Assessment of Quality of Life Using the Kidslife Scale in Individuals With Cornelia de Lange Syndrome

BACKGROUND

Cornelia de Lange syndrome (CdLS) is a rare polymalformative genetic disorder with multisystemic involvement. Despite numerous clinical and molecular studies, the specific evaluation of the quality of life (QoL) and its relationship with syndrome-specific risk factors has not been explored.

METHODS

The QoL of 33 individuals diagnosed with CdLS, aged between 4 and 21 years, was assessed using the Kidslife questionnaire. Specifically, the influence of 14 risk factors on overall QoL and 8 of its domains was analyzed.

RESULTS

The study revealed below-median QoL (45.3 percentile), with the most affected domains being physical well-being, personal development, and self-determination. When classifying patients based on their QoL and affected domains, variants in the NIPBL gene, clinical scores ≥11, and severe behavioral and communication issues were found to be the main risk factors.

CONCLUSIONS

We emphasize the need for a comprehensive approach to CdLS that encompasses clinical, molecular, psychosocial, and emotional aspects. The "Kidslife questionnaire" proved to be a useful tool for evaluating QoL, risk factors, and the effectiveness of implemented strategies. In this study, we underscore the importance of implementing corrective measures to improve the clinical score. Furthermore, we highlight the necessity of applying specific therapies for behavioral problems after ruling out underlying causes such as pain or gastroesophageal reflux and implementing measures that facilitate communication and promote social interaction.

Epidemiology of hamstring injuries in 538 cases from an FC Barcelona multi sports club

OBJECTIVES

Hamstring injuries are the most common muscle injuries in team sports. The aims of this study were to describe the epidemiology of hamstring muscle injuries in the professional and amateur sport sections of a multi-sport club Football Club Barcelona (FCB) and to determine any potential correlation between return-to-play (RTP) and injury location, severity of connective tissue damage, age, sex, and athlete's level of competition.

METHODS

This descriptive epidemiological study with data collected from September 2007 to September 2017 stored in the FCB database. The study included non-contact hamstring injuries sustained during training or competition.

RESULTS

A total of 538 hamstring injuries were reported in the club's database, of which 240 were structurally verified by imaging as hamstring injuries. The overall incidence for the 17 sports studied was 1.29 structurally verified hamstring injuries per 100 athletes per year. The muscle most commonly involved in hamstring injuries was the biceps femoris, and the connective tissue most frequently involved was the myofascial. There was no evidence of a statistically significant association between age and RTP after injury, and no statistically significant difference between sex and RTP. However, the time loss by professionals was shorter than for amateurs, and proximal hamstring injuries took longer RTP than distal ones.

CONCLUSION

In the 17 sports practiced at multi-sport club, the incidence of hamstring injury was 1.29 per 100 athletes per year. Players from sports in which high-speed sprinting and kicking are necessary, and amateurs, were at higher risk of suffering a hamstring injury. In addition, proximally located hamstring injuries involving tendinous connective tissue showed the longest RTP time. Age did not seem to have any influence on RTP. Documenting location and the exact tissue involved in hamstring injuries may be beneficial for determining the prognosis and RTP.

Comparison of heart, grace and TIMI scores to predict major adverse cardiac events from chest pain in a Spanish health care region

Acute non-traumatic chest pain (ANTCP) is the second cause of consultation in the Emergency department (ED). About 70% of all Acute Myocardial Infarctions present as non persistent ST-elevation acute coronary syndrome (NSTE-ACS) in the electrocardiogram. Our aim was to compare whether the HEART risk score is more effective than the GRACE and TIMI scores for the diagnosis and prognosis of Major Adverse Cardiac Events (MACE) at six weeks in patients with ANTCP and NSTE-ACS. A prospective cohort study was conducted with patients with ANTCP that attended an ED and a Primary Care Emergency Center (PCEC) from April 2018 to December 2020. The primary outcome was MACE at six weeks. Diagnostic performance was calculated for each scale as the Area under the Receiver Operating Characteristic (ROC) curve (AUC), sensitivity (SE), specificity (SP), and predictive values (PV). Qualitative variables were compared using the Chi-square test, and continuous variables were compared using the nonparametric Kruskal-Wallis test. We adjusted a logistic regression for risk groups, age, and gender to determine the effect of the HEART, GRACE, and TIMI scores on MACE. The degree of agreement (kappa index) was calculated in the categorical classification of patients according to the three risk scales. Cox proportional hazards regressions were performed for each scale and were compared using partial likelihood ratio tests for non-nested models. From a sample of 317 patients with ANTCP, 14.82% had MACE at six weeks. The AUC was 0.743 (95% CI 0.67-0.81) for the HEART score, 0.717 (95% CI 0.64-0.79) for the TIMI score, and 0.649 (95% CI 0.561-0.738) for the GRACE score. The HEART scale identified low-risk patients with a higher SE and negative PV than the GRACE and TIMI scores. The HEART scale was better than the GRACE and TIMI scores at diagnosing and predicting MACE at six weeks in patients with ANTCP and probable NSTE-ACS. It was also a reliable tool for risk stratification in low-risk patients. Its application is feasible in EDs and PCECs, avoiding the need for complementary tests and their associated costs without compromising patient health.

