A case history of training outside the hospital and its future.

Hospital-based medical residencies are slowly changing to include experience in ambulatory practices. This development is traced by the case example of the medical residency at the Massachusetts General Hospital which has always contained a component of ambulatory training. The history reveals that the institution's economy and work have determined the content and sequence of residency training, perhaps more than educational ideas on the proper training of the doctor. The early ambulatory experience (1900–1940) was prompted by the need to take care of a large number of outpatients, supported by the view of training for future private practice in the office. The residency became hospital-based (1940–1972) with the expansion of hospital beds, hospital insurance and of specialized jobs in academic medical centers. Residents were now required to care for more hospital patients, to manage the technology of acute care and to aid the growth of subspecialization. Although public interests today demand more ambulatory care, the future of expanded ambulatory training remains problematic, although a modern necessity if medicine is to redirect its attention to prevention and care outside the hospital. Expanded training outside the hospital depends on better financing of ambulatory services, the modernization of outpatient departments as group practices providing primary care, the development of teaching units and research in ambulatory practices, along with support for the idea of the generalist, which itself has had limited support by treatment and learning institutions.     

Right ventricular function in adults with pulmonary hypertension with and without atrial septal defect

Using equilibrium (gated) radionuclide ventriculography, right ventricular (RV) function was studied in 22 adults with pulmonary hypertension and in 16 patients without evidence of cardiac disease. To assess the effect of volume overload on RV performance in pulmonary hypertension, RV ejection fractions were compared in patients with and without left-to-right shunts due to atrial septal defect (ASD). In addition, the effect of ASD repair on RV function was examined. In 14 patients with pulmonary hypertension without RV volume overload (group I), the RV ejection fraction (0.35 ± 0.11, mean ± standard deviation [SD]) was significantly lower than in the normal group (0.47 ± 0.11, p < 0.01). In 8 patients with left-to-right shunts due to ASD (group II) and with RV systolic pressures similar to those in group I, the mean RV ejection fraction (0.53 ± 0.15) was normal and was significantly higher than in group I (p < 0.01). Right ventricular end-diastolic volumes, estimated from combined radionuclide and hemodynamic data, were higher (p < 0.01) in group II patients (171 ± 70 ml/m²) than in group I patients (70 ± 13 ml/m²). In 5 patients who underwent isolated shunt repair, mean RV ejection fraction decreased postoperatively from 0.57 ± 0.17 to 0.40 ± 0.12 (p < 0.05). It is concluded that (1) pulmonary hypertension frequently causes a decrease in RV systolic function due to abnormal afterload; (2) in patients with RV volume overload due to left-to-right shunt, systolic function, as measured by the ejection fraction, remains normal despite pulmonary hypertension, possibly through the Starling mechanism; and (3) RV systolic function often decreases after repair of an ASD.     

Monozygotic twins discordant for systemic lupus erythematosus.

A pair of monozygotic twins discordant for systemic lupus erythematosus(SLE) were studied and no differences noted in their immune respose to tetanus toxoid, keyhole lympet hemocyanin, DNCB, delayed sensitivity, or antibody titers to viruses. Both were noted to have biologically false positive serology at an early age, but only one twon developed SLE. The clinically unaffected twin underwent castration at an early age, suggesting that ovarian hormones may play an important role in the development of SLE.     

Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model

BackgroundGeneral medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients’ risk stratification. The aim of this study was to develop a mortality prediction machine learning model using data available at the time of admission to the medical ward.MethodsWe included consecutive patients (ages 18-100) admitted to medical wards at a single medical center (January 1, 2013-December 31, 2018). We constructed a machine learning model using patient characteristics, comorbidities, laboratory tests, and patients’ emergency department (ED) management. The model was trained on data from the years 2013 to 2017 and validated on data from the year 2018. The area under the curve (AUC) for mortality prediction was used as an outcome metric. Youden index was used to find an optimal sensitivity-specificity cutoff point.ResultsOf the 118,262 patients admitted to the medical ward, 6311 died (5.3%). The single variables with the highest AUCs were medications administered in the ED (AUC = 0.74), ED diagnosis (AUC = 0.74), and albumin (AUC = 0.73). The machine learning model yielded an AUC of 0.924 (95% confidence interval [CI]: 0.917-0.930). For Youden index, a sensitivity of 0.88 (95% CI: 0.86-0.89) and specificity of 0.83 (95% CI: 0.83–0.83) were observed. This corresponds to a false-positive rate of 1:5.9 and negative predictive value of 0.99.ConclusionA machine learning model outperforms single variables predictions of in-hospital mortality at the time of admission to the medical ward. Such a decision support tool has the potential to augment clinical decision-making regarding level of care needed for admitted patients.