy of the aforementioned scores to predict major events in patients with heart failure is significantly reduced when they are applied to patients included in a multidisciplinary heart failure management programme.

Prediction of underlying atrial fibrillation in patients with a cryptogenic stroke: results from the NOR-FIB Study.

BACKGROUND: Atrial fibrillation (AF) detection and treatment are key elements to reduce recurrence risk in cryptogenic stroke (CS) with underlying arrhythmia. The purpose of the present study was to assess the predictors of AF in CS and the utility of existing AF-predicting scores in The Nordic Atrial Fibrillation and Stroke (NOR-FIB) Study. METHOD: The NOR-FIB study was an international prospective observational multicenter study designed to detect and quantify AF in CS and cryptogenic transient ischaemic attack (TIA) patients monitored by the insertable cardiac monitor (ICM), and to identify AF-predicting biomarkers. The utility of the following AF-predicting scores was tested: AS5F, Brown ESUS-AF, CHA2DS2-VASc, CHASE-LESS, HATCH, HAVOC, STAF and SURF. RESULTS: In univariate analyses increasing age, hypertension, left ventricle hypertrophy, dyslipidaemia, antiarrhythmic drugs usage, valvular heart disease, and neuroimaging findings of stroke due to intracranial vessel occlusions and previous ischemic lesions were associated with a higher likelihood of detected AF. In multivariate analysis, age was the only independent predictor of AF. All the AF-predicting scores showed significantly higher score levels for AF than non-AF patients. The STAF and the SURF scores provided the highest sensitivity and negative predictive values, while the AS5F and SURF reached an area under the receiver operating curve (AUC) > 0.7. CONCLUSION: Clinical risk scores may guide a personalized evaluation approach in CS patients. Increasing awareness of the usage of available AF-predicting scores may optimize the arrhythmia detection pathway in stroke units.

Development and Validation of a Prediction Model for Incident Hypothyroidism in a National Chronic Kidney Disease Cohort.

CONTEXT: Hypothyroidism is a common yet under-recognized condition in patients with chronic kidney disease (CKD), which may lead to end-organ complications if left untreated. OBJECTIVE: We developed a prediction tool to identify CKD patients at risk for incident hypothyroidism. METHODS: Among 15 642 patients with stages 4 to 5 CKD without evidence of pre-existing thyroid disease, we developed and validated a risk prediction tool for the development of incident hypothyroidism (defined as thyrotropin [TSH] > 5.0 mIU/L) using the Optum Labs Data Warehouse, which contains de-identified administrative claims, including medical and pharmacy claims and enrollment records for commercial and Medicare Advantage enrollees as well as electronic health record data. Patients were divided into a two-thirds development set and a one-third validation set. Prediction models were developed using Cox models to estimate probability of incident hypothyroidism. RESULTS: There were 1650 (11%) cases of incident hypothyroidism during a median follow-up of 3.4 years. Characteristics associated with hypothyroidism included older age, White race, higher body mass index, low serum albumin, higher baseline TSH, hypertension, congestive heart failure, exposure to iodinated contrast via angiogram or computed tomography scan, and amiodarone use. Model discrimination was good with similar C-statistics in the development and validation datasets: 0.77 (95% CI 0.75-0.78) and 0.76 (95% CI 0.74-0.78), respectively. Model goodness-of-fit tests showed adequate fit in the overall cohort (P = .47) as well as in a subcohort of patients with stage 5 CKD (P = .33). CONCLUSION: In a national cohort of CKD patients, we developed a clinical prediction tool identifying those at risk for incident hypothyroidism to inform prioritized screening, monitoring, and treatment in this population.

Validation of extracorporeal membrane oxygenation mortality prediction and severity of illness scores in an international COVID-19 cohort.

BACKGROUND: Veno-venous extracorporeal membrane oxygenation (V-V ECMO) is a lifesaving support modality for severe respiratory failure, but its resource-intensive nature led to significant controversy surrounding its use during the COVID-19 pandemic. We report the performance of several ECMO mortality prediction and severity of illness scores at discriminating survival in a large COVID-19 V-V ECMO cohort. METHODS: We validated ECMOnet, PRESET (PREdiction of Survival on ECMO Therapy-Score), Roch, SOFA (Sequential Organ Failure Assessment), APACHE II (acute physiology and chronic health evaluation), 4C (Coronavirus Clinical Characterisation Consortium), and CURB-65 (Confusion, Urea nitrogen, Respiratory Rate, Blood Pressure, age >65 years) scores on the ISARIC (International Severe Acute Respiratory and emerging Infection Consortium) database. We report discrimination via Area Under the Receiver Operative Curve (AUROC) and Area under the Precision Recall Curve (AURPC) and calibration via Brier score. RESULTS: We included 1147 patients and scores were calculated on patients with sufficient variables. ECMO mortality scores had AUROC (0.58-0.62), AUPRC (0.62-0.74), and Brier score (0.286-0.303). Roch score had the highest accuracy (AUROC 0.62), precision (AUPRC 0.74) yet worst calibration (Brier score of 0.3) despite being calculated on the fewest patients (144). Severity of illness scores had AUROC (0.52-0.57), AURPC (0.59-0.64), and Brier Score (0.265-0.471). APACHE II had the highest accuracy (AUROC 0.58), precision (AUPRC 0.64), and best calibration (Brier score 0.26). CONCLUSION: Within a large international multicenter COVID-19 cohort, the evaluated ECMO mortality prediction and severity of illness scores demonstrated inconsistent discrimination and calibration highlighting the need for better clinically applicable decision support tools.