Preventive isolation criteria for the detection of multidrug-resistant bacteria in patients admitted to the Intensive Care Unit: A multicenter study within the Zero Resistance program

OBJECTIVE

To verify the validity of a checklist of risk factors (RFs) proposed by the Spanish "Zero Resistance" project (ZR) in the detection of multidrug-resistant bacteria (MRB), and to identify other possible RFs for colonization and infection by MRB on admission to the Intensive Care Unit (ICU).

DESIGN

A prospective cohort study, conducted in 2016.

SETTING

Multicenter study, patients requiring admission to adult ICUs that applied the ZR protocol and accepted the invitation for participating in the study.

PATIENTS OR PARTICIPANTS

Consecutive sample of patients admitted to the ICU and who underwent surveillance (nasal, pharyngeal, axillary and rectal) or clinical cultures.

INTERVENTIONS

Analysis of the RFs of the ZR project, in addition to other comorbidities, included in the ENVIN registry. A univariate and multivariate analysis was performed, with binary logistic regression methodology (significance considered for p < 0.05). Sensitivity and specificity analyses were performed for each of the selected factors.

MAIN VARIABLES OF INTEREST

Carrier of MRB on admission to the ICU, RFs (previous MRB colonization/infection, hospital admission in the previous 3 months, antibiotic use in the past month, institutionalization, dialysis, and other chronic conditions) and comorbidities.

RESULTS

A total of 2270 patients from 9 Spanish ICUs were included. We identified MRB in 288 (12.6% of the total patients admitted). In turn, 193 (68.2%) had some RF (OR 4.6, 95%CI: 3.5-6.0). All 6 RFs from the checklist reached statistical significance in the univariate analysis (sensitivity 66%, specificity 79%). Immunosuppression, antibiotic use on admission to the ICU and the male gender were additional RFs for MRB. MRB were isolated in 87 patients without RF (31.8%).

CONCLUSIONS

Patients with at least one RF had an increased risk of being carriers of MRB. However, almost 32% of the MRB were isolated in patients without RFs. Other comorbidities such as immunosuppression, antibiotic use on admission to the ICU and the male gender could be considered as additional RFs.

Development of a new score for early mortality prediction in trauma ICU patients: RETRASCORE

Background

Severity scores are commonly used for outcome adjustment and benchmarking of trauma care provided. No specific models performed only with critically ill patients are available. Our objective was to develop a new score for early mortality prediction in trauma ICU patients.

Methods

This is a retrospective study using the Spanish Trauma ICU registry (RETRAUCI) 2015–2019. Patients were divided and analysed into the derivation (2015–2017) and validation sets (2018–2019). We used as candidate variables to be associated with mortality those available in RETRAUCI that could be collected in the first 24 h after ICU admission. Using logistic regression methodology, a simple score (RETRASCORE) was created with points assigned to each selected variable. The performance of the model was carried out according to global measures, discrimination and calibration.

Results

The analysis included 9465 patients: derivation set 5976 and validation set 3489. Thirty-day mortality was 12.2%. The predicted probability of 30-day mortality was determined by the following equation: 1/(1 + exp (− y)), where y = 0.598 (Age 50–65) + 1.239 (Age 66–75) + 2.198 (Age > 75) + 0.349 (PRECOAG) + 0.336 (Pre-hospital intubation) + 0.662 (High-risk mechanism) + 0.950 (unilateral mydriasis) + 3.217 (bilateral mydriasis) + 0.841 (Glasgow ≤ 8) + 0.495 (MAIS-Head) − 0.271 (MAIS-Thorax) + 1.148 (Haemodynamic failure) + 0.708 (Respiratory failure) + 0.567 (Coagulopathy) + 0.580 (Mechanical ventilation) + 0.452 (Massive haemorrhage) − 5.432. The AUROC was 0.913 (0.903–0.923) in the derivation set and 0.929 (0.918–0.940) in the validation set.

Conclusions

The newly developed RETRASCORE is an early, easy-to-calculate and specific score to predict in-hospital mortality in trauma ICU patients. Although it has achieved adequate internal validation, it must be externally validated.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03845-6.

Machine learning techniques for mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study

Background

Interest in models for calculating the risk of death in traumatic patients admitted to ICUs remains high. These models use variables derived from the deviation of physiological parameters and/or the severity of anatomical lesions with respect to the affected body areas. Our objective is to create different predictive models of the mortality of critically traumatic patients using machine learning techniques.

Methods

We used 9625 records from the RETRAUCI database (National Trauma Registry of 52 Spanish ICUs in the period of 2015–2019). Hospital mortality was 12.6%. Data on demographic variables, affected anatomical areas and physiological repercussions were used. The Weka Platform was used, along with a ten-fold cross-validation for the construction of nine supervised algorithms: logistic regression binary (LR), neural network (NN), sequential minimal optimization (SMO), classification rules (JRip), classification trees (CT), Bayesian networks (BN), adaptive boosting (ADABOOST), bootstrap aggregating (BAGGING) and random forest (RFOREST). The performance of the models was evaluated by accuracy, specificity, precision, recall, F-measure, and AUC.

