Bipolar disorder: a troubled diagnosis

Bipolar disorder is indeed a troubled diagnosis. Conceived from manic-depressive illness, bipolar disorder is a much narrower concept by virtue of the emphasis placed in modern psychiatric taxonomy on polarity rather than recurrence of mood episodes. At first, this "new diagnosis" floundered and received little attention, but once it established itself, it steadily gained interest throughout the "decade of the brain." By the beginning of the new millennium bipolar disorder was perfectly poised for a phenomenal expansion. Its rapid growth led to a proliferation of bipolar subtypes, each of which quickly gained disorder status, wrongly insinuating a disease entity. Prompted by the recent launch of DSM-5 and the imminent arrival of ICD-11, questions are being asked about this complex diagnosis, which has been so problematic, especially in children. This chapter discusses the evolution of bipolar disorder, in the hope that an understanding of its origins will shed light on why it remains such a troublesome diagnosis.

On the validity of measuring change over time in routine clinical assessment: a close examination of item-level response shifts in psychosomatic inpatients

Objective: Significant life events such as severe health status changes or intensive medical treatment often trigger response shifts in individuals that may hamper the comparison of measurements over time. Drawing from the Oort model, this study aims at detecting response shift at the item level in psychosomatic inpatients and evaluating its impact on the validity of comparing repeated measurements. Study design and setting: Complete pretest and posttest data were available from 1188 patients who had filled out the ICD-10 Symptom Rating (ISR) scale at admission and discharge, on average 24 days after intake. Reconceptualization, reprioritization, and recalibration response shifts were explored applying tests of measurement invariance. In the item-level approach, all model parameters were constrained to be equal between pretest and posttest. If non-invariance was detected, these were linked to the different types of response shift. Results: When constraining across-occasion model parameters, model fit worsened as indicated by a significant Satorra–Bentler Chi-square difference test suggesting potential presence of response shifts. A close examination revealed presence of two types of response shift, i.e., (non)uniform recalibration and both higher- and lower-level reconceptualization response shifts leading to four model adjustments. Conclusions: Our analyses suggest that psychosomatic inpatients experienced some response shifts during their hospital stay. According to the hierarchy of measurement invariance, however, only one of the detected non-invariances is critical for unbiased mean comparisons over time, which did not have a substantial impact on estimating change. Hence, the use of the ISR can be recommended for outcomes assessment in clinical routine, as change score estimates do not seem hampered by response shift effects.

Predicting risk of suicide attempt using history of physical illnesses from electronic medical records

BACKGROUND: Although physical illnesses, routinely documented in electronic medical records (EMR), have been found to be a contributing factor to suicides, no automated systems use this information to predict suicide risk.

OBJECTIVE: The aim of this study is to quantify the impact of physical illnesses on suicide risk, and develop a predictive model that captures this relationship using EMR data.

METHODS: We used history of physical illnesses (except chapter V: Mental and behavioral disorders) from EMR data over different time-periods to build a lookup table that contains the probability of suicide risk for each chapter of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) codes. The lookup table was then used to predict the probability of suicide risk for any new assessment. Based on the different lengths of history of physical illnesses, we developed six different models to predict suicide risk. We tested the performance of developed models to predict 90-day risk using historical data over differing time-periods ranging from 3 to 48 months. A total of 16,858 assessments from 7399 mental health patients with at least one risk assessment was used for the validation of the developed model. The performance was measured using area under the receiver operating characteristic curve (AUC).

RESULTS: The best predictive results were derived (AUC=0.71) using combined data across all time-periods, which significantly outperformed the clinical baseline derived from routine risk assessment (AUC=0.56). The proposed approach thus shows potential to be incorporated in the broader risk assessment processes used by clinicians.

CONCLUSIONS: This study provides a novel approach to exploit the history of physical illnesses extracted from EMR (ICD-10 codes without chapter V-mental and behavioral disorders) to predict suicide risk, and this model outperforms existing clinical assessments of suicide risk.