Performance and analysis of four pediatric mortality prediction scores among critically ill children: A multicenter prospective observational study in four PICUs.

OBJECTIVE: We aimed to evaluate and compare the prognostic performance of common pediatric mortality scoring systems (the Pediatric Index of Mortality 2 [PIM2], PIM3, Pediatric Risk of Mortality [PRISM], and PRISM4 scores) to determine which is the most applicable score in our pediatric study cohort. METHODS: This prospective observational multicenter cohort study was conducted in four tertiary-care pediatric intensive care units (PICUs) in Turkey. All children, between 1 month and 16 years old, admitted to the participating PICUs between October 1, 2019, and March 31, 2020, were included in the study. Discrimination between death and survival was assessed by area under the receiver operating characteristic plot (AUC) for each model. The Hosmer-Lemeshow goodness-of-fit (GOF) test was used to assess the calibration of the models, RESULTS: A total of 570 patients (median age 35 months) were enrolled in the study. The observed mortality rate was 8.2% (47/570). The standardized mortality ratio (SMR) of PIM2, PIM3, PRISM, and PRISM4 with 95% confidence interval (CI) were 0.94 (0.68-1.23), 1.27 (0.93-1.68), 0.86 (0.63-1.13), and 1.5 (1.10-1.97), respectively. The AUC with 95% CI was 0.934 (0.91-0.96) for PIM2, 0.934 (0.91-0.96) for PIM3, 0.917 (0.88-0.95) for PRISM, and 0.926 (0.88-0.97) for PRISM4 models. The Hosmer-Lemeshow test showed that the difference between observed and predicted mortality by PIM3 (p = 0.003) and PRISM4 (p = 0.008) was statistically significant whereas PIM2 (p = 0.28) and PRISM (p = 0.62) showed good calibration. CONCLUSION: The overall performance of (both discrimination and calibration) PRISM and PIM2 scoring systems in Turkish pediatric patients aged 1 month to 16 years was accurate and had the best fit for risk groups according to our study. Although PIM3 and PRISM4 have good discriminatory power, their calibration was very poor in our study cohort.

Structured, Harmonized, and Interoperable Integration of Clinical Routine Data to Compute Heart Failure Risk Scores.

Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.

Performance Comparison of Infection Prediction Scores in a South African Neonatal Unit: A Retrospective Case-Control Study.

Background and objectives: Infection prediction scores are useful ancillary tests in determining the likelihood of neonatal hospital-acquired infection (HAI), particularly in very low birth weight (VLBW; <1,500 g) infants who are most vulnerable to HAI and have high antibiotic utilization rates. None of the existing infection prediction scores were developed for or evaluated in South African VLBW neonates. Methods: We identified existing infection prediction scores through literature searches and assessed each score for suitability and feasibility of use in resource-limited settings. Performance of suitable scores were compared using a retrospective dataset of VLBW infants (2016-2017) from a tertiary hospital neonatal unit in Cape Town, South Africa. Sensitivity, specificity, predictive values, and likelihood ratios were calculated for each score. Results: Eleven infection prediction scores were identified, but only five were suitable for use in resource-limited settings (NOSEP1, Singh, Rosenberg, and Bekhof scores). The five selected scores were evaluated using data from 841 episodes of HAI in 659 VLBW infants. The sensitivity for the scores ranged between 3% (NOSEP1 ≥14; proven and presumed infection), to a maximum of 74% (Singh score ≥1; proven infection). The specificity of these scores ranged from 31% (Singh score ≥1; proven and presumed infection) to 100% (NOSEP1 ≥11 and ≥14, NOSEP-NEW-1 ≥11; proven and presumed infection). Conclusion: Existing infection prediction scores did not achieve comparable predictive performance in South African VLBW infants and should therefore only be used as an adjunct to clinical judgment in antimicrobial decision making. Future studies should develop infection prediction scores that have high diagnostic accuracy and are feasible to implement in resource-limited neonatal units.

Development of an electronic Poor Outcome Screening (ePOS) Score to identify critically ill patients with potential palliative care needs.