Results

In all algorithms, the most important factors are those associated with traumatic brain injury (TBI) and organic failures. The LR finds thorax and limb injuries as independent protective factors of mortality. The CT generates 24 decision rules and uses those related to TBI as the first variables (range 2.0–81.6%). The JRip detects the eight rules with the highest risk of mortality (65.0–94.1%). The NN model uses a hidden layer of ten nodes, which requires 200 weights for its interpretation. The BN find the relationships between the different factors that identify different patient profiles. Models with the ensemble methodology (ADABOOST, BAGGING and RandomForest) do not have greater performance. All models obtain high values ​​in accuracy, specificity, and AUC, but obtain lower values ​​in recall. The greatest precision is achieved by the SMO model, and the BN obtains the best recall, F-measure, and AUC.

Conclusion

Machine learning techniques are useful for creating mortality classification models in critically traumatic patients. With clinical interpretation, the algorithms establish different patient profiles according to the relationship between the variables used, determine groups of patients with different evolutions, and alert clinicians to the presence of rules that indicate the greatest severity.

A prospective pilot study using metabolomics discloses specific fatty acid, catecholamine and tryptophan metabolic pathways as possible predictors for a negative outcome after severe trauma

Background

We wanted to define metabolomic patterns in plasma to predict a negative outcome in severe trauma patients.

Methods

A prospective pilot study was designed to evaluate plasma metabolomic patterns, established by liquid chromatography coupled to mass spectrometry, in patients allocated to an intensive care unit (in the University Hospital Arnau de Vilanova, Lleida, Spain) in the first hours after a severe trauma (n = 48). Univariate and multivariate statistics were employed to establish potential predictors of mortality.

Results

Plasma of patients non surviving to trauma (n = 5) exhibited a discriminating metabolomic pattern, involving basically metabolites belonging to fatty acid and catecholamine synthesis as well as tryptophan degradation pathways. Thus, concentration of several metabolites exhibited an area under the receiver operating curve (ROC) higher than 0.84, including 3-indolelactic acid, hydroxyisovaleric acid, phenylethanolamine, cortisol, epinephrine and myristic acid. Multivariate binary regression logistic revealed that patients with higher myristic acid concentrations had a non-survival odds ratio of 2.1 (CI 95% 1.1–3.9).

Conclusions

Specific fatty acids, catecholamine synthesis and tryptophan degradation pathways could be implicated in a negative outcome after trauma. The metabolomic study of severe trauma patients could be helpful for biomarker proposal.

Electronic supplementary material

The online version of this article (10.1186/s13049-019-0631-5) contains supplementary material, which is available to authorized users.

Location-dependent effects of trauma on oxidative stress in humans

Though circulating antioxidant capacity in plasma is homeostatically regulated, it is not known whether acute stressors (i.e. trauma) affecting different anatomical locations could have quantitatively different impacts. For this reason, we evaluated the relationship between the anatomical location of trauma and plasma total antioxidant capacity (TAC) in a prospective study, where the anatomical locations of trauma in polytraumatic patients (n = 66) were categorized as primary affecting the brain -traumatic brain injury (TBI)-, thorax, abdomen and pelvis or extremities. We measured the following: plasma TAC by 2 independent methods, the contribution of selected antioxidant molecules (uric acid, bilirubin and albumin) to these values and changes after 1 week of progression. Surprisingly, TBI lowered TAC (919 ± 335 μM Trolox equivalents (TE)) in comparison with other groups (thoracic trauma 1187 ± 270 μM TE; extremities 1025 ± 276 μM TE; p = 0.004). The latter 2 presented higher hypoxia (PaO₂/FiO₂ 272 ± 87 mmHg) and hemodynamic instability (inotrope use required in 54.5%) as well. Temporal changes in TAC are also dependent on anatomical location, as thoracic and extremity trauma patients’ TAC values decreased (1187 ± 270 to 1045 ± 263 μM TE; 1025 ± 276 to 918 ± 331 μM TE) after 1 week (p < 0.01), while in TBI these values increased (919 ± 335 to 961 ± 465 μM TE). Our results show that the response of plasma antioxidant capacity in trauma patients is strongly dependent on time after trauma and location, with TBI failing to induce such a response.

The Spanish Adaptation of the Palliative Performance Scale (Version 2) Among Cancer Patients at the End of Life: Psychometric Properties

BACKGROUND

Palliative Performance Scale (PPS) is a reliable tool to assess performance status in cancer patients receiving palliative care (PC). Spanish validated and culturally adapted tools are needed.

OBJECTIVES

The objectives are to develop PPS translation and cross-cultural adaptation into Spanish and to assess its psychometric properties.

DESIGN

Translation process with cross-cultural adaptation to produce Spanish Palliative Performance Scale (PPS-SPANISH).

SETTINGS

PC Team at one University hospital in Spain.

