Identification of predictive factors of diabetic ketoacidosis in type 1 diabetes using a subgroup discovery algorithm.

AIM: To identify predictive factors for diabetic ketoacidosis (DKA) by retrospective analysis of registry data and the use of a subgroup discovery algorithm. MATERIALS AND METHODS: Data from adults and children with type 1 diabetes and more than two diabetes-related visits were analysed from the Diabetes Prospective Follow-up Registry. Q-Finder, a supervised non-parametric proprietary subgroup discovery algorithm, was used to identify subgroups with clinical characteristics associated with increased DKA risk. DKA was defined as pH less than 7.3 during a hospitalization event. RESULTS: Data for 108 223 adults and children, of whom 5609 (5.2%) had DKA, were studied. Q-Finder analysis identified 11 profiles associated with an increased risk of DKA: low body mass index standard deviation score; DKA at diagnosis; age 6-10 years; age 11-15 years; an HbA1c of 8.87% or higher (≥ 73 mmol/mol); no fast-acting insulin intake; age younger than 15 years and not using a continuous glucose monitoring system; physician diagnosis of nephrotic kidney disease; severe hypoglycaemia; hypoglycaemic coma; and autoimmune thyroiditis. Risk of DKA increased with the number of risk profiles matching patients' characteristics. CONCLUSIONS: Q-Finder confirmed common risk profiles identified by conventional statistical methods and allowed the generation of new profiles that may help predict patients with type 1 diabetes who are at a greater risk of experiencing DKA.

Q-Finder: An Algorithm for Credible Subgroup Discovery in Clinical Data Analysis - An Application to the International Diabetes Management Practice Study.

Addressing the heterogeneity of both the outcome of a disease and the treatment response to an intervention is a mandatory pathway for regulatory approval of medicines. In randomized clinical trials (RCTs), confirmatory subgroup analyses focus on the assessment of drugs in predefined subgroups, while exploratory ones allow a posteriori the identification of subsets of patients who respond differently. Within the latter area, subgroup discovery (SD) data mining approach is widely used-particularly in precision medicine-to evaluate treatment effect across different groups of patients from various data sources (be it from clinical trials or real-world data). However, both the limited consideration by standard SD algorithms of recommended criteria to define credible subgroups and the lack of statistical power of the findings after correcting for multiple testing hinder the generation of hypothesis and their acceptance by healthcare authorities and practitioners. In this paper, we present the Q-Finder algorithm that aims to generate statistically credible subgroups to answer clinical questions, such as finding drivers of natural disease progression or treatment response. It combines an exhaustive search with a cascade of filters based on metrics assessing key credibility criteria, including relative risk reduction assessment, adjustment on confounding factors, individual feature's contribution to the subgroup's effect, interaction tests for assessing between-subgroup treatment effect interactions and tests adjustment (multiple testing). This allows Q-Finder to directly target and assess subgroups on recommended credibility criteria. The top-k credible subgroups are then selected, while accounting for subgroups' diversity and, possibly, clinical relevance. Those subgroups are tested on independent data to assess their consistency across databases, while preserving statistical power by limiting the number of tests. To illustrate this algorithm, we applied it on the database of the International Diabetes Management Practice Study (IDMPS) to better understand the drivers of improved glycemic control and rate of episodes of hypoglycemia in type 2 diabetics patients. We compared Q-Finder with state-of-the-art approaches from both Subgroup Identification and Knowledge Discovery in Databases literature. The results demonstrate its ability to identify and support a short list of highly credible and diverse data-driven subgroups for both prognostic and predictive tasks.

MMS19 as a potential predictive marker of adjuvant chemotherapy benefit in resected non-small cell lung cancer.