PURPOSE: To develop and validate an electronic poor outcome screening (ePOS) score to identify critically ill patients with potentially unmet palliative care (PC) needs at 48 hours after ICU admission. MATERIALS AND METHODS: Retrospective single-centre cohort study of 1'772 critically ill adult patients admitted to a tertiary academic ICU in Switzerland between 2017 and 2018. We used data available from electronic health records (EHR) in the first 48 hours and least absolute shrinkage and selection operator (LASSO) logistic regression to develop a prediction model and generate a score to predict the risk of all cause 6-month mortality. RESULTS: Within 6 months of the ICU admission, 598 patients (33.7%) had died. At a cut-off of 20 points, the ePOS score (range 0-46 points) had a sensitivity of 0.81 (95% CI 0.78 to 0.84) and a specificity of 0.51 (0.48 to 0.54) for predicting 6-month mortality and showed good discriminatory performance (AUROC 0.72, 0.67 to 0.77). CONCLUSIONS: The ePOS score can easily be implemented in EHR and can be used for automated screening and stratification of ICU patients, pinpointing those in whom a comprehensive PC assessment should be performed. However, it should not replace clinical judgement.

Comparison of mortality prediction scores in elderly patients with ICD for heart failure with reduced ejection fraction.

BACKGROUND: This investigation aimed to examine and compare the predictive value of MADIT-II, FADES, PACE and SHOCKED scores in predicting one-year and long-term all-cause mortality in implantable cardioverter-defibrillator (ICD) implanted patients, 75 years old and older, since there has been an area of uncertainty about the utility and usefulness of these available risk scores in such cases. METHODS: In this observational, retrospective study, 189 ICD implanted geriatric patients were divided into two groups according to the presence of long-term mortality in follow-up. The baseline characteristics and laboratory variables were compared between the groups. MADIT-II, FADES, PACE and SHOCKED scores were calculated at the time of ICD implantation. One-year and long-term predictive values of these scores were compared by a receiver-operating curve (ROC) analysis. RESULTS: A ROC analysis showed that the best cutoff value of the MADIT-II score to predict one-year mortality was ≥ 3 with 87% sensitivity and 74% specificity (AUC 0.83; 95% CI 0.73-0.94; p < 0.001) and that for long-term mortality was ≥ 2 with 83% sensitivity and 43% specificity (AUC 0.68; 95% CI 0.60-0.76; p < 0.001). The predictive value of MADIT-II was superior to FADES, PACE and SHOCKED scores in ICD implanted patients who are 75 years and older. CONCLUSION: MADIT-II score has a significant prognostic value as compared to FADES, PACE and SHOCKED scores for the prediction of one-year and long-term follow-up in geriatric patients with implanted ICDs for heart failure with reduced ejection fraction.

External validation of predictive scores for diabetes remission after metabolic surgery.

PURPOSE: Bariatric surgery has proven to be the most efficient treatment for obesity and type 2 diabetes mellitus (T2DM). Despite detailed qualification, desirable outcome after an intervention is not achieved by every patient. Various risk prediction models of diabetes remission after metabolic surgery have been established to facilitate the decision-making process. The purpose of the study is to validate the performance of available risk prediction scores for diabetes remission a year after surgical treatment and to determine the optimal model. METHODS: A retrospective analysis comprised 252 patients who underwent Roux-en-Y gastric bypass (RYGB) or sleeve gastrectomy (SG) between 2009 and 2017 and completed 1-year follow-up. The literature review revealed 5 models, which were subsequently explored in our study. Each score relationship with diabetes remission was assessed using logistic regression. Discrimination was evaluated by area under the receiver operating characteristic (AUROC) curve, whereas calibration by the Hosmer-Lemeshow test and predicted versus observed remission ratio. RESULTS: One year after surgery, 68.7% partial and 21.8% complete diabetes remission and 53.4% excessive weight loss were observed. DiaBetter demonstrated the best predictive performance (AUROC 0.81; 95% confidence interval (CI) 0.71-0.90; p-value > 0.05 in the Hosmer-Lemeshow test; predicted-to-observed ratio 1.09). The majority of models showed acceptable discrimination power. In calibration, only the DiaBetter score did not lose goodness-of-fit in all analyzed groups. CONCLUSION: The DiaBetter score seems to be the most appropriate tool to predict diabetes remission after metabolic surgery since it presents adequate accuracy and is convenient to use in clinical practice. There are no accurate models to predict T2DM remission in a patient with advanced diabetes.

Comparison of inflammation markers with prediction scores in patients with community-acquired pneumonia.