PARTICIPANTS

Fifteen advanced cancer patients (60 assessments) were included for PPS translation and validation and 250 patients for cross-sectional analysis. All participants were recruited at oncology ward, emergency area, and outpatient clinic by PC team professionals. Informed consent was given. Average age was 66.4 ± 13 years (60% men).

METHODS

The process is designed in three steps. In Step 1, PPS translation and reverse translation into Spanish (three bilingual speakers) and linguistic complexity measurement were performed. In Step 2, readability and intelligibility assessment was carried out. In Step 3, a pilot study was conducted to assess test-retest reliability followed by a cross-sectional study to measure internal consistency. Inclusion criteria were the same for two samples. Demographic data were also analyzed by descriptive statistics.

RESULTS

Following cultural, linguistic, and grammatical adaptation, PPS-SPANISH was readable and reliable. The analysis of the test-retest reliability after 48 hours showed intraclass correlations >0.60. Cronbach's alpha coefficient was 0.99 (0.988-0.992). There was high agreement with other functional assessment tools (Barthel Index and Karnofsky Performance Status Index).

CONCLUSIONS

PPS-SPANISH showed reliability and validity, and it is suitable to assess performance status in cancer patients receiving PC.

Left carotid adventitial vasa vasorum signal correlates directly with age and with left carotid intima-media thickness in individuals without atheromatous risk factors

Objective

The early identification of the onset of subclinical atheromatosis is essential in reducing the high mortality risk from cardiovascular disease (CVD) worldwide. Although carotid intima-media thickness (cIMT) is the most commonly used early predictor of ongoing atherosclerosis, an experimental model of atherosclerosis, demonstrated that increases in adventitial microvessels (vasa vasorum (VV)) precede endothelial dysfunction. Using the reported accuracy of contrast-enhanced ultrasound (CEU) to measure carotid adventitial VV, this study assessed whether measurements of carotid adventitial VV serve as a marker of subclinical atherosclerotic lesions in a control population with none of the classical risk factors for CVD.

Methods and results

Measurements of cIMT (B-mode ultrasound) and adventitial VV (CEU) were conducted in 65 subjects, 30–70 years old, 48% men, with none of the classical risk factors for CVD. Adventitial VV strongly correlated with its own cIMT only in the left carotid artery. Importantly, the left carotid adventitial VV directly correlated with age.

Conclusions

The increases with age in left carotid adventitial VV in individuals with zero risk for atheromatosis suggest that the measurement of carotid adventitial VV could be an accurate and sensitive marker for the diagnosis of subclinical atheromatosis and therefore a prominent tool for monitoring the efficacy of anti-atheromatous therapies.

New symptom-based predictive tool for survival at seven and thirty days developed by palliative home care teams

AIM

This study sought to develop models to predict survival at 7 and 30 days based on symptoms detected by palliative home care teams (PHCTs).

MATERIALS AND METHODS

This prospective analytic study included a 6-month recruitment period with patient monitoring until death or 180 days after recruitment. The inclusion criteria consisted of age greater than 18 years, advanced cancer, and treatment provided by participating PHCTs between April and July 2009. The study variables included death at 7 or 30 days, survival time, age, gender, place of residence, type of tumor and extension, presence of 11 signs and symptoms measured with a 0-3 Likert scale, functional and cognitive status, and use of a subcutaneous butterfly needle. The statistics applied included a descriptive analysis according to the percentage or mean±standard deviation. For symptom comparison between surviving and nonsurviving patients, the χ(2) test was used. Classification and regression tree (CART) methodology was used for model development. An internal validation system (cross-validation with 10 partitions) was used to ensure generalization of the models. The area under the receiver operating characteristics (ROC) curve was calculated (with a 95% confidence interval) to assess the validation of the models.

RESULTS

A total of 698 patients were included. The mean age of the patients was 73.7±12 years, and 60.3% were male. The most frequent type of neoplasm was digestive (37.6%). The mean Karnofsky score was 51.8±14, the patients' cognitive status according to the Pfeiffer test was 2.6±4 errors, and 8.3% of patients required a subcutaneous butterfly needle. Each model provided 8 decision rules with a probability assignment range between 2.2% and 99.1%. The model used to predict the probability of death at 7 days included the presence of anorexia and dysphagia and the level of consciousness, and this model produced areas under the curve (AUCs) of 0.88 (0.86-0.90) and 0.81 (0.79-0.83). The model used to predict the probability of death at 30 days included the presence of asthenia and anorexia and the level of consciousness, and this model produced AUCs of 0.78 (0.77-0.80) and 0.77 (0.75-0.79).

CONCLUSION

For patients with advanced cancer treated by PHCTs, the use of classification schemes and decision trees based on specific symptoms can help clinicians predict survival at 7 and 30 days.

Classification of dermatological disorders in critical care patients: a prospective observational study

PURPOSE

The objective of this study was to identify dermatological disorders detected in the intensive care unit (ICU), to analyze their specific characteristics, and to define a useful classification for intensive care physicians.

