Efficacious treatments are available for major depressive disorder (MDD), but treatment dropout is common and decreases their effectiveness. However, knowledge about prevalence of treatment dropout ...and its risk factors in routine care is limited. The objective of this study was to determine the prevalence of and risk factors for dropout in a large outpatient sample. In this retrospective cohort analysis, routinely collected data from 2235 outpatients with MDD who had a diagnostic work-up between 2014 and 2016 were examined. Dropout was defined as treatment termination without achieving remission before the fourth session within six months after its start. Total and item scores on the Dutch Measure for Quantification of Treatment Resistance in Depression (DM-TRD) at baseline, and demographic variables were analyzed for their association with dropout using logistic regression and elastic net analyses. Data of 987 subjects who started routine outpatient depression treatment were included in the analyses of which 143 (14.5%) dropped out. Higher DM-TRD-scores were predictive for lower dropout odds OR = 0.78, 95% CI = (0.70–0.86),
p
< 0.001. The elastic net analysis revealed several clinical variables predictive for dropout. Higher SES, higher depression severity, comorbid personality pathology and a comorbid anxiety disorder were significantly associated with less dropout in the sample. In this observational study, treatment dropout was relatively low. The DM-TRD, an easy-to-use clinical instrument, revealed several variables associated with less dropout. When applied in daily practice and combined with demographical information, this instrument may help to reduce dropout and increase treatment effectiveness.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Background
Immunodeficiency, centromeric instability, and facial anomalies (ICF) syndrome is a rare, genetically heterogeneous, autosomal recessive disorder. Patients suffer from recurrent infections ...caused by reduced levels or absence of serum immunoglobulins. Genetically, 4 subtypes of ICF syndrome have been identified to date: ICF1 (DNMT3B mutations), ICF2 (ZBTB24 mutations), ICF3 (CDCA7 mutations), and ICF4 (HELLS mutations).
Aim
To study the mutation spectrum in ICF syndrome.
Materials and methods
Genetic studies were performed in peripheral blood lymphocyte DNA from suspected ICF patients and family members.
Results
We describe 7 ICF1 patients and 6 novel missense mutations in DNMT3B, affecting highly conserved residues in the catalytic domain. We also describe 5 new ICF2 patients, one of them carrying a homozygous deletion of the complete ZBTB24 locus. In a meta‐analysis of all published ICF cases, we observed a gender bias in ICF2 with 79% male patients.
Discussion
The biallelic deletion of ZBTB24 provides strong support for the hypothesis that most ICF2 patients suffer from a ZBTB24 loss of function mechanism and confirms that complete absence of ZBTB24 is compatible with human life. This is in contrast to the observed early embryonic lethality in mice lacking functional Zbtb24. The observed gender bias seems to be restricted to ICF2 as it is not observed in the ICF1 cohort.
Conclusion
Our study expands the mutation spectrum in ICF syndrome and supports that DNMT3B and ZBTB24 are the most common disease genes.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
We aimed to identify novel deletions and variants of TP63 associated with orofacial clefting (OFC). Copy number variants were assessed in three OFC families using microarray analysis. Subsequently, ...we analyzed TP63 in a cohort of 1072 individuals affected with OFC and 706 population-based controls using molecular inversion probes (MIPs). We identified partial deletions of TP63 in individuals from three families affected with OFC. In the OFC cohort, we identified several TP63 variants predicting to cause loss-of-function alleles, including a frameshift variant c.569_576del (p.(Ala190Aspfs*5)) and a nonsense variant c.997C>T (p.(Gln333*)) that introduces a premature stop codon in the DNA-binding domain. In addition, we identified the first missense variants in the oligomerization domain c.1213G>A (p.(Val405Met)), which occurred in individuals with OFC. This variant was shown to abrogate oligomerization of mutant p63 protein into oligomeric complexes, and therefore likely represents a loss-of-function allele rather than a dominant-negative. All of these variants were inherited from an unaffected parent, suggesting reduced penetrance of such loss-of-function alleles. Our data indicate that loss-of-function alleles in TP63 can also give rise to OFC as the main phenotype. We have uncovered the dosage-dependent functions of p63, which were previously rejected.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Purpose
Recalibration and determining discriminative power, internationally, of the existing delirium prediction model (PRE-DELIRIC) for intensive care patients.
Methods
A prospective multicenter ...cohort study was performed in eight intensive care units (ICUs) in six countries. The ten predictors (age, APACHE-II, urgent and admission category, infection, coma, sedation, morphine use, urea level, metabolic acidosis) were collected within 24 h after ICU admission. The confusion assessment method for the intensive care unit (CAM-ICU) was used to identify ICU delirium. CAM-ICU screening compliance and inter-rater reliability measurements were used to secure the quality of the data.