OBJECTIVE: The lymphocyte-to-C-reactive protein ratio (LCRP) and Systemic Immune-Inflammation Index (SII) can successfully predict 28-day mortality rates with community-acquired pneumoniaMETHODS: This prospective study was conducted in 2018. Hospitalized patients underwent follow-up evaluations 28 days after admission. RESULTS: A total of 345 patients with CAP were enrolled in this study. All-cause mortality at the 28th day of follow-up was 13.6 %. There were statistically significant results between the 2 groups (survivors and non-survivors), in terms of the LCRP, SII, PSI, and CURB-65 values. Moreover, the optimal LCRP cutoff for predicting 28-day mortality was determined to be 4, with 89 % sensitivity, 73 % specificity. Based on the average SII>3551for predicting 28-day mortality, the sensitivity, specificity was 63.8 %, 68.1 % respectively. When the value of the cutoff PSI was ≥130 points, the sensitivity, specificity was 68 %, 65 %, respectively. Based on 3 points and above as the cutoff value of the CURB-65 score, the sensitivity, specificity was 80 %, 68 %, respectively. ROC curve analysis revealed that the areas of LCRP, SII, PSI, and CURB-65 under the AUC in terms of 28-day mortality were 0,820,0,737,681, and 0,773, respectively,CONCLUSIONS: LCRP and SII level are valuable for predicting the mortality rate among patients with CAP at ED admission (Tab. 3, Fig. 3, Ref. 27).

Underperformance of clinical risk scores in identifying vascular ultrasound-based high cardiovascular risk in systemic lupus erythematosus.

AIMS: The aim of this study was to assess the performance of eight clinical risk prediction scores to identify individuals with systemic lupus erythematosus (SLE) at high cardiovascular disease (CVD) risk, as defined by the presence of atherosclerotic plaques. METHODS: CVD risk was estimated in 210 eligible SLE patients without prior CVD or diabetes mellitus (female: 93.3%, mean age: 44.8 ± 12 years) using five generic (Systematic Coronary Risk Evaluation (SCORE), Framingham Risk Score (FRS), Pooled Cohort Risk Equations (ASCVD), Globorisk, Prospective Cardiovascular Münster Study risk calculator (PROCAM)) and three 'SLE-adapted' (modified-SCORE, modified-FRS, QRESEARCH risk estimator, version 3 (QRISK3)) CVD risk scores, as well as ultrasound examination of the carotid and femoral arteries. Calibration, discrimination and classification measures to identify high CVD risk based on the presence of atherosclerotic plaques were assessed for all risk models. CVD risk reclassification was applied for all scores by incorporating ultrasound results. RESULTS: Moderate calibration (p-value range from 0.38 to 0.63) and discrimination (area under the curve 0.73-0.84), and low-to-moderate sensitivity (8.3-71.4%) and classification ability (Matthews correlation coefficient (MCC) 0.25-0.47) were observed for all risk models to identify patients with plaques at any arterial site as high-risk. MCC was improved for modified-FRS versus FRS (0.43 vs 0.36), but not for modified-SCORE versus SCORE (0.25 vs 0.25). Based on plaque presence, CVD risk was upgraded to high-risk in 10%, 16.1%, 20.5%, 21.5%, 24%, 28.2% and 28.6% of cases classified as non-high-risk by QRISK3, modified-FRS, Globorisk, FRS/PROCAM, ASCVD, modified-SCORE and SCORE, respectively. CONCLUSIONS: Most of the five generic and three 'SLE-adapted' clinical risk scores underestimated high CVD risk defined by atherosclerotic plaque presence in patients with SLE.

The comparative ability of commonly used disease severity scores to predict death or a requirement for ICU care in patients hospitalised with possible sepsis in Yangon, Myanmar.

OBJECTIVES: To determine the comparative prognostic utility of commonly used disease prediction scores in adults with presumed community-acquired sepsis in a resource-limited tropical setting. METHODS: This prospective, observational study was performed on the medical ward of a tertiary-referral hospital in Yangon, Myanmar. The ability of the National Early Warning Score 2 (NEWS2), quick NEWS (qNEWS), quick Sequential Organ Failure Assessment (qSOFA) score, Universal Vital Assessment (UVA) and Sequential Organ Failure Assessment (SOFA) scores to predict a complicated inpatient course (death or requirement for intensive care unit (ICU) support) in patients with two or more systemic inflammatory response syndrome criteria was determined. RESULTS: Among the 509 patients, 30 (6%) were HIV-seropositive. The most commonly confirmed diagnoses were tuberculosis (30/509, 5.9%) and measles (26/509, 5.1%). Overall, 75/509 (14.7%) died or required ICU support. All the scores except the qSOFA score, which was inferior, had a similar ability to predict a complicated inpatient course. CONCLUSIONS: In this resource-limited tropical setting, disease severity scores calculated at presentation using only vital signs-such as the NEWS2 score-identified high-risk sepsis patient as well as the SOFA score, which is calculated at 24 h and which also requires laboratory data. Use of these simple clinical scores can be used to facilitate recognition of the high-risk patient and to optimise the use of finite resources.