MATERIALS AND METHODS

This was a prospective, observational study over a 3-year period (2006-2009) in a mixed ICU. This included all patients presenting with dermatological disorders that were detected at the time of ICU admission or developed along the ICU stay. We recorded the specific characteristics of the disorders and its evolution and treatment, which enabled us to classify the different observed conditions. As general variables, we analyzed demographic factors, the principal diagnosis, ICU procedures, the severity score (Acute Physiology and Chronic Health Evaluation II), length of stay, and mortality.

RESULTS

One hundred thirty-three patients showed at least one dermatological disorder (9.3%) and were classified into (1) preexisting dermatological disorders, (2) life-threatening dermatologic disorders, (3) systemic dermatological disorders, (4) infectious dermatological disorders, (5) reactive dermatological disorders, and (6) others.

CONCLUSIONS

Dermatological disorders are a frequent problem in the ICU, and their recognition is key to set up an appropriate care plan. We propose a classification and description of the different types of dermatological disorders that are most commonly found in ICUs.

[Classification of mental disorders in the Intensive Care Unit]

PURPOSE

To determine the incidence and characteristics of mental disorders (MD) in the Intensive Care Unit (ICU), and to define a classification system adapted to the ICU environment.

DESIGN

A retrospective, descriptive analysis.

SETTING

Intensive Care Unit, Arnau de Vilanova Hospital in Lérida (Spain).

PATIENTS

All patients with MD admitted during 5-year period (January, 1 2004 to December 31, 2008).

MAIN VARIABLES

General variables included clinical-demographic data, diagnostic variables, procedures, severity score, length of stay and mortality. Specific variables included psychiatric history, screening for substance abuse, psychiatric assessment, monitoring and transfer to a psychiatric center. Classification of the MD was as follows: 1) acute substance intoxication (SI); 2) suicide attempts (SA); and 3) MD associated with the main diagnosis (AMD).

RESULTS

A total of 146 patients had MD (7.8%); they were predominantly male (74%) and were younger than the general ICU population (43.9 vs. 55.3 years, p<0.001). The ICU stays of the patients with MDs were shorter (4 days vs. 7 days, p<0.001), and there was less hospital mortality (17.1 vs. 25%, p<0.05). They also showed a higher incidence of pneumonia (19.9 vs. 13.8%, p<0.05), but no differences in the level of severity were observed. The SI group (24.7%) contained the highest number of young people; the SA group (36.3%) showed a predominance of women; and the AMD (39%) group had the longest stays and the highest mortality. Psychiatric consultation was carried out mainly in the SA group (62.3%).

CONCLUSIONS

MD is a relatively common problem in the ICU. Collaboration with the Psychiatry Department seldom occurs, but must be encouraged to develop fully integrated management of critical patients with MD.

[Incidence of hyponatremia and its causes in neurological patients]

INTRODUCTION

Hyponatremia is considered the most frequent electrolyte disorder found in hospitalized patients and seems to be a prognostic factor during hospitalization.

METHODS

A prospective observational study was carried out in consecutive neurological patients admitted to our hospital over a 3-month period. Blood and urinary ionogram and osmolality were determined at entry and 3-5 days after admission in all patients with hyponatremia.

RESULTS

Of the 130 patients admitted, 19 (14.6%) had hyponatremia. The causes of hyponatremia were as follows: inappropriate fluid replacement in 4 patients (21%), antihypertensive drugs in 4 (21%), syndrome of inappropriate secretion of antidiuretic hormone in 4 (21%), cerebral salt wasting syndrome in 2 (10%), and edematous status caused by liver disease in one and digestive loss in one (5%) each. Mortality was one (5%) and 0 (0%) among patients with and without hyponatremia, respectively.

CONCLUSION

Hyponatremia is common in hospitalized neurological patients and can be misdiagnosed as a worsening of the main illness.

Stratification of the severity of critically ill patients with classification trees

Background

Development of three classification trees (CT) based on the CART (Classification and Regression Trees), CHAID (Chi-Square Automatic Interaction Detection) and C4.5 methodologies for the calculation of probability of hospital mortality; the comparison of the results with the APACHE II, SAPS II and MPM II-24 scores, and with a model based on multiple logistic regression (LR).

Methods

Retrospective study of 2864 patients. Random partition (70:30) into a Development Set (DS) n = 1808 and Validation Set (VS) n = 808. Their properties of discrimination are compared with the ROC curve (AUC CI 95%), Percent of correct classification (PCC CI 95%); and the calibration with the Calibration Curve and the Standardized Mortality Ratio (SMR CI 95%).

Results

CTs are produced with a different selection of variables and decision rules: CART (5 variables and 8 decision rules), CHAID (7 variables and 15 rules) and C4.5 (6 variables and 10 rules). The common variables were: inotropic therapy, Glasgow, age, (A-a)O2 gradient and antecedent of chronic illness. In VS: all the models achieved acceptable discrimination with AUC above 0.7. CT: CART (0.75(0.71-0.81)), CHAID (0.76(0.72-0.79)) and C4.5 (0.76(0.73-0.80)). PCC: CART (72(69-75)), CHAID (72(69-75)) and C4.5 (76(73-79)). Calibration (SMR) better in the CT: CART (1.04(0.95-1.31)), CHAID (1.06(0.97-1.15) and C4.5 (1.08(0.98-1.16)).