Results
A total of 2,852 adult ICU patients were screened of which 1,824 (64 %) were eligible for the study. Main reasons for exclusion were length of stay <1 day (19.1 %) and sustained coma (4.1 %). CAM-ICU compliance was mean (SD) 82 ± 16 % and inter-rater reliability 0.87 ± 0.17. The median delirium incidence was 22.5 % (IQR 12.8–36.6 %). Although the incidence of all ten predictors differed significantly between centers, the area under the receiver operating characteristic (AUROC) curve of the eight participating centers remained good: 0.77 (95 % CI 0.74–0.79). The linear predictor and intercept of the prediction rule were adjusted and resulted in improved re-calibration of the PRE-DELIRIC model.
Conclusions
In this multinational study, we recalibrated the PRE-DELIRIC model. Despite differences in the incidence of predictors between the centers in the different countries, the performance of the PRE-DELIRIC-model remained good. Following validation of the PRE-DELIRIC model, it may facilitate implementation of strategies to prevent delirium and aid improvements in delirium management of ICU patients.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Long-term untreated major depressive disorder (MDD) is associated with a less favorable clinical course. Waiting time, defined as the interval between diagnostic workup and treatment initiation, may ...be clinically relevant given the prolongation of the pre-existing duration of untreated MDD. However, it is currently unknown whether and to what extent waiting time affects treatment course in routine outpatient care.
Retrospectively extracted data from 715 outpatients with MDD who received naturalistic outpatient MDD treatment were examined. Treatment outcome was defined as the difference in depression severity at the start of treatment and six months thereafter. Clinical course during waiting time was defined by the difference in severity at diagnostic workup and at treatment initiation. We analyzed the association between waiting time and treatment outcome and between waiting time and clinical course during this waiting time using multivariable regression analyses. We adjusted for severity and suicidality as potential confounders.
An increased duration of the waiting time was associated with a less favorable treatment outcome (B = 0.049, SE = 0.019, p = 0.01). This association persisted after adjustment for potential confounders (B = 0.053, SE = 0.02, p = 0.01). No association was found between length of waiting time and clinical course during waiting time.
Strict definitions resulted in smaller sample sizes for the final analyses. The uncontrolled design may be questionable to definitively establish the impact of waiting time on treatment outcome.
A prolonged waiting time is significantly associated with less favorable treatment outcome. Reduction of waiting time deserves priority in depression treatment planning to improve clinical outcomes.
•A longer waiting time was significantly associated with a worse treatment outcome.•This association persisted after adjustment for potential confounders.•Clinical course during waiting time was not associated with duration of waiting time.•Minimizing waiting times should be a priority to improve MDD treatment outcomes.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Background
The Dutch Measure for Quantification of Treatment Resistance in Depression (DM‐TRD) is a promising prediction tool for major depressive disorder (MDD) based on variables associated with ...treatment outcome. The objective of our study was to examine the association between the DM‐TRD and clinical course in a large cohort of MDD outpatients receiving treatment as usual. Furthermore, we examined whether the addition of an item measuring the presence of childhood adversity improved this association.
Methods
We included 1115 subjects with MDD (according to the DSM‐IV) who were naturalistically treated at seven outpatient departments of a secondary mental healthcare center in the Netherlands. Data on subjects who had a diagnostic work‐up between June 2014 and June 2016 were analyzed. Multilevel analyses were performed to examine the association between the DM‐TRD score at baseline and clinical course, defined by symptom severity according to scores on the Quick Inventory of Depressive Symptomatology‐Self Report (QIDS‐SR) over time. We also investigated whether an extra item measuring childhood adversity improved the model.
Results
The model including the DM‐TRD and its interaction with time was superior to previous models. The addition of childhood adversity and its interaction with time did not improve the model.
Conclusions
In depressed outpatients receiving treatment as usual, the solid longer‐term association between higher DM‐TRD scores and worse clinical course supports its usefulness in clinical practice. Childhood adversity did not improve the model value indicating that—counterintuitively—this parameter offers no additional predictive power to the variables included.
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DOBA, FZAB, GIS, IJS, IZUM, KILJ, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBMB, UILJ, UKNU, UL, UM, UPUK
Rationale
Delirium incidence in intensive care unit (ICU) patients is high and associated with poor outcome. Identification of high-risk patients may facilitate its prevention.
Purpose
To develop and ...validate a model based on data available at ICU admission to predict delirium development during a patient’s complete ICU stay and to determine the predictive value of this model in relation to the time of delirium development.
Methods
Prospective cohort study in 13 ICUs from seven countries. Multiple logistic regression analysis was used to develop the early prediction (E-PRE-DELIRIC) model on data of the first two-thirds and validated on data of the last one-third of the patients from every participating ICU.