BACKGROUND: Resectable non-small cell lung cancer (NSCLC) treatment options most often consist of surgical resection along with adjuvant chemotherapy (ACT). The benefit of ACT however is modest and is accompanied by important side effects. OBJECTIVE: One central quest in the field is therefore the identification of a predictive marker of the response to ACT. METHODS: We applied an unbiased approach based on high content analysis of expression data generated from a discovery patient cohort. RESULTS: We identified MMS19, a component of the cytoplasmic Iron-Sulfur Assembly (CIA) machinery important for the Nucleotide Excision Repair (NER) pathway as a pivotal gene for cisplatin toxicity. We then confirmed the association between MMS19 expression and the response to Cisplatin treatment in a panel of NSCLC cell lines. Finally we validated these pre-clinical data in a subgroup of JBR.10 trial patients through a hypothesis-driven analysis, and showed that MMS19 levels associated with ACT benefit. CONCLUSIONS: We therefore propose the expression level of MMS19 as a candidate predictive marker of ACT benefit in resected NSCLC patients.

Radiographic analysis of the sagittal alignment and balance of the spine in asymptomatic subjects.

BACKGROUND: There is an increasing recognition of the clinical importance of the sagittal plane alignment of the spine. A prospective study of several radiographic parameters of the sagittal profile of the spine was conducted to determine the physiological values of these parameters, to calculate the variations of these parameters according to epidemiological and morphological data, and to study the relationships among all of these parameters. METHODS: Sagittal radiographs of the head, spine, and pelvis of 300 asymptomatic volunteers, made with the subject standing, were evaluated. The following parameters were measured: lumbar lordosis, thoracic kyphosis, T9 sagittal offset, sacral slope, pelvic incidence, pelvic tilt, intervertebral angulation, and vertebral wedging angle from T9 to S1. The radiographs were digitized, and all measurements were performed with use of a software program. Two different analyses, a descriptive analysis characterizing these parameters and a multivariate analysis, were performed in order to study the relationships among all of them. RESULTS: The mean values (and standard deviations) were 60 degrees 10 degrees for maximum lumbar lordosis, 41 degrees +/- 8.4 degrees for sacral slope, 13 degrees +/- 6 degrees for pelvic tilt, 55 degrees +/-10.6 degrees for pelvic incidence, and 10.3 degrees +/- 3.1 degrees for T9 sagittal offset. A strong correlation was found between the sacral slope and the pelvic incidence (r = 0.8); between maximum lumbar lordosis and sacral slope (r = 0.86); between pelvic incidence and pelvic tilt (r = 0.66); between maximum lumbar lordosis and pelvic incidence, pelvic tilt, and maximum thoracic kyphosis (r = 0.9); and, finally, between pelvic incidence and T9 sagittal offset, sacral slope, pelvic tilt, maximum lumbar lordosis, and thoracic kyphosis (r = 0.98). The T9 sagittal offset, reflecting the sagittal balance of the spine, was dependent on three separate factors: a linear combination of the pelvic incidence, maximum lumbar lordosis, and sacral slope; the pelvic tilt; and the thoracic kyphosis. CONCLUSIONS AND CLINICAL RELEVANCE: This description of the physiological spinal sagittal balance should serve as a baseline in the evaluation of pathological conditions associated with abnormal angular parameter values. Before a patient with spinal sagittal imbalance is treated, the reciprocal balance between various spinal angular parameters needs to be taken into account. The correlations between angular parameters may also be useful in calculating the corrections to be obtained during treatment.

The association of sagittal spinal and pelvic parameters in asymptomatic persons and patients with isthmic spondylolisthesis.

Using a specialized orthopedic software package, the authors investigated the sagittal spinal shape and the position of the pelvis in the space in patients with isthmic spondylolisthesis and in persons with no such symptoms. Digitized lateral spinal radiographs of 30 healthy volunteers and 48 patients were evaluated. The absolute values and significant correlations between parameters were analyzed. The pelvic parameters correlated well with lordosis, which shows sagittal balance in the asymptomatic group. The hyperlordosis and the horizontally positioned sacrum in isthmic spondylolisthesis enlarge the tensile force component of gravity, which may cause the lysis. Finally, the authors developed a new balance between the pelvis and the spine after slipping of the vertebral body. The degree of slipping correlated well with the sacrofemoral anatomic constant (incidence), which is unique in each person.