Conclusion

With different methodologies of CTs, trees are generated with different selection of variables and decision rules. The CTs are easy to interpret, and they stratify the risk of hospital mortality. The CTs should be taken into account for the classification of the prognosis of critically ill patients.

[Long distance interhospital transport. Accuracy of severity scoring system]

OBJECTIVE

To evaluate the hospital mortality risk for patients transported from a regional hospital to its second-level reference hospital using several scoring systems: Rapid Acute Physiology Score (RAPS), Rapid Emergency Medicine Score (REMS), SAPS II and APACHE II.

DESIGN AND SETTING

Prospective observational study of patients transferred from the Sant Hospital in la Seu d'Urgell to the University Hospital Arnau de Vilanova in Lleida, at a distance of 132 km.

PATIENTS

Consecutive cohort of 134 patients transferred between October 2005 and July 2007.

MAIN VARIABLES

Several data were collected, such as variables on demography, stay, severity score, diagnosis on admission, destination service and procedures, such as mechanical ventilation, inotropics, sedation, neuromuscular blockers and antiarrhythmics. Variable of result was hospital mortality.

RESULTS

The average transfer time was 105 +/- 14 minutes; 31.6% of the patients were admitted to an ICU; 16 (11,9%) patients died during hospital stay. The APACHE II and SAPS II scores got significantly higher values in those patients who died. The RAPS and REMS scores showed no significant differences among dead and survivors. The higher the APACHE II and SAPS II scores, the higher the proportion of mortality. The RAPS and REMS scores did not prove to have that tendency. Area under ROC curve was higher for APACHE II (0.76; 95% CI, 0.63-0.89) and SAPS II (0.78; 95% CI, 0.67-0.89), compared to those of RAPS (0.59; 95% CI, 0.43-0.75) and REMS (0.63; 95% CI, 0.49-0.78).

CONCLUSIONS

The severity of illness measured with APACHE II and SAPS II is able to identify those patients with a higher predictive of mortality. It is a priority to have the right previous stabilization and the adequately trained team to provide care during the transfer, when facing lengthy journey times.

A prediction rule for estimating the risk of bacteremia in patients with community-acquired pneumonia

BACKGROUND

We endeavored to construct a simple score based entirely on epidemiological and clinical variables that would stratify patients who require hospital admission because of community-acquired pneumonia into groups with a low or high risk of developing bacteremia.

METHODS

Derivation and internal validation cohorts were obtained by retrospective analysis of a database that included 3116 consecutive patients with community-acquired pneumonia from 2 university hospitals. Potential predictive factors were determined by means of a multivariate logistic regression equation applied to a cohort consisting of 60% of the patients. Points were assigned to significant parameters to generate the score. It was then internally validated with the remaining 40% of patients and was externally validated using an independent multicenter cohort of 1369 patients.

RESULTS

The overall rates of bacteremia were 12%-16% in the cohorts. The clinical probability estimate of developing bacteremia was based on 6 variables: liver disease, pleuritic pain, tachycardia, tachypnea, systolic hypotension, and absence of prior antibiotic treatment. For the score, 1 point was assigned to each predictive factor. In the derivation cohort, a cutoff score of 2 best identified the risk of bacteremia. In the validation cohorts, rates of bacteremia were <8% for patients with a score 1 (43%-49% of patients), whereas blood culture results were positive in 14%-63% of cases for patients with a score 2.

CONCLUSIONS

This clinical score, based on readily available and objective variables, provides a useful tool to predict bacteremia. The score has been internally and externally validated and may be useful to guide diagnostic decisions for community-acquired pneumonia.

[Risk factors of the hepatic dysfunction associated with parenteral nutrition]

BACKGROUND AND OBJECTIVE

The objective of this study is to describe the incidence of hepatic dysfunction (HD) in our hospital and evaluate the possible risk factors associated with HD development as an improvement of the caring process received by patients treated with parenteral nutrition (PN).

PATIENTS AND METHOD

A prospective study of patients (n=994) who required PN during the period 2000-2004. HD is the identification of an increase above 1,5 of the top reference value of alkaline phosphatase (40-450U/l) and gamma glutamyl transpeptidase (11-49U/l) associated with an increase of transaminases (5-32U/l) and a total bilirrubin higher than 1,2mg/dl.

RESULTS

The incidence of HD was 4,9% (n=49). Days with PN were significantly higher in the HD group: median (interquartile range): 30 (20-59) vs 15 (8-25) days (p<0.001). In the univariated HD analysis, the variables that reached significant odds ratio were: the critical patient condition, the PN duration, the total calorie contribution higher than 25kcal/kg, to exceed 3g of carbohydrates/kg, to administer more than 0.8g/kg of lipids and to exceed 0.16g of nitrogen/kg. In the multivariated analysis, the variables selected as independent risk factors were: to exceed 3 weeks of PN, to be a critical patient and a contribution over 0.16g of nitrogen/kg.