Results
In total, 2914 patients were included. Delirium incidence was 23.6 %. The E-PRE-DELIRIC model consists of nine predictors assessed at ICU admission: age, history of cognitive impairment, history of alcohol abuse, blood urea nitrogen, admission category, urgent admission, mean arterial blood pressure, use of corticosteroids, and respiratory failure. The area under the receiver operating characteristic curve (AUROC) was 0.76 95 % confidence interval (CI) 0.73–0.77 in the development dataset and 0.75 (95 % CI 0.71–0.79) in the validation dataset. The model was well calibrated. AUROC increased from 0.70 (95 % CI 0.67–0.74), for delirium that developed <2 days, to 0.81 (95 % CI 0.78–0.84), for delirium that developed >6 days.
Conclusion
Patients’ delirium risk for the complete ICU length of stay can be predicted at admission using the E-PRE-DELIRIC model, allowing early preventive interventions aimed to reduce incidence and severity of ICU delirium.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Increasing evidence indicates that harmful effects are associated with the use of physical restraint.
To characterize the use of physical restraint in intensive care units. Prevalence, adherence to ...protocols, and correlates of the use of physical restraint were determined. For comparisons between ICUs, adjustments were made for differences in patients' characteristics.
A prospective, cross-sectional, observational multicenter study with a representative sample (n = 25) of all Dutch intensive care units, ranging from local hospitals to academic centers. Each unit was visited twice, and all 379 patients admitted during these visits were included and were examined for use of physical restraint.
Physical restraint was used in 23% of all patients (range, 0%-56% for different units). Of all 346 nurses interviewed, 31% reported using a protocol when applying physical restraint. When corrections were made for clustering within units, the risk for use of physical restraint was increased in patients with delirium or coma, in patients who could not communicate verbally, and in patients receiving psychoactive or sedative medications. Sex, severity of illness, and nurse to patient ratio were not independently related to use of physical restraint. In 11 units (44%), use of physical restraint was more frequent than expected on the basis of patients' characteristics, although this finding was not significant.
Physical restraint is frequently used in Dutch intensive care units. The differences in frequency between units suggest that opportunities exist to limit the use of physical restraint.
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NUK, OILJ, UL, UM, UPUK, VSZLJ
In a multiple-dose-ranging trial, we previously evaluated higher doses of rifampin in patients for 2 weeks. The objectives of the current study were to administer higher doses of rifampin for a ...longer period to compare the pharmacokinetics, safety/tolerability, and bacteriological activity of such regimens. In a double-blind, randomized, placebo-controlled, phase II clinical trial, 150 Tanzanian patients with tuberculosis (TB) were randomized to receive either 600 mg (approximately 10 mg/kg of body weight), 900 mg, or 1,200 mg rifampin combined with standard doses of isoniazid, pyrazinamide, and ethambutol administered daily for 2 months. Intensive pharmacokinetic sampling occurred in 63 patients after 6 weeks of treatment, and safety/tolerability was assessed. The bacteriological response was assessed by culture conversion in liquid and solid media. Geometric mean total exposures (area under the concentration-versus-time curve up to 24 h after the dose) were 24.6, 50.8, and 76.1 mg · h/liter in the 600-mg, 900-mg, and 1,200-mg groups, respectively, reflecting a nonlinear increase in exposure with the dose (
< 0.001). Grade 3 adverse events occurred in only 2 patients in the 600-mg arm, 4 patients in the 900-mg arm, and 5 patients in the 1,200-mg arm. No significant differences in the bacteriological response were observed. Higher daily doses of rifampin (900 and 1,200 mg) resulted in a more than proportional increase in rifampin exposure in plasma and were safe and well tolerated when combined with other first-line anti-TB drugs for 2 months, but they did not result in improved bacteriological responses in patients with pulmonary TB. These findings have warranted evaluation of even higher doses of rifampin in follow-up trials. (This study has been registered at ClinicalTrials.gov under identifier NCT00760149.).
Recurrent somatic variants in SPOP are cancer specific; endometrial and prostate cancers result from gain-of-function and dominant-negative effects toward BET proteins, respectively. By using ...clinical exome sequencing, we identified six de novo pathogenic missense variants in SPOP in seven individuals with developmental delay and/or intellectual disability, facial dysmorphisms, and congenital anomalies. Two individuals shared craniofacial dysmorphisms, including congenital microcephaly, that were strikingly different from those of the other five individuals, who had (relative) macrocephaly and hypertelorism. We measured the effect of SPOP variants on BET protein amounts in human Ishikawa endometrial cancer cells and patient-derived cell lines because we hypothesized that variants would lead to functional divergent effects on BET proteins. The de novo variants c.362G>A (p.Arg121Gln) and c. 430G>A (p.Asp144Asn), identified in the first two individuals, resulted in a gain of function, and conversely, the c.73A>G (p.Thr25Ala), c.248A>G (p.Tyr83Cys), c.395G>T (p.Gly132Val), and c.412C>T (p.Arg138Cys) variants resulted in a dominant-negative effect. Our findings suggest that these opposite functional effects caused by the variants in SPOP result in two distinct and clinically recognizable syndromic forms of intellectual disability with contrasting craniofacial dysmorphisms.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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