CONCLUSIONS

The present profiles of the patients who will develop HD are those with prolonged PN. These patients undergo processes and critical therapy, where the specialists must monitor, not only calorie contribution, carbohydrates or lipids, but proteins as well.

Classification tree analysis for the discrimination of pleural exudates and transudates

BACKGROUND

Classification and regression tree (CART) analysis is a non-parametric technique suitable for the generation of clinical decision rules. We have studied the performance of CART analysis in the separation of pleural exudates and transudates.

METHODS

Basic demographic, radiologic and laboratory data were retrospectively evaluated in 1257 pleural effusions (204 transudates and 1053 exudates, according to standard clinical criteria) and submitted for CART analysis. The model's discriminative ability was compared with that of Light's criteria, in both the original formulation and an abbreviated version, i.e., deleting the pleural fluid (PF)/serum lactate dehydrogenase (LDH) ratio from the triad.

RESULTS

A first CART model built starting from all available data identified PF/serum protein ratio and PF LDH ratios as the two best discriminatory parameters. This algorithm achieved a sensitivity of 96.8%, slightly lower than that of classical Light's criteria (98.5%) and comparable to that of the abbreviated Light's criteria (97.0%), and significantly better specificity (85.3%) compared to both classical (74.0%) and abbreviated (79.4%) Light's criteria. A second CART model developed after excluding serum measurements selected PF protein and PF LDH as the most discriminatory variables, and correctly classified 97.2% of exudates and 77.0% of transudates.

CONCLUSIONS

CART-based algorithms can efficiently discriminate between pleural exudates and transudates.

A more flexible parametric estimation of univariate reference intervals: a new method based on the GS-distribution

BACKGROUND

Reference interval estimation is an important issue in clinical laboratories. Present methods are based either on data transformation or on non-parametric approaches.

METHODS

We present a new technique based in a family of statistical distributions known as GS-distributions that provide a suitable model for continuous unimodal variables. We compare, both by simulation studies an on actual data, the reference intervals estimated by using non-parametric methods and data transformations suggested by the IFCC and those obtained by fitting a GS-distribution. Simulated data are generated from various distributions to evaluate the accuracy of these methods. In each case, confidence intervals for the resulting reference intervals are obtained by bootstrap.

RESULTS

In all the cases, the GS-distribution based method provides comparable or more accurate results than the non-parametric methods. In most cases, the proposed method produces better results than those obtained by transforming the original data.

CONCLUSIONS

Our results suggest that the method for computing reference intervals based on GS-distribution is a valid alternative for the current non-parametric methods.

[Acute parotiditis after tracheostomy in Intensive Care]

Post-anesthesic parotiditis is a little known entity related with anesthesic procedures but described in any situation that motivates manipulation of the oropharyngeal cavity. Its physiopathological mechanism is not well-defined, although it could have a multifactorial origin. A case of a male who was admitted for post-operative control of brain tumor exeresis and who had preauricular and submaxillary inflammation after a routinely performed tracheostomy is presented. Coincidence with the performing of a tracheostomy required us to propose the differential diagnosis with the complications associated to said surgical act. Post-anesthesic parotiditis, even though it is a rare complication and has no clinical significance, should be kept in mind when there is facial edema after any manipulation of the oropharyngeal cavity.

[Methodological approach to the use of artificial neural networks for predicting results in medicine]

In clinical practice, there is an increasing interest in obtaining adequate models of prediction. Within the possible available alternatives, the artificial neural networks (ANN) are progressively more used. In this review we first introduce the ANN methodology, describing the most common type of ANN, the Multilayer Perceptron trained with backpropagation algorithm (MLP). Then we compare the MLP with the Logistic Regression (LR). Finally, we show a practical scheme to make an application based on ANN by means of an example with actual data. The main advantage of the RN is its capacity to incorporate nonlinear effects and interactions between the variables of the model without need to include them a priori. As greater disadvantages, they show a difficult interpretation of their parameters and large empiricism in their process of construction and training. ANN are useful for the computation of probabilities of a given outcome based on a set of predicting variables. Furthermore, in some cases, they obtain better results than LR. Both methodologies, ANN and LR, are complementary and they help us to obtain more valid models.

Estimating conditional distributions using a method based on S-distributions reference percentile curves for body mass index in Spanish children

BACKGROUND

Reference intervals are a fundamental tool for characterizing the health status in a given population and play a central role in defining diagnostic values in clinical applications. Estimation of the conditional distribution of a variable, as the body mass index (BMI), is necessary for providing reference values when there is a trend as a function of the covariate.

SUBJECTS AND METHOD

We studied 1453 boys and young between 5 and 16 years old measured in a study carried out in the schools of Lleida (Spain). BMI conditional distributions with age have been derived using a new parametric method based on the one proposed by Sorribas et al. [Stat. Med. (2000) 19:697-713]. This method is based on S-distributions as a parametric model for the distribution and uses maximum likelihood estimation of the conditional distribution.

RESULTS

The methods commonly used for estimating reference curves are based on a smoothing of sample quantiles using different techniques. However, these methods do not provide information on the conditional distribution of the target variable. Our method provides an estimation of such distribution and the corresponding reference curves for the quantiles as a function of a covariate, in our case age.

CONCLUSIONS

The suggested methodology provides appropriate reference quantiles for the BMI. Our results allow characterizing the change in distribution within the age range considered. Besides describing a raise in BMI with age, we observe an increase in dispersion around puberty. This must be considered when using BMI as a diagnostic variable.

Aplicación de las redes neuronales artificiales para la estratificación de riesgo de mortalidad hospitalaria

OBJECTIVE

To compare the ability of an artificial neural network (ANN) to predict hospital mortality with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) system and multiple logistic regression (LR). A secondary objective was to compare the allocation of individual probability among the models.

METHOD

The variables required for calculating the APACHE II were prospectively collected. A total of 1146 patients were divided (randomly 70% and 30%) into the Development (800) and the Validation (346) sets. With the same variables an LR model and an ANN were carried out (a 3-layer perceptron trained by algorithm backpropagation with bootstrap resampling and with 9 nodes in the hidden layer) in the Development set. The models developed were contrasted with the Validation set and their discrimination properties were evaluated using the area under the ROC curve (AUC [95% CI]) and calibration with the Hosmer-Lemeshow C (HLC) test. Differences between the probabilities were evaluated using the Bland-Altman test.

RESULTS

The Validation set showed an APACHE II with an AUC = 0.79 (0.75-0.84) and HLC = 11 (p = 0.329); LR model AUC = 0.81 (0.76-0.85) and HLC = 29 (p = 0.0001) and an ANN AUC = 0.82 (0.77-0.86) and HLC = 10 (p = 0.404). The patients with the most important differences in the allocation of probability between LR and ANN (8% of the total) were neurological. The worst results were found in trauma patients with an AUC of not greater than 0.75 in all the models. In respiratory patients, the ANN achieved the best AUC = 0.87 (0.78-0.91).

CONCLUSIONS

The ANN was able to stratify hospital mortality risk by using the APACHE II system variables. The ANN tended to achieve better results than LR, since, in order to work, it does not require lineal restrictions or independent variables. Allocation of individual probability differed in each model.

A new parametric method based on S-distributions for computing receiver operating characteristic curves for continuous diagnostic tests

Receiver operating characteristic (ROC) curves provides a method for evaluating the performance of a diagnostic test. These curves represent the true positive ratio, that is, the true positives among those affected by the disease, as a function of the false positive ratio, that is, the false positives among the healthy, corresponding to each possible value of the diagnostic variable. When the diagnostic variable is continuous, the corresponding ROC curve is also continuous. However, estimation of such curve through the analysis of sample data yields a step-line, unless some assumption is made on the underlying distribution of the considered variable. Since the actual distribution of the diagnostic test is seldom known, it is difficult to select an appropriate distribution for practical use. Data transformation may offer a solution but also may introduce a distortion on the evaluation of the diagnostic test. In this paper we show that the distribution family known as the S-distribution can be used to solve this problem. The S-distribution is defined as a differential equation in which the dependent variable is the cumulative. This special form provides a highly flexible family of distributions that can be used as models for unknown distributions. It has been shown that classical statistical distributions can be represented accurately as S-distributions and that they occur in a definite subspace of the parameter space corresponding to the whole S-distribution family. Consequently, many other distributional forms that do not correspond to known distributions are provided by the S-distribution. This property can be used to model observed data for unknown distributions and is very useful in constructing parametric ROC curves in those cases. After fitting an S-distribution to the observed samples of diseased and healthy populations, ROC curve computation is straightforward. A ROC curve can be considered as the solution of a differential equation in which the dependent variable is the ratio of true positives and the independent variable is the ratio of false positives. This equation can be easily obtained from the S-distributions fitted to observed data. Using these results, we can compute pointwise confidence bands for the ROC curve and the corresponding area under the curve. We shall compare this approach with the empirical and the binormal methods for estimating a ROC curve to show that the S-distribution based method is a useful parametric procedure.

Skin lesions in the ICU

OBJECTIVE

The objective of this study is to identify the dermatological disorders (DDs) responsible for the most common skin lesions in the ICU, their incidence and their impact on mortality, degree of severity and length of stay in the ICU.

DESIGN AND SETTING

We performed a 2-year prospective study in a general medical and surgical ICU including, exceptionally, paediatric cases.

PATIENTS

We included all patients who presented skin lesions upon admission or developed them during their ICU stay.

RESULTS

Forty-six patients (10% of all admissions) were enrolled, with 51 DDs. SAPS II score (43) and mean length of stay (19 days) were significantly higher than in the general group of ICU admissions. Differences in mortality rates (26% versus 29%) were not statistically significant.

CONCLUSIONS

DDs are entities that should be borne in mind in the critically ill patient; their incidence is by no means negligible and makes careful examination of the skin mandatory both on admission and during a patient's ICU stay.